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%>px;\" alt=\"\"/>
<%= contributor.name %>", - "skipCi": true, - "contributors": [ - { - "login": "alshedivat", - "name": "Maruan", - "avatar_url": "https://avatars.githubusercontent.com/u/2126561?v=4", - "profile": "http://maruan.alshedivat.com", - "contributions": [ - "design", - "code" - ] - }, - { - "login": "rohandebsarkar", - "name": "Rohan Deb Sarkar", - "avatar_url": "https://avatars.githubusercontent.com/u/50144004?v=4", - "profile": "http://rohandebsarkar.github.io", - "contributions": [ - "code" - ] - }, - { - "login": "pourmand1376", - "name": "Amir Pourmand", - "avatar_url": "https://avatars.githubusercontent.com/u/32064808?v=4", - "profile": "https://amirpourmand.ir", - "contributions": [ - "code" - ] - } - ] -} diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml deleted file mode 100644 index c78502f4..00000000 --- a/.github/FUNDING.yml +++ /dev/null @@ -1,12 +0,0 @@ -# These are supported funding model platforms - -github: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2] -patreon: # Replace with a single Patreon username -open_collective: # Replace with a single Open Collective username -ko_fi: alshedivat -tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel -community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry -liberapay: # Replace with a single Liberapay username -issuehunt: # Replace with a single IssueHunt username -otechie: # Replace with a single Otechie username -custom: # ['https://www.buymeacoffee.com/TkFxuKo'] diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md deleted file mode 100644 index 511f5851..00000000 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ /dev/null @@ -1,38 +0,0 @@ ---- -name: Bug report -about: Create a report to help us improve -title: '' -labels: bug -assignees: '' - ---- - -**Acknowledge the following** -- [ ] I carefully read and followed the [Getting Started](https://github.com/alshedivat/al-folio#getting-started) guide. -- [ ] I read through [FAQ](https://github.com/alshedivat/al-folio#faq) and searched through the [past issues](https://github.com/alshedivat/al-folio/issues), none of which addressed my issue. -- [ ] The issue I am raising is a potential bug in al-folio and not just a usage question.
[For usage questions, please post in the [Discussions](https://github.com/alshedivat/al-folio/discussions) instead of raising an issue.] - -**Describe the bug** -A clear and concise description of what the bug is. - -**To Reproduce** -Steps to reproduce the behavior: -1. Go to '...' -2. Click on '....' -3. Scroll down to '....' -4. See error - -**Expected behavior** -A clear and concise description of what you expected to happen. - -**Screenshots** -If applicable, add screenshots to help explain your problem. - -**System (please complete the following information):** - - OS: [e.g. iOS] - - Browser (and its version) [e.g. chrome, safari] - - Jekyll version [e.g. 3.8.7] -- Ruby version [e.g. 2.6.5] - -**Additional context** -Add any other context about the problem here. diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md deleted file mode 100644 index 11fc491e..00000000 --- a/.github/ISSUE_TEMPLATE/feature_request.md +++ /dev/null @@ -1,20 +0,0 @@ ---- -name: Feature request -about: Suggest an idea for this project -title: '' -labels: enhancement -assignees: '' - ---- - -**Is your feature request related to a problem? Please describe.** -A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] - -**Describe the solution you'd like** -A clear and concise description of what you want to happen. - -**Describe alternatives you've considered** -A clear and concise description of any alternative solutions or features you've considered. - -**Additional context** -Add any other context or screenshots about the feature request here. diff --git a/.github/stale.yml b/.github/stale.yml deleted file mode 100644 index 8ec2004d..00000000 --- a/.github/stale.yml +++ /dev/null @@ -1,18 +0,0 @@ -# Number of days of inactivity before an issue becomes stale -daysUntilStale: 60 -# Number of days of inactivity before a stale issue is closed -daysUntilClose: 7 -# Issues with these labels will never be considered stale -exemptLabels: - - pinned - - security - - enhancement -# Label to use when marking an issue as stale -staleLabel: wontfix -# Comment to post when marking an issue as stale. Set to `false` to disable -markComment: > - This issue has been automatically marked as stale because it has not had - recent activity. It will be closed if no further activity occurs. Thank you - for your contributions. -# Comment to post when closing a stale issue. Set to `false` to disable -closeComment: false diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml deleted file mode 100644 index 79a7218a..00000000 --- a/.github/workflows/deploy.yml +++ /dev/null @@ -1,42 +0,0 @@ -name: deploy - -on: - push: - branches: - - master - - main - pull_request: - branches: - - master - - main - -jobs: - deploy: - runs-on: ubuntu-latest - steps: - - name: Checkout code - uses: actions/checkout@v3 - - name: Setup Ruby - uses: ruby/setup-ruby@v1 - with: - ruby-version: '3.0.2' - bundler-cache: true - - name: Install deps - run: | - npm install -g mermaid.cli - - name: Setup deploy options - id: setup - run: | - git config --global user.name "GitHub Action" - git config --global user.email "41898282+github-actions[bot]@users.noreply.github.com" - if [[ ${GITHUB_REF} = refs/pull/*/merge ]]; then # pull request - echo "SRC_BRANCH=${GITHUB_HEAD_REF}" >> $GITHUB_OUTPUT - echo "NO_PUSH=--no-push" >> $GITHUB_OUTPUT - elif [[ ${GITHUB_REF} = refs/heads/* ]]; then # branch, e.g. master, source etc - echo "SRC_BRANCH=${GITHUB_REF#refs/heads/}" >> $GITHUB_OUTPUT - fi - echo "DEPLOY_BRANCH=gh-pages" >> $GITHUB_OUTPUT - - name: Deploy website - run: yes | bash bin/deploy --verbose ${{ steps.setup.outputs.NO_PUSH }} - --src ${{ steps.setup.outputs.SRC_BRANCH }} - --deploy ${{ steps.setup.outputs.DEPLOY_BRANCH }} \ No newline at end of file diff --git a/.nojekyll b/.nojekyll new file mode 100644 index 00000000..e69de29b diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml deleted file mode 100644 index ca7e4654..00000000 --- a/.pre-commit-config.yaml +++ /dev/null @@ -1,10 +0,0 @@ -# See https://pre-commit.com for more information -# See https://pre-commit.com/hooks.html for more hooks -repos: -- repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.3.0 - hooks: - - id: trailing-whitespace - - id: end-of-file-fixer - - id: check-yaml - - id: check-added-large-files diff --git a/404.html b/404.html index 0da4ee0b..103eba0d 100644 --- a/404.html +++ b/404.html @@ -1,9 +1 @@ ---- -layout: page -permalink: /404.html -title: "Page not found" -description: "Looks like there has been a mistake. Nothing exists here." -redirect: true ---- - -

You will be redirected to the main page within 3 seconds. If not redirected, please click here.

+ Page not found | Sebastian Daza

Page not found

Looks like there has been a mistake. Nothing exists here.

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\ No newline at end of file diff --git a/Gemfile b/Gemfile deleted file mode 100644 index 0cb2a8af..00000000 --- a/Gemfile +++ /dev/null @@ -1,23 +0,0 @@ -source 'https://rubygems.org' -group :jekyll_plugins do - gem 'jekyll' - gem 'jekyll-archives' - gem 'jekyll-diagrams' - gem 'jekyll-email-protect' - gem 'jekyll-feed' - gem 'jekyll-imagemagick' - gem 'jekyll-minifier' - gem 'jekyll-paginate-v2' - gem 'jekyll-scholar' - gem 'jekyll-sitemap' - gem 'jekyll-link-attributes' - gem 'jekyll-twitter-plugin' - gem 'jemoji' - gem 'mini_racer' - gem 'unicode_utils' - gem 'webrick' -end -group :other_plugins do - gem 'httparty' - gem 'feedjira' -end diff --git a/_R/2012-08-25-raking.Rmd b/_R/2012-08-25-raking.Rmd deleted file mode 100755 index 4d9bdf6e..00000000 --- a/_R/2012-08-25-raking.Rmd +++ /dev/null @@ -1,235 +0,0 @@ ---- -title: "Raking weights with R" -description: '' -layout: post -output: html_document -tags: -- raking -- home -category: survey -bibliography: raking.bib ---- - -When I was working with public opinion surveys, I usually had to adjust the data according to population parameters such as sex, age, socioeconomic status, or region. There are several ways to do this. One of them is *raking*. Using the package [anesrake](http://cran.r-project.org/web/packages/anesrake/index.html) by [Josh Pasek](http://joshpasek.com) is easy to compute raking weights in R. I show a brief and straightforward raking example in this post. - -*** - -### What is raking? - -It is common to find differences between the population and sample distribution in some key demographic variables when analyzing surveys. These differences might be due to the sampling design, non-coverage issues or non-response. To avoid biases of point estimates, we generally adjust those differences using weights so that the marginal totals agree (at least in part) with the population. These adjustments can be done using different methods: post-stratification or cell-weighting, raking, general regression estimator GREG (Battaglia et al. 2004, Valliant et al. 2013). Here I will show an example for raking adjustment. Raking has some advantages over cell-weighting: - -1. We have to know only the population totals of the specific variables, not all cells of the cross-classification. -2. It is more flexible feasible when we use several variables at once. - -It is important to note that raking adjustments are useful when we want to estimate a proportion or mean from a population, but they are not necessarily the best when modeling (see Gelman 2007). In that case, model specification (i.e., inclusion of all the key variables and interactions) may be a better solution. It is important to note that the raking procedure may affect the *dependence structure* of variables, what eventually may bias model estimates. - -*** - -### Some rules of thumb - -DeBell and Krosnick (2009) provide useful recommendations regarding variable selection when implementing *raking*: - -- Identify a set of variable likely to be measured with little error and a low item nonresponse rate (less than 5%), and compare them with *reliable* data sources (e.g., Census). Some typical variables are age groups, gender, race, SES, census region. -- Implement weighting when substantive demography discrepancies are observed. For instance, differences higher than five percentage points. When differences are less than two percentage points this adjustment would not be necessary. -- When selected variables have categories with less than 5% in the sample, it would be recommended to collapse them. - -Remember, however, that the ultimate goal of raking is to calibrate and improve the efficiency of our estimates (reduce standard errors). In general, there is trade-off between these two objectives. Anyway, weight adjustments should be variable-dependent: they should explore how different key variables of our survey behave after adjusting. In this post I only show how to implement *raking*. A more complete analysis will be surely necessary. - -Once the raking procedure is implemented, it is a good idea to follow these steps in an iterative fashion: - -- Identify extreme weights and truncate them. For example, those greater than five times the mean of weights. -- Truncate large values, but not small ones (i.e., those near zero). While large values increase the effect of outliers and are more likely to inflate variance, low values do not. -- Examine the design effect using the new weights. If the *new design effect* is greater than the *old design effect* by more than 0.5, it is a good idea to review the raking adjustment. In other words, calibration may cost too much in terms of efficiency (standard errors). - -Three things can be done to improve the weighting procedure: - -- Collapse categories of the variables -- Eliminate or replace weighting variables -- Increase the weight truncation criterion ( > 5 ) - -*** - -### Raking using R - -In this example I use data from Chile to estimate the **presidential approval** ([Opinion Public Survey CEP, July-August 2012](http://www.cepchile.cl/bannerscep/bdatos_encuestas_cep/base_datos.php)). Below, you can see the characteristics of the dataset: - -```{r, echo=FALSE} -options(replace.assign = TRUE, width = 120, tidy =TRUE, scipen = 4, digits = 3) -``` - - -```{r} -dat <- read.csv("cep.csv") -str(dat) -``` - -I use five variables to define raking weights: `sex`, `agecat`, `ses`, `region`, and `area`. I get relative frequencies of these variables using the package `weights` . Most of the variables have more than five percent in each category, with the exception of `region` and `ses`. For illustrative purposes, I will use those variables without collapsing their categories. - -```{r, message=F, warning=F} -library(weights) -# sex: 1 male, 2 female -wpct(dat$sex) -# agecat: 1 18-24, 2 25-34, 3 35-44, 4 45-54, 5 55+ -wpct(dat$agecat) -# ses: 1 abc1, 2 c2, 3 c3, 4 d, 5 e -wpct(dat$ses) -# region: 1 to 15 -wpct(dat$region) -# area: 1 urban, 2 rural -wpct(dat$area) -``` - -The next step is to specify the population distribution of the selected variables in a target *list*. I use two sources to obtain population values: the Chilean Census 2002, and the *Bicentenario* Survey 2009. It is hard to find reliable SES population parameters. The *Bicentenario* data provide, at least, an approximation. - -```{r} -# chilean census 2002 -sex <- c(.49,.51) -agecat <- c(.163, .203, .195, .187, .253) -region <- c(0.015, 0.031, 0.016, 0.039, 0.102, 0.051, 0.059, 0.123, - 0.056, 0.046, 0.006, 0.010, 0.408, 0.023 , 0.013) -area <- c(.869, .131) - -# bicentenario survey 2009 -ses <- c(.109, .184, .261, .364, .083) - -# definitions of target list -targets <- list(sex, agecat,ses, region, area) -# important: to use the same variable names of the dataset -names(targets) <- c("sex", "agecat", "ses", "region", "area") -# id variable -dat$caseid <- 1:length(dat$sex) -``` - -The output shows that some distributions do not sum one. This is because of rounding. We can force any proportion distribution to sum one using `force1 = TRUE`. The differences between the population and the sample distribution are all greater than five percentage points. This meets the variable selection criterion discussed above. - -```{r, warning=FALSE, message=FALSE} -sum(region) -``` - -```{r, warning=FALSE, message=FALSE} -library(anesrake) -anesrakefinder(targets, dat, choosemethod = "total") -``` - -I apply the *anesrake* function as follows: - -- The maximum weight value is five, weights greater than five will be truncated (`cap = 5`). -- The total differences between population and sample have to be greater than 0.05 so that to include a variable (`pctlim = .05`). -- The maximum number of variables included in the raking procedure is five (`nlim = 5`). - -```{r, warning=FALSE, results='hide', message=FALSE} -outsave <- anesrake(targets, dat, caseid = dat$caseid, - verbose= FALSE, cap = 5, choosemethod = "total", - type = "pctlim", pctlim = .05 , nlim = 5, - iterate = TRUE , force1 = TRUE) -``` - -```{r, warning=FALSE, message=FALSE} -summary(outsave) -``` - -The procedure reaches good convergence, weights lower than 5, and very small differences between the sample and population distribution. In addition, the design effect is 1.38. This gives us an idea of the weighting loss due to the raking procedure. The weighting loss ($L_w$) is the inflation in the variance of sample estimates that can be attributed to weighting (Heeringa et. al. 2010). A simple approximation is: - -$$ L_w(\bar{y}) \approx \frac{\sigma^{2}(w)}{\bar{w}^2} = \Bigg( \frac{\sum\limits_{i=1}^n w_i^2}{\big(\sum\limits_{i=1}^n w_i\big)^2} \cdot n \Bigg) - 1 $$ - -This simple formula was introduced by Kish (1965) in the context of proportionated stratified sampling. It represents the proportional increase in the variances of means and proportions, ignoring any clustering that may be included in the sample plan. It assumes that: - -- Proportionate allocation is the optimal stratified design, i.e., variances of $y$ are approximately equal in all strata. -- Weights are uncorrelated with the values of the random variable $y$. - -Thus, values from this formula only represents an approximation to weighting loss provided that model assumptions are met. As can be seen in the `anesrake` output, the design effect is 1.38. That value is equal to $1 + L_w$. - -```{r, message=FALSE} -# add weights to the dataset -dat$weightvec <- unlist(outsave[1]) -n <- length(dat$region) - -# weighting loss -((sum(dat$weightvec ^ 2) / (sum(dat$weightvec)) ^ 2) * n) - 1 -``` - -For a more precise estimation of the design effect it would be necessary to use a package such as [survey](http://cran.fhcrc.org/web/packages/survey/index.html), specifying the characteristics of the sample design. Unfortunately, I do not have enough information to specify the sample design and to estimate the design effect of the presidential approval. There are also no cluster variables or replicate weights in the CEP dataset. - -Anyway, the weighting loss approximation denotes an increase in the design effect lower than .5, what meets the recommendations mentioned above. However, it is important to keep in mind that this weighting procedure does not take into account any clustering effect associated with the sample design. - -Finally, we can estimate the presidential approval with and without weights: - -```{r} -unweighted <- wpct(dat$approval) -weighted <- wpct(dat$approval, dat$weightvec) -tab <- data.frame(unweighted, weighted) -rownames(tab) <- c("approve", "disapprove", "unsure", "dk") -tab -``` - -The difference between the weighted and unweighted presidential approval is not big, only `r sum(abs(unweighted-weighted))` percentage points. - -*** - -### Raking using given survey weights - -The CEP dataset includes its own weights (variable named `pond`). Unfortunately, there is no detailed information about the weighting construction in the methodological documentation of this survey ("Survey weighting is a mess", Gelman 2007). For illustrative purposes, however, I will show how to estimate raking weights using previous weights. - -Exploring the `pond` variable, we can see that there are some big weights (e.g., 17.6). This makes me think that they are just scaled design weights, probably with some non-response adjustment, but I am not really sure. - -```{r, warning=FALSE, message=FALSE} -summary(dat$pond) -``` - -The weighting loss associated with the pond weights is relatively high, denoting a design effect of 2.09. - -```{r, warning=FALSE, message=FALSE} -# weighting loss -((sum(dat$pond ^ 2) / (sum(dat$pond)) ^ 2) * n) - 1 -``` - -We can explore the total differences between the sample and population distributions using `pond` weights. Only **ses** and **region** meet the criterion of total differences greater than 5 percentage points. However, I will keep all the variables used in the previous section and specify that total differences between the population and sample should be greater than .05 (`pctlim = .05`). - -```{r, warning=FALSE, message=FALSE} -anesrakefinder(targets, dat, weightvec = dat$pond, choosemethod = "total") -``` - -In order to get raking weights using previous weights, I only have to add `weightvec = dat$pond` to the `anesrake` command. - -```{r, warning=FALSE, results='hide', message=FALSE} -outsave <- anesrake(targets, dat, caseid = dat$caseid, - weightvec = dat$pond, verbose = FALSE, cap = 5, - choosemethod = "total", type = "pctlim", - pctlim = .05 , nlim = 5, iterate = TRUE, force1 = TRUE) -``` - -```{r, message=FALSE} -summary(outsave) -``` - -Only **ses** and **region** are used in the raking procedure. Some weights were truncated to 5, and the design effect decreased from 2.09 to 1.91. The differences between the sample and population distribution are not big. - -```{r} -# add weights to the dataset -dat$weightvec2 <- unlist(outsave[1]) -pond <- wpct(dat$approval, dat$pond) -weighted_pond <- wpct(dat$approval, dat$weightvec2) -tab <- data.frame(unweighted, pond, weighted, weighted_pond) -rownames(tab) <- c("approve", "disapprove", "unsure", "dk") -tab -``` - -There are no great differences between the estimates `weighted` and `weighted_pond` (`r sum(abs(weighted - weighted_pond))`). There are greater discrepancies between `pond` and `weighted` estimates (0.049). This reveals the importance of the socioeconomic status in the presidential approval. - -*** - -**Last Update: `r format(Sys.time(), '%m/%d/%Y')`** - -### References - -Battaglia, M., D. Izrael, D. C. Hoaglin, and M. Frankel. 2004. "Tips and Tricks for Raking Survey Data (Aka Sample Balancing)." American Association of Public Opinion Research. - -DeBell, Matthew and Jon A. Krosnick. 2009. "Computing Weights for American National Election Study Survey Data." Stanford University. - -Gelman, Andrew. 2007. "Struggles with Survey Weighting and Regression Modeling." Statistical Science 22(2):153-64. - -Heeringa, Steven, Brady T. West, and Patricia A. Berglund. 2010. Applied Survey Data Analysis. Boca Raton, FL: Chapman & Hall/CRC. - -Valliant, Richard, Jill A. Dever, and Frauke Kreuter. 2013. Practical Tools for Designing and Weighting Survey Samples. New York, NY: Springer New York. - - diff --git a/_R/2012-08-28-lifetables.Rmd b/_R/2012-08-28-lifetables.Rmd deleted file mode 100755 index 4bbb8b67..00000000 --- a/_R/2012-08-28-lifetables.Rmd +++ /dev/null @@ -1,53 +0,0 @@ ---- -layout: post -title: "Life tables: brief introduction" -description: "" -category: demography -tags: [prelim, R, home] ---- - -A _life table_ is just one way of summarizing a cohort's mortality experience. Below a simple way to build a life table using R. - -```{r, echo=FALSE} -options(replace.assign=TRUE, tidy=TRUE, width=60, scipen = 4, digits = 3) -``` - -```{r} -dat <- read.csv("frmales85.csv", sep=",", header=T) -# length of intervals -dat$n <- c(diff(dat$x),NA) -# number of intervals -max <- length(dat$x) -# age 85 is an open interval -dat -``` - -```{r} -# age-specific mortality rates assuming $_nM_x=_nm_x$ -dat$nmx <- dat$nDx/dat$nNx -# age-specific probability to die -dat$nqx <- (dat$n*dat$nmx)/(1+(dat$n-dat$nax)*dat$nmx) -dat$nqx[max] <- 1 -dat$npx <- 1-dat$nqx -``` - -```{r} -# definition of radix -radix <- 100000 -# easy way to get lx without a loop -dat$lx <- radix * c(1, cumprod(dat[1:max-1,"npx"])) -dat$ndx <- dat$lx * dat$nqx -dat[, c(-2,-3)] -``` - -```{r} -# Lx for the open interval -dat$nLx <- dat$lx * dat$n - (dat$n-dat$nax)*dat$ndx -dat$nLx[max] <- dat$lx[max]/dat$nmx[max] -``` - -```{r} -dat$Tx <- rev(cumsum(rev(dat$nLx))) -dat$ex <- dat$Tx/dat$lx -dat[, c(-2:-6)] -``` \ No newline at end of file diff --git a/_R/2012-09-05-demQ01.Rmd b/_R/2012-09-05-demQ01.Rmd deleted file mode 100755 index 3481703e..00000000 --- a/_R/2012-09-05-demQ01.Rmd +++ /dev/null @@ -1,128 +0,0 @@ ---- -layout: post -title: "Life tables: Question 1" -description: "" -category: demography -tags: [R, prelim, home] ---- - - -```{r, echo=FALSE} -options(replace.assign=TRUE, width=60, scipen = 4, digits = 3) -``` - -You collected retrospective survey data on the age at first birth for twenty-five women who are age 50 at interview in 2009. You are to construct a life table where the state of interest is childlessness. An empty table is available on the next page. Start the life table at age 15 and terminate it at exact age 50. Use five year intervals and, for speed, assume that “deaths” occur halfway through the 5-yr age interval (despite the fact that you have actual ages).Their ages (last birthday) at first birth are 29, 22, 43, 31, 26, 20, no birth, 25, 23, 30, no birth, 37, 21, 25, 28, no birth, 23 (twins), 27, 34, 25, 24, 21, 17, no birth, no birth. On the basis of this life table, answer the following questions. - -#### What is the probability that a childless woman at exact age 30 would still be childless at age 40? - -First, we have to build the corresponding life table where the event of interest is childbearing. For childless women during the period I assign 50 years old. In the case of twins it is enough to specify just one event (first birth). - - -```{r} -ages <- c(29, 22, 43, 31, 26, 20, 50, 25, 23, 30, 50, 37, 21, 25, 28, 50, 23, 27, 34, 25, 24, 21, 17, 50, 50) -# age -x <- seq(15,49,5) -# width of intervals -n <- rep(5,7) -# births -b <- rep(NA,7) - -(dat <- data.frame(x, n, b)) -``` - -```{r} -# computing births: equivalent to counting logic values -for (i in 1:7){ - dat$b[i] <- sum(ages >= dat$x[i] & ages <= (dat$x[i+1]-1), na.rm=TRUE) - } - -# computing lx -dat$lx <- NA -# childless women at the beginning of the period -dat$lx[1] <- length(ages) - -for (i in 1:6) { - dat$lx[i+1] <- dat$lx[i] - dat$b[i] -} - -dat -``` - -So the probability a childless woman at exact age 30 would still be childless at age 40 is: - -```{r} -dat$l[dat$x==40]/dat$l[dat$x==30] -``` - -#### What was the expected number of years of childlessness (prior to age 50) for a 25 year old childless woman? - -We have to calculate the equivalent to life expectancy but for childlessness. For that, we need $L_x$ and $T_x$. Following the assumptions specified in the question: _“deaths” occur halfway through the 5-yr age interval (despite the fact that you have actual ages)_, we can compute readily $L_x$: - -```{r} -# computing Lx assuming nax = interval/2 -dat$Lx <- NA -for (i in 1:6) { - dat$Lx[i] <- dat$lx[i+1]*dat$n[i] + (dat$n[i]/2)*dat$b[i] -} - -# Lx for the last interval -dat$Lx[7] <- 5*5 - -# computing Tx -dat$Tx <- rev(cumsum(rev(dat$Lx))) -dat -``` - -The expected number of years of childlessness would be $\frac{T_{25}}{l_{25}}$ - - -```{r} -dat$T[dat$x==25]/dat$l[dat$x==25] -``` - -#### What fraction of years between ages 15 and 49.99 were spent childless? - - -That would be $\frac{e_{15}}{35}$: - - -```{r} -(dat$T[dat$x==15]/dat$l[dat$x==15])/35 -``` - -#### You observe that the parity progression ratios for this cohort take the following form. What is the TFR of this cohort? - -A straightforward way to do it: - -```{r} -# given parity progression ratios -ppr <- c(NA, .8, .75,.25, 0) -# estimation PPR1 -# the total first births were 20 -# the total number of women is 25, so... -(ppr[1] <- (20/25)) -(TFR <- sum(cumprod(ppr))) -``` - -#### How might your data collection method affect the accuracy of your answer to the former questions? Be specific, referencing the possible direction of bias if applicable. - - -In the survey we are only taking into account surviving women. We will underestimate the probability of remaining childless in this cohort because single and childless women have higher mortality. - - -#### Assume that births happened exactly half-way through the year of age in which a woman reported a birth (e.g., a birth reported at age 34 happened at exact age 34.5). How inaccurate is the short-cut estimate of $_{5}a_{20}$ you used above? - -We have to compute the average number of years lived childless according to the specification for the question. - -```{r} -# ages included in 5a20 -(age <- seq(20, 24, 1)) -# years lived childless according to assumptions of the question -(x <- (age - 19.5)) -# births between 20 and 24 -(y <- table (ages [ages >= 20 & ages <= 24])) -# average number years lived childless in the interval -mean(x*y) -``` - -Thus, the short-cut estimate is inaccurate by `3.5 - (5/2) = 1`. diff --git a/_R/2012-10-04-facebook-sna.Rmd b/_R/2012-10-04-facebook-sna.Rmd deleted file mode 100755 index 4369e439..00000000 --- a/_R/2012-10-04-facebook-sna.Rmd +++ /dev/null @@ -1,101 +0,0 @@ ---- -layout: post -title: "My Facebook Network" -description: "" -category: SNA -tags: [igraph, home] - ---- - - -I am taking a [coursera](https://www.coursera.org) class on Social Network Analysis. The first assignment consisted of plotting your Facebook network. In order to get your Facebook Network you can use the [Netvizz](https://apps.facebook.com/netvizz/) app. This application allows you to create gdf files (a simple text format that specifies an undirected graph) from the friendship relations. I used this [simplified Netvizz version](http://snacourse.com/getnet/) from [coursera](https://www.coursera.org) that generates gml files directly. - - -```{r, echo=FALSE, warning=FALSE} -options(replace.assign=TRUE, width=60, scipen = 4, digits = 3) -graph.basic.stats <- function(g) { - # check g object - if (!is.igraph(g)) stop("given grapg is not an igraph object") - - # compute some stats - n <- vcount(g) # number of nodes - e <- ecount(g) # number of edges - deg <- degree(g) # degrees - isol <- sum(deg==0) # number of isolated nodes - comps <- clusters(g, mode="strong") # Calculate the maximal (weakly or strongly) connected components of a graph - - # component sizes - compsize <- sort(comps$csize, decreasing=TRUE) - c1 <- compsize[1] # get the biggest component size - c2 <- compsize[2] # get the next biggest component size - - # print information - cat("Number of nodes:", n, "\n") - cat("Number of edges:", e, "\n\n") - - cat("Degree\n") - cat(" Average:", mean(deg), "\n\n") - - cat("\n") - cat("Giant component Size:", c1, "\n") - cat("Giant component As perc.:", c1/n, "\n") - cat("Second Giant component:", c2, "\n") - cat("Second Giant component As perc.:", c2/n, "\n\n") - - cat("Isolated nodes\n") - cat(" Number:", isol, "\n") - cat(" As perc.:", isol/n, "\n\n") -} - -``` - - -```{r} -library(igraph) - -# read the facebook network from a gml file -f <- read.graph("sdaza.gml",format="gml") -``` - -In order to plot my Facebook network, I extract the following attributes: gender, wall post counts, and interface language. I also assign some colors and shapes to those attributes. - -```{r} -# gender, assigning shapes -ssex <- V(f)$sex -table(ssex) -ssex[ssex=="female"] <- "circle" -ssex[ssex=="male"] <- "square" -table(ssex) - -# interface language, assigning colors -cloc <- V(f)$locale -table(cloc) - -cloc[cloc %in% c("es_ES", "it_IT", "nl_NL", "en_GB", "gl_ES", "ko_KR")] <- "Yellow" -cloc[cloc %in% c("es_CL", "pt_BR","es_LA")] <- "Green" -cloc[cloc==c("en_US")] <- "Blue" -table(cloc) - -# wall posts count, assigning node size -nsize <- (V(f)$wallcount/ max(V(f)$wallcount) + .1 )* 15 -``` - -Now, I can plot the network: - -```{r fig1} -plot(f, layout=layout.fruchterman.reingold, edge.arrow.size=.5, vertex.label=NA, vertex.size=3, vertex.color=cloc, vertex.shape=ssex) -``` - -If we use __wall post counts__ to size the nodes: - -```{r fig2} -plot(f, layout=layout.fruchterman.reingold, edge.arrow.size=.5, vertex.label=NA, vertex.size=nsize, vertex.color=cloc, vertex.shape=ssex) -``` - -We can also obtain some basic descriptive statistics of the network using a __graph.basic.stats__ function (see [here](http://www.isk.kth.se/~shahabm/WSAnalysis/networks/NetworkAnalysis.r)): - -```{r} -graph.basic.stats(f) -``` - -As can be seen, I have relatively compact and homogeneous components based on language interface. You can see how your own Facebook network looks like. \ No newline at end of file diff --git a/_R/2013-01-29-lookvar.Rmd b/_R/2013-01-29-lookvar.Rmd deleted file mode 100755 index 7cef52a9..00000000 --- a/_R/2013-01-29-lookvar.Rmd +++ /dev/null @@ -1,35 +0,0 @@ ---- -layout: post -title: "Looking for variables in R" -description: "" -category: R -tags: [R, home] ---- - - -Recently, I have been working with big databases. After reading their codebooks (usually very long pdf files), -I thought it would be useful to have a function to find variable names in R. I wrote a simply function -that looks for variable names in __data.frame__ and __data.table__ objects. - -Here an example: - -```{r, echo=FALSE} -options(replace.assign=TRUE, tidy=TRUE, width=110, scipen = 4, digits = 3) -``` - -```{r, warning=FALSE, message=FALSE} -library(devtools); source_gist("4661324") -``` - -```{r, warning=FALSE, message=FALSE} -library(data.table) -``` - -```{r} -dat <- data.table(infert) -(var <- lookvar(dat, c("par", "spon"))) - -dat[, var, with=FALSE] -``` - -Pretty useful, at least for me. You can also use _regular expressions_ to get variables, for instance, something like `lookvar(dat, "p5[0-2]_[a-z]+_2")`. \ No newline at end of file diff --git a/_R/2013-07-02-projections.Rmd b/_R/2013-07-02-projections.Rmd deleted file mode 100755 index d3721448..00000000 --- a/_R/2013-07-02-projections.Rmd +++ /dev/null @@ -1,163 +0,0 @@ ---- -layout: post -title: "Cohort component projection" -description: "" -category: demography -tags: [prelim, R, home] ---- - - -In this post I present an example of a cohort component projection using a closed female population (Sweden 1993), taken from the the Preston et al.'s book (Demography 2001, page 125). I use R and basic matrix algebra to replicate their results. The advantage of the procedure presented below, is that allows to compute easily the *intrinsic growth rate* and *age-proportionate distribution* of the "stable equivalent" population. All we need is the population by age at time 0 (from a census), survivorship ratios (from a life table), and age-specific fertility rates. - -The data: - -```{r echo=FALSE} -options(replace.assign=TRUE, tidy=TRUE, width=120, scipen =4, digits = 3) -setwd("/Users/sdaza/Dropbox/sdaza.github.com/Rmd/") -``` - -```{r} -dat <- read.csv("sweden1993.csv", sep=",", header=T) -attach(dat) -``` - -```{r echo=FALSE} -dat -``` - -As can be seen, the data have five-year-interval age groups, so each projection forward will involve 5 years. The steps are very simple: - -1. Project forward the population of each age group (estimation of people alive) -2. Calculate the number of births of each age group based on fertility rates, adjusting by mortality (estimation of children alive) -3. Create a Leslie matrix, and then multiple it by the population vector (population by age at time 0) - -### Survivorship ratios - -We have to estimate life table survival ratios, that is, proportions of birth cohorts surviving from one age interval to the next in a **stationary population**. Basically, we are summarizing the mortality experience of different cohorts assuming stationarity. Because census statistics refer to age "last birthday" (rather than exact age), I estimate ratios using $L_x$ (average number of survivors in an age interval) instead of $l_x$. - -$$S_x = \frac{_5L_x}{_5L_{x-5}}$$ - -I compute the survival ratios using a loop in R. The estimation of the open-ended survival ratio is slightly different but still straightforward ($\frac{T_{85}}{T_{80}}$). - -```{r} -Sf <- NA -for (i in 1:(length(Lf)-1)) { -Sf[i] <- Lf[i+1]/Lf[i] -} - -# open-ended survival ratio -Sf[length(Sf)] <- Lf[18]/(Lf[17] + Lf[18]) -``` - -```{r echo=FALSE} -Sf -``` -### Number of children - -This is the tricky part. Because census statistics refer to age "last birthday", and we are projecting every 5 years, the estimation of the number of person-years lived by women in each age group consists of the average number of women alive at the beginning and end of the period (assuming a linear change over the period). To take advantage of the Leslie matrix, I define the births in R using a loop as follows: - -```{r} -Bf <- rep(0, 18) -for (i in 1:length(Lf)) { -Bf[i] <- 1/(1+1.05) * Lf[1]/(100000*2) * sum(f[i+1]*Sf[i], f[i], na.rm=TRUE) -} -``` - -```{r echo=FALSE} -Bf -``` - -1/(1+1.05) corresponds to a transformation of age-specific fertility rates (son and daughters) to maternity rates (only daughters), assuming that the ratio of male to female births (SBR) is constant across mothers' ages. The number of births is also adjusted by the corresponding survival ratio from 0 to 5 years old ($\frac{_5L_0}{5 \times l_0}$), the number 5 goes away due to simplifying). - -### Leslie matrix - -I construct a Leslie matrix by replacing specific cells of a 18 x 18 matrix (18 age groups) by the vectors defined above (survival ratios and maternity rates): - -```{r} -m <- matrix(0,18, 18) -m[1,] <- Bf -s <- diag(17) * Sf -m[2:18,1:17] <- s -m[18,18] <- Sf[17] -``` - -Here we have the Leslie matrix: - -```{r echo=FALSE} -m -``` - -Note that the last survival ratio is repeated in the last column (0.518). This is because the estimation of the open-ended survival ratio is: - -$$ (N_{80} + N_{85}) \times \frac{T_{85}}{T_{80}} $$ - -### Now, let's do some projections - -Using the R multiplication operator for matrices, I do a 5-year projection by simply multiplying the Leslie matrix by the population vector (remember that matrix multiplication is not commutative). - -```{r} -m %*% Nf -``` - -I obtain the same results of the book. Raising this multiplication I can get the projected population of subsequent periods. Because R doesn't have a power operator for matrices, I define a function called *mp* to raise matrices (it is not very efficient, but for this example it's still useful). - -```{r} -mp <- function(mat,pow){ -ans <- mat -for ( i in 1:(pow-1)){ -ans <- mat%*%ans -} -return(ans) -} -``` - -Let's project the initial population for two periods (10 years): - -```{r} -(mp(m,2) %*% Nf) -``` - -Again, I get the same result of the book. The nice thing of all this is that estimating eigenvalues and eigenvectors, I can obtain the intrinsic growth rate and age-distribution of the "stable equivalent" population. Using the *eigen* function in R, I can identify the dominant eigenvalue (higher absolute number), and the corresponding eigenvector: - -```{r} -e <- eigen(m) - -# intrinsic growth rate -(max(abs(e$values))-1)/5 # 5-year-projection - -# intrinsic proportionate age distribution -as.numeric(e$vector[,1]/sum(e$vector[,1])) -``` - -The population is growing but little. - -### What about the population momentum? - -The population momentum corresponds to the growth of a population after imposing replacement fertility conditions, that is, NRR=1. Thus, the first thing we have to do is to estimate NRR. - -```{r} -# calculating NRR -(NRR <- sum(f*Lf/100000*(1/(1+1.05)), na.rm=TRUE)) -``` -We can quickly estimate the intrinsic growth rate using NRR: - -```{r} -# quick estimation of the intrinsic growth rate -log(NRR)/27 -``` - -Very close to our estimation using cohort component projection. To impose the replacement condition, I just have to divide the first row of the Leslie matrix by NRR. - -```{r} -m[1,] <- m[1,]/NRR -``` - -To get the population momentum we have to project the initial population until the growth is zero (here I raised the matrix 100 times), and then to compute the ratio between the initial population and the non-growing population (stationary). - - -```{r} -# population momentum -sum(mp(m,100) %*% Nf) / sum(Nf) -``` - -After imposing the replacement condition, the population grew 1%. diff --git a/_R/2015-09-30-sampler.Rmd b/_R/2015-09-30-sampler.Rmd deleted file mode 100755 index cdedac69..00000000 --- a/_R/2015-09-30-sampler.Rmd +++ /dev/null @@ -1,148 +0,0 @@ ---- -layout: post -title: "Simple R package to define sample sizes and MOEs" -description: "" -category: survey -tags: [sampling, home] ---- - -In this post I present a simple R package called [**sampler**](https://github.com/sdaza/sampler). The package defines **sample sizes** and **margins of error (MOE)** for a proportion, as usually is done when designing public opinion surveys. [In a previous post](http://sdaza.com/survey/2014/01/19/samplesize/), I presented some functions that do mostly the same thing. This new package, however, includes some new features that might be useful. - -## Installation - -```{r, eval = FALSE} -# you have to install devtools first -devtools::install_github("sdaza/sampler") -library(sampler) -``` - -## Functions - -The packages contains four functions: - -- **ssize**: computes sample size. -- **serr**: computes MOE. -- **astrata**: assigns sample sizes to strata. -- **serrst**: computes MOE for stratified samples. - - - -```{r, echo = FALSE} -library(sampler) -``` - -## Define sample size: *ssize* - -```{r} -ssize(.05) -# design effect (deff) and response rate (rr) -ssize(.05, deff = 1.2, rr = .90) -# finite population correction -ssize(.05, deff = 1.2, rr = .90, N = 1000) -# warning message -ssize(.05, deff = 1.2, rr = .90, N = 100) -``` - -## Define sampling error: *serr* - -```{r} -serr(384) -serr(512, deff = 1.2, rr = .90) -serr(370, deff = 1.2, rr = .90, N = 1000) -# we still get an answer -serr(100, deff = 1.2, rr = .90, N = 100) -``` - -## Strata allocation: *astrata* - -These examples show how to allocate a sample size across strata. Look at *?astrata* in **R** for definitions of the allocation procedures that are available. - -```{r} -# I will use data.table -library(data.table) -chile <- data.table(chile) -chile -``` - -```{r} -# proportional for a sample of 1000 -chile[, aprop := astrata(1000, wp = 1, N = pob)] - -# fixed (same number by stratum) -chile[, afixed := astrata(1000, wp = 0, N = pob)] - -# 40% proportional, 60% fixed -chile[, a40 := astrata(1000, wp =.4, N = pob)] - -# 60% proportional, 40% fixed -chile[, a60 := astrata(1000, wp =.6, N = pob)] - -# square-root -chile[, aroot := astrata(1000, method = "root", N = pob)] - -# neyman -chile[, aneyman := astrata(1000, method = "neyman", N = pob, p = pr)] - -# standard deviation -chile[, astdev := astrata(1000, method = "stdev", N = pob, p = pr)] - -# error -chile[, aerr := astrata(e = .11, method = "error", N = pob, p = pr)] -``` - -```{r, echo = FALSE} -chile -``` - -## Getting sampling error from a stratified sample: *serrst* - -```{r} -# the second most efficient allocation -serrst(n = chile$aprop, N = chile$pob, p = chile$pr) -# the worst solution -serrst(n = chile$afixed, N = chile$pob, p = chile$pr) -serrst(n = chile$a40, N = chile$pob, p = chile$pr) -serrst(n = chile$a60, N = chile$pob, p = chile$pr) -serrst(n = chile$aroot, N = chile$pob, p = chile$pr) -# the most efficient allocation -serrst(n = chile$aneyman, N = chile$pob, p = chile$pr) -serrst(n = chile$astdev, N = chile$pob, p = chile$pr) -serrst(n = chile$aerr, N = chile$pob, p = chile$pr) -``` - -## Combining criteria - -```{r} -# get error for 60% proportional / 40% fixed allocation for each strata -chile[, error_a60 := serr(a60, p = pr)] - -# assign sample sizes assuming 13% error for each strata -chile[, serr13 := astrata(e = .13, method = "error", N = pob, p = pr)] - -# total error, not that good! -serrst(n = chile$serr13, N = chile$pob, p = chile$pr) -chile[, .(reg, pob, pr, a60, error_a60, serr13)] -``` - -We can adjust a bit more: - -```{r} -# when error is higher than .13, use serr13 -chile[, sfinal := ifelse(error_a60 > .13, serr13, a60)] - -# new error by stratum -chile[, error_sfinal := serr(sfinal, p = pr)] - -# total error, much better! -serrst(n = chile$sfinal, N = chile$pob, p = chile$pr) - -# although the total sample size is now bigger -sum(chile$sfinal) -``` -```{r, echo = FALSE} -# details -chile[, .(reg, pob, pr, sfinal, error_sfinal)] -``` - -That's it. A simple package to do simple calculations. - diff --git a/_R/2015-10-14-imputation_parcels.Rmd b/_R/2015-10-14-imputation_parcels.Rmd deleted file mode 100755 index 853fe5bb..00000000 --- a/_R/2015-10-14-imputation_parcels.Rmd +++ /dev/null @@ -1,168 +0,0 @@ ---- -layout: post -title: "Imputing scales using parcels of items as auxiliary variables" -description: "" -category: imputation -tags: [home] ---- - -```{r, echo=FALSE} -options(replace.assign = FALSE, width = 200, tidy = FALSE, scipen = 4, digits = 3) -``` - -Multiple imputation when variables are scales generated from several items might be challenging. Fortunately, to impute every single item is not the only way to solve this problem. There are some practical and *theoretically* attractive alternatives! In this post, I show a simple implementation of what Enders (2010) calls **duplicated-scale imputation**. The specific method I show here was proposed by Eekhout et al. (2011). Thanks [Iris Eekhout](http://www.iriseekhout.com) for replying my e-mails! - -## Procedure - -For each scale, I define a number (or proportion) of items (let's say **p**) to create parcels (i.e., average of items). These parcels are used as auxiliary variables to impute the original scales. There are different ways to define parcels. I implemented a solution: see the function *rowscore* available in my R package [sdazar](http://github.com/sdaza/sdazar). - -The function `rowscore` select **p** items with less missing data. For each case (row), it computes the parcels using the available information of the selected items. If only one item has information, only that one will be used. If there are more than one item with valid data, it will average the available items. If there are no items available, it will pick **p** items from the rest of items to impute the original scale. In this particular example I created parcels using half of the items: - -```{r, echo = TRUE, warning = FALSE, message = FALSE, eval = FALSE} -rowscore(data, items, p = 1/2, type = "parcel") -``` -The idea of using a proportion of the original items is to include as much as information possible but preventing strong linear dependencies between the variables. Ideally, after this procedure, *parcels* should be complete. However, because in some cases all the items are missing, parcels can still have missing records (although less than the original scales). - -**Why not just to use the average of the available items?** That solution would implicitly assume that items perfectly correlates with the scale. We know that is not a good assumption. That is why, after all, we worry about creating scales. Using parcels takes advantage of the available information (items with complete information) and the relationship between a portion of items and the scale. - -Here I show a simple example using the [National Longitudinal Study of Adolescent to Adult Health (Add Health)](http://www.cpc.unc.edu/projects/addhealth). First, let's look some descriptives of the variables included in the imputation. I am using information from Wave 1 and 2. The key scales/scores are depression (19 items) and GPA (4 items). Variables ending with `.p` are parcels with 1/2 of the items of the original scale. - -```{r, echo = FALSE, warning = FALSE, message = FALSE, results = 'hide'} -rm(list=ls(all=TRUE)) - -library(sdazar) -library(texreg) -library(mice) -library(miceadds) -library(tables) -library(VIM) -load("/Users/sdaza/Documents/UWSociology/00Projects/01UnfinishedOLD/Meier/usrdata/addhealth_work01.Rdata") - -dats <- dat[rape1 == 0 & rape2 == 0& gswgt2 > 0 & gswgt1 > 0, ] # not raped people - -names(dats) -vars <- c("h2fs4", "h2fs8", "h2fs11", "h2fs15") -vardep <- lookvar(dats,"h2fs") -vars -vardep <- vardep[!(vardep %in% vars)] - -``` - -```{r, echo = FALSE, warning = FALSE, message = FALSE} -nvars <- c("female", "age", "race", "publicass", "bmi", "gpa1", "gpa2", "gpa1.p", "gpa2.p", "dep1.p", "dep1", "dep2.p", "dep2", "ppvt") - -dats <- dats[, nvars, with = FALSE] -dats[, female := factor(female)] -dats[, race := factor(race)] -dats[, publicass := factor(publicass)] - -``` - - - -```{r, echo = TRUE, warning = FALSE, message = FALSE} -dim(dats) -str(dats[, nvars, with = FALSE]) -``` - -```{r, echo = TRUE, warning = FALSE, message = FALSE} -# create summary table using package tables -missing <- function (x) {sum(is.na(x))} -tabular( - ( dep2 + dep2.p + dep1 + dep1.p + gpa2 + gpa2.p + - gpa1 + gpa1.p + age + bmi + ppvt ) - ~ (Format(digit = 2) * ( Heading("Mean") - * sdazar::Mean + Heading("SD") - * sdazar::Sd + Heading("Min") - * sdazar::Min + Heading("Max") - * sdazar::Max + (Heading("Missing") * missing ))), - data = dats) -``` - -As expected, the correlation between the scales and parcels is high. GPA variables have most of the problems. Note that parcels `.p` still have missing records, although much less than the original scales. - -```{r, echo = TRUE, warning = FALSE, message = FALSE} -cor(dats[, .(dep1, dep1.p)], use = "complete") -cor(dats[, .(dep2, dep2.p)], use = "complete") -cor(dats[, .(gpa1, gpa1.p)], use = "complete") -cor(dats[, .(gpa2, gpa2.p)], use = "complete") -``` - -I use the R package *MICE* to impute the data. - -```{r, echo = TRUE, warning = FALSE, message = FALSE} -ini <- mice(dats[, nvars, with = FALSE], m = 1, maxit = 0) - -# get methods -(meth <- ini$meth) - -# get predictor matrix -pred <- ini$pred -``` - -I adjusted the predictor matrix to avoid feedbacks during the imputation (circularity between variables). The main adjustment is to use only complete variables when imputing *parcels*. - -```{r, echo = TRUE, warning = FALSE, message = FALSE} -# predict parcels only with complete variables to avoid feedbacks -pred["gpa1.p", ] <- 0 -pred["gpa2.p", ] <- 0 -pred["dep1.p", ] <- 0 -pred["dep2.p", ] <- 0 - -pred["gpa1.p", c("female", "race")] <- 1 -pred["gpa2.p", c("female", "race")] <- 1 -pred["dep1.p", c("female", "race")] <- 1 -pred["dep2.p", c("female", "race")] <- 1 - -# predict scales using parcels -pred[, "gpa1.p"] <- 0 -pred[, "gpa2.p"] <- 0 -pred[, "dep1.p"] <- 0 -pred[, "dep2.p"] <- 0 - -pred["gpa1", c("gpa1.p")] <- 1 -pred["gpa2", c("gpa2.p")] <- 1 -pred["dep1", c("dep1.p")] <- 1 -pred["dep2", c("dep2.p")] <- 1 -``` - -Here the adjusted predictor matrix: - -```{r, echo = FALSE, warning = FALSE, message = FALSE} -pred -``` - -Let's impute the data! - -```{r, warning = FALSE, message = FALSE, results = 'hide'} -imp <- mice(dats[, nvars, with = FALSE], pred = pred, m = 5, maxit = 10) -``` - -Some plots to explore how the imputation went. - -```{r, echo = TRUE, warning = FALSE, message = FALSE} -plot(imp, c("gpa1", "gpa2", "dep1", "dep2")) -``` - -I don't see any problematic pattern. It looks as I got a proper solution. The distribution of the variables also looks right. - -```{r, echo = TRUE, warning = FALSE, message = FALSE} -densityplot(imp, ~ gpa1 + gpa2 + dep1 + dep2) -bwplot(imp, gpa1 + gpa2 + dep1 + dep2 ~ .imp) -``` - -**Last Update: `r format(Sys.time(), '%m/%d/%Y')`** - ------ - -### References - -Enders, Craig K. 2010. *Applied Missing Data Analysis*. The Guilford Press. - -Eekhout, Iris, Craig K. Enders, Jos W. R. Twisk, Michiel R. de Boer, Henrica -C. W. de Vet, and Martijn W. Heymans. 2015. "Analyzing Incomplete Item Scores -in Longitudinal Data by Including Item Score Information as Auxiliary -Variables." *Structural Equation Modeling: A Multidisciplinary Journal* 22 -(4):588-602. - - diff --git a/_R/2016-07-06-acsr.Rmd b/_R/2016-07-06-acsr.Rmd deleted file mode 100644 index 3d7b5c93..00000000 --- a/_R/2016-07-06-acsr.Rmd +++ /dev/null @@ -1,233 +0,0 @@ ---- -layout: post -title: "R package to compute statistics from the American Community Survey (ACS) and Decennial US Census" -description: "" -category: "R" -tags: [ACS, R] ---- - -```{r, echo=FALSE} -options(replace.assign=TRUE, tidy=TRUE, width=110, scipen = 4, digits = 3) -``` - -The `acsr` package helps extracting variables and computing statistics using the America Community Survey and Decennial US Census. It was created for the [Applied Population Laboratory](http://www.apl.wisc.edu/) (APL) at the University of Wisconsin-Madison. - -

Installation

- -The functions depend on the `acs` and `data.table` packages, so it is necessary to install then before using `acsr`. The `acsr` package is hosted on a github repository and can be installed using `devtools`: - -```{r, eval = FALSE} -devtools::install_github("sdaza/acsr") -library(acsr) -``` - -```{r, echo = FALSE, warning = FALSE, message = FALSE} -library(acsr) -``` - -Remember to set the ACS API key, to check the help documentation and the default values of the `acsr` functions. - -```{r, eval = FALSE} -api.key.install(key="*") -?sumacs -?acsdata -``` - -The default dataset is `acs`, the level is `state` (Wisconsin, `state = "WI"`), the `endyear` is 2014, and the confidence level to compute margins of error (MOEs) is 90%. - -

Levels

- -The `acsr` functions can extract all the levels available in the `acs` package. The table below shows the summary and required levels when using the `acsdata` and `sumacs` functions: - -| summary number | levels | -|---------------- |----------------------------------------- | -| 010 | us | -| 020 | region | -| 030 | division | -| 040 | state | -| 050 | state, county | -| 060 | state, county, county.subdivision | -| 140 | state, county, tract | -| 150 | state, county, tract, block.group | -| 160 | state, place | -| 250 | american.indian.area | -| 320 | state, msa | -| 340 | state, csa | -| 350 | necta | -| 400 | urban.area | -| 500 | state, congressional.district | -| 610 | state, state.legislative.district.upper | -| 620 | state, state.legislative.district.lower | -| 795 | state, puma | -| 860 | zip.code | -| 950 | state, school.district.elementary | -| 960 | state, school.district.secondary | -| 970 | state, school.district.unified | - - -

Getting variables and statistics

- -We can use the `sumacs` function to extract variable and statistics. We have to specify the corresponding method (e.g., *proportion* or just *variable*), and the name of the statistic or variable to be included in the output. - -```{r} -sumacs(formula = c("(b16004_004 + b16004_026 + b16004_048 / b16004_001)", "b16004_026"), - varname = c("mynewvar", "myvar"), - method = c("prop", "variable"), - level = c("division")) -``` - -To download the data can be slow, especially when many levels are being used (e.g., blockgroup). A better approach in those cases is, first, download the data using the function `acsdata` , and then use them as input. - -```{r} -mydata <- acsdata(formula = c("(b16004_004 + b16004_026 + b16004_048 / b16004_001)", - "b16004_026"), - level = c("division")) - -sumacs(formula = c("(b16004_004 + b16004_026 + b16004_048 / b16004_001)", "b16004_026"), - varname = c("mynewvar", "myvar"), - method = c("prop", "variable"), - level = c("division"), - data = mydata) -``` - -

Standard errors

- -When computing statistics there are two ways to define the standard errors: - -- Including all standard errors of the variables used to compute a statistic (`one.zero = FALSE`) -- Include all standard errors except those of variables that are equal to zero. Only the maximum standard error of the variables equal to zero is included (`one.zero = TRUE`) -- The default value is `one.zero = TRUE` - -For more details about how standard errors are computed for proportions, ratios and aggregations look at [A Compass for Understanding and Using American Community Survey Data](https://www.census.gov/content/dam/Census/library/publications/2008/acs/ACSGeneralHandbook.pdf). - -Below an example when estimating proportions and using `one.zero = FALSE`: - -```{r, warning=FALSE, message=FALSE} -sumacs(formula = "(b16004_004 + b16004_026 + b16004_048) / b16004_001", - varname = "mynewvar", - method = "prop", - level = "tract", - county = 1, - tract = 950501, - endyear = 2013, - one.zero = FALSE) -``` - -$$ SE = \sqrt{ \frac{(5.47 ^ 2 + 22.49 ^ 2 + 5.47 ^ 2) - ( 0.02 ^ 2 \times 102.13 ^ 2)}{1546} } \times 1.645 = 0.0252 $$ - -When `one.zero = TRUE`: - -```{r, warning=FALSE, message=FALSE} -sumacs(formula = "(b16004_004 + b16004_026 + b16004_048) / b16004_001", - varname = "mynewvar", - method = "prop", - level = "tract", - county = 1, - tract = 950501, - endyear = 2013, - one.zero = TRUE) -``` - -$$ SE_{\text{ one.zero}} \sqrt{ \frac{(5.47 ^ 2 + 22.49 ^ 2) - ( 0.02 ^ 2 \times 102.13 ^ 2)}{1546} } \times 1.645 = 0.0245 $$ - -When the square root value in the standard error formula doesn't exist (e.g., the square root of a negative number), the ratio formula is instead used. The ratio adjustment is done **variable by variable** . - -It can also be that the `one.zero` option makes the square root undefinable. In those cases, the function uses again the **ratio** formula to compute standard errors. There is also a possibility that the standard error estimates using the **ratio** formula are higher than the **proportion** estimates without the `one.zero` option. - -

Decennial Data from the US Census

- -Let's get the African American and Hispanic population by state. In this case, we don't have any estimation of margin of error. - -```{r, warning=FALSE, message=FALSE} -sumacs(formula = c("p0080004", "p0090002"), - method = "variable", - dataset = "sf1", - level = "state", - state = "*", - endyear = 2010) -``` - -

Output

- -The output can be formatted using a wide or long format: - -```{r } -sumacs(formula = "(b16004_004 + b16004_026 + b16004_048 / b16004_001)", - varname = "mynewvar", - method = "prop", - level = "division", - format.out = "long") -``` - -And it can also be exported to a csv file: - -```{r} -sumacs(formula = "(b16004_004 + b16004_026 + b16004_048 / b16004_001)", - varname = "mynewvar", - method = "prop", - level = "division", - file = "myfile.out") -``` - -

Combining geographic levels

- -We can combine geographic levels using two methods: (1) `sumacs` and (2) `combine.output`. The first one allows only single combinations, the second multiple ones. - -If I want to combine two states (e.g., Wisconsin and Minnesota) I can use: - -```{r, warning=FALSE, message=FALSE} -sumacs("(b16004_004 + b16004_026 + b16004_048 / b16004_001)", - varname = "mynewvar", - method = "prop", - level = "state", - state = list("WI", "MN"), - combine = TRUE, - print.levels = FALSE) -``` - -If I want to put together multiple combinations (e.g., groups of states): - -```{r, warning=FALSE, message=FALSE} -combine.output("(b16004_004 + b16004_026 + b16004_048 / b16004_001)", - varname = "mynewvar", - method = "prop", - level = list("state", "state"), - state = list( list("WI", "MN"), list("CA", "OR")), # nested list - combine.names = c("WI+MN", "CA+OR"), - print.levels = FALSE) -``` - - -

A map?

- -Let's color a map using poverty by county: - -```{r, warning=FALSE, message=FALSE} -pov <- sumacs(formula = "b17001_002 / b17001_001 * 100", - varname = c("pov"), - method = c("prop"), - level = c("county"), - state = "*") -``` - -```{r, warning=FALSE, message=FALSE} -library(choroplethr) -library(choroplethrMaps) -pov[, region := as.numeric(geoid)] -setnames(pov, "pov_est", "value") -county_choropleth(pov, num_colors = 5) -``` - -In sum, the `acsr` package: - -- Reads formulas directly and extracts any ACS/Census variable -- Provides an automatized and tailored way to obtain indicators and MOEs -- Allows different outputs' formats (wide and long, csv) -- Provides an easy way to adjust MOEs to different confidence levels -- Includes a variable-by-variable ratio adjustment of standard errors -- Includes the zero-option when computing standard errors for proportions, ratios, and aggregations -- Combines geographic levels flexibly - - - -**Last Update: `r format(Sys.time(), '%m/%d/%Y')`** diff --git a/_R/2016-10-05-readMortalityData.Rmd b/_R/2016-10-05-readMortalityData.Rmd deleted file mode 100644 index f36c1e3f..00000000 --- a/_R/2016-10-05-readMortalityData.Rmd +++ /dev/null @@ -1,85 +0,0 @@ ---- -layout: post -title: "Reading CDC mortality files using R" -description: "" -category: "R" -tags: [CDC, Mortality, R] ---- - -```{r, echo=FALSE} -options(replace.assign = TRUE, tidy = TRUE, width = 110, scipen = 4, digits = 3) -``` - -Reading fixed-width text files might be challenging, specially when we don't have a dictionary file. In this post, I show steps to read CDC files in a more systematic way. In this example, I import a compress mortality file (CMF 1979-1988) available [here](http://www.cdc.gov/nchs/data_access/cmf.htm) and whose codebook (or layout) is [here](http://www.cdc.gov/nchs/data/mortab/filelayout68_88.pdf). - -To read this file, usually with extension `.txt` or `.dat`, I first need to know where each column starts and finishes. What I get from the pdf file is something like this: - -![](/images/mortalityLayout.png) - -The layout is usually a codebook in Word/PDF or just plain text file. Here, I copy the PDF text and put it in a plain text file. I use a text editor (e.g., [Sublime Text](https://www.sublimetext.com/)) and [regular expressions](https://en.wikipedia.org/wiki/Regular_expression) to extract the information I need. - -I have to select every row with this pattern: `1-2 2 FIPS State code Numeric`. That is, a number followed by a hyphen (although not always, particularly when the width of the column is one), spaces, another number, spaces, and then any text. I use the following regular expression to get that pattern: `(^[0-9]+).([0-9]+)\s+([0-9])\s+(.+)`. Using the Sublime package [Filter Lines](https://packagecontrol.io/packages/Filter%20Lines) I get something like this (you can also just copy the selected lines): - -``` -1-2 2 FIPS State code Numeric -3-5 FIPS county code Numeric -6-9 4 Year of death Numeric -11-12 2 Age at death Numeric -13-16 4 ICD code for underlying cause-of-death 3 digits: Numeric -17-19 3 Cause-of-Death Recode Numeric -20-23 4 Number of deaths Numeric -``` - -This approach might be particularly useful when you have a long PDF/Word file and you want to extract most of the variables. You would need to adapt the regular expressions I'm using to the particular patterns of your codebook. - -To simplify, I format this text as a comma-separated values file (csv). Replacing this regular expression `([0-9]+)(-)([0-9]+)(\s)([0-9]+)(\s)(.+)(\s)(Numeric)` by `\1,\3,\5,\7,\9` I get: - -``` -1,2,2,FIPS State code,Numeric -3,5,3,FIPS county code,Numeric -6,9,4,Year of death,Numeric -11,12,2,Age at death,Numeric -13,16,4,ICD code for underlying cause-of-death 3 digits:,Numeric -17,19,3,Cause-of-Death Recode,Numeric -20,23,4,Number of deaths,Numeric -``` - -Then, I read the layout file: - - ```{r} - # define names of columns - colnames <- c("start", "end", "width", "name", "type") - dict <- read.csv("data/dictMortality.csv", col.names = colnames, header = FALSE) - ``` - - ```{r, echo = FALSE} - dict - ``` - -Now, I can read the fixed-width data file. I use the [readr](https://github.com/hadley/readr) package (in my experience relatively fast for big datasets ~ 1 GB). - - ```{r, results = 'hide', warning = FALSE, message = FALSE} - library(readr) - - # create name of variables - cnames <- c("state", "county", "year", "age", "icd", "cause", "deaths") - - # read mortality file - mort <- read_fwf("data/mort7988.txt", fwf_positions(dict$start, dict$end, cnames)) - ``` - - ```{r, echo = FALSE} - mort - ``` - - ```{r, echo = TRUE} - # year distribution - table(mort$year) - - # number of deaths - sum(mort$deaths) - ``` - -Hopefully, this might save you some time! - -**Last Update: `r format(Sys.time(), '%m/%d/%Y')`** diff --git a/_R/2020-05-12-descriptive-tables.Rmd b/_R/2020-05-12-descriptive-tables.Rmd deleted file mode 100644 index 15e3fb41..00000000 --- a/_R/2020-05-12-descriptive-tables.Rmd +++ /dev/null @@ -1,147 +0,0 @@ ---- -layout: post -title: "Oh, descriptive tables (R + Latex)!" -author: Sebastian Daza -comments: true -date: 2020-05-12 ---- - -It's been a while since my last post. It's time to catch up! - -This one is short. Every time I write a paper or report, I need to create descriptive tables using Latex. Over and over I create these Adhoc tables, I say to myself: -*Write a general function so you can save time in the next paper!* -I know there are some solutions out there, but in general, I feel they are not flexible enough. - -I introduce a far from perfect function to create descriptive tables in Latex. The steps and structure are quite simple: - -1. Write a function to summarize your data with any stats you want -2. Define a list with the data plus column names (labels) - -That's it. Some useful features of the function: - -- It deals automatically with factors (categorical variables) -- You can use different datasets at the same time -- You can group columns using a variable (e.g., year) -- You can add long notes at the bottom of the table -- You can specify your descriptive function - -Let's start creating some fake data: - -- 5 variables -- Variable 3 is a factor (i.e., categorical) -- Variable 5 is a grouping column - -```{r warning=FALSE, message=FALSE, include=TRUE} -library(data.table) -set.seed(14332) -devtools::source_gist("c4d1089a501d3567be9fb784b1c5a6ab") -``` - -```{r echo=TRUE, message=FALSE, warning=FALSE} -# create two fake datasets -n1 = 100 -dat1 = data.table( - var1 = runif(n1), - var2 = sample(0:1, n1, prob = c(0.8, 0.2), replace = TRUE), - var3 = factor(sample(1:4, n1, prob = c(0.1, 0.4, 0.3, 0.2), - replace = TRUE), - labels = c("Low", "Medium", "High", "Very high")), - var4 = rnorm(n1, 1000, 100), - var5 = sample(1:4, n1, replace = TRUE) -) - -n2 = 200 -dat2 = data.table( - var1 = runif(n2, min = 3, max = 5), - var2 = sample(0:1, n2, prob = c(0.5, 0.5), replace = TRUE), - var3 = factor(sample(1:5, n2, prob = c(0.05, 0.1, 0.35, 0.3, 0.2), - replace = TRUE), - labels = c("Very low", "Low", "Medium", "High", "Very high")), - var4 = rnorm(n2, 2000, 300), - var5 = sample(1:4, n2, replace = TRUE) -) - -datasets = list("Data 1" = dat1, "Data 2" = dat2) -variables = list(paste0("var", 1:4), paste0("var", 1:4)) -labels = list(paste0("Variable ", 1:4), paste0("Variable ", 1:4)) -colnames = c("Mean", "Median", "SD") -``` - -We can define a descriptive function: - -```{r echo=TRUE, warning=FALSE, message=FALSE, include=TRUE} -# descriptive function -myDescriptives = function(x) { - x = as.numeric(x) - m = mean(x, na.rm = TRUE) - md = median(x, na.rm = TRUE) - sd = sd(x, na.rm = TRUE) - return(c(m, md, sd)) -} -``` - -```{r results='hide', warning=FALSE, message = FALSE, include = TRUE} -# create table -createDescriptiveTable(datasets, - summary_function = myDescriptives, - column_names = colnames, - variable_names = variables, - variable_labels = labels, - arraystretch = 1.3, - title = "Descriptive statistics", - label = "tab:descriptive", - file = "example_01.tex") -``` - -
- -
- - -Thus, the grouping of rows is defined by the name of each dataset in the list. We can add a note, -just remember to add `\usepackage[flushleft]{threeparttable}` to your Latex document: - -```{r results='hide', warning=FALSE, message = FALSE, include = TRUE} -# table + note -createDescriptiveTable(datasets, - summary_function = myDescriptives, - column_names = colnames, - variable_names = variables, - variable_labels = labels, - arraystretch = 1.3, - note = "This is a very long long long long long long long long long note.", - title = "Descriptive statistics", - label = "tab:descriptive", - file = "example_02.tex") -``` - -
- -
- -We can also slice the descriptives by group: - -```{r results='hide', warning = FALSE, message = FALSE, include = TRUE} -# group columns -createDescriptiveTable(datasets, - summary_function = myDescriptives, - column_names = colnames, - variable_names = variables, - variable_labels = labels, - group_variable = "var5", - arraystretch = 1.3, - tabcolsep = 3, - note = "This is a very long long long long long long long long long note.", - title = "Descriptive statistics", - label = "tab:descriptive", - file = "example_03.tex") -``` - -
- -
- -It's just a first version. I am adding more features in the future. \ No newline at end of file diff --git a/_R/data/cep.csv b/_R/data/cep.csv deleted file mode 100755 index 32e2eb5a..00000000 --- a/_R/data/cep.csv +++ /dev/null @@ -1,1513 +0,0 @@ -"fnu","area","region","comuna","sex","age","ses","phone","edu","pond","agecat","approval" -1580,1,13,13123,1,30,2,1,4,1.977,2,2 -1579,1,13,13123,1,60,2,1,4,1.243,5,1 -890,2,9,9110,2,28,3,9,4,0.514,2,2 -871,1,9,9102,1,72,3,2,1,0.421,5,1 -1031,1,10,10202,1,55,4,2,2,0.526,5,1 -104,1,2,2201,1,35,3,2,3,8.956,3,2 -1103,1,13,13101,2,33,2,2,4,1.083,2,2 -263,1,5,5106,1,25,3,1,4,3.304,2,2 -924,1,9,9201,2,73,4,2,2,0.258,5,2 -105,1,2,2201,1,43,2,1,4,8.956,3,2 -346,1,5,5402,1,43,4,2,3,1.347,3,3 -997,1,10,10101,2,71,3,1,2,0.755,5,2 -109,1,2,2301,2,77,4,2,1,0.219,5,2 -698,1,8,8110,2,28,3,1,4,0.127,2,2 -641,1,8,8101,2,41,3,2,4,0.764,3,2 -1195,1,13,13106,2,39,4,2,4,0.517,3,1 -653,1,8,8102,1,59,3,1,2,1.372,5,2 -1508,1,13,13120,2,64,3,1,3,1.572,5,2 -1054,1,10,10301,2,46,4,2,1,0.899,4,2 -72,1,2,2101,1,29,3,2,3,3.061,2,3 -1331,1,13,13113,2,73,1,1,2,1.035,5,1 -1075,1,12,12101,1,79,4,1,2,0.322,5,2 -416,1,6,6101,1,30,4,2,3,0.563,2,2 -627,1,8,8101,1,63,1,2,4,2.457,5,2 -30,1,1,1101,2,43,4,1,3,1.37,3,1 -1236,1,13,13109,1,61,4,1,4,1.178,5,1 -8,1,15,15101,2,57,4,2,4,0.84,5,3 -1620,1,13,13125,2,52,4,2,2,0.804,4,2 -864,1,9,9101,1,61,3,1,3,0.376,5,3 -1073,1,11,11202,1,29,4,2,2,1.111,2,2 -775,2,8,8307,1,80,4,2,1,0.772,5,3 -635,1,8,8101,2,48,4,2,3,0.471,4,3 -711,1,8,8110,1,21,3,1,4,0.671,1,2 -345,1,5,5402,2,42,3,1,4,1.137,3,1 -1586,1,13,13123,2,86,2,9,4,0.35,5,1 -1002,1,10,10101,2,75,4,1,2,0.504,5,1 -998,1,10,10101,1,61,5,2,1,0.248,5,1 -1766,1,13,13201,2,28,2,1,4,0.41,2,1 -1524,1,13,13120,2,47,1,1,4,0.513,4,1 -83,1,2,2101,2,36,3,2,3,0.268,3,1 -479,1,6,6301,2,19,4,2,3,0.429,1,1 -750,1,8,8301,2,29,3,2,4,0.338,2,2 -59,1,2,2101,2,21,3,2,4,0.716,1,2 -64,1,2,2101,1,51,4,2,3,1.146,4,2 -412,1,6,6101,2,33,4,2,3,0.568,2,1 -1211,1,13,13106,2,54,3,1,4,0.279,4,2 -1053,1,10,10301,2,49,3,1,2,1.798,4,1 -235,1,5,5101,2,25,3,2,4,3.065,2,3 -1635,1,13,13126,2,19,3,1,4,1.994,1,2 -1278,1,13,13110,1,31,2,1,4,0.776,2,2 -1196,1,13,13106,2,53,3,1,3,0.464,4,1 -112,1,2,2301,1,39,3,2,3,0.29,3,1 -457,1,6,6115,1,36,4,2,3,0.528,3,1 -28,1,1,1101,2,75,4,2,2,0.92,5,3 -1418,1,13,13117,2,70,5,2,2,0.416,5,2 -719,1,8,8110,1,44,3,1,4,0.916,3,9 -1657,1,13,13128,1,66,4,1,2,1.173,5,1 -620,1,8,8101,1,32,4,1,3,1.428,2,1 -1230,1,13,13108,1,38,5,2,2,1.129,3,2 -131,1,3,3101,2,23,3,1,3,0.638,1,1 -128,1,3,3101,1,44,3,1,3,1.98,3,3 -856,1,9,9101,1,27,2,9,4,2.137,2,2 -1824,1,13,13402,2,42,4,9,3,1.133,3,1 -1557,1,13,13122,1,62,3,9,2,1.495,5,2 -138,1,3,3202,2,34,2,1,3,0.973,2,1 -886,1,9,9109,2,48,4,2,2,1.005,4,1 -1830,1,13,13404,2,38,3,1,4,0.209,3,1 -782,1,8,8312,2,62,4,1,1,0.466,5,2 -1333,1,13,13113,2,46,1,1,4,0.37,4,2 -1596,1,13,13124,1,48,4,2,3,2.924,4,2 -717,1,8,8110,2,44,4,2,3,0.773,3,2 -1585,1,13,13123,2,21,2,2,4,0.597,1,2 -18,1,15,15101,2,24,3,2,3,1.343,1,3 -510,1,7,7101,2,57,3,2,3,2.055,5,2 -1562,1,13,13122,2,37,3,2,2,0.13,3,2 -203,1,4,4302,1,58,3,2,3,0.825,5,1 -1839,1,13,13501,1,59,4,1,3,0.327,5,2 -1086,1,12,12101,1,24,3,9,4,0.407,1,2 -1052,1,10,10301,2,45,4,1,3,1.348,4,3 -1612,1,13,13125,2,53,3,1,4,0.272,4,2 -1210,1,13,13106,2,31,3,1,2,1.642,2,1 -1104,1,13,13101,2,21,2,1,4,1.251,1,2 -1001,1,10,10101,2,43,5,2,2,0.563,3,2 -1148,1,13,13103,2,52,4,2,3,0.201,4,2 -1275,1,13,13110,2,47,3,1,4,1.116,4,1 -1622,1,13,13125,2,29,3,1,4,2.766,2,2 -947,1,14,14101,2,45,4,1,2,0.018,4,9 -1823,1,13,13402,1,50,4,1,1,1.242,4,2 -1219,1,13,13107,1,30,3,1,4,0.05,2,3 -515,1,7,7101,2,43,4,2,2,0.387,3,1 -1856,1,13,13602,1,41,4,1,3,1.309,3,2 -1228,1,13,13108,2,80,5,1,3,0.427,5,2 -1791,1,13,13401,1,38,2,9,4,2.173,3,1 -708,1,8,8110,2,48,3,2,4,0.407,4,2 -1521,1,13,13120,1,37,1,2,4,0.903,3,3 -330,1,5,5201,1,70,3,1,2,0.203,5,3 -68,1,2,2101,1,31,3,2,4,0.437,2,1 -1069,1,11,11202,2,64,2,2,3,0.708,5,1 -77,1,2,2101,1,49,2,2,3,0.779,4,1 -966,2,14,14103,2,46,4,2,3,0.851,4,1 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source 371 - target 372 - ] - edge [ - source 371 - target 374 - ] - edge [ - source 371 - target 382 - ] - edge [ - source 371 - target 385 - ] - edge [ - source 372 - target 374 - ] - edge [ - source 372 - target 382 - ] - edge [ - source 372 - target 385 - ] - edge [ - source 374 - target 382 - ] - edge [ - source 374 - target 385 - ] - edge [ - source 378 - target 389 - ] - edge [ - source 379 - target 380 - ] - edge [ - source 379 - target 384 - ] - edge [ - source 380 - target 384 - ] - edge [ - source 383 - target 389 - ] - edge [ - source 363 - target 393 - ] - edge [ - source 363 - target 394 - ] - edge [ - source 363 - target 401 - ] - edge [ - source 363 - target 402 - ] - edge [ - source 367 - target 390 - ] - edge [ - source 367 - target 397 - ] - edge [ - source 368 - target 394 - ] - edge [ - source 368 - target 404 - ] - edge [ - source 370 - target 393 - ] - edge [ - source 370 - target 401 - ] - edge [ - source 371 - target 393 - ] - edge [ - source 371 - target 394 - ] - edge [ - source 371 - target 401 - ] - edge [ - source 371 - target 404 - ] - edge [ - source 372 - target 394 - ] - edge [ - source 372 - target 404 - ] - edge [ - source 373 - target 390 - ] - edge [ - source 373 - target 391 - ] - edge [ - source 374 - target 394 - ] - edge [ - source 374 - target 401 - ] - edge [ - source 374 - target 404 - ] - edge [ - source 374 - target 405 - ] - edge [ - source 375 - target 390 - ] - edge [ - source 377 - target 400 - ] - edge [ - source 379 - target 400 - ] - edge [ - source 382 - target 401 - ] - edge [ - source 382 - target 405 - ] - edge [ - source 383 - target 396 - ] - edge [ - source 383 - target 397 - ] - edge [ - source 385 - target 394 - ] - edge [ - source 385 - target 402 - ] - edge [ - source 385 - target 404 - ] - edge [ - source 388 - target 392 - ] - edge [ - source 390 - target 391 - ] - edge [ - source 391 - target 399 - ] - edge [ - source 391 - target 403 - ] - edge [ - source 396 - target 397 - ] -] diff --git a/_R/data/sweden1993.csv b/_R/data/sweden1993.csv deleted file mode 100755 index a035e717..00000000 --- a/_R/data/sweden1993.csv +++ /dev/null @@ -1 +0,0 @@ -age,Nf,Lf,f 0,293395,497487, 5,248369,497138, 10,240012,496901, 15,261346,496531,0.012 20,285209,495902,0.0908 25,314388,495168,0.1499 30,281290,494213,0.1125 35,286923,492760,0.0441 40,304108,490447,0.0074 45,324946,486613,0.0003 50,247613,480665, 55,211351,471786, 60,215140,457852, 65,221764,436153, 70,223506,402775, 75,183654,350358, 80,141990,271512, 85,112424,291707, \ No newline at end of file diff --git a/_R/data/us1991.csv b/_R/data/us1991.csv deleted file mode 100755 index be108a68..00000000 --- a/_R/data/us1991.csv +++ /dev/null @@ -1 +0,0 @@ -age,c,L,fm 0,0.0726,495804, 5,0.0689,495002, 10,0.0667,494603, 15,0.0648,493804,0.0007 20,0.0729,492552,0.0303 25,0.0799,491138,0.0566 30,0.0861,489356,0.0578 35,0.0801,486941,0.0388 40,0.0735,483577,0.0157 45,0.0556,478475,0.0027 50,0.0464,470374,0.0001 55,0.0421,457712, 60,0.0436,438502, 65,0.0429,410756, 70,0.0365,371990, 75,0.0294,319192, 80,0.0203,249203, 85,0.0176,273044, \ No newline at end of file diff --git a/_R/drafts/2014-01-27-samplestrat.Rmd b/_R/drafts/2014-01-27-samplestrat.Rmd deleted file mode 100755 index fe28a227..00000000 --- a/_R/drafts/2014-01-27-samplestrat.Rmd +++ /dev/null @@ -1,116 +0,0 @@ ---- -layout: post -title: "Functions for sample size and error" -description: "" -category: survey -tags: [sampling, home] ---- - - -Here two handy functions in R to compute sample sizes and errors for a proportion taking into account design effect (deff), response rate (rr), and finite population correction (fpc). Just there for those times when one needs to do these calculations quickly. - -[Here](https://gist.github.com/sdaza/7896840) you can see my code. - -```{r, echo=FALSE} -options(replace.assign=TRUE, tidy=TRUE, width=110, scipen = 4, digits = 3) -source("/Users/sdaza/Documents/Workplace/Functions/MyFunctions.R") -``` - - -The inputs of these functions are: - -- **n** = sample size -- **e** = sampling error -- **deff** = design effect, by default 1 (SRS) -- **rr** = response rate, by default 1 -- **N** = population size, by default NULL (infinite population) -- **cl** = confidence level , by default .95 -- **p** = proportion, by default 0.5 (maximum variance of a proportion) -- **relative** = to estimate relative error, by default FALSE - -## serr: sampling error - -An example for n = 400 and all the inputs at their default values: - -```{r} -serr(400) -``` - -The output is rounded to 4 decimals. A more complete example: - -- **n** = 400 -- **deff** = 1.5 -- **response rate** = 80% -- **population size** = 1000 - -```{r} -serr(n=400, deff=1.5, rr=.8, N=1000) -``` - -The sample size (n) has to be always lower than the population (N). It is important to note that the final sample size to compute the sampling error comes from: $n = \frac{N}{deff} * rr$. - -```{r} -serr(n=400, N=350) -``` - -## ssize: sample size - -Let's get a sample size with an error of .03, a population of 1000 elements, a response rate of 0.80, and an effect design of 1.2: - -```{r} -ssize(e=.03, deff=1.2, rr=.8, N=1000) -``` - -If the the sample size is bigger than the population because of low response rates or big design effects, the sample size will be fixed to N: - -```{r} -ssize(e=.03, deff=5, rr=.6, N=1000) -``` - -## Working with strata - -Finally, we can estimate different sample sizes by strata using vectors or a data frame: - -```{r} -# example sampling frame (4 strata) -frame <- data.frame( - strata = 1:4, - N =c(10000, 5000, 2000, 1000), - deff =c(1.1, 1, 1.3, .8), - rr = c(.8, .9, .85,.8), - p = c(.3, .6, .1, .2)) -``` - -```{r, echo=FALSE} -frame -``` - -```{r} -frame$n1 <- ssize(e=.02, deff=frame$deff, rr=frame$rr, N=frame$N, p=frame$p) -frame$e1 <- serr(n=frame$n1, deff=frame$deff, rr=frame$rr, N=frame$N, p=frame$p) -``` - -```{r, echo=FALSE} -frame -``` - - -## astrata: allocation of stratified sample - -**astrata** is a function to define different allocations of a given total sample size across strata. So far, I have specified three options: - -- Allocation by error: the sample size by strata is defined based on a given sampling error -- Square-root allocation: $\frac{sqrt(N)}{sum(sqrt(N)} -- Mixed: a combination between a proportional and equal-size allocation, where wp=1 means proportional, and wp=0 equal-size. - - -## serrst: sampling error with strata - -The function **serrst** computes the sampling error of a stratified sample. We only have to define the sample of each stratum. Optionally we can specify the distribution of strata (N could be a number or proportion), and the proportion of the variable of interest (by default p=.05). Based on the example used above: - -```{r} -serrst(n=frame$n1, N=frame$N, p=frame$p) -``` - - - diff --git a/_R/drafts/2015-10-20-ABM-thoughts.Rmd b/_R/drafts/2015-10-20-ABM-thoughts.Rmd deleted file mode 100644 index afa78d65..00000000 --- a/_R/drafts/2015-10-20-ABM-thoughts.Rmd +++ /dev/null @@ -1,115 +0,0 @@ ---- -layout: post -title: "We need more stupid models... oh, right!" -description: "" -category: "agent-based modeling" -tags: [modeling, ABM, home] ---- - - -A good way to learn the *nitty gritty* of agent-based modeling (ABM) is to *reproduce* re-implement a by others. As we start *replicating*, we quickly realize *things* could be done in many different ways. Theory always has to be our guideline, but theories are full of ambiguities, especially in social sciences. So, most of the time, we are going to have a lot of degrees of freedom to create our our models. How these **modeling idiosyncrasies** affect our results and conclusions is key to assess the quality and credibility of our computational models. For good reviews about replication and understanding agent-based models see XX. - -In this post I implement the model proposed by Birks et al. (2012, 2014) to explain three crime regularities: spatial concentration, repeated victimization, and journey to crime curve. I didn't intend to replicate *exactly* this model; indeed, I introduce several changes. However, I think this is still a nice exercise on learning form ABM and avoid make fool ourselves. The key message: we need to spend a reasonable time of our modeling process trying to understand what the model is telling us and checking how changes in the way we model affect our results. - -# Agent-based models, and some of their uses - -robustness checks, to finally discuss what we learn from models like this. - -Otherwise, the common critics to ABM such as XXX, are reasonable and get support. - -First, I describe the original model, then I define my version of the model, and then I discusses the similarities and differences between both. - -Both papers show experiments combining these mechanisms (eight combinations in total). I show the same kind of experiments but using *my version* of the model. Then, I discuss the insights can get from ABMs, but also their limitations and misuses. Let's start. - -## Good quotations - -"The behavior of a simulation is not understandable by simple inspection, on the contrary, efforts towards the results of a simulation must be expendend, since there is no guarantee that what goes on in it is going to be obvious" - -(Di Paolo, Noble, and Bullock 2000) - -Which particular mechanisms in the simulation caused the interesting patterns and reconceptualize it to se what insight this causality may give us into the real-world system. - -In most cases the real value of a simulation study depends on a subjective answer to the question: was the model useful? the answer is "yes" if the model told us something new, challenged some deeply entrenched assumptions, showed surprising implications of a conceptual model, suggested a new way of approaching a research question or highlighted gaps in our knowledge. - -"Although information about the relationship of the set-up of a simulation and its global outcomes can be useful, this fall short of a full scientific understanding, which must explain *how* there are connected. - -## Model and Implementation - -- Transportation network: I use a different approach -- Number of routine activities: 5 -- Radius of offender activity space: 25 -- Learning rate logistic function (4) -- Return home probability 0.8 -- World size 100 x 100 -- Potential offenders 25 -- Number of potential targets: 2500 -- Number of transport nodes: 1000 -- Model end condition: 1000 crimes -- Motivation offenders: 0.10 - -## My implementation - -- Transportation network: grid - radial design based on XX -- Movement: shortest path (weighted and non-weighted) 50% -- Same number of routine activities and prob return home -- Similar number of other parameter but not exactly the same - -## Comparison - -- Main differences of my results -- Rational-choice processes seem more important -- All the rest it is kind of obvious - -Some differences so far, possible explanations - -### Gini - -- In my non-neighbor models, crime-patterns mode doesn't generate high gini coefficients, why? Awareness is being set so that nodes don't have higher scores. -- So my conclusion is that how agents move is essential regarding the results obtained - -### Skewness - -- It is hard to compare measures, I don't get similar results. -- How the transportation network affects the results: the network has boundaries (it is not a torus) -- As expected: routine activities have the most important impact. Similar substantial conclusion, different magnitud of the metric. - -### NNI - -- This index is very related to the Gini coefficient or repeated victimization (in my case even more). The magnitude of my scores is very different from what Birks gets, although, I don't get the high reduction due to crime-patterns. This is due to, again, the restrictions I have imposed to the definition of awareness. -- The point here is that an important relationship between how agents move and repeated and spatial concentration of crime exists. In the second case, drivers are responding to each others' behavior and influencing each other's behavior. - -## Some notes that can be useful: - -- Schelling: There are easy cases, of course, in which the aggregate is merely an extrapolation from the individual. If we know that every driver, on his own, turns his lights on at sundown, we can guess that from our helicopter we shall see all the car lights in a local area going on at about the same time. But if most people turn their lights on when some fraction of the oncoming cars already have their lights on, we'll get a different picture form our helicopter. -## Do we really need an ABM for this? - -2. Regularities observed across offense types, localities and contexts. - - Offending is not distributed uniformly in space or time - - A small number of people, paces and targets experience the majority of the victimization - - The distances offenders travel are typically short and exhibit the characteristics of a distance decay function - -3. ABM: the aim is to assess whether a particular hypothesized decision calculus is sufficient to provide a viable explanation for a specific known regularity of crime: not a replace of empirical research, theoretical prototyping, not a definitive causal explanation, but allows to eliminate those hypothesized mechanisms that are insufficient! -2. They argue that ABM can be used as a means to operationalize and test hypothetical mechanisms that offer a potential explanation for observed criminological phenomena. -3. ABM of residential burglary: potential offenders and potential targets that behaves according to what the environmental criminology proposes: - - Impact of this mechanisms on patterns of offending - + Spatial concentration of crime (nearest neighborhood index) - + Repeat victimization (Gini coefficient) - + Journey to crime curve (JTC skewness): Euclidean distance between offense location and offender home, distribution of these distances (the distances offenders travel to offend are typically short and exhibit the characteristics of a distance decay function) - - Mechanisms - + Routine activity approach - + Rational choice - + - - -## References - -Birks, Daniel, Michael Townsley, and Anna Stewart. 2012. “Generative -Explanations of Crime: Using Simulation to Test Criminological Theory.” -*Criminology* 50 (1): 221–54. There is a new version of this paper - -Birks, Daniel, Michael Townsley, and Anna Stewart. 2014. “Emergent -Regularities of Interpersonal Victimization An Agent-Based Investigation.” -*Journal of Research in Crime and Delinquency* 51 (1): 119–40. - - -**Last Update: `r format(Sys.time(), '%m/%d/%Y')`** diff --git a/_R/drafts/2016-01-20-logit.Rmd b/_R/drafts/2016-01-20-logit.Rmd deleted file mode 100644 index 531ca5cb..00000000 --- a/_R/drafts/2016-01-20-logit.Rmd +++ /dev/null @@ -1,174 +0,0 @@ ---- -layout: post -title: "Comparing logit/probit coefficients across nested models" -description: "" -category: "regression" -tags: [modeling, R, home, monte-carlo] ---- - - -When we look a table with nested logistic or probit models, we have the temptation to compare coefficients directly. We know, however, that that could be misleading: unlike OLS, changes in coefficients in logit/probit models cannot be attributed directly to the inclusion of confounders. Why? In a nutshell, the variance of the underlying latent variable is not identified and will differ between models with different covariates. - -In this post I show how coefficients change when including predictors in logit models using Monte Carlo simulations. I also compare different solutions available in the *market*, following the analyses discussed by Karlson, Holm, and Breen (khb). - -First, I define a latent logistic model to simulate data: - -$$ -y^* = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + u \\ -y = 1 \text{ if } y^* > percentile(y^*), 0 \text{ otherwise} -$$ - -$\beta_0$ is the intercept. $x_1$ and $x_2$ can be normal distributed or binary. $u$, the error term, has a standard logistic distribution. When $y^*$ is higher than a certain percentile, it becomes 1, it is 0 otherwise. At the botton of this post, I show a function to easily run different scenarios and compare solutions. We will need the R packages `khb`, `mfx`, `MASS`, `ggplot2` and `data.table`. - -The function allows different cut-points for the dependent variable $y^*$, different distributions for $x_1$ and $x_2$ (normal or binary) and correlation between them. The function compares five solutions: naive (just the traditional logit coefficients), $y$ standardization, the khb, average partial effects (ape), and linear probability model. - -I compute ratios $\frac{\beta_1^{\text{ full}}}{\beta_1^{\text{ restricted}}}$, that is, the $x_1$ coefficient when **controlling** for $x_2$ over the $x_1$ coefficient **without controlling** for $x_2$. I worried only about estimates, not their uncertainty (standard errors). - - - -```{r, echo = FALSE, warning = FALSE, message = FALSE} -library(sdazar) -library(ggplot2) -# library(texreg) -# install.packages("/Users/sdaza/Downloads/08ZIP/khb_0.1.tar.gz",repos = NULL, type="source") -# library(arm) -library(khb) -library(MASS) -library(mfx) -``` - -

First scenario

- - - -```{r, echo = FALSE, warning = FALSE, message = FALSE} -sim <- function(b0 = 1, b1 = 0.5, b2 = 1, n = 1000, nsim = 1, cutpoint = 0.5, corx = 0.5, - x1 = "normal", prop1 = 0.5, x2 = "normal", prop2 = 0.5) { - -# function for plot -data_summary <- function(x) { - m <- mean(x) - ymin <- m - 1 *sd(x) - ymax <- m + 1* sd(x) - return(c( y = m ,ymin = ymin,ymax = ymax)) -} - -# set vector for save results -out <- NULL -mcor <- rep(NA, nsim) - -for (i in 1:nsim) { - -# matrix to save results -ratio <- matrix(NA, 1, 6) -mnames <- c("latent", "naive", "y std", "khb", "marginal", "lpm") -colnames(ratio) <- mnames - -# create predictors -mu <- rep(0, 2) -sigma <- matrix(corx, nrow = 2, ncol = 2) -diag(sigma) <- 1 - -dat <- data.table(MASS::mvrnorm(n = n, mu = mu, Sigma = sigma)) -setnames(dat, c("x1", "x2")) - -# dichotomize predictors if *binary* -if ( x1 == "binary") { -dat$x1 <- qbinom(pnorm(dat$x1), 1, prop1) -} - -if ( x2 == "binary" ) { -dat$x2 <- qbinom(pnorm(dat$x2), 1, prop2) -} - - -# latent logistic model -dat[, y := b0 + b1 * x1 + b2 * x2 + rlogis(n, 0, 1)] -dat[, yb := ifelse(y > quantile(y, cutpoint), 1, 0)] # definition of cutpoint - -# estimate ratios by method - -# latent -m1a <- lm(y ~ x1, data = dat) -m1b <- lm(y ~ x1 + x2, data = dat) -ratio[1, 1] <- m1b$coeff[2]/m1a$coeff[2] - -# naive -m2a <- glm( yb ~ x1, data = dat, family = "binomial") -m2b <- glm( yb ~ x1 + x2, data = dat, family = "binomial") -ratio[1, 2] <- m2b$coeff[2]/m2a$coeff[2] - -# y-standardization -ratio[1, 3] <- khb::ystandcoef(m2b)[2]/khb::ystandcoef(m2a)[2] - -# khb -ratio[1, 4] <- 1 / suppressMessages(khb::khb(m2a, m2b)$sum_conf$Ratio) - -# marginal -m3a <- mfx::logitmfx( yb ~ x1, data = dat, atmean = FALSE) -m3b <- mfx::logitmfx( yb ~ x1 + x2, data = dat, atmean = FALSE) -ratio[1, 5] <- m3b$mfxest[1]/m3a$mfxest[1] - -# linear probability model -m4a <- lm(yb ~ x1, data = dat) -m4b <- lm(yb ~ x1 + x2, data = dat) -ratio[1, 6] <- m4b$coeff[2]/m4a$coeff[2] - -# create outputs -out <- rbind(out, ratio) -mout <- apply(out, 2, mean) - -} - -# create plot (only if the number simulations is higher than 10) -if ( nsim > 10 ) { - -# change format -mdat <- suppressWarnings(melt(data.table(out))) -mdat[, variable := factor(variable, levels = mnames)] -means <- mdat[, .(mvalue = mean(value)), by = variable] - -# ggplot -mplot <- ggplot(mdat, aes(variable, value)) + - geom_violin(trim = TRUE) + - stat_summary(fun.data = data_summary, color = "red", alpha = 0.5) + - annotate("text", x = means$variable , y = means$mvalue + .02, label = round(means$mvalue, 4), col = "blue", size = 2.7) + - theme_bw() + - labs( y = "Ratio", x = " ", title = "Ratio Partial / Gross Effect", subtitle = paste0("N = ", n, ", Simulations = ", nsim)) - -# return list with three objects -return(list(out, mout, mplot)) - -} - -else { return(out) } - - -} -``` - -```{r, eval = FALSE} - -sim(corx = 0, cutpoint = 0.5, x1 = "normal", x2 = "normal", n = 1000, nsim = 100) - -sim(corx = 0, cutpoint = 0.8, x1 = "normal", x2 = "normal", n = 1000, nsim = 100) - -sim(corx = 0.5, cutpoint = 0.9, x1 = "normal", x2 = "normal", n = 1000, nsim = 100) - -sim(corx = 0.5, cutpoint = 0.9, x1 = "binary", prop1 = 0.5, x2 = "binary", prop2 = 0.5, n = 1000, nsim = 100) - -``` - - -**Last Update: `r format(Sys.time(), '%m/%d/%Y')`** - ------ - -### References - -Karlson, Kristian Bernt, Anders Holm, and Richard Breen. 2012. "Comparing -Regression Coefficients Between Same-Sample Nested Models Using Logit and -Probit A New Method". *Sociological Methodology* 42 (1):286-313. - - -### Function diff --git a/_R/drafts/2017-05-18-predictions.Rmd b/_R/drafts/2017-05-18-predictions.Rmd deleted file mode 100644 index fcc2e630..00000000 --- a/_R/drafts/2017-05-18-predictions.Rmd +++ /dev/null @@ -1,13 +0,0 @@ ---- -layout: post -title: "Explanations vs Predictions" -description: "" -category: "" -tags: [] ---- - - -Two tradition or cultures that seems to be reconciling nowdays. Still I have several questions I would like to discuss briefly here. - - -**Last Update: `r format(Sys.time(), '%m/%d/%Y')`** diff --git a/_R/drafts/2019-11-01-overleaf-dissertation.md b/_R/drafts/2019-11-01-overleaf-dissertation.md deleted file mode 100644 index 879e674e..00000000 --- a/_R/drafts/2019-11-01-overleaf-dissertation.md +++ /dev/null @@ -1,44 +0,0 @@ ---- -layout: post -title: "Overleaf = Dissertation + Papers" -author: Sebastian Daza -comments: true -date: 2019-11-01 ---- - - -It's been a while since my last post. It's time to catch up. I am working on my dissertation and using a handy workflow where you can both writing independent papers and chapters of your thesis using Overleaf and Github. The advantage of this workflow is: - -- Keep your in you dissertation or book repository all the files necessary to run analysis -- Keep independent papers for each chapter, almost ready for reviewing and publishing -- Allow collaboration and reviews for each chapter via Overleaf -The set up a simple: - -- I use [Overleaf](/www.overleaf.com) to write my papers and dissertation. Each chapter or article is an independent project. -- The structure of files would be something like: - -``` -project -│ dissertation.tex -│ ref.bib -│ -└───ch01 -│ │ manuscript.tex -│ │ ch01.bib -│ │ -│ └───tables -│ │ table01.tex -│ │ table02.tex -│ │ ... -│ -└───ch02 -| │ manuscript.tex -| │ ch02.bib -| | -│ └───tables -│ │ table01.tex -│ │ table02.tex -│ │ ... -``` - -- `dissertation.tex` is the main manuscript. In order diff --git a/_R/drafts/2020--03-01-non-academic-jobs.md b/_R/drafts/2020--03-01-non-academic-jobs.md deleted file mode 100644 index cc80ac81..00000000 --- a/_R/drafts/2020--03-01-non-academic-jobs.md +++ /dev/null @@ -1,44 +0,0 @@ ---- -layout: post -title: "Non-academic jobs: A must for academics!" -author: Sebastian Daza -comments: true -date: 2019-03-01 ---- - - -It's been a while since my last post. It's time to catch up. I am working on my dissertation and using a handy workflow where you can both writing independent papers and chapters of your thesis using Overleaf and Github. The advantage of this workflow is: - -- Keep your in you dissertation or book repository all the files necessary to run analysis -- Keep independent papers for each chapter, almost ready for reviewing and publishing -- Allow collaboration and reviews for each chapter via Overleaf -The set up a simple: - -- I use [Overleaf](/www.overleaf.com) to write my papers and dissertation. Each chapter or article is an independent project. -- The structure of files would be something like: - -``` -project -│ dissertation.tex -│ ref.bib -│ -└───ch01 -│ │ manuscript.tex -│ │ ch01.bib -│ │ -│ └───tables -│ │ table01.tex -│ │ table02.tex -│ │ ... -│ -└───ch02 -| │ manuscript.tex -| │ ch02.bib -| | -│ └───tables -│ │ table01.tex -│ │ table02.tex -│ │ ... -``` - -- `dissertation.tex` is the main manuscript. In order diff --git a/_bibliography/reentry.bib b/_bibliography/reentry.bib deleted file mode 100644 index 64d1b65c..00000000 --- a/_bibliography/reentry.bib +++ /dev/null @@ -1,71 +0,0 @@ -@report{daza2018, - title = {Evaluaci\'on de Sesgo en L\'inea de Base}, - author = {Daza, Sebastian and Larroulet, Pilar}, - date = {2018}, - series = {Estudio Reinserci\'on, Desistimiento y Reincidencia en Mujeres Privadas de Libertad en Chile}, - location = {Chile}, - bibtex_show = {true}, - code = {https://github.com/sdaza/reentry-chile/blob/master/reports/bias_baseline/bias_baseline.ipynb}, - pdf = {reentry_bias_baseline.pdf} -} - -@report{daza2019, - title = {Integraci\'on Social}, - author = {Daza, Sebastian and Larroulet, Pilar and Villar, Paloma Del and Droppelmann, Catalina and Figueroa, Ana}, - date = {2019}, - series = {Estudio Reinserci\'on, Desistimiento y Reincidencia en Mujeres Privadas de Libertad en Chile}, - location = {Chile}, - bibtex_show = {true}, - code = {https://github.com/sdaza/reentry-social-integration}, - langid = {spanish}, - pdf = {reentry_social_integration.pdf}, - file = {/Users/sdaza/Zotero/storage/T7296U6J/daza_et_al_2019_integracion_social.pdf} -} - -@report{larroulet2019a, - title = {Dise\~no, Metodolog\'ia y Caracterizaci\'on de la Muestra}, - author = {Larroulet, Pilar and Daza, Sebasti\'an and Del Villar, Paloma and Droppelmann, Catalina and Figueroa, Ana and Valenzuela, Eduardo}, - date = {2019}, - series = {Estudio Reinserci\'on, Desistimiento y Reincidencia en Mujeres Privadas de Libertad en Chile}, - location = {Chile}, - bibtex_show = {true}, - pdf = {reentry_presentation.pdf} -} - -@article{larroulet2020, - title = {Who is transitioning out of prison? Characterising female offenders and their needs in Chile}, - author = {Larroulet, Pilar and Droppelmann, Catalina and Del Villar, Paloma and Daza, Sebastian and Figueroa, Ana and Osorio, Victoria}, - date = {2020}, - journaltitle = {International Journal of Crime, Justice and Social Democracy}, - abbr = {ICJSD}, - abstract = {The last decades' increase in female incarceration has translated into an increasing number of women being released from prison. Understanding their characteristics and criminal trajectories can enlighten us regarding the different needs of women upon re-entering society after incarceration. Drawing on data from the Reinserci\'on, Desistimiento y Reincidencia en Mujeres Privadas de Libertad en Chile study, this article identifies different profiles among a cohort of 225 women who were released from prison in Santiago, Chile, and demonstrates that significant heterogeneity exists among them in terms of their criminal trajectories and the intervention needs to support their transition out of prison.}, - bibtex_show = {true}, - html = {https://www.crimejusticejournal.com/article/view/1467}, - pdf = {who_is_transitioning_out_of_prison.pdf}, - file = {/Users/sdaza/Zotero/storage/PQHNIH4B/Larroulet et al. - 2020 - Who is Transitioning out of Prison Characterising.pdf} -} - -@report{larroulet2021, - title = {Reinserci\'on, Desistimiento y Reincidencia en Mujeres Privadas de Libertad en Chile}, - author = {Larroulet, Pilar and Daza, Sebasti\'an and Del Villar, Paloma and Droppelmann, Catalina and Figueroa, Ana}, - date = {2021}, - series = {Estudio Reinserci\'on, Desistimiento y Reincidencia en Mujeres Privadas de Libertad en Chile}, - institution = {Centro de Estudios Justicia y Sociedad del Instituto de Sociologia, Pontificia Universidad Cat\'olica de Chile}, - location = {Chile}, - bibtex_show = {true}, - pdf = {final-report-reentry.pdf}, - file = {/Users/sdaza/Zotero/storage/TSC4JU5U/larroulet_et_al_2021_reinserción,_desistimientoy_reincidencia_en_mujeres_privadas_de_libertad_en.pdf} -} - -@article{larroulet2023a, - title = {From Prison to Work: Job Patterns for women in Chile}, - author = {Larroulet, Pilar and Daza, Sebastian and Borquez, Ignacio}, - date = {2023}, - shortjournal = {Social Science Research}, - abbr = {SSR}, - abstract = {Finding and keeping a job is one of the key challenges individuals face as they move back into society. Research suggests that former inmates access mostly jobs on the margins of the labor market, however there is limited research on the dynamics of post-prison employment that account for differences in types of jobs. Using data from the study ``Reintegration, desistance and recidivism among female inmates in Chile'' (RDFC), our paper describes patterns of employment among a cohort of 207 women released from prison in Santiago, Chile and followed during the first year after release. We use sequence analysis to describe monthly patterns of employment considering different types of work (i.e., self-employed / employed, legitimate / under-the-table). To better account for the complex relationship between work and crime, we include offending as another type of income-generating activity. Finally, we use cluster analysis to explore which individual characteristics are associated with the distinct patterns of employment and offending. Our results show a significant level of heterogeneity in employment trajectories by job type, and the importance of considering work and offending to obtain a more complete picture of the dynamics of employment during reentry.}, - bibtex_show = {true}, - code = {https://github.com/sdaza/reentry-work-lifecourse}, - pdf = {reentry-job-trajectories.pdf}, - preprint = {https://osf.io/preprints/socarxiv/k5zyd/} -} diff --git a/_bibliography/sdaza.bib b/_bibliography/sdaza.bib deleted file mode 100644 index c4f07e1a..00000000 --- a/_bibliography/sdaza.bib +++ /dev/null @@ -1,245 +0,0 @@ -@inproceedings{daza2016, - title = {Consequences of {{Childbearing}} in {{Delinquency}} and {{Substance Use}}}, - author = {Daza, Sebastian and Fletcher, Jason}, - date = {2016}, - location = {{Washington, DC}}, - abstract = {This paper uses the National Longitudinal Study of Adolescent to Adult Health (Add Health) and recent empirical strategies to examine the dynamic consequences of parenthood on delinquency and substance use. We take advantage of the miscarriage information available to form comparison groups. Our contribution is to extend the analysis of the effects of childbearing to deviant outcomes such as delinquency and substance use, and explore the differences between motherhood and fatherhood. Our preliminary results suggest reductions in risky behaviors from childbearing. We do not find evidence of heterogeneity across socioeconomic status.}, - bibtex_show = {true}, - conference = {true}, - eventtitle = {Population {{Association}} of {{America}} ({{PAA}})}, - pdf = {childbearing-crime-2016.pdf} -} - -@report{daza2018, - title = {Evaluación de {{Sesgo}} En {{Línea}} de {{Base}}}, - author = {Daza, Sebastian and Larroulet, Pilar}, - date = {2018}, - location = {{Chile}}, - bibtex_show = {true}, - code = {https://github.com/sdaza/reentry-chile/blob/master/reports/bias_baseline/bias_baseline.ipynb}, - pdf = {reentry_bias_baseline.pdf}, - study = {Estudio Reinserciόn, Desistimiento y Reincidencia en Mujeres} -} - -@inproceedings{daza2018a, - title = {Income {{Inequality}}, {{Social Mobility}} and {{Mortality}} in the {{US}}}, - author = {Daza, Sebastian and Palloni, Alberto}, - date = {2018}, - location = {{Chicago, IL}}, - abstract = {We assess the magnitude of the association between intergenerational income mobility and US adult mortality by gender, age group, race/ethnicity and the causes of death. We use a data set from The Health Inequality Project and CDC mortality data at the county level. We find that under different model specifications the association between income mobility and adult mortality is strong, properly signed, and consistent with our hypotheses. If the association we find reflects a causal effect it would translate into shifts in life expectancy at age 40 of as much as 2.0-4.8 years among males and 0.1-2.0 among females, equivalent to 5.1-12.5 and 0.2-4.7 percent of the U.S. male and female life expectancy at age 40 respectively. On average, these effects are 1.5 to 2.5 times as large as those of income inequality and represent between 40 (males) and 25 (females) percent of the magnitude of an income shift from the lowest to the highest quartile of the U.S. income distribution.}, - bibtex_show = {true}, - code = {https://github.com/sdaza/dissertation/tree/master/ch02}, - conference = {true}, - eventtitle = {Population {{Association}} of {{America}} ({{PAA}})}, - preprint = {https://osf.io/preprints/socarxiv/gdz2a} -} - -@article{daza2019, - title = {Agent-{{Based Models}} for {{Assessing Complex Statistical Models}}: {{An Example Evaluating Selection}} and {{Social Influence Estimates}} from {{SIENA}}}, - shorttitle = {Agent-{{Based Models}} for {{Assessing Complex Statistical Models}}}, - author = {Daza, Sebastian and Kreuger, L. Kurt}, - date = {2019}, - journaltitle = {Sociological Methods and Research}, - shortjournal = {SMR}, - issn = {0049-1241}, - doi = {10.1177/0049124119826147}, - url = {https://doi.org/10.1177/0049124119826147}, - urldate = {2019-04-24}, - abbr = {SMR}, - abstract = {Although agent-based models (ABMs) have been increasingly accepted in social sciences as a valid tool to formalize theory, propose mechanisms able to recreate regularities, and guide empirical research, we are not aware of any research using ABMs to assess the robustness of our statistical methods. We argue that ABMs can be extremely helpful to assess models when the phenomena under study are complex. As an example, we create an ABM to evaluate the estimation of selection and influence effects by SIENA, a stochastic actor-oriented model proposed by Tom A. B. Snijders and colleagues. It is a prominent network analysis method that has gained popularity during the last 10 years and been applied to estimate selection and influence for a broad range of behaviors and traits such as substance use, delinquency, violence, health, and educational attainment. However, we know little about the conditions for which this method is reliable or the particular biases it might have. The results from our analysis show that selection and influence are estimated by SIENA asymmetrically and that, with very simple assumptions, we can generate data where selection estimates are highly sensitive to misspecification, suggesting caution when interpreting SIENA analyses.}, - bibtex_show = {true}, - code = {https://github.com/sdaza/isa-siena}, - html = {https://doi.org/10.1177/0049124119826147}, - langid = {english}, - preprint = {https://osf.io/preprints/socarxiv/98fp4} -} - -@report{daza2019a, - title = {Integración Social}, - author = {Daza, Sebastian and Larroulet, Pilar and Villar, Paloma Del and Droppelmann, Catalina and Figueroa, Ana}, - date = {2019}, - location = {{Chile}}, - bibtex_show = {true}, - code = {https://github.com/sdaza/reentry-social-integration}, - langid = {spanish}, - pdf = {reentry_social_integration.pdf}, - study = {Estudio Reinserciόn, Desistimiento y Reincidencia en Mujeres}, - file = {/Users/sdaza/Zotero/storage/J75ZVUDW/daza_et_al_2019_integracion_social.pdf} -} - -@article{daza2020, - title = {The {{Consequences}} of {{Incarceration}} for {{Mortality}} in the {{United States}}}, - author = {Daza, Sebastian and Palloni, Alberto and Jones, Jerrett}, - date = {2020}, - journaltitle = {Demography}, - shortjournal = {Demography}, - volume = {57}, - number = {2}, - pages = {577--598}, - doi = {10.1007/s13524-020-00869-5}, - abbr = {Demography}, - abstract = {Previous research suggests that incarceration has negative implications for individuals’ well-being, health, and mortality. Most of these studies, though, do not follow former prisoners over extended periods of time and into older adult ages when it is more likely that cumulative consequences of incarceration will be felt. This paper contributes to this literature by employing for the first time the Panel Study of Income Dynamics (PSID) to estimate the long run association between individual incarceration and mortality over nearly 40 years, and supplementing those analyses with the National Longitudinal Survey of Youth 1979 (NLSY79). We then use these estimates to investigate the implications of the US incarceration regime and the post-1980 incarceration boom for the US health and mortality disadvantage relative to industrialized peer countries (the United Kingdom).}, - bibtex_show = {true}, - code = {https://github.com/sdaza/mortality-incarceration-paper}, - html = {https://rdcu.be/b278V}, - preprint = {https://osf.io/preprints/socarxiv/b8xe6/}, - selected = {true} -} - -@unpublished{daza2021, - title = {Exploring the Link between Place’s Income Mobility and Mortality Using an Agent-Based Model}, - author = {Daza, Sebastian and Palloni, Alberto}, - date = {2021}, - abstract = {Some scholars have recently suggested that contextual income mobility –- defined as individuals’ ability to exceed their parents’ income within the place of residence — may play an essential role in explaining health disparities in the U.S. Previous research provides some evidence for the link between the rigidity of the stratification system and health. This paper proposes an agent-based model to formalize, explore, and describe the dynamic between a place’s income mobility and health. By combining empirical information and using an exploratory model, we first assess the population-level consequences of changes in income mobility effects and regime for health (life expectancy). We then examine under which residential mobility conditions, and data collection and modeling strategies we can retrieve the effect of income mobility on mortality.}, - code = {https://github.com/sdaza/income-mobility-mortality-abm} -} - -@unpublished{daza2022, - title = {Distinguishing between Interaction and Dispersion Effects in {{GxE}} Analysis: {{A}} Review of Strategies}, - shorttitle = {Distinguishing between Interaction and Dispersion Effects in {{GxE}} Analysis}, - author = {Daza, Sebastian and Palloni, Alberto}, - date = {2022}, - url = {https://osf.io/preprints/socarxiv/tes5x/}, - urldate = {2022-04-27}, - abstract = {The study of GxE is messy. Multiple and challenging obstacles must be overcome to reach a clean estimate, including confounding, selection, insufficient statistical power, model misspecification, measurement error, and difficulties in discriminating between quantitative and qualitative changes when using polygenic scores. In this paper, we focus on one specific challenge: the importance of distinguishing between interaction and dispersion effects in any analysis of gene-environment interactions. We first argue that a critical decision that researchers must make is choosing between indicators of genetic penetrance, slopes of G, or variance decomposition, h2 or correlation. Sec- ond, we use simulation to assess alternative methods to identify discrepancies between slopes and dispersion effects when studying GxE. Based on our results, we suggest a strategy that requires a precise definition of the research question regarding GxE, a strong theoretical justification of the model that specifies GxE, and the full use of simple visualizations and Bayesian distributional models to evaluate variations patterns in slopes and phenotypical variance.}, - altmetric = {10.1001/jamainternmed.2019.6532}, - bibtex_show = {true}, - code = {https://gitlab.com/csic-echo/gxe-dispersion}, - langid = {american}, - preprint = {https://osf.io/preprints/socarxiv/tes5x}, - status = {Under review}, - file = {/Users/sdaza/Zotero/storage/F6C8GYJB/daza_palloni_2022_distinguishing_between_interaction_and_dispersion_effects_in_gxe_analysis.pdf} -} - -@article{daza2022a, - title = {Early {{Exposure}} to {{County Income Mobility}} and {{Adult Individual Health}} in the {{United States}}}, - author = {Daza, Sebastian and Palloni, Alberto}, - date = {2022}, - journaltitle = {The Journals of Gerontology: Series B}, - shortjournal = {JGSS}, - volume = {77}, - pages = {S199--S208}, - doi = {10.1093/geronb/gbab240}, - url = {https://osf.io/preprints/socarxiv/5fdzs/}, - abbr = {JGSS}, - abstract = {Despite substantial research, drivers of the widening gap in life expectancy between rich and poor in the U.S. -- the so-called longevity gap -- remain unknown. Recent research has suggested that contextual income mobility (e.g., county-level socioeconomic mobility) may play an essential role in explaining the longevity gap. Previous studies -- based mostly on aggregate and cross-sectional individual data -- show an association between county income mobility and county mortality and individual's health. However, inferring individual effects from aggregate (county-level) data can be problematic (i.e., ecological fallacy), and measuring exposure to income mobility using the county where respondents currently live or die, might overlook the selection process associated with residential mobility. This paper aims to extend previous research by estimating the effect of average exposure to mobility regimes during childhood and adolescence on adult health using longitudinal data and accounting for selection into counties over time (i.e., residential mobility). We use both the National Longitudinal Survey of Youth 1997 (NLSY97) and the Panel Study of Income Dynamics (PSID) with geocoded data to assess the link between county-level income mobility (Chetty's estimates), behaviors (smoking) and health conditions and status (self-reported health, BMI, depressive symptoms). Furthermore, we use cohorts optimally match Chetty's estimates of income mobility in the U.S. (1980-1982) and account for selection and time-varying confounders using marginal structural models (MSM). Overall, we provide a more precise test of the hypothesis that childhood exposure to income mobility regimes may determine health status through behavior (i.e., smoking) later in life and contribute to longevity gaps.}, - bibtex_show = {true}, - code = {https://github.com/sdaza/dissertation/tree/master/ch03}, - html = {https://academic.oup.com/psychsocgerontology/advance-article/doi/10.1093/geronb/gbab240/6519680?guestAccessKey=42f4029e-6430-4752-8f31-188b10a75141}, - issue = {Supplement\_2}, - preprint = {https://osf.io/preprints/socarxiv/5fdzs}, - selected = {true} -} - -@unpublished{daza2022b, - title = {Consequences of Heritability, Assortative Mating and Fertility Differentials for the Obesity Pandemic}, - author = {Daza, Sebastian and Palloni, Alberto and Ruiz, Sebastian and Povedano, Elena}, - date = {2022}, - abstract = {Although genetic heritability, the environment shared by family members (sociocultural heritability), assortative mating, and fertility differentials by body mass index (BMI) have been proposed as relevant factors that could sustain the obesity pandemic, their theoretical and empirical impact remain unclear. This paper proposes a formal model to assess how robust and significant are the influences of these factors at the population level. We explore under which conditions assortative mating and fertility differentials can significantly impact obesity trends and, as a result, relevant demographic outcomes such as the prevalence of type 2 diabetes (T2B), cardiovascular disease (CVD) and mortality. Using parameters based on empirical studies (e.g., BMI assortative mating and fertility differentials) and theoretical scenarios (random mating, entirely endogenous mating), we provide a range of estimates of the potential effect of heritability, mating, and fertility, on time trends of obesity prevalence at the population level.}, - bibtex_show = {true}, - code = {https://gitlab.com/csic-echo/heritability-dem}, - howpublished = {Working paper} -} - -@report{larroulet2019, - title = {Diseño, {{Metodología}} y {{Caracterizaciόn}} de La {{Muestra}}}, - author = {Larroulet, Pilar and Daza, Sebastián and Del Villar, Paloma and Droppelmann, Catalina and Figueroa, Ana and Valenzuela, Eduardo}, - date = {2019}, - location = {{Chile}}, - bibtex_show = {true}, - pdf = {reentry_presentation.pdf}, - study = {Estudio Reinserciόn, Desistimiento y Reincidencia en Mujeres} -} - -@article{larroulet2020, - title = {Who Is Transitioning out of Prison? {{Characterising}} Female Offenders and Their Needs in {{Chile}}}, - author = {Larroulet, Pilar and Droppelmann, Catalina and Del Villar, Paloma and Daza, Sebastian and Figueroa, Ana and Osorio, Victoria}, - date = {2020}, - journaltitle = {International Journal of Crime, Justice and Social Democracy}, - abbr = {ICJSD}, - abstract = {The last decades’ increase in female incarceration has translated into an increasing number of women being released from prison. Understanding their characteristics and criminal trajectories can enlighten us regarding the different needs of women upon re-entering society after incarceration. Drawing on data from the Reinserción, Desistimiento y Reincidencia en Mujeres Privadas de Libertad en Chile study, this article identifies different profiles among a cohort of 225 women who were released from prison in Santiago, Chile, and demonstrates that significant heterogeneity exists among them in terms of their criminal trajectories and the intervention needs to support their transition out of prison.}, - bibtex_show = {true}, - html = {https://www.crimejusticejournal.com/article/view/1467}, - pdf = {who_is_transitioning_out_of_prison.pdf} -} - -@report{larroulet2021, - title = {Reinserción, {{Desistimiento}} y {{Reincidencia}} En {{Mujeres Privadas}} de {{Libertad}} En {{Chile}}}, - author = {Larroulet, Pilar and Daza, Sebastián and Del Villar, Paloma and Droppelmann, Catalina and Figueroa, Ana}, - date = {2021}, - institution = {{Centro de Estudios Justicia y Sociedad del Instituto de Sociologia, Pontificia Universidad Católica de Chile}}, - location = {{Chile}}, - bibtex_show = {true}, - pdf = {final-report-reentry.pdf}, - study = {Estudio Reinserciόn, Desistimiento y Reincidencia en Mujeres}, - file = {/Users/sdaza/Zotero/storage/43SFNCF8/larroulet_et_al_2021_reinserción,_desistimiento_y_reincidencia_en_mujeres_privadas_de_libertad_en.pdf} -} - -@article{larroulet2023, - title = {From {{Prison}} to {{Work}}: {{Job Patterns}} for Women in {{Chile}}}, - author = {Larroulet, Pilar and Daza, Sebastian and Borquez, Ignacio}, - date = {2023}, - journaltitle = {Social Science Research}, - shortjournal = {SSR}, - abbr = {SSR}, - abstract = {Finding and keeping a job is one of the key challenges individuals face as they move back into society. Research suggests that former inmates access mostly jobs on the margins of the labor market, however there is limited research on the dynamics of post-prison employment that account for differences in types of jobs. Using data from the study “Reintegration, desistance and recidivism among female inmates in Chile” (RDFC), our paper describes patterns of employment among a cohort of 207 women released from prison in Santiago, Chile and followed during the first year after release. We use sequence analysis to describe monthly patterns of employment considering different types of work (i.e., self-employed / employed, legitimate / under-the-table). To better account for the complex relationship between work and crime, we include offending as another type of income-generating activity. Finally, we use cluster analysis to explore which individual characteristics are associated with the distinct patterns of employment and offending. Our results show a significant level of heterogeneity in employment trajectories by job type, and the importance of considering work and offending to obtain a more complete picture of the dynamics of employment during reentry.}, - bibtex_show = {true}, - code = {https://github.com/sdaza/reentry-work-lifecourse}, - html = {https://www.sciencedirect.com/science/article/abs/pii/S0049089X22001594?via%3Dihub}, - preprint = {https://osf.io/preprints/socarxiv/k5zyd/} -} - -@inproceedings{palloni2014, - title = {Intergenerational {{Transfers}} in the {{U}}.{{S}}., {{Economic Inequality}}, and {{Social Stratification}}}, - author = {Palloni, Alberto and Daza, Sebastian}, - date = {2014}, - location = {{Boston, MA}}, - abstract = {Only a handful of studies examine intergenerational transfer patterns over long stretches of time to determine how they change with family dynamic, the experience of personal events (changes in employment, health, deaths), and external shocks, mostly associated with the business cycle. This paper contributes to the literature on intergenerational transfers in three ways. First, we estimate the existence, direction and size of transfer patterns of money and time of elderly individuals in the US over a period of nearly 18 years (1992-2010). Second, we gather empirical evidence to identify the effects of the great 2008 recession on the direction and magnitude of transfers. Third, we estimate differentials in transfers across three ethnic groups, Non Hispanic Whites, African Americans and other ethnic groups (mostly Hispanic White). We formulate alternative models to falsify competing conjectures regarding the nature of transfers and use 10 waves of the Health and Retirement Study to test these models. We focus on parent-child dyads as units of observations and account for withinhousehold interdyad dependency of transfers as well as for reciprocal influence of transfers from parents to children (PtC), and children to parents (CtP). Our results confirm findings from previous research indicating that PtC transfers exceed CtP transfers and that the bulk of CtP transfers adopt the form of supply of time for caregiving. We discuss evidence of a modest but tangible effect of the 2008 recession as it led to a drop of PtC monetary transfers and modest increases in the time children spent caring for parents. We find a sharp ethnic divide in transfer regimes: PtC transfers are more prevalent among Non Hispanic Whites whereas CtP transfers and coresidence are dominant among African Americans and other ethnic groups. Finally, the effects of the 2008 recession are quite similar across ethnic groups.}, - bibtex_show = {true}, - conference = {true}, - eventtitle = {Population {{Association}} of {{America}} ({{PAA}})}, - pdf = {intergenerational-transfers-2014.pdf} -} - -@incollection{palloni2023, - title = {Early Childhood Origins of Modern Social Class Health Disparities}, - booktitle = {Handbook of {{Health Inequalities Across}} the {{Life Course}}}, - author = {Palloni, Alberto and Ramirez, Daniel and Daza, Sebastian}, - editor = {Hoffmann, Rasmus}, - date = {2023}, - series = {Research {{Handbooks}} in {{Sociology}}}, - url = {https://www.e-elgar.com/shop/usd/handbook-of-health-inequalities-across-the-life-course-9781800888159.html}, - urldate = {2023-04-25}, - bibtex_show = {true}, - pdf = {palloni-ramirez-daza-chapter16.pdf}, - html = {https://www.e-elgar.com/shop/usd/handbook-of-health-inequalities-across-the-life-course-9781800888159.html}, - langid = {english} -} - -@inproceedings{somma2016, - title = {The {{Impact}} of {{Age}}, {{Egotropic Vote}}, and {{Political Divisions}} in the 2009-2010 {{Chilean Election}}}, - author = {Somma, Nicolás and Daza, Sebastian}, - date = {2016}, - abbr = {ISUC}, - abstract = {Through a telephone survey, nationally representative of the Chilean adult population, we study the determinants of electoral choice in the first round of the 2009-2010 national election – which granted power to the political right (Alianza por Chile coalition) after two decades of dominance of center-left governments (Concertación por la Democracia coalition). Age differences and the egotropic vote explain the two main “leaks” of votes suffered by the Concertación candidate Eduardo Frei towards his contenders. Younger voters leaned towards the independent leftist candidate Marco Enríquez Ominami, and economically unsatisfied voters did so towards the rightist candidate Sebastián Piñera. We also show that the vote in the 1988 plebiscite still shapes the vote in 2009-2010, but more strongly among the upper classes than among the lower classes. We suggest this results from the stronger involvement of the former in the political conflict around the military regime.}, - bibtex_show = {true}, - pdf = {articulo-nicolas-somma.pdf}, - pub = {Cuadernos ISUC} -} - -@article{venkataramani2020, - title = {Association of {{Social Mobility}} with the {{Income-Based Longevity Gap}} in the {{United States}}. {{A Cross-Sectional}}, {{County-Level Study}}}, - author = {Venkataramani, Atheendar S. and Daza, Sebastian and Ezekiel, Emanuel}, - date = {2020}, - journaltitle = {JAMA Internal Medicine}, - shortjournal = {JAMA Int Med}, - volume = {180}, - number = {3}, - pages = {429--436}, - abbr = {JAMA Int Med}, - abstract = {Background: Despite substantial research, the causes of the widening gap in life expectancy between rich and poor Americans—the longevity gap— remain unknown. We hypothesized that social mobility may play an important role in explaining the longevity gap. Methods: We estimated the association between social mobility - here operationalized as the correlation between parent and child income - and life expectancy at age 40 among U.S. men and women in different income quartiles using newly available county-level data, adjusting for a range of economic, social, and demographic characteristics. We used these estimates to predict the longevity gap between the top and bottom income quartiles if all counties had the same social mobility as the best performing county. Results: In our sample of 1,559 counties (representing 93\% of the U.S. population), each increase of 1 standard deviation in the social mobility measure was associated with a 0.38 (95\% CI: 0.29, 0.47) and 0.29 year (95\% CI: 0.21, 0.38) increase in life expectancy among bottom income quartile men and women, respectively. Estimates for the top income quartile were smaller, and not robust to covariate adjustment. If all counties had the highest level of social mobility, the predicted longevity gap between the lowest and highest income quartiles would be smaller by 1.4 years for men (95\% CI: 0.7, 2.1) and 1.1 years (95\% CI: 0.5,1.6) for women, representing a 20\% decrease. Conclusion: Social mobility may explain an important portion of incomerelated life expectancy gaps in the United States.}, - bibtex_show = {true}, - code = {https://github.com/sdaza/income-mobility-le-gap}, - html = {https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2759132}, - pdf = {paper_longevity_gap_US.pdf} -} diff --git a/_config.yml b/_config.yml deleted file mode 100644 index d6d5634a..00000000 --- a/_config.yml +++ /dev/null @@ -1,384 +0,0 @@ -# ----------------------------------------------------------------------------- -# Site settings -# ----------------------------------------------------------------------------- - -title: blank # the website title (if blank, full name will be used instead) -first_name: Sebastian -middle_name: -last_name: Daza -email: sebastian.daza@gmail.com -description: Demography, Sociology, Simulation, Data Science -footer_text: > - Powered by Jekyll with al-folio theme. - Hosted by GitHub Pages. -keywords: - -lang: en # the language of your site (for example: en, fr, cn, ru, etc.) -icon: 🎯 # the emoji used as the favicon (alternatively, provide image name in /assets/img/) - -url: https://sdaza.com # the base hostname & protocol for your site -baseurl: # the subpath of your site, e.g. /blog/ -last_updated: true # set to true if you want to display last updated in the footer -impressum_path: # set to path to include impressum link in the footer, use the same path as permalink in a page, helps to conform with EU GDPR - -# ----------------------------------------------------------------------------- -# Theme -# ----------------------------------------------------------------------------- - -# code highlighter theme -highlight_theme_light: github # https://github.com/jwarby/jekyll-pygments-themes -highlight_theme_dark: native # https://github.com/jwarby/jekyll-pygments-themes - -# repo color theme -repo_theme_light: default # https://github.com/anuraghazra/github-readme-stats/blob/master/themes/README.md -repo_theme_dark: dark # https://github.com/anuraghazra/github-readme-stats/blob/master/themes/README.md - -# ----------------------------------------------------------------------------- -# RSS Feed -# ----------------------------------------------------------------------------- -# will use title and url fields -# Take a look to https://github.com/jekyll/jekyll-feed for more customization - -rss_icon: true - -# ----------------------------------------------------------------------------- -# Layout -# ----------------------------------------------------------------------------- - -navbar_fixed: true -footer_fixed: true - -# Dimensions -max_width: 800px - -# TODO: add layout settings (single page vs. multi-page) - -# ----------------------------------------------------------------------------- -# Open Graph & Schema.org -# ----------------------------------------------------------------------------- -# Display links to the page with a preview object on social media. -serve_og_meta: false # Include Open Graph meta tags in the HTML head -serve_schema_org: false # Include Schema.org in the HTML head -og_image: # The site-wide (default for all links) Open Graph preview image - -# ----------------------------------------------------------------------------- -# Social integration -# ----------------------------------------------------------------------------- - -github_username: sdaza -gitlab_username: sebastian.daza -twitter_username: sebadaza -mastodon_username: # your mastodon instance+username in the format instance.tld/@username -linkedin_username: sdaza -telegram_username: # your Telegram user name -scholar_userid: VaCkLrYAAAAJ # your Google Scholar ID -semanticscholar_id: # your Semantic Scholar ID -whatsapp_number: # your WhatsApp number (full phone number in international format. Omit any zeroes, brackets, or dashes when adding the phone number in international format.) -orcid_id: # your ORCID ID -medium_username: # your Medium username -quora_username: # your Quora username -publons_id: # your ID on Publons -research_gate_profile: # your profile on ResearchGate -blogger_url: # your blogger URL -work_url: # work page URL -keybase_username: # your keybase user name -wikidata_id: # your wikidata id -dblp_url: # your DBLP profile url -stackoverflow_id: # your stackoverflow id -kaggle_id: # your kaggle id -lastfm_id: # your lastfm id -spotify_id: # your spotify id -pinterest_id: # your pinterest id -unsplash_id: # your unsplash id -instagram_id: # your instagram id -facebook_id: # your facebook id -youtube_id: # your youtube channel id (youtube.com/@) -discord_id: # your discord id (18-digit unique numerical identifier) - -contact_note: > - You can even add a little note about which of these is the best way to reach you. - -# ----------------------------------------------------------------------------- -# Analytics and search engine verification -# ----------------------------------------------------------------------------- - 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Kurt", "K."] - url: https://scholar.google.ca/citations?user=S0CXF8EAAAAJ&hl=en - -"Somma": - - firstname: ["Nicolás", "N."] - url: https://sites.google.com/site/nsommag/home - -"Beltrán-Sánchez": - - firstname: ["Hiram", "H."] - url: https://ph.ucla.edu/faculty/beltr-n-s-nchez \ No newline at end of file diff --git a/_data/cv.yml b/_data/cv.yml deleted file mode 100644 index 5b115724..00000000 --- a/_data/cv.yml +++ /dev/null @@ -1,97 +0,0 @@ -- title: General Information - type: map - contents: - - name: Full Name - value: Albert Einstein - - name: Date of Birth - value: 14th March 1879 - - name: Languages - value: English, German - -- title: Education - type: time_table - contents: - - title: PhD - institution: University of Zurich, Zurich, Switzerland - year: 1905 - description: - - Description 1. - - Description 2. - - title: Description 3. - contents: - - Sub-description 1. - - Sub-description 2. - - title: Federal teaching diploma - institution: Eidgenössische Technische Hochschule, Zurich, Switzerland - year: 1900 - description: - - Description 1. - - Description 2. - -- title: Experience - type: time_table - contents: - - title: Professor of Theoretical Physics - institution: Institute for Advanced Study, Princeton University - year: 1933 - 1955 - description: - - Description 1. - - Description 2. - - title: Description 3. - contents: - - Sub-description 1. - - Sub-description 2. - - title: Visiting Professor - institution: California Institute of Technology, Pasadena, California, US - year: 1933 - description: - - Description 1. - - Description 2. - - - title: Director - institution: Kaiser Wilhelm Institute for Physics, Berlin, Germany. - year: 1917-1933 - - - title: Professor of Theoretical Physics - institution: Karl-Ferdinand University, Prague, Czechoslovakia - year: 1911 - 1917 - description: - - - title: Associate Professor of Theoretical Physics - institution: University of Zurich, Zurich, Switzerland - year: 1909 - 1911 - -- title: Open Source Projects - type: time_table - contents: - - title: al-folio - year: 2015-now - description: A beautiful, simple, clean, and responsive Jekyll theme for academics. - -- title: Honors and Awards - type: time_table - contents: - - year: 1921 - items: - - Nobel Prize in Physics - - Matteucci Medal - - year: 2029 - items: - - Max Planck Medal - -- title: Academic Interests - type: nested_list - contents: - - title: Topic 1. - items: - - Description 1. - - Description 2. - - title: Topic 2. - items: - - Description 1. - - Description 2. - -- title: Other Interests - type: list - contents: - - Hobbies: Hobby 1, Hobby 2, etc. diff --git a/_data/repositories.yml b/_data/repositories.yml deleted file mode 100644 index 83fda67d..00000000 --- a/_data/repositories.yml +++ /dev/null @@ -1,8 +0,0 @@ -github_users: - - sdaza - -github_repos: - - sdaza/acsr - - sdaza/zotnote - - sdaza/sampler - - sdaza/survey-methods \ No newline at end of file diff --git a/_data/venues.yml b/_data/venues.yml deleted file mode 100644 index 6c16ad5d..00000000 --- a/_data/venues.yml +++ /dev/null @@ -1,6 +0,0 @@ -"AJP": - url: https://aapt.scitation.org/journal/ajp - color: "#00369f" - -"PhysRev": - url: https://journals.aps.org/ diff --git a/_includes/cv/list.html b/_includes/cv/list.html deleted file mode 100644 index 75625859..00000000 --- a/_includes/cv/list.html +++ /dev/null @@ -1,5 +0,0 @@ -
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    • - {% endfor %} -
\ No newline at end of file diff --git a/_includes/cv/time_table.html b/_includes/cv/time_table.html deleted file mode 100644 index 123b9d09..00000000 --- a/_includes/cv/time_table.html +++ /dev/null @@ -1,59 +0,0 @@ -
    - {% for content in entry.contents %} -
  • -
    - {% if content.year %} -
    - - {{ content.year }} - -
    - {% endif %} -
    - {% if content.title %} -
    {{content.title}}
    - {% endif %} - {% if content.institution %} -
    {{content.institution}}
    - {% endif %} - {% if content.description %} -
      - {% for item in content.description %} -
    • - {% if item.contents %} - {{ item.title }} -
        - {% for subitem in item.contents %} -
      • {{ subitem }}
        • - {% endfor %} -
      - {% else %} - {{ item }} - {% endif %} -
      • - {% endfor %} -
    - {% endif %} - {% if content.items %} -
      - {% for item in content.items %} -
    • - {% if item.contents %} - {{ item.title }} -
        - {% for subitem in item.contents %} -
      • {{ subitem }}
        • - {% endfor %} -
      - {% else %} - {{ item }} - {% endif %} -
      • - {% endfor %} -
    - {% endif %} -
    -
    -
    • - {% endfor %} -
\ No newline at end of file diff --git a/_includes/disqus.html b/_includes/disqus.html deleted file mode 100644 index 73fe9538..00000000 --- a/_includes/disqus.html +++ /dev/null @@ -1,13 +0,0 @@ -
- - -
diff --git a/_includes/embedpdf.html b/_includes/embedpdf.html deleted file mode 100755 index e8d38f7c..00000000 --- a/_includes/embedpdf.html +++ /dev/null @@ -1 +0,0 @@ - diff --git a/_includes/figure.html b/_includes/figure.html deleted file mode 100644 index e67e8043..00000000 --- a/_includes/figure.html +++ /dev/null @@ -1,36 +0,0 @@ -{%- assign img_path = include.path | remove: ".jpg" | remove: ".jpeg" | remove: ".png" | remove: ".tiff" -%} - -
- - - {% if site.imagemagick.enabled %} - {% for i in site.imagemagick.widths -%} - - {% endfor -%} - {% endif %} - - - - - - - {%- if include.caption -%}
{{ include.caption }}
{%- endif %} - -
diff --git a/_includes/footer.html b/_includes/footer.html deleted file mode 100644 index acc4688f..00000000 --- a/_includes/footer.html +++ /dev/null @@ -1,25 +0,0 @@ - {% if site.footer_fixed %} -
-
- © Copyright {{ site.time | date: '%Y' }} {{ site.first_name }} {{ site.middle_name }} {{ site.last_name }}. {{ site.footer_text }} - {%- if site.impressum_path -%} - Impressum. - {%- endif -%} - {%- if site.last_updated -%} - Last updated: {{ "now" | date: '%B %d, %Y' }}. - {%- endif %} -
-
- {%- else -%} -
-
- © Copyright {{ site.time | date: '%Y' }} {{ site.first_name }} {{ site.middle_name }} {{ site.last_name }}. {{ site.footer_text }} - {%- if site.impressum_path -%} - Impressum. - {%- endif -%} - {%- if site.last_updated -%} - Last updated: {{ "now" | date: '%B %d, %Y' }}. - {%- endif %} -
-
- {%- endif %} \ No newline at end of file diff --git a/_includes/giscus.html b/_includes/giscus.html deleted file mode 100644 index bb504f64..00000000 --- a/_includes/giscus.html +++ /dev/null @@ -1,27 +0,0 @@ -
- - -
diff --git a/_includes/head.html b/_includes/head.html deleted file mode 100644 index 3796eb38..00000000 --- a/_includes/head.html +++ /dev/null @@ -1,31 +0,0 @@ - - {% include metadata.html %} - - - - - - - - - - - - - - - {% if site.icon.size < 3 %} - - {% elsif site.icon != blank %} - - {% endif %} - - - - - {% if site.enable_darkmode %} - - - - - {% endif %} diff --git a/_includes/header.html b/_includes/header.html deleted file mode 100644 index 79779f6a..00000000 --- a/_includes/header.html +++ /dev/null @@ -1,119 +0,0 @@ - -
- - - -{% if site.enable_progressbar %} - - -
- -
- -{%- endif %} -
diff --git a/_includes/metadata.html b/_includes/metadata.html deleted file mode 100644 index a404ca97..00000000 --- a/_includes/metadata.html +++ /dev/null @@ -1,200 +0,0 @@ -{% if site.enable_google_verification or site.enable_bing_verification %} - - {% if site.enable_google_verification -%} - - {%- endif -%} - {% if site.enable_bing_verification -%} - - {%- endif -%} -{%- endif %} - - - - - - - {%- if site.title == "blank" -%} - {%- capture title -%}{{ site.first_name }} {{ site.middle_name }} {{ site.last_name }}{%- endcapture -%} - {%- else -%} - {%- capture title -%}{{ site.title }}{%- endcapture -%} - {%- endif -%} - {% if page.url == '/blog/index.html' %} - {{ site.blog_nav_title }} | {{ title }} - {%- elsif page.title != "blank" and page.url != "/" -%} - {%- if page.title == nil or page.title == "" -%} - {{ page.date | date: "%Y" }} | {{ title }} - {%- else -%} - {{ page.title }} | {{ title }} - {%- endif -%} - {%- else -%} - {{ title }} - {%- endif -%} - - - -{%- if page.keywords or site.keywords %} - -{%- endif %} - -{%- if site.serve_og_meta %} - - - - - - - - {% if page.og_image or site.og_image -%} - - {%- endif %} - - - - - - - {% if page.og_image or site.og_image -%} - - {%- endif %} - {% if site.twitter_username -%} - - - {%- endif %} -{%- endif %} - -{%- if site.serve_schema_org %} - - - {%- comment -%} Social links generator for "sameAs schema" {%- endcomment %} - {% assign sameaslinks = "" | split: "," %} - {%- if site.orcid_id -%} - {%- capture link -%}https://orcid.org/{{ site.orcid_id }}{%- endcapture -%} - {%- assign sameaslinks = sameaslinks | push: link -%} - {%- endif -%} - {%- if site.scholar_userid -%} - {%- capture link -%}https://scholar.google.com/citations?user={{ site.scholar_userid }}{%- endcapture -%} - {%- assign sameaslinks = sameaslinks | push: link -%} - {%- endif -%} - {%- if site.semanticscholar_id -%} - {%- capture link -%}https://www.semanticscholar.org/author/{{ site.semanticscholar_id }}{%- endcapture -%} - {%- assign sameaslinks = sameaslinks | push: link -%} - {%- endif -%} - {%- if site.publons_id -%} - {%- capture link -%}https://publons.com/a/{{ site.publons_id }}/{%- endcapture -%} - {%- assign sameaslinks = sameaslinks | push: link -%} - {%- endif -%} - {%- if site.research_gate_profile -%} - {%- capture link -%}https://www.researchgate.net/profile/{{site.research_gate_profile}}{%- endcapture -%} - {%- assign sameaslinks = sameaslinks | push: link -%} - {%- endif -%} - {%- if site.github_username -%} - {%- capture link -%}https://github.com/{{ site.github_username }}{%- endcapture -%} - {%- assign sameaslinks = sameaslinks | push: link -%} - {%- endif -%} - {%- if site.telegram_username -%} - {%- capture link -%}https://telegram.me/{{ site.telegram_username }}{%- endcapture -%} - {%- assign sameaslinks = sameaslinks | push: link -%} - {%- endif -%} - {%- if site.linkedin_username -%} - {%- capture link -%}https://www.linkedin.com/in/{{ site.linkedin_username }}{%- endcapture -%} - {%- assign sameaslinks = sameaslinks | push: link -%} - {%- endif -%} - {%- if site.twitter_username -%} - {%- capture link -%}https://twitter.com/{{ site.twitter_username }}{%- endcapture -%} - {%- assign sameaslinks = sameaslinks | push: link -%} - {%- endif -%} - {%- if site.medium_username -%} - {%- capture link -%}https://medium.com/@{{ site.medium_username }}{%- endcapture -%} - {%- assign sameaslinks = sameaslinks | push: link -%} - {%- endif -%} - {%- if site.quora_username -%} - {%- capture link -%}https://www.quora.com/profile/{{ site.quora_username }}{%- endcapture -%} - {%- assign sameaslinks = sameaslinks | push: link -%} - {%- endif -%} - {%- if site.blogger_url -%} - {%- capture link -%}{{ site.blogger_url }}{%- endcapture -%} - {%- assign sameaslinks = sameaslinks | push: link -%} - {%- endif -%} - {%- if site.work_url -%} - {%- capture link -%}{{ site.work_url }}{%- endcapture -%} - {%- assign sameaslinks = sameaslinks | push: link -%} - {%- endif -%} - {%- if site.wikidata_id -%} - {%- capture link -%}https://www.wikidata.org/wiki/{{ site.wikidata_id }}{%- endcapture -%} - {%- assign sameaslinks = sameaslinks | push: link -%} - {%- endif -%} - {%- if site.strava_userid -%} - {%- capture link -%}https://www.strava.com/athletes/{{ site.strava_userid }}{%- endcapture -%} - {%- assign sameaslinks = sameaslinks | push: link -%} - {%- endif -%} - {%- if site.keybase_username -%} - {%- capture link -%}https://keybase.io/{{ site.keybase_username }}{%- endcapture -%} - {%- assign sameaslinks = sameaslinks | push: link -%} - {%- endif -%} - {%- if site.gitlab_username -%} - {%- capture link -%}https://gitlab.com/{{ site.gitlab_username }}{%- endcapture -%} - {%- assign sameaslinks = sameaslinks | push: link -%} - {%- endif -%} - {%- if site.dblp_url -%} - {%- capture link -%}{{ site.dblp_url }}{%- endcapture -%} - {%- assign sameaslinks = sameaslinks | push: link -%} - {%- endif -%} - {%- if site.stackoverflow_id -%} - {%- capture link -%}https://stackoverflow.com/users/{{ site.stackoverflow_id }}{%- endcapture -%} - {%- assign sameaslinks = sameaslinks | push: link -%} - {%- endif -%} - {%- if site.kaggle_id -%} - {%- capture link -%}https://www.kaggle.com/{{ site.kaggle_id }}{%- endcapture -%} - {%- assign sameaslinks = sameaslinks | push: link -%} - {%- endif -%} - {%- if site.lastfm_id -%} - {%- capture link -%}https://www.last.fm/user/{{ site.lastfm_id }}{%- endcapture -%} - {%- assign sameaslinks = sameaslinks | push: link -%} - {%- endif -%} - {%- if site.spotify_id -%} - {%- capture link -%}https://open.spotify.com/user/{{ site.spotify_id }}{%- endcapture -%} - {%- assign sameaslinks = sameaslinks | push: link -%} - {%- endif -%} - {%- if site.pinterest_id -%} - {%- capture link -%}https://www.pinterest.com/{{ site.pinterest_id }}{%- endcapture -%} - {%- assign sameaslinks = sameaslinks | push: link -%} - {%- endif -%} - {%- if site.unsplash_id -%} - {%- capture link -%}https://unsplash.com/@{{ site.unsplash_id }}{%- endcapture -%} - {%- assign sameaslinks = sameaslinks | push: link -%} - {%- endif -%} - {%- if site.instagram_id -%} - {%- capture link -%}https://instagram.com/{{ site.instagram_id }}{%- endcapture -%} - {%- assign sameaslinks = sameaslinks | push: link -%} - {%- endif -%} - {%- if site.facebook_id -%} - {%- capture link -%}https://facebook.com/{{ site.facebook_id }}{%- endcapture -%} - {%- assign sameaslinks = sameaslinks | push: link -%} - {%- endif -%} - {%- if site.discord_id -%} - {%- capture link -%}https://discord.com/users/{{ site.discord_id }}{%- endcapture -%} - {%- assign sameaslinks = sameaslinks | push: link -%} - {%- endif -%} - {%- if sameaslinks != blank -%} - {%- assign sameaslinks = sameaslinks | split: "" -%} - {%- endif -%} - - -{%- endif %} diff --git a/_includes/news.html b/_includes/news.html deleted file mode 100644 index 307e532d..00000000 --- a/_includes/news.html +++ /dev/null @@ -1,31 +0,0 @@ - -
-

news

- {% if site.news != blank -%} - {%- assign news_size = site.news | size -%} -
3 %}style="max-height: 10vw"{% endif %}> - - {%- assign news = site.news | reverse -%} - {% if site.news_limit %} - {% assign news_limit = site.news_limit %} - {% else %} - {% assign news_limit = news_size %} - {% endif %} - {% for item in news limit: news_limit %} - - - - - {%- endfor %} -
{{ item.date | date: "%b %-d, %Y" }} - {% if item.inline -%} - {{ item.content | remove: '

' | remove: '

' | emojify }} - {%- else -%} - {{ item.title }} - {%- endif %} -
-
- {%- else -%} -

No news so far...

- {%- endif %} -
diff --git a/_includes/pagination.html b/_includes/pagination.html deleted file mode 100644 index 4b8d27e3..00000000 --- a/_includes/pagination.html +++ /dev/null @@ -1,17 +0,0 @@ -{%- if paginator.total_pages > 1 -%} - -{%- endif -%} diff --git a/_includes/projects.html b/_includes/projects.html deleted file mode 100644 index 503146e2..00000000 --- a/_includes/projects.html +++ /dev/null @@ -1,36 +0,0 @@ - -
-
- {% if project.redirect -%} - - {%- else -%} - - {%- endif %} -
- {%- if project.img %} - {%- include figure.html - path=project.img - alt="project thumbnail" -%} - {%- endif %} -
-

{{ project.title }}

-

{{ project.description }}

-
- {%- if project.github -%} -
-
- -
- {%- if project.github_stars -%} - - - - - {%- endif %} -
- {%- endif %} -
-
-
- -
\ No newline at end of file diff --git a/_includes/projects_horizontal.html b/_includes/projects_horizontal.html deleted file mode 100644 index ddf74058..00000000 --- a/_includes/projects_horizontal.html +++ /dev/null @@ -1,40 +0,0 @@ -
- {%- if project.redirect -%} - - {%- else -%} - - {%- endif -%} -
-
- {%- if project.img -%} -
- {% include figure.html path=project.img alt="project thumbnail" %} -
-
- {%- else -%} -
- {%- endif -%} -
-

{{ project.title }}

-

{{ project.description }}

-
- {%- if project.github -%} -
-
- -
- {%- if project.github_stars -%} - - - - - {%- endif -%} -
- {%- endif -%} -
-
-
-
-
- -
diff --git a/_includes/repository/repo.html b/_includes/repository/repo.html deleted file mode 100644 index a0881c0b..00000000 --- a/_includes/repository/repo.html +++ /dev/null @@ -1,14 +0,0 @@ -{% assign repo_url = include.repository | split: '/' %} - -{% if site.data.repositories.github_users contains repo_url.first %} - {% assign show_owner = false %} -{% else %} - {% assign show_owner = true %} -{% endif %} - -
- - {{ include.repository }} - {{ include.repository }} - -
diff --git a/_includes/repository/repo_user.html b/_includes/repository/repo_user.html deleted file mode 100644 index ae06a058..00000000 --- a/_includes/repository/repo_user.html +++ /dev/null @@ -1,6 +0,0 @@ -
- - {{ include.username }} - {{ include.username }} - -
diff --git a/_includes/scripts/analytics.html b/_includes/scripts/analytics.html deleted file mode 100644 index db2aeef9..00000000 --- a/_includes/scripts/analytics.html +++ /dev/null @@ -1,18 +0,0 @@ -{%- if site.enable_google_analytics -%} - - - -{%- endif -%} -{%- if site.enable_panelbear_analytics -%} - - - -{%- endif -%} diff --git a/_includes/scripts/bootstrap.html b/_includes/scripts/bootstrap.html deleted file mode 100644 index 1c213650..00000000 --- a/_includes/scripts/bootstrap.html +++ /dev/null @@ -1,3 +0,0 @@ - - - diff --git a/_includes/scripts/jquery.html b/_includes/scripts/jquery.html deleted file mode 100644 index f84a2f22..00000000 --- a/_includes/scripts/jquery.html +++ /dev/null @@ -1,2 +0,0 @@ - - diff --git a/_includes/scripts/masonry.html b/_includes/scripts/masonry.html deleted file mode 100644 index 804389d3..00000000 --- a/_includes/scripts/masonry.html +++ /dev/null @@ -1,6 +0,0 @@ - {%- if site.enable_masonry -%} - - - - - {%- endif -%} diff --git a/_includes/scripts/mathjax.html b/_includes/scripts/mathjax.html deleted file mode 100644 index c55ec056..00000000 --- a/_includes/scripts/mathjax.html +++ /dev/null @@ -1,12 +0,0 @@ - {%- if site.enable_math -%} - - - - - {%- endif %} diff --git a/_includes/scripts/misc.html b/_includes/scripts/misc.html deleted file mode 100644 index 08ba49f0..00000000 --- a/_includes/scripts/misc.html +++ /dev/null @@ -1,14 +0,0 @@ -{% if site.enable_tooltips %} - - -{%- endif %} -{%- if site.enable_medium_zoom %} - - - -{%- endif -%} - - - diff --git a/_includes/scripts/progressBar.html b/_includes/scripts/progressBar.html deleted file mode 100644 index 88bb73cd..00000000 --- a/_includes/scripts/progressBar.html +++ /dev/null @@ -1,80 +0,0 @@ -{% if site.enable_progressbar %} - - - - -{%- endif %} diff --git a/_includes/selected_papers.html b/_includes/selected_papers.html deleted file mode 100644 index 61457dbc..00000000 --- a/_includes/selected_papers.html +++ /dev/null @@ -1,5 +0,0 @@ - -
-

selected publications

- {% bibliography -f papers -q @*[selected=true]* %} -
diff --git a/_includes/social.html b/_includes/social.html deleted file mode 100644 index 6938bd48..00000000 --- a/_includes/social.html +++ /dev/null @@ -1,96 +0,0 @@ - {%- if site.email -%} - - {% endif %} - {%- if site.linkedin_username -%} - - {% endif %} - {%- if site.telegram_username -%} - - {% endif %} - {%- if site.whatsapp_number -%} - - {% endif %} - {%- if site.orcid_id -%} - - {% endif %} - {%- if site.semanticscholar_id -%} - - {% endif %} - {%- if site.publons_id -%} - - {% endif %} - {%- if site.research_gate_profile -%} - - {% endif %} - {%- if site.github_username -%} - - {% endif %} - {%- if site.gitlab_username -%} - - {% endif %} - {%- if site.mastodon_username -%} - - {% endif %} - {%- if site.medium_username -%} - - {% endif %} - {%- if site.quora_username -%} - - {% endif %} - {%- if site.blogger_url -%} - - {% endif %} - {%- if site.work_url -%} - - {% endif %} - {%- if site.wikidata_id -%} - - {% endif %} - {%- if site.strava_userid -%} - - {% endif %} - {%- 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A data.table: 6 × 8
idfromtotransTstartTstopstatustreat
<dbl><dbl><dbl><dbl><dbl><dbl><dbl><fct>
1213 01510Placebo
1234 01511Placebo
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2234 02510Placebo
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\n", - "text/latex": "A data.table: 6 × 8\n\\begin{tabular}{llllllll}\n id & from & to & trans & Tstart & Tstop & status & treat\\\\\n & & & & & & & \\\\\n\\hline\n\t 1 & 2 & 1 & 3 & 0 & 151 & 0 & Placebo\\\\\n\t 1 & 2 & 3 & 4 & 0 & 151 & 1 & Placebo\\\\\n\t 2 & 2 & 1 & 3 & 0 & 251 & 1 & Placebo\\\\\n\t 2 & 2 & 3 & 4 & 0 & 251 & 0 & Placebo\\\\\n\t 2 & 1 & 2 & 1 & 251 & 434 & 1 & Placebo\\\\\n\t 2 & 1 & 3 & 2 & 251 & 434 & 0 & Placebo\\\\\n\\end{tabular}\n", - "text/markdown": "\nA data.table: 6 × 8\n\n| id <dbl> | from <dbl> | to <dbl> | trans <dbl> | Tstart <dbl> | Tstop <dbl> | status <dbl> | treat <fct> |\n|---|---|---|---|---|---|---|---|\n| 1 | 2 | 1 | 3 | 0 | 151 | 0 | Placebo |\n| 1 | 2 | 3 | 4 | 0 | 151 | 1 | Placebo |\n| 2 | 2 | 1 | 3 | 0 | 251 | 1 | Placebo |\n| 2 | 2 | 3 | 4 | 0 | 251 | 0 | Placebo |\n| 2 | 1 | 2 | 1 | 251 | 434 | 1 | Placebo |\n| 2 | 1 | 3 | 2 | 251 | 434 | 0 | Placebo |\n\n", - "text/plain": " id from to trans Tstart Tstop status treat \n1 1 2 1 3 0 151 0 Placebo\n2 1 2 3 4 0 151 1 Placebo\n3 2 2 1 3 0 251 1 Placebo\n4 2 2 3 4 0 251 0 Placebo\n5 2 1 2 1 251 434 1 Placebo\n6 2 1 3 2 251 434 0 Placebo" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# read data\n", - "data(prothr)\n", - "dat = data.table(prothr)\n", - "head(dat)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "comments": [ - { - "body": [ - { - "created": "2020-07-20T09:53:58.223Z", - "creator": { - "image": "https://avatars1.githubusercontent.com/u/2096246?v=4", - "name": "Sebastian Daza", - "user": "sdaza" - }, - "edited": false, - "value": "This is another test" - } - ], - "id": "anno/1", - "resolved": true, - "total": 1 - } - ] - }, - "outputs": [ - { - "data": { - "text/plain": "\n 1 2 3 4 \n532 532 544 544 " - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "table(dat$trans)\n", - "dat[, years := Tstop - Tstart][years == 0, years := 0.001][, years := years / 365.25]\n", - "dat[, trans := as.factor(trans)]\n", - "tmat = attr(prothr, \"trans\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Flexsurv\n", - "\n", - "- Does this take into account uncertainty?\n", - "- No, we have to use bootstrap\n", - "- The bootstrap function might limit us\n", - "\n", - "```\n", - "summfn <- function(x, ...){\n", - " sim <- sim.fmsm(x, ...) \n", - " ## [[ compute the summary you want here. ]] \n", - "}\n", - "mod <- flexsurvreg(...)\n", - "\n", - "bootci.fmsm(mod, fn=summfn, ) \n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\t\n", - "\n", - "\n", - "\t\n", - "\t\n", - "\t\n", - "\n", - "
A matrix: 3 × 3 of type int
NormalLowDeath
NormalNA 1 2
Low 3NA 4
DeathNANANA
\n" - ], - "text/latex": [ - "A matrix: 3 × 3 of type int\n", - "\\begin{tabular}{r|lll}\n", - " & Normal & Low & Death\\\\\n", - "\\hline\n", - "\tNormal & NA & 1 & 2\\\\\n", - "\tLow & 3 & NA & 4\\\\\n", - "\tDeath & NA & NA & NA\\\\\n", - "\\end{tabular}\n" - ], - "text/markdown": [ - "\n", - "A matrix: 3 × 3 of type int\n", - "\n", - "| | Normal | Low | Death |\n", - "|---|---|---|---|\n", - "| Normal | NA | 1 | 2 |\n", - "| Low | 3 | NA | 4 |\n", - "| Death | NA | NA | NA |\n", - "\n" - ], - "text/plain": [ - " to\n", - "from Normal Low Death\n", - " Normal NA 1 2 \n", - " Low 3 NA 4 \n", - " Death NA NA NA " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "tmat" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# models \n", - "\n", - "# parametric models\n", - "crexp = flexsurvreg(Surv(years, status) ~ trans + treat, data = dat, dist = \"exp\") \n", - "crwei = flexsurvreg(Surv(years, status) ~ 1 + treat, anc = list(shape = ~ trans), data = dat, dist = \"weibull\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Call:\n", - "flexsurvreg(formula = Surv(years, status) ~ 1 + treat, anc = list(shape = ~trans), \n", - " data = dat, dist = \"weibull\")\n", - "\n", - "Estimates: \n", - " data mean est L95% U95% se exp(est)\n", - "shape NA 0.7393 0.6721 0.8132 0.0360 NA \n", - "scale NA 5.1336 4.4882 5.8718 0.3519 NA \n", - "treatPrednisone 0.5028 0.1081 -0.0680 0.2843 0.0899 1.1142 \n", - "shape(trans2) 0.2472 0.2956 0.1411 0.4501 0.0788 1.3440 \n", - "shape(trans3) 0.2528 -0.1045 -0.2329 0.0240 0.0655 0.9008 \n", - "shape(trans4) 0.2528 -0.2584 -0.3946 -0.1221 0.0695 0.7723 \n", - " L95% U95% \n", - "shape NA NA\n", - "scale NA NA\n", - "treatPrednisone 0.9342 1.3288\n", - "shape(trans2) 1.1515 1.5685\n", - "shape(trans3) 0.7922 1.0243\n", - "shape(trans4) 0.6740 0.8850\n", - "\n", - "N = 2152, Events: 880, Censored: 1272\n", - "Total time at risk: 3555.401\n", - "Log-likelihood = -1991.459, df = 6\n", - "AIC = 3994.918\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "crwei" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "-211.833807932969" - ], - "text/latex": [ - "-211.833807932969" - ], - "text/markdown": [ - "-211.833807932969" - ], - "text/plain": [ - "[1] -211.8338" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "AIC(crexp) - AIC(crwei)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# list of independent models\n", - "crwei.list <- list()\n", - "trans = order(unique(dat$trans))\n", - "for (i in trans) {\n", - " crwei.list[[i]] = flexsurvreg(Surv(years, status) ~ 1 + treat, subset=(trans==i), data = dat, dist = \"weibull\")\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulate trajectories\n", - "\n", - "- The function `sim.fsms` simulates one or more trajectories for the same set of covariates (nrow == 1)\n", - "- To simulate different individual with different characteristics, we need to loop over those characteristics\n", - "- For parameter uncertainty, we can use bootstrap" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "metadata": {}, - "outputs": [], - "source": [ - "simulateMS = function(model, newdata, time, idvar = \"id\", tmat = NULL, iterations = 1,\n", - " checkDuplicates = TRUE) {\n", - " \n", - " # some initial check\n", - " if (checkDuplicates && anyDuplicated(newdata[, get(idvar)]) > 0) {\n", - " stop(\"There are duplicated ids in newdata\")\n", - " }\n", - " \n", - " nsimulations = nrow(newdata)\n", - " output = list()\n", - " print(paste0(\":::: Number of individuals to simulate over \", time, \" units of time: \", nsimulations))\n", - " for (i in 1:nsimulations) {\n", - " if (i %% 100 == 0) {print(paste0(\":::: Simulating individual number \", i))}\n", - " \n", - " temp = sim.fmsm(model, M = iterations, t = time, newdata = newdata[i, ], trans = tmat)\n", - " \n", - " a = data.table(temp$st)[, iter := 1:.N]; setnames(a, gsub(\"V\", \"s\", names(a)))\n", - " b = data.table(temp$t)[, iter := 1:.N]; setnames(b, gsub(\"V\", \"t\", names(b)))\n", - " temp = merge(a, b, on = \"iter\")\n", - " \n", - " sid = newdata[i, get(idvar)]\n", - " temp = melt(temp, id.vars = \"iter\", measure = patterns(\"^s\", \"^t\"), \n", - " value.name = c(\"state\", \"time\"))[, \n", - " `:=` (variable = NULL, id = sid)]\n", - "\n", - " temp = unique(temp, by = c(\"iter\", \"state\" , \"time\"))\n", - " setorder(temp, id, iter) \n", - " output[[i]] = temp\n", - " \n", - " }\n", - " return(rbindlist(output))\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1] \":::: Number of individuals to simulate over 60 units of time: 488\"\n", - "[1] \":::: Simulating individual number 100\"\n", - "[1] \":::: Simulating individual number 200\"\n", - "[1] \":::: Simulating individual number 300\"\n", - "[1] \":::: Simulating individual number 400\"\n" - ] - } - ], - "source": [ - "newdata = unique(dat, by = \"id\")[, .(id, treat)]\n", - "test = simulateMS(crwei.list, newdata = newdata, time = 60, tmat = tmat, iterations = 10)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "comments": [ - { - "body": [ - { - "created": "2020-07-20T09:53:38.449Z", - "creator": { - "image": "https://avatars1.githubusercontent.com/u/2096246?v=4", - "name": "Sebastian Daza", - "user": "sdaza" - }, - "edited": false, - "value": "This is a test" - } - ], - "id": "anno/0", - "resolved": true, - "total": 1 - } - ] - }, - "source": [ - "### Resampling\n", - "\n", - "- Create sampling\n", - "- Run models\n", - "- Simulation transitions" - ] - } - ], - "metadata": { - "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": "4.0.4" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/_jupyter/power_tools.py b/_jupyter/power_tools.py deleted file mode 100644 index 30125007..00000000 --- a/_jupyter/power_tools.py +++ /dev/null @@ -1,484 +0,0 @@ -import pandas as pd -import numpy as np -import itertools - -import matplotlib.pyplot as plt -import seaborn as sns -from multiprocess.pool import ThreadPool - -import multiprocess as mp -import scipy.stats as stats - -import statsmodels.api as sm -from statsmodels.stats.proportion import proportions_ztest as prop_test -from statsmodels.stats.weightstats import ttest_ind as ttest -import pandas as pd - - -class PowerSim: - ''' - PowerSim class for simulation of power analysis. - ''' - - def __init__(self, metric='proportion', relative_effect=False, nsim=100, - variants=None, comparisons=None, alternative='two-tailed', alpha=0.05, - correction='bonferroni', fdr_method='indep'): - """ - PowerSim class for simulation of power analysis. - - Parameters - ---------- - metric : str - Count, proportion, or average - relative effect : bool - True when change is percentual (not absolute). - variants : int - Number of cohorts or variants to use (remember, total number of groups = control + number of variants) - comparisons : list - List of tuple with the tests to run - nsim : int - Number of replicates to simulate power - alternative : str - Alternative hypothesis, 'two-tailed', 'greater', 'smaller' - alpha : float - One minus statistical confidence - correction : str - Type of correction: 'bonferroni', 'holm', 'fdr' or None - fdr_method : 'indep' | 'negcorr' - If 'indep' it implements Benjamini/Hochberg for independent or if - 'negcorr' it corresponds to Benjamini/Yekutieli. - """ - - self.metric = metric - self.relative_effect = relative_effect - self.variants = variants - self.comparisons = list(itertools.combinations(range(self.variants+1), 2)) if comparisons is None else comparisons - self.nsim = nsim - self.alternative = alternative - self.alpha = alpha - self.correction = correction - self.fdr_method = fdr_method - - # simulate data - def __run_experiment(self, baseline=[1.0], sample_size=[100], effect=[0.10], - compliance=[1.0], standard_deviation=[1]): - """ - Simulate data to run power analysis. - - Parameters - ---------- - baseline : list - List baseline rates for counts or proportions, or base average for mean comparisons - sample_size : list - List with sample for control and arm groups - effect : list - List with effect sizes - standard_deviation : list - List of standard deviations by groups - compliance : list - List with compliance values - - Returns - ------- - Two vectors with simulated data - """ - - # initial checks - if len(effect) != self.variants: - if len(effect)>1: - raise ValueError('Effects should be same length as the number of self.variants or length 1!') - effect = list(itertools.repeat(effect[0], self.variants)) - - if len(compliance) != self.variants: - if len(compliance)>1: - raise ValueError('Compliance rates should be same length as the number of self.variants or length 1!') - compliance = list(itertools.repeat(compliance[0], self.variants)) - - if len(standard_deviation) != self.variants+1: - if len(standard_deviation)>1: - raise ValueError('Standard deviations should be same length as the number of self.variants+1 or length 1!') - standard_deviation = list(itertools.repeat(standard_deviation[0], self.variants+1)) - - if len(sample_size) != self.variants+1: - if len(sample_size)>1: - raise ValueError('N should be same length as the number of self.variants+1 or length 1!') - sample_size = list(itertools.repeat(sample_size[0], self.variants+1)) - - if len(baseline) != self.variants+1: - if len(baseline)>1: - raise ValueError('Baseline values should be same length as the number of self.variants+1 or length 1!') - baseline = list(itertools.repeat(baseline[0], self.variants+1)) - - re = list(range(self.variants)) - - # outputs - dd = np.array([]) - - # index of condition - vv = np.array([]) - - if self.metric == 'count': - c_data = np.random.poisson(baseline[0], sample_size[0]) - dd = c_data - vv = list(itertools.repeat(0, len(c_data))) - - for i in range(self.variants): - if self.relative_effect: - re[i] = baseline[i+1] * (1.00 + effect[i]) - else: - re[i] = baseline[i+1] + effect[i] - t_data_c = np.random.poisson(re[i], int(np.round(sample_size[i+1] * compliance[i]))) - t_data_nc = np.random.poisson(baseline[i+1], int(np.round(sample_size[i+1] * (1 - compliance[i])))) - t_data = np.append(t_data_c, t_data_nc) - dd = np.append(dd, t_data) - vv = np.append(vv, list(itertools.repeat(i + 1, len(t_data)))) - - if self.metric == 'proportion': - c_data = np.random.binomial(n=1, size=int(sample_size[0]), p=baseline[0]) - dd = c_data - vv = list(itertools.repeat(0, len(c_data))) - - for i in range(self.variants): - if self.relative_effect: - re[i] = baseline[i+1] * (1.00 + effect[i]) - else: - re[i] = baseline[i+1] + effect[i] - - t_data_c = np.random.binomial(n=1, size=int(np.round(sample_size[i+1] * compliance[i])), p=re[i]) - t_data_nc = np.random.binomial(n=1, size=int(np.round(sample_size[i+1] * (1 - compliance[i]))), p=baseline[i+1]) - t_data = np.append(t_data_c, t_data_nc) - dd = np.append(dd, t_data) - vv = np.append(vv, list(itertools.repeat(i + 1, len(t_data)))) - - if self.metric == 'average': - c_data = np.random.normal(baseline[0], standard_deviation[0], sample_size[0]) - dd = c_data - vv = list(itertools.repeat(0, len(c_data))) - - for i in range(self.variants): - if self.relative_effect: - re[i] = baseline[i+1] * (1.00 + effect[i]) - else: - re[i] = baseline[i+1] + effect[i] - - t_data_c = np.random.normal(re[i], standard_deviation[i+1], int(np.round(sample_size[i+1] * compliance[i]))) - t_data_nc = np.random.normal(baseline[i+1], standard_deviation[i+1], int(np.round(sample_size[i+1] * (1 - compliance[i])))) - - t_data = np.append(t_data_c, t_data_nc) - dd = np.append(dd, t_data) - vv = np.append(vv, list(itertools.repeat(i + 1, len(t_data)))) - - return dd, vv - - - def get_power(self, baseline=[1.0], effect=[0.10], sample_size=[1000], compliance=[1.0], standard_deviation=[1]): - ''' - Estimate power using simulation. - - Parameters - ---------- - baseline : list - List baseline rates for counts or proportions, or base average for mean comparisons. - effect : list - List with effect sizes. - sample_size : list - List with sample for control and arm groups. - compliance : list - List with compliance values. - standard_deviation : list - List of standard deviations of control and variants. - - Returns - ------- - power : float - ''' - - # create empty values for results - results = [] - ncomparisons = len(self.comparisons) - - pvalues = {} - for c in range(len(self.comparisons)): - pvalues[c] = [] - - # iterate over simulations - for i in range(self.nsim): - # y = output, x = index of condition - y, x = self.__run_experiment(baseline=baseline, effect=effect, - sample_size=sample_size, compliance=compliance, - standard_deviation=standard_deviation) - - # iterate over variants - l_pvalues = [] - for j, h in self.comparisons: - - # getting pvalues - if self.metric == 'count': - - ty = np.append(y[np.isin(x, j)], y[np.isin(x, h)]) - tx = np.append(x[np.isin(x, j)], x[np.isin(x, h)]) - tx[np.isin(tx, j)] = 0 - tx[np.isin(tx, h)] = 1 - - model = sm.Poisson(ty, sm.add_constant(tx)) - pm = model.fit(disp=False) - pvalue = pm.pvalues[1] - z = pm.params[1] - - elif self.metric == 'proportion': - z, pvalue = sm.stats.proportions_ztest( - [np.sum(y[np.isin(x, h)]), np.sum(y[np.isin(x, j)])], - [len(y[np.isin(x, h)]), len(y[np.isin(x, j)])]) - - elif self.metric == 'average': - z, pvalue = stats.ttest_ind(y[np.isin(x, h)], y[np.isin(x, j)], equal_var=False) - - l_pvalues.append(pvalue) - - pvalue_adjustment = { - 'two-tailed': 1, - 'greater': 0.5, - 'smaller': 0.5 - } - - correction_methods = { - 'bonferroni': self.bonferroni, - 'holm': self.holm_bonferroni, - 'hochberg': self.hochberg, - 'sidak': self.sidak, - 'fdr': self.lsu - } - - if self.correction in correction_methods: - significant = correction_methods[self.correction](np.array(l_pvalues), self.alpha/pvalue_adjustment[self.alternative]) - - for v, p in enumerate(significant): - pvalues[v].append(p) - - # results.append(int(np.sum(pvalues)) >= len(self.comparisons)) - power = pd.DataFrame(pd.DataFrame(pvalues).mean()).reset_index() - power.columns = ['comparisons', 'power'] - power['comparisons'] = power['comparisons'].map(dict(enumerate(self.comparisons))) - - return power - - - def grid_sim_power(self, baseline_rates=None, effects=None, sample_sizes=None, - compliances=[[1]], standard_deviations=[[1]], threads=3, plot=False): - """ - Return Pandas DataFrame with parameter combinations and statistical power - - Parameters - ---------- - baseline_rates : list - List of baseline rates for counts or proportions, or base average for mean comparisons. - effects : list - List with effect sizes. - sample_sizes : list - List with sample for control and variants. - compliances : list - List with compliance values. - standard_deviations : list - List of standard deviations of control and variants. - threads : int - Number of threads for parallelization. - plot : bool - Whether to plot the results. - """ - - pdict = {'baseline': baseline_rates, 'effect': effects, 'sample_size': sample_sizes, - 'compliance': compliances, 'standard_deviation': standard_deviations} - grid = self.__expand_grid(pdict) - - parameters = list(grid.itertuples(index=False, name=None)) - - grid['nsim'] = self.nsim - grid['alpha'] = self.alpha - grid['alternative'] = self.alternative - grid['metric'] = self.metric - grid['variants'] = self.variants - grid['comparisons'] = str(self.comparisons) - grid['relative_effect'] = self.relative_effect - grid = grid.loc[:, ['baseline', 'effect', 'sample_size', 'compliance', 'standard_deviation', - 'variants', 'comparisons', 'nsim', 'alpha', 'alternative', 'metric', 'relative_effect']] - pool = ThreadPool(processes=threads) - results = pool.starmap(self.get_power, parameters) - pool.close() - pool.join() - - results = pd.concat(results) - - # create index - index = [] - repeating_number = grid.shape[0] - repeating_count = len(self.comparisons) - for i in range(0, repeating_number): - index.extend([i] * repeating_count) - - results['index'] = index - results = results.pivot(index=['index'], columns=['comparisons'], values=['power']) - results.columns = [str((i,j)) for i,j in self.comparisons] - - grid = pd.concat([grid, results], axis=1) - grid.sample_size = grid.sample_size.map(str) - grid.effect = grid.effect.map(str) - if plot: - self.plot_power(grid) - return grid - - - # plot power simulation - def plot_power(self, data): - ''' - Plot statistical power by scenario - ''' - - value_vars = [str((i,j)) for i,j in self.comparisons] - - cols = ['baseline', 'effect', 'sample_size', 'compliance', 'standard_deviation', - 'variants', 'comparisons', 'nsim', 'alpha', 'alternative', 'metric', 'relative_effect'] - - temp = pd.melt(data, id_vars=cols, var_name='comparison', value_name='power', value_vars=value_vars) - - d_relative_effect = {True: 'relative', False: 'absolute'} - effects = list(temp.effect.unique()) - for i in effects: - plot = sns.lineplot(x='sample_size', y='power', hue='comparison', errorbar=None, data=temp[temp['effect'] == i], legend='full') - plt.hlines(y=0.8, linestyles='dashed', xmin=0, xmax=len(temp.sample_size.unique()) - 1, colors='gray') - plt.title(f'Simulated power estimation for {self.metric}s, {d_relative_effect[self.relative_effect]} effects {str(i)}\n (sims per scenario:{self.nsim})') - plt.legend(bbox_to_anchor=(1.05, 1), title='comparison', loc='upper left') - plt.xlabel('\n sample size') - plt.ylabel('power\n') - plt.setp(plot.get_xticklabels(), rotation=45) - plt.show() - - - # create grid dataframe - def __expand_grid(self, dictionary): - ''' - Auxiliary function to expand a dictionary - ''' - return pd.DataFrame([row for row in itertools.product(*dictionary.values())], - columns=dictionary.keys()) - - - # pvalue adjustment functions - def bonferroni(self, pvals, alpha=0.05): - """A function for controlling the FWER at some level alpha using the - classical Bonferroni procedure. - - Parameters - ---------- - pvals : array_like - Set of p-values of the individual tests. - alpha: float - The desired family-wise error rate. - - Output: - significant: array, bool - True if a hypothesis is rejected, False if not. - """ - m, pvals = len(pvals), np.asarray(pvals) - return pvals < alpha/float(m) - - - def hochberg(self, pvals, alpha=0.05): - """A function for controlling the FWER using Hochberg's procedure. - - Parameters - ---------- - pvals : array_like - Set of p-values of the individual tests. - alpha: float - The desired family-wise error rate. - - Output - ------- - significant: array, bool - True if a hypothesis is rejected, False if not. - """ - m, pvals = len(pvals), np.asarray(pvals) - # sort the p-values into ascending order - ind = np.argsort(pvals) - - """Here we have k+1 (and not just k) since Python uses zero-based - indexing.""" - test = [p <= alpha/(m+1-(k+1)) for k, p in enumerate(pvals[ind])] - significant = np.zeros(np.shape(pvals), dtype='bool') - significant[ind[0:np.sum(test)]] = True - return significant - - - def holm_bonferroni(self, pvals, alpha=0.05): - """A function for controlling the FWER using the Holm-Bonferroni - procedure. - - Parameters - ---------- - pvals : array_like - Set of p-values of the individual tests. - alpha: float - The desired family-wise error rate. - - Output - ------- - significant: array, bool - True if a hypothesis is rejected, False if not. - """ - - m, pvals = len(pvals), np.asarray(pvals) - ind = np.argsort(pvals) - test = [p > alpha/(m+1-k) for k, p in enumerate(pvals[ind])] - - """The minimal index k is m-np.sum(test)+1 and the hypotheses 1, ..., k-1 - are rejected. Hence m-np.sum(test) gives the correct number.""" - significant = np.zeros(np.shape(pvals), dtype='bool') - significant[ind[0:m-np.sum(test)]] = True - return significant - - - def sidak(self, pvals, alpha=0.05): - """A function for controlling the FWER at some level alpha using the - procedure by Sidak. - - Parameters - ---------- - pvals : array_like - Set of p-values of the individual tests. - alpha: float - The desired family-wise error rate. - - Output - ------ - significant: array, bool - True if a hypothesis is rejected, False if not. - """ - n, pvals = len(pvals), np.asarray(pvals) - return pvals < 1. - (1.-alpha) ** (1./n) - - - def lsu(self, pvals, q=0.05): - """The (non-adaptive) one-stage linear step-up procedure (LSU) for - controlling the false discovery rate, i.e. the classic FDR method - proposed by Benjamini & Hochberg (1995). - - Parameters - ---------- - pvals: array_like - Set of p-values of the individual tests. - q: float - The desired false discovery rate. - - Output: - -------- - significant: array, bool - True if a hypothesis is rejected, False if not. - """ - - m = len(pvals) - sort_ind = np.argsort(pvals) - k = [i for i, p in enumerate(pvals[sort_ind]) if p < (i+1.)*q/m] - significant = np.zeros(m, dtype='bool') - if k: - significant[sort_ind[0:k[-1]+1]] = True - return significant diff --git a/_jupyter/segregation.ipynb b/_jupyter/segregation.ipynb deleted file mode 100644 index 2fea656a..00000000 --- a/_jupyter/segregation.ipynb +++ /dev/null @@ -1,405 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ABM: Residential segregation mechanim" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The segregation mechanism is an adaptation of Schelling's segregation model. \n", - "\n", - "\n", - "Agents live in neighborhoods. At rate *t*, agents decide whether to move or stay in their neighborhood based on the proportion of people within the same quintile of income (e.g., 5 groups of income). Agents have a tolerance threshold (e.g., 20%) of people in the same quintile of income living in the same neighborhood. If the proportion of people of that quintile is lower than the tolerance threshold, agents move to another neighborhood **chosen randomly** from a pool of neighborhood that has not reach its population limit (e.g., more than 30% its original size).\n", - "\n", - "Changes in segregation are very sensitive to changes in the values of parameters and number of income groups. In this example, I use: \n", - "\n", - "- 20 neighbors with an initial population of 100 agents.\n", - "- 5 income groups.\n", - "- Population limit by neighborhood of 1.30 * 100.\n", - "- Moving rate is 0.1 per year.\n", - "- 100 replicates for each scenario. \n", - "- Income distribution comes from CPS data. \n", - "\n", - "To measure segregation I use the **neighborhood sorting index or NSI** (Jargowsky's 1996), that compares the income variation across all neighborhoods in a metro area with the income variation across all households in that metro area. If households are segregated across neighborhoods by income, the income variation across\n", - "neighborhoods will be similar to the income variation across households, and the NSI will equal almost 1. If all neighborhoods are perfectly economically integrated (i.e., each neighborhood is a microcosm of the entire metro area) the NSI will be almost 0. Because the NSI is based on relative variances in income, measured income segregation will be influenced by the metro area’s overall inequality. I also use the **average proportion of similar agents**. " - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "import pysdaza as sd\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# examining some data\n", - "ind = sd.read_files('../output/example01/indiv*.csv')\n", - "agg = sd.read_files('../output/example01/aggregate_data*.csv')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The income distribution looks as expected and the average Gini coefficient of this distribution is 0.36. " - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# income distribution from all replicates\n", - "sns.distplot(ind.income);" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Gini 0.36 SD = 0.0046\n" - ] - } - ], - "source": [ - "print('Gini', round(agg.gini.mean(),2), 'SD =', round(agg.gini.std(), 4))" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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E5Ih7w5ih9dbH1by6fh+/uqWMS0pHRTodY6KWiKxW1bL++tmKBDGgtqmDEWmB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\nnF2cx5mT8sjPiNkrIgxWdKL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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# income distribution highest quintile\n", - "sns.distplot(ind.loc[ind.quintile==5, 'income']);" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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FJSdyFxE26iJCkarV3VZHX3KcqbSuBZkvBUgFJcdSNOoiQpGqtq6tDuc0K28p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\nvgJ83Z8kHyIXCPh6j5E74Z4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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# income distribution lower quintile\n", - "sns.distplot(ind.loc[ind.quintile==1, 'income']);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Segregation measures" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Most of replicates (73%) reach convergence (all agents satisfy the moving threshold)." - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True 583\n", - "False 217\n", - "Name: unhappy, dtype: int64" - ] - }, - "execution_count": 78, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(agg.unhappy==0).value_counts()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "NSI changes dramatically due to small changes in the moving threshold. In other words, segregation is very sensitive to changes in the moving threshold. This is related to the way the segregation model is implemented and the number of groups (income quintiles). Standard deviation is between 0.04 and 0.09. So there is about 7% of the variability of segregation due to the stochasticity of the simulation. " - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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9dWiGAdM1vxwMtBaEIPAEzbB6m/j9pKCXFESBRy3wUMayNsyYqgUsTciu2yUF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\n0+dARK4DdgJ/2uc41xxrP1xKSWEvcHzJ/RP5tvNS1W8Af0H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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.regplot(agg['threshold'], agg['nsi'], scatter_kws={'alpha':.10}, line_kws={'linestyle':'--', 'linewidth':0.6});" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "iter\n", - "1 0.402524\n", - "2 0.473832\n", - "3 0.548531\n", - "4 0.611097\n", - "5 0.731174\n", - "6 0.809421\n", - "7 0.858013\n", - "8 0.891403\n", - "Name: nsi, dtype: float64" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "agg_group = agg.groupby('iter')\n", - "agg_group.nsi.mean() # mean" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "iter\n", - "1 0.053013\n", - "2 0.065235\n", - "3 0.063023\n", - "4 0.073683\n", - "5 0.088100\n", - "6 0.079391\n", - "7 0.078770\n", - "8 0.064671\n", - "Name: nsi, dtype: float64" - ] - }, - "execution_count": 81, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# standard deviation\n", - "agg_group.nsi.std()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "I obtain similar results when observing the proportion of neighbors of similar income quintile. This time, variability increases with the moving threshold. That is, when the moving threshold is higher it becomes more difficult to satisfy that threshold and there is a higher chance agents will move, increasing the variability of similarity. This is confirmed by the plot of moving threshold and number of unhappy agent (i.e., who hasn't satisfied that threshold). The variability of **NSI** is more robust to higher thresholds. " - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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t9aTnxkqpG4EbATo6OmamOiEqWHM6jmUoDoyUyJR8qmIWa1prqJ/FSxw7Xspy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\n5XEhonSi7KMbx/74deAerfW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lHepSNletW8jC2uRclyaEOAXzoimMFFye2D2AH5bn/edK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\nzMwsx0XBzMxyXBTMzCzHRcHMzHJcFMzMLOf/hzb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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.regplot(agg['threshold'], agg['unhappy'], fit_reg=False, scatter_kws={'alpha':0.1});" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In sum, the segregation mechanism seems to work properly. " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [default]", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.1" - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/_jupyter/statistical-power.ipynb b/_jupyter/statistical-power.ipynb deleted file mode 100644 index 7a8183cf..00000000 --- a/_jupyter/statistical-power.ipynb +++ /dev/null @@ -1,492 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "This post will discuss tools for designing and evaluating experiments. These tools offer user-friendly methods to estimate statistical power when working with multiple treatments or variants. They also help with sample allocation, including determining sample sizes for treatment and control groups. Lastly, they estimate power based on different expected effects (e.g., minimum detectable effects or MDE).\n", - "\n", - "Calculating power for complex and uplift models using machine learning can be challenging. I will provide some general guidelines and references at the end of this post.\n", - "\n", - "## Let's start with some terms\n", - "\n", - "When conducting a statistical test to estimate an effect, we can have any of the following outcomes:\n", - "\n", - "

\n", - " \"Image\"\n", - "

\n", - "\n", - "These errors and success probabilities are associated with standard statistical terms used in making inferences.\n", - "\n", - "- **Type I Error ($$\\alpha$$)**: the probability of rejecting the null hypothesis\n", - "when it's true.\n", - "- **Type II Error ($$\\beta$$)**: the probability of failing to reject the null\n", - "hypothesis when it's false.\n", - "- **Power ($$1-\\beta$$)**: the probability of rejecting the null hypothesis when\n", - "it's false.\n", - "- **Confidence ($$1-\\alpha$$)**: the probability of failing to reject the null\n", - "hypothesis when it's true.\n", - "\n", - "As a standard, $$\\alpha$$ is set to 0.05 and power to .80. Also, we will generally\n", - "have a control group (no intervention) and different treatments (variants). " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## There are some challenges\n", - "\n", - "There are some challenges associated with basic power calculations (e.g., web\n", - "calculators): \n", - "\n", - "- **(Simultaneous) multiple comparisons**\n", - "- **Sample allocation**\n", - "- **Different MDEs by variant**\n", - "\n", - "When conducting multiple statistical tests or comparisons within a study or analysis, there is a risk of obtaining false positive results. To address this, we need to adjust our $$\\alpha$$ level based on the number of tests performed. This adjustment increases the required sample size to achieve statistical significance. It is important to consider this issue during experiment design and data analysis, including post hoc analyses.\n", - "\n", - "To gain an understanding of why multiple comparison testing poses a challenge, let's consider a simple example. Suppose we have a control group and three treatment variants. In this example, we will simulate data where there are no effects or differences between the control and treatment groups. In other words, the null hypothesis (i.e., no effect) is always true. In each iteration, we test the control group against each treatment and count the number of significant results we obtain using a significance level ($$\\alpha=0.05$$). It is important to note that we already know there are no differences between the groups." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Probability of at least one Type I error in a batch of 3 tests: 12.6%\n" - ] - } - ], - "source": [ - "from scipy.stats import ttest_ind\n", - "import pandas as pd\n", - "pd.set_option(\"display.notebook_repr_html\", False)\n", - "import numpy as np\n", - "np.random.seed(0)\n", - "\n", - "# Number of iterations for simulation\n", - "iterations = 1000\n", - "\n", - "# Number of treatment groups\n", - "groups = 3\n", - "\n", - "# Number of samples in each group\n", - "samples = 1000\n", - "\n", - "# Count of batches where at least one false positive (Type I error) occurs\n", - "batches_with_errors = 0\n", - "\n", - "# perform iterations\n", - "for _ in range(iterations):\n", - " # Control group data\n", - " control_group = np.random.normal(0, 1, samples)\n", - "\n", - " # Treatment group data - no actual difference from control\n", - " treatment_groups = [np.random.normal(0, 1, samples) for _ in range(groups)]\n", - "\n", - " # A flag for any error within this batch of tests\n", - " error_in_batch = False\n", - "\n", - " # perform t-test for each treatment group compared to control group\n", - " for treatment in treatment_groups:\n", - " t_stat, p_value = ttest_ind(control_group, treatment)\n", - "\n", - " # Check if p-value is less than 0.05 (significance level)\n", - " if p_value < 0.05:\n", - " error_in_batch = True\n", - " break # no need to check remaining tests\n", - "\n", - " if error_in_batch:\n", - " batches_with_errors += 1\n", - "\n", - "print(f\"Probability of at least one Type I error in a batch of {groups} tests: {batches_with_errors / iterations * 100}%\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After 1000 iterations, the probability of encountering at least one Type I error in a batch of 3 tests is 12.6%. You can also use the formula: \n", - "\n", - "$$ 1 - (1 - \\alpha)^n$$\n", - "\n", - "Where $$n$$ is the number of comparisons. For this example, FWER will be 14.2%. This probability is referred to as the **Family-wise Error Rate (FWER)**, which signifies the likelihood of committing at least one Type I error within the entire family (or batch) of comparisons. It is evident that 12.6% and 14.2% are higher than the assumed α value of 5% for a single test. \n", - "\n", - "This is the essence of the multiple comparison problem. When conducting multiple tests, even if you maintain an $$\\alpha$$ level of approximately 0.05 for each individual test, the likelihood of experiencing at least one false discovery among the entire set of tests is significantly higher and increases as the number of tests in the batch grows. Consequently, some of our significant findings may actually be false positives and difficult to replicate.\n", - "\n", - "The key to adjusting multiple comparison testing is to strike a balance between reducing false positives (incorrectly rejecting the null hypothesis) and maintaining test power (the ability to correctly reject the null hypothesis when it's false). Some methods, such as *Bonferroni* or *Holm*, minimize the **Family-Wise Error Rate (FWER)**. Others, like *Benjamini-Hochberg*, minimize the **False Discovery Rate (FDR)**, which is the expected proportion of Type I errors among all declared significant hypotheses. FDR methods aim to reduce the ratio of Type I errors among significant results without eliminating all false discoveries like FWER methods. FDR methods generally offer higher statistical power and are less conservative.\n", - "\n", - "A crucial element of experimental design lies in sample allocation. Variations in the distribution of samples among groups can influence the statistical power of our tests. Sometimes, the goal is to limit the potential disruption from an intervention by scaling down the sample size across various variants. Alternatively, we might opt to shrink the control group's size or allot more units to each variant, contingent on their Minimum Detectable Effect (MDE).\n", - "\n", - "Let's not forget to consider expected effects. In figuring out the smallest minimum detectable effect (MDE), think about the least significant change that would still make the intervention worthwhile. It's all about balancing the books: estimate the return on investment (ROI) from the intervention, then determine the tiniest shift in a key metric (like conversion rates) that would still make the effort profitable. The MDE doesn't have to be identical across all variants - after all, not all interventions or campaigns cost the same.\n", - "\n", - "## Tools can help\n", - "\n", - "I've developed a set of straightforward simulation methods in Python, primarily to construct and evaluate our experimental data. The choice to use simulation was driven by its flexibility in handling diverse scenarios and metrics. However, it does come with a trade-off, as it demands a higher computational power. We can live with that :smile:. [Feel free to explore the code for the power class](https://github.com/sdaza/sdaza.github.io/tree/main/_jupyter/power_tools.py).\n", - "\n", - "These methods are useful, particularly for handling multiple variants, different allocations, or MDE by group. However, it's important to note that these methods only offer a power estimate based on a given set of parameters. Therefore, we will need to conduct a grid search to evaluate various scenarios and determine the optimal one.\n", - "\n", - "Let's start with a simple example using a proportion as the metric of interest:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - " comparisons power\n", - "0 (0, 1) 0.706" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from power_tools import * \n", - "\n", - "# load class\n", - "p = PowerSim(metric='proportion', relative_effect=False, variants=1, nsim=1000, alpha=0.05, alternative='two-tailed')\n", - "\n", - "# get power\n", - "p.get_power(baseline=[0.33], effect=[0.03], sample_size=[3000])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`nsim` represents the number of simulations. Variants are set to 1, so we are\n", - "only comparing control and treatment: `comparisons = (0,1)`. This example is too\n", - "simple. Let's now assume two variants (treatments): " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - " comparisons power\n", - "0 (0, 1) 0.531\n", - "1 (0, 2) 0.530\n", - "2 (1, 2) 0.015" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "p = PowerSim(metric='proportion', relative_effect=False, variants=2, \n", - " nsim=1000, alpha=0.05, alternative='two-tailed')\n", - "p.get_power(baseline=[0.33], effect=[0.03], sample_size=[3000])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The function calculates the power of group comparisons with a sample size of 3000 for each group. Multiple comparisons result in reduced power. Since the effect of each variant is the same (0.03), comparing *variants 1 and 2* is not meaningful here (it will always be 0 in practice). Custom comparisons can be defined using a list of tuples, such as `comparisons=[(0,1), (0,2)]`." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - " comparisons power\n", - "0 (0, 1) 0.558\n", - "1 (0, 2) 0.563" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "p = PowerSim(metric='proportion', relative_effect=False, variants=2, \n", - " nsim=1000, alpha=0.05, alternative='two-tailed',\n", - " comparisons=[(0,1), (0,2)])\n", - "p.get_power(baseline=[0.33], effect=[0.03], sample_size=[3000])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using only two comparisons (each variant with a control), the power significantly decreases compared to the one-variant example. I have currently implemented the following p-value corrections:\n", - "\n", - "- `bonferroni`\n", - "- `holm_bonferroni`\n", - "- `hochberg`\n", - "- `sidak`\n", - "- `fdr`\n", - "\n", - "You can find an introduction to different methods [here](https://en.wikipedia.org/wiki/Multiple_comparisons_problem). In general, Bonferroni is more conservative, while Holm provides higher power. The `fdr` method aims to minimize the false discovery rate.\n", - "\n", - "The function corrects only for the comparisons specified in the `comparisons` parameter. If you conduct additional tests, such as comparing the performance of group A versus group B or examining differences among demographic groups, you need to apply additional multiple comparison corrections in your analysis.\n", - "\n", - "We can introduce greater variability into the experimental design. We can then plot the expected power for different scenarios. To specify parameters, we'll utilize nested lists and the `grid_sim_power` method. However, things can quickly become convoluted.\n", - "\n", - "Let's consider a more complex example. The impact of the first and second variants on the control group varies as follows: `[0.01, 0.03]`, `[0.03, 0.05]`, `[0.03, 0.07]`. The sample sizes are equal for each group, but they increase linearly. We apply the `holm` correction." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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+NYmJicrb29uoReBFW4ajo6NTxNiyZUtVvHhxo7K0+gx/8sknys7OLkUfpjFjxigzMzND68jatWsVoGbOnGm0Pw0aNMhQy3C1atWMjuvMmTMVoNatW6eUUur+/fvK0tJStWjRwugb9fz58xWgfvjhB6VUUuuuo6OjocVXr9crV1dX1blzZ2VmZqYeP36slFLqyy+/NGpB3rt3rwJStI5u2bIlRXlyi/qWLVueuW/J2y9ZsmSK10F0dLTy9fVVzZs3N5Q5OTmpQYMGPXN9afULTKtlGFDTp083lD169EjZ2NgojUajli9fbii/ePFiipaQ2NjYFK0XAQEBysrKSk2ZMsVQ9qw+w0+3DCe/XqZOnWpUr1OnTkqj0airV68aygBlaWlpVHbq1CkFqK+++irFtp7Gv62nTxo+fHiK1pvHjx8rX19fVaxYMcP+Jr//ihcvnup76WnJx9/GxkbduXPHUH748OEUv0okPy9jxowxWkdGjw2gjh07Zii7efOmsra2Vq+//rqh7LXXXlOWlpbq2rVrhrK7d+8qBwcH1bBhQ0NZ8vuwfv36KfqdP6vP8NPnvYweX39/f6N+inPnzlWAoTU9+RemrOq7m97XdHJ8Xl5eKiIiwlC+cuVKBai5c+emO76TJ08qQPXr18+o/MMPP1SA+uuvvwxlmW0ZVirtc8PSpUuVVqtN0WKZ3H9z//79acaekfNXWv2sk/fh6NGjmVpvz549lVarNVr+yfUolXaf4TVr1qTYdno9fT5M1qZNmxSfo0opFRUVler7+ml2dnaqb9++Kco3btyY6ufKwYMHjVq8S5cunem+0cnn6p9//jnFYyNHjlTAM1umk88FT/86opRSNWrUULVr105z2a+++koBatOmTak+Li3DLy7bRpNo3rw5Bw8epH379pw6dYqZM2fSsmVLvLy8+PPPP9O1jrffftto2LNatWqhlOLtt982lJmZmVG9enWuX7+eZbE/2X81PDychw8f0qhRI65fv054ePhzl1+1ahUNGjSgQIECPHz40PDXrFkzdDode/bsAWDTpk2Ym5szcOBAo/0ZMmRIhuIdMGCAUcvrwIEDMTc3N3xT3bFjB/Hx8QwfPtzoG3X//v1xdHRk48aNAGi1WurWrWuI78KFC4SEhDBmzBiUUhw8eBBIai0uX768oR/bqlWrcHJyonnz5kb7W61aNezt7dm1a5dRvL6+vrRs2fK5+3Xy5EmuXLnCW2+9RUhIiGG9UVFRvPLKK+zZswe9Xg8k9ak7fPgwd+/ezdCxe55+/foZbjs7O1O6dGns7Ozo0qWLobx06dI4OzsbvQatrKwMx1qn0xESEoK9vT2lS5dO9crn9Ni0aRNmZmYMHTrUqPyDDz5AKcXmzZuNyps1a2ZoSQKoWLEijo6OmX6vbNq0iZo1axq13Njb2zNgwABu3LjB+fPnjer36tUrQ33BX3vtNby8vAz3a9asSa1atVJtcXnyPZMcW0aOTZ06dahWrZrhftGiRenQoQNbt25Fp9Oh0+nYtm0br732GsWLFzfU8/Dw4K233mLfvn1EREQYrbN///7P7O/4PBk9vn369DFqbU/+ZSH5+U1uWd26dSvR0dGZjitZRl/TPXv2xMHBwXC/U6dOeHh4GJ7P9MSXXHfEiBFG5R988AGA4dyVXVatWoW/vz9lypQxOrc1bdoUIMW57UkZOX9lRHrXq9frWbt2Le3atUu1NfN5Q4omn983bNhAQkJChuNMTUxMTKp95ZOv2YiJicnS5cuWLcv27dtZu3Yto0aNws7OLtOjSSSvO7PxP2/5Zy27bNky3NzcUu1LLLJGtnWTAKhRowZ//PEH8fHxnDp1ijVr1jB79mw6derEyZMnKVu27DOXT+6gniz55FmkSJEU5Y8ePcqyuPfv38/EiRM5ePBgipN0eHj4c39evHLlCqdPn07z59DkiyJu3ryJh4dHip/rn9VtIDUlS5Y0um9vb4+Hh4dhqJybN2+mul5LS0uKFy9ueBySPlAnTZpETEwMe/fuxcPDg6pVq1KpUiX27t1L8+bN2bdvn1EyeOXKFcLDw3F3d3/m/iZL7krwPFeuXAF4ZreR8PBwChQowMyZM+nVqxdFihShWrVqtG7dmp49exolMhllbW2d4jl0cnLC29s7xQfJ069BvV7P3Llz+frrrwkICECn0xkeS6uLwvPcvHkTT09PowQDMPzk+OTzCCnfPwAFChTI9Hvl5s2bKboGPL39J7tEpfd5Tvb06xigVKlSrFy50qjM3Nwcb2/vFLFl5Nikta3o6GjDMEnR0dGpvhf9/f3R6/Xcvn2bcuXKGcozur9Py+jxffr5LVCgAIDh+fX19WXEiBF8+eWX/PrrrzRo0ID27dvzv//9L8NdJCDjr+mnj7FGo6FEiRKG81J64rt58yZarZYSJUoYratw4cI4OzuneF6z2pUrV7hw4cJzz+VpLQvpO39lNKb0rDc+Pp6IiIhMd1Ns1KgRHTt2ZPLkycyePZvGjRvz2muv8dZbb2X64k8bGxvi4uJSlMfGxhoez8rlHR0dadasGQAdOnRg2bJldOjQgePHj1OpUqUMxw5kOv7nLZ/WstevX+fgwYMMHjzYcNGdyHov5chaWlpSo0YNatSoQalSpejTpw+rVq1i4sSJz1wurVaW1MqVUobbaX3jffLknZZr167xyiuvUKZMGb788kuKFCmCpaUlmzZtYvbs2en6Jq/X62nevDmjRo1K9fFSpUo9dx2mUr9+fRISEjh48CB79+41tDY1aNCAvXv3cvHiRR48eGAoh6T9dXd359dff011nU9/kKS3tTD5WM+aNYvKlSunWif5i0SXLl1o0KABa9asYdu2bcyaNYvPPvuMP/74g1atWqVre0/LyOsPjF+D06dP5+OPP6Zv37588sknuLi4oNVqGT58eKZagzIjPXFmp+waIeTJFsqc5GWPiJKe5/eLL76gd+/erFu3jm3btjF06FA+/fRTDh06lOILxfNkx2s6vfFlZmKkF/kcSKbX66lQoQJffvllqo8/3TDz9LKQvvNXRqR3vaGhoRle95OSJ045dOgQ69evZ+vWrfTt25cvvviCQ4cOZSp2Dw8Pdu3ahVLK6Pm5d+8eQIp+56ktn1z3Seld/o033qBHjx4sX748w8mwh4eH0bae3r6Li8szvyQ8ufzTr5t79+6l2Y85eYQMGUUie730rxnJP9ek9oLKKsnftJ8e6D49rQjr168nLi6OP//806jlJbWfw9I62fr5+REZGWn4RpoWHx8fdu7cSWRkpNGJ5dKlS8+N80lXrlyhSZMmhvuRkZHcu3eP1q1bG7aTvN4nW0rj4+MJCAgwirNmzZpYWlqyd+9e9u7dy8iRI4GkC6sWL17Mzp07Dfef3N8dO3ZQr169LE0Ikn/if/Lb/bN4eHjw3nvv8d5773H//n2qVq3KtGnTDMnwy5xpcPXq1TRp0oTvv//eqDwsLMxwIVVGY/Lx8WHHjh08fvzYqAX04sWLhsezk4+PT6qvzazafnKL15MuX76crln4Mnps0tqWra2t4cubra1tmvur1WqfmQgly+jzmx3Ht0KFClSoUIGPPvqIAwcOUK9ePb755humTp2aofWk9zWd7OljrJTi6tWrVKxYMd3x+fj4oNfruXLlitFFV8HBwYSFhT3zmGTkc+BZ5/JTp07xyiuvZPj8kdHzV1av183NDUdHR86ePfvM9T1vv2rXrk3t2rWZNm0ay5Yto3v37ixfvtyoC1l6Va5cme+++44LFy4Y/TKcfNFnWsn9k8vv3bsXvV5v9IX48OHD2NraPrehKS4uDr1en67ujk/z8vLCzc2NY8eOpXjsyJEj6Yod4NixY0aJ7927d7lz506aw7ItW7YMPz+/TA0FJ9Iv25pXkr/9PS25D1hGuwJkhI+PD2ZmZoa+r8m+/vrr5y6b3NryZOzh4eH8+OOPKera2dmlOrNUly5dOHjwIFu3bk3xWFhYmGG2mdatW5OYmMjChQsNj+t0ugzP0Ldo0SKjPl0LFy4kMTHRkAQ2a9YMS0tL5s2bZ7Rf33//PeHh4UYzBFlbW1OjRg1+++03bt26ZdQyHBMTw7x58/Dz8zN8y03eX51OxyeffJIituSreTOjWrVq+Pn58fnnn6fazyv552ydTpfi5Obu7o6np6fRT1J2dnaZOglmhpmZWYrX/6pVqwgMDDQqS74yOD3HqHXr1uh0OsP4nMlmz56NRqPJdAt4erVu3ZojR44Y+o5D0ni6ixYtolixYs/t9vQ8a9euNTo+R44c4fDhw+nar4wem4MHDxr1c719+zbr1q2jRYsWmJmZYWZmRosWLVi3bp3RzFzBwcEsW7aM+vXr4+jo+Ny4Mvr8ZuXxjYiISDGzVYUKFdBqtan+VPs86X1NJ0seHSTZ6tWruXfvnuG5SE98yV/on54dLrml9unZzZ6UnDQ++Tmg0+lYtGhRirppnRu6dOlCYGAgixcvTvFYTEwMUVFRaW4/veevjErverVaLa+99hrr169PNYFLfi7Teo0+evQoxfOdnNBl5vUDSV0VLCwsjD6LlVJ88803eHl5GY2kc+/ePS5evGj02dapUyeCg4ONRrR4+PAhq1atol27doaW2bCwsFT7OX/33XdAyhEd0qtjx45s2LCB27dvG8p27tzJ5cuX6dy5s6EsISGBixcvGjX6lStXjjJlyrBo0SKjXycWLlyIRqMxjJv8pBMnTnDhwgXeeuutTMUr0i/bWoaHDBlCdHQ0r7/+OmXKlCE+Pp4DBw6wYsUKihUrRp8+fbJr0zg5OdG5c2e++uorNBoNfn5+bNiw4Zn9u5K1aNECS0tL2rVrxzvvvENkZCSLFy/G3d09RWt2tWrVWLhwIVOnTqVEiRK4u7vTtGlTRo4cyZ9//knbtm3p3bs31apVIyoqijNnzrB69Wpu3LhBwYIFadeuHfXq1WPMmDHcuHGDsmXL8scff2Q4YYuPj+eVV16hS5cuXLp0ia+//pr69evTvn17IKmFYOzYsUyePJlXX32V9u3bG+rVqFGD//3vf0bra9CgATNmzMDJyckwTI27uzulS5fm0qVL9O7d26h+o0aNeOedd/j00085efIkLVq0wMLCgitXrrBq1Srmzp2b6hv9ebRaLd999x2tWrWiXLly9OnTBy8vLwIDA9m1axeOjo6sX7+ex48f4+3tTadOnahUqRL29vbs2LGDo0eP8sUXXxg9XytWrGDEiBHUqFEDe3t72rVrl+G40qNt27ZMmTKFPn36ULduXc6cOcOvv/6aog+zn58fzs7OfPPNNzg4OGBnZ0etWrVS7X/arl07mjRpwvjx47lx4waVKlVi27ZtrFu3juHDhxtdLJcdxowZw2+//UarVq0YOnQoLi4u/PTTTwQEBPD777+/cNeFEiVKUL9+fQYOHEhcXBxz5szB1dU1ze5GT8rosSlfvjwtW7Y0GloNYPLkyYY6U6dOZfv27dSvX5/33nsPc3Nzvv32W+Li4pg5c2a69in5Ir3x48fz5ptvYmFhQbt27VIdHimrj+9ff/3F4MGD6dy5M6VKlSIxMZGlS5diZmZGx44dDfWSh13ctWsXjRs3TnN96X1NJ3NxcaF+/fr06dOH4OBg5syZQ4kSJejfv3+646tUqRK9evVi0aJFhIWF0ahRI44cOcJPP/3Ea6+9ZvSL2NPKlStH7dq1GTt2LKGhobi4uLB8+fIUCTikfW7o0aMHK1eu5N1332XXrl3Uq1cPnU7HxYsXWblyJVu3bk0zsUrv+SujMrLe6dOns23bNho1amQYGu7evXusWrWKffv24ezsTOXKlTEzM+Ozzz4jPDwcKysrmjZtyrJly/j66695/fXX8fPz4/HjxyxevBhHR0fDl5SM8vb2Zvjw4cyaNYuEhARq1KjB2rVr2bt3L7/++qtR15+xY8caXv/Jvw516tSJ2rVr06dPH86fP0/BggX5+uuv0el0Ru/d3bt3M3ToUDp16kTJkiWJj49n7969/PHHH1SvXj3FZ55Go6FRo0bs3r37mfGPGzeOVatW0aRJE4YNG0ZkZCSzZs2iQoUKRjlNYGAg/v7+9OrViyVLlhjKZ82aRfv27WnRogVvvvkmZ8+eZf78+fTr1y/FcHOAoeuhdJF4CbJrmIrNmzervn37qjJlyih7e3vD1MxDhgxRwcHBRnXTGlrt6SFdkodBe/DggVF58kD8T3rw4IHq2LGjsrW1VQUKFFDvvPOOOnv2bLqGVvvzzz9VxYoVDYOMf/bZZ+qHH35IMUxPUFCQatOmjXJwcDAMlJ7s8ePHauzYsapEiRLK0tJSFSxYUNWtW1d9/vnnRsOghYSEqB49ehgm3ejRo0emJ90oUKCAsre3V927d1chISEp6s+fP1+VKVNGWVhYqEKFCqmBAwemOhFC8jA1rVq1Mirv16+fAtT333+fajyLFi1S1apVUzY2NsrBwUFVqFBBjRo1St29e9dQJ3nSjYw4ceKEeuONN5Srq6uysrJSPj4+qkuXLmrnzp1KKaXi4uLUyJEjVaVKlQyTI1SqVCnFRCmRkZHqrbfeUs7OzgrSN+nG0xo1aqTKlSuXovzp/YqNjVUffPCB8vDwUDY2NqpevXrq4MGDqU4Zu27dOlW2bFllbm5uFEdqk248fvxYvf/++8rT01NZWFiokiVLPnNiidTifHqih9SktXzypBDOzs7K2tpa1axZM81JIdI7pNeTwwJ98cUXqkiRIsrKyko1aNBAnTp1yqhuWs+LUhk/Nr/88osqWbKksrKyUlWqVEl1yKXjx4+rli1bKnt7e2Vra6uaNGmiDhw4YFQnrfNVsk8++UR5eXkprVZrdA551qQbmTm+T7+Or1+/rvr27av8/PyUtbW1cnFxUU2aNFE7duwwWu6DDz5QGo1GXbhwIdX4k6X3Nf3kpCtjx45V7u7uysbGRrVp00bdvHnTUC+98SUkJKjJkycrX19fZWFhoYoUKZKuSTeSj2ezZs2UlZWVKlSokBo3bpzavn17iqHE0jo3KJU0Lfpnn32mypUrp6ysrFSBAgVUtWrV1OTJk1V4ePgzj5lSzz9/PXnM0jO0WkbWq1TSsIE9e/ZUbm5uysrKShUvXlwNGjTIaFi+xYsXq+LFiyszMzPDsTl+/Ljq1q2bKlq0qGEipbZt2xoNSZgW0hhaTamkITynT59umCCpXLly6pdffklRL3kYxaeHxgsNDVVvv/22cnV1Vba2tqpRo0Ypjs/Vq1dVz549VfHixZWNjY2ytrZW5cqVUxMnTkwxacXjx48VoN58883n7pdSSp09e1a1aNFC2draKmdnZ9W9e3cVFBRkVCf5vZjauXbNmjWqcuXKysrKSnl7e6uPPvoo1SFndTqd8vLyUlWrVn1uTDK02ovTKPWSrqYRWS55koujR49m+mcfIUztxo0b+Pr6MmvWLD788MNs355GozGaDja/q1mzJj4+PqxatcrUoYg8QqPRMHLkSMNwZi/74tKM2LRpE23btuXUqVNGE3bkBkopQkJCuH37NlWrVn1p59C8KOddki2EEOKliIiI4NSpU0yZMsXUoYg8ZtasWbi5ubFgwQJTh/JMu3bt4s0338x1iTAkXc/k5uZG1apVTR1KrieD1gkhRD7l6OiY6YuhhEjL9u3bDbdz8lCikJS051b29va56ljnZJIMCyGEECLLZOVQciJt5ubmcqyziPQZFkIIIYQQ+Zb0GRZCCCGEEPmWJMNCCCGEECLfkmRYiBziyJEjWFpapmva8CdpNBomTZqUPUGJHKV3797pmp46rwkJCcHOzs4wg6kQQmQlSYaFyCHGjx9Pt27d8PHxMXUoIh9ZuHAhnTt3pmjRomg0mhQzTD4pLCyMAQMG4Obmhp2dHU2aNDGa1vpJf/75J1WrVsXa2pqiRYsyceLEVGd/S886XV1d6devHx9//PEL7asQQqRGLqATIgc4efIkVapU4cCBA9SpUydDy8bGxmJubo65uQwOk9clJCSg1+uxsrLKsnUWK1aMx48fU7NmTXbs2EH37t2NppBNptfradCgAadOnWLkyJGGqXBv377NP//8Q8mSJQ11N2/eTJs2bWjcuDHdunXjzJkzLFiwgAEDBrBw4cJMrfPChQuULVuWnTt30rRp0yzbfyGEyLbpmIUQ6Td06FBVtGjRFFMHi5Senk41P8jOfb5x44bhdWdnZ5fmdN0rVqxIMWXw/fv3lbOzs+rWrZtR3bJly6pKlSqphIQEQ9n48eNTTPuckXUqpVT58uVVjx49MrWfQgiRFukmIUQOsHbtWpo2bYpGozEqP3bsGC1btqRgwYLY2Njg6+tL3759jeo83Wd40qRJaDQaLl++zP/+9z+cnJxwc3Pj448/RinF7du36dChA46OjhQuXJgvvvgiRTxfffUV5cqVw9bWlgIFClC9enWWLVv2zH3YvXs3Go2GFStWMG7cOAoXLoydnR3t27fn9u3bKeofPnyYV199FScnJ2xtbWnUqBH79+83qpO8L+fPn+ett96iQIEC1K9fP80YEhISmDx5MiVLlsTa2hpXV1fq169vNDA9wMWLF+nSpQtubm7Y2NhQunRpxo8fb1QnMDCQvn37UqhQIaysrChXrhw//PBDqvu8cuVKpk2bhre3N9bW1rzyyitcvXrVqO7evXsN3RGsrKwoUqQI77//PjExMUb1evfujb29PdeuXaN169Y4ODjQvXt3w2NP9xmOiorigw8+oEiRIlhZWVG6dGk+//xz1FM/+j18+JCLFy8SHR1tVO7j45PidZea1atXU6hQId544w1DmZubG126dGHdunWGyTvOnz/P+fPnGTBggNGvFe+99x5KKVavXp3hdSZr3rw569evT7FvQgjxIiQZFsLEAgMDuXXrVoopNe/fv0+LFi24ceMGY8aM4auvvqJ79+4cOnQoXevt2rUrer2eGTNmUKtWLaZOncqcOXNo3rw5Xl5efPbZZ5QoUYIPP/yQPXv2GJZbvHgxQ4cOpWzZssyZM4fJkydTuXJlDh8+nK7tTps2jY0bNzJ69GiGDh3K9u3badasmVHS99dff9GwYUMiIiKYOHEi06dPJywsjKZNm3LkyJEU6+zcuTPR0dFMnz6d/v37p7ntSZMmMXnyZJo0acL8+fMZP348RYsWNeqDevr0aWrVqsVff/1F//79mTt3Lq+99hrr16831AkODqZ27drs2LGDwYMHM3fuXEqUKMHbb7/NnDlzUmx3xowZrFmzhg8//JCxY8dy6NAhQwKbbNWqVURHRzNw4EC++uorWrZsyVdffUXPnj1TrC8xMZGWLVvi7u7O559/TseOHVPdX6UU7du3Z/bs2bz66qt8+eWXlC5dmpEjRzJixAijuvPnz8ff3z/V45seJ06coGrVqmi1xh8bNWvWJDo6msuXLxvqAVSvXt2onqenJ97e3obHM7LOZNWqVSMsLIxz585lah+EECJVJm2XFkKoHTt2KECtX7/eqHzNmjUKUEePHn3m8oCaOHGi4f7EiRMVoAYMGGAoS0xMVN7e3kqj0agZM2YYyh89eqRsbGyMfhrv0KGDKleuXIb3Y9euXQpQXl5eKiIiwlC+cuVKBai5c+cqpZTS6/WqZMmSqmXLlkbdQqKjo5Wvr69q3rx5in1J7Sfz1FSqVEm1adPmmXUaNmyoHBwc1M2bN43Kn4zl7bffVh4eHurhw4dGdd58803l5OSkoqOjjfbZ399fxcXFGerNnTtXAerMmTNG+/e0Tz/9VGk0GqNYevXqpQA1ZsyYFPV79eqlfHx8DPfXrl2rADV16lSjep06dVIajUZdvXrVUJZ8LHft2pXaYVFKPbubhJ2dnerbt2+K8o0bNypAbdmyRSml1KxZsxSgbt26laJujRo1VO3atTO8zmQHDhxQgFqxYkWa+yCEEBklLcNCmFhISAgABQoUMCp3dnYGYMOGDSQkJGR4vf369TPcNjMzo3r16iilePvtt422Ubp0aa5fv25UdufOHY4ePZrhbQL07NkTBwcHw/1OnTrh4eFhGBbr5MmTXLlyhbfeeouQkBAePnzIw4cPiYqK4pVXXmHPnj3o9Xqjdb777rvp2razszPnzp3jypUrqT7+4MED9uzZQ9++fSlatKjRY8ldBZRS/P7777Rr1w6llCG+hw8f0rJlS8LDw1OMdtCnTx8sLS0N9xs0aABgdFxtbGwMt6Oionj48CF169ZFKWXUWpps4MCBz93fTZs2YWZmxtChQ43KP/jgA5RSbN682VA2adIklFI0btz4uetNTUxMTKoX7llbWxsef/J/WnWf/IUgvetMlvweefjwYWZ2QQghUiXJsBA5hHqqH2SjRo3o2LEjkydPpmDBgnTo0IEff/wxRT/KtDyd7Dk5OWFtbU3BggVTlD969Mhwf/To0djb21OzZk1KlizJoEGDUvTlfZYnRwCApCSzRIkS3LhxA8CQqPbq1Qs3Nzejv++++464uDjCw8ON1uHr65uubU+ZMoWwsDBKlSpFhQoVGDlyJKdPnzY8npycli9fPs11PHjwgLCwMBYtWpQivj59+gBJXVie9PSxTk7anjyut27donfv3ri4uGBvb4+bmxuNGjUCSLG/5ubmeHt7P3d/b968iaenp9GXDwB/f3/D41nFxsYm1ddebGys4fEn/6dV98kvBeldZ7Lk90h6+jgLIUR6yVhMQpiYq6srYJw4QdIH/urVqzl06BDr169n69at9O3bly+++IJDhw5hb2//zPWamZmlqwyME3F/f38uXbrEhg0b2LJlC7///jtff/01EyZMYPLkyRndvRSSW31nzZpF5cqVU63z9L49nRSlpWHDhly7do1169axbds2vvvuO2bPns0333xj1FKenvj+97//0atXr1TrVKxY0ej+846rTqejefPmhIaGMnr0aMqUKYOdnR2BgYH07t07RUu4lZVVin60pubh4cG9e/dSlCeXeXp6GuollxcpUiRF3Zo1a2Z4ncmS3yNPf6ETQogXIcmwECZWpkwZAAICAlJ9vHbt2tSuXZtp06axbNkyunfvzvLly9Od3GWGnZ0dXbt2pWvXrsTHx/PGG28wbdo0xo4da/gJOy1Pd1FQSnH16lVDAunn5weAo6MjzZo1y/LYXVxc6NOnD3369CEyMpKGDRsyadIk+vXrR/HixQE4e/Zsmsu7ubnh4OCATqfLsvjOnDnD5cuX+emnn4wumHt6lIuM8vHxYceOHTx+/NiodfjixYuGx7NK5cqV2bt3L3q93ihRP3z4MLa2tpQqVcpQD5JGQnky8b179y537txhwIABGV5nsuT3SHLLtxBCZIWc1fQgRD7k5eVFkSJFOHbsmFH5o0ePUnSdSE400ttVIjOS+zAns7S0pGzZsiil0tV3+eeff+bx48eG+6tXr+bevXu0atUKSBoRwM/Pj88//5zIyMgUyz948CDLYre3t6dEiRKG4+Xm5kbDhg354YcfuHXrllHd5GNtZmZGx44d+f3331NNmjMTX3LL8ZPPp1KKuXPnZnhdT2rdujU6nY758+cblc+ePRuNRmM45pD20Grp1alTJ4KDg/njjz+M1rlq1SratWtn6Ptbrlw5ypQpw6JFi9DpdIa6CxcuRKPR0KlTpwyvM9k///yDk5MT5cqVy9Q+CCFEaqRlWIgcoEOHDqxZswallKE/5E8//cTXX3/N66+/jp+fH48fP2bx4sU4OjrSunXrbIulRYsWFC5cmHr16lGoUCEuXLjA/PnzadOmTYq+qalxcXGhfv369OnTh+DgYObMmUOJEiUMQ6JptVq+++47WrVqRbly5ejTpw9eXl4EBgaya9cuHB0djYY5y4iyZcvSuHFjqlWrhouLC8eOHWP16tUMHjzYUGfevHnUr1+fqlWrMmDAAHx9fblx4wYbN27k5MmTQNJQabt27aJWrVr079+fsmXLEhoayvHjx9mxYwehoaEZiqtMmTL4+fnx4YcfEhgYiKOjI7///nuKrjEZ1a5dO5o0acL48eO5ceMGlSpVYtu2baxbt47hw4cbWuEhaWi1yZMns2vXLqOL6NavX8+pU6eApHGaT58+zdSpUwFo3769oUW/U6dO1K5dmz59+nD+/HnDbHE6nS5F95lZs2bRvn17WrRowZtvvsnZs2eZP38+/fr1M2rVzcg6IaklvV27dtJnWAiRtV76+BVCiBSOHz+uALV3716jsm7duqmiRYsqKysr5e7urtq2bauOHTtmtCxpDK324MEDo3q9evVSdnZ2KbbdqFEjo6HUvv32W9WwYUPl6uqqrKyslJ+fnxo5cqQKDw9/5j4kDzP222+/qbFjxyp3d3dlY2Oj2rRpk2IYM6WUOnHihHrjjTcM2/Hx8VFdunRRO3fufO6+pGXq1KmqZs2aytnZWdnY2KgyZcqoadOmqfj4eKN6Z8+eVa+//rpydnZW1tbWqnTp0urjjz82qhMcHKwGDRqkihQpoiwsLFThwoXVK6+8ohYtWpRin5+cQU0ppQICAhSgfvzxR0PZ+fPnVbNmzZS9vb0qWLCg6t+/vzp16lSKemk9T8mPPTm0mlJKPX78WL3//vvK09NTWVhYqJIlS6pZs2almM0wraHVkodyS+3vybiUUio0NFS9/fbbytXVVdna2qpGjRqlOfTfmjVrVOXKlZWVlZXy9vZWH330UYrnISPrvHDhggLUjh07Ut2eEEJklkYpmcpHiJzglVdewdPTk6VLl5o6lEzZvXs3TZo0YdWqVUY/hQuRFYYPH86ePXv4559/pGVYCJGlpM+wEDnE9OnTWbFiRZYOhyVEXhASEsJ3333H1KlTJREWQmQ56TMsRA5Rq1Yt4uPjTR2GEDmOq6trqhdbCiFEVpCWYSGEEEIIkW9Jn2EhhBBCCJFvScuwEEIIIYTIt6TPcAbp9Xru3r2Lg4ODXMghhBBC5BJKKR4/foynp2eOm+5cmJYkwxl09+5dihQpYuowhBBCCJEJt2/fxtvb29RhiBxEkuEMSp6B6/bt2zg6Opo4GiGEEEKkR0REBEWKFEnXTJoif5FkOIOSu0Y4OjpKMiyEEELkMtLFUTxNOs0IIYQQQoh8S5JhIYQQQgiRb0kyLIQQQggh8i3pMyyEEEII8RLpdDoSEhJMHUaeZmlpme4h9CQZFkIIIYR4CZRSBAUFERYWZupQ8jytVouvry+WlpbPrSvJsBBCCCHES5CcCLu7u2NraysjW2ST5AnS7t27R9GiRZ97nCUZFkIIIYTIZjqdzpAIu7q6mjqcPM/NzY27d++SmJiIhYXFM+vKBXRCCCGEENksuY+wra2tiSPJH5K7R+h0uufWlWRYCCGEEOIlka4RL0dGjrMkw0IIIYQQIt/K1cnwnj17aNeuHZ6enmg0GtauXfvcZXbv3k3VqlWxsrKiRIkSLFmyJNvjFEIIIYQQOVOuToajoqKoVKkSCxYsSFf9gIAA2rRpQ5MmTTh58iTDhw+nX79+bN26NZsjFUIIIYTIX4oVK8acOXNMHcZz5erRJFq1akWrVq3SXf+bb77B19eXL774AgB/f3/27dvH7NmzadmyZXaFKYQQQgiR7xw9ehQ7OztTh/FcuToZzqiDBw/SrFkzo7KWLVsyfPjwNJeJi4sjLi7OcD8iIiK7whM5kVKQEAMxjyAm9N//T/xFP1kWlvRfn2jqqIUQ4oUpFDpI8ZeoAT2gQ5HIv7c1KevpNE88DkZ1k24nPa4D9P+W6f6tg1ZL9/7HXt7OiiwVHx+PpaUlbm5upg4lXfJVMhwUFEShQoWMygoVKkRERAQxMTHY2NikWObTTz9l8uTJLytEkV2UgoToZySyycluWMrHdXHPXb0QIvdKTtZ0Gg16kpI9HRpD2X+JnubfpC2pTI/mv7qa/9ZhtK6nylL//5x1GcqStwuJ/z6m12ie2kbK+v/tSxqPpRJ78rpNxUIpupts66aj1+v5/PPPWbRoEbdv36ZQoUK88847jB8/njNnzjBs2DAOHjyIra0tHTt25Msvv8Te3h6A3r17ExYWRs2aNZk7dy5xcXGMGDGCcePGMXbsWL7//ntsbW355JNP6NOnDwA3btzA19eX3377jXnz5nH8+HFKlCjBggULaNSoEZA0NNmAAQP466+/CAoKomjRorz33nsMGzbMEHfytmvUqMGCBQuwsrIiICCAYsWKMXz4cIYPH45SismTJ/PDDz8QHByMq6srnTp1Yt68eQA8evSIYcOGsX79euLi4mjUqBHz5s2jZMmSACxZsoThw4ezYsUKhg8fzu3bt6lfvz4//vgjHh4eL3Tc81UynBljx45lxIgRhvsREREUKVLEhBHlc08mtakms0+00j79+IsktVpzsCnw75/LE7f//bP997+1M5g9f+pHIXI7pRSJSkeCSiRBrydBJSbd1+sM5Yl6HQkq6X7S7WfXSdD/+7ihTiIJSm9UnuZ6nrifqHTolZ5EpUdn9KdDx9NlehTK1Icz1zFDi5nmyT8zzDSaf/9rU31cq9Fg/u/jWrSY//uYVpN828xQ30KbP9OTsWPHsnjxYmbPnk39+vW5d+8eFy9eJCoqipYtW1KnTh2OHj3K/fv36devH4MHDzYaCOCvv/7C29ubPXv2sH//ft5++20OHDhAw4YNOXz4MCtWrOCdd96hefPmeHt7G5YbOXIkc+bMoWzZsnz55Ze0a9eOgIAAXF1d0ev1eHt7s2rVKlxdXTlw4AADBgzAw8ODLl26GNaxc+dOHB0d2b59e6r79vvvvzN79myWL19OuXLlCAoK4tSpU4bHe/fuzZUrV/jzzz9xdHRk9OjRtG7dmvPnzxsmzYiOjubzzz9n6dKlaLVa/ve///Hhhx/y66+/vtBxz1evtsKFCxMcHGxUFhwcjKOjY6qtwgBWVlZYWVm9jPDyF6UgPiqNRDY50Q1L/XFdfOa3qzU3TmZtn0xsnVNPdm1dwNIeZGxIkYuFxobyT/A/HA8+TlhcGAn6BBL1iSn/6xKSktAn/6dSL1Hlj+5A5hpztBotZlqzpNvapKTN6Lb23zpP3DbXmGOmNUv/7QysI7U6Zhqz//7/ezutdTxZJ/m2Vvvsx43qarQyVm42ePz4MXPnzmX+/Pn06tULAD8/P+rXr8/ixYuJjY3l559/NvTBnT9/Pu3ateOzzz4z/Ort4uLCvHnz0Gq1lC5dmpkzZxIdHc24ceOApGR7xowZ7Nu3jzfffNOw7cGDB9OxY0cAFi5cyJYtW/j+++8ZNWoUFhYWRr+Q+/r6cvDgQVauXGmUDNvZ2fHdd98ZJrt42q1btyhcuDDNmjXDwsKCokWLUrNmTQBDErx//37q1q0LwK+//kqRIkVYu3YtnTt3BpImLvnmm2/w8/MzxD1lypQXPPL5LBmuU6cOmzZtMirbvn07derUMVFE+czlrfDXJxB5PwuSWounEtnkJNY5jUT33z9JakU+ERobyrGgYxwNOsqx4GNcDbua7ds015pjobVI8T+1stT+p6eO4b+ZBeYac8NtC42FUdmzkrr0JLLaf1ssJekTL8uFCxeIi4vjlVdeSfWxSpUqGV2MVq9ePfR6PZcuXTIkw+XKlUOr/W+gsEKFClG+fHnDfTMzM1xdXbl//77R+p/Mg8zNzalevToXLlwwlC1YsIAffviBW7duERMTQ3x8PJUrVzZaR4UKFdJMhAE6d+7MnDlzKF68OK+++iqtW7emXbt2mJubc+HCBczNzalVq5ahvqurK6VLlzaKw9bW1pAIA3h4eKTYl8zI1clwZGQkV6/+d4IPCAjg5MmTuLi4ULRoUcaOHUtgYCA///wzAO+++y7z589n1KhR9O3bl7/++ouVK1eyceNGU+1C/vHgMqzqndTF4Ulmls/udpBW1wRLO0lqhXhCSEwIx4KPcSzoWJrJbwnnElQvVB1vB+/0Ja3/JpdPJ56GsicTWY25JI5CvIC0fqHOiOTuBMk0Gk2qZXq9Pt3rXL58OR9++CFffPEFderUwcHBgVmzZnH48GGjes8bNaJIkSJcunSJHTt2sH37dt577z1mzZrF33//ne5YUtsXpV68m1OuToaPHTtGkyZNDPeT+/b26tWLJUuWcO/ePW7dumV43NfXl40bN/L+++8zd+5cvL29+e6772RYteyWEAur+yQlwr4NoeX0/5JaC1tJaoXIhOTk92jQUY4FHeNa+LUUdUoWKEmNQjWoXrg61QpVw8XaxQSRCiHSo2TJktjY2LBz50769etn9Ji/vz9LliwhKirKkHTu37/f0B3iRR06dIiGDRsCkJiYyD///MPgwYMN26lbty7vvfeeof61aynPN+lhY2NDu3btaNeuHYMGDaJMmTKcOXMGf39/EhMTOXz4sKGbREhICJcuXaJs2bIvuHfPl6uT4caNGz/zG0Fqs8s1btyYEydOZGNUIoXtH0PwWbAtCG8sBofCpo5IiFznYcxDQ8vv0aCjXA+/nqJOqQKlqF6oOjUK16BaoWoUsC5ggkiFEJlhbW3N6NGjGTVqFJaWltSrV48HDx5w7tw5unfvzsSJE+nVqxeTJk3iwYMHDBkyhB49eqQYJSszFixYQMmSJfH392f27Nk8evSIvn37AklJ+s8//8zWrVvx9fVl6dKlHD16FF9f3wxtY8mSJeh0OmrVqoWtrS2//PILNjY2+Pj44OrqSocOHejfvz/ffvstDg4OjBkzBi8vLzp06PDC+/c8uToZFrnAxY1wZFHS7de/kURYiHR6GPPQqM9vWslvjcI1qFGoBlULVZXkV+RqUXGJBDyM4tqDSMJjEuhZp5ipQ3rpPv74Y8zNzZkwYQJ3797Fw8ODd999F1tbW7Zu3cqwYcOoUaOG0dBqWWHGjBnMmDGDkydPUqJECf78808KFiwIwDvvvMOJEyfo2rUrGo2Gbt268d5777F58+YMbcPZ2ZkZM2YwYsQIdDodFSpUYP369bi6ugLw448/MmzYMNq2bUt8fDwNGzZk06ZNKbpGZAeNyorOFvlIREQETk5OhIeH4+joaOpwcrbwQPimXtLFcnUGQ8tppo5IiBzrQfSD/1p+g48SEB6Qok7pAqWpUfjfbg/u1XC2dn75gQrxApRSPHgcx9X7kVx7EMm1B0nJ77X7kdwNjzXUs7bQcn7yq2i1WdeNztSf37GxsQQEBODr64u1tfVL335qkscZPnHiRIoL4nK7jBxvaRkW2UOXCL/3S0qEPavAKxNNHZEQOUpy8ns06ChHg45yI+KG0eMaNJR2KU31QtUl+RW5TnyinluhUVy9/2+y+2/ie/1+JI/j0h6Wz9XOEj83e/zc7YlN1GFrKWmKyH7yKhPZY88suHUgaSizjt+DuUxEIfK3+9H3Da2+x4KOpZr8lnEpQ7VC1Qx9fp2snEwTrBDpFB6TYGjZfbKV92ZoNDp96j88azXg42qHn5tdUuLrZo+fux3FC9pTwE4+K8TLJ8mwyHo39sGemUm3284GV79n1xciD7offd/Q6vtP8D9pJr/VC1c39PmV5FfkRHq94m54TFKyez+Sq08kvw8j057Z087SDD/3f5Pd5MTX3R4fV1uszM1e4h6ItBQrVixLhibL7SQZFlkrOhR+7w9KD5W7Q8Uuz19GiDwgOCr4v6HOgo9xM+Km0ePJyW+NwjWoUbgGVdyrSPIrcpTYBB3XHxh3a7h2P5LrDyOJTUh7XNrCjtb4uT/RyutmTwl3ewo5WsnY0yJXkGRYZB2lYN0geHwXXEtCq5mmjkiIbBMUFWQ01Nmtx7eMHtdqtEnJ77/j/FYtVBVHS7noVpiWUoqQqHjjbg3//t15FENajYQWZhqKudoZujQkJ7y+Be1wsM7+q/2FyE6SDIusc2QxXNqUNKtcpx/Ayt7UEQmRZYKiggytvseCjqWa/Pq7+BvG+a1SqIokv8JkEnV6bj+K+Tfp/a+l9+r9pGHL0uJobU4J9/9ad5O7NhQpYIO5mTbN5YTIzSQZFlnj3mnYNj7pdvNPwKOiaeMR4gU9mfweDTrK7ce3jR5PTn6f7PbgYOlgomhFfhUZl8j1B5H/DVX27+gNN0KiSNCl3syr0YB3ARujbg1+bnb4udvjamcpXRtEviPJsHhx8VGwui/o4qFUK6j1jqkjEiLD9ErPkaAjbLq+iaNBR7kTecfoca1GS1mXsoZxfiX5FZml1ytiE3XEJuiJTdD9+6f/t0xH3L/lMQlP1Pm3flyCjsi4RG6ERHHtfhRBEbFpbsfaQkvxgvb/XsT2X59e34J22FjKBWxCJJNkWLy4TaMg5Ao4eEKHBUnNDkLkEmGxYay7to5Vl1cZXfRmpjGjrGtZqheuTvVC1anqXhV7S+n6kxclJ6cx8TpiE40T1LgnEtGnE9Skx55KaP+tE/dEcmuU9CbqiU9M+2K0zChob2Vo2X1y5AYvZ5ssnbRCiLxKkmHxYk6vgpO/gEYLHReDnaupIxLiuZRSnHxwkpWXVrLtxjbi9fEA2FnY0bZ4Wxp5N6KKexVJfnMZpRTbzgez/+rDVBLblK2sya2w8bqsTU4zwsJMg7W5GVYWZlhbaLFO/m9u9u/tlOU2lmYUcbFN6tfrZo+TrVzAJl6ekJAQ/P39OXLkCMWKFXup265duzYjR46kY8eOWbpeSYZF5oVehw3vJ91uOBKK1TdtPEI8x+P4x2y4voGVl1ZyNeyqodzfxZ8upbvQ2rc1tha2JoxQZNbZwHCmrD/PkRuhL7QeSzMtVqkmpdonklMzrM21Kcqt/i2zSSWJtUptPeZauShN5DrTpk2jQ4cORonwrVu3GDhwILt27cLe3p5evXrx6aefYm6e/jRzz549zJo1i3/++Yd79+6xZs0aXnvtNaM6H330Ee+//z6vv/46Wm3WvXckGRaZkxif1E84/jEUrQsNR5k6IiHSdO7hOVZeXsnmgM3EJMYAYG1mTevirelSqgvlCpYzcYQis+5HxDJr6yVWH7+DUmBlruXNGkVwd7RO0ZqafNsqlaQ0+baZdCsQIk3R0dF8//33bN261VCm0+lo06YNhQsX5sCBA9y7d4+ePXtiYWHB9OnT073uqKgoKlWqRN++fXnjjTdSrdOqVSv69evH5s2badOmzQvvTzJJhkXm/DUF7p4Aa+ek7hFm8lISOUt0QjSbAzaz8vJKzoecN5SXcC5B51KdaevXVoY+y8ViE3R8vy+ABbuuEh2vA6BDZU9GvVoGL2cbE0cnRMYopYhJ0Jlk2zYWZukeQWTTpk1YWVlRu3ZtQ9m2bds4f/48O3bsoFChQlSuXJlPPvmE0aNHM2nSJCwt0zfFdqtWrWjVqtUz65iZmdG6dWuWL18uybAwsSs74MBXSbc7LAAnb9PGI8QTLj+6zKpLq9hwfQORCZEAWGgtaFGsBV1KdaGKexUZOioXU0qx4fQ9Zmy+SGBYUit/5SLOfNy2LNV8Cpg4OiEyJyZBR9kJW59fMRucn9ISW8v0pYN79+6lWrVqRmUHDx6kQoUKFCpUyFDWsmVLBg4cyLlz56hSpUqWxluzZk1mzJiRpeuUZFhkzOMgWPPv0Gk1+oN/W9PGIwQQp4tj241trLq8ihP3TxjKizoUpXOpznQo0YEC1pIo5XanbofxyYbzHLv5CAAPJ2tGv1qG9pU8ZdQEIV6Cmzdv4unpaVQWFBRklAgDhvtBQUFZHoOnpye3b99Gr9dnWb9hSYZF+un1SYlw9EMoVB5aTDV1RCKfuxF+g9WXV7P22lrC48KBpCHRmhZtSudSnanlUQutRi5Qyu2CwmOZufUifxwPBJJ+1n23kR8DGhaX8XJFnmBjYcb5KS1Ntu30iomJwdraOhujeT4bGxv0ej1xcXHY2GRNlyhJhkX67Z8D13eDhW3SdMsWpn1DiPwpQZ/Arlu7WHl5JYfvHTaUF7YrTKeSnXi95Ou427qbMEKRVWLidSzee52Fu68Z+lO+UcWLka+WxsNJ+gWLvEOj0aS7q4IpFSxYkEePHhmVFS5cmCNHjhiVBQcHGx7LaqGhodjZ2WVZIgySDIv0un0U/vq3JbjVZ+BW2rTxiHznbuRdVl9ezZqra3gY8xAADRoaeDegS6ku1Peqj5lWWgnzAqUUf566y2ebL3I3PGmGtapFnZnQrhyVizibNjgh8rEqVarwyy+/GJXVqVOHadOmcf/+fdzdkxoitm/fjqOjI2XLls3yGM6ePZvl/ZAlGRbPFxMGv/cFpYPyHaFKD1NHJPIJnV7HvsB9rLi0gn2B+1AoAAraFOSNkm/QsWRHPO09n7MWkZucuPWIKRvOc+JWGABezjaMaVWGthU95MJHIUysZcuWjB07lkePHlGgQNJ1GC1atKBs2bL06NGDmTNnEhQUxEcffcSgQYOwsrJK97ojIyO5evW/8d8DAgI4efIkLi4uFC1a1FC+d+9eWrRokXU7hSTD4nmUgvXDIOwWOPtA29ky3bLIdvej7/PHlT/4/crvBEX9dwFGbY/adCndhcZFGmOhlVm38pK7YTHM3HKRtSfvAmBracZ7jf3o16A41hno0yiEyD4VKlSgatWqrFy5knfeSbqY3szMjA0bNjBw4EDq1KmDnZ0dvXr1YsqUKYblbty4ga+vL7t27aJx48aprvvYsWM0adLEcH/EiBEA9OrViyVLlgAQGBjIgQMHUrROvyhJhsWzHf8Jzq8FrTl0+hGsnUwdkcij9ErPoXuHWHVpFbtu70KnkvqIOls581qJ1+hUqhM+jj4mjlJktej4RL79+zrf7rlGbELStMidqnkzsmVpCjnKdQlC5DQTJkxg5MiR9O/f3zCag4+PD5s2bUpzmYCAAJydnalUqVKadRo3boxS6pnbnjdvHr1798bbO2uHdJVkWKTt/kXYPCbpdtOPwbvas+sLkQmhsaGsu7qOVZdXcfvxbUN5VfeqdC7dmeY+zbEyS/9PbSJ30OsV604F8tnmSwRFJPULrlGsABPalqOCt3zpFiKnatOmDVeuXCEwMJAiRYqka5lNmzYxbtw4Q9eKzHJ3dze0GGcljXpeGi6MRERE4OTkRHh4OI6OeXj2qoQYWNwU7p8Hv6bQ/XfIwnnARf6mlOL4/eOsvLSS7Te3k6BPAMDewp52fu3oXKozJQuUNHGUIrv8czOpX/Cp22EAeBewYVxrf1qVLyz9gkW2MfXnd2xsLAEBAfj6+pp8eLL8ICPHW1qGReq2jk9KhO3c4fVvJREWWSIiPoL119az6tIqroVfM5SXcy1Hl9JdeLXYq9ha2JowQpGd7jyK5rMtl1h/KqlfsJ2lGYOalqBvPV/pFyyEMBlJhkVK5/+EY98n3X79G7CXMVtF5imlOPvwLCsvr2RLwBZidUk/iduY29DatzWdS3WmXMFyJo5SZKeouES++fsai/ZcJy5Rj0YDXaoV4YOWpXB3kBYyIYRpSTIsjIXdgj8HJ92uNwxKvGLaeESuFZ0QzcaAjay6tIoLoRcM5SWcS9C1dFfaFG+Dg6WDCSMU2U2vV/xxIpCZWy5y/3EcALV8Xfi4bVnKe0m/YCFEziDJsPiPLhF+7w+x4eBVLemiOSEy6FLoJVZdXsWG6xuISogCwFJrSctiLelSuguV3CpJv9B84OiNUKasP8+ZwKRpsou42DC+tT8ty0m/YCFEziLJsPjP3zPg9iGwcoSO34OZjOMq0ic2MZZtN7ex8tJKTj04ZSj3cfShc6nOdPDrgLO1s+kCFC/N7dBoZmy+yMYz9wCwtzJnSNMS9K5XDCtz6RcshMh5JBkWSQL2wJ7Pk263mwMuviYNR+QOAeEBrLq8inVX1xERHwGAucacpkWb0qV0F2oWrimtgPlEZFwiX++6ynf7AohP1KPVQNcaRRnRvBRuDjI0nhAi55JkWEDUQ/hjAKCSplou39HUEYkcLEGXwM5bO1l1eRVHgo4Yyj3tPOlUqhOvl3ydgjYFTRiheJl0esXqf24za+tlHkYm9QuuU9yVj9uWpaxnHh5+UgiRZ0gynN8pBWvfg8f3oGApaPWZqSMSOVSiPpEl55aw9PxSQmNDAdBqtDT0bkiXUl2o61kXM638DJ6fHLoewpT15zl/L+lXgWKutoxr7U/zsoXkFwEh8qiQkBD8/f05cuQIxYoVe2nbjY+Pp1SpUqxevZrq1atn6bpl8Nj87tBCuLIVzKySplu2tDN1RCIHCooK4u2tbzP3+FxCY0Nxs3Hj3UrvsrXjVr5q+hUNvBtIIpyP3AqJ5t2l//DmokOcvxeBg7U5H7XxZ9v7jWghF8gJkadNmzaNDh06GCXCt27dok2bNtja2uLu7s7IkSNJTEzM0Ho//fRTatSogYODA+7u7rz22mtcunTJ8LilpSUffvgho0ePzqpdMZCW4fzs7knYPiHpdstpULi8ScMROdOOmzuYeGAiEfER2JrbMqbmGNr6tcVCKxdY5jePYxOYv+sqP+67QbwuqV/wW7WK8n6zUrjaS79gIfK66Ohovv/+e7Zu3Woo0+l0tGnThsKFC3PgwAHu3btHz549sbCwYPr06ele999//82gQYOoUaMGiYmJjBs3jhYtWnD+/Hns7JIa6rp3784HH3zAuXPnKFcu68anl2Q4v4p7DKv7gj4ByrSFGv1MHZHIYWITY5l1dBYrL68EkmaJm9lwJkUdi5o4MvGy6fSKlcdu88W2SzyMjAegQcmCfNSmLKULy1jRQrwwpSAh2jTbtrCFdP6as2nTJqysrKhdu7ahbNu2bZw/f54dO3ZQqFAhKleuzCeffMLo0aOZNGkSlpaW6Vr3li1bjO4vWbIEd3d3/vnnHxo2bAhAgQIFqFevHsuXL+eTTz5J5w4+nyTD+dXGDyH0Gjh6Q/uv0v1GEPnDlUdXGLVnFFfDrgLQp3wfhlQegoUMt5fvHLj6kCkbznMx6DEAxQva8VFbf5qUdpfuEEJklYRomO5pmm2Pu5vuLpJ79+6lWrVqRmUHDx6kQoUKFCpUyFDWsmVLBg4cyLlz56hSpUqmwgoPTxqj3MXFxai8Zs2a7N27N1PrTIskw/nRqeVwejlotNBxMdi6PH8ZkS8opVh5aSWzjs0iTheHq7Ur0+tPp65XXVOHJl6ygIdRTN90ge3ngwFwtDZneLNS/K+2D5bmcrmJEPnRzZs38fQ0TtqDgoKMEmHAcD8oKChT29Hr9QwfPpx69epRvrxxF05PT09u3ryZqfWmRZLh/CbkGmwYkXS70RjwkSRHJAmPC2fC/gn8dfsvAOp51WNavWm42riaODLxMoXHJDD/ryssOXCDBJ3CTKvhf7WKMrxZKQrYpe/nTiFEBlnYJrXQmmrb6RQTE4O1tXU2BpNk0KBBnD17ln379qV4zMbGhujorO1SIslwfpIYB6v7QEIU+NSHhh+aOiKRQxwNOsrYvWMJjg7GXGvO+1Xf539l/4dWIy2A+UWiTs/yo7f5cvtlQqOS+gU3KuXGR238KVlI+gULka00mlwxmlPBggV59OiRUVnhwoU5cuSIUVlwcLDhsYwaPHgwGzZsYM+ePXh7e6d4PDQ0FDc3twyv91kkGc5PdkyGe6fAxgXeWAQyFFa+l6hP5NvT37Lo9CL0So+Pow8zG86krGtZU4cmXqK9Vx4wdcMFLgUn9Qv2c7Pjo7ZlaVLa3cSRCSFykipVqvDLL78YldWpU4dp06Zx//593N2Tzhnbt2/H0dGRsmXT/1milGLIkCGsWbOG3bt34+ub+ky4Z8+ezXQ/5LRIMpxfXN4KhxYk3X7ta3DyMm08wuTuRt5lzN4xnLh/AoAOfh0YV2scthn4yUzkbtceRDJ94wV2XrwPgLOtBe83K8VbtYpiYSa/CgghjLVs2ZKxY8fy6NEjChQoAECLFi0oW7YsPXr0YObMmQQFBfHRRx8xaNAgrKzSP+TioEGDWLZsGevWrcPBwcHQ39jJyQkbGxtDvb1792bpSBIgk27kDxH3YO3ApNu13oXSrUwbjzC5bTe20Wl9J07cP4GdhR0zGsxgav2pkgjnE+HRCUxZf56Ws/ew8+J9zLUa+tQrxu4PG9OrbjFJhIUQqapQoQJVq1Zl5cqVhjIzMzM2bNiAmZkZderU4X//+x89e/ZkypQphjo3btxAo9Gwe/fuNNe9cOFCwsPDady4MR4eHoa/FStWGOocPHiQ8PBwOnXqlKX7JS3DeZ1eB3/0h+gQKFwBmk95/jIiz4pJjGHm0ZmsvrwagAoFK/BZw88o4lDExJGJ7KaU4vrDKHZeCObr3dcIi04AoGkZd8a19qeEu72JIxRC5AYTJkxg5MiR9O/fH6026Yuzj48PmzZtSnOZgIAAnJ2dqVSpUpp1lFLP3facOXMYOXKkUUtxVpBkOK/b9yXc2AsWdknTLZvLLFH51aXQS4zaM4rr4dfRoKFv+b4MqjJIZpLLo5RSXHsQxaHrIRy6HsLhgFAePI4zPF6qkD0ftSlLw1JZeyGKECJva9OmDVeuXCEwMJAiRdLXkLJp0ybGjRtn6FqRGfHx8VSoUIH3338/0+tIi0alJxUXBhERETg5OREeHo6jo6Opw3m2W4fgx9agdPDaQqj8lqkjEiaglGL5peV8fvRz4vXxFLQpyPT606njWcfUoYks9HTye+h6KA8j44zqWJprqVrUmXaVPOlavQjm0h1C5COm/vyOjY0lICAAX1/flzI8WX6XkeMtLcN5Vcwj+L1fUiJcoQtU6mbqiIQJPIp9xIQDE9h9ezcADbwaMLX+VFysZaKV3C4p+Y3k4PXQpJbfZyS/tYu7Uru4K5WLOGNtIaPICCHEkyQZzouUgj+HQPhtKOALbb+U6ZbzoSP3jjB271jux9zHQmvBiGoj6O7fXabQzaWUUly9H2lo9T0cEMLDyHijOlbmWqoWLfBv8utCJUl+hRDiuSQZzouO/QAX1oPWAjr9AFYyYH5+kqhPZOGphSw+vRiFophjMWY2nIm/q7+pQxMZoJTiyr/J7+F/W39DolImv9V8ChhafisVccLKXJJfIYTICEmG85rg87B1XNLtZhPBq6pp4xEvVWBkIKP3jObUg1MAvF7idcbUHCNDpuUCTya/yQlwaslv9WIFqO3rSi1JfoUQIktIMpyXxEcnTbecGAslmkPtQaaOSLxEW25sYcqBKTxOeIy9hT0T6kygla+MKZ1T6fVPJb8BoYZpkJNZW/zb8uvrSm0/Vyp6S/IrhBBZTZLhvGTLGHhwEewLJY0eoZUrxfOD6IRoZh6dye9XfgegoltFPmvwGd4OKed0F6aj1ysu33/MoWtJfX6P3Eg9+a3u40Lt4i7ULu5KRW9nLM3lfSyEENlJkuG84twaOP4ToIE3FoG9jB2aH1wKvcTIPSMJCA9Ag4Z+FfoxsPJAGTs4B9DrFZeCHxu6PBwOCOHRvxNdJLOxMEvq9vDvBW8VvCT5FULkbCEhIfj7+3PkyBGKFSv2Urddu3ZtRo4cSceOHbN0vZIM5wWPbsKfw5Ju138fijc2aTgi+ymlWHZxGV8c+4IEfQLuNu5MbzCdWh61TB1avvVk8pvc7SFMkl8hRB4zbdo0OnToYJQIDx06lP3793P27Fn8/f05efJkhte7ePFifv75Z86ePQtAtWrVmD59OjVr1jTU+eijj3j//fd5/fXXDbPfZQVJhnM7XQL8/jbEhYN3DWgyztQRiWwWGhvKhP0T+PvO3wA09m7MlHpTKGCd+Zl9RMbp9YqLQf8lv0dupEx+bS3NjEZ7qOjthIVMdCGEyKWio6P5/vvv2bp1a4rH+vbty+HDhzl9+nSm1r179266detG3bp1sba25rPPPqNFixacO3cOLy8vAFq1akW/fv3YvHkzbdq0eaF9eZIkw7ndrmlw5yhYOUHH78FMfh7Pyw7fO8zYvWN5EPMAS60lH1T/gG5lusnYwS+BXq+4EBTBoX+HOTsSEEp4TMrkt3qx//r8VvCS5FcIkXds2rQJKysrateubVQ+b948AB48eJDpZPjXX381uv/dd9/x+++/s3PnTnr27AmAmZkZrVu3Zvny5ZIMi39d2wX75iTdbj8PCviYNByRfRL0CXx98mu+P/M9CoWvky+zGs6itEtpU4eWZ+n0igv3IgxdHlJLfu0MyW9St4fykvwKITJBKUVMYoxJtm1jbpPuBpW9e/dSrVq1bI4oSXR0NAkJCbi4GM+YWrNmTWbMmJGl25JkOLeKfABr3gEUVOsN5V4zcUAiu9x5fIfRe0Zz+mHSt+2OJTsyqsYoGTs4G0TEJrDq2B0OXgvhSEAIEbGJRo/bWZpRwzcp+a3lK8mvECJrxCTGUGuZaa75OPzW4XR/nty8eRNPT89sjijJ6NGj8fT0pFmzZkblnp6e3L59G71en2X9hiUZzo30elj7LkQGg5s/tPzU1BGJbLI5YDNTDk4hMiESBwsHJtadSMtiLU0dVp504tYjhvx2gjuP/mudsbcyf+KCN1fKezpiLsmvECKfiomJwdraOtu3M2PGDJYvX87u3btTbM/Gxga9Xk9cXBw2NjZZsj1JhnOjQwvg6g4wt06abtlSWgjzmuiEaD498ilrr64FoLJbZT5r+Bme9i/nG3l+otcrvtt3nZlbLpGoVxRxseF/tXyoXdyVcpL8CiFeAhtzGw6/ddhk206vggUL8ujRo2yMBj7//HNmzJjBjh07qFixYorHQ0NDsbOzy7JEGCQZzn0C/4Edk5Juv/opFCpr0nBE1rsQcoFRe0ZxI+IGGjQMqDiAdyu9i7lW3q5ZLTQqng9WnmTXpQcAtKnowadvVMDRWi5EFUK8PBqNJld0fatSpQq//PJLtq1/5syZTJs2ja1bt1K9evVU65w9e5YqVapk6Xbl0zU3iY2A1X1Bnwj+7aFaH1NHJLKQUopfLvzC7H9mJ40dbOvOjAYzqFG4hqlDy5MOXQ9h2PITBEfEYWWuZWK7cnSrWURG5hBCiDS0bNmSsWPH8ujRIwoU+G84z6tXrxIZGUlQUBAxMTGGcYbLli2LpaVlutb92WefMWHCBJYtW0axYsUICgoCwN7eHnt7e0O9vXv30qJFi6zbKUB+/8stlIKNI+DRDXAqkjR6hHxo5xkhMSEM2jmImUdnkqBPoEmRJvze7ndJhLOBTq+Yt/MKby0+RHBEHH5udqwbXI+3ahWVRFgIIZ6hQoUKVK1alZUrVxqV9+vXjypVqvDtt99y+fJlqlSpQpUqVbh7966hjkajYcmSJWmue+HChcTHx9OpUyc8PDwMf59//rmhTmBgIAcOHKBPn6xtDJSW4dzi5DI4swo0ZknjCdvIBAt5xcG7Bxm3bxwPYx5iqbVkZI2RdC3dVRKzbHA/IpbhK05y4FoIAJ2qeTOlQzlsLeVUKIQQ6TFhwgRGjhxJ//79DaM57N69+5nLBAQEYG5uTr169dKsc+PGjedue968efTu3Rtvb++MhPxc8gmQGzy8Aps+TLrdZCwUlSl384IEfQLzT8znx7M/olD4Ofkxs9FMShUoZerQ8qQ9lx/w/oqThETFY2tpxtTXyvNG1aw9oQohRF7Xpk0brly5QmBgIEWKFEnXMps2bWLAgAGULFnyhbbt7u7OiBEjXmgdqdEopVSWrzUPi4iIwMnJifDwcBwdHbN/gwmx8F0zCD4Dvg2hx1rQmmX/dkW2uh1xm9F7R3Pm4RkAOpfqzMgaIzN0Va9InwSdni+3X2bh7msAlCnswILuVfFzs3/OkkKIvOSlf34/JTY2loCAAHx9fV/K8GT5XUaOt7QM53TbJyQlwrau8PoiSYTzgI3XN/LJoU+ISojCwdKByXUn09ynuanDypMCw2IY+tsJ/rmZNBRQj9o+jG/jj7WFvI+EEEIkkWQ4J7u4CY58m3T7tW/A0cO08YgXEpUQxfTD0/nz2p8AVHWvyowGM/Cwl+c1O2w7F8TI1acJj0nAwcqczzpVpHUFOdZCCCGMSTKcU4UHwrr3km7XHgSlsnYYEfFynQs5x+g9o7kZcROtRss7Fd9hQMUBMnZwNohL1DFj80V+3H8DgEpFnJnfrQpFXHL+GJ5CCCFePvkkzon0OvijP8Q8Ao9K0GyiqSMSmaRXepaeX8qc43NI1CdSyLYQMxrMoHrh1AcTFy/mxsMoBv92nLOBEQD0b+DLyJZlsDSXUSSFEDmDXq83dQj5QkYuicsTyfCCBQuYNWsWQUFBVKpUia+++oqaNWumWX/OnDksXLiQW7duUbBgQTp16sSnn36aczq075kFN/eDpT10+hHMrUwdkciEhzEP+Wj/R+wP3A/AK0VfYXLdyThZOZk4srzpz1N3GffHGSLjEilga8EXXSrRtEwhU4clhBAAWFpaotVquXv3Lm5ublhaWsoQmtlEKcWDBw/QaDRYWDx/RtFcnwyvWLGCESNG8M0331CrVi3mzJlDy5YtuXTpEu7u7inqL1u2jDFjxvDDDz9Qt25dLl++TO/evdFoNHz55Zcm2ANj8Vf2wN9fAObQ8nNwKALx8QBotVrMzf97yuL/LU/N0y+AjNRNSEhI8xtVdtUFjGapyUjdxMTEZ37TzkhdCwsLw8npReoevnuYiQcnEhobiq2ZLSNqjqBL6S5oNBp0Oh06nS5d631eXXNzc8M4jzmhrl6vJzExMc26ZmZmmJmZZWndmHgdM7Zc4rdjgQDU9CnA553LUdjRJtXX/ZPrVUqRkJCQrhieV/fJ92d21YVnv5flHJF63Zx4jnhWXTlH5M0LXLVaLb6+vty7d89oMgqRPTQaDd7e3ul6PeX6odVq1apFjRo1mD9/PpD0RitSpAhDhgxhzJgxKeoPHjyYCxcusHPnTkPZBx98wOHDh9m3b1+K+nFxccTFxRnuR0REUKRIkewZmiU6lMmzvkrz4ZIlS/LWW28Z7k+fPj3ND1EfHx969+5tuD9r1iyio6NTrevp6Un//v0N9+fMmUN4eHiqdd3c3HjvvfcM97/++msePHiQal0nJyeGDx9uuL948eI0TwC2traMHDnScH/JkiXcvHkz1boWFhaMGzfOcH/ZsmVcuXIl1boAEyf+181k1apVnD9/Ps26Y8eONXwwrl27llOnTqVZ98MPP8TOzg6AjRs3cuzYsTTrDhs2DGdnZwC2bdvGwYMH06w7cOBAwxe53bt38/fff6dZt1+/fnh5eQGwf/9+duzYkWbdXr16UaxYMQCOHDnC5s2b06zbrVs3SpVKGu/45MmTrFu3Ls26nTp1oly5cgCcO3eO1atXp1m3Q4cOVK5cGYDLly/z22+/pVm3VatWhl94bty4wU8//ZRm3aMJ3pzTFWZIkxJ08rfjxx++T7Nuo0aNaNy4MQD3799n4cKFadatU6eOYdrPsLAw5s6dm2bd6tWr06ZNGwCioqKMZk16WqVKlXjttdeApCT0008/TbNu2bJl6dy5s+H+5MmT06wr54gkco74T24+R2Q1Uw+tlkwpRWJi4jO/QIgXZ2Fhke4vVrm6ZTg+Pp5//vmHsWPHGsq0Wi3NmjVL80RSt25dfvnlF44cOULNmjW5fv06mzZtokePHqnW//TTT5/54ZNllIJ1g4HS2b8tIfIYO0szfnmrFvVKFCQwMNDU4QghRJqSfxFJz8/34uXI1S3Dd+/excvLiwMHDlCnTh1D+ahRo/j77785fPhwqsvNmzePDz/80PDt7N13302zdeiltQzfPQGLXyEeS+i9ATwqpqgiP4GmXjcn/AQaERPBwO0DORdyDkdLRz6q9RGNizZOta78BPpi3SSi4hKZvP4cG07fA6CunyszO1ehsLNthtcr3STkHJHRutJN4sXqmrKbRE5pGRY5T65uGc6M3bt3M336dL7++mtq1arF1atXGTZsGJ988gkff/xxivpWVlZYWb2EC9g8q0CfzViGXAWf9I008OQJPCvrZuTbak6o++SHvynqJuoTGXtgLKcfncbJxoklrZZQ3Kl4mvUzcrLPbXW1Wm26X2uZqXvubjiDl50g4GEUZlpzRjQvxcBGfmi1mhR100Oj0eSqupB973s5R+ScujnhvZwTzhFCvCy5OhkuWLAgZmZmBAcHG5UHBwdTuHDhVJf5+OOP6dGjB/369QOgQoUKREVFMWDAAMaPH2/49moSRWsl/YlcQynF1ENT2XNnD1ZmVsxvOv+ZibDIHKUUSw/dZOrGC8Qn6vF0smZetypUL+Zi6tCEEELkcrl68E1LS0uqVatmdDGcXq9n586dRt0mnhQdHZ0i4X3yZ1AhMuKb09/w+5Xf0Wq0zGw4k8rulU0dUp4THpPAwF+OM2HdOeIT9TTzd2fj0AaSCAshhMgSubplGGDEiBH06tWL6tWrU7NmTebMmUNUVBR9+vQBoGfPnnh5eRmu1m7Xrh1ffvklVapUMXST+Pjjj2nXrl2eHc5FZI81V9bw9cmvARhfazxNizY1cUR5z4lbjxjy2wnuPIrBwkzDmFb+9K1XTMbmFEIIkWVyfTLctWtXHjx4wIQJEwgKCqJy5cps2bKFQoWSBtu/deuWUUvwRx99hEaj4aOPPiIwMBA3NzfatWvHtGnTTLULIhfac2cPkw8mjTLSv0J/upTuYuKI8ha9XvHdvuvM3HKJRL2iqIst89+qQkVvZ1OHJoQQIo/J1aNJmIJcjSrOPjxL3619iUmMob1fe6bWmyotlVkoNCqeD1aeZNelpLFp21T04NM3KuBoLcMQCSEyTz6/RVpyfcuwEC/T7YjbDNo5iJjEGOp61mVS3UmSCGehQ9dDGLb8BMERcViZa5nYrhzdahaRYyyEECLbSDIsRDqFxoby7o53CY0Nxd/Fny8bf4mFVlors4JOr5j/11Xm7ryMXoGfmx0LulelTGFpvRFCCJG9JBkWIh2iE6IZvHMwtx7fwsvei6+bfY2dhZ2pw8oT7kfEMmz5SQ5eDwGgUzVvpnQoh62lnJ6EEEJkP/m0EeI5EvWJjNozijMPz+Bk5cTCZgspaFPQ1GHlCX9ffsCIFScJiYrH1tKMqa+V542q3qYOSwghRD4iybAQz5A8qcbfd/42TKrh6+Rr6rByvQSdni+3X2bh7msA+Hs4Mv+tKvi52Zs4MiGEEPmNJMNCPMO3p781TKrxWcPPZFKNLBAYFsOQZcc5fisMgB61fRjfxh9rCxnnWwghxMsnybAQaVhzZQ0LTi4AYFzNcbxS9BUTR5T7bTsXxMjVpwmPScDBypzPOlWkdQUPU4clhBAiH5NkWIhU7L2z12hSja5lupo4otwtLlHHjM0X+XH/DQAqFXFmfrcqFHGxNW1gQggh8j1JhoV4yrmH5/jg7w/QKR3t/dozpMoQU4eUq914GMXg345zNjACgP4NfBnZsgyW5trnLCmEEEJkP0mGhXjC7YjbvLfzPWISY6jjUYdJdWRSjRfx56m7jPvjDJFxiRSwteCLLpVoWqaQqcMSQgghDCQZFuJfT0+qMbvJbCzMZFKNzIiJ1zFlwzl+O3IbgJrFXJjbrTIeTjYmjkwIIYQwJsmwEBhPquFp58mCVxbIpBqZdCX4MYOWHedycCQaDQxpUoKhr5TE3Ey6RQghhMh5JBkW+V6KSTWaL8TN1s3UYeU6SilWHbvDhD/PEpugx83BijldK1OvhExQIoQQIueSZFjka6lNqlHcqbipw8p1IuMS+WjNGdaevAtAg5IF+bJLZdwcrEwcmRBCCPFskgyLfE0m1Xhx5+6GM3jZCQIeRmGm1fBBi1K829APrVYuPBRCCJHzSTIs8q0nJ9UYW3OsTKqRQUoplh66ydQNF4jX6fF0smZetypUL+Zi6tCEEEKIdJNkWORLT06q0a9CP94s86aJI8pdwmMSGL36NFvOBQHQzL8Qn3euiLOtpYkjE0IIITJGkmGR7zw5qUa74u0YWmWoqUPKVY7fesSQZScIDIvBwkzD2Fb+9KlXTMZjFkIIkStJMizylduPjSfVmFx3siRxGbBkfwBTN14gUa8o6mLL/LeqUNHb2dRhCSGEEJkmybDIN0JjQxm4YyChsaGUcSkjk2pk0C+HbjJp/XkA2lT04NM3KuBoLcdPCCFE7ibJsMgXYhJjGLJzCDcjbuJp58nXr3wtk2pkwMbT9/h43VkABjcpwQctSkmLuhBCiDxBpoQSeV6iPpFRf4/i9MPTMqlGJuy78pDhK06gFHSvVVQSYSGEEHmKJMMiT1NKMe3wNHbf2Y2VmRVfNf1KJtXIgFO3wxiw9BgJOkWbih5M6VBeEmEhhBB5iiTDIk9bdHoRqy+vRoOGzxp8RhX3KqYOKde4ej+S3j8eITpeR/0SBfmySyXMZCINIYQQeYwkwyLPWnNlDfNPzgdgbK2xvOIjk2qk192wGHp+f5hH0QlU8nbi2x7VsDI3M3VYQgghRJaTZFjkSU9OqvF2+bfpVqabiSPKPR5FxdPzhyPcDY+luJsdP/apiZ2VXGsrhBAib5JkWOQ5T0+qMazqMFOHlGtExSXSZ8lRrt6PxMPJmqVv18LFTmaVE0IIkXdJMizylCcn1ajtUVsm1ciA+EQ97/7yDydvh+Fsa8HSt2vi5Wxj6rCEEEKIbCXJsMgzUkyq0Vgm1UgvvV7xwapT7L3yEFtLM37sXYMS7g6mDksIIYTIdpIMizwhtUk17C3tTR1WrqCUYtL6c6w/dRcLMw3f/K8aVYoWMHVYQgghxEshybDI9WRSjRczd+cVfj54E40GvuxSmYal5NgJIYTIPyQZFrmaTKrxYpYevMGcHVcAmNK+HO0qeZo4IiGEEOLlkmRY5GoyqUbm/XnqLhP+PAfA8GYl6VGnmGkDEkIIIUxAkmGRa629ulYm1cikPZcf8MHKkygFver4MOyVkqYOSQghhDAJSYZFrrQvcB+TDkwCZFKNjDpx6xHvLP2HBJ2iXSVPJrYrJ8PPCSGEyLckGRa5zrmQc4zYPQKd0tG2eFuZVCMDrgQ/ps+So8Qk6GhYyo0vOldCq5VEWAghRP4lybDIVW4/vs17O/6bVGNK3SnSqplOgWEx9PzhCGHRCVQu4sw3/6uKpbmcAoQQQuRv8kkoco1HsY9kUo1MComMo8f3h7kXHksJd3t+7F0DW0tzU4clhBBCmJwkwyJXiEmMYfBfgw2Taix4ZYFMqpFOkXGJ9FlylOsPovB0subnvjUpYGdp6rCEEEKIHEGSYZHjJeoTGbVnFKcfnMbR0pGFzRbibutu6rByhbhEHe8sPcbpO+EUsLXg57dr4elsY+qwhBBCiBxDkmGRoymlmH54OrtvJ02qMf+V+RR3lkk10kOnV4xYcYr9V0OwtTRjSZ+alHCX1nQhhBDiSZIMixxt8ZnFrLq8SibVyCClFBPWnWXjmXtYmmlZ1KM6lYo4mzosIYQQIseRZFjkWGuvruWrE18BMKbmGJlUIwNmb7/Mr4dvodHAnDcrU79kQVOHJIQQQuRIkgyLHGl/4H4mH5gMQN/yfXnL/y0TR5R7/Lg/gHl/XQVg6mvlaV3Bw8QRCSGEEDmXJMMixzkXco73d79PokqUSTUyaN3JQCavPw/AB81L0b2Wj4kjEkIIIXI2SYZFjnLn8R0G7RhkNKmGViMv0/TYdek+H6w8BUDvusUY3LSEiSMSQgghcj7JMkSOkTypRkhsCKULlJZJNTLgn5uPGPjLPyTqFa9V9mRC27IyM58QQgiRDpIMixwheVKNGxE38LDz4OtmX8ukGul0OfgxfZccJTZBT+PSbszqXAmtVhJhIYQQIj0kGRYm9/SkGt80+0Ym1Uin26HR9Pj+MOExCVQt6szX3atiYSZvayGEECK95FNTmJRSik8Pf8ru27ux1FrKpBoZ8DAyjp4/HCE4Io5Shez5oXcNbC3NTR2WEEIIkatIMixM6rsz37Hy8sqkSTUayqQa6fU4NoHePx4h4GEUXs42/Ny3Fs62lqYOSwghhMh1JBkWJrPu6jrmnZgHJE2q0cynmYkjyh1iE3QM+PkfzgZG4GpnydK3a1LYydrUYQkhhBC5kiTDwiT2B+5n0oFJgEyqkRE6vWL48pMcvB6CvZU5S/rUpLibXGgohBBCZJYkw+KlOx9y3jCpRpvibWRSjXRSSjF+zRm2nAvC0kzLop7VqODtZOqwhBBCiFxNkmHxUt15fIf3drxHTGIMtTxq8UndT2RSjXT6fNsllh+9jVYD87pVpq5fQVOHJIQQQuR6koWIl+bpSTXmNJ4jk2qk0/f7Aliw6xoA016vwKvlPUwckRBCCJE3SDIsXpqZR2fKpBqZ8MfxO3yy4TwAI1uWplvNoiaOSAghhMg7JBkWL8XDmIdsubEFgC8afSGTaqTTXxeDGbn6NABv1/flvcZ+Jo5ICCGEyFskGRYvxR9X/iBRn0glt0pUcKtg6nByhWM3Qnnv1+Po9Io3qngxvrU/Go1MsyyEEEJkJUmGRbZL1Cey6vIqALqW7mriaHKHi0ER9F1ylNgEPU3LuPNZp4potZIICyGEEFlNkmGR7f6+8zdBUUEUsCpAi2ItTB1Ojnc7NJqe3x8hIjaR6j4FWPBWVSzM5K0qhBBCZAf5hBXZbsXFFQC8UfINrMysTBxNzvbgcRz/+/4w9x/HUaawA9/3qoGNpZmpwxJCCCHyrAwnwwkJCZibm3P27NnsiEfkMTfCb3Dw3kE0aOhcurOpw8nRImIT6PXDEW6GRONdwIaf+tbEyVaGnhNCCCGyU4aTYQsLC4oWLYpOp8uOeEQes+JSUqtwQ++GeNl7mTianCs2QUf/n45x/l4EBe0t+eXtWhRytDZ1WEIIIUSel6luEuPHj2fcuHGEhoZmdTwiD4lOiGbd1XUAvFnmTRNHk3Ml6vQM+e0EhwNCcbAyZ0mfmhQraGfqsIQQQoh8wTwzC82fP5+rV6/i6emJj48PdnbGH9zHjx/PkuBE7rY5YDOPEx7jbe9NXc+6pg4nR1JKMW7NGbafD8bSXMviXtUp7+Vk6rCEEEKIfCNTyfBrr72WxWGIvEYpxfJLy4Gk4dS0GrlWMzUztlxk5bE7aDXwVbcq1C7uauqQhBBCiHwlU8nwxIkTszoOkcecfniai6EXsTKz4rUSr5k6nBxp0Z5rfPv3dQBmvFGRluUKmzgiIYQQIv/JdHNdWFgY3333HWPHjjX0HT5+/DiBgYFZFpzIvZZfTGoVfrXYqzhbO5s2mBxo1bHbTN90EYAxrcrQpUYRE0ckhBBC5E+ZSoZPnz5NqVKl+Oyzz/j8888JCwsD4I8//mDs2LFZGV+6LFiwgGLFimFtbU2tWrU4cuTIM+uHhYUxaNAgPDw8sLKyolSpUmzatOklRZv3hcaGsvXGVkAunEvNjvPBjPnjDAADGhbn3UZ+Jo5ICCGEyL8ylQyPGDGC3r17c+XKFayt/xv+qXXr1uzZsyfLgkuPFStWMGLECCZOnMjx48epVKkSLVu25P79+6nWj4+Pp3nz5ty4cYPVq1dz6dIlFi9ejJeXDPuVVdZcWUOCPoHyruUpX7C8qcPJUQ5fD2HQsuPo9IqOVb0Z26qMqUMSQggh8rVM9Rk+evQo3377bYpyLy8vgoKCXjiojPjyyy/p378/ffr0AeCbb75h48aN/PDDD4wZMyZF/R9++IHQ0FAOHDiAhUXShAbFihV7mSHnaTq9jpWXVgLQtUxXE0eTs5y/G0G/n44Rl6inmb87n3WsgEajMXVYQgghRL6WqZZhKysrIiIiUpRfvnwZNze3Fw4qveLj4/nnn39o1qyZoUyr1dKsWTMOHjyY6jJ//vknderUYdCgQRQqVIjy5cszffr0NCcRiYuLIyIiwuhPpG1f4D7uRt3FycqJV4u9aupwcoybIVH0/OEIj+MSqVnMhflvVcXcTEbYEEIIIUwtU5/G7du3Z8qUKSQkJACg0Wi4desWo0ePpmPHjlka4LM8fPgQnU5HoUKFjMoLFSqUZgv19evXWb16NTqdjk2bNvHxxx/zxRdfMHXq1FTrf/rppzg5ORn+ihSRC52eJXk4tddLvI61ucygBnA/IpYe3x/hYWQc/h6OLO5VHWsLM1OHJYQQQggymQx/8cUXREZG4u7uTkxMDI0aNaJEiRI4ODgwbdq0rI4xS+n1etzd3Vm0aBHVqlWja9eujB8/nm+++SbV+mPHjiU8PNzwd/v27Zccce5xO+I2+wP3A9ClVBcTR5MzhMck0POHI9wKjaaoiy0/9a2Bk42FqcMSQgghxL8y1WfYycmJ7du3s2/fPk6fPk1kZCRVq1Y16q7wMhQsWBAzMzOCg4ONyoODgylcOPUxWz08PLCwsMDM7L+WOX9/f4KCgoiPj8fS0tKovpWVFVZWVlkffB608vJKFIp6XvUo4igt6LEJOvr/dIyLQY8paG/F0rdr4u4greVCCCFETpKpZDg2NhZra2vq169P/fr1szqmdLO0tKRatWrs3LnTMCueXq9n586dDB48ONVl6tWrx7Jly9Dr9Wi1SQ3jly9fxsPDI0UiLNIvNjGWNVfXANCtdDcTR2N6iTo9g5cd58iNUByszfm5b018XO2ev6AQQgghXqpMdZNwdnamYcOGfPzxx/z111/ExMRkdVzpNmLECBYvXsxPP/3EhQsXGDhwIFFRUYbRJXr27Gk09vHAgQMJDQ1l2LBhXL58mY0bNzJ9+nQGDRpkql3IE7bc2EJ4XDiedp7U9zLdF6ScQK9XjP79DDsu3MfKXMv3vWpQ1tPR1GEJIYQQIhWZahnesWMHe/bsYffu3cyePZvExESqV69Oo0aNaNy4Mc2bN8/qONPUtWtXHjx4wIQJEwgKCqJy5cps2bLFcFHdrVu3DC3AAEWKFGHr1q28//77VKxYES8vL4YNG8bo0aNfWsx50YqLKwDoXLozZtr8e3GYUopPN1/g9+N3MNNqWPBWVWr6upg6LCGEEEKkQaOUUi+ygsTERMO4w7/++it6vT7NYcrygoiICJycnAgPD8fRUVr7AM4+PEu3jd2w0Fqwo/MOXKzzb/K3cPc1PtuSNM3y550r0amat4kjEkIIAfL5LdKWqZZhSOpnu3v3bsNfXFwcbdu2pXHjxlkYnsgNll9MGk6tZbGW+ToR3nI2yJAIj2/tL4mwEEIIkQtkKhn28vIiJiaGxo0b07hxY0aPHk3FihVlNq18KCw2jC03tgDQtXT+nXEuMCyGUatPAdCnXjH6Nyxu4oiEEEIIkR6ZuoDOzc2N6OhogoKCCAoKIjg42KQX0QnTWXdtHXG6OPxd/KnkVsnU4ZhEok7PsN9OEBGbSCVvJ8a28jd1SEIIIYRIp0wlwydPniQoKIgxY8YQFxfHuHHjKFiwIHXr1mX8+PFZHaPIofRKz4pLSRfOdS3dNd/+MjB35xWO3XyEg5U5X3WriqW5TLMshBBC5BYvfAFdSEgIu3fvZt26dfz2229yAV0+si9wHwN3DMTBwoEdnXdga2Fr6pBeugPXHtL9u8MoBfO6VaF9JU9ThySEECIV8vkt0pKpPsN//PGH4cK58+fP4+LiQv369fniiy9o1KhRVscocqjk4dQ6lOiQLxPh0Kh43l9xEqWgS3VvSYSFEEKIXChTyfC7775Lw4YNGTBgAI0aNaJChQpZHZfI4QIjA/n7zt8AdCndxcTRvHxKKUauOkVwRBx+bnZMal/O1CEJIYQQIhMylQzfv38/q+MQucyqS6tQKGp71MbXydfU4bx0P+6/wc6L97E01/JVt6rYWmZ6lEIhhBBCmFCmP8F1Oh1r167lwoULAJQtW5YOHTpgZpZ/Zx/LL+J0cfxx5Q8A3izzpomjefnOBobz6eak1/1HbfxlqmUhhBAiF8tUMnz16lVat25NYGAgpUuXBuDTTz+lSJEibNy4ET8/vywNUuQs225s41HcIwrZFqKRd/7qIx4Zl8iQ306QoFO0KFuIHrV9TB2SEEIIIV5ApsaAGjp0KH5+fty+fZvjx49z/Phxbt26ha+vL0OHDs3qGEUOkzycWudSnTHX5q/uARPWnSXgYRQeTtbM7CQTzQghhBC5XaYymb///ptDhw7h4vLf1Luurq7MmDGDevXqZVlwIue5EHKBUw9OYa41p2OpjqYO56Vac+IOfxwPRKuBuW9WwdnW0tQhCSGEEOIFZapl2MrKisePH6coj4yMxNJSEoS8LLlVuHnR5hS0KWjiaF6egIdRfLTmLADDXilFTV+X5ywhhBBCiNwgU8lw27ZtGTBgAIcPH0YphVKKQ4cO8e6779K+ffusjlHkEBHxEWy8vhGArmW6mjialycuUceQ344TFa+jlq8Lg5uWMHVIQgghhMgimUqG582bh5+fH3Xq1MHa2hpra2vq1q1LiRIlmDt3blbHKHKIdVfXEauLpWSBklR1r2rqcF6amVsucTYwAmdbC+a8WRkzrfQTFkIIIfKKTPUZdnZ2Zt26dVy9epXz588DSUOrlSghLWZ5lV7pDV0k3iz9Zr65cGzXxft8vy8AgFmdKuHhZGPiiIQQQgiRlTI9FMD333/P7NmzuXLlCgAlS5Zk+PDh9OvXL8uCEznH4XuHuRlxEzsLO9oWb2vqcF6K4IhYPlh1CoDedYvRvGwhE0ckhBBCiKyWqWR4woQJfPnllwwZMoQ6deoAcPDgQd5//31u3brFlClTsjRIYXrLLy4HoL1fe2wtbE0cTfbT6RXDl58kNCqesh6OjGlVxtQhCSGEECIbZCoZXrhwIYsXL6Zbt26Gsvbt21OxYkWGDBkiyXAeExQVxO47uwHoWjp/XDi3cPdVDl4PwdbSjK/eqoK1hcysKIQQQuRFmbqALiEhgerVq6cor1atGomJiS8clMhZVl1ehV7pqVm4Jn7OeX92wX9uhjJ7R1L3n8nty+HnZm/iiIQQQgiRXTKVDPfo0YOFCxemKF+0aBHdu3d/4aBEzpGgS+D3y78D+aNVODw6gaG/nUSnV3So7Emnat6mDkkIIYQQ2eiFLqDbtm0btWvXBuDw4cPcunWLnj17MmLECEO9L7/88sWjFCaz49YOQmJDcLdxp0nRJqYOJ1sppRjzx2kCw2LwcbVl6mvl882oGUIIIUR+lalk+OzZs1StmjTO7LVr1wAoWLAgBQsW5OzZs4Z6kkjkfskXznUq1QkLrYWJo8levx6+xeazQViYafiqWxUcrPP2/gohhBAik8nwrl27sjoOkQNdfnSZ4/ePY6Yxo2OpjqYOJ1tdCnrMJxuSxswe1bIMFb2dTRuQEEIIIV6KTPUZFvnDiotJk2w0LdoUd1t3E0eTfWLidQxedpy4RD2NSrnxdn1fU4ckhBBCiJdEkmGRqsj4SNZfXw9AtzLdnlM7d5uy4TxX7kfi5mDFF10qoZXploUQQoh8Q5Jhkao/r/1JTGIMxZ2KU71QymH08oqNp+/x25FbaDQwu0tlCtpbmTokIYQQQrxEkgyLFJRSrLiU1EWia+muefZCyNuh0Yz54zQAAxv5Ub9kQRNHJIQQQoiXTZJhkcLRoKNcD7+OjbkN7f3amzqcbJGg0zN0+QkexyZSpagz7zcvZeqQhBBCCGECkgyLFJZfShpOrV3xdthb5s3Z12Zvv8yJW2E4WJsz780qWJjJW0EIIYTIjyQDEEbuR9/nr1t/AdC1TN6ccW7/1Ycs/DtpfOwZb1SkiIutiSMSQgghhKlIMiyMrL68Gp3SUdW9KqUK5L2uAw8j4xi+4iRKQbeaRWlT0cPUIQkhhBDChCQZFgYJ+gRWX14NwJtl3jRxNFlPr1d8sPIUDx7HUaqQPRPaljV1SEIIIYQwMUmGhcGuW7t4EPMAV2tXmhVtZupwstz3+wL4+/IDrMy1fNWtKjaWZqYOSQghhBAmJsmwMEi+cK5jqY5YmFmYOJqsdfpOGDO3XgTg47ZlKV3YwcQRCSGEECInkGRYAHAt7BpHg46i1WjpXKqzqcPJUo9jExjy2wkSdIpW5QvTvVZRU4ckhBBCiBxCkmEBYJhko7F3YwrbFTZxNFlHKcVHa89yMyQaL2cbZrxRMc9OIiKEEEKIjJNkWBCVEMWf1/4E8t6Fc6v/ucO6k3cx02qY160yTrZ5q/uHEEIIIV6MJMOCjdc3EpUQRTHHYtTyqGXqcLLMtQeRTFh3DoD3m5Wkmo+LiSMSQgghRE4jyXA+p5QyXDjXpXQXtJq88ZKIS9QxZNkJYhJ01CnuysDGJUwdkhBCCCFyoLyR+YhMO37/OFceXcHazJoOJTqYOpws8+mmi5y/F4GLnSVz3qyMmVb6CQshhBAiJUmG87kVF5MunGtTvA2Olo4mjiZrbD8fzJIDNwD4onMlCjlamzYgIYQQQuRYkgznYw9jHrL91nYAupbuauJossa98BhGrj4FwNv1fWlSxt3EEQkhhBAiJ5NkOB/7/fLvJOoTqeRWCX9Xf1OH88J0esXw5ScJi06gvJcjo14tbeqQhBBCCJHDSTKcTyXqE1l1eRWQd1qF5/91lcMBodhZmvFVt6pYmct0y0IIIYR4NkmG86m/7/xNcHQwBawK0KJYC1OH88KOBIQyd+dlAKa+Xh7fgnYmjkgIIYQQuYEkw/nU8otJw6m9UfINrMysTBzNi3kUFc+w5SfQK3ijqhevV/E2dUhCCCGEyCUkGc6HAsIDOHTvEBo0dC7d2dThvBClFKN+P8298Fh8C9rxSYfypg5JCCGEELmIJMP50MpLKwFo6N0QL3svE0fzYpYeusn288FYmGn4qlsV7KzMTR2SEEIIIXIRSYbzmeiEaNZdXQfAm2XeNHE0L+b83QimbrwAwNhW/pT3cjJxREIIIYTIbSQZzmc2B2zmccJjvO29qetZ19ThZFp0fCJDfjtOfKKeV8q406deMVOHJIQQQohcSJLhfEQpxfJLSRfOdS3dFa0m9z79k/48x7UHURRytGJW50poNDLdshBCCCEyLvdmQyLDTj04xcXQi1iZWfFaiddMHU6m/XnqLiuP3UGjgdldK+NiZ2nqkIQQQgiRS0kynI+suLQCgFeLvYqztbNpg8mkWyHRjPvjDABDmpSgrl9BE0ckhBBCiNxMkuF8IiQmhK03tgK598K5+EQ9Q5afIDIukRrFCjD0lZKmDkkIIYQQuZwkw/nEmqtrSNAnUN61POUL5s6xeL/YdolTt8NwsrFgzptVMDeTl68QQgghXoxkE/mATq9j1aVVAHQt09XE0WTO35cf8O2e6wB81rEiXs42Jo5ICCGEEHmBJMP5wN7AvdyNuouTlROvFnvV1OFk2P3HsXyw8iQA/6tdlFfLFzZtQEIIIYTIMyQZzgeSh1N7vcTrWJtbmziajNHrFR+sPMXDyHjKFHbgozZlTR2SEEIIIfIQSYbzuNsRt9kfuB8NGrqU6mLqcDJs0d7r7L3yEGsLLfPfqoK1hZmpQxJCCCFEHiLJcB6XPJxaPa96FHEsYuJoMubErUd8vvUSAJPalaOEu4OJIxJCCCFEXiPJcB4WmxjLmqtrAHizdO4aTi0iNoEhv50gUa9oU9GDrjVyVyIvhBBCiNxBkuE8bMuNLUTER+Bp50l9r/qmDifdlFKM++MMdx7F4F3Ahk/fqCDTLQshhBAiW0gynIctv5h04VyX0l0w0+aevrYrj91mw+l7mGs1zOtWBUdrC1OHJIQQQog8SpLhPOrsw7OcCzmHhdaC10u+bupw0u3q/cdM/PMcAB+0KE3VogVMHJEQQggh8jJJhvOo5FbhlsVa4mLtYuJo0ic2QcfgZSeITdDToGRB3mlY3NQhCSGEECKPk2Q4DwqLDWPLjS0AvFkm91w4N23jBS4GPaagvSVfdKmEViv9hIUQQgiRvSQZzoPWXl1LnC4Ofxd/KhasaOpw0mXL2SCWHroJwBddKuPukLsmBxFCCCFE7iTJcB6jV3rD2MJdS3fNFaMwBIbFMPr30wC807A4jUq5mTgiIYQQQuQXeSIZXrBgAcWKFcPa2ppatWpx5MiRdC23fPly/t/encdFVe//A38NIPsOiqnkBoIrqKjhDdM0qeu+kqZWV03NrDT9mmmu/dT27Ga38rpkN9NMzTRzF3HJRBFxCXAD3ABXNlln3r8/aCYmwCWVM+fM6/l48CjOnAPvlx/4zJsz53xGp9Ohd+/eD7fAKrTv4j5cyL0At2pu+GeDfypdzh2V6A147bsjyMovRkgdD7zRNUjpkoiIiMiKqL4ZXrVqFSZMmIAZM2YgLi4OISEhiIyMRGZm5m2PS0lJwcSJExEREVFFlVYN41nhXgG94GTnpHA1d/bpjlM4lHoDrg52+PegVrC3U/2PJBEREamI6juPjz76CCNHjsSLL76IJk2a4IsvvoCzszOWLFlS6TF6vR7PPfccZs2ahQYNtLNiwYWcC4i5EAOg9BIJS/frmWv4967TAID/16cZHvVxVrgiIiIisjaqboaLiopw+PBhdOnSxbTNxsYGXbp0wa+//lrpcbNnz0aNGjUwfPjwO36PwsJCZGdnm31YqtXJqyEQhD8Sjnoe9ZQu57au5xXh9VVHIAIMDKuDXqG1lS6JiIiIrJCqm+GrV69Cr9fDz8/PbLufnx/S09MrPGbv3r1YvHgxFi1adFffY968efDw8DB9+Pv733fdD0OhvhDrTq0DAEQFW/ZZYRHBpNVHkZFdiIbVXTCzZ1OlSyIiIiIrpepm+F7l5ORg6NChWLRoEXx9fe/qmClTpiArK8v0cf78+Ydc5d+zNWUrbhTeQE2XmniizhNKl3NbS/elYEdiJuztbPDvQa3gbG+ndElERERkpVTdhfj6+sLW1hYZGRlm2zMyMlCzZs1y+585cwYpKSno0aOHaZvBYAAA2NnZISkpCQ0bNjQ7xsHBAQ4ODg+h+gdrZVLpO84NaDQAdjaWO6zHL2Zh/i+JAICp/2yMJrXcFa6IiIiIrJmqzwzb29ujdevW2LFjh2mbwWDAjh07EB4eXm7/4OBgHDt2DPHx8aaPnj17olOnToiPj7fYSyDu5OS1k0i4kgA7Gzv0DeyrdDmVyisswbjvjqBIb8BTTfwwLLyu0iURERGRlbPcU4h3acKECXj++ecRFhaGtm3b4pNPPkFeXh5efPFFAMCwYcNQu3ZtzJs3D46OjmjWrJnZ8Z6engBQbruaGJdTe+rRp+DrdHeXfyjhnZ9/x7mreXjEwxHv92+hijcEISIiIm1TfTMcFRWFK1euYPr06UhPT0doaCg2b95suqkuLS0NNjaqPgF+W1mFWdh0dhMA4NngZxWupnKxKdfx3cE0AMBHA0Ph6WyvcEVEREREgE5EROki1CQ7OxseHh7IysqCu7vy17t+c/IbvBf7HgK9ArGmxxqLPNtaVGJAt0/34FRmLp5t44/5/VooXRIREVkZS3v+Jsuh3VOmVsAgBtMlEs8GPWuRjTAALNpzFqcyc+HjYo83nwlWuhwiIiIiEzbDKnbg8gGkZqfCpZoLujfornQ5FUq9lodPd5wCALzdvQkvjyAiIiKLwmZYxVYllp4V7tmwJ5yrWd5bGYsIpv14HIUlBjwe4IteobWULomIiIjIDJthlbqcexnRF6IBAFFBlvmOcz8dvYQ9p67C3s4Gc3o3s9jLOIiIiMh6sRlWqdXJq2EQA9rWbIuGng3vfEAVy7pVjDkbTwIAxnUKQH1fF4UrIiIiIiqPzbAKFeuLsebUGgCWe1b43S2JuJpbhIAarnjpiQZKl0NERERUITbDKrQtdRuuF1xHDaca6PRoJ6XLKedw6nWs+K10TeH/17sZHOxsFa6IiIiIqGJshlXIuJxa/0b9Uc2mmsLVmCvWG/DW2uMAgIFhddCugY/CFRERERFVjs2wyiRdT0JcZhxsdbbo16if0uWUs2jPWSRl5MDbxR5TnmmsdDlEREREt8VmWGWMZ4WffPRJ1HCuoXA15tKu3TKtKTytW2N4uXBNYSIiIrJsbIZVJKcoBxvPbgQADAoepHA15kQE09YfR0GxAe0b+qBPy9pKl0RERER0R2yGVWTDmQ3IL8lHA48GCPMLU7ocMxsTLiMm+QrsbW3wDtcUJiIiIpVgM6wSImK6RCIqKMqims2s/GLM2lC6pvDYTgFoUN1V4YqIiIiI7g6bYZWITY/F2ayzcLJzQs+GPZUux8z7WxJxNbcQDaq7YHRHrilMRERE6sFmWCVWJq0EAPRo0AOu9pZz5vVw6g18a1pTuDnXFCYiIiJVYTOsAhl5GdiZthMAEBVsOe84V6w3YOq6YxAB+reug/CGXFOYiIiI1IXNsAqsObUGetGjVY1WaOTVSOlyTBbvPYfE9Bx4OVfDW//kmsJERESkPmyGLVyxoRg/JP8AAHg2+FmFq/nT+eu38Mn2ZADA1G5N4M01hYmIiEiF2AxbuJ1pO3El/wp8HH3Q5dEuSpcDoHRli+l/rCn8WANv9GvFNYWJiIhIndgMWzjjcmr9GvVDNdtqCldTatOxdOxKMq4p3NyilnkjIiIiuhdshi3Y6RunEZseCxudDQY0GqB0OQCA7IJizNxwAgAwpmNDBNSwnJUtiIiIiO4Vm2ELZjwr3Mm/E2q61FS4mlIfbEnClZxCNPB1wZiODZUuh4iIiOi+sBm2UHnFedhwdgOA0necswRH0m7gmwOpAIB3+jSDYzWuKUxERETqxmbYQm08sxF5xXmo514P7R5pp3Q5KNYbMGVt6ZrCfVvVRvuGvkqXRERERHTf2AxbIBExveNcVFAUbHTKD9PSfaVrCns6V8NUrilMREREGqF8l0XlxGXG4fTN03C0dUTPgJ5Kl4MLN27h422nAABv/bMxfFwdFK6IiIiI6MFgM2yBViaWnhXu1qAb3O3dFa2ldE3hE8gv1qNtfW8MaF1H0XqIiIiIHiQ2wxbmav5VbE/dDsAy3nFu8/F07EzMRDVbHeb2acY1hYmIiEhT2AxbmDXJa1AiJQipHoJg72BFa8kpu6bwEw0RUMNN0XqIiIiIHjQ2wxakxFCC1cmrAVjGcmofbk1GRnYh6vk44+VOAUqXQ0RERPTAsRm2ILvP70bGrQx4OXghsl6korUcPX8TX/+aAgB4p3dzrilMREREmsRm2IIYl1PrG9gX9rb2itVRUmZN4T4ta+PxQK4pTERERNrEZthCnMs6hwOXD0AHHQYEDVC0lmX7U3DycjY8nKphajeuKUxERETaZad0AVTK380fn3T8BIk3ElHbtbZidVy8mY+PtiUDAKY8EwxfrilMREREGsZm2ELY2dihc93O6Fy3s2I1iAhmrD+OW0V6tKnnhYFh/orVQkRERFQVeJkEmWw5kYHtvxvXFG4OGxuuKUxERETaxmaYAAC5hSWY+VPpmsKjOjREoB/XFCYiIiLtYzNMAIAPtyYhPbsAdX2c8cqTXFOYiIiIrAObYULChZv4en8KAGBOr2ZcU5iIiIisBpthK1eiN+CtdcdgEKBXaC10aFRd6ZKIiIiIqgybYSv39a+pOH4xG+6OdpjWrYnS5RARERFVKTbDVuzSzXx8tDUJAPDmM41R3Y1rChMREZF1YTNsxWb+dAJ5RXq0ruuFZ9twTWEiIiKyPmyGrdSWE+nYejIDdjZcU5iIiIisF5thK1R2TeGXOjRAUE2uKUxERETWic2wFfp4WzIuZxXA39sJ454MVLocIiIiIsWwGbYyxy9mYem+cwBK1xR2sueawkRERGS92AxbEb1BMGVt6ZrCPUJqoWNQDaVLIiIiIlIUm2ErsvzXFBy7mAU3Rzu83b2x0uUQERERKY7NsJW4nJWPD7cmAwAmPx2MGm6OCldEREREpDw2w1Zi1k8nkVtYgpaPemJw20eVLoeIiIjIIrAZtgLbTmZg84l02NnoMK8v1xQmIiIiMmIzrHF5hSWYsf44AGBERAME13RXuCIiIiIiy8FmWOM+2Z6MS1kFqOPlhNc6c01hIiIiorLYDGvY8YtZWLIvBQAwpzfXFCYiIiL6KzbDGqU3CKauOwa9QdCtxSPoxDWFiYiIiMphM6xR3/6WiqMXsuDmYIcZ3ZsoXQ4RERGRRWIzrEEZ2QV4b3MSAOD/ng5CDXeuKUxERERUETbDGjRrwwnkFpYg1N8Tg9vVVbocIiIiIovFZlhjdiZmYNOxdNja6DC3T3PYck1hIiIiokqxGdaQW0UlePvHEwCAEY/XR5NaXFOYiIiI6HbYDGvIgu2ncPFmPmp7OuG1LlxTmIiIiOhO2AxrxMlL2fjv3nMAgNm9msLZ3k7hioiIiIgsH5thDdAbBG/9sabwP5vXROfGfkqXRERERKQKbIY1YMXBNMSfvwlXBzvM6NFU6XKIiIiIVIPNsMplZhfgvV8SAQCTIoPgxzWFiYiIiO4am2GVm7XxJHIKSxBSxwNDHuOawkRERET3gs2wiu1KysTPCZdL1xTuyzWFiYiIiO4Vm2GVyi/S4+0fjwMA/vWPemhay0PhioiIiIjUh82wSi3YcQoXbuSjlocjXu/SSOlyiIiIiFRJE83wwoULUa9ePTg6OqJdu3Y4ePBgpfsuWrQIERER8PLygpeXF7p06XLb/S1RYno2/rvnLABgdq9mcHHgmsJEREREf4fqm+FVq1ZhwoQJmDFjBuLi4hASEoLIyEhkZmZWuH90dDQGDRqEXbt24ddff4W/vz+6du2KixcvVnHlf4/BIJiy9hhKDIKnm9ZElyZcU5iIiIjo79KJiChdxP1o164d2rRpg88++wwAYDAY4O/vj3HjxuHNN9+84/F6vR5eXl747LPPMGzYsHKPFxYWorCw0PR5dnY2/P39kZWVBXd39wcX5C59+1sqpq47DlcHO2yf8ARqenApNSIiojvJzs6Gh4eHYs/fZLlUfWa4qKgIhw8fRpcuXUzbbGxs0KVLF/z666939TVu3bqF4uJieHt7V/j4vHnz4OHhYfrw9/d/ILX/HZk5BZj/x5rCb3RtxEaYiIiI6D6puhm+evUq9Ho9/PzMLxXw8/NDenr6XX2NyZMno1atWmYNdVlTpkxBVlaW6eP8+fP3XfffNWfj78gpKEHz2h4YFl5PsTqIiIiItMKq77yaP38+Vq5ciejoaDg6VnyW1cHBAQ4ODlVcWXm7k69gw9FLsNEB87imMBEREdEDoepm2NfXF7a2tsjIyDDbnpGRgZo1a9722A8++ADz58/H9u3b0aJFi4dZ5n3LL9Jj2o/HAAAv/qM+mtXmmsJERERED4KqL5Owt7dH69atsWPHDtM2g8GAHTt2IDw8vNLj3nvvPcyZMwebN29GWFhYVZR6X/698xTOX8/HIx6OmPAU1xQmIiIielBUfWYYACZMmIDnn38eYWFhaNu2LT755BPk5eXhxRdfBAAMGzYMtWvXxrx58wAA7777LqZPn44VK1agXr16pmuLXV1d4erqqliOyiSl5+CrmNI1hWf1bMo1hYmIiIgeINV3VlFRUbhy5QqmT5+O9PR0hIaGYvPmzaab6tLS0mBj8+cJ8P/85z8oKipC//79zb7OjBkzMHPmzKos/Y4MBsFb60rXFO7axA9dm97+0g8iIiIiujeqX2e4qlXlOoXfHUzDlLXH4GJvi20TnkAtT6eH+v2IiIi0iusMU2VUfc2wll3JKcS8Tb8DACZ0DWIjTERERPQQsBm2UO/8fBLZBSVoVtsdz4fXVbocIiIiIk1iM2yBYpKvYH38H2sK92kBO1sOExEREdHDwC7LwhQU6/H2+uMAgOfb10PzOlxTmIiIiOhhYTNsYT7beRqp126hprsj3ugapHQ5RERERJrGZtiCJGfk4MuYMwCAmT2bwpVrChMRERE9VGyGLYTBIJi67hiK9YIujf0Q2dRP6ZKIiIiINI/NsIVITM/B0QtZcLa3xaxeTaHT6ZQuiYiIiEjz+Dq8hWhSyx1bXu+ApPRs1OaawkRERERVgs2wBanv64L6vi5Kl0FERERkNXiZBBERERFZLTbDRERERGS12AwTERERkdViM0xEREREVovNMBERERFZLTbDRERERGS12AwTERERkdViM0xEREREVovNMBERERFZLTbDRERERGS12AwTERERkdViM0xEREREVovNMBERERFZLTulC1AbEQEAZGdnK1wJERER3S3j87bxeZzIiM3wPcrJyQEA+Pv7K1wJERER3aucnBx4eHgoXQZZEJ3wT6R7YjAYcOnSJbi5uUGn0z3Qr52dnQ1/f3+cP38e7u7uD/RrWwKt5wO0n5H51E/rGbWeD9B+xoeVT0SQk5ODWrVqwcaGV4nSn3hm+B7Z2NigTp06D/V7uLu7a3KCM9J6PkD7GZlP/bSeUev5AO1nfBj5eEaYKsI/jYiIiIjIarEZJiIiIiKrxWbYgjg4OGDGjBlwcHBQupSHQuv5AO1nZD7103pGrecDtJ9R6/nI8vAGOiIiIiKyWjwzTERERERWi80wEREREVktNsNEREREZLXYDBMRERGR1WIzrELWcM+j1jMyn/ppPaPW8wHaz8h8RHeHzbAKpaenK13CQ3f16lWlS3iomE/9tJ6R84z6aX0MtZ6Pqg6bYZVZvnw5ateujW+//VbpUh6aFStWICwsDJs2bVK6lIeC+dRP6xk5z6if1sdQ6/moigmpxsqVK0Wn00nXrl3F29tbvv32W6VLeuBWrFghOp1OwsPDJSQkRDZt2qR0SQ8U86mf1jNynlE/rY+h1vNR1WMzrCLXr1+X6Ohoyc7OlvHjx4ubm5vmJoEjR47I7t27JSkpSYYMGSJNmzbV1BMV86mf1jNynlE/rY+h1vNR1WMzrBLFxcVmn6elpWl+Ejh48KAmn6iMmE/9tJaR8wzH0NJpPR8pg82wBdPr9eW2lZSUmP4/LS1NJkyYoOpJwGAwlNtWdrJT+xMV86k7n4j2M3Ke4RhaOq3nI+WxGbZQxl/0W7duyc6dO2X16tWSmZkpBQUFZvupeRIwZszPz5dDhw5JdHS05Ofnl9tPrU9UzFdKrflEtJ+R88yfOIaWSev5yDKwGbZAxrMYWVlZ0rRpUwkKChJvb2/x9fWV6dOnS2Jiotn+apwEymYMDQ2VwMBA8fHxEX9/f1m6dKlcunTJbH+1PVExn7rziWg/I+cZjqGl03o+shxshi1UcXGx9OzZU/r27Svnz5+XwsJCmTVrlrRr104GDBggCQkJZvunpaXJ66+/Lm5ubrJixQqFqr43RUVF0rlzZxkwYIAkJSVJSkqKjBo1Sho2bCgTJ06UlJQUs/0PHjwozz33nGqeqJhP3flEtJ+R8wzH0NJpPR9ZBjbDFio7O1tCQkJk8eLFZtuXL18uERERMmTIkHKTeGpqqsyePVt0Op1s3LixKsv9WzIzM6Vx48aybt06s+3z58+XZs2aydSpU+X69etmjx08eFDGjBkjtWvXlt27d1dhtfeO+dSdT0T7GTnPcAwtfQy1no8sA990w0LpdDq4urri0qVLAICSkhIAwNChQ/HCCy/gyJEj2LJli9ljjz76KAoLC+Hs7AxbW1tlCr8HxcXFMBgMyM3NBQAUFBQAACZPnoyoqCgsW7YMhw4dAvBnxjZt2sDGxga3bt2y+LfiZD515wO0n5HzDMfQ0sdQ6/nIQijaipOIVHyns4jImDFjpG7dupKamioi5nc/jx49WgIDA02fl5SUSFFRkYSFhVnkS0OVZezdu7c0btzY9HjZmyJ69+4t7du3N/sa169fl5CQEPnuu+8ebsH3iPnUnU9E+xk5z3AMRSx7DLWejywXm2GFGe+ULSwslDNnzkhycrLk5uaaHm/ZsqW0bNmy3Mt4GzZskKCgoHLbi4qKHn7R98iYsaioSK5duybp6emmxy5evCj169eXzp07m7YZMyxcuFBatWolhYWFZl/v5s2bVVD13WM+decT0X5GzjMcQ0sfQ63nI8vGyyQUJCKwtbVFTk4OHn/8cfTt2xeNGzfG0KFD8d///hcAsGrVKuTn5+OJJ55AQkICCgsLAQAnTpyAs7NzuZfwqlWrVuU5bqdsxs6dO6Nr165o0KABxowZg19++QW1atXCl19+iaSkJHTq1Ak5OTmmDFevXoWLiwuKiorMcnp4eCgVpxzmU3c+QPsZOc9wDC19DLWej1SgqrtvMldYWCht27aV3r17S1xcnKxZs0aGDh0qdevWldmzZ4tI6d2x7du3l9q1a0tERIQMGjRIHBwc5IcfflC4+rtTUFAgISEh0rt3b9m6dassWrRIIiIiJCwszHRTRHR0tAQFBYm/v79ERUXJiBEjpFq1arJ27VqFq78z5lN3PhHtZ+Q8wzG0dFrPR5aNzbDCTp8+LSEhIRIfH2/alpqaKvPmzRNvb2+ZO3euafvChQvljTfekEmTJsmOHTtEpPJrrCxJbGysNG/e3HS9l4hIfHy8jBkzRoKDg+Xrr78WkdLrwN566y0ZOnSo/Otf/5LNmzeLiOVnZD515xPRfkbOMxxD5iOqHJthhZ05c0acnZ3L3aiRmZkps2fPluDg4HJLAhkZDAZVTABxcXHi6uoq27dvN9uelJQkL730knTo0EEOHDhg9pgxlxoyMp+684loPyPnGY4h8xFVjtcMVyGpYIkeHx8fPPXUU9i2bRsuXLhg2l69enUMHjwYNWrUQGxsrNnxxv/qdDrodLoqqPzuVZTR29sbwcHBiImJQX5+vml7o0aNMHLkSKSlpZky/pWlZWQ+decDtJ+R8wzH0NLHUOv5SIWqvP22UsY7ZUtKSuTGjRuSl5dnemz58uXi6ekpc+bMkWvXrpkd9/rrr0toaGi592G3RMaMer3eLJ+IyEcffSQ2NjayfPnycn/BDxkyRDp37mw63lIxn7rziWg/I+cZjqGlj6HW85E62SndjFsDg8EAW1tbZGdnY8iQIUhLS4OLiwuaNGmCzz//HEOHDsXVq1cxceJE6PV6PPfccwgICAAA2NvbIygoCDY2ln0S35gxJycHo0aNQmpqKnx9fdGmTRtMmzYN48ePx4ULFzBy5EgUFRWhX79+8PT0BAA4ODggICDAohdHZz515wO0n5HzDMfQ0sdQ6/lIvdgMVwEbGxvk5+ejffv2aNSoESZOnIjk5GSsXr0azZo1w4YNGzB+/HjY2tri/fffx6+//or69evD09MTCxYswPfff2/xy8QY362pTZs2CAgIQL9+/XDkyBF888032LRpE6Kjo/Hhhx+iWrVqePnll7F//374+/vDwcEB//vf/7B27VqlI9wW86k7H6D9jJxnOIaWPoZaz0cqpvSpaWsRExMjTZo0kbS0NNO2M2fOyD/+8Q+pV6+eafuGDRtk2rRp0qZNGxk8eLCsX79eRNRxp+z69eslNDRUrl69KiKlL4Pt3LlTgoODJTQ0VPR6vYiILFu2TIYNGyaNGzeWZ555xrSskaVnZD515xPRfkbOMxxD5iO6d2yGq8iaNWvE1dXV9C5Hxgk7MzNT2rZtKyEhIWb7FxUVmd5BRy13yn755Zfi4+Njts1gMEhsbKwEBQVJ165dTdsLCwslPz9fcnJyTPtZekbmU3c+Ee1n5DzDMWQ+onvHi2+qyJNPPgkfHx/MmTMHQOnLRQaDAdWrV8cXX3yB/Px8fP755wBKr6uqVq2a6eUgS79T1mAwAAA6duwIT09PLFq0yPSYTqdDaGgo5s6di4sXL+Lnn38GANjZ2cHR0RGurq6m/Sw1ozHfE088oZl8UuZubq2OnzFXWVrKWFE+rc0zer0egHlWrf4eGnXq1Ane3t6aGUNjRuMYdurUSVM/o6QNbIYfsIqeoADA0dERgwcPRnR0NJYvXw4AphsBmjZtCh8fHyQlJZltt1R/zWis19vbG2FhYVi7di127dpletzOzg6RkZHIy8tDXFyc2TGWyJjPOImXlJQA0FY+nU5nejtTGxsbiAiqV6+uiXxAaRNlY2ODvLw8xMTEmJoqrWQsm+/TTz/F9evXAWhrntHr9aab5Tp27GgaF19fX82MoU6nQ0lJCXJzc5GVlQWgdAyfe+451Y+hcR79a/Nqb2+PwYMHY9euXarOR9rCn7QHyGAwmG4QOHnyJIDShspgMMDR0RGjRo2Ct7c3lixZgsWLF5uOs7e3R0BAABwcHEzHWKqyT8ILFizAG2+8gU8//RQXLlyAr68v3n77bVy8eBEffvghtmzZYjrOxcUFISEhcHZ2VrD6OzPmy83Nxfjx4zFo0CAMGTIEycnJqF69uurzAX/ehFS3bl10794dQOkTlpeXF2bMmKH6fMYmKjs7G7Vr18aWLVtga2sLEdFERhEx5QsLC8PevXtx+vRpiAgcHR0xcuRIeHt7Y/HixaqeZ4wZH3vsMezduxdfffUVCgoK4OPjo/oxLLsqRv/+/REZGYnAwECsX78eTk5OGDFihKqfK4zPhbm5ufi///s/jB49GuPGjUNcXBxcXFwwduxY+Pr6qvpnlDRGkYszNMh4HVNubq40b95catWqJfv27TM9Zlxb8dSpUzJw4EBp1aqVvPDCC7Jx40aZO3euODg4lHvnJEtjzJidnS3BwcESGRkpYWFh8thjj0mPHj3kypUrIiKSkJAgbdq0kQ4dOsi0adPkwIED8vHHH4uTk5PExMQoGeG2jNeuZWdnS0BAgPTo0UNGjhwpXbp0kQ4dOkhWVpaIlL7FqxrzlXX8+HHx9vYWNzc3iYyMNHssPj5e2rZtKxEREarLZ/w9y8rKkkcffVT69Olj9rjxZzguLk7VY1hQUCDh4eHSs2fPcttFSt/aNioqSlq2bKm6eabsGNapU0cGDhwo06dPl4YNG8qNGzdM+6l1njHKycmRoKAg6d+/v3z99dcyevRo8fDwkMzMTBERSUxMlIEDB6pyDEVK8wUGBkqPHj1kxIgR0qtXL7GxsZEPP/xQiouLJTU1VbXPhaQ9bIYfoIKCAhk0aJC0aNFCevToIaGhobJ3714RKW20jJP8xYsX5fPPP5eQkBBp1KiRtG7dWn744QcRsfw7ZfPz86Vjx44ycOBAKSgoEL1eL99//720aNFC4uLiTPudOnVKJk6cKI0aNZK6detK48aN5fvvv1ew8ruTl5cn4eHhEhUVZWqOlyxZIr169RIRkVu3bomISHJyskycOFECAwNVlU+ktNkw3r29efNmqV+/vjzzzDOmxw0Gg1y8eFEmTZqkuvETKf1jpl69emaN4ubNm+XLL7+Ur7/+WlJSUkRE5Pfff1flz6hI6R8s7du3N/0BOmvWLBkyZIi0bdtWli1bJsXFxXL16lX5/PPPpUWLFqqbZ27cuCG1atWSAQMGiEjpvOPn5yeTJ08WkT/rV+s8YzAYZPLkyfLkk0+atp0+fVq6du0q165dk4yMDBERuXLlivznP/9R3RgaDAYZO3asdO/e3bStpKREnnzySXFxcZEZM2aISOlz4cKFC1X5XEjawmb4ATCerUhISJCnnnpK1q5dK9HR0dK/f38JCQmpsCE2unbtmul4S75T1ljjzz//LB07dpSjR4+aHtPr9RIQECBffvmliJi/w1B+fr6kpKSYJndLzWjMt2nTJomMjJQzZ86YHvviiy8kODhYnnrqKfnHP/4hGzduFJE/71RXU76yunfvLrGxsbJz50555JFHpF+/fjJ//nwZPny46PV6KS4uloKCAlXkE/kz44oVK0Sn08nixYtFRGTo0KHSsmVLqVOnjgQHB4uPj4/ExsaKSOkfsGocw927d0tgYKDk5eXJsGHDpGXLljJhwgR54YUXxNnZWSZNmmS6A19EXfOMXq+X4cOHm/0xU1hYKOPGjZOIiAjJzMw0e7WtpKRENT+nZcdwyJAhMmLECNPn3377rbi6ukrr1q3F09NTJk+ebLa/GsbQWJ9er5du3brJO++8IyKlf8yIiEyYMEHat28vNjY2smLFCrNj1ZCPtIvN8H369NNPpWPHjpKbmysiIvv37zctGbN7927p16+fhISEyJ49e0REnb/kxow5OTmyf/9++eCDD0wvxxqfkNq1ayeffPKJiJj/Ra+GrGXHUK/XS3x8vBQXF4tI6VqXOp1OJk6cKJ9++qmMHj1abG1t5dChQwpXfffKjp9I6ZgUFRVJp06dZOnSpSIikpSUJB4eHqLT6WTDhg0iIqZ/AzX46+/h7Nmzxd7eXlq1aiXh4eESGxsrV69elcTEROnfv7/4+fnJpUuXFK767v11DA8ePCg+Pj6yefNmGTx4sJw8edK075IlS6RatWqyZcsWpcr9W4wZs7Oz5ffffzdtN84hBw4cEDs7O/nf//5ntl0Nc4xI+TGcMmWKuLm5yaJFi2TevHlia2src+fOld9++02WLFkizs7OsmzZMoWrvnt/zRcVFSWdO3c2vcImIhIUFCSbNm2ScePGyaOPPio3b95UzfiRtrEZvg+bNm0SnU4n8+fPr3SfmJgYU0NsPEO8cOFC1VwTZcw4b948ESl94jE2+2UnsR49esiCBQvMjjO+HG3JbjeGBQUF8tprr5k9IZWUlEhgYKBMmzatKsv82yrKZ3xymj17tukPmJkzZ4qDg4P4+PiYLglRi8rG8J133hEvLy/ZsWOH2fbffvtN/Pz85JdffqnKMv+2yvL1799fPD095ZFHHpGkpCSzxzp27ChjxoypyjLvizHj3LlzK3zcONeMGDFCHn/8cdMZYLWoaAxv3LghI0eOlGeeeUbatWsnL730ktkx3bt3l8jIyHKvJlqiivKtXLlS2rdvL+Hh4fLuu+9KvXr1pFOnTiIiEhsbK/Xr15dz584pVDGROa4mcR9q1aqFgIAAXLp0CcePHzd7TP64CzYiIgKvvfYaAgMD8frrr2PcuHF45ZVXkJ2drUTJ98yY8fLly0hISIBOp4O9vb1peS6joqIi5OfnAwCWL1+Obt26ITExUamy71rZMTx27Jhpu4jAwcEB7733Hp5//nnTeF67dg2+vr5o1qyZUiXfk4p+RssuhXfixAnMmTMH8+fPx6pVq7Bx40bs3LkTkZGRSpZ9Tyr7PZw6dSo2bNiAFi1aAPjzd9LNzQ3Ozs7w8PBQpN57VVm+sWPHolWrVrh58yYyMzPNjvH09ET9+vWrutS/zZgxPT293FwK/Lk8V4cOHZCYmIgLFy4AqHwpS0tT0Tzj6emJr776Chs3bkT9+vXRuHFjAEBBQQEAwN3dHU2bNoWtra1idd+tsvkSEhIAAP3798f48ePh7++P3bt3o0+fPti5cycAoLCwEMXFxaYlD4kUp2wvrn5HjhyRVq1ayYgRI+T48eNmj5U9c7pnzx5p1KiR6HQ61d0gcDcZH3vsMVm4cKFs3LhRdDpduevBLFll+Sq6pGXJkiUSEBBgds20pass3/bt26V69eri5eUlP/74o2l7TEyMal65MLrdz+hfff7559K8eXNVnZUqm+/YsWMiUvrzuWbNGgkNDRVvb2/58ccfJSYmRpYtWyY+Pj6ye/duhau+N3c7hhEREeVWQFGDisbQaNy4cdKyZUvJzc2V7OxsWbFihXh7e6vq97Bsvr/Oj8Ybj43ef/99adOmjWnlDCKlsRl+AOLi4u7YLH722Wei0+nM3l9dLc2wSOUZjRkGDRokLVu2FDs7O/nmm29Mj6kl4+3GUETk7NmzsmDBAnF2dpbVq1crUOH9qShfcXGxjBs3rtJrS9UydkZ3GsMzZ87Ixx9/LM7OzrJmzRoFKrw/ZfMlJCSISOkYHT58WIYMGSLe3t7SoEEDCQwMlFWrVilc7d9zuzE0Xt7z/vvvS1BQkJw/f16JEu9LZfm2bdsm4eHh4uDgIO3bt5eaNWuaVsVQ0+9h2XxlG37j2B08eFA++OADcXJyUuXvIGkXm+EH5HaT+OnTp6VJkyby3XffiYi6msSybpexT58+otPpZN26dSKizoyV5Tt37py8++670rRpU1m7dq2IqOsJyqhsvvj4eBFRZ47bqWwMz549K3PmzJEGDRqo7pWZsipqiI0SEhLk7NmzpjPeavwdFLnzH96XL1+W/fv3K1XefauoYTQYDHLq1ClZsGCBLF++3HSDrhrHsGy+EydOmD32yy+/SLNmzVQ9j5I2sRl+gG7XLF68eFFE1Dm5lVVZxu+++850g6CaM1Y0kRsMBvn9998lOTnZ9LkW8v31pVqtqGwMExISTKsUaHEM1ZqnIpXNM2pa4eR2bvdHjRbc7rkwPT1dRNT9O0jaw2b4AatsklPbMkC3U9EZxrLUntGa8mnxiViEY6gFWs9YNp+a7kG4W3d6LiSyJGyGH4Kyk8DNmzdl27Zt5ZY+Uru/ZtyyZYumMv4139atW5lPZbSeUev5RLSf0dryafG5kLSBzfBDEhcXJ+3atZNWrVqJTqeT6OhopUt64LSekfnUT+sZtZ5PRPsZmY9IeWyGH6JDhw5Jq1at5KefflK6lIdG6xmZT/20nlHr+US0n5H5iJSlE/ljJXp6KHJycuDm5qZ0GQ+V1jMyn/ppPaPW8wHaz8h8RMphM0xEREREVotvx0xEREREVovNMBERERFZLTbDRERERGS12AwTERERkdViM0xEREREVovNMBERERFZLTbDRERERGS12AwTERERkdViM0xEREREVovNMBERERFZLTbDRERERGS12AwTERERkdViM0xEREREVovNMBERERFZLTbDRERERGS12AwTERERkdViM0xEREREVovNMBERERFZLTbDRERERGS12AwTERERkdViM0xEREREVovNMBFRGdHR0dDpdLh58+ZD+frLli2Dp6fnQ/naRER079gMExFVoaioKCQnJytdBhER/cFO6QKIiKyJk5MTnJyclC6DiIj+wDPDRKSI1NRU9OjRA15eXnBxcUHTpk2xadMmAIBer8fw4cNRv359ODk5ISgoCAsWLDA7/oUXXkDv3r0xd+5c+Pn5wdPTE7Nnz0ZJSQkmTZoEb29v1KlTB0uXLjUdk5KSAp1Oh5UrV6J9+/ZwdHREs2bNsHv37tvWunfvXkRERMDJyQn+/v549dVXkZeXV+n+R48eRadOneDm5gZ3d3e0bt0ahw4dAlD+Mol69epBp9OV+zA6f/48Bg4cCE9PT3h7e6NXr15ISUm5239mIiK6AzbDRKSIsWPHorCwEDExMTh27BjeffdduLq6AgAMBgPq1KmD1atX4+TJk5g+fTreeustfP/992ZfY+fOnbh06RJiYmLw0UcfYcaMGejevTu8vLzw22+/YfTo0Rg1ahQuXLhgdtykSZPwxhtv4MiRIwgPD0ePHj1w7dq1Cus8c+YMnn76afTr1w8JCQlYtWoV9u7di1deeaXSbM899xzq1KmD2NhYHD58GG+++SaqVatW4b6xsbG4fPkyLl++jAsXLuCxxx5DREQEAKC4uBiRkZFwc3PDnj17sG/fPri6uuLpp59GUVHRXf9bExHRbQgRkQKaN28uM2fOvOv9x44dK/369TN9/vzzz0vdunVFr9ebtgUFBUlERITp85KSEnFxcZHvvvtORETOnTsnAGT+/PmmfYqLi6VOnTry7rvviojIrl27BIDcuHFDRESGDx8uL730klkte/bsERsbG8nPz6+wVjc3N1m2bFmFjy1dulQ8PDwqfOzVV1+VunXrSmZmpoiIfPPNNxIUFCQGg8G0T2FhoTg5OcmWLVsq/BpERHRveM0wESni1VdfxZgxY7B161Z06dIF/fr1Q4sWLUyPL1y4EEuWLEFaWhry8/NRVFSE0NBQs6/RtGlT2Nj8+QKXn58fmjVrZvrc1tYWPj4+yMzMNDsuPDzc9P92dnYICwvD77//XmGdR48eRUJCAr799lvTNhGBwWDAuXPn0Lhx43LHTJgwASNGjMA333yDLl26YMCAAWjYsOFt/z2++uorLF68GPv370f16tVN3/v06dNwc3Mz27egoABnzpy57dcjIqK7w8skiEgRI0aMwNmzZzF06FAcO3YMYWFh+Pe//w0AWLlyJSZOnIjhw4dj69atiI+Px4svvlju0oC/Xnqg0+kq3GYwGP52nbm5uRg1ahTi4+NNH0ePHsWpU6cqbXBnzpyJEydOoFu3bti5cyeaNGmCdevWVfo9du3ahXHjxmH58uVmfxDk5uaidevWZt87Pj4eycnJGDx48N/OREREf+KZYSJSjL+/P0aPHo3Ro0djypQpWLRoEcaNG4d9+/ahffv2ePnll037PsgzoQcOHECHDh0AACUlJTh8+HCl1wC3atUKJ0+eREBAwD19j0aNGqFRo0YYP348Bg0ahKVLl6JPnz7l9jt9+jT69++Pt956C3379i33vVetWoUaNWrA3d39nr4/ERHdHZ4ZJiJFvP7669iyZQvOnTuHuLg47Nq1y3TJQWBgIA4dOoQtW7YgOTkZb7/9NmJjYx/Y9164cCHWrVuHxMREjB07Fjdu3MC//vWvCvedPHky9u/fj1deeQXx8fE4deoU1q9fX2nznJ+fj1deeQXR0dFITU3Fvn37EBsbW+HlFPn5+ejRowdatmyJl156Cenp6aYPoPRGPF9fX/Tq1Qt79uzBuXPnEB0djVdffbXcTYFERPT38MwwESlCr9dj7NixuHDhAtzd3fH000/j448/BgCMGjUKR44cQVRUFHQ6HQYNGoSXX34Zv/zyywP53vPnz8f8+fMRHx+PgIAA/PTTT/D19a1w3xYtWmD37t2YOnUqIiIiICJo2LAhoqKiKtzf1tYW165dw7Bhw5CRkQFfX1/07dsXs2bNKrdvRkYGEhMTkZiYiFq1apk9JiJwdnZGTEwMJk+ejL59+yInJwe1a9dG586deaaYiOgB0YmIKF0EEVFVSElJQf369XHkyJFyN+MREZF14mUSRERERGS12AwTERERkdXiZRJEREREZLV4ZpiIiIiIrBabYSIiIiKyWmyGiYiIiMhqsRkmIiIiIqvFZpiIiIiIrBabYSIiIiKyWmyGiYiIiMhqsRkmIiIiIqv1/wHjudL0I0X8NQAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "p = PowerSim(metric='proportion', relative_effect=False, variants=2, alternative='two-tailed', \n", - "\tnsim=1000, correction='holm')\n", - "rr = p.grid_sim_power(baseline_rates=[[0.33]], \n", - " effects=[[0.01, 0.03], [0.03, 0.05], [0.03, 0.07]],\n", - " sample_sizes= [[1000], [2000], [3000], [4000], [5000], [6000], [7000], [8000], [9000]], \n", - " threads=16, \n", - " plot=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As expected, the best scenario occurs when the effect sizes are large enough to be detected. We can also analyze the results of different sample allocations. For example, when we look at the effects `[0.03, 0.05]`, the allocation `[3000, 7000, 7000]` produces fairly good results, although it doesn't always meet the 0.8 threshold." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "p = PowerSim(metric='proportion', relative_effect=False, variants=2, \n", - " alternative='two-tailed', nsim=1000, correction='holm')\n", - "rr = p.grid_sim_power(baseline_rates=[[0.33]], \n", - " effects=[[0.01, 0.03], [0.03, 0.05], [0.03, 0.07]],\n", - " sample_sizes= [[1000, 3000, 3000], \n", - " [2000, 5000, 5000], \n", - " [3000, 7000, 7000], \n", - " [2000, 8000, 8000],\n", - " [5000, 8000, 8000]],\n", - " threads=16, \n", - " plot=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also use other metrics (e.g., average or counts).  For instance, we can\n", - "design an experiment where the outcome is the number of visits (counts). The simulator will use a\n", - "Poisson distribution with the parameter, $$\\lambda$$ (lambda) or the mean\n", - "number of events. In this example, I set a baseline rate (lambda) of 1.2 visits\n", - "and a relative increase of $$0.05 (1.2*1.05) = 1.26$$, with a control group of\n", - "3000 users and a treatment of 5000 users:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - " comparisons power\n", - "0 (0, 1) 0.59" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "p = PowerSim(metric='count', relative_effect=True, variants=1, \n", - " alpha=0.05, alternative='two-tailed')\n", - "p.get_power(baseline=[1.2], effect=[0.05], sample_size=[3000, 5000])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The effect is small, so our test is underpowered (<0.80). We can also use\n", - "averages (e.g., revenue), but in that case, we need to specify the standard\n", - "deviation of the groups:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - " comparisons power\n", - "0 (0, 1) 0.6592" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "p = PowerSim(metric='average', relative_effect=False, variants=1, \n", - " alpha=0.05, alternative='two-tailed', nsim=5000)\n", - "p.get_power(baseline=[1000], effect=[100], standard_deviation=[600], sample_size=[400])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Complex models \n", - "\n", - "When using uplift or mixed models, things become more complicated. As Aleksander\n", - "Molak put it: \n", - "\n", - "> The question of defining a \"safe\" dataset size for S-Learner and\n", - "other causal models is difficult to answer. Power calculations for machine\n", - "learning models are often difficult, if possible at all. \n", - "\n", - "There are some tricks we can apply, though, as Harrell suggests: \n", - "\n", - "> If you can afford a pilot study or you have some historical data that represents a problem similar to the one that\n", - "you’re interested in, you can find a subgroup in your data that is as homogenous\n", - "as possible. You can estimate the sample size for this group using some of the\n", - "traditional statistical power tools. Finally, scale your overall sample size so\n", - "that this subgroup is properly powered relative to the entire sample \n", - "\n", - "There are also some traditional ways to optimize the power of our tests:\n", - "\n", - "- Blocking design\n", - "- Stratification and covariate adjustments (regression)\n", - "\n", - "For instance, after learning from our uplift models, we can identify the key\n", - "features associated to users' responses. We can use those features to\n", - "design our experiments (blocking). We can also evaluate the results of our\n", - "uplift models in a new sample, and see if we can replicate the expected\n", - "`uplift`.\n", - "\n", - "Here's a brief and general overview of the key features to keep in mind when designing experiments or improving models. I hope it's helpful.\n", - "\n", - "
\n", - "\n", - "*** \n", - "\n", - "## References\n", - "\n", - "- Molak, Aleksander. *Causal Inference and Discovery in Python: Unlock the\n", - "secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more*\n", - ". \n", - "- https://www.fharrell.com/\n", - "- Ron Kohavi. *Trustworthy Online Controlled Experiments*." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "causal-inference", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.4" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/_jupyter/zotero-notes.csv b/_jupyter/zotero-notes.csv deleted file mode 100644 index d65c7506..00000000 --- a/_jupyter/zotero-notes.csv +++ /dev/null @@ -1,24 +0,0 @@ -id,citation,tags,title,reviewer,research_question,model,data,methods,conclusions,the_good,limitations,results,context,next -4ESWAPZY,van Dijk et al. 2015,,Recent developments on the role of epigenetics in obesity and metabolic disease,Elena,Knowledge about epigenetic marks related to obesity and metabolic disease in both animal and human models,Human and animal,Review,,,Good summary tables of studies carried out in both animal and human models and their main discoveries (includes whether or not they carry out a validation study),"So far, a causal role of epigenetics in the development of the disease, or the mode of transmission of these phenotypic effects over successive generations, cannot be confirmed.",,, -5JNF33U8,Fernandez-Twinn et al. 2019,"intrauterine effects, epigenetics",Intrauterine programming of obesity and type 2 diabetes,Elena,Which are the relevant exposures related to the development of obesity and t2d that affect or imply epigenetic changes?,Human and also murine models,,,"-          The main exposures involved: fetal undernutrition and overnutrition, infections and inflammation (enteroviruses), environmental chemicals (such asbisphenol A), maternal stress (project ice storm), gut microbiota and paternal factors (such as paternal smoking or paternal increased body fat). --          The mechanisms involved: dna methylation (most studied in humans) , histone modifications (mostly in murine models) and miRNAs and lncRNA. --          From animal studies: effects of under/overnutrition in sperm and seminal fluid, effects of obesity in oocyte mitochondrial damage, also effects in pancreatic islets, adipose tissue and brain (related to feeding behaviour) -Effects of lifestyle in epigenetics: diet or fasting periods of 36h and physical activity affects dna methylation",,limited evidence for  a causal role for epigenetic variation in mediating the effects of adverse in utero environment,,, -S8FZS32L,Ling 2019,Elena,Epigenetics in Human Obesity and Type 2 Diabetes,Elena,"Summarizes epigenetic signatures from human tissues (adipose tissue, muscle, pancreatic islets, liver and blood). Supports the idea of a role for epigenetics in disease development.",,Human,Different large-scale methylation studies using illumina 450k arrays for BMI (mainly),,"Also covers diet (methyl donnors) and epigenetics, physical activity and age","In epigenetic studies it’s important to understand the fact that the epigenome is tissue specific and it changes over time. Also we can’t test the whole epigenome, arrays cover aprox 450000 CpG sites (1.5%). Many studies are performed in blood cells, which most not likely do not contribute to obesity. The tissue should be carefully selected.","1) Evidence for DNA methylation sites that contribute to increased BMI (overlaps in multiple studies)2) Obesity is associated with differential DNAm and increased epigenetic variability (dysregulation)3) Alterations in DNAm are more often a result of obesity than the opposite4) Diets with higer folic acid alter the methylation pattern of genes associated with obesity and t2d in mice5) Diet affects methylation. Not only overfeeding/fasting, the fat composition affects.6) The epigenetic signature is altered in response to exercise. Epigenetics may be a link between exercise and adipocyte metabolism.",, -QY3Y7V6X,Sharp et al. 2017,Must read,Maternal BMI at the start of pregnancy and offspring epigenome-wide DNA methylation: findings from the pregnancy and childhood epigenetics (PACE) consortium,Elena,Is maternal bmi related with changes in the offspring methylation patterns?,,"meta-analysis across 19 cohorts using 450k illumina arrays (ALSPAC, CBC HISPANICS, CBC CAUCASIANS, CHAMACOS, EARLI, GOYA, GECKO, GENR, GEN3G, IOW, F2, MEDALL (INMA + EDEN), MOBA 1, 2 Y 3, NEST, NFCS, NHBCS, RICHS, VIVA n=7930",2 models: primary análisis used continous bmi (n=7523) and the secondary análisis used overweight mothers (n= 2855) and underwaight (n= 211) mothers separately,"They found evicence for a causal intrauterine effect of maternal bmi on newborn methylation a 8 of 86 sites. Adjusting for cell % reduces lambda in almost every cohort (and also reduces the number of hits when corrected by Bonferroni) a cell may be an important confounder when methylation is measured in the whole blood. Maternal BMI-associated newborn blood methylation sites are not enriched for certain biological processes or pathways. -Associations between maternal BMI at the start of pregnancy and newborn DNA methylation were reproduced in the whole blood of adolescents at most sites","Large sample size n=7523. Strong confounder correction by shared environment or genetics: they used a negative control design in which both exposures (paternal and maternal) were tested. They also correct by using mqtls a if there is evidence of a snp related to methylation changes, there is not a causal maternal intrauterine effect in that one because it could be driven by a shared genetic effect.","Effects sizes were very small, les tan a 0.15% change in methylation per one-unit increase in maternal bmi. The biological significance of such small effects is nuclear. Without integration with gene expression data it’s imposible to truly infer if the results are functionally important.",,, -5AMCHQMB,Sulc et al. 2020,,Quantification of the overall contribution of gene-environment interaction for obesity-related traits,Sebastian,Can we use a method to estimate GxE without measuring all the environments?,,Simulation and application to complex traits from the UK bank.,"Detect GxE based on the variance heterogeneity of the outcome across different genotype groups. -No need to observed the interacting environment. Environment is defined as a random effect. -Maximum likelihood approach -Software: https://github.com/zkutalik/GRSxE_software",,Interesting approach to GxE and GxG. The method could be used as a tool to establish the contribution of G×E to different traits and subsequently prioritize those with substantial global interaction effect for follow-up G×E analysis with specific environmental factors.,"The interaction effect estimates may depend too strongly on the choice of the fitted link function, thus this application requires further research. -How meaningful is to estimate the interactions this way? We are not able to identify which factors are the important ones. -Some GxE1 may represent G×G1 or E×E1 interactions, where G1 is a genetic factor associated with E1 and G with environment E, respectively. Still, such identified G × E is an informative starting point for further investigations narrowing down the most plausible E. -Mostly useful for polygenic scores analysis (SNP analysis most of the time underpowered) -The method assumes that any outcome heteroscedasticity (conditional on the GRS) is driven by G×E interaction, although it could be due to variance controlling genetic effects.","Simulation results seem to show the method is robust -Application to real data suggests that GxE contribute ~0.1-2% of genetic risk -n = 20,000 studies are well powered. -The heteroscedastic nature of BMI is most likely due to a transformation and not driven by G×E.","GxE are challenging, E is not measured accurately, potential crucial factors are hard to define. They proposed a method to establish statistical interactions between genetic risk (continuous outcome) and all environmental variables, and estimate the total GxE contribution to the trait. -Previous GxE studies indicated that the contribution of G×E compared with marginal genetic effects is very modest (less than a quarter of the explained variance of the marginal model).",What would be the implications of these results to the GxE heterocedasticity discussion? -RGDC9R9R,Abadi et al. 2017,"Genomics, GxG, GxE, BMI",Penetrance of Polygenic Obesity Susceptibility Loci across the Body Mass Index Distribution,Sebastian,Are the effects of obesity-susceptibility loci uniform across the BMI distribution? The study aims to investigate variations in the impact of 37 BMI-associated SNPs across the distribution of BMI.,,"Several studies, including the Framingham cohort (75,230 adults of European ancestry.) Includes >18 and >92. Outcomes: BMI. They describe quality control (QC) procedures. They compute different cumulative gene scores (GS) to estimate also the combined effect of SNPs.","Conditional quantile regression (CQR) to investigate the effects (association) of 37 BMI-associated SNPs at multiple percentiles. Also, meta-regression to combine results. All models were adjusted for age (years), sex (female, male), and study (factor).",,Interesting approach to GxE and GxG.,"European ancestry, how these results hold in developing countries?","Significant positive associations (adjusting for multiple comparisons) between BMI percentile and CQR estimates were detected for 9 of 37 SNPs. Positive meta-regression estimates indicate that the effects of SNPs vary systemically by BMI percentile because unadjusted interactions inflate the impact of SNPs in participants with a high BMI. CQR models for GS-BMI showed steadily increasing effects with increasing percentiles, whereas CQR models for GS-height did not vary across percentiles. -Suppose unadjusted interactions in the effects of BMI-associated SNPs are predominantly due to GXG interactions. In that case, it is reasonable to suppose that these un-adjusted interactions would be detected at a similar frequency in other quantitative traits such as height. On the other hand, if GXE interactions predominate, then these unadjusted interactions might be less frequently detected in quantitative traits with a smaller environmental component (i.e., height). -The simplest explanation for the discrepancy between the results for GS-BMI and GS-height is that the unadjusted interactions detected from GS-BMI were predominantly GXE interactions.","The role of the individual and compound gene-environment and gene-gene interactions in determining BMI (obesity) has not been fully elucidated (data limitations, etc.) FTO (rs1421085) has been shown to jointly interact with diet, physical activity, salt and alcohol consumption, and sleep duration.",What would be the implications of these results to the GxE discussion? diff --git a/_jupyter/zotero-notes.ipynb b/_jupyter/zotero-notes.ipynb deleted file mode 100644 index fb338e09..00000000 --- a/_jupyter/zotero-notes.ipynb +++ /dev/null @@ -1,396 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "When reviewing the literature, it's nice to have the notes linked to Zotero references. It would also be great to compare them systematically. The idea would be to have your notes in Zotero so that everyone in the group can access and edit them and create a data file to explore your notes using, for instance, text analysis techniques.\n", - "\n", - "This small package extracts notes from a collection and creates a CSV file that can be easily read using Excel. You only need to specify the collection ID. For instance, if the location of my collection is `https://www.zotero.org/groups/2406179/csic-echo/collections/M8N2VMAP`, the collection ID would be `M8N2VMAP`. We also need the Zotero API's credentials. \n", - "\n", - "To create a clean CSV, notes' headers would need a suitable separator. The default is `#`. In this case, the text between headings mustn't include `#`. Below an example:\n", - "\n", - "```\n", - "# Research question\n", - "\n", - "Estimates interaction effects between PGS of obesity and cohorts using HRS.\n", - "\n", - "# Data\n", - "\n", - "HRS\n", - "\n", - "# Methods\n", - "\n", - "Uses a HLM whereby they estimate effects of age and cohorts while making the intercepts and slopes a function of individual factors.\n", - "```\n", - "\n", - "## Installation\n", - "\n", - "```\n", - "pip install git+https://github.com/sdaza/zotnote.git\n", - "```\n", - "\n", - "## Credentials\n", - "\n", - "You can save your Zotero API credentials in a `config.py` and load them using `import config`: \n", - "\n", - "```yaml\n", - "library_id = \"0000000\"\n", - "api_key = \"key\"\n", - "library_type = \"group\"\n", - "```\n", - "\n", - "## Example\n", - "\n", - "Let's try to extract the notes and read the using Pandas: \n" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 1, - "source": [ - "import pandas as pd\n", - "import config\n", - "import zotnote as zn" - ], - "outputs": [], - "metadata": { - "tags": [] - } - }, - { - "cell_type": "code", - "execution_count": 4, - "source": [ - "zn.exportNotes(collection = \"M8N2VMAP\", \n", - " library_id = config.library_id, api_key = config.api_key)" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Notes saved in zotero-notes.csv\n" - ] - } - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 3, - "source": [ - "pd.read_csv(\"zotero-notes.csv\")\n", - "\n" - ], - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " id citation tags \\\n", - "0 4ESWAPZY van Dijk et al. 2015 NaN \n", - "1 5JNF33U8 Fernandez-Twinn et al. 2019 intrauterine effects, epigenetics \n", - "2 S8FZS32L Ling 2019 Elena \n", - "3 QY3Y7V6X Sharp et al. 2017 Must read \n", - "4 5AMCHQMB Sulc et al. 2020 NaN \n", - "5 U4MNK7QT Walter et al. 2016 Genomics, HRS, GxE, BMI \n", - "6 RGDC9R9R Abadi et al. 2017 Genomics, GxG, GxE, BMI \n", - "\n", - " title reviewer \\\n", - "0 Recent developments on the role of epigenetics... Elena \n", - "1 Intrauterine programming of obesity and type 2... Elena \n", - "2 Epigenetics in Human Obesity and Type 2 Diabetes Elena \n", - "3 Maternal BMI at the start of pregnancy and off... Elena \n", - "4 Quantification of the overall contribution of ... Sebastian \n", - "5 Association of a Genetic Risk Score With Body ... Sebastian \n", - "6 Penetrance of Polygenic Obesity Susceptibility... 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2S8FZS32LLing 2019ElenaEpigenetics in Human Obesity and Type 2 DiabetesElenaSummarizes epigenetic signatures from human ti...NaNHumanDifferent large-scale methylation studies usin...NaNAlso covers diet (methyl donnors) and epigenet...In epigenetic studies it’s important to unders...1) Evidence for DNA methylation sites that con...NaNNaN
3QY3Y7V6XSharp et al. 2017Must readMaternal BMI at the start of pregnancy and off...ElenaIs maternal bmi related with changes in the of...NaNmeta-analysis across 19 cohorts using 450k ill...2 models: primary análisis used continous bmi ...They found evicence for a causal intrauterine ...Large sample size n=7523. Strong confounder co...Effects sizes were very small, les tan a 0.15%...NaNNaNNaN
45AMCHQMBSulc et al. 2020NaNQuantification of the overall contribution of ...SebastianCan we use a method to estimate GxE without me...NaNSimulation and application to complex traits f...Detect GxE based on the variance heterogeneity...NaNInteresting approach to GxE and GxG. The metho...The interaction effect estimates may depend to...Simulation results seem to show the method is ...GxE are challenging, E is not measured accurat...What would be the implications of these result...
5U4MNK7QTWalter et al. 2016Genomics, HRS, GxE, BMIAssociation of a Genetic Risk Score With Body ...SebastianEstimates interaction effects between PGS of o...NaNHRSUses a HLM whereby they estimate effects of ag...NaNNaNPays little attention to selection due to surv...NaNNaNAnything
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- {%- endif %} - -
- {{ content }} -
- - {% if page.news -%} - - {%- include news.html %} - {%- endif %} - {% if page.selected_papers -%} - - {%- include selected_papers.html %} - {%- endif %} - {%- if page.social %} - - - {%- endif %} -
- -
diff --git a/_layouts/archive-category.html b/_layouts/archive-category.html deleted file mode 100644 index 79aad74f..00000000 --- a/_layouts/archive-category.html +++ /dev/null @@ -1,27 +0,0 @@ ---- -layout: default ---- - -
- -
-

{{ page.title }}

-

an archive of posts in this category

-
- -
-
- - {% for post in page.posts %} - - - - - {% endfor %} -
{{ post.date | date: "%b %-d, %Y" }} - {{ post.title }} -
-
-
- -
diff --git a/_layouts/archive-tag.html b/_layouts/archive-tag.html deleted file mode 100644 index 66abaebb..00000000 --- a/_layouts/archive-tag.html +++ /dev/null @@ -1,27 +0,0 @@ ---- -layout: default ---- - -
- -
-

{{ page.title }}

-

an archive of posts with this tag

-
- -
-
- - {% for post in page.posts %} - - - - - {% endfor %} -
{{ post.date | date: "%b %-d, %Y" }} - {{ post.title }} -
-
-
- -
diff --git a/_layouts/archive-year.html b/_layouts/archive-year.html deleted file mode 100644 index 8af1d29b..00000000 --- a/_layouts/archive-year.html +++ /dev/null @@ -1,27 +0,0 @@ ---- -layout: default ---- - -
- -
-

{{ page.date | date: "%Y" }}

-

an archive of posts from this year

-
- -
-
- - {% for post in page.posts %} - - - - - {% endfor %} -
{{ post.date | date: "%b %-d, %Y" }} - {{ post.title }} -
-
-
- -
diff --git a/_layouts/bib.html b/_layouts/bib.html deleted file mode 100644 index 1d0f3569..00000000 --- a/_layouts/bib.html +++ /dev/null @@ -1,189 +0,0 @@ ---- ---- - -
-
- {% if entry.abbr %} - {% if site.data.venues[entry.abbr] %} - {{entry.abbr}} - {% else %} - {{entry.abbr}} - {% endif %} - {% endif %} -
- -
- {% if entry.type == "thesis" %} - {{reference}} - {% else %} -
{{entry.title}}
- - -
- {% assign author_array_size = entry.author_array | size %} - - {% assign author_array_limit = author_array_size %} - {%- if site.max_author_limit and author_array_size > site.max_author_limit %} - {% assign author_array_limit = site.max_author_limit %} - {% endif %} - - {%- for author in entry.author_array limit: author_array_limit -%} - {%- assign author_is_self = false -%} - {%- assign author_last_name = author.last | remove: "¶" | remove: "&" | remove: "*" | remove: "†" | remove: "^" -%} - {%- if site.scholar.last_name contains author_last_name -%} - {%- if site.scholar.first_name contains author.first -%} - {%- assign author_is_self = true -%} - {%- endif -%} - {%- endif -%} - {%- assign coauthor_url = nil -%} - {%- if site.data.coauthors[author_last_name] -%} - {%- for coauthor in site.data.coauthors[author_last_name] -%} - {%- if coauthor.firstname contains author.first -%} - {%- assign coauthor_url = coauthor.url -%} - {%- break -%} - {%- endif -%} - {%- endfor -%} - {%- endif -%} - - {%- if forloop.length > 1 -%} - {%- if forloop.first == false -%}, {%- endif -%} - {%- if forloop.last and author_array_limit == author_array_size -%}and {%- endif -%} - {%- endif -%} - {%- if author_is_self -%} - {{author.first}} {{author.last}} - {%- else -%} - {%- if coauthor_url -%} - {{author.first}} {{author.last}} - {%- else -%} - {{author.first}} {{author.last}} - {%- endif -%} - {%- endif -%} - {%- endfor -%} - {%- assign more_authors = author_array_size | minus: author_array_limit -%} - - {%- assign more_authors_hide = more_authors | append: " more author" -%} - {%- if more_authors > 0 -%} - {%- if more_authors > 1 -%} - {%- assign more_authors_hide = more_authors_hide | append: "s" -%} - {%- endif -%} - {%- assign more_authors_show = '' -%} - {%- for author in entry.author_array offset: author_array_limit -%} - {%- assign more_authors_show = more_authors_show | append: author.first | append: " " | append: author.last -%} - {%- unless forloop.last -%} - {%- assign more_authors_show = more_authors_show | append: ", " -%} - {%- endunless -%} - {%- endfor -%} - , and - {{more_authors_hide}} - {%- endif -%} - -
- -
- {% if entry.type == "article" %} - {{entry.journaltitle}} - {% elsif entry.type == "inproceedings" and entry.conference %} - Presented in {{entry.eventtitle}} - {% elsif entry.type == "incollection" %} - In: {{entry.booktitle}} - {% elsif entry.type == "inproceedings" %} - {{entry.pub}} - {% elsif entry.type == "unpublished" %} - Working paper - {% elsif entry.type == "report" or entry.type == "techreport" %} - {{entry.study}} - {% endif %} - {% if entry.date %} - {{ entry.date }} - {% endif %} - {% if entry.status %} - ({{ entry.status) }}) - {% endif %} -
- {% endif %} - -
- {% if entry.abstract %} - Abs - {% endif %} - {% if entry.bibtex_show %} - Bib - {% endif %} - {% if entry.html %} - HTML - {% endif %} - {% if entry.preprint %} - Preprint - {% endif %} - {% if entry.pdf %} - {% if entry.pdf contains '://' %} - PDF - {% else %} - PDF - {% endif %} - {% endif %} - {% if entry.supp %} - {% if entry.supp contains '://' %} - Supp - {% else %} - Supp - {% endif %} - {% endif %} - {% if entry.blog %} - Blog - {% endif %} - {% if entry.code %} - Code - {% endif %} - {% if entry.poster %} - {% if entry.poster contains '://' %} - Poster - {% else %} - Poster - {% endif %} - {% endif %} - {% if entry.slides %} - {% if entry.slides contains '://' %} - Slides - {% else %} - Slides - {% endif %} - {% endif %} - {% if entry.website %} - Website - {% endif %} - {%- if entry.altmetric %} -
- {%- endif %} -
- - - {% if entry.abstract %} - - {% endif %} - - - {% if entry.bibtex_show %} - - {% endif %} -
-
\ No newline at end of file diff --git a/_layouts/cv.html b/_layouts/cv.html deleted file mode 100644 index bb3d85af..00000000 --- a/_layouts/cv.html +++ /dev/null @@ -1,35 +0,0 @@ ---- -layout: default ---- - -
- -
-

{{ page.title }} {% if page.cv_pdf %}{% endif %}

-

{{ page.description }}

-
- -
-
- {% for entry in site.data.cv %} -
-

{{ entry.title }}

-
- {% if entry.type == "list" %} - {% include cv/list.html %} - {% elsif entry.type == "map" %} - {% include cv/map.html %} - {% elsif entry.type == "nested_list" %} - {% include cv/nested_list.html %} - {% elsif entry.type == "time_table" %} - {% include cv/time_table.html %} - {% else %} - {{ entry.contents }} - {% endif %} -
-
- {% endfor %} -
-
- -
diff --git a/_layouts/default.html b/_layouts/default.html deleted file mode 100644 index 2887107c..00000000 --- a/_layouts/default.html +++ /dev/null @@ -1,35 +0,0 @@ - - - - - - {%- if page.redirect -%} - - {%- endif -%} - {% include head.html %} - - - - - - - {%- include header.html %} - - -
- {{ content }} -
- - - {%- include footer.html %} - - - {% include scripts/jquery.html %} - {% include scripts/bootstrap.html %} - {% include scripts/masonry.html %} - {% include scripts/misc.html %} - {% include scripts/mathjax.html %} - {% include scripts/analytics.html %} - {% include scripts/progressBar.html %} - - diff --git a/_layouts/distill.html b/_layouts/distill.html deleted file mode 100644 index bc323ce8..00000000 --- a/_layouts/distill.html +++ /dev/null @@ -1,117 +0,0 @@ - - - - - {%- include head.html %} - - {% include scripts/jquery.html %} - {% include scripts/mathjax.html %} - - - - - {% if page._styles %} - - - {%- endif %} - - - - - - - - - - {%- include header.html %} - - -
- - -

{{ page.title }}

-

{{ page.description }}

- - - - - - {% if page.toc -%} - - - - {%- endif %} - - {{ content }} - - - - - - - - - - {%- if site.disqus_shortname and page.disqus_comments -%} - {% include disqus.html %} - {%- endif %} - {%- if site.giscus.repo and page.giscus_comments -%} - {% include giscus.html %} - {%- endif -%} - -
- - - {%- include footer.html %} - - {% include scripts/bootstrap.html %} - {% include scripts/analytics.html %} - {% include scripts/progressBar.html %} - - diff --git a/_layouts/none.html b/_layouts/none.html deleted file mode 100644 index b92f6522..00000000 --- a/_layouts/none.html +++ /dev/null @@ -1 +0,0 @@ -{{content}} diff --git a/_layouts/page.html b/_layouts/page.html deleted file mode 100644 index 9e34f406..00000000 --- a/_layouts/page.html +++ /dev/null @@ -1,16 +0,0 @@ ---- -layout: default ---- - -
- -
-

{{ page.title }}

-

{{ page.description }}

-
- -
- {{ content }} -
- -
diff --git a/_layouts/post.html b/_layouts/post.html deleted file mode 100644 index ad513ff4..00000000 --- a/_layouts/post.html +++ /dev/null @@ -1,53 +0,0 @@ ---- -layout: default ---- - -{%- assign year = page.date | date: "%Y" -%} -{%- assign tags = page.tags | join: "" -%} -{%- assign categories = page.categories | join: "" -%} - -{% if page._styles %} - - -{% endif %} - -
- -
-

{{ page.title }}

-

{{ page.date | date: "%B %-d, %Y" }}{%- if page.author -%} • {{ page.author }}{%- endif -%}{%- if page.meta -%} • {{ page.meta }}{%- endif -%}

-

- {{ year }} - {%- if tags != "" %} -   ·   - {% for tag in page.tags -%} - - {{ tag }}   - {% endfor -%} - {% endif %} - - {%- if categories != "" %} -   ·   - {% for category in page.categories -%} - - {{ category }}   - {% endfor -%} - {% endif %} - -

-
- -
- {{ content }} -
- - {%- if site.disqus_shortname and page.disqus_comments -%} - {% include disqus.html %} - {%- endif %} - {%- if site.giscus.repo and page.giscus_comments -%} - {% include giscus.html %} - {%- endif -%} - -
diff --git a/_news/announcement_1.md b/_news/announcement_1.md deleted file mode 100644 index 98e5af5c..00000000 --- a/_news/announcement_1.md +++ /dev/null @@ -1,7 +0,0 @@ ---- -layout: post -date: 2015-10-22 15:59:00-0400 -inline: true ---- - -A simple inline announcement. diff --git a/_pages/about.md b/_pages/about.md deleted file mode 100644 index 81225942..00000000 --- a/_pages/about.md +++ /dev/null @@ -1,24 +0,0 @@ ---- -layout: about -title: about -permalink: / -description: Demography, Sociology, Simulation, Data Science - -profile: - align: right - image: me.jpg - address: > -

Lost somewhere in Santiago, Chile

- -news: false # includes a list of news items -selected_papers: false # includes a list of papers marked as "selected={true}" -social: false # includes social icons at the bottom of the page ---- - -I'm a data scientist, demographer, and sociologist working on industry and academic projects. - -I received my Ph.D. in sociology from the [University of Wisconsin-Madison](http://www.ssc.wisc.edu/soc/) and my Bachelor's and Master's in Sociology from the [Catholic University of Chile](http://sociologia.uc.cl/). - -My **academic work** focuses on health disparities, criminal justice system's consequences for health and social integration, and the link between health inequalities and genetics. My research relies on statistical and computational methods, emphasizing data science and agent-based modeling (ABM). - -Currently, I am working as a **Senior Data Scientist**. I am also an **external faculty** in the Master in [Computational Social Science UC3M](https://www.uc3m.es/master/computational-social-science) (Fall 2022-2023), and collaborate with the [Center for the Study of Justice and Society](http://justiciaysociedad.uc.cl/) of the Catholic University of Chile. \ No newline at end of file diff --git a/_pages/cv.md b/_pages/cv.md deleted file mode 100644 index 6eeb7435..00000000 --- a/_pages/cv.md +++ /dev/null @@ -1,23 +0,0 @@ ---- -layout: page -title: cv -permalink: /cv/ -description: -nav: true ---- - - -- [Download Resume ](/assets/pdf/sdaza_resume.pdf) -- [Download CV](/assets/pdf/cv_sdaza.pdf) - - -
- - - - - - - - \ No newline at end of file diff --git a/_pages/dropdown.md b/_pages/dropdown.md deleted file mode 100644 index ff8b1f70..00000000 --- a/_pages/dropdown.md +++ /dev/null @@ -1,16 +0,0 @@ ---- -layout: page -title: academia -nav: true -nav_order: 6 -dropdown: true -children: - - title: publications - permalink: /publications/ - - title: divider - - title: projects - permalink: /projects/ - - title: divider - - title: teaching - permalink: /teaching/ ---- \ No newline at end of file diff --git a/_pages/dropdown/index.html b/_pages/dropdown/index.html new file mode 100644 index 00000000..e40954c2 --- /dev/null +++ b/_pages/dropdown/index.html @@ -0,0 +1 @@ + academia | Sebastian Daza

academia

© Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
\ No newline at end of file diff --git a/_pages/projects.md b/_pages/projects.md deleted file mode 100644 index 5e41ae62..00000000 --- a/_pages/projects.md +++ /dev/null @@ -1,58 +0,0 @@ ---- -layout: page -title: projects -permalink: /projects/ -description: -nav: false -nav_order: 2 -display_categories: [current, previous] -horizontal: false ---- - - -
-{%- if site.enable_project_categories and page.display_categories %} - - {%- for category in page.display_categories %} -

{{ category }}

- {%- assign categorized_projects = site.projects | where: "category", category -%} - {%- assign sorted_projects = categorized_projects | sort: "importance" %} - - {% if page.horizontal -%} -
-
- {%- for project in sorted_projects -%} - {% include projects_horizontal.html %} - {%- endfor %} -
-
- {%- else -%} -
- {%- for project in sorted_projects -%} - {% include projects.html %} - {%- endfor %} -
- {%- endif -%} - {% endfor %} - -{%- else -%} - - {%- assign sorted_projects = site.projects | sort: "importance" -%} - - {% if page.horizontal -%} -
-
- {%- for project in sorted_projects -%} - {% include projects_horizontal.html %} - {%- endfor %} -
-
- {%- else -%} -
- {%- for project in sorted_projects -%} - {% include projects.html %} - {%- endfor %} -
- {%- endif -%} -{%- endif -%} -
diff --git a/_pages/publications.md b/_pages/publications.md deleted file mode 100644 index ab6a098d..00000000 --- a/_pages/publications.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -layout: page -permalink: /publications/ -title: publications -description: -# years: [2021, 2020, 2019, 2018, 2016, 2014] -nav: false ---- - -
- {% bibliography -f sdaza --group_by type %} -
\ No newline at end of file diff --git a/_pages/repos.md b/_pages/repos.md deleted file mode 100644 index 15d61c50..00000000 --- a/_pages/repos.md +++ /dev/null @@ -1,30 +0,0 @@ ---- -layout: page -permalink: /repos/ -title: repos -description: -nav: true -nav_order: 7 ---- - -## GitHub users - -{% if site.data.repositories.github_users %} -
- {% for user in site.data.repositories.github_users %} - {% include repository/repo_user.html username=user %} - {% endfor %} -
-{% endif %} - ---- - -## GitHub Repositories - -{% if site.data.repositories.github_repos %} -
- {% for repo in site.data.repositories.github_repos %} - {% include repository/repo.html repository=repo %} - {% endfor %} -
-{% endif %} diff --git a/_pages/teaching.md b/_pages/teaching.md deleted file mode 100644 index 61022ba0..00000000 --- a/_pages/teaching.md +++ /dev/null @@ -1,20 +0,0 @@ ---- -layout: page -permalink: /teaching/ -title: teaching -description: -nav: false -nav_order: 6 ---- - - -## Courses - - -- [Survey Methods I (Fall 2022-23)](https://github.com/sdaza/survey-methods) - -## Mentorship - -- Ulda Figueroa. Ph.D. candidate in Sociology. Catholic University in Chile. Dissertation: *Selectividad y control del delito en el sistema de justicia criminal: El funcionamiento de las salidas alternativas al proceso penal y sus consecuencias en la reiteración de delitos*. - - diff --git a/_plugins/external-posts.rb b/_plugins/external-posts.rb deleted file mode 100644 index e4fd5eb6..00000000 --- a/_plugins/external-posts.rb +++ /dev/null @@ -1,36 +0,0 @@ -require 'feedjira' -require 'httparty' -require 'jekyll' - -module ExternalPosts - class ExternalPostsGenerator < Jekyll::Generator - safe true - priority :high - - def generate(site) - if site.config['external_sources'] != nil - site.config['external_sources'].each do |src| - p "Fetching external posts from #{src['name']}:" - xml = HTTParty.get(src['rss_url']).body - feed = Feedjira.parse(xml) - feed.entries.each do |e| - p "...fetching #{e.url}" - slug = e.title.downcase.strip.gsub(' ', '-').gsub(/[^\w-]/, '') - path = site.in_source_dir("_posts/#{slug}.md") - doc = Jekyll::Document.new( - path, { :site => site, :collection => site.collections['posts'] } - ) - doc.data['external_source'] = src['name']; - doc.data['feed_content'] = e.content; - doc.data['title'] = "#{e.title}"; - doc.data['description'] = e.summary; - doc.data['date'] = e.published; - doc.data['redirect'] = e.url; - site.collections['posts'].docs << doc - end - end - end - end - end - -end diff --git a/_plugins/hideCustomBibtex.rb b/_plugins/hideCustomBibtex.rb deleted file mode 100644 index 4a852fde..00000000 --- a/_plugins/hideCustomBibtex.rb +++ /dev/null @@ -1,15 +0,0 @@ - module Jekyll - module HideCustomBibtex - def hideCustomBibtex(input) - keywords = @context.registers[:site].config['filtered_bibtex_keywords'] - - keywords.each do |keyword| - input = input.gsub(/^.*#{keyword}.*$\n/, '') - end - - return input - end - end -end - -Liquid::Template.register_filter(Jekyll::HideCustomBibtex) diff --git a/_posts/2012-08-25-raking.md b/_posts/2012-08-25-raking.md deleted file mode 100755 index 43eaf778..00000000 --- a/_posts/2012-08-25-raking.md +++ /dev/null @@ -1,508 +0,0 @@ ---- -layout: post -author: Sebastian Daza -title: "Raking weights with R" -date: 2012-08-25 -tag: - - R - - survey - - data science -giscus_comments: true ---- - -When I was working with public opinion surveys, I usually had to adjust the data according to population parameters such as sex, age, socioeconomic status, or region. There are several ways to do this. One of them is *raking*. Using the package [anesrake](http://cran.r-project.org/web/packages/anesrake/index.html) by [Josh Pasek](http://joshpasek.com) is easy to compute raking weights in R. I show a brief and straightforward raking example in this post. - -*** - -### What is raking? - -It is common to find differences between the population and sample distribution in some key demographic variables when analyzing surveys. These differences might be due to the sampling design, non-coverage issues or non-response. To avoid biases of point estimates, we generally adjust those differences using weights so that the marginal totals agree (at least in part) with the population. These adjustments can be done using different methods: post-stratification or cell-weighting, raking, general regression estimator GREG (Battaglia et al. 2004, Valliant et al. 2013). Here I will show an example for raking adjustment. Raking has some advantages over cell-weighting: - -1. We have to know only the population totals of the specific variables, not all cells of the cross-classification. -2. It is more flexible feasible when we use several variables at once. - -It is important to note that raking adjustments are useful when we want to estimate a proportion or mean from a population, but they are not necessarily the best when modeling (see Gelman 2007). In that case, model specification (i.e., inclusion of all the key variables and interactions) may be a better solution. It is important to note that the raking procedure may affect the *dependence structure* of variables, what eventually may bias model estimates. - -*** - -### Some rules of thumb - -DeBell and Krosnick (2009) provide useful recommendations regarding variable selection when implementing *raking*: - -- Identify a set of variable likely to be measured with little error and a low item nonresponse rate (less than 5%), and compare them with *reliable* data sources (e.g., Census). Some typical variables are age groups, gender, race, SES, census region. -- Implement weighting when substantive demography discrepancies are observed. For instance, differences higher than five percentage points. When differences are less than two percentage points this adjustment would not be necessary. -- When selected variables have categories with less than 5% in the sample, it would be recommended to collapse them. - -Remember, however, that the ultimate goal of raking is to calibrate and improve the efficiency of our estimates (reduce standard errors). In general, there is trade-off between these two objectives. Anyway, weight adjustments should be variable-dependent: they should explore how different key variables of our survey behave after adjusting. In this post I only show how to implement *raking*. A more complete analysis will be surely necessary. - -Once the raking procedure is implemented, it is a good idea to follow these steps in an iterative fashion: - -- Identify extreme weights and truncate them. For example, those greater than five times the mean of weights. -- Truncate large values, but not small ones (i.e., those near zero). While large values increase the effect of outliers and are more likely to inflate variance, low values do not. -- Examine the design effect using the new weights. If the *new design effect* is greater than the *old design effect* by more than 0.5, it is a good idea to review the raking adjustment. In other words, calibration may cost too much in terms of efficiency (standard errors). - -Three things can be done to improve the weighting procedure: - -- Collapse categories of the variables -- Eliminate or replace weighting variables -- Increase the weight truncation criterion ( > 5 ) - -*** - -### Raking using R - -In this example I use data from Chile to estimate the **presidential approval** ([Opinion Public Survey CEP, July-August 2012](http://www.cepchile.cl/bannerscep/bdatos_encuestas_cep/base_datos.php)). Below, you can see the characteristics of the dataset: - - - - - -{% highlight r %} -dat <- read.csv("cep.csv") -str(dat) -{% endhighlight %} - - - -{% highlight text %} -## 'data.frame': 1512 obs. of 12 variables: -## $ fnu : int 1580 1579 890 871 1031 104 1103 263 924 105 ... -## $ area : int 1 1 2 1 1 1 1 1 1 1 ... -## $ region : int 13 13 9 9 10 2 13 5 9 2 ... -## $ comuna : int 13123 13123 9110 9102 10202 2201 13101 5106 9201 2201 ... -## $ sex : int 1 1 2 1 1 1 2 1 2 1 ... -## $ age : int 30 60 28 72 55 35 33 25 73 43 ... -## $ ses : int 2 2 3 3 4 3 2 3 4 2 ... -## $ phone : int 1 1 9 2 2 2 2 1 2 1 ... -## $ edu : int 4 4 4 1 2 3 4 4 2 4 ... -## $ pond : num 1.977 1.243 0.514 0.421 0.526 ... -## $ agecat : int 2 5 2 5 5 3 2 2 5 3 ... -## $ approval: int 2 1 2 1 1 2 2 2 2 2 ... -{% endhighlight %} - -I use five variables to define raking weights: `sex`, `agecat`, `ses`, `region`, and `area`. I get relative frequencies of these variables using the package `weights` . Most of the variables have more than five percent in each category, with the exception of `region` and `ses`. For illustrative purposes, I will use those variables without collapsing their categories. - - -{% highlight r %} -library(weights) -# sex: 1 male, 2 female -wpct(dat$sex) -{% endhighlight %} - - - -{% highlight text %} -## 1 2 -## 0.407 0.593 -{% endhighlight %} - - - -{% highlight r %} -# agecat: 1 18-24, 2 25-34, 3 35-44, 4 45-54, 5 55+ -wpct(dat$agecat) -{% endhighlight %} - - - -{% highlight text %} -## 1 2 3 4 5 -## 0.124 0.159 0.177 0.194 0.346 -{% endhighlight %} - - - -{% highlight r %} -# ses: 1 abc1, 2 c2, 3 c3, 4 d, 5 e -wpct(dat$ses) -{% endhighlight %} - - - -{% highlight text %} -## 1 2 3 4 5 -## 0.0390 0.1078 0.3651 0.4484 0.0397 -{% endhighlight %} - - - -{% highlight r %} -# region: 1 to 15 -wpct(dat$region) -{% endhighlight %} - - - -{% highlight text %} -## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 -## 0.01323 0.04233 0.01389 0.04167 0.09921 0.05489 0.06151 0.13095 0.06349 0.04894 0.00397 0.01058 0.37632 0.02579 0.01323 -{% endhighlight %} - - - -{% highlight r %} -# area: 1 urban, 2 rural -wpct(dat$area) -{% endhighlight %} - - - -{% highlight text %} -## 1 2 -## 0.837 0.163 -{% endhighlight %} - -The next step is to specify the population distribution of the selected variables in a target *list*. I use two sources to obtain population values: the Chilean Census 2002, and the *Bicentenario* Survey 2009. It is hard to find reliable SES population parameters. The *Bicentenario* data provide, at least, an approximation. - - -{% highlight r %} -# chilean census 2002 -sex <- c(.49,.51) -agecat <- c(.163, .203, .195, .187, .253) -region <- c(0.015, 0.031, 0.016, 0.039, 0.102, 0.051, 0.059, 0.123, - 0.056, 0.046, 0.006, 0.010, 0.408, 0.023 , 0.013) -area <- c(.869, .131) - -# bicentenario survey 2009 -ses <- c(.109, .184, .261, .364, .083) - -# definitions of target list -targets <- list(sex, agecat,ses, region, area) -# important: to use the same variable names of the dataset -names(targets) <- c("sex", "agecat", "ses", "region", "area") -# id variable -dat$caseid <- 1:length(dat$sex) -{% endhighlight %} - -The output shows that some distributions do not sum one. This is because of rounding. We can force any proportion distribution to sum one using `force1 = TRUE`. The differences between the population and the sample distribution are all greater than five percentage points. This meets the variable selection criterion discussed above. - - -{% highlight r %} -sum(region) -{% endhighlight %} - - - -{% highlight text %} -## [1] 0.998 -{% endhighlight %} - - -{% highlight r %} -library(anesrake) -anesrakefinder(targets, dat, choosemethod = "total") -{% endhighlight %} - - - -{% highlight text %} -## sex agecat ses region area -## 0.1652 0.2017 0.3780 0.0828 0.0634 -{% endhighlight %} - -I apply the *anesrake* function as follows: - -- The maximum weight value is five, weights greater than five will be truncated (`cap = 5`). -- The total differences between population and sample have to be greater than 0.05 so that to include a variable (`pctlim = .05`). -- The maximum number of variables included in the raking procedure is five (`nlim = 5`). - - -{% highlight r %} -outsave <- anesrake(targets, dat, caseid = dat$caseid, - verbose= FALSE, cap = 5, choosemethod = "total", - type = "pctlim", pctlim = .05 , nlim = 5, - iterate = TRUE , force1 = TRUE) -{% endhighlight %} - - -{% highlight r %} -summary(outsave) -{% endhighlight %} - - - -{% highlight text %} -## $convergence -## [1] "Complete convergence was achieved after 48 iterations" -## -## $raking.variables -## [1] "sex" "agecat" "ses" "region" "area" -## -## $weight.summary -## Min. 1st Qu. Median Mean 3rd Qu. Max. -## 0.33 0.62 0.80 1.00 1.10 4.30 -## -## $selection.method -## [1] "variable selection conducted using _pctlim_ - discrepancies selected using _total_." -## -## $general.design.effect -## [1] 1.38 -## -## $sex -## Target Unweighted N Unweighted % Wtd N Wtd % Change in % Resid. Disc. Orig. Disc. -## 1 0.49 616 0.407 741 0.49 0.0826 0 0.0826 -## 2 0.51 896 0.593 771 0.51 -0.0826 0 -0.0826 -## -## $agecat -## Target Unweighted N Unweighted % Wtd N Wtd % Change in % Resid. Disc. Orig. Disc. -## 1 0.163 187 0.124 246 0.163 0.03916 2.78e-17 0.03916 -## 2 0.203 240 0.159 307 0.203 0.04407 -2.78e-17 0.04407 -## 3 0.195 268 0.177 295 0.195 0.01756 0.00e+00 0.01756 -## 4 0.187 294 0.194 282 0.187 -0.00763 -2.78e-17 -0.00763 -## 5 0.253 523 0.346 382 0.253 -0.09315 0.00e+00 -0.09315 -## -## $ses -## Target Unweighted N Unweighted % Wtd N Wtd % Change in % Resid. Disc. Orig. Disc. -## 1 0.1089 59 0.0390 165 0.1089 0.0699 1.39e-17 0.0699 -## 2 0.1838 163 0.1078 278 0.1838 0.0760 2.78e-17 0.0760 -## 3 0.2607 552 0.3651 394 0.2607 -0.1043 5.55e-17 -0.1043 -## 4 0.3636 678 0.4484 550 0.3636 -0.0848 5.55e-17 -0.0848 -## 5 0.0829 60 0.0397 125 0.0829 0.0432 1.39e-17 0.0432 -## -## $region -## Target Unweighted N Unweighted % Wtd N Wtd % Change in % Resid. Disc. Orig. Disc. -## 1 0.01503 20 0.01323 22.73 0.01503 0.001803 0.00e+00 0.001803 -## 2 0.03106 64 0.04233 46.97 0.03106 -0.011266 0.00e+00 -0.011266 -## 3 0.01603 21 0.01389 24.24 0.01603 0.002143 0.00e+00 0.002143 -## 4 0.03908 63 0.04167 59.09 0.03908 -0.002589 0.00e+00 -0.002589 -## 5 0.10220 150 0.09921 154.53 0.10220 0.002998 -2.78e-17 0.002998 -## 6 0.05110 83 0.05489 77.27 0.05110 -0.003792 -6.94e-18 -0.003792 -## 7 0.05912 93 0.06151 89.39 0.05912 -0.002390 0.00e+00 -0.002390 -## 8 0.12325 198 0.13095 186.35 0.12325 -0.007706 0.00e+00 -0.007706 -## 9 0.05611 96 0.06349 84.84 0.05611 -0.007380 0.00e+00 -0.007380 -## 10 0.04609 74 0.04894 69.69 0.04609 -0.002850 0.00e+00 -0.002850 -## 11 0.00601 6 0.00397 9.09 0.00601 0.002044 0.00e+00 0.002044 -## 12 0.01002 16 0.01058 15.15 0.01002 -0.000562 0.00e+00 -0.000562 -## 13 0.40882 569 0.37632 618.13 0.40882 0.032495 -5.55e-17 0.032495 -## 14 0.02305 39 0.02579 34.85 0.02305 -0.002748 0.00e+00 -0.002748 -## 15 0.01303 20 0.01323 19.70 0.01303 -0.000201 -3.47e-18 -0.000201 -## -## $area -## Target Unweighted N Unweighted % Wtd N Wtd % Change in % Resid. Disc. Orig. Disc. -## 1 0.869 1266 0.837 1314 0.869 0.0317 1.11e-16 0.0317 -## 2 0.131 246 0.163 198 0.131 -0.0317 0.00e+00 -0.0317 -{% endhighlight %} - -The procedure reaches good convergence, weights lower than 5, and very small differences between the sample and population distribution. In addition, the design effect is 1.38. This gives us an idea of the weighting loss due to the raking procedure. The weighting loss ($$ L_w $$) is the inflation in the variance of sample estimates that can be attributed to weighting (Heeringa et. al. 2010). A simple approximation is: - -$$ L_w(\bar{y}) \approx \frac{\sigma^{2}(w)}{\bar{w}^2} = \Bigg( \frac{\sum\limits_{i=1}^n w_i^2}{\big(\sum\limits_{i=1}^n w_i\big)^2} \cdot n \Bigg) - 1 $$ - -This simple formula was introduced by Kish (1965) in the context of proportionated stratified sampling. It represents the proportional increase in the variances of means and proportions, ignoring any clustering that may be included in the sample plan. It assumes that: - -- Proportionate allocation is the optimal stratified design, i.e., variances of $$y$$ are approximately equal in all strata. -- Weights are uncorrelated with the values of the random variable $$y$$. - -Thus, values from this formula only represents an approximation to weighting loss provided that model assumptions are met. As can be seen in the `anesrake` output, the design effect is 1.38. That value is equal to $$1 + L_w$$. - - -{% highlight r %} -# add weights to the dataset -dat$weightvec <- unlist(outsave[1]) -n <- length(dat$region) - -# weighting loss -((sum(dat$weightvec ^ 2) / (sum(dat$weightvec)) ^ 2) * n) - 1 -{% endhighlight %} - - - -{% highlight text %} -## [1] 0.38 -{% endhighlight %} - -For a more precise estimation of the design effect it would be necessary to use a package such as [survey](http://cran.fhcrc.org/web/packages/survey/index.html), specifying the characteristics of the sample design. Unfortunately, I do not have enough information to specify the sample design and to estimate the design effect of the presidential approval. There are also no cluster variables or replicate weights in the CEP dataset. - -Anyway, the weighting loss approximation denotes an increase in the design effect lower than .5, what meets the recommendations mentioned above. However, it is important to keep in mind that this weighting procedure does not take into account any clustering effect associated with the sample design. - -Finally, we can estimate the presidential approval with and without weights: - - -{% highlight r %} -unweighted <- wpct(dat$approval) -weighted <- wpct(dat$approval, dat$weightvec) -tab <- data.frame(unweighted, weighted) -rownames(tab) <- c("approve", "disapprove", "unsure", "dk") -tab -{% endhighlight %} - - - -{% highlight text %} -## unweighted weighted -## approve 0.2864 0.2983 -## disapprove 0.5119 0.5206 -## unsure 0.1779 0.1609 -## dk 0.0238 0.0201 -{% endhighlight %} - -The difference between the weighted and unweighted presidential approval is not big, only 0.041 percentage points. - -*** - -### Raking using given survey weights - -The CEP dataset includes its own weights (variable named `pond`). Unfortunately, there is no detailed information about the weighting construction in the methodological documentation of this survey ("Survey weighting is a mess", Gelman 2007). For illustrative purposes, however, I will show how to estimate raking weights using previous weights. - -Exploring the `pond` variable, we can see that there are some big weights (e.g., 17.6). This makes me think that they are just scaled design weights, probably with some non-response adjustment, but I am not really sure. - - -{% highlight r %} -summary(dat$pond) -{% endhighlight %} - - - -{% highlight text %} -## Min. 1st Qu. Median Mean 3rd Qu. Max. -## 0.02 0.46 0.79 1.00 1.24 17.60 -{% endhighlight %} - -The weighting loss associated with the pond weights is relatively high, denoting a design effect of 2.09. - - -{% highlight r %} -# weighting loss -((sum(dat$pond ^ 2) / (sum(dat$pond)) ^ 2) * n) - 1 -{% endhighlight %} - - - -{% highlight text %} -## [1] 1.09 -{% endhighlight %} - -We can explore the total differences between the sample and population distributions using `pond` weights. Only **ses** and **region** meet the criterion of total differences greater than 5 percentage points. However, I will keep all the variables used in the previous section and specify that total differences between the population and sample should be greater than .05 (`pctlim = .05`). - - -{% highlight r %} -anesrakefinder(targets, dat, weightvec = dat$pond, choosemethod = "total") -{% endhighlight %} - - - -{% highlight text %} -## sex agecat ses region area -## 0.000405 0.014836 0.317979 0.169910 0.001149 -{% endhighlight %} - -In order to get raking weights using previous weights, I only have to add `weightvec = dat$pond` to the `anesrake` command. - - -{% highlight r %} -outsave <- anesrake(targets, dat, caseid = dat$caseid, - weightvec = dat$pond, verbose = FALSE, cap = 5, - choosemethod = "total", type = "pctlim", - pctlim = .05 , nlim = 5, iterate = TRUE, force1 = TRUE) -{% endhighlight %} - - -{% highlight r %} -summary(outsave) -{% endhighlight %} - - - -{% highlight text %} -## $convergence -## [1] "Complete convergence was achieved after 50 iterations" -## -## $raking.variables -## [1] "ses" "region" -## -## $weight.summary -## Min. 1st Qu. Median Mean 3rd Qu. Max. -## 0.01 0.41 0.71 1.00 1.23 5.00 -## -## $selection.method -## [1] "variable selection conducted using _pctlim_ - discrepancies selected using _total_." -## -## $general.design.effect -## [1] 1.91 -## -## $sex -## Target Old Weights N Old Weights % Wtd N Wtd % Change in % Resid. Disc. Orig. Disc. -## 1 0.49 741 0.49 763 0.504 0.0147 -0.0145 0.000202 -## 2 0.51 771 0.51 749 0.496 -0.0147 0.0145 -0.000202 -## -## $agecat -## Target Old Weights N Old Weights % Wtd N Wtd % Change in % Resid. Disc. Orig. Disc. -## 1 0.163 242 0.160 230 0.152 -0.00768 0.01049 0.002815 -## 2 0.203 309 0.204 314 0.208 0.00388 -0.00521 -0.001321 -## 3 0.195 289 0.191 297 0.197 0.00565 -0.00193 0.003715 -## 4 0.187 281 0.186 267 0.176 -0.00956 0.01041 0.000843 -## 5 0.253 391 0.259 403 0.267 0.00771 -0.01376 -0.006053 -## -## $ses -## Target Old Weights N Old Weights % Wtd N Wtd % Change in % Resid. Disc. Orig. Disc. -## 1 0.1089 77.1 0.0510 165 0.1089 0.0579 1.39e-17 0.0579 -## 2 0.1838 209.7 0.1387 278 0.1838 0.0451 2.78e-17 0.0451 -## 3 0.2607 588.5 0.3892 394 0.2607 -0.1284 5.55e-17 -0.1284 -## 4 0.3636 596.3 0.3943 550 0.3636 -0.0307 5.55e-17 -0.0307 -## 5 0.0829 40.5 0.0268 125 0.0829 0.0561 1.39e-17 0.0561 -## -## $region -## Target Old Weights N Old Weights % Wtd N Wtd % Change in % Resid. Disc. Orig. Disc. -## 1 0.01503 31.70 0.02097 22.73 0.01503 -0.00594 0.00e+00 -0.00594 -## 2 0.03106 99.85 0.06604 46.97 0.03106 -0.03497 3.47e-18 -0.03497 -## 3 0.01603 19.56 0.01293 24.24 0.01603 0.00310 0.00e+00 0.00310 -## 4 0.03908 72.79 0.04814 59.09 0.03908 -0.00906 6.94e-18 -0.00906 -## 5 0.10220 206.06 0.13627 154.53 0.10220 -0.03407 0.00e+00 -0.03407 -## 6 0.05110 69.77 0.04614 77.27 0.05110 0.00496 6.94e-18 0.00496 -## 7 0.05912 74.20 0.04907 89.39 0.05912 0.01005 0.00e+00 0.01005 -## 8 0.12325 163.69 0.10826 186.35 0.12325 0.01499 0.00e+00 0.01499 -## 9 0.05611 79.51 0.05258 84.84 0.05611 0.00353 -6.94e-18 0.00353 -## 10 0.04609 67.87 0.04489 69.69 0.04609 0.00121 0.00e+00 0.00121 -## 11 0.00601 5.33 0.00352 9.09 0.00601 0.00249 8.67e-19 0.00249 -## 12 0.01002 11.90 0.00787 15.15 0.01002 0.00215 0.00e+00 0.00215 -## 13 0.40882 559.46 0.36999 618.13 0.40882 0.03883 0.00e+00 0.03883 -## 14 0.02305 28.42 0.01879 34.85 0.02305 0.00425 0.00e+00 0.00425 -## 15 0.01303 21.99 0.01454 19.70 0.01303 -0.00151 0.00e+00 -0.00151 -## -## $area -## Target Old Weights N Old Weights % Wtd N Wtd % Change in % Resid. Disc. Orig. Disc. -## 1 0.869 1315 0.87 1296 0.857 -0.0124 0.0119 -0.000575 -## 2 0.131 197 0.13 216 0.143 0.0124 -0.0119 0.000575 -{% endhighlight %} - -Only **ses** and **region** are used in the raking procedure. Some weights were truncated to 5, and the design effect decreased from 2.09 to 1.91. The differences between the sample and population distribution are not big. - - -{% highlight r %} -# add weights to the dataset -dat$weightvec2 <- unlist(outsave[1]) -pond <- wpct(dat$approval, dat$pond) -weighted_pond <- wpct(dat$approval, dat$weightvec2) -tab <- data.frame(unweighted, pond, weighted, weighted_pond) -rownames(tab) <- c("approve", "disapprove", "unsure", "dk") -tab -{% endhighlight %} - - - -{% highlight text %} -## unweighted pond weighted weighted_pond -## approve 0.2864 0.2739 0.2983 0.2945 -## disapprove 0.5119 0.5231 0.5206 0.5310 -## unsure 0.1779 0.1758 0.1609 0.1520 -## dk 0.0238 0.0272 0.0201 0.0224 -{% endhighlight %} - -There are no great differences between the estimates `weighted` and `weighted_pond` (0.025). There are greater discrepancies between `pond` and `weighted` estimates (0.049). This reveals the importance of the socioeconomic status in the presidential approval. - -__Last Update: 7/30/15__ - -*** - -### References - -Battaglia, M., D. Izrael, D. C. Hoaglin, and M. Frankel. 2004. "Tips and Tricks for Raking Survey Data (Aka Sample Balancing)." American Association of Public Opinion Research. - -DeBell, Matthew and Jon A. Krosnick. 2009. "Computing Weights for American National Election Study Survey Data." Stanford University. - -Gelman, Andrew. 2007. "Struggles with Survey Weighting and Regression Modeling." Statistical Science 22(2):153-64. - -Heeringa, Steven, Brady T. West, and Patricia A. Berglund. 2010. Applied Survey Data Analysis. Boca Raton, FL: Chapman & Hall/CRC. - -Valliant, Richard, Jill A. Dever, and Frauke Kreuter. 2013. Practical Tools for Designing and Weighting Survey Samples. New York, NY: Springer New York. - ---- diff --git a/_posts/2012-08-28-lifetables.md b/_posts/2012-08-28-lifetables.md deleted file mode 100755 index f3dc6775..00000000 --- a/_posts/2012-08-28-lifetables.md +++ /dev/null @@ -1,137 +0,0 @@ ---- -layout: post -title: "Life tables: A super brief introduction" -date: 2012-08-28 -author: Sebastian Daza -giscus_comments: true -tags: demography ---- - -A _life table_ is just one way of summarizing a cohort's mortality experience. Below a simple way to build a life table using R. - - - - - -{% highlight r %} -dat <- read.csv("frmales85.csv", sep = ",", header = T) -# length of intervals -dat$n <- c(diff(dat$x), NA) -# number of intervals -max <- length(dat$x) -# age 85 is an open interval -dat -{% endhighlight %} - - - -{% highlight text %} -## x nNx nDx nax n -## 1 0 379985 3741 0.087 1 -## 2 1 1559722 770 1.500 4 -## 3 5 1896295 532 2.500 5 -## 4 10 2160190 673 2.966 5 -## 5 15 2179837 2138 2.769 5 -## 6 20 2159556 3432 2.574 5 -## 7 25 2106750 3291 2.512 5 -## 8 30 2147845 3657 2.586 5 -## 9 35 2165387 4956 2.657 5 -## 10 40 1516952 5269 2.697 5 -## 11 45 1498630 8654 2.695 5 -## 12 50 1552746 14490 2.663 5 -## 13 55 1476770 20831 2.625 5 -## 14 60 1350479 26805 2.601 5 -## 15 65 722430 20233 2.615 5 -## 16 70 842589 38315 2.598 5 -## 17 75 636848 46903 2.538 5 -## 18 80 372059 44443 2.466 5 -## 19 85 175169 37759 4.639 NA -{% endhighlight %} - - - -{% highlight r %} -# age-specific mortality rates assuming $_nM_x=_nm_x$ -dat$nmx <- dat$nDx/dat$nNx -# age-specific probability to die -dat$nqx <- (dat$n * dat$nmx)/(1 + (dat$n - dat$nax) * dat$nmx) -dat$nqx[max] <- 1 -dat$npx <- 1 - dat$nqx -{% endhighlight %} - - - -{% highlight r %} -# definition of radix -radix <- 100000 -# easy way to get lx without a loop -dat$lx <- radix * c(1, cumprod(dat[1:max - 1, "npx"])) -dat$ndx <- dat$lx * dat$nqx -dat[, c(-2, -3)] -{% endhighlight %} - - - -{% highlight text %} -## x nax n nmx nqx npx lx ndx -## 1 0 0.087 1 0.009845 0.00976 0.990 100000 976 -## 2 1 1.500 4 0.000494 0.00197 0.998 99024 195 -## 3 5 2.500 5 0.000281 0.00140 0.999 98829 139 -## 4 10 2.966 5 0.000312 0.00156 0.998 98690 154 -## 5 15 2.769 5 0.000981 0.00489 0.995 98537 482 -## 6 20 2.574 5 0.001589 0.00792 0.992 98055 776 -## 7 25 2.512 5 0.001562 0.00778 0.992 97278 757 -## 8 30 2.586 5 0.001703 0.00848 0.992 96522 818 -## 9 35 2.657 5 0.002289 0.01138 0.989 95703 1089 -## 10 40 2.697 5 0.003473 0.01723 0.983 94614 1630 -## 11 45 2.695 5 0.005775 0.02849 0.972 92984 2649 -## 12 50 2.663 5 0.009332 0.04566 0.954 90334 4125 -## 13 55 2.625 5 0.014106 0.06824 0.932 86209 5883 -## 14 60 2.601 5 0.019849 0.09473 0.905 80326 7609 -## 15 65 2.615 5 0.028007 0.13127 0.869 72717 9545 -## 16 70 2.598 5 0.045473 0.20498 0.795 63171 12949 -## 17 75 2.538 5 0.073649 0.31172 0.688 50223 15656 -## 18 80 2.466 5 0.119451 0.45848 0.542 34567 15848 -## 19 85 4.639 NA 0.215558 1.00000 0.000 18719 18719 -{% endhighlight %} - - - -{% highlight r %} -# Lx for the open interval -dat$nLx <- dat$lx * dat$n - (dat$n - dat$nax) * dat$ndx -dat$nLx[max] <- dat$lx[max]/dat$nmx[max] -{% endhighlight %} - - - -{% highlight r %} -dat$Tx <- rev(cumsum(rev(dat$nLx))) -dat$ex <- dat$Tx/dat$lx -dat[, c(-2:-6)] -{% endhighlight %} - - - -{% highlight text %} -## x nqx npx lx ndx nLx Tx ex -## 1 0 0.00976 0.990 100000 976 99109 7131028 71.31 -## 2 1 0.00197 0.998 99024 195 395609 7031919 71.01 -## 3 5 0.00140 0.999 98829 139 493798 6636310 67.15 -## 4 10 0.00156 0.998 98690 154 493140 6142512 62.24 -## 5 15 0.00489 0.995 98537 482 491608 5649372 57.33 -## 6 20 0.00792 0.992 98055 776 488390 5157764 52.60 -## 7 25 0.00778 0.992 97278 757 484509 4669374 48.00 -## 8 30 0.00848 0.992 96522 818 480632 4184865 43.36 -## 9 35 0.01138 0.989 95703 1089 475964 3704232 38.71 -## 10 40 0.01723 0.983 94614 1630 469315 3228268 34.12 -## 11 45 0.02849 0.972 92984 2649 458812 2758953 29.67 -## 12 50 0.04566 0.954 90334 4125 442031 2300141 25.46 -## 13 55 0.06824 0.932 86209 5883 417074 1858110 21.55 -## 14 60 0.09473 0.905 80326 7609 383376 1441035 17.94 -## 15 65 0.13127 0.869 72717 9545 340818 1057660 14.54 -## 16 70 0.20498 0.795 63171 12949 284755 716841 11.35 -## 17 75 0.31172 0.688 50223 15656 212570 432087 8.60 -## 18 80 0.45848 0.542 34567 15848 132677 219516 6.35 -## 19 85 1.00000 0.000 18719 18719 86839 86839 4.64 -{% endhighlight %} \ No newline at end of file diff --git a/_posts/2012-09-05-demQ01.md b/_posts/2012-09-05-demQ01.md deleted file mode 100755 index 73c6ce82..00000000 --- a/_posts/2012-09-05-demQ01.md +++ /dev/null @@ -1,260 +0,0 @@ ---- -layout: post -title: "Life tables: Using survey data" -date: 2012-09-05 -author: Sebastian Daza -giscus_comments: true -tags: demography ---- - - - -Here a life table exercise using fake retrospective survey data: - -
You collected retrospective survey data on the age at first birth for twenty-five women who are age 50 at interview in 2009. Construct a life table where the state of interest is childlessness. Start the life table at age 15 and terminate it at exact age 50. Use five year intervals and, for speed, assume that “deaths” occur halfway through the 5-yr age interval (despite the fact that you have actual ages).Their ages (last birthday) at first birth are 29, 22, 43, 31, 26, 20, no birth, 25, 23, 30, no birth, 37, 21, 25, 28, no birth, 23 (twins), 27, 34, 25, 24, 21, 17, no birth, no birth. On the basis of this information, answer the following questions.
- -

What is the probability that a childless woman at exact age 30 would still be childless at age 40?

- -

First, we have to build the corresponding life table where the event of interest is childbearing. For childless women during the period I assign 50 years old. In the case of twins it is enough to specify just one event (first birth).

- - - -{% highlight r %} -ages <- c(29, 22, 43, 31, 26, 20, 50, 25, 23, 30, 50, 37, 21, - 25, 28, 50, 23, 27, 34, 25, 24, 21, 17, 50, 50) -# age -x <- seq(15, 49, 5) -# width of intervals -n <- rep(5, 7) -# births -b <- rep(NA, 7) - -(dat <- data.frame(x, n, b)) -{% endhighlight %} - - - -{% highlight text %} -## x n b -## 1 15 5 NA -## 2 20 5 NA -## 3 25 5 NA -## 4 30 5 NA -## 5 35 5 NA -## 6 40 5 NA -## 7 45 5 NA -{% endhighlight %} - - - -{% highlight r %} -# computing births: equivalent to counting logic values -for (i in 1:7) { - dat$b[i] <- sum(ages >= dat$x[i] & ages <= (dat$x[i + 1] - - 1), na.rm = TRUE) -} - -# computing lx -dat$lx <- NA -# childless women at the beginning of the period -dat$lx[1] <- length(ages) - -for (i in 1:6) { - dat$lx[i + 1] <- dat$lx[i] - dat$b[i] -} - -dat -{% endhighlight %} - - - -{% highlight text %} -## x n b lx -## 1 15 5 1 25 -## 2 20 5 7 24 -## 3 25 5 7 17 -## 4 30 5 3 10 -## 5 35 5 1 7 -## 6 40 5 1 6 -## 7 45 5 0 5 -{% endhighlight %} - - -So the probability a childless woman at exact age 30 would still be childless at age 40 is: - - -{% highlight r %} -dat$l[dat$x == 40]/dat$l[dat$x == 30] -{% endhighlight %} - - - -{% highlight text %} -## [1] 0.6 -{% endhighlight %} - - -

What was the expected number of years of childlessness (prior to age 50) for a 25 year old childless woman?

- -We have to calculate the equivalent to life expectancy but for childlessness. For that, we need $L_x$ and $T_x$. Following the assumptions specified in the question: _“deaths” occur halfway through the 5-yr age interval (despite the fact that you have actual ages)_, we can compute readily $L_x$: - - -{% highlight r %} -# computing Lx assuming nax = interval/2 -dat$Lx <- NA -for (i in 1:6) { - dat$Lx[i] <- dat$lx[i + 1] * dat$n[i] + (dat$n[i]/2) * dat$b[i] -} - -# Lx for the last interval -dat$Lx[7] <- 5 * 5 - -# computing Tx -dat$Tx <- rev(cumsum(rev(dat$Lx))) -dat -{% endhighlight %} - - - -{% highlight text %} -## x n b lx Lx Tx -## 1 15 5 1 25 122.5 420.0 -## 2 20 5 7 24 102.5 297.5 -## 3 25 5 7 17 67.5 195.0 -## 4 30 5 3 10 42.5 127.5 -## 5 35 5 1 7 32.5 85.0 -## 6 40 5 1 6 27.5 52.5 -## 7 45 5 0 5 25.0 25.0 -{% endhighlight %} - - -The expected number of years of childlessness would be $\frac{T_{25}}{l_{25}}$ - - - -{% highlight r %} -dat$T[dat$x == 25]/dat$l[dat$x == 25] -{% endhighlight %} - - - -{% highlight text %} -## [1] 11.5 -{% endhighlight %} - - -

What fraction of years between ages 15 and 49.99 were spent childless?

- -That would be $\frac{e_{15}}{35}$: - - - -{% highlight r %} -(dat$T[dat$x == 15]/dat$l[dat$x == 15])/35 -{% endhighlight %} - - - -{% highlight text %} -## [1] 0.48 -{% endhighlight %} - - -

You observe that the parity progression ratios for this cohort take the following form. What is the TFR of this cohort?

- -A straightforward way to do it: - - -{% highlight r %} -# given parity progression ratios -ppr <- c(NA, 0.8, 0.75, 0.25, 0) -# estimation PPR1 -# the total number of first births was 20 -# the total number of women is 25, so... -(ppr[1] <- (20/25)) -{% endhighlight %} - - - -{% highlight text %} -## [1] 0.8 -{% endhighlight %} - - - -{% highlight r %} -(TFR <- sum(cumprod(ppr))) -{% endhighlight %} - - - -{% highlight text %} -## [1] 2.04 -{% endhighlight %} - -

How might your data collection method affect the accuracy of your answer to the former questions? Be specific, referencing the possible direction of bias if applicable.

- - -

In the survey we are only taking into account surviving women. We will und erestimate the probability of remaining childless in this cohort because single and childless women have higher mortality.

- -

Assume that births happened exactly half-way through the year of age in which a woman reported a birth (e.g., a birth reported at age 34 happened at exact age 34.5). How inaccurate is the short-cut estimate of $_{5}a_{20}$ you used above?

- - -We have to compute the average number of years lived childless according to the specification for the question. - - -{% highlight r %} -# ages included in 5a20 -(age <- seq(20, 24, 1)) -{% endhighlight %} - - - -{% highlight text %} -## [1] 20 21 22 23 24 -{% endhighlight %} - - - -{% highlight r %} -# years lived childless according to assumptions of the -# question -(x <- (age - 19.5)) -{% endhighlight %} - - - -{% highlight text %} -## [1] 0.5 1.5 2.5 3.5 4.5 -{% endhighlight %} - - - -{% highlight r %} -# births between 20 and 24 -(y <- table(ages[ages >= 20 & ages <= 24])) -{% endhighlight %} - - - -{% highlight text %} -## -## 20 21 22 23 24 -## 1 2 1 2 1 -{% endhighlight %} - - - -{% highlight r %} -# average number years lived childless in the interval -mean(x * y) -{% endhighlight %} - - - -{% highlight text %} -## [1] 3.5 -{% endhighlight %} - - -Thus, the short-cut estimate is inaccurate by `3.5 - (5/2) = 1`. \ No newline at end of file diff --git a/_posts/2012-10-04-facebook-sna.md b/_posts/2012-10-04-facebook-sna.md deleted file mode 100644 index 639dab8e..00000000 --- a/_posts/2012-10-04-facebook-sna.md +++ /dev/null @@ -1,157 +0,0 @@ ---- -layout: post -title: "My Facebook Network" -date: 2012-10-04 -author: Sebastian Daza -giscus_comments: true -tags: R ---- - - -I am taking a [coursera](https://www.coursera.org) class on Social Network Analysis. The first assignment consisted of ploting my Facebook network. Below I show how I did it using the R package __igraph__. In order to get your Facebook Network you can use the [Netvizz](https://apps.facebook.com/netvizz/) app. This application allows you to create gdf files (a simple text format that specifies an undirected graph) from the friendship relations. I used this [simplified Netvizz version](http://snacourse.com/getnet/) from [coursera](https://www.coursera.org) that generates .gml files directly. - - - - - - - -{% highlight r %} -library(igraph) - -# read the facebook network from a .gml file -f <- read.graph("sdaza.gml", format = "gml") -{% endhighlight %} - - - -In order to plot my Facebook network, I extract the following attributes: gender, wall posts count, and interface language. I also assign some colors and shapes to those attributes. - - -{% highlight r %} -# gender, assigning shapes -ssex <- V(f)$sex -table(ssex) -{% endhighlight %} - - - -{% highlight text %} -## ssex -## female male -## 218 188 -{% endhighlight %} - - - -{% highlight r %} -ssex[ssex == "female"] <- "circle" -ssex[ssex == "male"] <- "square" -table(ssex) -{% endhighlight %} - - - -{% highlight text %} -## ssex -## circle square -## 218 188 -{% endhighlight %} - - - -{% highlight r %} - -# interface language, assigning colors -cloc <- V(f)$locale -table(cloc) -{% endhighlight %} - - - -{% highlight text %} -## cloc -## en_GB en_US es_CL es_ES es_LA it_IT nl_NL pt_BR -## 13 132 2 9 245 1 1 3 -{% endhighlight %} - - - -{% highlight r %} - -cloc[cloc %in% c("es_ES", "it_IT", "nl_NL", "en_GB", "gl_ES", "ko_KR")] <- "Yellow" -cloc[cloc %in% c("es_CL", "pt_BR", "es_LA")] <- "Green" -cloc[cloc == c("en_US")] <- "Blue" -table(cloc) -{% endhighlight %} - - - -{% highlight text %} -## cloc -## Blue Green Yellow -## 132 250 24 -{% endhighlight %} - - - -{% highlight r %} - -# wall posts count, assigning node size -nsize <- (V(f)$wallcount/max(V(f)$wallcount) + 0.1) * 15 -{% endhighlight %} - - -Now, I can plot the network: - - -{% highlight r %} -plot(f, layout = layout.fruchterman.reingold, edge.arrow.size = 0.5, - vertex.label = NA, vertex.size = 3, vertex.color = cloc, - vertex.shape = ssex) -{% endhighlight %} - -![center](/assets/img/2012-10-04-facebook-sna/fig1.png) - - -If we use __wall posts count__ to size the nodes: - - -{% highlight r %} -plot(f, layout = layout.fruchterman.reingold, edge.arrow.size = 0.5, - vertex.label = NA, vertex.size = nsize, vertex.color = cloc, - vertex.shape = ssex) -{% endhighlight %} - -![center](/assets/img/2012-10-04-facebook-sna/fig2.png) - - -We can also obtain some basic descriptive statistics of the network using a `graph.basic.stats` function (see [here](http://www.isk.kth.se/~shahabm/WSAnalysis/networks/NetworkAnalysis.r) to obtain it): - - -{% highlight r %} -graph.basic.stats(f) -{% endhighlight %} - - - -{% highlight text %} -## Number of nodes: 406 -## Number of edges: 4415 -## -## Degree -## Average: 21.7 -## -## -## Giant component Size: 397 -## Giant component As perc.: 0.978 -## Second Giant component: 1 -## Second Giant component As perc.: 0.00246 -## -## Isolated nodes -## Number: 9 -## As perc.: 0.0222 -{% endhighlight %} - - -As can be seen, I have relatively compact and homogeneous components (or clusters) regarding language interface on Facebook. You can see now how your own Facebook network looks like. \ No newline at end of file diff --git a/_posts/2013-01-29-lookvar.md b/_posts/2013-01-29-lookvar.md deleted file mode 100755 index 2fc9844d..00000000 --- a/_posts/2013-01-29-lookvar.md +++ /dev/null @@ -1,60 +0,0 @@ ---- -layout: post -title: "Looking for variables in R" -date: 2013-01-29 -author: Sebastian Daza -giscus_comments: true -tags: R ---- - - -Recently, I have been working with big databases. After reading their codebooks (usually very long pdf files), -I thought it would be useful to have a function to find variable names in R. I wrote a simply function -that looks for variable names in __data.frame__ and __data.table__ objects. - -Here an example: - -{% highlight r %} -library(devtools); source_gist("4661324") -{% endhighlight %} - - -{% highlight r %} -library(data.table) -{% endhighlight %} - - -{% highlight r %} -dat <- data.table(infert) -(var <- lookvar(dat, c("par", "spon"))) -{% endhighlight %} - - -{% highlight text %} -## [1] "parity" "spontaneous" -{% endhighlight %} - - - -{% highlight r %} -dat[, var, with=FALSE] -{% endhighlight %} - - - -{% highlight text %} -## parity spontaneous -## 1: 6 2 -## 2: 1 0 -## 3: 6 0 -## 4: 4 0 -## 5: 3 1 -## --- -## 244: 1 1 -## 245: 1 0 -## 246: 2 0 -## 247: 1 1 -## 248: 1 1 -{% endhighlight %} - -Pretty useful, at least for me. You can also use _regular expressions_ to get variables, for instance, something like `lookvar(dat, "p5[0-2]_[a-z]+_2")`. diff --git a/_posts/2013-07-02-projections.md b/_posts/2013-07-02-projections.md deleted file mode 100755 index d0460b0a..00000000 --- a/_posts/2013-07-02-projections.md +++ /dev/null @@ -1,317 +0,0 @@ ---- -layout: post -title: "Cohort component projection" -description: "A simple example using R and matrices" -author: Sebastian Daza -giscus_comments: true -tags: demography ---- - - -I present an example of a cohort component projection using a closed female population (Sweden 1993), taken from Preston et al.'s book (Demography 2001, page 125). I use R and basic matrix algebra to replicate their results. The advantage of this procedure is that allows to compute easily the *intrinsic growth rate* and *age-proportionate distribution* of the stable equivalent population. All we need is the population by age at time 0 (from a census), survivorship ratios (from a life table), and age-specific fertility rates. - -The data: - - - - - -{% highlight r %} -dat <- read.csv("sweden1993.csv", sep = ",", header = T) -attach(dat) -{% endhighlight %} - - - -{% highlight text %} -## age Nf Lf f -## 1 0 293395 497487 NA -## 2 5 248369 497138 NA -## 3 10 240012 496901 NA -## 4 15 261346 496531 0.0120 -## 5 20 285209 495902 0.0908 -## 6 25 314388 495168 0.1499 -## 7 30 281290 494213 0.1125 -## 8 35 286923 492760 0.0441 -## 9 40 304108 490447 0.0074 -## 10 45 324946 486613 0.0003 -## 11 50 247613 480665 NA -## 12 55 211351 471786 NA -## 13 60 215140 457852 NA -## 14 65 221764 436153 NA -## 15 70 223506 402775 NA -## 16 75 183654 350358 NA -## 17 80 141990 271512 NA -## 18 85 112424 291707 NA -{% endhighlight %} - - -As can be seen, the data have five-year-interval age groups, so each projection forward will involve 5 years. The steps are very simple: - -1. Project forward the population of each age group (estimation of people alive) -2. Calculate the number of births of each age group based on fertility rates, adjusting by mortality (estimation of children alive) -3. Create a Leslie matrix, and then multiple it by the population vector (population by age at time 0) - -### Survivorship ratios - -We have to estimate life table survival ratios, that is, proportions of birth cohorts surviving from one age interval to the next in a **stationary population**. Basically, we are summarizing the mortality experience of different cohorts assuming stationarity. Because census statistics refer to age "last birthday" (rather than exact age), I estimate ratios using $L_x$ (average number of survivors in an age interval) instead of $l_x$. - -$$S_x = \frac{_5L_x}{_5L_{x-5}}$$ - -I compute the survival ratios using a loop in R. The estimation of the open-ended survival ratio is slightly different but still straightforward: - -$$\frac{T_{85}}{T_{80}}$$ - - -{% highlight r %} -Sf <- NA -for (i in 1:(length(Lf) - 1)) { - Sf[i] <- Lf[i + 1]/Lf[i] -} - -# open-ended survival ratio -Sf[length(Sf)] <- Lf[18]/(Lf[17] + Lf[18]) -{% endhighlight %} - - - -{% highlight text %} -## [1] 0.999 1.000 0.999 0.999 0.999 0.998 0.997 0.995 0.992 0.988 0.982 0.970 0.953 0.923 0.870 0.775 0.518 -{% endhighlight %} - -### Number of children - -This is the tricky part. Because census statistics refer to age "last birthday", and we are projecting every 5 years, the estimation of the number of person-years lived by women in each age group consists of the average number of women alive at the beginning and end of the period (assuming a linear change over the period). To take advantage of the Leslie matrix, I define the births in R using a loop as follows: - - -{% highlight r %} -Bf <- rep(0, 18) -for (i in 1:length(Lf)) { - Bf[i] <- 1/(1 + 1.05) * Lf[1]/(100000 * 2) * sum(f[i + 1] * Sf[i], f[i], na.rm = TRUE) -} -{% endhighlight %} - - - -{% highlight text %} -## [1] 0.000000 0.000000 0.014550 0.124596 0.291792 0.318128 0.189858 0.062447 0.009340 0.000364 0.000000 0.000000 -## [13] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -{% endhighlight %} - - -1/(1+1.05) corresponds to a transformation of age-specific fertility rates (son and daughters) to maternity rates (only daughters), assuming that the ratio of male to female births (SBR) is constant across mothers' ages. The number of births is also adjusted by the corresponding survival ratio from 0 to 5 years old ($\frac{_5L_0}{5 \times l_0}$), the number 5 goes away due to simplifying). - -### Leslie matrix - -I construct a Leslie matrix by replacing specific cells of a 18 x 18 matrix (18 age groups) by the vectors defined above (survival ratios and maternity rates): - - -{% highlight r %} -m <- matrix(0, 18, 18) -m[1, ] <- Bf -s <- diag(17) * Sf -m[2:18, 1:17] <- s -m[18, 18] <- Sf[17] -{% endhighlight %} - - -Here we have the Leslie matrix: - - -{% highlight text %} -## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18] -## [1,] 0.000 0 0.0145 0.125 0.292 0.318 0.190 0.0624 0.00934 0.000364 0.000 0.00 0.000 0.000 0.00 0.000 0.000 0.000 -## [2,] 0.999 0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000 0.00 0.000 0.000 0.00 0.000 0.000 0.000 -## [3,] 0.000 1 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000 0.00 0.000 0.000 0.00 0.000 0.000 0.000 -## [4,] 0.000 0 0.9993 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000 0.00 0.000 0.000 0.00 0.000 0.000 0.000 -## [5,] 0.000 0 0.0000 0.999 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000 0.00 0.000 0.000 0.00 0.000 0.000 0.000 -## [6,] 0.000 0 0.0000 0.000 0.999 0.000 0.000 0.0000 0.00000 0.000000 0.000 0.00 0.000 0.000 0.00 0.000 0.000 0.000 -## [7,] 0.000 0 0.0000 0.000 0.000 0.998 0.000 0.0000 0.00000 0.000000 0.000 0.00 0.000 0.000 0.00 0.000 0.000 0.000 -## [8,] 0.000 0 0.0000 0.000 0.000 0.000 0.997 0.0000 0.00000 0.000000 0.000 0.00 0.000 0.000 0.00 0.000 0.000 0.000 -## [9,] 0.000 0 0.0000 0.000 0.000 0.000 0.000 0.9953 0.00000 0.000000 0.000 0.00 0.000 0.000 0.00 0.000 0.000 0.000 -## [10,] 0.000 0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.99218 0.000000 0.000 0.00 0.000 0.000 0.00 0.000 0.000 0.000 -## [11,] 0.000 0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.987777 0.000 0.00 0.000 0.000 0.00 0.000 0.000 0.000 -## [12,] 0.000 0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.982 0.00 0.000 0.000 0.00 0.000 0.000 0.000 -## [13,] 0.000 0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000 0.97 0.000 0.000 0.00 0.000 0.000 0.000 -## [14,] 0.000 0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000 0.00 0.953 0.000 0.00 0.000 0.000 0.000 -## [15,] 0.000 0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000 0.00 0.000 0.923 0.00 0.000 0.000 0.000 -## [16,] 0.000 0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000 0.00 0.000 0.000 0.87 0.000 0.000 0.000 -## [17,] 0.000 0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000 0.00 0.000 0.000 0.00 0.775 0.000 0.000 -## [18,] 0.000 0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000 0.00 0.000 0.000 0.00 0.000 0.518 0.518 -{% endhighlight %} - - -Note that the last survival ratio is repeated in the last column (0.518). This is because the estimation of the open-ended survival ratio is: - -$$ (N_{80} + N_{85}) \times \frac{T_{85}}{T_{80}} $$ - -### Now, let's do some projections - -Using the R multiplication operator for matrices, I do a 5-year projection by simply multiplying the Leslie matrix by the population vector (remember that matrix multiplication is not commutative). - - -{% highlight r %} -m %*% Nf -{% endhighlight %} - - - -{% highlight text %} -## [,1] -## [1,] 293574 -## [2,] 293189 -## [3,] 248251 -## [4,] 239833 -## [5,] 261015 -## [6,] 284787 -## [7,] 313782 -## [8,] 280463 -## [9,] 285576 -## [10,] 301731 -## [11,] 320974 -## [12,] 243039 -## [13,] 205109 -## [14,] 204944 -## [15,] 204793 -## [16,] 194419 -## [17,] 142324 -## [18,] 131768 -{% endhighlight %} - - -I obtain the same results of the book. Raising this multiplication I can get the projected population of subsequent periods. Because R doesn't have a power operator for matrices, I define a function called *mp* to raise matrices (it is not very efficient, but for this example it's still useful). - - -{% highlight r %} -mp <- function(mat, pow) { - ans <- mat - for (i in 1:(pow - 1)) { - ans <- mat %*% ans - } - return(ans) -} -{% endhighlight %} - - -Let's project the initial population for two periods (10 years): - - -{% highlight r %} -(mp(m, 2) %*% Nf) -{% endhighlight %} - - - -{% highlight text %} -## [,1] -## [1,] 280121 -## [2,] 293368 -## [3,] 293049 -## [4,] 248066 -## [5,] 239529 -## [6,] 260629 -## [7,] 284238 -## [8,] 312859 -## [9,] 279147 -## [10,] 283344 -## [11,] 298043 -## [12,] 315045 -## [13,] 235861 -## [14,] 195388 -## [15,] 189260 -## [16,] 178141 -## [17,] 150666 -## [18,] 141960 -{% endhighlight %} - - -Again, I get the same result of the book. The nice thing of all this is that estimating eigenvalues and eigenvectors, I can obtain the intrinsic growth rate and age-distribution of the "stable equivalent" population. Using the *eigen* function in R, I can identify the dominant eigenvalue (higher absolute number), and the corresponding eigenvector: - - -{% highlight r %} -e <- eigen(m) - -# intrinsic growth rate -(max(abs(e$values)) - 1)/5 # 5-year-projection -{% endhighlight %} - - - -{% highlight text %} -## [1] 0.000223 -{% endhighlight %} - - - -{% highlight r %} - -# intrinsic proportionate age distribution -as.numeric(e$vector[, 1]/sum(e$vector[, 1])) -{% endhighlight %} - - - -{% highlight text %} -## [1] 0.0619 0.0618 0.0617 0.0616 0.0614 0.0613 0.0611 0.0608 0.0605 0.0600 0.0592 0.0580 0.0562 0.0535 0.0494 0.0429 -## [17] 0.0332 0.0356 -{% endhighlight %} - - -The population is growing but little. - -### What about the population momentum? - -The population momentum corresponds to the growth of a population after imposing replacement fertility conditions, that is, NRR=1. Thus, the first thing we have to do is to estimate NRR. - - -{% highlight r %} -# calculating NRR -(NRR <- sum(f * Lf/100000 * (1/(1 + 1.05)), na.rm = TRUE)) -{% endhighlight %} - - - -{% highlight text %} -## [1] 1.01 -{% endhighlight %} - -We can quickly estimate the intrinsic growth rate using NRR: - - -{% highlight r %} -# quick estimation of the intrinsic growth rate -log(NRR)/27 -{% endhighlight %} - - - -{% highlight text %} -## [1] 0.000237 -{% endhighlight %} - - -Very close to our estimation using cohort component projection. To impose the replacement condition, I just have to divide the first row of the Leslie matrix by NRR. - - -{% highlight r %} -m[1, ] <- m[1, ]/NRR -{% endhighlight %} - - -To get the population momentum we have to project the initial population until the growth is zero (here I raised the matrix 100 times), and then to compute the ratio between the initial population and the non-growing population (stationary). - - - -{% highlight r %} -# population momentum -sum(mp(m, 100) %*% Nf)/sum(Nf) -{% endhighlight %} - - - -{% highlight text %} -## [1] 1.01 -{% endhighlight %} - - -After imposing the replacement condition, the population grew 1%. diff --git a/_posts/2014-01-19-samplesize.md b/_posts/2014-01-19-samplesize.md deleted file mode 100755 index 609ed4cd..00000000 --- a/_posts/2014-01-19-samplesize.md +++ /dev/null @@ -1,160 +0,0 @@ ---- -layout: post -title: "Functions for sample size and error" -author: Sebastian Daza -date: 2014-01-19 -giscus_comments: true -tags: - - R - - survey ---- - - -Here I show two functions in R to define sample sizes and errors of a proportion, taking into account design effect, response rate, finite population correction, and stratification. They are useful when one needs to do these calculations quickly. - -**Note: I created a package with similar functions. [See here](/survey/2015/09/30/sampler/).** - -The inputs are: - -- **n** = sample size -- **e** = sampling error -- **deff** = design effect, by default 1 (SRS) -- **rr** = response rate, by default 1 -- **N** = population size, by default NULL (infinite population) -- **cl** = confidence level , by default .95 -- **p** = proportion, by default 0.5 (maximum variance of a proportion) -- **relative** = to estimate relative error, by default FALSE - -## first, load the functions - - - - -{% highlight r %} -library(devtools); source_gist("7896840") -{% endhighlight %} - -## serr: sampling error - -An example for n = 400 and all inputs at their default values: - - -{% highlight r %} -serr(400) -{% endhighlight %} - - - -{% highlight text %} -## [1] 0.049 -{% endhighlight %} - -The output is rounded to 4 decimals. A more complete example: - -- **n** = 400 -- **deff** = 1.5 -- **response rate** = 80% -- **population size** = 1000 - - -{% highlight r %} -serr(n=400, deff=1.5, rr=.8, N=1000) -{% endhighlight %} - - - -{% highlight text %} -## [1] 0.0595 -{% endhighlight %} - -The sample size (n) has always to be lower than the population (N). It is important to note that the final sample size used to compute the sampling error is: - -$$ n = \frac{N}{deff} * rr$$ - - -{% highlight r %} -serr(n=400, N=350) -{% endhighlight %} - - - -{% highlight text %} -## Error: n is bigger than N -{% endhighlight %} - -## ssize: sample size - -Let's get a sample size with an error of .03, a population of 1000 elements, a response rate of 0.80, and an effect design of 1.2: - - -{% highlight r %} -ssize(e=.03, deff=1.2, rr=.8, N=1000) -{% endhighlight %} - - - -{% highlight text %} -## [1] 775 -{% endhighlight %} - -If the the sample size is bigger than the population because of low response rates or big design effects, the sample size will be fixed to N: - - -{% highlight r %} -ssize(e=.03, deff=5, rr=.6, N=1000) -{% endhighlight %} - - - -{% highlight text %} -## n is bigger than N in some rows: n = N -{% endhighlight %} - - - -{% highlight text %} -## [1] 1000 -{% endhighlight %} - -## Working with strata - -Finally, we can estimate different sample sizes by strata using vectors or a data frame: - - -{% highlight r %} -# example sampling frame (4 strata) -frame <- data.frame( - strata = 1:4, - N =c(10000, 5000, 2000, 1000), - deff =c(1.1, 1, 1.3, .8), - rr = c(.8, .9, .85,.8), - p = c(.3, .6, .1, .2)) -{% endhighlight %} - - -{% highlight text %} -## strata N deff rr p -## 1 1 10000 1.1 0.80 0.3 -## 2 2 5000 1.0 0.90 0.6 -## 3 3 2000 1.3 0.85 0.1 -## 4 4 1000 0.8 0.80 0.2 -{% endhighlight %} - - -{% highlight r %} -frame$n1 <- ssize(e=.02, deff=frame$deff, rr=frame$rr, N=frame$N, p=frame$p) -frame$e1 <- serr(n=frame$n1, deff=frame$deff, rr=frame$rr, N=frame$N, p=frame$p) -{% endhighlight %} - - -{% highlight text %} -## strata N deff rr p n1 e1 -## 1 1 10000 1.1 0.80 0.3 2308 0.02 -## 2 2 5000 1.0 0.90 0.6 1753 0.02 -## 3 3 2000 1.3 0.85 0.1 923 0.02 -## 4 4 1000 0.8 0.80 0.2 606 0.02 -{% endhighlight %} - -As easy as falling off a log! - - diff --git a/_posts/2015-09-30-sampler.md b/_posts/2015-09-30-sampler.md deleted file mode 100644 index ae9201cb..00000000 --- a/_posts/2015-09-30-sampler.md +++ /dev/null @@ -1,425 +0,0 @@ ---- -layout: post -title: "Simple R package to define sample sizes and MOEs" -author: Sebastian Daza -giscus_comments: true -date: 2015-09-30 -tags: - - survey - - R ---- - - -I present a simple R package called [**sampler**](https://github.com/sdaza/sampler). The package defines **sample sizes** and **margins of error (MOE)** for proportions, as usually it is needed when designing public opinion surveys. [In a previous post](/survey/2014/01/19/samplesize/), I showed some functions that do mostly the same thing. This new package, though, includes some new features that can be useful when allocating a sample. - -## Installation - - -{% highlight r %} -# you have to install devtools first -devtools::install_github("sdaza/sampler") -library(sampler) -{% endhighlight %} - -## Functions - -The packages contains four functions: - -- **ssize**: computes sample size. -- **serr**: computes MOE. -- **astrata**: assigns sample sizes to strata. -- **serrst**: computes MOE for stratified samples. - - - - - -## Define sample size: *ssize* - - -{% highlight r %} -ssize(.05) -{% endhighlight %} - - - -{% highlight text %} -## [1] 384 -{% endhighlight %} - - - -{% highlight r %} -# design effect (deff) and response rate (rr) -ssize(.05, deff = 1.2, rr = .90) -{% endhighlight %} - - - -{% highlight text %} -## [1] 512 -{% endhighlight %} - - - -{% highlight r %} -# finite population correction -ssize(.05, deff = 1.2, rr = .90, N = 1000) -{% endhighlight %} - - - -{% highlight text %} -## [1] 370 -{% endhighlight %} - - - -{% highlight r %} -# warning message -ssize(.05, deff = 1.2, rr = .90, N = 100) -{% endhighlight %} - - - -{% highlight text %} -## n is bigger than N in some rows: n = N -{% endhighlight %} - - - -{% highlight text %} -## [1] 100 -{% endhighlight %} - -## Define sampling error: *serr* - - -{% highlight r %} -serr(384) -{% endhighlight %} - - - -{% highlight text %} -## [1] 0.05 -{% endhighlight %} - - - -{% highlight r %} -serr(512, deff = 1.2, rr = .90) -{% endhighlight %} - - - -{% highlight text %} -## [1] 0.05 -{% endhighlight %} - - - -{% highlight r %} -serr(370, deff = 1.2, rr = .90, N = 1000) -{% endhighlight %} - - - -{% highlight text %} -## [1] 0.05 -{% endhighlight %} - - - -{% highlight r %} -# we still get an answer -serr(100, deff = 1.2, rr = .90, N = 100) -{% endhighlight %} - - - -{% highlight text %} -## [1] 0.0569 -{% endhighlight %} - -## Strata allocation: *astrata* - -These examples show how to allocate a sample size into strata. Look at *?astrata* in **R** for definitions of the allocation procedures that are available. - - -{% highlight r %} -# I will use data.table -library(data.table) -chile <- data.table(chile) -chile -{% endhighlight %} - - - -{% highlight text %} -## reg pob pr -## 1: 1 328782 0.3 -## 2: 2 613328 0.4 -## 3: 3 308247 0.5 -## 4: 4 759228 0.5 -## 5: 5 1808300 0.5 -## 6: 6 910577 0.6 -## 7: 7 1035593 0.3 -## 8: 8 2100494 0.1 -## 9: 9 983499 0.2 -## 10: 10 834714 0.5 -## 11: 11 107334 0.5 -## 12: 12 163748 0.4 -## 13: 13 7228581 0.6 -## 14: 14 401548 0.2 -## 15: 15 235081 0.3 -{% endhighlight %} - - -{% highlight r %} -# proportional for a sample of 1000 -chile[, aprop := astrata(1000, wp = 1, N = pob)] - -# fixed (same number by stratum) -chile[, afixed := astrata(1000, wp = 0, N = pob)] - -# 40% proportional, 60% fixed -chile[, a40 := astrata(1000, wp =.4, N = pob)] - -# 60% proportional, 40% fixed -chile[, a60 := astrata(1000, wp =.6, N = pob)] - -# square-root -chile[, aroot := astrata(1000, method = "root", N = pob)] - -# neyman -chile[, aneyman := astrata(1000, method = "neyman", N = pob, p = pr)] - -# standard deviation -chile[, astdev := astrata(1000, method = "stdev", N = pob, p = pr)] - -# error -chile[, aerr := astrata(e = .11, method = "error", N = pob, p = pr)] -{% endhighlight %} - - -{% highlight text %} -## reg pob pr aprop afixed a40 a60 aroot aneyman astdev aerr -## 1: 1 328782 0.3 18 67 47 38 41 18 66 67 -## 2: 2 613328 0.4 34 67 54 47 56 37 71 76 -## 3: 3 308247 0.5 17 67 47 37 40 19 72 79 -## 4: 4 759228 0.5 43 67 57 53 62 46 72 79 -## 5: 5 1808300 0.5 101 67 81 87 96 110 72 79 -## 6: 6 910577 0.6 51 67 61 57 68 54 71 76 -## 7: 7 1035593 0.3 58 67 63 62 73 58 66 67 -## 8: 8 2100494 0.1 118 67 87 98 104 77 43 29 -## 9: 9 983499 0.2 55 67 62 60 71 48 58 51 -## 10: 10 834714 0.5 47 67 59 55 65 51 72 79 -## 11: 11 107334 0.5 6 67 43 30 23 7 72 79 -## 12: 12 163748 0.4 9 67 44 32 29 10 71 76 -## 13: 13 7228581 0.6 406 67 203 270 192 432 71 76 -## 14: 14 401548 0.2 23 67 49 41 45 20 58 51 -## 15: 15 235081 0.3 13 67 45 35 35 13 66 67 -{% endhighlight %} - -## Getting sampling error from a stratified sample: *serrst* - - -{% highlight r %} -# the second most efficient allocation -serrst(n = chile$aprop, N = chile$pob, p = chile$pr) -{% endhighlight %} - - - -{% highlight text %} -## [1] 0.0288 -{% endhighlight %} - - - -{% highlight r %} -# the worst solution -serrst(n = chile$afixed, N = chile$pob, p = chile$pr) -{% endhighlight %} - - - -{% highlight text %} -## [1] 0.0518 -{% endhighlight %} - - - -{% highlight r %} -serrst(n = chile$a40, N = chile$pob, p = chile$pr) -{% endhighlight %} - - - -{% highlight text %} -## [1] 0.0339 -{% endhighlight %} - - - -{% highlight r %} -serrst(n = chile$a60, N = chile$pob, p = chile$pr) -{% endhighlight %} - - - -{% highlight text %} -## [1] 0.0311 -{% endhighlight %} - - - -{% highlight r %} -serrst(n = chile$aroot, N = chile$pob, p = chile$pr) -{% endhighlight %} - - - -{% highlight text %} -## [1] 0.0339 -{% endhighlight %} - - - -{% highlight r %} -# the most efficient allocation -serrst(n = chile$aneyman, N = chile$pob, p = chile$pr) -{% endhighlight %} - - - -{% highlight text %} -## [1] 0.0285 -{% endhighlight %} - - - -{% highlight r %} -serrst(n = chile$astdev, N = chile$pob, p = chile$pr) -{% endhighlight %} - - - -{% highlight text %} -## [1] 0.0508 -{% endhighlight %} - - - -{% highlight r %} -serrst(n = chile$aerr, N = chile$pob, p = chile$pr) -{% endhighlight %} - - - -{% highlight text %} -## [1] 0.0498 -{% endhighlight %} - -## Combining criteria - - -{% highlight r %} -# get error for 60% proportional / 40% fixed allocation for each strata -chile[, error_a60 := serr(a60, p = pr)] - -# assign sample sizes assuming 13% error for each strata -chile[, serr13 := astrata(e = .13, method = "error", N = pob, p = pr)] - -# total error, not that good! -serrst(n = chile$serr13, N = chile$pob, p = chile$pr) -{% endhighlight %} - - - -{% highlight text %} -## [1] 0.0586 -{% endhighlight %} - - - -{% highlight r %} -chile[, .(reg, pob, pr, a60, error_a60, serr13)] -{% endhighlight %} - - - -{% highlight text %} -## reg pob pr a60 error_a60 serr13 -## 1: 1 328782 0.3 38 0.1457 48 -## 2: 2 613328 0.4 47 0.1401 55 -## 3: 3 308247 0.5 37 0.1611 57 -## 4: 4 759228 0.5 53 0.1346 57 -## 5: 5 1808300 0.5 87 0.1051 57 -## 6: 6 910577 0.6 57 0.1272 55 -## 7: 7 1035593 0.3 62 0.1141 48 -## 8: 8 2100494 0.1 98 0.0594 20 -## 9: 9 983499 0.2 60 0.1012 36 -## 10: 10 834714 0.5 55 0.1321 57 -## 11: 11 107334 0.5 30 0.1789 57 -## 12: 12 163748 0.4 32 0.1697 55 -## 13: 13 7228581 0.6 270 0.0584 55 -## 14: 14 401548 0.2 41 0.1224 36 -## 15: 15 235081 0.3 35 0.1518 48 -{% endhighlight %} - -We can adjust a bit more: - - -{% highlight r %} -# when error is higher than .13, use serr13 -chile[, sfinal := ifelse(error_a60 > .13, serr13, a60)] - -# new error by stratum -chile[, error_sfinal := serr(sfinal, p = pr)] - -# total error, much better! -serrst(n = chile$sfinal, N = chile$pob, p = chile$pr) -{% endhighlight %} - - - -{% highlight text %} -## [1] 0.0309 -{% endhighlight %} - - - -{% highlight r %} -# although the total sample size is now bigger -sum(chile$sfinal) -{% endhighlight %} - - - -{% highlight text %} -## [1] 1109 -{% endhighlight %} - -{% highlight text %} -## reg pob pr sfinal error_sfinal -## 1: 1 328782 0.3 48 0.1296 -## 2: 2 613328 0.4 55 0.1295 -## 3: 3 308247 0.5 57 0.1298 -## 4: 4 759228 0.5 57 0.1298 -## 5: 5 1808300 0.5 87 0.1051 -## 6: 6 910577 0.6 57 0.1272 -## 7: 7 1035593 0.3 62 0.1141 -## 8: 8 2100494 0.1 98 0.0594 -## 9: 9 983499 0.2 60 0.1012 -## 10: 10 834714 0.5 57 0.1298 -## 11: 11 107334 0.5 57 0.1298 -## 12: 12 163748 0.4 55 0.1295 -## 13: 13 7228581 0.6 270 0.0584 -## 14: 14 401548 0.2 41 0.1224 -## 15: 15 235081 0.3 48 0.1296 -{% endhighlight %} - -That's it. A simple package to do simple calculations. - diff --git a/_posts/2015-10-14-imputation-parcels.md b/_posts/2015-10-14-imputation-parcels.md deleted file mode 100644 index a38b646b..00000000 --- a/_posts/2015-10-14-imputation-parcels.md +++ /dev/null @@ -1,271 +0,0 @@ ---- -layout: post -title: "Imputing scales using parcels of items as auxiliary variables" -author: Sebastian Daza -date: 2015-10-14 -giscus_comments: true -tags: - - data science - - R ---- - - - -Multiple imputation of scales generated by several items can be challenging. Fortunately, to impute every single item is not the only solution to the missing data problem. Some practical and *theoretically* attractive alternative have already been proposed. In this post, I show a simple implementation of what Enders (2010) calls **duplicated-scale imputation**, a method orginally suggested by Eekhout et al. (2011). By the way, thanks [Iris Eekhout](http://www.iriseekhout.com) for replying my e-mails! - -## Procedure - -For each scale, I define a number or proportion of items (let's say **p**) to create parcels (i.e., average of items although not the whole scale). These parcels are, then, used as auxiliary variables to *impute* the original scales. There are different ways to define parcels. I implemented a solution in my R package [sdazar](http://github.com/sdaza/sdazar), see the function `rowscore` for more details. - -The function `rowscore` selects **p** items with the least missing data. For each case (row), it computes the parcels using the available information of the selected items. If only one item has information, only that one will be used. If there are more than one item with valid data, it will average all the selected items. If there are no items available in my initial selection, it picks **p** items from the rest of unselected items to impute the original scale. In this particular example, I create parcels using half of the items: - - -{% highlight r %} -rowscore(data, items, p = 1/2, type = "parcel") -{% endhighlight %} - -The reason for using a proportion of the original items is to include as much information as possible, but preventing strong linear dependencies between variables. Ideally, parcels are complete (no missing values). However, in some cases all the items are missing, so parcels can still have missing records (although less than the original scales). - -**Why not just to use the average of the available items?** That solution would implicitly assume that items perfectly correlate with the scale. We know that's not a good assumption. That is why we worry about creating scales in the first place, right? Using parcels takes advantage of the available information (items with complete information) and the relationship between a portion of items and the scale. - -Here I show a simple example using the [National Longitudinal Study of Adolescent to Adult Health (Add Health)](http://www.cpc.unc.edu/projects/addhealth). First, let's look some descriptives of the variables included in the imputation. I am using information from Wave 1 and 2. The scales/scores I am imputing are depression (19 items) and GPA (4 items). Variables ending with `.p` are parcels with 1/2 of the items of the original scale. - - -{% highlight r %} -dim(dats) -{% endhighlight %} - - -{% highlight text %} -## [1] 12976 14 -{% endhighlight %} - - -{% highlight r %} -str(dats[, nvars, with = FALSE]) -{% endhighlight %} - - - -{% highlight text %} -## Classes 'data.table' and 'data.frame': 12976 obs. of 14 variables: -## $ female : Factor w/ 2 levels "0","1": 1 1 1 2 1 1 1 1 1 1 ... -## $ age : int 16 16 14 13 14 17 14 17 17 14 ... -## $ race : Factor w/ 5 levels "white","black",..: 2 1 1 1 2 1 3 3 3 2 ... -## $ publicass: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 NA ... -## $ bmi : num 27.4 16.3 22.2 18.2 21.9 ... -## $ gpa1 : num 2 3.25 3.75 3.25 2.25 1 NA 2.25 NA 2.5 ... -## $ gpa2 : num 2.5 2.5 4 3.75 1.75 1.5 NA 3 1 4 ... -## $ gpa1.p : num 2.5 3.5 3.5 3 2.5 1 3.5 3 1 3.5 ... -## $ gpa2.p : num 2.5 2.5 4 3.5 1.5 2 1.5 3 1 4 ... -## $ dep1.p : num 0.2 0.7 0 0.3 0.4 0.4 0.1 1 0.6 1.2 ... -## $ dep1 : num 0.263 0.789 0 0.211 0.474 ... -## $ dep2.p : num 0.6 0.6 0.1 0.3 0.4 0.6 0.2 1.1 0.8 0.5 ... -## $ dep2 : num 0.6316 0.5263 0.0526 0.1579 0.3684 ... -## $ ppvt : int 101 75 121 96 79 97 103 89 82 120 ... -## - attr(*, ".internal.selfref")= -{% endhighlight %} - - -{% highlight r %} -# create summary table using package tables -missing <- function (x) {sum(is.na(x))} -tabular( - ( dep2 + dep2.p + dep1 + dep1.p + gpa2 + gpa2.p + - gpa1 + gpa1.p + age + bmi + ppvt ) - ~ (Format(digit = 2) * ( Heading("Mean") - * sdazar::Mean + Heading("SD") - * sdazar::Sd + Heading("Min") - * sdazar::Min + Heading("Max") - * sdazar::Max + (Heading("Missing") * missing ))), - data = dats) -{% endhighlight %} - - - -{% highlight text %} -## -## Mean SD Min Max Missing -## dep2 0.58 0.39 0.00 2.95 68.00 -## dep2.p 0.58 0.43 0.00 3.00 6.00 -## dep1 0.58 0.39 0.00 2.84 81.00 -## dep1.p 0.62 0.44 0.00 3.00 18.00 -## gpa2 2.86 0.74 1.00 4.00 4710.00 -## gpa2.p 2.76 0.84 1.00 4.00 1238.00 -## gpa1 2.82 0.76 1.00 4.00 2788.00 -## gpa1.p 2.74 0.85 1.00 4.00 342.00 -## age 15.28 1.61 11.00 21.00 9.00 -## bmi 22.37 4.42 11.47 63.47 353.00 -## ppvt 100.17 15.00 13.00 146.00 568.00 -{% endhighlight %} - -As expected, the correlation between the scales and parcels is high. GPA has most of the problems. Note that parcels `.p` still have missing records, although much less than the original scales. - - -{% highlight r %} -cor(dats[, .(dep1, dep1.p)], use = "complete") -{% endhighlight %} - - - -{% highlight text %} -## dep1 dep1.p -## dep1 1.000 0.947 -## dep1.p 0.947 1.000 -{% endhighlight %} - - - -{% highlight r %} -cor(dats[, .(dep2, dep2.p)], use = "complete") -{% endhighlight %} - - - -{% highlight text %} -## dep2 dep2.p -## dep2 1.000 0.948 -## dep2.p 0.948 1.000 -{% endhighlight %} - - - -{% highlight r %} -cor(dats[, .(gpa1, gpa1.p)], use = "complete") -{% endhighlight %} - - - -{% highlight text %} -## gpa1 gpa1.p -## gpa1 1.000 0.888 -## gpa1.p 0.888 1.000 -{% endhighlight %} - - - -{% highlight r %} -cor(dats[, .(gpa2, gpa2.p)], use = "complete") -{% endhighlight %} - - - -{% highlight text %} -## gpa2 gpa2.p -## gpa2 1.000 0.885 -## gpa2.p 0.885 1.000 -{% endhighlight %} - -Let’s now impute the scales/scores using the R package *MICE*. - -{% highlight r %} -ini <- mice(dats[, nvars, with = FALSE], m = 1, maxit = 0) - -# get methods -(meth <- ini$meth) -{% endhighlight %} - - -{% highlight text %} -## female age race publicass bmi gpa1 gpa2 gpa1.p gpa2.p dep1.p dep1 dep2.p dep2 ppvt -## "" "pmm" "" "logreg" "pmm" "pmm" "pmm" "pmm" "pmm" "pmm" "pmm" "pmm" "pmm" "pmm" -{% endhighlight %} - - -{% highlight r %} -# get predictor matrix -pred <- ini$pred -{% endhighlight %} - -I adjust the predictor matrix to avoid feedbacks during the imputation (circularity between variables). The trick is to use only complete variables when imputing *parcels*. - - -{% highlight r %} -# predict parcels only with complete variables to avoid feedbacks -pred["gpa1.p", ] <- 0 -pred["gpa2.p", ] <- 0 -pred["dep1.p", ] <- 0 -pred["dep2.p", ] <- 0 - -pred["gpa1.p", c("female", "race")] <- 1 -pred["gpa2.p", c("female", "race")] <- 1 -pred["dep1.p", c("female", "race")] <- 1 -pred["dep2.p", c("female", "race")] <- 1 - -# predict scales using parcels -pred[, "gpa1.p"] <- 0 -pred[, "gpa2.p"] <- 0 -pred[, "dep1.p"] <- 0 -pred[, "dep2.p"] <- 0 - -pred["gpa1", c("gpa1.p")] <- 1 -pred["gpa2", c("gpa2.p")] <- 1 -pred["dep1", c("dep1.p")] <- 1 -pred["dep2", c("dep2.p")] <- 1 -{% endhighlight %} - -Here the adjusted predictor matrix: - -{% highlight text %} -## female age race publicass bmi gpa1 gpa2 gpa1.p gpa2.p dep1.p dep1 dep2.p dep2 ppvt -## female 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -## age 1 0 1 1 1 1 1 0 0 0 1 0 1 1 -## race 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -## publicass 1 1 1 0 1 1 1 0 0 0 1 0 1 1 -## bmi 1 1 1 1 0 1 1 0 0 0 1 0 1 1 -## gpa1 1 1 1 1 1 0 1 1 0 0 1 0 1 1 -## gpa2 1 1 1 1 1 1 0 0 1 0 1 0 1 1 -## gpa1.p 1 0 1 0 0 0 0 0 0 0 0 0 0 0 -## gpa2.p 1 0 1 0 0 0 0 0 0 0 0 0 0 0 -## dep1.p 1 0 1 0 0 0 0 0 0 0 0 0 0 0 -## dep1 1 1 1 1 1 1 1 0 0 1 0 0 1 1 -## dep2.p 1 0 1 0 0 0 0 0 0 0 0 0 0 0 -## dep2 1 1 1 1 1 1 1 0 0 0 1 1 0 1 -## ppvt 1 1 1 1 1 1 1 0 0 0 1 0 1 0 -{% endhighlight %} - -Let's impute the data! - - -{% highlight r %} -imp <- mice(dats[, nvars, with = FALSE], pred = pred, m = 5, maxit = 10) -{% endhighlight %} - -Below some plots to explore how the imputation goes. - - -{% highlight r %} -plot(imp, c("gpa1", "gpa2", "dep1", "dep2")) -{% endhighlight %} - -![center](/assets/img/2015-10-14-imputation_parcels/unnamed-chunk-12-1.png)![center](/assets/img/2015-10-14-imputation_parcels/unnamed-chunk-12-2.png) - -I don't see any problematic pattern. It seems I get a proper solution. The distribution of the variables also looks right. - -{% highlight r %} -densityplot(imp, ~ gpa1 + gpa2 + dep1 + dep2) -{% endhighlight %} - -![center](/assets/img/2015-10-14-imputation_parcels/unnamed-chunk-13-1.png) - -{% highlight r %} -bwplot(imp, gpa1 + gpa2 + dep1 + dep2 ~ .imp) -{% endhighlight %} - -![center](/assets/img/2015-10-14-imputation_parcels/unnamed-chunk-13-2.png) - -**Last Update: 06/02/2017** - ------ - -### References - -Enders, Craig K. 2010. *Applied Missing Data Analysis*. The Guilford Press. - -Eekhout, Iris, Craig K. Enders, Jos W. R. Twisk, Michiel R. de Boer, Henrica -C. W. de Vet, and Martijn W. Heymans. 2015. "Analyzing Incomplete Item Scores -in Longitudinal Data by Including Item Score Information as Auxiliary -Variables." *Structural Equation Modeling: A Multidisciplinary Journal* 22 -(4):588-602. - - diff --git a/_posts/2016-07-06-acsr.md b/_posts/2016-07-06-acsr.md deleted file mode 100644 index 40f7c0cc..00000000 --- a/_posts/2016-07-06-acsr.md +++ /dev/null @@ -1,532 +0,0 @@ ---- -layout: post -title: "R package to compute statistics from the American Community Survey (ACS) and Decennial US Census" -author: Sebastian Daza -date: 2016-07-06 -giscus_comments: true -tags: - - R - - demography - - data science ---- - - - -The `acsr` package helps extracting variables and computing statistics using the America Community Survey and Decennial US Census. It was created for the [Applied Population Laboratory](http://www.apl.wisc.edu/) (APL) at the University of Wisconsin-Madison. - -

Installation

- -The functions depend on the `acs` and `data.table` packages, so it is necessary to install then before using `acsr`. The `acsr` package is hosted on a github repository and can be installed using `devtools`: - - -{% highlight r %} -devtools::install_github("sdaza/acsr") -library(acsr) -{% endhighlight %} - - - -Remember to set the ACS API key, to check the help documentation and the default values of the `acsr` functions. - - -{% highlight r %} -api.key.install(key="*") -?sumacs -?acsdata -{% endhighlight %} - -The default dataset is `acs`, the level is `state` (Wisconsin, `state = "WI"`), the `endyear` is 2014, and the confidence level to compute margins of error (MOEs) is 90%. - -

Levels

- -The `acsr` functions can extract all the levels available in the `acs` package. The table below shows the summary and required levels when using the `acsdata` and `sumacs` functions: - -| summary number | levels | -|---------------- |----------------------------------------- | -| 010 | us | -| 020 | region | -| 030 | division | -| 040 | state | -| 050 | state, county | -| 060 | state, county, county.subdivision | -| 140 | state, county, tract | -| 150 | state, county, tract, block.group | -| 160 | state, place | -| 250 | american.indian.area | -| 320 | state, msa | -| 340 | state, csa | -| 350 | necta | -| 400 | urban.area | -| 500 | state, congressional.district | -| 610 | state, state.legislative.district.upper | -| 620 | state, state.legislative.district.lower | -| 795 | state, puma | -| 860 | zip.code | -| 950 | state, school.district.elementary | -| 960 | state, school.district.secondary | -| 970 | state, school.district.unified | - - -

Getting variables and statistics

- -We can use the `sumacs` function to extract variable and statistics. We have to specify the corresponding method (e.g., *proportion* or just *variable*), and the name of the statistic or variable to be included in the output. - - -{% highlight r %} -sumacs(formula = c("(b16004_004 + b16004_026 + b16004_048 / b16004_001)", "b16004_026"), - varname = c("mynewvar", "myvar"), - method = c("prop", "variable"), - level = c("division")) -{% endhighlight %} - - - -{% highlight text %} -## [1] "Extracting data from: acs 2014" -## [1] ". . . . . . ACS/Census variables : 4" -## [1] ". . . . . . Levels : 1" -## [1] ". . . . . . New variables : 2" -## [1] ". . . . . . Getting division data" -## [1] ". . . . . . Creating variables" -## [1] ". . . . . . 50%" -## [1] ". . . . . . 100%" -## [1] ". . . . . . Formatting output" -{% endhighlight %} - - - -{% highlight text %} -## sumlevel geoid division mynewvar_est mynewvar_moe myvar_est myvar_moe -## 1: 030 NA 1 0.0762 0.000347 770306 3490 -## 2: 030 NA 2 0.1182 0.000278 3332150 9171 -## 3: 030 NA 3 0.0599 0.000196 1819417 7209 -## 4: 030 NA 4 0.0411 0.000277 547577 4461 -## 5: 030 NA 5 0.1108 0.000246 4526480 11869 -## 6: 030 NA 6 0.0320 0.000265 402475 3781 -## 7: 030 NA 7 0.2203 0.000469 5318126 13044 -## 8: 030 NA 8 0.1582 0.000602 2279303 10746 -## 9: 030 NA 9 0.2335 0.000501 7765838 20289 -{% endhighlight %} - -To download the data can be slow, especially when many levels are being used (e.g., blockgroup). A better approach in those cases is, first, download the data using the function `acsdata` , and then use them as input. - - -{% highlight r %} -mydata <- acsdata(formula = c("(b16004_004 + b16004_026 + b16004_048 / b16004_001)", - "b16004_026"), - level = c("division")) -{% endhighlight %} - - - -{% highlight text %} -## [1] ". . . . . . Getting division data" -{% endhighlight %} - - - -{% highlight r %} -sumacs(formula = c("(b16004_004 + b16004_026 + b16004_048 / b16004_001)", "b16004_026"), - varname = c("mynewvar", "myvar"), - method = c("prop", "variable"), - level = c("division"), - data = mydata) -{% endhighlight %} - - - -{% highlight text %} -## [1] "Extracting data from: acs 2014" -## [1] ". . . . . . ACS/Census variables : 4" -## [1] ". . . . . . Levels : 1" -## [1] ". . . . . . New variables : 2" -## [1] ". . . . . . Creating variables" -## [1] ". . . . . . 50%" -## [1] ". . . . . . 100%" -## [1] ". . . . . . Formatting output" -{% endhighlight %} - - - -{% highlight text %} -## sumlevel geoid division mynewvar_est mynewvar_moe myvar_est myvar_moe -## 1: 030 NA 1 0.0762 0.000347 770306 3490 -## 2: 030 NA 2 0.1182 0.000278 3332150 9171 -## 3: 030 NA 3 0.0599 0.000196 1819417 7209 -## 4: 030 NA 4 0.0411 0.000277 547577 4461 -## 5: 030 NA 5 0.1108 0.000246 4526480 11869 -## 6: 030 NA 6 0.0320 0.000265 402475 3781 -## 7: 030 NA 7 0.2203 0.000469 5318126 13044 -## 8: 030 NA 8 0.1582 0.000602 2279303 10746 -## 9: 030 NA 9 0.2335 0.000501 7765838 20289 -{% endhighlight %} - -

Standard errors

- -When computing statistics there are two ways to define the standard errors: - -- Including all standard errors of the variables used to compute a statistic (`one.zero = FALSE`) -- Include all standard errors except those of variables that are equal to zero. Only the maximum standard error of the variables equal to zero is included (`one.zero = TRUE`) -- The default value is `one.zero = TRUE` - -For more details about how standard errors are computed for proportions, ratios and aggregations look at [A Compass for Understanding and Using American Community Survey Data](https://www.census.gov/content/dam/Census/library/publications/2008/acs/ACSGeneralHandbook.pdf). - -Below an example when estimating proportions and using `one.zero = FALSE`: - - -{% highlight r %} -sumacs(formula = "(b16004_004 + b16004_026 + b16004_048) / b16004_001", - varname = "mynewvar", - method = "prop", - level = "tract", - county = 1, - tract = 950501, - endyear = 2013, - one.zero = FALSE) -{% endhighlight %} - - - -{% highlight text %} -## [1] "Extracting data from: acs 2013" -## [1] ". . . . . . ACS/Census variables : 4" -## [1] ". . . . . . Levels : 1" -## [1] ". . . . . . New variables : 1" -## [1] ". . . . . . Getting tract data" -## [1] ". . . . . . Creating variables" -## [1] ". . . . . . 100%" -## [1] ". . . . . . Formatting output" -{% endhighlight %} - - - -{% highlight text %} -## sumlevel geoid st_fips cnty_fips tract_fips mynewvar_est mynewvar_moe -## 1: 140 55001950501 55 1 950501 0.0226 0.0252 -{% endhighlight %} - -$$ SE = \sqrt{ \frac{(5.47 ^ 2 + 22.49 ^ 2 + 5.47 ^ 2) - ( 0.02 ^ 2 \times 102.13 ^ 2)}{1546} } \times 1.645 = 0.0252 $$ - -When `one.zero = TRUE`: - - -{% highlight r %} -sumacs(formula = "(b16004_004 + b16004_026 + b16004_048) / b16004_001", - varname = "mynewvar", - method = "prop", - level = "tract", - county = 1, - tract = 950501, - endyear = 2013, - one.zero = TRUE) -{% endhighlight %} - - - -{% highlight text %} -## [1] "Extracting data from: acs 2013" -## [1] ". . . . . . ACS/Census variables : 4" -## [1] ". . . . . . Levels : 1" -## [1] ". . . . . . New variables : 1" -## [1] ". . . . . . Getting tract data" -## [1] ". . . . . . Creating variables" -## [1] ". . . . . . 100%" -## [1] ". . . . . . Formatting output" -{% endhighlight %} - - - -{% highlight text %} -## sumlevel geoid st_fips cnty_fips tract_fips mynewvar_est mynewvar_moe -## 1: 140 55001950501 55 1 950501 0.0226 0.0245 -{% endhighlight %} - -$$ SE_{\text{ one.zero}} \sqrt{ \frac{(5.47 ^ 2 + 22.49 ^ 2) - ( 0.02 ^ 2 \times 102.13 ^ 2)}{1546} } \times 1.645 = 0.0245 $$ - -When the square root value in the standard error formula doesn't exist (e.g., the square root of a negative number), the ratio formula is instead used. The ratio adjustment is done **variable by variable** . - -It can also be that the `one.zero` option makes the square root undefinable. In those cases, the function uses again the **ratio** formula to compute standard errors. There is also a possibility that the standard error estimates using the **ratio** formula are higher than the **proportion** estimates without the `one.zero` option. - -

Decennial Data from the US Census

- -Let's get the African American and Hispanic population by state. In this case, we don't have any estimation of margin of error. - - -{% highlight r %} -sumacs(formula = c("p0080004", "p0090002"), - method = "variable", - dataset = "sf1", - level = "state", - state = "*", - endyear = 2010) -{% endhighlight %} - - - -{% highlight text %} -## [1] "Extracting data from: sf1 2010" -## [1] ". . . . . . ACS/Census variables : 2" -## [1] ". . . . . . Levels : 1" -## [1] ". . . . . . New variables : 2" -## [1] ". . . . . . Getting state data" -## [1] ". . . . . . Creating variables" -## [1] ". . . . . . 50%" -## [1] ". . . . . . 100%" -## [1] ". . . . . . Formatting output" -{% endhighlight %} - - - -{% highlight text %} -## sumlevel geoid st_fips p0080004 p0090002 -## 1: 040 01 01 1251311 185602 -## 2: 040 02 02 23263 39249 -## 3: 040 04 04 259008 1895149 -## 4: 040 05 05 449895 186050 -## 5: 040 06 06 2299072 14013719 -## 6: 040 08 08 201737 1038687 -## 7: 040 09 09 362296 479087 -## 8: 040 10 10 191814 73221 -## 9: 040 11 11 305125 54749 -## 10: 040 12 12 2999862 4223806 -## 11: 040 13 13 2950435 853689 -## 12: 040 15 15 21424 120842 -## 13: 040 16 16 9810 175901 -## 14: 040 17 17 1866414 2027578 -## 15: 040 18 18 591397 389707 -## 16: 040 19 19 89148 151544 -## 17: 040 20 20 167864 300042 -## 18: 040 21 21 337520 132836 -## 19: 040 22 22 1452396 192560 -## 20: 040 23 23 15707 16935 -## 21: 040 24 24 1700298 470632 -## 22: 040 25 25 434398 627654 -## 23: 040 26 26 1400362 436358 -## 24: 040 27 27 274412 250258 -## 25: 040 28 28 1098385 81481 -## 26: 040 29 29 693391 212470 -## 27: 040 30 30 4027 28565 -## 28: 040 31 31 82885 167405 -## 29: 040 32 32 218626 716501 -## 30: 040 33 33 15035 36704 -## 31: 040 34 34 1204826 1555144 -## 32: 040 35 35 42550 953403 -## 33: 040 36 36 3073800 3416922 -## 34: 040 37 37 2048628 800120 -## 35: 040 38 38 7960 13467 -## 36: 040 39 39 1407681 354674 -## 37: 040 40 40 277644 332007 -## 38: 040 41 41 69206 450062 -## 39: 040 42 42 1377689 719660 -## 40: 040 44 44 60189 130655 -## 41: 040 45 45 1290684 235682 -## 42: 040 46 46 10207 22119 -## 43: 040 47 47 1057315 290059 -## 44: 040 48 48 2979598 9460921 -## 45: 040 49 49 29287 358340 -## 46: 040 50 50 6277 9208 -## 47: 040 51 51 1551399 631825 -## 48: 040 53 53 240042 755790 -## 49: 040 54 54 63124 22268 -## 50: 040 55 55 359148 336056 -## 51: 040 56 56 4748 50231 -## 52: 040 72 72 461498 3688455 -## sumlevel geoid st_fips p0080004 p0090002 -{% endhighlight %} - -

Output

- -The output can be formatted using a wide or long format: - - -{% highlight r %} -sumacs(formula = "(b16004_004 + b16004_026 + b16004_048 / b16004_001)", - varname = "mynewvar", - method = "prop", - level = "division", - format.out = "long") -{% endhighlight %} - - - -{% highlight text %} -## [1] "Extracting data from: acs 2014" -## [1] ". . . . . . ACS/Census variables : 4" -## [1] ". . . . . . Levels : 1" -## [1] ". . . . . . New variables : 1" -## [1] ". . . . . . Getting division data" -## [1] ". . . . . . Creating variables" -## [1] ". . . . . . 100%" -## [1] ". . . . . . Formatting output" -{% endhighlight %} - - - -{% highlight text %} -## geoid sumlevel division var_name est moe -## 1: NA 030 1 mynewvar 0.0762 0.000347 -## 2: NA 030 2 mynewvar 0.1182 0.000278 -## 3: NA 030 3 mynewvar 0.0599 0.000196 -## 4: NA 030 4 mynewvar 0.0411 0.000277 -## 5: NA 030 5 mynewvar 0.1108 0.000246 -## 6: NA 030 6 mynewvar 0.0320 0.000265 -## 7: NA 030 7 mynewvar 0.2203 0.000469 -## 8: NA 030 8 mynewvar 0.1582 0.000602 -## 9: NA 030 9 mynewvar 0.2335 0.000501 -{% endhighlight %} - -And it can also be exported to a csv file: - - -{% highlight r %} -sumacs(formula = "(b16004_004 + b16004_026 + b16004_048 / b16004_001)", - varname = "mynewvar", - method = "prop", - level = "division", - file = "myfile.out") -{% endhighlight %} - - - -{% highlight text %} -## [1] "Extracting data from: acs 2014" -## [1] ". . . . . . ACS/Census variables : 4" -## [1] ". . . . . . Levels : 1" -## [1] ". . . . . . New variables : 1" -## [1] ". . . . . . Getting division data" -## [1] ". . . . . . Creating variables" -## [1] ". . . . . . 100%" -## [1] ". . . . . . Formatting output" -## [1] "Data exported to a CSV file! " -{% endhighlight %} - -

Combining geographic levels

- -We can combine geographic levels using two methods: (1) `sumacs` and (2) `combine.output`. The first one allows only single combinations, the second multiple ones. - -If I want to combine two states (e.g., Wisconsin and Minnesota) I can use: - - -{% highlight r %} -sumacs("(b16004_004 + b16004_026 + b16004_048 / b16004_001)", - varname = "mynewvar", - method = "prop", - level = "state", - state = list("WI", "MN"), - combine = TRUE, - print.levels = FALSE) -{% endhighlight %} - - - -{% highlight text %} -## [1] "Extracting data from: acs 2014" -## [1] ". . . . . . ACS/Census variables : 4" -## [1] ". . . . . . Levels : 1" -## [1] ". . . . . . New variables : 1" -## [1] ". . . . . . Getting combined data" -## [1] ". . . . . . Creating variables" -## [1] ". . . . . . 100%" -## [1] ". . . . . . Formatting output" -{% endhighlight %} - - - -{% highlight text %} -## geoid combined_group mynewvar_est mynewvar_moe -## 1: NA aggregate 0.042 0.000331 -{% endhighlight %} - -If I want to put together multiple combinations (e.g., groups of states): - - -{% highlight r %} -combine.output("(b16004_004 + b16004_026 + b16004_048 / b16004_001)", - varname = "mynewvar", - method = "prop", - level = list("state", "state"), - state = list( list("WI", "MN"), list("CA", "OR")), # nested list - combine.names = c("WI+MN", "CA+OR"), - print.levels = FALSE) -{% endhighlight %} - - - -{% highlight text %} -## [1] ". . . . . . Defining WI+MN" -## [1] "Extracting data from: acs 2014" -## [1] ". . . . . . ACS/Census variables : 4" -## [1] ". . . . . . Levels : 1" -## [1] ". . . . . . New variables : 1" -## [1] ". . . . . . Getting combined data" -## [1] ". . . . . . Creating variables" -## [1] ". . . . . . 100%" -## [1] ". . . . . . Formatting output" -## [1] ". . . . . . Defining CA+OR" -## [1] "Extracting data from: acs 2014" -## [1] ". . . . . . ACS/Census variables : 4" -## [1] ". . . . . . Levels : 1" -## [1] ". . . . . . New variables : 1" -## [1] ". . . . . . Getting combined data" -## [1] ". . . . . . Creating variables" -## [1] ". . . . . . 100%" -## [1] ". . . . . . Formatting output" -{% endhighlight %} - - - -{% highlight text %} -## combined_group mynewvar_est mynewvar_moe -## 1: WI+MN 0.042 0.000331 -## 2: CA+OR 0.269 0.000565 -{% endhighlight %} - - -

A map?

- -Let's color a map using poverty by county: - - -{% highlight r %} -pov <- sumacs(formula = "b17001_002 / b17001_001 * 100", - varname = c("pov"), - method = c("prop"), - level = c("county"), - state = "*") -{% endhighlight %} - - - -{% highlight text %} -## [1] "Extracting data from: acs 2014" -## [1] ". . . . . . ACS/Census variables : 2" -## [1] ". . . . . . Levels : 1" -## [1] ". . . . . . New variables : 1" -## [1] ". . . . . . Getting county data" -## [1] ". . . . . . Creating variables" -## [1] ". . . . . . 100%" -## [1] ". . . . . . Formatting output" -{% endhighlight %} - - -{% highlight r %} -library(choroplethr) -library(choroplethrMaps) -pov[, region := as.numeric(geoid)] -setnames(pov, "pov_est", "value") -county_choropleth(pov, num_colors = 5) -{% endhighlight %} - -![center](/assets/img/2016-07-06-acsr/unnamed-chunk-15-1.png) - -In sum, the `acsr` package: - -- Reads formulas directly and extracts any ACS/Census variable -- Provides an automatized and tailored way to obtain indicators and MOEs -- Allows different outputs' formats (wide and long, csv) -- Provides an easy way to adjust MOEs to different confidence levels -- Includes a variable-by-variable ratio adjustment of standard errors -- Includes the zero-option when computing standard errors for proportions, ratios, and aggregations -- Combines geographic levels flexibly - -**Last Update: 02/07/2016** diff --git a/_posts/2016-10-05-read-mortality-data.md b/_posts/2016-10-05-read-mortality-data.md deleted file mode 100644 index d7686dfb..00000000 --- a/_posts/2016-10-05-read-mortality-data.md +++ /dev/null @@ -1,127 +0,0 @@ ---- -layout: post -title: "Reading CDC mortality files using R" -author: Sebastian Daza -date: 2016-10-05 -giscus_comments: true -tags: - - R - - demography ---- - - -Reading fixed-width text files might be challenging, specially when we don't have a dictionary file. In this post, I show steps to read CDC files in a more systematic way. In this example, I import a compress mortality file (CMF 1979-1988) available [here](http://www.cdc.gov/nchs/data_access/cmf.htm) and whose codebook (or layout) is [here](http://www.cdc.gov/nchs/data/mortab/filelayout68_88.pdf). - -To read this file, usually with extension `.txt` or `.dat`, I first need to know where each column starts and finishes. What I get from the pdf file is something like this: - - -![](/assets/img/mortalityLayout.png) - -The layout is usually a codebook in Word/PDF or just plain text file. Here, I copy the PDF text and put it in a plain text file. I use a text editor (e.g., [Sublime Text](https://www.sublimetext.com/)) and [regular expressions](https://en.wikipedia.org/wiki/Regular_expression) to extract the information I need. - -I have to select every row with this pattern: `1-2 2 FIPS State code Numeric`. That is, a number followed by a hyphen (although not always, particularly when the width of the column is one), spaces, another number, spaces, and then any text. I use the following regular expression to get that pattern: `(^[0-9]+).([0-9]+)\s+([0-9])\s+(.+)`. Using the Sublime package [Filter Lines](https://packagecontrol.io/packages/Filter%20Lines) I get something like this (you can also just copy the selected lines): - -``` -1-2 2 FIPS State code Numeric -3-5 FIPS county code Numeric -6-9 4 Year of death Numeric -11-12 2 Age at death Numeric -13-16 4 ICD code for underlying cause-of-death 3 digits: Numeric -17-19 3 Cause-of-Death Recode Numeric -20-23 4 Number of deaths Numeric -``` - -This approach might be particularly useful when you have a long PDF/Word file and you want to extract most of the variables. You would need to adapt the regular expressions I'm using to the particular patterns of your codebook. - -To simplify, I format this text as a comma-separated values file (csv). Replacing this regular expression `([0-9]+)(-)([0-9]+)(\s)([0-9]+)(\s)(.+)(\s)(Numeric)` by `\1,\3,\5,\7,\9` I get: - -``` -1,2,2,FIPS State code,Numeric -3,5,3,FIPS county code,Numeric -6,9,4,Year of death,Numeric -11,12,2,Age at death,Numeric -13,16,4,ICD code for underlying cause-of-death 3 digits:,Numeric -17,19,3,Cause-of-Death Recode,Numeric -20,23,4,Number of deaths,Numeric -``` - -Then, I read the layout file: - - -{% highlight r %} -# define names of columns -colnames <- c("start", "end", "width", "name", "type") -dict <- read.csv("data/dictMortality.csv", col.names = colnames, header = FALSE) -{% endhighlight %} - - -{% highlight text %} -## start end width name type -## 1 1 2 2 FIPS State code Numeric -## 2 3 5 3 FIPS county code Numeric -## 3 6 9 4 Year of death Numeric -## 4 11 12 2 Age at death Numeric -## 5 13 16 4 ICD code for underlying cause-of-death 3 digits: Numeric -## 6 17 19 3 Cause-of-Death Recode Numeric -## 7 20 23 4 Number of deaths Numeric -{% endhighlight %} - -Now, I can read the fixed-width data file. I use the [readr](https://github.com/hadley/readr) package (in my experience relatively fast for big datasets ~ 1 GB). - - -{% highlight r %} -library(readr) - -# create name of variables -cnames <- c("state", "county", "year", "age", "icd", "cause", "deaths") - -# read mortality file -mort <- read_fwf("data/mort7988.txt", fwf_positions(dict$start, dict$end, cnames)) -{% endhighlight %} - - -{% highlight text %} -## # A tibble: 8,776,385 x 7 -## state county year age icd cause deaths -## -## 1 01 001 1979 04 5789 780 1 -## 2 01 001 1979 04 7980 770 1 -## 3 01 001 1979 08 8121 800 1 -## 4 01 001 1979 09 3439 780 1 -## 5 01 001 1979 09 8120 800 2 -## 6 01 001 1979 09 8189 800 1 -## 7 01 001 1979 10 1629 180 1 -## 8 01 001 1979 10 2396 250 1 -## 9 01 001 1979 10 4289 410 1 -## 10 01 001 1979 10 8070 810 1 -## # ... with 8,776,375 more rows -{% endhighlight %} - - -{% highlight r %} -# year distribution -table(mort$year) -{% endhighlight %} - - - -{% highlight text %} -## 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 -## 831605 854860 854198 850505 867280 875607 894176 905736 912551 929867 -{% endhighlight %} - - -{% highlight r %} -# number of deaths -sum(mort$deaths) -{% endhighlight %} - - - -{% highlight text %} -## [1] 20398153 -{% endhighlight %} - -Hopefully, this might save you some time! - -**Last Update: 06/29/2017** diff --git a/_posts/2020-05-12-descriptive-tables.md b/_posts/2020-05-12-descriptive-tables.md deleted file mode 100644 index d2f8d8a5..00000000 --- a/_posts/2020-05-12-descriptive-tables.md +++ /dev/null @@ -1,158 +0,0 @@ ---- -layout: post -title: "Oh, descriptive tables (R + Latex)!" -description: "How to create latex descriptive tables in R" -author: Sebastian Daza -giscus_comments: true -date: 2020-05-12 -tags: - - R - - latex - - ---- - -It's been a while since my last post. It's time to catch up! - -Every time I write a paper or report, I need to create descriptive tables using Latex. Over and over I create Adhoc tables, and I say to myself: -*Write a general function so you can save time in the next paper!* -I know there are some solutions out there, but in general, I feel they are not flexible enough. - -I introduce a far from perfect function to create descriptive tables in Latex. The steps and structure are quite simple: - -1. Write a function to summarize your data with any stats you want -2. Define a list with the data plus column names (labels) - -That's it. You can see the function [here](https://gist.github.com/sdaza/c4d1089a501d3567be9fb784b1c5a6ab). It has some features might be useful: - -- It deals automatically with factors (categorical variables) -- You can use different datasets at the same time -- You can group columns using a variable (e.g., year) -- You can add long notes at the bottom of the table -- You can specify your own descriptive function - -Let's start creating some fake data: - -- 5 variables -- Variable 3 is a factor (i.e., categorical) -- Variable 5 is a grouping column - - -{% highlight r %} -library(data.table) -set.seed(14332) -devtools::source_gist("c4d1089a501d3567be9fb784b1c5a6ab") -{% endhighlight %} - - -{% highlight r %} -# create two fake datasets -n1 = 100 -dat1 = data.table( - var1 = runif(n1), - var2 = sample(0:1, n1, prob = c(0.8, 0.2), replace = TRUE), - var3 = factor(sample(1:4, n1, prob = c(0.1, 0.4, 0.3, 0.2), - replace = TRUE), - labels = c("Low", "Medium", "High", "Very high")), - var4 = rnorm(n1, 1000, 100), - var5 = sample(1:4, n1, replace = TRUE) -) - -n2 = 200 -dat2 = data.table( - var1 = runif(n2, min = 3, max = 5), - var2 = sample(0:1, n2, prob = c(0.5, 0.5), replace = TRUE), - var3 = factor(sample(1:5, n2, prob = c(0.05, 0.1, 0.35, 0.3, 0.2), - replace = TRUE), - labels = c("Very low", "Low", "Medium", "High", "Very high")), - var4 = rnorm(n2, 2000, 300), - var5 = sample(1:4, n2, replace = TRUE) -) - -datasets = list("Data 1" = dat1, "Data 2" = dat2) -variables = list(paste0("var", 1:4), paste0("var", 1:4)) -labels = list(paste0("Variable ", 1:4), paste0("Variable ", 1:4)) -colnames = c("Mean", "Median", "SD") -{% endhighlight %} - -We can define a descriptive function: - - -{% highlight r %} -# descriptive function -myDescriptives = function(x) { - x = as.numeric(x) - m = mean(x, na.rm = TRUE) - md = median(x, na.rm = TRUE) - sd = sd(x, na.rm = TRUE) - return(c(m, md, sd)) -} -{% endhighlight %} - - -{% highlight r %} -# create table -createDescriptiveTable(datasets, - summary_function = myDescriptives, - column_names = colnames, - variable_names = variables, - variable_labels = labels, - arraystretch = 1.3, - title = "Descriptive statistics", - label = "tab:descriptive", - file = "example_01.tex") -{% endhighlight %} - -
- -
- - -Thus, the grouping of rows is defined by the name of each dataset in the list. We can add a note, -just remember to add `\usepackage[flushleft]{threeparttable}` to your Latex document: - - -{% highlight r %} -# table + note -createDescriptiveTable(datasets, - summary_function = myDescriptives, - column_names = colnames, - variable_names = variables, - variable_labels = labels, - arraystretch = 1.3, - note = "This is a very long long long long long long long long long note.", - title = "Descriptive statistics", - label = "tab:descriptive", - file = "example_02.tex") -{% endhighlight %} - -
- -
- -We can also slice the descriptives by group: - - -{% highlight r %} -# group columns -createDescriptiveTable(datasets, - summary_function = myDescriptives, - column_names = colnames, - variable_names = variables, - variable_labels = labels, - group_variable = "var5", - arraystretch = 1.3, - tabcolsep = 3, - note = "This is a very long long long long long long long long long note.", - title = "Descriptive statistics", - label = "tab:descriptive", - file = "example_03.tex") -{% endhighlight %} - -
- -
- -It's just a first version. I will add more features soon. diff --git a/_posts/2020-11-03-anylogic-database.md b/_posts/2020-11-03-anylogic-database.md deleted file mode 100644 index ff61b18e..00000000 --- a/_posts/2020-11-03-anylogic-database.md +++ /dev/null @@ -1,149 +0,0 @@ ---- -layout: post -title: "From database to CSV using Anylogic" -description: "How to transform a database into CSV using Anylogic" -author: Sebastian Daza -giscus_comments: true -date: 2020-11-03 -published: true -tags: - - anylogic - - simulation ---- - -When running a parameter variation experiment, that is, simulating over several iterations and replicates using parallelization, we usually need to collect a huge amount of data and have them in a format that then we can process using Python or R. - -The best way to do this in Anylogic would be using a database and then export, read, or connect to the database to process simulation results (although, see the section **update** below). We can do that easily in Anylogic. Every time an experiment finishes, we can export the data (from a database) to an Excel file manually. - -The issue with Excel files is, on the one hand, they are Excel files, and on the other, they are not suitable for big data (more than 1 million rows). We can create a function to save all the simulation tables into an Excel file as our experiment finishes. However, we will still have the limit-of-rows limitation (check the Anylogic file linked below for a function to create Excel files from a database). - -Here I follow a different approach by exporting an Anylogic database table to a CSV file within Java. The general setup using Anylogic PLE 8.6 would be: - -- Create the databases you need for your experiment, and be sure you add the columns iteration and replicate. -- Create a function to save the data of your simulation runs (e.g., agent's status, age, etc.) -- Define a parameter variation experiment. -- Define a variable to specify where to save the data (i.e., path). -- Write code in the experiment Java Actions section so that to save data every time you run an experiment. -- Import functions in the advanced Java section of the experiment. - -## Databases - -I define two tables (`data1` and `data2`). Each agent saves its data at a given rate. After 5 years, the simulation will finish. - -{% highlight java %} -insertInto(data1) - .columns(data1.iteration, data1.replicate, data1.id, data1.dtime, data1.drandom) - .values(main.v_iteration, main.v_replicate, this.getIndex(), time(), normal(1.0)) - .execute(); - -insertInto(data2) - .columns(data2.iteration, data2.replicate, data2.id, data2.dtime, data2.drandom) - .values(main.v_iteration, main.v_replicate, this.getIndex(), time(), normal(0.3)) - .execute(); - -if (time() > 5) { - finishSimulation(); -} -{% endhighlight %} - -The tables include a column with the experiment iteration and replicate, in addition to the agent's index, time, and a random value from a normal distribution. - -## From DB to CSV - -The key function to export the data to a CSV file is `f_SQLToCSV`. It uses two arguments, a SQL query (`query`) and the path to an output file (`filename`). For instance, we can write: - -{% highlight java %} -f_SQLToCSV("select * from data1", "output/data1") -{% endhighlight %} - -You can use any query for your data, giving you a lot of flexibility on what to export to a CSV file. The `f_SQLToCSV` method is: - -{% highlight java %} -ResultSet rs = selectResultSet(query); -try { - File file = new File(filename + ".csv"); - file.getParentFile().mkdirs(); - FileWriter fw = new FileWriter(file); - int cols = rs.getMetaData().getColumnCount(); - for(int i = 1; i <= cols; i ++){ - fw.append(rs.getMetaData().getColumnLabel(i).toLowerCase()); - if(i < cols) fw.append(';'); - else fw.append('\n'); - } - while (rs.next()) { - for(int i = 1; i <= cols; i ++){ - fw.append(rs.getString(i)); - if(i < cols) fw.append(';'); - } - fw.append('\n'); - } - fw.flush(); - fw.close(); -} catch (Exception e) { - getEngine().pause(); - traceln("--> An Exception happened, continue? ..."); - e.printStackTrace(); -} -{% endhighlight %} - -The next step would be to create an experiment and complete the Java actions accordingly. First, we clear our tables. - -{% highlight java %} -// inital experiment setup -deleteFrom(data1).execute(); -deleteFrom(data2).execute(); -{% endhighlight %} - -Then, we collect information on the iteration and replicate of the simulation run: - -{% highlight java %} -// before simulation run -root.v_iteration = getCurrentIteration(); -root.v_replicate = getCurrentReplication(); -{% endhighlight %} - -Finally, at the end of the experiment, we save the data and clear the tables again: - -{% highlight java %} -// after experiment -f_exportTables(v_path); -deleteFrom(data1).execute(); -deleteFrom(data1).execute(); -{% endhighlight %} - -The method `f_exportTables` is just a function that goes through each table and export them to a CSV file. `v_tables` is string array with the name of the tables I want to export `{"data1", "data2"}`: - -{% highlight java %} -ArrayList tables = new ArrayList(); -for(String tab : v_tables) { - tables.add(tab); -} -for (String t : tables) { - f_SQLToCSV("select * from " + t, path + t); -} -{% endhighlight %} - -Remember to import some functions in the `imports section`: - -{% highlight java %} -// imports section -import java.io.BufferedWriter; -import java.io.File; -import java.io.FileWriter; -import java.io.BufferedReader; -import java.io.FileReader; -import java.io.IOException; -import java.text.*; -{% endhighlight %} - - -From there, we can create additional functions to select the tables to be exported. For more details, download the [Anylogic File here](/assets/files/DBToCSV.zip). - -## Update - -When running several replicates of my simulation, saving the information in a database didn't work as expected. My simulation just crashed, and I was not able to keep the data. I finally decided to follow my previous approach: create many CSV files -- one per iteration and replicate -- and read them using an R or Python function. I know you end up with a lot of CSV files, but at least the simulation doesn't crash, and you can recover the output of your simulation as it goes. - - - - - diff --git a/_posts/2021-07-06-zotero-notes.md b/_posts/2021-07-06-zotero-notes.md deleted file mode 100644 index 0645fc11..00000000 --- a/_posts/2021-07-06-zotero-notes.md +++ /dev/null @@ -1,87 +0,0 @@ ---- -layout: post -title: "Extracting Zotero's notes" -description: "How to extract zotero notes to CSV / Excel file" -author: Sebastian Daza -giscus_comments: true -date: 2021-07-06 -tags: zotero ---- -When reviewing the literature, it's nice to have the notes linked to Zotero -references. It would also be great to compare them systematically. The idea -would be to have your notes in Zotero so that everyone in the group can access -and edit them and create a data file to explore your notes using, for instance, -text analysis techniques. - -This small package extracts notes from a collection and creates a CSV file that -can be easily read using Excel. You only need to specify the collection ID. For -instance, if the location of my collection is -`https://www.zotero.org/groups/2406179/csic-echo/collections/M8N2VMAP`, the -collection ID would be `M8N2VMAP`. We also need the Zotero API's credentials. - -To create a clean CSV, notes' headers would need a suitable separator. The -default is `#`. In this case, the text between headings mustn't include `#`. -Below an example: - -``` -# Research question - -Estimates interaction effects between PGS of obesity and cohorts using HRS. - -# Data - -HRS - -# Methods - -Uses a HLM whereby they estimate effects of age and cohorts while making the -intercepts and slopes a function of individual factors. -``` - -## Installation - -``` -pip install git+https://github.com/sdaza/zotnote.git -``` - -## Credentials - -You can save your Zotero API credentials in a `config.py` and load them using -`import config`: - -```yaml -library_id = "0000000" -api_key = "key" -library_type = "group" -``` - -## Example - -Let's try to extract some notes and read them using Pandas: - - - -{% highlight python %} -import pandas as pd -import config -import zotnote as zn -{% endhighlight %} - - -{% highlight python %} -zn.exportNotes(collection = "M8N2VMAP", - library_id = config.library_id, api_key = config.api_key) -{% endhighlight %} - - Notes saved in zotero-notes.csv - - - -{% highlight python %} -pd.read_csv("zotero-notes.csv") - - -{% endhighlight %} - - -![center](/assets/img/zotnotes.png) diff --git a/_posts/2023-09-26-statistical-power.md b/_posts/2023-09-26-statistical-power.md deleted file mode 100644 index 1ac48fdb..00000000 --- a/_posts/2023-09-26-statistical-power.md +++ /dev/null @@ -1,432 +0,0 @@ ---- -layout: post -author: Sebastian Daza -title: "Tools for power analysis with multiple comparisons" -date: 2023-09-26 -giscus_comments: true -tags: - - simulation - - python - - experiments ---- - -This post will discuss tools for designing and evaluating experiments. These -tools offer user-friendly methods to estimate statistical power when working -with multiple treatments or variants. They also help with sample allocation, -including determining sample sizes for treatment and control groups. Lastly, -they estimate power based on different expected effects (e.g., minimum -detectable effects or MDE). - -Calculating power for complex and uplift models using machine learning can be -challenging. I will provide some general guidelines and references at the end of -this post. - -## Let's start with some terms - -When conducting a statistical test to estimate an effect, we can have any of the -following outcomes: - -

- Image -

- -These errors and success probabilities are associated with standard statistical -terms used in making inferences. - -- **Type I Error ($$\alpha$$)**: the probability of rejecting the null -hypothesis -when it's true. -- **Type II Error ($$\beta$$)**: the probability of failing to reject the null -hypothesis when it's false. -- **Power ($$1-\beta$$)**: the probability of rejecting the null hypothesis when -it's false. -- **Confidence ($$1-\alpha$$)**: the probability of failing to reject the null -hypothesis when it's true. - -As a standard, $$\alpha$$ is set to 0.05 and power to .80. Also, we will -generally -have a control group (no intervention) and different treatments (variants).  - -## There are some challenges - -There are some challenges associated with basic power calculations (e.g., web -calculators):  - -- **(Simultaneous) multiple comparisons** -- **Sample allocation** -- **Different MDEs by variant** - -When conducting multiple statistical tests or comparisons within a study or -analysis, there is a risk of obtaining false positive results. To address this, -we need to adjust our $$\alpha$$ level based on the number of tests performed. -This adjustment increases the required sample size to achieve statistical -significance. It is important to consider this issue during experiment design -and data analysis, including post hoc analyses. - -To gain an understanding of why multiple comparison testing poses a challenge, -let's consider a simple example. Suppose we have a control group and three -treatment variants. In this example, we will simulate data where there are no -effects or differences between the control and treatment groups. In other words, -the null hypothesis (i.e., no effect) is always true. In each iteration, we test -the control group against each treatment and count the number of significant -results we obtain using a significance level ($$\alpha=0.05$$). It is important -to note that we already know there are no differences between the groups. - - -{% highlight python %} -from scipy.stats import ttest_ind -import pandas as pd -pd.set_option("display.notebook_repr_html", False) -import numpy as np -np.random.seed(0) - -# Number of iterations for simulation -iterations = 1000 - -# Number of treatment groups -groups = 3 - -# Number of samples in each group -samples = 1000 - -# Count of batches where at least one false positive (Type I error) occurs -batches_with_errors = 0 - -# perform iterations -for _ in range(iterations): - # Control group data - control_group = np.random.normal(0, 1, samples) - - # Treatment group data - no actual difference from control - treatment_groups = [np.random.normal(0, 1, samples) for _ in range(groups)] - - # A flag for any error within this batch of tests - error_in_batch = False - - # perform t-test for each treatment group compared to control group - for treatment in treatment_groups: - t_stat, p_value = ttest_ind(control_group, treatment) - - # Check if p-value is less than 0.05 (significance level) - if p_value < 0.05: - error_in_batch = True - break # no need to check remaining tests - - if error_in_batch: - batches_with_errors += 1 - -print(f"Probability of at least one Type I error in a batch of {groups} tests: {batches_with_errors / iterations * 100}%") -{% endhighlight %} - - Probability of at least one Type I error in a batch of 3 tests: 12.6% - - -After 1000 iterations, the probability of encountering at least one Type I error -in a batch of 3 tests is 12.6%. You can also use the formula: - -$$ 1 - (1 - \alpha)^n$$ - -Where $$n$$ is the number of comparisons. For this example, FWER will be 14.2%. -This probability is referred to as the **Family-wise Error Rate (FWER)**, which -signifies the likelihood of committing at least one Type I error within the -entire family (or batch) of comparisons. It is evident that 12.6% and 14.2% are -higher than the assumed α value of 5% for a single test. - -This is the essence of the multiple comparison problem. When conducting multiple -tests, even if you maintain an $$\alpha$$ level of approximately 0.05 for each -individual test, the likelihood of experiencing at least one false discovery -among the entire set of tests is significantly higher and increases as the -number of tests in the batch grows. Consequently, some of our significant -findings may actually be false positives and difficult to replicate. - -The key to adjusting multiple comparison testing is to strike a balance between -reducing false positives (incorrectly rejecting the null hypothesis) and -maintaining test power (the ability to correctly reject the null hypothesis when -it's false). Some methods, such as *Bonferroni* or *Holm*, minimize the -**Family-Wise Error Rate (FWER)**. Others, like *Benjamini-Hochberg*, minimize -the **False Discovery Rate (FDR)**, which is the expected proportion of Type I -errors among all declared significant hypotheses. FDR methods aim to reduce the -ratio of Type I errors among significant results without eliminating all false -discoveries like FWER methods. FDR methods generally offer higher statistical -power and are less conservative. - -A crucial element of experimental design lies in sample allocation. Variations -in the distribution of samples among groups can influence the statistical power -of our tests. Sometimes, the goal is to limit the potential disruption from an -intervention by scaling down the sample size across various variants. -Alternatively, we might opt to shrink the control group's size or allot more -units to each variant, contingent on their Minimum Detectable Effect (MDE). - -Let's not forget to consider expected effects. In figuring out the smallest -minimum detectable effect (MDE), think about the least significant change that -would still make the intervention worthwhile. It's all about balancing the -books: estimate the return on investment (ROI) from the intervention, then -determine the tiniest shift in a key metric (like conversion rates) that would -still make the effort profitable. The MDE doesn't have to be identical across -all variants - after all, not all interventions or campaigns cost the same. - -## Tools can help - -I've developed a set of straightforward simulation methods in Python, primarily -to construct and evaluate our experimental data. The choice to use simulation -was driven by its flexibility in handling diverse scenarios and metrics. -However, it does come with a trade-off, as it demands a higher computational -power. We can live with that :smile:. [Feel free to explore the code for the -power class](https://github.com/sdaza/sdaza.github.io/tree/main/_jupyter/power_tools.py). - -These methods are useful, particularly for handling multiple variants, different -allocations, or MDE by group. However, it's important to note that these methods -only offer a power estimate based on a given set of parameters. Therefore, we -will need to conduct a grid search to evaluate various scenarios and determine -the optimal one. - -Let's start with a simple example using a proportion as the metric of interest: - - -{% highlight python %} -from power_tools import * - -# load class -p = PowerSim(metric='proportion', relative_effect=False, variants=1, nsim=1000, alpha=0.05, alternative='two-tailed') - -# get power -p.get_power(baseline=[0.33], effect=[0.03], sample_size=[3000]) -{% endhighlight %} - - - - - comparisons power - 0 (0, 1) 0.706 - - - -`nsim` represents the number of simulations. Variants are set to 1, so we are -only comparing control and treatment: `comparisons = (0,1)`. This example is too -simple. Let's now assume two variants (treatments):  - - -{% highlight python %} -p = PowerSim(metric='proportion', relative_effect=False, variants=2, - nsim=1000, alpha=0.05, alternative='two-tailed') -p.get_power(baseline=[0.33], effect=[0.03], sample_size=[3000]) -{% endhighlight %} - - - - - comparisons power - 0 (0, 1) 0.531 - 1 (0, 2) 0.530 - 2 (1, 2) 0.015 - - - -The function calculates the power of group comparisons with a sample size of -3000 for each group. Multiple comparisons result in reduced power. Since the -effect of each variant is the same (0.03), comparing *variants 1 and 2* is not -meaningful here (it will always be 0 in practice). Custom comparisons can be -defined using a list of tuples, such as `comparisons=[(0,1), (0,2)]`. - - -{% highlight python %} -p = PowerSim(metric='proportion', relative_effect=False, variants=2, - nsim=1000, alpha=0.05, alternative='two-tailed', - comparisons=[(0,1), (0,2)]) -p.get_power(baseline=[0.33], effect=[0.03], sample_size=[3000]) -{% endhighlight %} - - - - - comparisons power - 0 (0, 1) 0.558 - 1 (0, 2) 0.563 - - - -Using only two comparisons (each variant with a control), the power -significantly decreases compared to the one-variant example. I have currently -implemented the following p-value corrections: - -- `bonferroni` -- `holm_bonferroni` -- `hochberg` -- `sidak` -- `fdr` - -You can find an introduction to different methods -[here](https://en.wikipedia.org/wiki/Multiple_comparisons_problem). In general, -Bonferroni is more conservative, while Holm provides higher power. The `fdr` -method aims to minimize the false discovery rate. - -The function corrects only for the comparisons specified in the `comparisons` -parameter. If you conduct additional tests, such as comparing the performance of -group A versus group B or examining differences among demographic groups, you -need to apply additional multiple comparison corrections in your analysis. - -We can introduce greater variability into the experimental design. We can then -plot the expected power for different scenarios. To specify parameters, we'll -utilize nested lists and the `grid_sim_power` method. However, things can -quickly become convoluted. - -Let's consider a more complex example. The impact of the first and second -variants on the control group varies as follows: `[0.01, 0.03]`, `[0.03, 0.05]`, -`[0.03, 0.07]`. The sample sizes are equal for each group, but they increase -linearly. We apply the `holm` correction. - - -{% highlight python %} -p = PowerSim(metric='proportion', relative_effect=False, variants=2, alternative='two-tailed', - nsim=1000, correction='holm') -rr = p.grid_sim_power(baseline_rates=[[0.33]], - effects=[[0.01, 0.03], [0.03, 0.05], [0.03, 0.07]], - sample_sizes= [[1000], [2000], [3000], [4000], [5000], [6000], [7000], [8000], [9000]], - threads=16, - plot=True) -{% endhighlight %} - - - -![png](/assets/img/2023-09-26-statistical-power_files/2023-09-26-statistical-power_10_0.png) - - - - - -![png](/assets/img/2023-09-26-statistical-power_files/2023-09-26-statistical-power_10_1.png) - - - - - -![png](/assets/img/2023-09-26-statistical-power_files/2023-09-26-statistical-power_10_2.png) - - - -As expected, the best scenario occurs when the effect sizes are large enough to -be detected. We can also analyze the results of different sample allocations. -For example, when we look at the effects `[0.03, 0.05]`, the allocation `[3000, -7000, 7000]` produces fairly good results, although it doesn't always meet the -0.8 threshold. - - -{% highlight python %} -p = PowerSim(metric='proportion', relative_effect=False, variants=2, - alternative='two-tailed', nsim=1000, correction='holm') -rr = p.grid_sim_power(baseline_rates=[[0.33]], - effects=[[0.01, 0.03], [0.03, 0.05], [0.03, 0.07]], - sample_sizes= [[1000, 3000, 3000], - [2000, 5000, 5000], - [3000, 7000, 7000], - [2000, 8000, 8000], - [5000, 8000, 8000]], - threads=16, - plot=True) -{% endhighlight %} - - - -![png](/assets/img/2023-09-26-statistical-power_files/2023-09-26-statistical-power_12_0.png) - - - - - -![png](/assets/img/2023-09-26-statistical-power_files/2023-09-26-statistical-power_12_1.png) - - - - - -![png](/assets/img/2023-09-26-statistical-power_files/2023-09-26-statistical-power_12_2.png) - - - -We can also use other metrics (e.g., average or counts).  For instance, we can -design an experiment where the outcome is the number of visits (counts). The -simulator will use a -Poisson distribution with the parameter, $$\lambda$$ (lambda) or the mean -number of events. In this example, I set a baseline rate (lambda) of 1.2 visits -and a relative increase of $$0.05 (1.2*1.05) = 1.26$$, with a control group of -3000 users and a treatment of 5000 users: - - -{% highlight python %} -p = PowerSim(metric='count', relative_effect=True, variants=1, - alpha=0.05, alternative='two-tailed') -p.get_power(baseline=[1.2], effect=[0.05], sample_size=[3000, 5000]) -{% endhighlight %} - - - - - comparisons power - 0 (0, 1) 0.59 - - - -The effect is small, so our test is underpowered (<0.80). We can also use -averages (e.g., revenue), but in that case, we need to specify the standard -deviation of the groups: - - - -{% highlight python %} -p = PowerSim(metric='average', relative_effect=False, variants=1, - alpha=0.05, alternative='two-tailed', nsim=5000) -p.get_power(baseline=[1000], effect=[100], standard_deviation=[600], sample_size=[400]) -{% endhighlight %} - - - - - comparisons power - 0 (0, 1) 0.6592 - - - -## Complex models  - -When using uplift or mixed models, things become more complicated. As Aleksander -Molak put it: - -> The question of defining a "safe" dataset size for S-Learner and -other causal models is difficult to answer. Power calculations for machine -learning models are often difficult, if possible at all.  - -There are some tricks we can apply, though, as Harrell suggests: - -> If you can afford a pilot study or you have some historical data that -represents a problem similar to the one that -you’re interested in, you can find a subgroup in your data that is as homogenous -as possible. You can estimate the sample size for this group using some of the -traditional statistical power tools. Finally, scale your overall sample size so -that this subgroup is properly powered relative to the entire sample - -There are also some traditional ways to optimize the power of our tests: - -- Blocking design -- Stratification and covariate adjustments (regression) - -For instance, after learning from our uplift models, we can identify the key -features associated to users' responses. We can use those features to -design our experiments (blocking). We can also evaluate the results of our -uplift models in a new sample, and see if we can replicate the expected -`uplift`. - -Here's a brief and general overview of the key features to keep in mind when -designing experiments or improving models. I hope it's helpful. - -
- -*** - -## References - -- Molak, Aleksander. *Causal Inference and Discovery in Python: Unlock the -secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more* -.  -- https://www.fharrell.com/ -- Ron Kohavi. *Trustworthy Online Controlled Experiments*. diff --git a/_projects/01-echo.markdown b/_projects/01-echo.markdown deleted file mode 100644 index c47169b4..00000000 --- a/_projects/01-echo.markdown +++ /dev/null @@ -1,28 +0,0 @@ ---- -layout: page -title: ECHO -description: Early conditions, delayed adult effects and morbidity, disability and mortality in modern human populations -img: /assets/img/echo.jpeg -importance: 1 -category: previous ---- - - -I'm joinig the [Institute of Economics, Geography and Demography (IEGD) of the Spanish National Research Council (CSIC)](http://iegd.csic.es/es) to work as part of the team of the Advanced Grant **Early conditions, delayed adult effects and morbidity, disability and mortality in modern human populations (ECHO)**. This is an ambitious five-year project with a budget of more than EUR 2,800,000 aims to reformulate and generalize standard theories of human health and mortality. It proposes new formal models and a systematic agenda to empirically test hypotheses that link early biological growth and development, epigenetic changes across the life course, and adult human illness, disability and mortality. - -We seek to break new ground developing innovative formal models for illnesses and mortality, testing new hypotheses about the evolution of human health and, to the extent permitted by findings, reformulating standard theories to make them applicable to a less restrictive segment of populations than they are now. - -Over the past two decades there has been massive growth of research on the nature of delayed adult effects of conditions experienced in early life. This field of research is known as the Developmental Origins of Adult Health and Disease (DOHaD). Increasing evidence suggests that the mechanisms that are implicated are epigenetic and constitute an evolved adaptation selected over thousands of years to improve fitness in changing landscapes. - -The emergence of DOHaD is as close as we will ever come to a paradigmatic shift in the study of human health, disability and mortality. The most tantalizing possibility is that advances in our understanding of epigenetic mechanisms will shed light on pathways linking early exposures and delayed adult health thus fundamentally transforming our understanding of human illnesses and, in one fell swoop, bridge population health, epigenetics, and developmental and evolutionary biology. - -The overarching goal of this project is to contribute to this nascent area of study by: - -- Proposing new formal demographic models of health, disability and mortality; (b) empirically testing DOHaD predictions with population data. -- Designing and implementing a microsimulation model with modules that represent epigenetic changes and exposures over the life course as a means to verify DOHaD predictions about two conditions, obesity and Type 2 Diabetes. -- Asessing the adult health, disability and mortality toll implicated by relations between early conditions, obesity and T2D. - -The project will use a variety of global databases such as the Human Mortality Database, the Latin American Mortality Database or the Longitudinal Population Database of Andalusia, and has received formal support from the Spanish National Statistics Institute and the Andalusian Institute of Statistics and Cartography. - -More info [here](https://echo-erc.csic.es/). - diff --git a/_projects/02-reentry.markdown b/_projects/02-reentry.markdown deleted file mode 100755 index 628e743f..00000000 --- a/_projects/02-reentry.markdown +++ /dev/null @@ -1,30 +0,0 @@ ---- -layout: page -title: Women reentry -description: Social integration and inclusion after being in prison -img: /assets/img/women_reentry.jpeg -# years: [2020, 2019, 2018] -importance: 2 -category: current ---- - -*with Pilar Larroulet and Catalina Droppelmann* - -The re-entry study is an intensive longitudinal project that follows more than 200 women released from prison in Santiago, Chile. It is funded by the [San Carlos de Maipo](http://www.fsancarlos.cl/) and [Colunga](https://www.fundacioncolunga.org/) Foundation, and the [Inter-American Development Bank](http://www.iadb.org/). We used interviews over a period of one year to know their experiences on family life, housing, employment, social capital and health. Data collection finished in 2018. The final report is available [here](/assets/pdf/final-report-reentry.pdf), for more publications see [below](#publications). - -

- -

- -

- - -

- -## Publications - -
- {% bibliography -f sdaza --query @*[study=chile-reentry] --group_by type %} -
\ No newline at end of file diff --git a/_projects/03-health_income_mobility.markdown b/_projects/03-health_income_mobility.markdown deleted file mode 100755 index 30357f1e..00000000 --- a/_projects/03-health_income_mobility.markdown +++ /dev/null @@ -1,29 +0,0 @@ ---- -layout: page -title: Health and Income Mobility -description: How income mobility contributes to health disparities -img: /assets/img/cells.jpeg -importance: 3 -category: current ---- - -*with Alberto Palloni* - -Recent research on U.S. income mobility and health using community and individual data shows that higher mobility is associated with lower mortality risks. That relationship seems to be stronger and more consistent than the relationship between income inequality and health (a topic widely studied), although considerably smaller than the impact of income on mortality. This preliminary evidence suggests that income mobility might be a relevant determinant of health and mortality. Surprisingly, this potential pathway has received little attention in the literature. - -My **dissertation** builds on that small literature and examines the robustness of the relationship between income mobility and health at the aggregate and individual level. By using different data sources and modeling approaches, I describe the magnitude and variability of this association in the U.S., and explore the plausibility and consistency of explanations offered in the literature. The central argument is that the effect of income mobility on health is stronger and larger than the impact of income inequality and that the mechanisms behind it, although related to income inequality, are theoretically distinct and independent of those of income and inequality, and can have powerful and lasting consequences. - -

- -

- -

-
- Agents moving across counties / neighborhoods based on the income of their residents. -
- -

- -To ground this argument, I use two strategies. First, I analyze aggregate and individual data to assess the magnitude, robustness, and variability of the *effects* of income mobility, and empirically examine whether some of the potential pathways and mechanisms proposed in the literature are supported by the data. Secondly, building on this evidence, I use a theoretical model to assess the conditions and plausibility of the potential mechanisms involved in the association between income mobility and health. By formulating a generative model, where I precisely define (represent) a set of mechanisms (causal relationships) likely to bring about the observed patterns, I am able to assess the internal consistency of the theory and evaluate its generative sufficiency. - -[More info here.](https://github.com/sdaza/dissertation) diff --git a/_projects/04-incarceration.markdown b/_projects/04-incarceration.markdown deleted file mode 100644 index ac1fddfe..00000000 --- a/_projects/04-incarceration.markdown +++ /dev/null @@ -1,59 +0,0 @@ ---- -layout: page -title: Health and incarceration -description: Assessing the connection between mass incarceration and health -img: /assets/img/incarceration.jpeg -importance: 2 -category: previous ---- - -*with Alberto Palloni and Jerrett Jones* - -In light of the expansion of the criminal justice system, researchers have become increasingly interested in the social consequences of incarceration. A line of this research suggests that incarceration has negative implications for individuals’ health and well-being at older ages. However, prior studies are limited in that they have not adequately followed former prisoners over a sufficient time period to determine whether incarceration significantly increases mortality. - -This project contributes to this growing literature by employing the Panel Study of Income Dynamics (PSID), a long longitudinal data resource, to examine the consequences of incarceration for health and mortality. First, we estimate the effect of short and long-term effects of incarceration on mortality and disability over a period of nearly 40 years. Second, we use those estimates and different counterfactual scenarios to assess to what extent mass incarceration contributes to the so called U.S. health disadvantage. This project is funded by the The Panel Study of Income Dynamics (PSID), with support from the National Institute on Aging. - - - - - - -

- -

- -

-
- Cumulative probability of dying by years since being observed in the panel and incarceration status. -
- -

- -Thanks to an extension of this grant, we are also examining how contextual characteristics influence health and well-being over the life course. For this, we plan to expand the PSID data by merging them with incarceration information from the National Corrections Reporting Program (NCRP 2000-2014) at the county level, in addition to other crime and arrests data from Uniform Crime Reports (UCR), and socioeconomic information from the American Community Survey (e.g., employment, poverty, health, inequality). - - - - - - - - diff --git a/_projects/05-lambda.markdown b/_projects/05-lambda.markdown deleted file mode 100755 index 03b51aa2..00000000 --- a/_projects/05-lambda.markdown +++ /dev/null @@ -1,26 +0,0 @@ ---- -layout: page -title: Lambda -description: Latin American Mortality Database -img: /assets/img/population.jpeg -importance: 3 -category: previous ---- - -*with Alberto Palloni and Hiram Beltran-Sanchez* - -The [Latin American Mortality Database](https://www.ssc.wisc.edu/cdha/latinmortality/) (LAMBdA) was originally created to support the empirical study of the history of mortality trends in Latin American countries after independence. It now supports the study of very recent mortality trends and is particularly suited for the study of old age mortality during the post-WWII period. - -

- -

- -

-
- Model predicting life expectancy. -
- -

- -The database covers the interval between 1848 and 2014, it includes population censuses, age-specific (five year and single year age groups) total death counts (starting in 1900), and by causes of deaths (starting in 1945). It contains over 170 years of data including adjusted life tables (about 500 life tables). In this project, we assess estimate and model uncertainty of the association between life expectancy and economic progress using Bayesian models and stacking. [More info here.](https://github.com/sdaza/lambda) - diff --git a/_projects/06-abm-siena.markdown b/_projects/06-abm-siena.markdown deleted file mode 100755 index a7af3da6..00000000 --- a/_projects/06-abm-siena.markdown +++ /dev/null @@ -1,27 +0,0 @@ ---- -layout: page -title: ABM and Siena -description: Assesing complex statistical models using agent-based modeling -img: /assets/img/network.png -importance: 4 -category: previous ---- - -*with Kurt Kreuger* - -Although Agent-based models (ABM) have been increasingly accepted in social sciences as a valid tool to formalize theory, propose mechanisms able to recreate regularities, and guide empirical research, we are not aware of any research using ABMs to assess the robustness of our statistical methods. We argue that ABMs can be extremely helpful to assess models when the phenomena under study is complex. - -
- -
-
- Action chart of network formation, selection and influence. -
- -

- -As an example, we create an agent-based model (ABM) to evaluate Stochastic Actor-Oriented Model (SIENA) estimation of selection and influence effects. The SIENA Model, proposed by Tom A. B. Snijders and colleagues, is a prominent network analysis method that has gained popularity during the last ten years and been applied to estimate selection and influence for a broad range of behaviors and traits such as substance use, delinquency, violence, health, and educational attainment. However, we know little about the conditions for which this method is reliable or the particular biases it might have. - -The results from our analysis show that selection and influence are estimated by SIENA asymmetrically, and that with very simple assumptions, we can generate data where selection estimates are highly sensitive to mis-specification, suggesting caution when interpreting SIENA analyses. - - diff --git a/_sass/_base.scss b/_sass/_base.scss deleted file mode 100644 index ede239ee..00000000 --- a/_sass/_base.scss +++ /dev/null @@ -1,714 +0,0 @@ -/******************************************************************************* - * Styles for the base elements of the theme. - ******************************************************************************/ - -// Typography - -p, h1, h2, h3, h4, h5, h6, em, div, li, span, strong { - color: var(--global-text-color); -} - -hr { - border-top: 1px solid var(--global-divider-color); -} - -table { - td, th { - color: var(--global-text-color); - } - td { - font-size: 1rem; - } -} - -a, table.table a { - color: var(--global-theme-color); - &:hover { - color: var(--global-theme-color); - text-decoration: underline; - } - &:hover:after :not(.nav-item.dropdown) { - width: 100%; - } -} - -figure, img { - max-width: 90vw; -} - -blockquote { - background: var(--global-bg-color); - border-left: 2px solid var(--global-theme-color); - margin: 1.5em 10px; - padding: 0.5em 10px; - font-size: 1.2rem; -} - -// Math - -.equation { - margin-bottom: 1rem; - text-align: center; -} - -// Caption - -.caption { - font-size: 0.875rem; - margin-top: 0.75rem; - margin-bottom: 1.5rem; - text-align: center; -} - -// Card - -.card { - background-color: var(--global-card-bg-color); - - img { - width: 100%; - } - - .card-title { - color: var(--global-text-color); - } - - .card-item { - width: auto; - margin-bottom: 10px; - - .row { - display: flex; - align-items: center; - } - } -} - -// Citation - -.citation, .citation-number { - color: var(--global-theme-color); -} - -// Profile - -.profile { - width: 100%; - - .address { - margin-bottom: 5px; - margin-top: 5px; - font-family: monospace; - p { - display: inline-block; - margin: 0; - } - } -} -.profile.float-right{ - margin-left: 1rem; -} -.profile.float-left{ - margin-right: 1rem; -} - -@media (min-width: 576px) { - .profile { - width: 30%; - .address { - p { display: block; } - } - } -} - -.post-description { - margin-bottom: 2rem; - font-size: 0.875rem; - a { - color: inherit; - &:hover { - color: var(--global-theme-color); - text-decoration: none; - } - } -} - - -// Navbar customization - -.navbar { - box-shadow: none; - border-bottom: 1px solid var(--global-divider-color); - background-color: var(--global-bg-color); - opacity: 0.95; -} -.navbar .dropdown-menu { - background-color: var(--global-bg-color); - border: 1px solid var(--global-divider-color); - a:not(.active) { - color: var(--global-text-color); - } - a:hover { - color: var(--global-hover-color); - } - .dropdown-divider { - border-top: 1px solid var(--global-divider-color) !important; - } -} -.dropdown-item { - color: var(--global-text-color); - &:hover { - color: var(--global-hover-color); - background-color: var(--global-bg-color); - } -} -.navbar.navbar-light { - a { - &:hover { - text-decoration: none; - } - } - .navbar-brand { - color: var(--global-text-color); - } - .navbar-nav .nav-item .nav-link { - color: var(--global-text-color); - &:hover { - color: var(--global-hover-color); - } - } - .navbar-nav .nav-item.active>.nav-link { - background-color: inherit; - font-weight: bolder; - color: var(--global-theme-color); - &:hover { - color: var(--global-hover-color); - } - } - .navbar-brand.social { - padding-bottom: 0; - padding-top: 0; - font-size: 1.7rem; - a { - i::before { - color: #b2b7bd; - transition-property: all 0.2s ease-in-out; - } - &:hover { - i::before { - color: var(--global-theme-color); - } - } - } - } -} - -.navbar-toggler { - .icon-bar { - display: block; - width: 22px; - height: 2px; - background-color: var(--global-text-color); - border-radius: 1px; - margin-bottom: 4px; - transition: all 0.2s; - } - .top-bar { - transform: rotate(45deg); - transform-origin: 10% 10%; - } - .middle-bar { - opacity: 0; - } - .bottom-bar { - transform: rotate(-45deg); - transform-origin: 10% 90%; - } -} - -.navbar-toggler.collapsed { - .top-bar { - transform: rotate(0); - } - .middle-bar { - opacity: 1; - } - .bottom-bar { - transform: rotate(0); - } -} - -#light-toggle { - padding: 0; - border: 0; - background-color: inherit; - color: var(--global-text-color); - &:hover { - color: var(--global-hover-color); - } -} - -// Social (bottom) - -.social { - text-align: center; - .contact-icons { - font-size: 4rem; - a { - i::before { - color: var(--global-text-color); - transition-property: all 0.2s ease-in-out; - } - &:hover { - i::before { - color: var(--global-theme-color); - } - } - } - } - .contact-note { - font-size: 0.8rem; - } -} - - -// Footer -footer.fixed-bottom { - background-color: var(--global-footer-bg-color); - font-size: 0.75rem; - .container { - color: var(--global-footer-text-color); - padding-top: 9px; - padding-bottom: 8px; - } - a { - color: var(--global-footer-link-color); - &:hover { - color: var(--global-theme-color); - text-decoration: none; - } - } -} - -footer.sticky-bottom { - border-top: 1px solid var(--global-divider-color); - padding-top: 40px; - padding-bottom: 40px; - font-size: 0.9rem; -} - -// CV - -.cv { - margin-bottom: 40px; - - .card { - background-color: var(--global-card-bg-color); - border: 1px solid var(--global-divider-color); - - .list-group-item { - background-color: inherit; - border-color: var(--global-divider-color); - - .badge { - color: var(--global-card-bg-color) !important; - background-color: var(--global-theme-color) !important; - } - } - } -} - -// Repositories - -@media (min-width: 768px) { - .repo { - max-width: 50%; - } -} - -// Blog - -.header-bar { - border-bottom: 1px solid var(--global-divider-color); - text-align: center; - padding-top: 2rem; - padding-bottom: 3rem; - h1 { - color: var(--global-theme-color); - font-size: 5rem; - } -} - -.tag-list { - border-bottom: 1px solid var(--global-divider-color); - text-align: center; - padding-top: 1rem; - - ul { - justify-content: center; - display: flow-root; - - p, li { - list-style: none; - display: inline-block; - padding: 1rem 0.5rem; - color: var(--global-text-color-light); - } - } -} - -.post-list { - margin: 0; - margin-bottom: 40px; - padding: 0; - li { - border-bottom: 1px solid var(--global-divider-color); - list-style: none; - padding-top: 2rem; - padding-bottom: 2rem; - .post-meta { - color: var(--global-text-color-light); - font-size: 0.875rem; - margin-bottom: 0; - } - .post-tags { - color: var(--global-text-color-light); - font-size: 0.875rem; - padding-top: 0.25rem; - padding-bottom: 0; - } - a { - color: var(--global-text-color); - text-decoration: none; - &:hover { - color: var(--global-theme-color); - } - } - } -} - -.pagination { - .page-item { - .page-link { - color: var(--global-text-color); - &:hover { - color: $black-color; - } - } - &.active .page-link { - color: $white-color; - background-color: var(--global-theme-color); - &:hover { - background-color: var(--global-theme-color); - } - } - } -} - - -// Distill - -.distill { - a:hover { - border-bottom-color: var(--global-theme-color); - text-decoration: none; - } -} - - -// Projects - -.projects { - a { - text-decoration: none; - - &:hover { - .card-title { - color: var(--global-theme-color); - } - } - } - - .card { - img { - width: 100%; - } - } - - .card-item { - width: auto; - margin-bottom: 10px; - - .row { - display: flex; - align-items: center; - } - } - - .grid-sizer, .grid-item { - width: 250px; - margin-bottom: 10px; - } - - h2.category { - color: var(--global-divider-color); - border-bottom: 1px solid var(--global-divider-color); - padding-top: 0.5rem; - margin-top: 2rem; - margin-bottom: 1rem; - text-align: right; - } -} - - -// Publications - -.publications { - margin-top: 2rem; - h1 { - color: var(--global-theme-color); - font-size: 2rem; - text-align: center; - margin-top: 1em; - margin-bottom: 1em; - } - h2 { - margin-bottom: 1rem; - span { - font-size: 1.5rem; - } - } - h2.year { - color: var(--global-divider-color); - border-top: 1px solid var(--global-divider-color); - padding-top: 1rem; - margin-top: 2rem; - margin-bottom: -2rem; - text-align: right; - } - ol.bibliography { - list-style: none; - padding: 0; - margin-top: 0; - - li { - margin-bottom: 1rem; - .preview { - width: 100%; - min-width: 80px; - max-width: 200px; - } - .abbr { - height: 2rem; - margin-bottom: 0.5rem; - abbr { - display: inline-block; - background-color: var(--global-theme-color); - padding-left: 1rem; - padding-right: 1rem; - a { - color: white; - &:hover { - text-decoration: none; - } - } - } - .award { - color: var(--global-theme-color) !important; - border: 1px solid var(--global-theme-color); - } - } - .title { - font-weight: bolder; - } - .author { - a { - border-bottom: 1px dashed var(--global-theme-color); - &:hover { - border-bottom-style: solid; - text-decoration: none; - } - } - > em { - border-bottom: 1px solid; - font-style: normal; - } - > span.more-authors { - color: var(--global-text-color-light); - border-bottom: 1px dashed var(--global-text-color-light); - cursor: pointer; - &:hover { - color: var(--global-text-color); - border-bottom: 1px dashed var(--global-text-color); - } - } - } - .links { - a.btn { - color: var(--global-text-color); - border: 1px solid var(--global-text-color); - padding-left: 1rem; - padding-right: 1rem; - padding-top: 0.25rem; - padding-bottom: 0.25rem; - &:hover { - color: var(--global-theme-color); - border-color: var(--global-theme-color); - } - } - } - .hidden { - font-size: 0.875rem; - max-height: 0px; - overflow: hidden; - text-align: justify; - transition-property: 0.15s ease; - -moz-transition: 0.15s ease; - -ms-transition: 0.15s ease; - -o-transition: 0.15s ease; - transition: all 0.15s ease; - - p { - line-height: 1.4em; - margin: 10px; - } - pre { - font-size: 1em; - line-height: 1.4em; - padding: 10px; - } - } - .hidden.open { - max-height: 100em; - transition-property: 0.15s ease; - -moz-transition: 0.15s ease; - -ms-transition: 0.15s ease; - -o-transition: 0.15s ease; - transition: all 0.15s ease; - } - div.abstract.hidden { - border: dashed 1px var(--global-bg-color); - } - div.abstract.hidden.open { - border-color: var(--global-text-color); - } - } - } -} - -// Rouge Color Customization -figure.highlight { - margin: 0 0 1rem; -} - -pre { - color: var(--global-theme-color); - background-color: var(--global-code-bg-color); - border-radius: 6px; - padding: 6px 12px; - pre, code { - background-color: transparent; - border-radius: 0; - padding: 0; - } -} - -code { - color: var(--global-theme-color); - background-color: var(--global-code-bg-color); - border-radius: 3px; - padding: 3px 3px; -} - - -// Transitioning Themes -html.transition, -html.transition *, -html.transition *:before, -html.transition *:after { - transition: all 750ms !important; - transition-delay: 0 !important; -} - -// Extra Markdown style (post Customization) -.post{ - .post-meta{ - color: var(--global-text-color-light); - font-size: 0.875rem; - margin-bottom: 0; - } - .post-tags{ - color: var(--global-text-color-light); - font-size: 0.875rem; - padding-top: 0.25rem; - padding-bottom: 1rem; - a { - color: var(--global-text-color-light); - text-decoration: none; - &:hover { - color: var(--global-theme-color); - } - } - } - .post-content{ - blockquote { - border-left: 5px solid var(--global-theme-color); - padding: 8px; - } - } -} - -progress { - /* Positioning */ - position: fixed; - left: 0; - top: 56px; - z-index: 10; - - /* Dimensions */ - width: 100%; - height: 1px; - - /* Reset the appearance */ - -webkit-appearance: none; - -moz-appearance: none; - appearance: none; - - /* Get rid of the default border in Firefox/Opera. */ - border: none; - - /* Progress bar container for Firefox/IE10 */ - background-color: transparent; - - /* Progress bar value for IE10 */ - color: var(--global-theme-color); - } - - progress::-webkit-progress-bar { - background-color: transparent; - } - - progress::-webkit-progress-value { - background-color: var(--global-theme-color); - } - - progress::-moz-progress-bar { - background-color: var(--global-theme-color); - } - - .progress-container { - width: 100%; - background-color: transparent; - position: fixed; - top: 56px; - left: 0; - height: 5px; - display: block; - } - - .progress-bar { - background-color: var(--global-theme-color); - width: 0%; - display: block; - height: inherit; - } diff --git a/_sass/_distill.scss b/_sass/_distill.scss deleted file mode 100644 index d83fafd4..00000000 --- a/_sass/_distill.scss +++ /dev/null @@ -1,126 +0,0 @@ -/******************************************************************************* - * Style overrides for distill blog posts. - ******************************************************************************/ - -d-byline { - border-top-color: var(--global-divider-color) !important; -} - -d-byline h3 { - color: var(--global-text-color) !important; -} - -d-byline a, d-article d-byline a { - color: var(--global-text-color) !important; - &:hover { - color: var(--global-hover-color) !important; - } -} - -d-article { - border-top-color: var(--global-divider-color) !important; - a, p, h1, h2, h3, h4, h5, h6, li, table { - color: var(--global-text-color) !important; - } - a, h1, h2, hr, table, table th, table td { - border-bottom-color: var(--global-divider-color) !important; - } - a:hover { - border-bottom-color: var(--global-hover-color) !important; - } - b i { - display: inline; - } - - d-contents { - align-self: start; - grid-column: 1 / 4; - grid-row: auto / span 4; - justify-self: end; - margin-top: 0em; - padding-left: 2em; - padding-right: 3em; - border-right: 1px solid var(--global-divider-color); - width: calc(max(70%, 300px)); - margin-right: 0px; - margin-top: 0em; - display: grid; - grid-template-columns: - minmax(8px, 1fr) [toc] auto - minmax(8px, 1fr) [toc-line] 1px - minmax(32px, 2fr); - - nav { - grid-column: toc; - a { - border-bottom: none !important; - &:hover { - border-bottom: 1px solid var(--global-text-color) !important; - } - } - h3 { - margin-top: 0; - margin-bottom: 1em; - } - div { - display: block; - outline: none; - margin-bottom: 0.8em; - color: rgba(0, 0, 0, 0.8); - font-weight: bold; - } - ul { - padding-left: 1em; - margin-top: 0; - margin-bottom: 6px; - list-style-type: none; - li { - margin-bottom: 0.25em; - } - } - } - .figcaption { - line-height: 1.4em; - } - toc-line { - border-right: 1px solid var(--global-divider-color); - grid-column: toc-line; - } - } - - d-footnote { - scroll-margin-top: 66px; - } -} - -d-appendix { - border-top-color: var(--global-divider-color) !important; - color: var(--global-distill-app-color) !important; - h3, li, span { - color: var(--global-distill-app-color) !important; - } - a, a.footnote-backlink { - color: var(--global-distill-app-color) !important; - &:hover { - color: var(--global-hover-color) !important; - } - } -} - -@media (max-width: 1024px) { - d-article { - d-contents { - display: block; - grid-column-start: 2; - grid-column-end: -2; - padding-bottom: 0.5em; - margin-bottom: 1em; - padding-top: 0.5em; - width: 100%; - border: 1px solid var(--global-divider-color); - nav { - grid-column: none; - } - } - } -} diff --git a/_sass/_layout.scss b/_sass/_layout.scss deleted file mode 100644 index bd19667e..00000000 --- a/_sass/_layout.scss +++ /dev/null @@ -1,177 +0,0 @@ -/****************************************************************************** - * Content - ******************************************************************************/ - -body { - padding-bottom: 70px; - color: var(--global-text-color); - background-color: var(--global-bg-color); - - h1, h2, h3, h4, h5, h6 { - scroll-margin-top: 66px; - } -} - -body.fixed-top-nav { - // Add some padding for the nav-bar. - padding-top: 56px; -} - -body.sticky-bottom-footer { - // Remove padding below footer. - padding-bottom: 0; -} - -.container { - max-width: $max-content-width; -} - -// Profile -.profile { - img { - width: 100%; - } -} - -// TODO: redefine content layout. - - -/****************************************************************************** - * Publications - ******************************************************************************/ - -// TODO: redefine publications layout. - -.publications { - margin-top: 2rem; - h1 { - color: var(--global-theme-color); - font-size: 2rem; - text-align: center; - margin-top: 1em; - margin-bottom: 1em; - } - h2 { - margin-bottom: 1rem; - span { - font-size: 1.5rem; - } - } - h2.bibliography { - color: $grey-color-light; - border-top: 1px solid $grey-color-light; - padding-top: 1rem; - margin-top: 2rem; - margin-bottom: 2rem; - text-align: right; - } - h2.year { - color: $grey-color-light; - border-top: 1px solid $grey-color-light; - padding-top: 1rem; - margin-top: 2rem; - margin-bottom: -2rem; - text-align: right; - } - ol.bibliography { - list-style: none; - padding: 0; - margin-top: 0; - - li { - margin-bottom: 1rem; - .abbr { - height: 2rem; - margin-bottom: 0.5rem; - abbr { - display: inline-block; - background-color: var(--global-theme-color); - padding-left: 1rem; - padding-right: 1rem; - a { - color: white; - &:hover { - text-decoration: none; - } - } - } - .award { - color: var(--global-theme-color) !important; - border: 1px solid var(--global-theme-color); - } - } - .title { - font-weight: bolder; - } - .author { - a { - border-bottom: 1px dashed var(--global-theme-color); - &:hover { - border-bottom-style: solid; - text-decoration: none; - } - } - > em { - border-bottom: 1px solid; - font-style: normal; - } - } - .links { - a.btn { - color: var(--global-text-color); - border: 1px solid var(--global-text-color); - padding-left: 1rem; - padding-right: 1rem; - padding-top: 0.25rem; - padding-bottom: 0.25rem; - &:hover { - color: var(--global-theme-color); - border-color: var(--global-theme-color); - } - } - } - .hidden { - font-size: 0.875rem; - max-height: 0px; - overflow: hidden; - text-align: justify; - -webkit-transition: 0.15s ease; - -moz-transition: 0.15s ease; - -ms-transition: 0.15s ease; - -o-transition: 0.15s ease; - transition: all 0.15s ease; - - p { - line-height: 1.4em; - margin: 10px; - } - pre { - font-size: 1em; - line-height: 1.4em; - padding: 10px; - } - } - .hidden.open { - max-height: 100em; - -webkit-transition: 0.15s ease; - -moz-transition: 0.15s ease; - -ms-transition: 0.15s ease; - -o-transition: 0.15s ease; - transition: all 0.15s ease; - } - div.abstract.hidden { - border: dashed 1px var(--global-bg-color); - } - div.abstract.hidden.open { - border-color: var(--global-text-color); - } - } - } -} - - -/***************************************************************************** -* Projects -*****************************************************************************/ - -// TODO: redefine projects layout. diff --git a/_sass/_themes.scss b/_sass/_themes.scss deleted file mode 100644 index ddab07fd..00000000 --- a/_sass/_themes.scss +++ /dev/null @@ -1,65 +0,0 @@ -/******************************************************************************* - * Themes - ******************************************************************************/ - -:root { - --global-bg-color: #{$white-color}; - --global-code-bg-color: #{$code-bg-color-light}; - --global-text-color: #{$black-color}; - --global-text-color-light: #{$grey-color}; - --global-theme-color: #{$purple-color}; - --global-hover-color: #{$purple-color}; - --global-footer-bg-color: #{$grey-color-dark}; - --global-footer-text-color: #{$grey-color-light}; - --global-footer-link-color: #{$white-color}; - --global-distill-app-color: #{$grey-color}; - --global-divider-color: rgba(0,0,0,.1); - --global-card-bg-color: #{$white-color}; - - .fa-sun { - display : none; - } - .fa-moon { - padding-left: 10px; - padding-top: 12px; - display : block; - } - - .repo-img-light { - display: block; - } - .repo-img-dark { - display: none; - } -} - -html[data-theme='dark'] { - --global-bg-color: #{$grey-color-dark}; - --global-code-bg-color: #{$code-bg-color-dark}; - --global-text-color: #{$grey-color-light}; - --global-text-color-light: #{$grey-color-light}; - --global-theme-color: #{$cyan-color}; - --global-hover-color: #{$cyan-color}; - --global-footer-bg-color: #{$grey-color-light}; - --global-footer-text-color: #{$grey-color-dark}; - --global-footer-link-color: #{$black-color}; - --global-distill-app-color: #{$grey-color-light}; - --global-divider-color: #424246; - --global-card-bg-color: #{$grey-900}; - - .fa-sun { - padding-left: 10px; - padding-top: 12px; - display : block; - } - .fa-moon { - display : none; - } - - .repo-img-light { - display: none; - } - .repo-img-dark { - display: block; - } -} diff --git a/_sass/_variables.scss b/_sass/_variables.scss deleted file mode 100644 index b050aa6e..00000000 --- a/_sass/_variables.scss +++ /dev/null @@ -1,38 +0,0 @@ -/******************************************************************************* - * Variables used throughout the theme. - * To adjust anything, simply edit the variables below and rebuild the theme. - ******************************************************************************/ - - -// Colors -$red-color: #FF3636 !default; -$red-color-dark: #B71C1C !default; -$orange-color: #F29105 !default; -$blue-color: #0076df !default; -$blue-color-dark: #00369f !default; -$cyan-color: #2698BA !default; -$light-cyan-color: lighten($cyan-color, 25%); -$green-color: #00ab37 !default; -$green-color-lime: #B7D12A !default; -$green-color-dark: #009f06 !default; -$green-color-light: #ddffdd !default; -$green-color-bright: #11D68B !default; -$purple-color: #B509AC !default; -$light-purple-color: lighten($purple-color, 25%); -$pink-color: #f92080 !default; -$pink-color-light: #ffdddd !default; -$yellow-color: #efcc00 !default; - -$grey-color: #828282 !default; -$grey-color-light: lighten($grey-color, 40%); -$grey-color-dark: #1C1C1D; -$grey-900: #212529; - -$white-color: #ffffff !default; -$black-color: #000000 !default; - - -// Theme colors - -$code-bg-color-light: rgba($purple-color, 0.05); -$code-bg-color-dark: #2c3237 !default; diff --git a/_scripts/convert.sh b/_scripts/convert.sh deleted file mode 100755 index cc4c867c..00000000 --- a/_scripts/convert.sh +++ /dev/null @@ -1,59 +0,0 @@ -#!/bin/bash -# -# Convert Jupyter notebook files (in _jupyter folder) to markdown files (in _drafts folder). -# -# Arguments: -# $1 filename (excluding extension) -# Install: -# brew install gnu-sed -# Example: -# _scripts/convert.sh segregation - -# Generate a filename with today's date. -filename=$(date +%Y-%m-%d)-$1 - -# Jupyter will put all the assets associated with the notebook in a folder with this naming convention. -foldername=$filename"_files" - -# Do the conversion. -jupyter nbconvert ./_jupyter/$1.ipynb --to markdown --output-dir=./_posts --output=$filename --template=./_scripts/jekyll.tpl - -# Move the images. -echo "Moving images..." -mv ./_posts/$foldername ./assets/img/ - -# Remove the now empty folder. -# rmdir ./_posts/$foldername - -# Gets the title of the post -echo "What's the title of this post going to be?" -read ttl -gsed -ie "4 i title: \"$ttl\"" ./_posts/$filename.md -gsed -ie "5 i date: $(date +%Y-%m-%d)" ./_posts/$filename.md - -echo "added title $ttl in line 4" -rm ./_posts/$filename.mde - -echo "folder name $foldername" - -# Go through the markdown file and rewrite image paths. -# echo "Rewriting image paths..." - -# gsed -i.tmp -e "/assets/img/$foldername/" ./_posts/$filename.md -# 2018-02-08-segregation_files/2018-02-08-segregation_7_0.png -# gsed -i.tmp -e "s/_post/$foldername/\/assets/img/$foldername/" ./_posts/$filename.md -# gsed -i.tmp 's|$foldername|new_path|g' ./_posts/$filename.md -# gsed -i 's|2023-07-31-statistical-power_files/|assets/img/2023-07-31-statistical-power_files/|' your_markdown_file.md -gsed -i.tmp -e 's/'"$foldername"'/'"\/assets\/img\/$foldername"'/g' ./_posts/$filename.md - -# Remove backup file created by sed command. -rm ./_posts/$filename.md.tmp -rm -r ./_posts/$foldername -# Check if the conversion has left a blank line at the top of the file. -# firstline=$(head -n 1 ./_posts/$filename.md) -# if [ "$firstline" = "" ]; then -# # If it has, get rid of it. -# tail -n +2 "./_posts/$filename.md" > "./_posts/$filename.tmp" && mv "./_posts/$filename.tmp" "./_posts/$filename.md" -# fi - -echo "Done converting." diff --git a/_scripts/jekyll.tpl b/_scripts/jekyll.tpl deleted file mode 100644 index a76e37f8..00000000 --- a/_scripts/jekyll.tpl +++ /dev/null @@ -1,23 +0,0 @@ -{% extends 'markdown/index.md.j2' %} - -{%- block header -%} ---- -layout: post -author: Sebastian Daza -giscus_comments: true ---- -{%- endblock header -%} - -{% block input %} -{{ '{% highlight python %}' }} -{{ cell.source }} -{{ '{% endhighlight %}' }} -{% endblock input %} - -{% block markdowncell scoped %} -{{ cell.source | wrap_text(80) }} -{% endblock markdowncell %} - -{% block headingcell scoped %} -{{ '#' * cell.level }} {{ cell.source | replace('\n', ' ') }} -{% endblock headingcell %} diff --git a/_scripts/knit.R b/_scripts/knit.R deleted file mode 100755 index 32992f84..00000000 --- a/_scripts/knit.R +++ /dev/null @@ -1,45 +0,0 @@ -#!/usr/bin/Rscript --vanilla - -## Credit to David Robinson -## https://github.com/dgrtwo/dgrtwo.github.com/blob/master/_scripts/knitpages.R - -# setwd("~/gitrepos/dkmehrmann.github.io/") - -# compiles all .Rmd files in _R directory into .md files in posts directory, -# if the input file is older than the output file. - -# run ./knitpages.R to update all knitr files that need to be updated. - -KnitPost <- function(input, outfile, figsfolder, cachefolder, base.url="/") { - # this function is a modified version of an example here: - # http://jfisher-usgs.github.com/r/2012/07/03/knitr-jekyll/ - require(knitr); - opts_knit$set(base.url = base.url) - fig.path <- paste0(figsfolder, sub(".Rmd$", "", basename(input)), "/") - cache.path <- file.path(cachefolder, sub(".Rmd$", "", basename(input)), "/") - - opts_chunk$set(fig.path = fig.path) - opts_chunk$set(cache.path = cache.path) - opts_chunk$set(fig.cap = "center") - render_jekyll() - knit(input, outfile, envir = parent.frame()) -} - -knit_folder <- function(infolder, outfolder, figsfolder, cachefolder) { - for (infile in list.files(infolder, pattern = "*.Rmd", full.names = TRUE)) { - pattern = "\\d\\d\\d\\d\\-\\d\\d\\-\\d\\d\\-" - print(infile) - # folder = ifelse(grepl(pattern, infile), outfolder, "pages") - outfile = paste0(outfolder, "/", sub(".Rmd$", ".md", basename(infile))) - print(outfile) - - # knit only if the input file is the last one modified - if (!file.exists(outfile) | - file.info(infile)$mtime > file.info(outfile)$mtime) { - KnitPost(infile, outfile, figsfolder, cachefolder) - } - } -} - -knit_folder("_R", "_posts", "assets/img/", "_caches/") -#knit_folder("_R/drafts", "_drafts", "figs/drafts/") \ No newline at end of file diff --git a/_writing/2050-05-01-anylogic-slack.md b/_writing/2050-05-01-anylogic-slack.md deleted file mode 100644 index 3448c54b..00000000 --- a/_writing/2050-05-01-anylogic-slack.md +++ /dev/null @@ -1,143 +0,0 @@ ---- -layout: post -title: "Anylogic and Slack" -description: "Sending messages to Slack when simulation is done" -author: Sebastian Daza -comments: true -date: 2050-05-01 -tags: - - anylogic - - ABM ---- - -When running a parameter variation experiment, that is, simulating over several iterations and replicates using parallelization, we usually need to collect a huge amount of data and have them in a format that then we can process using Python or R. - -The best way to do this in Anylogic would be using a database and then export, read, or connect to the database to process simulation results (although, see the section **update** below). We can do that easily in Anylogic. Every time an experiment finishes, we can export the data (from a database) to an Excel file manually. - -The issue with Excel files is, on the one hand, they are Excel files, and on the other, they are not suitable for big data (more than 1 million rows). We can create a function to save all the simulation tables into an Excel file as our experiment finishes. However, we will still have the limit-of-rows limitation (check the Anylogic file linked below for a function to create Excel files from a database). - -Here I follow a different approach by exporting an Anylogic database table to a CSV file within Java. The general setup using Anylogic PLE 8.6 would be: - -- Create the databases you need for your experiment, and be sure you add the columns iteration and replicate. -- Create a function to save the data of your simulation runs (e.g., agent's status, age, etc.) -- Define a parameter variation experiment. -- Define a variable to specify where to save the data (i.e., path). -- Write code in the experiment Java Actions section so that to save data every time you run an experiment. -- Import functions in the advanced Java section of the experiment. - -## Databases - -I define two tables (`data1` and `data2`). Each agent saves its data at a given rate. After 5 years, the simulation will finish. - -{% highlight java %} -insertInto(data1) - .columns(data1.iteration, data1.replicate, data1.id, data1.dtime, data1.drandom) - .values(main.v_iteration, main.v_replicate, this.getIndex(), time(), normal(1.0)) - .execute(); - -insertInto(data2) - .columns(data2.iteration, data2.replicate, data2.id, data2.dtime, data2.drandom) - .values(main.v_iteration, main.v_replicate, this.getIndex(), time(), normal(0.3)) - .execute(); - -if (time() > 5) { - finishSimulation(); -} -{% endhighlight %} - -The tables include a column with the experiment iteration and replicate, in addition to the agent's index, time, and a random value from a normal distribution. - -## From DB to CSV - -The key function to export the data to a CSV file is `f_SQLToCSV`. It uses two arguments, a SQL query (`query`) and the path to an output file (`filename`). For instance, we can write: - -{% highlight java %} -f_SQLToCSV("select * from data1", "output/data1") -{% endhighlight %} - -You can use any query for your data, giving you a lot of flexibility on what to export to a CSV file. The `f_SQLToCSV` method is: - -{% highlight java %} -ResultSet rs = selectResultSet(query); -try { - File file = new File(filename + ".csv"); - file.getParentFile().mkdirs(); - FileWriter fw = new FileWriter(file); - int cols = rs.getMetaData().getColumnCount(); - for(int i = 1; i <= cols; i ++){ - fw.append(rs.getMetaData().getColumnLabel(i).toLowerCase()); - if(i < cols) fw.append(';'); - else fw.append('\n'); - } - while (rs.next()) { - for(int i = 1; i <= cols; i ++){ - fw.append(rs.getString(i)); - if(i < cols) fw.append(';'); - } - fw.append('\n'); - } - fw.flush(); - fw.close(); -} catch (Exception e) { - getEngine().pause(); - traceln("--> An Exception happened, continue? ..."); - e.printStackTrace(); -} -{% endhighlight %} - -The next step would be to create an experiment and complete the Java actions accordingly. First, we clear our tables. - -{% highlight java %} -// inital experiment setup -deleteFrom(data1).execute(); -deleteFrom(data2).execute(); -{% endhighlight %} - -Then, we collect information on the iteration and replicate of the simulation run: - -{% highlight java %} -// before simulation run -root.v_iteration = getCurrentIteration(); -root.v_replicate = getCurrentReplication(); -{% endhighlight %} - -Finally, at the end of the experiment, we save the data and clear the tables again: - -{% highlight java %} -// after experiment -f_exportTables(v_path); -deleteFrom(data1).execute(); -deleteFrom(data1).execute(); -{% endhighlight %} - -The method `f_exportTables` is just a function that goes through each table and export them to a CSV file. `v_tables` is string array with the name of the tables I want to export `{"data1", "data2"}`: - -{% highlight java %} -ArrayList tables = new ArrayList(); -for(String tab : v_tables) { - tables.add(tab); -} -for (String t : tables) { - f_SQLToCSV("select * from " + t, path + t); -} -{% endhighlight %} - -Remember to import some functions in the `imports section`: - -{% highlight java %} -// imports section -import java.io.BufferedWriter; -import java.io.File; -import java.io.FileWriter; -import java.io.BufferedReader; -import java.io.FileReader; -import java.io.IOException; -import java.text.*; -{% endhighlight %} - -From there, we can create additional functions to select the tables to be exported. For more details, download the [Anylogic File here](/assets/files/DBToCSV.zip). - - - - - diff --git a/assets/css/main.css b/assets/css/main.css new file mode 100644 index 00000000..726e2203 --- /dev/null +++ b/assets/css/main.css @@ -0,0 +1,4 @@ +:root{--global-bg-color:#fff;--global-code-bg-color:rgba(181,9,172,0.05);--global-text-color:#000;--global-text-color-light:#828282;--global-theme-color:#b509ac;--global-hover-color:#b509ac;--global-footer-bg-color:#1c1c1d;--global-footer-text-color:#e8e8e8;--global-footer-link-color:#fff;--global-distill-app-color:#828282;--global-divider-color:rgba(0,0,0,.1);--global-card-bg-color:#fff}:root .fa-sun{display:none}:root .fa-moon{padding-left:10px;padding-top:12px;display:block}:root .repo-img-light{display:block}:root .repo-img-dark{display:none}html[data-theme=dark]{--global-bg-color:#1c1c1d;--global-code-bg-color:#2c3237;--global-text-color:#e8e8e8;--global-text-color-light:#e8e8e8;--global-theme-color:#2698ba;--global-hover-color:#2698ba;--global-footer-bg-color:#e8e8e8;--global-footer-text-color:#1c1c1d;--global-footer-link-color:#000;--global-distill-app-color:#e8e8e8;--global-divider-color:#424246;--global-card-bg-color:#212529}html[data-theme=dark] .fa-sun{padding-left:10px;padding-top:12px;display:block}html[data-theme=dark] .fa-moon{display:none}html[data-theme=dark] .repo-img-light{display:none}html[data-theme=dark] .repo-img-dark{display:block}body{padding-bottom:70px;color:var(--global-text-color);background-color:var(--global-bg-color)}body h1,body h2,body h3,body h4,body h5,body h6{scroll-margin-top:66px}body.fixed-top-nav{padding-top:56px}body.sticky-bottom-footer{padding-bottom:0}.container{max-width:800px}.profile img{width:100%}.publications{margin-top:2rem}.publications 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!default;\n$black-color: #000000 !default;\n\n\n// Theme colors\n\n$code-bg-color-light: rgba($purple-color, 0.05);\n$code-bg-color-dark: #2c3237 !default;\n","/*******************************************************************************\r\n * Themes\r\n ******************************************************************************/\r\n \r\n:root {\r\n --global-bg-color: #{$white-color};\r\n --global-code-bg-color: #{$code-bg-color-light};\r\n --global-text-color: #{$black-color};\r\n --global-text-color-light: #{$grey-color};\r\n --global-theme-color: #{$purple-color};\r\n --global-hover-color: #{$purple-color};\r\n --global-footer-bg-color: #{$grey-color-dark};\r\n --global-footer-text-color: #{$grey-color-light};\r\n --global-footer-link-color: #{$white-color};\r\n --global-distill-app-color: #{$grey-color};\r\n --global-divider-color: rgba(0,0,0,.1);\r\n --global-card-bg-color: #{$white-color};\r\n\r\n .fa-sun {\r\n display : none;\r\n }\r\n .fa-moon {\r\n padding-left: 10px;\r\n padding-top: 12px;\r\n display : block;\r\n }\r\n\r\n .repo-img-light {\r\n display: block;\r\n }\r\n .repo-img-dark {\r\n display: none;\r\n }\r\n}\r\n\r\nhtml[data-theme='dark'] {\r\n --global-bg-color: #{$grey-color-dark};\r\n --global-code-bg-color: #{$code-bg-color-dark};\r\n --global-text-color: #{$grey-color-light};\r\n --global-text-color-light: #{$grey-color-light};\r\n --global-theme-color: #{$cyan-color};\r\n --global-hover-color: #{$cyan-color};\r\n --global-footer-bg-color: #{$grey-color-light};\r\n --global-footer-text-color: #{$grey-color-dark};\r\n --global-footer-link-color: #{$black-color};\r\n --global-distill-app-color: #{$grey-color-light};\r\n --global-divider-color: #424246;\r\n --global-card-bg-color: #{$grey-900};\r\n\r\n .fa-sun {\r\n padding-left: 10px;\r\n padding-top: 12px;\r\n display : block;\r\n }\r\n .fa-moon {\r\n display : none;\r\n }\r\n\r\n .repo-img-light {\r\n display: none;\r\n }\r\n .repo-img-dark {\r\n display: block;\r\n }\r\n}\r\n","/******************************************************************************\n * Content\n ******************************************************************************/\n\nbody {\n padding-bottom: 70px;\n color: var(--global-text-color);\n background-color: var(--global-bg-color);\n\n h1, h2, h3, h4, h5, h6 {\n scroll-margin-top: 66px;\n }\n}\n\nbody.fixed-top-nav {\n // Add some padding for the nav-bar.\n padding-top: 56px;\n}\n\nbody.sticky-bottom-footer {\n // Remove padding below footer.\n padding-bottom: 0;\n}\n\n.container {\n max-width: $max-content-width;\n}\n\n// Profile\n.profile {\n img {\n width: 100%;\n }\n}\n\n// TODO: redefine content layout.\n\n\n/******************************************************************************\n * Publications\n ******************************************************************************/\n\n// TODO: redefine publications layout.\n\n.publications {\n margin-top: 2rem;\n h1 {\n color: var(--global-theme-color);\n font-size: 2rem;\n text-align: center;\n margin-top: 1em;\n margin-bottom: 1em;\n }\n h2 {\n margin-bottom: 1rem;\n span {\n font-size: 1.5rem;\n }\n }\n h2.bibliography {\n color: $grey-color-light;\n border-top: 1px solid $grey-color-light;\n padding-top: 1rem;\n margin-top: 2rem;\n margin-bottom: 2rem;\n text-align: right;\n }\n h2.year {\n color: $grey-color-light;\n border-top: 1px solid $grey-color-light;\n padding-top: 1rem;\n margin-top: 2rem;\n margin-bottom: -2rem;\n text-align: right;\n }\n ol.bibliography {\n list-style: none;\n padding: 0;\n margin-top: 0;\n\n li {\n margin-bottom: 1rem;\n .abbr {\n height: 2rem;\n margin-bottom: 0.5rem;\n abbr {\n display: inline-block;\n background-color: var(--global-theme-color);\n padding-left: 1rem;\n padding-right: 1rem;\n a {\n color: white;\n &:hover {\n text-decoration: none;\n }\n }\n }\n .award {\n color: var(--global-theme-color) !important;\n border: 1px solid var(--global-theme-color);\n }\n }\n .title {\n font-weight: bolder;\n }\n .author {\n a {\n border-bottom: 1px dashed var(--global-theme-color);\n &:hover {\n border-bottom-style: solid;\n text-decoration: none;\n }\n }\n > em {\n border-bottom: 1px solid;\n font-style: normal;\n }\n }\n .links {\n a.btn {\n color: var(--global-text-color);\n border: 1px solid var(--global-text-color);\n padding-left: 1rem;\n padding-right: 1rem;\n padding-top: 0.25rem;\n padding-bottom: 0.25rem;\n &:hover {\n color: var(--global-theme-color);\n border-color: var(--global-theme-color);\n }\n }\n }\n .hidden {\n font-size: 0.875rem;\n max-height: 0px;\n overflow: hidden;\n text-align: justify;\n -webkit-transition: 0.15s ease;\n -moz-transition: 0.15s ease;\n -ms-transition: 0.15s ease;\n -o-transition: 0.15s ease;\n transition: all 0.15s ease;\n\n p {\n line-height: 1.4em;\n margin: 10px;\n }\n pre {\n font-size: 1em;\n line-height: 1.4em;\n padding: 10px;\n }\n }\n .hidden.open {\n max-height: 100em;\n -webkit-transition: 0.15s ease;\n -moz-transition: 0.15s ease;\n -ms-transition: 0.15s ease;\n -o-transition: 0.15s ease;\n transition: all 0.15s ease;\n }\n div.abstract.hidden {\n border: dashed 1px var(--global-bg-color);\n }\n div.abstract.hidden.open {\n border-color: var(--global-text-color);\n }\n }\n }\n}\n\n\n/*****************************************************************************\n* Projects\n*****************************************************************************/\n\n// TODO: redefine projects layout.\n","@charset \"utf-8\";\n\n// Dimensions\n$max-content-width: 800px;\n\n@import\n \"variables\",\n \"themes\",\n \"layout\",\n \"base\",\n \"distill\"\n;\n","/*******************************************************************************\n * Styles for the base elements of the theme.\n ******************************************************************************/\n\n// Typography\n\np, h1, h2, h3, h4, h5, h6, em, div, li, span, strong {\n color: var(--global-text-color);\n}\n\nhr {\n border-top: 1px solid var(--global-divider-color);\n}\n\ntable {\n td, th {\n color: var(--global-text-color);\n }\n td {\n font-size: 1rem;\n }\n}\n\na, table.table a {\n color: var(--global-theme-color);\n &:hover {\n color: var(--global-theme-color);\n text-decoration: underline;\n }\n &:hover:after :not(.nav-item.dropdown) {\n width: 100%;\n }\n}\n\nfigure, img {\n max-width: 90vw;\n}\n\nblockquote {\n background: var(--global-bg-color);\n border-left: 2px solid var(--global-theme-color);\n margin: 1.5em 10px;\n padding: 0.5em 10px;\n font-size: 1.2rem;\n}\n\n// Math\n\n.equation {\n margin-bottom: 1rem;\n text-align: center;\n}\n\n// Caption\n\n.caption {\n font-size: 0.875rem;\n margin-top: 0.75rem;\n margin-bottom: 1.5rem;\n text-align: center;\n}\n\n// Card\n\n.card {\n background-color: var(--global-card-bg-color);\n\n img {\n width: 100%;\n }\n\n .card-title {\n color: var(--global-text-color);\n }\n\n .card-item {\n width: auto;\n margin-bottom: 10px;\n\n .row {\n display: flex;\n align-items: center;\n }\n }\n}\n\n// Citation\n\n.citation, .citation-number {\n color: var(--global-theme-color);\n}\n\n// Profile\n\n.profile {\n width: 100%;\n\n .address {\n margin-bottom: 5px;\n margin-top: 5px;\n font-family: monospace;\n p {\n display: inline-block;\n margin: 0;\n }\n }\n}\n.profile.float-right{\n margin-left: 1rem;\n}\n.profile.float-left{\n margin-right: 1rem;\n}\n\n@media (min-width: 576px) {\n .profile {\n width: 30%;\n .address {\n p { display: block; }\n }\n }\n}\n\n.post-description {\n margin-bottom: 2rem;\n font-size: 0.875rem;\n a {\n color: inherit;\n &:hover {\n color: var(--global-theme-color);\n text-decoration: none;\n }\n }\n}\n\n\n// Navbar customization\n\n.navbar {\n box-shadow: none;\n border-bottom: 1px solid var(--global-divider-color);\n background-color: var(--global-bg-color);\n opacity: 0.95;\n}\n.navbar .dropdown-menu {\n background-color: var(--global-bg-color);\n border: 1px solid var(--global-divider-color);\n a:not(.active) {\n color: var(--global-text-color);\n }\n a:hover {\n color: var(--global-hover-color);\n }\n .dropdown-divider {\n border-top: 1px solid var(--global-divider-color) !important;\n }\n}\n.dropdown-item {\n color: var(--global-text-color);\n &:hover {\n color: var(--global-hover-color);\n background-color: var(--global-bg-color);\n }\n}\n.navbar.navbar-light {\n a {\n &:hover {\n text-decoration: none;\n }\n }\n .navbar-brand {\n color: var(--global-text-color);\n }\n .navbar-nav .nav-item .nav-link {\n color: var(--global-text-color);\n &:hover {\n color: var(--global-hover-color);\n }\n }\n .navbar-nav .nav-item.active>.nav-link {\n background-color: inherit;\n font-weight: bolder;\n color: var(--global-theme-color);\n &:hover {\n color: var(--global-hover-color);\n }\n }\n .navbar-brand.social {\n padding-bottom: 0;\n padding-top: 0;\n font-size: 1.7rem;\n a {\n i::before {\n color: #b2b7bd;\n transition-property: all 0.2s ease-in-out;\n }\n &:hover {\n i::before {\n color: var(--global-theme-color);\n }\n }\n }\n }\n}\n\n.navbar-toggler {\n .icon-bar {\n display: block;\n width: 22px;\n height: 2px;\n background-color: var(--global-text-color);\n border-radius: 1px;\n margin-bottom: 4px;\n transition: all 0.2s;\n }\n .top-bar {\n transform: rotate(45deg);\n transform-origin: 10% 10%;\n }\n .middle-bar {\n opacity: 0;\n }\n .bottom-bar {\n transform: rotate(-45deg);\n transform-origin: 10% 90%;\n }\n}\n\n.navbar-toggler.collapsed {\n .top-bar {\n transform: rotate(0);\n }\n .middle-bar {\n opacity: 1;\n }\n .bottom-bar {\n transform: rotate(0);\n }\n}\n\n#light-toggle {\n padding: 0;\n border: 0;\n background-color: inherit;\n color: var(--global-text-color);\n &:hover {\n color: var(--global-hover-color);\n }\n}\n\n// Social (bottom)\n\n.social {\n text-align: center;\n .contact-icons {\n font-size: 4rem;\n a {\n i::before {\n color: var(--global-text-color);\n transition-property: all 0.2s ease-in-out;\n }\n &:hover {\n i::before {\n color: var(--global-theme-color);\n }\n }\n }\n }\n .contact-note {\n font-size: 0.8rem;\n }\n}\n\n\n// Footer\nfooter.fixed-bottom {\n background-color: var(--global-footer-bg-color);\n font-size: 0.75rem;\n .container {\n color: var(--global-footer-text-color);\n padding-top: 9px;\n padding-bottom: 8px;\n }\n a {\n color: var(--global-footer-link-color);\n &:hover {\n color: var(--global-theme-color);\n text-decoration: none;\n }\n }\n}\n\nfooter.sticky-bottom {\n border-top: 1px solid var(--global-divider-color);\n padding-top: 40px;\n padding-bottom: 40px;\n font-size: 0.9rem;\n}\n\n// CV\n\n.cv {\n margin-bottom: 40px;\n \n .card {\n background-color: var(--global-card-bg-color);\n border: 1px solid var(--global-divider-color);\n \n .list-group-item {\n background-color: inherit;\n border-color: var(--global-divider-color);\n\n .badge {\n color: var(--global-card-bg-color) !important;\n background-color: var(--global-theme-color) !important;\n }\n }\n }\n}\n\n// Repositories\n\n@media (min-width: 768px) {\n .repo {\n max-width: 50%;\n }\n}\n\n// Blog\n\n.header-bar {\n border-bottom: 1px solid var(--global-divider-color);\n text-align: center;\n padding-top: 2rem;\n padding-bottom: 3rem;\n h1 {\n color: var(--global-theme-color);\n font-size: 5rem;\n }\n}\n\n.tag-list {\n border-bottom: 1px solid var(--global-divider-color);\n text-align: center;\n padding-top: 1rem;\n\n ul {\n justify-content: center;\n display: flow-root;\n\n p, li {\n list-style: none;\n display: inline-block;\n padding: 1rem 0.5rem;\n color: var(--global-text-color-light);\n }\n }\n}\n\n.post-list {\n margin: 0;\n margin-bottom: 40px;\n padding: 0;\n li {\n border-bottom: 1px solid var(--global-divider-color);\n list-style: none;\n padding-top: 2rem;\n padding-bottom: 2rem;\n .post-meta {\n color: var(--global-text-color-light);\n font-size: 0.875rem;\n margin-bottom: 0;\n }\n .post-tags {\n color: var(--global-text-color-light);\n font-size: 0.875rem;\n padding-top: 0.25rem;\n padding-bottom: 0;\n }\n a {\n color: var(--global-text-color);\n text-decoration: none;\n &:hover {\n color: var(--global-theme-color);\n }\n }\n }\n}\n\n.pagination {\n .page-item {\n .page-link {\n color: var(--global-text-color);\n &:hover {\n color: $black-color;\n }\n }\n &.active .page-link {\n color: $white-color;\n background-color: var(--global-theme-color);\n &:hover {\n background-color: var(--global-theme-color);\n }\n }\n }\n}\n\n\n// Distill\n\n.distill {\n a:hover {\n border-bottom-color: var(--global-theme-color);\n text-decoration: none;\n }\n}\n\n\n// Projects\n\n.projects {\n a {\n text-decoration: none;\n\n &:hover {\n .card-title {\n color: var(--global-theme-color);\n }\n }\n }\n\n .card {\n img {\n width: 100%;\n }\n }\n\n .card-item {\n width: auto;\n margin-bottom: 10px;\n\n .row {\n display: flex;\n align-items: center;\n }\n }\n\n .grid-sizer, .grid-item {\n width: 250px;\n margin-bottom: 10px;\n }\n\n h2.category {\n color: var(--global-divider-color);\n border-bottom: 1px solid var(--global-divider-color);\n padding-top: 0.5rem;\n margin-top: 2rem;\n margin-bottom: 1rem;\n text-align: right;\n }\n}\n\n\n// Publications\n\n.publications {\n margin-top: 2rem;\n h1 {\n color: var(--global-theme-color);\n font-size: 2rem;\n text-align: center;\n margin-top: 1em;\n margin-bottom: 1em;\n }\n h2 {\n margin-bottom: 1rem;\n span {\n font-size: 1.5rem;\n }\n }\n h2.year {\n color: var(--global-divider-color);\n border-top: 1px solid var(--global-divider-color);\n padding-top: 1rem;\n margin-top: 2rem;\n margin-bottom: -2rem;\n text-align: right;\n }\n ol.bibliography {\n list-style: none;\n padding: 0;\n margin-top: 0;\n\n li {\n margin-bottom: 1rem;\n .preview {\n width: 100%;\n min-width: 80px;\n max-width: 200px;\n }\n .abbr {\n height: 2rem;\n margin-bottom: 0.5rem;\n abbr {\n display: inline-block;\n background-color: var(--global-theme-color);\n padding-left: 1rem;\n padding-right: 1rem;\n a {\n color: white;\n &:hover {\n text-decoration: none;\n }\n }\n }\n .award {\n color: var(--global-theme-color) !important;\n border: 1px solid var(--global-theme-color);\n }\n }\n .title {\n font-weight: bolder;\n }\n .author {\n a {\n border-bottom: 1px dashed var(--global-theme-color);\n &:hover {\n border-bottom-style: solid;\n text-decoration: none;\n }\n }\n > em {\n border-bottom: 1px solid;\n font-style: normal;\n }\n > span.more-authors {\n color: var(--global-text-color-light);\n border-bottom: 1px dashed var(--global-text-color-light);\n cursor: pointer;\n &:hover {\n color: var(--global-text-color);\n border-bottom: 1px dashed var(--global-text-color);\n }\n }\n }\n .links {\n a.btn {\n color: var(--global-text-color);\n border: 1px solid var(--global-text-color);\n padding-left: 1rem;\n padding-right: 1rem;\n padding-top: 0.25rem;\n padding-bottom: 0.25rem;\n &:hover {\n color: var(--global-theme-color);\n border-color: var(--global-theme-color);\n }\n }\n }\n .hidden {\n font-size: 0.875rem;\n max-height: 0px;\n overflow: hidden;\n text-align: justify;\n transition-property: 0.15s ease;\n -moz-transition: 0.15s ease;\n -ms-transition: 0.15s ease;\n -o-transition: 0.15s ease;\n transition: all 0.15s ease;\n\n p {\n line-height: 1.4em;\n margin: 10px;\n }\n pre {\n font-size: 1em;\n line-height: 1.4em;\n padding: 10px;\n }\n }\n .hidden.open {\n max-height: 100em;\n transition-property: 0.15s ease;\n -moz-transition: 0.15s ease;\n -ms-transition: 0.15s ease;\n -o-transition: 0.15s ease;\n transition: all 0.15s ease;\n }\n div.abstract.hidden {\n border: dashed 1px var(--global-bg-color);\n }\n div.abstract.hidden.open {\n border-color: var(--global-text-color);\n }\n }\n }\n}\n\n// Rouge Color Customization\nfigure.highlight {\n margin: 0 0 1rem;\n}\n\npre {\n color: var(--global-theme-color);\n background-color: var(--global-code-bg-color);\n border-radius: 6px;\n padding: 6px 12px;\n pre, code {\n background-color: transparent;\n border-radius: 0;\n padding: 0;\n }\n}\n\ncode {\n color: var(--global-theme-color);\n background-color: var(--global-code-bg-color);\n border-radius: 3px;\n padding: 3px 3px;\n}\n\n\n// Transitioning Themes\nhtml.transition,\nhtml.transition *,\nhtml.transition *:before,\nhtml.transition *:after {\n transition: all 750ms !important;\n transition-delay: 0 !important;\n}\n\n// Extra Markdown style (post Customization)\n.post{\n .post-meta{\n color: var(--global-text-color-light);\n font-size: 0.875rem;\n margin-bottom: 0;\n }\n .post-tags{\n color: var(--global-text-color-light);\n font-size: 0.875rem;\n padding-top: 0.25rem;\n padding-bottom: 1rem;\n a {\n color: var(--global-text-color-light);\n text-decoration: none;\n &:hover {\n color: var(--global-theme-color);\n }\n }\n }\n .post-content{\n blockquote {\n border-left: 5px solid var(--global-theme-color);\n padding: 8px;\n }\n }\n}\n\nprogress {\n /* Positioning */\n position: fixed;\n left: 0;\n top: 56px;\n z-index: 10;\n \n /* Dimensions */\n width: 100%;\n height: 1px;\n \n /* Reset the appearance */\n -webkit-appearance: none;\n -moz-appearance: none;\n appearance: none;\n \n /* Get rid of the default border in Firefox/Opera. */\n border: none;\n \n /* Progress bar container for Firefox/IE10 */\n background-color: transparent;\n \n /* Progress bar value for IE10 */\n color: var(--global-theme-color);\n }\n \n progress::-webkit-progress-bar {\n background-color: transparent;\n }\n \n progress::-webkit-progress-value {\n background-color: var(--global-theme-color);\n }\n \n progress::-moz-progress-bar {\n background-color: var(--global-theme-color);\n }\n \n .progress-container {\n width: 100%;\n background-color: transparent;\n position: fixed;\n top: 56px;\n left: 0;\n height: 5px;\n display: block;\n }\n \n .progress-bar {\n background-color: var(--global-theme-color);\n width: 0%;\n display: block;\n height: inherit;\n }\n","/*******************************************************************************\n * Style overrides for distill blog posts.\n ******************************************************************************/\n\nd-byline {\n border-top-color: var(--global-divider-color) !important;\n}\n\nd-byline h3 {\n color: var(--global-text-color) !important;\n}\n\nd-byline a, d-article d-byline a {\n color: var(--global-text-color) !important;\n &:hover {\n color: var(--global-hover-color) !important;\n }\n}\n\nd-article {\n border-top-color: var(--global-divider-color) !important;\n a, p, h1, h2, h3, h4, h5, h6, li, table {\n color: var(--global-text-color) !important;\n }\n a, h1, h2, hr, table, table th, table td {\n border-bottom-color: var(--global-divider-color) !important;\n }\n a:hover {\n border-bottom-color: var(--global-hover-color) !important;\n }\n b i {\n display: inline;\n }\n\n d-contents {\n align-self: start;\n grid-column: 1 / 4;\n grid-row: auto / span 4;\n justify-self: end;\n margin-top: 0em;\n padding-left: 2em;\n padding-right: 3em;\n border-right: 1px solid var(--global-divider-color);\n width: calc(max(70%, 300px));\n margin-right: 0px;\n margin-top: 0em;\n display: grid;\n grid-template-columns:\n minmax(8px, 1fr) [toc] auto\n minmax(8px, 1fr) [toc-line] 1px\n minmax(32px, 2fr);\n\n nav {\n grid-column: toc;\n a {\n border-bottom: none !important;\n &:hover {\n border-bottom: 1px solid var(--global-text-color) !important;\n }\n }\n h3 {\n margin-top: 0;\n margin-bottom: 1em;\n }\n div {\n display: block;\n outline: none;\n margin-bottom: 0.8em;\n color: rgba(0, 0, 0, 0.8);\n font-weight: bold;\n }\n ul {\n padding-left: 1em;\n margin-top: 0;\n margin-bottom: 6px;\n list-style-type: none;\n li {\n margin-bottom: 0.25em;\n }\n }\n }\n .figcaption {\n line-height: 1.4em;\n }\n toc-line {\n border-right: 1px solid var(--global-divider-color);\n grid-column: toc-line;\n }\n }\n\n d-footnote {\n scroll-margin-top: 66px;\n }\n}\n\nd-appendix {\n border-top-color: var(--global-divider-color) !important;\n color: var(--global-distill-app-color) !important;\n h3, li, span {\n color: var(--global-distill-app-color) !important;\n }\n a, a.footnote-backlink {\n color: var(--global-distill-app-color) !important;\n &:hover {\n color: var(--global-hover-color) !important;\n }\n }\n}\n\n@media (max-width: 1024px) {\n d-article {\n d-contents {\n display: block;\n grid-column-start: 2;\n grid-column-end: -2;\n padding-bottom: 0.5em;\n margin-bottom: 1em;\n padding-top: 0.5em;\n width: 100%;\n border: 1px solid var(--global-divider-color);\n nav {\n grid-column: none;\n }\n }\n }\n}\n"],"file":"main.css"} \ No newline at end of file diff --git a/assets/css/main.scss b/assets/css/main.scss deleted file mode 100644 index fd8c311c..00000000 --- a/assets/css/main.scss +++ /dev/null @@ -1,15 +0,0 @@ ---- -# Only the main Sass file needs front matter (the dashes are enough) ---- -@charset "utf-8"; - -// Dimensions -$max-content-width: {{ site.max_width }}; - -@import - "variables", - "themes", - "layout", - "base", - "distill" -; diff --git a/assets/img/1-1400.webp 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define(factory) : - factory(); -}((function () { 'use strict'; - - // Copyright 2018 The Distill Template Authors - // - // Licensed under the Apache License, Version 2.0 (the "License"); - // you may not use this file except in compliance with the License. - // You may obtain a copy of the License at - // - // http://www.apache.org/licenses/LICENSE-2.0 - // - // Unless required by applicable law or agreed to in writing, software - // distributed under the License is distributed on an "AS IS" BASIS, - // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - // See the License for the specific language governing permissions and - // limitations under the License. - - const days = ['Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday']; - const months = ['Jan.', 'Feb.', 'March', 'April', 'May', 'June', 'July', 'Aug.', 'Sept.', 'Oct.', 'Nov.', 'Dec.']; - const zeroPad = n => n < 10 ? '0' + n : n; - - const RFC = function(date) { - const day = days[date.getDay()].substring(0, 3); - const paddedDate = zeroPad(date.getDate()); - const month = months[date.getMonth()].substring(0,3); - const year = date.getFullYear().toString(); - const hours = date.getUTCHours().toString(); - const minutes = date.getUTCMinutes().toString(); - const seconds = date.getUTCSeconds().toString(); - return `${day}, ${paddedDate} ${month} ${year} ${hours}:${minutes}:${seconds} Z`; - }; - - const objectFromMap = function(map) { - const object = Array.from(map).reduce((object, [key, value]) => ( - Object.assign(object, { [key]: value }) // Be careful! Maps can have non-String keys; object literals can't. - ), {}); - return object; - }; - - const mapFromObject = function(object) { - const map = new Map(); - for (var property in object) { - if (object.hasOwnProperty(property)) { - map.set(property, object[property]); - } - } - return map; - }; - - class Author { - - // constructor(name='', personalURL='', affiliation='', affiliationURL='') { - // this.name = name; // 'Chris Olah' - // this.personalURL = personalURL; // 'https://colah.github.io' - // this.affiliation = affiliation; // 'Google Brain' - // this.affiliationURL = affiliationURL; // 'https://g.co/brain' - // } - - constructor(object) { - this.name = object.author; // 'Chris Olah' - this.personalURL = object.authorURL; // 'https://colah.github.io' - this.affiliation = object.affiliation; // 'Google Brain' - this.affiliationURL = object.affiliationURL; // 'https://g.co/brain' - this.affiliations = object.affiliations || []; // new-style affiliations - } - - // 'Chris' - get firstName() { - const names = this.name.split(' '); - return names.slice(0, names.length - 1).join(' '); - } - - // 'Olah' - get lastName() { - const names = this.name.split(' '); - return names[names.length -1]; - } - } - - function mergeFromYMLFrontmatter(target, source) { - target.title = source.title; - if (source.published) { - if (source.published instanceof Date) { - target.publishedDate = source.published; - } else if (source.published.constructor === String) { - target.publishedDate = new Date(source.published); - } - } - if (source.publishedDate) { - if (source.publishedDate instanceof Date) { - target.publishedDate = source.publishedDate; - } else if (source.publishedDate.constructor === String) { - target.publishedDate = new Date(source.publishedDate); - } else { - console.error('Don\'t know what to do with published date: ' + source.publishedDate); - } - } - target.description = source.description; - target.authors = source.authors.map( (authorObject) => new Author(authorObject)); - target.katex = source.katex; - target.password = source.password; - if (source.doi) { - target.doi = source.doi; - } - } - - class FrontMatter { - constructor() { - this.title = 'unnamed article'; // 'Attention and Augmented Recurrent Neural Networks' - this.description = ''; // 'A visual overview of neural attention...' - this.authors = []; // Array of Author(s) - - this.bibliography = new Map(); - this.bibliographyParsed = false; - // { - // 'gregor2015draw': { - // 'title': 'DRAW: A recurrent neural network for image generation', - // 'author': 'Gregor, Karol and Danihelka, Ivo and Graves, Alex and Rezende, Danilo Jimenez and Wierstra, Daan', - // 'journal': 'arXiv preprint arXiv:1502.04623', - // 'year': '2015', - // 'url': 'https://arxiv.org/pdf/1502.04623.pdf', - // 'type': 'article' - // }, - // } - - // Citation keys should be listed in the order that they are appear in the document. - // Each key refers to a key in the bibliography dictionary. - this.citations = []; // [ 'gregor2015draw', 'mercier2011humans' ] - this.citationsCollected = false; - - // - // Assigned from posts.csv - // - - // publishedDate: 2016-09-08T07:00:00.000Z, - // tags: [ 'rnn' ], - // distillPath: '2016/augmented-rnns', - // githubPath: 'distillpub/post--augmented-rnns', - // doiSuffix: 1, - - // - // Assigned from journal - // - this.journal = {}; - // journal: { - // 'title': 'Distill', - // 'full_title': 'Distill', - // 'abbrev_title': 'Distill', - // 'url': 'http://distill.pub', - // 'doi': '10.23915/distill', - // 'publisherName': 'Distill Working Group', - // 'publisherEmail': 'admin@distill.pub', - // 'issn': '2476-0757', - // 'editors': [...], - // 'committee': [...] - // } - // volume: 1, - // issue: 9, - - this.katex = {}; - - // - // Assigned from publishing process - // - - // githubCompareUpdatesUrl: 'https://github.com/distillpub/post--augmented-rnns/compare/1596e094d8943d2dc0ea445d92071129c6419c59...3bd9209e0c24d020f87cf6152dcecc6017cbc193', - // updatedDate: 2017-03-21T07:13:16.000Z, - // doi: '10.23915/distill.00001', - this.doi = undefined; - this.publishedDate = undefined; - } - - // Example: - // title: Demo Title Attention and Augmented Recurrent Neural Networks - // published: Jan 10, 2017 - // authors: - // - Chris Olah: - // - Shan Carter: http://shancarter.com - // affiliations: - // - Google Brain: - // - Google Brain: http://g.co/brain - - // - // Computed Properties - // - - // 'http://distill.pub/2016/augmented-rnns', - set url(value) { - this._url = value; - } - get url() { - if (this._url) { - return this._url; - } else if (this.distillPath && this.journal.url) { - return this.journal.url + '/' + this.distillPath; - } else if (this.journal.url) { - return this.journal.url; - } - } - - // 'https://github.com/distillpub/post--augmented-rnns', - get githubUrl() { - if (this.githubPath) { - return 'https://github.com/' + this.githubPath; - } else { - return undefined; - } - } - - // TODO resolve differences in naming of URL/Url/url. - // 'http://distill.pub/2016/augmented-rnns/thumbnail.jpg', - set previewURL(value) { - this._previewURL = value; - } - get previewURL() { - return this._previewURL ? this._previewURL : this.url + '/thumbnail.jpg'; - } - - // 'Thu, 08 Sep 2016 00:00:00 -0700', - get publishedDateRFC() { - return RFC(this.publishedDate); - } - - // 'Thu, 08 Sep 2016 00:00:00 -0700', - get updatedDateRFC() { - return RFC(this.updatedDate); - } - - // 2016, - get publishedYear() { - return this.publishedDate.getFullYear(); - } - - // 'Sept', - get publishedMonth() { - return months[this.publishedDate.getMonth()]; - } - - // 8, - get publishedDay() { - return this.publishedDate.getDate(); - } - - // '09', - get publishedMonthPadded() { - return zeroPad(this.publishedDate.getMonth() + 1); - } - - // '08', - get publishedDayPadded() { - return zeroPad(this.publishedDate.getDate()); - } - - get publishedISODateOnly() { - return this.publishedDate.toISOString().split('T')[0]; - } - - get volume() { - const volume = this.publishedYear - 2015; - if (volume < 1) { - throw new Error('Invalid publish date detected during computing volume'); - } - return volume; - } - - get issue() { - return this.publishedDate.getMonth() + 1; - } - - // 'Olah & Carter', - get concatenatedAuthors() { - if (this.authors.length > 2) { - return this.authors[0].lastName + ', et al.'; - } else if (this.authors.length === 2) { - return this.authors[0].lastName + ' & ' + this.authors[1].lastName; - } else if (this.authors.length === 1) { - return this.authors[0].lastName; - } - } - - // 'Olah, Chris and Carter, Shan', - get bibtexAuthors() { - return this.authors.map(author => { - return author.lastName + ', ' + author.firstName; - }).join(' and '); - } - - // 'olah2016attention' - get slug() { - let slug = ''; - if (this.authors.length) { - slug += this.authors[0].lastName.toLowerCase(); - slug += this.publishedYear; - slug += this.title.split(' ')[0].toLowerCase(); - } - return slug || 'Untitled'; - } - - get bibliographyEntries() { - return new Map(this.citations.map( citationKey => { - const entry = this.bibliography.get(citationKey); - return [citationKey, entry]; - })); - } - - set bibliography(bibliography) { - if (bibliography instanceof Map) { - this._bibliography = bibliography; - } else if (typeof bibliography === 'object') { - this._bibliography = mapFromObject(bibliography); - } - } - - get bibliography() { - return this._bibliography; - } - - static fromObject(source) { - const frontMatter = new FrontMatter(); - Object.assign(frontMatter, source); - return frontMatter; - } - - assignToObject(target) { - Object.assign(target, this); - target.bibliography = objectFromMap(this.bibliographyEntries); - target.url = this.url; - target.doi = this.doi; - target.githubUrl = this.githubUrl; - target.previewURL = this.previewURL; - if (this.publishedDate) { - target.volume = this.volume; - target.issue = this.issue; - target.publishedDateRFC = this.publishedDateRFC; - target.publishedYear = this.publishedYear; - target.publishedMonth = this.publishedMonth; - target.publishedDay = this.publishedDay; - target.publishedMonthPadded = this.publishedMonthPadded; - target.publishedDayPadded = this.publishedDayPadded; - } - if (this.updatedDate) { - target.updatedDateRFC = this.updatedDateRFC; - } - target.concatenatedAuthors = this.concatenatedAuthors; - target.bibtexAuthors = this.bibtexAuthors; - target.slug = this.slug; - } - - } - - // Copyright 2018 The Distill Template Authors - // - // Licensed under the Apache License, Version 2.0 (the "License"); - // you may not use this file except in compliance with the License. - // You may obtain a copy of the License at - // - // http://www.apache.org/licenses/LICENSE-2.0 - // - // Unless required by applicable law or agreed to in writing, software - // distributed under the License is distributed on an "AS IS" BASIS, - // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - // See the License for the specific language governing permissions and - // limitations under the License. - - const Mutating = (superclass) => { - return class extends superclass { - - constructor() { - super(); - - // set up mutation observer - const options = {childList: true, characterData: true, subtree: true}; - const observer = new MutationObserver( () => { - observer.disconnect(); - this.renderIfPossible(); - observer.observe(this, options); - }); - - // ...and listen for changes - observer.observe(this, options); - } - - connectedCallback() { - super.connectedCallback(); - - this.renderIfPossible(); - } - - // potential TODO: check if this is enough for all our usecases - // maybe provide a custom function to tell if we have enough information to render - renderIfPossible() { - if (this.textContent && this.root) { - this.renderContent(); - } - } - - renderContent() { - console.error(`Your class ${this.constructor.name} must provide a custom renderContent() method!` ); - } - - }; // end class - }; // end mixin function - - // Copyright 2018 The Distill Template Authors - // - // Licensed under the Apache License, Version 2.0 (the "License"); - // you may not use this file except in compliance with the License. - // You may obtain a copy of the License at - // - // http://www.apache.org/licenses/LICENSE-2.0 - // - // Unless required by applicable law or agreed to in writing, software - // distributed under the License is distributed on an "AS IS" BASIS, - // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - // See the License for the specific language governing permissions and - // limitations under the License. - - /*global ShadyCSS*/ - - const Template = (name, templateString, useShadow = true) => { - - return (superclass) => { - - const template = document.createElement('template'); - template.innerHTML = templateString; - - if (useShadow && 'ShadyCSS' in window) { - ShadyCSS.prepareTemplate(template, name); - } - - return class extends superclass { - - static get is() { return name; } - - constructor() { - super(); - - this.clone = document.importNode(template.content, true); - if (useShadow) { - this.attachShadow({mode: 'open'}); - this.shadowRoot.appendChild(this.clone); - } - } - - connectedCallback() { - if (this.hasAttribute('distill-prerendered')) { - return; - } - if (useShadow) { - if ('ShadyCSS' in window) { - ShadyCSS.styleElement(this); - } - } else { - this.insertBefore(this.clone, this.firstChild); - } - } - - get root() { - if (useShadow) { - return this.shadowRoot; - } else { - return this; - } - } - - /* TODO: Are we using these? Should we even? */ - $(query) { - return this.root.querySelector(query); - } - - $$(query) { - return this.root.querySelectorAll(query); - } - }; - }; - }; - - var math = "/*\n * Copyright 2018 The Distill Template Authors\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\nspan.katex-display {\n text-align: left;\n padding: 8px 0 8px 0;\n margin: 0.5em 0 0.5em 1em;\n}\n\nspan.katex {\n -webkit-font-smoothing: antialiased;\n color: rgba(0, 0, 0, 0.8);\n font-size: 1.18em;\n}\n"; - - // Copyright 2018 The Distill Template Authors - // - // Licensed under the Apache License, Version 2.0 (the "License"); - // you may not use this file except in compliance with the License. - // You may obtain a copy of the License at - // - // http://www.apache.org/licenses/LICENSE-2.0 - // - // Unless required by applicable law or agreed to in writing, software - // distributed under the License is distributed on an "AS IS" BASIS, - // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - // See the License for the specific language governing permissions and - // limitations under the License. - - // This is a straight concatenation of code from KaTeX's contrib folder, - // but we aren't using some of their helpers that don't work well outside a browser environment. - - /*global katex */ - - const findEndOfMath = function(delimiter, text, startIndex) { - // Adapted from - // https://github.com/Khan/perseus/blob/master/src/perseus-markdown.jsx - let index = startIndex; - let braceLevel = 0; - - const delimLength = delimiter.length; - - while (index < text.length) { - const character = text[index]; - - if ( - braceLevel <= 0 && - text.slice(index, index + delimLength) === delimiter - ) { - return index; - } else if (character === "\\") { - index++; - } else if (character === "{") { - braceLevel++; - } else if (character === "}") { - braceLevel--; - } - - index++; - } - - return -1; - }; - - const splitAtDelimiters = function(startData, leftDelim, rightDelim, display) { - const finalData = []; - - for (let i = 0; i < startData.length; i++) { - if (startData[i].type === "text") { - const text = startData[i].data; - - let lookingForLeft = true; - let currIndex = 0; - let nextIndex; - - nextIndex = text.indexOf(leftDelim); - if (nextIndex !== -1) { - currIndex = nextIndex; - finalData.push({ - type: "text", - data: text.slice(0, currIndex) - }); - lookingForLeft = false; - } - - while (true) { - // eslint-disable-line no-constant-condition - if (lookingForLeft) { - nextIndex = text.indexOf(leftDelim, currIndex); - if (nextIndex === -1) { - break; - } - - finalData.push({ - type: "text", - data: text.slice(currIndex, nextIndex) - }); - - currIndex = nextIndex; - } else { - nextIndex = findEndOfMath( - rightDelim, - text, - currIndex + leftDelim.length - ); - if (nextIndex === -1) { - break; - } - - finalData.push({ - type: "math", - data: text.slice(currIndex + leftDelim.length, nextIndex), - rawData: text.slice(currIndex, nextIndex + rightDelim.length), - display: display - }); - - currIndex = nextIndex + rightDelim.length; - } - - lookingForLeft = !lookingForLeft; - } - - finalData.push({ - type: "text", - data: text.slice(currIndex) - }); - } else { - finalData.push(startData[i]); - } - } - - return finalData; - }; - - const splitWithDelimiters = function(text, delimiters) { - let data = [{ type: "text", data: text }]; - for (let i = 0; i < delimiters.length; i++) { - const delimiter = delimiters[i]; - data = splitAtDelimiters( - data, - delimiter.left, - delimiter.right, - delimiter.display || false - ); - } - return data; - }; - - /* Note: optionsCopy is mutated by this method. If it is ever exposed in the - * API, we should copy it before mutating. - */ - const renderMathInText = function(text, optionsCopy) { - const data = splitWithDelimiters(text, optionsCopy.delimiters); - const fragment = document.createDocumentFragment(); - - for (let i = 0; i < data.length; i++) { - if (data[i].type === "text") { - fragment.appendChild(document.createTextNode(data[i].data)); - } else { - const tag = document.createElement("d-math"); - const math = data[i].data; - // Override any display mode defined in the settings with that - // defined by the text itself - optionsCopy.displayMode = data[i].display; - try { - tag.textContent = math; - if (optionsCopy.displayMode) { - tag.setAttribute("block", ""); - } - } catch (e) { - if (!(e instanceof katex.ParseError)) { - throw e; - } - optionsCopy.errorCallback( - "KaTeX auto-render: Failed to parse `" + data[i].data + "` with ", - e - ); - fragment.appendChild(document.createTextNode(data[i].rawData)); - continue; - } - fragment.appendChild(tag); - } - } - - return fragment; - }; - - const renderElem = function(elem, optionsCopy) { - for (let i = 0; i < elem.childNodes.length; i++) { - const childNode = elem.childNodes[i]; - if (childNode.nodeType === 3) { - // Text node - const text = childNode.textContent; - if (optionsCopy.mightHaveMath(text)) { - const frag = renderMathInText(text, optionsCopy); - i += frag.childNodes.length - 1; - elem.replaceChild(frag, childNode); - } - } else if (childNode.nodeType === 1) { - // Element node - const shouldRender = - optionsCopy.ignoredTags.indexOf(childNode.nodeName.toLowerCase()) === - -1; - - if (shouldRender) { - renderElem(childNode, optionsCopy); - } - } - // Otherwise, it's something else, and ignore it. - } - }; - - const defaultAutoRenderOptions = { - delimiters: [ - { left: "$$", right: "$$", display: true }, - { left: "\\[", right: "\\]", display: true }, - { left: "\\(", right: "\\)", display: false } - // LaTeX uses this, but it ruins the display of normal `$` in text: - // {left: '$', right: '$', display: false}, - ], - - ignoredTags: [ - "script", - "noscript", - "style", - "textarea", - "pre", - "code", - "svg" - ], - - errorCallback: function(msg, err) { - console.error(msg, err); - } - }; - - const renderMathInElement = function(elem, options) { - if (!elem) { - throw new Error("No element provided to render"); - } - - const optionsCopy = Object.assign({}, defaultAutoRenderOptions, options); - const delimiterStrings = optionsCopy.delimiters.flatMap(d => [ - d.left, - d.right - ]); - const mightHaveMath = text => - delimiterStrings.some(d => text.indexOf(d) !== -1); - optionsCopy.mightHaveMath = mightHaveMath; - renderElem(elem, optionsCopy); - }; - - // Copyright 2018 The Distill Template Authors - - const katexJSURL = 'https://distill.pub/third-party/katex/katex.min.js'; - const katexCSSTag = ''; - - const T = Template('d-math', ` -${katexCSSTag} - - -`); - - // DMath, not Math, because that would conflict with the JS built-in - class DMath extends Mutating(T(HTMLElement)) { - - static set katexOptions(options) { - DMath._katexOptions = options; - if (DMath.katexOptions.delimiters) { - if (!DMath.katexAdded) { - DMath.addKatex(); - } else { - DMath.katexLoadedCallback(); - } - } - } - - static get katexOptions() { - if (!DMath._katexOptions) { - DMath._katexOptions = { - delimiters: [ { 'left':'$$', 'right':'$$', 'display': false } ] - }; - } - return DMath._katexOptions; - } - - static katexLoadedCallback() { - // render all d-math tags - const mathTags = document.querySelectorAll('d-math'); - for (const mathTag of mathTags) { - mathTag.renderContent(); - } - // transform inline delimited math to d-math tags - if (DMath.katexOptions.delimiters) { - renderMathInElement(document.body, DMath.katexOptions); - } - } - - static addKatex() { - // css tag can use this convenience function - document.head.insertAdjacentHTML('beforeend', katexCSSTag); - // script tag has to be created to work properly - const scriptTag = document.createElement('script'); - scriptTag.src = katexJSURL; - scriptTag.async = true; - scriptTag.onload = DMath.katexLoadedCallback; - scriptTag.crossorigin = 'anonymous'; - document.head.appendChild(scriptTag); - - DMath.katexAdded = true; - } - - get options() { - const localOptions = { displayMode: this.hasAttribute('block') }; - return Object.assign(localOptions, DMath.katexOptions); - } - - connectedCallback() { - super.connectedCallback(); - if (!DMath.katexAdded) { - DMath.addKatex(); - } - } - - renderContent() { - if (typeof katex !== 'undefined') { - const container = this.root.querySelector('#katex-container'); - katex.render(this.textContent, container, this.options); - } - } - - } - - DMath.katexAdded = false; - DMath.inlineMathRendered = false; - window.DMath = DMath; // TODO: check if this can be removed, or if we should expose a distill global - - // Copyright 2018 The Distill Template Authors - // - // Licensed under the Apache License, Version 2.0 (the "License"); - // you may not use this file except in compliance with the License. - // You may obtain a copy of the License at - // - // http://www.apache.org/licenses/LICENSE-2.0 - // - // Unless required by applicable law or agreed to in writing, software - // distributed under the License is distributed on an "AS IS" BASIS, - // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - // See the License for the specific language governing permissions and - // limitations under the License. - - function collect_citations(dom = document) { - const citations = new Set(); - const citeTags = dom.querySelectorAll("d-cite"); - for (const tag of citeTags) { - const keyString = tag.getAttribute("key") || tag.getAttribute("bibtex-key"); - const keys = keyString.split(",").map(k => k.trim()); - for (const key of keys) { - citations.add(key); - } - } - return [...citations]; - } - - function author_string(ent, template, sep, finalSep) { - if (ent.author == null) { - return ""; - } - var names = ent.author.split(" and "); - let name_strings = names.map(name => { - name = name.trim(); - if (name.indexOf(",") != -1) { - var last = name.split(",")[0].trim(); - var firsts = name.split(",")[1]; - } else if (name.indexOf(" ") != -1) { - var last = name - .split(" ") - .slice(-1)[0] - .trim(); - var firsts = name - .split(" ") - .slice(0, -1) - .join(" "); - } else { - var last = name.trim(); - } - var initials = ""; - if (firsts != undefined) { - initials = firsts - .trim() - .split(" ") - .map(s => s.trim()[0]); - initials = initials.join(".") + "."; - } - return template - .replace("${F}", firsts) - .replace("${L}", last) - .replace("${I}", initials) - .trim(); // in case one of first or last was empty - }); - if (names.length > 1) { - var str = name_strings.slice(0, names.length - 1).join(sep); - str += (finalSep || sep) + name_strings[names.length - 1]; - return str; - } else { - return name_strings[0]; - } - } - - function venue_string(ent) { - var cite = ent.journal || ent.booktitle || ""; - if ("volume" in ent) { - var issue = ent.issue || ent.number; - issue = issue != undefined ? "(" + issue + ")" : ""; - cite += ", Vol " + ent.volume + issue; - } - if ("pages" in ent) { - cite += ", pp. " + ent.pages; - } - if (cite != "") cite += ". "; - if ("publisher" in ent) { - cite += ent.publisher; - if (cite[cite.length - 1] != ".") cite += "."; - } - return cite; - } - - function link_string(ent) { - if ("url" in ent) { - var url = ent.url; - var arxiv_match = /arxiv\.org\/abs\/([0-9\.]*)/.exec(url); - if (arxiv_match != null) { - url = `http://arxiv.org/pdf/${arxiv_match[1]}.pdf`; - } - - if (url.slice(-4) == ".pdf") { - var label = "PDF"; - } else if (url.slice(-5) == ".html") { - var label = "HTML"; - } - return `  [${label || "link"}]`; - } /* else if ("doi" in ent){ - return `  [DOI]`; - }*/ else { - return ""; - } - } - function doi_string(ent, new_line) { - if ("doi" in ent) { - return `${new_line ? "
" : ""} DOI: ${ent.doi}`; - } else { - return ""; - } - } - - function title_string(ent) { - return '' + ent.title + " "; - } - - function bibliography_cite(ent, fancy) { - if (ent) { - var cite = title_string(ent); - cite += link_string(ent) + "
"; - if (ent.author) { - cite += author_string(ent, "${L}, ${I}", ", ", " and "); - if (ent.year || ent.date) { - cite += ", "; - } - } - if (ent.year || ent.date) { - cite += (ent.year || ent.date) + ". "; - } else { - cite += ". "; - } - cite += venue_string(ent); - cite += doi_string(ent); - return cite; - /*var cite = author_string(ent, "${L}, ${I}", ", ", " and "); - if (ent.year || ent.date){ - cite += ", " + (ent.year || ent.date) + ". " - } else { - cite += ". " - } - cite += "" + ent.title + ". "; - cite += venue_string(ent); - cite += doi_string(ent); - cite += link_string(ent); - return cite*/ - } else { - return "?"; - } - } - - function hover_cite(ent) { - if (ent) { - var cite = ""; - cite += "" + ent.title + ""; - cite += link_string(ent); - cite += "
"; - - var a_str = author_string(ent, "${I} ${L}", ", ") + "."; - var v_str = - venue_string(ent).trim() + " " + ent.year + ". " + doi_string(ent, true); - - if ((a_str + v_str).length < Math.min(40, ent.title.length)) { - cite += a_str + " " + v_str; - } else { - cite += a_str + "
" + v_str; - } - return cite; - } else { - return "?"; - } - } - - function domContentLoaded() { - return ['interactive', 'complete'].indexOf(document.readyState) !== -1; - } - - // Copyright 2018 The Distill Template Authors - // - // Licensed under the Apache License, Version 2.0 (the "License"); - // you may not use this file except in compliance with the License. - // You may obtain a copy of the License at - // - // http://www.apache.org/licenses/LICENSE-2.0 - // - // Unless required by applicable law or agreed to in writing, software - // distributed under the License is distributed on an "AS IS" BASIS, - // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - // See the License for the specific language governing permissions and - // limitations under the License. - - function _moveLegacyAffiliationFormatIntoArray(frontMatter) { - // authors used to have propoerties "affiliation" and "affiliationURL". - // We now encourage using an array for affiliations containing objects with - // properties "name" and "url". - for (let author of frontMatter.authors) { - const hasOldStyle = Boolean(author.affiliation); - const hasNewStyle = Boolean(author.affiliations); - if (!hasOldStyle) continue; - if (hasNewStyle) { - console.warn(`Author ${author.author} has both old-style ("affiliation" & "affiliationURL") and new style ("affiliations") affiliation information!`); - } else { - let newAffiliation = { - "name": author.affiliation - }; - if (author.affiliationURL) newAffiliation.url = author.affiliationURL; - author.affiliations = [newAffiliation]; - } - } - return frontMatter - } - - function parseFrontmatter(element) { - const scriptTag = element.firstElementChild; - if (scriptTag) { - const type = scriptTag.getAttribute('type'); - if (type.split('/')[1] == 'json') { - const content = scriptTag.textContent; - const parsed = JSON.parse(content); - return _moveLegacyAffiliationFormatIntoArray(parsed); - } else { - console.error('Distill only supports JSON frontmatter tags anymore; no more YAML.'); - } - } else { - console.error('You added a frontmatter tag but did not provide a script tag with front matter data in it. Please take a look at our templates.'); - } - return {}; - } - - class FrontMatter$1 extends HTMLElement { - - static get is() { return 'd-front-matter'; } - - constructor() { - super(); - - const options = {childList: true, characterData: true, subtree: true}; - const observer = new MutationObserver( (entries) => { - for (const entry of entries) { - if (entry.target.nodeName === 'SCRIPT' || entry.type === 'characterData') { - const data = parseFrontmatter(this); - this.notify(data); - } - } - }); - observer.observe(this, options); - } - - notify(data) { - const options = { detail: data, bubbles: true }; - const event = new CustomEvent('onFrontMatterChanged', options); - document.dispatchEvent(event); - } - - } - - // Copyright 2018 The Distill Template Authors - // - // Licensed under the Apache License, Version 2.0 (the "License"); - // you may not use this file except in compliance with the License. - // You may obtain a copy of the License at - // - // http://www.apache.org/licenses/LICENSE-2.0 - // - // Unless required by applicable law or agreed to in writing, software - // distributed under the License is distributed on an "AS IS" BASIS, - // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - // See the License for the specific language governing permissions and - // limitations under the License. - - // no appendix -> add appendix - // title in front, no h1 -> add it - // no title in front, h1 -> read and put into frontMatter - // footnote -> footnote list - // break up bib - // if citation, no bib-list -> add citation-list - - // if authors, no byline -> add byline - - function optionalComponents(dom, data) { - const body = dom.body; - const article = body.querySelector('d-article'); - - // If we don't have an article tag, something weird is going on—giving up. - if (!article) { - console.warn('No d-article tag found; skipping adding optional components!'); - return; - } - - let byline = dom.querySelector('d-byline'); - if (!byline) { - if (data.authors) { - byline = dom.createElement('d-byline'); - body.insertBefore(byline, article); - } else { - console.warn('No authors found in front matter; please add them before submission!'); - } - } - - let title = dom.querySelector('d-title'); - if (!title) { - title = dom.createElement('d-title'); - body.insertBefore(title, byline); - } - - let h1 = title.querySelector('h1'); - if (!h1) { - h1 = dom.createElement('h1'); - h1.textContent = data.title; - title.insertBefore(h1, title.firstChild); - } - - const hasPassword = typeof data.password !== 'undefined'; - let interstitial = body.querySelector('d-interstitial'); - if (hasPassword && !interstitial) { - const inBrowser = typeof window !== 'undefined'; - const onLocalhost = inBrowser && window.location.hostname.includes('localhost'); - if (!inBrowser || !onLocalhost) { - interstitial = dom.createElement('d-interstitial'); - interstitial.password = data.password; - body.insertBefore(interstitial, body.firstChild); - } - } else if (!hasPassword && interstitial) { - interstitial.parentElement.removeChild(this); - } - - let appendix = dom.querySelector('d-appendix'); - if (!appendix) { - appendix = dom.createElement('d-appendix'); - dom.body.appendChild(appendix); - } - - let footnoteList = dom.querySelector('d-footnote-list'); - if (!footnoteList) { - footnoteList = dom.createElement('d-footnote-list'); - appendix.appendChild(footnoteList); - } - - let citationList = dom.querySelector('d-citation-list'); - if (!citationList) { - citationList = dom.createElement('d-citation-list'); - appendix.appendChild(citationList); - } - - } - - // Copyright 2018 The Distill Template Authors - - const frontMatter = new FrontMatter(); - - const Controller = { - frontMatter: frontMatter, - waitingOn: { - bibliography: [], - citations: [] - }, - listeners: { - onCiteKeyCreated(event) { - const [citeTag, keys] = event.detail; - - // ensure we have citations - if (!frontMatter.citationsCollected) { - // console.debug('onCiteKeyCreated, but unresolved dependency ("citations"). Enqueing.'); - Controller.waitingOn.citations.push(() => - Controller.listeners.onCiteKeyCreated(event) - ); - return; - } - - // ensure we have a loaded bibliography - if (!frontMatter.bibliographyParsed) { - // console.debug('onCiteKeyCreated, but unresolved dependency ("bibliography"). Enqueing.'); - Controller.waitingOn.bibliography.push(() => - Controller.listeners.onCiteKeyCreated(event) - ); - return; - } - - const numbers = keys.map(key => frontMatter.citations.indexOf(key)); - citeTag.numbers = numbers; - const entries = keys.map(key => frontMatter.bibliography.get(key)); - citeTag.entries = entries; - }, - - onCiteKeyChanged() { - // const [citeTag, keys] = event.detail; - - // update citations - frontMatter.citations = collect_citations(); - frontMatter.citationsCollected = true; - for (const waitingCallback of Controller.waitingOn.citations.slice()) { - waitingCallback(); - } - - // update bibliography - const citationListTag = document.querySelector("d-citation-list"); - const bibliographyEntries = new Map( - frontMatter.citations.map(citationKey => { - return [citationKey, frontMatter.bibliography.get(citationKey)]; - }) - ); - citationListTag.citations = bibliographyEntries; - - const citeTags = document.querySelectorAll("d-cite"); - for (const citeTag of citeTags) { - console.log(citeTag); - const keys = citeTag.keys; - const numbers = keys.map(key => frontMatter.citations.indexOf(key)); - citeTag.numbers = numbers; - const entries = keys.map(key => frontMatter.bibliography.get(key)); - citeTag.entries = entries; - } - }, - - onCiteKeyRemoved(event) { - Controller.listeners.onCiteKeyChanged(event); - }, - - onBibliographyChanged(event) { - const citationListTag = document.querySelector("d-citation-list"); - - const bibliography = event.detail; - - frontMatter.bibliography = bibliography; - frontMatter.bibliographyParsed = true; - for (const waitingCallback of Controller.waitingOn.bibliography.slice()) { - waitingCallback(); - } - - // ensure we have citations - if (!frontMatter.citationsCollected) { - Controller.waitingOn.citations.push(function() { - Controller.listeners.onBibliographyChanged({ - target: event.target, - detail: event.detail - }); - }); - return; - } - - if (citationListTag.hasAttribute("distill-prerendered")) { - console.debug("Citation list was prerendered; not updating it."); - } else { - const entries = new Map( - frontMatter.citations.map(citationKey => { - return [citationKey, frontMatter.bibliography.get(citationKey)]; - }) - ); - citationListTag.citations = entries; - } - }, - - onFootnoteChanged() { - // const footnote = event.detail; - //TODO: optimize to only update current footnote - const footnotesList = document.querySelector("d-footnote-list"); - if (footnotesList) { - const footnotes = document.querySelectorAll("d-footnote"); - footnotesList.footnotes = footnotes; - } - }, - - onFrontMatterChanged(event) { - const data = event.detail; - mergeFromYMLFrontmatter(frontMatter, data); - - const interstitial = document.querySelector("d-interstitial"); - if (interstitial) { - if (typeof frontMatter.password !== "undefined") { - interstitial.password = frontMatter.password; - } else { - interstitial.parentElement.removeChild(interstitial); - } - } - - const prerendered = document.body.hasAttribute("distill-prerendered"); - if (!prerendered && domContentLoaded()) { - optionalComponents(document, frontMatter); - - const appendix = document.querySelector("distill-appendix"); - if (appendix) { - appendix.frontMatter = frontMatter; - } - - const byline = document.querySelector("d-byline"); - if (byline) { - byline.frontMatter = frontMatter; - } - - if (data.katex) { - DMath.katexOptions = data.katex; - } - } - }, - - DOMContentLoaded() { - if (Controller.loaded) { - console.warn( - "Controller received DOMContentLoaded but was already loaded!" - ); - return; - } else if (!domContentLoaded()) { - console.warn( - "Controller received DOMContentLoaded at document.readyState: " + - document.readyState + - "!" - ); - return; - } else { - Controller.loaded = true; - console.debug("Runlevel 4: Controller running DOMContentLoaded"); - } - - const frontMatterTag = document.querySelector("d-front-matter"); - if (frontMatterTag) { - const data = parseFrontmatter(frontMatterTag); - Controller.listeners.onFrontMatterChanged({ detail: data }); - } - - // Resolving "citations" dependency due to initial DOM load - frontMatter.citations = collect_citations(); - frontMatter.citationsCollected = true; - for (const waitingCallback of Controller.waitingOn.citations.slice()) { - waitingCallback(); - } - - if (frontMatter.bibliographyParsed) { - for (const waitingCallback of Controller.waitingOn.bibliography.slice()) { - waitingCallback(); - } - } - - const footnotesList = document.querySelector("d-footnote-list"); - if (footnotesList) { - const footnotes = document.querySelectorAll("d-footnote"); - footnotesList.footnotes = footnotes; - } - } - } // listeners - }; // Controller - - var base = "/*\n * Copyright 2018 The Distill Template Authors\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\nhtml {\n font-size: 14px;\n\tline-height: 1.6em;\n /* font-family: \"Libre Franklin\", \"Helvetica Neue\", sans-serif; */\n font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Roboto, Oxygen, Ubuntu, Cantarell, \"Fira Sans\", \"Droid Sans\", \"Helvetica Neue\", Arial, sans-serif;\n /*, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\";*/\n text-size-adjust: 100%;\n -ms-text-size-adjust: 100%;\n -webkit-text-size-adjust: 100%;\n}\n\n@media(min-width: 768px) {\n html {\n font-size: 16px;\n }\n}\n\nbody {\n margin: 0;\n}\n\na {\n color: #004276;\n}\n\nfigure {\n margin: 0;\n}\n\ntable {\n\tborder-collapse: collapse;\n\tborder-spacing: 0;\n}\n\ntable th {\n\ttext-align: left;\n}\n\ntable thead {\n border-bottom: 1px solid rgba(0, 0, 0, 0.05);\n}\n\ntable thead th {\n padding-bottom: 0.5em;\n}\n\ntable tbody :first-child td {\n padding-top: 0.5em;\n}\n\npre {\n overflow: auto;\n max-width: 100%;\n}\n\np {\n margin-top: 0;\n margin-bottom: 1em;\n}\n\nsup, sub {\n vertical-align: baseline;\n position: relative;\n top: -0.4em;\n line-height: 1em;\n}\n\nsub {\n top: 0.4em;\n}\n\n.kicker,\n.marker {\n font-size: 15px;\n font-weight: 600;\n color: rgba(0, 0, 0, 0.5);\n}\n\n\n/* Headline */\n\n@media(min-width: 1024px) {\n d-title h1 span {\n display: block;\n }\n}\n\n/* Figure */\n\nfigure {\n position: relative;\n margin-bottom: 2.5em;\n margin-top: 1.5em;\n}\n\nfigcaption+figure {\n\n}\n\nfigure img {\n width: 100%;\n}\n\nfigure svg text,\nfigure svg tspan {\n}\n\nfigcaption,\n.figcaption {\n color: rgba(0, 0, 0, 0.6);\n font-size: 12px;\n line-height: 1.5em;\n}\n\n@media(min-width: 1024px) {\nfigcaption,\n.figcaption {\n font-size: 13px;\n }\n}\n\nfigure.external img {\n background: white;\n border: 1px solid rgba(0, 0, 0, 0.1);\n box-shadow: 0 1px 8px rgba(0, 0, 0, 0.1);\n padding: 18px;\n box-sizing: border-box;\n}\n\nfigcaption a {\n color: rgba(0, 0, 0, 0.6);\n}\n\nfigcaption b,\nfigcaption strong, {\n font-weight: 600;\n color: rgba(0, 0, 0, 1.0);\n}\n"; - - var layout = "/*\n * Copyright 2018 The Distill Template Authors\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@supports not (display: grid) {\n .base-grid,\n distill-header,\n d-title,\n d-abstract,\n d-article,\n d-appendix,\n distill-appendix,\n d-byline,\n d-footnote-list,\n d-citation-list,\n distill-footer {\n display: block;\n padding: 8px;\n }\n}\n\n.base-grid,\ndistill-header,\nd-title,\nd-abstract,\nd-article,\nd-appendix,\ndistill-appendix,\nd-byline,\nd-footnote-list,\nd-citation-list,\ndistill-footer {\n display: grid;\n justify-items: stretch;\n grid-template-columns: [screen-start] 8px [page-start kicker-start text-start gutter-start middle-start] 1fr 1fr 1fr 1fr 1fr 1fr 1fr 1fr [text-end page-end gutter-end kicker-end middle-end] 8px [screen-end];\n grid-column-gap: 8px;\n}\n\n.grid {\n display: grid;\n grid-column-gap: 8px;\n}\n\n@media(min-width: 768px) {\n .base-grid,\n distill-header,\n d-title,\n d-abstract,\n d-article,\n d-appendix,\n distill-appendix,\n d-byline,\n d-footnote-list,\n d-citation-list,\n distill-footer {\n grid-template-columns: [screen-start] 1fr [page-start kicker-start middle-start text-start] 45px 45px 45px 45px 45px 45px 45px 45px [ kicker-end text-end gutter-start] 45px [middle-end] 45px [page-end gutter-end] 1fr [screen-end];\n grid-column-gap: 16px;\n }\n\n .grid {\n grid-column-gap: 16px;\n }\n}\n\n@media(min-width: 1000px) {\n .base-grid,\n distill-header,\n d-title,\n d-abstract,\n d-article,\n d-appendix,\n distill-appendix,\n d-byline,\n d-footnote-list,\n d-citation-list,\n distill-footer {\n grid-template-columns: [screen-start] 1fr [page-start kicker-start] 50px [middle-start] 50px [text-start kicker-end] 50px 50px 50px 50px 50px 50px 50px 50px [text-end gutter-start] 50px [middle-end] 50px [page-end gutter-end] 1fr [screen-end];\n grid-column-gap: 16px;\n }\n\n .grid {\n grid-column-gap: 16px;\n }\n}\n\n@media(min-width: 1180px) {\n .base-grid,\n distill-header,\n d-title,\n d-abstract,\n d-article,\n d-appendix,\n distill-appendix,\n d-byline,\n d-footnote-list,\n d-citation-list,\n distill-footer {\n grid-template-columns: [screen-start] 1fr [page-start kicker-start] 60px [middle-start] 60px [text-start kicker-end] 60px 60px 60px 60px 60px 60px 60px 60px [text-end gutter-start] 60px [middle-end] 60px [page-end gutter-end] 1fr [screen-end];\n grid-column-gap: 32px;\n }\n\n .grid {\n grid-column-gap: 32px;\n }\n}\n\n\n\n\n.base-grid {\n grid-column: screen;\n}\n\n/* .l-body,\nd-article > * {\n grid-column: text;\n}\n\n.l-page,\nd-title > *,\nd-figure {\n grid-column: page;\n} */\n\n.l-gutter {\n grid-column: gutter;\n}\n\n.l-text,\n.l-body {\n grid-column: text;\n}\n\n.l-page {\n grid-column: page;\n}\n\n.l-body-outset {\n grid-column: middle;\n}\n\n.l-page-outset {\n grid-column: page;\n}\n\n.l-screen {\n grid-column: screen;\n}\n\n.l-screen-inset {\n grid-column: screen;\n padding-left: 16px;\n padding-left: 16px;\n}\n\n\n/* Aside */\n\nd-article aside {\n grid-column: gutter;\n font-size: 12px;\n line-height: 1.6em;\n color: rgba(0, 0, 0, 0.6)\n}\n\n@media(min-width: 768px) {\n aside {\n grid-column: gutter;\n }\n\n .side {\n grid-column: gutter;\n }\n}\n"; - - var print = "/*\n * Copyright 2018 The Distill Template Authors\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@media print {\n\n @page {\n size: 8in 11in;\n @bottom-right {\n content: counter(page) \" of \" counter(pages);\n }\n }\n\n html {\n /* no general margins -- CSS Grid takes care of those */\n }\n\n p, code {\n page-break-inside: avoid;\n }\n\n h2, h3 {\n page-break-after: avoid;\n }\n\n d-header {\n visibility: hidden;\n }\n\n d-footer {\n display: none!important;\n }\n\n}\n"; - - var byline = "/*\n * Copyright 2018 The Distill Template Authors\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\nd-byline {\n contain: style;\n overflow: hidden;\n border-top: 1px solid rgba(0, 0, 0, 0.1);\n font-size: 0.8rem;\n line-height: 1.8em;\n padding: 1.5rem 0;\n min-height: 1.8em;\n}\n\n\nd-byline .byline {\n grid-template-columns: 1fr 1fr;\n grid-column: text;\n}\n\n@media(min-width: 768px) {\n d-byline .byline {\n grid-template-columns: 1fr 1fr 1fr 1fr;\n }\n}\n\nd-byline .authors-affiliations {\n grid-column-end: span 2;\n grid-template-columns: 1fr 1fr;\n margin-bottom: 1em;\n}\n\n@media(min-width: 768px) {\n d-byline .authors-affiliations {\n margin-bottom: 0;\n }\n}\n\nd-byline h3 {\n font-size: 0.6rem;\n font-weight: 400;\n color: rgba(0, 0, 0, 0.5);\n margin: 0;\n text-transform: uppercase;\n}\n\nd-byline p {\n margin: 0;\n}\n\nd-byline a,\nd-article d-byline a {\n color: rgba(0, 0, 0, 0.8);\n text-decoration: none;\n border-bottom: none;\n}\n\nd-article d-byline a:hover {\n text-decoration: underline;\n border-bottom: none;\n}\n\nd-byline p.author {\n font-weight: 500;\n}\n\nd-byline .affiliations {\n\n}\n"; - - var article = "/*\n * Copyright 2018 The Distill Template Authors\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\nd-article {\n contain: layout style;\n overflow-x: hidden;\n border-top: 1px solid rgba(0, 0, 0, 0.1);\n padding-top: 2rem;\n color: rgba(0, 0, 0, 0.8);\n}\n\nd-article > * {\n grid-column: text;\n}\n\n@media(min-width: 768px) {\n d-article {\n font-size: 16px;\n }\n}\n\n@media(min-width: 1024px) {\n d-article {\n font-size: 1.06rem;\n line-height: 1.7em;\n }\n}\n\n\n/* H2 */\n\n\nd-article .marker {\n text-decoration: none;\n border: none;\n counter-reset: section;\n grid-column: kicker;\n line-height: 1.7em;\n}\n\nd-article .marker:hover {\n border: none;\n}\n\nd-article .marker span {\n padding: 0 3px 4px;\n border-bottom: 1px solid rgba(0, 0, 0, 0.2);\n position: relative;\n top: 4px;\n}\n\nd-article .marker:hover span {\n color: rgba(0, 0, 0, 0.7);\n border-bottom: 1px solid rgba(0, 0, 0, 0.7);\n}\n\nd-article h2 {\n font-weight: 600;\n font-size: 24px;\n line-height: 1.25em;\n margin: 2rem 0 1.5rem 0;\n border-bottom: 1px solid rgba(0, 0, 0, 0.1);\n padding-bottom: 1rem;\n}\n\n@media(min-width: 1024px) {\n d-article h2 {\n font-size: 36px;\n }\n}\n\n/* H3 */\n\nd-article h3 {\n font-weight: 700;\n font-size: 18px;\n line-height: 1.4em;\n margin-bottom: 1em;\n margin-top: 2em;\n}\n\n@media(min-width: 1024px) {\n d-article h3 {\n font-size: 20px;\n }\n}\n\n/* H4 */\n\nd-article h4 {\n font-weight: 600;\n text-transform: uppercase;\n font-size: 14px;\n line-height: 1.4em;\n}\n\nd-article a {\n color: inherit;\n}\n\nd-article p,\nd-article ul,\nd-article ol,\nd-article blockquote {\n margin-top: 0;\n margin-bottom: 1em;\n margin-left: 0;\n margin-right: 0;\n}\n\nd-article blockquote {\n border-left: 2px solid rgba(0, 0, 0, 0.2);\n padding-left: 2em;\n font-style: italic;\n color: rgba(0, 0, 0, 0.6);\n}\n\nd-article a {\n border-bottom: 1px solid rgba(0, 0, 0, 0.4);\n text-decoration: none;\n}\n\nd-article a:hover {\n border-bottom: 1px solid rgba(0, 0, 0, 0.8);\n}\n\nd-article .link {\n text-decoration: underline;\n cursor: pointer;\n}\n\nd-article ul,\nd-article ol {\n padding-left: 24px;\n}\n\nd-article li {\n margin-bottom: 1em;\n margin-left: 0;\n padding-left: 0;\n}\n\nd-article li:last-child {\n margin-bottom: 0;\n}\n\nd-article pre {\n font-size: 14px;\n margin-bottom: 20px;\n}\n\nd-article hr {\n grid-column: screen;\n width: 100%;\n border: none;\n border-bottom: 1px solid rgba(0, 0, 0, 0.1);\n margin-top: 60px;\n margin-bottom: 60px;\n}\n\nd-article section {\n margin-top: 60px;\n margin-bottom: 60px;\n}\n\nd-article span.equation-mimic {\n font-family: georgia;\n font-size: 115%;\n font-style: italic;\n}\n\nd-article > d-code,\nd-article section > d-code {\n display: block;\n}\n\nd-article > d-math[block],\nd-article section > d-math[block] {\n display: block;\n}\n\n@media (max-width: 768px) {\n d-article > d-code,\n d-article section > d-code,\n d-article > d-math[block],\n d-article section > d-math[block] {\n overflow-x: scroll;\n -ms-overflow-style: none; // IE 10+\n overflow: -moz-scrollbars-none; // Firefox\n }\n\n d-article > d-code::-webkit-scrollbar,\n d-article section > d-code::-webkit-scrollbar,\n d-article > d-math[block]::-webkit-scrollbar,\n d-article section > d-math[block]::-webkit-scrollbar {\n display: none; // Safari and Chrome\n }\n}\n\nd-article .citation {\n color: #668;\n cursor: pointer;\n}\n\nd-include {\n width: auto;\n display: block;\n}\n\nd-figure {\n contain: layout style;\n}\n\n/* KaTeX */\n\n.katex, .katex-prerendered {\n contain: style;\n display: inline-block;\n}\n\n/* Tables */\n\nd-article table {\n border-collapse: collapse;\n margin-bottom: 1.5rem;\n border-bottom: 1px solid rgba(0, 0, 0, 0.2);\n}\n\nd-article table th {\n border-bottom: 1px solid rgba(0, 0, 0, 0.2);\n}\n\nd-article table td {\n border-bottom: 1px solid rgba(0, 0, 0, 0.05);\n}\n\nd-article table tr:last-of-type td {\n border-bottom: none;\n}\n\nd-article table th,\nd-article table td {\n font-size: 15px;\n padding: 2px 8px;\n}\n\nd-article table tbody :first-child td {\n padding-top: 2px;\n}\n"; - - var title = "/*\n * Copyright 2018 The Distill Template Authors\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\nd-title {\n padding: 2rem 0 1.5rem;\n contain: layout style;\n overflow-x: hidden;\n}\n\n@media(min-width: 768px) {\n d-title {\n padding: 4rem 0 1.5rem;\n }\n}\n\nd-title h1 {\n grid-column: text;\n font-size: 40px;\n font-weight: 700;\n line-height: 1.1em;\n margin: 0 0 0.5rem;\n}\n\n@media(min-width: 768px) {\n d-title h1 {\n font-size: 50px;\n }\n}\n\nd-title p {\n font-weight: 300;\n font-size: 1.2rem;\n line-height: 1.55em;\n grid-column: text;\n}\n\nd-title .status {\n margin-top: 0px;\n font-size: 12px;\n color: #009688;\n opacity: 0.8;\n grid-column: kicker;\n}\n\nd-title .status span {\n line-height: 1;\n display: inline-block;\n padding: 6px 0;\n border-bottom: 1px solid #80cbc4;\n font-size: 11px;\n text-transform: uppercase;\n}\n"; - - // Copyright 2018 The Distill Template Authors - - const styles = base + layout + title + byline + article + math + print; - - function makeStyleTag(dom) { - - const styleTagId = 'distill-prerendered-styles'; - const prerenderedTag = dom.getElementById(styleTagId); - if (!prerenderedTag) { - const styleTag = dom.createElement('style'); - styleTag.id = styleTagId; - styleTag.type = 'text/css'; - const cssTextTag = dom.createTextNode(styles); - styleTag.appendChild(cssTextTag); - const firstScriptTag = dom.head.querySelector('script'); - dom.head.insertBefore(styleTag, firstScriptTag); - } - - } - - // Copyright 2018 The Distill Template Authors - // - // Licensed under the Apache License, Version 2.0 (the "License"); - // you may not use this file except in compliance with the License. - // You may obtain a copy of the License at - // - // http://www.apache.org/licenses/LICENSE-2.0 - // - // Unless required by applicable law or agreed to in writing, software - // distributed under the License is distributed on an "AS IS" BASIS, - // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - // See the License for the specific language governing permissions and - // limitations under the License. - - function addPolyfill(polyfill, polyfillLoadedCallback) { - console.debug('Runlevel 0: Polyfill required: ' + polyfill.name); - const script = document.createElement('script'); - script.src = polyfill.url; - script.async = false; - if (polyfillLoadedCallback) { - script.onload = function() { polyfillLoadedCallback(polyfill); }; - } - script.onerror = function() { - new Error('Runlevel 0: Polyfills failed to load script ' + polyfill.name); - }; - document.head.appendChild(script); - } - - const polyfills = [ - { - name: 'WebComponents', - support: function() { - return 'customElements' in window && - 'attachShadow' in Element.prototype && - 'getRootNode' in Element.prototype && - 'content' in document.createElement('template') && - 'Promise' in window && - 'from' in Array; - }, - url: 'https://distill.pub/third-party/polyfills/webcomponents-lite.js' - }, { - name: 'IntersectionObserver', - support: function() { - return 'IntersectionObserver' in window && - 'IntersectionObserverEntry' in window; - }, - url: 'https://distill.pub/third-party/polyfills/intersection-observer.js' - }, - ]; - - class Polyfills { - - static browserSupportsAllFeatures() { - return polyfills.every((poly) => poly.support()); - } - - static load(callback) { - // Define an intermediate callback that checks if all is loaded. - const polyfillLoaded = function(polyfill) { - polyfill.loaded = true; - console.debug('Runlevel 0: Polyfill has finished loading: ' + polyfill.name); - // console.debug(window[polyfill.name]); - if (Polyfills.neededPolyfills.every((poly) => poly.loaded)) { - console.debug('Runlevel 0: All required polyfills have finished loading.'); - console.debug('Runlevel 0->1.'); - window.distillRunlevel = 1; - callback(); - } - }; - // Add polyfill script tags - for (const polyfill of Polyfills.neededPolyfills) { - addPolyfill(polyfill, polyfillLoaded); - } - } - - static get neededPolyfills() { - if (!Polyfills._neededPolyfills) { - Polyfills._neededPolyfills = polyfills.filter((poly) => !poly.support()); - } - return Polyfills._neededPolyfills; - } - } - - // Copyright 2018 The Distill Template Authors - // - // Licensed under the Apache License, Version 2.0 (the "License"); - // you may not use this file except in compliance with the License. - // You may obtain a copy of the License at - // - // http://www.apache.org/licenses/LICENSE-2.0 - // - // Unless required by applicable law or agreed to in writing, software - // distributed under the License is distributed on an "AS IS" BASIS, - // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - // See the License for the specific language governing permissions and - // limitations under the License. - - // const marginSmall = 16; - // const marginLarge = 3 * marginSmall; - // const margin = marginSmall + marginLarge; - // const gutter = marginSmall; - // const outsetAmount = margin / 2; - // const numCols = 4; - // const numGutters = numCols - 1; - // const columnWidth = (768 - 2 * marginLarge - numGutters * gutter) / numCols; - // - // const screenwidth = 768; - // const pageWidth = screenwidth - 2 * marginLarge; - // const bodyWidth = pageWidth - columnWidth - gutter; - - function body(selector) { - return `${selector} { - grid-column: left / text; - } - `; - } - - // Copyright 2018 The Distill Template Authors - - const T$1 = Template('d-abstract', ` - - - -`); - - class Abstract extends T$1(HTMLElement) { - - } - - // Copyright 2018 The Distill Template Authors - - const T$2 = Template('d-appendix', ` - - -`, false); - - class Appendix extends T$2(HTMLElement) { - - } - - // Copyright 2018 The Distill Template Authors - // - // Licensed under the Apache License, Version 2.0 (the "License"); - // you may not use this file except in compliance with the License. - // You may obtain a copy of the License at - // - // http://www.apache.org/licenses/LICENSE-2.0 - // - // Unless required by applicable law or agreed to in writing, software - // distributed under the License is distributed on an "AS IS" BASIS, - // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - // See the License for the specific language governing permissions and - // limitations under the License. - - // import { Template } from '../mixins/template'; - // import { Controller } from '../controller'; - - const isOnlyWhitespace = /^\s*$/; - - class Article extends HTMLElement { - static get is() { return 'd-article'; } - - constructor() { - super(); - - new MutationObserver( (mutations) => { - for (const mutation of mutations) { - for (const addedNode of mutation.addedNodes) { - switch (addedNode.nodeName) { - case '#text': { // usually text nodes are only linebreaks. - const text = addedNode.nodeValue; - if (!isOnlyWhitespace.test(text)) { - console.warn('Use of unwrapped text in distill articles is discouraged as it breaks layout! Please wrap any text in a or

tag. We found the following text: ' + text); - const wrapper = document.createElement('span'); - wrapper.innerHTML = addedNode.nodeValue; - addedNode.parentNode.insertBefore(wrapper, addedNode); - addedNode.parentNode.removeChild(addedNode); - } - } break; - } - } - } - }).observe(this, {childList: true}); - } - - } - - var commonjsGlobal = typeof globalThis !== 'undefined' ? globalThis : typeof window !== 'undefined' ? window : typeof global !== 'undefined' ? global : typeof self !== 'undefined' ? self : {}; - - function createCommonjsModule(fn, module) { - return module = { exports: {} }, fn(module, module.exports), module.exports; - } - - var bibtexParse = createCommonjsModule(function (module, exports) { - /* start bibtexParse 0.0.22 */ - - //Original work by Henrik Muehe (c) 2010 - // - //CommonJS port by Mikola Lysenko 2013 - // - //Port to Browser lib by ORCID / RCPETERS - // - //Issues: - //no comment handling within strings - //no string concatenation - //no variable values yet - //Grammar implemented here: - //bibtex -> (string | preamble | comment | entry)*; - //string -> '@STRING' '{' key_equals_value '}'; - //preamble -> '@PREAMBLE' '{' value '}'; - //comment -> '@COMMENT' '{' value '}'; - //entry -> '@' key '{' key ',' key_value_list '}'; - //key_value_list -> key_equals_value (',' key_equals_value)*; - //key_equals_value -> key '=' value; - //value -> value_quotes | value_braces | key; - //value_quotes -> '"' .*? '"'; // not quite - //value_braces -> '{' .*? '"'; // not quite - (function(exports) { - - function BibtexParser() { - - this.months = ["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"]; - this.notKey = [',','{','}',' ','=']; - this.pos = 0; - this.input = ""; - this.entries = new Array(); - - this.currentEntry = ""; - - this.setInput = function(t) { - this.input = t; - }; - - this.getEntries = function() { - return this.entries; - }; - - this.isWhitespace = function(s) { - return (s == ' ' || s == '\r' || s == '\t' || s == '\n'); - }; - - this.match = function(s, canCommentOut) { - if (canCommentOut == undefined || canCommentOut == null) - canCommentOut = true; - this.skipWhitespace(canCommentOut); - if (this.input.substring(this.pos, this.pos + s.length) == s) { - this.pos += s.length; - } else { - throw "Token mismatch, expected " + s + ", found " - + this.input.substring(this.pos); - } this.skipWhitespace(canCommentOut); - }; - - this.tryMatch = function(s, canCommentOut) { - if (canCommentOut == undefined || canCommentOut == null) - canCommentOut = true; - this.skipWhitespace(canCommentOut); - if (this.input.substring(this.pos, this.pos + s.length) == s) { - return true; - } else { - return false; - } }; - - /* when search for a match all text can be ignored, not just white space */ - this.matchAt = function() { - while (this.input.length > this.pos && this.input[this.pos] != '@') { - this.pos++; - } - if (this.input[this.pos] == '@') { - return true; - } return false; - }; - - this.skipWhitespace = function(canCommentOut) { - while (this.isWhitespace(this.input[this.pos])) { - this.pos++; - } if (this.input[this.pos] == "%" && canCommentOut == true) { - while (this.input[this.pos] != "\n") { - this.pos++; - } this.skipWhitespace(canCommentOut); - } }; - - this.value_braces = function() { - var bracecount = 0; - this.match("{", false); - var start = this.pos; - var escaped = false; - while (true) { - if (!escaped) { - if (this.input[this.pos] == '}') { - if (bracecount > 0) { - bracecount--; - } else { - var end = this.pos; - this.match("}", false); - return this.input.substring(start, end); - } } else if (this.input[this.pos] == '{') { - bracecount++; - } else if (this.pos >= this.input.length - 1) { - throw "Unterminated value"; - } } if (this.input[this.pos] == '\\' && escaped == false) - escaped = true; - else - escaped = false; - this.pos++; - } }; - - this.value_comment = function() { - var str = ''; - var brcktCnt = 0; - while (!(this.tryMatch("}", false) && brcktCnt == 0)) { - str = str + this.input[this.pos]; - if (this.input[this.pos] == '{') - brcktCnt++; - if (this.input[this.pos] == '}') - brcktCnt--; - if (this.pos >= this.input.length - 1) { - throw "Unterminated value:" + this.input.substring(start); - } this.pos++; - } return str; - }; - - this.value_quotes = function() { - this.match('"', false); - var start = this.pos; - var escaped = false; - while (true) { - if (!escaped) { - if (this.input[this.pos] == '"') { - var end = this.pos; - this.match('"', false); - return this.input.substring(start, end); - } else if (this.pos >= this.input.length - 1) { - throw "Unterminated value:" + this.input.substring(start); - } } - if (this.input[this.pos] == '\\' && escaped == false) - escaped = true; - else - escaped = false; - this.pos++; - } }; - - this.single_value = function() { - var start = this.pos; - if (this.tryMatch("{")) { - return this.value_braces(); - } else if (this.tryMatch('"')) { - return this.value_quotes(); - } else { - var k = this.key(); - if (k.match("^[0-9]+$")) - return k; - else if (this.months.indexOf(k.toLowerCase()) >= 0) - return k.toLowerCase(); - else - throw "Value expected:" + this.input.substring(start) + ' for key: ' + k; - - } }; - - this.value = function() { - var values = []; - values.push(this.single_value()); - while (this.tryMatch("#")) { - this.match("#"); - values.push(this.single_value()); - } return values.join(""); - }; - - this.key = function() { - var start = this.pos; - while (true) { - if (this.pos >= this.input.length) { - throw "Runaway key"; - } // а-яА-Я is Cyrillic - //console.log(this.input[this.pos]); - if (this.notKey.indexOf(this.input[this.pos]) >= 0) { - return this.input.substring(start, this.pos); - } else { - this.pos++; - - } } }; - - this.key_equals_value = function() { - var key = this.key(); - if (this.tryMatch("=")) { - this.match("="); - var val = this.value(); - return [ key, val ]; - } else { - throw "... = value expected, equals sign missing:" - + this.input.substring(this.pos); - } }; - - this.key_value_list = function() { - var kv = this.key_equals_value(); - this.currentEntry['entryTags'] = {}; - this.currentEntry['entryTags'][kv[0]] = kv[1]; - while (this.tryMatch(",")) { - this.match(","); - // fixes problems with commas at the end of a list - if (this.tryMatch("}")) { - break; - } - kv = this.key_equals_value(); - this.currentEntry['entryTags'][kv[0]] = kv[1]; - } }; - - this.entry_body = function(d) { - this.currentEntry = {}; - this.currentEntry['citationKey'] = this.key(); - this.currentEntry['entryType'] = d.substring(1); - this.match(","); - this.key_value_list(); - this.entries.push(this.currentEntry); - }; - - this.directive = function() { - this.match("@"); - return "@" + this.key(); - }; - - this.preamble = function() { - this.currentEntry = {}; - this.currentEntry['entryType'] = 'PREAMBLE'; - this.currentEntry['entry'] = this.value_comment(); - this.entries.push(this.currentEntry); - }; - - this.comment = function() { - this.currentEntry = {}; - this.currentEntry['entryType'] = 'COMMENT'; - this.currentEntry['entry'] = this.value_comment(); - this.entries.push(this.currentEntry); - }; - - this.entry = function(d) { - this.entry_body(d); - }; - - this.bibtex = function() { - while (this.matchAt()) { - var d = this.directive(); - this.match("{"); - if (d == "@STRING") { - this.string(); - } else if (d == "@PREAMBLE") { - this.preamble(); - } else if (d == "@COMMENT") { - this.comment(); - } else { - this.entry(d); - } - this.match("}"); - } }; - } - exports.toJSON = function(bibtex) { - var b = new BibtexParser(); - b.setInput(bibtex); - b.bibtex(); - return b.entries; - }; - - /* added during hackathon don't hate on me */ - exports.toBibtex = function(json) { - var out = ''; - for ( var i in json) { - out += "@" + json[i].entryType; - out += '{'; - if (json[i].citationKey) - out += json[i].citationKey + ', '; - if (json[i].entry) - out += json[i].entry ; - if (json[i].entryTags) { - var tags = ''; - for (var jdx in json[i].entryTags) { - if (tags.length != 0) - tags += ', '; - tags += jdx + '= {' + json[i].entryTags[jdx] + '}'; - } - out += tags; - } - out += '}\n\n'; - } - return out; - - }; - - })( exports); - - /* end bibtexParse */ - }); - - // Copyright 2018 The Distill Template Authors - - function normalizeTag(string) { - return string - .replace(/[\t\n ]+/g, ' ') - .replace(/{\\["^`.'acu~Hvs]( )?([a-zA-Z])}/g, (full, x, char) => char) - .replace(/{\\([a-zA-Z])}/g, (full, char) => char); - } - - function parseBibtex(bibtex) { - const bibliography = new Map(); - const parsedEntries = bibtexParse.toJSON(bibtex); - for (const entry of parsedEntries) { - // normalize tags; note entryTags is an object, not Map - for (const [key, value] of Object.entries(entry.entryTags)) { - entry.entryTags[key.toLowerCase()] = normalizeTag(value); - } - entry.entryTags.type = entry.entryType; - // add to bibliography - bibliography.set(entry.citationKey, entry.entryTags); - } - return bibliography; - } - - function serializeFrontmatterToBibtex(frontMatter) { - return `@article{${frontMatter.slug}, - author = {${frontMatter.bibtexAuthors}}, - title = {${frontMatter.title}}, - journal = {${frontMatter.journal.title}}, - year = {${frontMatter.publishedYear}}, - note = {${frontMatter.url}}, - doi = {${frontMatter.doi}} -}`; - } - - // Copyright 2018 The Distill Template Authors - - class Bibliography extends HTMLElement { - - static get is() { return 'd-bibliography'; } - - constructor() { - super(); - - // set up mutation observer - const options = {childList: true, characterData: true, subtree: true}; - const observer = new MutationObserver( (entries) => { - for (const entry of entries) { - if (entry.target.nodeName === 'SCRIPT' || entry.type === 'characterData') { - this.parseIfPossible(); - } - } - }); - observer.observe(this, options); - } - - connectedCallback() { - requestAnimationFrame(() => { - this.parseIfPossible(); - }); - } - - parseIfPossible() { - const scriptTag = this.querySelector('script'); - if (!scriptTag) return; - if (scriptTag.type == 'text/bibtex') { - const newBibtex = scriptTag.textContent; - if (this.bibtex !== newBibtex) { - this.bibtex = newBibtex; - const bibliography = parseBibtex(this.bibtex); - this.notify(bibliography); - } - } else if (scriptTag.type == 'text/json') { - const bibliography = new Map(JSON.parse(scriptTag.textContent)); - this.notify(bibliography); - } else { - console.warn('Unsupported bibliography script tag type: ' + scriptTag.type); - } - } - - notify(bibliography) { - const options = { detail: bibliography, bubbles: true }; - const event = new CustomEvent('onBibliographyChanged', options); - this.dispatchEvent(event); - } - - /* observe 'src' attribute */ - - static get observedAttributes() { - return ['src']; - } - - receivedBibtex(event) { - const bibliography = parseBibtex(event.target.response); - this.notify(bibliography); - } - - attributeChangedCallback(name, oldValue, newValue) { - var oReq = new XMLHttpRequest(); - oReq.onload = (e) => this.receivedBibtex(e); - oReq.onerror = () => console.warn(`Could not load Bibtex! (tried ${newValue})`); - oReq.responseType = 'text'; - oReq.open('GET', newValue, true); - oReq.send(); - } - - - } - - // Copyright 2018 The Distill Template Authors - // - // Licensed under the Apache License, Version 2.0 (the "License"); - // you may not use this file except in compliance with the License. - // You may obtain a copy of the License at - // - // http://www.apache.org/licenses/LICENSE-2.0 - // - // Unless required by applicable law or agreed to in writing, software - // distributed under the License is distributed on an "AS IS" BASIS, - // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - // See the License for the specific language governing permissions and - // limitations under the License. - - // import style from '../styles/d-byline.css'; - - function bylineTemplate(frontMatter) { - return ` -

-
-

Authors

-

Affiliations

- ${frontMatter.authors.map(author => ` -

- ${author.personalURL ? ` - ${author.name}` : ` - ${author.name}`} -

-

- ${author.affiliations.map(affiliation => - affiliation.url ? `${affiliation.name}` : `${affiliation.name}` - ).join(', ')} -

- `).join('')} -
-
-

Published

- ${frontMatter.publishedDate ? ` -

${frontMatter.publishedMonth} ${frontMatter.publishedDay}, ${frontMatter.publishedYear}

` : ` -

Not published yet.

`} -
-
-`; - } - - class Byline extends HTMLElement { - - static get is() { return 'd-byline'; } - - set frontMatter(frontMatter) { - this.innerHTML = bylineTemplate(frontMatter); - } - - } - - // Copyright 2018 The Distill Template Authors - - const T$3 = Template( - "d-cite", - ` - - - - -
- -
-` - ); - - class Cite extends T$3(HTMLElement) { - /* Lifecycle */ - constructor() { - super(); - this._numbers = []; - this._entries = []; - } - - connectedCallback() { - this.outerSpan = this.root.querySelector("#citation-"); - this.innerSpan = this.root.querySelector(".citation-number"); - this.hoverBox = this.root.querySelector("d-hover-box"); - window.customElements.whenDefined("d-hover-box").then(() => { - this.hoverBox.listen(this); - }); - // in case this component got connected after values were set - if (this.numbers) { - this.displayNumbers(this.numbers); - } - if (this.entries) { - this.displayEntries(this.entries); - } - } - - //TODO This causes an infinite loop on firefox with polyfills. - // This is only needed for interactive editing so no priority. - // disconnectedCallback() { - // const options = { detail: [this, this.keys], bubbles: true }; - // const event = new CustomEvent('onCiteKeyRemoved', options); - // document.dispatchEvent(event); - // } - - /* observe 'key' attribute */ - - static get observedAttributes() { - return ["key", "bibtex-key"]; - } - - attributeChangedCallback(name, oldValue, newValue) { - const eventName = oldValue ? "onCiteKeyChanged" : "onCiteKeyCreated"; - const keys = newValue.split(",").map(k => k.trim()); - const options = { detail: [this, keys], bubbles: true }; - const event = new CustomEvent(eventName, options); - document.dispatchEvent(event); - } - - set key(value) { - this.setAttribute("key", value); - } - - get key() { - return this.getAttribute("key") || this.getAttribute("bibtex-key"); - } - - get keys() { - const result = this.key.split(","); - console.log(result); - return result; - } - - /* Setters & Rendering */ - - set numbers(numbers) { - this._numbers = numbers; - this.displayNumbers(numbers); - } - - get numbers() { - return this._numbers; - } - - displayNumbers(numbers) { - if (!this.innerSpan) return; - const numberStrings = numbers.map(index => { - return index == -1 ? "?" : index + 1 + ""; - }); - const textContent = "[" + numberStrings.join(", ") + "]"; - this.innerSpan.textContent = textContent; - } - - set entries(entries) { - this._entries = entries; - this.displayEntries(entries); - } - - get entries() { - return this._entries; - } - - displayEntries(entries) { - if (!this.hoverBox) return; - this.hoverBox.innerHTML = `
    - ${entries - .map(hover_cite) - .map(html => `
  • ${html}
    • `) - .join("\n")} -
`; - } - } - - // Copyright 2018 The Distill Template Authors - - const styles$1 = ` -d-citation-list { - contain: style; -} - -d-citation-list .references { - grid-column: text; -} - -d-citation-list .references .title { - font-weight: 500; -} -`; - - function renderCitationList(element, entries, dom=document) { - if (entries.size > 0) { - element.style.display = ''; - let list = element.querySelector('.references'); - if (list) { - list.innerHTML = ''; - } else { - const stylesTag = dom.createElement('style'); - stylesTag.innerHTML = styles$1; - element.appendChild(stylesTag); - - const heading = dom.createElement('h3'); - heading.id = 'references'; - heading.textContent = 'References'; - element.appendChild(heading); - - list = dom.createElement('ol'); - list.id = 'references-list'; - list.className = 'references'; - element.appendChild(list); - } - - for (const [key, entry] of entries) { - const listItem = dom.createElement('li'); - listItem.id = key; - listItem.innerHTML = bibliography_cite(entry); - list.appendChild(listItem); - } - } else { - element.style.display = 'none'; - } - } - - class CitationList extends HTMLElement { - - static get is() { return 'd-citation-list'; } - - connectedCallback() { - if (!this.hasAttribute('distill-prerendered')) { - this.style.display = 'none'; - } - } - - set citations(citations) { - renderCitationList(this, citations); - } - - } - - var prism = createCommonjsModule(function (module) { - /* ********************************************** - Begin prism-core.js - ********************************************** */ - - var _self = (typeof window !== 'undefined') - ? window // if in browser - : ( - (typeof WorkerGlobalScope !== 'undefined' && self instanceof WorkerGlobalScope) - ? self // if in worker - : {} // if in node js - ); - - /** - * Prism: Lightweight, robust, elegant syntax highlighting - * MIT license http://www.opensource.org/licenses/mit-license.php/ - * @author Lea Verou http://lea.verou.me - */ - - var Prism = (function (_self){ - - // Private helper vars - var lang = /\blang(?:uage)?-([\w-]+)\b/i; - var uniqueId = 0; - - - var _ = { - manual: _self.Prism && _self.Prism.manual, - disableWorkerMessageHandler: _self.Prism && _self.Prism.disableWorkerMessageHandler, - util: { - encode: function encode(tokens) { - if (tokens instanceof Token) { - return new Token(tokens.type, encode(tokens.content), tokens.alias); - } else if (Array.isArray(tokens)) { - return tokens.map(encode); - } else { - return tokens.replace(/&/g, '&').replace(/' + env.content + ''; - }; - - /** - * @param {string} text - * @param {LinkedList} tokenList - * @param {any} grammar - * @param {LinkedListNode} startNode - * @param {number} startPos - * @param {boolean} [oneshot=false] - * @param {string} [target] - */ - function matchGrammar(text, tokenList, grammar, startNode, startPos, oneshot, target) { - for (var token in grammar) { - if (!grammar.hasOwnProperty(token) || !grammar[token]) { - continue; - } - - var patterns = grammar[token]; - patterns = Array.isArray(patterns) ? patterns : [patterns]; - - for (var j = 0; j < patterns.length; ++j) { - if (target && target == token + ',' + j) { - return; - } - - var pattern = patterns[j], - inside = pattern.inside, - lookbehind = !!pattern.lookbehind, - greedy = !!pattern.greedy, - lookbehindLength = 0, - alias = pattern.alias; - - if (greedy && !pattern.pattern.global) { - // Without the global flag, lastIndex won't work - var flags = pattern.pattern.toString().match(/[imsuy]*$/)[0]; - pattern.pattern = RegExp(pattern.pattern.source, flags + 'g'); - } - - pattern = pattern.pattern || pattern; - - for ( // iterate the token list and keep track of the current token/string position - var currentNode = startNode.next, pos = startPos; - currentNode !== tokenList.tail; - pos += currentNode.value.length, currentNode = currentNode.next - ) { - - var str = currentNode.value; - - if (tokenList.length > text.length) { - // Something went terribly wrong, ABORT, ABORT! - return; - } - - if (str instanceof Token) { - continue; - } - - var removeCount = 1; // this is the to parameter of removeBetween - - if (greedy && currentNode != tokenList.tail.prev) { - pattern.lastIndex = pos; - var match = pattern.exec(text); - if (!match) { - break; - } - - var from = match.index + (lookbehind && match[1] ? match[1].length : 0); - var to = match.index + match[0].length; - var p = pos; - - // find the node that contains the match - p += currentNode.value.length; - while (from >= p) { - currentNode = currentNode.next; - p += currentNode.value.length; - } - // adjust pos (and p) - p -= currentNode.value.length; - pos = p; - - // the current node is a Token, then the match starts inside another Token, which is invalid - if (currentNode.value instanceof Token) { - continue; - } - - // find the last node which is affected by this match - for ( - var k = currentNode; - k !== tokenList.tail && (p < to || (typeof k.value === 'string' && !k.prev.value.greedy)); - k = k.next - ) { - removeCount++; - p += k.value.length; - } - removeCount--; - - // replace with the new match - str = text.slice(pos, p); - match.index -= pos; - } else { - pattern.lastIndex = 0; - - var match = pattern.exec(str); - } - - if (!match) { - if (oneshot) { - break; - } - - continue; - } - - if (lookbehind) { - lookbehindLength = match[1] ? match[1].length : 0; - } - - var from = match.index + lookbehindLength, - match = match[0].slice(lookbehindLength), - to = from + match.length, - before = str.slice(0, from), - after = str.slice(to); - - var removeFrom = currentNode.prev; - - if (before) { - removeFrom = addAfter(tokenList, removeFrom, before); - pos += before.length; - } - - removeRange(tokenList, removeFrom, removeCount); - - var wrapped = new Token(token, inside ? _.tokenize(match, inside) : match, alias, match, greedy); - currentNode = addAfter(tokenList, removeFrom, wrapped); - - if (after) { - addAfter(tokenList, currentNode, after); - } - - - if (removeCount > 1) - matchGrammar(text, tokenList, grammar, currentNode.prev, pos, true, token + ',' + j); - - if (oneshot) - break; - } - } - } - } - - /** - * @typedef LinkedListNode - * @property {T} value - * @property {LinkedListNode | null} prev The previous node. - * @property {LinkedListNode | null} next The next node. - * @template T - */ - - /** - * @template T - */ - function LinkedList() { - /** @type {LinkedListNode} */ - var head = { value: null, prev: null, next: null }; - /** @type {LinkedListNode} */ - var tail = { value: null, prev: head, next: null }; - head.next = tail; - - /** @type {LinkedListNode} */ - this.head = head; - /** @type {LinkedListNode} */ - this.tail = tail; - this.length = 0; - } - - /** - * Adds a new node with the given value to the list. - * @param {LinkedList} list - * @param {LinkedListNode} node - * @param {T} value - * @returns {LinkedListNode} The added node. - * @template T - */ - function addAfter(list, node, value) { - // assumes that node != list.tail && values.length >= 0 - var next = node.next; - - var newNode = { value: value, prev: node, next: next }; - node.next = newNode; - next.prev = newNode; - list.length++; - - return newNode; - } - /** - * Removes `count` nodes after the given node. The given node will not be removed. - * @param {LinkedList} list - * @param {LinkedListNode} node - * @param {number} count - * @template T - */ - function removeRange(list, node, count) { - var next = node.next; - for (var i = 0; i < count && next !== list.tail; i++) { - next = next.next; - } - node.next = next; - next.prev = node; - list.length -= i; - } - /** - * @param {LinkedList} list - * @returns {T[]} - * @template T - */ - function toArray(list) { - var array = []; - var node = list.head.next; - while (node !== list.tail) { - array.push(node.value); - node = node.next; - } - return array; - } - - - if (!_self.document) { - if (!_self.addEventListener) { - // in Node.js - return _; - } - - if (!_.disableWorkerMessageHandler) { - // In worker - _self.addEventListener('message', function (evt) { - var message = JSON.parse(evt.data), - lang = message.language, - code = message.code, - immediateClose = message.immediateClose; - - _self.postMessage(_.highlight(code, _.languages[lang], lang)); - if (immediateClose) { - _self.close(); - } - }, false); - } - - return _; - } - - //Get current script and highlight - var script = _.util.currentScript(); - - if (script) { - _.filename = script.src; - - if (script.hasAttribute('data-manual')) { - _.manual = true; - } - } - - function highlightAutomaticallyCallback() { - if (!_.manual) { - _.highlightAll(); - } - } - - if (!_.manual) { - // If the document state is "loading", then we'll use DOMContentLoaded. - // If the document state is "interactive" and the prism.js script is deferred, then we'll also use the - // DOMContentLoaded event because there might be some plugins or languages which have also been deferred and they - // might take longer one animation frame to execute which can create a race condition where only some plugins have - // been loaded when Prism.highlightAll() is executed, depending on how fast resources are loaded. - // See https://github.com/PrismJS/prism/issues/2102 - var readyState = document.readyState; - if (readyState === 'loading' || readyState === 'interactive' && script && script.defer) { - document.addEventListener('DOMContentLoaded', highlightAutomaticallyCallback); - } else { - if (window.requestAnimationFrame) { - window.requestAnimationFrame(highlightAutomaticallyCallback); - } else { - window.setTimeout(highlightAutomaticallyCallback, 16); - } - } - } - - return _; - - })(_self); - - if ( module.exports) { - module.exports = Prism; - } - - // hack for components to work correctly in node.js - if (typeof commonjsGlobal !== 'undefined') { - commonjsGlobal.Prism = Prism; - } - - - /* ********************************************** - Begin prism-markup.js - ********************************************** */ - - Prism.languages.markup = { - 'comment': //, - 'prolog': /<\?[\s\S]+?\?>/, - 'doctype': { - pattern: /"'[\]]|"[^"]*"|'[^']*')+(?:\[(?:(?!)*\]\s*)?>/i, - greedy: true - }, - 'cdata': //i, - 'tag': { - pattern: /<\/?(?!\d)[^\s>\/=$<%]+(?:\s(?:\s*[^\s>\/=]+(?:\s*=\s*(?:"[^"]*"|'[^']*'|[^\s'">=]+(?=[\s>]))|(?=[\s/>])))+)?\s*\/?>/i, - greedy: true, - inside: { - 'tag': { - pattern: /^<\/?[^\s>\/]+/i, - inside: { - 'punctuation': /^<\/?/, - 'namespace': /^[^\s>\/:]+:/ - } - }, - 'attr-value': { - pattern: /=\s*(?:"[^"]*"|'[^']*'|[^\s'">=]+)/i, - inside: { - 'punctuation': [ - /^=/, - { - pattern: /^(\s*)["']|["']$/, - lookbehind: true - } - ] - } - }, - 'punctuation': /\/?>/, - 'attr-name': { - pattern: /[^\s>\/]+/, - inside: { - 'namespace': /^[^\s>\/:]+:/ - } - } - - } - }, - 'entity': /&#?[\da-z]{1,8};/i - }; - - Prism.languages.markup['tag'].inside['attr-value'].inside['entity'] = - Prism.languages.markup['entity']; - - // Plugin to make entity title show the real entity, idea by Roman Komarov - Prism.hooks.add('wrap', function(env) { - - if (env.type === 'entity') { - env.attributes['title'] = env.content.replace(/&/, '&'); - } - }); - - Object.defineProperty(Prism.languages.markup.tag, 'addInlined', { - /** - * Adds an inlined language to markup. - * - * An example of an inlined language is CSS with ` - - - -`); - - class Code extends Mutating(T$4(HTMLElement)) { - - renderContent() { - - // check if language can be highlighted - this.languageName = this.getAttribute('language'); - if (!this.languageName) { - console.warn('You need to provide a language attribute to your block to let us know how to highlight your code; e.g.:\n zeros = np.zeros(shape).'); - return; - } - const language = prism.languages[this.languageName]; - if (language == undefined) { - console.warn(`Distill does not yet support highlighting your code block in "${this.languageName}'.`); - return; - } - - let content = this.textContent; - const codeTag = this.shadowRoot.querySelector('#code-container'); - - if (this.hasAttribute('block')) { - // normalize the tab indents - content = content.replace(/\n/, ''); - const tabs = content.match(/\s*/); - content = content.replace(new RegExp('\n' + tabs, 'g'), '\n'); - content = content.trim(); - // wrap code block in pre tag if needed - if (codeTag.parentNode instanceof ShadowRoot) { - const preTag = document.createElement('pre'); - this.shadowRoot.removeChild(codeTag); - preTag.appendChild(codeTag); - this.shadowRoot.appendChild(preTag); - } - - } - - codeTag.className = `language-${this.languageName}`; - codeTag.innerHTML = prism.highlight(content, language); - } - - } - - // Copyright 2018 The Distill Template Authors - - const T$5 = Template('d-footnote', ` - - - -
- -
- - - - - - -`); - - class Footnote extends T$5(HTMLElement) { - - constructor() { - super(); - - const options = {childList: true, characterData: true, subtree: true}; - const observer = new MutationObserver(this.notify); - observer.observe(this, options); - } - - notify() { - const options = { detail: this, bubbles: true }; - const event = new CustomEvent('onFootnoteChanged', options); - document.dispatchEvent(event); - } - - connectedCallback() { - // listen and notify about changes to slotted content - // const slot = this.shadowRoot.querySelector('#slot'); - // console.warn(slot.textContent); - // slot.addEventListener('slotchange', this.notify); - this.hoverBox = this.root.querySelector('d-hover-box'); - window.customElements.whenDefined('d-hover-box').then(() => { - this.hoverBox.listen(this); - }); - // create numeric ID - Footnote.currentFootnoteId += 1; - const IdString = Footnote.currentFootnoteId.toString(); - this.root.host.id = 'd-footnote-' + IdString; - - // set up hidden hover box - const id = 'dt-fn-hover-box-' + IdString; - this.hoverBox.id = id; - - // set up visible footnote marker - const span = this.root.querySelector('#fn-'); - span.setAttribute('id', 'fn-' + IdString); - span.setAttribute('data-hover-ref', id); - span.textContent = IdString; - } - - } - - Footnote.currentFootnoteId = 0; - - // Copyright 2018 The Distill Template Authors - - const T$6 = Template('d-footnote-list', ` - - -

Footnotes

-
    -`, false); - - class FootnoteList extends T$6(HTMLElement) { - - connectedCallback() { - super.connectedCallback(); - - this.list = this.root.querySelector('ol'); - // footnotes list is initially hidden - this.root.style.display = 'none'; - // look through document and register existing footnotes - // Store.subscribeTo('footnotes', (footnote) => { - // this.renderFootnote(footnote); - // }); - } - - // TODO: could optimize this to accept individual footnotes? - set footnotes(footnotes) { - this.list.innerHTML = ''; - if (footnotes.length) { - // ensure footnote list is visible - this.root.style.display = ''; - - for (const footnote of footnotes) { - // construct and append list item to show footnote - const listItem = document.createElement('li'); - listItem.id = footnote.id + '-listing'; - listItem.innerHTML = footnote.innerHTML; - - const backlink = document.createElement('a'); - backlink.setAttribute('class', 'footnote-backlink'); - backlink.textContent = '[↩]'; - backlink.href = '#' + footnote.id; - - listItem.appendChild(backlink); - this.list.appendChild(listItem); - } - } else { - // ensure footnote list is invisible - this.root.style.display = 'none'; - } - } - - } - - // Copyright 2018 The Distill Template Authors - - const T$7 = Template('d-hover-box', ` - - -
    -
    - -
    -
    -`); - - class HoverBox extends T$7(HTMLElement) { - - constructor() { - super(); - } - - connectedCallback() { - - } - - listen(element) { - // console.log(element) - this.bindDivEvents(this); - this.bindTriggerEvents(element); - // this.style.display = "block"; - } - - bindDivEvents(element) { - // For mice, same behavior as hovering on links - element.addEventListener('mouseover', () => { - if (!this.visible) this.showAtNode(element); - this.stopTimeout(); - }); - element.addEventListener('mouseout', () => { - this.extendTimeout(500); - }); - // Don't trigger body touchstart event when touching within box - element.addEventListener('touchstart', (event) => { - event.stopPropagation(); - }, {passive: true}); - // Close box when touching outside box - document.body.addEventListener('touchstart', () => { - this.hide(); - }, {passive: true}); - } - - bindTriggerEvents(node) { - node.addEventListener('mouseover', () => { - if (!this.visible) { - this.showAtNode(node); - } - this.stopTimeout(); - }); - - node.addEventListener('mouseout', () => { - this.extendTimeout(300); - }); - - node.addEventListener('touchstart', (event) => { - if (this.visible) { - this.hide(); - } else { - this.showAtNode(node); - } - // Don't trigger body touchstart event when touching link - event.stopPropagation(); - }, {passive: true}); - } - - show(position) { - this.visible = true; - this.style.display = 'block'; - // 10px extra offset from element - this.style.top = Math.round(position[1] + 10) + 'px'; - } - - showAtNode(node) { - // https://developer.mozilla.org/en-US/docs/Web/API/HTMLElement/offsetTop - const bbox = node.getBoundingClientRect(); - this.show([node.offsetLeft + bbox.width, node.offsetTop + bbox.height]); - } - - hide() { - this.visible = false; - this.style.display = 'none'; - this.stopTimeout(); - } - - stopTimeout() { - if (this.timeout) { - clearTimeout(this.timeout); - } - } - - extendTimeout(time) { - this.stopTimeout(); - this.timeout = setTimeout(() => { - this.hide(); - }, time); - } - - } - - // Copyright 2018 The Distill Template Authors - // - // Licensed under the Apache License, Version 2.0 (the "License"); - // you may not use this file except in compliance with the License. - // You may obtain a copy of the License at - // - // http://www.apache.org/licenses/LICENSE-2.0 - // - // Unless required by applicable law or agreed to in writing, software - // distributed under the License is distributed on an "AS IS" BASIS, - // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - // See the License for the specific language governing permissions and - // limitations under the License. - - class Title extends HTMLElement { - static get is() { return 'd-title'; } - } - - // Copyright 2018 The Distill Template Authors - - const T$8 = Template('d-references', ` - -`, false); - - class References extends T$8(HTMLElement) { - - } - - // Copyright 2018 The Distill Template Authors - // - // Licensed under the Apache License, Version 2.0 (the "License"); - // you may not use this file except in compliance with the License. - // You may obtain a copy of the License at - // - // http://www.apache.org/licenses/LICENSE-2.0 - // - // Unless required by applicable law or agreed to in writing, software - // distributed under the License is distributed on an "AS IS" BASIS, - // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - // See the License for the specific language governing permissions and - // limitations under the License. - - class TOC extends HTMLElement { - - static get is() { return 'd-toc'; } - - connectedCallback() { - if (!this.getAttribute('prerendered')) { - window.onload = () => { - const article = document.querySelector('d-article'); - const headings = article.querySelectorAll('h2, h3'); - renderTOC(this, headings); - }; - } - } - - } - - function renderTOC(element, headings) { - - let ToC =` - - -

    Table of contents

    -
      `; - - for (const el of headings) { - // should element be included in TOC? - const isInTitle = el.parentElement.tagName == 'D-TITLE'; - const isException = el.getAttribute('no-toc'); - if (isInTitle || isException) continue; - // create TOC entry - const title = el.textContent; - const link = '#' + el.getAttribute('id'); - - let newLine = '
    • ' + '' + title + '' + '
      • '; - if (el.tagName == 'H3') { - newLine = '
        ' + newLine + '
      '; - } else { - newLine += '
      '; - } - ToC += newLine; - - } - - ToC += '
    '; - element.innerHTML = ToC; - } - - // Copyright 2018 The Distill Template Authors - // - // Licensed under the Apache License, Version 2.0 (the "License"); - // you may not use this file except in compliance with the License. - // You may obtain a copy of the License at - // - // http://www.apache.org/licenses/LICENSE-2.0 - // - // Unless required by applicable law or agreed to in writing, software - // distributed under the License is distributed on an "AS IS" BASIS, - // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - // See the License for the specific language governing permissions and - // limitations under the License. - - // Figure - // - // d-figure provides a state-machine of visibility events: - // - // scroll out of view - // +----------------+ - // *do work here* | | - // +----------------+ +-+---------+ +-v---------+ - // | ready +----> onscreen | | offscreen | - // +----------------+ +---------^-+ +---------+-+ - // | | - // +----------------+ - // scroll into view - // - - class Figure extends HTMLElement { - - static get is() { return 'd-figure'; } - - static get readyQueue() { - if (!Figure._readyQueue) { - Figure._readyQueue = []; - } - return Figure._readyQueue; - } - - static addToReadyQueue(figure) { - if (Figure.readyQueue.indexOf(figure) === -1) { - Figure.readyQueue.push(figure); - Figure.runReadyQueue(); - } - } - - static runReadyQueue() { - // console.log("Checking to run readyQueue, length: " + Figure.readyQueue.length + ", scrolling: " + Figure.isScrolling); - // if (Figure.isScrolling) return; - // console.log("Running ready Queue"); - const figure = Figure.readyQueue - .sort((a,b) => a._seenOnScreen - b._seenOnScreen ) - .filter((figure) => !figure._ready) - .pop(); - if (figure) { - figure.ready(); - requestAnimationFrame(Figure.runReadyQueue); - } - - } - - constructor() { - super(); - // debugger - this._ready = false; - this._onscreen = false; - this._offscreen = true; - } - - connectedCallback() { - this.loadsWhileScrolling = this.hasAttribute('loadsWhileScrolling'); - Figure.marginObserver.observe(this); - Figure.directObserver.observe(this); - } - - disconnectedCallback() { - Figure.marginObserver.unobserve(this); - Figure.directObserver.unobserve(this); - } - - // We use two separate observers: - // One with an extra 1000px margin to warn if the viewpoint gets close, - // And one for the actual on/off screen events - - static get marginObserver() { - if (!Figure._marginObserver) { - // if (!('IntersectionObserver' in window)) { - // throw new Error('no interscetionobbserver!'); - // } - const viewportHeight = window.innerHeight; - const margin = Math.floor(2 * viewportHeight); - const options = {rootMargin: margin + 'px 0px ' + margin + 'px 0px', threshold: 0.01}; - const callback = Figure.didObserveMarginIntersection; - const observer = new IntersectionObserver(callback, options); - Figure._marginObserver = observer; - } - return Figure._marginObserver; - } - - static didObserveMarginIntersection(entries) { - for (const entry of entries) { - const figure = entry.target; - if (entry.isIntersecting && !figure._ready) { - Figure.addToReadyQueue(figure); - } - } - } - - static get directObserver() { - if (!Figure._directObserver) { - Figure._directObserver = new IntersectionObserver( - Figure.didObserveDirectIntersection, { - rootMargin: '0px', threshold: [0, 1.0], - } - ); - } - return Figure._directObserver; - } - - static didObserveDirectIntersection(entries) { - for (const entry of entries) { - const figure = entry.target; - if (entry.isIntersecting) { - figure._seenOnScreen = new Date(); - // if (!figure._ready) { figure.ready(); } - if (figure._offscreen) { figure.onscreen(); } - } else { - if (figure._onscreen) { figure.offscreen(); } - } - } - } - - // Notify listeners that registered late, too: - - addEventListener(eventName, callback) { - super.addEventListener(eventName, callback); - // if we had already dispatched something while presumingly no one was listening, we do so again - // debugger - if (eventName === 'ready') { - if (Figure.readyQueue.indexOf(this) !== -1) { - this._ready = false; - Figure.runReadyQueue(); - } - } - if (eventName === 'onscreen') { - this.onscreen(); - } - } - - // Custom Events - - ready() { - // debugger - this._ready = true; - Figure.marginObserver.unobserve(this); - const event = new CustomEvent('ready'); - this.dispatchEvent(event); - } - - onscreen() { - this._onscreen = true; - this._offscreen = false; - const event = new CustomEvent('onscreen'); - this.dispatchEvent(event); - } - - offscreen() { - this._onscreen = false; - this._offscreen = true; - const event = new CustomEvent('offscreen'); - this.dispatchEvent(event); - } - - } - - if (typeof window !== 'undefined') { - - Figure.isScrolling = false; - let timeout; - const resetTimer = () => { - Figure.isScrolling = true; - clearTimeout(timeout); - timeout = setTimeout(() => { - Figure.isScrolling = false; - Figure.runReadyQueue(); - }, 500); - }; - window.addEventListener('scroll', resetTimer, true); - - } - - // Copyright 2018 The Distill Template Authors - - // This overlay is not secure. - // It is only meant as a social deterrent. - - const productionHostname = 'distill.pub'; - const T$9 = Template('d-interstitial', ` - - -
    -
    -

    This article is in review.

    -

    Do not share this URL or the contents of this article. Thank you!

    - -

    Enter the password we shared with you as part of the review process to view the article.

    -
    -
    -`); - - class Interstitial extends T$9(HTMLElement) { - - connectedCallback() { - if (this.shouldRemoveSelf()) { - this.parentElement.removeChild(this); - } else { - const passwordInput = this.root.querySelector('#interstitial-password-input'); - passwordInput.oninput = (event) => this.passwordChanged(event); - } - } - - passwordChanged(event) { - const entered = event.target.value; - if (entered === this.password) { - console.log('Correct password entered.'); - this.parentElement.removeChild(this); - if (typeof(Storage) !== 'undefined') { - console.log('Saved that correct password was entered.'); - localStorage.setItem(this.localStorageIdentifier(), 'true'); - } - } - } - - shouldRemoveSelf() { - // should never be visible in production - if (window && window.location.hostname === productionHostname) { - console.warn('Interstitial found on production, hiding it.'); - return true - } - // should only have to enter password once - if (typeof(Storage) !== 'undefined') { - if (localStorage.getItem(this.localStorageIdentifier()) === 'true') { - console.log('Loaded that correct password was entered before; skipping interstitial.'); - return true; - } - } - // otherwise, leave visible - return false; - } - - localStorageIdentifier() { - const prefix = 'distill-drafts'; - const suffix = 'interstitial-password-correct'; - return prefix + (window ? window.location.pathname : '-') + suffix - } - - } - - function ascending(a, b) { - return a < b ? -1 : a > b ? 1 : a >= b ? 0 : NaN; - } - - function bisector(compare) { - if (compare.length === 1) compare = ascendingComparator(compare); - return { - left: function(a, x, lo, hi) { - if (lo == null) lo = 0; - if (hi == null) hi = a.length; - while (lo < hi) { - var mid = lo + hi >>> 1; - if (compare(a[mid], x) < 0) lo = mid + 1; - else hi = mid; - } - return lo; - }, - right: function(a, x, lo, hi) { - if (lo == null) lo = 0; - if (hi == null) hi = a.length; - while (lo < hi) { - var mid = lo + hi >>> 1; - if (compare(a[mid], x) > 0) hi = mid; - else lo = mid + 1; - } - return lo; - } - }; - } - - function ascendingComparator(f) { - return function(d, x) { - return ascending(f(d), x); - }; - } - - var ascendingBisect = bisector(ascending); - var bisectRight = ascendingBisect.right; - - function range(start, stop, step) { - start = +start, stop = +stop, step = (n = arguments.length) < 2 ? (stop = start, start = 0, 1) : n < 3 ? 1 : +step; - - var i = -1, - n = Math.max(0, Math.ceil((stop - start) / step)) | 0, - range = new Array(n); - - while (++i < n) { - range[i] = start + i * step; - } - - return range; - } - - var e10 = Math.sqrt(50), - e5 = Math.sqrt(10), - e2 = Math.sqrt(2); - - function ticks(start, stop, count) { - var reverse, - i = -1, - n, - ticks, - step; - - stop = +stop, start = +start, count = +count; - if (start === stop && count > 0) return [start]; - if (reverse = stop < start) n = start, start = stop, stop = n; - if ((step = tickIncrement(start, stop, count)) === 0 || !isFinite(step)) return []; - - if (step > 0) { - start = Math.ceil(start / step); - stop = Math.floor(stop / step); - ticks = new Array(n = Math.ceil(stop - start + 1)); - while (++i < n) ticks[i] = (start + i) * step; - } else { - start = Math.floor(start * step); - stop = Math.ceil(stop * step); - ticks = new Array(n = Math.ceil(start - stop + 1)); - while (++i < n) ticks[i] = (start - i) / step; - } - - if (reverse) ticks.reverse(); - - return ticks; - } - - function tickIncrement(start, stop, count) { - var step = (stop - start) / Math.max(0, count), - power = Math.floor(Math.log(step) / Math.LN10), - error = step / Math.pow(10, power); - return power >= 0 - ? (error >= e10 ? 10 : error >= e5 ? 5 : error >= e2 ? 2 : 1) * Math.pow(10, power) - : -Math.pow(10, -power) / (error >= e10 ? 10 : error >= e5 ? 5 : error >= e2 ? 2 : 1); - } - - function tickStep(start, stop, count) { - var step0 = Math.abs(stop - start) / Math.max(0, count), - step1 = Math.pow(10, Math.floor(Math.log(step0) / Math.LN10)), - error = step0 / step1; - if (error >= e10) step1 *= 10; - else if (error >= e5) step1 *= 5; - else if (error >= e2) step1 *= 2; - return stop < start ? -step1 : step1; - } - - function initRange(domain, range) { - switch (arguments.length) { - case 0: break; - case 1: this.range(domain); break; - default: this.range(range).domain(domain); break; - } - return this; - } - - function define(constructor, factory, prototype) { - constructor.prototype = factory.prototype = prototype; - prototype.constructor = constructor; - } - - function extend(parent, definition) { - var prototype = Object.create(parent.prototype); - for (var key in definition) prototype[key] = definition[key]; - return prototype; - } - - function Color() {} - - var darker = 0.7; - var brighter = 1 / darker; - - var reI = "\\s*([+-]?\\d+)\\s*", - reN = "\\s*([+-]?\\d*\\.?\\d+(?:[eE][+-]?\\d+)?)\\s*", - reP = "\\s*([+-]?\\d*\\.?\\d+(?:[eE][+-]?\\d+)?)%\\s*", - reHex = /^#([0-9a-f]{3,8})$/, - reRgbInteger = new RegExp("^rgb\\(" + [reI, reI, reI] + "\\)$"), - reRgbPercent = new RegExp("^rgb\\(" + [reP, reP, reP] + "\\)$"), - reRgbaInteger = new RegExp("^rgba\\(" + [reI, reI, reI, reN] + "\\)$"), - reRgbaPercent = new RegExp("^rgba\\(" + [reP, reP, reP, reN] + "\\)$"), - reHslPercent = new RegExp("^hsl\\(" + [reN, reP, reP] + "\\)$"), - reHslaPercent = new RegExp("^hsla\\(" + [reN, reP, reP, reN] + "\\)$"); - - var named = { - aliceblue: 0xf0f8ff, - antiquewhite: 0xfaebd7, - aqua: 0x00ffff, - aquamarine: 0x7fffd4, - azure: 0xf0ffff, - beige: 0xf5f5dc, - bisque: 0xffe4c4, - black: 0x000000, - blanchedalmond: 0xffebcd, - blue: 0x0000ff, - blueviolet: 0x8a2be2, - brown: 0xa52a2a, - burlywood: 0xdeb887, - cadetblue: 0x5f9ea0, - chartreuse: 0x7fff00, - chocolate: 0xd2691e, - coral: 0xff7f50, - cornflowerblue: 0x6495ed, - cornsilk: 0xfff8dc, - crimson: 0xdc143c, - cyan: 0x00ffff, - darkblue: 0x00008b, - darkcyan: 0x008b8b, - darkgoldenrod: 0xb8860b, - darkgray: 0xa9a9a9, - darkgreen: 0x006400, - darkgrey: 0xa9a9a9, - darkkhaki: 0xbdb76b, - darkmagenta: 0x8b008b, - darkolivegreen: 0x556b2f, - darkorange: 0xff8c00, - darkorchid: 0x9932cc, - darkred: 0x8b0000, - darksalmon: 0xe9967a, - darkseagreen: 0x8fbc8f, - darkslateblue: 0x483d8b, - darkslategray: 0x2f4f4f, - darkslategrey: 0x2f4f4f, - darkturquoise: 0x00ced1, - darkviolet: 0x9400d3, - deeppink: 0xff1493, - deepskyblue: 0x00bfff, - dimgray: 0x696969, - dimgrey: 0x696969, - dodgerblue: 0x1e90ff, - firebrick: 0xb22222, - floralwhite: 0xfffaf0, - forestgreen: 0x228b22, - fuchsia: 0xff00ff, - gainsboro: 0xdcdcdc, - ghostwhite: 0xf8f8ff, - gold: 0xffd700, - goldenrod: 0xdaa520, - gray: 0x808080, - green: 0x008000, - greenyellow: 0xadff2f, - grey: 0x808080, - honeydew: 0xf0fff0, - hotpink: 0xff69b4, - indianred: 0xcd5c5c, - indigo: 0x4b0082, - ivory: 0xfffff0, - khaki: 0xf0e68c, - lavender: 0xe6e6fa, - lavenderblush: 0xfff0f5, - lawngreen: 0x7cfc00, - lemonchiffon: 0xfffacd, - lightblue: 0xadd8e6, - lightcoral: 0xf08080, - lightcyan: 0xe0ffff, - lightgoldenrodyellow: 0xfafad2, - lightgray: 0xd3d3d3, - lightgreen: 0x90ee90, - lightgrey: 0xd3d3d3, - lightpink: 0xffb6c1, - lightsalmon: 0xffa07a, - lightseagreen: 0x20b2aa, - lightskyblue: 0x87cefa, - lightslategray: 0x778899, - lightslategrey: 0x778899, - lightsteelblue: 0xb0c4de, - lightyellow: 0xffffe0, - lime: 0x00ff00, - limegreen: 0x32cd32, - linen: 0xfaf0e6, - magenta: 0xff00ff, - maroon: 0x800000, - mediumaquamarine: 0x66cdaa, - mediumblue: 0x0000cd, - mediumorchid: 0xba55d3, - mediumpurple: 0x9370db, - mediumseagreen: 0x3cb371, - mediumslateblue: 0x7b68ee, - mediumspringgreen: 0x00fa9a, - mediumturquoise: 0x48d1cc, - mediumvioletred: 0xc71585, - midnightblue: 0x191970, - mintcream: 0xf5fffa, - mistyrose: 0xffe4e1, - moccasin: 0xffe4b5, - navajowhite: 0xffdead, - navy: 0x000080, - oldlace: 0xfdf5e6, - olive: 0x808000, - olivedrab: 0x6b8e23, - orange: 0xffa500, - orangered: 0xff4500, - orchid: 0xda70d6, - palegoldenrod: 0xeee8aa, - palegreen: 0x98fb98, - paleturquoise: 0xafeeee, - palevioletred: 0xdb7093, - papayawhip: 0xffefd5, - peachpuff: 0xffdab9, - peru: 0xcd853f, - pink: 0xffc0cb, - plum: 0xdda0dd, - powderblue: 0xb0e0e6, - purple: 0x800080, - rebeccapurple: 0x663399, - red: 0xff0000, - rosybrown: 0xbc8f8f, - royalblue: 0x4169e1, - saddlebrown: 0x8b4513, - salmon: 0xfa8072, - sandybrown: 0xf4a460, - seagreen: 0x2e8b57, - seashell: 0xfff5ee, - sienna: 0xa0522d, - silver: 0xc0c0c0, - skyblue: 0x87ceeb, - slateblue: 0x6a5acd, - slategray: 0x708090, - slategrey: 0x708090, - snow: 0xfffafa, - springgreen: 0x00ff7f, - steelblue: 0x4682b4, - tan: 0xd2b48c, - teal: 0x008080, - thistle: 0xd8bfd8, - tomato: 0xff6347, - turquoise: 0x40e0d0, - violet: 0xee82ee, - wheat: 0xf5deb3, - white: 0xffffff, - whitesmoke: 0xf5f5f5, - yellow: 0xffff00, - yellowgreen: 0x9acd32 - }; - - define(Color, color, { - copy: function(channels) { - return Object.assign(new this.constructor, this, channels); - }, - displayable: function() { - return this.rgb().displayable(); - }, - hex: color_formatHex, // Deprecated! Use color.formatHex. - formatHex: color_formatHex, - formatHsl: color_formatHsl, - formatRgb: color_formatRgb, - toString: color_formatRgb - }); - - function color_formatHex() { - return this.rgb().formatHex(); - } - - function color_formatHsl() { - return hslConvert(this).formatHsl(); - } - - function color_formatRgb() { - return this.rgb().formatRgb(); - } - - function color(format) { - var m, l; - format = (format + "").trim().toLowerCase(); - return (m = reHex.exec(format)) ? (l = m[1].length, m = parseInt(m[1], 16), l === 6 ? rgbn(m) // #ff0000 - : l === 3 ? new Rgb((m >> 8 & 0xf) | (m >> 4 & 0xf0), (m >> 4 & 0xf) | (m & 0xf0), ((m & 0xf) << 4) | (m & 0xf), 1) // #f00 - : l === 8 ? rgba(m >> 24 & 0xff, m >> 16 & 0xff, m >> 8 & 0xff, (m & 0xff) / 0xff) // #ff000000 - : l === 4 ? rgba((m >> 12 & 0xf) | (m >> 8 & 0xf0), (m >> 8 & 0xf) | (m >> 4 & 0xf0), (m >> 4 & 0xf) | (m & 0xf0), (((m & 0xf) << 4) | (m & 0xf)) / 0xff) // #f000 - : null) // invalid hex - : (m = reRgbInteger.exec(format)) ? new Rgb(m[1], m[2], m[3], 1) // rgb(255, 0, 0) - : (m = reRgbPercent.exec(format)) ? new Rgb(m[1] * 255 / 100, m[2] * 255 / 100, m[3] * 255 / 100, 1) // rgb(100%, 0%, 0%) - : (m = reRgbaInteger.exec(format)) ? rgba(m[1], m[2], m[3], m[4]) // rgba(255, 0, 0, 1) - : (m = reRgbaPercent.exec(format)) ? rgba(m[1] * 255 / 100, m[2] * 255 / 100, m[3] * 255 / 100, m[4]) // rgb(100%, 0%, 0%, 1) - : (m = reHslPercent.exec(format)) ? hsla(m[1], m[2] / 100, m[3] / 100, 1) // hsl(120, 50%, 50%) - : (m = reHslaPercent.exec(format)) ? hsla(m[1], m[2] / 100, m[3] / 100, m[4]) // hsla(120, 50%, 50%, 1) - : named.hasOwnProperty(format) ? rgbn(named[format]) // eslint-disable-line no-prototype-builtins - : format === "transparent" ? new Rgb(NaN, NaN, NaN, 0) - : null; - } - - function rgbn(n) { - return new Rgb(n >> 16 & 0xff, n >> 8 & 0xff, n & 0xff, 1); - } - - function rgba(r, g, b, a) { - if (a <= 0) r = g = b = NaN; - return new Rgb(r, g, b, a); - } - - function rgbConvert(o) { - if (!(o instanceof Color)) o = color(o); - if (!o) return new Rgb; - o = o.rgb(); - return new Rgb(o.r, o.g, o.b, o.opacity); - } - - function rgb(r, g, b, opacity) { - return arguments.length === 1 ? rgbConvert(r) : new Rgb(r, g, b, opacity == null ? 1 : opacity); - } - - function Rgb(r, g, b, opacity) { - this.r = +r; - this.g = +g; - this.b = +b; - this.opacity = +opacity; - } - - define(Rgb, rgb, extend(Color, { - brighter: function(k) { - k = k == null ? brighter : Math.pow(brighter, k); - return new Rgb(this.r * k, this.g * k, this.b * k, this.opacity); - }, - darker: function(k) { - k = k == null ? darker : Math.pow(darker, k); - return new Rgb(this.r * k, this.g * k, this.b * k, this.opacity); - }, - rgb: function() { - return this; - }, - displayable: function() { - return (-0.5 <= this.r && this.r < 255.5) - && (-0.5 <= this.g && this.g < 255.5) - && (-0.5 <= this.b && this.b < 255.5) - && (0 <= this.opacity && this.opacity <= 1); - }, - hex: rgb_formatHex, // Deprecated! Use color.formatHex. - formatHex: rgb_formatHex, - formatRgb: rgb_formatRgb, - toString: rgb_formatRgb - })); - - function rgb_formatHex() { - return "#" + hex(this.r) + hex(this.g) + hex(this.b); - } - - function rgb_formatRgb() { - var a = this.opacity; a = isNaN(a) ? 1 : Math.max(0, Math.min(1, a)); - return (a === 1 ? "rgb(" : "rgba(") - + Math.max(0, Math.min(255, Math.round(this.r) || 0)) + ", " - + Math.max(0, Math.min(255, Math.round(this.g) || 0)) + ", " - + Math.max(0, Math.min(255, Math.round(this.b) || 0)) - + (a === 1 ? ")" : ", " + a + ")"); - } - - function hex(value) { - value = Math.max(0, Math.min(255, Math.round(value) || 0)); - return (value < 16 ? "0" : "") + value.toString(16); - } - - function hsla(h, s, l, a) { - if (a <= 0) h = s = l = NaN; - else if (l <= 0 || l >= 1) h = s = NaN; - else if (s <= 0) h = NaN; - return new Hsl(h, s, l, a); - } - - function hslConvert(o) { - if (o instanceof Hsl) return new Hsl(o.h, o.s, o.l, o.opacity); - if (!(o instanceof Color)) o = color(o); - if (!o) return new Hsl; - if (o instanceof Hsl) return o; - o = o.rgb(); - var r = o.r / 255, - g = o.g / 255, - b = o.b / 255, - min = Math.min(r, g, b), - max = Math.max(r, g, b), - h = NaN, - s = max - min, - l = (max + min) / 2; - if (s) { - if (r === max) h = (g - b) / s + (g < b) * 6; - else if (g === max) h = (b - r) / s + 2; - else h = (r - g) / s + 4; - s /= l < 0.5 ? max + min : 2 - max - min; - h *= 60; - } else { - s = l > 0 && l < 1 ? 0 : h; - } - return new Hsl(h, s, l, o.opacity); - } - - function hsl(h, s, l, opacity) { - return arguments.length === 1 ? hslConvert(h) : new Hsl(h, s, l, opacity == null ? 1 : opacity); - } - - function Hsl(h, s, l, opacity) { - this.h = +h; - this.s = +s; - this.l = +l; - this.opacity = +opacity; - } - - define(Hsl, hsl, extend(Color, { - brighter: function(k) { - k = k == null ? brighter : Math.pow(brighter, k); - return new Hsl(this.h, this.s, this.l * k, this.opacity); - }, - darker: function(k) { - k = k == null ? darker : Math.pow(darker, k); - return new Hsl(this.h, this.s, this.l * k, this.opacity); - }, - rgb: function() { - var h = this.h % 360 + (this.h < 0) * 360, - s = isNaN(h) || isNaN(this.s) ? 0 : this.s, - l = this.l, - m2 = l + (l < 0.5 ? l : 1 - l) * s, - m1 = 2 * l - m2; - return new Rgb( - hsl2rgb(h >= 240 ? h - 240 : h + 120, m1, m2), - hsl2rgb(h, m1, m2), - hsl2rgb(h < 120 ? h + 240 : h - 120, m1, m2), - this.opacity - ); - }, - displayable: function() { - return (0 <= this.s && this.s <= 1 || isNaN(this.s)) - && (0 <= this.l && this.l <= 1) - && (0 <= this.opacity && this.opacity <= 1); - }, - formatHsl: function() { - var a = this.opacity; a = isNaN(a) ? 1 : Math.max(0, Math.min(1, a)); - return (a === 1 ? "hsl(" : "hsla(") - + (this.h || 0) + ", " - + (this.s || 0) * 100 + "%, " - + (this.l || 0) * 100 + "%" - + (a === 1 ? ")" : ", " + a + ")"); - } - })); - - /* From FvD 13.37, CSS Color Module Level 3 */ - function hsl2rgb(h, m1, m2) { - return (h < 60 ? m1 + (m2 - m1) * h / 60 - : h < 180 ? m2 - : h < 240 ? m1 + (m2 - m1) * (240 - h) / 60 - : m1) * 255; - } - - var deg2rad = Math.PI / 180; - var rad2deg = 180 / Math.PI; - - // https://observablehq.com/@mbostock/lab-and-rgb - var K = 18, - Xn = 0.96422, - Yn = 1, - Zn = 0.82521, - t0 = 4 / 29, - t1 = 6 / 29, - t2 = 3 * t1 * t1, - t3 = t1 * t1 * t1; - - function labConvert(o) { - if (o instanceof Lab) return new Lab(o.l, o.a, o.b, o.opacity); - if (o instanceof Hcl) return hcl2lab(o); - if (!(o instanceof Rgb)) o = rgbConvert(o); - var r = rgb2lrgb(o.r), - g = rgb2lrgb(o.g), - b = rgb2lrgb(o.b), - y = xyz2lab((0.2225045 * r + 0.7168786 * g + 0.0606169 * b) / Yn), x, z; - if (r === g && g === b) x = z = y; else { - x = xyz2lab((0.4360747 * r + 0.3850649 * g + 0.1430804 * b) / Xn); - z = xyz2lab((0.0139322 * r + 0.0971045 * g + 0.7141733 * b) / Zn); - } - return new Lab(116 * y - 16, 500 * (x - y), 200 * (y - z), o.opacity); - } - - function lab(l, a, b, opacity) { - return arguments.length === 1 ? labConvert(l) : new Lab(l, a, b, opacity == null ? 1 : opacity); - } - - function Lab(l, a, b, opacity) { - this.l = +l; - this.a = +a; - this.b = +b; - this.opacity = +opacity; - } - - define(Lab, lab, extend(Color, { - brighter: function(k) { - return new Lab(this.l + K * (k == null ? 1 : k), this.a, this.b, this.opacity); - }, - darker: function(k) { - return new Lab(this.l - K * (k == null ? 1 : k), this.a, this.b, this.opacity); - }, - rgb: function() { - var y = (this.l + 16) / 116, - x = isNaN(this.a) ? y : y + this.a / 500, - z = isNaN(this.b) ? y : y - this.b / 200; - x = Xn * lab2xyz(x); - y = Yn * lab2xyz(y); - z = Zn * lab2xyz(z); - return new Rgb( - lrgb2rgb( 3.1338561 * x - 1.6168667 * y - 0.4906146 * z), - lrgb2rgb(-0.9787684 * x + 1.9161415 * y + 0.0334540 * z), - lrgb2rgb( 0.0719453 * x - 0.2289914 * y + 1.4052427 * z), - this.opacity - ); - } - })); - - function xyz2lab(t) { - return t > t3 ? Math.pow(t, 1 / 3) : t / t2 + t0; - } - - function lab2xyz(t) { - return t > t1 ? t * t * t : t2 * (t - t0); - } - - function lrgb2rgb(x) { - return 255 * (x <= 0.0031308 ? 12.92 * x : 1.055 * Math.pow(x, 1 / 2.4) - 0.055); - } - - function rgb2lrgb(x) { - return (x /= 255) <= 0.04045 ? x / 12.92 : Math.pow((x + 0.055) / 1.055, 2.4); - } - - function hclConvert(o) { - if (o instanceof Hcl) return new Hcl(o.h, o.c, o.l, o.opacity); - if (!(o instanceof Lab)) o = labConvert(o); - if (o.a === 0 && o.b === 0) return new Hcl(NaN, 0 < o.l && o.l < 100 ? 0 : NaN, o.l, o.opacity); - var h = Math.atan2(o.b, o.a) * rad2deg; - return new Hcl(h < 0 ? h + 360 : h, Math.sqrt(o.a * o.a + o.b * o.b), o.l, o.opacity); - } - - function hcl(h, c, l, opacity) { - return arguments.length === 1 ? hclConvert(h) : new Hcl(h, c, l, opacity == null ? 1 : opacity); - } - - function Hcl(h, c, l, opacity) { - this.h = +h; - this.c = +c; - this.l = +l; - this.opacity = +opacity; - } - - function hcl2lab(o) { - if (isNaN(o.h)) return new Lab(o.l, 0, 0, o.opacity); - var h = o.h * deg2rad; - return new Lab(o.l, Math.cos(h) * o.c, Math.sin(h) * o.c, o.opacity); - } - - define(Hcl, hcl, extend(Color, { - brighter: function(k) { - return new Hcl(this.h, this.c, this.l + K * (k == null ? 1 : k), this.opacity); - }, - darker: function(k) { - return new Hcl(this.h, this.c, this.l - K * (k == null ? 1 : k), this.opacity); - }, - rgb: function() { - return hcl2lab(this).rgb(); - } - })); - - var A = -0.14861, - B = +1.78277, - C = -0.29227, - D = -0.90649, - E = +1.97294, - ED = E * D, - EB = E * B, - BC_DA = B * C - D * A; - - function cubehelixConvert(o) { - if (o instanceof Cubehelix) return new Cubehelix(o.h, o.s, o.l, o.opacity); - if (!(o instanceof Rgb)) o = rgbConvert(o); - var r = o.r / 255, - g = o.g / 255, - b = o.b / 255, - l = (BC_DA * b + ED * r - EB * g) / (BC_DA + ED - EB), - bl = b - l, - k = (E * (g - l) - C * bl) / D, - s = Math.sqrt(k * k + bl * bl) / (E * l * (1 - l)), // NaN if l=0 or l=1 - h = s ? Math.atan2(k, bl) * rad2deg - 120 : NaN; - return new Cubehelix(h < 0 ? h + 360 : h, s, l, o.opacity); - } - - function cubehelix(h, s, l, opacity) { - return arguments.length === 1 ? cubehelixConvert(h) : new Cubehelix(h, s, l, opacity == null ? 1 : opacity); - } - - function Cubehelix(h, s, l, opacity) { - this.h = +h; - this.s = +s; - this.l = +l; - this.opacity = +opacity; - } - - define(Cubehelix, cubehelix, extend(Color, { - brighter: function(k) { - k = k == null ? brighter : Math.pow(brighter, k); - return new Cubehelix(this.h, this.s, this.l * k, this.opacity); - }, - darker: function(k) { - k = k == null ? darker : Math.pow(darker, k); - return new Cubehelix(this.h, this.s, this.l * k, this.opacity); - }, - rgb: function() { - var h = isNaN(this.h) ? 0 : (this.h + 120) * deg2rad, - l = +this.l, - a = isNaN(this.s) ? 0 : this.s * l * (1 - l), - cosh = Math.cos(h), - sinh = Math.sin(h); - return new Rgb( - 255 * (l + a * (A * cosh + B * sinh)), - 255 * (l + a * (C * cosh + D * sinh)), - 255 * (l + a * (E * cosh)), - this.opacity - ); - } - })); - - function constant(x) { - return function() { - return x; - }; - } - - function linear(a, d) { - return function(t) { - return a + t * d; - }; - } - - function exponential(a, b, y) { - return a = Math.pow(a, y), b = Math.pow(b, y) - a, y = 1 / y, function(t) { - return Math.pow(a + t * b, y); - }; - } - - function gamma(y) { - return (y = +y) === 1 ? nogamma : function(a, b) { - return b - a ? exponential(a, b, y) : constant(isNaN(a) ? b : a); - }; - } - - function nogamma(a, b) { - var d = b - a; - return d ? linear(a, d) : constant(isNaN(a) ? b : a); - } - - var rgb$1 = (function rgbGamma(y) { - var color = gamma(y); - - function rgb$1(start, end) { - var r = color((start = rgb(start)).r, (end = rgb(end)).r), - g = color(start.g, end.g), - b = color(start.b, end.b), - opacity = nogamma(start.opacity, end.opacity); - return function(t) { - start.r = r(t); - start.g = g(t); - start.b = b(t); - start.opacity = opacity(t); - return start + ""; - }; - } - - rgb$1.gamma = rgbGamma; - - return rgb$1; - })(1); - - function numberArray(a, b) { - if (!b) b = []; - var n = a ? Math.min(b.length, a.length) : 0, - c = b.slice(), - i; - return function(t) { - for (i = 0; i < n; ++i) c[i] = a[i] * (1 - t) + b[i] * t; - return c; - }; - } - - function isNumberArray(x) { - return ArrayBuffer.isView(x) && !(x instanceof DataView); - } - - function genericArray(a, b) { - var nb = b ? b.length : 0, - na = a ? Math.min(nb, a.length) : 0, - x = new Array(na), - c = new Array(nb), - i; - - for (i = 0; i < na; ++i) x[i] = interpolate(a[i], b[i]); - for (; i < nb; ++i) c[i] = b[i]; - - return function(t) { - for (i = 0; i < na; ++i) c[i] = x[i](t); - return c; - }; - } - - function date(a, b) { - var d = new Date; - return a = +a, b = +b, function(t) { - return d.setTime(a * (1 - t) + b * t), d; - }; - } - - function interpolateNumber(a, b) { - return a = +a, b = +b, function(t) { - return a * (1 - t) + b * t; - }; - } - - function object(a, b) { - var i = {}, - c = {}, - k; - - if (a === null || typeof a !== "object") a = {}; - if (b === null || typeof b !== "object") b = {}; - - for (k in b) { - if (k in a) { - i[k] = interpolate(a[k], b[k]); - } else { - c[k] = b[k]; - } - } - - return function(t) { - for (k in i) c[k] = i[k](t); - return c; - }; - } - - var reA = /[-+]?(?:\d+\.?\d*|\.?\d+)(?:[eE][-+]?\d+)?/g, - reB = new RegExp(reA.source, "g"); - - function zero(b) { - return function() { - return b; - }; - } - - function one(b) { - return function(t) { - return b(t) + ""; - }; - } - - function string(a, b) { - var bi = reA.lastIndex = reB.lastIndex = 0, // scan index for next number in b - am, // current match in a - bm, // current match in b - bs, // string preceding current number in b, if any - i = -1, // index in s - s = [], // string constants and placeholders - q = []; // number interpolators - - // Coerce inputs to strings. - a = a + "", b = b + ""; - - // Interpolate pairs of numbers in a & b. - while ((am = reA.exec(a)) - && (bm = reB.exec(b))) { - if ((bs = bm.index) > bi) { // a string precedes the next number in b - bs = b.slice(bi, bs); - if (s[i]) s[i] += bs; // coalesce with previous string - else s[++i] = bs; - } - if ((am = am[0]) === (bm = bm[0])) { // numbers in a & b match - if (s[i]) s[i] += bm; // coalesce with previous string - else s[++i] = bm; - } else { // interpolate non-matching numbers - s[++i] = null; - q.push({i: i, x: interpolateNumber(am, bm)}); - } - bi = reB.lastIndex; - } - - // Add remains of b. - if (bi < b.length) { - bs = b.slice(bi); - if (s[i]) s[i] += bs; // coalesce with previous string - else s[++i] = bs; - } - - // Special optimization for only a single match. - // Otherwise, interpolate each of the numbers and rejoin the string. - return s.length < 2 ? (q[0] - ? one(q[0].x) - : zero(b)) - : (b = q.length, function(t) { - for (var i = 0, o; i < b; ++i) s[(o = q[i]).i] = o.x(t); - return s.join(""); - }); - } - - function interpolate(a, b) { - var t = typeof b, c; - return b == null || t === "boolean" ? constant(b) - : (t === "number" ? interpolateNumber - : t === "string" ? ((c = color(b)) ? (b = c, rgb$1) : string) - : b instanceof color ? rgb$1 - : b instanceof Date ? date - : isNumberArray(b) ? numberArray - : Array.isArray(b) ? genericArray - : typeof b.valueOf !== "function" && typeof b.toString !== "function" || isNaN(b) ? object - : interpolateNumber)(a, b); - } - - function interpolateRound(a, b) { - return a = +a, b = +b, function(t) { - return Math.round(a * (1 - t) + b * t); - }; - } - - function constant$1(x) { - return function() { - return x; - }; - } - - function number(x) { - return +x; - } - - var unit = [0, 1]; - - function identity(x) { - return x; - } - - function normalize(a, b) { - return (b -= (a = +a)) - ? function(x) { return (x - a) / b; } - : constant$1(isNaN(b) ? NaN : 0.5); - } - - function clamper(a, b) { - var t; - if (a > b) t = a, a = b, b = t; - return function(x) { return Math.max(a, Math.min(b, x)); }; - } - - // normalize(a, b)(x) takes a domain value x in [a,b] and returns the corresponding parameter t in [0,1]. - // interpolate(a, b)(t) takes a parameter t in [0,1] and returns the corresponding range value x in [a,b]. - function bimap(domain, range, interpolate) { - var d0 = domain[0], d1 = domain[1], r0 = range[0], r1 = range[1]; - if (d1 < d0) d0 = normalize(d1, d0), r0 = interpolate(r1, r0); - else d0 = normalize(d0, d1), r0 = interpolate(r0, r1); - return function(x) { return r0(d0(x)); }; - } - - function polymap(domain, range, interpolate) { - var j = Math.min(domain.length, range.length) - 1, - d = new Array(j), - r = new Array(j), - i = -1; - - // Reverse descending domains. - if (domain[j] < domain[0]) { - domain = domain.slice().reverse(); - range = range.slice().reverse(); - } - - while (++i < j) { - d[i] = normalize(domain[i], domain[i + 1]); - r[i] = interpolate(range[i], range[i + 1]); - } - - return function(x) { - var i = bisectRight(domain, x, 1, j) - 1; - return r[i](d[i](x)); - }; - } - - function copy(source, target) { - return target - .domain(source.domain()) - .range(source.range()) - .interpolate(source.interpolate()) - .clamp(source.clamp()) - .unknown(source.unknown()); - } - - function transformer() { - var domain = unit, - range = unit, - interpolate$1 = interpolate, - transform, - untransform, - unknown, - clamp = identity, - piecewise, - output, - input; - - function rescale() { - var n = Math.min(domain.length, range.length); - if (clamp !== identity) clamp = clamper(domain[0], domain[n - 1]); - piecewise = n > 2 ? polymap : bimap; - output = input = null; - return scale; - } - - function scale(x) { - return isNaN(x = +x) ? unknown : (output || (output = piecewise(domain.map(transform), range, interpolate$1)))(transform(clamp(x))); - } - - scale.invert = function(y) { - return clamp(untransform((input || (input = piecewise(range, domain.map(transform), interpolateNumber)))(y))); - }; - - scale.domain = function(_) { - return arguments.length ? (domain = Array.from(_, number), rescale()) : domain.slice(); - }; - - scale.range = function(_) { - return arguments.length ? (range = Array.from(_), rescale()) : range.slice(); - }; - - scale.rangeRound = function(_) { - return range = Array.from(_), interpolate$1 = interpolateRound, rescale(); - }; - - scale.clamp = function(_) { - return arguments.length ? (clamp = _ ? true : identity, rescale()) : clamp !== identity; - }; - - scale.interpolate = function(_) { - return arguments.length ? (interpolate$1 = _, rescale()) : interpolate$1; - }; - - scale.unknown = function(_) { - return arguments.length ? (unknown = _, scale) : unknown; - }; - - return function(t, u) { - transform = t, untransform = u; - return rescale(); - }; - } - - function continuous() { - return transformer()(identity, identity); - } - - // Computes the decimal coefficient and exponent of the specified number x with - // significant digits p, where x is positive and p is in [1, 21] or undefined. - // For example, formatDecimal(1.23) returns ["123", 0]. - function formatDecimal(x, p) { - if ((i = (x = p ? x.toExponential(p - 1) : x.toExponential()).indexOf("e")) < 0) return null; // NaN, ±Infinity - var i, coefficient = x.slice(0, i); - - // The string returned by toExponential either has the form \d\.\d+e[-+]\d+ - // (e.g., 1.2e+3) or the form \de[-+]\d+ (e.g., 1e+3). - return [ - coefficient.length > 1 ? coefficient[0] + coefficient.slice(2) : coefficient, - +x.slice(i + 1) - ]; - } - - function exponent(x) { - return x = formatDecimal(Math.abs(x)), x ? x[1] : NaN; - } - - function formatGroup(grouping, thousands) { - return function(value, width) { - var i = value.length, - t = [], - j = 0, - g = grouping[0], - length = 0; - - while (i > 0 && g > 0) { - if (length + g + 1 > width) g = Math.max(1, width - length); - t.push(value.substring(i -= g, i + g)); - if ((length += g + 1) > width) break; - g = grouping[j = (j + 1) % grouping.length]; - } - - return t.reverse().join(thousands); - }; - } - - function formatNumerals(numerals) { - return function(value) { - return value.replace(/[0-9]/g, function(i) { - return numerals[+i]; - }); - }; - } - - // [[fill]align][sign][symbol][0][width][,][.precision][~][type] - var re = /^(?:(.)?([<>=^]))?([+\-( ])?([$#])?(0)?(\d+)?(,)?(\.\d+)?(~)?([a-z%])?$/i; - - function formatSpecifier(specifier) { - if (!(match = re.exec(specifier))) throw new Error("invalid format: " + specifier); - var match; - return new FormatSpecifier({ - fill: match[1], - align: match[2], - sign: match[3], - symbol: match[4], - zero: match[5], - width: match[6], - comma: match[7], - precision: match[8] && match[8].slice(1), - trim: match[9], - type: match[10] - }); - } - - formatSpecifier.prototype = FormatSpecifier.prototype; // instanceof - - function FormatSpecifier(specifier) { - this.fill = specifier.fill === undefined ? " " : specifier.fill + ""; - this.align = specifier.align === undefined ? ">" : specifier.align + ""; - this.sign = specifier.sign === undefined ? "-" : specifier.sign + ""; - this.symbol = specifier.symbol === undefined ? "" : specifier.symbol + ""; - this.zero = !!specifier.zero; - this.width = specifier.width === undefined ? undefined : +specifier.width; - this.comma = !!specifier.comma; - this.precision = specifier.precision === undefined ? undefined : +specifier.precision; - this.trim = !!specifier.trim; - this.type = specifier.type === undefined ? "" : specifier.type + ""; - } - - FormatSpecifier.prototype.toString = function() { - return this.fill - + this.align - + this.sign - + this.symbol - + (this.zero ? "0" : "") - + (this.width === undefined ? "" : Math.max(1, this.width | 0)) - + (this.comma ? "," : "") - + (this.precision === undefined ? "" : "." + Math.max(0, this.precision | 0)) - + (this.trim ? "~" : "") - + this.type; - }; - - // Trims insignificant zeros, e.g., replaces 1.2000k with 1.2k. - function formatTrim(s) { - out: for (var n = s.length, i = 1, i0 = -1, i1; i < n; ++i) { - switch (s[i]) { - case ".": i0 = i1 = i; break; - case "0": if (i0 === 0) i0 = i; i1 = i; break; - default: if (!+s[i]) break out; if (i0 > 0) i0 = 0; break; - } - } - return i0 > 0 ? s.slice(0, i0) + s.slice(i1 + 1) : s; - } - - var prefixExponent; - - function formatPrefixAuto(x, p) { - var d = formatDecimal(x, p); - if (!d) return x + ""; - var coefficient = d[0], - exponent = d[1], - i = exponent - (prefixExponent = Math.max(-8, Math.min(8, Math.floor(exponent / 3))) * 3) + 1, - n = coefficient.length; - return i === n ? coefficient - : i > n ? coefficient + new Array(i - n + 1).join("0") - : i > 0 ? coefficient.slice(0, i) + "." + coefficient.slice(i) - : "0." + new Array(1 - i).join("0") + formatDecimal(x, Math.max(0, p + i - 1))[0]; // less than 1y! - } - - function formatRounded(x, p) { - var d = formatDecimal(x, p); - if (!d) return x + ""; - var coefficient = d[0], - exponent = d[1]; - return exponent < 0 ? "0." + new Array(-exponent).join("0") + coefficient - : coefficient.length > exponent + 1 ? coefficient.slice(0, exponent + 1) + "." + coefficient.slice(exponent + 1) - : coefficient + new Array(exponent - coefficient.length + 2).join("0"); - } - - var formatTypes = { - "%": function(x, p) { return (x * 100).toFixed(p); }, - "b": function(x) { return Math.round(x).toString(2); }, - "c": function(x) { return x + ""; }, - "d": function(x) { return Math.round(x).toString(10); }, - "e": function(x, p) { return x.toExponential(p); }, - "f": function(x, p) { return x.toFixed(p); }, - "g": function(x, p) { return x.toPrecision(p); }, - "o": function(x) { return Math.round(x).toString(8); }, - "p": function(x, p) { return formatRounded(x * 100, p); }, - "r": formatRounded, - "s": formatPrefixAuto, - "X": function(x) { return Math.round(x).toString(16).toUpperCase(); }, - "x": function(x) { return Math.round(x).toString(16); } - }; - - function identity$1(x) { - return x; - } - - var map = Array.prototype.map, - prefixes = ["y","z","a","f","p","n","µ","m","","k","M","G","T","P","E","Z","Y"]; - - function formatLocale(locale) { - var group = locale.grouping === undefined || locale.thousands === undefined ? identity$1 : formatGroup(map.call(locale.grouping, Number), locale.thousands + ""), - currencyPrefix = locale.currency === undefined ? "" : locale.currency[0] + "", - currencySuffix = locale.currency === undefined ? "" : locale.currency[1] + "", - decimal = locale.decimal === undefined ? "." : locale.decimal + "", - numerals = locale.numerals === undefined ? identity$1 : formatNumerals(map.call(locale.numerals, String)), - percent = locale.percent === undefined ? "%" : locale.percent + "", - minus = locale.minus === undefined ? "-" : locale.minus + "", - nan = locale.nan === undefined ? "NaN" : locale.nan + ""; - - function newFormat(specifier) { - specifier = formatSpecifier(specifier); - - var fill = specifier.fill, - align = specifier.align, - sign = specifier.sign, - symbol = specifier.symbol, - zero = specifier.zero, - width = specifier.width, - comma = specifier.comma, - precision = specifier.precision, - trim = specifier.trim, - type = specifier.type; - - // The "n" type is an alias for ",g". - if (type === "n") comma = true, type = "g"; - - // The "" type, and any invalid type, is an alias for ".12~g". - else if (!formatTypes[type]) precision === undefined && (precision = 12), trim = true, type = "g"; - - // If zero fill is specified, padding goes after sign and before digits. - if (zero || (fill === "0" && align === "=")) zero = true, fill = "0", align = "="; - - // Compute the prefix and suffix. - // For SI-prefix, the suffix is lazily computed. - var prefix = symbol === "$" ? currencyPrefix : symbol === "#" && /[boxX]/.test(type) ? "0" + type.toLowerCase() : "", - suffix = symbol === "$" ? currencySuffix : /[%p]/.test(type) ? percent : ""; - - // What format function should we use? - // Is this an integer type? - // Can this type generate exponential notation? - var formatType = formatTypes[type], - maybeSuffix = /[defgprs%]/.test(type); - - // Set the default precision if not specified, - // or clamp the specified precision to the supported range. - // For significant precision, it must be in [1, 21]. - // For fixed precision, it must be in [0, 20]. - precision = precision === undefined ? 6 - : /[gprs]/.test(type) ? Math.max(1, Math.min(21, precision)) - : Math.max(0, Math.min(20, precision)); - - function format(value) { - var valuePrefix = prefix, - valueSuffix = suffix, - i, n, c; - - if (type === "c") { - valueSuffix = formatType(value) + valueSuffix; - value = ""; - } else { - value = +value; - - // Determine the sign. -0 is not less than 0, but 1 / -0 is! - var valueNegative = value < 0 || 1 / value < 0; - - // Perform the initial formatting. - value = isNaN(value) ? nan : formatType(Math.abs(value), precision); - - // Trim insignificant zeros. - if (trim) value = formatTrim(value); - - // If a negative value rounds to zero after formatting, and no explicit positive sign is requested, hide the sign. - if (valueNegative && +value === 0 && sign !== "+") valueNegative = false; - - // Compute the prefix and suffix. - valuePrefix = (valueNegative ? (sign === "(" ? sign : minus) : sign === "-" || sign === "(" ? "" : sign) + valuePrefix; - valueSuffix = (type === "s" ? prefixes[8 + prefixExponent / 3] : "") + valueSuffix + (valueNegative && sign === "(" ? ")" : ""); - - // Break the formatted value into the integer “value” part that can be - // grouped, and fractional or exponential “suffix” part that is not. - if (maybeSuffix) { - i = -1, n = value.length; - while (++i < n) { - if (c = value.charCodeAt(i), 48 > c || c > 57) { - valueSuffix = (c === 46 ? decimal + value.slice(i + 1) : value.slice(i)) + valueSuffix; - value = value.slice(0, i); - break; - } - } - } - } - - // If the fill character is not "0", grouping is applied before padding. - if (comma && !zero) value = group(value, Infinity); - - // Compute the padding. - var length = valuePrefix.length + value.length + valueSuffix.length, - padding = length < width ? new Array(width - length + 1).join(fill) : ""; - - // If the fill character is "0", grouping is applied after padding. - if (comma && zero) value = group(padding + value, padding.length ? width - valueSuffix.length : Infinity), padding = ""; - - // Reconstruct the final output based on the desired alignment. - switch (align) { - case "<": value = valuePrefix + value + valueSuffix + padding; break; - case "=": value = valuePrefix + padding + value + valueSuffix; break; - case "^": value = padding.slice(0, length = padding.length >> 1) + valuePrefix + value + valueSuffix + padding.slice(length); break; - default: value = padding + valuePrefix + value + valueSuffix; break; - } - - return numerals(value); - } - - format.toString = function() { - return specifier + ""; - }; - - return format; - } - - function formatPrefix(specifier, value) { - var f = newFormat((specifier = formatSpecifier(specifier), specifier.type = "f", specifier)), - e = Math.max(-8, Math.min(8, Math.floor(exponent(value) / 3))) * 3, - k = Math.pow(10, -e), - prefix = prefixes[8 + e / 3]; - return function(value) { - return f(k * value) + prefix; - }; - } - - return { - format: newFormat, - formatPrefix: formatPrefix - }; - } - - var locale; - var format; - var formatPrefix; - - defaultLocale({ - decimal: ".", - thousands: ",", - grouping: [3], - currency: ["$", ""], - minus: "-" - }); - - function defaultLocale(definition) { - locale = formatLocale(definition); - format = locale.format; - formatPrefix = locale.formatPrefix; - return locale; - } - - function precisionFixed(step) { - return Math.max(0, -exponent(Math.abs(step))); - } - - function precisionPrefix(step, value) { - return Math.max(0, Math.max(-8, Math.min(8, Math.floor(exponent(value) / 3))) * 3 - exponent(Math.abs(step))); - } - - function precisionRound(step, max) { - step = Math.abs(step), max = Math.abs(max) - step; - return Math.max(0, exponent(max) - exponent(step)) + 1; - } - - function tickFormat(start, stop, count, specifier) { - var step = tickStep(start, stop, count), - precision; - specifier = formatSpecifier(specifier == null ? ",f" : specifier); - switch (specifier.type) { - case "s": { - var value = Math.max(Math.abs(start), Math.abs(stop)); - if (specifier.precision == null && !isNaN(precision = precisionPrefix(step, value))) specifier.precision = precision; - return formatPrefix(specifier, value); - } - case "": - case "e": - case "g": - case "p": - case "r": { - if (specifier.precision == null && !isNaN(precision = precisionRound(step, Math.max(Math.abs(start), Math.abs(stop))))) specifier.precision = precision - (specifier.type === "e"); - break; - } - case "f": - case "%": { - if (specifier.precision == null && !isNaN(precision = precisionFixed(step))) specifier.precision = precision - (specifier.type === "%") * 2; - break; - } - } - return format(specifier); - } - - function linearish(scale) { - var domain = scale.domain; - - scale.ticks = function(count) { - var d = domain(); - return ticks(d[0], d[d.length - 1], count == null ? 10 : count); - }; - - scale.tickFormat = function(count, specifier) { - var d = domain(); - return tickFormat(d[0], d[d.length - 1], count == null ? 10 : count, specifier); - }; - - scale.nice = function(count) { - if (count == null) count = 10; - - var d = domain(), - i0 = 0, - i1 = d.length - 1, - start = d[i0], - stop = d[i1], - step; - - if (stop < start) { - step = start, start = stop, stop = step; - step = i0, i0 = i1, i1 = step; - } - - step = tickIncrement(start, stop, count); - - if (step > 0) { - start = Math.floor(start / step) * step; - stop = Math.ceil(stop / step) * step; - step = tickIncrement(start, stop, count); - } else if (step < 0) { - start = Math.ceil(start * step) / step; - stop = Math.floor(stop * step) / step; - step = tickIncrement(start, stop, count); - } - - if (step > 0) { - d[i0] = Math.floor(start / step) * step; - d[i1] = Math.ceil(stop / step) * step; - domain(d); - } else if (step < 0) { - d[i0] = Math.ceil(start * step) / step; - d[i1] = Math.floor(stop * step) / step; - domain(d); - } - - return scale; - }; - - return scale; - } - - function linear$1() { - var scale = continuous(); - - scale.copy = function() { - return copy(scale, linear$1()); - }; - - initRange.apply(scale, arguments); - - return linearish(scale); - } - - var t0$1 = new Date, - t1$1 = new Date; - - function newInterval(floori, offseti, count, field) { - - function interval(date) { - return floori(date = arguments.length === 0 ? new Date : new Date(+date)), date; - } - - interval.floor = function(date) { - return floori(date = new Date(+date)), date; - }; - - interval.ceil = function(date) { - return floori(date = new Date(date - 1)), offseti(date, 1), floori(date), date; - }; - - interval.round = function(date) { - var d0 = interval(date), - d1 = interval.ceil(date); - return date - d0 < d1 - date ? d0 : d1; - }; - - interval.offset = function(date, step) { - return offseti(date = new Date(+date), step == null ? 1 : Math.floor(step)), date; - }; - - interval.range = function(start, stop, step) { - var range = [], previous; - start = interval.ceil(start); - step = step == null ? 1 : Math.floor(step); - if (!(start < stop) || !(step > 0)) return range; // also handles Invalid Date - do range.push(previous = new Date(+start)), offseti(start, step), floori(start); - while (previous < start && start < stop); - return range; - }; - - interval.filter = function(test) { - return newInterval(function(date) { - if (date >= date) while (floori(date), !test(date)) date.setTime(date - 1); - }, function(date, step) { - if (date >= date) { - if (step < 0) while (++step <= 0) { - while (offseti(date, -1), !test(date)) {} // eslint-disable-line no-empty - } else while (--step >= 0) { - while (offseti(date, +1), !test(date)) {} // eslint-disable-line no-empty - } - } - }); - }; - - if (count) { - interval.count = function(start, end) { - t0$1.setTime(+start), t1$1.setTime(+end); - floori(t0$1), floori(t1$1); - return Math.floor(count(t0$1, t1$1)); - }; - - interval.every = function(step) { - step = Math.floor(step); - return !isFinite(step) || !(step > 0) ? null - : !(step > 1) ? interval - : interval.filter(field - ? function(d) { return field(d) % step === 0; } - : function(d) { return interval.count(0, d) % step === 0; }); - }; - } - - return interval; - } - - var millisecond = newInterval(function() { - // noop - }, function(date, step) { - date.setTime(+date + step); - }, function(start, end) { - return end - start; - }); - - // An optimized implementation for this simple case. - millisecond.every = function(k) { - k = Math.floor(k); - if (!isFinite(k) || !(k > 0)) return null; - if (!(k > 1)) return millisecond; - return newInterval(function(date) { - date.setTime(Math.floor(date / k) * k); - }, function(date, step) { - date.setTime(+date + step * k); - }, function(start, end) { - return (end - start) / k; - }); - }; - - var durationSecond = 1e3; - var durationMinute = 6e4; - var durationHour = 36e5; - var durationDay = 864e5; - var durationWeek = 6048e5; - - var second = newInterval(function(date) { - date.setTime(date - date.getMilliseconds()); - }, function(date, step) { - date.setTime(+date + step * durationSecond); - }, function(start, end) { - return (end - start) / durationSecond; - }, function(date) { - return date.getUTCSeconds(); - }); - - var minute = newInterval(function(date) { - date.setTime(date - date.getMilliseconds() - date.getSeconds() * durationSecond); - }, function(date, step) { - date.setTime(+date + step * durationMinute); - }, function(start, end) { - return (end - start) / durationMinute; - }, function(date) { - return date.getMinutes(); - }); - - var hour = newInterval(function(date) { - date.setTime(date - date.getMilliseconds() - date.getSeconds() * durationSecond - date.getMinutes() * durationMinute); - }, function(date, step) { - date.setTime(+date + step * durationHour); - }, function(start, end) { - return (end - start) / durationHour; - }, function(date) { - return date.getHours(); - }); - - var day = newInterval(function(date) { - date.setHours(0, 0, 0, 0); - }, function(date, step) { - date.setDate(date.getDate() + step); - }, function(start, end) { - return (end - start - (end.getTimezoneOffset() - start.getTimezoneOffset()) * durationMinute) / durationDay; - }, function(date) { - return date.getDate() - 1; - }); - - function weekday(i) { - return newInterval(function(date) { - date.setDate(date.getDate() - (date.getDay() + 7 - i) % 7); - date.setHours(0, 0, 0, 0); - }, function(date, step) { - date.setDate(date.getDate() + step * 7); - }, function(start, end) { - return (end - start - (end.getTimezoneOffset() - start.getTimezoneOffset()) * durationMinute) / durationWeek; - }); - } - - var sunday = weekday(0); - var monday = weekday(1); - var tuesday = weekday(2); - var wednesday = weekday(3); - var thursday = weekday(4); - var friday = weekday(5); - var saturday = weekday(6); - - var month = newInterval(function(date) { - date.setDate(1); - date.setHours(0, 0, 0, 0); - }, function(date, step) { - date.setMonth(date.getMonth() + step); - }, function(start, end) { - return end.getMonth() - start.getMonth() + (end.getFullYear() - start.getFullYear()) * 12; - }, function(date) { - return date.getMonth(); - }); - - var year = newInterval(function(date) { - date.setMonth(0, 1); - date.setHours(0, 0, 0, 0); - }, function(date, step) { - date.setFullYear(date.getFullYear() + step); - }, function(start, end) { - return end.getFullYear() - start.getFullYear(); - }, function(date) { - return date.getFullYear(); - }); - - // An optimized implementation for this simple case. - year.every = function(k) { - return !isFinite(k = Math.floor(k)) || !(k > 0) ? null : newInterval(function(date) { - date.setFullYear(Math.floor(date.getFullYear() / k) * k); - date.setMonth(0, 1); - date.setHours(0, 0, 0, 0); - }, function(date, step) { - date.setFullYear(date.getFullYear() + step * k); - }); - }; - - var utcMinute = newInterval(function(date) { - date.setUTCSeconds(0, 0); - }, function(date, step) { - date.setTime(+date + step * durationMinute); - }, function(start, end) { - return (end - start) / durationMinute; - }, function(date) { - return date.getUTCMinutes(); - }); - - var utcHour = newInterval(function(date) { - date.setUTCMinutes(0, 0, 0); - }, function(date, step) { - date.setTime(+date + step * durationHour); - }, function(start, end) { - return (end - start) / durationHour; - }, function(date) { - return date.getUTCHours(); - }); - - var utcDay = newInterval(function(date) { - date.setUTCHours(0, 0, 0, 0); - }, function(date, step) { - date.setUTCDate(date.getUTCDate() + step); - }, function(start, end) { - return (end - start) / durationDay; - }, function(date) { - return date.getUTCDate() - 1; - }); - - function utcWeekday(i) { - return newInterval(function(date) { - date.setUTCDate(date.getUTCDate() - (date.getUTCDay() + 7 - i) % 7); - date.setUTCHours(0, 0, 0, 0); - }, function(date, step) { - date.setUTCDate(date.getUTCDate() + step * 7); - }, function(start, end) { - return (end - start) / durationWeek; - }); - } - - var utcSunday = utcWeekday(0); - var utcMonday = utcWeekday(1); - var utcTuesday = utcWeekday(2); - var utcWednesday = utcWeekday(3); - var utcThursday = utcWeekday(4); - var utcFriday = utcWeekday(5); - var utcSaturday = utcWeekday(6); - - var utcMonth = newInterval(function(date) { - date.setUTCDate(1); - date.setUTCHours(0, 0, 0, 0); - }, function(date, step) { - date.setUTCMonth(date.getUTCMonth() + step); - }, function(start, end) { - return end.getUTCMonth() - start.getUTCMonth() + (end.getUTCFullYear() - start.getUTCFullYear()) * 12; - }, function(date) { - return date.getUTCMonth(); - }); - - var utcYear = newInterval(function(date) { - date.setUTCMonth(0, 1); - date.setUTCHours(0, 0, 0, 0); - }, function(date, step) { - date.setUTCFullYear(date.getUTCFullYear() + step); - }, function(start, end) { - return end.getUTCFullYear() - start.getUTCFullYear(); - }, function(date) { - return date.getUTCFullYear(); - }); - - // An optimized implementation for this simple case. - utcYear.every = function(k) { - return !isFinite(k = Math.floor(k)) || !(k > 0) ? null : newInterval(function(date) { - date.setUTCFullYear(Math.floor(date.getUTCFullYear() / k) * k); - date.setUTCMonth(0, 1); - date.setUTCHours(0, 0, 0, 0); - }, function(date, step) { - date.setUTCFullYear(date.getUTCFullYear() + step * k); - }); - }; - - function localDate(d) { - if (0 <= d.y && d.y < 100) { - var date = new Date(-1, d.m, d.d, d.H, d.M, d.S, d.L); - date.setFullYear(d.y); - return date; - } - return new Date(d.y, d.m, d.d, d.H, d.M, d.S, d.L); - } - - function utcDate(d) { - if (0 <= d.y && d.y < 100) { - var date = new Date(Date.UTC(-1, d.m, d.d, d.H, d.M, d.S, d.L)); - date.setUTCFullYear(d.y); - return date; - } - return new Date(Date.UTC(d.y, d.m, d.d, d.H, d.M, d.S, d.L)); - } - - function newDate(y, m, d) { - return {y: y, m: m, d: d, H: 0, M: 0, S: 0, L: 0}; - } - - function formatLocale$1(locale) { - var locale_dateTime = locale.dateTime, - locale_date = locale.date, - locale_time = locale.time, - locale_periods = locale.periods, - locale_weekdays = locale.days, - locale_shortWeekdays = locale.shortDays, - locale_months = locale.months, - locale_shortMonths = locale.shortMonths; - - var periodRe = formatRe(locale_periods), - periodLookup = formatLookup(locale_periods), - weekdayRe = formatRe(locale_weekdays), - weekdayLookup = formatLookup(locale_weekdays), - shortWeekdayRe = formatRe(locale_shortWeekdays), - shortWeekdayLookup = formatLookup(locale_shortWeekdays), - monthRe = formatRe(locale_months), - monthLookup = formatLookup(locale_months), - shortMonthRe = formatRe(locale_shortMonths), - shortMonthLookup = formatLookup(locale_shortMonths); - - var formats = { - "a": formatShortWeekday, - "A": formatWeekday, - "b": formatShortMonth, - "B": formatMonth, - "c": null, - "d": formatDayOfMonth, - "e": formatDayOfMonth, - "f": formatMicroseconds, - "H": formatHour24, - "I": formatHour12, - "j": formatDayOfYear, - "L": formatMilliseconds, - "m": formatMonthNumber, - "M": formatMinutes, - "p": formatPeriod, - "q": formatQuarter, - "Q": formatUnixTimestamp, - "s": formatUnixTimestampSeconds, - "S": formatSeconds, - "u": formatWeekdayNumberMonday, - "U": formatWeekNumberSunday, - "V": formatWeekNumberISO, - "w": formatWeekdayNumberSunday, - "W": formatWeekNumberMonday, - "x": null, - "X": null, - "y": formatYear, - "Y": formatFullYear, - "Z": formatZone, - "%": formatLiteralPercent - }; - - var utcFormats = { - "a": formatUTCShortWeekday, - "A": formatUTCWeekday, - "b": formatUTCShortMonth, - "B": formatUTCMonth, - "c": null, - "d": formatUTCDayOfMonth, - "e": formatUTCDayOfMonth, - "f": formatUTCMicroseconds, - "H": formatUTCHour24, - "I": formatUTCHour12, - "j": formatUTCDayOfYear, - "L": formatUTCMilliseconds, - "m": formatUTCMonthNumber, - "M": formatUTCMinutes, - "p": formatUTCPeriod, - "q": formatUTCQuarter, - "Q": formatUnixTimestamp, - "s": formatUnixTimestampSeconds, - "S": formatUTCSeconds, - "u": formatUTCWeekdayNumberMonday, - "U": formatUTCWeekNumberSunday, - "V": formatUTCWeekNumberISO, - "w": formatUTCWeekdayNumberSunday, - "W": formatUTCWeekNumberMonday, - "x": null, - "X": null, - "y": formatUTCYear, - "Y": formatUTCFullYear, - "Z": formatUTCZone, - "%": formatLiteralPercent - }; - - var parses = { - "a": parseShortWeekday, - "A": parseWeekday, - "b": parseShortMonth, - "B": parseMonth, - "c": parseLocaleDateTime, - "d": parseDayOfMonth, - "e": parseDayOfMonth, - "f": parseMicroseconds, - "H": parseHour24, - "I": parseHour24, - "j": parseDayOfYear, - "L": parseMilliseconds, - "m": parseMonthNumber, - "M": parseMinutes, - "p": parsePeriod, - "q": parseQuarter, - "Q": parseUnixTimestamp, - "s": parseUnixTimestampSeconds, - "S": parseSeconds, - "u": parseWeekdayNumberMonday, - "U": parseWeekNumberSunday, - "V": parseWeekNumberISO, - "w": parseWeekdayNumberSunday, - "W": parseWeekNumberMonday, - "x": parseLocaleDate, - "X": parseLocaleTime, - "y": parseYear, - "Y": parseFullYear, - "Z": parseZone, - "%": parseLiteralPercent - }; - - // These recursive directive definitions must be deferred. - formats.x = newFormat(locale_date, formats); - formats.X = newFormat(locale_time, formats); - formats.c = newFormat(locale_dateTime, formats); - utcFormats.x = newFormat(locale_date, utcFormats); - utcFormats.X = newFormat(locale_time, utcFormats); - utcFormats.c = newFormat(locale_dateTime, utcFormats); - - function newFormat(specifier, formats) { - return function(date) { - var string = [], - i = -1, - j = 0, - n = specifier.length, - c, - pad, - format; - - if (!(date instanceof Date)) date = new Date(+date); - - while (++i < n) { - if (specifier.charCodeAt(i) === 37) { - string.push(specifier.slice(j, i)); - if ((pad = pads[c = specifier.charAt(++i)]) != null) c = specifier.charAt(++i); - else pad = c === "e" ? " " : "0"; - if (format = formats[c]) c = format(date, pad); - string.push(c); - j = i + 1; - } - } - - string.push(specifier.slice(j, i)); - return string.join(""); - }; - } - - function newParse(specifier, Z) { - return function(string) { - var d = newDate(1900, undefined, 1), - i = parseSpecifier(d, specifier, string += "", 0), - week, day$1; - if (i != string.length) return null; - - // If a UNIX timestamp is specified, return it. - if ("Q" in d) return new Date(d.Q); - if ("s" in d) return new Date(d.s * 1000 + ("L" in d ? d.L : 0)); - - // If this is utcParse, never use the local timezone. - if (Z && !("Z" in d)) d.Z = 0; - - // The am-pm flag is 0 for AM, and 1 for PM. - if ("p" in d) d.H = d.H % 12 + d.p * 12; - - // If the month was not specified, inherit from the quarter. - if (d.m === undefined) d.m = "q" in d ? d.q : 0; - - // Convert day-of-week and week-of-year to day-of-year. - if ("V" in d) { - if (d.V < 1 || d.V > 53) return null; - if (!("w" in d)) d.w = 1; - if ("Z" in d) { - week = utcDate(newDate(d.y, 0, 1)), day$1 = week.getUTCDay(); - week = day$1 > 4 || day$1 === 0 ? utcMonday.ceil(week) : utcMonday(week); - week = utcDay.offset(week, (d.V - 1) * 7); - d.y = week.getUTCFullYear(); - d.m = week.getUTCMonth(); - d.d = week.getUTCDate() + (d.w + 6) % 7; - } else { - week = localDate(newDate(d.y, 0, 1)), day$1 = week.getDay(); - week = day$1 > 4 || day$1 === 0 ? monday.ceil(week) : monday(week); - week = day.offset(week, (d.V - 1) * 7); - d.y = week.getFullYear(); - d.m = week.getMonth(); - d.d = week.getDate() + (d.w + 6) % 7; - } - } else if ("W" in d || "U" in d) { - if (!("w" in d)) d.w = "u" in d ? d.u % 7 : "W" in d ? 1 : 0; - day$1 = "Z" in d ? utcDate(newDate(d.y, 0, 1)).getUTCDay() : localDate(newDate(d.y, 0, 1)).getDay(); - d.m = 0; - d.d = "W" in d ? (d.w + 6) % 7 + d.W * 7 - (day$1 + 5) % 7 : d.w + d.U * 7 - (day$1 + 6) % 7; - } - - // If a time zone is specified, all fields are interpreted as UTC and then - // offset according to the specified time zone. - if ("Z" in d) { - d.H += d.Z / 100 | 0; - d.M += d.Z % 100; - return utcDate(d); - } - - // Otherwise, all fields are in local time. - return localDate(d); - }; - } - - function parseSpecifier(d, specifier, string, j) { - var i = 0, - n = specifier.length, - m = string.length, - c, - parse; - - while (i < n) { - if (j >= m) return -1; - c = specifier.charCodeAt(i++); - if (c === 37) { - c = specifier.charAt(i++); - parse = parses[c in pads ? specifier.charAt(i++) : c]; - if (!parse || ((j = parse(d, string, j)) < 0)) return -1; - } else if (c != string.charCodeAt(j++)) { - return -1; - } - } - - return j; - } - - function parsePeriod(d, string, i) { - var n = periodRe.exec(string.slice(i)); - return n ? (d.p = periodLookup[n[0].toLowerCase()], i + n[0].length) : -1; - } - - function parseShortWeekday(d, string, i) { - var n = shortWeekdayRe.exec(string.slice(i)); - return n ? (d.w = shortWeekdayLookup[n[0].toLowerCase()], i + n[0].length) : -1; - } - - function parseWeekday(d, string, i) { - var n = weekdayRe.exec(string.slice(i)); - return n ? (d.w = weekdayLookup[n[0].toLowerCase()], i + n[0].length) : -1; - } - - function parseShortMonth(d, string, i) { - var n = shortMonthRe.exec(string.slice(i)); - return n ? (d.m = shortMonthLookup[n[0].toLowerCase()], i + n[0].length) : -1; - } - - function parseMonth(d, string, i) { - var n = monthRe.exec(string.slice(i)); - return n ? (d.m = monthLookup[n[0].toLowerCase()], i + n[0].length) : -1; - } - - function parseLocaleDateTime(d, string, i) { - return parseSpecifier(d, locale_dateTime, string, i); - } - - function parseLocaleDate(d, string, i) { - return parseSpecifier(d, locale_date, string, i); - } - - function parseLocaleTime(d, string, i) { - return parseSpecifier(d, locale_time, string, i); - } - - function formatShortWeekday(d) { - return locale_shortWeekdays[d.getDay()]; - } - - function formatWeekday(d) { - return locale_weekdays[d.getDay()]; - } - - function formatShortMonth(d) { - return locale_shortMonths[d.getMonth()]; - } - - function formatMonth(d) { - return locale_months[d.getMonth()]; - } - - function formatPeriod(d) { - return locale_periods[+(d.getHours() >= 12)]; - } - - function formatQuarter(d) { - return 1 + ~~(d.getMonth() / 3); - } - - function formatUTCShortWeekday(d) { - return locale_shortWeekdays[d.getUTCDay()]; - } - - function formatUTCWeekday(d) { - return locale_weekdays[d.getUTCDay()]; - } - - function formatUTCShortMonth(d) { - return locale_shortMonths[d.getUTCMonth()]; - } - - function formatUTCMonth(d) { - return locale_months[d.getUTCMonth()]; - } - - function formatUTCPeriod(d) { - return locale_periods[+(d.getUTCHours() >= 12)]; - } - - function formatUTCQuarter(d) { - return 1 + ~~(d.getUTCMonth() / 3); - } - - return { - format: function(specifier) { - var f = newFormat(specifier += "", formats); - f.toString = function() { return specifier; }; - return f; - }, - parse: function(specifier) { - var p = newParse(specifier += "", false); - p.toString = function() { return specifier; }; - return p; - }, - utcFormat: function(specifier) { - var f = newFormat(specifier += "", utcFormats); - f.toString = function() { return specifier; }; - return f; - }, - utcParse: function(specifier) { - var p = newParse(specifier += "", true); - p.toString = function() { return specifier; }; - return p; - } - }; - } - - var pads = {"-": "", "_": " ", "0": "0"}, - numberRe = /^\s*\d+/, // note: ignores next directive - percentRe = /^%/, - requoteRe = /[\\^$*+?|[\]().{}]/g; - - function pad(value, fill, width) { - var sign = value < 0 ? "-" : "", - string = (sign ? -value : value) + "", - length = string.length; - return sign + (length < width ? new Array(width - length + 1).join(fill) + string : string); - } - - function requote(s) { - return s.replace(requoteRe, "\\$&"); - } - - function formatRe(names) { - return new RegExp("^(?:" + names.map(requote).join("|") + ")", "i"); - } - - function formatLookup(names) { - var map = {}, i = -1, n = names.length; - while (++i < n) map[names[i].toLowerCase()] = i; - return map; - } - - function parseWeekdayNumberSunday(d, string, i) { - var n = numberRe.exec(string.slice(i, i + 1)); - return n ? (d.w = +n[0], i + n[0].length) : -1; - } - - function parseWeekdayNumberMonday(d, string, i) { - var n = numberRe.exec(string.slice(i, i + 1)); - return n ? (d.u = +n[0], i + n[0].length) : -1; - } - - function parseWeekNumberSunday(d, string, i) { - var n = numberRe.exec(string.slice(i, i + 2)); - return n ? (d.U = +n[0], i + n[0].length) : -1; - } - - function parseWeekNumberISO(d, string, i) { - var n = numberRe.exec(string.slice(i, i + 2)); - return n ? (d.V = +n[0], i + n[0].length) : -1; - } - - function parseWeekNumberMonday(d, string, i) { - var n = numberRe.exec(string.slice(i, i + 2)); - return n ? (d.W = +n[0], i + n[0].length) : -1; - } - - function parseFullYear(d, string, i) { - var n = numberRe.exec(string.slice(i, i + 4)); - return n ? (d.y = +n[0], i + n[0].length) : -1; - } - - function parseYear(d, string, i) { - var n = numberRe.exec(string.slice(i, i + 2)); - return n ? (d.y = +n[0] + (+n[0] > 68 ? 1900 : 2000), i + n[0].length) : -1; - } - - function parseZone(d, string, i) { - var n = /^(Z)|([+-]\d\d)(?::?(\d\d))?/.exec(string.slice(i, i + 6)); - return n ? (d.Z = n[1] ? 0 : -(n[2] + (n[3] || "00")), i + n[0].length) : -1; - } - - function parseQuarter(d, string, i) { - var n = numberRe.exec(string.slice(i, i + 1)); - return n ? (d.q = n[0] * 3 - 3, i + n[0].length) : -1; - } - - function parseMonthNumber(d, string, i) { - var n = numberRe.exec(string.slice(i, i + 2)); - return n ? (d.m = n[0] - 1, i + n[0].length) : -1; - } - - function parseDayOfMonth(d, string, i) { - var n = numberRe.exec(string.slice(i, i + 2)); - return n ? (d.d = +n[0], i + n[0].length) : -1; - } - - function parseDayOfYear(d, string, i) { - var n = numberRe.exec(string.slice(i, i + 3)); - return n ? (d.m = 0, d.d = +n[0], i + n[0].length) : -1; - } - - function parseHour24(d, string, i) { - var n = numberRe.exec(string.slice(i, i + 2)); - return n ? (d.H = +n[0], i + n[0].length) : -1; - } - - function parseMinutes(d, string, i) { - var n = numberRe.exec(string.slice(i, i + 2)); - return n ? (d.M = +n[0], i + n[0].length) : -1; - } - - function parseSeconds(d, string, i) { - var n = numberRe.exec(string.slice(i, i + 2)); - return n ? (d.S = +n[0], i + n[0].length) : -1; - } - - function parseMilliseconds(d, string, i) { - var n = numberRe.exec(string.slice(i, i + 3)); - return n ? (d.L = +n[0], i + n[0].length) : -1; - } - - function parseMicroseconds(d, string, i) { - var n = numberRe.exec(string.slice(i, i + 6)); - return n ? (d.L = Math.floor(n[0] / 1000), i + n[0].length) : -1; - } - - function parseLiteralPercent(d, string, i) { - var n = percentRe.exec(string.slice(i, i + 1)); - return n ? i + n[0].length : -1; - } - - function parseUnixTimestamp(d, string, i) { - var n = numberRe.exec(string.slice(i)); - return n ? (d.Q = +n[0], i + n[0].length) : -1; - } - - function parseUnixTimestampSeconds(d, string, i) { - var n = numberRe.exec(string.slice(i)); - return n ? (d.s = +n[0], i + n[0].length) : -1; - } - - function formatDayOfMonth(d, p) { - return pad(d.getDate(), p, 2); - } - - function formatHour24(d, p) { - return pad(d.getHours(), p, 2); - } - - function formatHour12(d, p) { - return pad(d.getHours() % 12 || 12, p, 2); - } - - function formatDayOfYear(d, p) { - return pad(1 + day.count(year(d), d), p, 3); - } - - function formatMilliseconds(d, p) { - return pad(d.getMilliseconds(), p, 3); - } - - function formatMicroseconds(d, p) { - return formatMilliseconds(d, p) + "000"; - } - - function formatMonthNumber(d, p) { - return pad(d.getMonth() + 1, p, 2); - } - - function formatMinutes(d, p) { - return pad(d.getMinutes(), p, 2); - } - - function formatSeconds(d, p) { - return pad(d.getSeconds(), p, 2); - } - - function formatWeekdayNumberMonday(d) { - var day = d.getDay(); - return day === 0 ? 7 : day; - } - - function formatWeekNumberSunday(d, p) { - return pad(sunday.count(year(d) - 1, d), p, 2); - } - - function formatWeekNumberISO(d, p) { - var day = d.getDay(); - d = (day >= 4 || day === 0) ? thursday(d) : thursday.ceil(d); - return pad(thursday.count(year(d), d) + (year(d).getDay() === 4), p, 2); - } - - function formatWeekdayNumberSunday(d) { - return d.getDay(); - } - - function formatWeekNumberMonday(d, p) { - return pad(monday.count(year(d) - 1, d), p, 2); - } - - function formatYear(d, p) { - return pad(d.getFullYear() % 100, p, 2); - } - - function formatFullYear(d, p) { - return pad(d.getFullYear() % 10000, p, 4); - } - - function formatZone(d) { - var z = d.getTimezoneOffset(); - return (z > 0 ? "-" : (z *= -1, "+")) - + pad(z / 60 | 0, "0", 2) - + pad(z % 60, "0", 2); - } - - function formatUTCDayOfMonth(d, p) { - return pad(d.getUTCDate(), p, 2); - } - - function formatUTCHour24(d, p) { - return pad(d.getUTCHours(), p, 2); - } - - function formatUTCHour12(d, p) { - return pad(d.getUTCHours() % 12 || 12, p, 2); - } - - function formatUTCDayOfYear(d, p) { - return pad(1 + utcDay.count(utcYear(d), d), p, 3); - } - - function formatUTCMilliseconds(d, p) { - return pad(d.getUTCMilliseconds(), p, 3); - } - - function formatUTCMicroseconds(d, p) { - return formatUTCMilliseconds(d, p) + "000"; - } - - function formatUTCMonthNumber(d, p) { - return pad(d.getUTCMonth() + 1, p, 2); - } - - function formatUTCMinutes(d, p) { - return pad(d.getUTCMinutes(), p, 2); - } - - function formatUTCSeconds(d, p) { - return pad(d.getUTCSeconds(), p, 2); - } - - function formatUTCWeekdayNumberMonday(d) { - var dow = d.getUTCDay(); - return dow === 0 ? 7 : dow; - } - - function formatUTCWeekNumberSunday(d, p) { - return pad(utcSunday.count(utcYear(d) - 1, d), p, 2); - } - - function formatUTCWeekNumberISO(d, p) { - var day = d.getUTCDay(); - d = (day >= 4 || day === 0) ? utcThursday(d) : utcThursday.ceil(d); - return pad(utcThursday.count(utcYear(d), d) + (utcYear(d).getUTCDay() === 4), p, 2); - } - - function formatUTCWeekdayNumberSunday(d) { - return d.getUTCDay(); - } - - function formatUTCWeekNumberMonday(d, p) { - return pad(utcMonday.count(utcYear(d) - 1, d), p, 2); - } - - function formatUTCYear(d, p) { - return pad(d.getUTCFullYear() % 100, p, 2); - } - - function formatUTCFullYear(d, p) { - return pad(d.getUTCFullYear() % 10000, p, 4); - } - - function formatUTCZone() { - return "+0000"; - } - - function formatLiteralPercent() { - return "%"; - } - - function formatUnixTimestamp(d) { - return +d; - } - - function formatUnixTimestampSeconds(d) { - return Math.floor(+d / 1000); - } - - var locale$1; - var timeFormat; - var timeParse; - var utcFormat; - var utcParse; - - defaultLocale$1({ - dateTime: "%x, %X", - date: "%-m/%-d/%Y", - time: "%-I:%M:%S %p", - periods: ["AM", "PM"], - days: ["Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"], - shortDays: ["Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat"], - months: ["January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December"], - shortMonths: ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"] - }); - - function defaultLocale$1(definition) { - locale$1 = formatLocale$1(definition); - timeFormat = locale$1.format; - timeParse = locale$1.parse; - utcFormat = locale$1.utcFormat; - utcParse = locale$1.utcParse; - return locale$1; - } - - var isoSpecifier = "%Y-%m-%dT%H:%M:%S.%LZ"; - - function formatIsoNative(date) { - return date.toISOString(); - } - - var formatIso = Date.prototype.toISOString - ? formatIsoNative - : utcFormat(isoSpecifier); - - function parseIsoNative(string) { - var date = new Date(string); - return isNaN(date) ? null : date; - } - - var parseIso = +new Date("2000-01-01T00:00:00.000Z") - ? parseIsoNative - : utcParse(isoSpecifier); - - var noop = {value: function() {}}; - - function dispatch() { - for (var i = 0, n = arguments.length, _ = {}, t; i < n; ++i) { - if (!(t = arguments[i] + "") || (t in _) || /[\s.]/.test(t)) throw new Error("illegal type: " + t); - _[t] = []; - } - return new Dispatch(_); - } - - function Dispatch(_) { - this._ = _; - } - - function parseTypenames(typenames, types) { - return typenames.trim().split(/^|\s+/).map(function(t) { - var name = "", i = t.indexOf("."); - if (i >= 0) name = t.slice(i + 1), t = t.slice(0, i); - if (t && !types.hasOwnProperty(t)) throw new Error("unknown type: " + t); - return {type: t, name: name}; - }); - } - - Dispatch.prototype = dispatch.prototype = { - constructor: Dispatch, - on: function(typename, callback) { - var _ = this._, - T = parseTypenames(typename + "", _), - t, - i = -1, - n = T.length; - - // If no callback was specified, return the callback of the given type and name. - if (arguments.length < 2) { - while (++i < n) if ((t = (typename = T[i]).type) && (t = get(_[t], typename.name))) return t; - return; - } - - // If a type was specified, set the callback for the given type and name. - // Otherwise, if a null callback was specified, remove callbacks of the given name. - if (callback != null && typeof callback !== "function") throw new Error("invalid callback: " + callback); - while (++i < n) { - if (t = (typename = T[i]).type) _[t] = set(_[t], typename.name, callback); - else if (callback == null) for (t in _) _[t] = set(_[t], typename.name, null); - } - - return this; - }, - copy: function() { - var copy = {}, _ = this._; - for (var t in _) copy[t] = _[t].slice(); - return new Dispatch(copy); - }, - call: function(type, that) { - if ((n = arguments.length - 2) > 0) for (var args = new Array(n), i = 0, n, t; i < n; ++i) args[i] = arguments[i + 2]; - if (!this._.hasOwnProperty(type)) throw new Error("unknown type: " + type); - for (t = this._[type], i = 0, n = t.length; i < n; ++i) t[i].value.apply(that, args); - }, - apply: function(type, that, args) { - if (!this._.hasOwnProperty(type)) throw new Error("unknown type: " + type); - for (var t = this._[type], i = 0, n = t.length; i < n; ++i) t[i].value.apply(that, args); - } - }; - - function get(type, name) { - for (var i = 0, n = type.length, c; i < n; ++i) { - if ((c = type[i]).name === name) { - return c.value; - } - } - } - - function set(type, name, callback) { - for (var i = 0, n = type.length; i < n; ++i) { - if (type[i].name === name) { - type[i] = noop, type = type.slice(0, i).concat(type.slice(i + 1)); - break; - } - } - if (callback != null) type.push({name: name, value: callback}); - return type; - } - - var xhtml = "http://www.w3.org/1999/xhtml"; - - var namespaces = { - svg: "http://www.w3.org/2000/svg", - xhtml: xhtml, - xlink: "http://www.w3.org/1999/xlink", - xml: "http://www.w3.org/XML/1998/namespace", - xmlns: "http://www.w3.org/2000/xmlns/" - }; - - function namespace(name) { - var prefix = name += "", i = prefix.indexOf(":"); - if (i >= 0 && (prefix = name.slice(0, i)) !== "xmlns") name = name.slice(i + 1); - return namespaces.hasOwnProperty(prefix) ? {space: namespaces[prefix], local: name} : name; - } - - function creatorInherit(name) { - return function() { - var document = this.ownerDocument, - uri = this.namespaceURI; - return uri === xhtml && document.documentElement.namespaceURI === xhtml - ? document.createElement(name) - : document.createElementNS(uri, name); - }; - } - - function creatorFixed(fullname) { - return function() { - return this.ownerDocument.createElementNS(fullname.space, fullname.local); - }; - } - - function creator(name) { - var fullname = namespace(name); - return (fullname.local - ? creatorFixed - : creatorInherit)(fullname); - } - - function none() {} - - function selector(selector) { - return selector == null ? none : function() { - return this.querySelector(selector); - }; - } - - function selection_select(select) { - if (typeof select !== "function") select = selector(select); - - for (var groups = this._groups, m = groups.length, subgroups = new Array(m), j = 0; j < m; ++j) { - for (var group = groups[j], n = group.length, subgroup = subgroups[j] = new Array(n), node, subnode, i = 0; i < n; ++i) { - if ((node = group[i]) && (subnode = select.call(node, node.__data__, i, group))) { - if ("__data__" in node) subnode.__data__ = node.__data__; - subgroup[i] = subnode; - } - } - } - - return new Selection(subgroups, this._parents); - } - - function empty() { - return []; - } - - function selectorAll(selector) { - return selector == null ? empty : function() { - return this.querySelectorAll(selector); - }; - } - - function selection_selectAll(select) { - if (typeof select !== "function") select = selectorAll(select); - - for (var groups = this._groups, m = groups.length, subgroups = [], parents = [], j = 0; j < m; ++j) { - for (var group = groups[j], n = group.length, node, i = 0; i < n; ++i) { - if (node = group[i]) { - subgroups.push(select.call(node, node.__data__, i, group)); - parents.push(node); - } - } - } - - return new Selection(subgroups, parents); - } - - function matcher(selector) { - return function() { - return this.matches(selector); - }; - } - - function selection_filter(match) { - if (typeof match !== "function") match = matcher(match); - - for (var groups = this._groups, m = groups.length, subgroups = new Array(m), j = 0; j < m; ++j) { - for (var group = groups[j], n = group.length, subgroup = subgroups[j] = [], node, i = 0; i < n; ++i) { - if ((node = group[i]) && match.call(node, node.__data__, i, group)) { - subgroup.push(node); - } - } - } - - return new Selection(subgroups, this._parents); - } - - function sparse(update) { - return new Array(update.length); - } - - function selection_enter() { - return new Selection(this._enter || this._groups.map(sparse), this._parents); - } - - function EnterNode(parent, datum) { - this.ownerDocument = parent.ownerDocument; - this.namespaceURI = parent.namespaceURI; - this._next = null; - this._parent = parent; - this.__data__ = datum; - } - - EnterNode.prototype = { - constructor: EnterNode, - appendChild: function(child) { return this._parent.insertBefore(child, this._next); }, - insertBefore: function(child, next) { return this._parent.insertBefore(child, next); }, - querySelector: function(selector) { return this._parent.querySelector(selector); }, - querySelectorAll: function(selector) { return this._parent.querySelectorAll(selector); } - }; - - function constant$2(x) { - return function() { - return x; - }; - } - - var keyPrefix = "$"; // Protect against keys like “__proto__”. - - function bindIndex(parent, group, enter, update, exit, data) { - var i = 0, - node, - groupLength = group.length, - dataLength = data.length; - - // Put any non-null nodes that fit into update. - // Put any null nodes into enter. - // Put any remaining data into enter. - for (; i < dataLength; ++i) { - if (node = group[i]) { - node.__data__ = data[i]; - update[i] = node; - } else { - enter[i] = new EnterNode(parent, data[i]); - } - } - - // Put any non-null nodes that don’t fit into exit. - for (; i < groupLength; ++i) { - if (node = group[i]) { - exit[i] = node; - } - } - } - - function bindKey(parent, group, enter, update, exit, data, key) { - var i, - node, - nodeByKeyValue = {}, - groupLength = group.length, - dataLength = data.length, - keyValues = new Array(groupLength), - keyValue; - - // Compute the key for each node. - // If multiple nodes have the same key, the duplicates are added to exit. - for (i = 0; i < groupLength; ++i) { - if (node = group[i]) { - keyValues[i] = keyValue = keyPrefix + key.call(node, node.__data__, i, group); - if (keyValue in nodeByKeyValue) { - exit[i] = node; - } else { - nodeByKeyValue[keyValue] = node; - } - } - } - - // Compute the key for each datum. - // If there a node associated with this key, join and add it to update. - // If there is not (or the key is a duplicate), add it to enter. - for (i = 0; i < dataLength; ++i) { - keyValue = keyPrefix + key.call(parent, data[i], i, data); - if (node = nodeByKeyValue[keyValue]) { - update[i] = node; - node.__data__ = data[i]; - nodeByKeyValue[keyValue] = null; - } else { - enter[i] = new EnterNode(parent, data[i]); - } - } - - // Add any remaining nodes that were not bound to data to exit. - for (i = 0; i < groupLength; ++i) { - if ((node = group[i]) && (nodeByKeyValue[keyValues[i]] === node)) { - exit[i] = node; - } - } - } - - function selection_data(value, key) { - if (!value) { - data = new Array(this.size()), j = -1; - this.each(function(d) { data[++j] = d; }); - return data; - } - - var bind = key ? bindKey : bindIndex, - parents = this._parents, - groups = this._groups; - - if (typeof value !== "function") value = constant$2(value); - - for (var m = groups.length, update = new Array(m), enter = new Array(m), exit = new Array(m), j = 0; j < m; ++j) { - var parent = parents[j], - group = groups[j], - groupLength = group.length, - data = value.call(parent, parent && parent.__data__, j, parents), - dataLength = data.length, - enterGroup = enter[j] = new Array(dataLength), - updateGroup = update[j] = new Array(dataLength), - exitGroup = exit[j] = new Array(groupLength); - - bind(parent, group, enterGroup, updateGroup, exitGroup, data, key); - - // Now connect the enter nodes to their following update node, such that - // appendChild can insert the materialized enter node before this node, - // rather than at the end of the parent node. - for (var i0 = 0, i1 = 0, previous, next; i0 < dataLength; ++i0) { - if (previous = enterGroup[i0]) { - if (i0 >= i1) i1 = i0 + 1; - while (!(next = updateGroup[i1]) && ++i1 < dataLength); - previous._next = next || null; - } - } - } - - update = new Selection(update, parents); - update._enter = enter; - update._exit = exit; - return update; - } - - function selection_exit() { - return new Selection(this._exit || this._groups.map(sparse), this._parents); - } - - function selection_join(onenter, onupdate, onexit) { - var enter = this.enter(), update = this, exit = this.exit(); - enter = typeof onenter === "function" ? onenter(enter) : enter.append(onenter + ""); - if (onupdate != null) update = onupdate(update); - if (onexit == null) exit.remove(); else onexit(exit); - return enter && update ? enter.merge(update).order() : update; - } - - function selection_merge(selection) { - - for (var groups0 = this._groups, groups1 = selection._groups, m0 = groups0.length, m1 = groups1.length, m = Math.min(m0, m1), merges = new Array(m0), j = 0; j < m; ++j) { - for (var group0 = groups0[j], group1 = groups1[j], n = group0.length, merge = merges[j] = new Array(n), node, i = 0; i < n; ++i) { - if (node = group0[i] || group1[i]) { - merge[i] = node; - } - } - } - - for (; j < m0; ++j) { - merges[j] = groups0[j]; - } - - return new Selection(merges, this._parents); - } - - function selection_order() { - - for (var groups = this._groups, j = -1, m = groups.length; ++j < m;) { - for (var group = groups[j], i = group.length - 1, next = group[i], node; --i >= 0;) { - if (node = group[i]) { - if (next && node.compareDocumentPosition(next) ^ 4) next.parentNode.insertBefore(node, next); - next = node; - } - } - } - - return this; - } - - function selection_sort(compare) { - if (!compare) compare = ascending$1; - - function compareNode(a, b) { - return a && b ? compare(a.__data__, b.__data__) : !a - !b; - } - - for (var groups = this._groups, m = groups.length, sortgroups = new Array(m), j = 0; j < m; ++j) { - for (var group = groups[j], n = group.length, sortgroup = sortgroups[j] = new Array(n), node, i = 0; i < n; ++i) { - if (node = group[i]) { - sortgroup[i] = node; - } - } - sortgroup.sort(compareNode); - } - - return new Selection(sortgroups, this._parents).order(); - } - - function ascending$1(a, b) { - return a < b ? -1 : a > b ? 1 : a >= b ? 0 : NaN; - } - - function selection_call() { - var callback = arguments[0]; - arguments[0] = this; - callback.apply(null, arguments); - return this; - } - - function selection_nodes() { - var nodes = new Array(this.size()), i = -1; - this.each(function() { nodes[++i] = this; }); - return nodes; - } - - function selection_node() { - - for (var groups = this._groups, j = 0, m = groups.length; j < m; ++j) { - for (var group = groups[j], i = 0, n = group.length; i < n; ++i) { - var node = group[i]; - if (node) return node; - } - } - - return null; - } - - function selection_size() { - var size = 0; - this.each(function() { ++size; }); - return size; - } - - function selection_empty() { - return !this.node(); - } - - function selection_each(callback) { - - for (var groups = this._groups, j = 0, m = groups.length; j < m; ++j) { - for (var group = groups[j], i = 0, n = group.length, node; i < n; ++i) { - if (node = group[i]) callback.call(node, node.__data__, i, group); - } - } - - return this; - } - - function attrRemove(name) { - return function() { - this.removeAttribute(name); - }; - } - - function attrRemoveNS(fullname) { - return function() { - this.removeAttributeNS(fullname.space, fullname.local); - }; - } - - function attrConstant(name, value) { - return function() { - this.setAttribute(name, value); - }; - } - - function attrConstantNS(fullname, value) { - return function() { - this.setAttributeNS(fullname.space, fullname.local, value); - }; - } - - function attrFunction(name, value) { - return function() { - var v = value.apply(this, arguments); - if (v == null) this.removeAttribute(name); - else this.setAttribute(name, v); - }; - } - - function attrFunctionNS(fullname, value) { - return function() { - var v = value.apply(this, arguments); - if (v == null) this.removeAttributeNS(fullname.space, fullname.local); - else this.setAttributeNS(fullname.space, fullname.local, v); - }; - } - - function selection_attr(name, value) { - var fullname = namespace(name); - - if (arguments.length < 2) { - var node = this.node(); - return fullname.local - ? node.getAttributeNS(fullname.space, fullname.local) - : node.getAttribute(fullname); - } - - return this.each((value == null - ? (fullname.local ? attrRemoveNS : attrRemove) : (typeof value === "function" - ? (fullname.local ? attrFunctionNS : attrFunction) - : (fullname.local ? attrConstantNS : attrConstant)))(fullname, value)); - } - - function defaultView(node) { - return (node.ownerDocument && node.ownerDocument.defaultView) // node is a Node - || (node.document && node) // node is a Window - || node.defaultView; // node is a Document - } - - function styleRemove(name) { - return function() { - this.style.removeProperty(name); - }; - } - - function styleConstant(name, value, priority) { - return function() { - this.style.setProperty(name, value, priority); - }; - } - - function styleFunction(name, value, priority) { - return function() { - var v = value.apply(this, arguments); - if (v == null) this.style.removeProperty(name); - else this.style.setProperty(name, v, priority); - }; - } - - function selection_style(name, value, priority) { - return arguments.length > 1 - ? this.each((value == null - ? styleRemove : typeof value === "function" - ? styleFunction - : styleConstant)(name, value, priority == null ? "" : priority)) - : styleValue(this.node(), name); - } - - function styleValue(node, name) { - return node.style.getPropertyValue(name) - || defaultView(node).getComputedStyle(node, null).getPropertyValue(name); - } - - function propertyRemove(name) { - return function() { - delete this[name]; - }; - } - - function propertyConstant(name, value) { - return function() { - this[name] = value; - }; - } - - function propertyFunction(name, value) { - return function() { - var v = value.apply(this, arguments); - if (v == null) delete this[name]; - else this[name] = v; - }; - } - - function selection_property(name, value) { - return arguments.length > 1 - ? this.each((value == null - ? propertyRemove : typeof value === "function" - ? propertyFunction - : propertyConstant)(name, value)) - : this.node()[name]; - } - - function classArray(string) { - return string.trim().split(/^|\s+/); - } - - function classList(node) { - return node.classList || new ClassList(node); - } - - function ClassList(node) { - this._node = node; - this._names = classArray(node.getAttribute("class") || ""); - } - - ClassList.prototype = { - add: function(name) { - var i = this._names.indexOf(name); - if (i < 0) { - this._names.push(name); - this._node.setAttribute("class", this._names.join(" ")); - } - }, - remove: function(name) { - var i = this._names.indexOf(name); - if (i >= 0) { - this._names.splice(i, 1); - this._node.setAttribute("class", this._names.join(" ")); - } - }, - contains: function(name) { - return this._names.indexOf(name) >= 0; - } - }; - - function classedAdd(node, names) { - var list = classList(node), i = -1, n = names.length; - while (++i < n) list.add(names[i]); - } - - function classedRemove(node, names) { - var list = classList(node), i = -1, n = names.length; - while (++i < n) list.remove(names[i]); - } - - function classedTrue(names) { - return function() { - classedAdd(this, names); - }; - } - - function classedFalse(names) { - return function() { - classedRemove(this, names); - }; - } - - function classedFunction(names, value) { - return function() { - (value.apply(this, arguments) ? classedAdd : classedRemove)(this, names); - }; - } - - function selection_classed(name, value) { - var names = classArray(name + ""); - - if (arguments.length < 2) { - var list = classList(this.node()), i = -1, n = names.length; - while (++i < n) if (!list.contains(names[i])) return false; - return true; - } - - return this.each((typeof value === "function" - ? classedFunction : value - ? classedTrue - : classedFalse)(names, value)); - } - - function textRemove() { - this.textContent = ""; - } - - function textConstant(value) { - return function() { - this.textContent = value; - }; - } - - function textFunction(value) { - return function() { - var v = value.apply(this, arguments); - this.textContent = v == null ? "" : v; - }; - } - - function selection_text(value) { - return arguments.length - ? this.each(value == null - ? textRemove : (typeof value === "function" - ? textFunction - : textConstant)(value)) - : this.node().textContent; - } - - function htmlRemove() { - this.innerHTML = ""; - } - - function htmlConstant(value) { - return function() { - this.innerHTML = value; - }; - } - - function htmlFunction(value) { - return function() { - var v = value.apply(this, arguments); - this.innerHTML = v == null ? "" : v; - }; - } - - function selection_html(value) { - return arguments.length - ? this.each(value == null - ? htmlRemove : (typeof value === "function" - ? htmlFunction - : htmlConstant)(value)) - : this.node().innerHTML; - } - - function raise() { - if (this.nextSibling) this.parentNode.appendChild(this); - } - - function selection_raise() { - return this.each(raise); - } - - function lower() { - if (this.previousSibling) this.parentNode.insertBefore(this, this.parentNode.firstChild); - } - - function selection_lower() { - return this.each(lower); - } - - function selection_append(name) { - var create = typeof name === "function" ? name : creator(name); - return this.select(function() { - return this.appendChild(create.apply(this, arguments)); - }); - } - - function constantNull() { - return null; - } - - function selection_insert(name, before) { - var create = typeof name === "function" ? name : creator(name), - select = before == null ? constantNull : typeof before === "function" ? before : selector(before); - return this.select(function() { - return this.insertBefore(create.apply(this, arguments), select.apply(this, arguments) || null); - }); - } - - function remove() { - var parent = this.parentNode; - if (parent) parent.removeChild(this); - } - - function selection_remove() { - return this.each(remove); - } - - function selection_cloneShallow() { - var clone = this.cloneNode(false), parent = this.parentNode; - return parent ? parent.insertBefore(clone, this.nextSibling) : clone; - } - - function selection_cloneDeep() { - var clone = this.cloneNode(true), parent = this.parentNode; - return parent ? parent.insertBefore(clone, this.nextSibling) : clone; - } - - function selection_clone(deep) { - return this.select(deep ? selection_cloneDeep : selection_cloneShallow); - } - - function selection_datum(value) { - return arguments.length - ? this.property("__data__", value) - : this.node().__data__; - } - - var filterEvents = {}; - - var event = null; - - if (typeof document !== "undefined") { - var element = document.documentElement; - if (!("onmouseenter" in element)) { - filterEvents = {mouseenter: "mouseover", mouseleave: "mouseout"}; - } - } - - function filterContextListener(listener, index, group) { - listener = contextListener(listener, index, group); - return function(event) { - var related = event.relatedTarget; - if (!related || (related !== this && !(related.compareDocumentPosition(this) & 8))) { - listener.call(this, event); - } - }; - } - - function contextListener(listener, index, group) { - return function(event1) { - var event0 = event; // Events can be reentrant (e.g., focus). - event = event1; - try { - listener.call(this, this.__data__, index, group); - } finally { - event = event0; - } - }; - } - - function parseTypenames$1(typenames) { - return typenames.trim().split(/^|\s+/).map(function(t) { - var name = "", i = t.indexOf("."); - if (i >= 0) name = t.slice(i + 1), t = t.slice(0, i); - return {type: t, name: name}; - }); - } - - function onRemove(typename) { - return function() { - var on = this.__on; - if (!on) return; - for (var j = 0, i = -1, m = on.length, o; j < m; ++j) { - if (o = on[j], (!typename.type || o.type === typename.type) && o.name === typename.name) { - this.removeEventListener(o.type, o.listener, o.capture); - } else { - on[++i] = o; - } - } - if (++i) on.length = i; - else delete this.__on; - }; - } - - function onAdd(typename, value, capture) { - var wrap = filterEvents.hasOwnProperty(typename.type) ? filterContextListener : contextListener; - return function(d, i, group) { - var on = this.__on, o, listener = wrap(value, i, group); - if (on) for (var j = 0, m = on.length; j < m; ++j) { - if ((o = on[j]).type === typename.type && o.name === typename.name) { - this.removeEventListener(o.type, o.listener, o.capture); - this.addEventListener(o.type, o.listener = listener, o.capture = capture); - o.value = value; - return; - } - } - this.addEventListener(typename.type, listener, capture); - o = {type: typename.type, name: typename.name, value: value, listener: listener, capture: capture}; - if (!on) this.__on = [o]; - else on.push(o); - }; - } - - function selection_on(typename, value, capture) { - var typenames = parseTypenames$1(typename + ""), i, n = typenames.length, t; - - if (arguments.length < 2) { - var on = this.node().__on; - if (on) for (var j = 0, m = on.length, o; j < m; ++j) { - for (i = 0, o = on[j]; i < n; ++i) { - if ((t = typenames[i]).type === o.type && t.name === o.name) { - return o.value; - } - } - } - return; - } - - on = value ? onAdd : onRemove; - if (capture == null) capture = false; - for (i = 0; i < n; ++i) this.each(on(typenames[i], value, capture)); - return this; - } - - function customEvent(event1, listener, that, args) { - var event0 = event; - event1.sourceEvent = event; - event = event1; - try { - return listener.apply(that, args); - } finally { - event = event0; - } - } - - function dispatchEvent(node, type, params) { - var window = defaultView(node), - event = window.CustomEvent; - - if (typeof event === "function") { - event = new event(type, params); - } else { - event = window.document.createEvent("Event"); - if (params) event.initEvent(type, params.bubbles, params.cancelable), event.detail = params.detail; - else event.initEvent(type, false, false); - } - - node.dispatchEvent(event); - } - - function dispatchConstant(type, params) { - return function() { - return dispatchEvent(this, type, params); - }; - } - - function dispatchFunction(type, params) { - return function() { - return dispatchEvent(this, type, params.apply(this, arguments)); - }; - } - - function selection_dispatch(type, params) { - return this.each((typeof params === "function" - ? dispatchFunction - : dispatchConstant)(type, params)); - } - - var root = [null]; - - function Selection(groups, parents) { - this._groups = groups; - this._parents = parents; - } - - function selection() { - return new Selection([[document.documentElement]], root); - } - - Selection.prototype = selection.prototype = { - constructor: Selection, - select: selection_select, - selectAll: selection_selectAll, - filter: selection_filter, - data: selection_data, - enter: selection_enter, - exit: selection_exit, - join: selection_join, - merge: selection_merge, - order: selection_order, - sort: selection_sort, - call: selection_call, - nodes: selection_nodes, - node: selection_node, - size: selection_size, - empty: selection_empty, - each: selection_each, - attr: selection_attr, - style: selection_style, - property: selection_property, - classed: selection_classed, - text: selection_text, - html: selection_html, - raise: selection_raise, - lower: selection_lower, - append: selection_append, - insert: selection_insert, - remove: selection_remove, - clone: selection_clone, - datum: selection_datum, - on: selection_on, - dispatch: selection_dispatch - }; - - function select(selector) { - return typeof selector === "string" - ? new Selection([[document.querySelector(selector)]], [document.documentElement]) - : new Selection([[selector]], root); - } - - function sourceEvent() { - var current = event, source; - while (source = current.sourceEvent) current = source; - return current; - } - - function point(node, event) { - var svg = node.ownerSVGElement || node; - - if (svg.createSVGPoint) { - var point = svg.createSVGPoint(); - point.x = event.clientX, point.y = event.clientY; - point = point.matrixTransform(node.getScreenCTM().inverse()); - return [point.x, point.y]; - } - - var rect = node.getBoundingClientRect(); - return [event.clientX - rect.left - node.clientLeft, event.clientY - rect.top - node.clientTop]; - } - - function mouse(node) { - var event = sourceEvent(); - if (event.changedTouches) event = event.changedTouches[0]; - return point(node, event); - } - - function touch(node, touches, identifier) { - if (arguments.length < 3) identifier = touches, touches = sourceEvent().changedTouches; - - for (var i = 0, n = touches ? touches.length : 0, touch; i < n; ++i) { - if ((touch = touches[i]).identifier === identifier) { - return point(node, touch); - } - } - - return null; - } - - function nopropagation() { - event.stopImmediatePropagation(); - } - - function noevent() { - event.preventDefault(); - event.stopImmediatePropagation(); - } - - function nodrag(view) { - var root = view.document.documentElement, - selection = select(view).on("dragstart.drag", noevent, true); - if ("onselectstart" in root) { - selection.on("selectstart.drag", noevent, true); - } else { - root.__noselect = root.style.MozUserSelect; - root.style.MozUserSelect = "none"; - } - } - - function yesdrag(view, noclick) { - var root = view.document.documentElement, - selection = select(view).on("dragstart.drag", null); - if (noclick) { - selection.on("click.drag", noevent, true); - setTimeout(function() { selection.on("click.drag", null); }, 0); - } - if ("onselectstart" in root) { - selection.on("selectstart.drag", null); - } else { - root.style.MozUserSelect = root.__noselect; - delete root.__noselect; - } - } - - function constant$3(x) { - return function() { - return x; - }; - } - - function DragEvent(target, type, subject, id, active, x, y, dx, dy, dispatch) { - this.target = target; - this.type = type; - this.subject = subject; - this.identifier = id; - this.active = active; - this.x = x; - this.y = y; - this.dx = dx; - this.dy = dy; - this._ = dispatch; - } - - DragEvent.prototype.on = function() { - var value = this._.on.apply(this._, arguments); - return value === this._ ? this : value; - }; - - // Ignore right-click, since that should open the context menu. - function defaultFilter() { - return !event.ctrlKey && !event.button; - } - - function defaultContainer() { - return this.parentNode; - } - - function defaultSubject(d) { - return d == null ? {x: event.x, y: event.y} : d; - } - - function defaultTouchable() { - return navigator.maxTouchPoints || ("ontouchstart" in this); - } - - function drag() { - var filter = defaultFilter, - container = defaultContainer, - subject = defaultSubject, - touchable = defaultTouchable, - gestures = {}, - listeners = dispatch("start", "drag", "end"), - active = 0, - mousedownx, - mousedowny, - mousemoving, - touchending, - clickDistance2 = 0; - - function drag(selection) { - selection - .on("mousedown.drag", mousedowned) - .filter(touchable) - .on("touchstart.drag", touchstarted) - .on("touchmove.drag", touchmoved) - .on("touchend.drag touchcancel.drag", touchended) - .style("touch-action", "none") - .style("-webkit-tap-highlight-color", "rgba(0,0,0,0)"); - } - - function mousedowned() { - if (touchending || !filter.apply(this, arguments)) return; - var gesture = beforestart("mouse", container.apply(this, arguments), mouse, this, arguments); - if (!gesture) return; - select(event.view).on("mousemove.drag", mousemoved, true).on("mouseup.drag", mouseupped, true); - nodrag(event.view); - nopropagation(); - mousemoving = false; - mousedownx = event.clientX; - mousedowny = event.clientY; - gesture("start"); - } - - function mousemoved() { - noevent(); - if (!mousemoving) { - var dx = event.clientX - mousedownx, dy = event.clientY - mousedowny; - mousemoving = dx * dx + dy * dy > clickDistance2; - } - gestures.mouse("drag"); - } - - function mouseupped() { - select(event.view).on("mousemove.drag mouseup.drag", null); - yesdrag(event.view, mousemoving); - noevent(); - gestures.mouse("end"); - } - - function touchstarted() { - if (!filter.apply(this, arguments)) return; - var touches = event.changedTouches, - c = container.apply(this, arguments), - n = touches.length, i, gesture; - - for (i = 0; i < n; ++i) { - if (gesture = beforestart(touches[i].identifier, c, touch, this, arguments)) { - nopropagation(); - gesture("start"); - } - } - } - - function touchmoved() { - var touches = event.changedTouches, - n = touches.length, i, gesture; - - for (i = 0; i < n; ++i) { - if (gesture = gestures[touches[i].identifier]) { - noevent(); - gesture("drag"); - } - } - } - - function touchended() { - var touches = event.changedTouches, - n = touches.length, i, gesture; - - if (touchending) clearTimeout(touchending); - touchending = setTimeout(function() { touchending = null; }, 500); // Ghost clicks are delayed! - for (i = 0; i < n; ++i) { - if (gesture = gestures[touches[i].identifier]) { - nopropagation(); - gesture("end"); - } - } - } - - function beforestart(id, container, point, that, args) { - var p = point(container, id), s, dx, dy, - sublisteners = listeners.copy(); - - if (!customEvent(new DragEvent(drag, "beforestart", s, id, active, p[0], p[1], 0, 0, sublisteners), function() { - if ((event.subject = s = subject.apply(that, args)) == null) return false; - dx = s.x - p[0] || 0; - dy = s.y - p[1] || 0; - return true; - })) return; - - return function gesture(type) { - var p0 = p, n; - switch (type) { - case "start": gestures[id] = gesture, n = active++; break; - case "end": delete gestures[id], --active; // nobreak - case "drag": p = point(container, id), n = active; break; - } - customEvent(new DragEvent(drag, type, s, id, n, p[0] + dx, p[1] + dy, p[0] - p0[0], p[1] - p0[1], sublisteners), sublisteners.apply, sublisteners, [type, that, args]); - }; - } - - drag.filter = function(_) { - return arguments.length ? (filter = typeof _ === "function" ? _ : constant$3(!!_), drag) : filter; - }; - - drag.container = function(_) { - return arguments.length ? (container = typeof _ === "function" ? _ : constant$3(_), drag) : container; - }; - - drag.subject = function(_) { - return arguments.length ? (subject = typeof _ === "function" ? _ : constant$3(_), drag) : subject; - }; - - drag.touchable = function(_) { - return arguments.length ? (touchable = typeof _ === "function" ? _ : constant$3(!!_), drag) : touchable; - }; - - drag.on = function() { - var value = listeners.on.apply(listeners, arguments); - return value === listeners ? drag : value; - }; - - drag.clickDistance = function(_) { - return arguments.length ? (clickDistance2 = (_ = +_) * _, drag) : Math.sqrt(clickDistance2); - }; - - return drag; - } - - // Copyright 2018 The Distill Template Authors - - const T$a = Template('d-slider', ` - - -
    -
    -
    -
    -
    -
    -
    -
    -
    -`); - - // ARIA - // If the slider has a visible label, it is referenced by aria-labelledby on the slider element. Otherwise, the slider element has a label provided by aria-label. - // If the slider is vertically oriented, it has aria-orientation set to vertical. The default value of aria-orientation for a slider is horizontal. - - const keyCodes = { - left: 37, - up: 38, - right: 39, - down: 40, - pageUp: 33, - pageDown: 34, - end: 35, - home: 36 - }; - - class Slider extends T$a(HTMLElement) { - - - connectedCallback() { - this.connected = true; - this.setAttribute('role', 'slider'); - // Makes the element tab-able. - if (!this.hasAttribute('tabindex')) { this.setAttribute('tabindex', 0); } - - // Keeps track of keyboard vs. mouse interactions for focus rings - this.mouseEvent = false; - - // Handles to shadow DOM elements - this.knob = this.root.querySelector('.knob-container'); - this.background = this.root.querySelector('.background'); - this.trackFill = this.root.querySelector('.track-fill'); - this.track = this.root.querySelector('.track'); - - // Default values for attributes - this.min = this.min ? this.min : 0; - this.max = this.max ? this.max : 100; - this.scale = linear$1().domain([this.min, this.max]).range([0, 1]).clamp(true); - - this.origin = this.origin !== undefined ? this.origin : this.min; - this.step = this.step ? this.step : 1; - this.update(this.value ? this.value : 0); - - this.ticks = this.ticks ? this.ticks : false; - this.renderTicks(); - - this.drag = drag() - .container(this.background) - .on('start', () => { - this.mouseEvent = true; - this.background.classList.add('mousedown'); - this.changeValue = this.value; - this.dragUpdate(); - }) - .on('drag', () => { - this.dragUpdate(); - }) - .on('end', () => { - this.mouseEvent = false; - this.background.classList.remove('mousedown'); - this.dragUpdate(); - if (this.changeValue !== this.value) this.dispatchChange(); - this.changeValue = this.value; - }); - this.drag(select(this.background)); - - this.addEventListener('focusin', () => { - if(!this.mouseEvent) { - this.background.classList.add('focus'); - } - }); - this.addEventListener('focusout', () => { - this.background.classList.remove('focus'); - }); - this.addEventListener('keydown', this.onKeyDown); - - } - - static get observedAttributes() {return ['min', 'max', 'value', 'step', 'ticks', 'origin', 'tickValues', 'tickLabels']; } - - attributeChangedCallback(attr, oldValue, newValue) { - if (isNaN(newValue) || newValue === undefined || newValue === null) return; - if (attr == 'min') { - this.min = +newValue; - this.setAttribute('aria-valuemin', this.min); - } - if (attr == 'max') { - this.max = +newValue; - this.setAttribute('aria-valuemax', this.max); - } - if (attr == 'value') { - this.update(+newValue); - } - if (attr == 'origin') { - this.origin = +newValue; - // this.update(this.value); - } - if (attr == 'step') { - if (newValue > 0) { - this.step = +newValue; - } - } - if (attr == 'ticks') { - this.ticks = (newValue === '' ? true : newValue); - } - } - - onKeyDown(event) { - this.changeValue = this.value; - let stopPropagation = false; - switch (event.keyCode) { - case keyCodes.left: - case keyCodes.down: - this.update(this.value - this.step); - stopPropagation = true; - break; - case keyCodes.right: - case keyCodes.up: - this.update(this.value + this.step); - stopPropagation = true; - break; - case keyCodes.pageUp: - this.update(this.value + this.step * 10); - stopPropagation = true; - break; - - case keyCodes.pageDown: - this.update(this.value + this.step * 10); - stopPropagation = true; - break; - case keyCodes.home: - this.update(this.min); - stopPropagation = true; - break; - case keyCodes.end: - this.update(this.max); - stopPropagation = true; - break; - } - if (stopPropagation) { - this.background.classList.add('focus'); - event.preventDefault(); - event.stopPropagation(); - if (this.changeValue !== this.value) this.dispatchChange(); - } - } - - validateValueRange(min, max, value) { - return Math.max(Math.min(max, value), min); - } - - quantizeValue(value, step) { - return Math.round(value / step) * step; - } - - dragUpdate() { - const bbox = this.background.getBoundingClientRect(); - const x = event.x; - const width = bbox.width; - this.update(this.scale.invert(x / width)); - } - - update(value) { - let v = value; - if (this.step !== 'any') { - v = this.quantizeValue(value, this.step); - } - v = this.validateValueRange(this.min, this.max, v); - if (this.connected) { - this.knob.style.left = this.scale(v) * 100 + '%'; - this.trackFill.style.width = this.scale(this.min + Math.abs(v - this.origin)) * 100 + '%'; - this.trackFill.style.left = this.scale(Math.min(v, this.origin)) * 100 + '%'; - } - if (this.value !== v) { - this.value = v; - this.setAttribute('aria-valuenow', this.value); - this.dispatchInput(); - } - } - - // Dispatches only on a committed change (basically only on mouseup). - dispatchChange() { - const e = new Event('change'); - this.dispatchEvent(e, {}); - } - - // Dispatches on each value change. - dispatchInput() { - const e = new Event('input'); - this.dispatchEvent(e, {}); - } - - renderTicks() { - const ticksContainer = this.root.querySelector('.ticks'); - if (this.ticks !== false) { - let tickData = []; - if (this.ticks > 0) { - tickData = this.scale.ticks(this.ticks); - } else if (this.step === 'any') { - tickData = this.scale.ticks(); - } else { - tickData = range(this.min, this.max + 1e-6, this.step); - } - tickData.forEach(d => { - const tick = document.createElement('div'); - tick.classList.add('tick'); - tick.style.left = this.scale(d) * 100 + '%'; - ticksContainer.appendChild(tick); - }); - } else { - ticksContainer.style.display = 'none'; - } - } - } - - var logo = "\n \n\n"; - - const headerTemplate = ` - -
    - - -
    -`; - - // Copyright 2018 The Distill Template Authors - - const T$b = Template('distill-header', headerTemplate, false); - - class DistillHeader extends T$b(HTMLElement) { - - } - - // Copyright 2018 The Distill Template Authors - - const styles$2 = ` - -`; - - function appendixTemplate(frontMatter) { - let html = styles$2; - - if (typeof frontMatter.githubUrl !== 'undefined') { - html += ` -

    Updates and Corrections

    -

    `; - if (frontMatter.githubCompareUpdatesUrl) { - html += `View all changes to this article since it was first published.`; - } - html += ` - If you see mistakes or want to suggest changes, please create an issue on GitHub.

    - `; - } - - const journal = frontMatter.journal; - if (typeof journal !== 'undefined' && journal.title === 'Distill') { - html += ` -

    Reuse

    -

    Diagrams and text are licensed under Creative Commons Attribution CC-BY 4.0 with the source available on GitHub, unless noted otherwise. The figures that have been reused from other sources don’t fall under this license and can be recognized by a note in their caption: “Figure from …”.

    - `; - } - - if (typeof frontMatter.publishedDate !== 'undefined') { - html += ` -

    Citation

    -

    For attribution in academic contexts, please cite this work as

    - 
    ${frontMatter.concatenatedAuthors}, "${frontMatter.title}", Distill, ${frontMatter.publishedYear}.
    -

    BibTeX citation

    - 
    ${serializeFrontmatterToBibtex(frontMatter)}
    - `; - } - - return html; - } - - class DistillAppendix extends HTMLElement { - - static get is() { return 'distill-appendix'; } - - set frontMatter(frontMatter) { - this.innerHTML = appendixTemplate(frontMatter); - } - - } - - const footerTemplate = ` - - -
    - - is dedicated to clear explanations of machine learning - -
    - About - Submit - Prize - Archive - RSS - GitHub - Twitter -      ISSN 2476-0757 -
    - -
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- return names.slice(0, names.length - 1).join(' '); - } - - // 'Olah' - get lastName() { - const names = this.name.split(' '); - return names[names.length -1]; - } - } - - function mergeFromYMLFrontmatter(target, source) { - target.title = source.title; - if (source.published) { - if (source.published instanceof Date) { - target.publishedDate = source.published; - } else if (source.published.constructor === String) { - target.publishedDate = new Date(source.published); - } - } - if (source.publishedDate) { - if (source.publishedDate instanceof Date) { - target.publishedDate = source.publishedDate; - } else if (source.publishedDate.constructor === String) { - target.publishedDate = new Date(source.publishedDate); - } else { - console.error('Don\'t know what to do with published date: ' + source.publishedDate); - } - } - target.description = source.description; - target.authors = source.authors.map( (authorObject) => new Author(authorObject)); - target.katex = source.katex; - target.password = source.password; 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- - // - // Assigned from posts.csv - // - - // publishedDate: 2016-09-08T07:00:00.000Z, - // tags: [ 'rnn' ], - // distillPath: '2016/augmented-rnns', - // githubPath: 'distillpub/post--augmented-rnns', - // doiSuffix: 1, - - // - // Assigned from journal - // - this.journal = {}; - // journal: { - // 'title': 'Distill', - // 'full_title': 'Distill', - // 'abbrev_title': 'Distill', - // 'url': 'http://distill.pub', - // 'doi': '10.23915/distill', - // 'publisherName': 'Distill Working Group', - // 'publisherEmail': 'admin@distill.pub', - // 'issn': '2476-0757', - // 'editors': [...], - // 'committee': [...] - // } - // volume: 1, - // issue: 9, - - this.katex = {}; - - // - // Assigned from publishing process - // - - // githubCompareUpdatesUrl: 'https://github.com/distillpub/post--augmented-rnns/compare/1596e094d8943d2dc0ea445d92071129c6419c59...3bd9209e0c24d020f87cf6152dcecc6017cbc193', - // updatedDate: 2017-03-21T07:13:16.000Z, - // doi: '10.23915/distill.00001', - this.doi = undefined; - this.publishedDate = undefined; - } - - // Example: - // title: Demo Title Attention and Augmented Recurrent Neural Networks - // published: Jan 10, 2017 - // authors: - // - Chris Olah: - // - Shan Carter: http://shancarter.com - // affiliations: - // - Google Brain: - // - Google Brain: http://g.co/brain - - // - // Computed Properties - // - - // 'http://distill.pub/2016/augmented-rnns', - set url(value) { - this._url = value; - } - get url() { - if (this._url) { - return this._url; - } else if (this.distillPath && this.journal.url) { - return this.journal.url + '/' + this.distillPath; - } else if (this.journal.url) { - return this.journal.url; - } - } - - // 'https://github.com/distillpub/post--augmented-rnns', - get githubUrl() { - if (this.githubPath) { - return 'https://github.com/' + this.githubPath; - } else { - return undefined; - } - } - - // TODO resolve differences in naming of URL/Url/url. - // 'http://distill.pub/2016/augmented-rnns/thumbnail.jpg', - set previewURL(value) { - this._previewURL = value; 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- } - return volume; - } - - get issue() { - return this.publishedDate.getMonth() + 1; - } - - // 'Olah & Carter', - get concatenatedAuthors() { - if (this.authors.length > 2) { - return this.authors[0].lastName + ', et al.'; - } else if (this.authors.length === 2) { - return this.authors[0].lastName + ' & ' + this.authors[1].lastName; - } else if (this.authors.length === 1) { - return this.authors[0].lastName; - } - } - - // 'Olah, Chris and Carter, Shan', - get bibtexAuthors() { - return this.authors.map(author => { - return author.lastName + ', ' + author.firstName; - }).join(' and '); - } - - // 'olah2016attention' - get slug() { - let slug = ''; - if (this.authors.length) { - slug += this.authors[0].lastName.toLowerCase(); - slug += this.publishedYear; - slug += this.title.split(' ')[0].toLowerCase(); - } - return slug || 'Untitled'; - } - - get bibliographyEntries() { - return new Map(this.citations.map( citationKey => { - const entry = this.bibliography.get(citationKey); - return [citationKey, entry]; - })); - } - - set bibliography(bibliography) { - if (bibliography instanceof Map) { - this._bibliography = bibliography; - } else if (typeof bibliography === 'object') { - this._bibliography = mapFromObject(bibliography); - } - } - - get bibliography() { - return this._bibliography; - } - - static fromObject(source) { - const frontMatter = new FrontMatter(); - Object.assign(frontMatter, source); - return frontMatter; - } - - assignToObject(target) { - Object.assign(target, this); - target.bibliography = objectFromMap(this.bibliographyEntries); - target.url = this.url; - target.doi = this.doi; - target.githubUrl = this.githubUrl; - target.previewURL = this.previewURL; - if (this.publishedDate) { - target.volume = this.volume; - target.issue = this.issue; - target.publishedDateRFC = this.publishedDateRFC; - target.publishedYear = this.publishedYear; - target.publishedMonth = this.publishedMonth; - target.publishedDay = this.publishedDay; - target.publishedMonthPadded = this.publishedMonthPadded; - target.publishedDayPadded = this.publishedDayPadded; - } - if (this.updatedDate) { - target.updatedDateRFC = this.updatedDateRFC; - } - target.concatenatedAuthors = this.concatenatedAuthors; - target.bibtexAuthors = this.bibtexAuthors; - target.slug = this.slug; - } - - } - - // Copyright 2018 The Distill Template Authors - // - // Licensed under the Apache License, Version 2.0 (the "License"); - // you may not use this file except in compliance with the License. - // You may obtain a copy of the License at - // - // http://www.apache.org/licenses/LICENSE-2.0 - // - // Unless required by applicable law or agreed to in writing, software - // distributed under the License is distributed on an "AS IS" BASIS, - // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - // See the License for the specific language governing permissions and - // limitations under the License. - - function _moveLegacyAffiliationFormatIntoArray(frontMatter) { - // authors used to have propoerties "affiliation" and "affiliationURL". - // We now encourage using an array for affiliations containing objects with - // properties "name" and "url". - for (let author of frontMatter.authors) { - const hasOldStyle = Boolean(author.affiliation); - const hasNewStyle = Boolean(author.affiliations); - if (!hasOldStyle) continue; - if (hasNewStyle) { - console.warn(`Author ${author.author} has both old-style ("affiliation" & "affiliationURL") and new style ("affiliations") affiliation information!`); - } else { - let newAffiliation = { - "name": author.affiliation - }; - if (author.affiliationURL) newAffiliation.url = author.affiliationURL; - author.affiliations = [newAffiliation]; - } - } - return frontMatter - } - - function parseFrontmatter(element) { - const scriptTag = element.firstElementChild; - if (scriptTag) { - const type = scriptTag.getAttribute('type'); - if (type.split('/')[1] == 'json') { - const content = scriptTag.textContent; - const parsed = JSON.parse(content); - return _moveLegacyAffiliationFormatIntoArray(parsed); - } else { - console.error('Distill only supports JSON frontmatter tags anymore; no more YAML.'); - } - } else { - console.error('You added a frontmatter tag but did not provide a script tag with front matter data in it. Please take a look at our templates.'); - } - return {}; - } - - // Copyright 2018 The Distill Template Authors - - function ExtractFrontmatter(dom, data) { - const frontMatterTag = dom.querySelector('d-front-matter'); - if (!frontMatterTag) { - console.warn('No front matter tag found!'); - return; - } - const extractedData = parseFrontmatter(frontMatterTag); - mergeFromYMLFrontmatter(data, extractedData); - } - - function commonjsRequire () { - throw new Error('Dynamic requires are not currently supported by rollup-plugin-commonjs'); - } - - function unwrapExports (x) { - return x && x.__esModule && Object.prototype.hasOwnProperty.call(x, 'default') ? x['default'] : x; - } - - function createCommonjsModule(fn, module) { - return module = { exports: {} }, fn(module, module.exports), module.exports; - } - - var bibtexParse = createCommonjsModule(function (module, exports) { - /* start bibtexParse 0.0.22 */ - - //Original work by Henrik Muehe (c) 2010 - // - //CommonJS port by Mikola Lysenko 2013 - // - //Port to Browser lib by ORCID / RCPETERS - // - //Issues: - //no comment handling within strings - //no string concatenation - //no variable values yet - //Grammar implemented here: - //bibtex -> (string | preamble | comment | entry)*; - //string -> '@STRING' '{' key_equals_value '}'; - //preamble -> '@PREAMBLE' '{' value '}'; - //comment -> '@COMMENT' '{' value '}'; - //entry -> '@' key '{' key ',' key_value_list '}'; - //key_value_list -> key_equals_value (',' key_equals_value)*; - //key_equals_value -> key '=' value; - //value -> value_quotes | value_braces | key; - //value_quotes -> '"' .*? '"'; // not quite - //value_braces -> '{' .*? '"'; // not quite - (function(exports) { - - function BibtexParser() { - - this.months = ["jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct", "nov", "dec"]; - this.notKey = [',','{','}',' ','=']; - this.pos = 0; - this.input = ""; - this.entries = new Array(); - - this.currentEntry = ""; - - this.setInput = function(t) { - this.input = t; - }; - - this.getEntries = function() { - return this.entries; - }; - - this.isWhitespace = function(s) { - return (s == ' ' || s == '\r' || s == '\t' || s == '\n'); - }; - - this.match = function(s, canCommentOut) { - if (canCommentOut == undefined || canCommentOut == null) - canCommentOut = true; - this.skipWhitespace(canCommentOut); - if (this.input.substring(this.pos, this.pos + s.length) == s) { - this.pos += s.length; - } else { - throw "Token mismatch, expected " + s + ", found " - + this.input.substring(this.pos); - } this.skipWhitespace(canCommentOut); - }; - - this.tryMatch = function(s, canCommentOut) { - if (canCommentOut == undefined || canCommentOut == null) - canCommentOut = true; - this.skipWhitespace(canCommentOut); - if (this.input.substring(this.pos, this.pos + s.length) == s) { - return true; - } else { - return false; - } }; - - /* when search for a match all text can be ignored, not just white space */ - this.matchAt = function() { - while (this.input.length > this.pos && this.input[this.pos] != '@') { - this.pos++; - } - if (this.input[this.pos] == '@') { - return true; - } return false; - }; - - this.skipWhitespace = function(canCommentOut) { - while (this.isWhitespace(this.input[this.pos])) { - this.pos++; - } if (this.input[this.pos] == "%" && canCommentOut == true) { - while (this.input[this.pos] != "\n") { - this.pos++; - } this.skipWhitespace(canCommentOut); - } }; - - this.value_braces = function() { - var bracecount = 0; - this.match("{", false); - var start = this.pos; - var escaped = false; - while (true) { - if (!escaped) { - if (this.input[this.pos] == '}') { - if (bracecount > 0) { - bracecount--; - } else { - var end = this.pos; - this.match("}", false); - return this.input.substring(start, end); - } } else if (this.input[this.pos] == '{') { - bracecount++; - } else if (this.pos >= this.input.length - 1) { - throw "Unterminated value"; - } } if (this.input[this.pos] == '\\' && escaped == false) - escaped = true; - else - escaped = false; - this.pos++; - } }; - - this.value_comment = function() { - var str = ''; - var brcktCnt = 0; - while (!(this.tryMatch("}", false) && brcktCnt == 0)) { - str = str + this.input[this.pos]; - if (this.input[this.pos] == '{') - brcktCnt++; - if (this.input[this.pos] == '}') - brcktCnt--; - if (this.pos >= this.input.length - 1) { - throw "Unterminated value:" + this.input.substring(start); - } this.pos++; - } return str; - }; - - this.value_quotes = function() { - this.match('"', false); - var start = this.pos; - var escaped = false; - while (true) { - if (!escaped) { - if (this.input[this.pos] == '"') { - var end = this.pos; - this.match('"', false); - return this.input.substring(start, end); - } else if (this.pos >= this.input.length - 1) { - throw "Unterminated value:" + this.input.substring(start); - } } - if (this.input[this.pos] == '\\' && escaped == false) - escaped = true; - else - escaped = false; - this.pos++; - } }; - - this.single_value = function() { - var start = this.pos; - if (this.tryMatch("{")) { - return this.value_braces(); - } else if (this.tryMatch('"')) { - return this.value_quotes(); - } else { - var k = this.key(); - if (k.match("^[0-9]+$")) - return k; - else if (this.months.indexOf(k.toLowerCase()) >= 0) - return k.toLowerCase(); - else - throw "Value expected:" + this.input.substring(start) + ' for key: ' + k; - - } }; - - this.value = function() { - var values = []; - values.push(this.single_value()); - while (this.tryMatch("#")) { - this.match("#"); - values.push(this.single_value()); - } return values.join(""); - }; - - this.key = function() { - var start = this.pos; - while (true) { - if (this.pos >= this.input.length) { - throw "Runaway key"; - } // а-яА-Я is Cyrillic - //console.log(this.input[this.pos]); - if (this.notKey.indexOf(this.input[this.pos]) >= 0) { - return this.input.substring(start, this.pos); - } else { - this.pos++; - - } } }; - - this.key_equals_value = function() { - var key = this.key(); - if (this.tryMatch("=")) { - this.match("="); - var val = this.value(); - return [ key, val ]; - } else { - throw "... = value expected, equals sign missing:" - + this.input.substring(this.pos); - } }; - - this.key_value_list = function() { - var kv = this.key_equals_value(); - this.currentEntry['entryTags'] = {}; - this.currentEntry['entryTags'][kv[0]] = kv[1]; - while (this.tryMatch(",")) { - this.match(","); - // fixes problems with commas at the end of a list - if (this.tryMatch("}")) { - break; - } - kv = this.key_equals_value(); - this.currentEntry['entryTags'][kv[0]] = kv[1]; - } }; - - this.entry_body = function(d) { - this.currentEntry = {}; - this.currentEntry['citationKey'] = this.key(); - this.currentEntry['entryType'] = d.substring(1); - this.match(","); - this.key_value_list(); - this.entries.push(this.currentEntry); - }; - - this.directive = function() { - this.match("@"); - return "@" + this.key(); - }; - - this.preamble = function() { - this.currentEntry = {}; - this.currentEntry['entryType'] = 'PREAMBLE'; - this.currentEntry['entry'] = this.value_comment(); - this.entries.push(this.currentEntry); - }; - - this.comment = function() { - this.currentEntry = {}; - this.currentEntry['entryType'] = 'COMMENT'; - this.currentEntry['entry'] = this.value_comment(); - this.entries.push(this.currentEntry); - }; - - this.entry = function(d) { - this.entry_body(d); - }; - - this.bibtex = function() { - while (this.matchAt()) { - var d = this.directive(); - this.match("{"); - if (d == "@STRING") { - this.string(); - } else if (d == "@PREAMBLE") { - this.preamble(); - } else if (d == "@COMMENT") { - this.comment(); - } else { - this.entry(d); - } - this.match("}"); - } }; - } - exports.toJSON = function(bibtex) { - var b = new BibtexParser(); - b.setInput(bibtex); - b.bibtex(); - return b.entries; - }; - - /* added during hackathon don't hate on me */ - exports.toBibtex = function(json) { - var out = ''; - for ( var i in json) { - out += "@" + json[i].entryType; - out += '{'; - if (json[i].citationKey) - out += json[i].citationKey + ', '; - if (json[i].entry) - out += json[i].entry ; - if (json[i].entryTags) { - var tags = ''; - for (var jdx in json[i].entryTags) { - if (tags.length != 0) - tags += ', '; - tags += jdx + '= {' + json[i].entryTags[jdx] + '}'; - } - out += tags; - } - out += '}\n\n'; - } - return out; - - }; - - })( exports); - - /* end bibtexParse */ - }); - - // Copyright 2018 The Distill Template Authors - - function normalizeTag(string) { - return string - .replace(/[\t\n ]+/g, ' ') - .replace(/{\\["^`.'acu~Hvs]( )?([a-zA-Z])}/g, (full, x, char) => char) - .replace(/{\\([a-zA-Z])}/g, (full, char) => char); - } - - function parseBibtex(bibtex) { - const bibliography = new Map(); - const parsedEntries = bibtexParse.toJSON(bibtex); - for (const entry of parsedEntries) { - // normalize tags; note entryTags is an object, not Map - for (const [key, value] of Object.entries(entry.entryTags)) { - entry.entryTags[key.toLowerCase()] = normalizeTag(value); - } - entry.entryTags.type = entry.entryType; - // add to bibliography - bibliography.set(entry.citationKey, entry.entryTags); - } - return bibliography; - } - - function serializeFrontmatterToBibtex(frontMatter) { - return `@article{${frontMatter.slug}, - author = {${frontMatter.bibtexAuthors}}, - title = {${frontMatter.title}}, - journal = {${frontMatter.journal.title}}, - year = {${frontMatter.publishedYear}}, - note = {${frontMatter.url}}, - doi = {${frontMatter.doi}} -}`; - } - - // Copyright 2018 The Distill Template Authors - - function parseBibliography(element) { - const scriptTag = element.firstElementChild; - if (scriptTag && scriptTag.tagName === 'SCRIPT') { - if (scriptTag.type == 'text/bibtex') { - const bibtex = element.firstElementChild.textContent; - return parseBibtex(bibtex); - } else if (scriptTag.type == 'text/json') { - return new Map(JSON.parse(scriptTag.textContent)); - } else { - console.warn('Unsupported bibliography script tag type: ' + scriptTag.type); - } - } else { - console.warn('Bibliography did not have any script tag.'); - } - } - - // Copyright 2018 The Distill Template Authors - - function ExtractBibliography(dom, data) { - const bibliographyTag = dom.querySelector('d-bibliography'); - if (!bibliographyTag) { - console.warn('No bibliography tag found!'); - return; - } - - const src = bibliographyTag.getAttribute('src'); - if (src) { - const path = data.inputDirectory + '/' + src; - const text = fs.readFileSync(path, 'utf-8'); - const bibliography = parseBibtex(text); - const scriptTag = dom.createElement('script'); - scriptTag.type = 'text/json'; - scriptTag.textContent = JSON.stringify([...bibliography]); - bibliographyTag.appendChild(scriptTag); - bibliographyTag.removeAttribute('src'); - } - - data.bibliography = parseBibliography(bibliographyTag); - } - - // Copyright 2018 The Distill Template Authors - // - // Licensed under the Apache License, Version 2.0 (the "License"); - // you may not use this file except in compliance with the License. - // You may obtain a copy of the License at - // - // http://www.apache.org/licenses/LICENSE-2.0 - // - // Unless required by applicable law or agreed to in writing, software - // distributed under the License is distributed on an "AS IS" BASIS, - // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - // See the License for the specific language governing permissions and - // limitations under the License. - - function collect_citations(dom = document) { - const citations = new Set(); - const citeTags = dom.querySelectorAll("d-cite"); - for (const tag of citeTags) { - const keyString = tag.getAttribute("key") || tag.getAttribute("bibtex-key"); - const keys = keyString.split(",").map(k => k.trim()); - for (const key of keys) { - citations.add(key); - } - } - return [...citations]; - } - - function author_string(ent, template, sep, finalSep) { - if (ent.author == null) { - return ""; - } - var names = ent.author.split(" and "); - let name_strings = names.map(name => { - name = name.trim(); - if (name.indexOf(",") != -1) { - var last = name.split(",")[0].trim(); - var firsts = name.split(",")[1]; - } else if (name.indexOf(" ") != -1) { - var last = name - .split(" ") - .slice(-1)[0] - .trim(); - var firsts = name - .split(" ") - .slice(0, -1) - .join(" "); - } else { - var last = name.trim(); - } - var initials = ""; - if (firsts != undefined) { - initials = firsts - .trim() - .split(" ") - .map(s => s.trim()[0]); - initials = initials.join(".") + "."; - } - return template - .replace("${F}", firsts) - .replace("${L}", last) - .replace("${I}", initials) - .trim(); // in case one of first or last was empty - }); - if (names.length > 1) { - var str = name_strings.slice(0, names.length - 1).join(sep); - str += (finalSep || sep) + name_strings[names.length - 1]; - return str; - } else { - return name_strings[0]; - } - } - - function venue_string(ent) { - var cite = ent.journal || ent.booktitle || ""; - if ("volume" in ent) { - var issue = ent.issue || ent.number; - issue = issue != undefined ? "(" + issue + ")" : ""; - cite += ", Vol " + ent.volume + issue; - } - if ("pages" in ent) { - cite += ", pp. " + ent.pages; - } - if (cite != "") cite += ". "; - if ("publisher" in ent) { - cite += ent.publisher; - if (cite[cite.length - 1] != ".") cite += "."; - } - return cite; - } - - function link_string(ent) { - if ("url" in ent) { - var url = ent.url; - var arxiv_match = /arxiv\.org\/abs\/([0-9\.]*)/.exec(url); - if (arxiv_match != null) { - url = `http://arxiv.org/pdf/${arxiv_match[1]}.pdf`; - } - - if (url.slice(-4) == ".pdf") { - var label = "PDF"; - } else if (url.slice(-5) == ".html") { - var label = "HTML"; - } - return `  [${label || "link"}]`; - } /* else if ("doi" in ent){ - return `  [DOI]`; - }*/ else { - return ""; - } - } - function doi_string(ent, new_line) { - if ("doi" in ent) { - return `${new_line ? "
      " : ""} DOI: ${ent.doi}`; - } else { - return ""; - } - } - - function title_string(ent) { - return '' + ent.title + " "; - } - - function bibliography_cite(ent, fancy) { - if (ent) { - var cite = title_string(ent); - cite += link_string(ent) + "
      "; - if (ent.author) { - cite += author_string(ent, "${L}, ${I}", ", ", " and "); - if (ent.year || ent.date) { - cite += ", "; - } - } - if (ent.year || ent.date) { - cite += (ent.year || ent.date) + ". "; - } else { - cite += ". "; - } - cite += venue_string(ent); - cite += doi_string(ent); - return cite; - /*var cite = author_string(ent, "${L}, ${I}", ", ", " and "); - if (ent.year || ent.date){ - cite += ", " + (ent.year || ent.date) + ". " - } else { - cite += ". " - } - cite += "" + ent.title + ". "; - cite += venue_string(ent); - cite += doi_string(ent); - cite += link_string(ent); - return cite*/ - } else { - return "?"; - } - } - - // Copyright 2018 The Distill Template Authors - - function ExtractCitations(dom, data) { - const citations = new Set(data.citations); - const newCitations = collect_citations(dom); - for (const citation of newCitations) { - citations.add(citation); - } - data.citations = Array.from(citations); - } - - // Copyright 2018 The Distill Template Authors - // - // Licensed under the Apache License, Version 2.0 (the "License"); - // you may not use this file except in compliance with the License. - // You may obtain a copy of the License at - // - // http://www.apache.org/licenses/LICENSE-2.0 - // - // Unless required by applicable law or agreed to in writing, software - // distributed under the License is distributed on an "AS IS" BASIS, - // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - // See the License for the specific language governing permissions and - // limitations under the License. - - function HTML(dom) { - - const head = dom.querySelector('head'); - - // set language to 'en' - if (!dom.querySelector('html').getAttribute('lang')) { - dom.querySelector('html').setAttribute('lang', 'en'); - } - - // set charset to 'utf-8' - if (!dom.querySelector('meta[charset]')) { - const meta = dom.createElement('meta'); - meta.setAttribute('charset', 'utf-8'); - head.appendChild(meta); - } - - // set viewport - if (!dom.querySelector('meta[name=viewport]')) { - const meta = dom.createElement('meta'); - meta.setAttribute('name', 'viewport'); - meta.setAttribute('content', 'width=device-width, initial-scale=1'); - head.appendChild(meta); - } - } - - // Copyright 2018 The Distill Template Authors - // - // Licensed under the Apache License, Version 2.0 (the "License"); - // you may not use this file except in compliance with the License. - // You may obtain a copy of the License at - // - // http://www.apache.org/licenses/LICENSE-2.0 - // - // Unless required by applicable law or agreed to in writing, software - // distributed under the License is distributed on an "AS IS" BASIS, - // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - // See the License for the specific language governing permissions and - // limitations under the License. - - // import style from '../styles/d-byline.css'; - - function bylineTemplate(frontMatter) { - return ` -
      -
      -

      Authors

      -

      Affiliations

      - ${frontMatter.authors.map(author => ` -

      - ${author.personalURL ? ` - ${author.name}` : ` - ${author.name}`} -

      -

      - ${author.affiliations.map(affiliation => - affiliation.url ? `${affiliation.name}` : `${affiliation.name}` - ).join(', ')} -

      - `).join('')} -
      -
      -

      Published

      - ${frontMatter.publishedDate ? ` -

      ${frontMatter.publishedMonth} ${frontMatter.publishedDay}, ${frontMatter.publishedYear}

      ` : ` -

      Not published yet.

      `} -
      -
      -`; - } - - // Copyright 2018 The Distill Template Authors - - function Byline(dom, data) { - const byline = dom.querySelector('d-byline'); - if (byline) { - byline.innerHTML = bylineTemplate(data); - } - } - - // Copyright 2018 The Distill Template Authors - // - // Licensed under the Apache License, Version 2.0 (the "License"); - // you may not use this file except in compliance with the License. - // You may obtain a copy of the License at - // - // http://www.apache.org/licenses/LICENSE-2.0 - // - // Unless required by applicable law or agreed to in writing, software - // distributed under the License is distributed on an "AS IS" BASIS, - // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - // See the License for the specific language governing permissions and - // limitations under the License. - - // no appendix -> add appendix - // title in front, no h1 -> add it - // no title in front, h1 -> read and put into frontMatter - // footnote -> footnote list - // break up bib - // if citation, no bib-list -> add citation-list - - // if authors, no byline -> add byline - - function OptionalComponents(dom, data) { - const body = dom.body; - const article = body.querySelector('d-article'); - - // If we don't have an article tag, something weird is going on—giving up. - if (!article) { - console.warn('No d-article tag found; skipping adding optional components!'); - return; - } - - let byline = dom.querySelector('d-byline'); - if (!byline) { - if (data.authors) { - byline = dom.createElement('d-byline'); - body.insertBefore(byline, article); - } else { - console.warn('No authors found in front matter; please add them before submission!'); - } - } - - let title = dom.querySelector('d-title'); - if (!title) { - title = dom.createElement('d-title'); - body.insertBefore(title, byline); - } - - let h1 = title.querySelector('h1'); - if (!h1) { - h1 = dom.createElement('h1'); - h1.textContent = data.title; - title.insertBefore(h1, title.firstChild); - } - - const hasPassword = typeof data.password !== 'undefined'; - let interstitial = body.querySelector('d-interstitial'); - if (hasPassword && !interstitial) { - const inBrowser = typeof window !== 'undefined'; - const onLocalhost = inBrowser && window.location.hostname.includes('localhost'); - if (!inBrowser || !onLocalhost) { - interstitial = dom.createElement('d-interstitial'); - interstitial.password = data.password; - body.insertBefore(interstitial, body.firstChild); - } - } else if (!hasPassword && interstitial) { - interstitial.parentElement.removeChild(this); - } - - let appendix = dom.querySelector('d-appendix'); - if (!appendix) { - appendix = dom.createElement('d-appendix'); - dom.body.appendChild(appendix); - } - - let footnoteList = dom.querySelector('d-footnote-list'); - if (!footnoteList) { - footnoteList = dom.createElement('d-footnote-list'); - appendix.appendChild(footnoteList); - } - - let citationList = dom.querySelector('d-citation-list'); - if (!citationList) { - citationList = dom.createElement('d-citation-list'); - appendix.appendChild(citationList); - } - - } - - var katex$1 = createCommonjsModule(function (module, exports) { - (function(f){{module.exports=f();}})(function(){return (function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof commonjsRequire=="function"&&commonjsRequire;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error("Cannot find module '"+o+"'");throw f.code="MODULE_NOT_FOUND",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r);}return n[o].exports}var i=typeof commonjsRequire=="function"&&commonjsRequire;for(var o=0;o= 0; --i) { - tok = expansion[i]; - if (tok.text === "#") { - if (i === 0) { - throw new _ParseError2.default("Incomplete placeholder at end of macro body", tok); - } - tok = expansion[--i]; // next token on stack - if (tok.text === "#") { - // ## → # - expansion.splice(i + 1, 1); // drop first # - } else if (/^[1-9]$/.test(tok.text)) { - // expansion.splice(i, 2, arg[0], arg[1], …) - // to replace placeholder with the indicated argument. - // TODO: use spread once we move to ES2015 - expansion.splice.apply(expansion, [i, 2].concat(args[tok.text - 1])); - } else { - throw new _ParseError2.default("Not a valid argument number", tok); - } - } - } - } - this.stack = this.stack.concat(expansion); - } - } - }, { - key: "get", - value: function get(ignoreSpace) { - this.discardedWhiteSpace = []; - var token = this.nextToken(); - if (ignoreSpace) { - while (token.text === " ") { - this.discardedWhiteSpace.push(token); - token = this.nextToken(); - } - } - return token; - } - - /** - * Undo the effect of the preceding call to the get method. - * A call to this method MUST be immediately preceded and immediately followed - * by a call to get. Only used during mode switching, i.e. after one token - * was got in the old mode but should get got again in a new mode - * with possibly different whitespace handling. - */ - - }, { - key: "unget", - value: function unget(token) { - this.stack.push(token); - while (this.discardedWhiteSpace.length !== 0) { - this.stack.push(this.discardedWhiteSpace.pop()); - } - } - }]); - return MacroExpander; - }(); - - module.exports = MacroExpander; - - },{"./Lexer":26,"./ParseError":29,"./macros":44,"babel-runtime/helpers/classCallCheck":4,"babel-runtime/helpers/createClass":5,"object-assign":25}],28:[function(require,module,exports){ - - var _classCallCheck2 = require("babel-runtime/helpers/classCallCheck"); - - var _classCallCheck3 = _interopRequireDefault(_classCallCheck2); - - var _createClass2 = require("babel-runtime/helpers/createClass"); - - var _createClass3 = _interopRequireDefault(_createClass2); - - var _fontMetrics2 = require("./fontMetrics"); - - var _fontMetrics3 = _interopRequireDefault(_fontMetrics2); - - function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } - - var BASESIZE = 6; /** - * This file contains information about the options that the Parser carries - * around with it while parsing. Data is held in an `Options` object, and when - * recursing, a new `Options` object can be created with the `.with*` and - * `.reset` functions. - */ - - var sizeStyleMap = [ - // Each element contains [textsize, scriptsize, scriptscriptsize]. - // The size mappings are taken from TeX with \normalsize=10pt. - [1, 1, 1], // size1: [5, 5, 5] \tiny - [2, 1, 1], // size2: [6, 5, 5] - [3, 1, 1], // size3: [7, 5, 5] \scriptsize - [4, 2, 1], // size4: [8, 6, 5] \footnotesize - [5, 2, 1], // size5: [9, 6, 5] \small - [6, 3, 1], // size6: [10, 7, 5] \normalsize - [7, 4, 2], // size7: [12, 8, 6] \large - [8, 6, 3], // size8: [14.4, 10, 7] \Large - [9, 7, 6], // size9: [17.28, 12, 10] \LARGE - [10, 8, 7], // size10: [20.74, 14.4, 12] \huge - [11, 10, 9]]; - - var sizeMultipliers = [ - // fontMetrics.js:getFontMetrics also uses size indexes, so if - // you change size indexes, change that function. - 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.2, 1.44, 1.728, 2.074, 2.488]; - - var sizeAtStyle = function sizeAtStyle(size, style) { - return style.size < 2 ? size : sizeStyleMap[size - 1][style.size - 1]; - }; - - /** - * This is the main options class. It contains the current style, size, color, - * and font. - * - * Options objects should not be modified. To create a new Options with - * different properties, call a `.having*` method. - */ - - var Options = function () { - function Options(data) { - (0, _classCallCheck3.default)(this, Options); - - this.style = data.style; - this.color = data.color; - this.size = data.size || BASESIZE; - this.textSize = data.textSize || this.size; - this.phantom = data.phantom; - this.font = data.font; - this.sizeMultiplier = sizeMultipliers[this.size - 1]; - this._fontMetrics = null; - } - - /** - * Returns a new options object with the same properties as "this". Properties - * from "extension" will be copied to the new options object. - */ - - - (0, _createClass3.default)(Options, [{ - key: "extend", - value: function extend(extension) { - var data = { - style: this.style, - size: this.size, - textSize: this.textSize, - color: this.color, - phantom: this.phantom, - font: this.font - }; - - for (var key in extension) { - if (extension.hasOwnProperty(key)) { - data[key] = extension[key]; - } - } - - return new Options(data); - } - - /** - * Return an options object with the given style. If `this.style === style`, - * returns `this`. - */ - - }, { - key: "havingStyle", - value: function havingStyle(style) { - if (this.style === style) { - return this; - } else { - return this.extend({ - style: style, - size: sizeAtStyle(this.textSize, style) - }); - } - } - - /** - * Return an options object with a cramped version of the current style. If - * the current style is cramped, returns `this`. - */ - - }, { - key: "havingCrampedStyle", - value: function havingCrampedStyle() { - return this.havingStyle(this.style.cramp()); - } - - /** - * Return an options object with the given size and in at least `\textstyle`. - * Returns `this` if appropriate. - */ - - }, { - key: "havingSize", - value: function havingSize(size) { - if (this.size === size && this.textSize === size) { - return this; - } else { - return this.extend({ - style: this.style.text(), - size: size, - textSize: size - }); - } - } - - /** - * Like `this.havingSize(BASESIZE).havingStyle(style)`. If `style` is omitted, - * changes to at least `\textstyle`. - */ - - }, { - key: "havingBaseStyle", - value: function havingBaseStyle(style) { - style = style || this.style.text(); - var wantSize = sizeAtStyle(BASESIZE, style); - if (this.size === wantSize && this.textSize === BASESIZE && this.style === style) { - return this; - } else { - return this.extend({ - style: style, - size: wantSize, - baseSize: BASESIZE - }); - } - } - - /** - * Create a new options object with the given color. - */ - - }, { - key: "withColor", - value: function withColor(color) { - return this.extend({ - color: color - }); - } - - /** - * Create a new options object with "phantom" set to true. - */ - - }, { - key: "withPhantom", - value: function withPhantom() { - return this.extend({ - phantom: true - }); - } - - /** - * Create a new options objects with the give font. - */ - - }, { - key: "withFont", - value: function withFont(font) { - return this.extend({ - font: font || this.font - }); - } - - /** - * Return the CSS sizing classes required to switch from enclosing options - * `oldOptions` to `this`. Returns an array of classes. - */ - - }, { - key: "sizingClasses", - value: function sizingClasses(oldOptions) { - if (oldOptions.size !== this.size) { - return ["sizing", "reset-size" + oldOptions.size, "size" + this.size]; - } else { - return []; - } - } - - /** - * Return the CSS sizing classes required to switch to the base size. Like - * `this.havingSize(BASESIZE).sizingClasses(this)`. - */ - - }, { - key: "baseSizingClasses", - value: function baseSizingClasses() { - if (this.size !== BASESIZE) { - return ["sizing", "reset-size" + this.size, "size" + BASESIZE]; - } else { - return []; - } - } - - /** - * Return the font metrics for this size. - */ - - }, { - key: "fontMetrics", - value: function fontMetrics() { - if (!this._fontMetrics) { - this._fontMetrics = _fontMetrics3.default.getFontMetrics(this.size); - } - return this._fontMetrics; - } - - /** - * A map of color names to CSS colors. - * TODO(emily): Remove this when we have real macros - */ - - }, { - key: "getColor", - - - /** - * Gets the CSS color of the current options object, accounting for the - * `colorMap`. - */ - value: function getColor() { - if (this.phantom) { - return "transparent"; - } else { - return Options.colorMap[this.color] || this.color; - } - } - }]); - return Options; - }(); - - /** - * The base size index. - */ - - - Options.colorMap = { - "katex-blue": "#6495ed", - "katex-orange": "#ffa500", - "katex-pink": "#ff00af", - "katex-red": "#df0030", - "katex-green": "#28ae7b", - "katex-gray": "gray", - "katex-purple": "#9d38bd", - "katex-blueA": "#ccfaff", - "katex-blueB": "#80f6ff", - "katex-blueC": "#63d9ea", - "katex-blueD": "#11accd", - "katex-blueE": "#0c7f99", - "katex-tealA": "#94fff5", - "katex-tealB": "#26edd5", - "katex-tealC": "#01d1c1", - "katex-tealD": "#01a995", - "katex-tealE": "#208170", - "katex-greenA": "#b6ffb0", - "katex-greenB": "#8af281", - "katex-greenC": "#74cf70", - "katex-greenD": "#1fab54", - "katex-greenE": "#0d923f", - "katex-goldA": "#ffd0a9", - "katex-goldB": "#ffbb71", - "katex-goldC": "#ff9c39", - "katex-goldD": "#e07d10", - "katex-goldE": "#a75a05", - "katex-redA": "#fca9a9", - "katex-redB": "#ff8482", - "katex-redC": "#f9685d", - "katex-redD": "#e84d39", - "katex-redE": "#bc2612", - "katex-maroonA": "#ffbde0", - "katex-maroonB": "#ff92c6", - "katex-maroonC": "#ed5fa6", - "katex-maroonD": "#ca337c", - "katex-maroonE": "#9e034e", - "katex-purpleA": "#ddd7ff", - "katex-purpleB": "#c6b9fc", - "katex-purpleC": "#aa87ff", - "katex-purpleD": "#7854ab", - "katex-purpleE": "#543b78", - "katex-mintA": "#f5f9e8", - "katex-mintB": "#edf2df", - "katex-mintC": "#e0e5cc", - "katex-grayA": "#f6f7f7", - "katex-grayB": "#f0f1f2", - "katex-grayC": "#e3e5e6", - "katex-grayD": "#d6d8da", - "katex-grayE": "#babec2", - "katex-grayF": "#888d93", - "katex-grayG": "#626569", - "katex-grayH": "#3b3e40", - "katex-grayI": "#21242c", - "katex-kaBlue": "#314453", - "katex-kaGreen": "#71B307" - }; - Options.BASESIZE = BASESIZE; - - module.exports = Options; - - },{"./fontMetrics":41,"babel-runtime/helpers/classCallCheck":4,"babel-runtime/helpers/createClass":5}],29:[function(require,module,exports){ - - var _classCallCheck2 = require("babel-runtime/helpers/classCallCheck"); - - var _classCallCheck3 = _interopRequireDefault(_classCallCheck2); - - function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } - - /** - * This is the ParseError class, which is the main error thrown by KaTeX - * functions when something has gone wrong. This is used to distinguish internal - * errors from errors in the expression that the user provided. - * - * If possible, a caller should provide a Token or ParseNode with information - * about where in the source string the problem occurred. - * - * @param {string} message The error message - * @param {(Token|ParseNode)=} token An object providing position information - */ - var ParseError = function ParseError(message, token) { - (0, _classCallCheck3.default)(this, ParseError); - - var error = "KaTeX parse error: " + message; - var start = void 0; - var end = void 0; - - if (token && token.lexer && token.start <= token.end) { - // If we have the input and a position, make the error a bit fancier - - // Get the input - var input = token.lexer.input; - - // Prepend some information - start = token.start; - end = token.end; - if (start === input.length) { - error += " at end of input: "; - } else { - error += " at position " + (start + 1) + ": "; - } - - // Underline token in question using combining underscores - var underlined = input.slice(start, end).replace(/[^]/g, "$&\u0332"); - - // Extract some context from the input and add it to the error - var left = void 0; - if (start > 15) { - left = "…" + input.slice(start - 15, start); - } else { - left = input.slice(0, start); - } - var right = void 0; - if (end + 15 < input.length) { - right = input.slice(end, end + 15) + "…"; - } else { - right = input.slice(end); - } - error += left + underlined + right; - } - - // Some hackery to make ParseError a prototype of Error - // See http://stackoverflow.com/a/8460753 - var self = new Error(error); - self.name = "ParseError"; - self.__proto__ = ParseError.prototype; - - self.position = start; - return self; - }; - - // More hackery - - - ParseError.prototype.__proto__ = Error.prototype; - - module.exports = ParseError; - - },{"babel-runtime/helpers/classCallCheck":4}],30:[function(require,module,exports){ - - Object.defineProperty(exports, "__esModule", { - value: true - }); - - var _classCallCheck2 = require("babel-runtime/helpers/classCallCheck"); - - var _classCallCheck3 = _interopRequireDefault(_classCallCheck2); - - function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } - - /** - * The resulting parse tree nodes of the parse tree. - * - * It is possible to provide position information, so that a ParseNode can - * fulfil a role similar to a Token in error reporting. - * For details on the corresponding properties see Token constructor. - * Providing such information can lead to better error reporting. - * - * @param {string} type type of node, like e.g. "ordgroup" - * @param {?object} value type-specific representation of the node - * @param {string} mode parse mode in action for this node, - * "math" or "text" - * @param {Token=} firstToken first token of the input for this node, - * will omit position information if unset - * @param {Token=} lastToken last token of the input for this node, - * will default to firstToken if unset - */ - var ParseNode = function ParseNode(type, value, mode, firstToken, lastToken) { - (0, _classCallCheck3.default)(this, ParseNode); - - this.type = type; - this.value = value; - this.mode = mode; - if (firstToken && (!lastToken || lastToken.lexer === firstToken.lexer)) { - this.lexer = firstToken.lexer; - this.start = firstToken.start; - this.end = (lastToken || firstToken).end; - } - }; - - exports.default = ParseNode; - - },{"babel-runtime/helpers/classCallCheck":4}],31:[function(require,module,exports){ - - var _classCallCheck2 = require("babel-runtime/helpers/classCallCheck"); - - var _classCallCheck3 = _interopRequireDefault(_classCallCheck2); - - var _createClass2 = require("babel-runtime/helpers/createClass"); - - var _createClass3 = _interopRequireDefault(_createClass2); - - var _functions = require("./functions"); - - var _functions2 = _interopRequireDefault(_functions); - - var _environments = require("./environments"); - - var _environments2 = _interopRequireDefault(_environments); - - var _MacroExpander = require("./MacroExpander"); - - var _MacroExpander2 = _interopRequireDefault(_MacroExpander); - - var _symbols = require("./symbols"); - - var _symbols2 = _interopRequireDefault(_symbols); - - var _utils = require("./utils"); - - var _utils2 = _interopRequireDefault(_utils); - - var _units = require("./units"); - - var _units2 = _interopRequireDefault(_units); - - var _unicodeRegexes = require("./unicodeRegexes"); - - var _ParseNode = require("./ParseNode"); - - var _ParseNode2 = _interopRequireDefault(_ParseNode); - - var _ParseError = require("./ParseError"); - - var _ParseError2 = _interopRequireDefault(_ParseError); - - function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } - - /** - * This file contains the parser used to parse out a TeX expression from the - * input. Since TeX isn't context-free, standard parsers don't work particularly - * well. - * - * The strategy of this parser is as such: - * - * The main functions (the `.parse...` ones) take a position in the current - * parse string to parse tokens from. The lexer (found in Lexer.js, stored at - * this.lexer) also supports pulling out tokens at arbitrary places. When - * individual tokens are needed at a position, the lexer is called to pull out a - * token, which is then used. - * - * The parser has a property called "mode" indicating the mode that - * the parser is currently in. Currently it has to be one of "math" or - * "text", which denotes whether the current environment is a math-y - * one or a text-y one (e.g. inside \text). Currently, this serves to - * limit the functions which can be used in text mode. - * - * The main functions then return an object which contains the useful data that - * was parsed at its given point, and a new position at the end of the parsed - * data. The main functions can call each other and continue the parsing by - * using the returned position as a new starting point. - * - * There are also extra `.handle...` functions, which pull out some reused - * functionality into self-contained functions. - * - * The earlier functions return ParseNodes. - * The later functions (which are called deeper in the parse) sometimes return - * ParseFuncOrArgument, which contain a ParseNode as well as some data about - * whether the parsed object is a function which is missing some arguments, or a - * standalone object which can be used as an argument to another function. - */ - - /** - * An initial function (without its arguments), or an argument to a function. - * The `result` argument should be a ParseNode. - */ - function ParseFuncOrArgument(result, isFunction, token) { - this.result = result; - // Is this a function (i.e. is it something defined in functions.js)? - this.isFunction = isFunction; - this.token = token; - } /* eslint no-constant-condition:0 */ - - var Parser = function () { - function Parser(input, settings) { - (0, _classCallCheck3.default)(this, Parser); - - // Create a new macro expander (gullet) and (indirectly via that) also a - // new lexer (mouth) for this parser (stomach, in the language of TeX) - this.gullet = new _MacroExpander2.default(input, settings.macros); - // Use old \color behavior (same as LaTeX's \textcolor) if requested. - // We do this after the macros object has been copied by MacroExpander. - if (settings.colorIsTextColor) { - this.gullet.macros["\\color"] = "\\textcolor"; - } - // Store the settings for use in parsing - this.settings = settings; - // Count leftright depth (for \middle errors) - this.leftrightDepth = 0; - } - - /** - * Checks a result to make sure it has the right type, and throws an - * appropriate error otherwise. - * - * @param {boolean=} consume whether to consume the expected token, - * defaults to true - */ - - - (0, _createClass3.default)(Parser, [{ - key: "expect", - value: function expect(text, consume) { - if (this.nextToken.text !== text) { - throw new _ParseError2.default("Expected '" + text + "', got '" + this.nextToken.text + "'", this.nextToken); - } - if (consume !== false) { - this.consume(); - } - } - - /** - * Considers the current look ahead token as consumed, - * and fetches the one after that as the new look ahead. - */ - - }, { - key: "consume", - value: function consume() { - this.nextToken = this.gullet.get(this.mode === "math"); - } - }, { - key: "switchMode", - value: function switchMode(newMode) { - this.gullet.unget(this.nextToken); - this.mode = newMode; - this.consume(); - } - - /** - * Main parsing function, which parses an entire input. - * - * @return {?Array.} - */ - - }, { - key: "parse", - value: function parse() { - // Try to parse the input - this.mode = "math"; - this.consume(); - var parse = this.parseInput(); - return parse; - } - - /** - * Parses an entire input tree. - */ - - }, { - key: "parseInput", - value: function parseInput() { - // Parse an expression - var expression = this.parseExpression(false); - // If we succeeded, make sure there's an EOF at the end - this.expect("EOF", false); - return expression; - } - }, { - key: "parseExpression", - - - /** - * Parses an "expression", which is a list of atoms. - * - * @param {boolean} breakOnInfix Should the parsing stop when we hit infix - * nodes? This happens when functions have higher precendence - * than infix nodes in implicit parses. - * - * @param {?string} breakOnTokenText The text of the token that the expression - * should end with, or `null` if something else should end the - * expression. - * - * @return {ParseNode} - */ - value: function parseExpression(breakOnInfix, breakOnTokenText) { - var body = []; - // Keep adding atoms to the body until we can't parse any more atoms (either - // we reached the end, a }, or a \right) - while (true) { - var lex = this.nextToken; - if (Parser.endOfExpression.indexOf(lex.text) !== -1) { - break; - } - if (breakOnTokenText && lex.text === breakOnTokenText) { - break; - } - if (breakOnInfix && _functions2.default[lex.text] && _functions2.default[lex.text].infix) { - break; - } - var atom = this.parseAtom(); - if (!atom) { - if (!this.settings.throwOnError && lex.text[0] === "\\") { - var errorNode = this.handleUnsupportedCmd(); - body.push(errorNode); - continue; - } - - break; - } - body.push(atom); - } - return this.handleInfixNodes(body); - } - - /** - * Rewrites infix operators such as \over with corresponding commands such - * as \frac. - * - * There can only be one infix operator per group. If there's more than one - * then the expression is ambiguous. This can be resolved by adding {}. - * - * @returns {Array} - */ - - }, { - key: "handleInfixNodes", - value: function handleInfixNodes(body) { - var overIndex = -1; - var funcName = void 0; - - for (var i = 0; i < body.length; i++) { - var node = body[i]; - if (node.type === "infix") { - if (overIndex !== -1) { - throw new _ParseError2.default("only one infix operator per group", node.value.token); - } - overIndex = i; - funcName = node.value.replaceWith; - } - } - - if (overIndex !== -1) { - var numerNode = void 0; - var denomNode = void 0; - - var numerBody = body.slice(0, overIndex); - var denomBody = body.slice(overIndex + 1); - - if (numerBody.length === 1 && numerBody[0].type === "ordgroup") { - numerNode = numerBody[0]; - } else { - numerNode = new _ParseNode2.default("ordgroup", numerBody, this.mode); - } - - if (denomBody.length === 1 && denomBody[0].type === "ordgroup") { - denomNode = denomBody[0]; - } else { - denomNode = new _ParseNode2.default("ordgroup", denomBody, this.mode); - } - - var value = this.callFunction(funcName, [numerNode, denomNode], null); - return [new _ParseNode2.default(value.type, value, this.mode)]; - } else { - return body; - } - } - - // The greediness of a superscript or subscript - - }, { - key: "handleSupSubscript", - - - /** - * Handle a subscript or superscript with nice errors. - */ - value: function handleSupSubscript(name) { - var symbolToken = this.nextToken; - var symbol = symbolToken.text; - this.consume(); - var group = this.parseGroup(); - - if (!group) { - if (!this.settings.throwOnError && this.nextToken.text[0] === "\\") { - return this.handleUnsupportedCmd(); - } else { - throw new _ParseError2.default("Expected group after '" + symbol + "'", symbolToken); - } - } else if (group.isFunction) { - // ^ and _ have a greediness, so handle interactions with functions' - // greediness - var funcGreediness = _functions2.default[group.result].greediness; - if (funcGreediness > Parser.SUPSUB_GREEDINESS) { - return this.parseFunction(group); - } else { - throw new _ParseError2.default("Got function '" + group.result + "' with no arguments " + "as " + name, symbolToken); - } - } else { - return group.result; - } - } - - /** - * Converts the textual input of an unsupported command into a text node - * contained within a color node whose color is determined by errorColor - */ - - }, { - key: "handleUnsupportedCmd", - value: function handleUnsupportedCmd() { - var text = this.nextToken.text; - var textordArray = []; - - for (var i = 0; i < text.length; i++) { - textordArray.push(new _ParseNode2.default("textord", text[i], "text")); - } - - var textNode = new _ParseNode2.default("text", { - body: textordArray, - type: "text" - }, this.mode); - - var colorNode = new _ParseNode2.default("color", { - color: this.settings.errorColor, - value: [textNode], - type: "color" - }, this.mode); - - this.consume(); - return colorNode; - } - - /** - * Parses a group with optional super/subscripts. - * - * @return {?ParseNode} - */ - - }, { - key: "parseAtom", - value: function parseAtom() { - // The body of an atom is an implicit group, so that things like - // \left(x\right)^2 work correctly. - var base = this.parseImplicitGroup(); - - // In text mode, we don't have superscripts or subscripts - if (this.mode === "text") { - return base; - } - - // Note that base may be empty (i.e. null) at this point. - - var superscript = void 0; - var subscript = void 0; - while (true) { - // Lex the first token - var lex = this.nextToken; - - if (lex.text === "\\limits" || lex.text === "\\nolimits") { - // We got a limit control - if (!base || base.type !== "op") { - throw new _ParseError2.default("Limit controls must follow a math operator", lex); - } else { - var limits = lex.text === "\\limits"; - base.value.limits = limits; - base.value.alwaysHandleSupSub = true; - } - this.consume(); - } else if (lex.text === "^") { - // We got a superscript start - if (superscript) { - throw new _ParseError2.default("Double superscript", lex); - } - superscript = this.handleSupSubscript("superscript"); - } else if (lex.text === "_") { - // We got a subscript start - if (subscript) { - throw new _ParseError2.default("Double subscript", lex); - } - subscript = this.handleSupSubscript("subscript"); - } else if (lex.text === "'") { - // We got a prime - if (superscript) { - throw new _ParseError2.default("Double superscript", lex); - } - var prime = new _ParseNode2.default("textord", "\\prime", this.mode); - - // Many primes can be grouped together, so we handle this here - var primes = [prime]; - this.consume(); - // Keep lexing tokens until we get something that's not a prime - while (this.nextToken.text === "'") { - // For each one, add another prime to the list - primes.push(prime); - this.consume(); - } - // If there's a superscript following the primes, combine that - // superscript in with the primes. - if (this.nextToken.text === "^") { - primes.push(this.handleSupSubscript("superscript")); - } - // Put everything into an ordgroup as the superscript - superscript = new _ParseNode2.default("ordgroup", primes, this.mode); - } else { - // If it wasn't ^, _, or ', stop parsing super/subscripts - break; - } - } - - if (superscript || subscript) { - // If we got either a superscript or subscript, create a supsub - return new _ParseNode2.default("supsub", { - base: base, - sup: superscript, - sub: subscript - }, this.mode); - } else { - // Otherwise return the original body - return base; - } - } - - // A list of the size-changing functions, for use in parseImplicitGroup - - - // A list of the style-changing functions, for use in parseImplicitGroup - - - // Old font functions - - }, { - key: "parseImplicitGroup", - - - /** - * Parses an implicit group, which is a group that starts at the end of a - * specified, and ends right before a higher explicit group ends, or at EOL. It - * is used for functions that appear to affect the current style, like \Large or - * \textrm, where instead of keeping a style we just pretend that there is an - * implicit grouping after it until the end of the group. E.g. - * small text {\Large large text} small text again - * It is also used for \left and \right to get the correct grouping. - * - * @return {?ParseNode} - */ - value: function parseImplicitGroup() { - var start = this.parseSymbol(); - - if (start == null) { - // If we didn't get anything we handle, fall back to parseFunction - return this.parseFunction(); - } - - var func = start.result; - - if (func === "\\left") { - // If we see a left: - // Parse the entire left function (including the delimiter) - var left = this.parseFunction(start); - // Parse out the implicit body - ++this.leftrightDepth; - var body = this.parseExpression(false); - --this.leftrightDepth; - // Check the next token - this.expect("\\right", false); - var right = this.parseFunction(); - return new _ParseNode2.default("leftright", { - body: body, - left: left.value.value, - right: right.value.value - }, this.mode); - } else if (func === "\\begin") { - // begin...end is similar to left...right - var begin = this.parseFunction(start); - var envName = begin.value.name; - if (!_environments2.default.hasOwnProperty(envName)) { - throw new _ParseError2.default("No such environment: " + envName, begin.value.nameGroup); - } - // Build the environment object. Arguments and other information will - // be made available to the begin and end methods using properties. - var env = _environments2.default[envName]; - var args = this.parseArguments("\\begin{" + envName + "}", env); - var context = { - mode: this.mode, - envName: envName, - parser: this, - positions: args.pop() - }; - var result = env.handler(context, args); - this.expect("\\end", false); - var endNameToken = this.nextToken; - var end = this.parseFunction(); - if (end.value.name !== envName) { - throw new _ParseError2.default("Mismatch: \\begin{" + envName + "} matched " + "by \\end{" + end.value.name + "}", endNameToken); - } - result.position = end.position; - return result; - } else if (_utils2.default.contains(Parser.sizeFuncs, func)) { - // If we see a sizing function, parse out the implicit body - this.consumeSpaces(); - var _body = this.parseExpression(false); - return new _ParseNode2.default("sizing", { - // Figure out what size to use based on the list of functions above - size: _utils2.default.indexOf(Parser.sizeFuncs, func) + 1, - value: _body - }, this.mode); - } else if (_utils2.default.contains(Parser.styleFuncs, func)) { - // If we see a styling function, parse out the implicit body - this.consumeSpaces(); - var _body2 = this.parseExpression(true); - return new _ParseNode2.default("styling", { - // Figure out what style to use by pulling out the style from - // the function name - style: func.slice(1, func.length - 5), - value: _body2 - }, this.mode); - } else if (func in Parser.oldFontFuncs) { - var style = Parser.oldFontFuncs[func]; - // If we see an old font function, parse out the implicit body - this.consumeSpaces(); - var _body3 = this.parseExpression(true); - if (style.slice(0, 4) === 'text') { - return new _ParseNode2.default("text", { - style: style, - body: new _ParseNode2.default("ordgroup", _body3, this.mode) - }, this.mode); - } else { - return new _ParseNode2.default("font", { - font: style, - body: new _ParseNode2.default("ordgroup", _body3, this.mode) - }, this.mode); - } - } else if (func === "\\color") { - // If we see a styling function, parse out the implicit body - var color = this.parseColorGroup(false); - if (!color) { - throw new _ParseError2.default("\\color not followed by color"); - } - var _body4 = this.parseExpression(true); - return new _ParseNode2.default("color", { - type: "color", - color: color.result.value, - value: _body4 - }, this.mode); - } else if (func === "$") { - if (this.mode === "math") { - throw new _ParseError2.default("$ within math mode"); - } - this.consume(); - var outerMode = this.mode; - this.switchMode("math"); - var _body5 = this.parseExpression(false, "$"); - this.expect("$", true); - this.switchMode(outerMode); - return new _ParseNode2.default("styling", { - style: "text", - value: _body5 - }, "math"); - } else { - // Defer to parseFunction if it's not a function we handle - return this.parseFunction(start); - } - } - - /** - * Parses an entire function, including its base and all of its arguments. - * The base might either have been parsed already, in which case - * it is provided as an argument, or it's the next group in the input. - * - * @param {ParseFuncOrArgument=} baseGroup optional as described above - * @return {?ParseNode} - */ - - }, { - key: "parseFunction", - value: function parseFunction(baseGroup) { - if (!baseGroup) { - baseGroup = this.parseGroup(); - } - - if (baseGroup) { - if (baseGroup.isFunction) { - var func = baseGroup.result; - var funcData = _functions2.default[func]; - if (this.mode === "text" && !funcData.allowedInText) { - throw new _ParseError2.default("Can't use function '" + func + "' in text mode", baseGroup.token); - } else if (this.mode === "math" && funcData.allowedInMath === false) { - throw new _ParseError2.default("Can't use function '" + func + "' in math mode", baseGroup.token); - } - - var args = this.parseArguments(func, funcData); - var token = baseGroup.token; - var result = this.callFunction(func, args, args.pop(), token); - return new _ParseNode2.default(result.type, result, this.mode); - } else { - return baseGroup.result; - } - } else { - return null; - } - } - - /** - * Call a function handler with a suitable context and arguments. - */ - - }, { - key: "callFunction", - value: function callFunction(name, args, positions, token) { - var context = { - funcName: name, - parser: this, - positions: positions, - token: token - }; - return _functions2.default[name].handler(context, args); - } - - /** - * Parses the arguments of a function or environment - * - * @param {string} func "\name" or "\begin{name}" - * @param {{numArgs:number,numOptionalArgs:number|undefined}} funcData - * @return the array of arguments, with the list of positions as last element - */ - - }, { - key: "parseArguments", - value: function parseArguments(func, funcData) { - var totalArgs = funcData.numArgs + funcData.numOptionalArgs; - if (totalArgs === 0) { - return [[this.pos]]; - } - - var baseGreediness = funcData.greediness; - var positions = [this.pos]; - var args = []; - - for (var i = 0; i < totalArgs; i++) { - var nextToken = this.nextToken; - var argType = funcData.argTypes && funcData.argTypes[i]; - var arg = void 0; - if (i < funcData.numOptionalArgs) { - if (argType) { - arg = this.parseGroupOfType(argType, true); - } else { - arg = this.parseGroup(true); - } - if (!arg) { - args.push(null); - positions.push(this.pos); - continue; - } - } else { - if (argType) { - arg = this.parseGroupOfType(argType); - } else { - arg = this.parseGroup(); - } - if (!arg) { - if (!this.settings.throwOnError && this.nextToken.text[0] === "\\") { - arg = new ParseFuncOrArgument(this.handleUnsupportedCmd(this.nextToken.text), false); - } else { - throw new _ParseError2.default("Expected group after '" + func + "'", nextToken); - } - } - } - var argNode = void 0; - if (arg.isFunction) { - var argGreediness = _functions2.default[arg.result].greediness; - if (argGreediness > baseGreediness) { - argNode = this.parseFunction(arg); - } else { - throw new _ParseError2.default("Got function '" + arg.result + "' as " + "argument to '" + func + "'", nextToken); - } - } else { - argNode = arg.result; - } - args.push(argNode); - positions.push(this.pos); - } - - args.push(positions); - - return args; - } - - /** - * Parses a group when the mode is changing. - * - * @return {?ParseFuncOrArgument} - */ - - }, { - key: "parseGroupOfType", - value: function parseGroupOfType(innerMode, optional) { - var outerMode = this.mode; - // Handle `original` argTypes - if (innerMode === "original") { - innerMode = outerMode; - } - - if (innerMode === "color") { - return this.parseColorGroup(optional); - } - if (innerMode === "size") { - return this.parseSizeGroup(optional); - } - - this.switchMode(innerMode); - if (innerMode === "text") { - // text mode is special because it should ignore the whitespace before - // it - this.consumeSpaces(); - } - // By the time we get here, innerMode is one of "text" or "math". - // We switch the mode of the parser, recurse, then restore the old mode. - var res = this.parseGroup(optional); - this.switchMode(outerMode); - return res; - } - }, { - key: "consumeSpaces", - value: function consumeSpaces() { - while (this.nextToken.text === " ") { - this.consume(); - } - } - - /** - * Parses a group, essentially returning the string formed by the - * brace-enclosed tokens plus some position information. - * - * @param {string} modeName Used to describe the mode in error messages - * @param {boolean=} optional Whether the group is optional or required - */ - - }, { - key: "parseStringGroup", - value: function parseStringGroup(modeName, optional) { - if (optional && this.nextToken.text !== "[") { - return null; - } - var outerMode = this.mode; - this.mode = "text"; - this.expect(optional ? "[" : "{"); - var str = ""; - var firstToken = this.nextToken; - var lastToken = firstToken; - while (this.nextToken.text !== (optional ? "]" : "}")) { - if (this.nextToken.text === "EOF") { - throw new _ParseError2.default("Unexpected end of input in " + modeName, firstToken.range(this.nextToken, str)); - } - lastToken = this.nextToken; - str += lastToken.text; - this.consume(); - } - this.mode = outerMode; - this.expect(optional ? "]" : "}"); - return firstToken.range(lastToken, str); - } - - /** - * Parses a regex-delimited group: the largest sequence of tokens - * whose concatenated strings match `regex`. Returns the string - * formed by the tokens plus some position information. - * - * @param {RegExp} regex - * @param {string} modeName Used to describe the mode in error messages - */ - - }, { - key: "parseRegexGroup", - value: function parseRegexGroup(regex, modeName) { - var outerMode = this.mode; - this.mode = "text"; - var firstToken = this.nextToken; - var lastToken = firstToken; - var str = ""; - while (this.nextToken.text !== "EOF" && regex.test(str + this.nextToken.text)) { - lastToken = this.nextToken; - str += lastToken.text; - this.consume(); - } - if (str === "") { - throw new _ParseError2.default("Invalid " + modeName + ": '" + firstToken.text + "'", firstToken); - } - this.mode = outerMode; - return firstToken.range(lastToken, str); - } - - /** - * Parses a color description. - */ - - }, { - key: "parseColorGroup", - value: function parseColorGroup(optional) { - var res = this.parseStringGroup("color", optional); - if (!res) { - return null; - } - var match = /^(#[a-z0-9]+|[a-z]+)$/i.exec(res.text); - if (!match) { - throw new _ParseError2.default("Invalid color: '" + res.text + "'", res); - } - return new ParseFuncOrArgument(new _ParseNode2.default("color", match[0], this.mode), false); - } - - /** - * Parses a size specification, consisting of magnitude and unit. - */ - - }, { - key: "parseSizeGroup", - value: function parseSizeGroup(optional) { - var res = void 0; - if (!optional && this.nextToken.text !== "{") { - res = this.parseRegexGroup(/^[-+]? *(?:$|\d+|\d+\.\d*|\.\d*) *[a-z]{0,2} *$/, "size"); - } else { - res = this.parseStringGroup("size", optional); - } - if (!res) { - return null; - } - var match = /([-+]?) *(\d+(?:\.\d*)?|\.\d+) *([a-z]{2})/.exec(res.text); - if (!match) { - throw new _ParseError2.default("Invalid size: '" + res.text + "'", res); - } - var data = { - number: +(match[1] + match[2]), // sign + magnitude, cast to number - unit: match[3] - }; - if (!_units2.default.validUnit(data)) { - throw new _ParseError2.default("Invalid unit: '" + data.unit + "'", res); - } - return new ParseFuncOrArgument(new _ParseNode2.default("size", data, this.mode), false); - } - - /** - * If the argument is false or absent, this parses an ordinary group, - * which is either a single nucleus (like "x") or an expression - * in braces (like "{x+y}"). - * If the argument is true, it parses either a bracket-delimited expression - * (like "[x+y]") or returns null to indicate the absence of a - * bracket-enclosed group. - * - * @param {boolean=} optional Whether the group is optional or required - * @return {?ParseFuncOrArgument} - */ - - }, { - key: "parseGroup", - value: function parseGroup(optional) { - var firstToken = this.nextToken; - // Try to parse an open brace - if (this.nextToken.text === (optional ? "[" : "{")) { - // If we get a brace, parse an expression - this.consume(); - var expression = this.parseExpression(false, optional ? "]" : null); - var lastToken = this.nextToken; - // Make sure we get a close brace - this.expect(optional ? "]" : "}"); - if (this.mode === "text") { - this.formLigatures(expression); - } - return new ParseFuncOrArgument(new _ParseNode2.default("ordgroup", expression, this.mode, firstToken, lastToken), false); - } else { - // Otherwise, just return a nucleus, or nothing for an optional group - return optional ? null : this.parseSymbol(); - } - } - - /** - * Form ligature-like combinations of characters for text mode. - * This includes inputs like "--", "---", "``" and "''". - * The result will simply replace multiple textord nodes with a single - * character in each value by a single textord node having multiple - * characters in its value. The representation is still ASCII source. - * - * @param {Array.} group the nodes of this group, - * list will be moified in place - */ - - }, { - key: "formLigatures", - value: function formLigatures(group) { - var n = group.length - 1; - for (var i = 0; i < n; ++i) { - var a = group[i]; - var v = a.value; - if (v === "-" && group[i + 1].value === "-") { - if (i + 1 < n && group[i + 2].value === "-") { - group.splice(i, 3, new _ParseNode2.default("textord", "---", "text", a, group[i + 2])); - n -= 2; - } else { - group.splice(i, 2, new _ParseNode2.default("textord", "--", "text", a, group[i + 1])); - n -= 1; - } - } - if ((v === "'" || v === "`") && group[i + 1].value === v) { - group.splice(i, 2, new _ParseNode2.default("textord", v + v, "text", a, group[i + 1])); - n -= 1; - } - } - } - - /** - * Parse a single symbol out of the string. Here, we handle both the functions - * we have defined, as well as the single character symbols - * - * @return {?ParseFuncOrArgument} - */ - - }, { - key: "parseSymbol", - value: function parseSymbol() { - var nucleus = this.nextToken; - - if (_functions2.default[nucleus.text]) { - this.consume(); - // If there exists a function with this name, we return the function and - // say that it is a function. - return new ParseFuncOrArgument(nucleus.text, true, nucleus); - } else if (_symbols2.default[this.mode][nucleus.text]) { - this.consume(); - // Otherwise if this is a no-argument function, find the type it - // corresponds to in the symbols map - return new ParseFuncOrArgument(new _ParseNode2.default(_symbols2.default[this.mode][nucleus.text].group, nucleus.text, this.mode, nucleus), false, nucleus); - } else if (this.mode === "text" && _unicodeRegexes.cjkRegex.test(nucleus.text)) { - this.consume(); - return new ParseFuncOrArgument(new _ParseNode2.default("textord", nucleus.text, this.mode, nucleus), false, nucleus); - } else if (nucleus.text === "$") { - return new ParseFuncOrArgument(nucleus.text, false, nucleus); - } else { - return null; - } - } - }]); - return Parser; - }(); - - Parser.endOfExpression = ["}", "\\end", "\\right", "&", "\\\\", "\\cr"]; - Parser.SUPSUB_GREEDINESS = 1; - Parser.sizeFuncs = ["\\tiny", "\\sixptsize", "\\scriptsize", "\\footnotesize", "\\small", "\\normalsize", "\\large", "\\Large", "\\LARGE", "\\huge", "\\Huge"]; - Parser.styleFuncs = ["\\displaystyle", "\\textstyle", "\\scriptstyle", "\\scriptscriptstyle"]; - Parser.oldFontFuncs = { - "\\rm": "mathrm", - "\\sf": "mathsf", - "\\tt": "mathtt", - "\\bf": "mathbf", - "\\it": "mathit" - }; - - - Parser.prototype.ParseNode = _ParseNode2.default; - - module.exports = Parser; - - },{"./MacroExpander":27,"./ParseError":29,"./ParseNode":30,"./environments":40,"./functions":43,"./symbols":48,"./unicodeRegexes":49,"./units":50,"./utils":51,"babel-runtime/helpers/classCallCheck":4,"babel-runtime/helpers/createClass":5}],32:[function(require,module,exports){ - - var _classCallCheck2 = require("babel-runtime/helpers/classCallCheck"); - - var _classCallCheck3 = _interopRequireDefault(_classCallCheck2); - - var _utils = require("./utils"); - - var _utils2 = _interopRequireDefault(_utils); - - function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } - - /** - * The main Settings object - * - * The current options stored are: - * - displayMode: Whether the expression should be typeset as inline math - * (false, the default), meaning that the math starts in - * \textstyle and is placed in an inline-block); or as display - * math (true), meaning that the math starts in \displaystyle - * and is placed in a block with vertical margin. - */ - var Settings = function Settings(options) { - (0, _classCallCheck3.default)(this, Settings); - - // allow null options - options = options || {}; - this.displayMode = _utils2.default.deflt(options.displayMode, false); - this.throwOnError = _utils2.default.deflt(options.throwOnError, true); - this.errorColor = _utils2.default.deflt(options.errorColor, "#cc0000"); - this.macros = options.macros || {}; - this.colorIsTextColor = _utils2.default.deflt(options.colorIsTextColor, false); - }; /** - * This is a module for storing settings passed into KaTeX. It correctly handles - * default settings. - */ - - module.exports = Settings; - - },{"./utils":51,"babel-runtime/helpers/classCallCheck":4}],33:[function(require,module,exports){ - - var _classCallCheck2 = require("babel-runtime/helpers/classCallCheck"); - - var _classCallCheck3 = _interopRequireDefault(_classCallCheck2); - - var _createClass2 = require("babel-runtime/helpers/createClass"); - - var _createClass3 = _interopRequireDefault(_createClass2); - - function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } - - /** - * This file contains information and classes for the various kinds of styles - * used in TeX. It provides a generic `Style` class, which holds information - * about a specific style. It then provides instances of all the different kinds - * of styles possible, and provides functions to move between them and get - * information about them. - */ - - /** - * The main style class. Contains a unique id for the style, a size (which is - * the same for cramped and uncramped version of a style), and a cramped flag. - */ - var Style = function () { - function Style(id, size, cramped) { - (0, _classCallCheck3.default)(this, Style); - - this.id = id; - this.size = size; - this.cramped = cramped; - } - - /** - * Get the style of a superscript given a base in the current style. - */ - - - (0, _createClass3.default)(Style, [{ - key: "sup", - value: function sup() { - return styles[_sup[this.id]]; - } - - /** - * Get the style of a subscript given a base in the current style. - */ - - }, { - key: "sub", - value: function sub() { - return styles[_sub[this.id]]; - } - - /** - * Get the style of a fraction numerator given the fraction in the current - * style. - */ - - }, { - key: "fracNum", - value: function fracNum() { - return styles[_fracNum[this.id]]; - } - - /** - * Get the style of a fraction denominator given the fraction in the current - * style. - */ - - }, { - key: "fracDen", - value: function fracDen() { - return styles[_fracDen[this.id]]; - } - - /** - * Get the cramped version of a style (in particular, cramping a cramped style - * doesn't change the style). - */ - - }, { - key: "cramp", - value: function cramp() { - return styles[_cramp[this.id]]; - } - - /** - * Get a text or display version of this style. - */ - - }, { - key: "text", - value: function text() { - return styles[_text[this.id]]; - } - - /** - * Return if this style is tightly spaced (scriptstyle/scriptscriptstyle) - */ - - }, { - key: "isTight", - value: function isTight() { - return this.size >= 2; - } - }]); - return Style; - }(); - - // IDs of the different styles - - - var D = 0; - var Dc = 1; - var T = 2; - var Tc = 3; - var S = 4; - var Sc = 5; - var SS = 6; - var SSc = 7; - - // Instances of the different styles - var styles = [new Style(D, 0, false), new Style(Dc, 0, true), new Style(T, 1, false), new Style(Tc, 1, true), new Style(S, 2, false), new Style(Sc, 2, true), new Style(SS, 3, false), new Style(SSc, 3, true)]; - - // Lookup tables for switching from one style to another - var _sup = [S, Sc, S, Sc, SS, SSc, SS, SSc]; - var _sub = [Sc, Sc, Sc, Sc, SSc, SSc, SSc, SSc]; - var _fracNum = [T, Tc, S, Sc, SS, SSc, SS, SSc]; - var _fracDen = [Tc, Tc, Sc, Sc, SSc, SSc, SSc, SSc]; - var _cramp = [Dc, Dc, Tc, Tc, Sc, Sc, SSc, SSc]; - var _text = [D, Dc, T, Tc, T, Tc, T, Tc]; - - // We only export some of the styles. Also, we don't export the `Style` class so - // no more styles can be generated. - module.exports = { - DISPLAY: styles[D], - TEXT: styles[T], - SCRIPT: styles[S], - SCRIPTSCRIPT: styles[SS] - }; - - },{"babel-runtime/helpers/classCallCheck":4,"babel-runtime/helpers/createClass":5}],34:[function(require,module,exports){ - - var _domTree = require("./domTree"); - - var _domTree2 = _interopRequireDefault(_domTree); - - var _fontMetrics = require("./fontMetrics"); - - var _fontMetrics2 = _interopRequireDefault(_fontMetrics); - - var _symbols = require("./symbols"); - - var _symbols2 = _interopRequireDefault(_symbols); - - var _utils = require("./utils"); - - var _utils2 = _interopRequireDefault(_utils); - - function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } - - // The following have to be loaded from Main-Italic font, using class mainit - /* eslint no-console:0 */ - /** - * This module contains general functions that can be used for building - * different kinds of domTree nodes in a consistent manner. - */ - - var mainitLetters = ["\\imath", // dotless i - "\\jmath", // dotless j - "\\pounds"]; - - /** - * Looks up the given symbol in fontMetrics, after applying any symbol - * replacements defined in symbol.js - */ - var lookupSymbol = function lookupSymbol(value, fontFamily, mode) { - // Replace the value with its replaced value from symbol.js - if (_symbols2.default[mode][value] && _symbols2.default[mode][value].replace) { - value = _symbols2.default[mode][value].replace; - } - return { - value: value, - metrics: _fontMetrics2.default.getCharacterMetrics(value, fontFamily) - }; - }; - - /** - * Makes a symbolNode after translation via the list of symbols in symbols.js. - * Correctly pulls out metrics for the character, and optionally takes a list of - * classes to be attached to the node. - * - * TODO: make argument order closer to makeSpan - * TODO: add a separate argument for math class (e.g. `mop`, `mbin`), which - * should if present come first in `classes`. - */ - var makeSymbol = function makeSymbol(value, fontFamily, mode, options, classes) { - var lookup = lookupSymbol(value, fontFamily, mode); - var metrics = lookup.metrics; - value = lookup.value; - - var symbolNode = void 0; - if (metrics) { - var italic = metrics.italic; - if (mode === "text") { - italic = 0; - } - symbolNode = new _domTree2.default.symbolNode(value, metrics.height, metrics.depth, italic, metrics.skew, classes); - } else { - // TODO(emily): Figure out a good way to only print this in development - typeof console !== "undefined" && console.warn("No character metrics for '" + value + "' in style '" + fontFamily + "'"); - symbolNode = new _domTree2.default.symbolNode(value, 0, 0, 0, 0, classes); - } - - if (options) { - symbolNode.maxFontSize = options.sizeMultiplier; - if (options.style.isTight()) { - symbolNode.classes.push("mtight"); - } - if (options.getColor()) { - symbolNode.style.color = options.getColor(); - } - } - - return symbolNode; - }; - - /** - * Makes a symbol in Main-Regular or AMS-Regular. - * Used for rel, bin, open, close, inner, and punct. - */ - var mathsym = function mathsym(value, mode, options, classes) { - // Decide what font to render the symbol in by its entry in the symbols - // table. - // Have a special case for when the value = \ because the \ is used as a - // textord in unsupported command errors but cannot be parsed as a regular - // text ordinal and is therefore not present as a symbol in the symbols - // table for text - if (value === "\\" || _symbols2.default[mode][value].font === "main") { - return makeSymbol(value, "Main-Regular", mode, options, classes); - } else { - return makeSymbol(value, "AMS-Regular", mode, options, classes.concat(["amsrm"])); - } - }; - - /** - * Makes a symbol in the default font for mathords and textords. - */ - var mathDefault = function mathDefault(value, mode, options, classes, type) { - if (type === "mathord") { - var fontLookup = mathit(value); - return makeSymbol(value, fontLookup.fontName, mode, options, classes.concat([fontLookup.fontClass])); - } else if (type === "textord") { - var font = _symbols2.default[mode][value] && _symbols2.default[mode][value].font; - if (font === "ams") { - return makeSymbol(value, "AMS-Regular", mode, options, classes.concat(["amsrm"])); - } else { - // if (font === "main") { - return makeSymbol(value, "Main-Regular", mode, options, classes.concat(["mathrm"])); - } - } else { - throw new Error("unexpected type: " + type + " in mathDefault"); - } - }; - - /** - * Determines which of the two font names (Main-Italic and Math-Italic) and - * corresponding style tags (mainit or mathit) to use for font "mathit", - * depending on the symbol. Use this function instead of fontMap for font - * "mathit". - */ - var mathit = function mathit(value, mode, options, classes) { - if (/[0-9]/.test(value.charAt(0)) || - // glyphs for \imath and \jmath do not exist in Math-Italic so we - // need to use Main-Italic instead - _utils2.default.contains(mainitLetters, value)) { - return { - fontName: "Main-Italic", - fontClass: "mainit" - }; - } else { - return { - fontName: "Math-Italic", - fontClass: "mathit" - }; - } - }; - - /** - * Makes either a mathord or textord in the correct font and color. - */ - var makeOrd = function makeOrd(group, options, type) { - var mode = group.mode; - var value = group.value; - - var classes = ["mord"]; - - var font = options.font; - if (font) { - var fontLookup = void 0; - if (font === "mathit" || _utils2.default.contains(mainitLetters, value)) { - fontLookup = mathit(value); - } else { - fontLookup = fontMap[font]; - } - if (lookupSymbol(value, fontLookup.fontName, mode).metrics) { - return makeSymbol(value, fontLookup.fontName, mode, options, classes.concat([fontLookup.fontClass || font])); - } else { - return mathDefault(value, mode, options, classes, type); - } - } else { - return mathDefault(value, mode, options, classes, type); - } - }; - - /** - * Calculate the height, depth, and maxFontSize of an element based on its - * children. - */ - var sizeElementFromChildren = function sizeElementFromChildren(elem) { - var height = 0; - var depth = 0; - var maxFontSize = 0; - - if (elem.children) { - for (var i = 0; i < elem.children.length; i++) { - if (elem.children[i].height > height) { - height = elem.children[i].height; - } - if (elem.children[i].depth > depth) { - depth = elem.children[i].depth; - } - if (elem.children[i].maxFontSize > maxFontSize) { - maxFontSize = elem.children[i].maxFontSize; - } - } - } - - elem.height = height; - elem.depth = depth; - elem.maxFontSize = maxFontSize; - }; - - /** - * Makes a span with the given list of classes, list of children, and options. - * - * TODO: Ensure that `options` is always provided (currently some call sites - * don't pass it). - * TODO: add a separate argument for math class (e.g. `mop`, `mbin`), which - * should if present come first in `classes`. - */ - var makeSpan = function makeSpan(classes, children, options) { - var span = new _domTree2.default.span(classes, children, options); - - sizeElementFromChildren(span); - - return span; - }; - - /** - * Prepends the given children to the given span, updating height, depth, and - * maxFontSize. - */ - var prependChildren = function prependChildren(span, children) { - span.children = children.concat(span.children); - - sizeElementFromChildren(span); - }; - - /** - * Makes a document fragment with the given list of children. - */ - var makeFragment = function makeFragment(children) { - var fragment = new _domTree2.default.documentFragment(children); - - sizeElementFromChildren(fragment); - - return fragment; - }; - - /** - * Makes a vertical list by stacking elements and kerns on top of each other. - * Allows for many different ways of specifying the positioning method. - * - * Arguments: - * - children: A list of child or kern nodes to be stacked on top of each other - * (i.e. the first element will be at the bottom, and the last at - * the top). Element nodes are specified as - * {type: "elem", elem: node} - * while kern nodes are specified as - * {type: "kern", size: size} - * - positionType: The method by which the vlist should be positioned. Valid - * values are: - * - "individualShift": The children list only contains elem - * nodes, and each node contains an extra - * "shift" value of how much it should be - * shifted (note that shifting is always - * moving downwards). positionData is - * ignored. - * - "top": The positionData specifies the topmost point of - * the vlist (note this is expected to be a height, - * so positive values move up) - * - "bottom": The positionData specifies the bottommost point - * of the vlist (note this is expected to be a - * depth, so positive values move down - * - "shift": The vlist will be positioned such that its - * baseline is positionData away from the baseline - * of the first child. Positive values move - * downwards. - * - "firstBaseline": The vlist will be positioned such that - * its baseline is aligned with the - * baseline of the first child. - * positionData is ignored. (this is - * equivalent to "shift" with - * positionData=0) - * - positionData: Data used in different ways depending on positionType - * - options: An Options object - * - */ - var makeVList = function makeVList(children, positionType, positionData, options) { - var depth = void 0; - var currPos = void 0; - var i = void 0; - if (positionType === "individualShift") { - var oldChildren = children; - children = [oldChildren[0]]; - - // Add in kerns to the list of children to get each element to be - // shifted to the correct specified shift - depth = -oldChildren[0].shift - oldChildren[0].elem.depth; - currPos = depth; - for (i = 1; i < oldChildren.length; i++) { - var diff = -oldChildren[i].shift - currPos - oldChildren[i].elem.depth; - var size = diff - (oldChildren[i - 1].elem.height + oldChildren[i - 1].elem.depth); - - currPos = currPos + diff; - - children.push({ type: "kern", size: size }); - children.push(oldChildren[i]); - } - } else if (positionType === "top") { - // We always start at the bottom, so calculate the bottom by adding up - // all the sizes - var bottom = positionData; - for (i = 0; i < children.length; i++) { - if (children[i].type === "kern") { - bottom -= children[i].size; - } else { - bottom -= children[i].elem.height + children[i].elem.depth; - } - } - depth = bottom; - } else if (positionType === "bottom") { - depth = -positionData; - } else if (positionType === "shift") { - depth = -children[0].elem.depth - positionData; - } else if (positionType === "firstBaseline") { - depth = -children[0].elem.depth; - } else { - depth = 0; - } - - // Create a strut that is taller than any list item. The strut is added to - // each item, where it will determine the item's baseline. Since it has - // `overflow:hidden`, the strut's top edge will sit on the item's line box's - // top edge and the strut's bottom edge will sit on the item's baseline, - // with no additional line-height spacing. This allows the item baseline to - // be positioned precisely without worrying about font ascent and - // line-height. - var pstrutSize = 0; - for (i = 0; i < children.length; i++) { - if (children[i].type === "elem") { - var child = children[i].elem; - pstrutSize = Math.max(pstrutSize, child.maxFontSize, child.height); - } - } - pstrutSize += 2; - var pstrut = makeSpan(["pstrut"], []); - pstrut.style.height = pstrutSize + "em"; - - // Create a new list of actual children at the correct offsets - var realChildren = []; - var minPos = depth; - var maxPos = depth; - currPos = depth; - for (i = 0; i < children.length; i++) { - if (children[i].type === "kern") { - currPos += children[i].size; - } else { - var _child = children[i].elem; - - var childWrap = makeSpan([], [pstrut, _child]); - childWrap.style.top = -pstrutSize - currPos - _child.depth + "em"; - if (children[i].marginLeft) { - childWrap.style.marginLeft = children[i].marginLeft; - } - if (children[i].marginRight) { - childWrap.style.marginRight = children[i].marginRight; - } - - realChildren.push(childWrap); - currPos += _child.height + _child.depth; - } - minPos = Math.min(minPos, currPos); - maxPos = Math.max(maxPos, currPos); - } - - // The vlist contents go in a table-cell with `vertical-align:bottom`. - // This cell's bottom edge will determine the containing table's baseline - // without overly expanding the containing line-box. - var vlist = makeSpan(["vlist"], realChildren); - vlist.style.height = maxPos + "em"; - - // A second row is used if necessary to represent the vlist's depth. - var rows = void 0; - if (minPos < 0) { - var depthStrut = makeSpan(["vlist"], []); - depthStrut.style.height = -minPos + "em"; - - // Safari wants the first row to have inline content; otherwise it - // puts the bottom of the *second* row on the baseline. - var topStrut = makeSpan(["vlist-s"], [new _domTree2.default.symbolNode("\u200B")]); - - rows = [makeSpan(["vlist-r"], [vlist, topStrut]), makeSpan(["vlist-r"], [depthStrut])]; - } else { - rows = [makeSpan(["vlist-r"], [vlist])]; - } - - var vtable = makeSpan(["vlist-t"], rows); - if (rows.length === 2) { - vtable.classes.push("vlist-t2"); - } - vtable.height = maxPos; - vtable.depth = -minPos; - return vtable; - }; - - // A map of spacing functions to their attributes, like size and corresponding - // CSS class - var spacingFunctions = { - "\\qquad": { - size: "2em", - className: "qquad" - }, - "\\quad": { - size: "1em", - className: "quad" - }, - "\\enspace": { - size: "0.5em", - className: "enspace" - }, - "\\;": { - size: "0.277778em", - className: "thickspace" - }, - "\\:": { - size: "0.22222em", - className: "mediumspace" - }, - "\\,": { - size: "0.16667em", - className: "thinspace" - }, - "\\!": { - size: "-0.16667em", - className: "negativethinspace" - } - }; - - /** - * Maps TeX font commands to objects containing: - * - variant: string used for "mathvariant" attribute in buildMathML.js - * - fontName: the "style" parameter to fontMetrics.getCharacterMetrics - */ - // A map between tex font commands an MathML mathvariant attribute values - var fontMap = { - // styles - "mathbf": { - variant: "bold", - fontName: "Main-Bold" - }, - "mathrm": { - variant: "normal", - fontName: "Main-Regular" - }, - "textit": { - variant: "italic", - fontName: "Main-Italic" - }, - - // "mathit" is missing because it requires the use of two fonts: Main-Italic - // and Math-Italic. This is handled by a special case in makeOrd which ends - // up calling mathit. - - // families - "mathbb": { - variant: "double-struck", - fontName: "AMS-Regular" - }, - "mathcal": { - variant: "script", - fontName: "Caligraphic-Regular" - }, - "mathfrak": { - variant: "fraktur", - fontName: "Fraktur-Regular" - }, - "mathscr": { - variant: "script", - fontName: "Script-Regular" - }, - "mathsf": { - variant: "sans-serif", - fontName: "SansSerif-Regular" - }, - "mathtt": { - variant: "monospace", - fontName: "Typewriter-Regular" - } - }; - - module.exports = { - fontMap: fontMap, - makeSymbol: makeSymbol, - mathsym: mathsym, - makeSpan: makeSpan, - makeFragment: makeFragment, - makeVList: makeVList, - makeOrd: makeOrd, - prependChildren: prependChildren, - spacingFunctions: spacingFunctions - }; - - },{"./domTree":39,"./fontMetrics":41,"./symbols":48,"./utils":51}],35:[function(require,module,exports){ - - var _stringify = require("babel-runtime/core-js/json/stringify"); - - var _stringify2 = _interopRequireDefault(_stringify); - - var _ParseError = require("./ParseError"); - - var _ParseError2 = _interopRequireDefault(_ParseError); - - var _Style = require("./Style"); - - var _Style2 = _interopRequireDefault(_Style); - - var _buildCommon = require("./buildCommon"); - - var _buildCommon2 = _interopRequireDefault(_buildCommon); - - var _delimiter = require("./delimiter"); - - var _delimiter2 = _interopRequireDefault(_delimiter); - - var _domTree = require("./domTree"); - - var _domTree2 = _interopRequireDefault(_domTree); - - var _units = require("./units"); - - var _units2 = _interopRequireDefault(_units); - - var _utils = require("./utils"); - - var _utils2 = _interopRequireDefault(_utils); - - var _stretchy = require("./stretchy"); - - var _stretchy2 = _interopRequireDefault(_stretchy); - - function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } - - /* eslint no-console:0 */ - /** - * This file does the main work of building a domTree structure from a parse - * tree. The entry point is the `buildHTML` function, which takes a parse tree. - * Then, the buildExpression, buildGroup, and various groupTypes functions are - * called, to produce a final HTML tree. - */ - - var isSpace = function isSpace(node) { - return node instanceof _domTree2.default.span && node.classes[0] === "mspace"; - }; - - // Binary atoms (first class `mbin`) change into ordinary atoms (`mord`) - // depending on their surroundings. See TeXbook pg. 442-446, Rules 5 and 6, - // and the text before Rule 19. - var isBin = function isBin(node) { - return node && node.classes[0] === "mbin"; - }; - - var isBinLeftCanceller = function isBinLeftCanceller(node, isRealGroup) { - // TODO: This code assumes that a node's math class is the first element - // of its `classes` array. A later cleanup should ensure this, for - // instance by changing the signature of `makeSpan`. - if (node) { - return _utils2.default.contains(["mbin", "mopen", "mrel", "mop", "mpunct"], node.classes[0]); - } else { - return isRealGroup; - } - }; - - var isBinRightCanceller = function isBinRightCanceller(node, isRealGroup) { - if (node) { - return _utils2.default.contains(["mrel", "mclose", "mpunct"], node.classes[0]); - } else { - return isRealGroup; - } - }; - - /** - * Splice out any spaces from `children` starting at position `i`, and return - * the spliced-out array. Returns null if `children[i]` does not exist or is not - * a space. - */ - var spliceSpaces = function spliceSpaces(children, i) { - var j = i; - while (j < children.length && isSpace(children[j])) { - j++; - } - if (j === i) { - return null; - } else { - return children.splice(i, j - i); - } - }; - - /** - * Take a list of nodes, build them in order, and return a list of the built - * nodes. documentFragments are flattened into their contents, so the - * returned list contains no fragments. `isRealGroup` is true if `expression` - * is a real group (no atoms will be added on either side), as opposed to - * a partial group (e.g. one created by \color). - */ - var buildExpression = function buildExpression(expression, options, isRealGroup) { - // Parse expressions into `groups`. - var groups = []; - for (var i = 0; i < expression.length; i++) { - var group = expression[i]; - var output = buildGroup(group, options); - if (output instanceof _domTree2.default.documentFragment) { - Array.prototype.push.apply(groups, output.children); - } else { - groups.push(output); - } - } - // At this point `groups` consists entirely of `symbolNode`s and `span`s. - - // Explicit spaces (e.g., \;, \,) should be ignored with respect to atom - // spacing (e.g., "add thick space between mord and mrel"). Since CSS - // adjacency rules implement atom spacing, spaces should be invisible to - // CSS. So we splice them out of `groups` and into the atoms themselves. - for (var _i = 0; _i < groups.length; _i++) { - var spaces = spliceSpaces(groups, _i); - if (spaces) { - // Splicing of spaces may have removed all remaining groups. - if (_i < groups.length) { - // If there is a following group, move space within it. - if (groups[_i] instanceof _domTree2.default.symbolNode) { - groups[_i] = (0, _buildCommon.makeSpan)([].concat(groups[_i].classes), [groups[_i]]); - } - _buildCommon2.default.prependChildren(groups[_i], spaces); - } else { - // Otherwise, put any spaces back at the end of the groups. - Array.prototype.push.apply(groups, spaces); - break; - } - } - } - - // Binary operators change to ordinary symbols in some contexts. - for (var _i2 = 0; _i2 < groups.length; _i2++) { - if (isBin(groups[_i2]) && (isBinLeftCanceller(groups[_i2 - 1], isRealGroup) || isBinRightCanceller(groups[_i2 + 1], isRealGroup))) { - groups[_i2].classes[0] = "mord"; - } - } - - // Process \\not commands within the group. - // TODO(kevinb): Handle multiple \\not commands in a row. - // TODO(kevinb): Handle \\not{abc} correctly. The \\not should appear over - // the 'a' instead of the 'c'. - for (var _i3 = 0; _i3 < groups.length; _i3++) { - if (groups[_i3].value === "\u0338" && _i3 + 1 < groups.length) { - var children = groups.slice(_i3, _i3 + 2); - - children[0].classes = ["mainrm"]; - // \u0338 is a combining glyph so we could reorder the children so - // that it comes after the other glyph. This works correctly on - // most browsers except for Safari. Instead we absolutely position - // the glyph and set its right side to match that of the other - // glyph which is visually equivalent. - children[0].style.position = "absolute"; - children[0].style.right = "0"; - - // Copy the classes from the second glyph to the new container. - // This is so it behaves the same as though there was no \\not. - var classes = groups[_i3 + 1].classes; - var container = (0, _buildCommon.makeSpan)(classes, children); - - // LaTeX adds a space between ords separated by a \\not. - if (classes.indexOf("mord") !== -1) { - // \glue(\thickmuskip) 2.77771 plus 2.77771 - container.style.paddingLeft = "0.277771em"; - } - - // Ensure that the \u0338 is positioned relative to the container. - container.style.position = "relative"; - groups.splice(_i3, 2, container); - } - } - - return groups; - }; - - // Return math atom class (mclass) of a domTree. - var getTypeOfDomTree = function getTypeOfDomTree(node) { - if (node instanceof _domTree2.default.documentFragment) { - if (node.children.length) { - return getTypeOfDomTree(node.children[node.children.length - 1]); - } - } else { - if (_utils2.default.contains(["mord", "mop", "mbin", "mrel", "mopen", "mclose", "mpunct", "minner"], node.classes[0])) { - return node.classes[0]; - } - } - return null; - }; - - /** - * Sometimes, groups perform special rules when they have superscripts or - * subscripts attached to them. This function lets the `supsub` group know that - * its inner element should handle the superscripts and subscripts instead of - * handling them itself. - */ - var shouldHandleSupSub = function shouldHandleSupSub(group, options) { - if (!group.value.base) { - return false; - } else { - var base = group.value.base; - if (base.type === "op") { - // Operators handle supsubs differently when they have limits - // (e.g. `\displaystyle\sum_2^3`) - return base.value.limits && (options.style.size === _Style2.default.DISPLAY.size || base.value.alwaysHandleSupSub); - } else if (base.type === "accent") { - return isCharacterBox(base.value.base); - } else if (base.type === "horizBrace") { - var isSup = group.value.sub ? false : true; - return isSup === base.value.isOver; - } else { - return null; - } - } - }; - - /** - * Sometimes we want to pull out the innermost element of a group. In most - * cases, this will just be the group itself, but when ordgroups and colors have - * a single element, we want to pull that out. - */ - var getBaseElem = function getBaseElem(group) { - if (!group) { - return false; - } else if (group.type === "ordgroup") { - if (group.value.length === 1) { - return getBaseElem(group.value[0]); - } else { - return group; - } - } else if (group.type === "color") { - if (group.value.value.length === 1) { - return getBaseElem(group.value.value[0]); - } else { - return group; - } - } else if (group.type === "font") { - return getBaseElem(group.value.body); - } else { - return group; - } - }; - - /** - * TeXbook algorithms often reference "character boxes", which are simply groups - * with a single character in them. To decide if something is a character box, - * we find its innermost group, and see if it is a single character. - */ - var isCharacterBox = function isCharacterBox(group) { - var baseElem = getBaseElem(group); - - // These are all they types of groups which hold single characters - return baseElem.type === "mathord" || baseElem.type === "textord" || baseElem.type === "bin" || baseElem.type === "rel" || baseElem.type === "inner" || baseElem.type === "open" || baseElem.type === "close" || baseElem.type === "punct"; - }; - - var makeNullDelimiter = function makeNullDelimiter(options, classes) { - var moreClasses = ["nulldelimiter"].concat(options.baseSizingClasses()); - return (0, _buildCommon.makeSpan)(classes.concat(moreClasses)); - }; - - /** - * This is a map of group types to the function used to handle that type. - * Simpler types come at the beginning, while complicated types come afterwards. - */ - var groupTypes = {}; - - groupTypes.mathord = function (group, options) { - return _buildCommon2.default.makeOrd(group, options, "mathord"); - }; - - groupTypes.textord = function (group, options) { - return _buildCommon2.default.makeOrd(group, options, "textord"); - }; - - groupTypes.bin = function (group, options) { - return _buildCommon2.default.mathsym(group.value, group.mode, options, ["mbin"]); - }; - - groupTypes.rel = function (group, options) { - return _buildCommon2.default.mathsym(group.value, group.mode, options, ["mrel"]); - }; - - groupTypes.open = function (group, options) { - return _buildCommon2.default.mathsym(group.value, group.mode, options, ["mopen"]); - }; - - groupTypes.close = function (group, options) { - return _buildCommon2.default.mathsym(group.value, group.mode, options, ["mclose"]); - }; - - groupTypes.inner = function (group, options) { - return _buildCommon2.default.mathsym(group.value, group.mode, options, ["minner"]); - }; - - groupTypes.punct = function (group, options) { - return _buildCommon2.default.mathsym(group.value, group.mode, options, ["mpunct"]); - }; - - groupTypes.ordgroup = function (group, options) { - return (0, _buildCommon.makeSpan)(["mord"], buildExpression(group.value, options, true), options); - }; - - groupTypes.text = function (group, options) { - var newOptions = options.withFont(group.value.style); - var inner = buildExpression(group.value.body, newOptions, true); - for (var i = 0; i < inner.length - 1; i++) { - if (inner[i].tryCombine(inner[i + 1])) { - inner.splice(i + 1, 1); - i--; - } - } - return (0, _buildCommon.makeSpan)(["mord", "text"], inner, newOptions); - }; - - groupTypes.color = function (group, options) { - var elements = buildExpression(group.value.value, options.withColor(group.value.color), false); - - // \color isn't supposed to affect the type of the elements it contains. - // To accomplish this, we wrap the results in a fragment, so the inner - // elements will be able to directly interact with their neighbors. For - // example, `\color{red}{2 +} 3` has the same spacing as `2 + 3` - return new _buildCommon2.default.makeFragment(elements); - }; - - groupTypes.supsub = function (group, options) { - // Superscript and subscripts are handled in the TeXbook on page - // 445-446, rules 18(a-f). - - // Here is where we defer to the inner group if it should handle - // superscripts and subscripts itself. - if (shouldHandleSupSub(group, options)) { - return groupTypes[group.value.base.type](group, options); - } - - var base = buildGroup(group.value.base, options); - var supm = void 0; - var subm = void 0; - - var metrics = options.fontMetrics(); - var newOptions = void 0; - - // Rule 18a - var supShift = 0; - var subShift = 0; - - if (group.value.sup) { - newOptions = options.havingStyle(options.style.sup()); - supm = buildGroup(group.value.sup, newOptions, options); - if (!isCharacterBox(group.value.base)) { - supShift = base.height - newOptions.fontMetrics().supDrop * newOptions.sizeMultiplier / options.sizeMultiplier; - } - } - - if (group.value.sub) { - newOptions = options.havingStyle(options.style.sub()); - subm = buildGroup(group.value.sub, newOptions, options); - if (!isCharacterBox(group.value.base)) { - subShift = base.depth + newOptions.fontMetrics().subDrop * newOptions.sizeMultiplier / options.sizeMultiplier; - } - } - - // Rule 18c - var minSupShift = void 0; - if (options.style === _Style2.default.DISPLAY) { - minSupShift = metrics.sup1; - } else if (options.style.cramped) { - minSupShift = metrics.sup3; - } else { - minSupShift = metrics.sup2; - } - - // scriptspace is a font-size-independent size, so scale it - // appropriately - var multiplier = options.sizeMultiplier; - var scriptspace = 0.5 / metrics.ptPerEm / multiplier + "em"; - - var supsub = void 0; - if (!group.value.sup) { - // Rule 18b - subShift = Math.max(subShift, metrics.sub1, subm.height - 0.8 * metrics.xHeight); - - var vlistElem = [{ type: "elem", elem: subm, marginRight: scriptspace }]; - // Subscripts shouldn't be shifted by the base's italic correction. - // Account for that by shifting the subscript back the appropriate - // amount. Note we only do this when the base is a single symbol. - if (base instanceof _domTree2.default.symbolNode) { - vlistElem[0].marginLeft = -base.italic + "em"; - } - - supsub = _buildCommon2.default.makeVList(vlistElem, "shift", subShift, options); - } else if (!group.value.sub) { - // Rule 18c, d - supShift = Math.max(supShift, minSupShift, supm.depth + 0.25 * metrics.xHeight); - - supsub = _buildCommon2.default.makeVList([{ type: "elem", elem: supm, marginRight: scriptspace }], "shift", -supShift, options); - } else { - supShift = Math.max(supShift, minSupShift, supm.depth + 0.25 * metrics.xHeight); - subShift = Math.max(subShift, metrics.sub2); - - var ruleWidth = metrics.defaultRuleThickness; - - // Rule 18e - if (supShift - supm.depth - (subm.height - subShift) < 4 * ruleWidth) { - subShift = 4 * ruleWidth - (supShift - supm.depth) + subm.height; - var psi = 0.8 * metrics.xHeight - (supShift - supm.depth); - if (psi > 0) { - supShift += psi; - subShift -= psi; - } - } - - var _vlistElem = [{ type: "elem", elem: subm, shift: subShift, marginRight: scriptspace }, { type: "elem", elem: supm, shift: -supShift, marginRight: scriptspace }]; - // See comment above about subscripts not being shifted - if (base instanceof _domTree2.default.symbolNode) { - _vlistElem[0].marginLeft = -base.italic + "em"; - } - - supsub = _buildCommon2.default.makeVList(_vlistElem, "individualShift", null, options); - } - - // We ensure to wrap the supsub vlist in a span.msupsub to reset text-align - var mclass = getTypeOfDomTree(base) || "mord"; - return (0, _buildCommon.makeSpan)([mclass], [base, (0, _buildCommon.makeSpan)(["msupsub"], [supsub])], options); - }; - - groupTypes.genfrac = function (group, options) { - // Fractions are handled in the TeXbook on pages 444-445, rules 15(a-e). - // Figure out what style this fraction should be in based on the - // function used - var style = options.style; - if (group.value.size === "display") { - style = _Style2.default.DISPLAY; - } else if (group.value.size === "text") { - style = _Style2.default.TEXT; - } - - var nstyle = style.fracNum(); - var dstyle = style.fracDen(); - var newOptions = void 0; - - newOptions = options.havingStyle(nstyle); - var numerm = buildGroup(group.value.numer, newOptions, options); - - newOptions = options.havingStyle(dstyle); - var denomm = buildGroup(group.value.denom, newOptions, options); - - var rule = void 0; - var ruleWidth = void 0; - var ruleSpacing = void 0; - if (group.value.hasBarLine) { - rule = makeLineSpan("frac-line", options); - ruleWidth = rule.height; - ruleSpacing = rule.height; - } else { - rule = null; - ruleWidth = 0; - ruleSpacing = options.fontMetrics().defaultRuleThickness; - } - - // Rule 15b - var numShift = void 0; - var clearance = void 0; - var denomShift = void 0; - if (style.size === _Style2.default.DISPLAY.size) { - numShift = options.fontMetrics().num1; - if (ruleWidth > 0) { - clearance = 3 * ruleSpacing; - } else { - clearance = 7 * ruleSpacing; - } - denomShift = options.fontMetrics().denom1; - } else { - if (ruleWidth > 0) { - numShift = options.fontMetrics().num2; - clearance = ruleSpacing; - } else { - numShift = options.fontMetrics().num3; - clearance = 3 * ruleSpacing; - } - denomShift = options.fontMetrics().denom2; - } - - var frac = void 0; - if (ruleWidth === 0) { - // Rule 15c - var candidateClearance = numShift - numerm.depth - (denomm.height - denomShift); - if (candidateClearance < clearance) { - numShift += 0.5 * (clearance - candidateClearance); - denomShift += 0.5 * (clearance - candidateClearance); - } - - frac = _buildCommon2.default.makeVList([{ type: "elem", elem: denomm, shift: denomShift }, { type: "elem", elem: numerm, shift: -numShift }], "individualShift", null, options); - } else { - // Rule 15d - var axisHeight = options.fontMetrics().axisHeight; - - if (numShift - numerm.depth - (axisHeight + 0.5 * ruleWidth) < clearance) { - numShift += clearance - (numShift - numerm.depth - (axisHeight + 0.5 * ruleWidth)); - } - - if (axisHeight - 0.5 * ruleWidth - (denomm.height - denomShift) < clearance) { - denomShift += clearance - (axisHeight - 0.5 * ruleWidth - (denomm.height - denomShift)); - } - - var midShift = -(axisHeight - 0.5 * ruleWidth); - - frac = _buildCommon2.default.makeVList([{ type: "elem", elem: denomm, shift: denomShift }, { type: "elem", elem: rule, shift: midShift }, { type: "elem", elem: numerm, shift: -numShift }], "individualShift", null, options); - } - - // Since we manually change the style sometimes (with \dfrac or \tfrac), - // account for the possible size change here. - newOptions = options.havingStyle(style); - frac.height *= newOptions.sizeMultiplier / options.sizeMultiplier; - frac.depth *= newOptions.sizeMultiplier / options.sizeMultiplier; - - // Rule 15e - var delimSize = void 0; - if (style.size === _Style2.default.DISPLAY.size) { - delimSize = options.fontMetrics().delim1; - } else { - delimSize = options.fontMetrics().delim2; - } - - var leftDelim = void 0; - var rightDelim = void 0; - if (group.value.leftDelim == null) { - leftDelim = makeNullDelimiter(options, ["mopen"]); - } else { - leftDelim = _delimiter2.default.customSizedDelim(group.value.leftDelim, delimSize, true, options.havingStyle(style), group.mode, ["mopen"]); - } - if (group.value.rightDelim == null) { - rightDelim = makeNullDelimiter(options, ["mclose"]); - } else { - rightDelim = _delimiter2.default.customSizedDelim(group.value.rightDelim, delimSize, true, options.havingStyle(style), group.mode, ["mclose"]); - } - - return (0, _buildCommon.makeSpan)(["mord"].concat(newOptions.sizingClasses(options)), [leftDelim, (0, _buildCommon.makeSpan)(["mfrac"], [frac]), rightDelim], options); - }; - - groupTypes.array = function (group, options) { - var r = void 0; - var c = void 0; - var nr = group.value.body.length; - var nc = 0; - var body = new Array(nr); - - // Horizontal spacing - var pt = 1 / options.fontMetrics().ptPerEm; - var arraycolsep = 5 * pt; // \arraycolsep in article.cls - - // Vertical spacing - var baselineskip = 12 * pt; // see size10.clo - // Default \jot from ltmath.dtx - // TODO(edemaine): allow overriding \jot via \setlength (#687) - var jot = 3 * pt; - // Default \arraystretch from lttab.dtx - // TODO(gagern): may get redefined once we have user-defined macros - var arraystretch = _utils2.default.deflt(group.value.arraystretch, 1); - var arrayskip = arraystretch * baselineskip; - var arstrutHeight = 0.7 * arrayskip; // \strutbox in ltfsstrc.dtx and - var arstrutDepth = 0.3 * arrayskip; // \@arstrutbox in lttab.dtx - - var totalHeight = 0; - for (r = 0; r < group.value.body.length; ++r) { - var inrow = group.value.body[r]; - var height = arstrutHeight; // \@array adds an \@arstrut - var depth = arstrutDepth; // to each tow (via the template) - - if (nc < inrow.length) { - nc = inrow.length; - } - - var outrow = new Array(inrow.length); - for (c = 0; c < inrow.length; ++c) { - var elt = buildGroup(inrow[c], options); - if (depth < elt.depth) { - depth = elt.depth; - } - if (height < elt.height) { - height = elt.height; - } - outrow[c] = elt; - } - - var gap = 0; - if (group.value.rowGaps[r]) { - gap = _units2.default.calculateSize(group.value.rowGaps[r].value, options); - if (gap > 0) { - // \@argarraycr - gap += arstrutDepth; - if (depth < gap) { - depth = gap; // \@xargarraycr - } - gap = 0; - } - } - // In AMS multiline environments such as aligned and gathered, rows - // correspond to lines that have additional \jot added to the - // \baselineskip via \openup. - if (group.value.addJot) { - depth += jot; - } - - outrow.height = height; - outrow.depth = depth; - totalHeight += height; - outrow.pos = totalHeight; - totalHeight += depth + gap; // \@yargarraycr - body[r] = outrow; - } - - var offset = totalHeight / 2 + options.fontMetrics().axisHeight; - var colDescriptions = group.value.cols || []; - var cols = []; - var colSep = void 0; - var colDescrNum = void 0; - for (c = 0, colDescrNum = 0; - // Continue while either there are more columns or more column - // descriptions, so trailing separators don't get lost. - c < nc || colDescrNum < colDescriptions.length; ++c, ++colDescrNum) { - - var colDescr = colDescriptions[colDescrNum] || {}; - - var firstSeparator = true; - while (colDescr.type === "separator") { - // If there is more than one separator in a row, add a space - // between them. - if (!firstSeparator) { - colSep = (0, _buildCommon.makeSpan)(["arraycolsep"], []); - colSep.style.width = options.fontMetrics().doubleRuleSep + "em"; - cols.push(colSep); - } - - if (colDescr.separator === "|") { - var separator = (0, _buildCommon.makeSpan)(["vertical-separator"], []); - separator.style.height = totalHeight + "em"; - separator.style.verticalAlign = -(totalHeight - offset) + "em"; - - cols.push(separator); - } else { - throw new _ParseError2.default("Invalid separator type: " + colDescr.separator); - } - - colDescrNum++; - colDescr = colDescriptions[colDescrNum] || {}; - firstSeparator = false; - } - - if (c >= nc) { - continue; - } - - var sepwidth = void 0; - if (c > 0 || group.value.hskipBeforeAndAfter) { - sepwidth = _utils2.default.deflt(colDescr.pregap, arraycolsep); - if (sepwidth !== 0) { - colSep = (0, _buildCommon.makeSpan)(["arraycolsep"], []); - colSep.style.width = sepwidth + "em"; - cols.push(colSep); - } - } - - var col = []; - for (r = 0; r < nr; ++r) { - var row = body[r]; - var elem = row[c]; - if (!elem) { - continue; - } - var shift = row.pos - offset; - elem.depth = row.depth; - elem.height = row.height; - col.push({ type: "elem", elem: elem, shift: shift }); - } - - col = _buildCommon2.default.makeVList(col, "individualShift", null, options); - col = (0, _buildCommon.makeSpan)(["col-align-" + (colDescr.align || "c")], [col]); - cols.push(col); - - if (c < nc - 1 || group.value.hskipBeforeAndAfter) { - sepwidth = _utils2.default.deflt(colDescr.postgap, arraycolsep); - if (sepwidth !== 0) { - colSep = (0, _buildCommon.makeSpan)(["arraycolsep"], []); - colSep.style.width = sepwidth + "em"; - cols.push(colSep); - } - } - } - body = (0, _buildCommon.makeSpan)(["mtable"], cols); - return (0, _buildCommon.makeSpan)(["mord"], [body], options); - }; - - groupTypes.spacing = function (group, options) { - if (group.value === "\\ " || group.value === "\\space" || group.value === " " || group.value === "~") { - // Spaces are generated by adding an actual space. Each of these - // things has an entry in the symbols table, so these will be turned - // into appropriate outputs. - if (group.mode === "text") { - return _buildCommon2.default.makeOrd(group, options, "textord"); - } else { - return (0, _buildCommon.makeSpan)(["mspace"], [_buildCommon2.default.mathsym(group.value, group.mode, options)], options); - } - } else { - // Other kinds of spaces are of arbitrary width. We use CSS to - // generate these. - return (0, _buildCommon.makeSpan)(["mspace", _buildCommon2.default.spacingFunctions[group.value].className], [], options); - } - }; - - groupTypes.llap = function (group, options) { - var inner = (0, _buildCommon.makeSpan)(["inner"], [buildGroup(group.value.body, options)]); - var fix = (0, _buildCommon.makeSpan)(["fix"], []); - return (0, _buildCommon.makeSpan)(["mord", "llap"], [inner, fix], options); - }; - - groupTypes.rlap = function (group, options) { - var inner = (0, _buildCommon.makeSpan)(["inner"], [buildGroup(group.value.body, options)]); - var fix = (0, _buildCommon.makeSpan)(["fix"], []); - return (0, _buildCommon.makeSpan)(["mord", "rlap"], [inner, fix], options); - }; - - groupTypes.op = function (group, options) { - // Operators are handled in the TeXbook pg. 443-444, rule 13(a). - var supGroup = void 0; - var subGroup = void 0; - var hasLimits = false; - if (group.type === "supsub") { - // If we have limits, supsub will pass us its group to handle. Pull - // out the superscript and subscript and set the group to the op in - // its base. - supGroup = group.value.sup; - subGroup = group.value.sub; - group = group.value.base; - hasLimits = true; - } - - var style = options.style; - - // Most operators have a large successor symbol, but these don't. - var noSuccessor = ["\\smallint"]; - - var large = false; - if (style.size === _Style2.default.DISPLAY.size && group.value.symbol && !_utils2.default.contains(noSuccessor, group.value.body)) { - - // Most symbol operators get larger in displaystyle (rule 13) - large = true; - } - - var base = void 0; - if (group.value.symbol) { - // If this is a symbol, create the symbol. - var fontName = large ? "Size2-Regular" : "Size1-Regular"; - base = _buildCommon2.default.makeSymbol(group.value.body, fontName, "math", options, ["mop", "op-symbol", large ? "large-op" : "small-op"]); - } else if (group.value.value) { - // If this is a list, compose that list. - var inner = buildExpression(group.value.value, options, true); - if (inner.length === 1 && inner[0] instanceof _domTree2.default.symbolNode) { - base = inner[0]; - base.classes[0] = "mop"; // replace old mclass - } else { - base = (0, _buildCommon.makeSpan)(["mop"], inner, options); - } - } else { - // Otherwise, this is a text operator. Build the text from the - // operator's name. - // TODO(emily): Add a space in the middle of some of these - // operators, like \limsup - var output = []; - for (var i = 1; i < group.value.body.length; i++) { - output.push(_buildCommon2.default.mathsym(group.value.body[i], group.mode)); - } - base = (0, _buildCommon.makeSpan)(["mop"], output, options); - } - - // If content of op is a single symbol, shift it vertically. - var baseShift = 0; - var slant = 0; - if (base instanceof _domTree2.default.symbolNode) { - // Shift the symbol so its center lies on the axis (rule 13). It - // appears that our fonts have the centers of the symbols already - // almost on the axis, so these numbers are very small. Note we - // don't actually apply this here, but instead it is used either in - // the vlist creation or separately when there are no limits. - baseShift = (base.height - base.depth) / 2 - options.fontMetrics().axisHeight; - - // The slant of the symbol is just its italic correction. - slant = base.italic; - } - - if (hasLimits) { - // IE 8 clips \int if it is in a display: inline-block. We wrap it - // in a new span so it is an inline, and works. - base = (0, _buildCommon.makeSpan)([], [base]); - - var supm = void 0; - var supKern = void 0; - var subm = void 0; - var subKern = void 0; - var newOptions = void 0; - // We manually have to handle the superscripts and subscripts. This, - // aside from the kern calculations, is copied from supsub. - if (supGroup) { - newOptions = options.havingStyle(style.sup()); - supm = buildGroup(supGroup, newOptions, options); - - supKern = Math.max(options.fontMetrics().bigOpSpacing1, options.fontMetrics().bigOpSpacing3 - supm.depth); - } - - if (subGroup) { - newOptions = options.havingStyle(style.sub()); - subm = buildGroup(subGroup, newOptions, options); - - subKern = Math.max(options.fontMetrics().bigOpSpacing2, options.fontMetrics().bigOpSpacing4 - subm.height); - } - - // Build the final group as a vlist of the possible subscript, base, - // and possible superscript. - var finalGroup = void 0; - var top = void 0; - var bottom = void 0; - if (!supGroup) { - top = base.height - baseShift; - - // Shift the limits by the slant of the symbol. Note - // that we are supposed to shift the limits by 1/2 of the slant, - // but since we are centering the limits adding a full slant of - // margin will shift by 1/2 that. - finalGroup = _buildCommon2.default.makeVList([{ type: "kern", size: options.fontMetrics().bigOpSpacing5 }, { type: "elem", elem: subm, marginLeft: -slant + "em" }, { type: "kern", size: subKern }, { type: "elem", elem: base }], "top", top, options); - } else if (!subGroup) { - bottom = base.depth + baseShift; - - finalGroup = _buildCommon2.default.makeVList([{ type: "elem", elem: base }, { type: "kern", size: supKern }, { type: "elem", elem: supm, marginLeft: slant + "em" }, { type: "kern", size: options.fontMetrics().bigOpSpacing5 }], "bottom", bottom, options); - } else if (!supGroup && !subGroup) { - // This case probably shouldn't occur (this would mean the - // supsub was sending us a group with no superscript or - // subscript) but be safe. - return base; - } else { - bottom = options.fontMetrics().bigOpSpacing5 + subm.height + subm.depth + subKern + base.depth + baseShift; - - finalGroup = _buildCommon2.default.makeVList([{ type: "kern", size: options.fontMetrics().bigOpSpacing5 }, { type: "elem", elem: subm, marginLeft: -slant + "em" }, { type: "kern", size: subKern }, { type: "elem", elem: base }, { type: "kern", size: supKern }, { type: "elem", elem: supm, marginLeft: slant + "em" }, { type: "kern", size: options.fontMetrics().bigOpSpacing5 }], "bottom", bottom, options); - } - - return (0, _buildCommon.makeSpan)(["mop", "op-limits"], [finalGroup], options); - } else { - if (baseShift) { - base.style.position = "relative"; - base.style.top = baseShift + "em"; - } - - return base; - } - }; - - groupTypes.mod = function (group, options) { - var inner = []; - - if (group.value.modType === "bmod") { - // “\nonscript\mskip-\medmuskip\mkern5mu” - if (!options.style.isTight()) { - inner.push((0, _buildCommon.makeSpan)(["mspace", "negativemediumspace"], [], options)); - } - inner.push((0, _buildCommon.makeSpan)(["mspace", "thickspace"], [], options)); - } else if (options.style.size === _Style2.default.DISPLAY.size) { - inner.push((0, _buildCommon.makeSpan)(["mspace", "quad"], [], options)); - } else if (group.value.modType === "mod") { - inner.push((0, _buildCommon.makeSpan)(["mspace", "twelvemuspace"], [], options)); - } else { - inner.push((0, _buildCommon.makeSpan)(["mspace", "eightmuspace"], [], options)); - } - - if (group.value.modType === "pod" || group.value.modType === "pmod") { - inner.push(_buildCommon2.default.mathsym("(", group.mode)); - } - - if (group.value.modType !== "pod") { - var modInner = [_buildCommon2.default.mathsym("m", group.mode), _buildCommon2.default.mathsym("o", group.mode), _buildCommon2.default.mathsym("d", group.mode)]; - if (group.value.modType === "bmod") { - inner.push((0, _buildCommon.makeSpan)(["mbin"], modInner, options)); - // “\mkern5mu\nonscript\mskip-\medmuskip” - inner.push((0, _buildCommon.makeSpan)(["mspace", "thickspace"], [], options)); - if (!options.style.isTight()) { - inner.push((0, _buildCommon.makeSpan)(["mspace", "negativemediumspace"], [], options)); - } - } else { - Array.prototype.push.apply(inner, modInner); - inner.push((0, _buildCommon.makeSpan)(["mspace", "sixmuspace"], [], options)); - } - } - - if (group.value.value) { - Array.prototype.push.apply(inner, buildExpression(group.value.value, options, false)); - } - - if (group.value.modType === "pod" || group.value.modType === "pmod") { - inner.push(_buildCommon2.default.mathsym(")", group.mode)); - } - - return _buildCommon2.default.makeFragment(inner); - }; - - groupTypes.katex = function (group, options) { - // The KaTeX logo. The offsets for the K and a were chosen to look - // good, but the offsets for the T, E, and X were taken from the - // definition of \TeX in TeX (see TeXbook pg. 356) - var k = (0, _buildCommon.makeSpan)(["k"], [_buildCommon2.default.mathsym("K", group.mode)], options); - var a = (0, _buildCommon.makeSpan)(["a"], [_buildCommon2.default.mathsym("A", group.mode)], options); - - a.height = (a.height + 0.2) * 0.75; - a.depth = (a.height - 0.2) * 0.75; - - var t = (0, _buildCommon.makeSpan)(["t"], [_buildCommon2.default.mathsym("T", group.mode)], options); - var e = (0, _buildCommon.makeSpan)(["e"], [_buildCommon2.default.mathsym("E", group.mode)], options); - - e.height = e.height - 0.2155; - e.depth = e.depth + 0.2155; - - var x = (0, _buildCommon.makeSpan)(["x"], [_buildCommon2.default.mathsym("X", group.mode)], options); - - return (0, _buildCommon.makeSpan)(["mord", "katex-logo"], [k, a, t, e, x], options); - }; - - var makeLineSpan = function makeLineSpan(className, options, thickness) { - var line = (0, _buildCommon.makeSpan)([className], [], options); - line.height = thickness || options.fontMetrics().defaultRuleThickness; - line.style.borderBottomWidth = line.height + "em"; - line.maxFontSize = 1.0; - return line; - }; - - groupTypes.overline = function (group, options) { - // Overlines are handled in the TeXbook pg 443, Rule 9. - - // Build the inner group in the cramped style. - var innerGroup = buildGroup(group.value.body, options.havingCrampedStyle()); - - // Create the line above the body - var line = makeLineSpan("overline-line", options); - - // Generate the vlist, with the appropriate kerns - var vlist = _buildCommon2.default.makeVList([{ type: "elem", elem: innerGroup }, { type: "kern", size: 3 * line.height }, { type: "elem", elem: line }, { type: "kern", size: line.height }], "firstBaseline", null, options); - - return (0, _buildCommon.makeSpan)(["mord", "overline"], [vlist], options); - }; - - groupTypes.underline = function (group, options) { - // Underlines are handled in the TeXbook pg 443, Rule 10. - // Build the inner group. - var innerGroup = buildGroup(group.value.body, options); - - // Create the line above the body - var line = makeLineSpan("underline-line", options); - - // Generate the vlist, with the appropriate kerns - var vlist = _buildCommon2.default.makeVList([{ type: "kern", size: line.height }, { type: "elem", elem: line }, { type: "kern", size: 3 * line.height }, { type: "elem", elem: innerGroup }], "top", innerGroup.height, options); - - return (0, _buildCommon.makeSpan)(["mord", "underline"], [vlist], options); - }; - - groupTypes.sqrt = function (group, options) { - // Square roots are handled in the TeXbook pg. 443, Rule 11. - - // First, we do the same steps as in overline to build the inner group - // and line - var inner = buildGroup(group.value.body, options.havingCrampedStyle()); - - // Some groups can return document fragments. Handle those by wrapping - // them in a span. - if (inner instanceof _domTree2.default.documentFragment) { - inner = (0, _buildCommon.makeSpan)([], [inner], options); - } - - // Calculate the minimum size for the \surd delimiter - var metrics = options.fontMetrics(); - var theta = metrics.defaultRuleThickness; - - var phi = theta; - if (options.style.id < _Style2.default.TEXT.id) { - phi = options.fontMetrics().xHeight; - } - - // Calculate the clearance between the body and line - var lineClearance = theta + phi / 4; - - var minDelimiterHeight = (inner.height + inner.depth + lineClearance + theta) * options.sizeMultiplier; - - // Create a sqrt SVG of the required minimum size - var img = _delimiter2.default.customSizedDelim("\\surd", minDelimiterHeight, false, options, group.mode); - - // Calculate the actual line width. - // This actually should depend on the chosen font -- e.g. \boldmath - // should use the thicker surd symbols from e.g. KaTeX_Main-Bold, and - // have thicker rules. - var ruleWidth = options.fontMetrics().sqrtRuleThickness * img.sizeMultiplier; - - var delimDepth = img.height - ruleWidth; - - // Adjust the clearance based on the delimiter size - if (delimDepth > inner.height + inner.depth + lineClearance) { - lineClearance = (lineClearance + delimDepth - inner.height - inner.depth) / 2; - } - - // Shift the sqrt image - var imgShift = img.height - inner.height - lineClearance - ruleWidth; - - // We add a special case here, because even when `inner` is empty, we - // still get a line. So, we use a simple heuristic to decide if we - // should omit the body entirely. (note this doesn't work for something - // like `\sqrt{\rlap{x}}`, but if someone is doing that they deserve for - // it not to work. - var body = void 0; - if (inner.height === 0 && inner.depth === 0) { - body = (0, _buildCommon.makeSpan)(); - } else { - inner.style.paddingLeft = img.surdWidth + "em"; - - // Overlay the image and the argument. - body = _buildCommon2.default.makeVList([{ type: "elem", elem: inner }, { type: "kern", size: -(inner.height + imgShift) }, { type: "elem", elem: img }, { type: "kern", size: ruleWidth }], "firstBaseline", null, options); - body.children[0].children[0].classes.push("svg-align"); - } - - if (!group.value.index) { - return (0, _buildCommon.makeSpan)(["mord", "sqrt"], [body], options); - } else { - // Handle the optional root index - - // The index is always in scriptscript style - var newOptions = options.havingStyle(_Style2.default.SCRIPTSCRIPT); - var rootm = buildGroup(group.value.index, newOptions, options); - - // The amount the index is shifted by. This is taken from the TeX - // source, in the definition of `\r@@t`. - var toShift = 0.6 * (body.height - body.depth); - - // Build a VList with the superscript shifted up correctly - var rootVList = _buildCommon2.default.makeVList([{ type: "elem", elem: rootm }], "shift", -toShift, options); - // Add a class surrounding it so we can add on the appropriate - // kerning - var rootVListWrap = (0, _buildCommon.makeSpan)(["root"], [rootVList]); - - return (0, _buildCommon.makeSpan)(["mord", "sqrt"], [rootVListWrap, body], options); - } - }; - - function sizingGroup(value, options, baseOptions) { - var inner = buildExpression(value, options, false); - var multiplier = options.sizeMultiplier / baseOptions.sizeMultiplier; - - // Add size-resetting classes to the inner list and set maxFontSize - // manually. Handle nested size changes. - for (var i = 0; i < inner.length; i++) { - var pos = _utils2.default.indexOf(inner[i].classes, "sizing"); - if (pos < 0) { - Array.prototype.push.apply(inner[i].classes, options.sizingClasses(baseOptions)); - } else if (inner[i].classes[pos + 1] === "reset-size" + options.size) { - // This is a nested size change: e.g., inner[i] is the "b" in - // `\Huge a \small b`. Override the old size (the `reset-` class) - // but not the new size. - inner[i].classes[pos + 1] = "reset-size" + baseOptions.size; - } - - inner[i].height *= multiplier; - inner[i].depth *= multiplier; - } - - return _buildCommon2.default.makeFragment(inner); - } - - groupTypes.sizing = function (group, options) { - // Handle sizing operators like \Huge. Real TeX doesn't actually allow - // these functions inside of math expressions, so we do some special - // handling. - var newOptions = options.havingSize(group.value.size); - return sizingGroup(group.value.value, newOptions, options); - }; - - groupTypes.styling = function (group, options) { - // Style changes are handled in the TeXbook on pg. 442, Rule 3. - - // Figure out what style we're changing to. - var styleMap = { - "display": _Style2.default.DISPLAY, - "text": _Style2.default.TEXT, - "script": _Style2.default.SCRIPT, - "scriptscript": _Style2.default.SCRIPTSCRIPT - }; - - var newStyle = styleMap[group.value.style]; - var newOptions = options.havingStyle(newStyle); - return sizingGroup(group.value.value, newOptions, options); - }; - - groupTypes.font = function (group, options) { - var font = group.value.font; - return buildGroup(group.value.body, options.withFont(font)); - }; - - groupTypes.delimsizing = function (group, options) { - var delim = group.value.value; - - if (delim === ".") { - // Empty delimiters still count as elements, even though they don't - // show anything. - return (0, _buildCommon.makeSpan)([group.value.mclass]); - } - - // Use delimiter.sizedDelim to generate the delimiter. - return _delimiter2.default.sizedDelim(delim, group.value.size, options, group.mode, [group.value.mclass]); - }; - - groupTypes.leftright = function (group, options) { - // Build the inner expression - var inner = buildExpression(group.value.body, options, true); - - var innerHeight = 0; - var innerDepth = 0; - var hadMiddle = false; - - // Calculate its height and depth - for (var i = 0; i < inner.length; i++) { - if (inner[i].isMiddle) { - hadMiddle = true; - } else { - innerHeight = Math.max(inner[i].height, innerHeight); - innerDepth = Math.max(inner[i].depth, innerDepth); - } - } - - // The size of delimiters is the same, regardless of what style we are - // in. Thus, to correctly calculate the size of delimiter we need around - // a group, we scale down the inner size based on the size. - innerHeight *= options.sizeMultiplier; - innerDepth *= options.sizeMultiplier; - - var leftDelim = void 0; - if (group.value.left === ".") { - // Empty delimiters in \left and \right make null delimiter spaces. - leftDelim = makeNullDelimiter(options, ["mopen"]); - } else { - // Otherwise, use leftRightDelim to generate the correct sized - // delimiter. - leftDelim = _delimiter2.default.leftRightDelim(group.value.left, innerHeight, innerDepth, options, group.mode, ["mopen"]); - } - // Add it to the beginning of the expression - inner.unshift(leftDelim); - - // Handle middle delimiters - if (hadMiddle) { - for (var _i4 = 1; _i4 < inner.length; _i4++) { - var middleDelim = inner[_i4]; - if (middleDelim.isMiddle) { - // Apply the options that were active when \middle was called - inner[_i4] = _delimiter2.default.leftRightDelim(middleDelim.isMiddle.value, innerHeight, innerDepth, middleDelim.isMiddle.options, group.mode, []); - // Add back spaces shifted into the delimiter - var spaces = spliceSpaces(middleDelim.children, 0); - if (spaces) { - _buildCommon2.default.prependChildren(inner[_i4], spaces); - } - } - } - } - - var rightDelim = void 0; - // Same for the right delimiter - if (group.value.right === ".") { - rightDelim = makeNullDelimiter(options, ["mclose"]); - } else { - rightDelim = _delimiter2.default.leftRightDelim(group.value.right, innerHeight, innerDepth, options, group.mode, ["mclose"]); - } - // Add it to the end of the expression. - inner.push(rightDelim); - - return (0, _buildCommon.makeSpan)(["minner"], inner, options); - }; - - groupTypes.middle = function (group, options) { - var middleDelim = void 0; - if (group.value.value === ".") { - middleDelim = makeNullDelimiter(options, []); - } else { - middleDelim = _delimiter2.default.sizedDelim(group.value.value, 1, options, group.mode, []); - middleDelim.isMiddle = { value: group.value.value, options: options }; - } - return middleDelim; - }; - - groupTypes.rule = function (group, options) { - // Make an empty span for the rule - var rule = (0, _buildCommon.makeSpan)(["mord", "rule"], [], options); - - // Calculate the shift, width, and height of the rule, and account for units - var shift = 0; - if (group.value.shift) { - shift = _units2.default.calculateSize(group.value.shift, options); - } - - var width = _units2.default.calculateSize(group.value.width, options); - var height = _units2.default.calculateSize(group.value.height, options); - - // Style the rule to the right size - rule.style.borderRightWidth = width + "em"; - rule.style.borderTopWidth = height + "em"; - rule.style.bottom = shift + "em"; - - // Record the height and width - rule.width = width; - rule.height = height + shift; - rule.depth = -shift; - // Font size is the number large enough that the browser will - // reserve at least `absHeight` space above the baseline. - // The 1.125 factor was empirically determined - rule.maxFontSize = height * 1.125 * options.sizeMultiplier; - - return rule; - }; - - groupTypes.kern = function (group, options) { - // Make an empty span for the rule - var rule = (0, _buildCommon.makeSpan)(["mord", "rule"], [], options); - - if (group.value.dimension) { - var dimension = _units2.default.calculateSize(group.value.dimension, options); - rule.style.marginLeft = dimension + "em"; - } - - return rule; - }; - - groupTypes.accent = function (group, options) { - // Accents are handled in the TeXbook pg. 443, rule 12. - var base = group.value.base; - - var supsubGroup = void 0; - if (group.type === "supsub") { - // If our base is a character box, and we have superscripts and - // subscripts, the supsub will defer to us. In particular, we want - // to attach the superscripts and subscripts to the inner body (so - // that the position of the superscripts and subscripts won't be - // affected by the height of the accent). We accomplish this by - // sticking the base of the accent into the base of the supsub, and - // rendering that, while keeping track of where the accent is. - - // The supsub group is the group that was passed in - var supsub = group; - // The real accent group is the base of the supsub group - group = supsub.value.base; - // The character box is the base of the accent group - base = group.value.base; - // Stick the character box into the base of the supsub group - supsub.value.base = base; - - // Rerender the supsub group with its new base, and store that - // result. - supsubGroup = buildGroup(supsub, options); - } - - // Build the base group - var body = buildGroup(base, options.havingCrampedStyle()); - - // Does the accent need to shift for the skew of a character? - var mustShift = group.value.isShifty && isCharacterBox(base); - - // Calculate the skew of the accent. This is based on the line "If the - // nucleus is not a single character, let s = 0; otherwise set s to the - // kern amount for the nucleus followed by the \skewchar of its font." - // Note that our skew metrics are just the kern between each character - // and the skewchar. - var skew = 0; - if (mustShift) { - // If the base is a character box, then we want the skew of the - // innermost character. To do that, we find the innermost character: - var baseChar = getBaseElem(base); - // Then, we render its group to get the symbol inside it - var baseGroup = buildGroup(baseChar, options.havingCrampedStyle()); - // Finally, we pull the skew off of the symbol. - skew = baseGroup.skew; - // Note that we now throw away baseGroup, because the layers we - // removed with getBaseElem might contain things like \color which - // we can't get rid of. - // TODO(emily): Find a better way to get the skew - } - - // calculate the amount of space between the body and the accent - var clearance = Math.min(body.height, options.fontMetrics().xHeight); - - // Build the accent - var accentBody = void 0; - if (!group.value.isStretchy) { - var accent = _buildCommon2.default.makeSymbol(group.value.label, "Main-Regular", group.mode, options); - // Remove the italic correction of the accent, because it only serves to - // shift the accent over to a place we don't want. - accent.italic = 0; - - // The \vec character that the fonts use is a combining character, and - // thus shows up much too far to the left. To account for this, we add a - // specific class which shifts the accent over to where we want it. - // TODO(emily): Fix this in a better way, like by changing the font - // Similarly, text accent \H is a combining character and - // requires a different adjustment. - var accentClass = null; - if (group.value.label === "\\vec") { - accentClass = "accent-vec"; - } else if (group.value.label === '\\H') { - accentClass = "accent-hungarian"; - } - - accentBody = (0, _buildCommon.makeSpan)([], [accent]); - accentBody = (0, _buildCommon.makeSpan)(["accent-body", accentClass], [accentBody]); - - // Shift the accent over by the skew. Note we shift by twice the skew - // because we are centering the accent, so by adding 2*skew to the left, - // we shift it to the right by 1*skew. - accentBody.style.marginLeft = 2 * skew + "em"; - - accentBody = _buildCommon2.default.makeVList([{ type: "elem", elem: body }, { type: "kern", size: -clearance }, { type: "elem", elem: accentBody }], "firstBaseline", null, options); - } else { - accentBody = _stretchy2.default.svgSpan(group, options); - - accentBody = _buildCommon2.default.makeVList([{ type: "elem", elem: body }, { type: "elem", elem: accentBody }], "firstBaseline", null, options); - - var styleSpan = accentBody.children[0].children[0].children[1]; - styleSpan.classes.push("svg-align"); // text-align: left; - if (skew > 0) { - // Shorten the accent and nudge it to the right. - styleSpan.style.width = "calc(100% - " + 2 * skew + "em)"; - styleSpan.style.marginLeft = 2 * skew + "em"; - } - } - - var accentWrap = (0, _buildCommon.makeSpan)(["mord", "accent"], [accentBody], options); - - if (supsubGroup) { - // Here, we replace the "base" child of the supsub with our newly - // generated accent. - supsubGroup.children[0] = accentWrap; - - // Since we don't rerun the height calculation after replacing the - // accent, we manually recalculate height. - supsubGroup.height = Math.max(accentWrap.height, supsubGroup.height); - - // Accents should always be ords, even when their innards are not. - supsubGroup.classes[0] = "mord"; - - return supsubGroup; - } else { - return accentWrap; - } - }; - - groupTypes.horizBrace = function (group, options) { - var style = options.style; - - var hasSupSub = group.type === "supsub"; - var supSubGroup = void 0; - var newOptions = void 0; - if (hasSupSub) { - // Ref: LaTeX source2e: }}}}\limits} - // i.e. LaTeX treats the brace similar to an op and passes it - // with \limits, so we need to assign supsub style. - if (group.value.sup) { - newOptions = options.havingStyle(style.sup()); - supSubGroup = buildGroup(group.value.sup, newOptions, options); - } else { - newOptions = options.havingStyle(style.sub()); - supSubGroup = buildGroup(group.value.sub, newOptions, options); - } - group = group.value.base; - } - - // Build the base group - var body = buildGroup(group.value.base, options.havingBaseStyle(_Style2.default.DISPLAY)); - - // Create the stretchy element - var braceBody = _stretchy2.default.svgSpan(group, options); - - // Generate the vlist, with the appropriate kerns ┏━━━━━━━━┓ - // This first vlist contains the subject matter and the brace: equation - var vlist = void 0; - if (group.value.isOver) { - vlist = _buildCommon2.default.makeVList([{ type: "elem", elem: body }, { type: "kern", size: 0.1 }, { type: "elem", elem: braceBody }], "firstBaseline", null, options); - vlist.children[0].children[0].children[1].classes.push("svg-align"); - } else { - vlist = _buildCommon2.default.makeVList([{ type: "elem", elem: braceBody }, { type: "kern", size: 0.1 }, { type: "elem", elem: body }], "bottom", body.depth + 0.1 + braceBody.height, options); - vlist.children[0].children[0].children[0].classes.push("svg-align"); - } - - if (hasSupSub) { - // In order to write the supsub, wrap the first vlist in another vlist: - // They can't all go in the same vlist, because the note might be wider - // than the equation. We want the equation to control the brace width. - - // note long note long note - // ┏━━━━━━━━┓ or ┏━━━┓ not ┏━━━━━━━━━┓ - // equation eqn eqn - - var vSpan = (0, _buildCommon.makeSpan)(["mord", group.value.isOver ? "mover" : "munder"], [vlist], options); - - if (group.value.isOver) { - vlist = _buildCommon2.default.makeVList([{ type: "elem", elem: vSpan }, { type: "kern", size: 0.2 }, { type: "elem", elem: supSubGroup }], "firstBaseline", null, options); - } else { - vlist = _buildCommon2.default.makeVList([{ type: "elem", elem: supSubGroup }, { type: "kern", size: 0.2 }, { type: "elem", elem: vSpan }], "bottom", vSpan.depth + 0.2 + supSubGroup.height, options); - } - } - - return (0, _buildCommon.makeSpan)(["mord", group.value.isOver ? "mover" : "munder"], [vlist], options); - }; - - groupTypes.accentUnder = function (group, options) { - // Treat under accents much like underlines. - var innerGroup = buildGroup(group.value.body, options); - - var accentBody = _stretchy2.default.svgSpan(group, options); - var kern = /tilde/.test(group.value.label) ? 0.12 : 0; - - // Generate the vlist, with the appropriate kerns - var vlist = _buildCommon2.default.makeVList([{ type: "elem", elem: accentBody }, { type: "kern", size: kern }, { type: "elem", elem: innerGroup }], "bottom", accentBody.height + kern, options); - - vlist.children[0].children[0].children[0].classes.push("svg-align"); - - return (0, _buildCommon.makeSpan)(["mord", "accentunder"], [vlist], options); - }; - - groupTypes.enclose = function (group, options) { - // \cancel, \bcancel, \xcancel, \sout, \fbox - var inner = buildGroup(group.value.body, options); - - var label = group.value.label.substr(1); - var scale = options.sizeMultiplier; - var img = void 0; - var pad = 0; - var imgShift = 0; - - if (label === "sout") { - img = (0, _buildCommon.makeSpan)(["stretchy", "sout"]); - img.height = options.fontMetrics().defaultRuleThickness / scale; - imgShift = -0.5 * options.fontMetrics().xHeight; - } else { - // Add horizontal padding - inner.classes.push(label === "fbox" ? "boxpad" : "cancel-pad"); - - // Add vertical padding - var isCharBox = isCharacterBox(group.value.body); - // ref: LaTeX source2e: \fboxsep = 3pt; \fboxrule = .4pt - // ref: cancel package: \advance\totalheight2\p@ % "+2" - pad = label === "fbox" ? 0.34 : isCharBox ? 0.2 : 0; - imgShift = inner.depth + pad; - - img = _stretchy2.default.encloseSpan(inner, label, pad, options); - } - - var vlist = _buildCommon2.default.makeVList([{ type: "elem", elem: inner, shift: 0 }, { type: "elem", elem: img, shift: imgShift }], "individualShift", null, options); - - if (label !== "fbox") { - vlist.children[0].children[0].children[1].classes.push("svg-align"); - } - - if (/cancel/.test(label)) { - // cancel does not create horiz space for its line extension. - // That is, not when adjacent to a mord. - return (0, _buildCommon.makeSpan)(["mord", "cancel-lap"], [vlist], options); - } else { - return (0, _buildCommon.makeSpan)(["mord"], [vlist], options); - } - }; - - groupTypes.xArrow = function (group, options) { - var style = options.style; - - // Build the argument groups in the appropriate style. - // Ref: amsmath.dtx: \hbox{$\scriptstyle\mkern#3mu{#6}\mkern#4mu$}% - - var newOptions = options.havingStyle(style.sup()); - var upperGroup = buildGroup(group.value.body, newOptions, options); - upperGroup.classes.push("x-arrow-pad"); - - var lowerGroup = void 0; - if (group.value.below) { - // Build the lower group - newOptions = options.havingStyle(style.sub()); - lowerGroup = buildGroup(group.value.below, newOptions, options); - lowerGroup.classes.push("x-arrow-pad"); - } - - var arrowBody = _stretchy2.default.svgSpan(group, options); - - var arrowShift = -options.fontMetrics().axisHeight + arrowBody.depth; - var upperShift = -options.fontMetrics().axisHeight - arrowBody.height - 0.111; // 2 mu. Ref: amsmath.dtx: #7\if0#2\else\mkern#2mu\fi - - // Generate the vlist - var vlist = void 0; - if (group.value.below) { - var lowerShift = -options.fontMetrics().axisHeight + lowerGroup.height + arrowBody.height + 0.111; - vlist = _buildCommon2.default.makeVList([{ type: "elem", elem: upperGroup, shift: upperShift }, { type: "elem", elem: arrowBody, shift: arrowShift }, { type: "elem", elem: lowerGroup, shift: lowerShift }], "individualShift", null, options); - } else { - vlist = _buildCommon2.default.makeVList([{ type: "elem", elem: upperGroup, shift: upperShift }, { type: "elem", elem: arrowBody, shift: arrowShift }], "individualShift", null, options); - } - - vlist.children[0].children[0].children[1].classes.push("svg-align"); - - return (0, _buildCommon.makeSpan)(["mrel", "x-arrow"], [vlist], options); - }; - - groupTypes.phantom = function (group, options) { - var elements = buildExpression(group.value.value, options.withPhantom(), false); - - // \phantom isn't supposed to affect the elements it contains. - // See "color" for more details. - return new _buildCommon2.default.makeFragment(elements); - }; - - groupTypes.mclass = function (group, options) { - var elements = buildExpression(group.value.value, options, true); - - return (0, _buildCommon.makeSpan)([group.value.mclass], elements, options); - }; - - /** - * buildGroup is the function that takes a group and calls the correct groupType - * function for it. It also handles the interaction of size and style changes - * between parents and children. - */ - var buildGroup = function buildGroup(group, options, baseOptions) { - if (!group) { - return (0, _buildCommon.makeSpan)(); - } - - if (groupTypes[group.type]) { - // Call the groupTypes function - var groupNode = groupTypes[group.type](group, options); - - // If the size changed between the parent and the current group, account - // for that size difference. - if (baseOptions && options.size !== baseOptions.size) { - groupNode = (0, _buildCommon.makeSpan)(options.sizingClasses(baseOptions), [groupNode], options); - - var multiplier = options.sizeMultiplier / baseOptions.sizeMultiplier; - - groupNode.height *= multiplier; - groupNode.depth *= multiplier; - } - - return groupNode; - } else { - throw new _ParseError2.default("Got group of unknown type: '" + group.type + "'"); - } - }; - - /** - * Take an entire parse tree, and build it into an appropriate set of HTML - * nodes. - */ - var buildHTML = function buildHTML(tree, options) { - // buildExpression is destructive, so we need to make a clone - // of the incoming tree so that it isn't accidentally changed - tree = JSON.parse((0, _stringify2.default)(tree)); - - // Build the expression contained in the tree - var expression = buildExpression(tree, options, true); - var body = (0, _buildCommon.makeSpan)(["base"], expression, options); - - // Add struts, which ensure that the top of the HTML element falls at the - // height of the expression, and the bottom of the HTML element falls at the - // depth of the expression. - var topStrut = (0, _buildCommon.makeSpan)(["strut"]); - var bottomStrut = (0, _buildCommon.makeSpan)(["strut", "bottom"]); - - topStrut.style.height = body.height + "em"; - bottomStrut.style.height = body.height + body.depth + "em"; - // We'd like to use `vertical-align: top` but in IE 9 this lowers the - // baseline of the box to the bottom of this strut (instead staying in the - // normal place) so we use an absolute value for vertical-align instead - bottomStrut.style.verticalAlign = -body.depth + "em"; - - // Wrap the struts and body together - var htmlNode = (0, _buildCommon.makeSpan)(["katex-html"], [topStrut, bottomStrut, body]); - - htmlNode.setAttribute("aria-hidden", "true"); - - return htmlNode; - }; - - module.exports = buildHTML; - - },{"./ParseError":29,"./Style":33,"./buildCommon":34,"./delimiter":38,"./domTree":39,"./stretchy":47,"./units":50,"./utils":51,"babel-runtime/core-js/json/stringify":2}],36:[function(require,module,exports){ - - var _buildCommon = require("./buildCommon"); - - var _buildCommon2 = _interopRequireDefault(_buildCommon); - - var _fontMetrics = require("./fontMetrics"); - - var _fontMetrics2 = _interopRequireDefault(_fontMetrics); - - var _mathMLTree = require("./mathMLTree"); - - var _mathMLTree2 = _interopRequireDefault(_mathMLTree); - - var _ParseError = require("./ParseError"); - - var _ParseError2 = _interopRequireDefault(_ParseError); - - var _Style = require("./Style"); - - var _Style2 = _interopRequireDefault(_Style); - - var _symbols = require("./symbols"); - - var _symbols2 = _interopRequireDefault(_symbols); - - var _utils = require("./utils"); - - var _utils2 = _interopRequireDefault(_utils); - - var _stretchy = require("./stretchy"); - - var _stretchy2 = _interopRequireDefault(_stretchy); - - function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } - - /** - * Takes a symbol and converts it into a MathML text node after performing - * optional replacement from symbols.js. - */ - /** - * This file converts a parse tree into a cooresponding MathML tree. The main - * entry point is the `buildMathML` function, which takes a parse tree from the - * parser. - */ - - var makeText = function makeText(text, mode) { - if (_symbols2.default[mode][text] && _symbols2.default[mode][text].replace) { - text = _symbols2.default[mode][text].replace; - } - - return new _mathMLTree2.default.TextNode(text); - }; - - /** - * Returns the math variant as a string or null if none is required. - */ - var getVariant = function getVariant(group, options) { - var font = options.font; - if (!font) { - return null; - } - - var mode = group.mode; - if (font === "mathit") { - return "italic"; - } - - var value = group.value; - if (_utils2.default.contains(["\\imath", "\\jmath"], value)) { - return null; - } - - if (_symbols2.default[mode][value] && _symbols2.default[mode][value].replace) { - value = _symbols2.default[mode][value].replace; - } - - var fontName = _buildCommon.fontMap[font].fontName; - if (_fontMetrics2.default.getCharacterMetrics(value, fontName)) { - return _buildCommon.fontMap[options.font].variant; - } - - return null; - }; - - /** - * Functions for handling the different types of groups found in the parse - * tree. Each function should take a parse group and return a MathML node. - */ - var groupTypes = {}; - - var defaultVariant = { - "mi": "italic", - "mn": "normal", - "mtext": "normal" - }; - - groupTypes.mathord = function (group, options) { - var node = new _mathMLTree2.default.MathNode("mi", [makeText(group.value, group.mode)]); - - var variant = getVariant(group, options) || "italic"; - if (variant !== defaultVariant[node.type]) { - node.setAttribute("mathvariant", variant); - } - return node; - }; - - groupTypes.textord = function (group, options) { - var text = makeText(group.value, group.mode); - - var variant = getVariant(group, options) || "normal"; - - var node = void 0; - if (group.mode === 'text') { - node = new _mathMLTree2.default.MathNode("mtext", [text]); - } else if (/[0-9]/.test(group.value)) { - // TODO(kevinb) merge adjacent nodes - // do it as a post processing step - node = new _mathMLTree2.default.MathNode("mn", [text]); - } else if (group.value === "\\prime") { - node = new _mathMLTree2.default.MathNode("mo", [text]); - } else { - node = new _mathMLTree2.default.MathNode("mi", [text]); - } - if (variant !== defaultVariant[node.type]) { - node.setAttribute("mathvariant", variant); - } - - return node; - }; - - groupTypes.bin = function (group) { - var node = new _mathMLTree2.default.MathNode("mo", [makeText(group.value, group.mode)]); - - return node; - }; - - groupTypes.rel = function (group) { - var node = new _mathMLTree2.default.MathNode("mo", [makeText(group.value, group.mode)]); - - return node; - }; - - groupTypes.open = function (group) { - var node = new _mathMLTree2.default.MathNode("mo", [makeText(group.value, group.mode)]); - - return node; - }; - - groupTypes.close = function (group) { - var node = new _mathMLTree2.default.MathNode("mo", [makeText(group.value, group.mode)]); - - return node; - }; - - groupTypes.inner = function (group) { - var node = new _mathMLTree2.default.MathNode("mo", [makeText(group.value, group.mode)]); - - return node; - }; - - groupTypes.punct = function (group) { - var node = new _mathMLTree2.default.MathNode("mo", [makeText(group.value, group.mode)]); - - node.setAttribute("separator", "true"); - - return node; - }; - - groupTypes.ordgroup = function (group, options) { - var inner = buildExpression(group.value, options); - - var node = new _mathMLTree2.default.MathNode("mrow", inner); - - return node; - }; - - groupTypes.text = function (group, options) { - var body = group.value.body; - - // Convert each element of the body into MathML, and combine consecutive - // outputs into a single tag. In this way, we don't - // nest non-text items (e.g., $nested-math$) within an . - var inner = []; - var currentText = null; - for (var i = 0; i < body.length; i++) { - var _group = buildGroup(body[i], options); - if (_group.type === 'mtext' && currentText != null) { - Array.prototype.push.apply(currentText.children, _group.children); - } else { - inner.push(_group); - if (_group.type === 'mtext') { - currentText = _group; - } - } - } - - // If there is a single tag in the end (presumably ), - // just return it. Otherwise, wrap them in an . - if (inner.length === 1) { - return inner[0]; - } else { - return new _mathMLTree2.default.MathNode("mrow", inner); - } - }; - - groupTypes.color = function (group, options) { - var inner = buildExpression(group.value.value, options); - - var node = new _mathMLTree2.default.MathNode("mstyle", inner); - - node.setAttribute("mathcolor", group.value.color); - - return node; - }; - - groupTypes.supsub = function (group, options) { - // Is the inner group a relevant horizonal brace? - var isBrace = false; - var isOver = void 0; - var isSup = void 0; - if (group.value.base) { - if (group.value.base.value.type === "horizBrace") { - isSup = group.value.sup ? true : false; - if (isSup === group.value.base.value.isOver) { - isBrace = true; - isOver = group.value.base.value.isOver; - } - } - } - - var removeUnnecessaryRow = true; - var children = [buildGroup(group.value.base, options, removeUnnecessaryRow)]; - - if (group.value.sub) { - children.push(buildGroup(group.value.sub, options, removeUnnecessaryRow)); - } - - if (group.value.sup) { - children.push(buildGroup(group.value.sup, options, removeUnnecessaryRow)); - } - - var nodeType = void 0; - if (isBrace) { - nodeType = isOver ? "mover" : "munder"; - } else if (!group.value.sub) { - nodeType = "msup"; - } else if (!group.value.sup) { - nodeType = "msub"; - } else { - var base = group.value.base; - if (base && base.value.limits && options.style === _Style2.default.DISPLAY) { - nodeType = "munderover"; - } else { - nodeType = "msubsup"; - } - } - - var node = new _mathMLTree2.default.MathNode(nodeType, children); - - return node; - }; - - groupTypes.genfrac = function (group, options) { - var node = new _mathMLTree2.default.MathNode("mfrac", [buildGroup(group.value.numer, options), buildGroup(group.value.denom, options)]); - - if (!group.value.hasBarLine) { - node.setAttribute("linethickness", "0px"); - } - - if (group.value.leftDelim != null || group.value.rightDelim != null) { - var withDelims = []; - - if (group.value.leftDelim != null) { - var leftOp = new _mathMLTree2.default.MathNode("mo", [new _mathMLTree2.default.TextNode(group.value.leftDelim)]); - - leftOp.setAttribute("fence", "true"); - - withDelims.push(leftOp); - } - - withDelims.push(node); - - if (group.value.rightDelim != null) { - var rightOp = new _mathMLTree2.default.MathNode("mo", [new _mathMLTree2.default.TextNode(group.value.rightDelim)]); - - rightOp.setAttribute("fence", "true"); - - withDelims.push(rightOp); - } - - var outerNode = new _mathMLTree2.default.MathNode("mrow", withDelims); - - return outerNode; - } - - return node; - }; - - groupTypes.array = function (group, options) { - return new _mathMLTree2.default.MathNode("mtable", group.value.body.map(function (row) { - return new _mathMLTree2.default.MathNode("mtr", row.map(function (cell) { - return new _mathMLTree2.default.MathNode("mtd", [buildGroup(cell, options)]); - })); - })); - }; - - groupTypes.sqrt = function (group, options) { - var node = void 0; - if (group.value.index) { - node = new _mathMLTree2.default.MathNode("mroot", [buildGroup(group.value.body, options), buildGroup(group.value.index, options)]); - } else { - node = new _mathMLTree2.default.MathNode("msqrt", [buildGroup(group.value.body, options)]); - } - - return node; - }; - - groupTypes.leftright = function (group, options) { - var inner = buildExpression(group.value.body, options); - - if (group.value.left !== ".") { - var leftNode = new _mathMLTree2.default.MathNode("mo", [makeText(group.value.left, group.mode)]); - - leftNode.setAttribute("fence", "true"); - - inner.unshift(leftNode); - } - - if (group.value.right !== ".") { - var rightNode = new _mathMLTree2.default.MathNode("mo", [makeText(group.value.right, group.mode)]); - - rightNode.setAttribute("fence", "true"); - - inner.push(rightNode); - } - - var outerNode = new _mathMLTree2.default.MathNode("mrow", inner); - - return outerNode; - }; - - groupTypes.middle = function (group, options) { - var middleNode = new _mathMLTree2.default.MathNode("mo", [makeText(group.value.middle, group.mode)]); - middleNode.setAttribute("fence", "true"); - return middleNode; - }; - - groupTypes.accent = function (group, options) { - var accentNode = void 0; - if (group.value.isStretchy) { - accentNode = _stretchy2.default.mathMLnode(group.value.label); - } else { - accentNode = new _mathMLTree2.default.MathNode("mo", [makeText(group.value.label, group.mode)]); - } - - var node = new _mathMLTree2.default.MathNode("mover", [buildGroup(group.value.base, options), accentNode]); - - node.setAttribute("accent", "true"); - - return node; - }; - - groupTypes.spacing = function (group) { - var node = void 0; - - if (group.value === "\\ " || group.value === "\\space" || group.value === " " || group.value === "~") { - node = new _mathMLTree2.default.MathNode("mtext", [new _mathMLTree2.default.TextNode("\xA0")]); - } else { - node = new _mathMLTree2.default.MathNode("mspace"); - - node.setAttribute("width", _buildCommon2.default.spacingFunctions[group.value].size); - } - - return node; - }; - - groupTypes.op = function (group, options) { - var node = void 0; - - // TODO(emily): handle big operators using the `largeop` attribute - - if (group.value.symbol) { - // This is a symbol. Just add the symbol. - node = new _mathMLTree2.default.MathNode("mo", [makeText(group.value.body, group.mode)]); - } else if (group.value.value) { - // This is an operator with children. Add them. - node = new _mathMLTree2.default.MathNode("mo", buildExpression(group.value.value, options)); - } else { - // This is a text operator. Add all of the characters from the - // operator's name. - // TODO(emily): Add a space in the middle of some of these - // operators, like \limsup. - node = new _mathMLTree2.default.MathNode("mi", [new _mathMLTree2.default.TextNode(group.value.body.slice(1))]); - } - - return node; - }; - - groupTypes.mod = function (group, options) { - var inner = []; - - if (group.value.modType === "pod" || group.value.modType === "pmod") { - inner.push(new _mathMLTree2.default.MathNode("mo", [makeText("(", group.mode)])); - } - if (group.value.modType !== "pod") { - inner.push(new _mathMLTree2.default.MathNode("mo", [makeText("mod", group.mode)])); - } - if (group.value.value) { - var space = new _mathMLTree2.default.MathNode("mspace"); - space.setAttribute("width", "0.333333em"); - inner.push(space); - inner = inner.concat(buildExpression(group.value.value, options)); - } - if (group.value.modType === "pod" || group.value.modType === "pmod") { - inner.push(new _mathMLTree2.default.MathNode("mo", [makeText(")", group.mode)])); - } - - return new _mathMLTree2.default.MathNode("mo", inner); - }; - - groupTypes.katex = function (group) { - var node = new _mathMLTree2.default.MathNode("mtext", [new _mathMLTree2.default.TextNode("KaTeX")]); - - return node; - }; - - groupTypes.font = function (group, options) { - var font = group.value.font; - return buildGroup(group.value.body, options.withFont(font)); - }; - - groupTypes.delimsizing = function (group) { - var children = []; - - if (group.value.value !== ".") { - children.push(makeText(group.value.value, group.mode)); - } - - var node = new _mathMLTree2.default.MathNode("mo", children); - - if (group.value.mclass === "mopen" || group.value.mclass === "mclose") { - // Only some of the delimsizing functions act as fences, and they - // return "mopen" or "mclose" mclass. - node.setAttribute("fence", "true"); - } else { - // Explicitly disable fencing if it's not a fence, to override the - // defaults. - node.setAttribute("fence", "false"); - } - - return node; - }; - - groupTypes.styling = function (group, options) { - // Figure out what style we're changing to. - // TODO(kevinb): dedupe this with buildHTML.js - // This will be easier of handling of styling nodes is in the same file. - var styleMap = { - "display": _Style2.default.DISPLAY, - "text": _Style2.default.TEXT, - "script": _Style2.default.SCRIPT, - "scriptscript": _Style2.default.SCRIPTSCRIPT - }; - - var newStyle = styleMap[group.value.style]; - var newOptions = options.havingStyle(newStyle); - - var inner = buildExpression(group.value.value, newOptions); - - var node = new _mathMLTree2.default.MathNode("mstyle", inner); - - var styleAttributes = { - "display": ["0", "true"], - "text": ["0", "false"], - "script": ["1", "false"], - "scriptscript": ["2", "false"] - }; - - var attr = styleAttributes[group.value.style]; - - node.setAttribute("scriptlevel", attr[0]); - node.setAttribute("displaystyle", attr[1]); - - return node; - }; - - groupTypes.sizing = function (group, options) { - var newOptions = options.havingSize(group.value.size); - var inner = buildExpression(group.value.value, newOptions); - - var node = new _mathMLTree2.default.MathNode("mstyle", inner); - - // TODO(emily): This doesn't produce the correct size for nested size - // changes, because we don't keep state of what style we're currently - // in, so we can't reset the size to normal before changing it. Now - // that we're passing an options parameter we should be able to fix - // this. - node.setAttribute("mathsize", newOptions.sizeMultiplier + "em"); - - return node; - }; - - groupTypes.overline = function (group, options) { - var operator = new _mathMLTree2.default.MathNode("mo", [new _mathMLTree2.default.TextNode("\u203E")]); - operator.setAttribute("stretchy", "true"); - - var node = new _mathMLTree2.default.MathNode("mover", [buildGroup(group.value.body, options), operator]); - node.setAttribute("accent", "true"); - - return node; - }; - - groupTypes.underline = function (group, options) { - var operator = new _mathMLTree2.default.MathNode("mo", [new _mathMLTree2.default.TextNode("\u203E")]); - operator.setAttribute("stretchy", "true"); - - var node = new _mathMLTree2.default.MathNode("munder", [buildGroup(group.value.body, options), operator]); - node.setAttribute("accentunder", "true"); - - return node; - }; - - groupTypes.accentUnder = function (group, options) { - var accentNode = _stretchy2.default.mathMLnode(group.value.label); - var node = new _mathMLTree2.default.MathNode("munder", [buildGroup(group.value.body, options), accentNode]); - node.setAttribute("accentunder", "true"); - return node; - }; - - groupTypes.enclose = function (group, options) { - var node = new _mathMLTree2.default.MathNode("menclose", [buildGroup(group.value.body, options)]); - var notation = ""; - switch (group.value.label) { - case "\\bcancel": - notation = "downdiagonalstrike"; - break; - case "\\sout": - notation = "horizontalstrike"; - break; - case "\\fbox": - notation = "box"; - break; - default: - notation = "updiagonalstrike"; - } - node.setAttribute("notation", notation); - return node; - }; - - groupTypes.horizBrace = function (group, options) { - var accentNode = _stretchy2.default.mathMLnode(group.value.label); - return new _mathMLTree2.default.MathNode(group.value.isOver ? "mover" : "munder", [buildGroup(group.value.base, options), accentNode]); - }; - - groupTypes.xArrow = function (group, options) { - var arrowNode = _stretchy2.default.mathMLnode(group.value.label); - var node = void 0; - var lowerNode = void 0; - - if (group.value.body) { - var upperNode = buildGroup(group.value.body, options); - if (group.value.below) { - lowerNode = buildGroup(group.value.below, options); - node = new _mathMLTree2.default.MathNode("munderover", [arrowNode, lowerNode, upperNode]); - } else { - node = new _mathMLTree2.default.MathNode("mover", [arrowNode, upperNode]); - } - } else if (group.value.below) { - lowerNode = buildGroup(group.value.below, options); - node = new _mathMLTree2.default.MathNode("munder", [arrowNode, lowerNode]); - } else { - node = new _mathMLTree2.default.MathNode("mover", [arrowNode]); - } - return node; - }; - - groupTypes.rule = function (group) { - // TODO(emily): Figure out if there's an actual way to draw black boxes - // in MathML. - var node = new _mathMLTree2.default.MathNode("mrow"); - - return node; - }; - - groupTypes.kern = function (group) { - // TODO(kevin): Figure out if there's a way to add space in MathML - var node = new _mathMLTree2.default.MathNode("mrow"); - - return node; - }; - - groupTypes.llap = function (group, options) { - var node = new _mathMLTree2.default.MathNode("mpadded", [buildGroup(group.value.body, options)]); - - node.setAttribute("lspace", "-1width"); - node.setAttribute("width", "0px"); - - return node; - }; - - groupTypes.rlap = function (group, options) { - var node = new _mathMLTree2.default.MathNode("mpadded", [buildGroup(group.value.body, options)]); - - node.setAttribute("width", "0px"); - - return node; - }; - - groupTypes.phantom = function (group, options) { - var inner = buildExpression(group.value.value, options); - return new _mathMLTree2.default.MathNode("mphantom", inner); - }; - - groupTypes.mclass = function (group, options) { - var inner = buildExpression(group.value.value, options); - return new _mathMLTree2.default.MathNode("mstyle", inner); - }; - - /** - * Takes a list of nodes, builds them, and returns a list of the generated - * MathML nodes. A little simpler than the HTML version because we don't do any - * previous-node handling. - */ - var buildExpression = function buildExpression(expression, options) { - var groups = []; - for (var i = 0; i < expression.length; i++) { - var group = expression[i]; - groups.push(buildGroup(group, options)); - } - - // TODO(kevinb): combine \\not with mrels and mords - - return groups; - }; - - /** - * Takes a group from the parser and calls the appropriate groupTypes function - * on it to produce a MathML node. - */ - // TODO(kevinb): determine if removeUnnecessaryRow should always be true - var buildGroup = function buildGroup(group, options) { - var removeUnnecessaryRow = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : false; - - if (!group) { - return new _mathMLTree2.default.MathNode("mrow"); - } - - if (groupTypes[group.type]) { - // Call the groupTypes function - var result = groupTypes[group.type](group, options); - if (removeUnnecessaryRow) { - if (result.type === "mrow" && result.children.length === 1) { - return result.children[0]; - } - } - return result; - } else { - throw new _ParseError2.default("Got group of unknown type: '" + group.type + "'"); - } - }; - - /** - * Takes a full parse tree and settings and builds a MathML representation of - * it. In particular, we put the elements from building the parse tree into a - * tag so we can also include that TeX source as an annotation. - * - * Note that we actually return a domTree element with a `` inside it so - * we can do appropriate styling. - */ - var buildMathML = function buildMathML(tree, texExpression, options) { - var expression = buildExpression(tree, options); - - // Wrap up the expression in an mrow so it is presented in the semantics - // tag correctly. - var wrapper = new _mathMLTree2.default.MathNode("mrow", expression); - - // Build a TeX annotation of the source - var annotation = new _mathMLTree2.default.MathNode("annotation", [new _mathMLTree2.default.TextNode(texExpression)]); - - annotation.setAttribute("encoding", "application/x-tex"); - - var semantics = new _mathMLTree2.default.MathNode("semantics", [wrapper, annotation]); - - var math = new _mathMLTree2.default.MathNode("math", [semantics]); - - // You can't style nodes, so we wrap the node in a span. - return (0, _buildCommon.makeSpan)(["katex-mathml"], [math]); - }; - - module.exports = buildMathML; - - },{"./ParseError":29,"./Style":33,"./buildCommon":34,"./fontMetrics":41,"./mathMLTree":45,"./stretchy":47,"./symbols":48,"./utils":51}],37:[function(require,module,exports){ - - var _buildHTML = require("./buildHTML"); - - var _buildHTML2 = _interopRequireDefault(_buildHTML); - - var _buildMathML = require("./buildMathML"); - - var _buildMathML2 = _interopRequireDefault(_buildMathML); - - var _buildCommon = require("./buildCommon"); - - var _Options = require("./Options"); - - var _Options2 = _interopRequireDefault(_Options); - - var _Settings = require("./Settings"); - - var _Settings2 = _interopRequireDefault(_Settings); - - var _Style = require("./Style"); - - var _Style2 = _interopRequireDefault(_Style); - - function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } - - var buildTree = function buildTree(tree, expression, settings) { - settings = settings || new _Settings2.default({}); - - var startStyle = _Style2.default.TEXT; - if (settings.displayMode) { - startStyle = _Style2.default.DISPLAY; - } - - // Setup the default options - var options = new _Options2.default({ - style: startStyle - }); - - // `buildHTML` sometimes messes with the parse tree (like turning bins -> - // ords), so we build the MathML version first. - var mathMLNode = (0, _buildMathML2.default)(tree, expression, options); - var htmlNode = (0, _buildHTML2.default)(tree, options); - - var katexNode = (0, _buildCommon.makeSpan)(["katex"], [mathMLNode, htmlNode]); - - if (settings.displayMode) { - return (0, _buildCommon.makeSpan)(["katex-display"], [katexNode]); - } else { - return katexNode; - } - }; - - module.exports = buildTree; - - },{"./Options":28,"./Settings":32,"./Style":33,"./buildCommon":34,"./buildHTML":35,"./buildMathML":36}],38:[function(require,module,exports){ - - var _ParseError = require("./ParseError"); - - var _ParseError2 = _interopRequireDefault(_ParseError); - - var _Style = require("./Style"); - - var _Style2 = _interopRequireDefault(_Style); - - var _buildCommon = require("./buildCommon"); - - var _buildCommon2 = _interopRequireDefault(_buildCommon); - - var _fontMetrics = require("./fontMetrics"); - - var _fontMetrics2 = _interopRequireDefault(_fontMetrics); - - var _symbols = require("./symbols"); - - var _symbols2 = _interopRequireDefault(_symbols); - - var _utils = require("./utils"); - - var _utils2 = _interopRequireDefault(_utils); - - function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } - - /** - * Get the metrics for a given symbol and font, after transformation (i.e. - * after following replacement from symbols.js) - */ - /** - * This file deals with creating delimiters of various sizes. The TeXbook - * discusses these routines on page 441-442, in the "Another subroutine sets box - * x to a specified variable delimiter" paragraph. - * - * There are three main routines here. `makeSmallDelim` makes a delimiter in the - * normal font, but in either text, script, or scriptscript style. - * `makeLargeDelim` makes a delimiter in textstyle, but in one of the Size1, - * Size2, Size3, or Size4 fonts. `makeStackedDelim` makes a delimiter out of - * smaller pieces that are stacked on top of one another. - * - * The functions take a parameter `center`, which determines if the delimiter - * should be centered around the axis. - * - * Then, there are three exposed functions. `sizedDelim` makes a delimiter in - * one of the given sizes. This is used for things like `\bigl`. - * `customSizedDelim` makes a delimiter with a given total height+depth. It is - * called in places like `\sqrt`. `leftRightDelim` makes an appropriate - * delimiter which surrounds an expression of a given height an depth. It is - * used in `\left` and `\right`. - */ - - var getMetrics = function getMetrics(symbol, font) { - if (_symbols2.default.math[symbol] && _symbols2.default.math[symbol].replace) { - return _fontMetrics2.default.getCharacterMetrics(_symbols2.default.math[symbol].replace, font); - } else { - return _fontMetrics2.default.getCharacterMetrics(symbol, font); - } - }; - - /** - * Puts a delimiter span in a given style, and adds appropriate height, depth, - * and maxFontSizes. - */ - var styleWrap = function styleWrap(delim, toStyle, options, classes) { - var newOptions = options.havingBaseStyle(toStyle); - - var span = (0, _buildCommon.makeSpan)((classes || []).concat(newOptions.sizingClasses(options)), [delim], options); - - span.delimSizeMultiplier = newOptions.sizeMultiplier / options.sizeMultiplier; - span.height *= span.delimSizeMultiplier; - span.depth *= span.delimSizeMultiplier; - span.maxFontSize = newOptions.sizeMultiplier; - - return span; - }; - - var centerSpan = function centerSpan(span, options, style) { - var newOptions = options.havingBaseStyle(style); - var shift = (1 - options.sizeMultiplier / newOptions.sizeMultiplier) * options.fontMetrics().axisHeight; - - span.classes.push("delimcenter"); - span.style.top = shift + "em"; - span.height -= shift; - span.depth += shift; - }; - - /** - * Makes a small delimiter. This is a delimiter that comes in the Main-Regular - * font, but is restyled to either be in textstyle, scriptstyle, or - * scriptscriptstyle. - */ - var makeSmallDelim = function makeSmallDelim(delim, style, center, options, mode, classes) { - var text = _buildCommon2.default.makeSymbol(delim, "Main-Regular", mode, options); - var span = styleWrap(text, style, options, classes); - if (center) { - centerSpan(span, options, style); - } - return span; - }; - - /** - * Builds a symbol in the given font size (note size is an integer) - */ - var mathrmSize = function mathrmSize(value, size, mode, options) { - return _buildCommon2.default.makeSymbol(value, "Size" + size + "-Regular", mode, options); - }; - - /** - * Makes a large delimiter. This is a delimiter that comes in the Size1, Size2, - * Size3, or Size4 fonts. It is always rendered in textstyle. - */ - var makeLargeDelim = function makeLargeDelim(delim, size, center, options, mode, classes) { - var inner = mathrmSize(delim, size, mode, options); - var span = styleWrap((0, _buildCommon.makeSpan)(["delimsizing", "size" + size], [inner], options), _Style2.default.TEXT, options, classes); - if (center) { - centerSpan(span, options, _Style2.default.TEXT); - } - return span; - }; - - /** - * Make an inner span with the given offset and in the given font. This is used - * in `makeStackedDelim` to make the stacking pieces for the delimiter. - */ - var makeInner = function makeInner(symbol, font, mode) { - var sizeClass = void 0; - // Apply the correct CSS class to choose the right font. - if (font === "Size1-Regular") { - sizeClass = "delim-size1"; - } else if (font === "Size4-Regular") { - sizeClass = "delim-size4"; - } - - var inner = (0, _buildCommon.makeSpan)(["delimsizinginner", sizeClass], [(0, _buildCommon.makeSpan)([], [_buildCommon2.default.makeSymbol(symbol, font, mode)])]); - - // Since this will be passed into `makeVList` in the end, wrap the element - // in the appropriate tag that VList uses. - return { type: "elem", elem: inner }; - }; - - /** - * Make a stacked delimiter out of a given delimiter, with the total height at - * least `heightTotal`. This routine is mentioned on page 442 of the TeXbook. - */ - var makeStackedDelim = function makeStackedDelim(delim, heightTotal, center, options, mode, classes) { - // There are four parts, the top, an optional middle, a repeated part, and a - // bottom. - var top = void 0; - var middle = void 0; - var repeat = void 0; - var bottom = void 0; - top = repeat = bottom = delim; - middle = null; - // Also keep track of what font the delimiters are in - var font = "Size1-Regular"; - - // We set the parts and font based on the symbol. Note that we use - // '\u23d0' instead of '|' and '\u2016' instead of '\\|' for the - // repeats of the arrows - if (delim === "\\uparrow") { - repeat = bottom = "\u23D0"; - } else if (delim === "\\Uparrow") { - repeat = bottom = "\u2016"; - } else if (delim === "\\downarrow") { - top = repeat = "\u23D0"; - } else if (delim === "\\Downarrow") { - top = repeat = "\u2016"; - } else if (delim === "\\updownarrow") { - top = "\\uparrow"; - repeat = "\u23D0"; - bottom = "\\downarrow"; - } else if (delim === "\\Updownarrow") { - top = "\\Uparrow"; - repeat = "\u2016"; - bottom = "\\Downarrow"; - } else if (delim === "[" || delim === "\\lbrack") { - top = "\u23A1"; - repeat = "\u23A2"; - bottom = "\u23A3"; - font = "Size4-Regular"; - } else if (delim === "]" || delim === "\\rbrack") { - top = "\u23A4"; - repeat = "\u23A5"; - bottom = "\u23A6"; - font = "Size4-Regular"; - } else if (delim === "\\lfloor") { - repeat = top = "\u23A2"; - bottom = "\u23A3"; - font = "Size4-Regular"; - } else if (delim === "\\lceil") { - top = "\u23A1"; - repeat = bottom = "\u23A2"; - font = "Size4-Regular"; - } else if (delim === "\\rfloor") { - repeat = top = "\u23A5"; - bottom = "\u23A6"; - font = "Size4-Regular"; - } else if (delim === "\\rceil") { - top = "\u23A4"; - repeat = bottom = "\u23A5"; - font = "Size4-Regular"; - } else if (delim === "(") { - top = "\u239B"; - repeat = "\u239C"; - bottom = "\u239D"; - font = "Size4-Regular"; - } else if (delim === ")") { - top = "\u239E"; - repeat = "\u239F"; - bottom = "\u23A0"; - font = "Size4-Regular"; - } else if (delim === "\\{" || delim === "\\lbrace") { - top = "\u23A7"; - middle = "\u23A8"; - bottom = "\u23A9"; - repeat = "\u23AA"; - font = "Size4-Regular"; - } else if (delim === "\\}" || delim === "\\rbrace") { - top = "\u23AB"; - middle = "\u23AC"; - bottom = "\u23AD"; - repeat = "\u23AA"; - font = "Size4-Regular"; - } else if (delim === "\\lgroup") { - top = "\u23A7"; - bottom = "\u23A9"; - repeat = "\u23AA"; - font = "Size4-Regular"; - } else if (delim === "\\rgroup") { - top = "\u23AB"; - bottom = "\u23AD"; - repeat = "\u23AA"; - font = "Size4-Regular"; - } else if (delim === "\\lmoustache") { - top = "\u23A7"; - bottom = "\u23AD"; - repeat = "\u23AA"; - font = "Size4-Regular"; - } else if (delim === "\\rmoustache") { - top = "\u23AB"; - bottom = "\u23A9"; - repeat = "\u23AA"; - font = "Size4-Regular"; - } - - // Get the metrics of the four sections - var topMetrics = getMetrics(top, font); - var topHeightTotal = topMetrics.height + topMetrics.depth; - var repeatMetrics = getMetrics(repeat, font); - var repeatHeightTotal = repeatMetrics.height + repeatMetrics.depth; - var bottomMetrics = getMetrics(bottom, font); - var bottomHeightTotal = bottomMetrics.height + bottomMetrics.depth; - var middleHeightTotal = 0; - var middleFactor = 1; - if (middle !== null) { - var middleMetrics = getMetrics(middle, font); - middleHeightTotal = middleMetrics.height + middleMetrics.depth; - middleFactor = 2; // repeat symmetrically above and below middle - } - - // Calcuate the minimal height that the delimiter can have. - // It is at least the size of the top, bottom, and optional middle combined. - var minHeight = topHeightTotal + bottomHeightTotal + middleHeightTotal; - - // Compute the number of copies of the repeat symbol we will need - var repeatCount = Math.ceil((heightTotal - minHeight) / (middleFactor * repeatHeightTotal)); - - // Compute the total height of the delimiter including all the symbols - var realHeightTotal = minHeight + repeatCount * middleFactor * repeatHeightTotal; - - // The center of the delimiter is placed at the center of the axis. Note - // that in this context, "center" means that the delimiter should be - // centered around the axis in the current style, while normally it is - // centered around the axis in textstyle. - var axisHeight = options.fontMetrics().axisHeight; - if (center) { - axisHeight *= options.sizeMultiplier; - } - // Calculate the depth - var depth = realHeightTotal / 2 - axisHeight; - - // Now, we start building the pieces that will go into the vlist - - // Keep a list of the inner pieces - var inners = []; - - // Add the bottom symbol - inners.push(makeInner(bottom, font, mode)); - - if (middle === null) { - // Add that many symbols - for (var i = 0; i < repeatCount; i++) { - inners.push(makeInner(repeat, font, mode)); - } - } else { - // When there is a middle bit, we need the middle part and two repeated - // sections - for (var _i = 0; _i < repeatCount; _i++) { - inners.push(makeInner(repeat, font, mode)); - } - inners.push(makeInner(middle, font, mode)); - for (var _i2 = 0; _i2 < repeatCount; _i2++) { - inners.push(makeInner(repeat, font, mode)); - } - } - - // Add the top symbol - inners.push(makeInner(top, font, mode)); - - // Finally, build the vlist - var newOptions = options.havingBaseStyle(_Style2.default.TEXT); - var inner = _buildCommon2.default.makeVList(inners, "bottom", depth, newOptions); - - return styleWrap((0, _buildCommon.makeSpan)(["delimsizing", "mult"], [inner], newOptions), _Style2.default.TEXT, options, classes); - }; - - var sqrtInnerSVG = { - // The main path geometry is from glyph U221A in the font KaTeX Main - main: "", - - // size1 is from glyph U221A in the font KaTeX_Size1-Regular - 1: "", - - // size2 is from glyph U221A in the font KaTeX_Size2-Regular - 2: "", - - // size3 is from glyph U221A in the font KaTeX_Size3-Regular - 3: "", - - // size4 is from glyph U221A in the font KaTeX_Size4-Regular - 4: "", - - // tall is from glyph U23B7 in the font KaTeX_Size4-Regular - tall: "l-4 4-4 4c-.667.667-2 1.5-4 2.5s-4.167 1.833-6.5 2.5-5.5 1-9.5 1h\n-12l-28-84c-16.667-52-96.667 -294.333-240-727l-212 -643 -85 170c-4-3.333-8.333\n-7.667-13 -13l-13-13l77-155 77-156c66 199.333 139 419.667 219 661 l218 661z\nM702 0H400000v40H742z'/>" - }; - - var sqrtSpan = function sqrtSpan(height, delim, options) { - // Create a span containing an SVG image of a sqrt symbol. - var span = _buildCommon2.default.makeSpan([], [], options); - var sizeMultiplier = options.sizeMultiplier; // default - - if (delim.type === "small") { - // Get an SVG that is derived from glyph U+221A in font KaTeX-Main. - var newOptions = options.havingBaseStyle(delim.style); - sizeMultiplier = newOptions.sizeMultiplier / options.sizeMultiplier; - - span.height = 1 * sizeMultiplier; - span.style.height = span.height + "em"; - span.surdWidth = 0.833 * sizeMultiplier; // from the font. - //In the font, the glyph is 1000 units tall. The font scale is 1:1000. - - span.innerHTML = "\n " + sqrtInnerSVG['main'] + ""; - } else if (delim.type === "large") { - // These SVGs come from fonts: KaTeX_Size1, _Size2, etc. - // Get sqrt height from font data - span.height = sizeToMaxHeight[delim.size] / sizeMultiplier; - span.style.height = span.height + "em"; - span.surdWidth = 1.0 / sizeMultiplier; // from the font - - span.innerHTML = "\n " + sqrtInnerSVG[delim.size] + ""; - } else { - // Tall sqrt. In TeX, this would be stacked using multiple glyphs. - // We'll use a single SVG to accomplish the same thing. - span.height = height / sizeMultiplier; - span.style.height = span.height + "em"; - span.surdWidth = 1.056 / sizeMultiplier; - var viewBoxHeight = Math.floor(span.height * 1000); // scale = 1:1000 - var vertSegment = viewBoxHeight - 54; - - // This \sqrt is customized in both height and width. We set the - // height now. Then CSS will stretch the image to the correct width. - // This SVG path comes from glyph U+23B7, font KaTeX_Size4-Regular. - span.innerHTML = "\n \n "; - } - - span.sizeMultiplier = sizeMultiplier; - - return span; - }; - - // There are three kinds of delimiters, delimiters that stack when they become - // too large - var stackLargeDelimiters = ["(", ")", "[", "\\lbrack", "]", "\\rbrack", "\\{", "\\lbrace", "\\}", "\\rbrace", "\\lfloor", "\\rfloor", "\\lceil", "\\rceil", "\\surd"]; - - // delimiters that always stack - var stackAlwaysDelimiters = ["\\uparrow", "\\downarrow", "\\updownarrow", "\\Uparrow", "\\Downarrow", "\\Updownarrow", "|", "\\|", "\\vert", "\\Vert", "\\lvert", "\\rvert", "\\lVert", "\\rVert", "\\lgroup", "\\rgroup", "\\lmoustache", "\\rmoustache"]; - - // and delimiters that never stack - var stackNeverDelimiters = ["<", ">", "\\langle", "\\rangle", "/", "\\backslash", "\\lt", "\\gt"]; - - // Metrics of the different sizes. Found by looking at TeX's output of - // $\bigl| // \Bigl| \biggl| \Biggl| \showlists$ - // Used to create stacked delimiters of appropriate sizes in makeSizedDelim. - var sizeToMaxHeight = [0, 1.2, 1.8, 2.4, 3.0]; - - /** - * Used to create a delimiter of a specific size, where `size` is 1, 2, 3, or 4. - */ - var makeSizedDelim = function makeSizedDelim(delim, size, options, mode, classes) { - // < and > turn into \langle and \rangle in delimiters - if (delim === "<" || delim === "\\lt") { - delim = "\\langle"; - } else if (delim === ">" || delim === "\\gt") { - delim = "\\rangle"; - } - - // Sized delimiters are never centered. - if (_utils2.default.contains(stackLargeDelimiters, delim) || _utils2.default.contains(stackNeverDelimiters, delim)) { - return makeLargeDelim(delim, size, false, options, mode, classes); - } else if (_utils2.default.contains(stackAlwaysDelimiters, delim)) { - return makeStackedDelim(delim, sizeToMaxHeight[size], false, options, mode, classes); - } else { - throw new _ParseError2.default("Illegal delimiter: '" + delim + "'"); - } - }; - - /** - * There are three different sequences of delimiter sizes that the delimiters - * follow depending on the kind of delimiter. This is used when creating custom - * sized delimiters to decide whether to create a small, large, or stacked - * delimiter. - * - * In real TeX, these sequences aren't explicitly defined, but are instead - * defined inside the font metrics. Since there are only three sequences that - * are possible for the delimiters that TeX defines, it is easier to just encode - * them explicitly here. - */ - - // Delimiters that never stack try small delimiters and large delimiters only - var stackNeverDelimiterSequence = [{ type: "small", style: _Style2.default.SCRIPTSCRIPT }, { type: "small", style: _Style2.default.SCRIPT }, { type: "small", style: _Style2.default.TEXT }, { type: "large", size: 1 }, { type: "large", size: 2 }, { type: "large", size: 3 }, { type: "large", size: 4 }]; - - // Delimiters that always stack try the small delimiters first, then stack - var stackAlwaysDelimiterSequence = [{ type: "small", style: _Style2.default.SCRIPTSCRIPT }, { type: "small", style: _Style2.default.SCRIPT }, { type: "small", style: _Style2.default.TEXT }, { type: "stack" }]; - - // Delimiters that stack when large try the small and then large delimiters, and - // stack afterwards - var stackLargeDelimiterSequence = [{ type: "small", style: _Style2.default.SCRIPTSCRIPT }, { type: "small", style: _Style2.default.SCRIPT }, { type: "small", style: _Style2.default.TEXT }, { type: "large", size: 1 }, { type: "large", size: 2 }, { type: "large", size: 3 }, { type: "large", size: 4 }, { type: "stack" }]; - - /** - * Get the font used in a delimiter based on what kind of delimiter it is. - */ - var delimTypeToFont = function delimTypeToFont(type) { - if (type.type === "small") { - return "Main-Regular"; - } else if (type.type === "large") { - return "Size" + type.size + "-Regular"; - } else if (type.type === "stack") { - return "Size4-Regular"; - } - }; - - /** - * Traverse a sequence of types of delimiters to decide what kind of delimiter - * should be used to create a delimiter of the given height+depth. - */ - var traverseSequence = function traverseSequence(delim, height, sequence, options) { - // Here, we choose the index we should start at in the sequences. In smaller - // sizes (which correspond to larger numbers in style.size) we start earlier - // in the sequence. Thus, scriptscript starts at index 3-3=0, script starts - // at index 3-2=1, text starts at 3-1=2, and display starts at min(2,3-0)=2 - var start = Math.min(2, 3 - options.style.size); - for (var i = start; i < sequence.length; i++) { - if (sequence[i].type === "stack") { - // This is always the last delimiter, so we just break the loop now. - break; - } - - var metrics = getMetrics(delim, delimTypeToFont(sequence[i])); - var heightDepth = metrics.height + metrics.depth; - - // Small delimiters are scaled down versions of the same font, so we - // account for the style change size. - - if (sequence[i].type === "small") { - var newOptions = options.havingBaseStyle(sequence[i].style); - heightDepth *= newOptions.sizeMultiplier; - } - - // Check if the delimiter at this size works for the given height. - if (heightDepth > height) { - return sequence[i]; - } - } - - // If we reached the end of the sequence, return the last sequence element. - return sequence[sequence.length - 1]; - }; - - /** - * Make a delimiter of a given height+depth, with optional centering. Here, we - * traverse the sequences, and create a delimiter that the sequence tells us to. - */ - var makeCustomSizedDelim = function makeCustomSizedDelim(delim, height, center, options, mode, classes) { - if (delim === "<" || delim === "\\lt") { - delim = "\\langle"; - } else if (delim === ">" || delim === "\\gt") { - delim = "\\rangle"; - } - - // Decide what sequence to use - var sequence = void 0; - if (_utils2.default.contains(stackNeverDelimiters, delim)) { - sequence = stackNeverDelimiterSequence; - } else if (_utils2.default.contains(stackLargeDelimiters, delim)) { - sequence = stackLargeDelimiterSequence; - } else { - sequence = stackAlwaysDelimiterSequence; - } - - // Look through the sequence - var delimType = traverseSequence(delim, height, sequence, options); - - if (delim === "\\surd") { - // Get an SVG image for - return sqrtSpan(height, delimType, options); - } else { - // Get the delimiter from font glyphs. - // Depending on the sequence element we decided on, call the - // appropriate function. - if (delimType.type === "small") { - return makeSmallDelim(delim, delimType.style, center, options, mode, classes); - } else if (delimType.type === "large") { - return makeLargeDelim(delim, delimType.size, center, options, mode, classes); - } else if (delimType.type === "stack") { - return makeStackedDelim(delim, height, center, options, mode, classes); - } - } - }; - - /** - * Make a delimiter for use with `\left` and `\right`, given a height and depth - * of an expression that the delimiters surround. - */ - var makeLeftRightDelim = function makeLeftRightDelim(delim, height, depth, options, mode, classes) { - // We always center \left/\right delimiters, so the axis is always shifted - var axisHeight = options.fontMetrics().axisHeight * options.sizeMultiplier; - - // Taken from TeX source, tex.web, function make_left_right - var delimiterFactor = 901; - var delimiterExtend = 5.0 / options.fontMetrics().ptPerEm; - - var maxDistFromAxis = Math.max(height - axisHeight, depth + axisHeight); - - var totalHeight = Math.max( - // In real TeX, calculations are done using integral values which are - // 65536 per pt, or 655360 per em. So, the division here truncates in - // TeX but doesn't here, producing different results. If we wanted to - // exactly match TeX's calculation, we could do - // Math.floor(655360 * maxDistFromAxis / 500) * - // delimiterFactor / 655360 - // (To see the difference, compare - // x^{x^{\left(\rule{0.1em}{0.68em}\right)}} - // in TeX and KaTeX) - maxDistFromAxis / 500 * delimiterFactor, 2 * maxDistFromAxis - delimiterExtend); - - // Finally, we defer to `makeCustomSizedDelim` with our calculated total - // height - return makeCustomSizedDelim(delim, totalHeight, true, options, mode, classes); - }; - - module.exports = { - sizedDelim: makeSizedDelim, - customSizedDelim: makeCustomSizedDelim, - leftRightDelim: makeLeftRightDelim - }; - - },{"./ParseError":29,"./Style":33,"./buildCommon":34,"./fontMetrics":41,"./symbols":48,"./utils":51}],39:[function(require,module,exports){ - - var _classCallCheck2 = require("babel-runtime/helpers/classCallCheck"); - - var _classCallCheck3 = _interopRequireDefault(_classCallCheck2); - - var _createClass2 = require("babel-runtime/helpers/createClass"); - - var _createClass3 = _interopRequireDefault(_createClass2); - - var _unicodeRegexes = require("./unicodeRegexes"); - - var _unicodeRegexes2 = _interopRequireDefault(_unicodeRegexes); - - var _utils = require("./utils"); - - var _utils2 = _interopRequireDefault(_utils); - - function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } - - /** - * Create an HTML className based on a list of classes. In addition to joining - * with spaces, we also remove null or empty classes. - */ - /** - * These objects store the data about the DOM nodes we create, as well as some - * extra data. They can then be transformed into real DOM nodes with the - * `toNode` function or HTML markup using `toMarkup`. They are useful for both - * storing extra properties on the nodes, as well as providing a way to easily - * work with the DOM. - * - * Similar functions for working with MathML nodes exist in mathMLTree.js. - */ - var createClass = function createClass(classes) { - classes = classes.slice(); - for (var i = classes.length - 1; i >= 0; i--) { - if (!classes[i]) { - classes.splice(i, 1); - } - } - - return classes.join(" "); - }; - - /** - * This node represents a span node, with a className, a list of children, and - * an inline style. It also contains information about its height, depth, and - * maxFontSize. - */ - - var span = function () { - function span(classes, children, options) { - (0, _classCallCheck3.default)(this, span); - - this.classes = classes || []; - this.children = children || []; - this.height = 0; - this.depth = 0; - this.maxFontSize = 0; - this.style = {}; - this.attributes = {}; - this.innerHTML; // used for inline SVG code. - if (options) { - if (options.style.isTight()) { - this.classes.push("mtight"); - } - if (options.getColor()) { - this.style.color = options.getColor(); - } - } - } - - /** - * Sets an arbitrary attribute on the span. Warning: use this wisely. Not all - * browsers support attributes the same, and having too many custom attributes - * is probably bad. - */ - - - (0, _createClass3.default)(span, [{ - key: "setAttribute", - value: function setAttribute(attribute, value) { - this.attributes[attribute] = value; - } - }, { - key: "tryCombine", - value: function tryCombine(sibling) { - return false; - } - - /** - * Convert the span into an HTML node - */ - - }, { - key: "toNode", - value: function toNode() { - var span = document.createElement("span"); - - // Apply the class - span.className = createClass(this.classes); - - // Apply inline styles - for (var style in this.style) { - if (Object.prototype.hasOwnProperty.call(this.style, style)) { - span.style[style] = this.style[style]; - } - } - - // Apply attributes - for (var attr in this.attributes) { - if (Object.prototype.hasOwnProperty.call(this.attributes, attr)) { - span.setAttribute(attr, this.attributes[attr]); - } - } - - if (this.innerHTML) { - span.innerHTML = this.innerHTML; - } - - // Append the children, also as HTML nodes - for (var i = 0; i < this.children.length; i++) { - span.appendChild(this.children[i].toNode()); - } - - return span; - } - - /** - * Convert the span into an HTML markup string - */ - - }, { - key: "toMarkup", - value: function toMarkup() { - var markup = " 0 || createClass(this.classes) !== createClass(sibling.classes) || this.skew !== sibling.skew || this.maxFontSize !== sibling.maxFontSize) { - return false; - } - for (var style in this.style) { - if (this.style.hasOwnProperty(style) && this.style[style] !== sibling.style[style]) { - return false; - } - } - for (var _style in sibling.style) { - if (sibling.style.hasOwnProperty(_style) && this.style[_style] !== sibling.style[_style]) { - return false; - } - } - this.value += sibling.value; - this.height = Math.max(this.height, sibling.height); - this.depth = Math.max(this.depth, sibling.depth); - this.italic = sibling.italic; - return true; - } - - /** - * Creates a text node or span from a symbol node. Note that a span is only - * created if it is needed. - */ - - }, { - key: "toNode", - value: function toNode() { - var node = document.createTextNode(this.value); - var span = null; - - if (this.italic > 0) { - span = document.createElement("span"); - span.style.marginRight = this.italic + "em"; - } - - if (this.classes.length > 0) { - span = span || document.createElement("span"); - span.className = createClass(this.classes); - } - - for (var style in this.style) { - if (this.style.hasOwnProperty(style)) { - span = span || document.createElement("span"); - span.style[style] = this.style[style]; - } - } - - if (span) { - span.appendChild(node); - return span; - } else { - return node; - } - } - - /** - * Creates markup for a symbol node. - */ - - }, { - key: "toMarkup", - value: function toMarkup() { - // TODO(alpert): More duplication than I'd like from - // span.prototype.toMarkup and symbolNode.prototype.toNode... - var needsSpan = false; - - var markup = " 0) { - styles += "margin-right:" + this.italic + "em;"; - } - for (var style in this.style) { - if (this.style.hasOwnProperty(style)) { - styles += _utils2.default.hyphenate(style) + ":" + this.style[style] + ";"; - } - } - - if (styles) { - needsSpan = true; - markup += " style=\"" + _utils2.default.escape(styles) + "\""; - } - - var escaped = _utils2.default.escape(this.value); - if (needsSpan) { - markup += ">"; - markup += escaped; - markup += "      "; - return markup; - } else { - return escaped; - } - } - }]); - return symbolNode; - }(); - - module.exports = { - span: span, - documentFragment: documentFragment, - symbolNode: symbolNode - }; - - },{"./unicodeRegexes":49,"./utils":51,"babel-runtime/helpers/classCallCheck":4,"babel-runtime/helpers/createClass":5}],40:[function(require,module,exports){ - - var _ParseNode = require("./ParseNode"); - - var _ParseNode2 = _interopRequireDefault(_ParseNode); - - var _ParseError = require("./ParseError"); - - var _ParseError2 = _interopRequireDefault(_ParseError); - - function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } - - /** - * Parse the body of the environment, with rows delimited by \\ and - * columns delimited by &, and create a nested list in row-major order - * with one group per cell. If given an optional argument style - * ("text", "display", etc.), then each cell is cast into that style. - */ - /* eslint no-constant-condition:0 */ - function parseArray(parser, result, style) { - var row = []; - var body = [row]; - var rowGaps = []; - while (true) { - var cell = parser.parseExpression(false, null); - cell = new _ParseNode2.default("ordgroup", cell, parser.mode); - if (style) { - cell = new _ParseNode2.default("styling", { - style: style, - value: [cell] - }, parser.mode); - } - row.push(cell); - var next = parser.nextToken.text; - if (next === "&") { - parser.consume(); - } else if (next === "\\end") { - break; - } else if (next === "\\\\" || next === "\\cr") { - var cr = parser.parseFunction(); - rowGaps.push(cr.value.size); - row = []; - body.push(row); - } else { - throw new _ParseError2.default("Expected & or \\\\ or \\end", parser.nextToken); - } - } - result.body = body; - result.rowGaps = rowGaps; - return new _ParseNode2.default(result.type, result, parser.mode); - } - - /* - * An environment definition is very similar to a function definition: - * it is declared with a name or a list of names, a set of properties - * and a handler containing the actual implementation. - * - * The properties include: - * - numArgs: The number of arguments after the \begin{name} function. - * - argTypes: (optional) Just like for a function - * - allowedInText: (optional) Whether or not the environment is allowed inside - * text mode (default false) (not enforced yet) - * - numOptionalArgs: (optional) Just like for a function - * A bare number instead of that object indicates the numArgs value. - * - * The handler function will receive two arguments - * - context: information and references provided by the parser - * - args: an array of arguments passed to \begin{name} - * The context contains the following properties: - * - envName: the name of the environment, one of the listed names. - * - parser: the parser object - * - lexer: the lexer object - * - positions: the positions associated with these arguments from args. - * The handler must return a ParseResult. - */ - function defineEnvironment(names, props, handler) { - if (typeof names === "string") { - names = [names]; - } - if (typeof props === "number") { - props = { numArgs: props }; - } - // Set default values of environments - var data = { - numArgs: props.numArgs || 0, - argTypes: props.argTypes, - greediness: 1, - allowedInText: !!props.allowedInText, - numOptionalArgs: props.numOptionalArgs || 0, - handler: handler - }; - for (var i = 0; i < names.length; ++i) { - module.exports[names[i]] = data; - } - } - - // Decides on a style for cells in an array according to whether the given - // environment name starts with the letter 'd'. - function dCellStyle(envName) { - if (envName.substr(0, 1) === "d") { - return "display"; - } else { - return "text"; - } - } - - // Arrays are part of LaTeX, defined in lttab.dtx so its documentation - // is part of the source2e.pdf file of LaTeX2e source documentation. - // {darray} is an {array} environment where cells are set in \displaystyle, - // as defined in nccmath.sty. - defineEnvironment(["array", "darray"], { - numArgs: 1 - }, function (context, args) { - var colalign = args[0]; - colalign = colalign.value.map ? colalign.value : [colalign]; - var cols = colalign.map(function (node) { - var ca = node.value; - if ("lcr".indexOf(ca) !== -1) { - return { - type: "align", - align: ca - }; - } else if (ca === "|") { - return { - type: "separator", - separator: "|" - }; - } - throw new _ParseError2.default("Unknown column alignment: " + node.value, node); - }); - var res = { - type: "array", - cols: cols, - hskipBeforeAndAfter: true }; - res = parseArray(context.parser, res, dCellStyle(context.envName)); - return res; - }); - - // The matrix environments of amsmath builds on the array environment - // of LaTeX, which is discussed above. - defineEnvironment(["matrix", "pmatrix", "bmatrix", "Bmatrix", "vmatrix", "Vmatrix"], {}, function (context) { - var delimiters = { - "matrix": null, - "pmatrix": ["(", ")"], - "bmatrix": ["[", "]"], - "Bmatrix": ["\\{", "\\}"], - "vmatrix": ["|", "|"], - "Vmatrix": ["\\Vert", "\\Vert"] - }[context.envName]; - var res = { - type: "array", - hskipBeforeAndAfter: false }; - res = parseArray(context.parser, res, dCellStyle(context.envName)); - if (delimiters) { - res = new _ParseNode2.default("leftright", { - body: [res], - left: delimiters[0], - right: delimiters[1] - }, context.mode); - } - return res; - }); - - // A cases environment (in amsmath.sty) is almost equivalent to - // \def\arraystretch{1.2}% - // \left\{\begin{array}{@{}l@{\quad}l@{}} … \end{array}\right. - // {dcases} is a {cases} environment where cells are set in \displaystyle, - // as defined in mathtools.sty. - defineEnvironment(["cases", "dcases"], {}, function (context) { - var res = { - type: "array", - arraystretch: 1.2, - cols: [{ - type: "align", - align: "l", - pregap: 0, - // TODO(kevinb) get the current style. - // For now we use the metrics for TEXT style which is what we were - // doing before. Before attempting to get the current style we - // should look at TeX's behavior especially for \over and matrices. - postgap: 1.0 }, { - type: "align", - align: "l", - pregap: 0, - postgap: 0 - }] - }; - res = parseArray(context.parser, res, dCellStyle(context.envName)); - res = new _ParseNode2.default("leftright", { - body: [res], - left: "\\{", - right: "." - }, context.mode); - return res; - }); - - // An aligned environment is like the align* environment - // except it operates within math mode. - // Note that we assume \nomallineskiplimit to be zero, - // so that \strut@ is the same as \strut. - defineEnvironment("aligned", {}, function (context) { - var res = { - type: "array", - cols: [], - addJot: true - }; - res = parseArray(context.parser, res, "display"); - // Count number of columns = maximum number of cells in each row. - // At the same time, prepend empty group {} at beginning of every second - // cell in each row (starting with second cell) so that operators become - // binary. This behavior is implemented in amsmath's \start@aligned. - var emptyGroup = new _ParseNode2.default("ordgroup", [], context.mode); - var numCols = 0; - res.value.body.forEach(function (row) { - for (var i = 1; i < row.length; i += 2) { - // Modify ordgroup node within styling node - var ordgroup = row[i].value.value[0]; - ordgroup.value.unshift(emptyGroup); - } - if (numCols < row.length) { - numCols = row.length; - } - }); - for (var i = 0; i < numCols; ++i) { - var align = "r"; - var pregap = 0; - if (i % 2 === 1) { - align = "l"; - } else if (i > 0) { - pregap = 2; // one \qquad between columns - } - res.value.cols[i] = { - type: "align", - align: align, - pregap: pregap, - postgap: 0 - }; - } - return res; - }); - - // A gathered environment is like an array environment with one centered - // column, but where rows are considered lines so get \jot line spacing - // and contents are set in \displaystyle. - defineEnvironment("gathered", {}, function (context) { - var res = { - type: "array", - cols: [{ - type: "align", - align: "c" - }], - addJot: true - }; - res = parseArray(context.parser, res, "display"); - return res; - }); - - },{"./ParseError":29,"./ParseNode":30}],41:[function(require,module,exports){ - - var _unicodeRegexes = require("./unicodeRegexes"); - - var _fontMetricsData = require("./fontMetricsData"); - - var _fontMetricsData2 = _interopRequireDefault(_fontMetricsData); - - function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } - - /** - * This file contains metrics regarding fonts and individual symbols. The sigma - * and xi variables, as well as the metricMap map contain data extracted from - * TeX, TeX font metrics, and the TTF files. These data are then exposed via the - * `metrics` variable and the getCharacterMetrics function. - */ - - // In TeX, there are actually three sets of dimensions, one for each of - // textstyle (size index 5 and higher: >=9pt), scriptstyle (size index 3 and 4: - // 7-8pt), and scriptscriptstyle (size index 1 and 2: 5-6pt). These are - // provided in the the arrays below, in that order. - // - // The font metrics are stored in fonts cmsy10, cmsy7, and cmsy5 respsectively. - // This was determined by running the following script: - // - // latex -interaction=nonstopmode \ - // '\documentclass{article}\usepackage{amsmath}\begin{document}' \ - // '$a$ \expandafter\show\the\textfont2' \ - // '\expandafter\show\the\scriptfont2' \ - // '\expandafter\show\the\scriptscriptfont2' \ - // '\stop' - // - // The metrics themselves were retreived using the following commands: - // - // tftopl cmsy10 - // tftopl cmsy7 - // tftopl cmsy5 - // - // The output of each of these commands is quite lengthy. The only part we - // care about is the FONTDIMEN section. Each value is measured in EMs. - var sigmasAndXis = { - slant: [0.250, 0.250, 0.250], // sigma1 - space: [0.000, 0.000, 0.000], // sigma2 - stretch: [0.000, 0.000, 0.000], // sigma3 - shrink: [0.000, 0.000, 0.000], // sigma4 - xHeight: [0.431, 0.431, 0.431], // sigma5 - quad: [1.000, 1.171, 1.472], // sigma6 - extraSpace: [0.000, 0.000, 0.000], // sigma7 - num1: [0.677, 0.732, 0.925], // sigma8 - num2: [0.394, 0.384, 0.387], // sigma9 - num3: [0.444, 0.471, 0.504], // sigma10 - denom1: [0.686, 0.752, 1.025], // sigma11 - denom2: [0.345, 0.344, 0.532], // sigma12 - sup1: [0.413, 0.503, 0.504], // sigma13 - sup2: [0.363, 0.431, 0.404], // sigma14 - sup3: [0.289, 0.286, 0.294], // sigma15 - sub1: [0.150, 0.143, 0.200], // sigma16 - sub2: [0.247, 0.286, 0.400], // sigma17 - supDrop: [0.386, 0.353, 0.494], // sigma18 - subDrop: [0.050, 0.071, 0.100], // sigma19 - delim1: [2.390, 1.700, 1.980], // sigma20 - delim2: [1.010, 1.157, 1.420], // sigma21 - axisHeight: [0.250, 0.250, 0.250], // sigma22 - - // These font metrics are extracted from TeX by using tftopl on cmex10.tfm; - // they correspond to the font parameters of the extension fonts (family 3). - // See the TeXbook, page 441. In AMSTeX, the extension fonts scale; to - // match cmex7, we'd use cmex7.tfm values for script and scriptscript - // values. - defaultRuleThickness: [0.04, 0.049, 0.049], // xi8; cmex7: 0.049 - bigOpSpacing1: [0.111, 0.111, 0.111], // xi9 - bigOpSpacing2: [0.166, 0.166, 0.166], // xi10 - bigOpSpacing3: [0.2, 0.2, 0.2], // xi11 - bigOpSpacing4: [0.6, 0.611, 0.611], // xi12; cmex7: 0.611 - bigOpSpacing5: [0.1, 0.143, 0.143], // xi13; cmex7: 0.143 - - // The \sqrt rule width is taken from the height of the surd character. - // Since we use the same font at all sizes, this thickness doesn't scale. - sqrtRuleThickness: [0.04, 0.04, 0.04], - - // This value determines how large a pt is, for metrics which are defined - // in terms of pts. - // This value is also used in katex.less; if you change it make sure the - // values match. - ptPerEm: [10.0, 10.0, 10.0], - - // The space between adjacent `|` columns in an array definition. From - // `\showthe\doublerulesep` in LaTeX. Equals 2.0 / ptPerEm. - doubleRuleSep: [0.2, 0.2, 0.2] - }; - - // This map contains a mapping from font name and character code to character - // metrics, including height, depth, italic correction, and skew (kern from the - // character to the corresponding \skewchar) - // This map is generated via `make metrics`. It should not be changed manually. - - - // These are very rough approximations. We default to Times New Roman which - // should have Latin-1 and Cyrillic characters, but may not depending on the - // operating system. The metrics do not account for extra height from the - // accents. In the case of Cyrillic characters which have both ascenders and - // descenders we prefer approximations with ascenders, primarily to prevent - // the fraction bar or root line from intersecting the glyph. - // TODO(kevinb) allow union of multiple glyph metrics for better accuracy. - var extraCharacterMap = { - // Latin-1 - 'À': 'A', - 'Á': 'A', - 'Â': 'A', - 'Ã': 'A', - 'Ä': 'A', - 'Å': 'A', - 'Æ': 'A', - 'Ç': 'C', - 'È': 'E', - 'É': 'E', - 'Ê': 'E', - 'Ë': 'E', - 'Ì': 'I', - 'Í': 'I', - 'Î': 'I', - 'Ï': 'I', - 'Ð': 'D', - 'Ñ': 'N', - 'Ò': 'O', - 'Ó': 'O', - 'Ô': 'O', - 'Õ': 'O', - 'Ö': 'O', - 'Ø': 'O', - 'Ù': 'U', - 'Ú': 'U', - 'Û': 'U', - 'Ü': 'U', - 'Ý': 'Y', - 'Þ': 'o', - 'ß': 'B', - 'à': 'a', - 'á': 'a', - 'â': 'a', - 'ã': 'a', - 'ä': 'a', - 'å': 'a', - 'æ': 'a', - 'ç': 'c', - 'è': 'e', - 'é': 'e', - 'ê': 'e', - 'ë': 'e', - 'ì': 'i', - 'í': 'i', - 'î': 'i', - 'ï': 'i', - 'ð': 'd', - 'ñ': 'n', - 'ò': 'o', - 'ó': 'o', - 'ô': 'o', - 'õ': 'o', - 'ö': 'o', - 'ø': 'o', - 'ù': 'u', - 'ú': 'u', - 'û': 'u', - 'ü': 'u', - 'ý': 'y', - 'þ': 'o', - 'ÿ': 'y', - - // Cyrillic - 'А': 'A', - 'Б': 'B', - 'В': 'B', - 'Г': 'F', - 'Д': 'A', - 'Е': 'E', - 'Ж': 'K', - 'З': '3', - 'И': 'N', - 'Й': 'N', - 'К': 'K', - 'Л': 'N', - 'М': 'M', - 'Н': 'H', - 'О': 'O', - 'П': 'N', - 'Р': 'P', - 'С': 'C', - 'Т': 'T', - 'У': 'y', - 'Ф': 'O', - 'Х': 'X', - 'Ц': 'U', - 'Ч': 'h', - 'Ш': 'W', - 'Щ': 'W', - 'Ъ': 'B', - 'Ы': 'X', - 'Ь': 'B', - 'Э': '3', - 'Ю': 'X', - 'Я': 'R', - 'а': 'a', - 'б': 'b', - 'в': 'a', - 'г': 'r', - 'д': 'y', - 'е': 'e', - 'ж': 'm', - 'з': 'e', - 'и': 'n', - 'й': 'n', - 'к': 'n', - 'л': 'n', - 'м': 'm', - 'н': 'n', - 'о': 'o', - 'п': 'n', - 'р': 'p', - 'с': 'c', - 'т': 'o', - 'у': 'y', - 'ф': 'b', - 'х': 'x', - 'ц': 'n', - 'ч': 'n', - 'ш': 'w', - 'щ': 'w', - 'ъ': 'a', - 'ы': 'm', - 'ь': 'a', - 'э': 'e', - 'ю': 'm', - 'я': 'r' - }; - - /** - * This function is a convenience function for looking up information in the - * metricMap table. It takes a character as a string, and a style. - * - * Note: the `width` property may be undefined if fontMetricsData.js wasn't - * built using `Make extended_metrics`. - */ - var getCharacterMetrics = function getCharacterMetrics(character, style) { - var ch = character.charCodeAt(0); - if (character[0] in extraCharacterMap) { - ch = extraCharacterMap[character[0]].charCodeAt(0); - } else if (_unicodeRegexes.cjkRegex.test(character[0])) { - ch = 'M'.charCodeAt(0); - } - var metrics = _fontMetricsData2.default[style][ch]; - if (metrics) { - return { - depth: metrics[0], - height: metrics[1], - italic: metrics[2], - skew: metrics[3], - width: metrics[4] - }; - } - }; - - var fontMetricsBySizeIndex = {}; - - /** - * Get the font metrics for a given size. - */ - var getFontMetrics = function getFontMetrics(size) { - var sizeIndex = void 0; - if (size >= 5) { - sizeIndex = 0; - } else if (size >= 3) { - sizeIndex = 1; - } else { - sizeIndex = 2; - } - if (!fontMetricsBySizeIndex[sizeIndex]) { - var metrics = fontMetricsBySizeIndex[sizeIndex] = {}; - for (var key in sigmasAndXis) { - if (sigmasAndXis.hasOwnProperty(key)) { - metrics[key] = sigmasAndXis[key][sizeIndex]; - } - } - metrics.cssEmPerMu = metrics.quad / 18; - } - return fontMetricsBySizeIndex[sizeIndex]; - }; - - module.exports = { - getFontMetrics: getFontMetrics, - getCharacterMetrics: getCharacterMetrics - }; - - },{"./fontMetricsData":42,"./unicodeRegexes":49}],42:[function(require,module,exports){ - - module.exports = { - "AMS-Regular": { - "65": [0, 0.68889, 0, 0], - "66": [0, 0.68889, 0, 0], - "67": [0, 0.68889, 0, 0], - "68": [0, 0.68889, 0, 0], - "69": [0, 0.68889, 0, 0], - "70": [0, 0.68889, 0, 0], - "71": [0, 0.68889, 0, 0], - "72": [0, 0.68889, 0, 0], - "73": [0, 0.68889, 0, 0], - "74": [0.16667, 0.68889, 0, 0], - "75": [0, 0.68889, 0, 0], - "76": [0, 0.68889, 0, 0], - "77": [0, 0.68889, 0, 0], - "78": [0, 0.68889, 0, 0], - "79": [0.16667, 0.68889, 0, 0], - "80": [0, 0.68889, 0, 0], - "81": [0.16667, 0.68889, 0, 0], - "82": [0, 0.68889, 0, 0], - "83": [0, 0.68889, 0, 0], - "84": [0, 0.68889, 0, 0], - "85": [0, 0.68889, 0, 0], - "86": [0, 0.68889, 0, 0], - "87": [0, 0.68889, 0, 0], - "88": [0, 0.68889, 0, 0], - "89": [0, 0.68889, 0, 0], - "90": [0, 0.68889, 0, 0], - "107": [0, 0.68889, 0, 0], - "165": [0, 0.675, 0.025, 0], - "174": [0.15559, 0.69224, 0, 0], - "240": [0, 0.68889, 0, 0], - "295": [0, 0.68889, 0, 0], - "710": [0, 0.825, 0, 0], - "732": [0, 0.9, 0, 0], - "770": [0, 0.825, 0, 0], - "771": [0, 0.9, 0, 0], - "989": [0.08167, 0.58167, 0, 0], - "1008": [0, 0.43056, 0.04028, 0], - "8245": [0, 0.54986, 0, 0], - "8463": [0, 0.68889, 0, 0], - "8487": [0, 0.68889, 0, 0], - "8498": [0, 0.68889, 0, 0], - "8502": [0, 0.68889, 0, 0], - "8503": [0, 0.68889, 0, 0], - "8504": [0, 0.68889, 0, 0], - "8513": [0, 0.68889, 0, 0], - "8592": [-0.03598, 0.46402, 0, 0], - "8594": [-0.03598, 0.46402, 0, 0], - "8602": [-0.13313, 0.36687, 0, 0], - "8603": [-0.13313, 0.36687, 0, 0], - "8606": [0.01354, 0.52239, 0, 0], - "8608": [0.01354, 0.52239, 0, 0], - "8610": [0.01354, 0.52239, 0, 0], - "8611": [0.01354, 0.52239, 0, 0], - "8619": [0, 0.54986, 0, 0], - "8620": [0, 0.54986, 0, 0], - "8621": [-0.13313, 0.37788, 0, 0], - "8622": [-0.13313, 0.36687, 0, 0], - "8624": [0, 0.69224, 0, 0], - "8625": [0, 0.69224, 0, 0], - "8630": [0, 0.43056, 0, 0], - "8631": [0, 0.43056, 0, 0], - "8634": [0.08198, 0.58198, 0, 0], - "8635": [0.08198, 0.58198, 0, 0], - "8638": [0.19444, 0.69224, 0, 0], - "8639": [0.19444, 0.69224, 0, 0], - "8642": [0.19444, 0.69224, 0, 0], - "8643": [0.19444, 0.69224, 0, 0], - "8644": [0.1808, 0.675, 0, 0], - "8646": [0.1808, 0.675, 0, 0], - "8647": [0.1808, 0.675, 0, 0], - "8648": [0.19444, 0.69224, 0, 0], - "8649": [0.1808, 0.675, 0, 0], - "8650": [0.19444, 0.69224, 0, 0], - "8651": [0.01354, 0.52239, 0, 0], - "8652": [0.01354, 0.52239, 0, 0], - "8653": [-0.13313, 0.36687, 0, 0], - "8654": [-0.13313, 0.36687, 0, 0], - "8655": [-0.13313, 0.36687, 0, 0], - "8666": [0.13667, 0.63667, 0, 0], - "8667": [0.13667, 0.63667, 0, 0], - "8669": [-0.13313, 0.37788, 0, 0], - "8672": [-0.064, 0.437, 0, 0], - "8674": [-0.064, 0.437, 0, 0], - "8705": [0, 0.825, 0, 0], - "8708": [0, 0.68889, 0, 0], - "8709": [0.08167, 0.58167, 0, 0], - "8717": [0, 0.43056, 0, 0], - "8722": [-0.03598, 0.46402, 0, 0], - "8724": [0.08198, 0.69224, 0, 0], - "8726": [0.08167, 0.58167, 0, 0], - "8733": [0, 0.69224, 0, 0], - "8736": [0, 0.69224, 0, 0], - "8737": [0, 0.69224, 0, 0], - "8738": [0.03517, 0.52239, 0, 0], - "8739": [0.08167, 0.58167, 0, 0], - "8740": [0.25142, 0.74111, 0, 0], - "8741": [0.08167, 0.58167, 0, 0], - "8742": [0.25142, 0.74111, 0, 0], - "8756": [0, 0.69224, 0, 0], - "8757": [0, 0.69224, 0, 0], - "8764": [-0.13313, 0.36687, 0, 0], - "8765": [-0.13313, 0.37788, 0, 0], - "8769": [-0.13313, 0.36687, 0, 0], - "8770": [-0.03625, 0.46375, 0, 0], - "8774": [0.30274, 0.79383, 0, 0], - "8776": [-0.01688, 0.48312, 0, 0], - "8778": [0.08167, 0.58167, 0, 0], - "8782": [0.06062, 0.54986, 0, 0], - "8783": [0.06062, 0.54986, 0, 0], - "8785": [0.08198, 0.58198, 0, 0], - "8786": [0.08198, 0.58198, 0, 0], - "8787": [0.08198, 0.58198, 0, 0], - "8790": [0, 0.69224, 0, 0], - "8791": [0.22958, 0.72958, 0, 0], - "8796": [0.08198, 0.91667, 0, 0], - "8806": [0.25583, 0.75583, 0, 0], - "8807": [0.25583, 0.75583, 0, 0], - "8808": [0.25142, 0.75726, 0, 0], - "8809": [0.25142, 0.75726, 0, 0], - "8812": [0.25583, 0.75583, 0, 0], - "8814": [0.20576, 0.70576, 0, 0], - "8815": [0.20576, 0.70576, 0, 0], - "8816": [0.30274, 0.79383, 0, 0], - "8817": [0.30274, 0.79383, 0, 0], - "8818": [0.22958, 0.72958, 0, 0], - "8819": [0.22958, 0.72958, 0, 0], - "8822": [0.1808, 0.675, 0, 0], - "8823": [0.1808, 0.675, 0, 0], - "8828": [0.13667, 0.63667, 0, 0], - "8829": [0.13667, 0.63667, 0, 0], - "8830": [0.22958, 0.72958, 0, 0], - "8831": [0.22958, 0.72958, 0, 0], - "8832": [0.20576, 0.70576, 0, 0], - "8833": [0.20576, 0.70576, 0, 0], - "8840": [0.30274, 0.79383, 0, 0], - "8841": [0.30274, 0.79383, 0, 0], - "8842": [0.13597, 0.63597, 0, 0], - "8843": [0.13597, 0.63597, 0, 0], - "8847": [0.03517, 0.54986, 0, 0], - "8848": [0.03517, 0.54986, 0, 0], - "8858": [0.08198, 0.58198, 0, 0], - "8859": [0.08198, 0.58198, 0, 0], - "8861": [0.08198, 0.58198, 0, 0], - "8862": [0, 0.675, 0, 0], - "8863": [0, 0.675, 0, 0], - "8864": [0, 0.675, 0, 0], - "8865": [0, 0.675, 0, 0], - "8872": [0, 0.69224, 0, 0], - "8873": [0, 0.69224, 0, 0], - "8874": [0, 0.69224, 0, 0], - "8876": [0, 0.68889, 0, 0], - "8877": [0, 0.68889, 0, 0], - "8878": [0, 0.68889, 0, 0], - "8879": [0, 0.68889, 0, 0], - "8882": [0.03517, 0.54986, 0, 0], - "8883": [0.03517, 0.54986, 0, 0], - "8884": [0.13667, 0.63667, 0, 0], - "8885": [0.13667, 0.63667, 0, 0], - "8888": [0, 0.54986, 0, 0], - "8890": [0.19444, 0.43056, 0, 0], - "8891": [0.19444, 0.69224, 0, 0], - "8892": [0.19444, 0.69224, 0, 0], - "8901": [0, 0.54986, 0, 0], - "8903": [0.08167, 0.58167, 0, 0], - "8905": [0.08167, 0.58167, 0, 0], - "8906": [0.08167, 0.58167, 0, 0], - "8907": [0, 0.69224, 0, 0], - "8908": [0, 0.69224, 0, 0], - "8909": [-0.03598, 0.46402, 0, 0], - "8910": [0, 0.54986, 0, 0], - "8911": [0, 0.54986, 0, 0], - "8912": [0.03517, 0.54986, 0, 0], - "8913": [0.03517, 0.54986, 0, 0], - "8914": [0, 0.54986, 0, 0], - "8915": [0, 0.54986, 0, 0], - "8916": [0, 0.69224, 0, 0], - "8918": [0.0391, 0.5391, 0, 0], - "8919": [0.0391, 0.5391, 0, 0], - "8920": [0.03517, 0.54986, 0, 0], - "8921": [0.03517, 0.54986, 0, 0], - "8922": [0.38569, 0.88569, 0, 0], - "8923": [0.38569, 0.88569, 0, 0], - "8926": [0.13667, 0.63667, 0, 0], - "8927": [0.13667, 0.63667, 0, 0], - "8928": [0.30274, 0.79383, 0, 0], - "8929": [0.30274, 0.79383, 0, 0], - "8934": [0.23222, 0.74111, 0, 0], - "8935": [0.23222, 0.74111, 0, 0], - "8936": [0.23222, 0.74111, 0, 0], - "8937": [0.23222, 0.74111, 0, 0], - "8938": [0.20576, 0.70576, 0, 0], - "8939": [0.20576, 0.70576, 0, 0], - "8940": [0.30274, 0.79383, 0, 0], - "8941": [0.30274, 0.79383, 0, 0], - "8994": [0.19444, 0.69224, 0, 0], - "8995": [0.19444, 0.69224, 0, 0], - "9416": [0.15559, 0.69224, 0, 0], - "9484": [0, 0.69224, 0, 0], - "9488": [0, 0.69224, 0, 0], - "9492": [0, 0.37788, 0, 0], - "9496": [0, 0.37788, 0, 0], - "9585": [0.19444, 0.68889, 0, 0], - "9586": [0.19444, 0.74111, 0, 0], - "9632": [0, 0.675, 0, 0], - "9633": [0, 0.675, 0, 0], - "9650": [0, 0.54986, 0, 0], - "9651": [0, 0.54986, 0, 0], - "9654": [0.03517, 0.54986, 0, 0], - "9660": [0, 0.54986, 0, 0], - "9661": [0, 0.54986, 0, 0], - "9664": [0.03517, 0.54986, 0, 0], - "9674": [0.11111, 0.69224, 0, 0], - "9733": [0.19444, 0.69224, 0, 0], - "10003": [0, 0.69224, 0, 0], - "10016": [0, 0.69224, 0, 0], - "10731": [0.11111, 0.69224, 0, 0], - "10846": [0.19444, 0.75583, 0, 0], - "10877": [0.13667, 0.63667, 0, 0], - "10878": [0.13667, 0.63667, 0, 0], - "10885": [0.25583, 0.75583, 0, 0], - "10886": [0.25583, 0.75583, 0, 0], - "10887": [0.13597, 0.63597, 0, 0], - "10888": [0.13597, 0.63597, 0, 0], - "10889": [0.26167, 0.75726, 0, 0], - "10890": [0.26167, 0.75726, 0, 0], - "10891": [0.48256, 0.98256, 0, 0], - "10892": [0.48256, 0.98256, 0, 0], - "10901": [0.13667, 0.63667, 0, 0], - 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- /* This file contains a list of functions that we parse, identified by - * the calls to defineFunction. - * - * The first argument to defineFunction is a single name or a list of names. - * All functions named in such a list will share a single implementation. - * - * Each declared function can have associated properties, which - * include the following: - * - * - numArgs: The number of arguments the function takes. - * If this is the only property, it can be passed as a number - * instead of an element of a properties object. - * - argTypes: (optional) An array corresponding to each argument of the - * function, giving the type of argument that should be parsed. Its - * length should be equal to `numArgs + numOptionalArgs`. Valid - * types: - * - "size": A size-like thing, such as "1em" or "5ex" - * - "color": An html color, like "#abc" or "blue" - * - "original": The same type as the environment that the - * function being parsed is in (e.g. used for the - * bodies of functions like \textcolor where the - * first argument is special and the second - * argument is parsed normally) - * Other possible types (probably shouldn't be used) - * - "text": Text-like (e.g. \text) - * - "math": Normal math - * If undefined, this will be treated as an appropriate length - * array of "original" strings - * - greediness: (optional) The greediness of the function to use ungrouped - * arguments. - * - * E.g. if you have an expression - * \sqrt \frac 1 2 - * since \frac has greediness=2 vs \sqrt's greediness=1, \frac - * will use the two arguments '1' and '2' as its two arguments, - * then that whole function will be used as the argument to - * \sqrt. On the other hand, the expressions - * \frac \frac 1 2 3 - * and - * \frac \sqrt 1 2 - * will fail because \frac and \frac have equal greediness - * and \sqrt has a lower greediness than \frac respectively. To - * make these parse, we would have to change them to: - * \frac {\frac 1 2} 3 - * and - * \frac {\sqrt 1} 2 - * - * The default value is `1` - * - allowedInText: (optional) Whether or not the function is allowed inside - * text mode (default false) - * - numOptionalArgs: (optional) The number of optional arguments the function - * should parse. If the optional arguments aren't found, - * `null` will be passed to the handler in their place. - * (default 0) - * - infix: (optional) Must be true if the function is an infix operator. - * - * The last argument is that implementation, the handler for the function(s). - * It is called to handle these functions and their arguments. - * It receives two arguments: - * - context contains information and references provided by the parser - * - args is an array of arguments obtained from TeX input - * The context contains the following properties: - * - funcName: the text (i.e. name) of the function, including \ - * - parser: the parser object - * - lexer: the lexer object - * - positions: the positions in the overall string of the function - * and the arguments. - * The latter three should only be used to produce error messages. - * - * The function should return an object with the following keys: - * - type: The type of element that this is. This is then used in - * buildHTML/buildMathML to determine which function - * should be called to build this node into a DOM node - * Any other data can be added to the object, which will be passed - * in to the function in buildHTML/buildMathML as `group.value`. - */ - - function defineFunction(names, props, handler) { - if (typeof names === "string") { - names = [names]; - } - if (typeof props === "number") { - props = { numArgs: props }; - } - // Set default values of functions - var data = { - numArgs: props.numArgs, - argTypes: props.argTypes, - greediness: props.greediness === undefined ? 1 : props.greediness, - allowedInText: !!props.allowedInText, - allowedInMath: props.allowedInMath, - numOptionalArgs: props.numOptionalArgs || 0, - infix: !!props.infix, - handler: handler - }; - for (var i = 0; i < names.length; ++i) { - module.exports[names[i]] = data; - } - } - - // Since the corresponding buildHTML/buildMathML function expects a - // list of elements, we normalize for different kinds of arguments - var ordargument = function ordargument(arg) { - if (arg.type === "ordgroup") { - return arg.value; - } else { - return [arg]; - } - }; - - // A normal square root - defineFunction("\\sqrt", { - numArgs: 1, - numOptionalArgs: 1 - }, function (context, args) { - var index = args[0]; - var body = args[1]; - return { - type: "sqrt", - body: body, - index: index - }; - }); - - // Non-mathy text, possibly in a font - var textFunctionStyles = { - "\\text": undefined, "\\textrm": "mathrm", "\\textsf": "mathsf", - "\\texttt": "mathtt", "\\textnormal": "mathrm", "\\textbf": "mathbf", - "\\textit": "textit" - }; - - defineFunction(["\\text", "\\textrm", "\\textsf", "\\texttt", "\\textnormal", "\\textbf", "\\textit"], { - numArgs: 1, - argTypes: ["text"], - greediness: 2, - allowedInText: true - }, function (context, args) { - var body = args[0]; - return { - type: "text", - body: ordargument(body), - style: textFunctionStyles[context.funcName] - }; - }); - - // A two-argument custom color - defineFunction("\\textcolor", { - numArgs: 2, - allowedInText: true, - greediness: 3, - argTypes: ["color", "original"] - }, function (context, args) { - var color = args[0]; - var body = args[1]; - return { - type: "color", - color: color.value, - value: ordargument(body) - }; - }); - - // \color is handled in Parser.js's parseImplicitGroup - defineFunction("\\color", { - numArgs: 1, - allowedInText: true, - greediness: 3, - argTypes: ["color"] - }, null); - - // An overline - defineFunction("\\overline", { - numArgs: 1 - }, function (context, args) { - var body = args[0]; - return { - type: "overline", - body: body - }; - }); - - // An underline - defineFunction("\\underline", { - numArgs: 1 - }, function (context, args) { - var body = args[0]; - return { - type: "underline", - body: body - }; - }); - - // A box of the width and height - defineFunction("\\rule", { - numArgs: 2, - numOptionalArgs: 1, - argTypes: ["size", "size", "size"] - }, function (context, args) { - var shift = args[0]; - var width = args[1]; - var height = args[2]; - return { - type: "rule", - shift: shift && shift.value, - width: width.value, - height: height.value - }; - }); - - // TODO: In TeX, \mkern only accepts mu-units, and \kern does not accept - // mu-units. In current KaTeX we relax this; both commands accept any unit. - defineFunction(["\\kern", "\\mkern"], { - numArgs: 1, - argTypes: ["size"] - }, function (context, args) { - return { - type: "kern", - dimension: args[0].value - }; - }); - - // A KaTeX logo - defineFunction("\\KaTeX", { - numArgs: 0 - }, function (context) { - return { - type: "katex" - }; - }); - - defineFunction("\\phantom", { - numArgs: 1 - }, function (context, args) { - var body = args[0]; - return { - type: "phantom", - value: ordargument(body) - }; - }); - - // Math class commands except \mathop - defineFunction(["\\mathord", "\\mathbin", "\\mathrel", "\\mathopen", "\\mathclose", "\\mathpunct", "\\mathinner"], { - numArgs: 1 - }, function (context, args) { - var body = args[0]; - return { - type: "mclass", - mclass: "m" + context.funcName.substr(5), - value: ordargument(body) - }; - }); - - // Build a relation by placing one symbol on top of another - defineFunction("\\stackrel", { - numArgs: 2 - }, function (context, args) { - var top = args[0]; - var bottom = args[1]; - - var bottomop = new _ParseNode2.default("op", { - type: "op", - limits: true, - alwaysHandleSupSub: true, - symbol: false, - value: ordargument(bottom) - }, bottom.mode); - - var supsub = new _ParseNode2.default("supsub", { - base: bottomop, - sup: top, - sub: null - }, top.mode); - - return { - type: "mclass", - mclass: "mrel", - value: [supsub] - }; - }); - - // \mod-type functions - defineFunction("\\bmod", { - numArgs: 0 - }, function (context, args) { - return { - type: "mod", - modType: "bmod", - value: null - }; - }); - - defineFunction(["\\pod", "\\pmod", "\\mod"], { - numArgs: 1 - }, function (context, args) { - var body = args[0]; - return { - type: "mod", - modType: context.funcName.substr(1), - value: ordargument(body) - }; - }); - - // Extra data needed for the delimiter handler down below - var delimiterSizes = { - "\\bigl": { mclass: "mopen", size: 1 }, - "\\Bigl": { mclass: "mopen", size: 2 }, - "\\biggl": { mclass: "mopen", size: 3 }, - "\\Biggl": { mclass: "mopen", size: 4 }, - "\\bigr": { mclass: "mclose", size: 1 }, - "\\Bigr": { mclass: "mclose", size: 2 }, - "\\biggr": { mclass: "mclose", size: 3 }, - "\\Biggr": { mclass: "mclose", size: 4 }, - "\\bigm": { mclass: "mrel", size: 1 }, - "\\Bigm": { mclass: "mrel", size: 2 }, - "\\biggm": { mclass: "mrel", size: 3 }, - "\\Biggm": { mclass: "mrel", size: 4 }, - "\\big": { mclass: "mord", size: 1 }, - "\\Big": { mclass: "mord", size: 2 }, - "\\bigg": { mclass: "mord", size: 3 }, - "\\Bigg": { mclass: "mord", size: 4 } - }; - - var delimiters = ["(", ")", "[", "\\lbrack", "]", "\\rbrack", "\\{", "\\lbrace", "\\}", "\\rbrace", "\\lfloor", "\\rfloor", "\\lceil", "\\rceil", "<", ">", "\\langle", "\\rangle", "\\lt", "\\gt", "\\lvert", "\\rvert", "\\lVert", "\\rVert", "\\lgroup", "\\rgroup", "\\lmoustache", "\\rmoustache", "/", "\\backslash", "|", "\\vert", "\\|", "\\Vert", "\\uparrow", "\\Uparrow", "\\downarrow", "\\Downarrow", "\\updownarrow", "\\Updownarrow", "."]; - - var fontAliases = { - "\\Bbb": "\\mathbb", - "\\bold": "\\mathbf", - "\\frak": "\\mathfrak" - }; - - // Single-argument color functions - defineFunction(["\\blue", "\\orange", "\\pink", "\\red", "\\green", "\\gray", "\\purple", "\\blueA", "\\blueB", "\\blueC", "\\blueD", "\\blueE", "\\tealA", "\\tealB", "\\tealC", "\\tealD", "\\tealE", "\\greenA", "\\greenB", "\\greenC", "\\greenD", "\\greenE", "\\goldA", "\\goldB", "\\goldC", "\\goldD", "\\goldE", "\\redA", "\\redB", "\\redC", "\\redD", "\\redE", "\\maroonA", "\\maroonB", "\\maroonC", "\\maroonD", "\\maroonE", "\\purpleA", "\\purpleB", "\\purpleC", "\\purpleD", "\\purpleE", "\\mintA", "\\mintB", "\\mintC", "\\grayA", "\\grayB", "\\grayC", "\\grayD", "\\grayE", "\\grayF", "\\grayG", "\\grayH", "\\grayI", "\\kaBlue", "\\kaGreen"], { - numArgs: 1, - allowedInText: true, - greediness: 3 - }, function (context, args) { - var body = args[0]; - return { - type: "color", - color: "katex-" + context.funcName.slice(1), - value: ordargument(body) - }; - }); - - // There are 2 flags for operators; whether they produce limits in - // displaystyle, and whether they are symbols and should grow in - // displaystyle. These four groups cover the four possible choices. - - // No limits, not symbols - defineFunction(["\\arcsin", "\\arccos", "\\arctan", "\\arctg", "\\arcctg", "\\arg", "\\ch", "\\cos", "\\cosec", "\\cosh", "\\cot", "\\cotg", "\\coth", "\\csc", "\\ctg", "\\cth", "\\deg", "\\dim", "\\exp", "\\hom", "\\ker", "\\lg", "\\ln", "\\log", "\\sec", "\\sin", "\\sinh", "\\sh", "\\tan", "\\tanh", "\\tg", "\\th"], { - numArgs: 0 - }, function (context) { - return { - type: "op", - limits: false, - symbol: false, - body: context.funcName - }; - }); - - // Limits, not symbols - defineFunction(["\\det", "\\gcd", "\\inf", "\\lim", "\\liminf", "\\limsup", "\\max", "\\min", "\\Pr", "\\sup"], { - numArgs: 0 - }, function (context) { - return { - type: "op", - limits: true, - symbol: false, - body: context.funcName - }; - }); - - // No limits, symbols - defineFunction(["\\int", "\\iint", "\\iiint", "\\oint"], { - numArgs: 0 - }, function (context) { - return { - type: "op", - limits: false, - symbol: true, - body: context.funcName - }; - }); - - // Limits, symbols - defineFunction(["\\coprod", "\\bigvee", "\\bigwedge", "\\biguplus", "\\bigcap", "\\bigcup", "\\intop", "\\prod", "\\sum", "\\bigotimes", "\\bigoplus", "\\bigodot", "\\bigsqcup", "\\smallint"], { - numArgs: 0 - }, function (context) { - return { - type: "op", - limits: true, - symbol: true, - body: context.funcName - }; - }); - - // \mathop class command - defineFunction("\\mathop", { - numArgs: 1 - }, function (context, args) { - var body = args[0]; - return { - type: "op", - limits: false, - symbol: false, - value: ordargument(body) - }; - }); - - // Fractions - defineFunction(["\\dfrac", "\\frac", "\\tfrac", "\\dbinom", "\\binom", "\\tbinom", "\\\\atopfrac"], { - numArgs: 2, - greediness: 2 - }, function (context, args) { - var numer = args[0]; - var denom = args[1]; - var hasBarLine = void 0; - var leftDelim = null; - var rightDelim = null; - var size = "auto"; - - switch (context.funcName) { - case "\\dfrac": - case "\\frac": - case "\\tfrac": - hasBarLine = true; - break; - case "\\\\atopfrac": - hasBarLine = false; - break; - case "\\dbinom": - case "\\binom": - case "\\tbinom": - hasBarLine = false; - leftDelim = "("; - rightDelim = ")"; - break; - default: - throw new Error("Unrecognized genfrac command"); - } - - switch (context.funcName) { - case "\\dfrac": - case "\\dbinom": - size = "display"; - break; - case "\\tfrac": - case "\\tbinom": - size = "text"; - break; - } - - return { - type: "genfrac", - numer: numer, - denom: denom, - hasBarLine: hasBarLine, - leftDelim: leftDelim, - rightDelim: rightDelim, - size: size - }; - }); - - // Left and right overlap functions - defineFunction(["\\llap", "\\rlap"], { - numArgs: 1, - allowedInText: true - }, function (context, args) { - var body = args[0]; - return { - type: context.funcName.slice(1), - body: body - }; - }); - - // Delimiter functions - var checkDelimiter = function checkDelimiter(delim, context) { - if (_utils2.default.contains(delimiters, delim.value)) { - return delim; - } else { - throw new _ParseError2.default("Invalid delimiter: '" + delim.value + "' after '" + context.funcName + "'", delim); - } - }; - - defineFunction(["\\bigl", "\\Bigl", "\\biggl", "\\Biggl", "\\bigr", "\\Bigr", "\\biggr", "\\Biggr", "\\bigm", "\\Bigm", "\\biggm", "\\Biggm", "\\big", "\\Big", "\\bigg", "\\Bigg"], { - numArgs: 1 - }, function (context, args) { - var delim = checkDelimiter(args[0], context); - - return { - type: "delimsizing", - size: delimiterSizes[context.funcName].size, - mclass: delimiterSizes[context.funcName].mclass, - value: delim.value - }; - }); - - defineFunction(["\\left", "\\right"], { - numArgs: 1 - }, function (context, args) { - var delim = checkDelimiter(args[0], context); - - // \left and \right are caught somewhere in Parser.js, which is - // why this data doesn't match what is in buildHTML. - return { - type: "leftright", - value: delim.value - }; - }); - - defineFunction("\\middle", { - numArgs: 1 - }, function (context, args) { - var delim = checkDelimiter(args[0], context); - if (!context.parser.leftrightDepth) { - throw new _ParseError2.default("\\middle without preceding \\left", delim); - } - - return { - type: "middle", - value: delim.value - }; - }); - - // Sizing functions (handled in Parser.js explicitly, hence no handler) - defineFunction(["\\tiny", "\\scriptsize", "\\footnotesize", "\\small", "\\normalsize", "\\large", "\\Large", "\\LARGE", "\\huge", "\\Huge"], 0, null); - - // Style changing functions (handled in Parser.js explicitly, hence no - // handler) - defineFunction(["\\displaystyle", "\\textstyle", "\\scriptstyle", "\\scriptscriptstyle"], 0, null); - - // Old font changing functions - defineFunction(["\\rm", "\\sf", "\\tt", "\\bf", "\\it"], 0, null); - - defineFunction([ - // styles - "\\mathrm", "\\mathit", "\\mathbf", - - // families - "\\mathbb", "\\mathcal", "\\mathfrak", "\\mathscr", "\\mathsf", "\\mathtt", - - // aliases - "\\Bbb", "\\bold", "\\frak"], { - numArgs: 1, - greediness: 2 - }, function (context, args) { - var body = args[0]; - var func = context.funcName; - if (func in fontAliases) { - func = fontAliases[func]; - } - return { - type: "font", - font: func.slice(1), - body: body - }; - }); - - // Accents - defineFunction(["\\acute", "\\grave", "\\ddot", "\\tilde", "\\bar", "\\breve", "\\check", "\\hat", "\\vec", "\\dot", "\\widehat", "\\widetilde", "\\overrightarrow", "\\overleftarrow", "\\Overrightarrow", "\\overleftrightarrow", "\\overgroup", "\\overlinesegment", "\\overleftharpoon", "\\overrightharpoon"], { - numArgs: 1 - }, function (context, args) { - var base = args[0]; - - var isStretchy = !_utils2.default.contains(["\\acute", "\\grave", "\\ddot", "\\tilde", "\\bar", "\\breve", "\\check", "\\hat", "\\vec", "\\dot"], context.funcName); - - var isShifty = !isStretchy || _utils2.default.contains(["\\widehat", "\\widetilde"], context.funcName); - - return { - type: "accent", - label: context.funcName, - isStretchy: isStretchy, - isShifty: isShifty, - value: ordargument(base), - base: base - }; - }); - - // Text-mode accents - defineFunction(["\\'", "\\`", "\\^", "\\~", "\\=", "\\u", "\\.", '\\"', "\\r", "\\H", "\\v"], { - numArgs: 1, - allowedInText: true, - allowedInMath: false - }, function (context, args) { - var base = args[0]; - - return { - type: "accent", - label: context.funcName, - isStretchy: false, - isShifty: true, - value: ordargument(base), - base: base - }; - }); - - // Horizontal stretchy braces - defineFunction(["\\overbrace", "\\underbrace"], { - numArgs: 1 - }, function (context, args) { - var base = args[0]; - return { - type: "horizBrace", - label: context.funcName, - isOver: /^\\over/.test(context.funcName), - base: base - }; - }); - - // Stretchy accents under the body - defineFunction(["\\underleftarrow", "\\underrightarrow", "\\underleftrightarrow", "\\undergroup", "\\underlinesegment", "\\undertilde"], { - numArgs: 1 - }, function (context, args) { - var body = args[0]; - return { - type: "accentUnder", - label: context.funcName, - value: ordargument(body), - body: body - }; - }); - - // Stretchy arrows with an optional argument - defineFunction(["\\xleftarrow", "\\xrightarrow", "\\xLeftarrow", "\\xRightarrow", "\\xleftrightarrow", "\\xLeftrightarrow", "\\xhookleftarrow", "\\xhookrightarrow", "\\xmapsto", "\\xrightharpoondown", "\\xrightharpoonup", "\\xleftharpoondown", "\\xleftharpoonup", "\\xrightleftharpoons", "\\xleftrightharpoons", "\\xLongequal", "\\xtwoheadrightarrow", "\\xtwoheadleftarrow", "\\xLongequal", "\\xtofrom"], { - numArgs: 1, - numOptionalArgs: 1 - }, function (context, args) { - var below = args[0]; - var body = args[1]; - return { - type: "xArrow", // x for extensible - label: context.funcName, - body: body, - below: below - }; - }); - - // enclose - defineFunction(["\\cancel", "\\bcancel", "\\xcancel", "\\sout", "\\fbox"], { - numArgs: 1 - }, function (context, args) { - var body = args[0]; - return { - type: "enclose", - label: context.funcName, - body: body - }; - }); - - // Infix generalized fractions - defineFunction(["\\over", "\\choose", "\\atop"], { - numArgs: 0, - infix: true - }, function (context) { - var replaceWith = void 0; - switch (context.funcName) { - case "\\over": - replaceWith = "\\frac"; - break; - case "\\choose": - replaceWith = "\\binom"; - break; - case "\\atop": - replaceWith = "\\\\atopfrac"; - break; - default: - throw new Error("Unrecognized infix genfrac command"); - } - return { - type: "infix", - replaceWith: replaceWith, - token: context.token - }; - }); - - // Row breaks for aligned data - defineFunction(["\\\\", "\\cr"], { - numArgs: 0, - numOptionalArgs: 1, - argTypes: ["size"] - }, function (context, args) { - var size = args[0]; - return { - type: "cr", - size: size - }; - }); - - // Environment delimiters - defineFunction(["\\begin", "\\end"], { - numArgs: 1, - argTypes: ["text"] - }, function (context, args) { - var nameGroup = args[0]; - if (nameGroup.type !== "ordgroup") { - throw new _ParseError2.default("Invalid environment name", nameGroup); - } - var name = ""; - for (var i = 0; i < nameGroup.value.length; ++i) { - name += nameGroup.value[i].value; - } - return { - type: "environment", - name: name, - nameGroup: nameGroup - }; - }); - - },{"./ParseError":29,"./ParseNode":30,"./utils":51}],44:[function(require,module,exports){ - - /** - * Predefined macros for KaTeX. - * This can be used to define some commands in terms of others. - */ - - // This function might one day accept additional argument and do more things. - function defineMacro(name, body) { - module.exports[name] = body; - } - - ////////////////////////////////////////////////////////////////////// - // basics - defineMacro("\\bgroup", "{"); - defineMacro("\\egroup", "}"); - defineMacro("\\begingroup", "{"); - defineMacro("\\endgroup", "}"); - - // We don't distinguish between math and nonmath kerns. - // (In TeX, the mu unit works only with \mkern.) - defineMacro("\\mkern", "\\kern"); - - ////////////////////////////////////////////////////////////////////// - // amsmath.sty - - // \def\overset#1#2{\binrel@{#2}\binrel@@{\mathop{\kern\z@#2}\limits^{#1}}} - defineMacro("\\overset", "\\mathop{#2}\\limits^{#1}"); - defineMacro("\\underset", "\\mathop{#2}\\limits_{#1}"); - - // \newcommand{\boxed}[1]{\fbox{\m@th$\displaystyle#1$}} - defineMacro("\\boxed", "\\fbox{\\displaystyle{#1}}"); - - //TODO: When implementing \dots, should ideally add the \DOTSB indicator - // into the macro, to indicate these are binary operators. - // \def\iff{\DOTSB\;\Longleftrightarrow\;} - // \def\implies{\DOTSB\;\Longrightarrow\;} - // \def\impliedby{\DOTSB\;\Longleftarrow\;} - defineMacro("\\iff", "\\;\\Longleftrightarrow\\;"); - defineMacro("\\implies", "\\;\\Longrightarrow\\;"); - defineMacro("\\impliedby", "\\;\\Longleftarrow\\;"); - - ////////////////////////////////////////////////////////////////////// - // mathtools.sty - - //\providecommand\ordinarycolon{:} - defineMacro("\\ordinarycolon", ":"); - //\def\vcentcolon{\mathrel{\mathop\ordinarycolon}} - //TODO(edemaine): Not yet centered. Fix via \raisebox or #726 - defineMacro("\\vcentcolon", "\\mathrel{\\mathop\\ordinarycolon}"); - // \providecommand*\dblcolon{\vcentcolon\mathrel{\mkern-.9mu}\vcentcolon} - defineMacro("\\dblcolon", "\\vcentcolon\\mathrel{\\mkern-.9mu}\\vcentcolon"); - // \providecommand*\coloneqq{\vcentcolon\mathrel{\mkern-1.2mu}=} - defineMacro("\\coloneqq", "\\vcentcolon\\mathrel{\\mkern-1.2mu}="); - // \providecommand*\Coloneqq{\dblcolon\mathrel{\mkern-1.2mu}=} - defineMacro("\\Coloneqq", "\\dblcolon\\mathrel{\\mkern-1.2mu}="); - // \providecommand*\coloneq{\vcentcolon\mathrel{\mkern-1.2mu}\mathrel{-}} - defineMacro("\\coloneq", "\\vcentcolon\\mathrel{\\mkern-1.2mu}\\mathrel{-}"); - // \providecommand*\Coloneq{\dblcolon\mathrel{\mkern-1.2mu}\mathrel{-}} - defineMacro("\\Coloneq", "\\dblcolon\\mathrel{\\mkern-1.2mu}\\mathrel{-}"); - // \providecommand*\eqqcolon{=\mathrel{\mkern-1.2mu}\vcentcolon} - defineMacro("\\eqqcolon", "=\\mathrel{\\mkern-1.2mu}\\vcentcolon"); - // \providecommand*\Eqqcolon{=\mathrel{\mkern-1.2mu}\dblcolon} - defineMacro("\\Eqqcolon", "=\\mathrel{\\mkern-1.2mu}\\dblcolon"); - // \providecommand*\eqcolon{\mathrel{-}\mathrel{\mkern-1.2mu}\vcentcolon} - defineMacro("\\eqcolon", "\\mathrel{-}\\mathrel{\\mkern-1.2mu}\\vcentcolon"); - // \providecommand*\Eqcolon{\mathrel{-}\mathrel{\mkern-1.2mu}\dblcolon} - defineMacro("\\Eqcolon", "\\mathrel{-}\\mathrel{\\mkern-1.2mu}\\dblcolon"); - // \providecommand*\colonapprox{\vcentcolon\mathrel{\mkern-1.2mu}\approx} - defineMacro("\\colonapprox", "\\vcentcolon\\mathrel{\\mkern-1.2mu}\\approx"); - // \providecommand*\Colonapprox{\dblcolon\mathrel{\mkern-1.2mu}\approx} - defineMacro("\\Colonapprox", "\\dblcolon\\mathrel{\\mkern-1.2mu}\\approx"); - // \providecommand*\colonsim{\vcentcolon\mathrel{\mkern-1.2mu}\sim} - defineMacro("\\colonsim", "\\vcentcolon\\mathrel{\\mkern-1.2mu}\\sim"); - // \providecommand*\Colonsim{\dblcolon\mathrel{\mkern-1.2mu}\sim} - defineMacro("\\Colonsim", "\\dblcolon\\mathrel{\\mkern-1.2mu}\\sim"); - - ////////////////////////////////////////////////////////////////////// - // colonequals.sty - - // Alternate names for mathtools's macros: - defineMacro("\\ratio", "\\vcentcolon"); - defineMacro("\\coloncolon", "\\dblcolon"); - defineMacro("\\colonequals", "\\coloneqq"); - defineMacro("\\coloncolonequals", "\\Coloneqq"); - defineMacro("\\equalscolon", "\\eqqcolon"); - defineMacro("\\equalscoloncolon", "\\Eqqcolon"); - defineMacro("\\colonminus", "\\coloneq"); - defineMacro("\\coloncolonminus", "\\Coloneq"); - defineMacro("\\minuscolon", "\\eqcolon"); - defineMacro("\\minuscoloncolon", "\\Eqcolon"); - // \colonapprox name is same in mathtools and colonequals. - defineMacro("\\coloncolonapprox", "\\Colonapprox"); - // \colonsim name is same in mathtools and colonequals. - defineMacro("\\coloncolonsim", "\\Colonsim"); - - // Additional macros, implemented by analogy with mathtools definitions: - defineMacro("\\simcolon", "\\sim\\mathrel{\\mkern-1.2mu}\\vcentcolon"); - defineMacro("\\simcoloncolon", "\\sim\\mathrel{\\mkern-1.2mu}\\dblcolon"); - defineMacro("\\approxcolon", "\\approx\\mathrel{\\mkern-1.2mu}\\vcentcolon"); - defineMacro("\\approxcoloncolon", "\\approx\\mathrel{\\mkern-1.2mu}\\dblcolon"); - - },{}],45:[function(require,module,exports){ - - var _classCallCheck2 = require("babel-runtime/helpers/classCallCheck"); - - var _classCallCheck3 = _interopRequireDefault(_classCallCheck2); - - var _createClass2 = require("babel-runtime/helpers/createClass"); - - var _createClass3 = _interopRequireDefault(_createClass2); - - var _utils = require("./utils"); - - var _utils2 = _interopRequireDefault(_utils); - - function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } - - /** - * This node represents a general purpose MathML node of any type. The - * constructor requires the type of node to create (for example, `"mo"` or - * `"mspace"`, corresponding to `` and `` tags). - */ - var MathNode = function () { - function MathNode(type, children) { - (0, _classCallCheck3.default)(this, MathNode); - - this.type = type; - this.attributes = {}; - this.children = children || []; - } - - /** - * Sets an attribute on a MathML node. MathML depends on attributes to convey a - * semantic content, so this is used heavily. - */ - - - (0, _createClass3.default)(MathNode, [{ - key: "setAttribute", - value: function setAttribute(name, value) { - this.attributes[name] = value; - } - - /** - * Converts the math node into a MathML-namespaced DOM element. - */ - - }, { - key: "toNode", - value: function toNode() { - var node = document.createElementNS("http://www.w3.org/1998/Math/MathML", this.type); - - for (var attr in this.attributes) { - if (Object.prototype.hasOwnProperty.call(this.attributes, attr)) { - node.setAttribute(attr, this.attributes[attr]); - } - } - - for (var i = 0; i < this.children.length; i++) { - node.appendChild(this.children[i].toNode()); - } - - return node; - } - - /** - * Converts the math node into an HTML markup string. - */ - - }, { - key: "toMarkup", - value: function toMarkup() { - var markup = "<" + this.type; - - // Add the attributes - for (var attr in this.attributes) { - if (Object.prototype.hasOwnProperty.call(this.attributes, attr)) { - markup += " " + attr + "=\""; - markup += _utils2.default.escape(this.attributes[attr]); - markup += "\""; - } - } - - markup += ">"; - - for (var i = 0; i < this.children.length; i++) { - markup += this.children[i].toMarkup(); - } - - markup += ""; - - return markup; - } - }]); - return MathNode; - }(); - - /** - * This node represents a piece of text. - */ - /** - * These objects store data about MathML nodes. This is the MathML equivalent - * of the types in domTree.js. Since MathML handles its own rendering, and - * since we're mainly using MathML to improve accessibility, we don't manage - * any of the styling state that the plain DOM nodes do. - * - * The `toNode` and `toMarkup` functions work simlarly to how they do in - * domTree.js, creating namespaced DOM nodes and HTML text markup respectively. - */ - - var TextNode = function () { - function TextNode(text) { - (0, _classCallCheck3.default)(this, TextNode); - - this.text = text; - } - - /** - * Converts the text node into a DOM text node. - */ - - - (0, _createClass3.default)(TextNode, [{ - key: "toNode", - value: function toNode() { - return document.createTextNode(this.text); - } - - /** - * Converts the text node into HTML markup (which is just the text itself). - */ - - }, { - key: "toMarkup", - value: function toMarkup() { - return _utils2.default.escape(this.text); - } - }]); - return TextNode; - }(); - - module.exports = { - MathNode: MathNode, - TextNode: TextNode - }; - - },{"./utils":51,"babel-runtime/helpers/classCallCheck":4,"babel-runtime/helpers/createClass":5}],46:[function(require,module,exports){ - - var _Parser = require('./Parser'); - - var _Parser2 = _interopRequireDefault(_Parser); - - function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } - - /** - * Parses an expression using a Parser, then returns the parsed result. - */ - var parseTree = function parseTree(toParse, settings) { - if (!(typeof toParse === 'string' || toParse instanceof String)) { - throw new TypeError('KaTeX can only parse string typed expression'); - } - var parser = new _Parser2.default(toParse, settings); - - return parser.parse(); - }; /** - * Provides a single function for parsing an expression using a Parser - * TODO(emily): Remove this - */ - - module.exports = parseTree; - - },{"./Parser":31}],47:[function(require,module,exports){ - - /** - * This file provides support to buildMathML.js and buildHTML.js - * for stretchy wide elements rendered from SVG files - * and other CSS trickery. - */ - - var buildCommon = require("./buildCommon"); - var mathMLTree = require("./mathMLTree"); - var utils = require("./utils"); - - var stretchyCodePoint = { - widehat: "^", - widetilde: "~", - undertilde: "~", - overleftarrow: "\u2190", - underleftarrow: "\u2190", - xleftarrow: "\u2190", - overrightarrow: "\u2192", - underrightarrow: "\u2192", - xrightarrow: "\u2192", - underbrace: "\u23B5", - overbrace: "\u23DE", - overleftrightarrow: "\u2194", - underleftrightarrow: "\u2194", - xleftrightarrow: "\u2194", - Overrightarrow: "\u21D2", - xRightarrow: "\u21D2", - overleftharpoon: "\u21BC", - xleftharpoonup: "\u21BC", - overrightharpoon: "\u21C0", - xrightharpoonup: "\u21C0", - xLeftarrow: "\u21D0", - xLeftrightarrow: "\u21D4", - xhookleftarrow: "\u21A9", - xhookrightarrow: "\u21AA", - xmapsto: "\u21A6", - xrightharpoondown: "\u21C1", - xleftharpoondown: "\u21BD", - xrightleftharpoons: "\u21CC", - xleftrightharpoons: "\u21CB", - xtwoheadleftarrow: "\u219E", - xtwoheadrightarrow: "\u21A0", - xLongequal: "=", - xtofrom: "\u21C4" - }; - - var mathMLnode = function mathMLnode(label) { - var node = new mathMLTree.MathNode("mo", [new mathMLTree.TextNode(stretchyCodePoint[label.substr(1)])]); - node.setAttribute("stretchy", "true"); - return node; - }; - - // In the katexImagesData object just below, the dimensions all - // correspond to path geometry inside the relevant SVG. - // For example, \rightarrow uses the same arrowhead as glyph U+2192 - // from the KaTeX Main font. The scaling factor is 1000. - // That is, inside the font, that arrowhead is 522 units tall, which - // corresponds to 0.522 em inside the document. - // And for extensible arrows, we split that distance around the math axis. - - var katexImagesData = { - // height, depth, imageName, minWidth - overleftarrow: [0.522, 0, "leftarrow", 0.5], - underleftarrow: [0.522, 0, "leftarrow", 0.5], - xleftarrow: [0.261, 0.261, "leftarrow", 0.783], - overrightarrow: [0.522, 0, "rightarrow", 0.5], - underrightarrow: [0.522, 0, "rightarrow", 0.5], - xrightarrow: [0.261, 0.261, "rightarrow", 0.783], - overbrace: [0.548, 0, "overbrace", 1.6], - underbrace: [0.548, 0, "underbrace", 1.6], - overleftrightarrow: [0.522, 0, "leftrightarrow", 0.5], - underleftrightarrow: [0.522, 0, "leftrightarrow", 0.5], - xleftrightarrow: [0.261, 0.261, "leftrightarrow", 0.783], - Overrightarrow: [0.56, 0, "doublerightarrow", 0.5], - xLeftarrow: [0.28, 0.28, "doubleleftarrow", 0.783], - xRightarrow: [0.28, 0.28, "doublerightarrow", 0.783], - xLeftrightarrow: [0.28, 0.28, "doubleleftrightarrow", 0.955], - overleftharpoon: [0.522, 0, "leftharpoon", 0.5], - overrightharpoon: [0.522, 0, "rightharpoon", 0.5], - xleftharpoonup: [0.261, 0.261, "leftharpoon", 0.783], - xrightharpoonup: [0.261, 0.261, "rightharpoon", 0.783], - xhookleftarrow: [0.261, 0.261, "hookleftarrow", 0.87], - xhookrightarrow: [0.261, 0.261, "hookrightarrow", 0.87], - overlinesegment: [0.414, 0, "linesegment", 0.5], - underlinesegment: [0.414, 0, "linesegment", 0.5], - xmapsto: [0.261, 0.261, "mapsto", 0.783], - xrightharpoondown: [0.261, 0.261, "rightharpoondown", 0.783], - xleftharpoondown: [0.261, 0.261, "leftharpoondown", 0.783], - xrightleftharpoons: [0.358, 0.358, "rightleftharpoons", 0.716], - xleftrightharpoons: [0.358, 0.358, "leftrightharpoons", 0.716], - overgroup: [0.342, 0, "overgroup", 0.87], - undergroup: [0.342, 0, "undergroup", 0.87], - xtwoheadleftarrow: [0.167, 0.167, "twoheadleftarrow", 0.86], - xtwoheadrightarrow: [0.167, 0.167, "twoheadrightarrow", 0.86], - xLongequal: [0.167, 0.167, "longequal", 0.5], - xtofrom: [0.264, 0.264, "tofrom", 0.86] - }; - - // Many of the KaTeX SVG images have been adapted from glyphs in KaTeX fonts. - // Copyright (c) 2009-2010, Design Science, Inc. () - // Copyright (c) 2014-2017 Khan Academy () - // Licensed under the SIL Open Font License, Version 1.1. - // See \nhttp://scripts.sil.org/OFL - - // Nested SVGs - // Many of the KaTeX SVG images contain a nested SVG. This is done to - // achieve a stretchy image while avoiding distortion of arrowheads or - // brace corners. - - // The inner SVG typically contains a very long (400 em) arrow. - - // The outer SVG acts like a window that exposes only part of the inner SVG. - // The outer SVG will grow or shrink to match the dimensions set by CSS. - - // The inner SVG always has a longer, thinner aspect ratio than the outer - // SVG. After the inner SVG fills 100% of the height of the outer SVG, - // there is a long arrow shaft left over. That left-over shaft is not shown. - // Instead, it is sliced off because the inner SVG is set to - // "preserveAspectRatio='... slice'". - - // Thus, the reader sees an arrow that matches the subject matter width - // without distortion. - - // Some functions, such as \cancel, need to vary their aspect ratio. These - // functions do not get the nested SVG treatment. - - // Second Brush Stroke - // Low resolution monitors struggle to display images in fine detail. - // So browsers apply anti-aliasing. A long straight arrow shaft therefore - // will sometimes appear as if it has a blurred edge. - - // To mitigate this, these SVG files contain a second "brush-stroke" on the - // arrow shafts. That is, a second long thin rectangular SVG path has been - // written directly on top of each arrow shaft. This reinforcement causes - // some of the screen pixels to display as black instead of the anti-aliased - // gray pixel that a single path would generate. So we get arrow shafts - // whose edges appear to be sharper. - - var svgPath = { - doubleleftarrow: "", - - doublerightarrow: "", - - leftarrow: "", - - rightarrow: "" - }; - - var innerSVG = { - // Since bcancel's SVG is inline and it omits the viewBox attribute, - // it's stroke-width will not vary with span area. - bcancel: "", - - cancel: "", - - // The doubleleftarrow geometry is from glyph U+21D0 in the font KaTeX Main - doubleleftarrow: ">" + svgPath["doubleleftarrow"] + "", - - // doubleleftrightarrow is from glyph U+21D4 in font KaTeX Main - doubleleftrightarrow: ">" + svgPath["doubleleftarrow"] + "\n" + svgPath["doublerightarrow"] + "", - - // doublerightarrow is from glyph U+21D2 in font KaTeX Main - doublerightarrow: ">" + svgPath["doublerightarrow"] + "", - - // hookleftarrow is from glyph U+21A9 in font KaTeX Main - hookleftarrow: ">" + svgPath["leftarrow"] + "\n", - - // hookrightarrow is from glyph U+21AA in font KaTeX Main - hookrightarrow: ">" + svgPath["rightarrow"] + "", - - // leftarrow is from glyph U+2190 in font KaTeX Main - leftarrow: ">" + svgPath["leftarrow"] + "", - - // leftharpoon is from glyph U+21BD in font KaTeX Main - leftharpoon: ">", - - // leftharpoondown is from glyph U+21BD in font KaTeX Main - leftharpoondown: ">", - - // leftrightarrow is from glyph U+2194 in font KaTeX Main - leftrightarrow: ">" + svgPath["leftarrow"] + "\n" + svgPath["rightarrow"] + "", - - // leftrightharpoons is from glyphs U+21BC/21B1 in font KaTeX Main - leftrightharpoons: ">\n", - - linesegment: ">\n", - - longequal: " viewBox='0 0 100 334' preserveAspectRatio='none'>\n", - - // mapsto is from glyph U+21A6 in font KaTeX Main - mapsto: ">" + svgPath["rightarrow"] + "", - - // overbrace is from glyphs U+23A9/23A8/23A7 in font KaTeX_Size4-Regular - overbrace: ">\n", - - // overgroup is from the MnSymbol package (public domain) - overgroup: ">", - - // rightarrow is from glyph U+2192 in font KaTeX Main - rightarrow: ">" + svgPath["rightarrow"] + "", - - // rightharpoon is from glyph U+21C0 in font KaTeX Main - rightharpoon: ">", - - // rightharpoondown is from glyph U+21C1 in font KaTeX Main - rightharpoondown: ">", - - // rightleftharpoons is from glyph U+21CC in font KaTeX Main - rightleftharpoons: ">", - - // tilde1 is a modified version of a glyph from the MnSymbol package - tilde1: " viewBox='0 0 600 260' preserveAspectRatio='none'>\n", - - // Ditto tilde2, tilde3, and tilde 4 - tilde2: " viewBox='0 0 1033 286' preserveAspectRatio='none'>\n", - - tilde3: " viewBox='0 0 2339 306' preserveAspectRatio='none'>\n", - - tilde4: " viewBox='0 0 2340 312' preserveAspectRatio='none'>\n", - - // tofrom is from glyph U+21C4 in font KaTeX AMS Regular - tofrom: ">", - - // twoheadleftarrow is from glyph U+219E in font KaTeX AMS Regular - twoheadleftarrow: ">\n", - - // twoheadrightarrow is from glyph U+21A0 in font KaTeX AMS Regular - twoheadrightarrow: ">\n", - - // underbrace is from glyphs U+23A9/23A8/23A7 in font KaTeX_Size4-Regular - underbrace: ">\n", - - // undergroup is from the MnSymbol package (public domain) - undergroup: ">", - - // widehat1 is a modified version of a glyph from the MnSymbol package - widehat1: " viewBox='0 0 1062 239' preserveAspectRatio='none'>\n", - - // Ditto widehat2, widehat3, and widehat4 - widehat2: " viewBox='0 0 2364 300' preserveAspectRatio='none'>\n", - - widehat3: " viewBox='0 0 2364 360' preserveAspectRatio='none'>\n", - - widehat4: " viewBox='0 0 2364 420' preserveAspectRatio='none'>\n", - - xcancel: "\n" - }; - - var svgSpan = function svgSpan(group, options) { - // Create a span with inline SVG for the element. - var label = group.value.label.substr(1); - var height = 0; - var depth = 0; - var imageName = ""; - var minWidth = 0; - - if (utils.contains(["widehat", "widetilde", "undertilde"], label)) { - // There are four SVG images available for each function. - // Choose a taller image when there are more characters. - var numChars = group.value.value.length; - if (numChars > 5) { - height = 0.312; - imageName = (label === "widehat" ? "widehat" : "tilde") + "4"; - } else { - var imgIndex = [1, 1, 2, 2, 3, 3][numChars]; - if (label === "widehat") { - height = [0, 0.24, 0.30, 0.30, 0.36, 0.36][numChars]; - imageName = "widehat" + imgIndex; - } else { - height = [0, 0.26, 0.30, 0.30, 0.34, 0.34][numChars]; - imageName = "tilde" + imgIndex; - } - } - } else { - var imgData = katexImagesData[label]; - height = imgData[0]; - depth = imgData[1]; - imageName = imgData[2]; - minWidth = imgData[3]; - } - - var span = buildCommon.makeSpan([], [], options); - span.height = height; - span.depth = depth; - var totalHeight = height + depth; - span.style.height = totalHeight + "em"; - if (minWidth > 0) { - span.style.minWidth = minWidth + "em"; - } - - span.innerHTML = ""; - - return span; - }; - - var encloseSpan = function encloseSpan(inner, label, pad, options) { - // Return an image span for \cancel, \bcancel, \xcancel, or \fbox - var img = void 0; - var totalHeight = inner.height + inner.depth + 2 * pad; - - if (label === "fbox") { - img = buildCommon.makeSpan(["stretchy", label], [], options); - if (options.color) { - img.style.borderColor = options.getColor(); - } - } else { - img = buildCommon.makeSpan([], [], options); - img.innerHTML = "" + innerSVG[label] + ""; - } - - img.height = totalHeight; - img.style.height = totalHeight + "em"; - - return img; - }; - - module.exports = { - encloseSpan: encloseSpan, - mathMLnode: mathMLnode, - svgSpan: svgSpan - }; - - },{"./buildCommon":34,"./mathMLTree":45,"./utils":51}],48:[function(require,module,exports){ - - /** - * This file holds a list of all no-argument functions and single-character - * symbols (like 'a' or ';'). - * - * For each of the symbols, there are three properties they can have: - * - font (required): the font to be used for this symbol. Either "main" (the - normal font), or "ams" (the ams fonts). - * - group (required): the ParseNode group type the symbol should have (i.e. - "textord", "mathord", etc). - See https://github.com/Khan/KaTeX/wiki/Examining-TeX#group-types - * - replace: the character that this symbol or function should be - * replaced with (i.e. "\phi" has a replace value of "\u03d5", the phi - * character in the main font). - * - * The outermost map in the table indicates what mode the symbols should be - * accepted in (e.g. "math" or "text"). - */ - - module.exports = { - math: {}, - text: {} - }; - - function defineSymbol(mode, font, group, replace, name, acceptUnicodeChar) { - module.exports[mode][name] = { - font: font, - group: group, - replace: replace - }; - - if (acceptUnicodeChar) { - module.exports[mode][replace] = module.exports[mode][name]; - } - } - - // Some abbreviations for commonly used strings. - // This helps minify the code, and also spotting typos using jshint. - - // modes: - var math = "math"; - var text = "text"; - - // fonts: - var main = "main"; - var ams = "ams"; - - // groups: - var accent = "accent"; - var bin = "bin"; - var close = "close"; - var inner = "inner"; - var mathord = "mathord"; - var op = "op"; - var open = "open"; - var punct = "punct"; - var rel = "rel"; - var spacing = "spacing"; - var textord = "textord"; - - // Now comes the symbol table - - // Relation Symbols - defineSymbol(math, main, rel, "\u2261", "\\equiv"); - defineSymbol(math, main, rel, "\u227A", "\\prec"); - defineSymbol(math, main, rel, "\u227B", "\\succ"); - defineSymbol(math, main, rel, "\u223C", "\\sim"); - defineSymbol(math, main, rel, "\u22A5", "\\perp"); - defineSymbol(math, main, rel, "\u2AAF", "\\preceq"); - defineSymbol(math, main, rel, "\u2AB0", "\\succeq"); - defineSymbol(math, main, rel, "\u2243", "\\simeq"); - defineSymbol(math, main, rel, "\u2223", "\\mid"); - defineSymbol(math, main, rel, "\u226A", "\\ll"); - defineSymbol(math, main, rel, "\u226B", "\\gg"); - defineSymbol(math, main, rel, "\u224D", "\\asymp"); - defineSymbol(math, main, rel, "\u2225", "\\parallel"); - defineSymbol(math, main, rel, "\u22C8", "\\bowtie"); - defineSymbol(math, main, rel, "\u2323", "\\smile"); - defineSymbol(math, main, rel, "\u2291", "\\sqsubseteq"); - defineSymbol(math, main, rel, "\u2292", "\\sqsupseteq"); - defineSymbol(math, main, rel, "\u2250", "\\doteq"); - defineSymbol(math, main, rel, "\u2322", "\\frown"); - defineSymbol(math, main, rel, "\u220B", "\\ni"); - defineSymbol(math, main, rel, "\u221D", "\\propto"); - defineSymbol(math, main, rel, "\u22A2", "\\vdash"); - defineSymbol(math, main, rel, "\u22A3", "\\dashv"); - defineSymbol(math, main, rel, "\u220B", "\\owns"); - - // Punctuation - defineSymbol(math, main, punct, ".", "\\ldotp"); - defineSymbol(math, main, punct, "\u22C5", "\\cdotp"); - - // Misc Symbols - defineSymbol(math, main, textord, "#", "\\#"); - defineSymbol(text, main, textord, "#", "\\#"); - defineSymbol(math, main, textord, "&", "\\&"); - defineSymbol(text, main, textord, "&", "\\&"); - defineSymbol(math, main, textord, "\u2135", "\\aleph"); - defineSymbol(math, main, textord, "\u2200", "\\forall"); - defineSymbol(math, main, textord, "\u210F", "\\hbar"); - defineSymbol(math, main, textord, "\u2203", "\\exists"); - defineSymbol(math, main, textord, "\u2207", "\\nabla"); - defineSymbol(math, main, textord, "\u266D", "\\flat"); - defineSymbol(math, main, textord, "\u2113", "\\ell"); - defineSymbol(math, main, textord, "\u266E", "\\natural"); - defineSymbol(math, main, textord, "\u2663", "\\clubsuit"); - defineSymbol(math, main, textord, "\u2118", "\\wp"); - defineSymbol(math, main, textord, "\u266F", "\\sharp"); - defineSymbol(math, main, textord, "\u2662", "\\diamondsuit"); - defineSymbol(math, main, textord, "\u211C", "\\Re"); - defineSymbol(math, main, textord, "\u2661", "\\heartsuit"); - defineSymbol(math, main, textord, "\u2111", "\\Im"); - defineSymbol(math, main, textord, "\u2660", "\\spadesuit"); - - // Math and Text - defineSymbol(math, main, textord, "\u2020", "\\dag"); - defineSymbol(text, main, textord, "\u2020", "\\dag"); - defineSymbol(text, main, textord, "\u2020", "\\textdagger"); - defineSymbol(math, main, textord, "\u2021", "\\ddag"); - defineSymbol(text, main, textord, "\u2021", "\\ddag"); - defineSymbol(text, main, textord, "\u2020", "\\textdaggerdbl"); - - // Large Delimiters - defineSymbol(math, main, close, "\u23B1", "\\rmoustache"); - defineSymbol(math, main, open, "\u23B0", "\\lmoustache"); - defineSymbol(math, main, close, "\u27EF", "\\rgroup"); - defineSymbol(math, main, open, "\u27EE", "\\lgroup"); - - // Binary Operators - defineSymbol(math, main, bin, "\u2213", "\\mp"); - defineSymbol(math, main, bin, "\u2296", "\\ominus"); - defineSymbol(math, main, bin, "\u228E", "\\uplus"); - defineSymbol(math, main, bin, "\u2293", "\\sqcap"); - defineSymbol(math, main, bin, "\u2217", "\\ast"); - defineSymbol(math, main, bin, "\u2294", "\\sqcup"); - defineSymbol(math, main, bin, "\u25EF", "\\bigcirc"); - defineSymbol(math, main, bin, "\u2219", "\\bullet"); - defineSymbol(math, main, bin, "\u2021", "\\ddagger"); - defineSymbol(math, main, bin, "\u2240", "\\wr"); - defineSymbol(math, main, bin, "\u2A3F", "\\amalg"); - - // Arrow Symbols - defineSymbol(math, main, rel, "\u27F5", "\\longleftarrow"); - defineSymbol(math, main, rel, "\u21D0", "\\Leftarrow"); - defineSymbol(math, main, rel, "\u27F8", "\\Longleftarrow"); - defineSymbol(math, main, rel, "\u27F6", "\\longrightarrow"); - defineSymbol(math, main, rel, "\u21D2", "\\Rightarrow"); - defineSymbol(math, main, rel, "\u27F9", "\\Longrightarrow"); - defineSymbol(math, main, rel, "\u2194", "\\leftrightarrow"); - defineSymbol(math, main, rel, "\u27F7", "\\longleftrightarrow"); - defineSymbol(math, main, rel, "\u21D4", "\\Leftrightarrow"); - defineSymbol(math, main, rel, "\u27FA", "\\Longleftrightarrow"); - defineSymbol(math, main, rel, "\u21A6", "\\mapsto"); - defineSymbol(math, main, rel, "\u27FC", "\\longmapsto"); - defineSymbol(math, main, rel, "\u2197", "\\nearrow"); - defineSymbol(math, main, rel, "\u21A9", "\\hookleftarrow"); - defineSymbol(math, main, rel, "\u21AA", "\\hookrightarrow"); - defineSymbol(math, main, rel, "\u2198", "\\searrow"); - defineSymbol(math, main, rel, "\u21BC", "\\leftharpoonup"); - defineSymbol(math, main, rel, "\u21C0", "\\rightharpoonup"); - defineSymbol(math, main, rel, "\u2199", "\\swarrow"); - defineSymbol(math, main, rel, "\u21BD", "\\leftharpoondown"); - defineSymbol(math, main, rel, "\u21C1", "\\rightharpoondown"); - defineSymbol(math, main, rel, "\u2196", "\\nwarrow"); - defineSymbol(math, main, rel, "\u21CC", "\\rightleftharpoons"); - - // AMS Negated Binary Relations - defineSymbol(math, ams, rel, "\u226E", "\\nless"); - defineSymbol(math, ams, rel, "\uE010", "\\nleqslant"); - defineSymbol(math, ams, rel, "\uE011", "\\nleqq"); - defineSymbol(math, ams, rel, "\u2A87", "\\lneq"); - defineSymbol(math, ams, rel, "\u2268", "\\lneqq"); - defineSymbol(math, ams, rel, "\uE00C", "\\lvertneqq"); - defineSymbol(math, ams, rel, "\u22E6", "\\lnsim"); - defineSymbol(math, ams, rel, "\u2A89", "\\lnapprox"); - defineSymbol(math, ams, rel, "\u2280", "\\nprec"); - defineSymbol(math, ams, rel, "\u22E0", "\\npreceq"); - defineSymbol(math, ams, rel, "\u22E8", "\\precnsim"); - defineSymbol(math, ams, rel, "\u2AB9", "\\precnapprox"); - defineSymbol(math, ams, rel, "\u2241", "\\nsim"); - defineSymbol(math, ams, rel, "\uE006", "\\nshortmid"); - defineSymbol(math, ams, rel, "\u2224", "\\nmid"); - defineSymbol(math, ams, rel, "\u22AC", "\\nvdash"); - defineSymbol(math, ams, rel, "\u22AD", "\\nvDash"); - defineSymbol(math, ams, rel, "\u22EA", "\\ntriangleleft"); - defineSymbol(math, ams, rel, "\u22EC", "\\ntrianglelefteq"); - defineSymbol(math, ams, rel, "\u228A", "\\subsetneq"); - defineSymbol(math, ams, rel, "\uE01A", "\\varsubsetneq"); - defineSymbol(math, ams, rel, "\u2ACB", "\\subsetneqq"); - defineSymbol(math, ams, rel, "\uE017", "\\varsubsetneqq"); - defineSymbol(math, ams, rel, "\u226F", "\\ngtr"); - defineSymbol(math, ams, rel, "\uE00F", "\\ngeqslant"); - defineSymbol(math, ams, rel, "\uE00E", "\\ngeqq"); - defineSymbol(math, ams, rel, "\u2A88", "\\gneq"); - defineSymbol(math, ams, rel, "\u2269", "\\gneqq"); - defineSymbol(math, ams, rel, "\uE00D", "\\gvertneqq"); - defineSymbol(math, ams, rel, "\u22E7", "\\gnsim"); - defineSymbol(math, ams, rel, "\u2A8A", "\\gnapprox"); - defineSymbol(math, ams, rel, "\u2281", "\\nsucc"); - defineSymbol(math, ams, rel, "\u22E1", "\\nsucceq"); - defineSymbol(math, ams, rel, "\u22E9", "\\succnsim"); - defineSymbol(math, ams, rel, "\u2ABA", "\\succnapprox"); - defineSymbol(math, ams, rel, "\u2246", "\\ncong"); - defineSymbol(math, ams, rel, "\uE007", "\\nshortparallel"); - defineSymbol(math, ams, rel, "\u2226", "\\nparallel"); - defineSymbol(math, ams, rel, "\u22AF", "\\nVDash"); - defineSymbol(math, ams, rel, "\u22EB", "\\ntriangleright"); - defineSymbol(math, ams, rel, "\u22ED", "\\ntrianglerighteq"); - defineSymbol(math, ams, rel, "\uE018", "\\nsupseteqq"); - defineSymbol(math, ams, rel, "\u228B", "\\supsetneq"); - defineSymbol(math, ams, rel, "\uE01B", "\\varsupsetneq"); - defineSymbol(math, ams, rel, "\u2ACC", "\\supsetneqq"); - defineSymbol(math, ams, rel, "\uE019", "\\varsupsetneqq"); - defineSymbol(math, ams, rel, "\u22AE", "\\nVdash"); - defineSymbol(math, ams, rel, "\u2AB5", "\\precneqq"); - defineSymbol(math, ams, rel, "\u2AB6", "\\succneqq"); - defineSymbol(math, ams, rel, "\uE016", "\\nsubseteqq"); - defineSymbol(math, ams, bin, "\u22B4", "\\unlhd"); - defineSymbol(math, ams, bin, "\u22B5", "\\unrhd"); - - // AMS Negated Arrows - defineSymbol(math, ams, rel, "\u219A", "\\nleftarrow"); - defineSymbol(math, ams, rel, "\u219B", "\\nrightarrow"); - defineSymbol(math, ams, rel, "\u21CD", "\\nLeftarrow"); - defineSymbol(math, ams, rel, "\u21CF", "\\nRightarrow"); - defineSymbol(math, ams, rel, "\u21AE", "\\nleftrightarrow"); - defineSymbol(math, ams, rel, "\u21CE", "\\nLeftrightarrow"); - - // AMS Misc - defineSymbol(math, ams, rel, "\u25B3", "\\vartriangle"); - defineSymbol(math, ams, textord, "\u210F", "\\hslash"); - defineSymbol(math, ams, textord, "\u25BD", "\\triangledown"); - defineSymbol(math, ams, textord, "\u25CA", "\\lozenge"); - defineSymbol(math, ams, textord, "\u24C8", "\\circledS"); - defineSymbol(math, ams, textord, "\xAE", "\\circledR"); - defineSymbol(text, ams, textord, "\xAE", "\\circledR"); - defineSymbol(math, ams, textord, "\u2221", "\\measuredangle"); - defineSymbol(math, ams, textord, "\u2204", "\\nexists"); - defineSymbol(math, ams, textord, "\u2127", "\\mho"); - defineSymbol(math, ams, textord, "\u2132", "\\Finv"); - defineSymbol(math, ams, textord, "\u2141", "\\Game"); - defineSymbol(math, ams, textord, "k", "\\Bbbk"); - defineSymbol(math, ams, textord, "\u2035", "\\backprime"); - defineSymbol(math, ams, textord, "\u25B2", "\\blacktriangle"); - defineSymbol(math, ams, textord, "\u25BC", "\\blacktriangledown"); - defineSymbol(math, ams, textord, "\u25A0", "\\blacksquare"); - defineSymbol(math, ams, textord, "\u29EB", "\\blacklozenge"); - defineSymbol(math, ams, textord, "\u2605", "\\bigstar"); - defineSymbol(math, ams, textord, "\u2222", "\\sphericalangle"); - defineSymbol(math, ams, textord, "\u2201", "\\complement"); - defineSymbol(math, ams, textord, "\xF0", "\\eth"); - defineSymbol(math, ams, textord, "\u2571", "\\diagup"); - defineSymbol(math, ams, textord, "\u2572", "\\diagdown"); - defineSymbol(math, ams, textord, "\u25A1", "\\square"); - defineSymbol(math, ams, textord, "\u25A1", "\\Box"); - defineSymbol(math, ams, textord, "\u25CA", "\\Diamond"); - defineSymbol(math, ams, textord, "\xA5", "\\yen"); - defineSymbol(math, ams, textord, "\u2713", "\\checkmark"); - defineSymbol(text, ams, textord, "\u2713", "\\checkmark"); - - // AMS Hebrew - defineSymbol(math, ams, textord, "\u2136", "\\beth"); - defineSymbol(math, ams, textord, "\u2138", "\\daleth"); - defineSymbol(math, ams, textord, "\u2137", "\\gimel"); - - // AMS Greek - defineSymbol(math, ams, textord, "\u03DD", "\\digamma"); - defineSymbol(math, ams, textord, "\u03F0", "\\varkappa"); - - // AMS Delimiters - defineSymbol(math, ams, open, "\u250C", "\\ulcorner"); - defineSymbol(math, ams, close, "\u2510", "\\urcorner"); - defineSymbol(math, ams, open, "\u2514", "\\llcorner"); - defineSymbol(math, ams, close, "\u2518", "\\lrcorner"); - - // AMS Binary Relations - defineSymbol(math, ams, rel, "\u2266", "\\leqq"); - defineSymbol(math, ams, rel, "\u2A7D", "\\leqslant"); - defineSymbol(math, ams, rel, "\u2A95", "\\eqslantless"); - defineSymbol(math, ams, rel, "\u2272", "\\lesssim"); - defineSymbol(math, ams, rel, "\u2A85", "\\lessapprox"); - defineSymbol(math, ams, rel, "\u224A", "\\approxeq"); - defineSymbol(math, ams, bin, "\u22D6", "\\lessdot"); - defineSymbol(math, ams, rel, "\u22D8", "\\lll"); - defineSymbol(math, ams, rel, "\u2276", "\\lessgtr"); - defineSymbol(math, ams, rel, "\u22DA", "\\lesseqgtr"); - defineSymbol(math, ams, rel, "\u2A8B", "\\lesseqqgtr"); - defineSymbol(math, ams, rel, "\u2251", "\\doteqdot"); - defineSymbol(math, ams, rel, "\u2253", "\\risingdotseq"); - defineSymbol(math, ams, rel, "\u2252", "\\fallingdotseq"); - defineSymbol(math, ams, rel, "\u223D", "\\backsim"); - defineSymbol(math, ams, rel, "\u22CD", "\\backsimeq"); - defineSymbol(math, ams, rel, "\u2AC5", "\\subseteqq"); - defineSymbol(math, ams, rel, "\u22D0", "\\Subset"); - defineSymbol(math, ams, rel, "\u228F", "\\sqsubset"); - defineSymbol(math, ams, rel, "\u227C", "\\preccurlyeq"); - defineSymbol(math, ams, rel, "\u22DE", "\\curlyeqprec"); - defineSymbol(math, ams, rel, "\u227E", "\\precsim"); - defineSymbol(math, ams, rel, "\u2AB7", "\\precapprox"); - defineSymbol(math, ams, rel, "\u22B2", "\\vartriangleleft"); - defineSymbol(math, ams, rel, "\u22B4", "\\trianglelefteq"); - defineSymbol(math, ams, rel, "\u22A8", "\\vDash"); - defineSymbol(math, ams, rel, "\u22AA", "\\Vvdash"); - defineSymbol(math, ams, rel, "\u2323", "\\smallsmile"); - defineSymbol(math, ams, rel, "\u2322", "\\smallfrown"); - defineSymbol(math, ams, rel, "\u224F", "\\bumpeq"); - defineSymbol(math, ams, rel, "\u224E", "\\Bumpeq"); - defineSymbol(math, ams, rel, "\u2267", "\\geqq"); - defineSymbol(math, ams, rel, "\u2A7E", "\\geqslant"); - defineSymbol(math, ams, rel, "\u2A96", "\\eqslantgtr"); - defineSymbol(math, ams, rel, "\u2273", "\\gtrsim"); - defineSymbol(math, ams, rel, "\u2A86", "\\gtrapprox"); - defineSymbol(math, ams, bin, "\u22D7", "\\gtrdot"); - defineSymbol(math, ams, rel, "\u22D9", "\\ggg"); - defineSymbol(math, ams, rel, "\u2277", "\\gtrless"); - defineSymbol(math, ams, rel, "\u22DB", "\\gtreqless"); - defineSymbol(math, ams, rel, "\u2A8C", "\\gtreqqless"); - defineSymbol(math, ams, rel, "\u2256", "\\eqcirc"); - defineSymbol(math, ams, rel, "\u2257", "\\circeq"); - defineSymbol(math, ams, rel, "\u225C", "\\triangleq"); - defineSymbol(math, ams, rel, "\u223C", "\\thicksim"); - defineSymbol(math, ams, rel, "\u2248", "\\thickapprox"); - defineSymbol(math, ams, rel, "\u2AC6", "\\supseteqq"); - defineSymbol(math, ams, rel, "\u22D1", "\\Supset"); - defineSymbol(math, ams, rel, "\u2290", "\\sqsupset"); - defineSymbol(math, ams, rel, "\u227D", "\\succcurlyeq"); - defineSymbol(math, ams, rel, "\u22DF", "\\curlyeqsucc"); - defineSymbol(math, ams, rel, "\u227F", "\\succsim"); - defineSymbol(math, ams, rel, "\u2AB8", "\\succapprox"); - defineSymbol(math, ams, rel, "\u22B3", "\\vartriangleright"); - defineSymbol(math, ams, rel, "\u22B5", "\\trianglerighteq"); - defineSymbol(math, ams, rel, "\u22A9", "\\Vdash"); - defineSymbol(math, ams, rel, "\u2223", "\\shortmid"); - defineSymbol(math, ams, rel, "\u2225", "\\shortparallel"); - defineSymbol(math, ams, rel, "\u226C", "\\between"); - defineSymbol(math, ams, rel, "\u22D4", "\\pitchfork"); - defineSymbol(math, ams, rel, "\u221D", "\\varpropto"); - defineSymbol(math, ams, rel, "\u25C0", "\\blacktriangleleft"); - defineSymbol(math, ams, rel, "\u2234", "\\therefore"); - defineSymbol(math, ams, rel, "\u220D", "\\backepsilon"); - defineSymbol(math, ams, rel, "\u25B6", "\\blacktriangleright"); - defineSymbol(math, ams, rel, "\u2235", "\\because"); - defineSymbol(math, ams, rel, "\u22D8", "\\llless"); - defineSymbol(math, ams, rel, "\u22D9", "\\gggtr"); - defineSymbol(math, ams, bin, "\u22B2", "\\lhd"); - defineSymbol(math, ams, bin, "\u22B3", "\\rhd"); - defineSymbol(math, ams, rel, "\u2242", "\\eqsim"); - defineSymbol(math, main, rel, "\u22C8", "\\Join"); - defineSymbol(math, ams, rel, "\u2251", "\\Doteq"); - - // AMS Binary Operators - defineSymbol(math, ams, bin, "\u2214", "\\dotplus"); - defineSymbol(math, ams, bin, "\u2216", "\\smallsetminus"); - defineSymbol(math, ams, bin, "\u22D2", "\\Cap"); - defineSymbol(math, ams, bin, "\u22D3", "\\Cup"); - defineSymbol(math, ams, bin, "\u2A5E", "\\doublebarwedge"); - defineSymbol(math, ams, bin, "\u229F", "\\boxminus"); - defineSymbol(math, ams, bin, "\u229E", "\\boxplus"); - defineSymbol(math, ams, bin, "\u22C7", "\\divideontimes"); - defineSymbol(math, ams, bin, "\u22C9", "\\ltimes"); - defineSymbol(math, ams, bin, "\u22CA", "\\rtimes"); - defineSymbol(math, ams, bin, "\u22CB", "\\leftthreetimes"); - defineSymbol(math, ams, bin, "\u22CC", "\\rightthreetimes"); - defineSymbol(math, ams, bin, "\u22CF", "\\curlywedge"); - defineSymbol(math, ams, bin, "\u22CE", "\\curlyvee"); - defineSymbol(math, ams, bin, "\u229D", "\\circleddash"); - defineSymbol(math, ams, bin, "\u229B", "\\circledast"); - defineSymbol(math, ams, bin, "\u22C5", "\\centerdot"); - defineSymbol(math, ams, bin, "\u22BA", "\\intercal"); - defineSymbol(math, ams, bin, "\u22D2", "\\doublecap"); - defineSymbol(math, ams, bin, "\u22D3", "\\doublecup"); - defineSymbol(math, ams, bin, "\u22A0", "\\boxtimes"); - - // AMS Arrows - defineSymbol(math, ams, rel, "\u21E2", "\\dashrightarrow"); - defineSymbol(math, ams, rel, "\u21E0", "\\dashleftarrow"); - defineSymbol(math, ams, rel, "\u21C7", "\\leftleftarrows"); - defineSymbol(math, ams, rel, "\u21C6", "\\leftrightarrows"); - defineSymbol(math, ams, rel, "\u21DA", "\\Lleftarrow"); - defineSymbol(math, ams, rel, "\u219E", "\\twoheadleftarrow"); - defineSymbol(math, ams, rel, "\u21A2", "\\leftarrowtail"); - defineSymbol(math, ams, rel, "\u21AB", "\\looparrowleft"); - defineSymbol(math, ams, rel, "\u21CB", "\\leftrightharpoons"); - defineSymbol(math, ams, rel, "\u21B6", "\\curvearrowleft"); - defineSymbol(math, ams, rel, "\u21BA", "\\circlearrowleft"); - defineSymbol(math, ams, rel, "\u21B0", "\\Lsh"); - defineSymbol(math, ams, rel, "\u21C8", "\\upuparrows"); - defineSymbol(math, ams, rel, "\u21BF", "\\upharpoonleft"); - defineSymbol(math, ams, rel, "\u21C3", "\\downharpoonleft"); - defineSymbol(math, ams, rel, "\u22B8", "\\multimap"); - defineSymbol(math, ams, rel, "\u21AD", "\\leftrightsquigarrow"); - defineSymbol(math, ams, rel, "\u21C9", "\\rightrightarrows"); - defineSymbol(math, ams, rel, "\u21C4", "\\rightleftarrows"); - defineSymbol(math, ams, rel, "\u21A0", "\\twoheadrightarrow"); - defineSymbol(math, ams, rel, "\u21A3", "\\rightarrowtail"); - defineSymbol(math, ams, rel, "\u21AC", "\\looparrowright"); - defineSymbol(math, ams, rel, "\u21B7", "\\curvearrowright"); - defineSymbol(math, ams, rel, "\u21BB", "\\circlearrowright"); - defineSymbol(math, ams, rel, "\u21B1", "\\Rsh"); - defineSymbol(math, ams, rel, "\u21CA", "\\downdownarrows"); - defineSymbol(math, ams, rel, "\u21BE", "\\upharpoonright"); - defineSymbol(math, ams, rel, "\u21C2", "\\downharpoonright"); - defineSymbol(math, ams, rel, "\u21DD", "\\rightsquigarrow"); - defineSymbol(math, ams, rel, "\u21DD", "\\leadsto"); - defineSymbol(math, ams, rel, "\u21DB", "\\Rrightarrow"); - defineSymbol(math, ams, rel, "\u21BE", "\\restriction"); - - defineSymbol(math, main, textord, "\u2018", "`"); - defineSymbol(math, main, textord, "$", "\\$"); - defineSymbol(text, main, textord, "$", "\\$"); - defineSymbol(text, main, textord, "$", "\\textdollar"); - defineSymbol(math, main, textord, "%", "\\%"); - defineSymbol(text, main, textord, "%", "\\%"); - defineSymbol(math, main, textord, "_", "\\_"); - defineSymbol(text, main, textord, "_", "\\_"); - defineSymbol(text, main, textord, "_", "\\textunderscore"); - defineSymbol(math, main, textord, "\u2220", "\\angle"); - defineSymbol(math, main, textord, "\u221E", "\\infty"); - defineSymbol(math, main, textord, "\u2032", "\\prime"); - defineSymbol(math, main, textord, "\u25B3", "\\triangle"); - defineSymbol(math, main, textord, "\u0393", "\\Gamma", true); - defineSymbol(math, main, textord, "\u0394", "\\Delta", true); - defineSymbol(math, main, textord, "\u0398", "\\Theta", true); - defineSymbol(math, main, textord, "\u039B", "\\Lambda", true); - defineSymbol(math, main, textord, "\u039E", "\\Xi", true); - defineSymbol(math, main, textord, "\u03A0", "\\Pi", true); - defineSymbol(math, main, textord, "\u03A3", "\\Sigma", true); - defineSymbol(math, main, textord, "\u03A5", "\\Upsilon", true); - defineSymbol(math, main, textord, "\u03A6", "\\Phi", true); - defineSymbol(math, main, textord, "\u03A8", "\\Psi", true); - defineSymbol(math, main, textord, "\u03A9", "\\Omega", true); - defineSymbol(math, main, textord, "\xAC", "\\neg"); - defineSymbol(math, main, textord, "\xAC", "\\lnot"); - defineSymbol(math, main, textord, "\u22A4", "\\top"); - defineSymbol(math, main, textord, "\u22A5", "\\bot"); - defineSymbol(math, main, textord, "\u2205", "\\emptyset"); - defineSymbol(math, ams, textord, "\u2205", "\\varnothing"); - defineSymbol(math, main, mathord, "\u03B1", "\\alpha", true); - defineSymbol(math, main, mathord, "\u03B2", "\\beta", true); - defineSymbol(math, main, mathord, "\u03B3", "\\gamma", true); - defineSymbol(math, main, mathord, "\u03B4", "\\delta", true); - defineSymbol(math, main, mathord, "\u03F5", "\\epsilon", true); - defineSymbol(math, main, mathord, "\u03B6", "\\zeta", true); - defineSymbol(math, main, mathord, "\u03B7", "\\eta", true); - defineSymbol(math, main, mathord, "\u03B8", "\\theta", true); - defineSymbol(math, main, mathord, "\u03B9", "\\iota", true); - defineSymbol(math, main, mathord, "\u03BA", "\\kappa", true); - defineSymbol(math, main, mathord, "\u03BB", "\\lambda", true); - defineSymbol(math, main, mathord, "\u03BC", "\\mu", true); - defineSymbol(math, main, mathord, "\u03BD", "\\nu", true); - defineSymbol(math, main, mathord, "\u03BE", "\\xi", true); - defineSymbol(math, main, mathord, "\u03BF", "\\omicron", true); - defineSymbol(math, main, mathord, "\u03C0", "\\pi", true); - defineSymbol(math, main, mathord, "\u03C1", "\\rho", true); - defineSymbol(math, main, mathord, "\u03C3", "\\sigma", true); - defineSymbol(math, main, mathord, "\u03C4", "\\tau", true); - defineSymbol(math, main, mathord, "\u03C5", "\\upsilon", true); - defineSymbol(math, main, mathord, "\u03D5", "\\phi", true); - defineSymbol(math, main, mathord, "\u03C7", "\\chi", true); - defineSymbol(math, main, mathord, "\u03C8", "\\psi", true); - defineSymbol(math, main, mathord, "\u03C9", "\\omega", true); - defineSymbol(math, main, mathord, "\u03B5", "\\varepsilon", true); - defineSymbol(math, main, mathord, "\u03D1", "\\vartheta", true); - defineSymbol(math, main, mathord, "\u03D6", "\\varpi", true); - defineSymbol(math, main, mathord, "\u03F1", "\\varrho", true); - defineSymbol(math, main, mathord, "\u03C2", "\\varsigma", true); - defineSymbol(math, main, mathord, "\u03C6", "\\varphi", true); - defineSymbol(math, main, bin, "\u2217", "*"); - defineSymbol(math, main, bin, "+", "+"); - defineSymbol(math, main, bin, "\u2212", "-"); - defineSymbol(math, main, bin, "\u22C5", "\\cdot"); - defineSymbol(math, main, bin, "\u2218", "\\circ"); - defineSymbol(math, main, bin, "\xF7", "\\div"); - defineSymbol(math, main, bin, "\xB1", "\\pm"); - defineSymbol(math, main, bin, "\xD7", "\\times"); - defineSymbol(math, main, bin, "\u2229", "\\cap"); - defineSymbol(math, main, bin, "\u222A", "\\cup"); - defineSymbol(math, main, bin, "\u2216", "\\setminus"); - defineSymbol(math, main, bin, "\u2227", "\\land"); - defineSymbol(math, main, bin, "\u2228", "\\lor"); - defineSymbol(math, main, bin, "\u2227", "\\wedge"); - defineSymbol(math, main, bin, "\u2228", "\\vee"); - defineSymbol(math, main, textord, "\u221A", "\\surd"); - defineSymbol(math, main, open, "(", "("); - defineSymbol(math, main, open, "[", "["); - defineSymbol(math, main, open, "\u27E8", "\\langle"); - defineSymbol(math, main, open, "\u2223", "\\lvert"); - defineSymbol(math, main, open, "\u2225", "\\lVert"); - defineSymbol(math, main, close, ")", ")"); - defineSymbol(math, main, close, "]", "]"); - defineSymbol(math, main, close, "?", "?"); - defineSymbol(math, main, close, "!", "!"); - defineSymbol(math, main, close, "\u27E9", "\\rangle"); - defineSymbol(math, main, close, "\u2223", "\\rvert"); - defineSymbol(math, main, close, "\u2225", "\\rVert"); - defineSymbol(math, main, rel, "=", "="); - defineSymbol(math, main, rel, "<", "<"); - defineSymbol(math, main, rel, ">", ">"); - defineSymbol(math, main, rel, ":", ":"); - defineSymbol(math, main, rel, "\u2248", "\\approx"); - defineSymbol(math, main, rel, "\u2245", "\\cong"); - defineSymbol(math, main, rel, "\u2265", "\\ge"); - defineSymbol(math, main, rel, "\u2265", "\\geq"); - defineSymbol(math, main, rel, "\u2190", "\\gets"); - defineSymbol(math, main, rel, ">", "\\gt"); - defineSymbol(math, main, rel, "\u2208", "\\in"); - defineSymbol(math, main, rel, "\u2209", "\\notin"); - defineSymbol(math, main, rel, "\u0338", "\\not"); - defineSymbol(math, main, rel, "\u2282", "\\subset"); - defineSymbol(math, main, rel, "\u2283", "\\supset"); - defineSymbol(math, main, rel, "\u2286", "\\subseteq"); - defineSymbol(math, main, rel, "\u2287", "\\supseteq"); - defineSymbol(math, ams, rel, "\u2288", "\\nsubseteq"); - defineSymbol(math, ams, rel, "\u2289", "\\nsupseteq"); - defineSymbol(math, main, rel, "\u22A8", "\\models"); - defineSymbol(math, main, rel, "\u2190", "\\leftarrow"); - defineSymbol(math, main, rel, "\u2264", "\\le"); - defineSymbol(math, main, rel, "\u2264", "\\leq"); - defineSymbol(math, main, rel, "<", "\\lt"); - defineSymbol(math, main, rel, "\u2260", "\\ne"); - defineSymbol(math, main, rel, "\u2260", "\\neq"); - defineSymbol(math, main, rel, "\u2192", "\\rightarrow"); - defineSymbol(math, main, rel, "\u2192", "\\to"); - defineSymbol(math, ams, rel, "\u2271", "\\ngeq"); - defineSymbol(math, ams, rel, "\u2270", "\\nleq"); - defineSymbol(math, main, spacing, null, "\\!"); - defineSymbol(math, main, spacing, "\xA0", "\\ "); - defineSymbol(math, main, spacing, "\xA0", "~"); - defineSymbol(math, main, spacing, null, "\\,"); - defineSymbol(math, main, spacing, null, "\\:"); - defineSymbol(math, main, spacing, null, "\\;"); - defineSymbol(math, main, spacing, null, "\\enspace"); - defineSymbol(math, main, spacing, null, "\\qquad"); - defineSymbol(math, main, spacing, null, "\\quad"); - defineSymbol(math, main, spacing, "\xA0", "\\space"); - defineSymbol(math, main, punct, ",", ","); - defineSymbol(math, main, punct, ";", ";"); - defineSymbol(math, main, punct, ":", "\\colon"); - defineSymbol(math, ams, bin, "\u22BC", "\\barwedge"); - defineSymbol(math, ams, bin, "\u22BB", "\\veebar"); - defineSymbol(math, main, bin, "\u2299", "\\odot"); - defineSymbol(math, main, bin, "\u2295", "\\oplus"); - defineSymbol(math, main, bin, "\u2297", "\\otimes"); - defineSymbol(math, main, textord, "\u2202", "\\partial"); - defineSymbol(math, main, bin, "\u2298", "\\oslash"); - defineSymbol(math, ams, bin, "\u229A", "\\circledcirc"); - defineSymbol(math, ams, bin, "\u22A1", "\\boxdot"); - defineSymbol(math, main, bin, "\u25B3", "\\bigtriangleup"); - defineSymbol(math, main, bin, "\u25BD", "\\bigtriangledown"); - defineSymbol(math, main, bin, "\u2020", "\\dagger"); - defineSymbol(math, main, bin, "\u22C4", "\\diamond"); - defineSymbol(math, main, bin, "\u22C6", "\\star"); - defineSymbol(math, main, bin, "\u25C3", "\\triangleleft"); - defineSymbol(math, main, bin, "\u25B9", "\\triangleright"); - defineSymbol(math, main, open, "{", "\\{"); - defineSymbol(text, main, textord, "{", "\\{"); - defineSymbol(text, main, textord, "{", "\\textbraceleft"); - defineSymbol(math, main, close, "}", "\\}"); - defineSymbol(text, main, textord, "}", "\\}"); - defineSymbol(text, main, textord, "}", "\\textbraceright"); - defineSymbol(math, main, open, "{", "\\lbrace"); - defineSymbol(math, main, close, "}", "\\rbrace"); - defineSymbol(math, main, open, "[", "\\lbrack"); - defineSymbol(math, main, close, "]", "\\rbrack"); - defineSymbol(text, main, textord, "<", "\\textless"); // in T1 fontenc - defineSymbol(text, main, textord, ">", "\\textgreater"); // in T1 fontenc - defineSymbol(math, main, open, "\u230A", "\\lfloor"); - defineSymbol(math, main, close, "\u230B", "\\rfloor"); - defineSymbol(math, main, open, "\u2308", "\\lceil"); - defineSymbol(math, main, close, "\u2309", "\\rceil"); - defineSymbol(math, main, textord, "\\", "\\backslash"); - defineSymbol(math, main, textord, "\u2223", "|"); - defineSymbol(math, main, textord, "\u2223", "\\vert"); - defineSymbol(text, main, textord, "|", "\\textbar"); // in T1 fontenc - defineSymbol(math, main, textord, "\u2225", "\\|"); - defineSymbol(math, main, textord, "\u2225", "\\Vert"); - defineSymbol(text, main, textord, "\u2225", "\\textbardbl"); - defineSymbol(math, main, rel, "\u2191", "\\uparrow"); - defineSymbol(math, main, rel, "\u21D1", "\\Uparrow"); - defineSymbol(math, main, rel, "\u2193", "\\downarrow"); - defineSymbol(math, main, rel, "\u21D3", "\\Downarrow"); - defineSymbol(math, main, rel, "\u2195", "\\updownarrow"); - defineSymbol(math, main, rel, "\u21D5", "\\Updownarrow"); - defineSymbol(math, main, op, "\u2210", "\\coprod"); - defineSymbol(math, main, op, "\u22C1", "\\bigvee"); - defineSymbol(math, main, op, "\u22C0", "\\bigwedge"); - defineSymbol(math, main, op, "\u2A04", "\\biguplus"); - defineSymbol(math, main, op, "\u22C2", "\\bigcap"); - defineSymbol(math, main, op, "\u22C3", "\\bigcup"); - defineSymbol(math, main, op, "\u222B", "\\int"); - defineSymbol(math, main, op, "\u222B", "\\intop"); - defineSymbol(math, main, op, "\u222C", "\\iint"); - defineSymbol(math, main, op, "\u222D", "\\iiint"); - defineSymbol(math, main, op, "\u220F", "\\prod"); - defineSymbol(math, main, op, "\u2211", "\\sum"); - defineSymbol(math, main, op, "\u2A02", "\\bigotimes"); - defineSymbol(math, main, op, "\u2A01", "\\bigoplus"); - defineSymbol(math, main, op, "\u2A00", "\\bigodot"); - defineSymbol(math, main, op, "\u222E", "\\oint"); - defineSymbol(math, main, op, "\u2A06", "\\bigsqcup"); - defineSymbol(math, main, op, "\u222B", "\\smallint"); - defineSymbol(text, main, inner, "\u2026", "\\textellipsis"); - defineSymbol(math, main, inner, "\u2026", "\\mathellipsis"); - defineSymbol(text, main, inner, "\u2026", "\\ldots", true); - defineSymbol(math, main, inner, "\u2026", "\\ldots", true); - defineSymbol(math, main, inner, "\u22EF", "\\cdots", true); - defineSymbol(math, main, inner, "\u22F1", "\\ddots", true); - defineSymbol(math, main, textord, "\u22EE", "\\vdots", true); - defineSymbol(math, main, accent, "\xB4", "\\acute"); - defineSymbol(math, main, accent, "`", "\\grave"); - defineSymbol(math, main, accent, "\xA8", "\\ddot"); - defineSymbol(math, main, accent, "~", "\\tilde"); - defineSymbol(math, main, accent, "\xAF", "\\bar"); - defineSymbol(math, main, accent, "\u02D8", "\\breve"); - defineSymbol(math, main, accent, "\u02C7", "\\check"); - defineSymbol(math, main, accent, "^", "\\hat"); - defineSymbol(math, main, accent, "\u20D7", "\\vec"); - defineSymbol(math, main, accent, "\u02D9", "\\dot"); - defineSymbol(math, main, mathord, "\u0131", "\\imath"); - defineSymbol(math, main, mathord, "\u0237", "\\jmath"); - defineSymbol(text, main, accent, "\u02CA", "\\'"); // acute - defineSymbol(text, main, accent, "\u02CB", "\\`"); // grave - defineSymbol(text, main, accent, "\u02C6", "\\^"); // circumflex - defineSymbol(text, main, accent, "\u02DC", "\\~"); // tilde - defineSymbol(text, main, accent, "\u02C9", "\\="); // macron - defineSymbol(text, main, accent, "\u02D8", "\\u"); // breve - defineSymbol(text, main, accent, "\u02D9", "\\."); // dot above - defineSymbol(text, main, accent, "\u02DA", "\\r"); // ring above - defineSymbol(text, main, accent, "\u02C7", "\\v"); // caron - defineSymbol(text, main, accent, "\xA8", '\\"'); // diaresis - defineSymbol(text, main, accent, "\u030B", "\\H"); // double acute - - defineSymbol(text, main, textord, "\u2013", "--"); - defineSymbol(text, main, textord, "\u2013", "\\textendash"); - defineSymbol(text, main, textord, "\u2014", "---"); - defineSymbol(text, main, textord, "\u2014", "\\textemdash"); - defineSymbol(text, main, textord, "\u2018", "`"); - defineSymbol(text, main, textord, "\u2018", "\\textquoteleft"); - defineSymbol(text, main, textord, "\u2019", "'"); - defineSymbol(text, main, textord, "\u2019", "\\textquoteright"); - defineSymbol(text, main, textord, "\u201C", "``"); - defineSymbol(text, main, textord, "\u201C", "\\textquotedblleft"); - defineSymbol(text, main, textord, "\u201D", "''"); - defineSymbol(text, main, textord, "\u201D", "\\textquotedblright"); - defineSymbol(math, main, textord, "\xB0", "\\degree"); - defineSymbol(text, main, textord, "\xB0", "\\degree"); - // TODO: In LaTeX, \pounds can generate a different character in text and math - // mode, but among our fonts, only Main-Italic defines this character "163". - defineSymbol(math, main, mathord, "\xA3", "\\pounds"); - defineSymbol(math, main, mathord, "\xA3", "\\mathsterling"); - defineSymbol(text, main, mathord, "\xA3", "\\pounds"); - defineSymbol(text, main, mathord, "\xA3", "\\textsterling"); - defineSymbol(math, ams, textord, "\u2720", "\\maltese"); - defineSymbol(text, ams, textord, "\u2720", "\\maltese"); - - defineSymbol(text, main, spacing, "\xA0", "\\ "); - defineSymbol(text, main, spacing, "\xA0", " "); - defineSymbol(text, main, spacing, "\xA0", "~"); - - // There are lots of symbols which are the same, so we add them in afterwards. - - // All of these are textords in math mode - var mathTextSymbols = "0123456789/@.\""; - for (var i = 0; i < mathTextSymbols.length; i++) { - var ch = mathTextSymbols.charAt(i); - defineSymbol(math, main, textord, ch, ch); - } - - // All of these are textords in text mode - var textSymbols = "0123456789!@*()-=+[]<>|\";:?/.,"; - for (var _i = 0; _i < textSymbols.length; _i++) { - var _ch = textSymbols.charAt(_i); - defineSymbol(text, main, textord, _ch, _ch); - } - - // All of these are textords in text mode, and mathords in math mode - var letters = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"; - for (var _i2 = 0; _i2 < letters.length; _i2++) { - var _ch2 = letters.charAt(_i2); - defineSymbol(math, main, mathord, _ch2, _ch2); - defineSymbol(text, main, textord, _ch2, _ch2); - } - - // Latin-1 letters - for (var _i3 = 0x00C0; _i3 <= 0x00D6; _i3++) { - var _ch3 = String.fromCharCode(_i3); - defineSymbol(math, main, mathord, _ch3, _ch3); - defineSymbol(text, main, textord, _ch3, _ch3); - } - - for (var _i4 = 0x00D8; _i4 <= 0x00F6; _i4++) { - var _ch4 = String.fromCharCode(_i4); - defineSymbol(math, main, mathord, _ch4, _ch4); - defineSymbol(text, main, textord, _ch4, _ch4); - } - - for (var _i5 = 0x00F8; _i5 <= 0x00FF; _i5++) { - var _ch5 = String.fromCharCode(_i5); - defineSymbol(math, main, mathord, _ch5, _ch5); - defineSymbol(text, main, textord, _ch5, _ch5); - } - - // Cyrillic - for (var _i6 = 0x0410; _i6 <= 0x044F; _i6++) { - var _ch6 = String.fromCharCode(_i6); - defineSymbol(text, main, textord, _ch6, _ch6); - } - - // Unicode versions of existing characters - defineSymbol(text, main, textord, "\u2013", "–"); - defineSymbol(text, main, textord, "\u2014", "—"); - defineSymbol(text, main, textord, "\u2018", "‘"); - defineSymbol(text, main, textord, "\u2019", "’"); - defineSymbol(text, main, textord, "\u201C", "“"); - defineSymbol(text, main, textord, "\u201D", "”"); - - },{}],49:[function(require,module,exports){ - - var hangulRegex = /[\uAC00-\uD7AF]/; - - // This regex combines - // - CJK symbols and punctuation: [\u3000-\u303F] - // - Hiragana: [\u3040-\u309F] - // - Katakana: [\u30A0-\u30FF] - // - CJK ideograms: [\u4E00-\u9FAF] - // - Hangul syllables: [\uAC00-\uD7AF] - // - Fullwidth punctuation: [\uFF00-\uFF60] - // Notably missing are halfwidth Katakana and Romanji glyphs. - var cjkRegex = /[\u3000-\u30FF\u4E00-\u9FAF\uAC00-\uD7AF\uFF00-\uFF60]/; - - module.exports = { - cjkRegex: cjkRegex, - hangulRegex: hangulRegex - }; - - },{}],50:[function(require,module,exports){ - - var _ParseError = require("./ParseError"); - - var _ParseError2 = _interopRequireDefault(_ParseError); - - function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } - - // This table gives the number of TeX pts in one of each *absolute* TeX unit. - // Thus, multiplying a length by this number converts the length from units - // into pts. Dividing the result by ptPerEm gives the number of ems - // *assuming* a font size of ptPerEm (normal size, normal style). - var ptPerUnit = { - // https://en.wikibooks.org/wiki/LaTeX/Lengths and - // https://tex.stackexchange.com/a/8263 - "pt": 1, // TeX point - "mm": 7227 / 2540, // millimeter - "cm": 7227 / 254, // centimeter - "in": 72.27, // inch - "bp": 803 / 800, // big (PostScript) points - "pc": 12, // pica - "dd": 1238 / 1157, // didot - "cc": 14856 / 1157, // cicero (12 didot) - "nd": 685 / 642, // new didot - "nc": 1370 / 107, // new cicero (12 new didot) - "sp": 1 / 65536, // scaled point (TeX's internal smallest unit) - // https://tex.stackexchange.com/a/41371 - "px": 803 / 800 }; - - // Dictionary of relative units, for fast validity testing. - /* eslint no-console:0 */ - - /** - * This file does conversion between units. In particular, it provides - * calculateSize to convert other units into ems. - */ - - var relativeUnit = { - "ex": true, - "em": true, - "mu": true - }; - - /** - * Determine whether the specified unit (either a string defining the unit - * or a "size" parse node containing a unit field) is valid. - */ - var validUnit = function validUnit(unit) { - if (unit.unit) { - unit = unit.unit; - } - return unit in ptPerUnit || unit in relativeUnit || unit === "ex"; - }; - - /* - * Convert a "size" parse node (with numeric "number" and string "unit" fields, - * as parsed by functions.js argType "size") into a CSS em value for the - * current style/scale. `options` gives the current options. - */ - var calculateSize = function calculateSize(sizeValue, options) { - var scale = void 0; - if (sizeValue.unit in ptPerUnit) { - // Absolute units - scale = ptPerUnit[sizeValue.unit] // Convert unit to pt - / options.fontMetrics().ptPerEm // Convert pt to CSS em - / options.sizeMultiplier; // Unscale to make absolute units - } else if (sizeValue.unit === "mu") { - // `mu` units scale with scriptstyle/scriptscriptstyle. - scale = options.fontMetrics().cssEmPerMu; - } else { - // Other relative units always refer to the *textstyle* font - // in the current size. - var unitOptions = void 0; - if (options.style.isTight()) { - // isTight() means current style is script/scriptscript. - unitOptions = options.havingStyle(options.style.text()); - } else { - unitOptions = options; - } - // TODO: In TeX these units are relative to the quad of the current - // *text* font, e.g. cmr10. KaTeX instead uses values from the - // comparably-sized *Computer Modern symbol* font. At 10pt, these - // match. At 7pt and 5pt, they differ: cmr7=1.138894, cmsy7=1.170641; - // cmr5=1.361133, cmsy5=1.472241. Consider $\scriptsize a\kern1emb$. - // TeX \showlists shows a kern of 1.13889 * fontsize; - // KaTeX shows a kern of 1.171 * fontsize. - if (sizeValue.unit === "ex") { - scale = unitOptions.fontMetrics().xHeight; - } else if (sizeValue.unit === "em") { - scale = unitOptions.fontMetrics().quad; - } else { - throw new _ParseError2.default("Invalid unit: '" + sizeValue.unit + "'"); - } - if (unitOptions !== options) { - scale *= unitOptions.sizeMultiplier / options.sizeMultiplier; - } - } - return sizeValue.number * scale; - }; - - module.exports = { - validUnit: validUnit, - calculateSize: calculateSize - }; - - },{"./ParseError":29}],51:[function(require,module,exports){ - - /** - * This file contains a list of utility functions which are useful in other - * files. - */ - - /** - * Provide an `indexOf` function which works in IE8, but defers to native if - * possible. - */ - var nativeIndexOf = Array.prototype.indexOf; - var indexOf = function indexOf(list, elem) { - if (list == null) { - return -1; - } - if (nativeIndexOf && list.indexOf === nativeIndexOf) { - return list.indexOf(elem); - } - var l = list.length; - for (var i = 0; i < l; i++) { - if (list[i] === elem) { - return i; - } - } - return -1; - }; - - /** - * Return whether an element is contained in a list - */ - var contains = function contains(list, elem) { - return indexOf(list, elem) !== -1; - }; - - /** - * Provide a default value if a setting is undefined - */ - var deflt = function deflt(setting, defaultIfUndefined) { - return setting === undefined ? defaultIfUndefined : setting; - }; - - // hyphenate and escape adapted from Facebook's React under Apache 2 license - - var uppercase = /([A-Z])/g; - var hyphenate = function hyphenate(str) { - return str.replace(uppercase, "-$1").toLowerCase(); - }; - - var ESCAPE_LOOKUP = { - "&": "&", - ">": ">", - "<": "<", - "\"": """, - "'": "'" - }; - - var ESCAPE_REGEX = /[&><"']/g; - - function escaper(match) { - return ESCAPE_LOOKUP[match]; - } - - /** - * Escapes text to prevent scripting attacks. - * - * @param {*} text Text value to escape. - * @return {string} An escaped string. - */ - function escape(text) { - return ("" + text).replace(ESCAPE_REGEX, escaper); - } - - /** - * A function to set the text content of a DOM element in all supported - * browsers. Note that we don't define this if there is no document. - */ - var setTextContent = void 0; - if (typeof document !== "undefined") { - var testNode = document.createElement("span"); - if ("textContent" in testNode) { - setTextContent = function setTextContent(node, text) { - node.textContent = text; - }; - } else { - setTextContent = function setTextContent(node, text) { - node.innerText = text; - }; - } - } - - /** - * A function to clear a node. - */ - function clearNode(node) { - setTextContent(node, ""); - } - - module.exports = { - contains: contains, - deflt: deflt, - escape: escape, - hyphenate: hyphenate, - indexOf: indexOf, - setTextContent: setTextContent, - clearNode: clearNode - }; - - },{}]},{},[1])(1) - }); - }); - - var katex$2 = unwrapExports(katex$1); - - // Copyright 2018 The Distill Template Authors - // - // Licensed under the Apache License, Version 2.0 (the "License"); - // you may not use this file except in compliance with the License. - // You may obtain a copy of the License at - // - // http://www.apache.org/licenses/LICENSE-2.0 - // - // Unless required by applicable law or agreed to in writing, software - // distributed under the License is distributed on an "AS IS" BASIS, - // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - // See the License for the specific language governing permissions and - // limitations under the License. - - // This is a straight concatenation of code from KaTeX's contrib folder, - // but we aren't using some of their helpers that don't work well outside a browser environment. - - /*global katex */ - - const findEndOfMath = function(delimiter, text, startIndex) { - // Adapted from - // https://github.com/Khan/perseus/blob/master/src/perseus-markdown.jsx - let index = startIndex; - let braceLevel = 0; - - const delimLength = delimiter.length; - - while (index < text.length) { - const character = text[index]; - - if ( - braceLevel <= 0 && - text.slice(index, index + delimLength) === delimiter - ) { - return index; - } else if (character === "\\") { - index++; - } else if (character === "{") { - braceLevel++; - } else if (character === "}") { - braceLevel--; - } - - index++; - } - - return -1; - }; - - const splitAtDelimiters = function(startData, leftDelim, rightDelim, display) { - const finalData = []; - - for (let i = 0; i < startData.length; i++) { - if (startData[i].type === "text") { - const text = startData[i].data; - - let lookingForLeft = true; - let currIndex = 0; - let nextIndex; - - nextIndex = text.indexOf(leftDelim); - if (nextIndex !== -1) { - currIndex = nextIndex; - finalData.push({ - type: "text", - data: text.slice(0, currIndex) - }); - lookingForLeft = false; - } - - while (true) { - // eslint-disable-line no-constant-condition - if (lookingForLeft) { - nextIndex = text.indexOf(leftDelim, currIndex); - if (nextIndex === -1) { - break; - } - - finalData.push({ - type: "text", - data: text.slice(currIndex, nextIndex) - }); - - currIndex = nextIndex; - } else { - nextIndex = findEndOfMath( - rightDelim, - text, - currIndex + leftDelim.length - ); - if (nextIndex === -1) { - break; - } - - finalData.push({ - type: "math", - data: text.slice(currIndex + leftDelim.length, nextIndex), - rawData: text.slice(currIndex, nextIndex + rightDelim.length), - display: display - }); - - currIndex = nextIndex + rightDelim.length; - } - - lookingForLeft = !lookingForLeft; - } - - finalData.push({ - type: "text", - data: text.slice(currIndex) - }); - } else { - finalData.push(startData[i]); - } - } - - return finalData; - }; - - const splitWithDelimiters = function(text, delimiters) { - let data = [{ type: "text", data: text }]; - for (let i = 0; i < delimiters.length; i++) { - const delimiter = delimiters[i]; - data = splitAtDelimiters( - data, - delimiter.left, - delimiter.right, - delimiter.display || false - ); - } - return data; - }; - - /* Note: optionsCopy is mutated by this method. If it is ever exposed in the - * API, we should copy it before mutating. - */ - const renderMathInText = function(text, optionsCopy) { - const data = splitWithDelimiters(text, optionsCopy.delimiters); - const fragment = document.createDocumentFragment(); - - for (let i = 0; i < data.length; i++) { - if (data[i].type === "text") { - fragment.appendChild(document.createTextNode(data[i].data)); - } else { - const tag = document.createElement("d-math"); - const math = data[i].data; - // Override any display mode defined in the settings with that - // defined by the text itself - optionsCopy.displayMode = data[i].display; - try { - tag.textContent = math; - if (optionsCopy.displayMode) { - tag.setAttribute("block", ""); - } - } catch (e) { - if (!(e instanceof katex.ParseError)) { - throw e; - } - optionsCopy.errorCallback( - "KaTeX auto-render: Failed to parse `" + data[i].data + "` with ", - e - ); - fragment.appendChild(document.createTextNode(data[i].rawData)); - continue; - } - fragment.appendChild(tag); - } - } - - return fragment; - }; - - const renderElem = function(elem, optionsCopy) { - for (let i = 0; i < elem.childNodes.length; i++) { - const childNode = elem.childNodes[i]; - if (childNode.nodeType === 3) { - // Text node - const text = childNode.textContent; - if (optionsCopy.mightHaveMath(text)) { - const frag = renderMathInText(text, optionsCopy); - i += frag.childNodes.length - 1; - elem.replaceChild(frag, childNode); - } - } else if (childNode.nodeType === 1) { - // Element node - const shouldRender = - optionsCopy.ignoredTags.indexOf(childNode.nodeName.toLowerCase()) === - -1; - - if (shouldRender) { - renderElem(childNode, optionsCopy); - } - } - // Otherwise, it's something else, and ignore it. - } - }; - - const defaultAutoRenderOptions = { - delimiters: [ - { left: "$$", right: "$$", display: true }, - { left: "\\[", right: "\\]", display: true }, - { left: "\\(", right: "\\)", display: false } - // LaTeX uses this, but it ruins the display of normal `$` in text: - // {left: '$', right: '$', display: false}, - ], - - ignoredTags: [ - "script", - "noscript", - "style", - "textarea", - "pre", - "code", - "svg" - ], - - errorCallback: function(msg, err) { - console.error(msg, err); - } - }; - - const renderMathInElement = function(elem, options) { - if (!elem) { - throw new Error("No element provided to render"); - } - - const optionsCopy = Object.assign({}, defaultAutoRenderOptions, options); - const delimiterStrings = optionsCopy.delimiters.flatMap(d => [ - d.left, - d.right - ]); - const mightHaveMath = text => - delimiterStrings.some(d => text.indexOf(d) !== -1); - optionsCopy.mightHaveMath = mightHaveMath; - renderElem(elem, optionsCopy); - }; - - // Copyright 2018 The Distill Template Authors - - function Mathematics(dom, data) { - let needsCSS = false; - const body = dom.querySelector('body'); - - if (!body) { - console.warn("No body tag found!"); - return; - } - - if (data.katex && data.katex.delimiters) { - global.document = dom; - renderMathInElement(body, data.katex); - } - - // render d-math tags - const mathTags = body.querySelectorAll('d-math'); - if (mathTags.length > 0) { - needsCSS = true; - console.warn(`Prerendering ${mathTags.length} math tags...`); 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+/*! * escape-html * Copyright(c) 2012-2013 TJ Holowaychuk * Copyright(c) 2015 Andreas Lubbe * Copyright(c) 2015 Tiancheng "Timothy" Gu * MIT Licensed */ - - /** - * Module variables. - * @private - */ - - var matchHtmlRegExp = /["'&<>]/; - - /** - * Module exports. - * @public - */ - - var escapeHtml_1 = escapeHtml; - - /** - * Escape special characters in the given string of html. - * - * @param {string} string The string to escape for inserting into HTML - * @return {string} - * @public - */ - - function escapeHtml(string) { - var str = '' + string; - var match = matchHtmlRegExp.exec(str); - - if (!match) { - return str; - } - - var escape; - var html = ''; - var index = 0; - var lastIndex = 0; - - for (index = match.index; index < str.length; index++) { - switch (str.charCodeAt(index)) { - case 34: // " - escape = '"'; - break; - case 38: // & - escape = '&'; - break; - case 39: // ' - escape = '''; - break; - case 60: // < - escape = '<'; - break; - case 62: // > - escape = '>'; - break; - default: - continue; - } - - if (lastIndex !== index) { - html += str.substring(lastIndex, index); - } - - lastIndex = index + 1; - html += escape; - } - - return lastIndex !== index - ? html + str.substring(lastIndex, index) - : html; - } - - // Copyright 2018 The Distill Template Authors - - function Meta(dom, data) { - let head = dom.querySelector('head'); - let appendHead = html => appendHtml(head, html); - - function meta(name, content, force) { - if (content || force) - appendHead(` \n`); - } - - appendHead(` - - - - `); - - if (data.title) { - appendHead(` - ${escapeHtml_1(data.title)} - `); - } - - if (data.url) { - appendHead(` - - `); - } - - - if (data.publishedDate){ - appendHead(` - - - - - `); - } - - if (data.updatedDate) { - appendHead(` - - `); - } - - (data.authors || []).forEach((a) => { - appendHtml(head, ` - `); - }); - - appendHead(` - - - - - - - - - `); - - appendHead(` - - - - - - - - - `); - - // if this is a proprer article, generate Google Scholar meta data - if (data.doiSuffix){ - appendHead(` - \n`); - - meta('citation_title', data.title); - meta('citation_fulltext_html_url', data.url); - meta('citation_volume', data.volume); - meta('citation_issue', data.issue); - meta('citation_firstpage', data.doiSuffix ? `e${data.doiSuffix}` : undefined); - meta('citation_doi', data.doi); - - let journal = data.journal || {}; - meta('citation_journal_title', journal.full_title || journal.title); - meta('citation_journal_abbrev', journal.abbrev_title); - meta('citation_issn', journal.issn); - meta('citation_publisher', journal.publisher); - meta('citation_fulltext_world_readable', '', true); - - if (data.publishedDate){ - meta('citation_online_date', `${data.publishedYear}/${data.publishedMonthPadded}/${data.publishedDayPadded}`); - meta('citation_publication_date', `${data.publishedYear}/${data.publishedMonthPadded}/${data.publishedDayPadded}`); - } - - (data.authors || []).forEach((a) => { - meta('citation_author', `${a.lastName}, ${a.firstName}`); - meta('citation_author_institution', a.affiliation); - }); - } else { - console.warn('No DOI suffix in data; not adding citation meta tags!'); - } - - if (data.citations) { - data.citations.forEach(key => { - if (data.bibliography && data.bibliography.has(key)) { - const entry = data.bibliography.get(key); - meta('citation_reference', citation_meta_content(entry) ); - } else { - console.warn('No bibliography data found for ' + key); - } - }); - } else { - console.warn('No citations found; not adding any references meta tags!'); - } - } - - function appendHtml(el, html) { - el.innerHTML += html; - } - - function citation_meta_content(ref){ - var content = `citation_title=${ref.title};`; - - if (ref.author && ref.author !== '') { - ref.author.split(' and ').forEach(name => { - name = name.trim(); - let last, firsts; - if (name.indexOf(',') != -1){ - last = name.split(',')[0].trim(); - firsts = name.split(',')[1].trim(); - } else { - last = name.split(' ').slice(-1)[0].trim(); - firsts = name.split(' ').slice(0,-1).join(' '); - } - content += `citation_author=${firsts} ${last};`; - }); - } - - if ('year' in ref) { - content += `citation_publication_date=${ref.year};`; - } - - // Special test for arxiv - let arxiv_id_search = /https?:\/\/arxiv\.org\/pdf\/([0-9]*\.[0-9]*)\.pdf/.exec(ref.url); - arxiv_id_search = arxiv_id_search || /https?:\/\/arxiv\.org\/abs\/([0-9]*\.[0-9]*)/.exec(ref.url); - arxiv_id_search = arxiv_id_search || /arXiv preprint arXiv:([0-9]*\.[0-9]*)/.exec(ref.journal); - if (arxiv_id_search && arxiv_id_search[1]){ - content += `citation_arxiv_id=${arxiv_id_search[1]};`; - return content; // arXiv is not considered a journal, so we don't need journal/volume/issue - } - if ('journal' in ref){ - content += `citation_journal_title=${escapeHtml_1(ref.journal)};`; - } - if ('volume' in ref) { - content += `citation_volume=${escapeHtml_1(ref.volume)};`; - } - if ('issue' in ref || 'number' in ref){ - content += `citation_number=${escapeHtml_1(ref.issue || ref.number)};`; - } - return content; - } - - var base = "/*\n * Copyright 2018 The Distill Template Authors\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\nhtml {\n font-size: 14px;\n\tline-height: 1.6em;\n /* font-family: \"Libre Franklin\", \"Helvetica Neue\", sans-serif; */\n font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Roboto, Oxygen, Ubuntu, Cantarell, \"Fira Sans\", \"Droid Sans\", \"Helvetica Neue\", Arial, sans-serif;\n /*, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\";*/\n text-size-adjust: 100%;\n -ms-text-size-adjust: 100%;\n -webkit-text-size-adjust: 100%;\n}\n\n@media(min-width: 768px) {\n html {\n font-size: 16px;\n }\n}\n\nbody {\n margin: 0;\n}\n\na {\n color: #004276;\n}\n\nfigure {\n margin: 0;\n}\n\ntable {\n\tborder-collapse: collapse;\n\tborder-spacing: 0;\n}\n\ntable th {\n\ttext-align: left;\n}\n\ntable thead {\n border-bottom: 1px solid rgba(0, 0, 0, 0.05);\n}\n\ntable thead th {\n padding-bottom: 0.5em;\n}\n\ntable tbody :first-child td {\n padding-top: 0.5em;\n}\n\npre {\n overflow: auto;\n max-width: 100%;\n}\n\np {\n margin-top: 0;\n margin-bottom: 1em;\n}\n\nsup, sub {\n vertical-align: baseline;\n position: relative;\n top: -0.4em;\n line-height: 1em;\n}\n\nsub {\n top: 0.4em;\n}\n\n.kicker,\n.marker {\n font-size: 15px;\n font-weight: 600;\n color: rgba(0, 0, 0, 0.5);\n}\n\n\n/* Headline */\n\n@media(min-width: 1024px) {\n d-title h1 span {\n display: block;\n }\n}\n\n/* Figure */\n\nfigure {\n position: relative;\n margin-bottom: 2.5em;\n margin-top: 1.5em;\n}\n\nfigcaption+figure {\n\n}\n\nfigure img {\n width: 100%;\n}\n\nfigure svg text,\nfigure svg tspan {\n}\n\nfigcaption,\n.figcaption {\n color: rgba(0, 0, 0, 0.6);\n font-size: 12px;\n line-height: 1.5em;\n}\n\n@media(min-width: 1024px) {\nfigcaption,\n.figcaption {\n font-size: 13px;\n }\n}\n\nfigure.external img {\n background: white;\n border: 1px solid rgba(0, 0, 0, 0.1);\n box-shadow: 0 1px 8px rgba(0, 0, 0, 0.1);\n padding: 18px;\n box-sizing: border-box;\n}\n\nfigcaption a {\n color: rgba(0, 0, 0, 0.6);\n}\n\nfigcaption b,\nfigcaption strong, {\n font-weight: 600;\n color: rgba(0, 0, 0, 1.0);\n}\n"; - - var layout = "/*\n * Copyright 2018 The Distill Template Authors\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@supports not (display: grid) {\n .base-grid,\n distill-header,\n d-title,\n d-abstract,\n d-article,\n d-appendix,\n distill-appendix,\n d-byline,\n d-footnote-list,\n d-citation-list,\n distill-footer {\n display: block;\n padding: 8px;\n }\n}\n\n.base-grid,\ndistill-header,\nd-title,\nd-abstract,\nd-article,\nd-appendix,\ndistill-appendix,\nd-byline,\nd-footnote-list,\nd-citation-list,\ndistill-footer {\n display: grid;\n justify-items: stretch;\n grid-template-columns: [screen-start] 8px [page-start kicker-start text-start gutter-start middle-start] 1fr 1fr 1fr 1fr 1fr 1fr 1fr 1fr [text-end page-end gutter-end kicker-end middle-end] 8px [screen-end];\n grid-column-gap: 8px;\n}\n\n.grid {\n display: grid;\n grid-column-gap: 8px;\n}\n\n@media(min-width: 768px) {\n .base-grid,\n distill-header,\n d-title,\n d-abstract,\n d-article,\n d-appendix,\n distill-appendix,\n d-byline,\n d-footnote-list,\n d-citation-list,\n distill-footer {\n grid-template-columns: [screen-start] 1fr [page-start kicker-start middle-start text-start] 45px 45px 45px 45px 45px 45px 45px 45px [ kicker-end text-end gutter-start] 45px [middle-end] 45px [page-end gutter-end] 1fr [screen-end];\n grid-column-gap: 16px;\n }\n\n .grid {\n grid-column-gap: 16px;\n }\n}\n\n@media(min-width: 1000px) {\n .base-grid,\n distill-header,\n d-title,\n d-abstract,\n d-article,\n d-appendix,\n distill-appendix,\n d-byline,\n d-footnote-list,\n d-citation-list,\n distill-footer {\n grid-template-columns: [screen-start] 1fr [page-start kicker-start] 50px [middle-start] 50px [text-start kicker-end] 50px 50px 50px 50px 50px 50px 50px 50px [text-end gutter-start] 50px [middle-end] 50px [page-end gutter-end] 1fr [screen-end];\n grid-column-gap: 16px;\n }\n\n .grid {\n grid-column-gap: 16px;\n }\n}\n\n@media(min-width: 1180px) {\n .base-grid,\n distill-header,\n d-title,\n d-abstract,\n d-article,\n d-appendix,\n distill-appendix,\n d-byline,\n d-footnote-list,\n d-citation-list,\n distill-footer {\n grid-template-columns: [screen-start] 1fr [page-start kicker-start] 60px [middle-start] 60px [text-start kicker-end] 60px 60px 60px 60px 60px 60px 60px 60px [text-end gutter-start] 60px [middle-end] 60px [page-end gutter-end] 1fr [screen-end];\n grid-column-gap: 32px;\n }\n\n .grid {\n grid-column-gap: 32px;\n }\n}\n\n\n\n\n.base-grid {\n grid-column: screen;\n}\n\n/* .l-body,\nd-article > * {\n grid-column: text;\n}\n\n.l-page,\nd-title > *,\nd-figure {\n grid-column: page;\n} */\n\n.l-gutter {\n grid-column: gutter;\n}\n\n.l-text,\n.l-body {\n grid-column: text;\n}\n\n.l-page {\n grid-column: page;\n}\n\n.l-body-outset {\n grid-column: middle;\n}\n\n.l-page-outset {\n grid-column: page;\n}\n\n.l-screen {\n grid-column: screen;\n}\n\n.l-screen-inset {\n grid-column: screen;\n padding-left: 16px;\n padding-left: 16px;\n}\n\n\n/* Aside */\n\nd-article aside {\n grid-column: gutter;\n font-size: 12px;\n line-height: 1.6em;\n color: rgba(0, 0, 0, 0.6)\n}\n\n@media(min-width: 768px) {\n aside {\n grid-column: gutter;\n }\n\n .side {\n grid-column: gutter;\n }\n}\n"; - - var print = "/*\n * Copyright 2018 The Distill Template Authors\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@media print {\n\n @page {\n size: 8in 11in;\n @bottom-right {\n content: counter(page) \" of \" counter(pages);\n }\n }\n\n html {\n /* no general margins -- CSS Grid takes care of those */\n }\n\n p, code {\n page-break-inside: avoid;\n }\n\n h2, h3 {\n page-break-after: avoid;\n }\n\n d-header {\n visibility: hidden;\n }\n\n d-footer {\n display: none!important;\n }\n\n}\n"; - - var byline = "/*\n * Copyright 2018 The Distill Template Authors\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\nd-byline {\n contain: style;\n overflow: hidden;\n border-top: 1px solid rgba(0, 0, 0, 0.1);\n font-size: 0.8rem;\n line-height: 1.8em;\n padding: 1.5rem 0;\n min-height: 1.8em;\n}\n\n\nd-byline .byline {\n grid-template-columns: 1fr 1fr;\n grid-column: text;\n}\n\n@media(min-width: 768px) {\n d-byline .byline {\n grid-template-columns: 1fr 1fr 1fr 1fr;\n }\n}\n\nd-byline .authors-affiliations {\n grid-column-end: span 2;\n grid-template-columns: 1fr 1fr;\n margin-bottom: 1em;\n}\n\n@media(min-width: 768px) {\n d-byline .authors-affiliations {\n margin-bottom: 0;\n }\n}\n\nd-byline h3 {\n font-size: 0.6rem;\n font-weight: 400;\n color: rgba(0, 0, 0, 0.5);\n margin: 0;\n text-transform: uppercase;\n}\n\nd-byline p {\n margin: 0;\n}\n\nd-byline a,\nd-article d-byline a {\n color: rgba(0, 0, 0, 0.8);\n text-decoration: none;\n border-bottom: none;\n}\n\nd-article d-byline a:hover {\n text-decoration: underline;\n border-bottom: none;\n}\n\nd-byline p.author {\n font-weight: 500;\n}\n\nd-byline .affiliations {\n\n}\n"; - - var article = "/*\n * Copyright 2018 The Distill Template Authors\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\nd-article {\n contain: layout style;\n overflow-x: hidden;\n border-top: 1px solid rgba(0, 0, 0, 0.1);\n padding-top: 2rem;\n color: rgba(0, 0, 0, 0.8);\n}\n\nd-article > * {\n grid-column: text;\n}\n\n@media(min-width: 768px) {\n d-article {\n font-size: 16px;\n }\n}\n\n@media(min-width: 1024px) {\n d-article {\n font-size: 1.06rem;\n line-height: 1.7em;\n }\n}\n\n\n/* H2 */\n\n\nd-article .marker {\n text-decoration: none;\n border: none;\n counter-reset: section;\n grid-column: kicker;\n line-height: 1.7em;\n}\n\nd-article .marker:hover {\n border: none;\n}\n\nd-article .marker span {\n padding: 0 3px 4px;\n border-bottom: 1px solid rgba(0, 0, 0, 0.2);\n position: relative;\n top: 4px;\n}\n\nd-article .marker:hover span {\n color: rgba(0, 0, 0, 0.7);\n border-bottom: 1px solid rgba(0, 0, 0, 0.7);\n}\n\nd-article h2 {\n font-weight: 600;\n font-size: 24px;\n line-height: 1.25em;\n margin: 2rem 0 1.5rem 0;\n border-bottom: 1px solid rgba(0, 0, 0, 0.1);\n padding-bottom: 1rem;\n}\n\n@media(min-width: 1024px) {\n d-article h2 {\n font-size: 36px;\n }\n}\n\n/* H3 */\n\nd-article h3 {\n font-weight: 700;\n font-size: 18px;\n line-height: 1.4em;\n margin-bottom: 1em;\n margin-top: 2em;\n}\n\n@media(min-width: 1024px) {\n d-article h3 {\n font-size: 20px;\n }\n}\n\n/* H4 */\n\nd-article h4 {\n font-weight: 600;\n text-transform: uppercase;\n font-size: 14px;\n line-height: 1.4em;\n}\n\nd-article a {\n color: inherit;\n}\n\nd-article p,\nd-article ul,\nd-article ol,\nd-article blockquote {\n margin-top: 0;\n margin-bottom: 1em;\n margin-left: 0;\n margin-right: 0;\n}\n\nd-article blockquote {\n border-left: 2px solid rgba(0, 0, 0, 0.2);\n padding-left: 2em;\n font-style: italic;\n color: rgba(0, 0, 0, 0.6);\n}\n\nd-article a {\n border-bottom: 1px solid rgba(0, 0, 0, 0.4);\n text-decoration: none;\n}\n\nd-article a:hover {\n border-bottom: 1px solid rgba(0, 0, 0, 0.8);\n}\n\nd-article .link {\n text-decoration: underline;\n cursor: pointer;\n}\n\nd-article ul,\nd-article ol {\n padding-left: 24px;\n}\n\nd-article li {\n margin-bottom: 1em;\n margin-left: 0;\n padding-left: 0;\n}\n\nd-article li:last-child {\n margin-bottom: 0;\n}\n\nd-article pre {\n font-size: 14px;\n margin-bottom: 20px;\n}\n\nd-article hr {\n grid-column: screen;\n width: 100%;\n border: none;\n border-bottom: 1px solid rgba(0, 0, 0, 0.1);\n margin-top: 60px;\n margin-bottom: 60px;\n}\n\nd-article section {\n margin-top: 60px;\n margin-bottom: 60px;\n}\n\nd-article span.equation-mimic {\n font-family: georgia;\n font-size: 115%;\n font-style: italic;\n}\n\nd-article > d-code,\nd-article section > d-code {\n display: block;\n}\n\nd-article > d-math[block],\nd-article section > d-math[block] {\n display: block;\n}\n\n@media (max-width: 768px) {\n d-article > d-code,\n d-article section > d-code,\n d-article > d-math[block],\n d-article section > d-math[block] {\n overflow-x: scroll;\n -ms-overflow-style: none; // IE 10+\n overflow: -moz-scrollbars-none; // Firefox\n }\n\n d-article > d-code::-webkit-scrollbar,\n d-article section > d-code::-webkit-scrollbar,\n d-article > d-math[block]::-webkit-scrollbar,\n d-article section > d-math[block]::-webkit-scrollbar {\n display: none; // Safari and Chrome\n }\n}\n\nd-article .citation {\n color: #668;\n cursor: pointer;\n}\n\nd-include {\n width: auto;\n display: block;\n}\n\nd-figure {\n contain: layout style;\n}\n\n/* KaTeX */\n\n.katex, .katex-prerendered {\n contain: style;\n display: inline-block;\n}\n\n/* Tables */\n\nd-article table {\n border-collapse: collapse;\n margin-bottom: 1.5rem;\n border-bottom: 1px solid rgba(0, 0, 0, 0.2);\n}\n\nd-article table th {\n border-bottom: 1px solid rgba(0, 0, 0, 0.2);\n}\n\nd-article table td {\n border-bottom: 1px solid rgba(0, 0, 0, 0.05);\n}\n\nd-article table tr:last-of-type td {\n border-bottom: none;\n}\n\nd-article table th,\nd-article table td {\n font-size: 15px;\n padding: 2px 8px;\n}\n\nd-article table tbody :first-child td {\n padding-top: 2px;\n}\n"; - - var title = "/*\n * Copyright 2018 The Distill Template Authors\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\nd-title {\n padding: 2rem 0 1.5rem;\n contain: layout style;\n overflow-x: hidden;\n}\n\n@media(min-width: 768px) {\n d-title {\n padding: 4rem 0 1.5rem;\n }\n}\n\nd-title h1 {\n grid-column: text;\n font-size: 40px;\n font-weight: 700;\n line-height: 1.1em;\n margin: 0 0 0.5rem;\n}\n\n@media(min-width: 768px) {\n d-title h1 {\n font-size: 50px;\n }\n}\n\nd-title p {\n font-weight: 300;\n font-size: 1.2rem;\n line-height: 1.55em;\n grid-column: text;\n}\n\nd-title .status {\n margin-top: 0px;\n font-size: 12px;\n color: #009688;\n opacity: 0.8;\n grid-column: kicker;\n}\n\nd-title .status span {\n line-height: 1;\n display: inline-block;\n padding: 6px 0;\n border-bottom: 1px solid #80cbc4;\n font-size: 11px;\n text-transform: uppercase;\n}\n"; - - var math = "/*\n * Copyright 2018 The Distill Template Authors\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\nspan.katex-display {\n text-align: left;\n padding: 8px 0 8px 0;\n margin: 0.5em 0 0.5em 1em;\n}\n\nspan.katex {\n -webkit-font-smoothing: antialiased;\n color: rgba(0, 0, 0, 0.8);\n font-size: 1.18em;\n}\n"; - - // Copyright 2018 The Distill Template Authors - - const styles = base + layout + title + byline + article + math + print; - - function makeStyleTag(dom) { - - const styleTagId = 'distill-prerendered-styles'; - const prerenderedTag = dom.getElementById(styleTagId); - if (!prerenderedTag) { - const styleTag = dom.createElement('style'); - styleTag.id = styleTagId; - styleTag.type = 'text/css'; - const cssTextTag = dom.createTextNode(styles); - styleTag.appendChild(cssTextTag); - const firstScriptTag = dom.head.querySelector('script'); - dom.head.insertBefore(styleTag, firstScriptTag); - } - - } - - // Copyright 2018 The Distill Template Authors - - function renderTOC(element, headings) { - - let ToC =` - - -

      Table of contents

      -
        `; - - for (const el of headings) { - // should element be included in TOC? - const isInTitle = el.parentElement.tagName == 'D-TITLE'; - const isException = el.getAttribute('no-toc'); - if (isInTitle || isException) continue; - // create TOC entry - const title = el.textContent; - const link = '#' + el.getAttribute('id'); - - let newLine = '
      • ' + '' + title + '' + '
        • '; - if (el.tagName == 'H3') { - newLine = '
          ' + newLine + '
        '; - } else { - newLine += '
        '; - } - ToC += newLine; - - } - - ToC += '
      '; - element.innerHTML = ToC; - } - - // Copyright 2018 The Distill Template Authors - - function TOC(dom) { - const article = dom.querySelector('d-article'); - const toc = dom.querySelector('d-toc'); - if (toc) { - const headings = article.querySelectorAll('h2, h3'); - renderTOC(toc, headings); - toc.setAttribute('prerendered', 'true'); - } - } - - // Copyright 2018 The Distill Template Authors - // - // Licensed under the Apache License, Version 2.0 (the "License"); - // you may not use this file except in compliance with the License. - // You may obtain a copy of the License at - // - // http://www.apache.org/licenses/LICENSE-2.0 - // - // Unless required by applicable law or agreed to in writing, software - // distributed under the License is distributed on an "AS IS" BASIS, - // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - // See the License for the specific language governing permissions and - // limitations under the License. - - function Typeset(dom) { - - var textNodes = dom.createTreeWalker( - dom.body, - dom.defaultView.NodeFilter.SHOW_TEXT - ); - while (textNodes.nextNode()) { - var n = textNodes.currentNode, - text = n.nodeValue; - if (text && acceptNode(n)) { - text = quotes(text); - text = punctuation(text); - // TODO: Add back support for ligatures once their uppercased versions don't hang Chrome search anymore - // see: https://bugs.chromium.org/p/chromium/issues/detail?id=862648 - // text = ligatures(text); - n.nodeValue = text; - } - } - } - - // 2018-07-11 shancarter@ and ludwigschubert@ no longer know what this was meant to accomplish - // if it was trying to not replace text in any child nodes of those listed here, - // then it does not accomplish that. - function acceptNode(node) { - var parent = node.parentElement; - var isMath = (parent && parent.getAttribute && parent.getAttribute('class')) ? parent.getAttribute('class').includes('katex') || parent.getAttribute('class').includes('MathJax') : false; - return parent && - parent.nodeName !== 'SCRIPT' && - parent.nodeName !== 'STYLE' && - parent.nodeName !== 'CODE' && - parent.nodeName !== 'PRE' && - parent.nodeName !== 'SPAN' && - parent.nodeName !== 'D-HEADER' && - parent.nodeName !== 'D-BYLINE' && - parent.nodeName !== 'D-MATH' && - parent.nodeName !== 'D-CODE' && - parent.nodeName !== 'D-BIBLIOGRAPHY' && - parent.nodeName !== 'D-FOOTER' && - parent.nodeName !== 'D-APPENDIX' && - parent.nodeName !== 'D-FRONTMATTER' && - parent.nodeName !== 'D-TOC' && - parent.nodeType !== 8 && //comment nodes - !isMath; - } - - - /*! - * typeset - Typesetting for the web - * @version v0.1.6 - * @link https://github.com/davidmerfield/Typeset.js - * @author David Merfield - */ - // which has a CC0 license - // http://creativecommons.org/publicdomain/zero/1.0/ - - - function punctuation(text){ - - // Dashes - text = text.replace(/--/g, '\u2014'); - text = text.replace(/\s*\u2014\s*/g,'\u2009\u2014\u2009'); //this has thin spaces - - // Elipses - text = text.replace(/\.\.\./g,'…'); - - // Nbsp for punc with spaces - var NBSP = '\u00a0'; - var NBSP_PUNCTUATION_START = /([«¿¡]) /g; - var NBSP_PUNCTUATION_END = / ([!?:;.,‽»])/g; - - text = text.replace(NBSP_PUNCTUATION_START, '$1' + NBSP); - text = text.replace(NBSP_PUNCTUATION_END, NBSP + '$1'); - - return text; - } - - function quotes(text) { - - text = text - .replace(/(\W|^)"([^\s!?:;.,‽»])/g, '$1\u201c$2') // beginning " - .replace(/(\u201c[^"]*)"([^"]*$|[^\u201c"]*\u201c)/g, '$1\u201d$2') // ending " - .replace(/([^0-9])"/g,'$1\u201d') // remaining " at end of word - .replace(/(\W|^)'(\S)/g, '$1\u2018$2') // beginning ' - .replace(/([a-z])'([a-z])/ig, '$1\u2019$2') // conjunction's possession - .replace(/((\u2018[^']*)|[a-z])'([^0-9]|$)/ig, '$1\u2019$3') // ending ' - .replace(/(\u2018)([0-9]{2}[^\u2019]*)(\u2018([^0-9]|$)|$|\u2019[a-z])/ig, '\u2019$2$3') // abbrev. years like '93 - .replace(/(\B|^)\u2018(?=([^\u2019]*\u2019\b)*([^\u2019\u2018]*\W[\u2019\u2018]\b|[^\u2019\u2018]*$))/ig, '$1\u2019') // backwards apostrophe - .replace(/'''/g, '\u2034') // triple prime - .replace(/("|'')/g, '\u2033') // double prime - .replace(/'/g, '\u2032'); - - // Allow escaped quotes - text = text.replace(/\\“/, '"'); - text = text.replace(/\\”/, '"'); - text = text.replace(/\\’/, '\''); - text = text.replace(/\\‘/, '\''); - - return text; - } - - // Copyright 2018 The Distill Template Authors - - // const template = ` - // if ('IntersectionObserver' in window && - // 'IntersectionObserverEntry' in window && - // 'intersectionRatio' in IntersectionObserverEntry.prototype) { - // // Platform supports IntersectionObserver natively! :-) - // if (!('isIntersecting' in IntersectionObserverEntry.prototype)) { - // Object.defineProperty(IntersectionObserverEntry.prototype, - // 'isIntersecting', { - // get: function () { - // return this.intersectionRatio > 0; - // } - // }); - // } - // } else { - // // Platform does not support webcomponents--loading polyfills synchronously. - // const scriptTag = document.createElement('script'); - // scriptTag.src = '${intersectionObserverPath}'; - // scriptTag.async = false; - // document.currentScript.parentNode.insertBefore(scriptTag, document.currentScript.nextSibling); - // } - // - // if ('registerElement' in document && - // 'import' in document.createElement('link') && - // 'content' in document.createElement('template')) { - // // Platform supports webcomponents natively! :-) - // } else { - // // Platform does not support webcomponents--loading polyfills synchronously. - // const scriptTag = document.createElement('script'); - // scriptTag.src = '${webcomponentPath}'; - // scriptTag.async = false; - // document.currentScript.parentNode.insertBefore(scriptTag, document.currentScript.nextSibling); - // } - // - // - // `; - - - const addBackIn = ` -window.addEventListener('WebComponentsReady', function() { - console.warn('WebComponentsReady'); - const loaderTag = document.createElement('script'); - loaderTag.src = 'https://distill.pub/template.v2.js'; - document.head.insertBefore(loaderTag, document.head.firstChild); -}); -`; - - function render(dom) { - // pull out template script tag - const templateTag = dom.querySelector('script[src*="template.v2.js"]'); - if (templateTag) { - templateTag.parentNode.removeChild(templateTag); - } else { - console.debug('FYI: Did not find template tag when trying to remove it. You may not have added it. Be aware that our polyfills will add it.'); - } - - // add loader - const loaderTag = dom.createElement('script'); - loaderTag.src = 'https://cdnjs.cloudflare.com/ajax/libs/webcomponentsjs/1.0.17/webcomponents-loader.js'; - dom.head.insertBefore(loaderTag, dom.head.firstChild); - - // add loader event listener to add tempalrte back in - const addTag = dom.createElement('script'); - addTag.innerHTML = addBackIn; - dom.head.insertBefore(addTag, dom.head.firstChild); - - - // create polyfill script tag - // const polyfillScriptTag = dom.createElement('script'); - // polyfillScriptTag.innerHTML = template; - // polyfillScriptTag.id = 'polyfills'; - - // insert at appropriate position--before any other script tag - // const firstScriptTag = dom.head.querySelector('script'); - // dom.head.insertBefore(polyfillScriptTag, firstScriptTag); - } - - // Copyright 2018 The Distill Template Authors - - const styles$1 = ` -d-citation-list { - contain: style; -} - -d-citation-list .references { - grid-column: text; -} - -d-citation-list .references .title { - font-weight: 500; -} -`; - - function renderCitationList(element, entries, dom=document) { - if (entries.size > 0) { - element.style.display = ''; - let list = element.querySelector('.references'); - if (list) { - list.innerHTML = ''; - } else { - const stylesTag = dom.createElement('style'); - stylesTag.innerHTML = styles$1; - element.appendChild(stylesTag); - - const heading = dom.createElement('h3'); - heading.id = 'references'; - heading.textContent = 'References'; - element.appendChild(heading); - - list = dom.createElement('ol'); - list.id = 'references-list'; - list.className = 'references'; - element.appendChild(list); - } - - for (const [key, entry] of entries) { - const listItem = dom.createElement('li'); - listItem.id = key; - listItem.innerHTML = bibliography_cite(entry); - list.appendChild(listItem); - } - } else { - element.style.display = 'none'; - } - } - - // Copyright 2018 The Distill Template Authors - - function CitationList(dom, data) { - const citationListTag = dom.querySelector('d-citation-list'); - if (citationListTag) { - const entries = new Map(data.citations.map( citationKey => { - return [citationKey, data.bibliography.get(citationKey)]; - })); - renderCitationList(citationListTag, entries, dom); - citationListTag.setAttribute('distill-prerendered', 'true'); - } - } - - // Copyright 2018 The Distill Template Authors - // - // Licensed under the Apache License, Version 2.0 (the "License"); - // you may not use this file except in compliance with the License. - // You may obtain a copy of the License at - // - // http://www.apache.org/licenses/LICENSE-2.0 - // - // Unless required by applicable law or agreed to in writing, software - // distributed under the License is distributed on an "AS IS" BASIS, - // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - // See the License for the specific language governing permissions and - // limitations under the License. - - /* - Try to only reorder things that MAY be user defined. - Try to use templates etc to define the order of our own tags. - */ - - function render$1(dom) { - const head = dom.head; - - const metaIE = head.querySelector('meta[http-equiv]'); - head.insertBefore(metaIE, head.firstChild); - - const metaViewport = head.querySelector('meta[name=viewport]'); - head.insertBefore(metaViewport, head.firstChild); - - const metaCharset = head.querySelector('meta[charset]'); - head.insertBefore(metaCharset, head.firstChild); - } - - var logo = "\n \n\n"; - - const headerTemplate = ` - -
      - - -
      -`; - - // Copyright 2018 The Distill Template Authors - - function DistillHeader(dom, data) { - const headerTag = dom.querySelector('distill-header'); - if (!headerTag) { - const header = dom.createElement('distill-header'); - header.innerHTML = headerTemplate; - header.setAttribute('distill-prerendered', ""); - const body = dom.querySelector('body'); - body.insertBefore(header, body.firstChild); - } - } - - // Copyright 2018 The Distill Template Authors - - const styles$2 = ` - -`; - - function appendixTemplate(frontMatter) { - let html = styles$2; - - if (typeof frontMatter.githubUrl !== 'undefined') { - html += ` -

      Updates and Corrections

      -

      `; - if (frontMatter.githubCompareUpdatesUrl) { - html += `View all changes to this article since it was first published.`; - } - html += ` - If you see mistakes or want to suggest changes, please create an issue on GitHub.

      - `; - } - - const journal = frontMatter.journal; - if (typeof journal !== 'undefined' && journal.title === 'Distill') { - html += ` -

      Reuse

      -

      Diagrams and text are licensed under Creative Commons Attribution CC-BY 4.0 with the source available on GitHub, unless noted otherwise. The figures that have been reused from other sources don’t fall under this license and can be recognized by a note in their caption: “Figure from …”.

      - `; - } - - if (typeof frontMatter.publishedDate !== 'undefined') { - html += ` -

      Citation

      -

      For attribution in academic contexts, please cite this work as

      - 
      ${frontMatter.concatenatedAuthors}, "${frontMatter.title}", Distill, ${frontMatter.publishedYear}.
      -

      BibTeX citation

      - 
      ${serializeFrontmatterToBibtex(frontMatter)}
      - `; - } - - return html; - } - - // Copyright 2018 The Distill Template Authors - - function DistillAppendix(dom, data) { - - const appendixTag = dom.querySelector('d-appendix'); - if (!appendixTag) { - console.warn('No appendix tag found!'); - return; - } - const distillAppendixTag = appendixTag.querySelector('distill-appendix'); - if (!distillAppendixTag) { - const distillAppendix = dom.createElement('distill-appendix'); - appendixTag.appendChild(distillAppendix); - distillAppendix.innerHTML = appendixTemplate(data); - } - - } - - const footerTemplate = ` - - -
      - - is dedicated to clear explanations of machine learning - -
      - About - Submit - Prize - Archive - RSS - GitHub - Twitter -      ISSN 2476-0757 -
      - -
      - -`; - - // Copyright 2018 The Distill Template Authors - - function DistillFooter(dom) { - const footerTag = dom.querySelector('distill-footer'); - if(!footerTag) { - const footer = dom.createElement('distill-footer'); - footer.innerHTML = footerTemplate; - const body = dom.querySelector('body'); - body.appendChild(footer); - } - } - - // Copyright 2018 The Distill Template Authors - - const extractors = new Map([ - ['ExtractFrontmatter', ExtractFrontmatter], - ['ExtractBibliography', ExtractBibliography], - ['ExtractCitations', ExtractCitations], - ]); - - const transforms = new Map([ - ['HTML', HTML], - ['makeStyleTag', makeStyleTag], - ['OptionalComponents', OptionalComponents], - ['TOC', TOC], - ['Byline', Byline], - ['Mathematics', Mathematics], - ['Meta', Meta], - ['Typeset', Typeset], - ['Polyfills', render], - ['CitationList', CitationList], - ['Reorder', render$1] // keep last - ]); - - const distillTransforms = new Map([ - ['DistillHeader', DistillHeader], - ['DistillAppendix', DistillAppendix], - ['DistillFooter', DistillFooter], - ]); - - /* Exported functions */ - - function render$2(dom, data, verbose=true) { - let frontMatter; - if (data instanceof FrontMatter) { - frontMatter = data; - } else { - frontMatter = FrontMatter.fromObject(data); - } - // first, we collect static data from the dom - for (const [name, extract] of extractors.entries()) { - if (verbose) console.warn('Running extractor: ' + name); - extract(dom, frontMatter, verbose); - } - // secondly we use it to transform parts of the dom - for (const [name, transform] of transforms.entries()) { - if (verbose) console.warn('Running transform: ' + name); - // console.warn('Running transform: ', transform); - transform(dom, frontMatter, verbose); - } - dom.body.setAttribute('distill-prerendered', ''); - // the function calling us can now use the transformed dom and filled data object - if (data instanceof FrontMatter) ; else { - frontMatter.assignToObject(data); - } - } - - function distillify(dom, data, verbose=true) { - // thirdly, we can use these additional transforms when publishing on the Distill website - for (const [name, transform] of distillTransforms.entries()) { - if (verbose) console.warn('Running distillify: ', name); - transform(dom, data, verbose); - } - } - - function usesTemplateV2(dom) { - const tags = dom.querySelectorAll('script'); - let usesV2 = undefined; - for (const tag of tags) { - const src = tag.src; - if (src.includes('template.v1.js')) { - usesV2 = false; - } else if (src.includes('template.v2.js')) { - usesV2 = true; - } else if (src.includes('template.')) { - throw new Error('Uses distill template, but unknown version?!'); - } - } - - if (usesV2 === undefined) { - throw new Error('Does not seem to use Distill template at all.'); - } else { - return usesV2; - } - } - - const testing = { - extractors: extractors, - transforms: transforms, - distillTransforms: distillTransforms - }; - - exports.FrontMatter = FrontMatter; - exports.distillify = distillify; - exports.render = render$2; - exports.testing = testing; - exports.usesTemplateV2 = usesTemplateV2; - - Object.defineProperty(exports, '__esModule', { value: true }); - -}))); -//# sourceMappingURL=transforms.v2.js.map +// Copyright 2018 The Distill Template Authors +const me='/*\n * Copyright 2018 The Distill Template Authors\n *\n * Licensed under the Apache License, Version 2.0 (the "License");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an "AS IS" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\nhtml {\n font-size: 14px;\n\tline-height: 1.6em;\n /* font-family: "Libre Franklin", "Helvetica Neue", sans-serif; */\n font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Oxygen, Ubuntu, Cantarell, "Fira Sans", "Droid Sans", "Helvetica Neue", Arial, sans-serif;\n /*, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";*/\n text-size-adjust: 100%;\n -ms-text-size-adjust: 100%;\n -webkit-text-size-adjust: 100%;\n}\n\n@media(min-width: 768px) {\n html {\n font-size: 16px;\n }\n}\n\nbody {\n margin: 0;\n}\n\na {\n color: #004276;\n}\n\nfigure {\n margin: 0;\n}\n\ntable {\n\tborder-collapse: collapse;\n\tborder-spacing: 0;\n}\n\ntable th {\n\ttext-align: left;\n}\n\ntable thead {\n border-bottom: 1px solid rgba(0, 0, 0, 0.05);\n}\n\ntable thead th {\n padding-bottom: 0.5em;\n}\n\ntable tbody :first-child td {\n padding-top: 0.5em;\n}\n\npre {\n overflow: auto;\n max-width: 100%;\n}\n\np {\n margin-top: 0;\n margin-bottom: 1em;\n}\n\nsup, sub {\n vertical-align: baseline;\n position: relative;\n top: -0.4em;\n line-height: 1em;\n}\n\nsub {\n top: 0.4em;\n}\n\n.kicker,\n.marker {\n font-size: 15px;\n font-weight: 600;\n color: rgba(0, 0, 0, 0.5);\n}\n\n\n/* Headline */\n\n@media(min-width: 1024px) {\n d-title h1 span {\n display: block;\n }\n}\n\n/* Figure */\n\nfigure {\n position: relative;\n margin-bottom: 2.5em;\n margin-top: 1.5em;\n}\n\nfigcaption+figure {\n\n}\n\nfigure img {\n width: 100%;\n}\n\nfigure svg text,\nfigure svg tspan {\n}\n\nfigcaption,\n.figcaption {\n color: rgba(0, 0, 0, 0.6);\n font-size: 12px;\n line-height: 1.5em;\n}\n\n@media(min-width: 1024px) {\nfigcaption,\n.figcaption {\n font-size: 13px;\n }\n}\n\nfigure.external img {\n background: white;\n border: 1px solid rgba(0, 0, 0, 0.1);\n box-shadow: 0 1px 8px rgba(0, 0, 0, 0.1);\n padding: 18px;\n box-sizing: border-box;\n}\n\nfigcaption a {\n color: rgba(0, 0, 0, 0.6);\n}\n\nfigcaption b,\nfigcaption strong, {\n font-weight: 600;\n color: rgba(0, 0, 0, 1.0);\n}\n'+'/*\n * Copyright 2018 The Distill Template Authors\n *\n * Licensed under the Apache License, Version 2.0 (the "License");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an "AS IS" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n@supports not (display: grid) {\n .base-grid,\n distill-header,\n d-title,\n d-abstract,\n d-article,\n d-appendix,\n distill-appendix,\n d-byline,\n d-footnote-list,\n d-citation-list,\n distill-footer {\n display: block;\n padding: 8px;\n }\n}\n\n.base-grid,\ndistill-header,\nd-title,\nd-abstract,\nd-article,\nd-appendix,\ndistill-appendix,\nd-byline,\nd-footnote-list,\nd-citation-list,\ndistill-footer {\n display: grid;\n 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[y/N] " response -if [[ ! $response =~ ^([yY][eE][sS]|[yY])+$ ]] -then - echo "Aborting." - [[ "$0" = "$BASH_SOURCE" ]] && exit 1 || return 1 -fi - -# Check if there are any uncommitted changes -if ! git diff-index --quiet HEAD --; then - echo "Changes to the following files are uncommitted:" - git diff-index --name-only HEAD -- - echo "Please commit the changes before proceeding." - echo "Aborting." - [[ "$0" = "$BASH_SOURCE" ]] && exit 1 || return 1 -fi - -# Check if there are any untracked files -if ! test -z "$(git ls-files --exclude-standard --others)"; then - echo "There are untracked files:" - git ls-files --exclude-standard --others - echo "Please commit those files or stash them before proceeding." - echo "Aborting." - [[ "$0" = "$BASH_SOURCE" ]] && exit 1 || return 1 -fi - -# Switch to source branch (creates it if necessary from the current branch) -if [ `git branch | grep $SRC_BRANCH | tr ' ' '\n' | tail -1` ] -then - git checkout $SRC_BRANCH -else - git checkout -b $SRC_BRANCH -fi - -# Checkout DEPLOY_BRANCH branch -if [ `git branch | grep $DEPLOY_BRANCH` ] -then - git branch -D $DEPLOY_BRANCH -fi -git checkout -b $DEPLOY_BRANCH - -# Export JEKYLL_ENV=production -export JEKYLL_ENV=production - -# Build site -bundle exec jekyll build - -# Delete and move files -find . -maxdepth 1 ! -name '_site' ! -name '.git' ! -name 'CNAME' ! -name '.gitignore' -exec rm -rf {} \; -mv _site/* . -rm -R _site/ - -# Create `.nojekyll` file (bypass GitHub Pages Jekyll processing) -touch .nojekyll - -# Push to DEPLOY_BRANCH -git add -fA -git commit --allow-empty -m "$(git log -1 --pretty=%B) [ci skip]" -[[ ${NO_PUSH} ]] || git push -f -q origin $DEPLOY_BRANCH - -# Move back to SRC_BRANCH -git checkout $SRC_BRANCH - -echo "Deployed successfully!" - -exit 0 diff --git a/bin/docker_build_image.sh b/bin/docker_build_image.sh deleted file mode 100755 index fe208afb..00000000 --- a/bin/docker_build_image.sh +++ /dev/null @@ -1,5 +0,0 @@ - FILE=Gemfile.lock -if [ -f "$FILE" ]; then - rm $FILE -fi - docker build -t "al-folio:latest" . \ No newline at end of file diff --git a/bin/docker_run.sh b/bin/docker_run.sh deleted file mode 100755 index 9c3d3d8a..00000000 --- a/bin/docker_run.sh +++ /dev/null @@ -1,8 +0,0 @@ -FILE=Gemfile.lock -if [ -f "$FILE" ]; then - rm $FILE -fi -docker run --rm -v "$PWD:/srv/jekyll/" -p "8080:8080" \ - -it al-folio:latest bundler \ - exec jekyll serve --watch --port=8080 --host=0.0.0.0 \ - --verbose --livereload \ No newline at end of file diff --git a/bin/dockerhub_run.sh b/bin/dockerhub_run.sh deleted file mode 100755 index ac6a14e9..00000000 --- a/bin/dockerhub_run.sh +++ /dev/null @@ -1,8 +0,0 @@ -FILE=Gemfile.lock -if [ -f "$FILE" ]; then - rm $FILE -fi -docker run --rm -v "$PWD:/srv/jekyll/" -p "8080:8080" \ - -it amirpourmand/al-folio bundler \ - exec jekyll serve --watch --port=8080 --host=0.0.0.0 \ - --verbose --livereload diff --git a/blog/2012/demQ01/index.html b/blog/2012/demQ01/index.html new file mode 100644 index 00000000..989dd579 --- /dev/null +++ b/blog/2012/demQ01/index.html @@ -0,0 +1,69 @@ + Life tables: Using survey data | Sebastian Daza

      Life tables: Using survey data

      September 5, 2012• Sebastian Daza

      2012   ·   demography  

      Here a life table exercise using fake retrospective survey data:

      You collected retrospective survey data on the age at first birth for twenty-five women who are age 50 at interview in 2009. Construct a life table where the state of interest is childlessness. Start the life table at age 15 and terminate it at exact age 50. Use five year intervals and, for speed, assume that “deaths” occur halfway through the 5-yr age interval (despite the fact that you have actual ages).Their ages (last birthday) at first birth are 29, 22, 43, 31, 26, 20, no birth, 25, 23, 30, no birth, 37, 21, 25, 28, no birth, 23 (twins), 27, 34, 25, 24, 21, 17, no birth, no birth. On the basis of this information, answer the following questions.

      What is the probability that a childless woman at exact age 30 would still be childless at age 40?

      First, we have to build the corresponding life table where the event of interest is childbearing. For childless women during the period I assign 50 years old. In the case of twins it is enough to specify just one event (first birth).

      ages <- c(29, 22, 43, 31, 26, 20, 50, 25, 23, 30, 50, 37, 21,
+    25, 28, 50, 23, 27, 34, 25, 24, 21, 17, 50, 50)
+# age
+x <- seq(15, 49, 5)
+# width of intervals
+n <- rep(5, 7)
+# births
+b <- rep(NA, 7)
+
+(dat <- data.frame(x, n, b))
      ##    x n  b
+## 1 15 5 NA
+## 2 20 5 NA
+## 3 25 5 NA
+## 4 30 5 NA
+## 5 35 5 NA
+## 6 40 5 NA
+## 7 45 5 NA
      # computing births: equivalent to counting logic values
+for (i in 1:7) {
+    dat$b[i] <- sum(ages >= dat$x[i] & ages <= (dat$x[i + 1] -
+        1), na.rm = TRUE)
+}
+
+# computing lx
+dat$lx <- NA
+# childless women at the beginning of the period
+dat$lx[1] <- length(ages)
+
+for (i in 1:6) {
+    dat$lx[i + 1] <- dat$lx[i] - dat$b[i]
+}
+
+dat
      ##    x n b lx
+## 1 15 5 1 25
+## 2 20 5 7 24
+## 3 25 5 7 17
+## 4 30 5 3 10
+## 5 35 5 1  7
+## 6 40 5 1  6
+## 7 45 5 0  5

      So the probability a childless woman at exact age 30 would still be childless at age 40 is:

      dat$l[dat$x == 40]/dat$l[dat$x == 30]
      ## [1] 0.6

      What was the expected number of years of childlessness (prior to age 50) for a 25 year old childless woman?

      We have to calculate the equivalent to life expectancy but for childlessness. For that, we need $L_x$ and $T_x$. Following the assumptions specified in the question: “deaths” occur halfway through the 5-yr age interval (despite the fact that you have actual ages), we can compute readily $L_x$:

      # computing Lx assuming nax = interval/2
+dat$Lx <- NA
+for (i in 1:6) {
+    dat$Lx[i] <- dat$lx[i + 1] * dat$n[i] + (dat$n[i]/2) * dat$b[i]
+}
+
+# Lx for the last interval
+dat$Lx[7] <- 5 * 5
+
+# computing Tx
+dat$Tx <- rev(cumsum(rev(dat$Lx)))
+dat
      ##    x n b lx    Lx    Tx
+## 1 15 5 1 25 122.5 420.0
+## 2 20 5 7 24 102.5 297.5
+## 3 25 5 7 17  67.5 195.0
+## 4 30 5 3 10  42.5 127.5
+## 5 35 5 1  7  32.5  85.0
+## 6 40 5 1  6  27.5  52.5
+## 7 45 5 0  5  25.0  25.0

      The expected number of years of childlessness would be $\frac{T_{25}}{l_{25}}$

      dat$T[dat$x == 25]/dat$l[dat$x == 25]
      ## [1] 11.5

      What fraction of years between ages 15 and 49.99 were spent childless?

      That would be $\frac{e_{15}}{35}$:

      (dat$T[dat$x == 15]/dat$l[dat$x == 15])/35
      ## [1] 0.48

      You observe that the parity progression ratios for this cohort take the following form. What is the TFR of this cohort?

      A straightforward way to do it:

      # given parity progression ratios
+ppr <- c(NA, 0.8, 0.75, 0.25, 0)
+# estimation PPR1
+# the total number of first births was 20
+# the total number of women is 25, so...
+(ppr[1] <- (20/25))
      ## [1] 0.8
      (TFR <- sum(cumprod(ppr)))
      ## [1] 2.04

      How might your data collection method affect the accuracy of your answer to the former questions? Be specific, referencing the possible direction of bias if applicable.

      In the survey we are only taking into account surviving women. We will und erestimate the probability of remaining childless in this cohort because single and childless women have higher mortality.

      Assume that births happened exactly half-way through the year of age in which a woman reported a birth (e.g., a birth reported at age 34 happened at exact age 34.5). How inaccurate is the short-cut estimate of $_{5}a_{20}$ you used above?

      We have to compute the average number of years lived childless according to the specification for the question.

      # ages included in 5a20
+(age <- seq(20, 24, 1))
      ## [1] 20 21 22 23 24
      # years lived childless according to assumptions of the
+# question
+(x <- (age - 19.5))
      ## [1] 0.5 1.5 2.5 3.5 4.5
      # births between 20 and 24
+(y <- table(ages[ages >= 20 & ages <= 24]))
      ##
+## 20 21 22 23 24
+##  1  2  1  2  1
      # average number years lived childless in the interval
+mean(x * y)
      ## [1] 3.5

      Thus, the short-cut estimate is inaccurate by 3.5 - (5/2) = 1.

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/2012/facebook-sna/index.html b/blog/2012/facebook-sna/index.html new file mode 100644 index 00000000..2058751f --- /dev/null +++ b/blog/2012/facebook-sna/index.html @@ -0,0 +1,40 @@ + My Facebook Network | Sebastian Daza

      My Facebook Network

      October 4, 2012• Sebastian Daza

      2012   ·   R  

      I am taking a coursera class on Social Network Analysis. The first assignment consisted of ploting my Facebook network. Below I show how I did it using the R package igraph. In order to get your Facebook Network you can use the Netvizz app. This application allows you to create gdf files (a simple text format that specifies an undirected graph) from the friendship relations. I used this simplified Netvizz version from coursera that generates .gml files directly.

      library(igraph)
+
+# read the facebook network from a .gml file
+f <- read.graph("sdaza.gml", format = "gml")

      In order to plot my Facebook network, I extract the following attributes: gender, wall posts count, and interface language. I also assign some colors and shapes to those attributes.

      # gender, assigning shapes
+ssex <- V(f)$sex
+table(ssex)
      ## ssex
+## female   male
+##    218    188
      ssex[ssex == "female"] <- "circle"
+ssex[ssex == "male"] <- "square"
+table(ssex)
      ## ssex
+## circle square
+##    218    188
      # interface language, assigning colors
+cloc <- V(f)$locale
+table(cloc)
      ## cloc
+## en_GB en_US es_CL es_ES es_LA it_IT nl_NL pt_BR
+##    13   132     2     9   245     1     1     3
      cloc[cloc %in% c("es_ES", "it_IT", "nl_NL", "en_GB", "gl_ES", "ko_KR")] <-  "Yellow"
+cloc[cloc %in% c("es_CL", "pt_BR", "es_LA")] <- "Green"
+cloc[cloc == c("en_US")] <- "Blue"
+table(cloc)
      ## cloc
+##   Blue  Green Yellow
+##    132    250     24
      # wall posts count, assigning node size
+nsize <- (V(f)$wallcount/max(V(f)$wallcount) + 0.1) * 15

      Now, I can plot the network:

      plot(f, layout = layout.fruchterman.reingold, edge.arrow.size = 0.5,
+    vertex.label = NA, vertex.size = 3, vertex.color = cloc,
+    vertex.shape = ssex)

      center

      If we use wall posts count to size the nodes:

      plot(f, layout = layout.fruchterman.reingold, edge.arrow.size = 0.5,
+    vertex.label = NA, vertex.size = nsize, vertex.color = cloc,
+    vertex.shape = ssex)

      center

      We can also obtain some basic descriptive statistics of the network using a graph.basic.stats function (see here to obtain it):

      graph.basic.stats(f)
      ## Number of nodes: 406
+## Number of edges: 4415
+##
+## Degree
+##   Average: 21.7
+##
+##
+## Giant component  Size: 397
+## Giant component  As perc.: 0.978
+## Second Giant component: 1
+## Second Giant component As perc.: 0.00246
+##
+## Isolated nodes
+##   Number: 9
+##   As perc.: 0.0222

      As can be seen, I have relatively compact and homogeneous components (or clusters) regarding language interface on Facebook. You can see now how your own Facebook network looks like.

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/2012/index.html b/blog/2012/index.html new file mode 100644 index 00000000..7fbc3d15 --- /dev/null +++ b/blog/2012/index.html @@ -0,0 +1 @@ + 2012 | Sebastian Daza

      2012

      an archive of posts from this year

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/2012/lifetables/index.html b/blog/2012/lifetables/index.html new file mode 100644 index 00000000..18f684da --- /dev/null +++ b/blog/2012/lifetables/index.html @@ -0,0 +1,78 @@ + Life tables: A super brief introduction | Sebastian Daza

      Life tables: A super brief introduction

      August 28, 2012• Sebastian Daza

      2012   ·   demography  

      A life table is just one way of summarizing a cohort’s mortality experience. Below a simple way to build a life table using R.

      dat <- read.csv("frmales85.csv", sep = ",", header = T)
+# length of intervals
+dat$n <- c(diff(dat$x), NA)
+# number of intervals
+max <- length(dat$x)
+# age 85 is an open interval
+dat
      ##     x     nNx   nDx   nax  n
+## 1   0  379985  3741 0.087  1
+## 2   1 1559722   770 1.500  4
+## 3   5 1896295   532 2.500  5
+## 4  10 2160190   673 2.966  5
+## 5  15 2179837  2138 2.769  5
+## 6  20 2159556  3432 2.574  5
+## 7  25 2106750  3291 2.512  5
+## 8  30 2147845  3657 2.586  5
+## 9  35 2165387  4956 2.657  5
+## 10 40 1516952  5269 2.697  5
+## 11 45 1498630  8654 2.695  5
+## 12 50 1552746 14490 2.663  5
+## 13 55 1476770 20831 2.625  5
+## 14 60 1350479 26805 2.601  5
+## 15 65  722430 20233 2.615  5
+## 16 70  842589 38315 2.598  5
+## 17 75  636848 46903 2.538  5
+## 18 80  372059 44443 2.466  5
+## 19 85  175169 37759 4.639 NA
      # age-specific mortality rates assuming $_nM_x=_nm_x$
+dat$nmx <- dat$nDx/dat$nNx
+# age-specific probability to die
+dat$nqx <- (dat$n * dat$nmx)/(1 + (dat$n - dat$nax) * dat$nmx)
+dat$nqx[max] <- 1
+dat$npx <- 1 - dat$nqx
      # definition of radix
+radix <- 100000
+# easy way to get lx without a loop
+dat$lx <- radix * c(1, cumprod(dat[1:max - 1, "npx"]))
+dat$ndx <- dat$lx * dat$nqx
+dat[, c(-2, -3)]
      ##     x   nax  n      nmx     nqx   npx     lx   ndx
+## 1   0 0.087  1 0.009845 0.00976 0.990 100000   976
+## 2   1 1.500  4 0.000494 0.00197 0.998  99024   195
+## 3   5 2.500  5 0.000281 0.00140 0.999  98829   139
+## 4  10 2.966  5 0.000312 0.00156 0.998  98690   154
+## 5  15 2.769  5 0.000981 0.00489 0.995  98537   482
+## 6  20 2.574  5 0.001589 0.00792 0.992  98055   776
+## 7  25 2.512  5 0.001562 0.00778 0.992  97278   757
+## 8  30 2.586  5 0.001703 0.00848 0.992  96522   818
+## 9  35 2.657  5 0.002289 0.01138 0.989  95703  1089
+## 10 40 2.697  5 0.003473 0.01723 0.983  94614  1630
+## 11 45 2.695  5 0.005775 0.02849 0.972  92984  2649
+## 12 50 2.663  5 0.009332 0.04566 0.954  90334  4125
+## 13 55 2.625  5 0.014106 0.06824 0.932  86209  5883
+## 14 60 2.601  5 0.019849 0.09473 0.905  80326  7609
+## 15 65 2.615  5 0.028007 0.13127 0.869  72717  9545
+## 16 70 2.598  5 0.045473 0.20498 0.795  63171 12949
+## 17 75 2.538  5 0.073649 0.31172 0.688  50223 15656
+## 18 80 2.466  5 0.119451 0.45848 0.542  34567 15848
+## 19 85 4.639 NA 0.215558 1.00000 0.000  18719 18719
      # Lx for the open interval
+dat$nLx <- dat$lx * dat$n - (dat$n - dat$nax) * dat$ndx
+dat$nLx[max] <- dat$lx[max]/dat$nmx[max]
      dat$Tx <- rev(cumsum(rev(dat$nLx)))
+dat$ex <- dat$Tx/dat$lx
+dat[, c(-2:-6)]
      ##     x     nqx   npx     lx   ndx    nLx      Tx    ex
+## 1   0 0.00976 0.990 100000   976  99109 7131028 71.31
+## 2   1 0.00197 0.998  99024   195 395609 7031919 71.01
+## 3   5 0.00140 0.999  98829   139 493798 6636310 67.15
+## 4  10 0.00156 0.998  98690   154 493140 6142512 62.24
+## 5  15 0.00489 0.995  98537   482 491608 5649372 57.33
+## 6  20 0.00792 0.992  98055   776 488390 5157764 52.60
+## 7  25 0.00778 0.992  97278   757 484509 4669374 48.00
+## 8  30 0.00848 0.992  96522   818 480632 4184865 43.36
+## 9  35 0.01138 0.989  95703  1089 475964 3704232 38.71
+## 10 40 0.01723 0.983  94614  1630 469315 3228268 34.12
+## 11 45 0.02849 0.972  92984  2649 458812 2758953 29.67
+## 12 50 0.04566 0.954  90334  4125 442031 2300141 25.46
+## 13 55 0.06824 0.932  86209  5883 417074 1858110 21.55
+## 14 60 0.09473 0.905  80326  7609 383376 1441035 17.94
+## 15 65 0.13127 0.869  72717  9545 340818 1057660 14.54
+## 16 70 0.20498 0.795  63171 12949 284755  716841 11.35
+## 17 75 0.31172 0.688  50223 15656 212570  432087  8.60
+## 18 80 0.45848 0.542  34567 15848 132677  219516  6.35
+## 19 85 1.00000 0.000  18719 18719  86839   86839  4.64
      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/2012/raking/index.html b/blog/2012/raking/index.html new file mode 100644 index 00000000..c41ba17a --- /dev/null +++ b/blog/2012/raking/index.html @@ -0,0 +1,190 @@ + Raking weights with R | Sebastian Daza

      Raking weights with R

      August 25, 2012• Sebastian Daza

      2012   ·   R   survey   data science  

      When I was working with public opinion surveys, I usually had to adjust the data according to population parameters such as sex, age, socioeconomic status, or region. There are several ways to do this. One of them is raking. Using the package anesrake by Josh Pasek is easy to compute raking weights in R. I show a brief and straightforward raking example in this post.

      What is raking?

      It is common to find differences between the population and sample distribution in some key demographic variables when analyzing surveys. These differences might be due to the sampling design, non-coverage issues or non-response. To avoid biases of point estimates, we generally adjust those differences using weights so that the marginal totals agree (at least in part) with the population. These adjustments can be done using different methods: post-stratification or cell-weighting, raking, general regression estimator GREG (Battaglia et al. 2004, Valliant et al. 2013). Here I will show an example for raking adjustment. Raking has some advantages over cell-weighting:

      1. We have to know only the population totals of the specific variables, not all cells of the cross-classification.
      2. It is more flexible feasible when we use several variables at once.

      It is important to note that raking adjustments are useful when we want to estimate a proportion or mean from a population, but they are not necessarily the best when modeling (see Gelman 2007). In that case, model specification (i.e., inclusion of all the key variables and interactions) may be a better solution. It is important to note that the raking procedure may affect the dependence structure of variables, what eventually may bias model estimates.

      Some rules of thumb

      DeBell and Krosnick (2009) provide useful recommendations regarding variable selection when implementing raking:

      • Identify a set of variable likely to be measured with little error and a low item nonresponse rate (less than 5%), and compare them with reliable data sources (e.g., Census). Some typical variables are age groups, gender, race, SES, census region.
      • Implement weighting when substantive demography discrepancies are observed. For instance, differences higher than five percentage points. When differences are less than two percentage points this adjustment would not be necessary.
      • When selected variables have categories with less than 5% in the sample, it would be recommended to collapse them.

      Remember, however, that the ultimate goal of raking is to calibrate and improve the efficiency of our estimates (reduce standard errors). In general, there is trade-off between these two objectives. Anyway, weight adjustments should be variable-dependent: they should explore how different key variables of our survey behave after adjusting. In this post I only show how to implement raking. A more complete analysis will be surely necessary.

      Once the raking procedure is implemented, it is a good idea to follow these steps in an iterative fashion:

      • Identify extreme weights and truncate them. For example, those greater than five times the mean of weights.
      • Truncate large values, but not small ones (i.e., those near zero). While large values increase the effect of outliers and are more likely to inflate variance, low values do not.
      • Examine the design effect using the new weights. If the new design effect is greater than the old design effect by more than 0.5, it is a good idea to review the raking adjustment. In other words, calibration may cost too much in terms of efficiency (standard errors).

      Three things can be done to improve the weighting procedure:

      • Collapse categories of the variables
      • Eliminate or replace weighting variables
      • Increase the weight truncation criterion ( > 5 )

      Raking using R

      In this example I use data from Chile to estimate the presidential approval (Opinion Public Survey CEP, July-August 2012). Below, you can see the characteristics of the dataset:

      dat  <- read.csv("cep.csv")
+str(dat)
      ## 'data.frame':	1512 obs. of  12 variables:
+##  $ fnu     : int  1580 1579 890 871 1031 104 1103 263 924 105 ...
+##  $ area    : int  1 1 2 1 1 1 1 1 1 1 ...
+##  $ region  : int  13 13 9 9 10 2 13 5 9 2 ...
+##  $ comuna  : int  13123 13123 9110 9102 10202 2201 13101 5106 9201 2201 ...
+##  $ sex     : int  1 1 2 1 1 1 2 1 2 1 ...
+##  $ age     : int  30 60 28 72 55 35 33 25 73 43 ...
+##  $ ses     : int  2 2 3 3 4 3 2 3 4 2 ...
+##  $ phone   : int  1 1 9 2 2 2 2 1 2 1 ...
+##  $ edu     : int  4 4 4 1 2 3 4 4 2 4 ...
+##  $ pond    : num  1.977 1.243 0.514 0.421 0.526 ...
+##  $ agecat  : int  2 5 2 5 5 3 2 2 5 3 ...
+##  $ approval: int  2 1 2 1 1 2 2 2 2 2 ...

      I use five variables to define raking weights: sex, agecat, ses, region, and area. I get relative frequencies of these variables using the package weights . Most of the variables have more than five percent in each category, with the exception of region and ses. For illustrative purposes, I will use those variables without collapsing their categories.

      library(weights)
+# sex: 1 male, 2 female
+wpct(dat$sex)
      ##     1     2
+## 0.407 0.593
      # agecat: 1 18-24, 2 25-34, 3 35-44, 4 45-54, 5 55+
+wpct(dat$agecat)
      ##     1     2     3     4     5
+## 0.124 0.159 0.177 0.194 0.346
      # ses: 1 abc1, 2 c2, 3 c3, 4 d, 5 e
+wpct(dat$ses)
      ##      1      2      3      4      5
+## 0.0390 0.1078 0.3651 0.4484 0.0397
      # region: 1 to 15
+wpct(dat$region)
      ##       1       2       3       4       5       6       7       8       9      10      11      12      13      14      15
+## 0.01323 0.04233 0.01389 0.04167 0.09921 0.05489 0.06151 0.13095 0.06349 0.04894 0.00397 0.01058 0.37632 0.02579 0.01323
      # area: 1 urban, 2 rural
+wpct(dat$area)
      ##     1     2
+## 0.837 0.163

      The next step is to specify the population distribution of the selected variables in a target list. I use two sources to obtain population values: the Chilean Census 2002, and the Bicentenario Survey 2009. It is hard to find reliable SES population parameters. The Bicentenario data provide, at least, an approximation.

      # chilean census 2002
+sex <- c(.49,.51)
+agecat  <- c(.163, .203, .195, .187, .253)
+region <- c(0.015, 0.031, 0.016, 0.039, 0.102, 0.051, 0.059, 0.123,
+            0.056, 0.046, 0.006, 0.010, 0.408, 0.023 , 0.013)
+area  <-  c(.869, .131)
+
+# bicentenario survey 2009
+ses  <- c(.109, .184, .261, .364, .083)
+
+# definitions of target list
+targets <- list(sex, agecat,ses, region, area)
+# important: to use the same variable names of the dataset
+names(targets) <- c("sex", "agecat", "ses", "region", "area")
+# id variable
+dat$caseid <- 1:length(dat$sex)

      The output shows that some distributions do not sum one. This is because of rounding. We can force any proportion distribution to sum one using force1 = TRUE. The differences between the population and the sample distribution are all greater than five percentage points. This meets the variable selection criterion discussed above.

      sum(region)
      ## [1] 0.998
      library(anesrake)
+anesrakefinder(targets, dat, choosemethod = "total")
      ##    sex agecat    ses region   area
+## 0.1652 0.2017 0.3780 0.0828 0.0634

      I apply the anesrake function as follows:

      • The maximum weight value is five, weights greater than five will be truncated (cap = 5).
      • The total differences between population and sample have to be greater than 0.05 so that to include a variable (pctlim = .05).
      • The maximum number of variables included in the raking procedure is five (nlim = 5).
      outsave <- anesrake(targets, dat, caseid = dat$caseid,
+  verbose= FALSE, cap = 5, choosemethod = "total",
+  type = "pctlim", pctlim = .05 , nlim = 5,
+  iterate = TRUE , force1 = TRUE)
      summary(outsave)
      ## $convergence
+## [1] "Complete convergence was achieved after 48 iterations"
+##
+## $raking.variables
+## [1] "sex"    "agecat" "ses"    "region" "area"
+##
+## $weight.summary
+##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
+##    0.33    0.62    0.80    1.00    1.10    4.30
+##
+## $selection.method
+## [1] "variable selection conducted using _pctlim_ - discrepancies selected using _total_."
+##
+## $general.design.effect
+## [1] 1.38
+##
+## $sex
+##   Target Unweighted N Unweighted % Wtd N Wtd % Change in % Resid. Disc. Orig. Disc.
+## 1   0.49          616        0.407   741  0.49      0.0826            0      0.0826
+## 2   0.51          896        0.593   771  0.51     -0.0826            0     -0.0826
+##
+## $agecat
+##   Target Unweighted N Unweighted % Wtd N Wtd % Change in % Resid. Disc. Orig. Disc.
+## 1  0.163          187        0.124   246 0.163     0.03916     2.78e-17     0.03916
+## 2  0.203          240        0.159   307 0.203     0.04407    -2.78e-17     0.04407
+## 3  0.195          268        0.177   295 0.195     0.01756     0.00e+00     0.01756
+## 4  0.187          294        0.194   282 0.187    -0.00763    -2.78e-17    -0.00763
+## 5  0.253          523        0.346   382 0.253    -0.09315     0.00e+00    -0.09315
+##
+## $ses
+##   Target Unweighted N Unweighted % Wtd N  Wtd % Change in % Resid. Disc. Orig. Disc.
+## 1 0.1089           59       0.0390   165 0.1089      0.0699     1.39e-17      0.0699
+## 2 0.1838          163       0.1078   278 0.1838      0.0760     2.78e-17      0.0760
+## 3 0.2607          552       0.3651   394 0.2607     -0.1043     5.55e-17     -0.1043
+## 4 0.3636          678       0.4484   550 0.3636     -0.0848     5.55e-17     -0.0848
+## 5 0.0829           60       0.0397   125 0.0829      0.0432     1.39e-17      0.0432
+##
+## $region
+##     Target Unweighted N Unweighted %  Wtd N   Wtd % Change in % Resid. Disc. Orig. Disc.
+## 1  0.01503           20      0.01323  22.73 0.01503    0.001803     0.00e+00    0.001803
+## 2  0.03106           64      0.04233  46.97 0.03106   -0.011266     0.00e+00   -0.011266
+## 3  0.01603           21      0.01389  24.24 0.01603    0.002143     0.00e+00    0.002143
+## 4  0.03908           63      0.04167  59.09 0.03908   -0.002589     0.00e+00   -0.002589
+## 5  0.10220          150      0.09921 154.53 0.10220    0.002998    -2.78e-17    0.002998
+## 6  0.05110           83      0.05489  77.27 0.05110   -0.003792    -6.94e-18   -0.003792
+## 7  0.05912           93      0.06151  89.39 0.05912   -0.002390     0.00e+00   -0.002390
+## 8  0.12325          198      0.13095 186.35 0.12325   -0.007706     0.00e+00   -0.007706
+## 9  0.05611           96      0.06349  84.84 0.05611   -0.007380     0.00e+00   -0.007380
+## 10 0.04609           74      0.04894  69.69 0.04609   -0.002850     0.00e+00   -0.002850
+## 11 0.00601            6      0.00397   9.09 0.00601    0.002044     0.00e+00    0.002044
+## 12 0.01002           16      0.01058  15.15 0.01002   -0.000562     0.00e+00   -0.000562
+## 13 0.40882          569      0.37632 618.13 0.40882    0.032495    -5.55e-17    0.032495
+## 14 0.02305           39      0.02579  34.85 0.02305   -0.002748     0.00e+00   -0.002748
+## 15 0.01303           20      0.01323  19.70 0.01303   -0.000201    -3.47e-18   -0.000201
+##
+## $area
+##   Target Unweighted N Unweighted % Wtd N Wtd % Change in % Resid. Disc. Orig. Disc.
+## 1  0.869         1266        0.837  1314 0.869      0.0317     1.11e-16      0.0317
+## 2  0.131          246        0.163   198 0.131     -0.0317     0.00e+00     -0.0317

      The procedure reaches good convergence, weights lower than 5, and very small differences between the sample and population distribution. In addition, the design effect is 1.38. This gives us an idea of the weighting loss due to the raking procedure. The weighting loss (\(L_w\)) is the inflation in the variance of sample estimates that can be attributed to weighting (Heeringa et. al. 2010). A simple approximation is:

      \[L_w(\bar{y}) \approx \frac{\sigma^{2}(w)}{\bar{w}^2} = \Bigg( \frac{\sum\limits_{i=1}^n w_i^2}{\big(\sum\limits_{i=1}^n w_i\big)^2} \cdot n \Bigg) - 1\]

      This simple formula was introduced by Kish (1965) in the context of proportionated stratified sampling. It represents the proportional increase in the variances of means and proportions, ignoring any clustering that may be included in the sample plan. It assumes that:

      • Proportionate allocation is the optimal stratified design, i.e., variances of \(y\) are approximately equal in all strata.
      • Weights are uncorrelated with the values of the random variable \(y\).

      Thus, values from this formula only represents an approximation to weighting loss provided that model assumptions are met. As can be seen in the anesrake output, the design effect is 1.38. That value is equal to \(1 + L_w\).

      # add weights to the dataset
+dat$weightvec  <- unlist(outsave[1])
+n  <- length(dat$region)
+
+# weighting loss
+((sum(dat$weightvec ^ 2) / (sum(dat$weightvec)) ^ 2) * n) - 1
      ## [1] 0.38

      For a more precise estimation of the design effect it would be necessary to use a package such as survey, specifying the characteristics of the sample design. Unfortunately, I do not have enough information to specify the sample design and to estimate the design effect of the presidential approval. There are also no cluster variables or replicate weights in the CEP dataset.

      Anyway, the weighting loss approximation denotes an increase in the design effect lower than .5, what meets the recommendations mentioned above. However, it is important to keep in mind that this weighting procedure does not take into account any clustering effect associated with the sample design.

      Finally, we can estimate the presidential approval with and without weights:

      unweighted <-  wpct(dat$approval)
+weighted  <-  wpct(dat$approval, dat$weightvec)
+tab  <- data.frame(unweighted, weighted)
+rownames(tab)  <- c("approve", "disapprove", "unsure", "dk")
+tab
      ##            unweighted weighted
+## approve        0.2864   0.2983
+## disapprove     0.5119   0.5206
+## unsure         0.1779   0.1609
+## dk             0.0238   0.0201

      The difference between the weighted and unweighted presidential approval is not big, only 0.041 percentage points.

      Raking using given survey weights

      The CEP dataset includes its own weights (variable named pond). Unfortunately, there is no detailed information about the weighting construction in the methodological documentation of this survey (“Survey weighting is a mess”, Gelman 2007). For illustrative purposes, however, I will show how to estimate raking weights using previous weights.

      Exploring the pond variable, we can see that there are some big weights (e.g., 17.6). This makes me think that they are just scaled design weights, probably with some non-response adjustment, but I am not really sure.

      summary(dat$pond)
      ##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
+##    0.02    0.46    0.79    1.00    1.24   17.60

      The weighting loss associated with the pond weights is relatively high, denoting a design effect of 2.09.

      # weighting loss
+((sum(dat$pond ^ 2) / (sum(dat$pond)) ^ 2) * n) - 1
      ## [1] 1.09

      We can explore the total differences between the sample and population distributions using pond weights. Only ses and region meet the criterion of total differences greater than 5 percentage points. However, I will keep all the variables used in the previous section and specify that total differences between the population and sample should be greater than .05 (pctlim = .05).

      anesrakefinder(targets, dat, weightvec = dat$pond, choosemethod = "total")
      ##      sex   agecat      ses   region     area
+## 0.000405 0.014836 0.317979 0.169910 0.001149

      In order to get raking weights using previous weights, I only have to add weightvec = dat$pond to the anesrake command.

      outsave <- anesrake(targets, dat, caseid = dat$caseid,
+  weightvec = dat$pond,  verbose = FALSE, cap = 5,
+  choosemethod = "total", type = "pctlim",
+  pctlim = .05 , nlim = 5, iterate = TRUE, force1 = TRUE)
      summary(outsave)
      ## $convergence
+## [1] "Complete convergence was achieved after 50 iterations"
+##
+## $raking.variables
+## [1] "ses"    "region"
+##
+## $weight.summary
+##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
+##    0.01    0.41    0.71    1.00    1.23    5.00
+##
+## $selection.method
+## [1] "variable selection conducted using _pctlim_ - discrepancies selected using _total_."
+##
+## $general.design.effect
+## [1] 1.91
+##
+## $sex
+##   Target Old Weights N Old Weights % Wtd N Wtd % Change in % Resid. Disc. Orig. Disc.
+## 1   0.49           741          0.49   763 0.504      0.0147      -0.0145    0.000202
+## 2   0.51           771          0.51   749 0.496     -0.0147       0.0145   -0.000202
+##
+## $agecat
+##   Target Old Weights N Old Weights % Wtd N Wtd % Change in % Resid. Disc. Orig. Disc.
+## 1  0.163           242         0.160   230 0.152    -0.00768      0.01049    0.002815
+## 2  0.203           309         0.204   314 0.208     0.00388     -0.00521   -0.001321
+## 3  0.195           289         0.191   297 0.197     0.00565     -0.00193    0.003715
+## 4  0.187           281         0.186   267 0.176    -0.00956      0.01041    0.000843
+## 5  0.253           391         0.259   403 0.267     0.00771     -0.01376   -0.006053
+##
+## $ses
+##   Target Old Weights N Old Weights % Wtd N  Wtd % Change in % Resid. Disc. Orig. Disc.
+## 1 0.1089          77.1        0.0510   165 0.1089      0.0579     1.39e-17      0.0579
+## 2 0.1838         209.7        0.1387   278 0.1838      0.0451     2.78e-17      0.0451
+## 3 0.2607         588.5        0.3892   394 0.2607     -0.1284     5.55e-17     -0.1284
+## 4 0.3636         596.3        0.3943   550 0.3636     -0.0307     5.55e-17     -0.0307
+## 5 0.0829          40.5        0.0268   125 0.0829      0.0561     1.39e-17      0.0561
+##
+## $region
+##     Target Old Weights N Old Weights %  Wtd N   Wtd % Change in % Resid. Disc. Orig. Disc.
+## 1  0.01503         31.70       0.02097  22.73 0.01503    -0.00594     0.00e+00    -0.00594
+## 2  0.03106         99.85       0.06604  46.97 0.03106    -0.03497     3.47e-18    -0.03497
+## 3  0.01603         19.56       0.01293  24.24 0.01603     0.00310     0.00e+00     0.00310
+## 4  0.03908         72.79       0.04814  59.09 0.03908    -0.00906     6.94e-18    -0.00906
+## 5  0.10220        206.06       0.13627 154.53 0.10220    -0.03407     0.00e+00    -0.03407
+## 6  0.05110         69.77       0.04614  77.27 0.05110     0.00496     6.94e-18     0.00496
+## 7  0.05912         74.20       0.04907  89.39 0.05912     0.01005     0.00e+00     0.01005
+## 8  0.12325        163.69       0.10826 186.35 0.12325     0.01499     0.00e+00     0.01499
+## 9  0.05611         79.51       0.05258  84.84 0.05611     0.00353    -6.94e-18     0.00353
+## 10 0.04609         67.87       0.04489  69.69 0.04609     0.00121     0.00e+00     0.00121
+## 11 0.00601          5.33       0.00352   9.09 0.00601     0.00249     8.67e-19     0.00249
+## 12 0.01002         11.90       0.00787  15.15 0.01002     0.00215     0.00e+00     0.00215
+## 13 0.40882        559.46       0.36999 618.13 0.40882     0.03883     0.00e+00     0.03883
+## 14 0.02305         28.42       0.01879  34.85 0.02305     0.00425     0.00e+00     0.00425
+## 15 0.01303         21.99       0.01454  19.70 0.01303    -0.00151     0.00e+00    -0.00151
+##
+## $area
+##   Target Old Weights N Old Weights % Wtd N Wtd % Change in % Resid. Disc. Orig. Disc.
+## 1  0.869          1315          0.87  1296 0.857     -0.0124       0.0119   -0.000575
+## 2  0.131           197          0.13   216 0.143      0.0124      -0.0119    0.000575

      Only ses and region are used in the raking procedure. Some weights were truncated to 5, and the design effect decreased from 2.09 to 1.91. The differences between the sample and population distribution are not big.

      # add weights to the dataset
+dat$weightvec2  <- unlist(outsave[1])
+pond <- wpct(dat$approval, dat$pond)
+weighted_pond <-  wpct(dat$approval, dat$weightvec2)
+tab  <- data.frame(unweighted, pond, weighted, weighted_pond)
+rownames(tab)  <- c("approve", "disapprove", "unsure", "dk")
+tab
      ##            unweighted   pond weighted weighted_pond
+## approve        0.2864 0.2739   0.2983        0.2945
+## disapprove     0.5119 0.5231   0.5206        0.5310
+## unsure         0.1779 0.1758   0.1609        0.1520
+## dk             0.0238 0.0272   0.0201        0.0224

      There are no great differences between the estimates weighted and weighted_pond (0.025). There are greater discrepancies between pond and weighted estimates (0.049). This reveals the importance of the socioeconomic status in the presidential approval.

      Last Update: 7/30/15

      References

      Battaglia, M., D. Izrael, D. C. Hoaglin, and M. Frankel. 2004. “Tips and Tricks for Raking Survey Data (Aka Sample Balancing).” American Association of Public Opinion Research.

      DeBell, Matthew and Jon A. Krosnick. 2009. “Computing Weights for American National Election Study Survey Data.” Stanford University.

      Gelman, Andrew. 2007. “Struggles with Survey Weighting and Regression Modeling.” Statistical Science 22(2):153-64.

      Heeringa, Steven, Brady T. West, and Patricia A. Berglund. 2010. Applied Survey Data Analysis. Boca Raton, FL: Chapman & Hall/CRC.

      Valliant, Richard, Jill A. Dever, and Frauke Kreuter. 2013. Practical Tools for Designing and Weighting Survey Samples. New York, NY: Springer New York.

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/2013/index.html b/blog/2013/index.html new file mode 100644 index 00000000..55d8000e --- /dev/null +++ b/blog/2013/index.html @@ -0,0 +1 @@ + 2013 | Sebastian Daza

      2013

      an archive of posts from this year

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/2013/lookvar/index.html b/blog/2013/lookvar/index.html new file mode 100644 index 00000000..2b2249b6 --- /dev/null +++ b/blog/2013/lookvar/index.html @@ -0,0 +1,13 @@ + Looking for variables in R | Sebastian Daza

      Looking for variables in R

      January 29, 2013• Sebastian Daza

      2013   ·   R  

      Recently, I have been working with big databases. After reading their codebooks (usually very long pdf files), I thought it would be useful to have a function to find variable names in R. I wrote a simply function that looks for variable names in data.frame and data.table objects.

      Here an example:

      library(devtools); source_gist("4661324")
      library(data.table)
      dat  <- data.table(infert)
+(var  <- lookvar(dat, c("par", "spon")))
      ## [1] "parity"      "spontaneous"
      dat[, var, with=FALSE]
      ##      parity spontaneous
+##   1:      6           2
+##   2:      1           0
+##   3:      6           0
+##   4:      4           0
+##   5:      3           1
+##  ---
+## 244:      1           1
+## 245:      1           0
+## 246:      2           0
+## 247:      1           1
+## 248:      1           1

      Pretty useful, at least for me. You can also use regular expressions to get variables, for instance, something like lookvar(dat, "p5[0-2]_[a-z]+_2").

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/2013/projections/index.html b/blog/2013/projections/index.html new file mode 100644 index 00000000..05a72aaa --- /dev/null +++ b/blog/2013/projections/index.html @@ -0,0 +1,102 @@ + Cohort component projection | Sebastian Daza

      Cohort component projection

      July 2, 2013• Sebastian Daza

      2013   ·   demography  

      I present an example of a cohort component projection using a closed female population (Sweden 1993), taken from Preston et al.’s book (Demography 2001, page 125). I use R and basic matrix algebra to replicate their results. The advantage of this procedure is that allows to compute easily the intrinsic growth rate and age-proportionate distribution of the stable equivalent population. All we need is the population by age at time 0 (from a census), survivorship ratios (from a life table), and age-specific fertility rates.

      The data:

      dat <- read.csv("sweden1993.csv", sep = ",", header = T)
+attach(dat)
      ##    age     Nf     Lf      f
+## 1    0 293395 497487     NA
+## 2    5 248369 497138     NA
+## 3   10 240012 496901     NA
+## 4   15 261346 496531 0.0120
+## 5   20 285209 495902 0.0908
+## 6   25 314388 495168 0.1499
+## 7   30 281290 494213 0.1125
+## 8   35 286923 492760 0.0441
+## 9   40 304108 490447 0.0074
+## 10  45 324946 486613 0.0003
+## 11  50 247613 480665     NA
+## 12  55 211351 471786     NA
+## 13  60 215140 457852     NA
+## 14  65 221764 436153     NA
+## 15  70 223506 402775     NA
+## 16  75 183654 350358     NA
+## 17  80 141990 271512     NA
+## 18  85 112424 291707     NA

      As can be seen, the data have five-year-interval age groups, so each projection forward will involve 5 years. The steps are very simple:

      1. Project forward the population of each age group (estimation of people alive)
      2. Calculate the number of births of each age group based on fertility rates, adjusting by mortality (estimation of children alive)
      3. Create a Leslie matrix, and then multiple it by the population vector (population by age at time 0)

      Survivorship ratios

      We have to estimate life table survival ratios, that is, proportions of birth cohorts surviving from one age interval to the next in a stationary population. Basically, we are summarizing the mortality experience of different cohorts assuming stationarity. Because census statistics refer to age “last birthday” (rather than exact age), I estimate ratios using $L_x$ (average number of survivors in an age interval) instead of $l_x$.

      \[S_x = \frac{_5L_x}{_5L_{x-5}}\]

      I compute the survival ratios using a loop in R. The estimation of the open-ended survival ratio is slightly different but still straightforward:

      \[\frac{T_{85}}{T_{80}}\] 
      Sf <- NA
+for (i in 1:(length(Lf) - 1)) {
+    Sf[i] <- Lf[i + 1]/Lf[i]
+}
+
+# open-ended survival ratio
+Sf[length(Sf)] <- Lf[18]/(Lf[17] + Lf[18])
      ##  [1] 0.999 1.000 0.999 0.999 0.999 0.998 0.997 0.995 0.992 0.988 0.982 0.970 0.953 0.923 0.870 0.775 0.518

      Number of children

      This is the tricky part. Because census statistics refer to age “last birthday”, and we are projecting every 5 years, the estimation of the number of person-years lived by women in each age group consists of the average number of women alive at the beginning and end of the period (assuming a linear change over the period). To take advantage of the Leslie matrix, I define the births in R using a loop as follows:

      Bf <- rep(0, 18)
+for (i in 1:length(Lf)) {
+    Bf[i] <- 1/(1 + 1.05) * Lf[1]/(100000 * 2) * sum(f[i + 1] * Sf[i], f[i], na.rm = TRUE)
+}
      ##  [1] 0.000000 0.000000 0.014550 0.124596 0.291792 0.318128 0.189858 0.062447 0.009340 0.000364 0.000000 0.000000
+## [13] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

      1/(1+1.05) corresponds to a transformation of age-specific fertility rates (son and daughters) to maternity rates (only daughters), assuming that the ratio of male to female births (SBR) is constant across mothers’ ages. The number of births is also adjusted by the corresponding survival ratio from 0 to 5 years old ($\frac{_5L_0}{5 \times l_0}$), the number 5 goes away due to simplifying).

      Leslie matrix

      I construct a Leslie matrix by replacing specific cells of a 18 x 18 matrix (18 age groups) by the vectors defined above (survival ratios and maternity rates):

      m <- matrix(0, 18, 18)
+m[1, ] <- Bf
+s <- diag(17) * Sf
+m[2:18, 1:17] <- s
+m[18, 18] <- Sf[17]

      Here we have the Leslie matrix:

      ##        [,1] [,2]   [,3]  [,4]  [,5]  [,6]  [,7]   [,8]    [,9]    [,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18]
+##  [1,] 0.000    0 0.0145 0.125 0.292 0.318 0.190 0.0624 0.00934 0.000364 0.000  0.00 0.000 0.000  0.00 0.000 0.000 0.000
+##  [2,] 0.999    0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000  0.00 0.000 0.000  0.00 0.000 0.000 0.000
+##  [3,] 0.000    1 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000  0.00 0.000 0.000  0.00 0.000 0.000 0.000
+##  [4,] 0.000    0 0.9993 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000  0.00 0.000 0.000  0.00 0.000 0.000 0.000
+##  [5,] 0.000    0 0.0000 0.999 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000  0.00 0.000 0.000  0.00 0.000 0.000 0.000
+##  [6,] 0.000    0 0.0000 0.000 0.999 0.000 0.000 0.0000 0.00000 0.000000 0.000  0.00 0.000 0.000  0.00 0.000 0.000 0.000
+##  [7,] 0.000    0 0.0000 0.000 0.000 0.998 0.000 0.0000 0.00000 0.000000 0.000  0.00 0.000 0.000  0.00 0.000 0.000 0.000
+##  [8,] 0.000    0 0.0000 0.000 0.000 0.000 0.997 0.0000 0.00000 0.000000 0.000  0.00 0.000 0.000  0.00 0.000 0.000 0.000
+##  [9,] 0.000    0 0.0000 0.000 0.000 0.000 0.000 0.9953 0.00000 0.000000 0.000  0.00 0.000 0.000  0.00 0.000 0.000 0.000
+## [10,] 0.000    0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.99218 0.000000 0.000  0.00 0.000 0.000  0.00 0.000 0.000 0.000
+## [11,] 0.000    0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.987777 0.000  0.00 0.000 0.000  0.00 0.000 0.000 0.000
+## [12,] 0.000    0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.982  0.00 0.000 0.000  0.00 0.000 0.000 0.000
+## [13,] 0.000    0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000  0.97 0.000 0.000  0.00 0.000 0.000 0.000
+## [14,] 0.000    0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000  0.00 0.953 0.000  0.00 0.000 0.000 0.000
+## [15,] 0.000    0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000  0.00 0.000 0.923  0.00 0.000 0.000 0.000
+## [16,] 0.000    0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000  0.00 0.000 0.000  0.87 0.000 0.000 0.000
+## [17,] 0.000    0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000  0.00 0.000 0.000  0.00 0.775 0.000 0.000
+## [18,] 0.000    0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000  0.00 0.000 0.000  0.00 0.000 0.518 0.518

      Note that the last survival ratio is repeated in the last column (0.518). This is because the estimation of the open-ended survival ratio is:

      \[(N_{80} + N_{85}) \times \frac{T_{85}}{T_{80}}\]

      Now, let’s do some projections

      Using the R multiplication operator for matrices, I do a 5-year projection by simply multiplying the Leslie matrix by the population vector (remember that matrix multiplication is not commutative).

      m %*% Nf
      ##         [,1]
+##  [1,] 293574
+##  [2,] 293189
+##  [3,] 248251
+##  [4,] 239833
+##  [5,] 261015
+##  [6,] 284787
+##  [7,] 313782
+##  [8,] 280463
+##  [9,] 285576
+## [10,] 301731
+## [11,] 320974
+## [12,] 243039
+## [13,] 205109
+## [14,] 204944
+## [15,] 204793
+## [16,] 194419
+## [17,] 142324
+## [18,] 131768

      I obtain the same results of the book. Raising this multiplication I can get the projected population of subsequent periods. Because R doesn’t have a power operator for matrices, I define a function called mp to raise matrices (it is not very efficient, but for this example it’s still useful).

      mp <- function(mat, pow) {
+    ans <- mat
+    for (i in 1:(pow - 1)) {
+        ans <- mat %*% ans
+    }
+    return(ans)
+}

      Let’s project the initial population for two periods (10 years):

      (mp(m, 2) %*% Nf)
      ##         [,1]
+##  [1,] 280121
+##  [2,] 293368
+##  [3,] 293049
+##  [4,] 248066
+##  [5,] 239529
+##  [6,] 260629
+##  [7,] 284238
+##  [8,] 312859
+##  [9,] 279147
+## [10,] 283344
+## [11,] 298043
+## [12,] 315045
+## [13,] 235861
+## [14,] 195388
+## [15,] 189260
+## [16,] 178141
+## [17,] 150666
+## [18,] 141960

      Again, I get the same result of the book. The nice thing of all this is that estimating eigenvalues and eigenvectors, I can obtain the intrinsic growth rate and age-distribution of the “stable equivalent” population. Using the eigen function in R, I can identify the dominant eigenvalue (higher absolute number), and the corresponding eigenvector:

      e <- eigen(m)
+
+# intrinsic growth rate
+(max(abs(e$values)) - 1)/5  # 5-year-projection
      ## [1] 0.000223
      # intrinsic proportionate age distribution
+as.numeric(e$vector[, 1]/sum(e$vector[, 1]))
      ##  [1] 0.0619 0.0618 0.0617 0.0616 0.0614 0.0613 0.0611 0.0608 0.0605 0.0600 0.0592 0.0580 0.0562 0.0535 0.0494 0.0429
+## [17] 0.0332 0.0356

      The population is growing but little.

      What about the population momentum?

      The population momentum corresponds to the growth of a population after imposing replacement fertility conditions, that is, NRR=1. Thus, the first thing we have to do is to estimate NRR.

      # calculating NRR
+(NRR <- sum(f * Lf/100000 * (1/(1 + 1.05)), na.rm = TRUE))
      ## [1] 1.01

      We can quickly estimate the intrinsic growth rate using NRR:

      # quick estimation of the intrinsic growth rate
+log(NRR)/27
      ## [1] 0.000237

      Very close to our estimation using cohort component projection. To impose the replacement condition, I just have to divide the first row of the Leslie matrix by NRR.

      m[1, ] <- m[1, ]/NRR

      To get the population momentum we have to project the initial population until the growth is zero (here I raised the matrix 100 times), and then to compute the ratio between the initial population and the non-growing population (stationary).

      # population momentum
+sum(mp(m, 100) %*% Nf)/sum(Nf)
      ## [1] 1.01

      After imposing the replacement condition, the population grew 1%.

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/2014/index.html b/blog/2014/index.html new file mode 100644 index 00000000..9b966b85 --- /dev/null +++ b/blog/2014/index.html @@ -0,0 +1 @@ + 2014 | Sebastian Daza

      2014

      an archive of posts from this year

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/2014/samplesize/index.html b/blog/2014/samplesize/index.html new file mode 100644 index 00000000..d049b1a1 --- /dev/null +++ b/blog/2014/samplesize/index.html @@ -0,0 +1,16 @@ + Functions for sample size and error | Sebastian Daza

      Functions for sample size and error

      January 19, 2014• Sebastian Daza

      2014   ·   R   survey  

      Here I show two functions in R to define sample sizes and errors of a proportion, taking into account design effect, response rate, finite population correction, and stratification. They are useful when one needs to do these calculations quickly.

      Note: I created a package with similar functions. See here.

      The inputs are:

      • n = sample size
      • e = sampling error
      • deff = design effect, by default 1 (SRS)
      • rr = response rate, by default 1
      • N = population size, by default NULL (infinite population)
      • cl = confidence level , by default .95
      • p = proportion, by default 0.5 (maximum variance of a proportion)
      • relative = to estimate relative error, by default FALSE

      first, load the functions

      library(devtools); source_gist("7896840")

      serr: sampling error

      An example for n = 400 and all inputs at their default values:

      serr(400)
      ## [1] 0.049

      The output is rounded to 4 decimals. A more complete example:

      • n = 400
      • deff = 1.5
      • response rate = 80%
      • population size = 1000
      serr(n=400, deff=1.5, rr=.8, N=1000)
      ## [1] 0.0595

      The sample size (n) has always to be lower than the population (N). It is important to note that the final sample size used to compute the sampling error is:

      \[n = \frac{N}{deff} * rr\] 
      serr(n=400, N=350)
      ## Error: n is bigger than N

      ssize: sample size

      Let’s get a sample size with an error of .03, a population of 1000 elements, a response rate of 0.80, and an effect design of 1.2:

      ssize(e=.03, deff=1.2, rr=.8, N=1000)
      ## [1] 775

      If the the sample size is bigger than the population because of low response rates or big design effects, the sample size will be fixed to N:

      ssize(e=.03, deff=5, rr=.6, N=1000)
      ## n is bigger than N in some rows: n = N
      ## [1] 1000

      Working with strata

      Finally, we can estimate different sample sizes by strata using vectors or a data frame:

      # example sampling frame (4 strata)
+frame <- data.frame(
+	strata = 1:4,
+	N =c(10000, 5000, 2000, 1000),
+	deff =c(1.1, 1, 1.3, .8),
+	rr = c(.8, .9, .85,.8),
+	p = c(.3, .6, .1, .2))
      ##   strata     N deff   rr   p
+## 1      1 10000  1.1 0.80 0.3
+## 2      2  5000  1.0 0.90 0.6
+## 3      3  2000  1.3 0.85 0.1
+## 4      4  1000  0.8 0.80 0.2
      frame$n1 <- ssize(e=.02, deff=frame$deff, rr=frame$rr, N=frame$N, p=frame$p)
+frame$e1 <- serr(n=frame$n1, deff=frame$deff, rr=frame$rr, N=frame$N, p=frame$p)
      ##   strata     N deff   rr   p   n1   e1
+## 1      1 10000  1.1 0.80 0.3 2308 0.02
+## 2      2  5000  1.0 0.90 0.6 1753 0.02
+## 3      3  2000  1.3 0.85 0.1  923 0.02
+## 4      4  1000  0.8 0.80 0.2  606 0.02

      As easy as falling off a log!

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/2015/imputation-parcels/index.html b/blog/2015/imputation-parcels/index.html new file mode 100644 index 00000000..84fbf586 --- /dev/null +++ b/blog/2015/imputation-parcels/index.html @@ -0,0 +1,85 @@ + Imputing scales using parcels of items as auxiliary variables | Sebastian Daza

      Imputing scales using parcels of items as auxiliary variables

      October 14, 2015• Sebastian Daza

      2015   ·   data science   R  

      Multiple imputation of scales generated by several items can be challenging. Fortunately, to impute every single item is not the only solution to the missing data problem. Some practical and theoretically attractive alternative have already been proposed. In this post, I show a simple implementation of what Enders (2010) calls duplicated-scale imputation, a method orginally suggested by Eekhout et al. (2011). By the way, thanks Iris Eekhout for replying my e-mails!

      Procedure

      For each scale, I define a number or proportion of items (let’s say p) to create parcels (i.e., average of items although not the whole scale). These parcels are, then, used as auxiliary variables to impute the original scales. There are different ways to define parcels. I implemented a solution in my R package sdazar, see the function rowscore for more details.

      The function rowscore selects p items with the least missing data. For each case (row), it computes the parcels using the available information of the selected items. If only one item has information, only that one will be used. If there are more than one item with valid data, it will average all the selected items. If there are no items available in my initial selection, it picks p items from the rest of unselected items to impute the original scale. In this particular example, I create parcels using half of the items:

      rowscore(data, items, p = 1/2, type = "parcel")

      The reason for using a proportion of the original items is to include as much information as possible, but preventing strong linear dependencies between variables. Ideally, parcels are complete (no missing values). However, in some cases all the items are missing, so parcels can still have missing records (although less than the original scales).

      Why not just to use the average of the available items? That solution would implicitly assume that items perfectly correlate with the scale. We know that’s not a good assumption. That is why we worry about creating scales in the first place, right? Using parcels takes advantage of the available information (items with complete information) and the relationship between a portion of items and the scale.

      Here I show a simple example using the National Longitudinal Study of Adolescent to Adult Health (Add Health). First, let’s look some descriptives of the variables included in the imputation. I am using information from Wave 1 and 2. The scales/scores I am imputing are depression (19 items) and GPA (4 items). Variables ending with .p are parcels with 1/2 of the items of the original scale.

      dim(dats)
      ## [1] 12976    14
      str(dats[, nvars, with = FALSE])
      ## Classes 'data.table' and 'data.frame':	12976 obs. of  14 variables:
+##  $ female   : Factor w/ 2 levels "0","1": 1 1 1 2 1 1 1 1 1 1 ...
+##  $ age      : int  16 16 14 13 14 17 14 17 17 14 ...
+##  $ race     : Factor w/ 5 levels "white","black",..: 2 1 1 1 2 1 3 3 3 2 ...
+##  $ publicass: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 NA ...
+##  $ bmi      : num  27.4 16.3 22.2 18.2 21.9 ...
+##  $ gpa1     : num  2 3.25 3.75 3.25 2.25 1 NA 2.25 NA 2.5 ...
+##  $ gpa2     : num  2.5 2.5 4 3.75 1.75 1.5 NA 3 1 4 ...
+##  $ gpa1.p   : num  2.5 3.5 3.5 3 2.5 1 3.5 3 1 3.5 ...
+##  $ gpa2.p   : num  2.5 2.5 4 3.5 1.5 2 1.5 3 1 4 ...
+##  $ dep1.p   : num  0.2 0.7 0 0.3 0.4 0.4 0.1 1 0.6 1.2 ...
+##  $ dep1     : num  0.263 0.789 0 0.211 0.474 ...
+##  $ dep2.p   : num  0.6 0.6 0.1 0.3 0.4 0.6 0.2 1.1 0.8 0.5 ...
+##  $ dep2     : num  0.6316 0.5263 0.0526 0.1579 0.3684 ...
+##  $ ppvt     : int  101 75 121 96 79 97 103 89 82 120 ...
+##  - attr(*, ".internal.selfref")=<externalptr>
      # create summary table using package tables
+missing <- function (x) {sum(is.na(x))}
+tabular(
+  ( dep2 + dep2.p + dep1 + dep1.p + gpa2 + gpa2.p +
+    gpa1 + gpa1.p  + age + bmi + ppvt )
+   ~  (Format(digit = 2) * ( Heading("Mean")
+   	* sdazar::Mean + Heading("SD")
+   	* sdazar::Sd + Heading("Min")
+   	* sdazar::Min + Heading("Max")
+   	* sdazar::Max + (Heading("Missing") * missing ))),
+  data = dats)
      ##
+##         Mean    SD      Min     Max     Missing
+##  dep2      0.58    0.39    0.00    2.95   68.00
+##  dep2.p    0.58    0.43    0.00    3.00    6.00
+##  dep1      0.58    0.39    0.00    2.84   81.00
+##  dep1.p    0.62    0.44    0.00    3.00   18.00
+##  gpa2      2.86    0.74    1.00    4.00 4710.00
+##  gpa2.p    2.76    0.84    1.00    4.00 1238.00
+##  gpa1      2.82    0.76    1.00    4.00 2788.00
+##  gpa1.p    2.74    0.85    1.00    4.00  342.00
+##  age      15.28    1.61   11.00   21.00    9.00
+##  bmi      22.37    4.42   11.47   63.47  353.00
+##  ppvt    100.17   15.00   13.00  146.00  568.00

      As expected, the correlation between the scales and parcels is high. GPA has most of the problems. Note that parcels .p still have missing records, although much less than the original scales.

      cor(dats[, .(dep1, dep1.p)], use = "complete")
      ##         dep1 dep1.p
+## dep1   1.000  0.947
+## dep1.p 0.947  1.000
      cor(dats[, .(dep2, dep2.p)], use = "complete")
      ##         dep2 dep2.p
+## dep2   1.000  0.948
+## dep2.p 0.948  1.000
      cor(dats[, .(gpa1, gpa1.p)], use = "complete")
      ##         gpa1 gpa1.p
+## gpa1   1.000  0.888
+## gpa1.p 0.888  1.000
      cor(dats[, .(gpa2, gpa2.p)], use = "complete")
      ##         gpa2 gpa2.p
+## gpa2   1.000  0.885
+## gpa2.p 0.885  1.000

      Let’s now impute the scales/scores using the R package MICE.

      ini <- mice(dats[, nvars, with = FALSE], m = 1, maxit = 0)
+
+# get methods
+(meth <- ini$meth)
      ##    female       age      race publicass       bmi      gpa1      gpa2    gpa1.p    gpa2.p    dep1.p      dep1    dep2.p      dep2      ppvt
+##        ""     "pmm"        ""  "logreg"     "pmm"     "pmm"     "pmm"     "pmm"     "pmm"     "pmm"     "pmm"     "pmm"     "pmm"     "pmm"
      # get predictor matrix
+pred <- ini$pred

      I adjust the predictor matrix to avoid feedbacks during the imputation (circularity between variables). The trick is to use only complete variables when imputing parcels.

      # predict parcels only with complete variables to avoid feedbacks
+pred["gpa1.p", ] <- 0
+pred["gpa2.p", ] <- 0
+pred["dep1.p", ] <- 0
+pred["dep2.p", ] <- 0
+
+pred["gpa1.p", c("female", "race")] <- 1
+pred["gpa2.p", c("female", "race")] <- 1
+pred["dep1.p", c("female", "race")] <- 1
+pred["dep2.p", c("female", "race")] <- 1
+
+# predict scales using parcels
+pred[, "gpa1.p"] <- 0
+pred[, "gpa2.p"] <- 0
+pred[, "dep1.p"] <- 0
+pred[, "dep2.p"] <- 0
+
+pred["gpa1", c("gpa1.p")] <- 1
+pred["gpa2", c("gpa2.p")] <- 1
+pred["dep1", c("dep1.p")] <- 1
+pred["dep2", c("dep2.p")] <- 1

      Here the adjusted predictor matrix:

      ##           female age race publicass bmi gpa1 gpa2 gpa1.p gpa2.p dep1.p dep1 dep2.p dep2 ppvt
+## female         0   0    0         0   0    0    0      0      0      0    0      0    0    0
+## age            1   0    1         1   1    1    1      0      0      0    1      0    1    1
+## race           0   0    0         0   0    0    0      0      0      0    0      0    0    0
+## publicass      1   1    1         0   1    1    1      0      0      0    1      0    1    1
+## bmi            1   1    1         1   0    1    1      0      0      0    1      0    1    1
+## gpa1           1   1    1         1   1    0    1      1      0      0    1      0    1    1
+## gpa2           1   1    1         1   1    1    0      0      1      0    1      0    1    1
+## gpa1.p         1   0    1         0   0    0    0      0      0      0    0      0    0    0
+## gpa2.p         1   0    1         0   0    0    0      0      0      0    0      0    0    0
+## dep1.p         1   0    1         0   0    0    0      0      0      0    0      0    0    0
+## dep1           1   1    1         1   1    1    1      0      0      1    0      0    1    1
+## dep2.p         1   0    1         0   0    0    0      0      0      0    0      0    0    0
+## dep2           1   1    1         1   1    1    1      0      0      0    1      1    0    1
+## ppvt           1   1    1         1   1    1    1      0      0      0    1      0    1    0

      Let’s impute the data!

      imp <- mice(dats[, nvars, with = FALSE], pred = pred, m = 5, maxit = 10)

      Below some plots to explore how the imputation goes.

      plot(imp, c("gpa1", "gpa2", "dep1", "dep2"))

      centercenter

      I don’t see any problematic pattern. It seems I get a proper solution. The distribution of the variables also looks right.

      densityplot(imp, ~ gpa1 + gpa2 + dep1 + dep2)

      center

      bwplot(imp, gpa1 + gpa2 + dep1 + dep2  ~ .imp)

      center

      Last Update: 06/02/2017

      References

      Enders, Craig K. 2010. Applied Missing Data Analysis. The Guilford Press.

      Eekhout, Iris, Craig K. Enders, Jos W. R. Twisk, Michiel R. de Boer, Henrica C. W. de Vet, and Martijn W. Heymans. 2015. “Analyzing Incomplete Item Scores in Longitudinal Data by Including Item Score Information as Auxiliary Variables.” Structural Equation Modeling: A Multidisciplinary Journal 22 (4):588-602.

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/2015/index.html b/blog/2015/index.html new file mode 100644 index 00000000..9db5ccc4 --- /dev/null +++ b/blog/2015/index.html @@ -0,0 +1 @@ + 2015 | Sebastian Daza

      2015

      an archive of posts from this year

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/2015/sampler/index.html b/blog/2015/sampler/index.html new file mode 100644 index 00000000..234fd747 --- /dev/null +++ b/blog/2015/sampler/index.html @@ -0,0 +1,110 @@ + Simple R package to define sample sizes and MOEs | Sebastian Daza

      Simple R package to define sample sizes and MOEs

      September 30, 2015• Sebastian Daza

      2015   ·   survey   R  

      I present a simple R package called sampler. The package defines sample sizes and margins of error (MOE) for proportions, as usually it is needed when designing public opinion surveys. In a previous post, I showed some functions that do mostly the same thing. This new package, though, includes some new features that can be useful when allocating a sample.

      Installation

      # you have to install devtools first
+devtools::install_github("sdaza/sampler")
+library(sampler)

      Functions

      The packages contains four functions:

      • ssize: computes sample size.
      • serr: computes MOE.
      • astrata: assigns sample sizes to strata.
      • serrst: computes MOE for stratified samples.

      Define sample size: ssize 

      ssize(.05)
      ## [1] 384
      # design effect (deff) and response rate (rr)
+ssize(.05, deff = 1.2, rr = .90)
      ## [1] 512
      # finite population correction
+ssize(.05, deff = 1.2, rr = .90, N = 1000)
      ## [1] 370
      # warning message
+ssize(.05, deff = 1.2, rr = .90, N = 100)
      ## n is bigger than N in some rows: n = N
      ## [1] 100

      Define sampling error: serr 

      serr(384)
      ## [1] 0.05
      serr(512, deff = 1.2, rr = .90)
      ## [1] 0.05
      serr(370, deff = 1.2, rr = .90, N = 1000)
      ## [1] 0.05
      # we still get an answer
+serr(100, deff = 1.2, rr = .90, N = 100)
      ## [1] 0.0569

      Strata allocation: astrata

      These examples show how to allocate a sample size into strata. Look at ?astrata in R for definitions of the allocation procedures that are available.

      # I will use data.table
+library(data.table)
+chile <- data.table(chile)
+chile
      ##     reg     pob  pr
+##  1:   1  328782 0.3
+##  2:   2  613328 0.4
+##  3:   3  308247 0.5
+##  4:   4  759228 0.5
+##  5:   5 1808300 0.5
+##  6:   6  910577 0.6
+##  7:   7 1035593 0.3
+##  8:   8 2100494 0.1
+##  9:   9  983499 0.2
+## 10:  10  834714 0.5
+## 11:  11  107334 0.5
+## 12:  12  163748 0.4
+## 13:  13 7228581 0.6
+## 14:  14  401548 0.2
+## 15:  15  235081 0.3
      # proportional for a sample of 1000
+chile[, aprop := astrata(1000, wp = 1, N = pob)]
+
+# fixed (same number by stratum)
+chile[, afixed := astrata(1000, wp = 0, N = pob)]
+
+# 40% proportional, 60% fixed
+chile[, a40 := astrata(1000, wp =.4, N = pob)]
+
+# 60% proportional, 40% fixed
+chile[, a60 := astrata(1000, wp =.6, N = pob)]
+
+# square-root
+chile[, aroot := astrata(1000, method = "root", N = pob)]
+
+# neyman
+chile[, aneyman := astrata(1000, method = "neyman", N = pob, p = pr)]
+
+# standard deviation
+chile[, astdev := astrata(1000, method = "stdev", N = pob, p = pr)]
+
+# error
+chile[, aerr := astrata(e = .11, method = "error", N = pob, p = pr)]
      ##     reg     pob  pr aprop afixed a40 a60 aroot aneyman astdev aerr
+##  1:   1  328782 0.3    18     67  47  38    41      18     66   67
+##  2:   2  613328 0.4    34     67  54  47    56      37     71   76
+##  3:   3  308247 0.5    17     67  47  37    40      19     72   79
+##  4:   4  759228 0.5    43     67  57  53    62      46     72   79
+##  5:   5 1808300 0.5   101     67  81  87    96     110     72   79
+##  6:   6  910577 0.6    51     67  61  57    68      54     71   76
+##  7:   7 1035593 0.3    58     67  63  62    73      58     66   67
+##  8:   8 2100494 0.1   118     67  87  98   104      77     43   29
+##  9:   9  983499 0.2    55     67  62  60    71      48     58   51
+## 10:  10  834714 0.5    47     67  59  55    65      51     72   79
+## 11:  11  107334 0.5     6     67  43  30    23       7     72   79
+## 12:  12  163748 0.4     9     67  44  32    29      10     71   76
+## 13:  13 7228581 0.6   406     67 203 270   192     432     71   76
+## 14:  14  401548 0.2    23     67  49  41    45      20     58   51
+## 15:  15  235081 0.3    13     67  45  35    35      13     66   67

      Getting sampling error from a stratified sample: serrst 

      # the second most efficient allocation
+serrst(n = chile$aprop, N = chile$pob, p = chile$pr)
      ## [1] 0.0288
      # the worst solution
+serrst(n = chile$afixed, N = chile$pob, p = chile$pr)
      ## [1] 0.0518
      serrst(n = chile$a40, N = chile$pob, p = chile$pr)
      ## [1] 0.0339
      serrst(n = chile$a60, N = chile$pob, p = chile$pr)
      ## [1] 0.0311
      serrst(n = chile$aroot, N = chile$pob, p = chile$pr)
      ## [1] 0.0339
      # the most efficient allocation
+serrst(n = chile$aneyman, N = chile$pob, p = chile$pr)
      ## [1] 0.0285
      serrst(n = chile$astdev, N = chile$pob, p = chile$pr)
      ## [1] 0.0508
      serrst(n = chile$aerr, N = chile$pob, p = chile$pr)
      ## [1] 0.0498

      Combining criteria

      # get error for 60% proportional / 40% fixed allocation for each strata
+chile[, error_a60 := serr(a60, p = pr)]
+
+# assign sample sizes assuming 13% error for each strata
+chile[, serr13 := astrata(e = .13, method = "error", N = pob, p = pr)]
+
+# total error, not that good!
+serrst(n = chile$serr13, N = chile$pob, p = chile$pr)
      ## [1] 0.0586
      chile[, .(reg, pob, pr, a60, error_a60, serr13)]
      ##     reg     pob  pr a60 error_a60 serr13
+##  1:   1  328782 0.3  38    0.1457     48
+##  2:   2  613328 0.4  47    0.1401     55
+##  3:   3  308247 0.5  37    0.1611     57
+##  4:   4  759228 0.5  53    0.1346     57
+##  5:   5 1808300 0.5  87    0.1051     57
+##  6:   6  910577 0.6  57    0.1272     55
+##  7:   7 1035593 0.3  62    0.1141     48
+##  8:   8 2100494 0.1  98    0.0594     20
+##  9:   9  983499 0.2  60    0.1012     36
+## 10:  10  834714 0.5  55    0.1321     57
+## 11:  11  107334 0.5  30    0.1789     57
+## 12:  12  163748 0.4  32    0.1697     55
+## 13:  13 7228581 0.6 270    0.0584     55
+## 14:  14  401548 0.2  41    0.1224     36
+## 15:  15  235081 0.3  35    0.1518     48

      We can adjust a bit more:

      # when error is higher than .13, use serr13
+chile[, sfinal := ifelse(error_a60 > .13, serr13, a60)]
+
+# new error by stratum
+chile[, error_sfinal := serr(sfinal, p = pr)]
+
+# total error, much better!
+serrst(n = chile$sfinal, N = chile$pob, p = chile$pr)
      ## [1] 0.0309
      # although the total sample size is now bigger
+sum(chile$sfinal)
      ## [1] 1109
      ##     reg     pob  pr sfinal error_sfinal
+##  1:   1  328782 0.3     48       0.1296
+##  2:   2  613328 0.4     55       0.1295
+##  3:   3  308247 0.5     57       0.1298
+##  4:   4  759228 0.5     57       0.1298
+##  5:   5 1808300 0.5     87       0.1051
+##  6:   6  910577 0.6     57       0.1272
+##  7:   7 1035593 0.3     62       0.1141
+##  8:   8 2100494 0.1     98       0.0594
+##  9:   9  983499 0.2     60       0.1012
+## 10:  10  834714 0.5     57       0.1298
+## 11:  11  107334 0.5     57       0.1298
+## 12:  12  163748 0.4     55       0.1295
+## 13:  13 7228581 0.6    270       0.0584
+## 14:  14  401548 0.2     41       0.1224
+## 15:  15  235081 0.3     48       0.1296

      That’s it. A simple package to do simple calculations.

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/2016/acsr/index.html b/blog/2016/acsr/index.html new file mode 100644 index 00000000..f9a423d9 --- /dev/null +++ b/blog/2016/acsr/index.html @@ -0,0 +1,228 @@ + R package to compute statistics from the American Community Survey (ACS) and Decennial US Census | Sebastian Daza

      R package to compute statistics from the American Community Survey (ACS) and Decennial US Census

      July 6, 2016• Sebastian Daza

      2016   ·   R   demography   data science  

      The acsr package helps extracting variables and computing statistics using the America Community Survey and Decennial US Census. It was created for the Applied Population Laboratory (APL) at the University of Wisconsin-Madison.

      Installation

      The functions depend on the acs and data.table packages, so it is necessary to install then before using acsr. The acsr package is hosted on a github repository and can be installed using devtools:

      devtools::install_github("sdaza/acsr")
+library(acsr)

      Remember to set the ACS API key, to check the help documentation and the default values of the acsr functions.

      api.key.install(key="*")
+?sumacs
+?acsdata

      The default dataset is acs, the level is state (Wisconsin, state = "WI"), the endyear is 2014, and the confidence level to compute margins of error (MOEs) is 90%.

      Levels

      The acsr functions can extract all the levels available in the acs package. The table below shows the summary and required levels when using the acsdata and sumacs functions:

      summary number levels
      010 us
      020 region
      030 division
      040 state
      050 state, county
      060 state, county, county.subdivision
      140 state, county, tract
      150 state, county, tract, block.group
      160 state, place
      250 american.indian.area
      320 state, msa
      340 state, csa
      350 necta
      400 urban.area
      500 state, congressional.district
      610 state, state.legislative.district.upper
      620 state, state.legislative.district.lower
      795 state, puma
      860 zip.code
      950 state, school.district.elementary
      960 state, school.district.secondary
      970 state, school.district.unified

      Getting variables and statistics

      We can use the sumacs function to extract variable and statistics. We have to specify the corresponding method (e.g., proportion or just variable), and the name of the statistic or variable to be included in the output.

      sumacs(formula = c("(b16004_004 + b16004_026 + b16004_048 / b16004_001)", "b16004_026"),
+        varname = c("mynewvar", "myvar"),
+        method = c("prop", "variable"),
+        level = c("division"))
      ## [1] "Extracting data from: acs 2014"
+## [1] ". . . . . .  ACS/Census variables : 4"
+## [1] ". . . . . .  Levels : 1"
+## [1] ". . . . . .  New variables : 2"
+## [1] ". . . . . .  Getting division data"
+## [1] ". . . . . .  Creating variables"
+## [1] ". . . . . .  50%"
+## [1] ". . . . . .  100%"
+## [1] ". . . . . .  Formatting output"
      ##    sumlevel geoid division mynewvar_est mynewvar_moe myvar_est myvar_moe
+## 1:      030    NA        1       0.0762     0.000347    770306      3490
+## 2:      030    NA        2       0.1182     0.000278   3332150      9171
+## 3:      030    NA        3       0.0599     0.000196   1819417      7209
+## 4:      030    NA        4       0.0411     0.000277    547577      4461
+## 5:      030    NA        5       0.1108     0.000246   4526480     11869
+## 6:      030    NA        6       0.0320     0.000265    402475      3781
+## 7:      030    NA        7       0.2203     0.000469   5318126     13044
+## 8:      030    NA        8       0.1582     0.000602   2279303     10746
+## 9:      030    NA        9       0.2335     0.000501   7765838     20289

      To download the data can be slow, especially when many levels are being used (e.g., blockgroup). A better approach in those cases is, first, download the data using the function acsdata , and then use them as input.

      mydata <- acsdata(formula = c("(b16004_004 + b16004_026 + b16004_048 /  b16004_001)",
+        "b16004_026"),
+        level = c("division"))
      ## [1] ". . . . . .  Getting division data"
      sumacs(formula = c("(b16004_004 + b16004_026 + b16004_048 / b16004_001)", "b16004_026"),
+        varname = c("mynewvar", "myvar"),
+        method = c("prop", "variable"),
+        level = c("division"),
+        data = mydata)
      ## [1] "Extracting data from: acs 2014"
+## [1] ". . . . . .  ACS/Census variables : 4"
+## [1] ". . . . . .  Levels : 1"
+## [1] ". . . . . .  New variables : 2"
+## [1] ". . . . . .  Creating variables"
+## [1] ". . . . . .  50%"
+## [1] ". . . . . .  100%"
+## [1] ". . . . . .  Formatting output"
      ##    sumlevel geoid division mynewvar_est mynewvar_moe myvar_est myvar_moe
+## 1:      030    NA        1       0.0762     0.000347    770306      3490
+## 2:      030    NA        2       0.1182     0.000278   3332150      9171
+## 3:      030    NA        3       0.0599     0.000196   1819417      7209
+## 4:      030    NA        4       0.0411     0.000277    547577      4461
+## 5:      030    NA        5       0.1108     0.000246   4526480     11869
+## 6:      030    NA        6       0.0320     0.000265    402475      3781
+## 7:      030    NA        7       0.2203     0.000469   5318126     13044
+## 8:      030    NA        8       0.1582     0.000602   2279303     10746
+## 9:      030    NA        9       0.2335     0.000501   7765838     20289

      Standard errors

      When computing statistics there are two ways to define the standard errors:

      • Including all standard errors of the variables used to compute a statistic (one.zero = FALSE)
      • Include all standard errors except those of variables that are equal to zero. Only the maximum standard error of the variables equal to zero is included (one.zero = TRUE)
      • The default value is one.zero = TRUE

      For more details about how standard errors are computed for proportions, ratios and aggregations look at A Compass for Understanding and Using American Community Survey Data.

      Below an example when estimating proportions and using one.zero = FALSE:

      sumacs(formula = "(b16004_004 + b16004_026 + b16004_048) / b16004_001",
+            varname =  "mynewvar",
+            method = "prop",
+            level = "tract",
+            county = 1,
+            tract = 950501,
+            endyear = 2013,
+            one.zero = FALSE)
      ## [1] "Extracting data from: acs 2013"
+## [1] ". . . . . .  ACS/Census variables : 4"
+## [1] ". . . . . .  Levels : 1"
+## [1] ". . . . . .  New variables : 1"
+## [1] ". . . . . .  Getting tract data"
+## [1] ". . . . . .  Creating variables"
+## [1] ". . . . . .  100%"
+## [1] ". . . . . .  Formatting output"
      ##    sumlevel       geoid st_fips cnty_fips tract_fips mynewvar_est mynewvar_moe
+## 1:      140 55001950501      55         1     950501       0.0226       0.0252
      \[SE = \sqrt{ \frac{(5.47 ^ 2 + 22.49 ^ 2 + 5.47 ^ 2) - ( 0.02 ^ 2 \times 102.13 ^ 2)}{1546} } \times 1.645 = 0.0252\]

      When one.zero = TRUE:

      sumacs(formula = "(b16004_004 + b16004_026 + b16004_048) / b16004_001",
+            varname = "mynewvar",
+            method = "prop",
+            level = "tract",
+            county = 1,
+            tract = 950501,
+            endyear = 2013,
+            one.zero = TRUE)
      ## [1] "Extracting data from: acs 2013"
+## [1] ". . . . . .  ACS/Census variables : 4"
+## [1] ". . . . . .  Levels : 1"
+## [1] ". . . . . .  New variables : 1"
+## [1] ". . . . . .  Getting tract data"
+## [1] ". . . . . .  Creating variables"
+## [1] ". . . . . .  100%"
+## [1] ". . . . . .  Formatting output"
      ##    sumlevel       geoid st_fips cnty_fips tract_fips mynewvar_est mynewvar_moe
+## 1:      140 55001950501      55         1     950501       0.0226       0.0245
      \[SE_{\text{ one.zero}} \sqrt{ \frac{(5.47 ^ 2 + 22.49 ^ 2) - ( 0.02 ^ 2 \times 102.13 ^ 2)}{1546} } \times 1.645 = 0.0245\]

      When the square root value in the standard error formula doesn’t exist (e.g., the square root of a negative number), the ratio formula is instead used. The ratio adjustment is done variable by variable .

      It can also be that the one.zero option makes the square root undefinable. In those cases, the function uses again the ratio formula to compute standard errors. There is also a possibility that the standard error estimates using the ratio formula are higher than the proportion estimates without the one.zero option.

      Decennial Data from the US Census

      Let’s get the African American and Hispanic population by state. In this case, we don’t have any estimation of margin of error.

      sumacs(formula = c("p0080004", "p0090002"),
+            method = "variable",
+            dataset = "sf1",
+            level = "state",
+            state = "*",
+            endyear = 2010)
      ## [1] "Extracting data from: sf1 2010"
+## [1] ". . . . . .  ACS/Census variables : 2"
+## [1] ". . . . . .  Levels : 1"
+## [1] ". . . . . .  New variables : 2"
+## [1] ". . . . . .  Getting state data"
+## [1] ". . . . . .  Creating variables"
+## [1] ". . . . . .  50%"
+## [1] ". . . . . .  100%"
+## [1] ". . . . . .  Formatting output"
      ##     sumlevel geoid st_fips p0080004 p0090002
+##  1:      040    01      01  1251311   185602
+##  2:      040    02      02    23263    39249
+##  3:      040    04      04   259008  1895149
+##  4:      040    05      05   449895   186050
+##  5:      040    06      06  2299072 14013719
+##  6:      040    08      08   201737  1038687
+##  7:      040    09      09   362296   479087
+##  8:      040    10      10   191814    73221
+##  9:      040    11      11   305125    54749
+## 10:      040    12      12  2999862  4223806
+## 11:      040    13      13  2950435   853689
+## 12:      040    15      15    21424   120842
+## 13:      040    16      16     9810   175901
+## 14:      040    17      17  1866414  2027578
+## 15:      040    18      18   591397   389707
+## 16:      040    19      19    89148   151544
+## 17:      040    20      20   167864   300042
+## 18:      040    21      21   337520   132836
+## 19:      040    22      22  1452396   192560
+## 20:      040    23      23    15707    16935
+## 21:      040    24      24  1700298   470632
+## 22:      040    25      25   434398   627654
+## 23:      040    26      26  1400362   436358
+## 24:      040    27      27   274412   250258
+## 25:      040    28      28  1098385    81481
+## 26:      040    29      29   693391   212470
+## 27:      040    30      30     4027    28565
+## 28:      040    31      31    82885   167405
+## 29:      040    32      32   218626   716501
+## 30:      040    33      33    15035    36704
+## 31:      040    34      34  1204826  1555144
+## 32:      040    35      35    42550   953403
+## 33:      040    36      36  3073800  3416922
+## 34:      040    37      37  2048628   800120
+## 35:      040    38      38     7960    13467
+## 36:      040    39      39  1407681   354674
+## 37:      040    40      40   277644   332007
+## 38:      040    41      41    69206   450062
+## 39:      040    42      42  1377689   719660
+## 40:      040    44      44    60189   130655
+## 41:      040    45      45  1290684   235682
+## 42:      040    46      46    10207    22119
+## 43:      040    47      47  1057315   290059
+## 44:      040    48      48  2979598  9460921
+## 45:      040    49      49    29287   358340
+## 46:      040    50      50     6277     9208
+## 47:      040    51      51  1551399   631825
+## 48:      040    53      53   240042   755790
+## 49:      040    54      54    63124    22268
+## 50:      040    55      55   359148   336056
+## 51:      040    56      56     4748    50231
+## 52:      040    72      72   461498  3688455
+##     sumlevel geoid st_fips p0080004 p0090002

      Output

      The output can be formatted using a wide or long format:

      sumacs(formula = "(b16004_004 + b16004_026 + b16004_048 / b16004_001)",
+            varname = "mynewvar",
+            method = "prop",
+            level = "division",
+            format.out = "long")
      ## [1] "Extracting data from: acs 2014"
+## [1] ". . . . . .  ACS/Census variables : 4"
+## [1] ". . . . . .  Levels : 1"
+## [1] ". . . . . .  New variables : 1"
+## [1] ". . . . . .  Getting division data"
+## [1] ". . . . . .  Creating variables"
+## [1] ". . . . . .  100%"
+## [1] ". . . . . .  Formatting output"
      ##    geoid sumlevel division var_name    est      moe
+## 1:    NA      030        1 mynewvar 0.0762 0.000347
+## 2:    NA      030        2 mynewvar 0.1182 0.000278
+## 3:    NA      030        3 mynewvar 0.0599 0.000196
+## 4:    NA      030        4 mynewvar 0.0411 0.000277
+## 5:    NA      030        5 mynewvar 0.1108 0.000246
+## 6:    NA      030        6 mynewvar 0.0320 0.000265
+## 7:    NA      030        7 mynewvar 0.2203 0.000469
+## 8:    NA      030        8 mynewvar 0.1582 0.000602
+## 9:    NA      030        9 mynewvar 0.2335 0.000501

      And it can also be exported to a csv file:

      sumacs(formula = "(b16004_004 + b16004_026 + b16004_048 / b16004_001)",
+            varname = "mynewvar",
+            method = "prop",
+            level = "division",
+            file = "myfile.out")
      ## [1] "Extracting data from: acs 2014"
+## [1] ". . . . . .  ACS/Census variables : 4"
+## [1] ". . . . . .  Levels : 1"
+## [1] ". . . . . .  New variables : 1"
+## [1] ". . . . . .  Getting division data"
+## [1] ". . . . . .  Creating variables"
+## [1] ". . . . . .  100%"
+## [1] ". . . . . .  Formatting output"
+## [1] "Data exported to a CSV file! "

      Combining geographic levels

      We can combine geographic levels using two methods: (1) sumacs and (2) combine.output. The first one allows only single combinations, the second multiple ones.

      If I want to combine two states (e.g., Wisconsin and Minnesota) I can use:

      sumacs("(b16004_004 + b16004_026 + b16004_048 / b16004_001)",
+    varname = "mynewvar",
+    method = "prop",
+    level = "state",
+    state = list("WI", "MN"),
+    combine = TRUE,
+    print.levels = FALSE)
      ## [1] "Extracting data from: acs 2014"
+## [1] ". . . . . .  ACS/Census variables : 4"
+## [1] ". . . . . .  Levels : 1"
+## [1] ". . . . . .  New variables : 1"
+## [1] ". . . . . .  Getting combined data"
+## [1] ". . . . . .  Creating variables"
+## [1] ". . . . . .  100%"
+## [1] ". . . . . .  Formatting output"
      ##    geoid combined_group mynewvar_est mynewvar_moe
+## 1:    NA      aggregate        0.042     0.000331

      If I want to put together multiple combinations (e.g., groups of states):

      combine.output("(b16004_004 + b16004_026 + b16004_048 / b16004_001)",
+    varname = "mynewvar",
+    method = "prop",
+    level = list("state", "state"),
+    state = list( list("WI", "MN"), list("CA", "OR")), # nested list
+    combine.names = c("WI+MN", "CA+OR"),
+    print.levels = FALSE)
      ## [1] ". . . . . .  Defining WI+MN"
+## [1] "Extracting data from: acs 2014"
+## [1] ". . . . . .  ACS/Census variables : 4"
+## [1] ". . . . . .  Levels : 1"
+## [1] ". . . . . .  New variables : 1"
+## [1] ". . . . . .  Getting combined data"
+## [1] ". . . . . .  Creating variables"
+## [1] ". . . . . .  100%"
+## [1] ". . . . . .  Formatting output"
+## [1] ". . . . . .  Defining CA+OR"
+## [1] "Extracting data from: acs 2014"
+## [1] ". . . . . .  ACS/Census variables : 4"
+## [1] ". . . . . .  Levels : 1"
+## [1] ". . . . . .  New variables : 1"
+## [1] ". . . . . .  Getting combined data"
+## [1] ". . . . . .  Creating variables"
+## [1] ". . . . . .  100%"
+## [1] ". . . . . .  Formatting output"
      ##    combined_group mynewvar_est mynewvar_moe
+## 1:          WI+MN        0.042     0.000331
+## 2:          CA+OR        0.269     0.000565

      A map?

      Let’s color a map using poverty by county:

      pov <- sumacs(formula = "b17001_002 / b17001_001 * 100",
+        varname = c("pov"),
+        method = c("prop"),
+        level = c("county"),
+        state = "*")
      ## [1] "Extracting data from: acs 2014"
+## [1] ". . . . . .  ACS/Census variables : 2"
+## [1] ". . . . . .  Levels : 1"
+## [1] ". . . . . .  New variables : 1"
+## [1] ". . . . . .  Getting county data"
+## [1] ". . . . . .  Creating variables"
+## [1] ". . . . . .  100%"
+## [1] ". . . . . .  Formatting output"
      library(choroplethr)
+library(choroplethrMaps)
+pov[, region := as.numeric(geoid)]
+setnames(pov, "pov_est", "value")
+county_choropleth(pov, num_colors = 5)

      center

      In sum, the acsr package:

      • Reads formulas directly and extracts any ACS/Census variable
      • Provides an automatized and tailored way to obtain indicators and MOEs
      • Allows different outputs’ formats (wide and long, csv)
      • Provides an easy way to adjust MOEs to different confidence levels
      • Includes a variable-by-variable ratio adjustment of standard errors
      • Includes the zero-option when computing standard errors for proportions, ratios, and aggregations
      • Combines geographic levels flexibly

      Last Update: 02/07/2016

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/2016/index.html b/blog/2016/index.html new file mode 100644 index 00000000..6f2ca236 --- /dev/null +++ b/blog/2016/index.html @@ -0,0 +1 @@ + 2016 | Sebastian Daza

      2016

      an archive of posts from this year

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/2016/read-mortality-data/index.html b/blog/2016/read-mortality-data/index.html new file mode 100644 index 00000000..3ec31fc6 --- /dev/null +++ b/blog/2016/read-mortality-data/index.html @@ -0,0 +1,46 @@ + Reading CDC mortality files using R | Sebastian Daza

      Reading CDC mortality files using R

      October 5, 2016• Sebastian Daza

      2016   ·   R   demography  

      Reading fixed-width text files might be challenging, specially when we don’t have a dictionary file. In this post, I show steps to read CDC files in a more systematic way. In this example, I import a compress mortality file (CMF 1979-1988) available here and whose codebook (or layout) is here.

      To read this file, usually with extension .txt or .dat, I first need to know where each column starts and finishes. What I get from the pdf file is something like this:

      The layout is usually a codebook in Word/PDF or just plain text file. Here, I copy the PDF text and put it in a plain text file. I use a text editor (e.g., Sublime Text) and regular expressions to extract the information I need.

      I have to select every row with this pattern: 1-2 2 FIPS State code Numeric. That is, a number followed by a hyphen (although not always, particularly when the width of the column is one), spaces, another number, spaces, and then any text. I use the following regular expression to get that pattern: (^[0-9]+).([0-9]+)\s+([0-9])\s+(.+). Using the Sublime package Filter Lines I get something like this (you can also just copy the selected lines):

      1-2 2 FIPS State code Numeric
+3-5 FIPS county code Numeric
+6-9 4 Year of death Numeric
+11-12 2 Age at death Numeric
+13-16 4 ICD code for underlying cause-of-death 3 digits: Numeric
+17-19 3 Cause-of-Death Recode Numeric
+20-23 4 Number of deaths Numeric
+

      This approach might be particularly useful when you have a long PDF/Word file and you want to extract most of the variables. You would need to adapt the regular expressions I’m using to the particular patterns of your codebook.

      To simplify, I format this text as a comma-separated values file (csv). Replacing this regular expression ([0-9]+)(-)([0-9]+)(\s)([0-9]+)(\s)(.+)(\s)(Numeric) by \1,\3,\5,\7,\9 I get:

      1,2,2,FIPS State code,Numeric
+3,5,3,FIPS county code,Numeric
+6,9,4,Year of death,Numeric
+11,12,2,Age at death,Numeric
+13,16,4,ICD code for underlying cause-of-death 3 digits:,Numeric
+17,19,3,Cause-of-Death Recode,Numeric
+20,23,4,Number of deaths,Numeric
+

      Then, I read the layout file:

      # define names of columns
+colnames <- c("start", "end", "width", "name", "type")
+dict <- read.csv("data/dictMortality.csv", col.names = colnames, header = FALSE)
      ##   start end width                                             name    type
+## 1     1   2     2                                  FIPS State code Numeric
+## 2     3   5     3                                 FIPS county code Numeric
+## 3     6   9     4                                    Year of death Numeric
+## 4    11  12     2                                     Age at death Numeric
+## 5    13  16     4 ICD code for underlying cause-of-death 3 digits: Numeric
+## 6    17  19     3                            Cause-of-Death Recode Numeric
+## 7    20  23     4                                 Number of deaths Numeric

      Now, I can read the fixed-width data file. I use the readr package (in my experience relatively fast for big datasets ~ 1 GB).

      library(readr)
+
+# create name of variables
+cnames <- c("state", "county", "year", "age", "icd", "cause", "deaths")
+
+# read mortality file
+mort <- read_fwf("data/mort7988.txt", fwf_positions(dict$start, dict$end, cnames))
      ## # A tibble: 8,776,385 x 7
+##    state county  year   age   icd cause deaths
+##    <chr>  <chr> <int> <chr> <chr> <chr>  <int>
+##  1    01    001  1979    04  5789   780      1
+##  2    01    001  1979    04  7980   770      1
+##  3    01    001  1979    08  8121   800      1
+##  4    01    001  1979    09  3439   780      1
+##  5    01    001  1979    09  8120   800      2
+##  6    01    001  1979    09  8189   800      1
+##  7    01    001  1979    10  1629   180      1
+##  8    01    001  1979    10  2396   250      1
+##  9    01    001  1979    10  4289   410      1
+## 10    01    001  1979    10  8070   810      1
+## # ... with 8,776,375 more rows
      # year distribution
+table(mort$year)
      ##   1979   1980   1981   1982   1983   1984   1985   1986   1987   1988
+## 831605 854860 854198 850505 867280 875607 894176 905736 912551 929867
      # number of deaths
+sum(mort$deaths)
      ## [1] 20398153

      Hopefully, this might save you some time!

      Last Update: 06/29/2017

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/2020/anylogic-database/index.html b/blog/2020/anylogic-database/index.html new file mode 100644 index 00000000..75d707aa --- /dev/null +++ b/blog/2020/anylogic-database/index.html @@ -0,0 +1,57 @@ + From database to CSV using Anylogic | Sebastian Daza

      From database to CSV using Anylogic

      November 3, 2020• Sebastian Daza

      2020   ·   anylogic   simulation  

      When running a parameter variation experiment, that is, simulating over several iterations and replicates using parallelization, we usually need to collect a huge amount of data and have them in a format that then we can process using Python or R.

      The best way to do this in Anylogic would be using a database and then export, read, or connect to the database to process simulation results (although, see the section update below). We can do that easily in Anylogic. Every time an experiment finishes, we can export the data (from a database) to an Excel file manually.

      The issue with Excel files is, on the one hand, they are Excel files, and on the other, they are not suitable for big data (more than 1 million rows). We can create a function to save all the simulation tables into an Excel file as our experiment finishes. However, we will still have the limit-of-rows limitation (check the Anylogic file linked below for a function to create Excel files from a database).

      Here I follow a different approach by exporting an Anylogic database table to a CSV file within Java. The general setup using Anylogic PLE 8.6 would be:

      • Create the databases you need for your experiment, and be sure you add the columns iteration and replicate.
      • Create a function to save the data of your simulation runs (e.g., agent’s status, age, etc.)
      • Define a parameter variation experiment.
      • Define a variable to specify where to save the data (i.e., path).
      • Write code in the experiment Java Actions section so that to save data every time you run an experiment.
      • Import functions in the advanced Java section of the experiment.

      Databases

      I define two tables (data1 and data2). Each agent saves its data at a given rate. After 5 years, the simulation will finish.

      insertInto(data1)
+    .columns(data1.iteration, data1.replicate, data1.id, data1.dtime, data1.drandom)
+    .values(main.v_iteration, main.v_replicate, this.getIndex(), time(), normal(1.0))
+    .execute();
+
+insertInto(data2)
+    .columns(data2.iteration, data2.replicate, data2.id, data2.dtime, data2.drandom)
+    .values(main.v_iteration, main.v_replicate, this.getIndex(), time(), normal(0.3))
+    .execute();
+
+if (time() > 5) {
+    finishSimulation();
+}

      The tables include a column with the experiment iteration and replicate, in addition to the agent’s index, time, and a random value from a normal distribution.

      From DB to CSV

      The key function to export the data to a CSV file is f_SQLToCSV. It uses two arguments, a SQL query (query) and the path to an output file (filename). For instance, we can write:

      f_SQLToCSV("select * from data1", "output/data1")

      You can use any query for your data, giving you a lot of flexibility on what to export to a CSV file. The f_SQLToCSV method is:

      ResultSet rs = selectResultSet(query);
+try {
+    File file = new File(filename + ".csv");
+    file.getParentFile().mkdirs();
+    FileWriter fw = new FileWriter(file);
+    int cols = rs.getMetaData().getColumnCount();
+    for(int i = 1; i <= cols; i ++){
+        fw.append(rs.getMetaData().getColumnLabel(i).toLowerCase());
+        if(i < cols) fw.append(';');
+        else fw.append('\n');
+    }
+    while (rs.next()) {
+        for(int i = 1; i <= cols; i ++){
+            fw.append(rs.getString(i));
+            if(i < cols) fw.append(';');
+        }
+        fw.append('\n');
+     }
+     fw.flush();
+     fw.close();
+} catch (Exception e) {
+    getEngine().pause();
+    traceln("--> An Exception happened, continue? ...");
+    e.printStackTrace();
+}

      The next step would be to create an experiment and complete the Java actions accordingly. First, we clear our tables.

      // inital experiment setup
+deleteFrom(data1).execute();
+deleteFrom(data2).execute();

      Then, we collect information on the iteration and replicate of the simulation run:

      // before simulation run
+root.v_iteration = getCurrentIteration();
+root.v_replicate = getCurrentReplication();

      Finally, at the end of the experiment, we save the data and clear the tables again:

      // after experiment
+f_exportTables(v_path);
+deleteFrom(data1).execute();
+deleteFrom(data1).execute();

      The method f_exportTables is just a function that goes through each table and export them to a CSV file. v_tables is string array with the name of the tables I want to export {"data1", "data2"}:

      ArrayList<String> tables = new ArrayList<String>();
+for(String tab : v_tables) {
+   tables.add(tab);
+}
+for (String t : tables) {
+    f_SQLToCSV("select * from " + t, path + t);
+}

      Remember to import some functions in the imports section:

      // imports section
+import java.io.BufferedWriter;
+import java.io.File;
+import java.io.FileWriter;
+import java.io.BufferedReader;
+import java.io.FileReader;
+import java.io.IOException;
+import java.text.*;

      From there, we can create additional functions to select the tables to be exported. For more details, download the Anylogic File here.

      Update

      When running several replicates of my simulation, saving the information in a database didn’t work as expected. My simulation just crashed, and I was not able to keep the data. I finally decided to follow my previous approach: create many CSV files – one per iteration and replicate – and read them using an R or Python function. I know you end up with a lot of CSV files, but at least the simulation doesn’t crash, and you can recover the output of your simulation as it goes.

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/2020/descriptive-tables/index.html b/blog/2020/descriptive-tables/index.html new file mode 100644 index 00000000..88a57cc4 --- /dev/null +++ b/blog/2020/descriptive-tables/index.html @@ -0,0 +1,67 @@ + Oh, descriptive tables (R + Latex)! | Sebastian Daza

      Oh, descriptive tables (R + Latex)!

      May 12, 2020• Sebastian Daza

      2020   ·   R   latex  

      It’s been a while since my last post. It’s time to catch up!

      Every time I write a paper or report, I need to create descriptive tables using Latex. Over and over I create Adhoc tables, and I say to myself: Write a general function so you can save time in the next paper! I know there are some solutions out there, but in general, I feel they are not flexible enough.

      I introduce a far from perfect function to create descriptive tables in Latex. The steps and structure are quite simple:

      1. Write a function to summarize your data with any stats you want
      2. Define a list with the data plus column names (labels)

      That’s it. You can see the function here. It has some features might be useful:

      • It deals automatically with factors (categorical variables)
      • You can use different datasets at the same time
      • You can group columns using a variable (e.g., year)
      • You can add long notes at the bottom of the table
      • You can specify your own descriptive function

      Let’s start creating some fake data:

      • 5 variables
      • Variable 3 is a factor (i.e., categorical)
      • Variable 5 is a grouping column
      library(data.table)
+set.seed(14332)
+devtools::source_gist("c4d1089a501d3567be9fb784b1c5a6ab")
      # create two fake datasets
+n1 = 100
+dat1 = data.table(
+    var1 = runif(n1),
+    var2 = sample(0:1, n1, prob = c(0.8, 0.2), replace = TRUE),
+    var3 = factor(sample(1:4, n1, prob = c(0.1, 0.4, 0.3, 0.2),
+        replace = TRUE),
+        labels = c("Low", "Medium", "High", "Very high")),
+    var4 = rnorm(n1, 1000, 100),
+    var5 = sample(1:4, n1, replace = TRUE)
+)
+
+n2 = 200
+dat2 = data.table(
+    var1 = runif(n2, min = 3, max = 5),
+    var2 = sample(0:1, n2, prob = c(0.5, 0.5), replace = TRUE),
+    var3 = factor(sample(1:5, n2, prob = c(0.05, 0.1, 0.35, 0.3, 0.2),
+        replace = TRUE),
+        labels = c("Very low", "Low", "Medium", "High", "Very high")),
+    var4 = rnorm(n2, 2000, 300),
+    var5 = sample(1:4, n2, replace = TRUE)
+)
+
+datasets = list("Data 1" = dat1, "Data 2" = dat2)
+variables = list(paste0("var", 1:4), paste0("var", 1:4))
+labels = list(paste0("Variable ", 1:4), paste0("Variable ", 1:4))
+colnames =  c("Mean", "Median", "SD")

      We can define a descriptive function:

      # descriptive function
+myDescriptives = function(x) {
+    x = as.numeric(x)
+    m = mean(x, na.rm = TRUE)
+    md = median(x, na.rm = TRUE)
+    sd = sd(x, na.rm = TRUE)
+    return(c(m, md, sd))
+}
      # create table
+createDescriptiveTable(datasets,
+    summary_function = myDescriptives,
+    column_names = colnames,
+    variable_names = variables,
+    variable_labels = labels,
+    arraystretch = 1.3,
+    title = "Descriptive statistics",
+    label = "tab:descriptive",
+    file = "example_01.tex")

      Thus, the grouping of rows is defined by the name of each dataset in the list. We can add a note, just remember to add \usepackage[flushleft]{threeparttable} to your Latex document:

      # table + note
+createDescriptiveTable(datasets,
+    summary_function = myDescriptives,
+    column_names = colnames,
+    variable_names = variables,
+    variable_labels = labels,
+    arraystretch = 1.3,
+    note = "This is a very long long long long long long long long long note.",
+    title = "Descriptive statistics",
+    label = "tab:descriptive",
+    file = "example_02.tex")

      We can also slice the descriptives by group:

      # group columns
+createDescriptiveTable(datasets,
+    summary_function = myDescriptives,
+    column_names = colnames,
+    variable_names = variables,
+    variable_labels = labels,
+    group_variable = "var5",
+    arraystretch = 1.3,
+    tabcolsep = 3,
+    note = "This is a very long long long long long long long long long note.",
+    title = "Descriptive statistics",
+    label = "tab:descriptive",
+    file = "example_03.tex")

      It’s just a first version. I will add more features soon.

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/2020/index.html b/blog/2020/index.html new file mode 100644 index 00000000..fb2b2d7c --- /dev/null +++ b/blog/2020/index.html @@ -0,0 +1 @@ + 2020 | Sebastian Daza

      2020

      an archive of posts from this year

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/2021/index.html b/blog/2021/index.html new file mode 100644 index 00000000..e4c197be --- /dev/null +++ b/blog/2021/index.html @@ -0,0 +1 @@ + 2021 | Sebastian Daza

      2021

      an archive of posts from this year

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/2021/zotero-notes/index.html b/blog/2021/zotero-notes/index.html new file mode 100644 index 00000000..cfbd1c7c --- /dev/null +++ b/blog/2021/zotero-notes/index.html @@ -0,0 +1,21 @@ + Extracting Zotero's notes | Sebastian Daza

      Extracting Zotero's notes

      July 6, 2021• Sebastian Daza

      2021   ·   zotero  

      When reviewing the literature, it’s nice to have the notes linked to Zotero references. It would also be great to compare them systematically. The idea would be to have your notes in Zotero so that everyone in the group can access and edit them and create a data file to explore your notes using, for instance, text analysis techniques.

      This small package extracts notes from a collection and creates a CSV file that can be easily read using Excel. You only need to specify the collection ID. For instance, if the location of my collection is https://www.zotero.org/groups/2406179/csic-echo/collections/M8N2VMAP, the collection ID would be M8N2VMAP. We also need the Zotero API’s credentials.

      To create a clean CSV, notes’ headers would need a suitable separator. The default is #. In this case, the text between headings mustn’t include #. Below an example:

      # Research question
+
+Estimates interaction effects between PGS of obesity and cohorts using HRS.
+
+# Data
+
+HRS
+
+# Methods
+
+Uses a HLM whereby they estimate effects of age and cohorts while making the
+intercepts and slopes a function of individual factors.
+

      Installation

      pip install git+https://github.com/sdaza/zotnote.git
+

      Credentials

      You can save your Zotero API credentials in a config.py and load them using import config:

      library_id = "0000000"
+api_key = "key"
+library_type = "group"
+

      Example

      Let’s try to extract some notes and read them using Pandas:

      import pandas as pd
+import config
+import zotnote as zn
      zn.exportNotes(collection = "M8N2VMAP", 
+    library_id = config.library_id, api_key = config.api_key)
      Notes saved in zotero-notes.csv
+
      pd.read_csv("zotero-notes.csv")

      center

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/2023/index.html b/blog/2023/index.html new file mode 100644 index 00000000..5b49baa6 --- /dev/null +++ b/blog/2023/index.html @@ -0,0 +1 @@ + 2023 | Sebastian Daza

      2023

      an archive of posts from this year

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/2023/statistical-power/index.html b/blog/2023/statistical-power/index.html new file mode 100644 index 00000000..67350594 --- /dev/null +++ b/blog/2023/statistical-power/index.html @@ -0,0 +1,87 @@ + Tools for power analysis with multiple comparisons | Sebastian Daza

      Tools for power analysis with multiple comparisons

      September 26, 2023• Sebastian Daza

      2023   ·   simulation   python   experiments  

      This post will discuss tools for designing and evaluating experiments. These tools offer user-friendly methods to estimate statistical power when working with multiple treatments or variants. They also help with sample allocation, including determining sample sizes for treatment and control groups. Lastly, they estimate power based on different expected effects (e.g., minimum detectable effects or MDE).

      Calculating power for complex and uplift models using machine learning can be challenging. I will provide some general guidelines and references at the end of this post.

      Let’s start with some terms

      When conducting a statistical test to estimate an effect, we can have any of the following outcomes:

      Image

      These errors and success probabilities are associated with standard statistical terms used in making inferences.

      • Type I Error (\(\alpha\)): the probability of rejecting the null hypothesis when it’s true.
      • Type II Error (\(\beta\)): the probability of failing to reject the null hypothesis when it’s false.
      • Power (\(1-\beta\)): the probability of rejecting the null hypothesis when it’s false.
      • Confidence (\(1-\alpha\)): the probability of failing to reject the null hypothesis when it’s true.

      As a standard, \(\alpha\) is set to 0.05 and power to .80. Also, we will generally have a control group (no intervention) and different treatments (variants). 

      There are some challenges

      There are some challenges associated with basic power calculations (e.g., web calculators): 

      • (Simultaneous) multiple comparisons
      • Sample allocation
      • Different MDEs by variant

      When conducting multiple statistical tests or comparisons within a study or analysis, there is a risk of obtaining false positive results. To address this, we need to adjust our \(\alpha\) level based on the number of tests performed. This adjustment increases the required sample size to achieve statistical significance. It is important to consider this issue during experiment design and data analysis, including post hoc analyses.

      To gain an understanding of why multiple comparison testing poses a challenge, let’s consider a simple example. Suppose we have a control group and three treatment variants. In this example, we will simulate data where there are no effects or differences between the control and treatment groups. In other words, the null hypothesis (i.e., no effect) is always true. In each iteration, we test the control group against each treatment and count the number of significant results we obtain using a significance level (\(\alpha=0.05\)). It is important to note that we already know there are no differences between the groups.

      from scipy.stats import ttest_ind
+import pandas as pd
+pd.set_option("display.notebook_repr_html", False)
+import numpy as np
+np.random.seed(0)
+
+# Number of iterations for simulation
+iterations = 1000
+
+# Number of treatment groups
+groups = 3
+
+# Number of samples in each group
+samples = 1000
+
+# Count of batches where at least one false positive (Type I error) occurs
+batches_with_errors = 0
+
+# perform iterations
+for _ in range(iterations):
+    # Control group data
+    control_group = np.random.normal(0, 1, samples)
+
+    # Treatment group data - no actual difference from control
+    treatment_groups = [np.random.normal(0, 1, samples) for _ in range(groups)]
+
+    # A flag for any error within this batch of tests
+    error_in_batch = False
+
+    # perform t-test for each treatment group compared to control group
+    for treatment in treatment_groups:
+        t_stat, p_value = ttest_ind(control_group, treatment)
+
+        # Check if p-value is less than 0.05 (significance level)
+        if p_value < 0.05:
+            error_in_batch = True
+            break  # no need to check remaining tests
+
+    if error_in_batch:
+        batches_with_errors += 1
+
+print(f"Probability of at least one Type I error in a batch of {groups} tests: {batches_with_errors / iterations * 100}%")
      Probability of at least one Type I error in a batch of 3 tests: 12.6%
+

      After 1000 iterations, the probability of encountering at least one Type I error in a batch of 3 tests is 12.6%. You can also use the formula:

      \[1 - (1 - \alpha)^n\]

      Where \(n\) is the number of comparisons. For this example, FWER will be 14.2%. This probability is referred to as the Family-wise Error Rate (FWER), which signifies the likelihood of committing at least one Type I error within the entire family (or batch) of comparisons. It is evident that 12.6% and 14.2% are higher than the assumed α value of 5% for a single test.

      This is the essence of the multiple comparison problem. When conducting multiple tests, even if you maintain an \(\alpha\) level of approximately 0.05 for each individual test, the likelihood of experiencing at least one false discovery among the entire set of tests is significantly higher and increases as the number of tests in the batch grows. Consequently, some of our significant findings may actually be false positives and difficult to replicate.

      The key to adjusting multiple comparison testing is to strike a balance between reducing false positives (incorrectly rejecting the null hypothesis) and maintaining test power (the ability to correctly reject the null hypothesis when it’s false). Some methods, such as Bonferroni or Holm, minimize the Family-Wise Error Rate (FWER). Others, like Benjamini-Hochberg, minimize the False Discovery Rate (FDR), which is the expected proportion of Type I errors among all declared significant hypotheses. FDR methods aim to reduce the ratio of Type I errors among significant results without eliminating all false discoveries like FWER methods. FDR methods generally offer higher statistical power and are less conservative.

      A crucial element of experimental design lies in sample allocation. Variations in the distribution of samples among groups can influence the statistical power of our tests. Sometimes, the goal is to limit the potential disruption from an intervention by scaling down the sample size across various variants. Alternatively, we might opt to shrink the control group’s size or allot more units to each variant, contingent on their Minimum Detectable Effect (MDE).

      Let’s not forget to consider expected effects. In figuring out the smallest minimum detectable effect (MDE), think about the least significant change that would still make the intervention worthwhile. It’s all about balancing the books: estimate the return on investment (ROI) from the intervention, then determine the tiniest shift in a key metric (like conversion rates) that would still make the effort profitable. The MDE doesn’t have to be identical across all variants - after all, not all interventions or campaigns cost the same.

      Tools can help

      I’ve developed a set of straightforward simulation methods in Python, primarily to construct and evaluate our experimental data. The choice to use simulation was driven by its flexibility in handling diverse scenarios and metrics. However, it does come with a trade-off, as it demands a higher computational power. We can live with that :smile:. Feel free to explore the code for the power class.

      These methods are useful, particularly for handling multiple variants, different allocations, or MDE by group. However, it’s important to note that these methods only offer a power estimate based on a given set of parameters. Therefore, we will need to conduct a grid search to evaluate various scenarios and determine the optimal one.

      Let’s start with a simple example using a proportion as the metric of interest:

      from power_tools import * 
+
+# load class
+p = PowerSim(metric='proportion', relative_effect=False, variants=1, nsim=1000, alpha=0.05, alternative='two-tailed')
+
+# get power
+p.get_power(baseline=[0.33], effect=[0.03], sample_size=[3000])
        comparisons  power
+0      (0, 1)  0.706
+

      nsim represents the number of simulations. Variants are set to 1, so we are only comparing control and treatment: comparisons = (0,1). This example is too simple. Let’s now assume two variants (treatments): 

      p = PowerSim(metric='proportion', relative_effect=False, variants=2, 
+             nsim=1000, alpha=0.05, alternative='two-tailed')
+p.get_power(baseline=[0.33], effect=[0.03], sample_size=[3000])
        comparisons  power
+0      (0, 1)  0.531
+1      (0, 2)  0.530
+2      (1, 2)  0.015
+

      The function calculates the power of group comparisons with a sample size of 3000 for each group. Multiple comparisons result in reduced power. Since the effect of each variant is the same (0.03), comparing variants 1 and 2 is not meaningful here (it will always be 0 in practice). Custom comparisons can be defined using a list of tuples, such as comparisons=[(0,1), (0,2)].

      p = PowerSim(metric='proportion', relative_effect=False, variants=2, 
+             nsim=1000, alpha=0.05, alternative='two-tailed',
+             comparisons=[(0,1), (0,2)])
+p.get_power(baseline=[0.33], effect=[0.03], sample_size=[3000])
        comparisons  power
+0      (0, 1)  0.558
+1      (0, 2)  0.563
+

      Using only two comparisons (each variant with a control), the power significantly decreases compared to the one-variant example. I have currently implemented the following p-value corrections:

      • bonferroni
      • holm_bonferroni
      • hochberg
      • sidak
      • fdr

      You can find an introduction to different methods here. In general, Bonferroni is more conservative, while Holm provides higher power. The fdr method aims to minimize the false discovery rate.

      The function corrects only for the comparisons specified in the comparisons parameter. If you conduct additional tests, such as comparing the performance of group A versus group B or examining differences among demographic groups, you need to apply additional multiple comparison corrections in your analysis.

      We can introduce greater variability into the experimental design. We can then plot the expected power for different scenarios. To specify parameters, we’ll utilize nested lists and the grid_sim_power method. However, things can quickly become convoluted.

      Let’s consider a more complex example. The impact of the first and second variants on the control group varies as follows: [0.01, 0.03], [0.03, 0.05], [0.03, 0.07]. The sample sizes are equal for each group, but they increase linearly. We apply the holm correction.

      p = PowerSim(metric='proportion', relative_effect=False, variants=2, alternative='two-tailed', 
+	nsim=1000, correction='holm')
+rr = p.grid_sim_power(baseline_rates=[[0.33]], 
+                effects=[[0.01, 0.03], [0.03, 0.05], [0.03, 0.07]],
+                sample_sizes= [[1000], [2000], [3000], [4000], [5000], [6000], [7000], [8000], [9000]], 
+                threads=16, 
+                plot=True)

      png

      png

      png

      As expected, the best scenario occurs when the effect sizes are large enough to be detected. We can also analyze the results of different sample allocations. For example, when we look at the effects [0.03, 0.05], the allocation [3000, 7000, 7000] produces fairly good results, although it doesn’t always meet the 0.8 threshold.

      p = PowerSim(metric='proportion', relative_effect=False, variants=2, 
+             alternative='two-tailed', nsim=1000, correction='holm')
+rr = p.grid_sim_power(baseline_rates=[[0.33]], 
+                effects=[[0.01, 0.03], [0.03, 0.05], [0.03, 0.07]],
+                sample_sizes= [[1000, 3000, 3000], 
+                               [2000, 5000, 5000], 
+                               [3000, 7000, 7000], 
+                               [2000, 8000, 8000],
+                               [5000, 8000, 8000]],
+                threads=16, 
+                plot=True)

      png

      png

      png

      We can also use other metrics (e.g., average or counts).  For instance, we can design an experiment where the outcome is the number of visits (counts). The simulator will use a Poisson distribution with the parameter, \(\lambda\) (lambda) or the mean number of events. In this example, I set a baseline rate (lambda) of 1.2 visits and a relative increase of \(0.05 (1.2*1.05) = 1.26\), with a control group of 3000 users and a treatment of 5000 users:

      p = PowerSim(metric='count', relative_effect=True, variants=1, 
+             alpha=0.05, alternative='two-tailed')
+p.get_power(baseline=[1.2], effect=[0.05], sample_size=[3000, 5000])
        comparisons  power
+0      (0, 1)   0.59
+

      The effect is small, so our test is underpowered (<0.80). We can also use averages (e.g., revenue), but in that case, we need to specify the standard deviation of the groups:

      p = PowerSim(metric='average', relative_effect=False, variants=1, 
+             alpha=0.05, alternative='two-tailed', nsim=5000)
+p.get_power(baseline=[1000], effect=[100], standard_deviation=[600], sample_size=[400])
        comparisons   power
+0      (0, 1)  0.6592
+

      Complex models 

      When using uplift or mixed models, things become more complicated. As Aleksander Molak put it:

      The question of defining a “safe” dataset size for S-Learner and other causal models is difficult to answer. Power calculations for machine learning models are often difficult, if possible at all. 

      There are some tricks we can apply, though, as Harrell suggests:

      If you can afford a pilot study or you have some historical data that represents a problem similar to the one that you’re interested in, you can find a subgroup in your data that is as homogenous as possible. You can estimate the sample size for this group using some of the traditional statistical power tools. Finally, scale your overall sample size so that this subgroup is properly powered relative to the entire sample

      There are also some traditional ways to optimize the power of our tests:

      • Blocking design
      • Stratification and covariate adjustments (regression)

      For instance, after learning from our uplift models, we can identify the key features associated to users’ responses. We can use those features to design our experiments (blocking). We can also evaluate the results of our uplift models in a new sample, and see if we can replicate the expected uplift.

      Here’s a brief and general overview of the key features to keep in mind when designing experiments or improving models. I hope it’s helpful.


      References

      • Molak, Aleksander. Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more . 
      • https://www.fharrell.com/
      • Ron Kohavi. Trustworthy Online Controlled Experiments.
      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/index.html b/blog/index.html index 35aaaa0b..1c675dd2 100644 --- a/blog/index.html +++ b/blog/index.html @@ -1,100 +1 @@ ---- -layout: default -title: blog -pagination: - enabled: true - collection: posts - permalink: /page/:num/ - per_page: 5 - sort_field: date - sort_reverse: true - trail: - before: 1 # The number of links before the current page - after: 3 # The number of links after the current page ---- - -
      - -
      -

      {{ site.blog_name }}

      -

      {{ site.blog_description }}

      -
      - - {% if site.display_tags %} -
      -
        - {% for tag in site.display_tags %} -
      • - {{ tag }} -
        • - {% unless forloop.last %} -

        •

        - {% endunless %} - {% endfor %} -
      -
      - {% endif %} - -
        - {% for post in paginator.posts %} - - {% if post.external_source == blank %} - {% assign read_time = post.content | number_of_words | divided_by: 180 | plus: 1 %} - {% else %} - {% assign read_time = post.feed_content | strip_html | number_of_words | divided_by: 180 | plus: 1 %} - {% endif %} - {% assign year = post.date | date: "%Y" %} - {% assign tags = post.tags | join: "" %} - {% assign categories = post.categories | join: "" %} - -
      • -

        - {% if post.redirect == blank %} - {{ post.title }} - {% else %} - {% if post.redirect contains '://' %} - {{ post.title }} - - - - {% else %} - {{ post.title }} - {% endif %} - {% endif %} -

        -

        {{ post.description }}

        -

        - {{ read_time }} min read   ·   - {{ post.date | date: '%B %-d, %Y' }} - {%- if post.external_source %} -   ·   {{ post.external_source }} - {%- endif %} -

        -

        - - {{ year }} - - {% if tags != "" %} -   ·   - {% for tag in post.tags %} - - {{ tag }}   - {% endfor %} - {% endif %} - - {% if categories != "" %} -   ·   - {% for category in post.categories %} - - {{ category }}   - {% endfor %} - {% endif %} -

        -
        • - - {% endfor %} -
      - - {% include pagination.html %} - -
      + blog | Sebastian Daza

      blog

      learnings, stats, demography, sociology

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/page/2/index.html b/blog/page/2/index.html new file mode 100644 index 00000000..13131c25 --- /dev/null +++ b/blog/page/2/index.html @@ -0,0 +1 @@ + blog - page 2 | Sebastian Daza

      blog

      learnings, stats, demography, sociology

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
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      blog

      learnings, stats, demography, sociology

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/tag/anylogic/index.html b/blog/tag/anylogic/index.html new file mode 100644 index 00000000..b914c66a --- /dev/null +++ b/blog/tag/anylogic/index.html @@ -0,0 +1 @@ + anylogic | Sebastian Daza

      anylogic

      an archive of posts with this tag

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/tag/data-science/index.html b/blog/tag/data-science/index.html new file mode 100644 index 00000000..bd233b74 --- /dev/null +++ b/blog/tag/data-science/index.html @@ -0,0 +1 @@ + data science | Sebastian Daza

      data science

      an archive of posts with this tag

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/tag/demography/index.html b/blog/tag/demography/index.html new file mode 100644 index 00000000..0429a1d5 --- /dev/null +++ b/blog/tag/demography/index.html @@ -0,0 +1 @@ + demography | Sebastian Daza

      demography

      an archive of posts with this tag

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/tag/experiments/index.html b/blog/tag/experiments/index.html new file mode 100644 index 00000000..139e2635 --- /dev/null +++ b/blog/tag/experiments/index.html @@ -0,0 +1 @@ + experiments | Sebastian Daza

      experiments

      an archive of posts with this tag

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/tag/latex/index.html b/blog/tag/latex/index.html new file mode 100644 index 00000000..1bd87bca --- /dev/null +++ b/blog/tag/latex/index.html @@ -0,0 +1 @@ + latex | Sebastian Daza

      latex

      an archive of posts with this tag

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/tag/python/index.html b/blog/tag/python/index.html new file mode 100644 index 00000000..5ea602ba --- /dev/null +++ b/blog/tag/python/index.html @@ -0,0 +1 @@ + python | Sebastian Daza

      python

      an archive of posts with this tag

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/tag/r/index.html b/blog/tag/r/index.html new file mode 100644 index 00000000..02ab0ac8 --- /dev/null +++ b/blog/tag/r/index.html @@ -0,0 +1 @@ + R | Sebastian Daza

      R

      an archive of posts with this tag

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/tag/simulation/index.html b/blog/tag/simulation/index.html new file mode 100644 index 00000000..63a2ce83 --- /dev/null +++ b/blog/tag/simulation/index.html @@ -0,0 +1 @@ + simulation | Sebastian Daza

      simulation

      an archive of posts with this tag

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/tag/survey/index.html b/blog/tag/survey/index.html new file mode 100644 index 00000000..6ccd2f26 --- /dev/null +++ b/blog/tag/survey/index.html @@ -0,0 +1 @@ + survey | Sebastian Daza

      survey

      an archive of posts with this tag

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/blog/tag/zotero/index.html b/blog/tag/zotero/index.html new file mode 100644 index 00000000..a86f972c --- /dev/null +++ b/blog/tag/zotero/index.html @@ -0,0 +1 @@ + zotero | Sebastian Daza

      zotero

      an archive of posts with this tag

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/cv/index.html b/cv/index.html new file mode 100644 index 00000000..02b6e8e1 --- /dev/null +++ b/cv/index.html @@ -0,0 +1 @@ + cv | Sebastian Daza

      cv

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
      \ No newline at end of file diff --git a/feed.xml b/feed.xml new file mode 100644 index 00000000..72fbcc56 --- /dev/null +++ b/feed.xml @@ -0,0 +1,810 @@ +Jekyll2024-11-28T14:43:46+00:00https://sdaza.com/feed.xmlblankDemography, Sociology, Simulation, Data ScienceTools for power analysis with multiple comparisons2023-09-26T00:00:00+00:002023-09-26T00:00:00+00:00https://sdaza.com/blog/2023/statistical-powerThis post will discuss tools for designing and evaluating experiments. These tools offer user-friendly methods to estimate statistical power when working with multiple treatments or variants. They also help with sample allocation, including determining sample sizes for treatment and control groups. Lastly, they estimate power based on different expected effects (e.g., minimum detectable effects or MDE).

      Calculating power for complex and uplift models using machine learning can be challenging. I will provide some general guidelines and references at the end of this post.

      Let’s start with some terms

      When conducting a statistical test to estimate an effect, we can have any of the following outcomes:

      Image

      These errors and success probabilities are associated with standard statistical terms used in making inferences.

      • Type I Error (\(\alpha\)): the probability of rejecting the null hypothesis when it’s true.
      • Type II Error (\(\beta\)): the probability of failing to reject the null hypothesis when it’s false.
      • Power (\(1-\beta\)): the probability of rejecting the null hypothesis when it’s false.
      • Confidence (\(1-\alpha\)): the probability of failing to reject the null hypothesis when it’s true.

      As a standard, \(\alpha\) is set to 0.05 and power to .80. Also, we will generally have a control group (no intervention) and different treatments (variants). 

      There are some challenges

      There are some challenges associated with basic power calculations (e.g., web calculators): 

      • (Simultaneous) multiple comparisons
      • Sample allocation
      • Different MDEs by variant

      When conducting multiple statistical tests or comparisons within a study or analysis, there is a risk of obtaining false positive results. To address this, we need to adjust our \(\alpha\) level based on the number of tests performed. This adjustment increases the required sample size to achieve statistical significance. It is important to consider this issue during experiment design and data analysis, including post hoc analyses.

      To gain an understanding of why multiple comparison testing poses a challenge, let’s consider a simple example. Suppose we have a control group and three treatment variants. In this example, we will simulate data where there are no effects or differences between the control and treatment groups. In other words, the null hypothesis (i.e., no effect) is always true. In each iteration, we test the control group against each treatment and count the number of significant results we obtain using a significance level (\(\alpha=0.05\)). It is important to note that we already know there are no differences between the groups.

      from scipy.stats import ttest_ind
+import pandas as pd
+pd.set_option("display.notebook_repr_html", False)
+import numpy as np
+np.random.seed(0)
+
+# Number of iterations for simulation
+iterations = 1000
+
+# Number of treatment groups
+groups = 3
+
+# Number of samples in each group
+samples = 1000
+
+# Count of batches where at least one false positive (Type I error) occurs
+batches_with_errors = 0
+
+# perform iterations
+for _ in range(iterations):
+    # Control group data
+    control_group = np.random.normal(0, 1, samples)
+
+    # Treatment group data - no actual difference from control
+    treatment_groups = [np.random.normal(0, 1, samples) for _ in range(groups)]
+
+    # A flag for any error within this batch of tests
+    error_in_batch = False
+
+    # perform t-test for each treatment group compared to control group
+    for treatment in treatment_groups:
+        t_stat, p_value = ttest_ind(control_group, treatment)
+
+        # Check if p-value is less than 0.05 (significance level)
+        if p_value < 0.05:
+            error_in_batch = True
+            break  # no need to check remaining tests
+
+    if error_in_batch:
+        batches_with_errors += 1
+
+print(f"Probability of at least one Type I error in a batch of {groups} tests: {batches_with_errors / iterations * 100}%")
      Probability of at least one Type I error in a batch of 3 tests: 12.6%
+

      After 1000 iterations, the probability of encountering at least one Type I error in a batch of 3 tests is 12.6%. You can also use the formula:

      \[1 - (1 - \alpha)^n\]

      Where \(n\) is the number of comparisons. For this example, FWER will be 14.2%. This probability is referred to as the Family-wise Error Rate (FWER), which signifies the likelihood of committing at least one Type I error within the entire family (or batch) of comparisons. It is evident that 12.6% and 14.2% are higher than the assumed α value of 5% for a single test.

      This is the essence of the multiple comparison problem. When conducting multiple tests, even if you maintain an \(\alpha\) level of approximately 0.05 for each individual test, the likelihood of experiencing at least one false discovery among the entire set of tests is significantly higher and increases as the number of tests in the batch grows. Consequently, some of our significant findings may actually be false positives and difficult to replicate.

      The key to adjusting multiple comparison testing is to strike a balance between reducing false positives (incorrectly rejecting the null hypothesis) and maintaining test power (the ability to correctly reject the null hypothesis when it’s false). Some methods, such as Bonferroni or Holm, minimize the Family-Wise Error Rate (FWER). Others, like Benjamini-Hochberg, minimize the False Discovery Rate (FDR), which is the expected proportion of Type I errors among all declared significant hypotheses. FDR methods aim to reduce the ratio of Type I errors among significant results without eliminating all false discoveries like FWER methods. FDR methods generally offer higher statistical power and are less conservative.

      A crucial element of experimental design lies in sample allocation. Variations in the distribution of samples among groups can influence the statistical power of our tests. Sometimes, the goal is to limit the potential disruption from an intervention by scaling down the sample size across various variants. Alternatively, we might opt to shrink the control group’s size or allot more units to each variant, contingent on their Minimum Detectable Effect (MDE).

      Let’s not forget to consider expected effects. In figuring out the smallest minimum detectable effect (MDE), think about the least significant change that would still make the intervention worthwhile. It’s all about balancing the books: estimate the return on investment (ROI) from the intervention, then determine the tiniest shift in a key metric (like conversion rates) that would still make the effort profitable. The MDE doesn’t have to be identical across all variants - after all, not all interventions or campaigns cost the same.

      Tools can help

      I’ve developed a set of straightforward simulation methods in Python, primarily to construct and evaluate our experimental data. The choice to use simulation was driven by its flexibility in handling diverse scenarios and metrics. However, it does come with a trade-off, as it demands a higher computational power. We can live with that :smile:. Feel free to explore the code for the power class.

      These methods are useful, particularly for handling multiple variants, different allocations, or MDE by group. However, it’s important to note that these methods only offer a power estimate based on a given set of parameters. Therefore, we will need to conduct a grid search to evaluate various scenarios and determine the optimal one.

      Let’s start with a simple example using a proportion as the metric of interest:

      from power_tools import * 
+
+# load class
+p = PowerSim(metric='proportion', relative_effect=False, variants=1, nsim=1000, alpha=0.05, alternative='two-tailed')
+
+# get power
+p.get_power(baseline=[0.33], effect=[0.03], sample_size=[3000])
        comparisons  power
+0      (0, 1)  0.706
+

      nsim represents the number of simulations. Variants are set to 1, so we are only comparing control and treatment: comparisons = (0,1). This example is too simple. Let’s now assume two variants (treatments): 

      p = PowerSim(metric='proportion', relative_effect=False, variants=2, 
+             nsim=1000, alpha=0.05, alternative='two-tailed')
+p.get_power(baseline=[0.33], effect=[0.03], sample_size=[3000])
        comparisons  power
+0      (0, 1)  0.531
+1      (0, 2)  0.530
+2      (1, 2)  0.015
+

      The function calculates the power of group comparisons with a sample size of 3000 for each group. Multiple comparisons result in reduced power. Since the effect of each variant is the same (0.03), comparing variants 1 and 2 is not meaningful here (it will always be 0 in practice). Custom comparisons can be defined using a list of tuples, such as comparisons=[(0,1), (0,2)].

      p = PowerSim(metric='proportion', relative_effect=False, variants=2, 
+             nsim=1000, alpha=0.05, alternative='two-tailed',
+             comparisons=[(0,1), (0,2)])
+p.get_power(baseline=[0.33], effect=[0.03], sample_size=[3000])
        comparisons  power
+0      (0, 1)  0.558
+1      (0, 2)  0.563
+

      Using only two comparisons (each variant with a control), the power significantly decreases compared to the one-variant example. I have currently implemented the following p-value corrections:

      • bonferroni
      • holm_bonferroni
      • hochberg
      • sidak
      • fdr

      You can find an introduction to different methods here. In general, Bonferroni is more conservative, while Holm provides higher power. The fdr method aims to minimize the false discovery rate.

      The function corrects only for the comparisons specified in the comparisons parameter. If you conduct additional tests, such as comparing the performance of group A versus group B or examining differences among demographic groups, you need to apply additional multiple comparison corrections in your analysis.

      We can introduce greater variability into the experimental design. We can then plot the expected power for different scenarios. To specify parameters, we’ll utilize nested lists and the grid_sim_power method. However, things can quickly become convoluted.

      Let’s consider a more complex example. The impact of the first and second variants on the control group varies as follows: [0.01, 0.03], [0.03, 0.05], [0.03, 0.07]. The sample sizes are equal for each group, but they increase linearly. We apply the holm correction.

      p = PowerSim(metric='proportion', relative_effect=False, variants=2, alternative='two-tailed', 
+	nsim=1000, correction='holm')
+rr = p.grid_sim_power(baseline_rates=[[0.33]], 
+                effects=[[0.01, 0.03], [0.03, 0.05], [0.03, 0.07]],
+                sample_sizes= [[1000], [2000], [3000], [4000], [5000], [6000], [7000], [8000], [9000]], 
+                threads=16, 
+                plot=True)

      png

      png

      png

      As expected, the best scenario occurs when the effect sizes are large enough to be detected. We can also analyze the results of different sample allocations. For example, when we look at the effects [0.03, 0.05], the allocation [3000, 7000, 7000] produces fairly good results, although it doesn’t always meet the 0.8 threshold.

      p = PowerSim(metric='proportion', relative_effect=False, variants=2, 
+             alternative='two-tailed', nsim=1000, correction='holm')
+rr = p.grid_sim_power(baseline_rates=[[0.33]], 
+                effects=[[0.01, 0.03], [0.03, 0.05], [0.03, 0.07]],
+                sample_sizes= [[1000, 3000, 3000], 
+                               [2000, 5000, 5000], 
+                               [3000, 7000, 7000], 
+                               [2000, 8000, 8000],
+                               [5000, 8000, 8000]],
+                threads=16, 
+                plot=True)

      png

      png

      png

      We can also use other metrics (e.g., average or counts).  For instance, we can design an experiment where the outcome is the number of visits (counts). The simulator will use a Poisson distribution with the parameter, \(\lambda\) (lambda) or the mean number of events. In this example, I set a baseline rate (lambda) of 1.2 visits and a relative increase of \(0.05 (1.2*1.05) = 1.26\), with a control group of 3000 users and a treatment of 5000 users:

      p = PowerSim(metric='count', relative_effect=True, variants=1, 
+             alpha=0.05, alternative='two-tailed')
+p.get_power(baseline=[1.2], effect=[0.05], sample_size=[3000, 5000])
        comparisons  power
+0      (0, 1)   0.59
+

      The effect is small, so our test is underpowered (<0.80). We can also use averages (e.g., revenue), but in that case, we need to specify the standard deviation of the groups:

      p = PowerSim(metric='average', relative_effect=False, variants=1, 
+             alpha=0.05, alternative='two-tailed', nsim=5000)
+p.get_power(baseline=[1000], effect=[100], standard_deviation=[600], sample_size=[400])
        comparisons   power
+0      (0, 1)  0.6592
+

      Complex models 

      When using uplift or mixed models, things become more complicated. As Aleksander Molak put it:

      The question of defining a “safe” dataset size for S-Learner and other causal models is difficult to answer. Power calculations for machine learning models are often difficult, if possible at all. 

      There are some tricks we can apply, though, as Harrell suggests:

      If you can afford a pilot study or you have some historical data that represents a problem similar to the one that you’re interested in, you can find a subgroup in your data that is as homogenous as possible. You can estimate the sample size for this group using some of the traditional statistical power tools. Finally, scale your overall sample size so that this subgroup is properly powered relative to the entire sample

      There are also some traditional ways to optimize the power of our tests:

      • Blocking design
      • Stratification and covariate adjustments (regression)

      For instance, after learning from our uplift models, we can identify the key features associated to users’ responses. We can use those features to design our experiments (blocking). We can also evaluate the results of our uplift models in a new sample, and see if we can replicate the expected uplift.

      Here’s a brief and general overview of the key features to keep in mind when designing experiments or improving models. I hope it’s helpful.


      References

      • Molak, Aleksander. Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more . 
      • https://www.fharrell.com/
      • Ron Kohavi. Trustworthy Online Controlled Experiments.
      ]]>      Sebastian Daza      Extracting Zotero’s notes2021-07-06T00:00:00+00:002021-07-06T00:00:00+00:00https://sdaza.com/blog/2021/zotero-notesWhen reviewing the literature, it’s nice to have the notes linked to Zotero references. It would also be great to compare them systematically. The idea would be to have your notes in Zotero so that everyone in the group can access and edit them and create a data file to explore your notes using, for instance, text analysis techniques.

      This small package extracts notes from a collection and creates a CSV file that can be easily read using Excel. You only need to specify the collection ID. For instance, if the location of my collection is https://www.zotero.org/groups/2406179/csic-echo/collections/M8N2VMAP, the collection ID would be M8N2VMAP. We also need the Zotero API’s credentials.

      To create a clean CSV, notes’ headers would need a suitable separator. The default is #. In this case, the text between headings mustn’t include #. Below an example:

      # Research question
+
+Estimates interaction effects between PGS of obesity and cohorts using HRS.
+
+# Data
+
+HRS
+
+# Methods
+
+Uses a HLM whereby they estimate effects of age and cohorts while making the
+intercepts and slopes a function of individual factors.
+

      Installation

      pip install git+https://github.com/sdaza/zotnote.git
+

      Credentials

      You can save your Zotero API credentials in a config.py and load them using import config:

      library_id = "0000000"
+api_key = "key"
+library_type = "group"
+

      Example

      Let’s try to extract some notes and read them using Pandas:

      import pandas as pd
+import config
+import zotnote as zn
      zn.exportNotes(collection = "M8N2VMAP", 
+    library_id = config.library_id, api_key = config.api_key)
      Notes saved in zotero-notes.csv
+
      pd.read_csv("zotero-notes.csv")

      center

      ]]>      Sebastian Daza      From database to CSV using Anylogic2020-11-03T00:00:00+00:002020-11-03T00:00:00+00:00https://sdaza.com/blog/2020/anylogic-databaseWhen running a parameter variation experiment, that is, simulating over several iterations and replicates using parallelization, we usually need to collect a huge amount of data and have them in a format that then we can process using Python or R.

      The best way to do this in Anylogic would be using a database and then export, read, or connect to the database to process simulation results (although, see the section update below). We can do that easily in Anylogic. Every time an experiment finishes, we can export the data (from a database) to an Excel file manually.

      The issue with Excel files is, on the one hand, they are Excel files, and on the other, they are not suitable for big data (more than 1 million rows). We can create a function to save all the simulation tables into an Excel file as our experiment finishes. However, we will still have the limit-of-rows limitation (check the Anylogic file linked below for a function to create Excel files from a database).

      Here I follow a different approach by exporting an Anylogic database table to a CSV file within Java. The general setup using Anylogic PLE 8.6 would be:

      • Create the databases you need for your experiment, and be sure you add the columns iteration and replicate.
      • Create a function to save the data of your simulation runs (e.g., agent’s status, age, etc.)
      • Define a parameter variation experiment.
      • Define a variable to specify where to save the data (i.e., path).
      • Write code in the experiment Java Actions section so that to save data every time you run an experiment.
      • Import functions in the advanced Java section of the experiment.

      Databases

      I define two tables (data1 and data2). Each agent saves its data at a given rate. After 5 years, the simulation will finish.

      insertInto(data1)
+    .columns(data1.iteration, data1.replicate, data1.id, data1.dtime, data1.drandom)
+    .values(main.v_iteration, main.v_replicate, this.getIndex(), time(), normal(1.0))
+    .execute();
+
+insertInto(data2)
+    .columns(data2.iteration, data2.replicate, data2.id, data2.dtime, data2.drandom)
+    .values(main.v_iteration, main.v_replicate, this.getIndex(), time(), normal(0.3))
+    .execute();
+
+if (time() > 5) {
+    finishSimulation();
+}

      The tables include a column with the experiment iteration and replicate, in addition to the agent’s index, time, and a random value from a normal distribution.

      From DB to CSV

      The key function to export the data to a CSV file is f_SQLToCSV. It uses two arguments, a SQL query (query) and the path to an output file (filename). For instance, we can write:

      f_SQLToCSV("select * from data1", "output/data1")

      You can use any query for your data, giving you a lot of flexibility on what to export to a CSV file. The f_SQLToCSV method is:

      ResultSet rs = selectResultSet(query);
+try {
+    File file = new File(filename + ".csv");
+    file.getParentFile().mkdirs();
+    FileWriter fw = new FileWriter(file);
+    int cols = rs.getMetaData().getColumnCount();
+    for(int i = 1; i <= cols; i ++){
+        fw.append(rs.getMetaData().getColumnLabel(i).toLowerCase());
+        if(i < cols) fw.append(';');
+        else fw.append('\n');
+    }
+    while (rs.next()) {
+        for(int i = 1; i <= cols; i ++){
+            fw.append(rs.getString(i));
+            if(i < cols) fw.append(';');
+        }
+        fw.append('\n');
+     }
+     fw.flush();
+     fw.close();
+} catch (Exception e) {
+    getEngine().pause();
+    traceln("--> An Exception happened, continue? ...");
+    e.printStackTrace();
+}

      The next step would be to create an experiment and complete the Java actions accordingly. First, we clear our tables.

      // inital experiment setup
+deleteFrom(data1).execute();
+deleteFrom(data2).execute();

      Then, we collect information on the iteration and replicate of the simulation run:

      // before simulation run
+root.v_iteration = getCurrentIteration();
+root.v_replicate = getCurrentReplication();

      Finally, at the end of the experiment, we save the data and clear the tables again:

      // after experiment
+f_exportTables(v_path);
+deleteFrom(data1).execute();
+deleteFrom(data1).execute();

      The method f_exportTables is just a function that goes through each table and export them to a CSV file. v_tables is string array with the name of the tables I want to export {"data1", "data2"}:

      ArrayList<String> tables = new ArrayList<String>();
+for(String tab : v_tables) {
+   tables.add(tab);
+}
+for (String t : tables) {
+    f_SQLToCSV("select * from " + t, path + t);
+}

      Remember to import some functions in the imports section:

      // imports section
+import java.io.BufferedWriter;
+import java.io.File;
+import java.io.FileWriter;
+import java.io.BufferedReader;
+import java.io.FileReader;
+import java.io.IOException;
+import java.text.*;

      From there, we can create additional functions to select the tables to be exported. For more details, download the Anylogic File here.

      Update

      When running several replicates of my simulation, saving the information in a database didn’t work as expected. My simulation just crashed, and I was not able to keep the data. I finally decided to follow my previous approach: create many CSV files – one per iteration and replicate – and read them using an R or Python function. I know you end up with a lot of CSV files, but at least the simulation doesn’t crash, and you can recover the output of your simulation as it goes.

      ]]>      Sebastian Daza      Oh, descriptive tables (R + Latex)!2020-05-12T00:00:00+00:002020-05-12T00:00:00+00:00https://sdaza.com/blog/2020/descriptive-tablesIt’s been a while since my last post. It’s time to catch up!

      Every time I write a paper or report, I need to create descriptive tables using Latex. Over and over I create Adhoc tables, and I say to myself: Write a general function so you can save time in the next paper! I know there are some solutions out there, but in general, I feel they are not flexible enough.

      I introduce a far from perfect function to create descriptive tables in Latex. The steps and structure are quite simple:

      1. Write a function to summarize your data with any stats you want
      2. Define a list with the data plus column names (labels)

      That’s it. You can see the function here. It has some features might be useful:

      • It deals automatically with factors (categorical variables)
      • You can use different datasets at the same time
      • You can group columns using a variable (e.g., year)
      • You can add long notes at the bottom of the table
      • You can specify your own descriptive function

      Let’s start creating some fake data:

      • 5 variables
      • Variable 3 is a factor (i.e., categorical)
      • Variable 5 is a grouping column
      library(data.table)
+set.seed(14332)
+devtools::source_gist("c4d1089a501d3567be9fb784b1c5a6ab")
      # create two fake datasets
+n1 = 100
+dat1 = data.table(
+    var1 = runif(n1),
+    var2 = sample(0:1, n1, prob = c(0.8, 0.2), replace = TRUE),
+    var3 = factor(sample(1:4, n1, prob = c(0.1, 0.4, 0.3, 0.2),
+        replace = TRUE),
+        labels = c("Low", "Medium", "High", "Very high")),
+    var4 = rnorm(n1, 1000, 100),
+    var5 = sample(1:4, n1, replace = TRUE)
+)
+
+n2 = 200
+dat2 = data.table(
+    var1 = runif(n2, min = 3, max = 5),
+    var2 = sample(0:1, n2, prob = c(0.5, 0.5), replace = TRUE),
+    var3 = factor(sample(1:5, n2, prob = c(0.05, 0.1, 0.35, 0.3, 0.2),
+        replace = TRUE),
+        labels = c("Very low", "Low", "Medium", "High", "Very high")),
+    var4 = rnorm(n2, 2000, 300),
+    var5 = sample(1:4, n2, replace = TRUE)
+)
+
+datasets = list("Data 1" = dat1, "Data 2" = dat2)
+variables = list(paste0("var", 1:4), paste0("var", 1:4))
+labels = list(paste0("Variable ", 1:4), paste0("Variable ", 1:4))
+colnames =  c("Mean", "Median", "SD")

      We can define a descriptive function:

      # descriptive function
+myDescriptives = function(x) {
+    x = as.numeric(x)
+    m = mean(x, na.rm = TRUE)
+    md = median(x, na.rm = TRUE)
+    sd = sd(x, na.rm = TRUE)
+    return(c(m, md, sd))
+}
      # create table
+createDescriptiveTable(datasets,
+    summary_function = myDescriptives,
+    column_names = colnames,
+    variable_names = variables,
+    variable_labels = labels,
+    arraystretch = 1.3,
+    title = "Descriptive statistics",
+    label = "tab:descriptive",
+    file = "example_01.tex")

      Thus, the grouping of rows is defined by the name of each dataset in the list. We can add a note, just remember to add \usepackage[flushleft]{threeparttable} to your Latex document:

      # table + note
+createDescriptiveTable(datasets,
+    summary_function = myDescriptives,
+    column_names = colnames,
+    variable_names = variables,
+    variable_labels = labels,
+    arraystretch = 1.3,
+    note = "This is a very long long long long long long long long long note.",
+    title = "Descriptive statistics",
+    label = "tab:descriptive",
+    file = "example_02.tex")

      We can also slice the descriptives by group:

      # group columns
+createDescriptiveTable(datasets,
+    summary_function = myDescriptives,
+    column_names = colnames,
+    variable_names = variables,
+    variable_labels = labels,
+    group_variable = "var5",
+    arraystretch = 1.3,
+    tabcolsep = 3,
+    note = "This is a very long long long long long long long long long note.",
+    title = "Descriptive statistics",
+    label = "tab:descriptive",
+    file = "example_03.tex")

      It’s just a first version. I will add more features soon.

      ]]>      Sebastian Daza      Reading CDC mortality files using R2016-10-05T00:00:00+00:002016-10-05T00:00:00+00:00https://sdaza.com/blog/2016/read-mortality-dataReading fixed-width text files might be challenging, specially when we don’t have a dictionary file. In this post, I show steps to read CDC files in a more systematic way. In this example, I import a compress mortality file (CMF 1979-1988) available here and whose codebook (or layout) is here.

      To read this file, usually with extension .txt or .dat, I first need to know where each column starts and finishes. What I get from the pdf file is something like this:

      The layout is usually a codebook in Word/PDF or just plain text file. Here, I copy the PDF text and put it in a plain text file. I use a text editor (e.g., Sublime Text) and regular expressions to extract the information I need.

      I have to select every row with this pattern: 1-2 2 FIPS State code Numeric. That is, a number followed by a hyphen (although not always, particularly when the width of the column is one), spaces, another number, spaces, and then any text. I use the following regular expression to get that pattern: (^[0-9]+).([0-9]+)\s+([0-9])\s+(.+). Using the Sublime package Filter Lines I get something like this (you can also just copy the selected lines):

      1-2 2 FIPS State code Numeric
+3-5 FIPS county code Numeric
+6-9 4 Year of death Numeric
+11-12 2 Age at death Numeric
+13-16 4 ICD code for underlying cause-of-death 3 digits: Numeric
+17-19 3 Cause-of-Death Recode Numeric
+20-23 4 Number of deaths Numeric
+

      This approach might be particularly useful when you have a long PDF/Word file and you want to extract most of the variables. You would need to adapt the regular expressions I’m using to the particular patterns of your codebook.

      To simplify, I format this text as a comma-separated values file (csv). Replacing this regular expression ([0-9]+)(-)([0-9]+)(\s)([0-9]+)(\s)(.+)(\s)(Numeric) by \1,\3,\5,\7,\9 I get:

      1,2,2,FIPS State code,Numeric
+3,5,3,FIPS county code,Numeric
+6,9,4,Year of death,Numeric
+11,12,2,Age at death,Numeric
+13,16,4,ICD code for underlying cause-of-death 3 digits:,Numeric
+17,19,3,Cause-of-Death Recode,Numeric
+20,23,4,Number of deaths,Numeric
+

      Then, I read the layout file:

      # define names of columns
+colnames <- c("start", "end", "width", "name", "type")
+dict <- read.csv("data/dictMortality.csv", col.names = colnames, header = FALSE)
      ##   start end width                                             name    type
+## 1     1   2     2                                  FIPS State code Numeric
+## 2     3   5     3                                 FIPS county code Numeric
+## 3     6   9     4                                    Year of death Numeric
+## 4    11  12     2                                     Age at death Numeric
+## 5    13  16     4 ICD code for underlying cause-of-death 3 digits: Numeric
+## 6    17  19     3                            Cause-of-Death Recode Numeric
+## 7    20  23     4                                 Number of deaths Numeric

      Now, I can read the fixed-width data file. I use the readr package (in my experience relatively fast for big datasets ~ 1 GB).

      library(readr)
+
+# create name of variables
+cnames <- c("state", "county", "year", "age", "icd", "cause", "deaths")
+
+# read mortality file
+mort <- read_fwf("data/mort7988.txt", fwf_positions(dict$start, dict$end, cnames))
      ## # A tibble: 8,776,385 x 7
+##    state county  year   age   icd cause deaths
+##    <chr>  <chr> <int> <chr> <chr> <chr>  <int>
+##  1    01    001  1979    04  5789   780      1
+##  2    01    001  1979    04  7980   770      1
+##  3    01    001  1979    08  8121   800      1
+##  4    01    001  1979    09  3439   780      1
+##  5    01    001  1979    09  8120   800      2
+##  6    01    001  1979    09  8189   800      1
+##  7    01    001  1979    10  1629   180      1
+##  8    01    001  1979    10  2396   250      1
+##  9    01    001  1979    10  4289   410      1
+## 10    01    001  1979    10  8070   810      1
+## # ... with 8,776,375 more rows
      # year distribution
+table(mort$year)
      ##   1979   1980   1981   1982   1983   1984   1985   1986   1987   1988
+## 831605 854860 854198 850505 867280 875607 894176 905736 912551 929867
      # number of deaths
+sum(mort$deaths)
      ## [1] 20398153

      Hopefully, this might save you some time!

      Last Update: 06/29/2017

      ]]>      Sebastian Daza      R package to compute statistics from the American Community Survey (ACS) and Decennial US Census2016-07-06T00:00:00+00:002016-07-06T00:00:00+00:00https://sdaza.com/blog/2016/acsrThe acsr package helps extracting variables and computing statistics using the America Community Survey and Decennial US Census. It was created for the Applied Population Laboratory (APL) at the University of Wisconsin-Madison.

      Installation

      The functions depend on the acs and data.table packages, so it is necessary to install then before using acsr. The acsr package is hosted on a github repository and can be installed using devtools:

      devtools::install_github("sdaza/acsr")
+library(acsr)

      Remember to set the ACS API key, to check the help documentation and the default values of the acsr functions.

      api.key.install(key="*")
+?sumacs
+?acsdata

      The default dataset is acs, the level is state (Wisconsin, state = "WI"), the endyear is 2014, and the confidence level to compute margins of error (MOEs) is 90%.

      Levels

      The acsr functions can extract all the levels available in the acs package. The table below shows the summary and required levels when using the acsdata and sumacs functions:

      summary number levels
      010 us
      020 region
      030 division
      040 state
      050 state, county
      060 state, county, county.subdivision
      140 state, county, tract
      150 state, county, tract, block.group
      160 state, place
      250 american.indian.area
      320 state, msa
      340 state, csa
      350 necta
      400 urban.area
      500 state, congressional.district
      610 state, state.legislative.district.upper
      620 state, state.legislative.district.lower
      795 state, puma
      860 zip.code
      950 state, school.district.elementary
      960 state, school.district.secondary
      970 state, school.district.unified

      Getting variables and statistics

      We can use the sumacs function to extract variable and statistics. We have to specify the corresponding method (e.g., proportion or just variable), and the name of the statistic or variable to be included in the output.

      sumacs(formula = c("(b16004_004 + b16004_026 + b16004_048 / b16004_001)", "b16004_026"),
+        varname = c("mynewvar", "myvar"),
+        method = c("prop", "variable"),
+        level = c("division"))
      ## [1] "Extracting data from: acs 2014"
+## [1] ". . . . . .  ACS/Census variables : 4"
+## [1] ". . . . . .  Levels : 1"
+## [1] ". . . . . .  New variables : 2"
+## [1] ". . . . . .  Getting division data"
+## [1] ". . . . . .  Creating variables"
+## [1] ". . . . . .  50%"
+## [1] ". . . . . .  100%"
+## [1] ". . . . . .  Formatting output"
      ##    sumlevel geoid division mynewvar_est mynewvar_moe myvar_est myvar_moe
+## 1:      030    NA        1       0.0762     0.000347    770306      3490
+## 2:      030    NA        2       0.1182     0.000278   3332150      9171
+## 3:      030    NA        3       0.0599     0.000196   1819417      7209
+## 4:      030    NA        4       0.0411     0.000277    547577      4461
+## 5:      030    NA        5       0.1108     0.000246   4526480     11869
+## 6:      030    NA        6       0.0320     0.000265    402475      3781
+## 7:      030    NA        7       0.2203     0.000469   5318126     13044
+## 8:      030    NA        8       0.1582     0.000602   2279303     10746
+## 9:      030    NA        9       0.2335     0.000501   7765838     20289

      To download the data can be slow, especially when many levels are being used (e.g., blockgroup). A better approach in those cases is, first, download the data using the function acsdata , and then use them as input.

      mydata <- acsdata(formula = c("(b16004_004 + b16004_026 + b16004_048 /  b16004_001)",
+        "b16004_026"),
+        level = c("division"))
      ## [1] ". . . . . .  Getting division data"
      sumacs(formula = c("(b16004_004 + b16004_026 + b16004_048 / b16004_001)", "b16004_026"),
+        varname = c("mynewvar", "myvar"),
+        method = c("prop", "variable"),
+        level = c("division"),
+        data = mydata)
      ## [1] "Extracting data from: acs 2014"
+## [1] ". . . . . .  ACS/Census variables : 4"
+## [1] ". . . . . .  Levels : 1"
+## [1] ". . . . . .  New variables : 2"
+## [1] ". . . . . .  Creating variables"
+## [1] ". . . . . .  50%"
+## [1] ". . . . . .  100%"
+## [1] ". . . . . .  Formatting output"
      ##    sumlevel geoid division mynewvar_est mynewvar_moe myvar_est myvar_moe
+## 1:      030    NA        1       0.0762     0.000347    770306      3490
+## 2:      030    NA        2       0.1182     0.000278   3332150      9171
+## 3:      030    NA        3       0.0599     0.000196   1819417      7209
+## 4:      030    NA        4       0.0411     0.000277    547577      4461
+## 5:      030    NA        5       0.1108     0.000246   4526480     11869
+## 6:      030    NA        6       0.0320     0.000265    402475      3781
+## 7:      030    NA        7       0.2203     0.000469   5318126     13044
+## 8:      030    NA        8       0.1582     0.000602   2279303     10746
+## 9:      030    NA        9       0.2335     0.000501   7765838     20289

      Standard errors

      When computing statistics there are two ways to define the standard errors:

      • Including all standard errors of the variables used to compute a statistic (one.zero = FALSE)
      • Include all standard errors except those of variables that are equal to zero. Only the maximum standard error of the variables equal to zero is included (one.zero = TRUE)
      • The default value is one.zero = TRUE

      For more details about how standard errors are computed for proportions, ratios and aggregations look at A Compass for Understanding and Using American Community Survey Data.

      Below an example when estimating proportions and using one.zero = FALSE:

      sumacs(formula = "(b16004_004 + b16004_026 + b16004_048) / b16004_001",
+            varname =  "mynewvar",
+            method = "prop",
+            level = "tract",
+            county = 1,
+            tract = 950501,
+            endyear = 2013,
+            one.zero = FALSE)
      ## [1] "Extracting data from: acs 2013"
+## [1] ". . . . . .  ACS/Census variables : 4"
+## [1] ". . . . . .  Levels : 1"
+## [1] ". . . . . .  New variables : 1"
+## [1] ". . . . . .  Getting tract data"
+## [1] ". . . . . .  Creating variables"
+## [1] ". . . . . .  100%"
+## [1] ". . . . . .  Formatting output"
      ##    sumlevel       geoid st_fips cnty_fips tract_fips mynewvar_est mynewvar_moe
+## 1:      140 55001950501      55         1     950501       0.0226       0.0252
      \[SE = \sqrt{ \frac{(5.47 ^ 2 + 22.49 ^ 2 + 5.47 ^ 2) - ( 0.02 ^ 2 \times 102.13 ^ 2)}{1546} } \times 1.645 = 0.0252\]

      When one.zero = TRUE:

      sumacs(formula = "(b16004_004 + b16004_026 + b16004_048) / b16004_001",
+            varname = "mynewvar",
+            method = "prop",
+            level = "tract",
+            county = 1,
+            tract = 950501,
+            endyear = 2013,
+            one.zero = TRUE)
      ## [1] "Extracting data from: acs 2013"
+## [1] ". . . . . .  ACS/Census variables : 4"
+## [1] ". . . . . .  Levels : 1"
+## [1] ". . . . . .  New variables : 1"
+## [1] ". . . . . .  Getting tract data"
+## [1] ". . . . . .  Creating variables"
+## [1] ". . . . . .  100%"
+## [1] ". . . . . .  Formatting output"
      ##    sumlevel       geoid st_fips cnty_fips tract_fips mynewvar_est mynewvar_moe
+## 1:      140 55001950501      55         1     950501       0.0226       0.0245
      \[SE_{\text{ one.zero}} \sqrt{ \frac{(5.47 ^ 2 + 22.49 ^ 2) - ( 0.02 ^ 2 \times 102.13 ^ 2)}{1546} } \times 1.645 = 0.0245\]

      When the square root value in the standard error formula doesn’t exist (e.g., the square root of a negative number), the ratio formula is instead used. The ratio adjustment is done variable by variable .

      It can also be that the one.zero option makes the square root undefinable. In those cases, the function uses again the ratio formula to compute standard errors. There is also a possibility that the standard error estimates using the ratio formula are higher than the proportion estimates without the one.zero option.

      Decennial Data from the US Census

      Let’s get the African American and Hispanic population by state. In this case, we don’t have any estimation of margin of error.

      sumacs(formula = c("p0080004", "p0090002"),
+            method = "variable",
+            dataset = "sf1",
+            level = "state",
+            state = "*",
+            endyear = 2010)
      ## [1] "Extracting data from: sf1 2010"
+## [1] ". . . . . .  ACS/Census variables : 2"
+## [1] ". . . . . .  Levels : 1"
+## [1] ". . . . . .  New variables : 2"
+## [1] ". . . . . .  Getting state data"
+## [1] ". . . . . .  Creating variables"
+## [1] ". . . . . .  50%"
+## [1] ". . . . . .  100%"
+## [1] ". . . . . .  Formatting output"
      ##     sumlevel geoid st_fips p0080004 p0090002
+##  1:      040    01      01  1251311   185602
+##  2:      040    02      02    23263    39249
+##  3:      040    04      04   259008  1895149
+##  4:      040    05      05   449895   186050
+##  5:      040    06      06  2299072 14013719
+##  6:      040    08      08   201737  1038687
+##  7:      040    09      09   362296   479087
+##  8:      040    10      10   191814    73221
+##  9:      040    11      11   305125    54749
+## 10:      040    12      12  2999862  4223806
+## 11:      040    13      13  2950435   853689
+## 12:      040    15      15    21424   120842
+## 13:      040    16      16     9810   175901
+## 14:      040    17      17  1866414  2027578
+## 15:      040    18      18   591397   389707
+## 16:      040    19      19    89148   151544
+## 17:      040    20      20   167864   300042
+## 18:      040    21      21   337520   132836
+## 19:      040    22      22  1452396   192560
+## 20:      040    23      23    15707    16935
+## 21:      040    24      24  1700298   470632
+## 22:      040    25      25   434398   627654
+## 23:      040    26      26  1400362   436358
+## 24:      040    27      27   274412   250258
+## 25:      040    28      28  1098385    81481
+## 26:      040    29      29   693391   212470
+## 27:      040    30      30     4027    28565
+## 28:      040    31      31    82885   167405
+## 29:      040    32      32   218626   716501
+## 30:      040    33      33    15035    36704
+## 31:      040    34      34  1204826  1555144
+## 32:      040    35      35    42550   953403
+## 33:      040    36      36  3073800  3416922
+## 34:      040    37      37  2048628   800120
+## 35:      040    38      38     7960    13467
+## 36:      040    39      39  1407681   354674
+## 37:      040    40      40   277644   332007
+## 38:      040    41      41    69206   450062
+## 39:      040    42      42  1377689   719660
+## 40:      040    44      44    60189   130655
+## 41:      040    45      45  1290684   235682
+## 42:      040    46      46    10207    22119
+## 43:      040    47      47  1057315   290059
+## 44:      040    48      48  2979598  9460921
+## 45:      040    49      49    29287   358340
+## 46:      040    50      50     6277     9208
+## 47:      040    51      51  1551399   631825
+## 48:      040    53      53   240042   755790
+## 49:      040    54      54    63124    22268
+## 50:      040    55      55   359148   336056
+## 51:      040    56      56     4748    50231
+## 52:      040    72      72   461498  3688455
+##     sumlevel geoid st_fips p0080004 p0090002

      Output

      The output can be formatted using a wide or long format:

      sumacs(formula = "(b16004_004 + b16004_026 + b16004_048 / b16004_001)",
+            varname = "mynewvar",
+            method = "prop",
+            level = "division",
+            format.out = "long")
      ## [1] "Extracting data from: acs 2014"
+## [1] ". . . . . .  ACS/Census variables : 4"
+## [1] ". . . . . .  Levels : 1"
+## [1] ". . . . . .  New variables : 1"
+## [1] ". . . . . .  Getting division data"
+## [1] ". . . . . .  Creating variables"
+## [1] ". . . . . .  100%"
+## [1] ". . . . . .  Formatting output"
      ##    geoid sumlevel division var_name    est      moe
+## 1:    NA      030        1 mynewvar 0.0762 0.000347
+## 2:    NA      030        2 mynewvar 0.1182 0.000278
+## 3:    NA      030        3 mynewvar 0.0599 0.000196
+## 4:    NA      030        4 mynewvar 0.0411 0.000277
+## 5:    NA      030        5 mynewvar 0.1108 0.000246
+## 6:    NA      030        6 mynewvar 0.0320 0.000265
+## 7:    NA      030        7 mynewvar 0.2203 0.000469
+## 8:    NA      030        8 mynewvar 0.1582 0.000602
+## 9:    NA      030        9 mynewvar 0.2335 0.000501

      And it can also be exported to a csv file:

      sumacs(formula = "(b16004_004 + b16004_026 + b16004_048 / b16004_001)",
+            varname = "mynewvar",
+            method = "prop",
+            level = "division",
+            file = "myfile.out")
      ## [1] "Extracting data from: acs 2014"
+## [1] ". . . . . .  ACS/Census variables : 4"
+## [1] ". . . . . .  Levels : 1"
+## [1] ". . . . . .  New variables : 1"
+## [1] ". . . . . .  Getting division data"
+## [1] ". . . . . .  Creating variables"
+## [1] ". . . . . .  100%"
+## [1] ". . . . . .  Formatting output"
+## [1] "Data exported to a CSV file! "

      Combining geographic levels

      We can combine geographic levels using two methods: (1) sumacs and (2) combine.output. The first one allows only single combinations, the second multiple ones.

      If I want to combine two states (e.g., Wisconsin and Minnesota) I can use:

      sumacs("(b16004_004 + b16004_026 + b16004_048 / b16004_001)",
+    varname = "mynewvar",
+    method = "prop",
+    level = "state",
+    state = list("WI", "MN"),
+    combine = TRUE,
+    print.levels = FALSE)
      ## [1] "Extracting data from: acs 2014"
+## [1] ". . . . . .  ACS/Census variables : 4"
+## [1] ". . . . . .  Levels : 1"
+## [1] ". . . . . .  New variables : 1"
+## [1] ". . . . . .  Getting combined data"
+## [1] ". . . . . .  Creating variables"
+## [1] ". . . . . .  100%"
+## [1] ". . . . . .  Formatting output"
      ##    geoid combined_group mynewvar_est mynewvar_moe
+## 1:    NA      aggregate        0.042     0.000331

      If I want to put together multiple combinations (e.g., groups of states):

      combine.output("(b16004_004 + b16004_026 + b16004_048 / b16004_001)",
+    varname = "mynewvar",
+    method = "prop",
+    level = list("state", "state"),
+    state = list( list("WI", "MN"), list("CA", "OR")), # nested list
+    combine.names = c("WI+MN", "CA+OR"),
+    print.levels = FALSE)
      ## [1] ". . . . . .  Defining WI+MN"
+## [1] "Extracting data from: acs 2014"
+## [1] ". . . . . .  ACS/Census variables : 4"
+## [1] ". . . . . .  Levels : 1"
+## [1] ". . . . . .  New variables : 1"
+## [1] ". . . . . .  Getting combined data"
+## [1] ". . . . . .  Creating variables"
+## [1] ". . . . . .  100%"
+## [1] ". . . . . .  Formatting output"
+## [1] ". . . . . .  Defining CA+OR"
+## [1] "Extracting data from: acs 2014"
+## [1] ". . . . . .  ACS/Census variables : 4"
+## [1] ". . . . . .  Levels : 1"
+## [1] ". . . . . .  New variables : 1"
+## [1] ". . . . . .  Getting combined data"
+## [1] ". . . . . .  Creating variables"
+## [1] ". . . . . .  100%"
+## [1] ". . . . . .  Formatting output"
      ##    combined_group mynewvar_est mynewvar_moe
+## 1:          WI+MN        0.042     0.000331
+## 2:          CA+OR        0.269     0.000565

      A map?

      Let’s color a map using poverty by county:

      pov <- sumacs(formula = "b17001_002 / b17001_001 * 100",
+        varname = c("pov"),
+        method = c("prop"),
+        level = c("county"),
+        state = "*")
      ## [1] "Extracting data from: acs 2014"
+## [1] ". . . . . .  ACS/Census variables : 2"
+## [1] ". . . . . .  Levels : 1"
+## [1] ". . . . . .  New variables : 1"
+## [1] ". . . . . .  Getting county data"
+## [1] ". . . . . .  Creating variables"
+## [1] ". . . . . .  100%"
+## [1] ". . . . . .  Formatting output"
      library(choroplethr)
+library(choroplethrMaps)
+pov[, region := as.numeric(geoid)]
+setnames(pov, "pov_est", "value")
+county_choropleth(pov, num_colors = 5)

      center

      In sum, the acsr package:

      • Reads formulas directly and extracts any ACS/Census variable
      • Provides an automatized and tailored way to obtain indicators and MOEs
      • Allows different outputs’ formats (wide and long, csv)
      • Provides an easy way to adjust MOEs to different confidence levels
      • Includes a variable-by-variable ratio adjustment of standard errors
      • Includes the zero-option when computing standard errors for proportions, ratios, and aggregations
      • Combines geographic levels flexibly

      Last Update: 02/07/2016

      ]]>      Sebastian Daza      Imputing scales using parcels of items as auxiliary variables2015-10-14T00:00:00+00:002015-10-14T00:00:00+00:00https://sdaza.com/blog/2015/imputation-parcelsMultiple imputation of scales generated by several items can be challenging. Fortunately, to impute every single item is not the only solution to the missing data problem. Some practical and theoretically attractive alternative have already been proposed. In this post, I show a simple implementation of what Enders (2010) calls duplicated-scale imputation, a method orginally suggested by Eekhout et al. (2011). By the way, thanks Iris Eekhout for replying my e-mails!

      Procedure

      For each scale, I define a number or proportion of items (let’s say p) to create parcels (i.e., average of items although not the whole scale). These parcels are, then, used as auxiliary variables to impute the original scales. There are different ways to define parcels. I implemented a solution in my R package sdazar, see the function rowscore for more details.

      The function rowscore selects p items with the least missing data. For each case (row), it computes the parcels using the available information of the selected items. If only one item has information, only that one will be used. If there are more than one item with valid data, it will average all the selected items. If there are no items available in my initial selection, it picks p items from the rest of unselected items to impute the original scale. In this particular example, I create parcels using half of the items:

      rowscore(data, items, p = 1/2, type = "parcel")

      The reason for using a proportion of the original items is to include as much information as possible, but preventing strong linear dependencies between variables. Ideally, parcels are complete (no missing values). However, in some cases all the items are missing, so parcels can still have missing records (although less than the original scales).

      Why not just to use the average of the available items? That solution would implicitly assume that items perfectly correlate with the scale. We know that’s not a good assumption. That is why we worry about creating scales in the first place, right? Using parcels takes advantage of the available information (items with complete information) and the relationship between a portion of items and the scale.

      Here I show a simple example using the National Longitudinal Study of Adolescent to Adult Health (Add Health). First, let’s look some descriptives of the variables included in the imputation. I am using information from Wave 1 and 2. The scales/scores I am imputing are depression (19 items) and GPA (4 items). Variables ending with .p are parcels with 1/2 of the items of the original scale.

      dim(dats)
      ## [1] 12976    14
      str(dats[, nvars, with = FALSE])
      ## Classes 'data.table' and 'data.frame':	12976 obs. of  14 variables:
+##  $ female   : Factor w/ 2 levels "0","1": 1 1 1 2 1 1 1 1 1 1 ...
+##  $ age      : int  16 16 14 13 14 17 14 17 17 14 ...
+##  $ race     : Factor w/ 5 levels "white","black",..: 2 1 1 1 2 1 3 3 3 2 ...
+##  $ publicass: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 NA ...
+##  $ bmi      : num  27.4 16.3 22.2 18.2 21.9 ...
+##  $ gpa1     : num  2 3.25 3.75 3.25 2.25 1 NA 2.25 NA 2.5 ...
+##  $ gpa2     : num  2.5 2.5 4 3.75 1.75 1.5 NA 3 1 4 ...
+##  $ gpa1.p   : num  2.5 3.5 3.5 3 2.5 1 3.5 3 1 3.5 ...
+##  $ gpa2.p   : num  2.5 2.5 4 3.5 1.5 2 1.5 3 1 4 ...
+##  $ dep1.p   : num  0.2 0.7 0 0.3 0.4 0.4 0.1 1 0.6 1.2 ...
+##  $ dep1     : num  0.263 0.789 0 0.211 0.474 ...
+##  $ dep2.p   : num  0.6 0.6 0.1 0.3 0.4 0.6 0.2 1.1 0.8 0.5 ...
+##  $ dep2     : num  0.6316 0.5263 0.0526 0.1579 0.3684 ...
+##  $ ppvt     : int  101 75 121 96 79 97 103 89 82 120 ...
+##  - attr(*, ".internal.selfref")=<externalptr>
      # create summary table using package tables
+missing <- function (x) {sum(is.na(x))}
+tabular(
+  ( dep2 + dep2.p + dep1 + dep1.p + gpa2 + gpa2.p +
+    gpa1 + gpa1.p  + age + bmi + ppvt )
+   ~  (Format(digit = 2) * ( Heading("Mean")
+   	* sdazar::Mean + Heading("SD")
+   	* sdazar::Sd + Heading("Min")
+   	* sdazar::Min + Heading("Max")
+   	* sdazar::Max + (Heading("Missing") * missing ))),
+  data = dats)
      ##
+##         Mean    SD      Min     Max     Missing
+##  dep2      0.58    0.39    0.00    2.95   68.00
+##  dep2.p    0.58    0.43    0.00    3.00    6.00
+##  dep1      0.58    0.39    0.00    2.84   81.00
+##  dep1.p    0.62    0.44    0.00    3.00   18.00
+##  gpa2      2.86    0.74    1.00    4.00 4710.00
+##  gpa2.p    2.76    0.84    1.00    4.00 1238.00
+##  gpa1      2.82    0.76    1.00    4.00 2788.00
+##  gpa1.p    2.74    0.85    1.00    4.00  342.00
+##  age      15.28    1.61   11.00   21.00    9.00
+##  bmi      22.37    4.42   11.47   63.47  353.00
+##  ppvt    100.17   15.00   13.00  146.00  568.00

      As expected, the correlation between the scales and parcels is high. GPA has most of the problems. Note that parcels .p still have missing records, although much less than the original scales.

      cor(dats[, .(dep1, dep1.p)], use = "complete")
      ##         dep1 dep1.p
+## dep1   1.000  0.947
+## dep1.p 0.947  1.000
      cor(dats[, .(dep2, dep2.p)], use = "complete")
      ##         dep2 dep2.p
+## dep2   1.000  0.948
+## dep2.p 0.948  1.000
      cor(dats[, .(gpa1, gpa1.p)], use = "complete")
      ##         gpa1 gpa1.p
+## gpa1   1.000  0.888
+## gpa1.p 0.888  1.000
      cor(dats[, .(gpa2, gpa2.p)], use = "complete")
      ##         gpa2 gpa2.p
+## gpa2   1.000  0.885
+## gpa2.p 0.885  1.000

      Let’s now impute the scales/scores using the R package MICE.

      ini <- mice(dats[, nvars, with = FALSE], m = 1, maxit = 0)
+
+# get methods
+(meth <- ini$meth)
      ##    female       age      race publicass       bmi      gpa1      gpa2    gpa1.p    gpa2.p    dep1.p      dep1    dep2.p      dep2      ppvt
+##        ""     "pmm"        ""  "logreg"     "pmm"     "pmm"     "pmm"     "pmm"     "pmm"     "pmm"     "pmm"     "pmm"     "pmm"     "pmm"
      # get predictor matrix
+pred <- ini$pred

      I adjust the predictor matrix to avoid feedbacks during the imputation (circularity between variables). The trick is to use only complete variables when imputing parcels.

      # predict parcels only with complete variables to avoid feedbacks
+pred["gpa1.p", ] <- 0
+pred["gpa2.p", ] <- 0
+pred["dep1.p", ] <- 0
+pred["dep2.p", ] <- 0
+
+pred["gpa1.p", c("female", "race")] <- 1
+pred["gpa2.p", c("female", "race")] <- 1
+pred["dep1.p", c("female", "race")] <- 1
+pred["dep2.p", c("female", "race")] <- 1
+
+# predict scales using parcels
+pred[, "gpa1.p"] <- 0
+pred[, "gpa2.p"] <- 0
+pred[, "dep1.p"] <- 0
+pred[, "dep2.p"] <- 0
+
+pred["gpa1", c("gpa1.p")] <- 1
+pred["gpa2", c("gpa2.p")] <- 1
+pred["dep1", c("dep1.p")] <- 1
+pred["dep2", c("dep2.p")] <- 1

      Here the adjusted predictor matrix:

      ##           female age race publicass bmi gpa1 gpa2 gpa1.p gpa2.p dep1.p dep1 dep2.p dep2 ppvt
+## female         0   0    0         0   0    0    0      0      0      0    0      0    0    0
+## age            1   0    1         1   1    1    1      0      0      0    1      0    1    1
+## race           0   0    0         0   0    0    0      0      0      0    0      0    0    0
+## publicass      1   1    1         0   1    1    1      0      0      0    1      0    1    1
+## bmi            1   1    1         1   0    1    1      0      0      0    1      0    1    1
+## gpa1           1   1    1         1   1    0    1      1      0      0    1      0    1    1
+## gpa2           1   1    1         1   1    1    0      0      1      0    1      0    1    1
+## gpa1.p         1   0    1         0   0    0    0      0      0      0    0      0    0    0
+## gpa2.p         1   0    1         0   0    0    0      0      0      0    0      0    0    0
+## dep1.p         1   0    1         0   0    0    0      0      0      0    0      0    0    0
+## dep1           1   1    1         1   1    1    1      0      0      1    0      0    1    1
+## dep2.p         1   0    1         0   0    0    0      0      0      0    0      0    0    0
+## dep2           1   1    1         1   1    1    1      0      0      0    1      1    0    1
+## ppvt           1   1    1         1   1    1    1      0      0      0    1      0    1    0

      Let’s impute the data!

      imp <- mice(dats[, nvars, with = FALSE], pred = pred, m = 5, maxit = 10)

      Below some plots to explore how the imputation goes.

      plot(imp, c("gpa1", "gpa2", "dep1", "dep2"))

      centercenter

      I don’t see any problematic pattern. It seems I get a proper solution. The distribution of the variables also looks right.

      densityplot(imp, ~ gpa1 + gpa2 + dep1 + dep2)

      center

      bwplot(imp, gpa1 + gpa2 + dep1 + dep2  ~ .imp)

      center

      Last Update: 06/02/2017

      References

      Enders, Craig K. 2010. Applied Missing Data Analysis. The Guilford Press.

      Eekhout, Iris, Craig K. Enders, Jos W. R. Twisk, Michiel R. de Boer, Henrica C. W. de Vet, and Martijn W. Heymans. 2015. “Analyzing Incomplete Item Scores in Longitudinal Data by Including Item Score Information as Auxiliary Variables.” Structural Equation Modeling: A Multidisciplinary Journal 22 (4):588-602.

      ]]>      Sebastian Daza      Simple R package to define sample sizes and MOEs2015-09-30T00:00:00+00:002015-09-30T00:00:00+00:00https://sdaza.com/blog/2015/samplerI present a simple R package called sampler. The package defines sample sizes and margins of error (MOE) for proportions, as usually it is needed when designing public opinion surveys. In a previous post, I showed some functions that do mostly the same thing. This new package, though, includes some new features that can be useful when allocating a sample.

      Installation

      # you have to install devtools first
+devtools::install_github("sdaza/sampler")
+library(sampler)

      Functions

      The packages contains four functions:

      • ssize: computes sample size.
      • serr: computes MOE.
      • astrata: assigns sample sizes to strata.
      • serrst: computes MOE for stratified samples.

      Define sample size: ssize

      ssize(.05)
      ## [1] 384
      # design effect (deff) and response rate (rr)
+ssize(.05, deff = 1.2, rr = .90)
      ## [1] 512
      # finite population correction
+ssize(.05, deff = 1.2, rr = .90, N = 1000)
      ## [1] 370
      # warning message
+ssize(.05, deff = 1.2, rr = .90, N = 100)
      ## n is bigger than N in some rows: n = N
      ## [1] 100

      Define sampling error: serr

      serr(384)
      ## [1] 0.05
      serr(512, deff = 1.2, rr = .90)
      ## [1] 0.05
      serr(370, deff = 1.2, rr = .90, N = 1000)
      ## [1] 0.05
      # we still get an answer
+serr(100, deff = 1.2, rr = .90, N = 100)
      ## [1] 0.0569

      Strata allocation: astrata

      These examples show how to allocate a sample size into strata. Look at ?astrata in R for definitions of the allocation procedures that are available.

      # I will use data.table
+library(data.table)
+chile <- data.table(chile)
+chile
      ##     reg     pob  pr
+##  1:   1  328782 0.3
+##  2:   2  613328 0.4
+##  3:   3  308247 0.5
+##  4:   4  759228 0.5
+##  5:   5 1808300 0.5
+##  6:   6  910577 0.6
+##  7:   7 1035593 0.3
+##  8:   8 2100494 0.1
+##  9:   9  983499 0.2
+## 10:  10  834714 0.5
+## 11:  11  107334 0.5
+## 12:  12  163748 0.4
+## 13:  13 7228581 0.6
+## 14:  14  401548 0.2
+## 15:  15  235081 0.3
      # proportional for a sample of 1000
+chile[, aprop := astrata(1000, wp = 1, N = pob)]
+
+# fixed (same number by stratum)
+chile[, afixed := astrata(1000, wp = 0, N = pob)]
+
+# 40% proportional, 60% fixed
+chile[, a40 := astrata(1000, wp =.4, N = pob)]
+
+# 60% proportional, 40% fixed
+chile[, a60 := astrata(1000, wp =.6, N = pob)]
+
+# square-root
+chile[, aroot := astrata(1000, method = "root", N = pob)]
+
+# neyman
+chile[, aneyman := astrata(1000, method = "neyman", N = pob, p = pr)]
+
+# standard deviation
+chile[, astdev := astrata(1000, method = "stdev", N = pob, p = pr)]
+
+# error
+chile[, aerr := astrata(e = .11, method = "error", N = pob, p = pr)]
      ##     reg     pob  pr aprop afixed a40 a60 aroot aneyman astdev aerr
+##  1:   1  328782 0.3    18     67  47  38    41      18     66   67
+##  2:   2  613328 0.4    34     67  54  47    56      37     71   76
+##  3:   3  308247 0.5    17     67  47  37    40      19     72   79
+##  4:   4  759228 0.5    43     67  57  53    62      46     72   79
+##  5:   5 1808300 0.5   101     67  81  87    96     110     72   79
+##  6:   6  910577 0.6    51     67  61  57    68      54     71   76
+##  7:   7 1035593 0.3    58     67  63  62    73      58     66   67
+##  8:   8 2100494 0.1   118     67  87  98   104      77     43   29
+##  9:   9  983499 0.2    55     67  62  60    71      48     58   51
+## 10:  10  834714 0.5    47     67  59  55    65      51     72   79
+## 11:  11  107334 0.5     6     67  43  30    23       7     72   79
+## 12:  12  163748 0.4     9     67  44  32    29      10     71   76
+## 13:  13 7228581 0.6   406     67 203 270   192     432     71   76
+## 14:  14  401548 0.2    23     67  49  41    45      20     58   51
+## 15:  15  235081 0.3    13     67  45  35    35      13     66   67

      Getting sampling error from a stratified sample: serrst

      # the second most efficient allocation
+serrst(n = chile$aprop, N = chile$pob, p = chile$pr)
      ## [1] 0.0288
      # the worst solution
+serrst(n = chile$afixed, N = chile$pob, p = chile$pr)
      ## [1] 0.0518
      serrst(n = chile$a40, N = chile$pob, p = chile$pr)
      ## [1] 0.0339
      serrst(n = chile$a60, N = chile$pob, p = chile$pr)
      ## [1] 0.0311
      serrst(n = chile$aroot, N = chile$pob, p = chile$pr)
      ## [1] 0.0339
      # the most efficient allocation
+serrst(n = chile$aneyman, N = chile$pob, p = chile$pr)
      ## [1] 0.0285
      serrst(n = chile$astdev, N = chile$pob, p = chile$pr)
      ## [1] 0.0508
      serrst(n = chile$aerr, N = chile$pob, p = chile$pr)
      ## [1] 0.0498

      Combining criteria

      # get error for 60% proportional / 40% fixed allocation for each strata
+chile[, error_a60 := serr(a60, p = pr)]
+
+# assign sample sizes assuming 13% error for each strata
+chile[, serr13 := astrata(e = .13, method = "error", N = pob, p = pr)]
+
+# total error, not that good!
+serrst(n = chile$serr13, N = chile$pob, p = chile$pr)
      ## [1] 0.0586
      chile[, .(reg, pob, pr, a60, error_a60, serr13)]
      ##     reg     pob  pr a60 error_a60 serr13
+##  1:   1  328782 0.3  38    0.1457     48
+##  2:   2  613328 0.4  47    0.1401     55
+##  3:   3  308247 0.5  37    0.1611     57
+##  4:   4  759228 0.5  53    0.1346     57
+##  5:   5 1808300 0.5  87    0.1051     57
+##  6:   6  910577 0.6  57    0.1272     55
+##  7:   7 1035593 0.3  62    0.1141     48
+##  8:   8 2100494 0.1  98    0.0594     20
+##  9:   9  983499 0.2  60    0.1012     36
+## 10:  10  834714 0.5  55    0.1321     57
+## 11:  11  107334 0.5  30    0.1789     57
+## 12:  12  163748 0.4  32    0.1697     55
+## 13:  13 7228581 0.6 270    0.0584     55
+## 14:  14  401548 0.2  41    0.1224     36
+## 15:  15  235081 0.3  35    0.1518     48

      We can adjust a bit more:

      # when error is higher than .13, use serr13
+chile[, sfinal := ifelse(error_a60 > .13, serr13, a60)]
+
+# new error by stratum
+chile[, error_sfinal := serr(sfinal, p = pr)]
+
+# total error, much better!
+serrst(n = chile$sfinal, N = chile$pob, p = chile$pr)
      ## [1] 0.0309
      # although the total sample size is now bigger
+sum(chile$sfinal)
      ## [1] 1109
      ##     reg     pob  pr sfinal error_sfinal
+##  1:   1  328782 0.3     48       0.1296
+##  2:   2  613328 0.4     55       0.1295
+##  3:   3  308247 0.5     57       0.1298
+##  4:   4  759228 0.5     57       0.1298
+##  5:   5 1808300 0.5     87       0.1051
+##  6:   6  910577 0.6     57       0.1272
+##  7:   7 1035593 0.3     62       0.1141
+##  8:   8 2100494 0.1     98       0.0594
+##  9:   9  983499 0.2     60       0.1012
+## 10:  10  834714 0.5     57       0.1298
+## 11:  11  107334 0.5     57       0.1298
+## 12:  12  163748 0.4     55       0.1295
+## 13:  13 7228581 0.6    270       0.0584
+## 14:  14  401548 0.2     41       0.1224
+## 15:  15  235081 0.3     48       0.1296

      That’s it. A simple package to do simple calculations.

      ]]>      Sebastian Daza      Functions for sample size and error2014-01-19T00:00:00+00:002014-01-19T00:00:00+00:00https://sdaza.com/blog/2014/samplesizeHere I show two functions in R to define sample sizes and errors of a proportion, taking into account design effect, response rate, finite population correction, and stratification. They are useful when one needs to do these calculations quickly.

      Note: I created a package with similar functions. See here.

      The inputs are:

      • n = sample size
      • e = sampling error
      • deff = design effect, by default 1 (SRS)
      • rr = response rate, by default 1
      • N = population size, by default NULL (infinite population)
      • cl = confidence level , by default .95
      • p = proportion, by default 0.5 (maximum variance of a proportion)
      • relative = to estimate relative error, by default FALSE

      first, load the functions

      library(devtools); source_gist("7896840")

      serr: sampling error

      An example for n = 400 and all inputs at their default values:

      serr(400)
      ## [1] 0.049

      The output is rounded to 4 decimals. A more complete example:

      • n = 400
      • deff = 1.5
      • response rate = 80%
      • population size = 1000
      serr(n=400, deff=1.5, rr=.8, N=1000)
      ## [1] 0.0595

      The sample size (n) has always to be lower than the population (N). It is important to note that the final sample size used to compute the sampling error is:

      \[n = \frac{N}{deff} * rr\] 
      serr(n=400, N=350)
      ## Error: n is bigger than N

      ssize: sample size

      Let’s get a sample size with an error of .03, a population of 1000 elements, a response rate of 0.80, and an effect design of 1.2:

      ssize(e=.03, deff=1.2, rr=.8, N=1000)
      ## [1] 775

      If the the sample size is bigger than the population because of low response rates or big design effects, the sample size will be fixed to N:

      ssize(e=.03, deff=5, rr=.6, N=1000)
      ## n is bigger than N in some rows: n = N
      ## [1] 1000

      Working with strata

      Finally, we can estimate different sample sizes by strata using vectors or a data frame:

      # example sampling frame (4 strata)
+frame <- data.frame(
+	strata = 1:4,
+	N =c(10000, 5000, 2000, 1000),
+	deff =c(1.1, 1, 1.3, .8),
+	rr = c(.8, .9, .85,.8),
+	p = c(.3, .6, .1, .2))
      ##   strata     N deff   rr   p
+## 1      1 10000  1.1 0.80 0.3
+## 2      2  5000  1.0 0.90 0.6
+## 3      3  2000  1.3 0.85 0.1
+## 4      4  1000  0.8 0.80 0.2
      frame$n1 <- ssize(e=.02, deff=frame$deff, rr=frame$rr, N=frame$N, p=frame$p)
+frame$e1 <- serr(n=frame$n1, deff=frame$deff, rr=frame$rr, N=frame$N, p=frame$p)
      ##   strata     N deff   rr   p   n1   e1
+## 1      1 10000  1.1 0.80 0.3 2308 0.02
+## 2      2  5000  1.0 0.90 0.6 1753 0.02
+## 3      3  2000  1.3 0.85 0.1  923 0.02
+## 4      4  1000  0.8 0.80 0.2  606 0.02

      As easy as falling off a log!

      ]]>      Sebastian Daza      Cohort component projection2013-07-02T00:00:00+00:002013-07-02T00:00:00+00:00https://sdaza.com/blog/2013/projectionsI present an example of a cohort component projection using a closed female population (Sweden 1993), taken from Preston et al.’s book (Demography 2001, page 125). I use R and basic matrix algebra to replicate their results. The advantage of this procedure is that allows to compute easily the intrinsic growth rate and age-proportionate distribution of the stable equivalent population. All we need is the population by age at time 0 (from a census), survivorship ratios (from a life table), and age-specific fertility rates.

      The data:

      dat <- read.csv("sweden1993.csv", sep = ",", header = T)
+attach(dat)
      ##    age     Nf     Lf      f
+## 1    0 293395 497487     NA
+## 2    5 248369 497138     NA
+## 3   10 240012 496901     NA
+## 4   15 261346 496531 0.0120
+## 5   20 285209 495902 0.0908
+## 6   25 314388 495168 0.1499
+## 7   30 281290 494213 0.1125
+## 8   35 286923 492760 0.0441
+## 9   40 304108 490447 0.0074
+## 10  45 324946 486613 0.0003
+## 11  50 247613 480665     NA
+## 12  55 211351 471786     NA
+## 13  60 215140 457852     NA
+## 14  65 221764 436153     NA
+## 15  70 223506 402775     NA
+## 16  75 183654 350358     NA
+## 17  80 141990 271512     NA
+## 18  85 112424 291707     NA

      As can be seen, the data have five-year-interval age groups, so each projection forward will involve 5 years. The steps are very simple:

      1. Project forward the population of each age group (estimation of people alive)
      2. Calculate the number of births of each age group based on fertility rates, adjusting by mortality (estimation of children alive)
      3. Create a Leslie matrix, and then multiple it by the population vector (population by age at time 0)

      Survivorship ratios

      We have to estimate life table survival ratios, that is, proportions of birth cohorts surviving from one age interval to the next in a stationary population. Basically, we are summarizing the mortality experience of different cohorts assuming stationarity. Because census statistics refer to age “last birthday” (rather than exact age), I estimate ratios using $L_x$ (average number of survivors in an age interval) instead of $l_x$.

      \[S_x = \frac{_5L_x}{_5L_{x-5}}\]

      I compute the survival ratios using a loop in R. The estimation of the open-ended survival ratio is slightly different but still straightforward:

      \[\frac{T_{85}}{T_{80}}\] 
      Sf <- NA
+for (i in 1:(length(Lf) - 1)) {
+    Sf[i] <- Lf[i + 1]/Lf[i]
+}
+
+# open-ended survival ratio
+Sf[length(Sf)] <- Lf[18]/(Lf[17] + Lf[18])
      ##  [1] 0.999 1.000 0.999 0.999 0.999 0.998 0.997 0.995 0.992 0.988 0.982 0.970 0.953 0.923 0.870 0.775 0.518

      Number of children

      This is the tricky part. Because census statistics refer to age “last birthday”, and we are projecting every 5 years, the estimation of the number of person-years lived by women in each age group consists of the average number of women alive at the beginning and end of the period (assuming a linear change over the period). To take advantage of the Leslie matrix, I define the births in R using a loop as follows:

      Bf <- rep(0, 18)
+for (i in 1:length(Lf)) {
+    Bf[i] <- 1/(1 + 1.05) * Lf[1]/(100000 * 2) * sum(f[i + 1] * Sf[i], f[i], na.rm = TRUE)
+}
      ##  [1] 0.000000 0.000000 0.014550 0.124596 0.291792 0.318128 0.189858 0.062447 0.009340 0.000364 0.000000 0.000000
+## [13] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

      1/(1+1.05) corresponds to a transformation of age-specific fertility rates (son and daughters) to maternity rates (only daughters), assuming that the ratio of male to female births (SBR) is constant across mothers’ ages. The number of births is also adjusted by the corresponding survival ratio from 0 to 5 years old ($\frac{_5L_0}{5 \times l_0}$), the number 5 goes away due to simplifying).

      Leslie matrix

      I construct a Leslie matrix by replacing specific cells of a 18 x 18 matrix (18 age groups) by the vectors defined above (survival ratios and maternity rates):

      m <- matrix(0, 18, 18)
+m[1, ] <- Bf
+s <- diag(17) * Sf
+m[2:18, 1:17] <- s
+m[18, 18] <- Sf[17]

      Here we have the Leslie matrix:

      ##        [,1] [,2]   [,3]  [,4]  [,5]  [,6]  [,7]   [,8]    [,9]    [,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18]
+##  [1,] 0.000    0 0.0145 0.125 0.292 0.318 0.190 0.0624 0.00934 0.000364 0.000  0.00 0.000 0.000  0.00 0.000 0.000 0.000
+##  [2,] 0.999    0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000  0.00 0.000 0.000  0.00 0.000 0.000 0.000
+##  [3,] 0.000    1 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000  0.00 0.000 0.000  0.00 0.000 0.000 0.000
+##  [4,] 0.000    0 0.9993 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000  0.00 0.000 0.000  0.00 0.000 0.000 0.000
+##  [5,] 0.000    0 0.0000 0.999 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000  0.00 0.000 0.000  0.00 0.000 0.000 0.000
+##  [6,] 0.000    0 0.0000 0.000 0.999 0.000 0.000 0.0000 0.00000 0.000000 0.000  0.00 0.000 0.000  0.00 0.000 0.000 0.000
+##  [7,] 0.000    0 0.0000 0.000 0.000 0.998 0.000 0.0000 0.00000 0.000000 0.000  0.00 0.000 0.000  0.00 0.000 0.000 0.000
+##  [8,] 0.000    0 0.0000 0.000 0.000 0.000 0.997 0.0000 0.00000 0.000000 0.000  0.00 0.000 0.000  0.00 0.000 0.000 0.000
+##  [9,] 0.000    0 0.0000 0.000 0.000 0.000 0.000 0.9953 0.00000 0.000000 0.000  0.00 0.000 0.000  0.00 0.000 0.000 0.000
+## [10,] 0.000    0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.99218 0.000000 0.000  0.00 0.000 0.000  0.00 0.000 0.000 0.000
+## [11,] 0.000    0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.987777 0.000  0.00 0.000 0.000  0.00 0.000 0.000 0.000
+## [12,] 0.000    0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.982  0.00 0.000 0.000  0.00 0.000 0.000 0.000
+## [13,] 0.000    0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000  0.97 0.000 0.000  0.00 0.000 0.000 0.000
+## [14,] 0.000    0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000  0.00 0.953 0.000  0.00 0.000 0.000 0.000
+## [15,] 0.000    0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000  0.00 0.000 0.923  0.00 0.000 0.000 0.000
+## [16,] 0.000    0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000  0.00 0.000 0.000  0.87 0.000 0.000 0.000
+## [17,] 0.000    0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000  0.00 0.000 0.000  0.00 0.775 0.000 0.000
+## [18,] 0.000    0 0.0000 0.000 0.000 0.000 0.000 0.0000 0.00000 0.000000 0.000  0.00 0.000 0.000  0.00 0.000 0.518 0.518

      Note that the last survival ratio is repeated in the last column (0.518). This is because the estimation of the open-ended survival ratio is:

      \[(N_{80} + N_{85}) \times \frac{T_{85}}{T_{80}}\]

      Now, let’s do some projections

      Using the R multiplication operator for matrices, I do a 5-year projection by simply multiplying the Leslie matrix by the population vector (remember that matrix multiplication is not commutative).

      m %*% Nf
      ##         [,1]
+##  [1,] 293574
+##  [2,] 293189
+##  [3,] 248251
+##  [4,] 239833
+##  [5,] 261015
+##  [6,] 284787
+##  [7,] 313782
+##  [8,] 280463
+##  [9,] 285576
+## [10,] 301731
+## [11,] 320974
+## [12,] 243039
+## [13,] 205109
+## [14,] 204944
+## [15,] 204793
+## [16,] 194419
+## [17,] 142324
+## [18,] 131768

      I obtain the same results of the book. Raising this multiplication I can get the projected population of subsequent periods. Because R doesn’t have a power operator for matrices, I define a function called mp to raise matrices (it is not very efficient, but for this example it’s still useful).

      mp <- function(mat, pow) {
+    ans <- mat
+    for (i in 1:(pow - 1)) {
+        ans <- mat %*% ans
+    }
+    return(ans)
+}

      Let’s project the initial population for two periods (10 years):

      (mp(m, 2) %*% Nf)
      ##         [,1]
+##  [1,] 280121
+##  [2,] 293368
+##  [3,] 293049
+##  [4,] 248066
+##  [5,] 239529
+##  [6,] 260629
+##  [7,] 284238
+##  [8,] 312859
+##  [9,] 279147
+## [10,] 283344
+## [11,] 298043
+## [12,] 315045
+## [13,] 235861
+## [14,] 195388
+## [15,] 189260
+## [16,] 178141
+## [17,] 150666
+## [18,] 141960

      Again, I get the same result of the book. The nice thing of all this is that estimating eigenvalues and eigenvectors, I can obtain the intrinsic growth rate and age-distribution of the “stable equivalent” population. Using the eigen function in R, I can identify the dominant eigenvalue (higher absolute number), and the corresponding eigenvector:

      e <- eigen(m)
+
+# intrinsic growth rate
+(max(abs(e$values)) - 1)/5  # 5-year-projection
      ## [1] 0.000223
      # intrinsic proportionate age distribution
+as.numeric(e$vector[, 1]/sum(e$vector[, 1]))
      ##  [1] 0.0619 0.0618 0.0617 0.0616 0.0614 0.0613 0.0611 0.0608 0.0605 0.0600 0.0592 0.0580 0.0562 0.0535 0.0494 0.0429
+## [17] 0.0332 0.0356

      The population is growing but little.

      What about the population momentum?

      The population momentum corresponds to the growth of a population after imposing replacement fertility conditions, that is, NRR=1. Thus, the first thing we have to do is to estimate NRR.

      # calculating NRR
+(NRR <- sum(f * Lf/100000 * (1/(1 + 1.05)), na.rm = TRUE))
      ## [1] 1.01

      We can quickly estimate the intrinsic growth rate using NRR:

      # quick estimation of the intrinsic growth rate
+log(NRR)/27
      ## [1] 0.000237

      Very close to our estimation using cohort component projection. To impose the replacement condition, I just have to divide the first row of the Leslie matrix by NRR.

      m[1, ] <- m[1, ]/NRR

      To get the population momentum we have to project the initial population until the growth is zero (here I raised the matrix 100 times), and then to compute the ratio between the initial population and the non-growing population (stationary).

      # population momentum
+sum(mp(m, 100) %*% Nf)/sum(Nf)
      ## [1] 1.01

      After imposing the replacement condition, the population grew 1%.

      ]]>      Sebastian Daza       \ No newline at end of file diff --git a/index.html b/index.html new file mode 100644 index 00000000..07f225ad --- /dev/null +++ b/index.html @@ -0,0 +1 @@ + Sebastian Daza

      Sebastian Daza

      Demography, Sociology, Simulation, Data Science


      me.jpg

      Lost somewhere in Santiago, Chile

      I’m a data scientist, demographer, and sociologist working on industry and academic projects.

      I received my Ph.D. in sociology from the University of Wisconsin-Madison and my Bachelor’s and Master’s in Sociology from the Catholic University of Chile.

      My academic work focuses on health disparities, criminal justice system’s consequences for health and social integration, and the link between health inequalities and genetics. My research relies on statistical and computational methods, emphasizing data science and agent-based modeling (ABM).

      Currently, I am working as a Senior Data Scientist. I am also an external faculty in the Master in Computational Social Science UC3M (Fall 2022-2023), and collaborate with the Center for the Study of Justice and Society of the Catholic University of Chile.

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
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      Announcement_1

      October 22, 2015

      2015

      A simple inline announcement.

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
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      ECHO

      Early conditions, delayed adult effects and morbidity, disability and mortality in modern human populations

      I’m joinig the Institute of Economics, Geography and Demography (IEGD) of the Spanish National Research Council (CSIC) to work as part of the team of the Advanced Grant Early conditions, delayed adult effects and morbidity, disability and mortality in modern human populations (ECHO). This is an ambitious five-year project with a budget of more than EUR 2,800,000 aims to reformulate and generalize standard theories of human health and mortality. It proposes new formal models and a systematic agenda to empirically test hypotheses that link early biological growth and development, epigenetic changes across the life course, and adult human illness, disability and mortality.

      We seek to break new ground developing innovative formal models for illnesses and mortality, testing new hypotheses about the evolution of human health and, to the extent permitted by findings, reformulating standard theories to make them applicable to a less restrictive segment of populations than they are now.

      Over the past two decades there has been massive growth of research on the nature of delayed adult effects of conditions experienced in early life. This field of research is known as the Developmental Origins of Adult Health and Disease (DOHaD). Increasing evidence suggests that the mechanisms that are implicated are epigenetic and constitute an evolved adaptation selected over thousands of years to improve fitness in changing landscapes.

      The emergence of DOHaD is as close as we will ever come to a paradigmatic shift in the study of human health, disability and mortality. The most tantalizing possibility is that advances in our understanding of epigenetic mechanisms will shed light on pathways linking early exposures and delayed adult health thus fundamentally transforming our understanding of human illnesses and, in one fell swoop, bridge population health, epigenetics, and developmental and evolutionary biology.

      The overarching goal of this project is to contribute to this nascent area of study by:

      • Proposing new formal demographic models of health, disability and mortality; (b) empirically testing DOHaD predictions with population data.
      • Designing and implementing a microsimulation model with modules that represent epigenetic changes and exposures over the life course as a means to verify DOHaD predictions about two conditions, obesity and Type 2 Diabetes.
      • Asessing the adult health, disability and mortality toll implicated by relations between early conditions, obesity and T2D.

      The project will use a variety of global databases such as the Human Mortality Database, the Latin American Mortality Database or the Longitudinal Population Database of Andalusia, and has received formal support from the Spanish National Statistics Institute and the Andalusian Institute of Statistics and Cartography.

      More info here.

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
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      Women reentry

      Social integration and inclusion after being in prison

      with Pilar Larroulet and Catalina Droppelmann

      The re-entry study is an intensive longitudinal project that follows more than 200 women released from prison in Santiago, Chile. It is funded by the San Carlos de Maipo and Colunga Foundation, and the Inter-American Development Bank. We used interviews over a period of one year to know their experiences on family life, housing, employment, social capital and health. Data collection finished in 2018. The final report is available here, for more publications see below.





      Publications

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
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      Health and Income Mobility

      How income mobility contributes to health disparities

      with Alberto Palloni

      Recent research on U.S. income mobility and health using community and individual data shows that higher mobility is associated with lower mortality risks. That relationship seems to be stronger and more consistent than the relationship between income inequality and health (a topic widely studied), although considerably smaller than the impact of income on mortality. This preliminary evidence suggests that income mobility might be a relevant determinant of health and mortality. Surprisingly, this potential pathway has received little attention in the literature.

      My dissertation builds on that small literature and examines the robustness of the relationship between income mobility and health at the aggregate and individual level. By using different data sources and modeling approaches, I describe the magnitude and variability of this association in the U.S., and explore the plausibility and consistency of explanations offered in the literature. The central argument is that the effect of income mobility on health is stronger and larger than the impact of income inequality and that the mechanisms behind it, although related to income inequality, are theoretically distinct and independent of those of income and inequality, and can have powerful and lasting consequences.



      Agents moving across counties / neighborhoods based on the income of their residents.



      To ground this argument, I use two strategies. First, I analyze aggregate and individual data to assess the magnitude, robustness, and variability of the effects of income mobility, and empirically examine whether some of the potential pathways and mechanisms proposed in the literature are supported by the data. Secondly, building on this evidence, I use a theoretical model to assess the conditions and plausibility of the potential mechanisms involved in the association between income mobility and health. By formulating a generative model, where I precisely define (represent) a set of mechanisms (causal relationships) likely to bring about the observed patterns, I am able to assess the internal consistency of the theory and evaluate its generative sufficiency.

      More info here.

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
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      Health and incarceration

      Assessing the connection between mass incarceration and health

      with Alberto Palloni and Jerrett Jones

      In light of the expansion of the criminal justice system, researchers have become increasingly interested in the social consequences of incarceration. A line of this research suggests that incarceration has negative implications for individuals’ health and well-being at older ages. However, prior studies are limited in that they have not adequately followed former prisoners over a sufficient time period to determine whether incarceration significantly increases mortality.

      This project contributes to this growing literature by employing the Panel Study of Income Dynamics (PSID), a long longitudinal data resource, to examine the consequences of incarceration for health and mortality. First, we estimate the effect of short and long-term effects of incarceration on mortality and disability over a period of nearly 40 years. Second, we use those estimates and different counterfactual scenarios to assess to what extent mass incarceration contributes to the so called U.S. health disadvantage. This project is funded by the The Panel Study of Income Dynamics (PSID), with support from the National Institute on Aging.



      Cumulative probability of dying by years since being observed in the panel and incarceration status.



      Thanks to an extension of this grant, we are also examining how contextual characteristics influence health and well-being over the life course. For this, we plan to expand the PSID data by merging them with incarceration information from the National Corrections Reporting Program (NCRP 2000-2014) at the county level, in addition to other crime and arrests data from Uniform Crime Reports (UCR), and socioeconomic information from the American Community Survey (e.g., employment, poverty, health, inequality).

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
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      Lambda

      Latin American Mortality Database

      with Alberto Palloni and Hiram Beltran-Sanchez

      The Latin American Mortality Database (LAMBdA) was originally created to support the empirical study of the history of mortality trends in Latin American countries after independence. It now supports the study of very recent mortality trends and is particularly suited for the study of old age mortality during the post-WWII period.



      Model predicting life expectancy.



      The database covers the interval between 1848 and 2014, it includes population censuses, age-specific (five year and single year age groups) total death counts (starting in 1900), and by causes of deaths (starting in 1945). It contains over 170 years of data including adjusted life tables (about 500 life tables). In this project, we assess estimate and model uncertainty of the association between life expectancy and economic progress using Bayesian models and stacking. More info here.

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
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      ABM and Siena

      Assesing complex statistical models using agent-based modeling

      with Kurt Kreuger

      Although Agent-based models (ABM) have been increasingly accepted in social sciences as a valid tool to formalize theory, propose mechanisms able to recreate regularities, and guide empirical research, we are not aware of any research using ABMs to assess the robustness of our statistical methods. We argue that ABMs can be extremely helpful to assess models when the phenomena under study is complex.

      Action chart of network formation, selection and influence.



      As an example, we create an agent-based model (ABM) to evaluate Stochastic Actor-Oriented Model (SIENA) estimation of selection and influence effects. The SIENA Model, proposed by Tom A. B. Snijders and colleagues, is a prominent network analysis method that has gained popularity during the last ten years and been applied to estimate selection and influence for a broad range of behaviors and traits such as substance use, delinquency, violence, health, and educational attainment. However, we know little about the conditions for which this method is reliable or the particular biases it might have.

      The results from our analysis show that selection and influence are estimated by SIENA asymmetrically, and that with very simple assumptions, we can generate data where selection estimates are highly sensitive to mis-specification, suggesting caution when interpreting SIENA analyses.

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
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      projects

      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
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      publications

      journal articles

      1. SSR
        From Prison to Work: Job Patterns for Women in Chile
        Pilar Larroulet, Sebastian Daza, and 1 more author
        Social Science Research 2023
        Abs Bib HTML Preprint Code
      2. JGSS
        Early Exposure to County Income Mobility and Adult Individual Health in the United States
        Sebastian Daza, and Alberto Palloni
        The Journals of Gerontology: Series B 2022
        Abs Bib HTML Preprint Code
      3. Demography
        The Consequences of Incarceration for Mortality in the United States
        Sebastian Daza, Alberto Palloni, and 1 more author
        Demography 2020
        Abs Bib HTML Preprint Code
      4. ICJSD
        Who Is Transitioning out of Prison? Characterising Female Offenders and Their Needs in Chile
        Pilar Larroulet, Catalina Droppelmann, and 4 more authors
        International Journal of Crime, Justice and Social Democracy 2020
        Abs Bib HTML PDF
      5. JAMA Int Med
        Association of Social Mobility with the Income-Based Longevity Gap in the United States. A Cross-Sectional, County-Level Study
        Atheendar S. Venkataramani, Sebastian Daza, and 1 more author
        JAMA Internal Medicine 2020
        Abs Bib HTML PDF Code
      6. SMR
        Agent-Based Models for Assessing Complex Statistical Models: An Example Evaluating Selection and Social Influence Estimates from SIENA
        Sebastian Daza, and L. Kurt Kreuger
        Sociological Methods and Research 2019
        Abs Bib HTML Preprint Code

      working papers

      1. Distinguishing between Interaction and Dispersion Effects in GxE Analysis: A Review of Strategies
        Sebastian Daza, and Alberto Palloni
        Working paper 2022 (Under review)
        Abs Bib Preprint Code
      2. Consequences of Heritability, Assortative Mating and Fertility Differentials for the Obesity Pandemic
        Sebastian Daza, Alberto Palloni, and 2 more authors
        Working paper 2022
        Abs Bib Code
      3. Exploring the Link between Place’s Income Mobility and Mortality Using an Agent-Based Model
        Sebastian Daza, and Alberto Palloni
        Working paper 2021
        Abs Code

      books & conferences

      1. Early Childhood Origins of Modern Social Class Health Disparities
        Alberto Palloni, Daniel Ramirez, and 1 more author
        In: Handbook of Health Inequalities Across the Life Course 2023
        Bib HTML PDF
      2. Income Inequality, Social Mobility and Mortality in the US
        Sebastian Daza, and Alberto Palloni
        Presented in Population Association of America (PAA) 2018
        Abs Bib Preprint Code
      3. Consequences of Childbearing in Delinquency and Substance Use
        Sebastian Daza, and Jason Fletcher
        Presented in Population Association of America (PAA) 2016
        Abs Bib PDF
      4. ISUC
        The Impact of Age, Egotropic Vote, and Political Divisions in the 2009-2010 Chilean Election
        Nicolás Somma, and Sebastian Daza
        Cuadernos ISUC 2016
        Abs Bib PDF
      5. Intergenerational Transfers in the U.S., Economic Inequality, and Social Stratification
        Alberto Palloni, and Sebastian Daza
        Presented in Population Association of America (PAA) 2014
        Abs Bib PDF

      technical reports

      1. Reinserción, Desistimiento y Reincidencia En Mujeres Privadas de Libertad En Chile
        Pilar Larroulet, Sebastián Daza, and 3 more authors
        Estudio Reinserciόn, Desistimiento y Reincidencia en Mujeres 2021
        Bib PDF
      2. Integración Social
        Sebastian Daza, Pilar Larroulet, and 3 more authors
        Estudio Reinserciόn, Desistimiento y Reincidencia en Mujeres 2019
        Bib PDF Code
      3. Diseño, Metodología y Caracterizaciόn de La Muestra
        Pilar Larroulet, Sebastián Daza, and 4 more authors
        Estudio Reinserciόn, Desistimiento y Reincidencia en Mujeres 2019
        Bib PDF
      4. Evaluación de Sesgo En Línea de Base
        Sebastian Daza, and Pilar Larroulet
        Estudio Reinserciόn, Desistimiento y Reincidencia en Mujeres 2018
        Bib PDF Code
      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
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      repos

      GitHub users

      sdaza sdaza

      GitHub Repositories

      sdaza/acsr sdaza/acsr
      sdaza/zotnote sdaza/zotnote
      sdaza/sampler sdaza/sampler
      sdaza/survey-methods sdaza/survey-methods
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      teaching

      Courses

      Mentorship

      • Ulda Figueroa. Ph.D. candidate in Sociology. Catholic University in Chile. Dissertation: Selectividad y control del delito en el sistema de justicia criminal: El funcionamiento de las salidas alternativas al proceso penal y sus consecuencias en la reiteración de delitos.
      © Copyright 2024 Sebastian Daza. Powered by Jekyll with al-folio theme. Hosted by GitHub Pages. Last updated: November 28, 2024.
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