diff --git a/.Rbuildignore b/.Rbuildignore index 8d5c8d8..5a362d1 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -5,3 +5,7 @@ cran-comments.md ^Meta$ ^CRAN-RELEASE$ ^\.github$ +^_pkgdown\.yml$ +^docs$ +^pkgdown$ +^vignettes \ No newline at end of file diff --git a/DESCRIPTION b/DESCRIPTION index a4bba12..d04e625 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -15,6 +15,7 @@ Imports: rlang Suggests: testthat, + bookdown, parallel, pbapply, knitr, @@ -23,7 +24,7 @@ Suggests: VignetteBuilder: knitr Type: Package Title: Distance Sampling Simulations -Version: 1.0.4 +Version: 1.0.4.9000 Authors@R: c( person("Laura", "Marshall", email = "lhm@st-and.ac.uk", role = c("aut", "cre")), person("Thomas", "Len", email = "len.thomas@st-andrews.ac.uk", role = "ctb")) @@ -48,7 +49,7 @@ Language: en-GB URL: https://github.com/DistanceDevelopment/dsims BugReports: https://github.com/DistanceDevelopment/dsims/issues Encoding: UTF-8 -RoxygenNote: 7.2.3 +RoxygenNote: 7.3.2 Collate: 'AICc.R' 'generic.functions.R' diff --git a/NEWS b/NEWS deleted file mode 100644 index 3ffe293..0000000 --- a/NEWS +++ /dev/null @@ -1,122 +0,0 @@ -dsims 1.0.4 -------------------- - -Bug Fixes - - * Fixed a bug when generating the simulation summary which meant that only the first value of mean.k and n.miss.dists was repeated rather than including all values in the summary tables. Issue #84 - * The make.simulation function now throws an error if the P2 ER variance estimator is used with line transects designs (rather than after the simulation has completed). Issue #61 - -dsims 1.0.3 -------------------- - -Bug Fixes - - * Simulations were crashing if there were zero detections - now fixed and warnings displayed instead. Issue #77 - * Errors were also occuring when there were no individuals generated in a stratum, now fixed. Issue #80 - * Detections are no longer permitted across stratum boundaries - this was causing errors due to NA area values in the data. This is inline with expected protocols on surveys. Issue #81 - * Remove dependence on sp and rgeos. Issue #42 - -dsims 1.0.2 -------------------- - -Bug Fixes - - * Fixed transparency issue with detection distance histograms when saving to wmf (generated a warning in Distance for Windows) - * Only print summary table for individuals if animals occur as individuals (and not as clusters) - * Updated references to examples - * Fixed grouped strata bugs - * Can now read transect shapefiles in from file and will convert all to one strata if global region used. This allows regional simulations from stratified designs in distance for windows. - - -dsims 1.0.1 -------------------- - -New Features - - * Added save.sim.results function so that simulation results can be written to .txt files. This is mainly useful for Distance for Windows users as R users would probably prefer to just save the whole simulation object to file. - * Can write the simulation progress to file - this allows the simulation progress to be displayed when simulations are being run from Distance for Windows using dsims. - * Add segmented trackline design as an option in simulation summary (currently these design can only be generated inside Distance for Windows for use in simulations). - -Bug Fixes - - * Partial fix to the bug relating to grouping strata at the analysis stage. Strata grouping should now work when detections are of individuals. Still needs to be fixed for when clusters are present. - -dsims 1.0.0 -------------------- - -New Features - - * Reading transects from file - this functionality is primarily envisionged for use from within Distance for Windows. - -Enhancements - - * New routine which will generate covariate values from a zero-truncated Poisson distribution for non integer values. - * There is now no lower limit on the number of detections in simulations as this was introducing bias. There is now a warning system in place. Very low numbers of detections may cause issues fitting. There must be more detections than there are parameters in the model for the model to have a chance of fitting successfully. Note that distance sampling good practice recommends minimum of 60-80 detections for estimating the detection function for line transects and more for points. - * Improved histogram.N.ests function will now plot either a histogram of estimates of individuals or clusters. It also provides the use.max.reps argument so that the plot can be consistent with the option selected for the simulation summary. - -Bug Fixes - - * Fixed simulations where cluster size was included - there was a formatting change in mrds output tables. - * Added a check for repeat model definitions. - * Add code to deal with equal model criteria values. - * Fixed bug when no simulation repetitions had been successful - * AICc method fixed - * Warning indexes from parallel runs are now fixed - -dsims 0.2.2 / 0.2.3 -------------------- - -Bug Fixes - - * Minor modifications to stay CRAN compliant. - -dsims 0.2.1 ----------- - -New Features - - * Now interfaces with new syntax in Distance >= 1.0.5 (it will remain backwards compatible with older versions of Distance for this release) - -Bug Fixes - - * Plus sampling simulations now issue a warning and modify to minus sampling - these should not have run in previous versions. - * Fixed default simulation truncation distance to 50 in the analyses (will fix dssd to be consistent with this in release 0.3.2) - * Fixed the recording of warning / error indexing in parallel simulations - -dsims 0.2.0 ----------- - -New Features - - * Delta selection criteria is now recorded as the difference in informtion criteria between the top 2 best fitting models as determined by the information criteria.] - * The iteration numbers generating warnings or errors are now stored and displayed so user can choose what to do with these results. - -Bug Fixes - - * Fixed missing RMSE values - * Fix strata re-ordering for cluster size - * Models with -Inf information criteria no longer selected - * Models with dht = NULL are no longer selected - * Models which predict detection values < 1 no longer cause errors and are correctly excluded. - * Detectibility parameters for continuous covariates are now checked and validated. - * Fix situation where all reps are to be excluded due to problematic model fitting. - * There was a bug in the underlying code on windows machines that meant that segmented lines were not being clipped properly. The dependencies of sf have not been updated and the issue fixed. Please update sf if you run into missing segment transects. - -dsims 0.1.0 ----------- - -Enhancements - - * Introducing the new Distance Sampling Simulation package. dsims is our latest simulation package which interfaces with dssd so designs can be generated within R, thus making the simulation process a lot easier. Dsims also makes use of ggplot to produce cleaner looking graphics. - * Region and Design: dsims can make use of the region creation and all the designs currently in dssd. - * Density: dsims can generate density objects from constant values for each strata, from fitted mgcv gam objects with x and y as explantory covariates and from formulas of x and y. - * Density: Density grids are stored as sf polygons with their associated x, y central coordinates and density value - * Population Description: Populations can either be created with fixed population sizes or based on the densities in the density grid. - * Population Description: Both discrete and continuous individual level covariates can be included in the population - * Detectablity: The detectability of the population can be described by either half normal, hazard rate or uniform detection shapes. Parameters can vary by stratum - * Detectablity: Covariate parameters can be included to modify the scale parameter for each individual based on their covariate values. - * Analyses:A number of detection function analyses can be incorporated in a simulation and the model with the lowest criterion (AIC / AICc / BIC) will be selected. - * Analyses:Defining analyses is based on the arguments which are passed to our Distance R library. - * Simulations: Simulations can be run in serial or parallel and their progress is output. - * Simulations: The function run.survey can be used to create a single instance of a survey and check the simulation setup. - diff --git a/NEWS.md b/NEWS.md new file mode 100644 index 0000000..ca3410b --- /dev/null +++ b/NEWS.md @@ -0,0 +1,119 @@ +# dsims 1.0.5 + +Bug Fixes + +* Fixed bug which generated NA's as scale parameters when factor covariates were included. Issue #89 + +# dsims 1.0.4 + +Bug Fixes + +* Fixed a bug when generating the simulation summary which meant that only the first value of mean.k and n.miss.dists was repeated rather than including all values in the summary tables. Issue #84 +* The make.simulation function now throws an error if the P2 ER variance estimator is used with line transects designs (rather than after the simulation has completed). Issue #61 + +# dsims 1.0.3 + +Bug Fixes + +* Simulations were crashing if there were zero detections - now fixed and warnings displayed instead. Issue #77 +* Errors were also occurring when there were no individuals generated in a stratum, now fixed. Issue #80 +* Detections are no longer permitted across stratum boundaries - this was causing errors due to NA area values in the data. This is inline with expected protocols on surveys. Issue #81 +* Remove dependence on sp and rgeos. Issue #42 + +# dsims 1.0.2 + +Bug Fixes + +* Fixed transparency issue with detection distance histograms when saving to wmf (generated a warning in Distance for Windows) +* Only print summary table for individuals if animals occur as individuals (and not as clusters) +* Updated references to examples +* Fixed grouped strata bugs +* Can now read transect shapefiles in from file and will convert all to one strata if global region used. This allows regional simulations from stratified designs in distance for windows. + + +# dsims 1.0.1 + +New Features + +* Added save.sim.results function so that simulation results can be written to .txt files. This is mainly useful for Distance for Windows users as R users would probably prefer to just save the whole simulation object to file. +* Can write the simulation progress to file - this allows the simulation progress to be displayed when simulations are being run from Distance for Windows using dsims. +* Add segmented trackline design as an option in simulation summary (currently these design can only be generated inside Distance for Windows for use in simulations). + +Bug Fixes + +* Partial fix to the bug relating to grouping strata at the analysis stage. Strata grouping should now work when detections are of individuals. Still needs to be fixed for when clusters are present. + +# dsims 1.0.0 + +New Features + +* Reading transects from file - this functionality is primarily envisioned for use from within Distance for Windows. + +Enhancements + +* New routine which will generate covariate values from a zero-truncated Poisson distribution for non integer values. +* There is now no lower limit on the number of detections in simulations as this was introducing bias. There is now a warning system in place. Very low numbers of detections may cause issues fitting. There must be more detections than there are parameters in the model for the model to have a chance of fitting successfully. Note that distance sampling good practice recommends minimum of 60-80 detections for estimating the detection function for line transects and more for points. +* Improved histogram.N.ests function will now plot either a histogram of estimates of individuals or clusters. It also provides the use.max.reps argument so that the plot can be consistent with the option selected for the simulation summary. + +Bug Fixes + +* Fixed simulations where cluster size was included - there was a formatting change in mrds output tables. +* Added a check for repeat model definitions. +* Add code to deal with equal model criteria values. +* Fixed bug when no simulation repetitions had been successful +* AICc method fixed +* Warning indexes from parallel runs are now fixed + +# dsims 0.2.2 / 0.2.3 + +Bug Fixes + +* Minor modifications to stay CRAN compliant. + +# dsims 0.2.1 + +New Features + +* Now interfaces with new syntax in Distance >= 1.0.5 (it will remain backwards compatible with older versions of Distance for this release) + +Bug Fixes + +* Plus sampling simulations now issue a warning and modify to minus sampling - these should not have run in previous versions. +* Fixed default simulation truncation distance to 50 in the analyses (will fix dssd to be consistent with this in release 0.3.2) +* Fixed the recording of warning / error indexing in parallel simulations + +# dsims 0.2.0 + +New Features + +* Delta selection criteria is now recorded as the difference in information criteria between the top 2 best fitting models as determined by the information criteria.] +* The iteration numbers generating warnings or errors are now stored and displayed so user can choose what to do with these results. + +Bug Fixes + +* Fixed missing RMSE values +* Fix strata re-ordering for cluster size +* Models with -Inf information criteria no longer selected +* Models with dht = NULL are no longer selected +* Models which predict detection values < 1 no longer cause errors and are correctly excluded. +* Detectibility parameters for continuous covariates are now checked and validated. +* Fix situation where all reps are to be excluded due to problematic model fitting. +* There was a bug in the underlying code on windows machines that meant that segmented lines were not being clipped properly. The dependencies of sf have not been updated and the issue fixed. Please update sf if you run into missing segment transects. + +# dsims 0.1.0 + +Enhancements + +* Introducing the new Distance Sampling Simulation package. # dsims is our latest simulation package which interfaces with dssd so designs can be generated within R, thus making the simulation process a lot easier. # dsims also makes use of ggplot to produce cleaner looking graphics. +* Region and Design: # dsims can make use of the region creation and all the designs currently in dssd. +* Density: # dsims can generate density objects from constant values for each strata, from fitted mgcv gam objects with x and y as explantory covariates and from formulas of x and y. +* Density: Density grids are stored as sf polygons with their associated x, y central coordinates and density value +* Population Description: Populations can either be created with fixed population sizes or based on the densities in the density grid. +* Population Description: Both discrete and continuous individual level covariates can be included in the population +* Detectablity: The detectability of the population can be described by either half normal, hazard rate or uniform detection shapes. Parameters can vary by stratum +* Detectablity: Covariate parameters can be included to modify the scale parameter for each individual based on their covariate values. +* Analyses:A number of detection function analyses can be incorporated in a simulation and the model with the lowest criterion (AIC / AICc / BIC) will be selected. +* Analyses:Defining analyses is based on the arguments which are passed to our Distance R library. +* Simulations: Simulations can be run in serial or parallel and their progress is output. +* Simulations: The function run.survey can be used to create a single instance of a survey and check the simulation setup. + diff --git a/R/calculate.scale.param.R b/R/calculate.scale.param.R index d1ec842..d3fea0e 100755 --- a/R/calculate.scale.param.R +++ b/R/calculate.scale.param.R @@ -76,7 +76,7 @@ calculate.scale.param <- function(pop.data, detectability, region){ factor.tab <- detectability@cov.param[[index[fac]]] if(!is.null(factor.tab$strata)){ # Subset table for strata - factor.tab <- factor.tab[factor.tab$strata == strata.names[strata.ids[strat]],] + factor.tab <- factor.tab[factor.tab$strata == strata.ids[strat],] } for(level in seq(along = factor.tab$level)){ diff --git a/R/dsims-package.R b/R/dsims-package.R index ed65109..534853e 100644 --- a/R/dsims-package.R +++ b/R/dsims-package.R @@ -28,8 +28,7 @@ #' #' @name dsims-package #' @aliases dsims-package dsims -#' @docType package #' @author Laura Marshall -#' @keywords package +#' "_PACKAGE" #' NULL diff --git a/README.md b/README.md index a9f4397..445dcd6 100644 --- a/README.md +++ b/README.md @@ -10,25 +10,14 @@ Distance Sampling Simulations # Using `dsims` -There is currently one vignette within the dsims package to help you get started using `dsims`: - - GettingStarted: "Getting Started with dsims" +There is currently three vignette within the dsims package to help you get started using `dsims`: -# Getting `dsims` - -We typically aim to keep `dsims` on CRAN, so it can be readily installed from within R-Studio or the R interface. However, at present there is an issue with our package `dssd` on which `dsims` relies (see [issue in dssd](https://github.com/DistanceDevelopment/dssd/issues/94) ) that prevents this. Therefore to obtain `dsims` at present, please use the following code. - - # First, ensure you have a copy of the devtools package - if (!nzchar(system.file(package = "devtools"))) install.packages("devtools") - # then ensure you have a copy of the dssd package: - if (!nzchar(system.file(package = "dssd"))) devtools::install_github("DistanceDevelopment/dssd", build_vignettes = TRUE) - # finally install dsims from github: - devtools::install_github("DistanceDevelopment/dsims", build_vignettes = TRUE) +- GettingStarted: *Getting Started with dsims* available from the navigation bar at top of the page +- Transition from `DSsim` to `dsims`: under *Articles* on the navigation bar +- Grouped strata: Combining abundance estimates across strata constructed for design purposes; under *Articles* on the navigation bar -### Troubleshooting tip - -During installation of packages, you may get the message "These packages have more recent versions available. It is recommended to update all of them. Which would you like to update?" and then a list of packages. We recommend you typically choose the option "CRAN packages only". Note you may then get the message that some packages cannot be installed because they are already loaded. In this case, a solution may be to note which packages these are, to open an R console (rather than R Studio) and to use the `Packages | Update packages` menu option (or the `update.packages` function) to update these packages. +# Getting `dsims` - + +### Troubleshooting tip + +During installation of packages, you may get the message "These packages have more recent versions available. It is recommended to update all of them. Which would you like to update?" and then a list of packages. We recommend you typically choose the option "CRAN packages only". Note you may then get the message that some packages cannot be installed because they are already loaded. In this case, a solution may be to note which packages these are, to open an R console (rather than R Studio) and to use the `Packages | Update packages` menu option (or the `update.packages` function) to update these packages. diff --git a/_pkgdown.yml b/_pkgdown.yml new file mode 100644 index 0000000..abf1507 --- /dev/null +++ b/_pkgdown.yml @@ -0,0 +1,44 @@ +url: ~ +template: + bootstrap: 5 + bslib: + bg: "#fcfaf2" + fg: "#14059e" + primary: "#0542a3" + base_font: {google: "Roboto"} + includes: + in_header: | + + +navbar: + bg: primary + structure: + right: [twitter, github] + components: + twitter: + icon: fa-twitter + href: https://twitter.com/distancesamp + aria-label: Twitter + left: + - text: Function reference + href: reference/index.html + - text: Getting started + href: articles/GettingStarted.html + - text: Articles + menu: + - text: Transition from `DSsim` to `dsims` + href: articles/dsims-examples.html + - text: Grouped strata + href: articles/dsims_grouped_strata.html + - text: News + href: news/index.html + +footer: + structure: + right: donate + left: clarity + components: + donate: "If you wish to donate to development and maintenance, please email us." + clarity: "We improve our site and software support by using Microsoft Clarity to see
+ how you use our website. By using our site, you agree that we and Microsoft
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We improve our site and software support by using Microsoft Clarity to see
how you use our website. By using our site, you agree that we and Microsoft
can collect and use this data. Clarity is GDPR compliant.

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If you wish to donate to development and maintenance, please email us.

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Getting Started with dsims

+

L. Marshall

+
+ CREEM, Univ of St Andrews

November 2024

+ + Source: vignettes/GettingStarted.Rmd +
GettingStarted.Rmd
+
+
+ + + +
+

Distance Sampling Simulations +

+

This vignette introduces the basic procedure for setting up and running a distance sampling simulation using ‘dsims’ (Laura Marshall 2023a). The ‘dsims’ package uses the distance sampling survey design package ‘dssd’ (Laura Marshall 2023b) to define the design and generate the surveys (sets of transects). For further details on defining designs please refer to the ‘dssd’ vignettes. ‘dsims’ was designed to be largely similar to the ‘DSsim’ package (L. Marshall 2020) in terms of work flow, functions and arguments. The main differences in terms of its use lie in the definition of the designs which can now be generated in R using the ‘dssd’ package (these packages are automatically linked) and the definition of analyses. Analyses are now defined using terminology based on the ‘Distance’ package (Miller et al. 2019). In addition, the underlying functionality now makes use of the ‘sf’ package (Pebesma and Baston 2021).

+

Distance Sampling techniques provide design based estimates of density and abundance for populations. The accuracy of these estimates relies on valid survey design. While general rules of thumb can help guide our design choices, simulations emulating a specific set of survey characteristics can often help us achieve more efficient and robust designs for individual studies. For example, simulations can help us investigate how effort allocation can affect our estimates or the effects of a more efficient design which has less uniform coverage probability. Due to the individual nature of each study, each with their specific set of characteristics, simulation can be a powerful tool in evaluating survey design.

+
+
+

Setting up the Region +

+

We will use the St Andrews bay area as an example study region for these simulations. This is a single strata study region which has been projected into metres. We will first load the ‘dsims’ package, this will also automatically load the ‘dssd’ package. As this shapefile does not have a projection recorded (in an associated .prj file) we tell ‘dsims’ that the units are metres.

+ +
## Loading required package: dssd
+
+# Find the file path to the example shapefile in dssd
+shapefile.name <- system.file("extdata", "StAndrew.shp", package = "dssd")
+# Create the survey region object
+region <- make.region(region.name = "St Andrews bay",
+                      shape = shapefile.name,
+                      units = "m")
+plot(region)
+
+ +The study region.

+Figure 1: The study region. +

+
+
+
+

Defining the study population +

+

To define a study population we require a number of intermediate steps. We describe these in turn below.

+
+

Population Density Grid +

+

The first step in defining your study population is to set up the density grid. One way to do this is to first create a flat surface and then add hot and low spots to represent where you think you might have areas of higher and lower density of animals.

+

If we were to assume that there were 300 groups in the St Andrews bay study area (which is a fairly large number!) this would only give us an average density of 3.04-07 groups per square metre. For this simulation, as we will use a fixed population size, we do not need to worry about the absolute values of the density surface. Instead, it can be simpler to work with larger values and be aware that we are defining a relative density surface. So where we create a surface to have a density of twice that in another area that relationship will be maintained (be it at much smaller absolute values) when we later generate the population.

+

For the purposes of simulation you will likely want to test over a range of plausible animal distributions (if you knew exactly how many you were going to find at any given location you probably wouldn’t be doing the study!). When testing non-uniform coverage designs it is advisable to try out worst case scenarios, i.e. set density in the area of higher or lower coverage to differ from the majority of the survey region. This will give an idea of the degree of potential bias which could be introduced.

+

In this example, for the equal spaced zigzag design, as it is generated in a convex hull the areas with differing coverage are likely to be at the very top and very bottom of the survey region. In the density grid below these areas are shown to have lower animal density than the rest of the survey region, a likely scenario when a study region has been constructed in order to catch the range of a population of interest.

+
+# We first create a flat density grid
+density <- make.density(region = region,
+                        x.space = 500,
+                        constant = 1)
+
+# Now we can add some high and low points to give some spatial variability
+density <- add.hotspot(object = density,
+                       centre = c(-170000, 6255000),
+                       sigma = 8000,
+                       amplitude = 4)
+
+density <- add.hotspot(object = density,
+                       centre = c(-160000, 6275000),
+                       sigma = 6000,
+                       amplitude = 4)
+
+density <- add.hotspot(object = density,
+                       centre = c(-155000, 6260000),
+                       sigma = 3000,
+                       amplitude = 2)
+
+density <- add.hotspot(object = density,
+                       centre = c(-150000, 6240000),
+                       sigma = 10000,
+                       amplitude = -0.9)
+
+density <- add.hotspot(object = density,
+                       centre = c(-155000, 6285000),
+                       sigma = 10000,
+                       amplitude = -1)
+
+# I will choose to plot in km rather than m (scale = 0.001)
+plot(density, region, scale = 0.001)
+
+ +A density map representing a plausible distributions of animals within the study region.

+Figure 2: A density map representing a plausible distributions of animals within the study region. +

+
+

In some situations you may not need to rely on constructing a density distribution from scratch. Now we will demonstrate how to use a gam to construct the density surface. As I do not have data for this area I will use the density grid I created above as an example dataset. I will fit a gam to this data and then use this to create a new density object. As I need to restrict the predicted values to be greater than zero, I will use a log link with the Gaussian error distribution. This can also be a useful trick if you want to turn something created using the above method, which can look a bit lumpy and bumpy, into a smoother distribution surface. The gam fitted must only use a smooth over x and y to fit the model as no other predictor covariates will be present in the density surface.

+
+# First extract the data above - this is simple in this case as we only have a single strata
+# Multi-strata regions will involve combining the density grids for each strata into a 
+# single dataset.
+density.data <- density@density.surface[[1]]
+head(density.data)
+
## Simple feature collection with 6 features and 4 fields
+## Geometry type: POLYGON
+## Dimension:     XY
+## Bounding box:  xmin: -157572.4 ymin: 6241463 xmax: -154890.4 ymax: 6241543
+## CRS:           NA
+##            strata   density         x       y                       geometry
+## 34 St Andrews bay 0.6128054 -157640.4 6241293 POLYGON ((-157390.4 6241543...
+## 35 St Andrews bay 0.5614958 -157140.4 6241293 POLYGON ((-157390.4 6241543...
+## 36 St Andrews bay 0.5125986 -156640.4 6241293 POLYGON ((-156890.4 6241543...
+## 37 St Andrews bay 0.4662975 -156140.4 6241293 POLYGON ((-156390.4 6241543...
+## 38 St Andrews bay 0.4227525 -155640.4 6241293 POLYGON ((-155890.4 6241543...
+## 39 St Andrews bay 0.3821010 -155140.4 6241293 POLYGON ((-155390.4 6241543...
+
+# Fit a simple gam to the data
+library(mgcv)
+
## Loading required package: nlme
+
## This is mgcv 1.9-1. For overview type 'help("mgcv-package")'.
+
+fit.gam <- gam(density ~ s(x,y), data = density.data, family = gaussian(link="log"))
+
+# Use the gam object to create a density object
+gam.density <- make.density(region = region,
+                            x.space = 500,
+                            fitted.model = fit.gam)
+
+plot(gam.density, region, scale = 0.001)
+
+ +A density map representing a plausible distributions of animals within the study region.

+Figure 3: A density map representing a plausible distributions of animals within the study region. +

+
+
+
+

Other Population Parameters +

+

Once we have created a plausible animal density distribution we can go on to define other population parameters. We do this by constructing a population description.

+

We will assume animals occur in small clusters so we will first create a covariate list and define the distribution for cluster size (which must be named “size”) as a zero-truncated Poisson distribution with mean equal to 3. For those of you familiar with ‘DSsim’ please note the simplified format for defining population covariates.

+

The other population value we have to define is the population size. As we have clusters in our population, N will refer to the number of clusters rather than individuals. We will set the number of clusters to be 100. We then leave the fixed.N argument as the default TRUE to say we would like to generate the population based on the population size rather than the density surface.

+
+# Create a covariate list describing the distribution of cluster sizes
+covariates <- list(size = list(distribution = "ztruncpois", mean = 3))
+
+# Define the population description
+pop.desc <- make.population.description(region = region,
+                                        density = gam.density,
+                                        covariates = covariates,
+                                        N = 300,
+                                        fixed.N = TRUE)
+
+
+
+

Coverage Grid +

+

It is good practice to create a coverage grid over your study area to assess how coverage probability varies spatially across your study area for any specified designs. For designs where there may be non-uniform coverage, we advise coverage probability is assessed prior to running any simulations. However, as this step is not essential for running simulations we will omit it here and refer you to the ‘dssd’ vignettes for further details.

+
+
+

Defining the Design +

+

‘dsims’ working together with ‘dssd’ provides a number of point and line transect designs. Further details on defining designs can be found in the ‘dssd’ help and vignettes. We also provide examples online at https://distancedevelopment.github.io/distancesamplingcom2/resources/vignettes.html .

+

For these simulations we will compare two line transect designs, systematically spaced parallel lines and equal spaced zigzag lines. The zigzag design will be generated within a convex hull to try to minimise the off-effort transit time between the ends of transects.

+

The design angles for each design were selected so that the transects run roughly perpendicular to the coast. The way the two designs are defined means that this is 90 degrees for the parallel line design and 0 for the zigzag design. Both designs assumed a minus sampling protocol and the truncation distance was set at 750m from the transect. The spacings for each design were selected to give the same trackline lengths of around 450 km (this was assessed by running the coverage simulations for these designs using ‘run.coverage’, see help in ‘dssd’). The trackline lengths can be thought of as an indicator of the cost of the survey as they give the total travel time (both on and off effort) from the beginning of the first transect to the end of the last transect.

+
+parallel.design <- make.design(region = region, 
+                               design = "systematic",
+                               spacing = 2500,
+                               edge.protocol = "minus",
+                               design.angle = 90,
+                               truncation = 750)
+
+zigzag.design <- make.design(region = region, 
+                             design = "eszigzag",
+                             spacing = 2233,
+                             edge.protocol = "minus",
+                             design.angle = 0,
+                             bounding.shape = "convex.hull",
+                             truncation = 750)
+
+

Generating a Set of Transects +

+

It is always a good idea to run a quick check that your design is as expected by generating a set of transects and plotting them.

+
+p.survey <- generate.transects(parallel.design)
+plot(region, p.survey)
+
+ +An example set of transects generated from the systematic parallel line design plotted within the study region.

+Figure 4: An example set of transects generated from the systematic parallel line design plotted within the study region. +

+
+
+z.survey <- generate.transects(zigzag.design)
+plot(region, z.survey)
+
+ +An example set of transects generated from the systematic parallel line design plotted within the study region.

+Figure 5: An example set of transects generated from the systematic parallel line design plotted within the study region. +

+
+
+
+
+

Defining Detectability +

+

Once we have defined both the population of interest and the design which we will use to survey our population we now need to provide information about how detectable the individuals or clusters are. For this example we will assume that larger clusters are more detectable. Take care when defining covariate parameters that the covariate names match those in the population description.

+

When setting the basic scale parameter along side covariate parameters values we need be aware of how the covariate parameter values are incorporated. The covariate parameter values provided adjust the value of the scale parameter on the log scale. The scale parameter for any individual (\(\sigma_j\)) can be calculated as:

+

\[\sigma_j = exp(log(\sigma_0)+\sum_{i=1}^{k}\beta_ix_{ij})\] +where \(j\) is the individual, \(\sigma_0\) is the base line scale parameter (passed in as argument ‘scale.param’ on the natural scale), the \(\beta_i\)’s are the covariate parameters passed in on the log scale for each covariate \(i\) and the \(x_{ij}\) values are the covariate values for covariate \(i\) and individual \(j\).

+

We will assume a half normal detection function with a scale parameter of 300. We will set the truncation distance to be the same as the design at 750 m. and set the covariate slope coefficient on the log scale to log(1.08) = 0.077. We can check what our detection functions will look like for the different covariate values by plotting them. To plot the example detection functions we need to provide the population description as well as detectability.

+
+# Define the covariate parameters on the log scale
+cov.param <- list(size = log(1.08))
+
+# Create the detectability description
+detect <- make.detectability(key.function = "hn",
+                             scale.param = 300,
+                             cov.param = cov.param,
+                             truncation = 750)
+
+# Plot the simulation detection functions
+plot(detect, pop.desc)
+
+ +Plot of the detection function for the mean group size (solid line) and for the 2.5 and 97.5 percentile values of group size (dashed lines) for this population.

+Figure 6: Plot of the detection function for the mean group size (solid line) and for the 2.5 and 97.5 percentile values of group size (dashed lines) for this population. +

+
+

We can also calculate the average detection function for our mean cluster size of 3 as defined in our population description:

+

\[\sigma_{size = 3} = exp(log(300)+log(1.05)*3) = 347.3 \]

+
+
+

Defining Analyses +

+

The final component to a simulation is the analysis or set of analyses you wish to fit to the simulated data. We will define a number of models and allow automatic model selection based on the minimum AIC value. The models included below are a half-normal with no covariates, a hazard rate with no covariates and a half-normal with cluster size as a covariate. We will leave the truncation value at 750 as previously defined (it must be \(\le\) to the truncation values used previously). We will use the default error variance estimator “R2”. See ?mrds::varn for descriptions of the various empirical variance estimators for encounter rate.

+
+analyses <- make.ds.analysis(dfmodel = list(~1, ~1, ~size),
+                             key = c("hn", "hr", "hn"),
+                             truncation = 750,
+                             er.var = "R2",
+                             criteria = "AIC")
+
+
+

Putting the Simulation Together +

+

Now we have all the simulation components defined we can create our simulation objects. We will create one for the systematic parallel line design and one for the equal spaced zigzag design.

+
+sim.parallel <- make.simulation(reps = 999,
+                                design = parallel.design,
+                                population.description = pop.desc,
+                                detectability = detect,
+                                ds.analysis = analyses)
+
+sim.zigzag <- make.simulation(reps = 999,
+                              design = zigzag.design,
+                              population.description = pop.desc,
+                              detectability = detect,
+                              ds.analysis = analyses)
+

Once you have created a simulation we recommend you check to see what a simulated survey might look like.

+
+# Generate a single instance of a survey: a population, set of transects 
+# and the resulting distance data
+eg.parallel.survey <- run.survey(sim.parallel)
+
+# Plot it to view a summary
+plot(eg.parallel.survey, region)
+
+ +Example survey from systematic parallel design. Panels showing: top left - transects, top right - population, bottom left - transects, population and survey detections (cyan dots), bottom right - histogram of detection distances

+Figure 7: Example survey from systematic parallel design. Panels showing: top left - transects, top right - population, bottom left - transects, population and survey detections (cyan dots), bottom right - histogram of detection distances +

+
+
+# Generate a single instance of a survey: a population, set of transects 
+# and the resulting distance data
+eg.zigzag.survey <- run.survey(sim.zigzag)
+
+# Plot it to view a summary
+plot(eg.zigzag.survey, region)
+
+ +Example survey from equal spaced zigzag design. Panels showing: top left - transects, top right - population, bottom left - transects, population and survey detections (cyan dots), bottom right - histogram of detection distances

+Figure 8: Example survey from equal spaced zigzag design. Panels showing: top left - transects, top right - population, bottom left - transects, population and survey detections (cyan dots), bottom right - histogram of detection distances +

+
+
+
+

Running the Simulation +

+

The simulations can be run as follows. Note that these will take some time to run!

+
+# Running the simulations
+sim.parallel <- run.simulation(sim.parallel)
+sim.zigzag <- run.simulation(sim.zigzag)
+
+
+

Simulation Results +

+

Once the simulations have run we can view a summary of the results. Viewing a summary of a simulation will first summarise the simulation setup and then if the simulation has been run provide a summary of the results. A glossary is also provided to aid interpretation of the results. Note that each run will produce slightly different results due to the random component of the generation of both the populations and the sets of survey transects.

+

Firstly, for the systematic parallel lines design we can see that there is very low bias 1.85% for the estimated abundance/density of individuals. The bias is even lower at only 0.16% for the estimated abundance/density of clusters. Also we can see that the analyses have done a good job at estimating the mean cluster size, there is only 1.72% bias.

+

We can also see that the 95% confidence intervals calculated for the abundance/density estimates are in fact capturing the true value around 97% of the time (CI.coverage.prob). We can also note that the observed standard deviation of the estimates of the mean is a bit lower than the mean se, meaning we are realising a lower variance than we would estimate. This is often seen with systematic designs as the default variance estimator assumes a completely random allocation of transect locations, systematic designs usually have lower variance.

+

Reassuringly, these results are as expected for the systematic parallel line design. We expect low bias, as by definition, parallel line designs produce a very uniform coverage probability. The only areas where this design might not produce uniform coverage is around the boundary where there could be minor edge effects due to the minus sampling.

+
+summary(sim.parallel)
+
## 
+## GLOSSARY
+## --------
+## 
+## Summary of Simulation Output
+## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+## 
+## Region          : the region name.
+## No. Repetitions : the number of times the simulation was repeated.
+## No. Excluded Repetitions : the number of times the simulation failed
+##                   (too few sightings, model fitting failure etc.)
+## 
+## Summary for Individuals
+## ~~~~~~~~~~~~~~~~~~~~~~~
+## 
+## Summary Statistics:
+##    mean.Cover.Area : mean covered across simulation.
+##    mean.Effort     : mean effort across simulation.
+##    mean.n          : mean number of observed objects across
+##                      simulation.
+##    mean.n.miss.dist: mean number of observed objects where no distance
+##                     was recorded (only displayed if value > 0).
+##    no.zero.n       : number of surveys in simulation where
+##                      nothing was detected (only displayed if value > 0).
+##    mean.ER         : mean encounter rate across simulation.
+##    mean.se.ER      : mean standard error of the encounter rates
+##                      across simulation.
+##    sd.mean.ER      : standard deviation of the encounter rates
+##                      across simulation.
+## 
+## Estimates of Abundance:
+##    Truth            : true population size, (or mean of true
+##                       population sizes across simulation for Poisson N.
+##    mean.Estimate    : mean estimate of abundance across simulation.
+##    percent.bias     : the percentage of bias in the estimates.
+##    RMSE             : root mean squared error/no. successful reps
+##    CI.coverage.prob : proportion of times the 95% confidence interval
+##                       contained the true value.
+##    mean.se          : the mean standard error of the estimates of
+##                       abundance
+##    sd.of.means      : the standard deviation of the estimates
+## 
+## Estimates of Density:
+##    Truth            : true average density.
+##    mean.Estimate    : mean estimate of density across simulation.
+##    percent.bias     : the percentage of bias in the estimates.
+##    RMSE             : root mean squared error/no. successful reps
+##    CI.coverage.prob : proportion of times the 95% confidence interval
+##                       contained the true value.
+##    mean.se          : the mean standard error of the estimates.
+##    sd.of.means      : the standard deviation of the estimates.
+## 
+## Detection Function Values
+## ~~~~~~~~~~~~~~~~~~~~~~~~~
+## 
+##  mean.observed.Pa : mean proportion of individuals/clusters observed in
+##                     the covered region.
+##  mean.estimte.Pa  : mean estimate of the proportion of individuals/
+##                     clusters observed in the covered region.
+##  sd.estimate.Pa   : standard deviation of the mean estimates of the
+##                     proportion of individuals/clusters observed in the
+##                     covered region.
+##  mean.ESW         : mean estimated strip width.
+##  sd.ESW           : standard deviation of the mean estimated strip widths.
+
## 
+## 
+## Region:  St Andrews bay
+## No. Repetitions:  999
+## No. Excluded Repetitions:  0
+## Using only repetitions where all models converged.
+## 
+## Design:  Systematic parallel line design
+##    design.type :  Systematic parallel line design
+##    bounding.shape :  rectangle
+##    spacing :  2500
+##    design.angle :  90
+##    edge.protocol :  minus
+## 
+## Individual Level Covariate Summary:
+##    size:ztruncpois , mean  =  3
+## Population Detectability Summary:
+##     key.function  =  hn
+##     scale.param  =  300
+##     truncation  =  750
+## 
+## Covariate Detectability Summary (params on log scale):
+##    size parameters: 
+## Strata St Andrews bay 
+##            0.07696104 
+## 
+## Analysis Summary:
+##    Candidate Models:
+##       Model 1: key function 'hn', formula '~1', was selected 474 time(s).
+##       Model 2: key function 'hr', formula '~1', was selected 201 time(s).
+##       Model 3: key function 'hn', formula '~size', was selected 324 time(s).
+##    criteria  =  AIC
+##    variance.estimator  =  R2
+##    truncation  =  750
+## 
+## Summary for Individuals
+## 
+## Estimates of Abundance (N)
+## 
+##   Truth mean.Estimate percent.bias   RMSE CI.coverage.prob mean.se sd.of.means
+## 1   900        916.67         1.85 149.89             0.97  155.99      149.04
+## 
+##      ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+## Estimates of Density (D)
+## 
+##          Truth mean.Estimate percent.bias         RMSE CI.coverage.prob
+## 1 9.113923e-07  9.282781e-07     1.852743 1.517923e-07         0.968969
+##        mean.se  sd.of.means
+## 1 1.579674e-07 1.509258e-07
+## 
+##      ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+##      ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+## 
+## Summary for Clusters
+## 
+## Summary Statistics
+## 
+##   mean.Cover.Area mean.Effort   mean.n   mean.k      mean.ER   mean.se.ER
+## 1       592153913    394769.3 106.7317 15.82883 0.0002704223 3.569565e-05
+##     sd.mean.ER
+## 1 2.137828e-05
+## 
+##      ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+## Estimates of Abundance (N)
+## 
+##   Truth mean.Estimate percent.bias  RMSE CI.coverage.prob mean.se sd.of.means
+## 1   300        300.49         0.16 45.02             0.97   49.01       45.04
+## 
+##      ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+## Estimates of Density (D)
+## 
+##          Truth mean.Estimate percent.bias        RMSE CI.coverage.prob
+## 1 3.037974e-07  3.042914e-07    0.1626056 4.55915e-08         0.970971
+##        mean.se  sd.of.means
+## 1 4.962823e-08 4.561166e-08
+## 
+##      ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+## Estimates of Expected Cluster Size
+## 
+##   Truth mean.Expected.S percent.bias mean.se.ExpS sd.mean.ExpS
+## 1     3            3.05         1.72         0.16          0.2
+## 
+##      ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+##      ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+## 
+## Detection Function Values
+## 
+##   mean.observed.Pa mean.estimate.Pa sd.estimate.Pa mean.ESW sd.ESW
+## 1              0.6              0.6           0.07   451.01  54.08
+

We can now check the results for the zigzag design. While zigzag designs generated inside a convex hull can be much more efficient than parallel line designs (less off-effort transit) there is the possibility of non-uniform coverage. The coverage can be assessed by running run.coverage but by itself this does not give much of an indication of the likely effects on the survey results. The degree to which non-uniform coverage may affect survey results is determined not only by the variability in coverage but also in how that combines with the density of animals in the region. Note that while we have run only one density scenario here, if you have non-uniform coverage probability it is advisable to test the effects under a range of plausible animal distributions.

+

Under this assumed distribution of animals, it looks like any effects of non-uniform coverage are going to have minimal effects on the estimates of abundance / density. For individuals the bias is around 2.5% and for clusters it is 0.65%. Similar to the parallel line design, the confidence intervals are also giving a coverage of 97%.

+

What we can note is that the improved efficiency of this design has increased our on effort line length and corresponding covered area and is thus giving us a bit better precision than the systematic parallel line design.

+
+summary(sim.zigzag)
+
## 
+## GLOSSARY
+## --------
+## 
+## Summary of Simulation Output
+## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+## 
+## Region          : the region name.
+## No. Repetitions : the number of times the simulation was repeated.
+## No. Excluded Repetitions : the number of times the simulation failed
+##                   (too few sightings, model fitting failure etc.)
+## 
+## Summary for Individuals
+## ~~~~~~~~~~~~~~~~~~~~~~~
+## 
+## Summary Statistics:
+##    mean.Cover.Area : mean covered across simulation.
+##    mean.Effort     : mean effort across simulation.
+##    mean.n          : mean number of observed objects across
+##                      simulation.
+##    mean.n.miss.dist: mean number of observed objects where no distance
+##                     was recorded (only displayed if value > 0).
+##    no.zero.n       : number of surveys in simulation where
+##                      nothing was detected (only displayed if value > 0).
+##    mean.ER         : mean encounter rate across simulation.
+##    mean.se.ER      : mean standard error of the encounter rates
+##                      across simulation.
+##    sd.mean.ER      : standard deviation of the encounter rates
+##                      across simulation.
+## 
+## Estimates of Abundance:
+##    Truth            : true population size, (or mean of true
+##                       population sizes across simulation for Poisson N.
+##    mean.Estimate    : mean estimate of abundance across simulation.
+##    percent.bias     : the percentage of bias in the estimates.
+##    RMSE             : root mean squared error/no. successful reps
+##    CI.coverage.prob : proportion of times the 95% confidence interval
+##                       contained the true value.
+##    mean.se          : the mean standard error of the estimates of
+##                       abundance
+##    sd.of.means      : the standard deviation of the estimates
+## 
+## Estimates of Density:
+##    Truth            : true average density.
+##    mean.Estimate    : mean estimate of density across simulation.
+##    percent.bias     : the percentage of bias in the estimates.
+##    RMSE             : root mean squared error/no. successful reps
+##    CI.coverage.prob : proportion of times the 95% confidence interval
+##                       contained the true value.
+##    mean.se          : the mean standard error of the estimates.
+##    sd.of.means      : the standard deviation of the estimates.
+## 
+## Detection Function Values
+## ~~~~~~~~~~~~~~~~~~~~~~~~~
+## 
+##  mean.observed.Pa : mean proportion of individuals/clusters observed in
+##                     the covered region.
+##  mean.estimte.Pa  : mean estimate of the proportion of individuals/
+##                     clusters observed in the covered region.
+##  sd.estimate.Pa   : standard deviation of the mean estimates of the
+##                     proportion of individuals/clusters observed in the
+##                     covered region.
+##  mean.ESW         : mean estimated strip width.
+##  sd.ESW           : standard deviation of the mean estimated strip widths.
+
## 
+## 
+## Region:  St Andrews bay
+## No. Repetitions:  999
+## No. Excluded Repetitions:  0
+## Using only repetitions where all models converged.
+## 
+## Design:  Equal spaced zigzag line design
+##    design.type :  Equal spaced zigzag line design
+##    bounding.shape :  convex.hull
+##    spacing :  2233
+##    design.angle :  0
+##    edge.protocol :  minus
+## 
+## Individual Level Covariate Summary:
+##    size:ztruncpois , mean  =  3
+## Population Detectability Summary:
+##     key.function  =  hn
+##     scale.param  =  300
+##     truncation  =  750
+## 
+## Covariate Detectability Summary (params on log scale):
+##    size parameters: 
+## Strata St Andrews bay 
+##            0.07696104 
+## 
+## Analysis Summary:
+##    Candidate Models:
+##       Model 1: key function 'hn', formula '~1', was selected 476 time(s).
+##       Model 2: key function 'hr', formula '~1', was selected 178 time(s).
+##       Model 3: key function 'hn', formula '~size', was selected 345 time(s).
+##    criteria  =  AIC
+##    variance.estimator  =  R2
+##    truncation  =  750
+## 
+## Summary for Individuals
+## 
+## Estimates of Abundance (N)
+## 
+##   Truth mean.Estimate percent.bias   RMSE CI.coverage.prob mean.se sd.of.means
+## 1   900        922.42         2.49 134.29             0.97  145.28      132.47
+## 
+##      ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+## Estimates of Density (D)
+## 
+##          Truth mean.Estimate percent.bias         RMSE CI.coverage.prob
+## 1 9.113923e-07  9.340926e-07     2.490729 1.359901e-07         0.971972
+##        mean.se  sd.of.means
+## 1 1.471139e-07 1.341493e-07
+## 
+##      ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+##      ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+## 
+## Summary for Clusters
+## 
+## Summary Statistics
+## 
+##   mean.Cover.Area mean.Effort   mean.n   mean.k      mean.ER   mean.se.ER
+## 1       663209990      442140 120.3654 18.47948 0.0002722232 3.346453e-05
+##     sd.mean.ER
+## 1 2.143639e-05
+## 
+##      ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+## Estimates of Abundance (N)
+## 
+##   Truth mean.Estimate percent.bias  RMSE CI.coverage.prob mean.se sd.of.means
+## 1   300        301.94         0.65 41.32             0.97   45.58       41.29
+## 
+##      ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+## Estimates of Density (D)
+## 
+##          Truth mean.Estimate percent.bias        RMSE CI.coverage.prob
+## 1 3.037974e-07  3.057631e-07    0.6470286 4.18412e-08         0.973974
+##        mean.se  sd.of.means
+## 1 4.616181e-08 4.181593e-08
+## 
+##      ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+## Estimates of Expected Cluster Size
+## 
+##   Truth mean.Expected.S percent.bias mean.se.ExpS sd.mean.ExpS
+## 1     3            3.06         1.97         0.15          0.2
+## 
+##      ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+##      ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+## 
+## Detection Function Values
+## 
+##   mean.observed.Pa mean.estimate.Pa sd.estimate.Pa mean.ESW sd.ESW
+## 1              0.6              0.6           0.07   450.54  49.27
+

Histograms of the estimates of abundance from each of the simulation replicates can also be viewed to check for the possible effects of extreme values or skewed distributions.

+
+oldparams <- par(mfrow = c(1,2))
+histogram.N.ests(sim.parallel)
+histogram.N.ests(sim.zigzag)
+
+ +Left - histogram of estimates of abundance of clusters for systematic parallel design. Right - histogram of estimates of abundance of clusters for zigzag design.

+Figure 9: Left - histogram of estimates of abundance of clusters for systematic parallel design. Right - histogram of estimates of abundance of clusters for zigzag design. +

+
+
+par(oldparams)
+

We can see in Figure 9 that there were a couple of high estimates generated >500 for both the parallel line and zigzag designs. These probably represent data sets that were difficult to fit a model too (perhaps a chance spiked data set). Most of the estimates are centered around truth but these occasional high estimates may have increased the mean value slightly and could be associated with the small amount of positive bias.

+
+
+

Simulation Conclusions +

+

Under these simulation assumptions it appears that the zigzag design will cost us a little in accuracy but allow us to gain some precision. It should be noted that the cost in accuracy will vary depending on the distribution of animals in the survey region.

+
+
+

References +

+
+
+Marshall, L. 2020. DSsim: Distance Sampling Simulations. https://CRAN.R-project.org/package=DSsim. +
+
+Marshall, Laura. 2023a. Dsims: Distance Sampling Simulations. https://CRAN.R-project.org/package=dsims. +
+
+———. 2023b. Dssd: Distance Sampling Survey Design. https://CRAN.R-project.org/package=dssd. +
+
+Miller, David L., Eric Rexstad, Len Thomas, Laura Marshall, and Jeffrey L. Laake. 2019. “Distance Sampling in R.” Journal of Statistical Software 89 (1): 1–28. https://doi.org/10.18637/jss.v089.i01. +
+
+Pebesma, Bivand, E., and D. Baston. 2021. Sf: Simple Features for r. https://CRAN.R-project.org/package=sf. +
+
+
+
+
+ + + +
+

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+ +
+
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+ + +
+
+ +

Transition from `DSsim` to `dsims`

+

L. Marshall

+
+ CREEM, Univ of St Andrews

November 2024

+ + Source: vignettes/dsims-examples.Rmd +
dsims-examples.Rmd
+
+
+ + + +
+

Preamble +

+

The first version of the simulation engine was a package called DSsim (Marshall, 2019); with improving GIS capabilities in R we have later released a second more efficient simulation package, dsims (Marshall, 2022a). This vignette was originally written for DSsim and so we use it now to not only demonstrate dsims but also as an example for users of DSsim showing how to transition to dsims. As the two packages have largely the same function names loading them together is not advised, this vignette will therefore leave the DSsim code in as comments for comparison with the dsims code. Please note that normal comments follow a single # and DSsim code follows double ##. It should also be noted that dsims now uses the distance sampling survey design package, dssd (Marshall, 2022b), to generate the transects based on the design so that shapefiles containing the transects no longer need to be created in advance.

+

If your goal is to transition to dsims from DSsim then you will find all you need in the sections up to and including the section on running simulations. The latter sections go on to run a series of additional simulations investigating pooling robustness and covariate parameter estimation with respect to truncation distance. If you are completely new to distance sampling simulations then an alternative place to start is the Getting Started vignette inside the dsims package. This vignette uses dsims to compare a systematic parallel design with a zigzag design to assess the accuracy/precision trade off. To view this open R and after installing dsims, enter the following code:

+
+vignette("GettingStarted", package = "dsims")
+
## Warning: vignette 'GettingStarted' not found
+
+
+

Introduction +

+

Distance sampling is a process in which a study area is surveyed to estimate the size of the population within it. It can be thought of as an extension to plot sampling. However, while plot sampling assumes that all objects within the plots are detected, distance sampling relaxes this assumption. To do this Distance sampling makes an assumptions about the distribution of objects with respect to the transects and to satisfy these assumptions the transects (the points or lines) must be randomly located within the study region. Note that for the purposes of distance sampling an object can either be an individual or a cluster or individuals.

+

The next step in distance sampling is then to record the distances from each detected object to the transect it was detected from and fit a detection function. From this function we can estimate how many objects were missed and hence the total number in the covered area. For example, Figure 1 shows histograms of distances that might be collected on a line transect survey, with a fitted detection function. If the lines have been placed at random within the study region then we would expect on average the same number of object to occur at any given distance from the transect. Therefore the drop in number of detection with increasing distance from the line can be attributed to a failure to detect all objects. We can therefore estimate from this detection function that the probability of seeing an object within the covered region out to a chosen truncation distance is the area under the curve (shaded grey) divided by the area of the rectangle.

+
+ +An example detection function. The histogram shows example distances recorded from a line transect. The smooth curve is the detection function. The grey shaded area represents the number of detected objects and the diagonal hash region represents the number of objects in the covered region that were not detected.

+Figure 1: An example detection function. The histogram shows example distances recorded from a line transect. The smooth curve is the detection function. The grey shaded area represents the number of detected objects and the diagonal hash region represents the number of objects in the covered region that were not detected. +

+
+

The R package dsims allows users to simulate both point and line transect surveys, and test out a range of design and analysis decisions specific to their population of interest. To simulate surveys the user must make some assumptions about the population of interest and the detection process giving rise to the observed distances. Simulations can be repeated over a range of assumptions so that the user can be confident that their chosen design will perform well despite any uncertainty.

+
+

Introduction to dsims +

+

dsims takes information from the user on the study region, population and detection process and uses it to generate distance sampling data. dsims can then be asked to fit detection functions to this data and produce estimates of density, abundance and the associated uncertainty. dsims splits this process into three stages. Firstly, it generates an instance of a population and a set of survey transects. Secondly, it simulates the distance sampling survey using the assumed detection function(s) provided by the user. Lastly, dsims analyses the data from the survey. Figure 2 illustrates the simulation process and highlights the information which must be provided by the user.

+

Distance sampling simulations can be very useful to researchers who wish to optimise their survey design for their specific study regions and species of interest in order to try and achieve the most accurate / precise estimates for their populations. Setting up and running such simulations to optimise a design is a very small cost in comparison to those associated with actually completing the survey!

+
+ +Illustrates the simulation process. Blue rectangles indicate information supplied by the user. Green rectangles are objects created by dsims in the simulation process. Orange diamonds indicate the processes carried out by dsims.

+Figure 2: Illustrates the simulation process. Blue rectangles indicate information supplied by the user. Green rectangles are objects created by dsims in the simulation process. Orange diamonds indicate the processes carried out by dsims. +

+
+

dsims is written using the S4 object orientated system in R. The S4 system is a more formal and rigorous style of object orientated programming than the more commonly implemented S3. The process of defining a simulation involves the specification of many variables relating to the survey region, population, survey design and finally the analysis. The design of dsims is based around each of these descriptions being contained in its own class and the formal S4 class definition procedure ensures that the objects created are of the correct format for the simulation. As the objects created by dsims are instances of S4 classes, if the user wishes to access information within them the symbol used is slightly different. To access named parts of S3 objects the “$” symbol would be used, while for S4 objects the “@” symbol must be used. The following code demonstrates this.

+
+# load simulation package
+## library(DSsim)
+library(dsims)
+
+# Make a default region object
+## eg.region <- make.region()
+eg.region <- make.region()
+
+# Let's check the structure of the object we have created
+str(eg.region)
+
## Formal class 'Region' [package "dssd"] with 5 slots
+##   ..@ region.name: chr "region"
+##   ..@ strata.name: chr "region"
+##   ..@ units      : chr(0) 
+##   ..@ area       : num 1e+06
+##   ..@ region     :Classes 'sf' and 'data.frame': 1 obs. of  2 variables:
+##   .. ..$ region  : chr "study_ar"
+##   .. ..$ geometry:sfc_POLYGON of length 1; first list element: List of 1
+##   .. .. ..$ : num [1:5, 1:2] 0 0 2000 2000 0 0 500 500 0 0
+##   .. .. ..- attr(*, "class")= chr [1:3] "XY" "POLYGON" "sfg"
+##   .. ..- attr(*, "sf_column")= chr "geometry"
+##   .. ..- attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA
+##   .. .. ..- attr(*, "names")= chr "region"
+
+# If we wanted to extract the area of the region we would use
+eg.region@area
+
## [1] 1e+06
+
+
+

Example Simulation Study: Which Truncation Distance? +

+

It is usual in distance sampling studies to truncate the data at some distance from the transect. This is because the observations far away from the transect are of lesser importance when fitting the detection function and also these sparse observations at large distances could have high influence on model selection and possibly increase variability in estimated abundance / density.

+

Buckland et al. (2001) suggest truncating the data where the probability of detection is around 0.15 as a general rule of thumb. However, distance sampling data is often costly to obtain and discarding some of the data points can feel counter intuitive. In this vignette we investigate truncation distance in distance sampling analyses. We will do this through a series of three simulations outlined below.

+

Firstly, this vignette will investigate data generated assuming a simple half normal detection function where every object has the same probability of detection at a specific distance from the transect. Figure 3 shows a simple half normal detection function with three possible truncation distances at \(1*\sigma\), \(2*\sigma\) and \(3*\sigma\) where \(\sigma\) is the scale parameter of the half normal detection function. The truncation distance at \(2*\sigma\) gives a probability of detection of 0.135 so close to the 0.15 rule of thumb.

+
+ +Half-normal detection function showing 3 proposed truncation distances at $1*\sigma$, $2*\sigma$ and $3*\sigma$. The truncation distance at twice sigma gives a probability of detection of 0.135 so close to the 0.15 rule of thumb.

+Figure 3: Half-normal detection function showing 3 proposed truncation distances at \(1*\sigma\), \(2*\sigma\) and \(3*\sigma\). The truncation distance at twice sigma gives a probability of detection of 0.135 so close to the 0.15 rule of thumb. +

+
+

While the first set of simulations assume a simple half normal detection function, in reality individual objects or clusters of objects will likely have varying probability of being detected based on certain characteristics. Perhaps the behaviour of males will make them easier to detect. It is also easy to see that larger clusters of individuals might be easier to spot at large distances than small clusters. We will also investigate the effects of truncation distance when individual level covariates affect the probability of detection. Figure 4 shows how covariates may affect detectability. We will use simulated distance data with one covariate (sex) to investigate both the effects of truncation when we assume that we were not able to measure the covariate affecting detectability and when we assume that we can and therefore will include the relevant covariate in the detection function model.

+
+ +Half-normal detection function which varies based on cluster size and animal sex.

+Figure 4: Half-normal detection function which varies based on cluster size and animal sex. +

+
+
+
+

Model Uncertainty and Pooling Robustness +

+

When we simulate data, we have to provide the detection function to generate detections, and we therefore know the underlying true detection function. When collecting data in the field, we will not have this information, and so we will have to rely on some form of model selection. One method of model selection is to compare information criterion, dsims allows the user to select either AIC, AICc or BIC as the model selection criteria. For these simulations we will use AIC and allow dsims to select between a half-normal and a hazard rate model in the first two sets of simulations.

+

In addition, if the probability of detection is affected by covariates then we may not only have a single underlying detection function but a combination of detection functions giving rise to our observed data. In this situation we can either model detectability as a function of these covariates or rely on a concept called pooling robustness. Pooling robustness refers to the fact that distance sampling techniques are robust to the pooling of multiple detection functions into one. This means that we do not necessarily need to include all the covariates which affect detectability in the detection function to accurately estimate density / abundance. This vignette will examine the concept of pooling robustness to see if it is affected by truncation distance.

+
+
+
+

Methods +

+

This vignette will guide you through the steps to create and run a series of simulations to investigate the effects of varying truncation distance on both data generated from a simple half-normal detection function and from a detection function where detectability is affected by a covariate.

+
+
+

Setup +

+

First we load the dsims library.

+
+## library(DSsim)
+library(dsims)
+
+
+

Simulation Components +

+

As detailed in Introduction to dsims a simulation comprises of a number of components. dsims is designed so that each of these components is defined individually before they are grouped together into a simulation. This helps keep the process clear and also allows reuse of simulation components between different simulations. Each of the function names to create a simulation component or simulation takes the form make.<component>.

+
+

Region +

+

These simulations will use a rectangular study region of 5 km by 20 km. Survey regions can be defined in any units but all units must be the same throughout the components of the simulation. If a shapefile is used to create the survey region, then information on the units will be taken from the .prj file. Here we will define the coordinates in m. As this is a simple study region (Figure 5) with few vertices we can simply provide the coordinates. A change from DSsim is that you now need to turn the coordinates into an sf polygon shape prior to creating the region. This step is documented below. Note that while standard shapefiles have their outer polygon coordinates given in a clockwise direction, sf uses counter clockwise for external polygons and clockwise for holes within polygons. Further details on creating multi-part or multi-strata sf objects can be found at the end of the multi-strata dssd vignette.

+

You will also note that units are no longer a plotting option. The plot functions have been modified to use ggplot2 and if additional plotting options are desired the ggplot object can be captured and modified.

+
+## # Create a polgon
+## poly1 <- data.frame(x = c(0,0,20000,20000,0), y = c(0,5000,5000,0,0))
+## 
+## # Create an empty list
+## # Store the polygon inside a list in the first element of the coords list referring to strata 1.
+## coords <- list()
+## coords[[1]] <- list(poly1)
+
+# Create an sf polgon
+library(sf)
+# Put the coordinates of the polygon in a matrix
+poly1 = matrix(c(0,0, 20000,0, 20000,5000, 0,5000, 0,0),ncol=2, byrow=TRUE)
+# Turn them into an sf polygon
+pl1 = st_polygon(list(poly1))
+
+## # Create the survey region
+## region <- make.region(region.name = "study area", 
+##                       units = "m",
+##                       coords = coords)
+## # The plot function allows plotting in km or m.
+## plot(region, plot.units = "km")
+
+# Create the survey region
+region <- make.region(region.name = "study area", 
+                      units = "m",
+                      shape = pl1)
+# The plot function allows plotting in km or m.
+plot(region)
+
+ +The study region.

+Figure 5: The study region. +

+
+
+
+

Population +

+

We will now define our population within our study region. Firstly, we must describe the distribution of the population by defining a density surface. For these simulations we will assume a uniform distribution of animals throughout the study region. dsims will generate an sf grid describing the density surface for us if we provide the x (and optionally the y) spacing and a constant density value for the surface. If the y spacing is omitted it will be assumed to be equal to the x spacing. In this example the value of the constant is not important as we will generate animals based on a fixed population size rather than using the exact values in the density grid.

+

There are two argument name changes in the make.density function: region.obj is now region and density.gam is now fitted model. The buffer argument is no longer needed and there is now an option to supply a formula for density based on x and y using density.formula.

+
+## # Create the density surface
+## density <- make.density(region.obj = region, 
+##                         x.space = 100, 
+##                         constant = 1)
+## 
+## # Plot the density surface
+## plot(density, style = "blocks")
+## plot(region, add = TRUE)
+
+density <- make.density(region = region, 
+                        x.space = 100, 
+                        constant = 1)
+
+# Plot the density surface
+plot(density, region)
+
+ +The density surface.

+Figure 6: The density surface. +

+
+

As an aside, if we wished to add areas of higher or lower density to our density surface we could do this using the add.hotspot function in dsims. This function adds these hot or low spots based on a Gaussian decay function. We have to provide the central coordinates and a sigma value to tell dsims about the location and shape of the hot/low spot. The amplitude argument gives the value of the hot or low spot at its centre and is combined with the existing density surface through addition.

+

The code used to do this in dsims is identical to that used with DSsim and so is not repeated in the code chunk below.

+
+# Add a hotspot to the density surface, centre located at x = 15000, y = 4000 with 
+# a Gaussian decay parameter sigma = 1500. The value at the centre point will now 
+# be 1 (the current value of the density surface defined above) + 0.5 = 1.5
+eg.density <- add.hotspot(density, centre = c(15000,4000), sigma = 1500, amplitude = 0.5)
+# Add a lowspot to this new density surface (eg.density)
+eg.density <- add.hotspot(eg.density, centre = c(10000,3000), sigma = 1000, amplitude = -0.25)
+# Plot the density surface
+plot(eg.density, region)
+
+ +The non-uniform density surface.

+Figure 7: The non-uniform density surface. +

+
+

We can now define other aspects of the population. For the simple case (with no covariates) we only need to define a fixed population size and provide the region and density grid we created above. This fixed population size of 200 was selected as a value sufficient to give around 100 detections per simulated survey while not so large as to cause the simulations to run more slowly. The minimum recommended number of detections for fitting a detection function to is 60 (Buckland et al., 2001).

+

There are only minor argument names changes to this function: region.obj is now region and density.obj is now density.

+
+## # Create the population description, with a population size N = 200
+## pop.desc <- make.population.description(region.obj = region, 
+##                                             density.obj = density, 
+##                                             N = 200,
+##                                             fixed.N = TRUE)
+
+# Create the population description, with a population size N = 200
+pop.desc <- make.population.description(region = region, 
+                                        density = density, 
+                                        N = 200,
+                                        fixed.N = TRUE)
+

For our simulations involving covariates we need to define how individuals will be allocated these covariate values. dsims allows the user to either define their own discrete distribution or alternatively provide a distribution (Normal, Poisson, Zero-truncated Poisson or Lognormal) with associated parameters. For these simulation we will use sex as a covariate and assume that 50% of the population are female and 50% are male.

+

In this example the sex covariate is defined in exactly the same way as in DSsim.

+
+# Create the covariate list
+covariate.list <- list()
+# The population will be 50% males and 50% females
+covariate.list$sex <- list(data.frame(level = c("female", "male"), 
+                                      prob = c(0.5,0.5)))
+
+
+## # Create the population description, with a population size N = 200
+## pop.desc.cov <- make.population.description(region = region, 
+##                                             density = density, 
+##                                             covariates = covariate.list, 
+##                                             N = 200)
+
+# Create the population description, with a population size N = 200
+pop.desc.cov <- make.population.description(region = region, 
+                                            density = density, 
+                                            covariates = covariate.list, 
+                                            N = 200)
+

Note that when defining covariates using distributions the format has changed slightly. An example is included below. In dsims the format has been simplified in that the covariate distribution list provided for each stratum is now just a list with named elements ‘distribution’ and the distribution parameters. Please refer to the help for which parameters should be defined for each distribution and further examples.

+
+## covariate.list <- list()
+## covariate.list$size <- list(list("poisson", list(lambda = 35)))
+
+covariate.list <- list()
+covariate.list$size <- list(list(distribution = "poisson", lambda = 35))
+
+
+

Detectability +

+

Detectability refers to the detection function or functions we feed into the simulation to generate the observations. In the simple case we can set all animals to have the same probability of detection given their distance from the transect. Here we define a half-normal detection function with scale parameter \(\sigma = 200\) and data generation truncation distance of 1000. The truncation distance defined here is to aid simulation efficiency and means that no detections can occur beyond this value. We can then plot this function to check we have defined it correctly. As we defined our survey region in m the scale parameter and truncation distance will also be assumed to be in metres.

+

The scale parameter of 200 was selected as on average it gives around 100 detections out to a truncation distance of 1000m with our chosen population size of 200.

+

Defining detectability in dsims uses identical code to that in DSsim and so the code is not repeated here.

+
+# Make a simple half normal detection function with a scale parameter of 200
+detect.hn <- make.detectability(key.function = "hn",
+                                 scale.param = 200, 
+                                 truncation = 1000)
+# We can now visualise these detection functions
+plot(detect.hn, pop.desc)
+
+ +The detection functions for males and females.

+Figure 8: The detection functions for males and females. +

+
+

When we have covariates in the population we may choose to vary the scale parameter of the detection function based on the covariate values. dsims assumes that the scale parameter is a function of the covariates as follows:

+

\[ \sigma = exp(\beta_0+\sum_{j=1}^{q}\beta_{j}z_{ij}) \]

+

where \(\beta_0\) is the log of the scale parameter supplied to make.detectability, the \(\beta_j\)’s are the covariate parameters supplied on the log scale and \(z_{ij}\) is the ith value of the jth covariate. This formula was taken from Buckland et al. (2004).

+

The covariate values were selected so that males had a higher probability of detection than females. The values selected in this example give a sample size of around 150 observations out to the 1000m truncation value for our population of 200.

+

Defining detectability in dsims uses identical code to that in DSsim and so the code is not repeated here.

+
+# Create the covariate parameter list
+cov.params <- list()
+# Note the covariate parameters are supplied on the log scale
+cov.params$sex = data.frame(level = c("female", "male"), 
+                            param = c(0, 1.5))
+
+detect.cov <- make.detectability(key.function = "hn" ,
+                                 scale.param = 120,
+                                 cov.param = cov.params, 
+                                 truncation = 1000)
+
+# This setup gives a scale parameter of around 120 for the females and 540 for 
+# the males. We can calculate the sigma for the males using the formula above:
+# exp(log(scale.param) + sex.male)
+exp(log(120) + 1.5)
+
## [1] 537.8027
+
+# We can now visualise these detection functions
+plot(detect.cov, pop.desc.cov)
+
+ +The detection functions for males and females.

+Figure 9: The detection functions for males and females. +

+
+
+
+

Design +

+

The design section of the simulations in dsims is the part which differs most significantly from DSsim. DSsim only generated very basic designs and anything more complex needed to be generated externally and loaded as shapefiles. dsims uses the dssd survey design package in R to specify designs and generate transects from them.

+

For this example we will use a systematic parallel line transect design. As the recommended minimum number of transects is between 10 and 20 (Buckland et al., 2001) we have set the spacing between the lines to be 1000 m to give 20 transects per survey.

+

For basic designs the arguments to the make.design function have only changed slightly: region.obj is now region and design.details is now design. Note, it is now important to define a truncation distance for the design, this allows design coverage to be assessed. dssd also now provides a more comprehensive set of arguments for defining designs. To investigate these further, please see our Getting Started with dssd vignette and our Multiple Strata in dssd vignette.

+
+## # Define the design
+## design <- make.design(region.obj = region,
+##                       transect.type = "line",
+##                       design.details = c("parallel", "systematic"),
+##                       spacing = 1000)
+
+# Define the design
+design <- make.design(region = region,
+                      transect.type = "line",
+                      design = "systematic",
+                      spacing = 1000,
+                      truncation = 1000)
+

The design objects now contain the survey region and so there is no need to supply this as a separate argument when generating transects. If you would like to plot the covered areas then the covered.area argument can be set to TRUE in the plot function, in this example the covered areas may not be obvious as the truncation distance is the same as the transect spacing.

+
+## transects <- generate.transects(design, region = region)
+## plot(region)
+## plot(transects, col = 4, lwd = 2)
+
+transects <- generate.transects(design)
+plot(region, transects)
+
+ +Example survey transects.

+Figure 10: Example survey transects. +

+
+
+
+

Analysis +

+

The final stage of the simulation is to analyse the distance sampling data that has been generated. As discussed above, when collecting data in the field we would not know the true underlying detection function and will therefore incorporate model uncertainty. We can ask the simulation to fit two models, a half-normal and a hazard rate, to the data and select the best model based on the minimum AIC.

+

There is a fairly substantial change to the syntax used to define the detection function models for the analyses as well as the function name itself. The syntax for DSsim was based on mrds which we felt was not as user friendly as the syntax used by the Distance R package (Miller, Rexstad, Thomas, Marshall, & Laake, 2019). We have therefore made the code in dsims more simililar to defining models for Distance.

+
+## ddf.analyses <- make.ddf.analysis.list(dsmodel = list(~cds(key = "hn", formula = ~1),
+##                                                       ~cds(key = "hr", formula = ~1)), 
+##                                        method = "ds",
+##                                        truncation = 600)
+##                                        criteria = "AIC",
+
+ddf.analyses <- make.ds.analysis(dfmodel = list(~1, ~1),
+                                 key = c("hn", "hr"),
+                                 criteria = "AIC",
+                                 truncation = 600)
+

In this code we have set the truncation distance to 600 but later we will vary this value to investigate the effects of truncation distance on our simulation results. Note that while the truncation distance can be set to any value, it should not exceed the truncation value defined in the detectability or design as no observations will occur beyond these values.

+

In addition, in the field it may be possible to identify the covariates that affect detectability so we may wish to fit a detection function that incorporates this. In this case, the following model would be appropriate:

+
+## ddf.analyses.cov <- make.ddf.analysis.list(dsmodel = list(~mcds(key = "hn", formula = ~sex)), 
+##                                            method = "ds",
+##                                            truncation = 600)
+
+ddf.analyses.cov <- make.ds.analysis(dfmodel = list(~sex),
+                                     key = c("hn"),
+                                     truncation = 600)
+
+
+
+

Simulations +

+

The simulation is created by grouping all these components together. We will create two simulations here, the first simple case will involve no difference in detectability between animals, the second will include the difference in detectability due to sex. Initially, we will only include the analyses which allow selection between a half-normal and hazard rate model, later we modify this to run a third set of simulations where we fit a detection function with sex included as a covariate.

+

Once we have created the simulation objects, it is a good idea to check that everything is as you intended. The function run.survey simulates a single survey and generates a set of transects and a population and then simulates the survey process to create a distance sampling data set. These can then be plotted (Figures 11 and 12).

+
+## sim <- make.simulation(reps = 999, 
+##                        region.obj = region,
+##                        design.obj = design,
+##                        detectability.obj = detect.hn,
+##                        ddf.analyses.list = ddf.analyses)
+##                        population.description.obj = pop.desc,
+## # Produce simulation setup plots
+## check.sim.setup(sim)
+
+sim <- make.simulation(reps = 999, 
+                       design = design,
+                       population.description = pop.desc,
+                       detectability = detect.hn,
+                       ds.analysis = ddf.analyses)
+# Produce survey and plot it
+survey <- run.survey(sim)
+plot(survey, region)
+
+ +Example survey. Top left - an example set of transects. Top right - an example population. Bottom left - the detections from the transects. Bottom right - A histogram of the distances from these observations to the transect it was detected.

+Figure 11: Example survey. Top left - an example set of transects. Top right - an example population. Bottom left - the detections from the transects. Bottom right - A histogram of the distances from these observations to the transect it was detected. +

+
+

We will now create a second simulation object for the simulations with covariates. We can re-use the design component and then add in the new population description and detectability to include the sex covariate. Here we include the same non-covariate analyses but for the final set of simulations we will change this to fit the covariate detection function model.

+
+## sim.cov <- make.simulation(reps = 999, 
+##                        region.obj = region,
+##                        design.obj = design,
+##                        population.description.obj = pop.desc.cov,
+##                        detectability.obj = detect.cov,
+##                        ddf.analyses.list = ddf.analyses)
+## # Produce simulation setup plots
+## check.sim.setup(sim.cov)
+
+sim.cov <- make.simulation(reps = 999, 
+                       design = design,
+                       population.description = pop.desc.cov,
+                       detectability = detect.cov,
+                       ds.analysis = ddf.analyses)
+# Produce survey and plot it
+survey.cov <- run.survey(sim.cov)
+plot(survey.cov, region)
+
+ +Example survey. Top left - an example set of transects. Top right - an example population. Bottom left - the detections from the transects. Bottom right - A histogram of the distances from these observations to the transect it was detected.

+Figure 12: Example survey. Top left - an example set of transects. Top right - an example population. Bottom left - the detections from the transects. Bottom right - A histogram of the distances from these observations to the transect it was detected. +

+
+

To check that our second simulation is correctly generating covariate values for our population we can examine the first few detections in the simulated distance data.

+
+head(survey.cov@dist.data)
+
##    object individual obs.Region.Label Sample.Label distance        x         y
+## 2       2          2       study area            1 439.6622 680.6209  833.2067
+## 15     15         13       study area            1 127.7175 113.2412 3562.4237
+## 16     16         14       study area            1 385.4299 626.3886 2696.8466
+## 36     36         35       study area            1 699.3118 940.2705 4044.2287
+## 47     47         45       study area            1 521.5709 762.5297  411.8589
+## 87     87         89       study area            1 349.0618 590.0205  710.0814
+##     sex Region.Label Effort  Area
+## 2  male   study area   5000 1e+08
+## 15 male   study area   5000 1e+08
+## 16 male   study area   5000 1e+08
+## 36 male   study area   5000 1e+08
+## 47 male   study area   5000 1e+08
+## 87 male   study area   5000 1e+08
+
+
+

Running Simulations +

+

To run simulations the syntax has changed slightly from run in DSsim to run.simulations in dsims and the object argument is now simulation. The simulations can still be run in parallel using run.parallel with the maximum cores set using max.cores and the counter argument is retained. The transect.path argument of the run.simulation function in dsims is where you can optionally supply a folder or filename if you wish to load pre-generated shapefiles (in DSsim this was specified in the design). This option is not expected to be widely used and was incorporated to allow simulations in Distance for Windows to be run using dsims.

+

Here we demonstrate how to run the basic simulation as an example. You will see the code to do this incorporated into the multiple simulations run within for loops in the following sections. Note that it is advisable to first run your simulation with a few iterations (<10) to give an indication that it should run without issues before setting it off on hundreds / thousands repetitions. Once you have run a simulation you can view the results using the summary function which provides a glossary to explain the output. A histogram of the estimates of abundance can also be viewed, Figure 13.

+
+## sim <- run(object = sim)
+
+sim <- run.simulation(simulation = sim, run.parallel = TRUE)
+# Display a summary of the simulation
+summary(sim)
+# Display a histogram of the estimates of abundance
+histogram.N.ests(sim)
+
## 
+## GLOSSARY
+## --------
+## 
+## Summary of Simulation Output
+## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+## 
+## Region          : the region name.
+## No. Repetitions : the number of times the simulation was repeated.
+## No. Excluded Repetitions : the number of times the simulation failed
+##                   (too few sightings, model fitting failure etc.)
+## 
+## Summary for Individuals
+## ~~~~~~~~~~~~~~~~~~~~~~~
+## 
+## Summary Statistics:
+##    mean.Cover.Area : mean covered across simulation.
+##    mean.Effort     : mean effort across simulation.
+##    mean.n          : mean number of observed objects across
+##                      simulation.
+##    mean.n.miss.dist: mean number of observed objects where no distance
+##                     was recorded (only displayed if value > 0).
+##    no.zero.n       : number of surveys in simulation where
+##                      nothing was detected (only displayed if value > 0).
+##    mean.ER         : mean encounter rate across simulation.
+##    mean.se.ER      : mean standard error of the encounter rates
+##                      across simulation.
+##    sd.mean.ER      : standard deviation of the encounter rates
+##                      across simulation.
+## 
+## Estimates of Abundance:
+##    Truth            : true population size, (or mean of true
+##                       population sizes across simulation for Poisson N.
+##    mean.Estimate    : mean estimate of abundance across simulation.
+##    percent.bias     : the percentage of bias in the estimates.
+##    RMSE             : root mean squared error/no. successful reps
+##    CI.coverage.prob : proportion of times the 95% confidence interval
+##                       contained the true value.
+##    mean.se          : the mean standard error of the estimates of
+##                       abundance
+##    sd.of.means      : the standard deviation of the estimates
+## 
+## Estimates of Density:
+##    Truth            : true average density.
+##    mean.Estimate    : mean estimate of density across simulation.
+##    percent.bias     : the percentage of bias in the estimates.
+##    RMSE             : root mean squared error/no. successful reps
+##    CI.coverage.prob : proportion of times the 95% confidence interval
+##                       contained the true value.
+##    mean.se          : the mean standard error of the estimates.
+##    sd.of.means      : the standard deviation of the estimates.
+## 
+## Detection Function Values
+## ~~~~~~~~~~~~~~~~~~~~~~~~~
+## 
+##  mean.observed.Pa : mean proportion of individuals/clusters observed in
+##                     the covered region.
+##  mean.estimte.Pa  : mean estimate of the proportion of individuals/
+##                     clusters observed in the covered region.
+##  sd.estimate.Pa   : standard deviation of the mean estimates of the
+##                     proportion of individuals/clusters observed in the
+##                     covered region.
+##  mean.ESW         : mean estimated strip width.
+##  sd.ESW           : standard deviation of the mean estimated strip widths.
+
## 
+## 
+## Region:  study area
+## No. Repetitions:  999
+## No. Excluded Repetitions:  0
+## Using only repetitions where all models converged.
+## 
+## Design:  Systematic parallel line design
+##    design.type :  Systematic parallel line design
+##    bounding.shape :  rectangle
+##    spacing :  1000
+##    design.angle :  0
+##    edge.protocol :  minus
+## 
+## Population Detectability Summary:
+##     key.function  =  hn
+##     scale.param  =  200
+##     truncation  =  1000
+## 
+## Analysis Summary:
+##    Candidate Models:
+##       Model 1: key function 'hn', formula '~1', was selected 798 time(s).
+##       Model 2: key function 'hr', formula '~1', was selected 201 time(s).
+##    criteria  =  AIC
+##    variance.estimator  =  R2
+##    truncation  =  600
+## 
+## Summary for Individuals
+## 
+## Summary Statistics
+## 
+##   mean.Cover.Area mean.Effort   mean.n mean.k      mean.ER   mean.se.ER
+## 1         1.2e+08       1e+05 99.11311     20 0.0009911311 9.838211e-05
+##     sd.mean.ER
+## 1 7.436991e-05
+## 
+##      ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+## Estimates of Abundance (N)
+## 
+##   Truth mean.Estimate percent.bias  RMSE CI.coverage.prob mean.se sd.of.means
+## 1   200        198.33        -0.84 26.17             0.94   25.36       26.13
+## 
+##      ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+## Estimates of Density (D)
+## 
+##   Truth mean.Estimate percent.bias         RMSE CI.coverage.prob      mean.se
+## 1 2e-06  1.983281e-06   -0.8359462 2.617061e-07        0.9379379 2.535869e-07
+##    sd.of.means
+## 1 2.613023e-07
+## 
+##      ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+##      ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+## 
+## Detection Function Values
+## 
+##   mean.observed.Pa mean.estimate.Pa sd.estimate.Pa mean.ESW sd.ESW
+## 1             0.42             0.42           0.05   252.75  27.29
+
+ +Histogram of abundance estimates from the simulation.

+Figure 13: Histogram of abundance estimates from the simulation. +

+
+

If your goal was to simply learn the syntax for switching from DSsim to dsims then you can finish here. The remainder of this vignette loops through further simulations to test how altering truncation distance affects pooling robustness and covariate parameter estimation. From now on only dsims code will be shown.

+
+
+

Running Multiple Simulations to investigate Truncation +

+

To investigate the effects of varying the truncation distance during analysis we do not simply need to run one simulation, but one for each truncation distance. The following code shows how we iterated over a number of different truncation distances and stored the simulation with its results and the simulation summaries as lists. In this first set of simulations detectability does not change with individual level covariates.

+
+# Truncation distances to iterate over
+truncation <- c(200, 400, 600)
+# Storage space for results
+results.list <- list()
+summary.list <- list()
+
+# We will now run the simulation for each of the analysis truncation distances.
+for(tdist in seq(along= truncation)){
+  # Screen display to indicate how far through the simulations we are
+  cat("\n Running for truncation = ", truncation[tdist], fill = T)
+  # Update analysis with new truncation distance
+  new.ds.analyses <- make.ds.analysis(dfmodel = list(~1, ~1),
+                                      key = c("hn", "hr"),
+                                      criteria = "AIC",
+                                      truncation = truncation[tdist])
+  # Update simulation to include new analysis component
+  # We can use the @ symbol to change the contents of a slot or alternatively we could have
+  # re-created the simulation with the new analyses using make.simulation().
+  sim@ds.analysis <- new.ds.analyses
+  # Run simulation and store the results in the appropriate list element
+  results.list[[tdist]] <- run.simulation(sim, run.parallel = TRUE)
+  # Store simulation summary in another list in the appropriate list element
+  # As we are storing the summary we do not need the description.summary displayed
+  summary.list[[tdist]] <- summary(results.list[[tdist]], description.summary = FALSE)
+}
+
+# Add names to the summary and results list so we know which truncation distance they
+# relate to
+names(results.list) <- paste("t", truncation, sep = "")
+names(summary.list) <- paste("t", truncation, sep = "")
+

We will now move on to investigate what happens when the sex covariate affects detectability. First, we need to select suitable candidate truncation distances; to do this we will plot some example data. Figure 12 shows data generated from a population size of 2500, this increase in population size will increase the number of detections and make the shape of the resulting data less variable. From this histogram five candidate truncation distances were selected and are shown by the red vertical lines. These were selected so that the truncation distances represent a range of values for the probability of detection starting at about 0.6 for the shortest truncation distance.

+
+ +Histogram of data from covariate simulation with an increased population size of 2500. The detection function shows the best fit to the data (the code was allowed to select between a half normal and hazard rate based on minimum AIC). The red lines indicate the manually selected candidate truncation distances.

+Figure 14: Histogram of data from covariate simulation with an increased population size of 2500. The detection function shows the best fit to the data (the code was allowed to select between a half normal and hazard rate based on minimum AIC). The red lines indicate the manually selected candidate truncation distances. +

+
+

We can now feed these candidate truncation distances into our covariate simulations in the same way as we did for the simple half normal simulation and again store the results and summaries as lists. Note that for now we are still fitting the half-normal and hazard rate intercept only models and are therefore testing pooling robustness.

+
+# Truncation distances to iterate over
+truncation <- c(200, 400, 600, 800, 1000)
+# Storage space for results
+cov.results.list <- list()
+cov.summary.list <- list()
+
+for(tdist in seq(along= truncation)){
+  # Screen display to indicate how far through the simulations we are
+  cat("\n Running for truncation = ", truncation[tdist], fill = T)
+  # Update analysis truncation distance
+  new.ds.analyses <- make.ds.analysis(dfmodel = list(~1, ~1),
+                                      key = c("hn", "hr"),
+                                      criteria = "AIC",
+                                      truncation = truncation[tdist])
+  # Update simulation
+  sim.cov@ds.analysis <- new.ds.analyses
+  # Run Simulation
+  cov.results.list[[tdist]] <- run.simulation(sim.cov, run.parallel = TRUE)
+  # Store simulation summaries
+  cov.summary.list[[tdist]] <- summary(cov.results.list[[tdist]], description.summary = FALSE)
+}
+# Add names to the summary and results list
+names(cov.results.list) <- paste("t", truncation, sep = "")
+names(cov.summary.list) <- paste("t", truncation, sep = "")
+

Finally, we may also wish to fit the covariate model we used to generate the data rather than the non covariate half-normal and hazard rate models. This will allow us to investigate the effects of truncation if in fact we were aware of and could “measure” the covariate that we knew to be affecting detectability.

+
+# Now include the ddf.analyses.cov in the simulation
+sim.cov <- make.simulation(reps = 999, 
+                           design = design,
+                           population.description = pop.desc.cov,
+                           detectability = detect.cov,
+                           ds.analysis = ddf.analyses.cov)
+
+# Truncation distances to iterate over
+truncation <- c(200, 400, 600, 800, 1000)
+
+# Storage space for results
+covmod.results.list <- list()
+covmod.summary.list <- list()
+
+for(tdist in seq(along= truncation)){
+  # Screen display to indicate how far through the simulations we are
+  cat("\n Running for truncation = ", truncation[tdist], fill = T)
+  # Update analysis truncation distance so that detecability is now modelled as a function of sex
+  new.ds.analyses <- make.ds.analysis(dfmodel = list(~sex),
+                                      key = c("hn"),
+                                      truncation = truncation[tdist])
+  # Update simulation
+  sim.cov@ds.analysis <- new.ds.analyses
+  # Run Simulation
+  covmod.results.list[[tdist]] <- run.simulation(sim.cov, run.parallel = TRUE)
+  # Store simulation summaries
+  covmod.summary.list[[tdist]] <- summary(covmod.results.list[[tdist]], description.summary = FALSE)
+}
+# Add names to the summary and results list
+names(covmod.results.list) <- paste("t", truncation, sep = "")
+names(covmod.summary.list) <- paste("t", truncation, sep = "")
+
+
+

Running Simulations to Check Detection Function Parameter Estimates +

+

The above simulations concentrate on the question of how accurately and precisely we can estimate the abundance and density of a population. However, we may also be interested in learning how individual level covariates affect detectability. To do this we require a different and slightly more advanced setup. dsims does not currently store the detection function parameter estimates therefore we need to do this manually, however dsims does provide functionality so that doing this is fairly straight forward. As before we create our simulation but then we need to get dsims to give us the survey data so that we can run the analyses and obtain the parameter estimates. Please note that the extraction of the parameter estimates from the ddf model is specific to this model, if you are adapting this code you will need to check the ddf documentation in mrds to understand the parameters for different models.

+
+sim.cov <- make.simulation(reps = 999, 
+                       design = design,
+                       population.description = pop.desc.cov,
+                       detectability = detect.cov,
+                       ds.analysis = ddf.analyses.cov)
+
+# Truncation distances to iterate over
+truncation <- c(200, 400, 600, 800, 1000)
+reps <- sim.cov@reps
+
+# To store values of interest
+sigma.est <- male.param <- array(NA, 
+                                 dim = c(length(truncation), reps), 
+                                 dimnames = list(truncation, 1:reps))
+
+# Iterate over truncation distances
+for(tdist in 2:5){#seq(along = truncation)){
+  # Screen display to indicate how far through the simulations we are
+  cat("\n Running for truncation = ", truncation[tdist], fill = T)
+  # Update truncation distance
+  new.ds.analyses <- make.ds.analysis(dfmodel = list(~sex),
+                                      key = c("hn"),
+                                      truncation = truncation[tdist])
+  # Update simulation
+  sim.cov@ds.analysis <- new.ds.analyses
+  # Simulation repetitions
+  for(i in 1:reps){
+    cat("\r", i, " out of ", reps,  " reps \r")
+    # Simulates the survey process 
+    simulated.data <- run.survey(sim.cov)
+    # Run analyses 
+    results <- analyse.data(new.ds.analyses, simulated.data)
+    # Obtain detection function model
+    ddf.results <- results$ddf
+    # Store values of interest
+    try(sigma.est[tdist,i] <- ddf.results$par[1])
+    try(male.param[tdist,i] <- ddf.results$par[2])
+  }
+}
+
+
+

Results +

+

As these simulations take a substantial amount of time to run we have saved the results and summaries; these can be downloaded as dsims_truncation_results.zip. Running one of these simulations with 999 repetitions for one truncation distance takes about 11 minutes on an i7-2600K 3.40GHz processor when running in parallel across 7 threads. When running in parallel the maximum number of cores (or threads) permitted is one less than the number on the machine, this is the default number used unless max.cores specifies a lower number.

+
+# Running simulations in parallel
+run.simulation(sim.cov, run.parallel = TRUE, max.cores = 7)
+

Once they have been downloaded and unzipped into a sub folder called results the results and summaries can be loaded as follows:

+
+# Simulations using a simple half normal detection function
+load("results/results_list.ROBJ")
+load("results/summary_list.ROBJ")
+
+# Covartiate simulations
+load("results/results_cov_list.ROBJ")
+load("results/summary_cov_list.ROBJ")
+
+# Covariate simulations with covariate model
+load("results/covmod_results_list.ROBJ")
+load("results/covmod_summary_list.ROBJ")
+load("results/sigma_est.ROBJ")
+load("results/male_param.ROBJ")
+

The objects this has loaded into the workspace include results.list, summary.list, cov.results.list, cov.summary.list, covmod.results.list, covmod.summary.list, sigma_est and male_param. results.list is a list of 3 simulation objects for the simple half normal simulations with truncation distances of 200, 400 and 600. summary.list is a list of the 3 simulation summaries associated with simulations in results.list. cov.results.list is a list of 5 simulation objects for the covariate simulations where detectability is affected by sex but sex is not included as a covariate in the detection function models. These simulations relate to truncation distances of 200, 400, 600, 800 and 1000. cov.summary.list is a list of the 5 simulation summaries associated with simulations in cov.results.list. covmod.results.list is a list of 5 simulation objects for the covariate simulations where detectability is affected by sex and with the analyses including the covariate sex in the detection function model. These simulations relate to truncation distances of 200, 400, 600, 800 and 1000. covmod.summary.list is a list of the 5 simulation summaries associated with simulations in covmod.results.list. sigma_est and male_param contains the parameter estimates from the same simulation set up as covmod.summary.list. sigma_est is a 2D array containing parameter estimates for sigma for the five truncation distances and male_param contains the parameter estimates for the male sex parameter for each truncation distance.

+
+# To view the full summary for the simple half normal simulation with a truncation distance of 200:
+summary.list$t200
+
+# To view the full summary for the covariate simulation with a truncation distance of 600:
+cov.summary.list$t600
+
+

Extracting Result Statistics +

+

To investigate how truncation distance affects the results we need to produce tables for comparison. This section details how this can be done using knitr. This section is provided for those interested but users can just skip to the next section where the results tables are actually presented. This code is only applicable to study regions which only have one strata, it would need to be modified to deal with multiple strata.

+
+library(knitr)
+
+N    <- unlist(lapply(summary.list, function(x){x@individuals$N$mean.Estimate}))
+n    <- unlist(lapply(summary.list, function(x){x@individuals$summary$mean.n}))
+se   <- unlist(lapply(summary.list, function(x){x@individuals$N$mean.se}))
+sd.N <- unlist(lapply(summary.list, function(x){x@individuals$N$sd.of.means}))
+bias <- unlist(lapply(summary.list, function(x){x@individuals$N$percent.bias}))
+RMSE <- unlist(lapply(summary.list, function(x){x@individuals$N$RMSE}))
+cov  <- unlist(lapply(summary.list, function(x){x@individuals$N$CI.coverage.prob}))
+
+sim.data <- data.frame(trunc = c(200,400,600), 
+                       n = round(n),
+                       N = round(N),
+                       se = round(se,2),
+                       sd.N = round(sd.N,2),
+                       bias = round(bias,2),
+                       RMSE = round(RMSE,2),
+                       cov = round(cov*100,1))
+
+kable(sim.data, 
+      col.names = c("$Truncation$", "$mean\\ n$", "$mean\\ \\hat{N}$", "$mean\\ se$", "$SD(\\hat{N})$", "$\\% Bias$", "$RMSE$", "$\\%\\ CI\\ Coverage$"),
+      row.names = FALSE,
+      align = c('c', 'c', 'c', 'c', 'c', 'c', 'c', 'c'),
+      caption = "Simulation Results for the simple half normal detection probability: The truncation distance, mean number of detections, mean estimated population size (N), mean standard error of $\\hat{N}$, the standard deviation of $\\hat{N}$, percentage bias, root mean squared error, percentage of times the true value of N was captured in the confidence intervals.",
+      table.placement="!h",
+      format = "html")
+
+
+
+

Simulation Results +

+
+

Simple Half-Normal Simulations +

+

For the simulations where the data were generated based on a single half-normal detection function the truncation distance used at the analysis stage made little difference to the estimates of abundance. There was perhaps some small decrease in coverage of the 95% confidence intervals as truncation distance was increased. A truncation distance of 400 or 600 didn’t quite capture truth 95% of the time, Table 1. The root mean squared error (RMSE) values suggested that the further away from the transect the distances were truncated the closer the abundance estimates were to truth, although bias appeared minimal for all three scenarios. Precision looked to improve with larger truncation distances.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+Table 1: Simulation Results for the simple half normal detection probability. The truncation distance, mean number of detections, mean estimated population size (N), mean standard error of \(\hat{N}\), the standard deviation of \(\hat{N}\), percentage bias, root mean squared error, percentage of times the true value of N was captured in the 95% confidence intervals. +
+\(Truncation\) + +\(mean\ n\) + +\(mean\ \hat{N}\) + +\(mean\ se\) + +\(SD(\hat{N})\) + +\(\% Bias\) + +\(RMSE\) + +\(\%\ CI\ Coverage\) +
+200 + +68 + +203 + +33.72 + +33.50 + +1.43 + +33.61 + +95.5 +
+400 + +95 + +201 + +29.12 + +33.34 + +0.71 + +33.35 + +93.1 +
+600 + +99 + +198 + +25.39 + +26.13 + +-1.11 + +26.21 + +93.8 +
+
+
+

Covariate Simulation Testing Pooling Robustness +

+

These simulations test whether or not we can rely on our assumption of pooling robustness in this situation. We have deliberately not provided the model used to generate the data as a candidate model in the analysis stage. We can see that for this setup, when we have pooled two quite distinct detection functions, there is some bias in the abundance estimates when the truncation distance is larger, Table 2. These results also show that our 95% confidence intervals capture the true abundance substantially less than 95% of the time when we use large truncation distances. This could be down to an underestimation of the variability, Table 2 shows that for large truncation values the mean se (mean of the estimated standard errors) is lower than the standard deviation of the estimates of abundance. If the analyses were correctly estimating the variability we would expected these values to be similar. In addition, the RMSE suggests that the larger the truncation distance the further away from truth the abundance estimates become, with the most significant jump between 800 and 1000m.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+Table 2: Simulation Results for the covariate detection probability, where detectability is affected by sex but the candidate models (half-normal and hazard rate) do not contain covariates. The truncation distance, mean number of detections, mean estimated population size (N), mean standard error of \(\hat{N}\), the standard deviation of \(\hat{N}\), percentage bias, root mean squared error, percentage of times the true value of N was captured in the 95% confidence intervals. +
+\(Truncation\) + +\(mean\ n\) + +\(mean\ \hat{N}\) + +\(mean\ se\) + +\(SD(\hat{N})\) + +\(\% Bias\) + +\(RMSE\) + +\(\%\ CI\ Coverage\) +
+200 + +66 + +199 + +34.25 + +34.98 + +-0.49 + +34.97 + +96.4 +
+400 + +102 + +190 + +31.36 + +34.69 + +-5.12 + +36.16 + +90.6 +
+600 + +128 + +188 + +33.94 + +35.95 + +-5.84 + +37.78 + +78.7 +
+800 + +144 + +191 + +34.85 + +37.17 + +-4.43 + +38.19 + +79.6 +
+1000 + +154 + +187 + +31.68 + +40.97 + +-6.41 + +42.91 + +69.7 +
+
+
+

Covariate Simulation with Covariate Model +

+

Finally we ran simulations and fitted the model we used to generate the data. In these simulations truncation distance had little influence on the accuracy of the estimates of abundance, with the exception of a small amount of bias for the smallest truncation distance, Table 3. The RMSE values suggest that the larger truncation distances did a better job at estimating abundance with the most significant improvement coming with the step from 200m truncation to 400m truncation. The 95% confidence intervals captured the true abundance at least 95% of the time for all truncation distances. In these simulations, the variability was always over estimated with the mean of the estimated standard errors always being higher than the standard deviation of the estimates.

+

While the estimates of abundance are not greatly affected by truncation distance for these simulations, the same cannot be said for the parameter estimates. Figure 15, suggests that parameter estimation is most accurate and reliable at maximum truncation distance. The unstable parameter estimates for the smallest truncation distance leading to sometimes very large estimates of sigma and a bimodal distribution for sex.male could explain the slight bias in abundance estimates for this truncation distance seen in Table 2. It is hoped that in practise this strange behaviour might be associated with a poor fit to the data and would be identified and such estimates rejected based on more extensive model selection criteria.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+Table 3: Simulation Results for the covariate detection probability, where detectability is affected by sex and this is modelled in the detection function. The truncation distance, mean number of detections, mean estimated population size (N), mean standard error of \(\hat{N}\), the standard deviation of the \(\hat{N}\), percentage bias, root mean squared error, percentage of times the true value of N was captured in the 95% confidence intervals. +
+\(Truncation\) + +\(mean\ n\) + +\(mean\ \hat{N}\) + +\(mean\ se\) + +\(SD(\hat{N})\) + +\(\% Bias\) + +\(RMSE\) + +\(\%\ CI\ Coverage\) +
+200 + +66 + +208 + +36.14 + +28.57 + +3.76 + +29.52 + +98.0 +
+400 + +102 + +204 + +29.46 + +23.85 + +1.81 + +24.11 + +98.5 +
+600 + +128 + +202 + +27.78 + +22.16 + +1.15 + +22.27 + +97.8 +
+800 + +144 + +202 + +26.97 + +20.66 + +0.88 + +20.73 + +98.5 +
+1000 + +154 + +202 + +26.52 + +20.97 + +0.85 + +21.03 + +98.4 +
+
+ +Histograms of the parameter estimates for sigma and sex.male for three of the five truncation distances investigated. Red lines indicate truth.

+Figure 15: Histograms of the parameter estimates for sigma and sex.male for three of the five truncation distances investigated. Red lines indicate truth. +

+
+
+
+
+

Discussion +

+

In these simulations we have pushed the concept of pooling robustness to the limit in that our two detection functions for males and females were very distinct from one another. This would have increased the potential for spiked data in our simulations, that is when the number of detections falls away quickly at small distances and can make fitting the detection function unreliable (and in fact there were numerous warnings when running some of the simulations about such a scenario). The recommendation when performing distance sampling surveys is to review your data frequently in the field as it is being collected. If you detect spiked data then field methods should be adapted to achieve a wider shoulder in the detection function. This practise will help ensure that pooling robustness holds.

+

The model selection (if any) applied in these simulations was done purely on the basis of AIC. In practise the AIC value is one of a number of diagnostic techniques researchers rely on to select an appropriate detection function model. It is likely, especially due to the potential for spiked data, that some of the models in these simulations were not good fits to the data and would not have been selected by a researcher. If model selection would have been manual then a researcher may have chosen to include adjustment terms in the half-normal or hazard rate models which may have improved the model fit and associated estimates of abundance when relying on pooling robustness.

+

These simulations do suggest that there is only a small cost in precision to the researcher in truncating the data. In fact, truncation may be beneficial if there are large differences in the underlying detection functions due to a covariate which have not been included in the detection function models. We suspect that this is because when there are multiple detection functions pooled together the tails of the observed combined detection function only represent some of these detection functions while other have already dropped to extremely low probabilities of detection closer to the transect. It is a general rule in distance sampling that the shape of the detection function close to the transect is of more importance that what is going on in the tail. And indeed detections made at large distances, if included, can have an undesired large influence on detection function parameters. The generally accepted rule of thumb is to truncate data where the probability of detection is around 0.15.

+

Conversely, if the researcher hopes to identify which covariates affect detectability and obtain reliable parameter estimates then minimal (if any) truncation appears to be preferable.

+

The effects of truncation distance on estimated abundance precision are interesting, especially the comparison between our estimated and observed variability. When we only allow the simulations to fit the half-normal and hazard rate models but detectability if affected by the sex covariate, as truncation distance increases the estimated variability (mean se) stays roughly the same while the observed variability \((SD(\hat{N}))\) increases. So at larger truncation distances the variability in our estimated abundance is underestimated and our confidence interval coverage is low. However, when fitting the covariate model our estimated variance is higher than our observed variance suggesting that for this model we are over estimating variability for all truncation distances and our confidence interval coverage is high.

+
+
+

Conclusions +

+
    +
  • Truncation can help ensure the concept of pooling robustness holds when there are differences in the detection functions of the individuals in the population and the covariates affecting detectability are not modelled.
    • +
  • The estimates of abundance are more accurate and precise when the covariate affecting detectability is included in the detection function model.
    • +
  • Larger truncation distances or no truncation is preferable when trying to accurately obtain the parameters for the covariates that affect detectability.
    • +
+
+
+

References +

+
+
+Buckland, S. T., Anderson, D. R., Burnham, K. P., Borchers, D. L., & Thomas, L. (2001). Introduction to distance sampling. Oxford University Press, Oxford, UK. +
+
+Buckland, S. T., Anderson, D. R., Burnham, K. P., Laake, J. L., Borchers, D. L., & Thomas, L. (2004). Advanced distance sampling. Oxford University Press. +
+
+Marshall, L. (2019). DSsim: Distance sampling simulations. Retrieved from https://CRAN.R-project.org/package=DSsim +
+
+Marshall, L. (2022a). Dsims: Distance sampling simulations. Retrieved from https://CRAN.R-project.org/package=dsims +
+
+Marshall, L. (2022b). Dssd: Distance sampling survey design. Retrieved from https://CRAN.R-project.org/package=dssd +
+
+Miller, D. L., Rexstad, E., Thomas, L., Marshall, L., & Laake, J. L. (2019). Distance sampling in R. Journal of Statistical Software, 89(1), 1–28. https://doi.org/10.18637/jss.v089.i01 +
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+ + +
+
+ +

Grouping strata during simulation

+

L. Marshall

+
+ CREEM, Univ of St Andrews

November 2024

+ + Source: vignettes/dsims_grouped_strata.Rmd +
dsims_grouped_strata.Rmd
+
+
+ + + + +

These example simulations demonstrate the option to group strata at the analysis stage during a simulation. There are different reasons why we may wish to divide our study region into strata, or perhaps strata into sub strata, but sometimes we might need to create strata purely to optimise the design. For example, if we have a narrow study region that follows a coastline and we wish to keep our lines perpendicular to the coast then we may need to divide the region into strata and use different design angles in each stratum. Assuming we keep the coverage constant across these strata, the data can then be grouped at the analysis stage. We will illustrate an example of grouping strata at the analysis stage below.

+
+

Getting started +

+

Ensure you have administrator privileges on your computer and install the necessary R packages.

+
+

Running the simulation and viewing the results for yourself +

+

It is advisable to download the .Rmd file if you would like to replicate the simulations for yourself. In addition, results from these simulations are provided to allow you to compile the .Rmd document. The results are included in a zip archive results.zip. Uncompressing the contents into a folder called results within the same folder as the .Rmd file should give you the required structure to run the code in the .Rmd file. You should end up with the file sim.results.ROBJ within the results folder.

+
+
+
+

Creating a grouped strata simulation +

+
+

Creating a region object +

+

First, we create the region object using a shapefile stored within the package directory. The shapefile provided contains a marine study area off the coast of Ireland. This region has already been projected into metres and dssd will detect that from the shapefile .prj file. The study region has also been divided into six strata and we will provide names in the code below to identify them (“North”, “NW”, “West Upper”, “West Lower”, “SW”, “South”). Care should be taken to check that the order of the strata is as expected by checking a plot of the study region.

+

The division of the study area into six strata was for design purposes, this allows us to specify design angles for each stratum individually. However, for analysis purposes we are interested in estimates for only two distinct areas in this study region, these will consist of the three northern strata grouped together and the three southern strata grouped together.

+
+# Find the full file path to the shapefile on the users machine
+shapefile.path <- system.file("extdata", "AreaRProjStrata.shp", package = "dssd")
+
+# Create the region object
+region <- make.region(region.name = "study area", strata.name = c("North", "NW",
+    "West Upper", "West Lower", "SW", "South"), shape = shapefile.path)
+
+# Plot the survey region
+plot(region)
+

+
+
+

Creating a design object +

+

As mentioned above, we have two sub regions of interest in this study area, for which we would like estimates of density / abundance (the northern three strata and the southern three strata). Let’s start by constructing our design as though we had only divided our study region into two strata. We expect more animals in the southern strata so we will implement a non-uniform coverage design by allocating more effort per unit area (i.e. higher coverage) to this strata than the northern strata.

+

Let’s assume that our effort calculations have suggested that we have sufficient resources to survey parallel lines with a spacing of 16,000m in the northern strata and a spacing of 8,000m in the southern strata. Note that as our shapefile units are metres, all our simulation measurements must also be provided in metres. We will supply a single design angle for the three northern strata and one for the three southern strata, let’s set these to be 135 and 70 degrees, respectively. We will also specify that we will be doing minus sampling and do not expect to observe animals beyond 1,500m.

+

We will generate a set of transects from this design and assess them for desirable design qualities.

+
+# Define a design based on only two strata
+design <- make.design(region = region, transect.type = "line", design = "systematic",
+    spacing = c(rep(16000, 3), rep(8000, 3)), design.angle = c(135, 135, 135, 70,
+        70, 70), edge.protocol = "minus", truncation = 1500)
+
+# Generate and plot a single set of transects
+survey <- generate.transects(design)
+plot(region, survey)
+

+

An optimal design will aim to both maximise the number of samplers (many short lines are better than fewer long lines) and place them parallel to any density gradients. In the case of a long thin study region such as this, we want to lay the transects across the short dimension of the region (i.e. perpendicular to the coast). It is also often the case that marine species are distributed in relation to the coast (usually having a particular depth preference) so again laying the transects perpendicular to the coast should align them parallel to any density gradient and thereby reduce variability in encounter rate between transects resulting in more precise estimates.

+

We can see from this first design, given the complexity of the region, choosing a single design angle for the northern and southern groups of strata is not going to achieve this goal. This is particularly problematic in the southern strata where selecting a design angle to give lines perpendicular to the coast in one area gives lines that are parallel to the coast in another. We now make use of the fact that we have six strata and select appropriate design angles in each with the aim of orientating the transects so they are perpendicular to the coast.

+
+# Define the design
+design <- make.design(region = region, transect.type = "line", design = "systematic",
+    spacing = c(rep(16000, 3), rep(8000, 3)), design.angle = c(160, 135, 80, 135,
+        50, 150), edge.protocol = "minus", truncation = 1500)
+
+
+# Create a single set of transects to check
+survey <- generate.transects(design)
+plot(region, survey)
+

+

We can see from the image above that further dividing the northern and southern regions of interest into substrata allows us to better orientate our lines to both maximise the number of samplers and place them perpendicular to the coast.

+

As this further stratification was purely for design purposes (so we could modify the design angle as we moved along the coast) we would still treat each of the three substrata as one when we come to analyse the data. However, it is important to note that we can only do this because we have kept a uniform coverage across the substrata. However, the above design would not allow us to simply group all 6 strata at the analysis stage. As the northern strata have lower coverage than the southern strata the full dataset will be more representative of the southern strata than the northern and we must therefore ensure that any differences in detectability are modelled.

+
+
+

Creating a density object +

+

We will create a density surface to represent a distribution of animals which is more abundant in the south and also prefers coastal waters.

+

In order to get an idea of where to place the hostpots we can first check the range of the coordinates on the projected scale. Note that the plot of the region gives the scale in lat and lon despite the region being projected. We can access this information by requesting the bounding box of the sf object stored within the dssd region.

+
+# Get the bounding box of the sf object within the region
+sf::st_bbox(region@region)
+

We can now create a density grid with a spacing of 2,500m in both dimensions and add two hotspots to simulate a potentially realistic distribution of animals which prefer to stick closely to the coast. Adding hotspots is largely done by trial and error once we know the range of the x-y coordinate values. Again all measurement values must be provided in metres. As we will later use a fixed population size in the simulations, we do not need to worry about the exact values we provide in the density grid only how they relate to one another. For example, an area with a density cell with a value twice that of another density cell will, on average, end up with twice as many animals when the population is generated.

+
+# Make a density grid with values of 1 across the region
+my.density <- make.density(region = region, x.space = 2500, y.space = 2500, constant = 1)
+
+# Add a hotspot at coordinates (0, 1900000)
+my.density <- add.hotspot(my.density, centre = c(0, 1900000), sigma = 70000, amplitude = 10)
+
+# Add a hotspot at coordinates (80000, 210000)
+my.density <- add.hotspot(my.density, centre = c(80000, 2100000), sigma = 1e+05,
+    amplitude = 5)
+
+# Plot this example density surface
+plot(my.density, region)
+

+
+

Population size +

+

We will base our simulation on a total population size of 2,500 animals. As the make.population command requires us to specify how many individuals per stratum, we will have to calculate this using the density summary.

+
+# View the density summary
+summary(my.density)
+
##       strata             area       ave.N    ave.D
+## 1      North 4176461143 [m^2]  8731625315 2.090676
+## 2         NW 8180996497 [m^2] 25656220203 3.136075
+## 3 West Upper 6316380968 [m^2] 17438152704 2.760782
+## 4 West Lower 8188111047 [m^2] 41315196625 5.045754
+## 5         SW 2654685511 [m^2] 13585880563 5.117699
+## 6      South 9291229356 [m^2] 48534037861 5.223640
+

We can see from the table that if we used the exact densities in the density grid we would generate a lot of animals (see ave.N column)! However, as mentioned above, the simulation will only use this density surface as a guide to relative density across the region. Therefore, we will use these value to decide how many animals to allocate to each strata by scaling them.

+
+# Extract average N values
+ave.N.vals <- summary(my.density)@summary$ave.N
+# Scale average N vals to sum to 2500
+N.per.stratum <- round(2500 * ave.N.vals/sum(ave.N.vals))
+
+# View the allocation per stratum
+N.per.stratum
+
## [1] 141 413 281 665 219 781
+
+# Check the total sums to 2500 (sometimes rounding may cause slight variation)
+sum(N.per.stratum)
+
## [1] 2500
+

At this point, we will also create an individual level covariate to indicate whether the animals are in the northern group of strata or the southern group of strata. We will do this to enable us to later model any differences in detectability between the northern and southern sub populations. Ignoring any differences would not only lead bias in our estimates of abundance for the northern and southern strata but also in our total estimates due to the non-uniform coverage design.

+
+# Create the population description
+covs <- list()
+# Adds a strata group entry allocating 'North' to all animals in the North, NW
+# and West Upper strata and allocating 'South' to all animals in the West
+# Lower, SW and South strata.
+covs$strata.group <- data.frame(level = c(rep("North", 3), rep("South", 3)), prob = rep(1,
+    6), strata = c("North", "NW", "West Upper", "West Lower", "SW", "South"))
+

We will now include the above information in our population description and set the fixed population size argument to be true.

+
+# Create the population description
+pop.description <- make.population.description(region = region, density = my.density,
+    covariates = covs, N = N.per.stratum, fixed.N = TRUE)
+
+
+

True detection function +

+

We will simulate using a half-normal detection function but change \(\sigma\) (scale.param) depending on stratum and use a truncation distance of 1500m. By changing the detection functions across strata we can demonstrate when pooling robustness applies. Pooling robustness refers to a property in distance sampling which allows us to obtain unbiased abundance estimates from a single ‘pooled’ detection function fitted across a number of sub populations, even when detectability may vary greatly, (Rexstad, Buckland, Marshall, & Borchers, 2023). Pooling robustness applies when our data are a representative sample across the population for which we are generating estimates. In this example, the data in our three northern sub-strata can be pooled and the data in our three southern sub- strata can be pooled as these have the same coverage as each other. We cannot pool detections from any strata / sub-strata where coverage varies (without accounting for the non-uniform coverage) as the resulting detection function will be more representative of the strata with higher coverage.

+
+# Create the detectability
+detect <- make.detectability(key.function = "hn", scale.param = c(950, 850, 750,
+    650, 550, 450), truncation = 1500)
+
+# Plot the detectability
+plot(detect, pop.description)
+

+
+
+
+

Creating the analyses object +

+

The simulation engine currently only fits one global detection function to each simulated dataset. In the scenario we have constructed, we know that pooling robustness does not apply across the study region as a whole as we have different levels of coverage between the northern and southern stratum groups. Given we cannot fit separate detection functions, we must allow our model to be able to vary the detection function across the two groups of strata. To achieve this we can include the strata.group covariate (which we included in the population description) in the model, this will allow a different scale parameter to be estimated for the northern three strata than for the southern three.

+

Note that we could have simply included Region.Label as a covariate in the detection function model, however, within the simulation at the stage of fitting the detection function all strata are included in the dataset and this would have resulted in a scale parameter being estimated for all 6 strata individually.

+

It is at the analysis stage that we also need to define how the strata will be grouped in order to obtain estimates for our regions of interest. The dataframe created in the code below tells the simulation how to group the strata.

+
+# Create a dataframe describing how the strata will be grouped
+group.strata <- data.frame(design.id = c("North", "NW", "West Upper", "West Lower",
+    "SW", "South"), analysis.id = c(rep("North", 3), rep("South", 3)))
+
+# View the dataframe
+print(group.strata)
+
##    design.id analysis.id
+## 1      North       North
+## 2         NW       North
+## 3 West Upper       North
+## 4 West Lower       South
+## 5         SW       South
+## 6      South       South
+

We will now define the analyses. As we are simulating detections from a range of difference detection functions, we will incorporate some model uncertainty by allowing the simulation to select between a half normal and a hazard rate model. Both these models will include the strata.group covariate and we will use the AIC as the criterion for model selection.

+
+# Define the analyses - both the hn and hr models use the ~strata.group formula
+ds.analyses <- make.ds.analysis(dfmodel = list(~strata.group, ~strata.group), key = c("hn",
+    "hr"), truncation = 1500, group.strata = group.strata, criteria = "AIC")
+
+
+

Running the simulation +

+

Before running the simulation we group all the components into a simulation object and define the number of repetitions. For this example we will simulate 1000 surveys from our simulation definition. Note that the first time you run a simulation you should limit the number of repetitions to only a few to check everything works as expected.

+
+# Create the simulation
+simulation <- make.simulation(reps = 1000, design = design, population.description = pop.description,
+    detectability = detect, ds.analysis = ds.analyses)
+

A useful way to check the simulation setup is to generate a single example survey, this may take a moment to complete.

+
+# Simulate the data generation for a single survey
+eg.survey <- run.survey(simulation)
+
+# Plot the example survey
+plot(eg.survey, region)
+

+

If the previous plots lead you to believe you have properly parameterised your simulation, it is time to run it. If you run it for a small number of repetitions it should only take a minute or two to complete, running for a 1000 repetitions will take considerably longer and so this simulation has already been run and the results can be loaded instead.

+
+# Run the simulation in parallel
+simulation <- run.simulation(simulation, run.parallel = TRUE)
+
+# Load the simulation object which has already been run
+load("files/sim.results.ROBJ")
+

After you have loaded the simulation with the results, you can view them. To view the full summary use summary(simulation), below we will store the simulation summary and look at specific tables within it. Firstly, we will view the summary table. Notice that as requested we have results for only a northern and a southern strata instead of all six of the substrata. The summary table indicates that around 98 detections were made on average in the northern strata and 275 in the southern strata. It is important to check that there are sufficient detections in each strata so that any differences in detectability can be accurately modelled in the detection function.

+
+# Create a summary (silently without the description)
+sim.summary <- summary(simulation, description.summary = FALSE)
+
+# Display the summary table
+sim.summary@individuals$summary
+
##       mean.Cover.Area mean.Effort  mean.n mean.k      mean.ER   mean.se.ER
+## North      3498729895     1166243  98.490 27.912 8.445558e-05 9.150347e-06
+## South      7551517574     2517173 274.998 71.931 1.092477e-04 7.244126e-06
+## Total     11050247469     3683416 373.488 99.843 9.731804e-05 5.812492e-06
+##         sd.mean.ER
+## North 7.782846e-06
+## South 5.842293e-06
+## Total 4.780433e-06
+

Next we will view the table giving the abundance estimates. There is only a small amount of negative bias for both strata and in the total estimate of abundance. However, the coverage of the confidence intervals (which should be 0.95) is only 0.92 for the southern strata and the total estimate. Sometimes reduced confidence interval coverage can be due to the variance being under estimated but in this case the mean.se (mean of the estimated standard error) and the sd.of.means (truth - observed standard error of the estimates) are very close suggesting the variance has been estimated accurately.

+
+# Display the table of abundance estimates
+round(sim.summary@individuals$N, 3)
+
##       Truth mean.Estimate percent.bias    RMSE CI.coverage.prob mean.se
+## North   835       819.344       -1.875 111.016            0.956 115.122
+## South  1665      1604.102       -3.658 145.372            0.924 132.674
+## Total  2500      2423.446       -3.062 193.676            0.915 176.878
+##       sd.of.means
+## North     109.962
+## South     132.068
+## Total     177.993
+
+
+

Discussion +

+

Even though the detectability of animals was varied across each of the six sub-strata, the estimates for the northern and southern groups of sub-strata combined had very low bias. This result was due to pooling robustness applying across both these groups of sub strata (coverage was the same in each group). Meanwhile, the difference in detectability between the northern and southern groups was modelled explicitly using the strata.group covariate in each of the models. If the simulation was repeated, but this covariate was omitted, we would expect to see bias in the abundance estimated for the northern and southern regions as well as the overall estimate. This would be due to the differences in coverage between the three northen sub-strata and the three southern sub-strata and the fitted detection function being more representative of the southern region (due to the higher coverage) than the northern region.

+

The setup for the analysis in this simulation is a little complex due to the restrictions of the simulation package (i.e. needing to include the stratum covariate in the population description). When analysing your own distance sampling data from the field, if you have a similar scenario, you will be able to either fit separate detection functions to the data from the different regions of interest or create any kind of stratum variable you want, giving you more analysis options.

+
+
+

References +

+
+
+Rexstad, E., Buckland, S., Marshall, L., & Borchers, D. (2023). Pooling robustness in distance sampling: Avoiding bias when there is unmodelled heterogeneity. Ecology and Evolution, 13(1), e9684. Retrieved fromhttps://onlinelibrary.wiley.com/doi/10.1002/ece3.9684 +
+
+
+
+
+
+ + + +
+

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+ + + + + + + diff --git a/docs/articles/dsims_grouped_strata_files/figure-html/density-1.png b/docs/articles/dsims_grouped_strata_files/figure-html/density-1.png new file mode 100644 index 0000000..0c14091 Binary files /dev/null and b/docs/articles/dsims_grouped_strata_files/figure-html/density-1.png differ diff --git a/docs/articles/dsims_grouped_strata_files/figure-html/design_one-1.png b/docs/articles/dsims_grouped_strata_files/figure-html/design_one-1.png new file mode 100644 index 0000000..3a49ec1 Binary files /dev/null and b/docs/articles/dsims_grouped_strata_files/figure-html/design_one-1.png differ diff --git a/docs/articles/dsims_grouped_strata_files/figure-html/design_two-1.png b/docs/articles/dsims_grouped_strata_files/figure-html/design_two-1.png new file mode 100644 index 0000000..f550da9 Binary files /dev/null and b/docs/articles/dsims_grouped_strata_files/figure-html/design_two-1.png differ diff --git a/docs/articles/dsims_grouped_strata_files/figure-html/egsurvey-1.png b/docs/articles/dsims_grouped_strata_files/figure-html/egsurvey-1.png new file mode 100644 index 0000000..32b7680 Binary files /dev/null and b/docs/articles/dsims_grouped_strata_files/figure-html/egsurvey-1.png differ diff --git a/docs/articles/dsims_grouped_strata_files/figure-html/makereg-1.png b/docs/articles/dsims_grouped_strata_files/figure-html/makereg-1.png new file mode 100644 index 0000000..8a6c867 Binary files /dev/null and b/docs/articles/dsims_grouped_strata_files/figure-html/makereg-1.png differ diff --git a/docs/articles/dsims_grouped_strata_files/figure-html/truedetect-1.png b/docs/articles/dsims_grouped_strata_files/figure-html/truedetect-1.png new file mode 100644 index 0000000..289de35 Binary files /dev/null and b/docs/articles/dsims_grouped_strata_files/figure-html/truedetect-1.png differ diff --git a/docs/articles/index.html b/docs/articles/index.html new file mode 100644 index 0000000..1fbadb5 --- /dev/null +++ b/docs/articles/index.html @@ -0,0 +1,68 @@ + +Articles • dsims + Skip to contents + + +
+
+
+ +

Articles

+
+ +
+

All vignettes

+
+ +
Transition from `DSsim` to `dsims`
+

Learning the distinction between the former simulation engine and the current simulation engine by using dsims to investigate truncation distances with individual level covariates

+
Grouping strata during simulation
+

Estimation when combining strata for logistical or design reasons.

+
Getting Started with dsims
+

Assessing behaviour of a survey before going into the field

+
+
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+ + + + + + + diff --git a/docs/authors.html b/docs/authors.html new file mode 100644 index 0000000..058701d --- /dev/null +++ b/docs/authors.html @@ -0,0 +1,88 @@ + +Authors and Citation • dsims + Skip to contents + + +
+
+
+ +

Authors and Citation

+
+ +
+

Authors

+ +
  • +

    Laura Marshall. Author, maintainer. +

    +
    • +
  • +

    Thomas Len. Contributor. +

    +
    • +
+ +
+

Citation

+

Source: DESCRIPTION

+ +

Marshall L (2024). +dsims: Distance Sampling Simulations. +R package version 1.0.4, https://github.com/DistanceDevelopment/dsims. +

+ 
@Manual{,
+  title = {dsims: Distance Sampling Simulations},
+  author = {Laura Marshall},
+  year = {2024},
+  note = {R package version 1.0.4},
+  url = {https://github.com/DistanceDevelopment/dsims},
+}
+
+ +
+ + +
+

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H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
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")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)N.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else 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object[0] : object\n }\n\n if (typeof object === 'string' && object.length > 0) {\n return document.querySelector(parseSelector(object))\n }\n\n return null\n}\n\nconst isVisible = element => {\n if (!isElement(element) || element.getClientRects().length === 0) {\n return false\n }\n\n const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible'\n // Handle `details` element as its content may falsie appear visible when it is closed\n const closedDetails = element.closest('details:not([open])')\n\n if (!closedDetails) {\n return elementIsVisible\n }\n\n if (closedDetails !== element) {\n const summary = element.closest('summary')\n if (summary && summary.parentNode !== closedDetails) {\n return false\n }\n\n if (summary === null) {\n return false\n }\n }\n\n return elementIsVisible\n}\n\nconst isDisabled = element => {\n if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n return true\n }\n\n if (element.classList.contains('disabled')) {\n return true\n }\n\n if (typeof element.disabled !== 'undefined') {\n return element.disabled\n }\n\n return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false'\n}\n\nconst findShadowRoot = element => {\n if (!document.documentElement.attachShadow) {\n return null\n }\n\n // Can find the shadow root otherwise it'll return the document\n if (typeof element.getRootNode === 'function') {\n const root = element.getRootNode()\n return root instanceof ShadowRoot ? root : null\n }\n\n if (element instanceof ShadowRoot) {\n return element\n }\n\n // when we don't find a shadow root\n if (!element.parentNode) {\n return null\n }\n\n return findShadowRoot(element.parentNode)\n}\n\nconst noop = () => {}\n\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\nconst reflow = element => {\n element.offsetHeight // eslint-disable-line no-unused-expressions\n}\n\nconst getjQuery = () => {\n if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n return window.jQuery\n }\n\n return null\n}\n\nconst DOMContentLoadedCallbacks = []\n\nconst onDOMContentLoaded = callback => {\n if (document.readyState === 'loading') {\n // add listener on the first call when the document is in loading state\n if (!DOMContentLoadedCallbacks.length) {\n document.addEventListener('DOMContentLoaded', () => {\n for (const callback of DOMContentLoadedCallbacks) {\n callback()\n }\n })\n }\n\n DOMContentLoadedCallbacks.push(callback)\n } else {\n callback()\n }\n}\n\nconst isRTL = () => document.documentElement.dir === 'rtl'\n\nconst defineJQueryPlugin = plugin => {\n onDOMContentLoaded(() => {\n const $ = getjQuery()\n /* istanbul ignore if */\n if ($) {\n const name = plugin.NAME\n const JQUERY_NO_CONFLICT = $.fn[name]\n $.fn[name] = plugin.jQueryInterface\n $.fn[name].Constructor = plugin\n $.fn[name].noConflict = () => {\n $.fn[name] = JQUERY_NO_CONFLICT\n return plugin.jQueryInterface\n }\n }\n })\n}\n\nconst execute = (possibleCallback, args = [], defaultValue = possibleCallback) => {\n return typeof possibleCallback === 'function' ? possibleCallback(...args) : defaultValue\n}\n\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n if (!waitForTransition) {\n execute(callback)\n return\n }\n\n const durationPadding = 5\n const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding\n\n let called = false\n\n const handler = ({ target }) => {\n if (target !== transitionElement) {\n return\n }\n\n called = true\n transitionElement.removeEventListener(TRANSITION_END, handler)\n execute(callback)\n }\n\n transitionElement.addEventListener(TRANSITION_END, handler)\n setTimeout(() => {\n if (!called) {\n triggerTransitionEnd(transitionElement)\n }\n }, emulatedDuration)\n}\n\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list The list of elements\n * @param activeElement The active element\n * @param shouldGetNext Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n const listLength = list.length\n let index = list.indexOf(activeElement)\n\n // if the element does not exist in the list return an element\n // depending on the direction and if cycle is allowed\n if (index === -1) {\n return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0]\n }\n\n index += shouldGetNext ? 1 : -1\n\n if (isCycleAllowed) {\n index = (index + listLength) % listLength\n }\n\n return list[Math.max(0, Math.min(index, listLength - 1))]\n}\n\nexport {\n defineJQueryPlugin,\n execute,\n executeAfterTransition,\n findShadowRoot,\n getElement,\n getjQuery,\n getNextActiveElement,\n getTransitionDurationFromElement,\n getUID,\n isDisabled,\n isElement,\n isRTL,\n isVisible,\n noop,\n onDOMContentLoaded,\n parseSelector,\n reflow,\n triggerTransitionEnd,\n toType\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { getjQuery } from '../util/index.js'\n\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/\nconst stripNameRegex = /\\..*/\nconst stripUidRegex = /::\\d+$/\nconst eventRegistry = {} // Events storage\nlet uidEvent = 1\nconst customEvents = {\n mouseenter: 'mouseover',\n mouseleave: 'mouseout'\n}\n\nconst nativeEvents = new Set([\n 'click',\n 'dblclick',\n 'mouseup',\n 'mousedown',\n 'contextmenu',\n 'mousewheel',\n 'DOMMouseScroll',\n 'mouseover',\n 'mouseout',\n 'mousemove',\n 'selectstart',\n 'selectend',\n 'keydown',\n 'keypress',\n 'keyup',\n 'orientationchange',\n 'touchstart',\n 'touchmove',\n 'touchend',\n 'touchcancel',\n 'pointerdown',\n 'pointermove',\n 'pointerup',\n 'pointerleave',\n 'pointercancel',\n 'gesturestart',\n 'gesturechange',\n 'gestureend',\n 'focus',\n 'blur',\n 'change',\n 'reset',\n 'select',\n 'submit',\n 'focusin',\n 'focusout',\n 'load',\n 'unload',\n 'beforeunload',\n 'resize',\n 'move',\n 'DOMContentLoaded',\n 'readystatechange',\n 'error',\n 'abort',\n 'scroll'\n])\n\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n return (uid && `${uid}::${uidEvent++}`) || element.uidEvent || uidEvent++\n}\n\nfunction getElementEvents(element) {\n const uid = makeEventUid(element)\n\n element.uidEvent = uid\n eventRegistry[uid] = eventRegistry[uid] || {}\n\n return eventRegistry[uid]\n}\n\nfunction bootstrapHandler(element, fn) {\n return function handler(event) {\n hydrateObj(event, { delegateTarget: element })\n\n if (handler.oneOff) {\n EventHandler.off(element, event.type, fn)\n }\n\n return fn.apply(element, [event])\n }\n}\n\nfunction bootstrapDelegationHandler(element, selector, fn) {\n return function handler(event) {\n const domElements = element.querySelectorAll(selector)\n\n for (let { target } = event; target && target !== this; target = target.parentNode) {\n for (const domElement of domElements) {\n if (domElement !== target) {\n continue\n }\n\n hydrateObj(event, { delegateTarget: target })\n\n if (handler.oneOff) {\n EventHandler.off(element, event.type, selector, fn)\n }\n\n return fn.apply(target, [event])\n }\n }\n }\n}\n\nfunction findHandler(events, callable, delegationSelector = null) {\n return Object.values(events)\n .find(event => event.callable === callable && event.delegationSelector === delegationSelector)\n}\n\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n const isDelegated = typeof handler === 'string'\n // TODO: tooltip passes `false` instead of selector, so we need to check\n const callable = isDelegated ? delegationFunction : (handler || delegationFunction)\n let typeEvent = getTypeEvent(originalTypeEvent)\n\n if (!nativeEvents.has(typeEvent)) {\n typeEvent = originalTypeEvent\n }\n\n return [isDelegated, callable, typeEvent]\n}\n\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return\n }\n\n let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction)\n\n // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n if (originalTypeEvent in customEvents) {\n const wrapFunction = fn => {\n return function (event) {\n if (!event.relatedTarget || (event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget))) {\n return fn.call(this, event)\n }\n }\n }\n\n callable = wrapFunction(callable)\n }\n\n const events = getElementEvents(element)\n const handlers = events[typeEvent] || (events[typeEvent] = {})\n const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null)\n\n if (previousFunction) {\n previousFunction.oneOff = previousFunction.oneOff && oneOff\n\n return\n }\n\n const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''))\n const fn = isDelegated ?\n bootstrapDelegationHandler(element, handler, callable) :\n bootstrapHandler(element, callable)\n\n fn.delegationSelector = isDelegated ? handler : null\n fn.callable = callable\n fn.oneOff = oneOff\n fn.uidEvent = uid\n handlers[uid] = fn\n\n element.addEventListener(typeEvent, fn, isDelegated)\n}\n\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n const fn = findHandler(events[typeEvent], handler, delegationSelector)\n\n if (!fn) {\n return\n }\n\n element.removeEventListener(typeEvent, fn, Boolean(delegationSelector))\n delete events[typeEvent][fn.uidEvent]\n}\n\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n const storeElementEvent = events[typeEvent] || {}\n\n for (const [handlerKey, event] of Object.entries(storeElementEvent)) {\n if (handlerKey.includes(namespace)) {\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector)\n }\n }\n}\n\nfunction getTypeEvent(event) {\n // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n event = event.replace(stripNameRegex, '')\n return customEvents[event] || event\n}\n\nconst EventHandler = {\n on(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, false)\n },\n\n one(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, true)\n },\n\n off(element, originalTypeEvent, handler, delegationFunction) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return\n }\n\n const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction)\n const inNamespace = typeEvent !== originalTypeEvent\n const events = getElementEvents(element)\n const storeElementEvent = events[typeEvent] || {}\n const isNamespace = originalTypeEvent.startsWith('.')\n\n if (typeof callable !== 'undefined') {\n // Simplest case: handler is passed, remove that listener ONLY.\n if (!Object.keys(storeElementEvent).length) {\n return\n }\n\n removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null)\n return\n }\n\n if (isNamespace) {\n for (const elementEvent of Object.keys(events)) {\n removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1))\n }\n }\n\n for (const [keyHandlers, event] of Object.entries(storeElementEvent)) {\n const handlerKey = keyHandlers.replace(stripUidRegex, '')\n\n if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector)\n }\n }\n },\n\n trigger(element, event, args) {\n if (typeof event !== 'string' || !element) {\n return null\n }\n\n const $ = getjQuery()\n const typeEvent = getTypeEvent(event)\n const inNamespace = event !== typeEvent\n\n let jQueryEvent = null\n let bubbles = true\n let nativeDispatch = true\n let defaultPrevented = false\n\n if (inNamespace && $) {\n jQueryEvent = $.Event(event, args)\n\n $(element).trigger(jQueryEvent)\n bubbles = !jQueryEvent.isPropagationStopped()\n nativeDispatch = !jQueryEvent.isImmediatePropagationStopped()\n defaultPrevented = jQueryEvent.isDefaultPrevented()\n }\n\n const evt = hydrateObj(new Event(event, { bubbles, cancelable: true }), args)\n\n if (defaultPrevented) {\n evt.preventDefault()\n }\n\n if (nativeDispatch) {\n element.dispatchEvent(evt)\n }\n\n if (evt.defaultPrevented && jQueryEvent) {\n jQueryEvent.preventDefault()\n }\n\n return evt\n }\n}\n\nfunction hydrateObj(obj, meta = {}) {\n for (const [key, value] of Object.entries(meta)) {\n try {\n obj[key] = value\n } catch {\n Object.defineProperty(obj, key, {\n configurable: true,\n get() {\n return value\n }\n })\n }\n }\n\n return obj\n}\n\nexport default EventHandler\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nfunction normalizeData(value) {\n if (value === 'true') {\n return true\n }\n\n if (value === 'false') {\n return false\n }\n\n if (value === Number(value).toString()) {\n return Number(value)\n }\n\n if (value === '' || value === 'null') {\n return null\n }\n\n if (typeof value !== 'string') {\n return value\n }\n\n try {\n return JSON.parse(decodeURIComponent(value))\n } catch {\n return value\n }\n}\n\nfunction normalizeDataKey(key) {\n return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`)\n}\n\nconst Manipulator = {\n setDataAttribute(element, key, value) {\n element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value)\n },\n\n removeDataAttribute(element, key) {\n element.removeAttribute(`data-bs-${normalizeDataKey(key)}`)\n },\n\n getDataAttributes(element) {\n if (!element) {\n return {}\n }\n\n const attributes = {}\n const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'))\n\n for (const key of bsKeys) {\n let pureKey = key.replace(/^bs/, '')\n pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length)\n attributes[pureKey] = normalizeData(element.dataset[key])\n }\n\n return attributes\n },\n\n getDataAttribute(element, key) {\n return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`))\n }\n}\n\nexport default Manipulator\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Manipulator from '../dom/manipulator.js'\nimport { isElement, toType } from './index.js'\n\n/**\n * Class definition\n */\n\nclass Config {\n // Getters\n static get Default() {\n return {}\n }\n\n static get DefaultType() {\n return {}\n }\n\n static get NAME() {\n throw new Error('You have to implement the static method \"NAME\", for each component!')\n }\n\n _getConfig(config) {\n config = this._mergeConfigObj(config)\n config = this._configAfterMerge(config)\n this._typeCheckConfig(config)\n return config\n }\n\n _configAfterMerge(config) {\n return config\n }\n\n _mergeConfigObj(config, element) {\n const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {} // try to parse\n\n return {\n ...this.constructor.Default,\n ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n ...(typeof config === 'object' ? config : {})\n }\n }\n\n _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n for (const [property, expectedTypes] of Object.entries(configTypes)) {\n const value = config[property]\n const valueType = isElement(value) ? 'element' : toType(value)\n\n if (!new RegExp(expectedTypes).test(valueType)) {\n throw new TypeError(\n `${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`\n )\n }\n }\n }\n}\n\nexport default Config\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Data from './dom/data.js'\nimport EventHandler from './dom/event-handler.js'\nimport Config from './util/config.js'\nimport { executeAfterTransition, getElement } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst VERSION = '5.3.1'\n\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n constructor(element, config) {\n super()\n\n element = getElement(element)\n if (!element) {\n return\n }\n\n this._element = element\n this._config = this._getConfig(config)\n\n Data.set(this._element, this.constructor.DATA_KEY, this)\n }\n\n // Public\n dispose() {\n Data.remove(this._element, this.constructor.DATA_KEY)\n EventHandler.off(this._element, this.constructor.EVENT_KEY)\n\n for (const propertyName of Object.getOwnPropertyNames(this)) {\n this[propertyName] = null\n }\n }\n\n _queueCallback(callback, element, isAnimated = true) {\n executeAfterTransition(callback, element, isAnimated)\n }\n\n _getConfig(config) {\n config = this._mergeConfigObj(config, this._element)\n config = this._configAfterMerge(config)\n this._typeCheckConfig(config)\n return config\n }\n\n // Static\n static getInstance(element) {\n return Data.get(getElement(element), this.DATA_KEY)\n }\n\n static getOrCreateInstance(element, config = {}) {\n return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null)\n }\n\n static get VERSION() {\n return VERSION\n }\n\n static get DATA_KEY() {\n return `bs.${this.NAME}`\n }\n\n static get EVENT_KEY() {\n return `.${this.DATA_KEY}`\n }\n\n static eventName(name) {\n return `${name}${this.EVENT_KEY}`\n }\n}\n\nexport default BaseComponent\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { isDisabled, isVisible, parseSelector } from '../util/index.js'\n\nconst getSelector = element => {\n let selector = element.getAttribute('data-bs-target')\n\n if (!selector || selector === '#') {\n let hrefAttribute = element.getAttribute('href')\n\n // The only valid content that could double as a selector are IDs or classes,\n // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n // `document.querySelector` will rightfully complain it is invalid.\n // See https://github.com/twbs/bootstrap/issues/32273\n if (!hrefAttribute || (!hrefAttribute.includes('#') && !hrefAttribute.startsWith('.'))) {\n return null\n }\n\n // Just in case some CMS puts out a full URL with the anchor appended\n if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n hrefAttribute = `#${hrefAttribute.split('#')[1]}`\n }\n\n selector = hrefAttribute && hrefAttribute !== '#' ? hrefAttribute.trim() : null\n }\n\n return parseSelector(selector)\n}\n\nconst SelectorEngine = {\n find(selector, element = document.documentElement) {\n return [].concat(...Element.prototype.querySelectorAll.call(element, selector))\n },\n\n findOne(selector, element = document.documentElement) {\n return Element.prototype.querySelector.call(element, selector)\n },\n\n children(element, selector) {\n return [].concat(...element.children).filter(child => child.matches(selector))\n },\n\n parents(element, selector) {\n const parents = []\n let ancestor = element.parentNode.closest(selector)\n\n while (ancestor) {\n parents.push(ancestor)\n ancestor = ancestor.parentNode.closest(selector)\n }\n\n return parents\n },\n\n prev(element, selector) {\n let previous = element.previousElementSibling\n\n while (previous) {\n if (previous.matches(selector)) {\n return [previous]\n }\n\n previous = previous.previousElementSibling\n }\n\n return []\n },\n // TODO: this is now unused; remove later along with prev()\n next(element, selector) {\n let next = element.nextElementSibling\n\n while (next) {\n if (next.matches(selector)) {\n return [next]\n }\n\n next = next.nextElementSibling\n }\n\n return []\n },\n\n focusableChildren(element) {\n const focusables = [\n 'a',\n 'button',\n 'input',\n 'textarea',\n 'select',\n 'details',\n '[tabindex]',\n '[contenteditable=\"true\"]'\n ].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',')\n\n return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el))\n },\n\n getSelectorFromElement(element) {\n const selector = getSelector(element)\n\n if (selector) {\n return SelectorEngine.findOne(selector) ? selector : null\n }\n\n return null\n },\n\n getElementFromSelector(element) {\n const selector = getSelector(element)\n\n return selector ? SelectorEngine.findOne(selector) : null\n },\n\n getMultipleElementsFromSelector(element) {\n const selector = getSelector(element)\n\n return selector ? SelectorEngine.find(selector) : []\n }\n}\n\nexport default SelectorEngine\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport { isDisabled } from './index.js'\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n const clickEvent = `click.dismiss${component.EVENT_KEY}`\n const name = component.NAME\n\n EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault()\n }\n\n if (isDisabled(this)) {\n return\n }\n\n const target = SelectorEngine.getElementFromSelector(this) || this.closest(`.${name}`)\n const instance = component.getOrCreateInstance(target)\n\n // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n instance[method]()\n })\n}\n\nexport {\n enableDismissTrigger\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'alert'\nconst DATA_KEY = 'bs.alert'\nconst EVENT_KEY = `.${DATA_KEY}`\n\nconst EVENT_CLOSE = `close${EVENT_KEY}`\nconst EVENT_CLOSED = `closed${EVENT_KEY}`\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\n\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME\n }\n\n // Public\n close() {\n const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE)\n\n if (closeEvent.defaultPrevented) {\n return\n }\n\n this._element.classList.remove(CLASS_NAME_SHOW)\n\n const isAnimated = this._element.classList.contains(CLASS_NAME_FADE)\n this._queueCallback(() => this._destroyElement(), this._element, isAnimated)\n }\n\n // Private\n _destroyElement() {\n this._element.remove()\n EventHandler.trigger(this._element, EVENT_CLOSED)\n this.dispose()\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Alert.getOrCreateInstance(this)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config](this)\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Alert, 'close')\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert)\n\nexport default Alert\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'button'\nconst DATA_KEY = 'bs.button'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst CLASS_NAME_ACTIVE = 'active'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"button\"]'\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle() {\n // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE))\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Button.getOrCreateInstance(this)\n\n if (config === 'toggle') {\n data[config]()\n }\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, event => {\n event.preventDefault()\n\n const button = event.target.closest(SELECTOR_DATA_TOGGLE)\n const data = Button.getOrCreateInstance(button)\n\n data.toggle()\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button)\n\nexport default Button\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport Config from './config.js'\nimport { execute } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'swipe'\nconst EVENT_KEY = '.bs.swipe'\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY}`\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY}`\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY}`\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY}`\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY}`\nconst POINTER_TYPE_TOUCH = 'touch'\nconst POINTER_TYPE_PEN = 'pen'\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event'\nconst SWIPE_THRESHOLD = 40\n\nconst Default = {\n endCallback: null,\n leftCallback: null,\n rightCallback: null\n}\n\nconst DefaultType = {\n endCallback: '(function|null)',\n leftCallback: '(function|null)',\n rightCallback: '(function|null)'\n}\n\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n constructor(element, config) {\n super()\n this._element = element\n\n if (!element || !Swipe.isSupported()) {\n return\n }\n\n this._config = this._getConfig(config)\n this._deltaX = 0\n this._supportPointerEvents = Boolean(window.PointerEvent)\n this._initEvents()\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n dispose() {\n EventHandler.off(this._element, EVENT_KEY)\n }\n\n // Private\n _start(event) {\n if (!this._supportPointerEvents) {\n this._deltaX = event.touches[0].clientX\n\n return\n }\n\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX\n }\n }\n\n _end(event) {\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX - this._deltaX\n }\n\n this._handleSwipe()\n execute(this._config.endCallback)\n }\n\n _move(event) {\n this._deltaX = event.touches && event.touches.length > 1 ?\n 0 :\n event.touches[0].clientX - this._deltaX\n }\n\n _handleSwipe() {\n const absDeltaX = Math.abs(this._deltaX)\n\n if (absDeltaX <= SWIPE_THRESHOLD) {\n return\n }\n\n const direction = absDeltaX / this._deltaX\n\n this._deltaX = 0\n\n if (!direction) {\n return\n }\n\n execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback)\n }\n\n _initEvents() {\n if (this._supportPointerEvents) {\n EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event))\n EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event))\n\n this._element.classList.add(CLASS_NAME_POINTER_EVENT)\n } else {\n EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event))\n EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event))\n EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event))\n }\n }\n\n _eventIsPointerPenTouch(event) {\n return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH)\n }\n\n // Static\n static isSupported() {\n return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0\n }\n}\n\nexport default Swipe\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n defineJQueryPlugin,\n getNextActiveElement,\n isRTL,\n isVisible,\n reflow,\n triggerTransitionEnd\n} from './util/index.js'\nimport Swipe from './util/swipe.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'carousel'\nconst DATA_KEY = 'bs.carousel'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst ARROW_LEFT_KEY = 'ArrowLeft'\nconst ARROW_RIGHT_KEY = 'ArrowRight'\nconst TOUCHEVENT_COMPAT_WAIT = 500 // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next'\nconst ORDER_PREV = 'prev'\nconst DIRECTION_LEFT = 'left'\nconst DIRECTION_RIGHT = 'right'\n\nconst EVENT_SLIDE = `slide${EVENT_KEY}`\nconst EVENT_SLID = `slid${EVENT_KEY}`\nconst EVENT_KEYDOWN = `keydown${EVENT_KEY}`\nconst EVENT_MOUSEENTER = `mouseenter${EVENT_KEY}`\nconst EVENT_MOUSELEAVE = `mouseleave${EVENT_KEY}`\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_CAROUSEL = 'carousel'\nconst CLASS_NAME_ACTIVE = 'active'\nconst CLASS_NAME_SLIDE = 'slide'\nconst CLASS_NAME_END = 'carousel-item-end'\nconst CLASS_NAME_START = 'carousel-item-start'\nconst CLASS_NAME_NEXT = 'carousel-item-next'\nconst CLASS_NAME_PREV = 'carousel-item-prev'\n\nconst SELECTOR_ACTIVE = '.active'\nconst SELECTOR_ITEM = '.carousel-item'\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM\nconst SELECTOR_ITEM_IMG = '.carousel-item img'\nconst SELECTOR_INDICATORS = '.carousel-indicators'\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]'\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]'\n\nconst KEY_TO_DIRECTION = {\n [ARROW_LEFT_KEY]: DIRECTION_RIGHT,\n [ARROW_RIGHT_KEY]: DIRECTION_LEFT\n}\n\nconst Default = {\n interval: 5000,\n keyboard: true,\n pause: 'hover',\n ride: false,\n touch: true,\n wrap: true\n}\n\nconst DefaultType = {\n interval: '(number|boolean)', // TODO:v6 remove boolean support\n keyboard: 'boolean',\n pause: '(string|boolean)',\n ride: '(boolean|string)',\n touch: 'boolean',\n wrap: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._interval = null\n this._activeElement = null\n this._isSliding = false\n this.touchTimeout = null\n this._swipeHelper = null\n\n this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element)\n this._addEventListeners()\n\n if (this._config.ride === CLASS_NAME_CAROUSEL) {\n this.cycle()\n }\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n next() {\n this._slide(ORDER_NEXT)\n }\n\n nextWhenVisible() {\n // FIXME TODO use `document.visibilityState`\n // Don't call next when the page isn't visible\n // or the carousel or its parent isn't visible\n if (!document.hidden && isVisible(this._element)) {\n this.next()\n }\n }\n\n prev() {\n this._slide(ORDER_PREV)\n }\n\n pause() {\n if (this._isSliding) {\n triggerTransitionEnd(this._element)\n }\n\n this._clearInterval()\n }\n\n cycle() {\n this._clearInterval()\n this._updateInterval()\n\n this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval)\n }\n\n _maybeEnableCycle() {\n if (!this._config.ride) {\n return\n }\n\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.cycle())\n return\n }\n\n this.cycle()\n }\n\n to(index) {\n const items = this._getItems()\n if (index > items.length - 1 || index < 0) {\n return\n }\n\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.to(index))\n return\n }\n\n const activeIndex = this._getItemIndex(this._getActive())\n if (activeIndex === index) {\n return\n }\n\n const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV\n\n this._slide(order, items[index])\n }\n\n dispose() {\n if (this._swipeHelper) {\n this._swipeHelper.dispose()\n }\n\n super.dispose()\n }\n\n // Private\n _configAfterMerge(config) {\n config.defaultInterval = config.interval\n return config\n }\n\n _addEventListeners() {\n if (this._config.keyboard) {\n EventHandler.on(this._element, EVENT_KEYDOWN, event => this._keydown(event))\n }\n\n if (this._config.pause === 'hover') {\n EventHandler.on(this._element, EVENT_MOUSEENTER, () => this.pause())\n EventHandler.on(this._element, EVENT_MOUSELEAVE, () => this._maybeEnableCycle())\n }\n\n if (this._config.touch && Swipe.isSupported()) {\n this._addTouchEventListeners()\n }\n }\n\n _addTouchEventListeners() {\n for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault())\n }\n\n const endCallBack = () => {\n if (this._config.pause !== 'hover') {\n return\n }\n\n // If it's a touch-enabled device, mouseenter/leave are fired as\n // part of the mouse compatibility events on first tap - the carousel\n // would stop cycling until user tapped out of it;\n // here, we listen for touchend, explicitly pause the carousel\n // (as if it's the second time we tap on it, mouseenter compat event\n // is NOT fired) and after a timeout (to allow for mouse compatibility\n // events to fire) we explicitly restart cycling\n\n this.pause()\n if (this.touchTimeout) {\n clearTimeout(this.touchTimeout)\n }\n\n this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval)\n }\n\n const swipeConfig = {\n leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n endCallback: endCallBack\n }\n\n this._swipeHelper = new Swipe(this._element, swipeConfig)\n }\n\n _keydown(event) {\n if (/input|textarea/i.test(event.target.tagName)) {\n return\n }\n\n const direction = KEY_TO_DIRECTION[event.key]\n if (direction) {\n event.preventDefault()\n this._slide(this._directionToOrder(direction))\n }\n }\n\n _getItemIndex(element) {\n return this._getItems().indexOf(element)\n }\n\n _setActiveIndicatorElement(index) {\n if (!this._indicatorsElement) {\n return\n }\n\n const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement)\n\n activeIndicator.classList.remove(CLASS_NAME_ACTIVE)\n activeIndicator.removeAttribute('aria-current')\n\n const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement)\n\n if (newActiveIndicator) {\n newActiveIndicator.classList.add(CLASS_NAME_ACTIVE)\n newActiveIndicator.setAttribute('aria-current', 'true')\n }\n }\n\n _updateInterval() {\n const element = this._activeElement || this._getActive()\n\n if (!element) {\n return\n }\n\n const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10)\n\n this._config.interval = elementInterval || this._config.defaultInterval\n }\n\n _slide(order, element = null) {\n if (this._isSliding) {\n return\n }\n\n const activeElement = this._getActive()\n const isNext = order === ORDER_NEXT\n const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap)\n\n if (nextElement === activeElement) {\n return\n }\n\n const nextElementIndex = this._getItemIndex(nextElement)\n\n const triggerEvent = eventName => {\n return EventHandler.trigger(this._element, eventName, {\n relatedTarget: nextElement,\n direction: this._orderToDirection(order),\n from: this._getItemIndex(activeElement),\n to: nextElementIndex\n })\n }\n\n const slideEvent = triggerEvent(EVENT_SLIDE)\n\n if (slideEvent.defaultPrevented) {\n return\n }\n\n if (!activeElement || !nextElement) {\n // Some weirdness is happening, so we bail\n // TODO: change tests that use empty divs to avoid this check\n return\n }\n\n const isCycling = Boolean(this._interval)\n this.pause()\n\n this._isSliding = true\n\n this._setActiveIndicatorElement(nextElementIndex)\n this._activeElement = nextElement\n\n const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END\n const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV\n\n nextElement.classList.add(orderClassName)\n\n reflow(nextElement)\n\n activeElement.classList.add(directionalClassName)\n nextElement.classList.add(directionalClassName)\n\n const completeCallBack = () => {\n nextElement.classList.remove(directionalClassName, orderClassName)\n nextElement.classList.add(CLASS_NAME_ACTIVE)\n\n activeElement.classList.remove(CLASS_NAME_ACTIVE, orderClassName, directionalClassName)\n\n this._isSliding = false\n\n triggerEvent(EVENT_SLID)\n }\n\n this._queueCallback(completeCallBack, activeElement, this._isAnimated())\n\n if (isCycling) {\n this.cycle()\n }\n }\n\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_SLIDE)\n }\n\n _getActive() {\n return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element)\n }\n\n _getItems() {\n return SelectorEngine.find(SELECTOR_ITEM, this._element)\n }\n\n _clearInterval() {\n if (this._interval) {\n clearInterval(this._interval)\n this._interval = null\n }\n }\n\n _directionToOrder(direction) {\n if (isRTL()) {\n return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT\n }\n\n return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV\n }\n\n _orderToDirection(order) {\n if (isRTL()) {\n return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT\n }\n\n return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Carousel.getOrCreateInstance(this, config)\n\n if (typeof config === 'number') {\n data.to(config)\n return\n }\n\n if (typeof config === 'string') {\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n }\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_SLIDE, function (event) {\n const target = SelectorEngine.getElementFromSelector(this)\n\n if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n return\n }\n\n event.preventDefault()\n\n const carousel = Carousel.getOrCreateInstance(target)\n const slideIndex = this.getAttribute('data-bs-slide-to')\n\n if (slideIndex) {\n carousel.to(slideIndex)\n carousel._maybeEnableCycle()\n return\n }\n\n if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n carousel.next()\n carousel._maybeEnableCycle()\n return\n }\n\n carousel.prev()\n carousel._maybeEnableCycle()\n})\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE)\n\n for (const carousel of carousels) {\n Carousel.getOrCreateInstance(carousel)\n }\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel)\n\nexport default Carousel\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n defineJQueryPlugin,\n getElement,\n reflow\n} from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'collapse'\nconst DATA_KEY = 'bs.collapse'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_COLLAPSE = 'collapse'\nconst CLASS_NAME_COLLAPSING = 'collapsing'\nconst CLASS_NAME_COLLAPSED = 'collapsed'\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal'\n\nconst WIDTH = 'width'\nconst HEIGHT = 'height'\n\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"collapse\"]'\n\nconst Default = {\n parent: null,\n toggle: true\n}\n\nconst DefaultType = {\n parent: '(null|element)',\n toggle: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._isTransitioning = false\n this._triggerArray = []\n\n const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE)\n\n for (const elem of toggleList) {\n const selector = SelectorEngine.getSelectorFromElement(elem)\n const filterElement = SelectorEngine.find(selector)\n .filter(foundElement => foundElement === this._element)\n\n if (selector !== null && filterElement.length) {\n this._triggerArray.push(elem)\n }\n }\n\n this._initializeChildren()\n\n if (!this._config.parent) {\n this._addAriaAndCollapsedClass(this._triggerArray, this._isShown())\n }\n\n if (this._config.toggle) {\n this.toggle()\n }\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle() {\n if (this._isShown()) {\n this.hide()\n } else {\n this.show()\n }\n }\n\n show() {\n if (this._isTransitioning || this._isShown()) {\n return\n }\n\n let activeChildren = []\n\n // find active children\n if (this._config.parent) {\n activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES)\n .filter(element => element !== this._element)\n .map(element => Collapse.getOrCreateInstance(element, { toggle: false }))\n }\n\n if (activeChildren.length && activeChildren[0]._isTransitioning) {\n return\n }\n\n const startEvent = EventHandler.trigger(this._element, EVENT_SHOW)\n if (startEvent.defaultPrevented) {\n return\n }\n\n for (const activeInstance of activeChildren) {\n activeInstance.hide()\n }\n\n const dimension = this._getDimension()\n\n this._element.classList.remove(CLASS_NAME_COLLAPSE)\n this._element.classList.add(CLASS_NAME_COLLAPSING)\n\n this._element.style[dimension] = 0\n\n this._addAriaAndCollapsedClass(this._triggerArray, true)\n this._isTransitioning = true\n\n const complete = () => {\n this._isTransitioning = false\n\n this._element.classList.remove(CLASS_NAME_COLLAPSING)\n this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW)\n\n this._element.style[dimension] = ''\n\n EventHandler.trigger(this._element, EVENT_SHOWN)\n }\n\n const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1)\n const scrollSize = `scroll${capitalizedDimension}`\n\n this._queueCallback(complete, this._element, true)\n this._element.style[dimension] = `${this._element[scrollSize]}px`\n }\n\n hide() {\n if (this._isTransitioning || !this._isShown()) {\n return\n }\n\n const startEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n if (startEvent.defaultPrevented) {\n return\n }\n\n const dimension = this._getDimension()\n\n this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`\n\n reflow(this._element)\n\n this._element.classList.add(CLASS_NAME_COLLAPSING)\n this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW)\n\n for (const trigger of this._triggerArray) {\n const element = SelectorEngine.getElementFromSelector(trigger)\n\n if (element && !this._isShown(element)) {\n this._addAriaAndCollapsedClass([trigger], false)\n }\n }\n\n this._isTransitioning = true\n\n const complete = () => {\n this._isTransitioning = false\n this._element.classList.remove(CLASS_NAME_COLLAPSING)\n this._element.classList.add(CLASS_NAME_COLLAPSE)\n EventHandler.trigger(this._element, EVENT_HIDDEN)\n }\n\n this._element.style[dimension] = ''\n\n this._queueCallback(complete, this._element, true)\n }\n\n _isShown(element = this._element) {\n return element.classList.contains(CLASS_NAME_SHOW)\n }\n\n // Private\n _configAfterMerge(config) {\n config.toggle = Boolean(config.toggle) // Coerce string values\n config.parent = getElement(config.parent)\n return config\n }\n\n _getDimension() {\n return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT\n }\n\n _initializeChildren() {\n if (!this._config.parent) {\n return\n }\n\n const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE)\n\n for (const element of children) {\n const selected = SelectorEngine.getElementFromSelector(element)\n\n if (selected) {\n this._addAriaAndCollapsedClass([element], this._isShown(selected))\n }\n }\n }\n\n _getFirstLevelChildren(selector) {\n const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent)\n // remove children if greater depth\n return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element))\n }\n\n _addAriaAndCollapsedClass(triggerArray, isOpen) {\n if (!triggerArray.length) {\n return\n }\n\n for (const element of triggerArray) {\n element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen)\n element.setAttribute('aria-expanded', isOpen)\n }\n }\n\n // Static\n static jQueryInterface(config) {\n const _config = {}\n if (typeof config === 'string' && /show|hide/.test(config)) {\n _config.toggle = false\n }\n\n return this.each(function () {\n const data = Collapse.getOrCreateInstance(this, _config)\n\n if (typeof config === 'string') {\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n }\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n // preventDefault only for elements (which change the URL) not inside the collapsible element\n if (event.target.tagName === 'A' || (event.delegateTarget && event.delegateTarget.tagName === 'A')) {\n event.preventDefault()\n }\n\n for (const element of SelectorEngine.getMultipleElementsFromSelector(this)) {\n Collapse.getOrCreateInstance(element, { toggle: false }).toggle()\n }\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse)\n\nexport default Collapse\n","export var top = 'top';\nexport var bottom = 'bottom';\nexport var right = 'right';\nexport var left = 'left';\nexport var auto = 'auto';\nexport var basePlacements = [top, bottom, right, left];\nexport var start = 'start';\nexport var end = 'end';\nexport var clippingParents = 'clippingParents';\nexport var viewport = 'viewport';\nexport var popper = 'popper';\nexport var reference = 'reference';\nexport var variationPlacements = /*#__PURE__*/basePlacements.reduce(function (acc, placement) {\n return acc.concat([placement + \"-\" + start, placement + \"-\" + end]);\n}, []);\nexport var placements = /*#__PURE__*/[].concat(basePlacements, [auto]).reduce(function (acc, placement) {\n return acc.concat([placement, placement + \"-\" + start, placement + \"-\" + end]);\n}, []); // modifiers that need to read the DOM\n\nexport var beforeRead = 'beforeRead';\nexport var read = 'read';\nexport var afterRead = 'afterRead'; // pure-logic modifiers\n\nexport var beforeMain = 'beforeMain';\nexport var main = 'main';\nexport var afterMain = 'afterMain'; // modifier with the purpose to write to the DOM (or write into a framework state)\n\nexport var beforeWrite = 'beforeWrite';\nexport var write = 'write';\nexport var afterWrite = 'afterWrite';\nexport var modifierPhases = [beforeRead, read, afterRead, beforeMain, main, afterMain, beforeWrite, write, afterWrite];","export default function getNodeName(element) {\n return element ? (element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n if (node == null) {\n return window;\n }\n\n if (node.toString() !== '[object Window]') {\n var ownerDocument = node.ownerDocument;\n return ownerDocument ? ownerDocument.defaultView || window : window;\n }\n\n return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n var OwnElement = getWindow(node).Element;\n return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n var OwnElement = getWindow(node).HTMLElement;\n return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n // IE 11 has no ShadowRoot\n if (typeof ShadowRoot === 'undefined') {\n return false;\n }\n\n var OwnElement = getWindow(node).ShadowRoot;\n return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n var state = _ref.state;\n Object.keys(state.elements).forEach(function (name) {\n var style = state.styles[name] || {};\n var attributes = state.attributes[name] || {};\n var element = state.elements[name]; // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n } // Flow doesn't support to extend this property, but it's the most\n // effective way to apply styles to an HTMLElement\n // $FlowFixMe[cannot-write]\n\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (name) {\n var value = attributes[name];\n\n if (value === false) {\n element.removeAttribute(name);\n } else {\n element.setAttribute(name, value === true ? '' : value);\n }\n });\n });\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state;\n var initialStyles = {\n popper: {\n position: state.options.strategy,\n left: '0',\n top: '0',\n margin: '0'\n },\n arrow: {\n position: 'absolute'\n },\n reference: {}\n };\n Object.assign(state.elements.popper.style, initialStyles.popper);\n state.styles = initialStyles;\n\n if (state.elements.arrow) {\n Object.assign(state.elements.arrow.style, initialStyles.arrow);\n }\n\n return function () {\n Object.keys(state.elements).forEach(function (name) {\n var element = state.elements[name];\n var attributes = state.attributes[name] || {};\n var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n var style = styleProperties.reduce(function (style, property) {\n style[property] = '';\n return style;\n }, {}); // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n }\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (attribute) {\n element.removeAttribute(attribute);\n });\n });\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'applyStyles',\n enabled: true,\n phase: 'write',\n fn: applyStyles,\n effect: effect,\n requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n var uaData = navigator.userAgentData;\n\n if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n return uaData.brands.map(function (item) {\n return item.brand + \"/\" + item.version;\n }).join(' ');\n }\n\n return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n if (includeScale === void 0) {\n includeScale = false;\n }\n\n if (isFixedStrategy === void 0) {\n isFixedStrategy = false;\n }\n\n var clientRect = element.getBoundingClientRect();\n var scaleX = 1;\n var scaleY = 1;\n\n if (includeScale && isHTMLElement(element)) {\n scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n }\n\n var _ref = isElement(element) ? getWindow(element) : window,\n visualViewport = _ref.visualViewport;\n\n var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n var width = clientRect.width / scaleX;\n var height = clientRect.height / scaleY;\n return {\n width: width,\n height: height,\n top: y,\n right: x + width,\n bottom: y + height,\n left: x,\n x: x,\n y: y\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n var width = element.offsetWidth;\n var height = element.offsetHeight;\n\n if (Math.abs(clientRect.width - width) <= 1) {\n width = clientRect.width;\n }\n\n if (Math.abs(clientRect.height - height) <= 1) {\n height = clientRect.height;\n }\n\n return {\n x: element.offsetLeft,\n y: element.offsetTop,\n width: width,\n height: height\n };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n if (parent.contains(child)) {\n return true;\n } // then fallback to custom implementation with Shadow DOM support\n else if (rootNode && isShadowRoot(rootNode)) {\n var next = child;\n\n do {\n if (next && parent.isSameNode(next)) {\n return true;\n } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n next = next.parentNode || next.host;\n } while (next);\n } // Give up, the result is false\n\n\n return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n if (getNodeName(element) === 'html') {\n return element;\n }\n\n return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n // $FlowFixMe[incompatible-return]\n // $FlowFixMe[prop-missing]\n element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n element.parentNode || ( // DOM Element detected\n isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n getDocumentElement(element) // fallback\n\n );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n getComputedStyle(element).position === 'fixed') {\n return null;\n }\n\n return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n var isFirefox = /firefox/i.test(getUAString());\n var isIE = /Trident/i.test(getUAString());\n\n if (isIE && isHTMLElement(element)) {\n // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n var elementCss = getComputedStyle(element);\n\n if (elementCss.position === 'fixed') {\n return null;\n }\n }\n\n var currentNode = getParentNode(element);\n\n if (isShadowRoot(currentNode)) {\n currentNode = currentNode.host;\n }\n\n while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n // create a containing block.\n // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n return currentNode;\n } else {\n currentNode = currentNode.parentNode;\n }\n }\n\n return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n var window = getWindow(element);\n var offsetParent = getTrueOffsetParent(element);\n\n while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n offsetParent = getTrueOffsetParent(offsetParent);\n }\n\n if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n return window;\n }\n\n return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n var v = within(min, value, max);\n return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n return {\n top: 0,\n right: 0,\n bottom: 0,\n left: 0\n };\n}","export default function expandToHashMap(value, keys) {\n return keys.reduce(function (hashMap, key) {\n hashMap[key] = value;\n return hashMap;\n }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n placement: state.placement\n })) : padding;\n return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n var _state$modifiersData$;\n\n var state = _ref.state,\n name = _ref.name,\n options = _ref.options;\n var arrowElement = state.elements.arrow;\n var popperOffsets = state.modifiersData.popperOffsets;\n var basePlacement = getBasePlacement(state.placement);\n var axis = getMainAxisFromPlacement(basePlacement);\n var isVertical = [left, right].indexOf(basePlacement) >= 0;\n var len = isVertical ? 'height' : 'width';\n\n if (!arrowElement || !popperOffsets) {\n return;\n }\n\n var paddingObject = toPaddingObject(options.padding, state);\n var arrowRect = getLayoutRect(arrowElement);\n var minProp = axis === 'y' ? top : left;\n var maxProp = axis === 'y' ? bottom : right;\n var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n var arrowOffsetParent = getOffsetParent(arrowElement);\n var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n // outside of the popper bounds\n\n var min = paddingObject[minProp];\n var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n var axisProp = axis;\n state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state,\n options = _ref2.options;\n var _options$element = options.element,\n arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n if (arrowElement == null) {\n return;\n } // CSS selector\n\n\n if (typeof arrowElement === 'string') {\n arrowElement = state.elements.popper.querySelector(arrowElement);\n\n if (!arrowElement) {\n return;\n }\n }\n\n if (!contains(state.elements.popper, arrowElement)) {\n return;\n }\n\n state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'arrow',\n enabled: true,\n phase: 'main',\n fn: arrow,\n effect: effect,\n requires: ['popperOffsets'],\n requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n top: 'auto',\n right: 'auto',\n bottom: 'auto',\n left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n var x = _ref.x,\n y = _ref.y;\n var dpr = win.devicePixelRatio || 1;\n return {\n x: round(x * dpr) / dpr || 0,\n y: round(y * dpr) / dpr || 0\n };\n}\n\nexport function mapToStyles(_ref2) {\n var _Object$assign2;\n\n var popper = _ref2.popper,\n popperRect = _ref2.popperRect,\n placement = _ref2.placement,\n variation = _ref2.variation,\n offsets = _ref2.offsets,\n position = _ref2.position,\n gpuAcceleration = _ref2.gpuAcceleration,\n adaptive = _ref2.adaptive,\n roundOffsets = _ref2.roundOffsets,\n isFixed = _ref2.isFixed;\n var _offsets$x = offsets.x,\n x = _offsets$x === void 0 ? 0 : _offsets$x,\n _offsets$y = offsets.y,\n y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n x: x,\n y: y\n }) : {\n x: x,\n y: y\n };\n\n x = _ref3.x;\n y = _ref3.y;\n var hasX = offsets.hasOwnProperty('x');\n var hasY = offsets.hasOwnProperty('y');\n var sideX = left;\n var sideY = top;\n var win = window;\n\n if (adaptive) {\n var offsetParent = getOffsetParent(popper);\n var heightProp = 'clientHeight';\n var widthProp = 'clientWidth';\n\n if (offsetParent === getWindow(popper)) {\n offsetParent = getDocumentElement(popper);\n\n if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n heightProp = 'scrollHeight';\n widthProp = 'scrollWidth';\n }\n } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n offsetParent = offsetParent;\n\n if (placement === top || (placement === left || placement === right) && variation === end) {\n sideY = bottom;\n var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n offsetParent[heightProp];\n y -= offsetY - popperRect.height;\n y *= gpuAcceleration ? 1 : -1;\n }\n\n if (placement === left || (placement === top || placement === bottom) && variation === end) {\n sideX = right;\n var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n offsetParent[widthProp];\n x -= offsetX - popperRect.width;\n x *= gpuAcceleration ? 1 : -1;\n }\n }\n\n var commonStyles = Object.assign({\n position: position\n }, adaptive && unsetSides);\n\n var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n x: x,\n y: y\n }, getWindow(popper)) : {\n x: x,\n y: y\n };\n\n x = _ref4.x;\n y = _ref4.y;\n\n if (gpuAcceleration) {\n var _Object$assign;\n\n return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n }\n\n return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n var state = _ref5.state,\n options = _ref5.options;\n var _options$gpuAccelerat = options.gpuAcceleration,\n gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n _options$adaptive = options.adaptive,\n adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n _options$roundOffsets = options.roundOffsets,\n roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n var commonStyles = {\n placement: getBasePlacement(state.placement),\n variation: getVariation(state.placement),\n popper: state.elements.popper,\n popperRect: state.rects.popper,\n gpuAcceleration: gpuAcceleration,\n isFixed: state.options.strategy === 'fixed'\n };\n\n if (state.modifiersData.popperOffsets != null) {\n state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.popperOffsets,\n position: state.options.strategy,\n adaptive: adaptive,\n roundOffsets: roundOffsets\n })));\n }\n\n if (state.modifiersData.arrow != null) {\n state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.arrow,\n position: 'absolute',\n adaptive: false,\n roundOffsets: roundOffsets\n })));\n }\n\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-placement': state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'computeStyles',\n enabled: true,\n phase: 'beforeWrite',\n fn: computeStyles,\n data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n passive: true\n};\n\nfunction effect(_ref) {\n var state = _ref.state,\n instance = _ref.instance,\n options = _ref.options;\n var _options$scroll = options.scroll,\n scroll = _options$scroll === void 0 ? true : _options$scroll,\n _options$resize = options.resize,\n resize = _options$resize === void 0 ? true : _options$resize;\n var window = getWindow(state.elements.popper);\n var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.addEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.addEventListener('resize', instance.update, passive);\n }\n\n return function () {\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.removeEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.removeEventListener('resize', instance.update, passive);\n }\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'eventListeners',\n enabled: true,\n phase: 'write',\n fn: function fn() {},\n effect: effect,\n data: {}\n};","var hash = {\n left: 'right',\n right: 'left',\n bottom: 'top',\n top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n return placement.replace(/left|right|bottom|top/g, function (matched) {\n return hash[matched];\n });\n}","var hash = {\n start: 'end',\n end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n return placement.replace(/start|end/g, function (matched) {\n return hash[matched];\n });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n var win = getWindow(node);\n var scrollLeft = win.pageXOffset;\n var scrollTop = win.pageYOffset;\n return {\n scrollLeft: scrollLeft,\n scrollTop: scrollTop\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n // If has a CSS width greater than the viewport, then this will be\n // incorrect for RTL.\n // Popper 1 is broken in this case and never had a bug report so let's assume\n // it's not an issue. I don't think anyone ever specifies width on \n // anyway.\n // Browsers where the left scrollbar doesn't cause an issue report `0` for\n // this (e.g. Edge 2019, IE11, Safari)\n return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n // Firefox wants us to check `-x` and `-y` variations as well\n var _getComputedStyle = getComputedStyle(element),\n overflow = _getComputedStyle.overflow,\n overflowX = _getComputedStyle.overflowX,\n overflowY = _getComputedStyle.overflowY;\n\n return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return node.ownerDocument.body;\n }\n\n if (isHTMLElement(node) && isScrollParent(node)) {\n return node;\n }\n\n return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n var _element$ownerDocumen;\n\n if (list === void 0) {\n list = [];\n }\n\n var scrollParent = getScrollParent(element);\n var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n var win = getWindow(scrollParent);\n var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n var updatedList = list.concat(target);\n return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n return Object.assign({}, rect, {\n left: rect.x,\n top: rect.y,\n right: rect.x + rect.width,\n bottom: rect.y + rect.height\n });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n rect.top = rect.top + element.clientTop;\n rect.left = rect.left + element.clientLeft;\n rect.bottom = rect.top + element.clientHeight;\n rect.right = rect.left + element.clientWidth;\n rect.width = element.clientWidth;\n rect.height = element.clientHeight;\n rect.x = rect.left;\n rect.y = rect.top;\n return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n var clippingParents = listScrollParents(getParentNode(element));\n var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n if (!isElement(clipperElement)) {\n return [];\n } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n return clippingParents.filter(function (clippingParent) {\n return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n var firstClippingParent = clippingParents[0];\n var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n accRect.top = max(rect.top, accRect.top);\n accRect.right = min(rect.right, accRect.right);\n accRect.bottom = min(rect.bottom, accRect.bottom);\n accRect.left = max(rect.left, accRect.left);\n return accRect;\n }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n clippingRect.width = clippingRect.right - clippingRect.left;\n clippingRect.height = clippingRect.bottom - clippingRect.top;\n clippingRect.x = clippingRect.left;\n clippingRect.y = clippingRect.top;\n return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n var win = getWindow(element);\n var html = getDocumentElement(element);\n var visualViewport = win.visualViewport;\n var width = html.clientWidth;\n var height = html.clientHeight;\n var x = 0;\n var y = 0;\n\n if (visualViewport) {\n width = visualViewport.width;\n height = visualViewport.height;\n var layoutViewport = isLayoutViewport();\n\n if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n x = visualViewport.offsetLeft;\n y = visualViewport.offsetTop;\n }\n }\n\n return {\n width: width,\n height: height,\n x: x + getWindowScrollBarX(element),\n y: y\n };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `` and `` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n var _element$ownerDocumen;\n\n var html = getDocumentElement(element);\n var winScroll = getWindowScroll(element);\n var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n var y = -winScroll.scrollTop;\n\n if (getComputedStyle(body || html).direction === 'rtl') {\n x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n }\n\n return {\n width: width,\n height: height,\n x: x,\n y: y\n };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n var reference = _ref.reference,\n element = _ref.element,\n placement = _ref.placement;\n var basePlacement = placement ? getBasePlacement(placement) : null;\n var variation = placement ? getVariation(placement) : null;\n var commonX = reference.x + reference.width / 2 - element.width / 2;\n var commonY = reference.y + reference.height / 2 - element.height / 2;\n var offsets;\n\n switch (basePlacement) {\n case top:\n offsets = {\n x: commonX,\n y: reference.y - element.height\n };\n break;\n\n case bottom:\n offsets = {\n x: commonX,\n y: reference.y + reference.height\n };\n break;\n\n case right:\n offsets = {\n x: reference.x + reference.width,\n y: commonY\n };\n break;\n\n case left:\n offsets = {\n x: reference.x - element.width,\n y: commonY\n };\n break;\n\n default:\n offsets = {\n x: reference.x,\n y: reference.y\n };\n }\n\n var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n if (mainAxis != null) {\n var len = mainAxis === 'y' ? 'height' : 'width';\n\n switch (variation) {\n case start:\n offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n break;\n\n case end:\n offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n break;\n\n default:\n }\n }\n\n return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n _options$placement = _options.placement,\n placement = _options$placement === void 0 ? state.placement : _options$placement,\n _options$strategy = _options.strategy,\n strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n _options$boundary = _options.boundary,\n boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n _options$rootBoundary = _options.rootBoundary,\n rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n _options$elementConte = _options.elementContext,\n elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n _options$altBoundary = _options.altBoundary,\n altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n _options$padding = _options.padding,\n padding = _options$padding === void 0 ? 0 : _options$padding;\n var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n var altContext = elementContext === popper ? reference : popper;\n var popperRect = state.rects.popper;\n var element = state.elements[altBoundary ? altContext : elementContext];\n var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n var referenceClientRect = getBoundingClientRect(state.elements.reference);\n var popperOffsets = computeOffsets({\n reference: referenceClientRect,\n element: popperRect,\n strategy: 'absolute',\n placement: placement\n });\n var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n // 0 or negative = within the clipping rect\n\n var overflowOffsets = {\n top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n };\n var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n if (elementContext === popper && offsetData) {\n var offset = offsetData[placement];\n Object.keys(overflowOffsets).forEach(function (key) {\n var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n overflowOffsets[key] += offset[axis] * multiply;\n });\n }\n\n return overflowOffsets;\n}","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n placement = _options.placement,\n boundary = _options.boundary,\n rootBoundary = _options.rootBoundary,\n padding = _options.padding,\n flipVariations = _options.flipVariations,\n _options$allowedAutoP = _options.allowedAutoPlacements,\n allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n var variation = getVariation(placement);\n var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n return getVariation(placement) === variation;\n }) : basePlacements;\n var allowedPlacements = placements.filter(function (placement) {\n return allowedAutoPlacements.indexOf(placement) >= 0;\n });\n\n if (allowedPlacements.length === 0) {\n allowedPlacements = placements;\n } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n var overflows = allowedPlacements.reduce(function (acc, placement) {\n acc[placement] = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding\n })[getBasePlacement(placement)];\n return acc;\n }, {});\n return Object.keys(overflows).sort(function (a, b) {\n return overflows[a] - overflows[b];\n });\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n if (getBasePlacement(placement) === auto) {\n return [];\n }\n\n var oppositePlacement = getOppositePlacement(placement);\n return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n\n if (state.modifiersData[name]._skip) {\n return;\n }\n\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n specifiedFallbackPlacements = options.fallbackPlacements,\n padding = options.padding,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n _options$flipVariatio = options.flipVariations,\n flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n allowedAutoPlacements = options.allowedAutoPlacements;\n var preferredPlacement = state.options.placement;\n var basePlacement = getBasePlacement(preferredPlacement);\n var isBasePlacement = basePlacement === preferredPlacement;\n var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n flipVariations: flipVariations,\n allowedAutoPlacements: allowedAutoPlacements\n }) : placement);\n }, []);\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var checksMap = new Map();\n var makeFallbackChecks = true;\n var firstFittingPlacement = placements[0];\n\n for (var i = 0; i < placements.length; i++) {\n var placement = placements[i];\n\n var _basePlacement = getBasePlacement(placement);\n\n var isStartVariation = getVariation(placement) === start;\n var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n var len = isVertical ? 'width' : 'height';\n var overflow = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n altBoundary: altBoundary,\n padding: padding\n });\n var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n if (referenceRect[len] > popperRect[len]) {\n mainVariationSide = getOppositePlacement(mainVariationSide);\n }\n\n var altVariationSide = getOppositePlacement(mainVariationSide);\n var checks = [];\n\n if (checkMainAxis) {\n checks.push(overflow[_basePlacement] <= 0);\n }\n\n if (checkAltAxis) {\n checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n }\n\n if (checks.every(function (check) {\n return check;\n })) {\n firstFittingPlacement = placement;\n makeFallbackChecks = false;\n break;\n }\n\n checksMap.set(placement, checks);\n }\n\n if (makeFallbackChecks) {\n // `2` may be desired in some cases – research later\n var numberOfChecks = flipVariations ? 3 : 1;\n\n var _loop = function _loop(_i) {\n var fittingPlacement = placements.find(function (placement) {\n var checks = checksMap.get(placement);\n\n if (checks) {\n return checks.slice(0, _i).every(function (check) {\n return check;\n });\n }\n });\n\n if (fittingPlacement) {\n firstFittingPlacement = fittingPlacement;\n return \"break\";\n }\n };\n\n for (var _i = numberOfChecks; _i > 0; _i--) {\n var _ret = _loop(_i);\n\n if (_ret === \"break\") break;\n }\n }\n\n if (state.placement !== firstFittingPlacement) {\n state.modifiersData[name]._skip = true;\n state.placement = firstFittingPlacement;\n state.reset = true;\n }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'flip',\n enabled: true,\n phase: 'main',\n fn: flip,\n requiresIfExists: ['offset'],\n data: {\n _skip: false\n }\n};","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n if (preventedOffsets === void 0) {\n preventedOffsets = {\n x: 0,\n y: 0\n };\n }\n\n return {\n top: overflow.top - rect.height - preventedOffsets.y,\n right: overflow.right - rect.width + preventedOffsets.x,\n bottom: overflow.bottom - rect.height + preventedOffsets.y,\n left: overflow.left - rect.width - preventedOffsets.x\n };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n return [top, right, bottom, left].some(function (side) {\n return overflow[side] >= 0;\n });\n}\n\nfunction hide(_ref) {\n var state = _ref.state,\n name = _ref.name;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var preventedOffsets = state.modifiersData.preventOverflow;\n var referenceOverflow = detectOverflow(state, {\n elementContext: 'reference'\n });\n var popperAltOverflow = detectOverflow(state, {\n altBoundary: true\n });\n var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n state.modifiersData[name] = {\n referenceClippingOffsets: referenceClippingOffsets,\n popperEscapeOffsets: popperEscapeOffsets,\n isReferenceHidden: isReferenceHidden,\n hasPopperEscaped: hasPopperEscaped\n };\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-reference-hidden': isReferenceHidden,\n 'data-popper-escaped': hasPopperEscaped\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'hide',\n enabled: true,\n phase: 'main',\n requiresIfExists: ['preventOverflow'],\n fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n var basePlacement = getBasePlacement(placement);\n var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n placement: placement\n })) : offset,\n skidding = _ref[0],\n distance = _ref[1];\n\n skidding = skidding || 0;\n distance = (distance || 0) * invertDistance;\n return [left, right].indexOf(basePlacement) >= 0 ? {\n x: distance,\n y: skidding\n } : {\n x: skidding,\n y: distance\n };\n}\n\nfunction offset(_ref2) {\n var state = _ref2.state,\n options = _ref2.options,\n name = _ref2.name;\n var _options$offset = options.offset,\n offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n var data = placements.reduce(function (acc, placement) {\n acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n return acc;\n }, {});\n var _data$state$placement = data[state.placement],\n x = _data$state$placement.x,\n y = _data$state$placement.y;\n\n if (state.modifiersData.popperOffsets != null) {\n state.modifiersData.popperOffsets.x += x;\n state.modifiersData.popperOffsets.y += y;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'offset',\n enabled: true,\n phase: 'main',\n requires: ['popperOffsets'],\n fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n var state = _ref.state,\n name = _ref.name;\n // Offsets are the actual position the popper needs to have to be\n // properly positioned near its reference element\n // This is the most basic placement, and will be adjusted by\n // the modifiers in the next step\n state.modifiersData[name] = computeOffsets({\n reference: state.rects.reference,\n element: state.rects.popper,\n strategy: 'absolute',\n placement: state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'popperOffsets',\n enabled: true,\n phase: 'read',\n fn: popperOffsets,\n data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n padding = options.padding,\n _options$tether = options.tether,\n tether = _options$tether === void 0 ? true : _options$tether,\n _options$tetherOffset = options.tetherOffset,\n tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n var overflow = detectOverflow(state, {\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n altBoundary: altBoundary\n });\n var basePlacement = getBasePlacement(state.placement);\n var variation = getVariation(state.placement);\n var isBasePlacement = !variation;\n var mainAxis = getMainAxisFromPlacement(basePlacement);\n var altAxis = getAltAxis(mainAxis);\n var popperOffsets = state.modifiersData.popperOffsets;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n placement: state.placement\n })) : tetherOffset;\n var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n mainAxis: tetherOffsetValue,\n altAxis: tetherOffsetValue\n } : Object.assign({\n mainAxis: 0,\n altAxis: 0\n }, tetherOffsetValue);\n var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n var data = {\n x: 0,\n y: 0\n };\n\n if (!popperOffsets) {\n return;\n }\n\n if (checkMainAxis) {\n var _offsetModifierState$;\n\n var mainSide = mainAxis === 'y' ? top : left;\n var altSide = mainAxis === 'y' ? bottom : right;\n var len = mainAxis === 'y' ? 'height' : 'width';\n var offset = popperOffsets[mainAxis];\n var min = offset + overflow[mainSide];\n var max = offset - overflow[altSide];\n var additive = tether ? -popperRect[len] / 2 : 0;\n var minLen = variation === start ? referenceRect[len] : popperRect[len];\n var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n // outside the reference bounds\n\n var arrowElement = state.elements.arrow;\n var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n width: 0,\n height: 0\n };\n var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n var arrowPaddingMin = arrowPaddingObject[mainSide];\n var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n // to include its full size in the calculation. If the reference is small\n // and near the edge of a boundary, the popper can overflow even if the\n // reference is not overflowing as well (e.g. virtual elements with no\n // width or height)\n\n var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n var tetherMax = offset + maxOffset - offsetModifierValue;\n var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n popperOffsets[mainAxis] = preventedOffset;\n data[mainAxis] = preventedOffset - offset;\n }\n\n if (checkAltAxis) {\n var _offsetModifierState$2;\n\n var _mainSide = mainAxis === 'x' ? top : left;\n\n var _altSide = mainAxis === 'x' ? bottom : right;\n\n var _offset = popperOffsets[altAxis];\n\n var _len = altAxis === 'y' ? 'height' : 'width';\n\n var _min = _offset + overflow[_mainSide];\n\n var _max = _offset - overflow[_altSide];\n\n var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n popperOffsets[altAxis] = _preventedOffset;\n data[altAxis] = _preventedOffset - _offset;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'preventOverflow',\n enabled: true,\n phase: 'main',\n fn: preventOverflow,\n requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n var rect = element.getBoundingClientRect();\n var scaleX = round(rect.width) / element.offsetWidth || 1;\n var scaleY = round(rect.height) / element.offsetHeight || 1;\n return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n if (isFixed === void 0) {\n isFixed = false;\n }\n\n var isOffsetParentAnElement = isHTMLElement(offsetParent);\n var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n var documentElement = getDocumentElement(offsetParent);\n var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n var scroll = {\n scrollLeft: 0,\n scrollTop: 0\n };\n var offsets = {\n x: 0,\n y: 0\n };\n\n if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n isScrollParent(documentElement)) {\n scroll = getNodeScroll(offsetParent);\n }\n\n if (isHTMLElement(offsetParent)) {\n offsets = getBoundingClientRect(offsetParent, true);\n offsets.x += offsetParent.clientLeft;\n offsets.y += offsetParent.clientTop;\n } else if (documentElement) {\n offsets.x = getWindowScrollBarX(documentElement);\n }\n }\n\n return {\n x: rect.left + scroll.scrollLeft - offsets.x,\n y: rect.top + scroll.scrollTop - offsets.y,\n width: rect.width,\n height: rect.height\n };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n if (node === getWindow(node) || !isHTMLElement(node)) {\n return getWindowScroll(node);\n } else {\n return getHTMLElementScroll(node);\n }\n}","export default function getHTMLElementScroll(element) {\n return {\n scrollLeft: element.scrollLeft,\n scrollTop: element.scrollTop\n };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n var map = new Map();\n var visited = new Set();\n var result = [];\n modifiers.forEach(function (modifier) {\n map.set(modifier.name, modifier);\n }); // On visiting object, check for its dependencies and visit them recursively\n\n function sort(modifier) {\n visited.add(modifier.name);\n var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n requires.forEach(function (dep) {\n if (!visited.has(dep)) {\n var depModifier = map.get(dep);\n\n if (depModifier) {\n sort(depModifier);\n }\n }\n });\n result.push(modifier);\n }\n\n modifiers.forEach(function (modifier) {\n if (!visited.has(modifier.name)) {\n // check for visited object\n sort(modifier);\n }\n });\n return result;\n}\n\nexport default function orderModifiers(modifiers) {\n // order based on dependencies\n var orderedModifiers = order(modifiers); // order based on phase\n\n return modifierPhases.reduce(function (acc, phase) {\n return acc.concat(orderedModifiers.filter(function (modifier) {\n return modifier.phase === phase;\n }));\n }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nvar DEFAULT_OPTIONS = {\n placement: 'bottom',\n modifiers: [],\n strategy: 'absolute'\n};\n\nfunction areValidElements() {\n for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n args[_key] = arguments[_key];\n }\n\n return !args.some(function (element) {\n return !(element && typeof element.getBoundingClientRect === 'function');\n });\n}\n\nexport function popperGenerator(generatorOptions) {\n if (generatorOptions === void 0) {\n generatorOptions = {};\n }\n\n var _generatorOptions = generatorOptions,\n _generatorOptions$def = _generatorOptions.defaultModifiers,\n defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n _generatorOptions$def2 = _generatorOptions.defaultOptions,\n defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n return function createPopper(reference, popper, options) {\n if (options === void 0) {\n options = defaultOptions;\n }\n\n var state = {\n placement: 'bottom',\n orderedModifiers: [],\n options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n modifiersData: {},\n elements: {\n reference: reference,\n popper: popper\n },\n attributes: {},\n styles: {}\n };\n var effectCleanupFns = [];\n var isDestroyed = false;\n var instance = {\n state: state,\n setOptions: function setOptions(setOptionsAction) {\n var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n cleanupModifierEffects();\n state.options = Object.assign({}, defaultOptions, state.options, options);\n state.scrollParents = {\n reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n popper: listScrollParents(popper)\n }; // Orders the modifiers based on their dependencies and `phase`\n // properties\n\n var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n state.orderedModifiers = orderedModifiers.filter(function (m) {\n return m.enabled;\n });\n runModifierEffects();\n return instance.update();\n },\n // Sync update – it will always be executed, even if not necessary. This\n // is useful for low frequency updates where sync behavior simplifies the\n // logic.\n // For high frequency updates (e.g. `resize` and `scroll` events), always\n // prefer the async Popper#update method\n forceUpdate: function forceUpdate() {\n if (isDestroyed) {\n return;\n }\n\n var _state$elements = state.elements,\n reference = _state$elements.reference,\n popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n // anymore\n\n if (!areValidElements(reference, popper)) {\n return;\n } // Store the reference and popper rects to be read by modifiers\n\n\n state.rects = {\n reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n popper: getLayoutRect(popper)\n }; // Modifiers have the ability to reset the current update cycle. The\n // most common use case for this is the `flip` modifier changing the\n // placement, which then needs to re-run all the modifiers, because the\n // logic was previously ran for the previous placement and is therefore\n // stale/incorrect\n\n state.reset = false;\n state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n // is filled with the initial data specified by the modifier. This means\n // it doesn't persist and is fresh on each update.\n // To ensure persistent data, use `${name}#persistent`\n\n state.orderedModifiers.forEach(function (modifier) {\n return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n });\n\n for (var index = 0; index < state.orderedModifiers.length; index++) {\n if (state.reset === true) {\n state.reset = false;\n index = -1;\n continue;\n }\n\n var _state$orderedModifie = state.orderedModifiers[index],\n fn = _state$orderedModifie.fn,\n _state$orderedModifie2 = _state$orderedModifie.options,\n _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n name = _state$orderedModifie.name;\n\n if (typeof fn === 'function') {\n state = fn({\n state: state,\n options: _options,\n name: name,\n instance: instance\n }) || state;\n }\n }\n },\n // Async and optimistically optimized update – it will not be executed if\n // not necessary (debounced to run at most once-per-tick)\n update: debounce(function () {\n return new Promise(function (resolve) {\n instance.forceUpdate();\n resolve(state);\n });\n }),\n destroy: function destroy() {\n cleanupModifierEffects();\n isDestroyed = true;\n }\n };\n\n if (!areValidElements(reference, popper)) {\n return instance;\n }\n\n instance.setOptions(options).then(function (state) {\n if (!isDestroyed && options.onFirstUpdate) {\n options.onFirstUpdate(state);\n }\n }); // Modifiers have the ability to execute arbitrary code before the first\n // update cycle runs. They will be executed in the same order as the update\n // cycle. This is useful when a modifier adds some persistent data that\n // other modifiers need to use, but the modifier is run after the dependent\n // one.\n\n function runModifierEffects() {\n state.orderedModifiers.forEach(function (_ref) {\n var name = _ref.name,\n _ref$options = _ref.options,\n options = _ref$options === void 0 ? {} : _ref$options,\n effect = _ref.effect;\n\n if (typeof effect === 'function') {\n var cleanupFn = effect({\n state: state,\n name: name,\n instance: instance,\n options: options\n });\n\n var noopFn = function noopFn() {};\n\n effectCleanupFns.push(cleanupFn || noopFn);\n }\n });\n }\n\n function cleanupModifierEffects() {\n effectCleanupFns.forEach(function (fn) {\n return fn();\n });\n effectCleanupFns = [];\n }\n\n return instance;\n };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n var pending;\n return function () {\n if (!pending) {\n pending = new Promise(function (resolve) {\n Promise.resolve().then(function () {\n pending = undefined;\n resolve(fn());\n });\n });\n }\n\n return pending;\n };\n}","export default function mergeByName(modifiers) {\n var merged = modifiers.reduce(function (merged, current) {\n var existing = merged[current.name];\n merged[current.name] = existing ? Object.assign({}, existing, current, {\n options: Object.assign({}, existing.options, current.options),\n data: Object.assign({}, existing.data, current.data)\n }) : current;\n return merged;\n }, {}); // IE11 does not support Object.values\n\n return Object.keys(merged).map(function (key) {\n return merged[key];\n });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","/**\n * --------------------------------------------------------------------------\n * Bootstrap dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport * as Popper from '@popperjs/core'\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n defineJQueryPlugin,\n execute,\n getElement,\n getNextActiveElement,\n isDisabled,\n isElement,\n isRTL,\n isVisible,\n noop\n} from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'dropdown'\nconst DATA_KEY = 'bs.dropdown'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst ESCAPE_KEY = 'Escape'\nconst TAB_KEY = 'Tab'\nconst ARROW_UP_KEY = 'ArrowUp'\nconst ARROW_DOWN_KEY = 'ArrowDown'\nconst RIGHT_MOUSE_BUTTON = 2 // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_DROPUP = 'dropup'\nconst CLASS_NAME_DROPEND = 'dropend'\nconst CLASS_NAME_DROPSTART = 'dropstart'\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center'\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center'\n\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)'\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE}.${CLASS_NAME_SHOW}`\nconst SELECTOR_MENU = '.dropdown-menu'\nconst SELECTOR_NAVBAR = '.navbar'\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav'\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)'\n\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start'\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end'\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start'\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end'\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start'\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start'\nconst PLACEMENT_TOPCENTER = 'top'\nconst PLACEMENT_BOTTOMCENTER = 'bottom'\n\nconst Default = {\n autoClose: true,\n boundary: 'clippingParents',\n display: 'dynamic',\n offset: [0, 2],\n popperConfig: null,\n reference: 'toggle'\n}\n\nconst DefaultType = {\n autoClose: '(boolean|string)',\n boundary: '(string|element)',\n display: 'string',\n offset: '(array|string|function)',\n popperConfig: '(null|object|function)',\n reference: '(string|element|object)'\n}\n\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._popper = null\n this._parent = this._element.parentNode // dropdown wrapper\n // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] ||\n SelectorEngine.prev(this._element, SELECTOR_MENU)[0] ||\n SelectorEngine.findOne(SELECTOR_MENU, this._parent)\n this._inNavbar = this._detectNavbar()\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle() {\n return this._isShown() ? this.hide() : this.show()\n }\n\n show() {\n if (isDisabled(this._element) || this._isShown()) {\n return\n }\n\n const relatedTarget = {\n relatedTarget: this._element\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, relatedTarget)\n\n if (showEvent.defaultPrevented) {\n return\n }\n\n this._createPopper()\n\n // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop)\n }\n }\n\n this._element.focus()\n this._element.setAttribute('aria-expanded', true)\n\n this._menu.classList.add(CLASS_NAME_SHOW)\n this._element.classList.add(CLASS_NAME_SHOW)\n EventHandler.trigger(this._element, EVENT_SHOWN, relatedTarget)\n }\n\n hide() {\n if (isDisabled(this._element) || !this._isShown()) {\n return\n }\n\n const relatedTarget = {\n relatedTarget: this._element\n }\n\n this._completeHide(relatedTarget)\n }\n\n dispose() {\n if (this._popper) {\n this._popper.destroy()\n }\n\n super.dispose()\n }\n\n update() {\n this._inNavbar = this._detectNavbar()\n if (this._popper) {\n this._popper.update()\n }\n }\n\n // Private\n _completeHide(relatedTarget) {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE, relatedTarget)\n if (hideEvent.defaultPrevented) {\n return\n }\n\n // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop)\n }\n }\n\n if (this._popper) {\n this._popper.destroy()\n }\n\n this._menu.classList.remove(CLASS_NAME_SHOW)\n this._element.classList.remove(CLASS_NAME_SHOW)\n this._element.setAttribute('aria-expanded', 'false')\n Manipulator.removeDataAttribute(this._menu, 'popper')\n EventHandler.trigger(this._element, EVENT_HIDDEN, relatedTarget)\n }\n\n _getConfig(config) {\n config = super._getConfig(config)\n\n if (typeof config.reference === 'object' && !isElement(config.reference) &&\n typeof config.reference.getBoundingClientRect !== 'function'\n ) {\n // Popper virtual elements require a getBoundingClientRect method\n throw new TypeError(`${NAME.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`)\n }\n\n return config\n }\n\n _createPopper() {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)')\n }\n\n let referenceElement = this._element\n\n if (this._config.reference === 'parent') {\n referenceElement = this._parent\n } else if (isElement(this._config.reference)) {\n referenceElement = getElement(this._config.reference)\n } else if (typeof this._config.reference === 'object') {\n referenceElement = this._config.reference\n }\n\n const popperConfig = this._getPopperConfig()\n this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig)\n }\n\n _isShown() {\n return this._menu.classList.contains(CLASS_NAME_SHOW)\n }\n\n _getPlacement() {\n const parentDropdown = this._parent\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n return PLACEMENT_RIGHT\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n return PLACEMENT_LEFT\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n return PLACEMENT_TOPCENTER\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n return PLACEMENT_BOTTOMCENTER\n }\n\n // We need to trim the value because custom properties can also include spaces\n const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end'\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP\n }\n\n return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM\n }\n\n _detectNavbar() {\n return this._element.closest(SELECTOR_NAVBAR) !== null\n }\n\n _getOffset() {\n const { offset } = this._config\n\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10))\n }\n\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element)\n }\n\n return offset\n }\n\n _getPopperConfig() {\n const defaultBsPopperConfig = {\n placement: this._getPlacement(),\n modifiers: [{\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n },\n {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n }]\n }\n\n // Disable Popper if we have a static display or Dropdown is in Navbar\n if (this._inNavbar || this._config.display === 'static') {\n Manipulator.setDataAttribute(this._menu, 'popper', 'static') // TODO: v6 remove\n defaultBsPopperConfig.modifiers = [{\n name: 'applyStyles',\n enabled: false\n }]\n }\n\n return {\n ...defaultBsPopperConfig,\n ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n }\n }\n\n _selectMenuItem({ key, target }) {\n const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element))\n\n if (!items.length) {\n return\n }\n\n // if target isn't included in items (e.g. when expanding the dropdown)\n // allow cycling to get the last item in case key equals ARROW_UP_KEY\n getNextActiveElement(items, target, key === ARROW_DOWN_KEY, !items.includes(target)).focus()\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Dropdown.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n })\n }\n\n static clearMenus(event) {\n if (event.button === RIGHT_MOUSE_BUTTON || (event.type === 'keyup' && event.key !== TAB_KEY)) {\n return\n }\n\n const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN)\n\n for (const toggle of openToggles) {\n const context = Dropdown.getInstance(toggle)\n if (!context || context._config.autoClose === false) {\n continue\n }\n\n const composedPath = event.composedPath()\n const isMenuTarget = composedPath.includes(context._menu)\n if (\n composedPath.includes(context._element) ||\n (context._config.autoClose === 'inside' && !isMenuTarget) ||\n (context._config.autoClose === 'outside' && isMenuTarget)\n ) {\n continue\n }\n\n // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n if (context._menu.contains(event.target) && ((event.type === 'keyup' && event.key === TAB_KEY) || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n continue\n }\n\n const relatedTarget = { relatedTarget: context._element }\n\n if (event.type === 'click') {\n relatedTarget.clickEvent = event\n }\n\n context._completeHide(relatedTarget)\n }\n }\n\n static dataApiKeydownHandler(event) {\n // If not an UP | DOWN | ESCAPE key => not a dropdown command\n // If input/textarea && if key is other than ESCAPE => not a dropdown command\n\n const isInput = /input|textarea/i.test(event.target.tagName)\n const isEscapeEvent = event.key === ESCAPE_KEY\n const isUpOrDownEvent = [ARROW_UP_KEY, ARROW_DOWN_KEY].includes(event.key)\n\n if (!isUpOrDownEvent && !isEscapeEvent) {\n return\n }\n\n if (isInput && !isEscapeEvent) {\n return\n }\n\n event.preventDefault()\n\n // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE) ?\n this :\n (SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE)[0] ||\n SelectorEngine.next(this, SELECTOR_DATA_TOGGLE)[0] ||\n SelectorEngine.findOne(SELECTOR_DATA_TOGGLE, event.delegateTarget.parentNode))\n\n const instance = Dropdown.getOrCreateInstance(getToggleButton)\n\n if (isUpOrDownEvent) {\n event.stopPropagation()\n instance.show()\n instance._selectMenuItem(event)\n return\n }\n\n if (instance._isShown()) { // else is escape and we check if it is shown\n event.stopPropagation()\n instance.hide()\n getToggleButton.focus()\n }\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE, Dropdown.dataApiKeydownHandler)\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler)\nEventHandler.on(document, EVENT_CLICK_DATA_API, Dropdown.clearMenus)\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus)\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n event.preventDefault()\n Dropdown.getOrCreateInstance(this).toggle()\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown)\n\nexport default Dropdown\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport Config from './config.js'\nimport { execute, executeAfterTransition, getElement, reflow } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'backdrop'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME}`\n\nconst Default = {\n className: 'modal-backdrop',\n clickCallback: null,\n isAnimated: false,\n isVisible: true, // if false, we use the backdrop helper without adding any element to the dom\n rootElement: 'body' // give the choice to place backdrop under different elements\n}\n\nconst DefaultType = {\n className: 'string',\n clickCallback: '(function|null)',\n isAnimated: 'boolean',\n isVisible: 'boolean',\n rootElement: '(element|string)'\n}\n\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n constructor(config) {\n super()\n this._config = this._getConfig(config)\n this._isAppended = false\n this._element = null\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n show(callback) {\n if (!this._config.isVisible) {\n execute(callback)\n return\n }\n\n this._append()\n\n const element = this._getElement()\n if (this._config.isAnimated) {\n reflow(element)\n }\n\n element.classList.add(CLASS_NAME_SHOW)\n\n this._emulateAnimation(() => {\n execute(callback)\n })\n }\n\n hide(callback) {\n if (!this._config.isVisible) {\n execute(callback)\n return\n }\n\n this._getElement().classList.remove(CLASS_NAME_SHOW)\n\n this._emulateAnimation(() => {\n this.dispose()\n execute(callback)\n })\n }\n\n dispose() {\n if (!this._isAppended) {\n return\n }\n\n EventHandler.off(this._element, EVENT_MOUSEDOWN)\n\n this._element.remove()\n this._isAppended = false\n }\n\n // Private\n _getElement() {\n if (!this._element) {\n const backdrop = document.createElement('div')\n backdrop.className = this._config.className\n if (this._config.isAnimated) {\n backdrop.classList.add(CLASS_NAME_FADE)\n }\n\n this._element = backdrop\n }\n\n return this._element\n }\n\n _configAfterMerge(config) {\n // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n config.rootElement = getElement(config.rootElement)\n return config\n }\n\n _append() {\n if (this._isAppended) {\n return\n }\n\n const element = this._getElement()\n this._config.rootElement.append(element)\n\n EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n execute(this._config.clickCallback)\n })\n\n this._isAppended = true\n }\n\n _emulateAnimation(callback) {\n executeAfterTransition(callback, this._getElement(), this._config.isAnimated)\n }\n}\n\nexport default Backdrop\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport Config from './config.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'focustrap'\nconst DATA_KEY = 'bs.focustrap'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst EVENT_FOCUSIN = `focusin${EVENT_KEY}`\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY}`\n\nconst TAB_KEY = 'Tab'\nconst TAB_NAV_FORWARD = 'forward'\nconst TAB_NAV_BACKWARD = 'backward'\n\nconst Default = {\n autofocus: true,\n trapElement: null // The element to trap focus inside of\n}\n\nconst DefaultType = {\n autofocus: 'boolean',\n trapElement: 'element'\n}\n\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n constructor(config) {\n super()\n this._config = this._getConfig(config)\n this._isActive = false\n this._lastTabNavDirection = null\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n activate() {\n if (this._isActive) {\n return\n }\n\n if (this._config.autofocus) {\n this._config.trapElement.focus()\n }\n\n EventHandler.off(document, EVENT_KEY) // guard against infinite focus loop\n EventHandler.on(document, EVENT_FOCUSIN, event => this._handleFocusin(event))\n EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event))\n\n this._isActive = true\n }\n\n deactivate() {\n if (!this._isActive) {\n return\n }\n\n this._isActive = false\n EventHandler.off(document, EVENT_KEY)\n }\n\n // Private\n _handleFocusin(event) {\n const { trapElement } = this._config\n\n if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n return\n }\n\n const elements = SelectorEngine.focusableChildren(trapElement)\n\n if (elements.length === 0) {\n trapElement.focus()\n } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n elements[elements.length - 1].focus()\n } else {\n elements[0].focus()\n }\n }\n\n _handleKeydown(event) {\n if (event.key !== TAB_KEY) {\n return\n }\n\n this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD\n }\n}\n\nexport default FocusTrap\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Manipulator from '../dom/manipulator.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport { isElement } from './index.js'\n\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top'\nconst SELECTOR_STICKY_CONTENT = '.sticky-top'\nconst PROPERTY_PADDING = 'padding-right'\nconst PROPERTY_MARGIN = 'margin-right'\n\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n constructor() {\n this._element = document.body\n }\n\n // Public\n getWidth() {\n // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n const documentWidth = document.documentElement.clientWidth\n return Math.abs(window.innerWidth - documentWidth)\n }\n\n hide() {\n const width = this.getWidth()\n this._disableOverFlow()\n // give padding to element to balance the hidden scrollbar width\n this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width)\n // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width)\n this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width)\n }\n\n reset() {\n this._resetElementAttributes(this._element, 'overflow')\n this._resetElementAttributes(this._element, PROPERTY_PADDING)\n this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING)\n this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN)\n }\n\n isOverflowing() {\n return this.getWidth() > 0\n }\n\n // Private\n _disableOverFlow() {\n this._saveInitialAttribute(this._element, 'overflow')\n this._element.style.overflow = 'hidden'\n }\n\n _setElementAttributes(selector, styleProperty, callback) {\n const scrollbarWidth = this.getWidth()\n const manipulationCallBack = element => {\n if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n return\n }\n\n this._saveInitialAttribute(element, styleProperty)\n const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty)\n element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`)\n }\n\n this._applyManipulationCallback(selector, manipulationCallBack)\n }\n\n _saveInitialAttribute(element, styleProperty) {\n const actualValue = element.style.getPropertyValue(styleProperty)\n if (actualValue) {\n Manipulator.setDataAttribute(element, styleProperty, actualValue)\n }\n }\n\n _resetElementAttributes(selector, styleProperty) {\n const manipulationCallBack = element => {\n const value = Manipulator.getDataAttribute(element, styleProperty)\n // We only want to remove the property if the value is `null`; the value can also be zero\n if (value === null) {\n element.style.removeProperty(styleProperty)\n return\n }\n\n Manipulator.removeDataAttribute(element, styleProperty)\n element.style.setProperty(styleProperty, value)\n }\n\n this._applyManipulationCallback(selector, manipulationCallBack)\n }\n\n _applyManipulationCallback(selector, callBack) {\n if (isElement(selector)) {\n callBack(selector)\n return\n }\n\n for (const sel of SelectorEngine.find(selector, this._element)) {\n callBack(sel)\n }\n }\n}\n\nexport default ScrollBarHelper\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport Backdrop from './util/backdrop.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport FocusTrap from './util/focustrap.js'\nimport { defineJQueryPlugin, isRTL, isVisible, reflow } from './util/index.js'\nimport ScrollBarHelper from './util/scrollbar.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'modal'\nconst DATA_KEY = 'bs.modal'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\nconst ESCAPE_KEY = 'Escape'\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_RESIZE = `resize${EVENT_KEY}`\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY}`\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY}`\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_OPEN = 'modal-open'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_STATIC = 'modal-static'\n\nconst OPEN_SELECTOR = '.modal.show'\nconst SELECTOR_DIALOG = '.modal-dialog'\nconst SELECTOR_MODAL_BODY = '.modal-body'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"modal\"]'\n\nconst Default = {\n backdrop: true,\n focus: true,\n keyboard: true\n}\n\nconst DefaultType = {\n backdrop: '(boolean|string)',\n focus: 'boolean',\n keyboard: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element)\n this._backdrop = this._initializeBackDrop()\n this._focustrap = this._initializeFocusTrap()\n this._isShown = false\n this._isTransitioning = false\n this._scrollBar = new ScrollBarHelper()\n\n this._addEventListeners()\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget)\n }\n\n show(relatedTarget) {\n if (this._isShown || this._isTransitioning) {\n return\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, {\n relatedTarget\n })\n\n if (showEvent.defaultPrevented) {\n return\n }\n\n this._isShown = true\n this._isTransitioning = true\n\n this._scrollBar.hide()\n\n document.body.classList.add(CLASS_NAME_OPEN)\n\n this._adjustDialog()\n\n this._backdrop.show(() => this._showElement(relatedTarget))\n }\n\n hide() {\n if (!this._isShown || this._isTransitioning) {\n return\n }\n\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n if (hideEvent.defaultPrevented) {\n return\n }\n\n this._isShown = false\n this._isTransitioning = true\n this._focustrap.deactivate()\n\n this._element.classList.remove(CLASS_NAME_SHOW)\n\n this._queueCallback(() => this._hideModal(), this._element, this._isAnimated())\n }\n\n dispose() {\n EventHandler.off(window, EVENT_KEY)\n EventHandler.off(this._dialog, EVENT_KEY)\n\n this._backdrop.dispose()\n this._focustrap.deactivate()\n\n super.dispose()\n }\n\n handleUpdate() {\n this._adjustDialog()\n }\n\n // Private\n _initializeBackDrop() {\n return new Backdrop({\n isVisible: Boolean(this._config.backdrop), // 'static' option will be translated to true, and booleans will keep their value,\n isAnimated: this._isAnimated()\n })\n }\n\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n })\n }\n\n _showElement(relatedTarget) {\n // try to append dynamic modal\n if (!document.body.contains(this._element)) {\n document.body.append(this._element)\n }\n\n this._element.style.display = 'block'\n this._element.removeAttribute('aria-hidden')\n this._element.setAttribute('aria-modal', true)\n this._element.setAttribute('role', 'dialog')\n this._element.scrollTop = 0\n\n const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog)\n if (modalBody) {\n modalBody.scrollTop = 0\n }\n\n reflow(this._element)\n\n this._element.classList.add(CLASS_NAME_SHOW)\n\n const transitionComplete = () => {\n if (this._config.focus) {\n this._focustrap.activate()\n }\n\n this._isTransitioning = false\n EventHandler.trigger(this._element, EVENT_SHOWN, {\n relatedTarget\n })\n }\n\n this._queueCallback(transitionComplete, this._dialog, this._isAnimated())\n }\n\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n if (event.key !== ESCAPE_KEY) {\n return\n }\n\n if (this._config.keyboard) {\n this.hide()\n return\n }\n\n this._triggerBackdropTransition()\n })\n\n EventHandler.on(window, EVENT_RESIZE, () => {\n if (this._isShown && !this._isTransitioning) {\n this._adjustDialog()\n }\n })\n\n EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n if (this._element !== event.target || this._element !== event2.target) {\n return\n }\n\n if (this._config.backdrop === 'static') {\n this._triggerBackdropTransition()\n return\n }\n\n if (this._config.backdrop) {\n this.hide()\n }\n })\n })\n }\n\n _hideModal() {\n this._element.style.display = 'none'\n this._element.setAttribute('aria-hidden', true)\n this._element.removeAttribute('aria-modal')\n this._element.removeAttribute('role')\n this._isTransitioning = false\n\n this._backdrop.hide(() => {\n document.body.classList.remove(CLASS_NAME_OPEN)\n this._resetAdjustments()\n this._scrollBar.reset()\n EventHandler.trigger(this._element, EVENT_HIDDEN)\n })\n }\n\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_FADE)\n }\n\n _triggerBackdropTransition() {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n if (hideEvent.defaultPrevented) {\n return\n }\n\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight\n const initialOverflowY = this._element.style.overflowY\n // return if the following background transition hasn't yet completed\n if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n return\n }\n\n if (!isModalOverflowing) {\n this._element.style.overflowY = 'hidden'\n }\n\n this._element.classList.add(CLASS_NAME_STATIC)\n this._queueCallback(() => {\n this._element.classList.remove(CLASS_NAME_STATIC)\n this._queueCallback(() => {\n this._element.style.overflowY = initialOverflowY\n }, this._dialog)\n }, this._dialog)\n\n this._element.focus()\n }\n\n /**\n * The following methods are used to handle overflowing modals\n */\n\n _adjustDialog() {\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight\n const scrollbarWidth = this._scrollBar.getWidth()\n const isBodyOverflowing = scrollbarWidth > 0\n\n if (isBodyOverflowing && !isModalOverflowing) {\n const property = isRTL() ? 'paddingLeft' : 'paddingRight'\n this._element.style[property] = `${scrollbarWidth}px`\n }\n\n if (!isBodyOverflowing && isModalOverflowing) {\n const property = isRTL() ? 'paddingRight' : 'paddingLeft'\n this._element.style[property] = `${scrollbarWidth}px`\n }\n }\n\n _resetAdjustments() {\n this._element.style.paddingLeft = ''\n this._element.style.paddingRight = ''\n }\n\n // Static\n static jQueryInterface(config, relatedTarget) {\n return this.each(function () {\n const data = Modal.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config](relatedTarget)\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n const target = SelectorEngine.getElementFromSelector(this)\n\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault()\n }\n\n EventHandler.one(target, EVENT_SHOW, showEvent => {\n if (showEvent.defaultPrevented) {\n // only register focus restorer if modal will actually get shown\n return\n }\n\n EventHandler.one(target, EVENT_HIDDEN, () => {\n if (isVisible(this)) {\n this.focus()\n }\n })\n })\n\n // avoid conflict when clicking modal toggler while another one is open\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR)\n if (alreadyOpen) {\n Modal.getInstance(alreadyOpen).hide()\n }\n\n const data = Modal.getOrCreateInstance(target)\n\n data.toggle(this)\n})\n\nenableDismissTrigger(Modal)\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal)\n\nexport default Modal\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport Backdrop from './util/backdrop.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport FocusTrap from './util/focustrap.js'\nimport {\n defineJQueryPlugin,\n isDisabled,\n isVisible\n} from './util/index.js'\nimport ScrollBarHelper from './util/scrollbar.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'offcanvas'\nconst DATA_KEY = 'bs.offcanvas'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\nconst ESCAPE_KEY = 'Escape'\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_SHOWING = 'showing'\nconst CLASS_NAME_HIDING = 'hiding'\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop'\nconst OPEN_SELECTOR = '.offcanvas.show'\n\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_RESIZE = `resize${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY}`\n\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"offcanvas\"]'\n\nconst Default = {\n backdrop: true,\n keyboard: true,\n scroll: false\n}\n\nconst DefaultType = {\n backdrop: '(boolean|string)',\n keyboard: 'boolean',\n scroll: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._isShown = false\n this._backdrop = this._initializeBackDrop()\n this._focustrap = this._initializeFocusTrap()\n this._addEventListeners()\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget)\n }\n\n show(relatedTarget) {\n if (this._isShown) {\n return\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, { relatedTarget })\n\n if (showEvent.defaultPrevented) {\n return\n }\n\n this._isShown = true\n this._backdrop.show()\n\n if (!this._config.scroll) {\n new ScrollBarHelper().hide()\n }\n\n this._element.setAttribute('aria-modal', true)\n this._element.setAttribute('role', 'dialog')\n this._element.classList.add(CLASS_NAME_SHOWING)\n\n const completeCallBack = () => {\n if (!this._config.scroll || this._config.backdrop) {\n this._focustrap.activate()\n }\n\n this._element.classList.add(CLASS_NAME_SHOW)\n this._element.classList.remove(CLASS_NAME_SHOWING)\n EventHandler.trigger(this._element, EVENT_SHOWN, { relatedTarget })\n }\n\n this._queueCallback(completeCallBack, this._element, true)\n }\n\n hide() {\n if (!this._isShown) {\n return\n }\n\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n if (hideEvent.defaultPrevented) {\n return\n }\n\n this._focustrap.deactivate()\n this._element.blur()\n this._isShown = false\n this._element.classList.add(CLASS_NAME_HIDING)\n this._backdrop.hide()\n\n const completeCallback = () => {\n this._element.classList.remove(CLASS_NAME_SHOW, CLASS_NAME_HIDING)\n this._element.removeAttribute('aria-modal')\n this._element.removeAttribute('role')\n\n if (!this._config.scroll) {\n new ScrollBarHelper().reset()\n }\n\n EventHandler.trigger(this._element, EVENT_HIDDEN)\n }\n\n this._queueCallback(completeCallback, this._element, true)\n }\n\n dispose() {\n this._backdrop.dispose()\n this._focustrap.deactivate()\n super.dispose()\n }\n\n // Private\n _initializeBackDrop() {\n const clickCallback = () => {\n if (this._config.backdrop === 'static') {\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n return\n }\n\n this.hide()\n }\n\n // 'static' option will be translated to true, and booleans will keep their value\n const isVisible = Boolean(this._config.backdrop)\n\n return new Backdrop({\n className: CLASS_NAME_BACKDROP,\n isVisible,\n isAnimated: true,\n rootElement: this._element.parentNode,\n clickCallback: isVisible ? clickCallback : null\n })\n }\n\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n })\n }\n\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n if (event.key !== ESCAPE_KEY) {\n return\n }\n\n if (this._config.keyboard) {\n this.hide()\n return\n }\n\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n })\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Offcanvas.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config](this)\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n const target = SelectorEngine.getElementFromSelector(this)\n\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault()\n }\n\n if (isDisabled(this)) {\n return\n }\n\n EventHandler.one(target, EVENT_HIDDEN, () => {\n // focus on trigger when it is closed\n if (isVisible(this)) {\n this.focus()\n }\n })\n\n // avoid conflict when clicking a toggler of an offcanvas, while another is open\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR)\n if (alreadyOpen && alreadyOpen !== target) {\n Offcanvas.getInstance(alreadyOpen).hide()\n }\n\n const data = Offcanvas.getOrCreateInstance(target)\n data.toggle(this)\n})\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n Offcanvas.getOrCreateInstance(selector).show()\n }\n})\n\nEventHandler.on(window, EVENT_RESIZE, () => {\n for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n if (getComputedStyle(element).position !== 'fixed') {\n Offcanvas.getOrCreateInstance(element).hide()\n }\n }\n})\n\nenableDismissTrigger(Offcanvas)\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas)\n\nexport default Offcanvas\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n// js-docs-start allow-list\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i\n\nexport const DefaultAllowlist = {\n // Global attributes allowed on any supplied element below.\n '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n a: ['target', 'href', 'title', 'rel'],\n area: [],\n b: [],\n br: [],\n col: [],\n code: [],\n div: [],\n em: [],\n hr: [],\n h1: [],\n h2: [],\n h3: [],\n h4: [],\n h5: [],\n h6: [],\n i: [],\n img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n li: [],\n ol: [],\n p: [],\n pre: [],\n s: [],\n small: [],\n span: [],\n sub: [],\n sup: [],\n strong: [],\n u: [],\n ul: []\n}\n// js-docs-end allow-list\n\nconst uriAttributes = new Set([\n 'background',\n 'cite',\n 'href',\n 'itemtype',\n 'longdesc',\n 'poster',\n 'src',\n 'xlink:href'\n])\n\n/**\n * A pattern that recognizes URLs that are safe wrt. XSS in URL navigation\n * contexts.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/15.2.8/packages/core/src/sanitization/url_sanitizer.ts#L38\n */\n// eslint-disable-next-line unicorn/better-regex\nconst SAFE_URL_PATTERN = /^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i\n\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n const attributeName = attribute.nodeName.toLowerCase()\n\n if (allowedAttributeList.includes(attributeName)) {\n if (uriAttributes.has(attributeName)) {\n return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue))\n }\n\n return true\n }\n\n // Check if a regular expression validates the attribute.\n return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp)\n .some(regex => regex.test(attributeName))\n}\n\nexport function sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n if (!unsafeHtml.length) {\n return unsafeHtml\n }\n\n if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n return sanitizeFunction(unsafeHtml)\n }\n\n const domParser = new window.DOMParser()\n const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html')\n const elements = [].concat(...createdDocument.body.querySelectorAll('*'))\n\n for (const element of elements) {\n const elementName = element.nodeName.toLowerCase()\n\n if (!Object.keys(allowList).includes(elementName)) {\n element.remove()\n continue\n }\n\n const attributeList = [].concat(...element.attributes)\n const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || [])\n\n for (const attribute of attributeList) {\n if (!allowedAttribute(attribute, allowedAttributes)) {\n element.removeAttribute(attribute.nodeName)\n }\n }\n }\n\n return createdDocument.body.innerHTML\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport SelectorEngine from '../dom/selector-engine.js'\nimport Config from './config.js'\nimport { DefaultAllowlist, sanitizeHtml } from './sanitizer.js'\nimport { execute, getElement, isElement } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'TemplateFactory'\n\nconst Default = {\n allowList: DefaultAllowlist,\n content: {}, // { selector : text , selector2 : text2 , }\n extraClass: '',\n html: false,\n sanitize: true,\n sanitizeFn: null,\n template: '
'\n}\n\nconst DefaultType = {\n allowList: 'object',\n content: 'object',\n extraClass: '(string|function)',\n html: 'boolean',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n template: 'string'\n}\n\nconst DefaultContentType = {\n entry: '(string|element|function|null)',\n selector: '(string|element)'\n}\n\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n constructor(config) {\n super()\n this._config = this._getConfig(config)\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n getContent() {\n return Object.values(this._config.content)\n .map(config => this._resolvePossibleFunction(config))\n .filter(Boolean)\n }\n\n hasContent() {\n return this.getContent().length > 0\n }\n\n changeContent(content) {\n this._checkContent(content)\n this._config.content = { ...this._config.content, ...content }\n return this\n }\n\n toHtml() {\n const templateWrapper = document.createElement('div')\n templateWrapper.innerHTML = this._maybeSanitize(this._config.template)\n\n for (const [selector, text] of Object.entries(this._config.content)) {\n this._setContent(templateWrapper, text, selector)\n }\n\n const template = templateWrapper.children[0]\n const extraClass = this._resolvePossibleFunction(this._config.extraClass)\n\n if (extraClass) {\n template.classList.add(...extraClass.split(' '))\n }\n\n return template\n }\n\n // Private\n _typeCheckConfig(config) {\n super._typeCheckConfig(config)\n this._checkContent(config.content)\n }\n\n _checkContent(arg) {\n for (const [selector, content] of Object.entries(arg)) {\n super._typeCheckConfig({ selector, entry: content }, DefaultContentType)\n }\n }\n\n _setContent(template, content, selector) {\n const templateElement = SelectorEngine.findOne(selector, template)\n\n if (!templateElement) {\n return\n }\n\n content = this._resolvePossibleFunction(content)\n\n if (!content) {\n templateElement.remove()\n return\n }\n\n if (isElement(content)) {\n this._putElementInTemplate(getElement(content), templateElement)\n return\n }\n\n if (this._config.html) {\n templateElement.innerHTML = this._maybeSanitize(content)\n return\n }\n\n templateElement.textContent = content\n }\n\n _maybeSanitize(arg) {\n return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg\n }\n\n _resolvePossibleFunction(arg) {\n return execute(arg, [this])\n }\n\n _putElementInTemplate(element, templateElement) {\n if (this._config.html) {\n templateElement.innerHTML = ''\n templateElement.append(element)\n return\n }\n\n templateElement.textContent = element.textContent\n }\n}\n\nexport default TemplateFactory\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport * as Popper from '@popperjs/core'\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport { defineJQueryPlugin, execute, findShadowRoot, getElement, getUID, isRTL, noop } from './util/index.js'\nimport { DefaultAllowlist } from './util/sanitizer.js'\nimport TemplateFactory from './util/template-factory.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'tooltip'\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn'])\n\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_MODAL = 'modal'\nconst CLASS_NAME_SHOW = 'show'\n\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner'\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`\n\nconst EVENT_MODAL_HIDE = 'hide.bs.modal'\n\nconst TRIGGER_HOVER = 'hover'\nconst TRIGGER_FOCUS = 'focus'\nconst TRIGGER_CLICK = 'click'\nconst TRIGGER_MANUAL = 'manual'\n\nconst EVENT_HIDE = 'hide'\nconst EVENT_HIDDEN = 'hidden'\nconst EVENT_SHOW = 'show'\nconst EVENT_SHOWN = 'shown'\nconst EVENT_INSERTED = 'inserted'\nconst EVENT_CLICK = 'click'\nconst EVENT_FOCUSIN = 'focusin'\nconst EVENT_FOCUSOUT = 'focusout'\nconst EVENT_MOUSEENTER = 'mouseenter'\nconst EVENT_MOUSELEAVE = 'mouseleave'\n\nconst AttachmentMap = {\n AUTO: 'auto',\n TOP: 'top',\n RIGHT: isRTL() ? 'left' : 'right',\n BOTTOM: 'bottom',\n LEFT: isRTL() ? 'right' : 'left'\n}\n\nconst Default = {\n allowList: DefaultAllowlist,\n animation: true,\n boundary: 'clippingParents',\n container: false,\n customClass: '',\n delay: 0,\n fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n html: false,\n offset: [0, 6],\n placement: 'top',\n popperConfig: null,\n sanitize: true,\n sanitizeFn: null,\n selector: false,\n template: '
' +\n '
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',\n title: '',\n trigger: 'hover focus'\n}\n\nconst DefaultType = {\n allowList: 'object',\n animation: 'boolean',\n boundary: '(string|element)',\n container: '(string|element|boolean)',\n customClass: '(string|function)',\n delay: '(number|object)',\n fallbackPlacements: 'array',\n html: 'boolean',\n offset: '(array|string|function)',\n placement: '(string|function)',\n popperConfig: '(null|object|function)',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n selector: '(string|boolean)',\n template: 'string',\n title: '(string|element|function)',\n trigger: 'string'\n}\n\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n constructor(element, config) {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)')\n }\n\n super(element, config)\n\n // Private\n this._isEnabled = true\n this._timeout = 0\n this._isHovered = null\n this._activeTrigger = {}\n this._popper = null\n this._templateFactory = null\n this._newContent = null\n\n // Protected\n this.tip = null\n\n this._setListeners()\n\n if (!this._config.selector) {\n this._fixTitle()\n }\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n enable() {\n this._isEnabled = true\n }\n\n disable() {\n this._isEnabled = false\n }\n\n toggleEnabled() {\n this._isEnabled = !this._isEnabled\n }\n\n toggle() {\n if (!this._isEnabled) {\n return\n }\n\n this._activeTrigger.click = !this._activeTrigger.click\n if (this._isShown()) {\n this._leave()\n return\n }\n\n this._enter()\n }\n\n dispose() {\n clearTimeout(this._timeout)\n\n EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler)\n\n if (this._element.getAttribute('data-bs-original-title')) {\n this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'))\n }\n\n this._disposePopper()\n super.dispose()\n }\n\n show() {\n if (this._element.style.display === 'none') {\n throw new Error('Please use show on visible elements')\n }\n\n if (!(this._isWithContent() && this._isEnabled)) {\n return\n }\n\n const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW))\n const shadowRoot = findShadowRoot(this._element)\n const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element)\n\n if (showEvent.defaultPrevented || !isInTheDom) {\n return\n }\n\n // TODO: v6 remove this or make it optional\n this._disposePopper()\n\n const tip = this._getTipElement()\n\n this._element.setAttribute('aria-describedby', tip.getAttribute('id'))\n\n const { container } = this._config\n\n if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n container.append(tip)\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED))\n }\n\n this._popper = this._createPopper(tip)\n\n tip.classList.add(CLASS_NAME_SHOW)\n\n // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop)\n }\n }\n\n const complete = () => {\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN))\n\n if (this._isHovered === false) {\n this._leave()\n }\n\n this._isHovered = false\n }\n\n this._queueCallback(complete, this.tip, this._isAnimated())\n }\n\n hide() {\n if (!this._isShown()) {\n return\n }\n\n const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE))\n if (hideEvent.defaultPrevented) {\n return\n }\n\n const tip = this._getTipElement()\n tip.classList.remove(CLASS_NAME_SHOW)\n\n // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop)\n }\n }\n\n this._activeTrigger[TRIGGER_CLICK] = false\n this._activeTrigger[TRIGGER_FOCUS] = false\n this._activeTrigger[TRIGGER_HOVER] = false\n this._isHovered = null // it is a trick to support manual triggering\n\n const complete = () => {\n if (this._isWithActiveTrigger()) {\n return\n }\n\n if (!this._isHovered) {\n this._disposePopper()\n }\n\n this._element.removeAttribute('aria-describedby')\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN))\n }\n\n this._queueCallback(complete, this.tip, this._isAnimated())\n }\n\n update() {\n if (this._popper) {\n this._popper.update()\n }\n }\n\n // Protected\n _isWithContent() {\n return Boolean(this._getTitle())\n }\n\n _getTipElement() {\n if (!this.tip) {\n this.tip = this._createTipElement(this._newContent || this._getContentForTemplate())\n }\n\n return this.tip\n }\n\n _createTipElement(content) {\n const tip = this._getTemplateFactory(content).toHtml()\n\n // TODO: remove this check in v6\n if (!tip) {\n return null\n }\n\n tip.classList.remove(CLASS_NAME_FADE, CLASS_NAME_SHOW)\n // TODO: v6 the following can be achieved with CSS only\n tip.classList.add(`bs-${this.constructor.NAME}-auto`)\n\n const tipId = getUID(this.constructor.NAME).toString()\n\n tip.setAttribute('id', tipId)\n\n if (this._isAnimated()) {\n tip.classList.add(CLASS_NAME_FADE)\n }\n\n return tip\n }\n\n setContent(content) {\n this._newContent = content\n if (this._isShown()) {\n this._disposePopper()\n this.show()\n }\n }\n\n _getTemplateFactory(content) {\n if (this._templateFactory) {\n this._templateFactory.changeContent(content)\n } else {\n this._templateFactory = new TemplateFactory({\n ...this._config,\n // the `content` var has to be after `this._config`\n // to override config.content in case of popover\n content,\n extraClass: this._resolvePossibleFunction(this._config.customClass)\n })\n }\n\n return this._templateFactory\n }\n\n _getContentForTemplate() {\n return {\n [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n }\n }\n\n _getTitle() {\n return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title')\n }\n\n // Private\n _initializeOnDelegatedTarget(event) {\n return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig())\n }\n\n _isAnimated() {\n return this._config.animation || (this.tip && this.tip.classList.contains(CLASS_NAME_FADE))\n }\n\n _isShown() {\n return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW)\n }\n\n _createPopper(tip) {\n const placement = execute(this._config.placement, [this, tip, this._element])\n const attachment = AttachmentMap[placement.toUpperCase()]\n return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment))\n }\n\n _getOffset() {\n const { offset } = this._config\n\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10))\n }\n\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element)\n }\n\n return offset\n }\n\n _resolvePossibleFunction(arg) {\n return execute(arg, [this._element])\n }\n\n _getPopperConfig(attachment) {\n const defaultBsPopperConfig = {\n placement: attachment,\n modifiers: [\n {\n name: 'flip',\n options: {\n fallbackPlacements: this._config.fallbackPlacements\n }\n },\n {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n },\n {\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n },\n {\n name: 'arrow',\n options: {\n element: `.${this.constructor.NAME}-arrow`\n }\n },\n {\n name: 'preSetPlacement',\n enabled: true,\n phase: 'beforeMain',\n fn: data => {\n // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n this._getTipElement().setAttribute('data-popper-placement', data.state.placement)\n }\n }\n ]\n }\n\n return {\n ...defaultBsPopperConfig,\n ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n }\n }\n\n _setListeners() {\n const triggers = this._config.trigger.split(' ')\n\n for (const trigger of triggers) {\n if (trigger === 'click') {\n EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK), this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event)\n context.toggle()\n })\n } else if (trigger !== TRIGGER_MANUAL) {\n const eventIn = trigger === TRIGGER_HOVER ?\n this.constructor.eventName(EVENT_MOUSEENTER) :\n this.constructor.eventName(EVENT_FOCUSIN)\n const eventOut = trigger === TRIGGER_HOVER ?\n this.constructor.eventName(EVENT_MOUSELEAVE) :\n this.constructor.eventName(EVENT_FOCUSOUT)\n\n EventHandler.on(this._element, eventIn, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event)\n context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true\n context._enter()\n })\n EventHandler.on(this._element, eventOut, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event)\n context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] =\n context._element.contains(event.relatedTarget)\n\n context._leave()\n })\n }\n }\n\n this._hideModalHandler = () => {\n if (this._element) {\n this.hide()\n }\n }\n\n EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler)\n }\n\n _fixTitle() {\n const title = this._element.getAttribute('title')\n\n if (!title) {\n return\n }\n\n if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n this._element.setAttribute('aria-label', title)\n }\n\n this._element.setAttribute('data-bs-original-title', title) // DO NOT USE IT. Is only for backwards compatibility\n this._element.removeAttribute('title')\n }\n\n _enter() {\n if (this._isShown() || this._isHovered) {\n this._isHovered = true\n return\n }\n\n this._isHovered = true\n\n this._setTimeout(() => {\n if (this._isHovered) {\n this.show()\n }\n }, this._config.delay.show)\n }\n\n _leave() {\n if (this._isWithActiveTrigger()) {\n return\n }\n\n this._isHovered = false\n\n this._setTimeout(() => {\n if (!this._isHovered) {\n this.hide()\n }\n }, this._config.delay.hide)\n }\n\n _setTimeout(handler, timeout) {\n clearTimeout(this._timeout)\n this._timeout = setTimeout(handler, timeout)\n }\n\n _isWithActiveTrigger() {\n return Object.values(this._activeTrigger).includes(true)\n }\n\n _getConfig(config) {\n const dataAttributes = Manipulator.getDataAttributes(this._element)\n\n for (const dataAttribute of Object.keys(dataAttributes)) {\n if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n delete dataAttributes[dataAttribute]\n }\n }\n\n config = {\n ...dataAttributes,\n ...(typeof config === 'object' && config ? config : {})\n }\n config = this._mergeConfigObj(config)\n config = this._configAfterMerge(config)\n this._typeCheckConfig(config)\n return config\n }\n\n _configAfterMerge(config) {\n config.container = config.container === false ? document.body : getElement(config.container)\n\n if (typeof config.delay === 'number') {\n config.delay = {\n show: config.delay,\n hide: config.delay\n }\n }\n\n if (typeof config.title === 'number') {\n config.title = config.title.toString()\n }\n\n if (typeof config.content === 'number') {\n config.content = config.content.toString()\n }\n\n return config\n }\n\n _getDelegateConfig() {\n const config = {}\n\n for (const [key, value] of Object.entries(this._config)) {\n if (this.constructor.Default[key] !== value) {\n config[key] = value\n }\n }\n\n config.selector = false\n config.trigger = 'manual'\n\n // In the future can be replaced with:\n // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n // `Object.fromEntries(keysWithDifferentValues)`\n return config\n }\n\n _disposePopper() {\n if (this._popper) {\n this._popper.destroy()\n this._popper = null\n }\n\n if (this.tip) {\n this.tip.remove()\n this.tip = null\n }\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Tooltip.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n })\n }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tooltip)\n\nexport default Tooltip\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Tooltip from './tooltip.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'popover'\n\nconst SELECTOR_TITLE = '.popover-header'\nconst SELECTOR_CONTENT = '.popover-body'\n\nconst Default = {\n ...Tooltip.Default,\n content: '',\n offset: [0, 8],\n placement: 'right',\n template: '
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' +\n '
' +\n '
',\n trigger: 'click'\n}\n\nconst DefaultType = {\n ...Tooltip.DefaultType,\n content: '(null|string|element|function)'\n}\n\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Overrides\n _isWithContent() {\n return this._getTitle() || this._getContent()\n }\n\n // Private\n _getContentForTemplate() {\n return {\n [SELECTOR_TITLE]: this._getTitle(),\n [SELECTOR_CONTENT]: this._getContent()\n }\n }\n\n _getContent() {\n return this._resolvePossibleFunction(this._config.content)\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Popover.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n })\n }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Popover)\n\nexport default Popover\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport { defineJQueryPlugin, getElement, isDisabled, isVisible } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'scrollspy'\nconst DATA_KEY = 'bs.scrollspy'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst EVENT_ACTIVATE = `activate${EVENT_KEY}`\nconst EVENT_CLICK = `click${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item'\nconst CLASS_NAME_ACTIVE = 'active'\n\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]'\nconst SELECTOR_TARGET_LINKS = '[href]'\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group'\nconst SELECTOR_NAV_LINKS = '.nav-link'\nconst SELECTOR_NAV_ITEMS = '.nav-item'\nconst SELECTOR_LIST_ITEMS = '.list-group-item'\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`\nconst SELECTOR_DROPDOWN = '.dropdown'\nconst SELECTOR_DROPDOWN_TOGGLE = '.dropdown-toggle'\n\nconst Default = {\n offset: null, // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: '0px 0px -25%',\n smoothScroll: false,\n target: null,\n threshold: [0.1, 0.5, 1]\n}\n\nconst DefaultType = {\n offset: '(number|null)', // TODO v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: 'string',\n smoothScroll: 'boolean',\n target: 'element',\n threshold: 'array'\n}\n\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n // this._element is the observablesContainer and config.target the menu links wrapper\n this._targetLinks = new Map()\n this._observableSections = new Map()\n this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element\n this._activeTarget = null\n this._observer = null\n this._previousScrollData = {\n visibleEntryTop: 0,\n parentScrollTop: 0\n }\n this.refresh() // initialize\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n refresh() {\n this._initializeTargetsAndObservables()\n this._maybeEnableSmoothScroll()\n\n if (this._observer) {\n this._observer.disconnect()\n } else {\n this._observer = this._getNewObserver()\n }\n\n for (const section of this._observableSections.values()) {\n this._observer.observe(section)\n }\n }\n\n dispose() {\n this._observer.disconnect()\n super.dispose()\n }\n\n // Private\n _configAfterMerge(config) {\n // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n config.target = getElement(config.target) || document.body\n\n // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin\n\n if (typeof config.threshold === 'string') {\n config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value))\n }\n\n return config\n }\n\n _maybeEnableSmoothScroll() {\n if (!this._config.smoothScroll) {\n return\n }\n\n // unregister any previous listeners\n EventHandler.off(this._config.target, EVENT_CLICK)\n\n EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n const observableSection = this._observableSections.get(event.target.hash)\n if (observableSection) {\n event.preventDefault()\n const root = this._rootElement || window\n const height = observableSection.offsetTop - this._element.offsetTop\n if (root.scrollTo) {\n root.scrollTo({ top: height, behavior: 'smooth' })\n return\n }\n\n // Chrome 60 doesn't support `scrollTo`\n root.scrollTop = height\n }\n })\n }\n\n _getNewObserver() {\n const options = {\n root: this._rootElement,\n threshold: this._config.threshold,\n rootMargin: this._config.rootMargin\n }\n\n return new IntersectionObserver(entries => this._observerCallback(entries), options)\n }\n\n // The logic of selection\n _observerCallback(entries) {\n const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`)\n const activate = entry => {\n this._previousScrollData.visibleEntryTop = entry.target.offsetTop\n this._process(targetElement(entry))\n }\n\n const parentScrollTop = (this._rootElement || document.documentElement).scrollTop\n const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop\n this._previousScrollData.parentScrollTop = parentScrollTop\n\n for (const entry of entries) {\n if (!entry.isIntersecting) {\n this._activeTarget = null\n this._clearActiveClass(targetElement(entry))\n\n continue\n }\n\n const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop\n // if we are scrolling down, pick the bigger offsetTop\n if (userScrollsDown && entryIsLowerThanPrevious) {\n activate(entry)\n // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n if (!parentScrollTop) {\n return\n }\n\n continue\n }\n\n // if we are scrolling up, pick the smallest offsetTop\n if (!userScrollsDown && !entryIsLowerThanPrevious) {\n activate(entry)\n }\n }\n }\n\n _initializeTargetsAndObservables() {\n this._targetLinks = new Map()\n this._observableSections = new Map()\n\n const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target)\n\n for (const anchor of targetLinks) {\n // ensure that the anchor has an id and is not disabled\n if (!anchor.hash || isDisabled(anchor)) {\n continue\n }\n\n const observableSection = SelectorEngine.findOne(decodeURI(anchor.hash), this._element)\n\n // ensure that the observableSection exists & is visible\n if (isVisible(observableSection)) {\n this._targetLinks.set(decodeURI(anchor.hash), anchor)\n this._observableSections.set(anchor.hash, observableSection)\n }\n }\n }\n\n _process(target) {\n if (this._activeTarget === target) {\n return\n }\n\n this._clearActiveClass(this._config.target)\n this._activeTarget = target\n target.classList.add(CLASS_NAME_ACTIVE)\n this._activateParents(target)\n\n EventHandler.trigger(this._element, EVENT_ACTIVATE, { relatedTarget: target })\n }\n\n _activateParents(target) {\n // Activate dropdown parents\n if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE, target.closest(SELECTOR_DROPDOWN))\n .classList.add(CLASS_NAME_ACTIVE)\n return\n }\n\n for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n // Set triggered links parents as active\n // With both
    and
')},createChildNavList:function(e){var t=this.createNavList();return e.append(t),t},generateNavEl:function(e,t){var n=a('');n.attr("href","#"+e),n.text(t);var r=a("
  • ");return r.append(n),r},generateNavItem:function(e){var t=this.generateAnchor(e),n=a(e),r=n.data("toc-text")||n.text();return this.generateNavEl(t,r)},getTopLevel:function(e){for(var t=1;t<=6;t++){if(1 + + + + + + + + + + + + + diff --git a/docs/deps/font-awesome-6.4.2/css/all.css b/docs/deps/font-awesome-6.4.2/css/all.css new file mode 100644 index 0000000..bdb6e3a --- /dev/null +++ b/docs/deps/font-awesome-6.4.2/css/all.css @@ -0,0 +1,7968 @@ +/*! + * Font Awesome Free 6.4.2 by @fontawesome - https://fontawesome.com + * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) + * Copyright 2023 Fonticons, Inc. + */ +.fa { + font-family: var(--fa-style-family, "Font Awesome 6 Free"); + font-weight: var(--fa-style, 900); } + +.fa, +.fa-classic, +.fa-sharp, +.fas, +.fa-solid, +.far, +.fa-regular, +.fab, +.fa-brands { + -moz-osx-font-smoothing: grayscale; + -webkit-font-smoothing: antialiased; + display: var(--fa-display, inline-block); + font-style: normal; + font-variant: normal; + line-height: 1; + text-rendering: auto; } + +.fas, +.fa-classic, +.fa-solid, +.far, +.fa-regular { + font-family: 'Font Awesome 6 Free'; } + +.fab, +.fa-brands { + font-family: 'Font Awesome 6 Brands'; } + +.fa-1x { + font-size: 1em; } + +.fa-2x { + font-size: 2em; } + +.fa-3x { + font-size: 3em; } + +.fa-4x { + font-size: 4em; } + +.fa-5x { + font-size: 5em; } + +.fa-6x { + font-size: 6em; } + +.fa-7x { + font-size: 7em; } + +.fa-8x { + font-size: 8em; } + +.fa-9x { + font-size: 9em; } + +.fa-10x { + font-size: 10em; } + +.fa-2xs { + font-size: 0.625em; + line-height: 0.1em; + vertical-align: 0.225em; } + +.fa-xs { + font-size: 0.75em; + line-height: 0.08333em; + vertical-align: 0.125em; } + +.fa-sm { + font-size: 0.875em; + line-height: 0.07143em; + vertical-align: 0.05357em; } + +.fa-lg { + font-size: 1.25em; + line-height: 0.05em; + vertical-align: -0.075em; } + +.fa-xl { + font-size: 1.5em; + line-height: 0.04167em; + vertical-align: -0.125em; } + +.fa-2xl { + font-size: 2em; + line-height: 0.03125em; + vertical-align: -0.1875em; } + +.fa-fw { + text-align: center; + width: 1.25em; } + +.fa-ul { + list-style-type: none; + margin-left: var(--fa-li-margin, 2.5em); + padding-left: 0; } + .fa-ul > li { + position: relative; } + +.fa-li { + left: calc(var(--fa-li-width, 2em) * -1); + position: absolute; + text-align: center; + width: var(--fa-li-width, 2em); + line-height: inherit; } + +.fa-border { + border-color: var(--fa-border-color, #eee); + border-radius: var(--fa-border-radius, 0.1em); + border-style: var(--fa-border-style, solid); + border-width: var(--fa-border-width, 0.08em); + padding: var(--fa-border-padding, 0.2em 0.25em 0.15em); } + +.fa-pull-left { + float: left; + margin-right: var(--fa-pull-margin, 0.3em); } + +.fa-pull-right { + float: right; + margin-left: var(--fa-pull-margin, 0.3em); } + +.fa-beat { + -webkit-animation-name: fa-beat; + animation-name: fa-beat; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); + animation-timing-function: var(--fa-animation-timing, ease-in-out); } + +.fa-bounce { + -webkit-animation-name: fa-bounce; + animation-name: fa-bounce; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); } + +.fa-fade { + -webkit-animation-name: fa-fade; + animation-name: fa-fade; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } + +.fa-beat-fade { + -webkit-animation-name: fa-beat-fade; + animation-name: fa-beat-fade; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } + +.fa-flip { + -webkit-animation-name: fa-flip; + animation-name: fa-flip; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); + animation-timing-function: var(--fa-animation-timing, ease-in-out); } + +.fa-shake { + -webkit-animation-name: fa-shake; + animation-name: fa-shake; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, linear); + animation-timing-function: var(--fa-animation-timing, linear); } + +.fa-spin { + -webkit-animation-name: fa-spin; + animation-name: fa-spin; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 2s); + animation-duration: var(--fa-animation-duration, 2s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, linear); + animation-timing-function: var(--fa-animation-timing, linear); } + +.fa-spin-reverse { + --fa-animation-direction: reverse; } + +.fa-pulse, +.fa-spin-pulse { + -webkit-animation-name: fa-spin; + animation-name: fa-spin; + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, steps(8)); + animation-timing-function: var(--fa-animation-timing, steps(8)); } + +@media (prefers-reduced-motion: reduce) { + .fa-beat, + .fa-bounce, + .fa-fade, + .fa-beat-fade, + .fa-flip, + .fa-pulse, + .fa-shake, + .fa-spin, + .fa-spin-pulse { + -webkit-animation-delay: -1ms; + animation-delay: -1ms; + -webkit-animation-duration: 1ms; + animation-duration: 1ms; + -webkit-animation-iteration-count: 1; + animation-iteration-count: 1; + -webkit-transition-delay: 0s; + transition-delay: 0s; + -webkit-transition-duration: 0s; + transition-duration: 0s; } } + +@-webkit-keyframes fa-beat { + 0%, 90% { + -webkit-transform: scale(1); + transform: scale(1); } + 45% { + -webkit-transform: scale(var(--fa-beat-scale, 1.25)); + transform: scale(var(--fa-beat-scale, 1.25)); } } + +@keyframes fa-beat { + 0%, 90% { + -webkit-transform: scale(1); + transform: scale(1); } + 45% { + -webkit-transform: scale(var(--fa-beat-scale, 1.25)); + transform: scale(var(--fa-beat-scale, 1.25)); } } + +@-webkit-keyframes fa-bounce { + 0% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 10% { + -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); + transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } + 30% { + -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); + transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } + 50% { + -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); + transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } + 57% { + -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); + transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } + 64% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 100% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } } + +@keyframes fa-bounce { + 0% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 10% { + -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); + transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } + 30% { + -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); + transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } + 50% { + -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); + transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } + 57% { + -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); + transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } + 64% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 100% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } } + +@-webkit-keyframes fa-fade { + 50% { + opacity: var(--fa-fade-opacity, 0.4); } } + +@keyframes fa-fade { + 50% { + opacity: var(--fa-fade-opacity, 0.4); } } + +@-webkit-keyframes fa-beat-fade { + 0%, 100% { + opacity: var(--fa-beat-fade-opacity, 0.4); + -webkit-transform: scale(1); + transform: scale(1); } + 50% { + opacity: 1; + -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); + transform: scale(var(--fa-beat-fade-scale, 1.125)); } } + +@keyframes fa-beat-fade { + 0%, 100% { + opacity: var(--fa-beat-fade-opacity, 0.4); + -webkit-transform: scale(1); + transform: scale(1); } + 50% { + opacity: 1; + -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); + transform: scale(var(--fa-beat-fade-scale, 1.125)); } } + +@-webkit-keyframes fa-flip { + 50% { + -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); + transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } + +@keyframes fa-flip { + 50% { + -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); + transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } + +@-webkit-keyframes fa-shake { + 0% { + -webkit-transform: rotate(-15deg); + transform: rotate(-15deg); } + 4% { + -webkit-transform: rotate(15deg); + transform: rotate(15deg); } + 8%, 24% { + -webkit-transform: rotate(-18deg); + transform: rotate(-18deg); } + 12%, 28% { + -webkit-transform: rotate(18deg); + transform: rotate(18deg); } + 16% { + -webkit-transform: rotate(-22deg); + transform: rotate(-22deg); } + 20% { + -webkit-transform: rotate(22deg); + transform: rotate(22deg); } + 32% { + -webkit-transform: rotate(-12deg); + transform: rotate(-12deg); } + 36% { + -webkit-transform: rotate(12deg); + transform: rotate(12deg); } + 40%, 100% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } } + +@keyframes fa-shake { + 0% { + -webkit-transform: rotate(-15deg); + transform: rotate(-15deg); } + 4% { + -webkit-transform: rotate(15deg); + transform: rotate(15deg); } + 8%, 24% { + -webkit-transform: rotate(-18deg); + transform: rotate(-18deg); } + 12%, 28% { + -webkit-transform: rotate(18deg); + transform: rotate(18deg); } + 16% { + -webkit-transform: rotate(-22deg); + transform: rotate(-22deg); } + 20% { + -webkit-transform: rotate(22deg); + transform: rotate(22deg); } + 32% { + -webkit-transform: rotate(-12deg); + transform: rotate(-12deg); } + 36% { + -webkit-transform: rotate(12deg); + transform: rotate(12deg); } + 40%, 100% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } } + +@-webkit-keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } + 100% { + -webkit-transform: rotate(360deg); + transform: rotate(360deg); } } + +@keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } + 100% { + -webkit-transform: rotate(360deg); + transform: rotate(360deg); } } + +.fa-rotate-90 { + -webkit-transform: rotate(90deg); + transform: rotate(90deg); } + +.fa-rotate-180 { + -webkit-transform: rotate(180deg); + transform: rotate(180deg); } + +.fa-rotate-270 { + -webkit-transform: rotate(270deg); + transform: rotate(270deg); } + +.fa-flip-horizontal { + -webkit-transform: scale(-1, 1); + transform: scale(-1, 1); } + +.fa-flip-vertical { + -webkit-transform: scale(1, -1); + transform: scale(1, -1); } + +.fa-flip-both, +.fa-flip-horizontal.fa-flip-vertical { + -webkit-transform: scale(-1, -1); + transform: scale(-1, -1); } + +.fa-rotate-by { + -webkit-transform: rotate(var(--fa-rotate-angle, none)); + transform: rotate(var(--fa-rotate-angle, none)); } + +.fa-stack { + display: inline-block; + height: 2em; + line-height: 2em; + position: relative; + vertical-align: middle; + width: 2.5em; } + +.fa-stack-1x, +.fa-stack-2x { + left: 0; + position: absolute; + text-align: center; + width: 100%; + z-index: var(--fa-stack-z-index, auto); } + +.fa-stack-1x { + line-height: inherit; } + +.fa-stack-2x { + font-size: 2em; } + +.fa-inverse { + color: var(--fa-inverse, #fff); } + +/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen +readers do not read off random characters that represent icons */ + +.fa-0::before { + content: "\30"; } + +.fa-1::before { + content: "\31"; } + +.fa-2::before { + content: "\32"; } + +.fa-3::before { + content: "\33"; } + +.fa-4::before { + content: "\34"; } + +.fa-5::before { + content: "\35"; } + +.fa-6::before { + content: "\36"; } + +.fa-7::before { + content: "\37"; } + +.fa-8::before { + content: "\38"; } + +.fa-9::before { + content: "\39"; } + +.fa-fill-drip::before { + content: "\f576"; } + +.fa-arrows-to-circle::before { + content: "\e4bd"; } + +.fa-circle-chevron-right::before { + content: "\f138"; } + +.fa-chevron-circle-right::before { + content: "\f138"; } + +.fa-at::before { + content: "\40"; } + +.fa-trash-can::before { + content: "\f2ed"; } + +.fa-trash-alt::before { + content: "\f2ed"; } + +.fa-text-height::before { + content: "\f034"; } + +.fa-user-xmark::before { + content: "\f235"; } + +.fa-user-times::before { + content: "\f235"; } + +.fa-stethoscope::before { + content: "\f0f1"; } + +.fa-message::before { + content: "\f27a"; } + +.fa-comment-alt::before { + content: "\f27a"; } + +.fa-info::before { + content: "\f129"; } + +.fa-down-left-and-up-right-to-center::before { + content: "\f422"; } + +.fa-compress-alt::before { + content: "\f422"; } + +.fa-explosion::before { + content: "\e4e9"; } + +.fa-file-lines::before { + content: "\f15c"; } + +.fa-file-alt::before { + content: "\f15c"; } + +.fa-file-text::before { + content: "\f15c"; } + +.fa-wave-square::before { + content: "\f83e"; } + +.fa-ring::before { + content: "\f70b"; } + +.fa-building-un::before { + content: "\e4d9"; } + +.fa-dice-three::before { + content: "\f527"; } + +.fa-calendar-days::before { + content: "\f073"; } + +.fa-calendar-alt::before { + content: "\f073"; } + +.fa-anchor-circle-check::before { + content: "\e4aa"; } + +.fa-building-circle-arrow-right::before { + content: "\e4d1"; } + +.fa-volleyball::before { + content: "\f45f"; } + +.fa-volleyball-ball::before { + content: "\f45f"; } + +.fa-arrows-up-to-line::before { + content: "\e4c2"; } + +.fa-sort-down::before { + content: "\f0dd"; } + +.fa-sort-desc::before { + content: "\f0dd"; } + +.fa-circle-minus::before { + content: "\f056"; } + +.fa-minus-circle::before { + content: "\f056"; } + +.fa-door-open::before { + content: "\f52b"; } + +.fa-right-from-bracket::before { + content: "\f2f5"; } + +.fa-sign-out-alt::before { + content: "\f2f5"; } + +.fa-atom::before { + content: "\f5d2"; } + +.fa-soap::before { + content: "\e06e"; } + +.fa-icons::before { + content: "\f86d"; } + +.fa-heart-music-camera-bolt::before { + content: "\f86d"; } + +.fa-microphone-lines-slash::before { + content: "\f539"; } + +.fa-microphone-alt-slash::before { + content: "\f539"; } + +.fa-bridge-circle-check::before { + content: "\e4c9"; } + +.fa-pump-medical::before { + content: "\e06a"; } + +.fa-fingerprint::before { + content: "\f577"; } + +.fa-hand-point-right::before { + content: "\f0a4"; } + +.fa-magnifying-glass-location::before { + content: "\f689"; } + +.fa-search-location::before { + content: "\f689"; } + +.fa-forward-step::before { + content: "\f051"; } + +.fa-step-forward::before { + content: "\f051"; } + +.fa-face-smile-beam::before { + content: "\f5b8"; } + +.fa-smile-beam::before { + content: "\f5b8"; } + +.fa-flag-checkered::before { + content: "\f11e"; } + +.fa-football::before { + content: "\f44e"; } + +.fa-football-ball::before { + content: "\f44e"; } + +.fa-school-circle-exclamation::before { + content: "\e56c"; } + +.fa-crop::before { + content: "\f125"; } + +.fa-angles-down::before { + content: "\f103"; } + +.fa-angle-double-down::before { + content: "\f103"; } + +.fa-users-rectangle::before { + content: "\e594"; } + +.fa-people-roof::before { + content: "\e537"; } + +.fa-people-line::before { + content: "\e534"; } + +.fa-beer-mug-empty::before { + content: "\f0fc"; } + +.fa-beer::before { + content: "\f0fc"; } + +.fa-diagram-predecessor::before { + content: "\e477"; } + +.fa-arrow-up-long::before { + content: "\f176"; } + +.fa-long-arrow-up::before { + content: "\f176"; } + +.fa-fire-flame-simple::before { + content: "\f46a"; } + +.fa-burn::before { + content: "\f46a"; } + +.fa-person::before { + content: "\f183"; } + +.fa-male::before { + content: "\f183"; } + +.fa-laptop::before { + content: "\f109"; } + +.fa-file-csv::before { + content: "\f6dd"; } + +.fa-menorah::before { + content: "\f676"; } + +.fa-truck-plane::before { + content: "\e58f"; } + +.fa-record-vinyl::before { + content: "\f8d9"; } + +.fa-face-grin-stars::before { + content: "\f587"; } + +.fa-grin-stars::before { + content: "\f587"; } + +.fa-bong::before { + content: "\f55c"; } + +.fa-spaghetti-monster-flying::before { + content: "\f67b"; } + +.fa-pastafarianism::before { + content: "\f67b"; } + +.fa-arrow-down-up-across-line::before { + content: "\e4af"; } + +.fa-spoon::before { + content: "\f2e5"; } + +.fa-utensil-spoon::before { + content: "\f2e5"; } + +.fa-jar-wheat::before { + content: "\e517"; } + +.fa-envelopes-bulk::before { + content: "\f674"; } + +.fa-mail-bulk::before { + content: "\f674"; } + +.fa-file-circle-exclamation::before { + content: "\e4eb"; } + +.fa-circle-h::before { + content: "\f47e"; } + +.fa-hospital-symbol::before { + content: "\f47e"; } + +.fa-pager::before { + content: "\f815"; } + +.fa-address-book::before { + content: "\f2b9"; } + +.fa-contact-book::before { + content: "\f2b9"; } + +.fa-strikethrough::before { + content: "\f0cc"; } + +.fa-k::before { + content: "\4b"; } + +.fa-landmark-flag::before { + content: "\e51c"; } + +.fa-pencil::before { + content: "\f303"; } + +.fa-pencil-alt::before { + content: "\f303"; } + +.fa-backward::before { + content: "\f04a"; } + +.fa-caret-right::before { + content: "\f0da"; } + +.fa-comments::before { + content: "\f086"; } + +.fa-paste::before { + content: "\f0ea"; } + +.fa-file-clipboard::before { + content: "\f0ea"; } + +.fa-code-pull-request::before { + content: "\e13c"; } + +.fa-clipboard-list::before { + content: "\f46d"; } + +.fa-truck-ramp-box::before { + content: "\f4de"; } + +.fa-truck-loading::before { + content: "\f4de"; } + +.fa-user-check::before { + content: "\f4fc"; } + +.fa-vial-virus::before { + content: "\e597"; } + +.fa-sheet-plastic::before { + content: "\e571"; } + +.fa-blog::before { + content: "\f781"; } + +.fa-user-ninja::before { + content: "\f504"; } + +.fa-person-arrow-up-from-line::before { + content: "\e539"; } + +.fa-scroll-torah::before { + content: "\f6a0"; } + +.fa-torah::before { + content: "\f6a0"; } + +.fa-broom-ball::before { + content: "\f458"; } + +.fa-quidditch::before { + content: "\f458"; } + +.fa-quidditch-broom-ball::before { + content: "\f458"; } + +.fa-toggle-off::before { + content: "\f204"; } + +.fa-box-archive::before { + content: "\f187"; } + +.fa-archive::before { + content: "\f187"; } + +.fa-person-drowning::before { + content: "\e545"; } + +.fa-arrow-down-9-1::before { + content: "\f886"; } + +.fa-sort-numeric-desc::before { + content: "\f886"; } + +.fa-sort-numeric-down-alt::before { + content: "\f886"; } + +.fa-face-grin-tongue-squint::before { + content: "\f58a"; } + +.fa-grin-tongue-squint::before { + content: "\f58a"; } + +.fa-spray-can::before { + content: "\f5bd"; } + +.fa-truck-monster::before { + content: "\f63b"; } + +.fa-w::before { + content: "\57"; } + +.fa-earth-africa::before { + content: "\f57c"; } + +.fa-globe-africa::before { + content: "\f57c"; } + +.fa-rainbow::before { + content: "\f75b"; } + +.fa-circle-notch::before { + content: "\f1ce"; } + +.fa-tablet-screen-button::before { + content: "\f3fa"; } + +.fa-tablet-alt::before { + content: "\f3fa"; } + +.fa-paw::before { + content: "\f1b0"; } + +.fa-cloud::before { + content: "\f0c2"; } + +.fa-trowel-bricks::before { + content: "\e58a"; } + +.fa-face-flushed::before { + content: "\f579"; } + +.fa-flushed::before { + content: "\f579"; } + +.fa-hospital-user::before { + content: "\f80d"; } + +.fa-tent-arrow-left-right::before { + content: "\e57f"; } + +.fa-gavel::before { + content: "\f0e3"; } + +.fa-legal::before { + content: "\f0e3"; } + +.fa-binoculars::before { + content: "\f1e5"; } + +.fa-microphone-slash::before { + content: "\f131"; } + +.fa-box-tissue::before { + content: "\e05b"; } + +.fa-motorcycle::before { + content: "\f21c"; } + +.fa-bell-concierge::before { + content: "\f562"; } + +.fa-concierge-bell::before { + content: "\f562"; } + +.fa-pen-ruler::before { + content: "\f5ae"; } + +.fa-pencil-ruler::before { + content: "\f5ae"; } + +.fa-people-arrows::before { + content: "\e068"; } + +.fa-people-arrows-left-right::before { + content: "\e068"; } + +.fa-mars-and-venus-burst::before { + content: "\e523"; } + +.fa-square-caret-right::before { + content: "\f152"; } + +.fa-caret-square-right::before { + content: "\f152"; } + +.fa-scissors::before { + content: "\f0c4"; } + +.fa-cut::before { + content: "\f0c4"; } + +.fa-sun-plant-wilt::before { + content: "\e57a"; } + +.fa-toilets-portable::before { + content: "\e584"; } + +.fa-hockey-puck::before { + content: "\f453"; } + +.fa-table::before { + content: "\f0ce"; } + +.fa-magnifying-glass-arrow-right::before { + content: "\e521"; } + +.fa-tachograph-digital::before { + content: "\f566"; } + +.fa-digital-tachograph::before { + content: "\f566"; } + +.fa-users-slash::before { + content: "\e073"; } + +.fa-clover::before { + content: "\e139"; } + +.fa-reply::before { + content: "\f3e5"; } + +.fa-mail-reply::before { + content: "\f3e5"; } + +.fa-star-and-crescent::before { + content: "\f699"; } + +.fa-house-fire::before { + content: "\e50c"; } + +.fa-square-minus::before { + content: "\f146"; } + +.fa-minus-square::before { + content: "\f146"; } + +.fa-helicopter::before { + content: "\f533"; } + +.fa-compass::before { + content: "\f14e"; } + +.fa-square-caret-down::before { + content: "\f150"; } + +.fa-caret-square-down::before { + content: "\f150"; } + +.fa-file-circle-question::before { + content: "\e4ef"; } + +.fa-laptop-code::before { + content: "\f5fc"; } + +.fa-swatchbook::before { + content: "\f5c3"; } + +.fa-prescription-bottle::before { + content: "\f485"; } + +.fa-bars::before { + content: "\f0c9"; } + +.fa-navicon::before { + content: "\f0c9"; } + +.fa-people-group::before { + content: "\e533"; } + +.fa-hourglass-end::before { + content: "\f253"; } + +.fa-hourglass-3::before { + content: "\f253"; } + +.fa-heart-crack::before { + content: "\f7a9"; } + +.fa-heart-broken::before { + content: "\f7a9"; } + +.fa-square-up-right::before { + content: "\f360"; } + +.fa-external-link-square-alt::before { + content: "\f360"; } + +.fa-face-kiss-beam::before { + content: "\f597"; } + +.fa-kiss-beam::before { + content: "\f597"; } + +.fa-film::before { + content: "\f008"; } + +.fa-ruler-horizontal::before { + content: "\f547"; } + +.fa-people-robbery::before { + content: "\e536"; } + +.fa-lightbulb::before { + content: "\f0eb"; } + +.fa-caret-left::before { + content: "\f0d9"; } + +.fa-circle-exclamation::before { + content: "\f06a"; } + +.fa-exclamation-circle::before { + content: "\f06a"; } + +.fa-school-circle-xmark::before { + content: "\e56d"; } + +.fa-arrow-right-from-bracket::before { + content: "\f08b"; } + +.fa-sign-out::before { + content: "\f08b"; } + +.fa-circle-chevron-down::before { + content: "\f13a"; } + +.fa-chevron-circle-down::before { + content: "\f13a"; } + +.fa-unlock-keyhole::before { + content: "\f13e"; } + +.fa-unlock-alt::before { + content: "\f13e"; } + +.fa-cloud-showers-heavy::before { + content: "\f740"; } + +.fa-headphones-simple::before { + content: "\f58f"; } + +.fa-headphones-alt::before { + content: "\f58f"; } + +.fa-sitemap::before { + content: "\f0e8"; } + +.fa-circle-dollar-to-slot::before { + content: "\f4b9"; } + +.fa-donate::before { + content: "\f4b9"; } + +.fa-memory::before { + content: "\f538"; } + +.fa-road-spikes::before { + content: "\e568"; } + +.fa-fire-burner::before { + content: "\e4f1"; } + +.fa-flag::before { + content: "\f024"; } + +.fa-hanukiah::before { + content: "\f6e6"; } + +.fa-feather::before { + content: "\f52d"; } + +.fa-volume-low::before { + content: "\f027"; } + +.fa-volume-down::before { + content: "\f027"; } + +.fa-comment-slash::before { + content: "\f4b3"; } + +.fa-cloud-sun-rain::before { + content: "\f743"; } + +.fa-compress::before { + content: "\f066"; } + +.fa-wheat-awn::before { + content: "\e2cd"; } + +.fa-wheat-alt::before { + content: "\e2cd"; } + +.fa-ankh::before { + content: "\f644"; } + +.fa-hands-holding-child::before { + content: "\e4fa"; } + +.fa-asterisk::before { + content: "\2a"; } + +.fa-square-check::before { + content: "\f14a"; } + +.fa-check-square::before { + content: "\f14a"; } + +.fa-peseta-sign::before { + content: "\e221"; } + +.fa-heading::before { + content: "\f1dc"; } + +.fa-header::before { + content: "\f1dc"; } + +.fa-ghost::before { + content: "\f6e2"; } + +.fa-list::before { + content: "\f03a"; } + +.fa-list-squares::before { + content: "\f03a"; } + +.fa-square-phone-flip::before { + content: "\f87b"; } + +.fa-phone-square-alt::before { + content: "\f87b"; } + +.fa-cart-plus::before { + content: "\f217"; } + +.fa-gamepad::before { + content: "\f11b"; } + +.fa-circle-dot::before { + content: "\f192"; } + +.fa-dot-circle::before { + content: "\f192"; } + +.fa-face-dizzy::before { + content: "\f567"; } + +.fa-dizzy::before { + content: "\f567"; } + +.fa-egg::before { + content: "\f7fb"; } + +.fa-house-medical-circle-xmark::before { + content: "\e513"; } + +.fa-campground::before { + content: "\f6bb"; } + +.fa-folder-plus::before { + content: "\f65e"; } + +.fa-futbol::before { + content: "\f1e3"; } + +.fa-futbol-ball::before { + content: "\f1e3"; } + +.fa-soccer-ball::before { + content: "\f1e3"; } + +.fa-paintbrush::before { + content: "\f1fc"; } + +.fa-paint-brush::before { + content: "\f1fc"; } + +.fa-lock::before { + content: "\f023"; } + +.fa-gas-pump::before { + content: "\f52f"; } + +.fa-hot-tub-person::before { + content: "\f593"; } + +.fa-hot-tub::before { + content: "\f593"; } + +.fa-map-location::before { + content: "\f59f"; } + +.fa-map-marked::before { + content: "\f59f"; } + +.fa-house-flood-water::before { + content: "\e50e"; } + +.fa-tree::before { + content: "\f1bb"; } + +.fa-bridge-lock::before { + content: "\e4cc"; } + +.fa-sack-dollar::before { + content: "\f81d"; } + +.fa-pen-to-square::before { + content: "\f044"; } + +.fa-edit::before { + content: "\f044"; } + +.fa-car-side::before { + content: "\f5e4"; } + +.fa-share-nodes::before { + content: "\f1e0"; } + +.fa-share-alt::before { + content: "\f1e0"; } + +.fa-heart-circle-minus::before { + content: "\e4ff"; } + +.fa-hourglass-half::before { + content: "\f252"; } + +.fa-hourglass-2::before { + content: "\f252"; } + +.fa-microscope::before { + content: "\f610"; } + +.fa-sink::before { + content: "\e06d"; } + +.fa-bag-shopping::before { + content: "\f290"; } + +.fa-shopping-bag::before { + content: "\f290"; } + +.fa-arrow-down-z-a::before { + content: "\f881"; } + +.fa-sort-alpha-desc::before { + content: "\f881"; } + +.fa-sort-alpha-down-alt::before { + content: "\f881"; } + +.fa-mitten::before { + content: "\f7b5"; } + 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content: "\f0a8"; } + +.fa-group-arrows-rotate::before { + content: "\e4f6"; } + +.fa-bowl-food::before { + content: "\e4c6"; } + +.fa-candy-cane::before { + content: "\f786"; } + +.fa-arrow-down-wide-short::before { + content: "\f160"; } + +.fa-sort-amount-asc::before { + content: "\f160"; } + +.fa-sort-amount-down::before { + content: "\f160"; } + +.fa-cloud-bolt::before { + content: "\f76c"; } + +.fa-thunderstorm::before { + content: "\f76c"; } + +.fa-text-slash::before { + content: "\f87d"; } + +.fa-remove-format::before { + content: "\f87d"; } + +.fa-face-smile-wink::before { + content: "\f4da"; } + +.fa-smile-wink::before { + content: "\f4da"; } + +.fa-file-word::before { + content: "\f1c2"; } + +.fa-file-powerpoint::before { + content: "\f1c4"; } + +.fa-arrows-left-right::before { + content: "\f07e"; } + +.fa-arrows-h::before { + content: "\f07e"; } + +.fa-house-lock::before { + content: "\e510"; } + +.fa-cloud-arrow-down::before { + content: "\f0ed"; } + 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content: "\f3ed"; } + +.fa-shield-alt::before { + content: "\f3ed"; } + +.fa-book-atlas::before { + content: "\f558"; } + +.fa-atlas::before { + content: "\f558"; } + +.fa-virus::before { + content: "\e074"; } + +.fa-envelope-circle-check::before { + content: "\e4e8"; } + +.fa-layer-group::before { + content: "\f5fd"; } + +.fa-arrows-to-dot::before { + content: "\e4be"; } + +.fa-archway::before { + content: "\f557"; } + +.fa-heart-circle-check::before { + content: "\e4fd"; } + +.fa-house-chimney-crack::before { + content: "\f6f1"; } + +.fa-house-damage::before { + content: "\f6f1"; } + +.fa-file-zipper::before { + content: "\f1c6"; } + +.fa-file-archive::before { + content: "\f1c6"; } + +.fa-square::before { + content: "\f0c8"; } + +.fa-martini-glass-empty::before { + content: "\f000"; } + +.fa-glass-martini::before { + content: "\f000"; } + +.fa-couch::before { + content: "\f4b8"; } + +.fa-cedi-sign::before { + content: "\e0df"; } + +.fa-italic::before { + content: "\f033"; } + 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} + +.fa-locust::before { + content: "\e520"; } + +.fa-sort::before { + content: "\f0dc"; } + +.fa-unsorted::before { + content: "\f0dc"; } + +.fa-list-ol::before { + content: "\f0cb"; } + +.fa-list-1-2::before { + content: "\f0cb"; } + +.fa-list-numeric::before { + content: "\f0cb"; } + +.fa-person-dress-burst::before { + content: "\e544"; } + +.fa-money-check-dollar::before { + content: "\f53d"; } + +.fa-money-check-alt::before { + content: "\f53d"; } + +.fa-vector-square::before { + content: "\f5cb"; } + +.fa-bread-slice::before { + content: "\f7ec"; } + +.fa-language::before { + content: "\f1ab"; } + +.fa-face-kiss-wink-heart::before { + content: "\f598"; } + +.fa-kiss-wink-heart::before { + content: "\f598"; } + +.fa-filter::before { + content: "\f0b0"; } + +.fa-question::before { + content: "\3f"; } + +.fa-file-signature::before { + content: "\f573"; } + +.fa-up-down-left-right::before { + content: "\f0b2"; } + +.fa-arrows-alt::before { + content: "\f0b2"; } + 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content: "\e0a9"; } + +.fa-f::before { + content: "\46"; } + +.fa-leaf::before { + content: "\f06c"; } + +.fa-road::before { + content: "\f018"; } + +.fa-taxi::before { + content: "\f1ba"; } + +.fa-cab::before { + content: "\f1ba"; } + +.fa-person-circle-plus::before { + content: "\e541"; } + +.fa-chart-pie::before { + content: "\f200"; } + +.fa-pie-chart::before { + content: "\f200"; } + +.fa-bolt-lightning::before { + content: "\e0b7"; } + +.fa-sack-xmark::before { + content: "\e56a"; } + +.fa-file-excel::before { + content: "\f1c3"; } + +.fa-file-contract::before { + content: "\f56c"; } + +.fa-fish-fins::before { + content: "\e4f2"; } + +.fa-building-flag::before { + content: "\e4d5"; } + +.fa-face-grin-beam::before { + content: "\f582"; } + +.fa-grin-beam::before { + content: "\f582"; } + +.fa-object-ungroup::before { + content: "\f248"; } + +.fa-poop::before { + content: "\f619"; } + +.fa-location-pin::before { + content: "\f041"; } + +.fa-map-marker::before { + content: "\f041"; } + +.fa-kaaba::before { + content: "\f66b"; } + +.fa-toilet-paper::before { + content: "\f71e"; } + +.fa-helmet-safety::before { + content: "\f807"; } + +.fa-hard-hat::before { + content: "\f807"; } + +.fa-hat-hard::before { + content: "\f807"; } + +.fa-eject::before { + content: "\f052"; } + +.fa-circle-right::before { + content: "\f35a"; } + +.fa-arrow-alt-circle-right::before { + content: "\f35a"; } + +.fa-plane-circle-check::before { + content: "\e555"; } + +.fa-face-rolling-eyes::before { + content: "\f5a5"; } + +.fa-meh-rolling-eyes::before { + content: "\f5a5"; } + +.fa-object-group::before { + content: "\f247"; } + +.fa-chart-line::before { + content: "\f201"; } + +.fa-line-chart::before { + content: "\f201"; } + +.fa-mask-ventilator::before { + content: "\e524"; } + +.fa-arrow-right::before { + content: "\f061"; } + +.fa-signs-post::before { + content: "\f277"; } + +.fa-map-signs::before { + content: "\f277"; } + +.fa-cash-register::before { + content: "\f788"; } + 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content: "\f885"; } + +.fa-house-medical::before { + content: "\e3b2"; } + +.fa-golf-ball-tee::before { + content: "\f450"; } + +.fa-golf-ball::before { + content: "\f450"; } + +.fa-circle-chevron-left::before { + content: "\f137"; } + +.fa-chevron-circle-left::before { + content: "\f137"; } + +.fa-house-chimney-window::before { + content: "\e00d"; } + +.fa-pen-nib::before { + content: "\f5ad"; } + +.fa-tent-arrow-turn-left::before { + content: "\e580"; } + +.fa-tents::before { + content: "\e582"; } + +.fa-wand-magic::before { + content: "\f0d0"; } + +.fa-magic::before { + content: "\f0d0"; } + +.fa-dog::before { + content: "\f6d3"; } + +.fa-carrot::before { + content: "\f787"; } + +.fa-moon::before { + content: "\f186"; } + +.fa-wine-glass-empty::before { + content: "\f5ce"; } + +.fa-wine-glass-alt::before { + content: "\f5ce"; } + +.fa-cheese::before { + content: "\f7ef"; } + +.fa-yin-yang::before { + content: "\f6ad"; } + +.fa-music::before { + content: "\f001"; } + 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{ + content: "\f234"; } + +.fa-check::before { + content: "\f00c"; } + +.fa-battery-three-quarters::before { + content: "\f241"; } + +.fa-battery-4::before { + content: "\f241"; } + +.fa-house-circle-check::before { + content: "\e509"; } + +.fa-angle-left::before { + content: "\f104"; } + +.fa-diagram-successor::before { + content: "\e47a"; } + +.fa-truck-arrow-right::before { + content: "\e58b"; } + +.fa-arrows-split-up-and-left::before { + content: "\e4bc"; } + +.fa-hand-fist::before { + content: "\f6de"; } + +.fa-fist-raised::before { + content: "\f6de"; } + +.fa-cloud-moon::before { + content: "\f6c3"; } + +.fa-briefcase::before { + content: "\f0b1"; } + +.fa-person-falling::before { + content: "\e546"; } + +.fa-image-portrait::before { + content: "\f3e0"; } + +.fa-portrait::before { + content: "\f3e0"; } + +.fa-user-tag::before { + content: "\f507"; } + +.fa-rug::before { + content: "\e569"; } + +.fa-earth-europe::before { + content: "\f7a2"; } + +.fa-globe-europe::before { + content: "\f7a2"; } + +.fa-cart-flatbed-suitcase::before { + content: "\f59d"; } + +.fa-luggage-cart::before { + content: "\f59d"; } + +.fa-rectangle-xmark::before { + content: "\f410"; } + +.fa-rectangle-times::before { + content: "\f410"; } + +.fa-times-rectangle::before { + content: "\f410"; } + +.fa-window-close::before { + content: "\f410"; } + +.fa-baht-sign::before { + content: "\e0ac"; } + +.fa-book-open::before { + content: "\f518"; } + +.fa-book-journal-whills::before { + content: "\f66a"; } + +.fa-journal-whills::before { + content: "\f66a"; } + +.fa-handcuffs::before { + content: "\e4f8"; } + +.fa-triangle-exclamation::before { + content: "\f071"; } + +.fa-exclamation-triangle::before { + content: "\f071"; } + +.fa-warning::before { + content: "\f071"; } + +.fa-database::before { + content: "\f1c0"; } + +.fa-share::before { + content: "\f064"; } + +.fa-arrow-turn-right::before { + content: "\f064"; } + +.fa-mail-forward::before { + content: "\f064"; } + 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+.fa-xmark-circle::before { + content: "\f057"; } + +.fa-gifts::before { + content: "\f79c"; } + +.fa-hotel::before { + content: "\f594"; } + +.fa-earth-asia::before { + content: "\f57e"; } + +.fa-globe-asia::before { + content: "\f57e"; } + +.fa-id-card-clip::before { + content: "\f47f"; } + +.fa-id-card-alt::before { + content: "\f47f"; } + +.fa-magnifying-glass-plus::before { + content: "\f00e"; } + +.fa-search-plus::before { + content: "\f00e"; } + +.fa-thumbs-up::before { + content: "\f164"; } + +.fa-user-clock::before { + content: "\f4fd"; } + +.fa-hand-dots::before { + content: "\f461"; } + +.fa-allergies::before { + content: "\f461"; } + +.fa-file-invoice::before { + content: "\f570"; } + +.fa-window-minimize::before { + content: "\f2d1"; } + +.fa-mug-saucer::before { + content: "\f0f4"; } + +.fa-coffee::before { + content: "\f0f4"; } + +.fa-brush::before { + content: "\f55d"; } + +.fa-mask::before { + content: "\f6fa"; } + +.fa-magnifying-glass-minus::before { + content: "\f010"; } + +.fa-search-minus::before { + content: "\f010"; } + +.fa-ruler-vertical::before { + content: "\f548"; } + +.fa-user-large::before { + content: "\f406"; } + +.fa-user-alt::before { + content: "\f406"; } + +.fa-train-tram::before { + content: "\e5b4"; } + +.fa-user-nurse::before { + content: "\f82f"; } + +.fa-syringe::before { + content: "\f48e"; } + +.fa-cloud-sun::before { + content: "\f6c4"; } + +.fa-stopwatch-20::before { + content: "\e06f"; } + +.fa-square-full::before { + content: "\f45c"; } + +.fa-magnet::before { + content: "\f076"; } + +.fa-jar::before { + content: "\e516"; } + +.fa-note-sticky::before { + content: "\f249"; } + +.fa-sticky-note::before { + content: "\f249"; } + +.fa-bug-slash::before { + content: "\e490"; } + +.fa-arrow-up-from-water-pump::before { + content: "\e4b6"; } + +.fa-bone::before { + content: "\f5d7"; } + +.fa-user-injured::before { + content: "\f728"; } + +.fa-face-sad-tear::before { + content: "\f5b4"; } + +.fa-sad-tear::before { + content: "\f5b4"; } + +.fa-plane::before { + content: "\f072"; } + +.fa-tent-arrows-down::before { + content: "\e581"; } + +.fa-exclamation::before { + content: "\21"; } + +.fa-arrows-spin::before { + content: "\e4bb"; } + +.fa-print::before { + content: "\f02f"; } + +.fa-turkish-lira-sign::before { + content: "\e2bb"; } + +.fa-try::before { + content: "\e2bb"; } + +.fa-turkish-lira::before { + content: "\e2bb"; } + +.fa-dollar-sign::before { + content: "\24"; } + +.fa-dollar::before { + content: "\24"; } + +.fa-usd::before { + content: "\24"; } + +.fa-x::before { + content: "\58"; } + +.fa-magnifying-glass-dollar::before { + content: "\f688"; } + +.fa-search-dollar::before { + content: "\f688"; } + +.fa-users-gear::before { + content: "\f509"; } + +.fa-users-cog::before { + content: "\f509"; } + +.fa-person-military-pointing::before { + content: "\e54a"; } + +.fa-building-columns::before { + content: "\f19c"; } + +.fa-bank::before { + content: "\f19c"; } + +.fa-institution::before { + content: "\f19c"; } + +.fa-museum::before { + content: "\f19c"; } + +.fa-university::before { + content: "\f19c"; } + +.fa-umbrella::before { + content: "\f0e9"; } + +.fa-trowel::before { + content: "\e589"; } + +.fa-d::before { + content: "\44"; } + +.fa-stapler::before { + content: "\e5af"; } + +.fa-masks-theater::before { + content: "\f630"; } + +.fa-theater-masks::before { + content: "\f630"; } + +.fa-kip-sign::before { + content: "\e1c4"; } + +.fa-hand-point-left::before { + content: "\f0a5"; } + +.fa-handshake-simple::before { + content: "\f4c6"; } + +.fa-handshake-alt::before { + content: "\f4c6"; } + +.fa-jet-fighter::before { + content: "\f0fb"; } + +.fa-fighter-jet::before { + content: "\f0fb"; } + +.fa-square-share-nodes::before { + content: "\f1e1"; } + +.fa-share-alt-square::before { + content: "\f1e1"; } + +.fa-barcode::before { + content: "\f02a"; } + +.fa-plus-minus::before { + content: "\e43c"; } + +.fa-video::before { + content: "\f03d"; } + +.fa-video-camera::before { + content: "\f03d"; } + +.fa-graduation-cap::before { + content: "\f19d"; } + +.fa-mortar-board::before { + content: "\f19d"; } + +.fa-hand-holding-medical::before { + content: "\e05c"; } + +.fa-person-circle-check::before { + content: "\e53e"; } + +.fa-turn-up::before { + content: "\f3bf"; } + +.fa-level-up-alt::before { + content: "\f3bf"; } + +.sr-only, +.fa-sr-only { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + white-space: nowrap; + border-width: 0; } + +.sr-only-focusable:not(:focus), +.fa-sr-only-focusable:not(:focus) { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + white-space: nowrap; + border-width: 0; } +:root, :host { + --fa-style-family-brands: 'Font Awesome 6 Brands'; + --fa-font-brands: normal 400 1em/1 'Font Awesome 6 Brands'; } + +@font-face { + font-family: 'Font Awesome 6 Brands'; + font-style: normal; + font-weight: 400; + font-display: block; + src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); } + +.fab, +.fa-brands { + font-weight: 400; } + +.fa-monero:before { + content: "\f3d0"; } + +.fa-hooli:before { + content: "\f427"; } + +.fa-yelp:before { + content: "\f1e9"; } + +.fa-cc-visa:before { + content: "\f1f0"; } + +.fa-lastfm:before { + content: "\f202"; } + +.fa-shopware:before { + content: "\f5b5"; } + +.fa-creative-commons-nc:before { + content: "\f4e8"; } + +.fa-aws:before { + content: "\f375"; } + +.fa-redhat:before { + content: "\f7bc"; } + +.fa-yoast:before { + content: "\f2b1"; } + +.fa-cloudflare:before { + content: "\e07d"; } + +.fa-ups:before { + content: "\f7e0"; } + +.fa-wpexplorer:before { + content: "\f2de"; } + +.fa-dyalog:before { + content: "\f399"; } + +.fa-bity:before { + content: "\f37a"; } + +.fa-stackpath:before { + content: "\f842"; } + +.fa-buysellads:before { + content: "\f20d"; } + +.fa-first-order:before { + content: "\f2b0"; } + +.fa-modx:before { + content: "\f285"; } + +.fa-guilded:before { + content: "\e07e"; } + +.fa-vnv:before { + content: "\f40b"; } + +.fa-square-js:before { + content: "\f3b9"; } + +.fa-js-square:before { + content: "\f3b9"; } + +.fa-microsoft:before { + content: "\f3ca"; } + +.fa-qq:before { + content: "\f1d6"; } + +.fa-orcid:before { + content: "\f8d2"; } + +.fa-java:before { + content: "\f4e4"; } + +.fa-invision:before { + content: "\f7b0"; } + +.fa-creative-commons-pd-alt:before { + content: "\f4ed"; } + +.fa-centercode:before { + content: "\f380"; } + +.fa-glide-g:before { + content: "\f2a6"; } + +.fa-drupal:before { + content: "\f1a9"; } + +.fa-hire-a-helper:before { + content: "\f3b0"; } + +.fa-creative-commons-by:before { + content: "\f4e7"; } + +.fa-unity:before { + content: "\e049"; } + +.fa-whmcs:before { + content: "\f40d"; } + +.fa-rocketchat:before { + content: "\f3e8"; } + +.fa-vk:before { + content: "\f189"; } + +.fa-untappd:before { + content: "\f405"; } + +.fa-mailchimp:before { + content: "\f59e"; } + +.fa-css3-alt:before { + content: "\f38b"; } + +.fa-square-reddit:before { + content: "\f1a2"; } + +.fa-reddit-square:before { + content: "\f1a2"; } + +.fa-vimeo-v:before { + content: "\f27d"; } + +.fa-contao:before { + content: "\f26d"; } + +.fa-square-font-awesome:before { + content: "\e5ad"; } + +.fa-deskpro:before { + content: "\f38f"; } + +.fa-sistrix:before { + content: "\f3ee"; } + +.fa-square-instagram:before { + content: "\e055"; } + +.fa-instagram-square:before { + content: "\e055"; } + +.fa-battle-net:before { + content: "\f835"; } + +.fa-the-red-yeti:before { + content: "\f69d"; } + +.fa-square-hacker-news:before { + content: "\f3af"; } + +.fa-hacker-news-square:before { + content: "\f3af"; } + +.fa-edge:before { + content: "\f282"; } + +.fa-threads:before { + content: "\e618"; } + +.fa-napster:before { + content: "\f3d2"; } + +.fa-square-snapchat:before { + content: "\f2ad"; } + 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+.fa.fa-thumb-tack:before { + content: "\f08d"; } + +.fa.fa-external-link:before { + content: "\f35d"; } + +.fa.fa-sign-in:before { + content: "\f2f6"; } + +.fa.fa-github-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-github-square:before { + content: "\f092"; } + +.fa.fa-lemon-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-lemon-o:before { + content: "\f094"; } + +.fa.fa-square-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-square-o:before { + content: "\f0c8"; } + +.fa.fa-bookmark-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-bookmark-o:before { + content: "\f02e"; } + +.fa.fa-twitter { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-facebook { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-facebook:before { + content: "\f39e"; } + +.fa.fa-facebook-f { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-facebook-f:before { + content: "\f39e"; } + +.fa.fa-github { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-credit-card { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-feed:before { + content: "\f09e"; } + +.fa.fa-hdd-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hdd-o:before { + content: "\f0a0"; } + +.fa.fa-hand-o-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-o-right:before { + content: "\f0a4"; } + +.fa.fa-hand-o-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-o-left:before { + content: "\f0a5"; } + +.fa.fa-hand-o-up { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-o-up:before { + content: "\f0a6"; } + +.fa.fa-hand-o-down { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-o-down:before { + content: "\f0a7"; } + +.fa.fa-globe:before { + content: "\f57d"; } + +.fa.fa-tasks:before { + content: "\f828"; } + +.fa.fa-arrows-alt:before { + content: "\f31e"; } + +.fa.fa-group:before { + content: "\f0c0"; } + +.fa.fa-chain:before { + content: "\f0c1"; } + +.fa.fa-cut:before { + content: "\f0c4"; } + +.fa.fa-files-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-files-o:before { + content: "\f0c5"; } + +.fa.fa-floppy-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-floppy-o:before { + content: "\f0c7"; } + +.fa.fa-save { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-save:before { + content: "\f0c7"; } + +.fa.fa-navicon:before { + content: "\f0c9"; } + +.fa.fa-reorder:before { + content: "\f0c9"; } + +.fa.fa-magic:before { + content: "\e2ca"; } + +.fa.fa-pinterest { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pinterest-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pinterest-square:before { + content: "\f0d3"; } + +.fa.fa-google-plus-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-plus-square:before { + content: "\f0d4"; } + +.fa.fa-google-plus { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-plus:before { + content: "\f0d5"; } + +.fa.fa-money:before { + content: "\f3d1"; } + +.fa.fa-unsorted:before { + content: "\f0dc"; } + +.fa.fa-sort-desc:before { + content: "\f0dd"; } + +.fa.fa-sort-asc:before { + content: "\f0de"; } + +.fa.fa-linkedin { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-linkedin:before { + content: "\f0e1"; } + +.fa.fa-rotate-left:before { + content: "\f0e2"; } + +.fa.fa-legal:before { + content: "\f0e3"; } + +.fa.fa-tachometer:before { + content: "\f625"; } + +.fa.fa-dashboard:before { + content: "\f625"; } + +.fa.fa-comment-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-comment-o:before { + content: "\f075"; } + +.fa.fa-comments-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-comments-o:before { + content: "\f086"; } + +.fa.fa-flash:before { + content: "\f0e7"; } + +.fa.fa-clipboard:before { + content: "\f0ea"; } + +.fa.fa-lightbulb-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-lightbulb-o:before { + content: "\f0eb"; } + +.fa.fa-exchange:before { + content: "\f362"; } + +.fa.fa-cloud-download:before { + content: "\f0ed"; } + +.fa.fa-cloud-upload:before { + content: "\f0ee"; } + +.fa.fa-bell-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-bell-o:before { + content: "\f0f3"; } + +.fa.fa-cutlery:before { + content: "\f2e7"; } + +.fa.fa-file-text-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-text-o:before { + content: "\f15c"; } + +.fa.fa-building-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-building-o:before { + content: "\f1ad"; } + +.fa.fa-hospital-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hospital-o:before { + content: "\f0f8"; } + +.fa.fa-tablet:before { + content: "\f3fa"; } + +.fa.fa-mobile:before { + content: "\f3cd"; } + +.fa.fa-mobile-phone:before { + content: "\f3cd"; } + +.fa.fa-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-circle-o:before { + content: "\f111"; } + +.fa.fa-mail-reply:before { + content: "\f3e5"; } + +.fa.fa-github-alt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-folder-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-folder-o:before { + content: "\f07b"; } + +.fa.fa-folder-open-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-folder-open-o:before { + content: "\f07c"; } + +.fa.fa-smile-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-smile-o:before { + content: "\f118"; } + +.fa.fa-frown-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-frown-o:before { + content: "\f119"; } + +.fa.fa-meh-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-meh-o:before { + content: "\f11a"; } + +.fa.fa-keyboard-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-keyboard-o:before { + content: "\f11c"; } + +.fa.fa-flag-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-flag-o:before { + content: "\f024"; } + +.fa.fa-mail-reply-all:before { + content: "\f122"; } + +.fa.fa-star-half-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-star-half-o:before { + content: "\f5c0"; } + +.fa.fa-star-half-empty { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-star-half-empty:before { + content: "\f5c0"; } + +.fa.fa-star-half-full { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-star-half-full:before { + content: "\f5c0"; } + +.fa.fa-code-fork:before { + content: "\f126"; } + +.fa.fa-chain-broken:before { + content: "\f127"; } + +.fa.fa-unlink:before { + content: "\f127"; } + +.fa.fa-calendar-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-o:before { + content: "\f133"; } + +.fa.fa-maxcdn { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-html5 { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-css3 { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-unlock-alt:before { + content: "\f09c"; } + +.fa.fa-minus-square-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-minus-square-o:before { + content: "\f146"; } + +.fa.fa-level-up:before { + content: "\f3bf"; } + +.fa.fa-level-down:before { + content: "\f3be"; } + +.fa.fa-pencil-square:before { + content: "\f14b"; } + +.fa.fa-external-link-square:before { + content: "\f360"; } + +.fa.fa-compass { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-down { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-down:before { + content: "\f150"; } + +.fa.fa-toggle-down { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-down:before { + content: "\f150"; } + +.fa.fa-caret-square-o-up { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-up:before { + content: "\f151"; } + +.fa.fa-toggle-up { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-up:before { + content: "\f151"; } + +.fa.fa-caret-square-o-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-right:before { + content: "\f152"; } + +.fa.fa-toggle-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-right:before { + content: "\f152"; } + +.fa.fa-eur:before { + content: "\f153"; } + +.fa.fa-euro:before { + content: "\f153"; } + +.fa.fa-gbp:before { + content: "\f154"; } + +.fa.fa-usd:before { + content: "\24"; } + +.fa.fa-dollar:before { + content: "\24"; } + +.fa.fa-inr:before { + content: "\e1bc"; } + +.fa.fa-rupee:before { + content: "\e1bc"; } + +.fa.fa-jpy:before { + content: "\f157"; } + +.fa.fa-cny:before { + content: "\f157"; } + +.fa.fa-rmb:before { + content: "\f157"; } + +.fa.fa-yen:before { + content: "\f157"; } + +.fa.fa-rub:before { + content: "\f158"; } + +.fa.fa-ruble:before { + content: "\f158"; } + +.fa.fa-rouble:before { + content: "\f158"; } + +.fa.fa-krw:before { + content: "\f159"; } + +.fa.fa-won:before { + content: "\f159"; } + +.fa.fa-btc { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitcoin { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitcoin:before { + content: "\f15a"; } + +.fa.fa-file-text:before { + content: "\f15c"; } + +.fa.fa-sort-alpha-asc:before { + content: "\f15d"; } + +.fa.fa-sort-alpha-desc:before { + content: "\f881"; } + +.fa.fa-sort-amount-asc:before { + content: "\f884"; } + +.fa.fa-sort-amount-desc:before { + content: "\f160"; } + +.fa.fa-sort-numeric-asc:before { + content: "\f162"; } + +.fa.fa-sort-numeric-desc:before { + content: "\f886"; } + +.fa.fa-youtube-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-youtube-square:before { + content: "\f431"; } + +.fa.fa-youtube { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-xing { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-xing-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-xing-square:before { + content: "\f169"; } + +.fa.fa-youtube-play { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-youtube-play:before { + content: "\f167"; } + +.fa.fa-dropbox { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-stack-overflow { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-instagram { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-flickr { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-adn { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitbucket { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitbucket-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitbucket-square:before { + content: "\f171"; } + +.fa.fa-tumblr { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-tumblr-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-tumblr-square:before { + content: "\f174"; } + +.fa.fa-long-arrow-down:before { + content: "\f309"; } + +.fa.fa-long-arrow-up:before { + content: "\f30c"; } + +.fa.fa-long-arrow-left:before { + content: "\f30a"; } + +.fa.fa-long-arrow-right:before { + content: "\f30b"; } + +.fa.fa-apple { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-windows { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-android { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-linux { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-dribbble { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-skype { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-foursquare { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-trello { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gratipay { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gittip { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gittip:before { + content: "\f184"; } + +.fa.fa-sun-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-sun-o:before { + content: "\f185"; } + +.fa.fa-moon-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-moon-o:before { + content: "\f186"; } + +.fa.fa-vk { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-weibo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-renren { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pagelines { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-stack-exchange { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-arrow-circle-o-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-arrow-circle-o-right:before { + content: "\f35a"; } + +.fa.fa-arrow-circle-o-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-arrow-circle-o-left:before { + content: "\f359"; } + +.fa.fa-caret-square-o-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-left:before { + content: "\f191"; } + +.fa.fa-toggle-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-left:before { + content: "\f191"; } + +.fa.fa-dot-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-dot-circle-o:before { + content: "\f192"; } + +.fa.fa-vimeo-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-vimeo-square:before { + content: "\f194"; } + +.fa.fa-try:before { + content: "\e2bb"; } + +.fa.fa-turkish-lira:before { + content: "\e2bb"; } + +.fa.fa-plus-square-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-plus-square-o:before { + content: "\f0fe"; } + +.fa.fa-slack { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wordpress { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-openid { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-institution:before { + content: "\f19c"; } + +.fa.fa-bank:before { + content: "\f19c"; } + +.fa.fa-mortar-board:before { + content: "\f19d"; } + +.fa.fa-yahoo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit-square:before { + content: "\f1a2"; } + +.fa.fa-stumbleupon-circle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-stumbleupon { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-delicious { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-digg { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pied-piper-pp { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pied-piper-alt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-drupal { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-joomla { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-behance { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-behance-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-behance-square:before { + content: "\f1b5"; } + +.fa.fa-steam { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-steam-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-steam-square:before { + content: "\f1b7"; } + +.fa.fa-automobile:before { + content: "\f1b9"; } + +.fa.fa-cab:before { + content: "\f1ba"; } + +.fa.fa-spotify { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-deviantart { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-soundcloud { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-file-pdf-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-pdf-o:before { + content: "\f1c1"; } + +.fa.fa-file-word-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-word-o:before { + content: "\f1c2"; } + +.fa.fa-file-excel-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-excel-o:before { + content: "\f1c3"; } + +.fa.fa-file-powerpoint-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-powerpoint-o:before { + content: "\f1c4"; } + +.fa.fa-file-image-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-image-o:before { + content: "\f1c5"; } + +.fa.fa-file-photo-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-photo-o:before { + content: "\f1c5"; } + +.fa.fa-file-picture-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-picture-o:before { + content: "\f1c5"; } + +.fa.fa-file-archive-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-archive-o:before { + content: "\f1c6"; } + +.fa.fa-file-zip-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-zip-o:before { + content: "\f1c6"; } + +.fa.fa-file-audio-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-audio-o:before { + content: "\f1c7"; } + +.fa.fa-file-sound-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-sound-o:before { + content: "\f1c7"; } + +.fa.fa-file-video-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-video-o:before { + content: "\f1c8"; } + +.fa.fa-file-movie-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-movie-o:before { + content: "\f1c8"; } + +.fa.fa-file-code-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-code-o:before { + content: "\f1c9"; } + +.fa.fa-vine { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-codepen { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-jsfiddle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-life-bouy:before { + content: "\f1cd"; } + +.fa.fa-life-buoy:before { + content: "\f1cd"; } + +.fa.fa-life-saver:before { + content: "\f1cd"; } + +.fa.fa-support:before { + content: "\f1cd"; } + +.fa.fa-circle-o-notch:before { + content: "\f1ce"; } + +.fa.fa-rebel { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ra { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ra:before { + content: "\f1d0"; } + +.fa.fa-resistance { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-resistance:before { + content: "\f1d0"; } + +.fa.fa-empire { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ge { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ge:before { + content: "\f1d1"; } + +.fa.fa-git-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-git-square:before { + content: "\f1d2"; } + +.fa.fa-git { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-hacker-news { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-y-combinator-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-y-combinator-square:before { + content: "\f1d4"; } + +.fa.fa-yc-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yc-square:before { + content: "\f1d4"; } + +.fa.fa-tencent-weibo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-qq { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-weixin { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wechat { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wechat:before { + content: "\f1d7"; } + +.fa.fa-send:before { + content: "\f1d8"; } + +.fa.fa-paper-plane-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-paper-plane-o:before { + content: "\f1d8"; } + +.fa.fa-send-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-send-o:before { + content: "\f1d8"; } + +.fa.fa-circle-thin { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-circle-thin:before { + content: "\f111"; } + +.fa.fa-header:before { + content: "\f1dc"; } + +.fa.fa-futbol-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-futbol-o:before { + content: "\f1e3"; } + +.fa.fa-soccer-ball-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-soccer-ball-o:before { + content: "\f1e3"; } + +.fa.fa-slideshare { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-twitch { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yelp { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-newspaper-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-newspaper-o:before { + content: "\f1ea"; } + +.fa.fa-paypal { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-wallet { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-visa { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-mastercard { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-discover { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-amex { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-paypal { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-stripe { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bell-slash-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-bell-slash-o:before { + content: "\f1f6"; } + +.fa.fa-trash:before { + content: "\f2ed"; } + +.fa.fa-copyright { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-eyedropper:before { + content: "\f1fb"; } + +.fa.fa-area-chart:before { + content: "\f1fe"; } + +.fa.fa-pie-chart:before { + content: "\f200"; } + +.fa.fa-line-chart:before { + content: "\f201"; } + +.fa.fa-lastfm { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-lastfm-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-lastfm-square:before { + content: "\f203"; } + +.fa.fa-ioxhost { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-angellist { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-cc:before { + content: "\f20a"; } + +.fa.fa-ils:before { + content: "\f20b"; } + +.fa.fa-shekel:before { + content: "\f20b"; } + +.fa.fa-sheqel:before { + content: "\f20b"; } + +.fa.fa-buysellads { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-connectdevelop { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-dashcube { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-forumbee { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-leanpub { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-sellsy { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-shirtsinbulk { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-simplybuilt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-skyatlas { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-diamond { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-diamond:before { + content: "\f3a5"; } + +.fa.fa-transgender:before { + content: "\f224"; } + +.fa.fa-intersex:before { + content: "\f224"; } + +.fa.fa-transgender-alt:before { + content: "\f225"; } + +.fa.fa-facebook-official { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-facebook-official:before { + content: "\f09a"; } + +.fa.fa-pinterest-p { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-whatsapp { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-hotel:before { + content: "\f236"; } + +.fa.fa-viacoin { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-medium { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-y-combinator { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yc { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yc:before { + content: "\f23b"; } + +.fa.fa-optin-monster { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-opencart { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-expeditedssl { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-battery-4:before { + content: "\f240"; } + +.fa.fa-battery:before { + content: "\f240"; } + +.fa.fa-battery-3:before { + content: "\f241"; } + +.fa.fa-battery-2:before { + content: "\f242"; } + +.fa.fa-battery-1:before { + content: "\f243"; } + +.fa.fa-battery-0:before { + content: "\f244"; } + +.fa.fa-object-group { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-object-ungroup { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-sticky-note-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-sticky-note-o:before { + content: "\f249"; } + +.fa.fa-cc-jcb { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-diners-club { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-clone { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hourglass-o:before { + content: "\f254"; } + +.fa.fa-hourglass-1:before { + content: "\f251"; } + +.fa.fa-hourglass-2:before { + content: "\f252"; } + +.fa.fa-hourglass-3:before { + content: "\f253"; } + +.fa.fa-hand-rock-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-rock-o:before { + content: "\f255"; } + +.fa.fa-hand-grab-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-grab-o:before { + content: "\f255"; } + +.fa.fa-hand-paper-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-paper-o:before { + content: "\f256"; } + +.fa.fa-hand-stop-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-stop-o:before { + content: "\f256"; } + +.fa.fa-hand-scissors-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-scissors-o:before { + content: "\f257"; } + +.fa.fa-hand-lizard-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-lizard-o:before { + content: "\f258"; } + +.fa.fa-hand-spock-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-spock-o:before { + content: "\f259"; } + +.fa.fa-hand-pointer-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-pointer-o:before { + content: "\f25a"; } + +.fa.fa-hand-peace-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-peace-o:before { + content: "\f25b"; } + +.fa.fa-registered { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-creative-commons { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gg { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gg-circle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-odnoklassniki { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-odnoklassniki-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-odnoklassniki-square:before { + content: "\f264"; } + +.fa.fa-get-pocket { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wikipedia-w { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-safari { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-chrome { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-firefox { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-opera { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-internet-explorer { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-television:before { + content: "\f26c"; } + +.fa.fa-contao { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-500px { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-amazon { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-calendar-plus-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-plus-o:before { + content: "\f271"; } + +.fa.fa-calendar-minus-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-minus-o:before { + content: "\f272"; } + +.fa.fa-calendar-times-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-times-o:before { + content: "\f273"; } + +.fa.fa-calendar-check-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-check-o:before { + content: "\f274"; } + +.fa.fa-map-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-map-o:before { + content: "\f279"; } + +.fa.fa-commenting:before { + content: "\f4ad"; } + +.fa.fa-commenting-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-commenting-o:before { + content: "\f4ad"; } + +.fa.fa-houzz { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-vimeo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-vimeo:before { + content: "\f27d"; } + +.fa.fa-black-tie { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-fonticons { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit-alien { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-edge { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-credit-card-alt:before { + content: "\f09d"; } + +.fa.fa-codiepie { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-modx { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-fort-awesome { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-usb { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-product-hunt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-mixcloud { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-scribd { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pause-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-pause-circle-o:before { + content: "\f28b"; } + +.fa.fa-stop-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-stop-circle-o:before { + content: "\f28d"; } + +.fa.fa-bluetooth { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bluetooth-b { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gitlab { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wpbeginner { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wpforms { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-envira { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wheelchair-alt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wheelchair-alt:before { + content: "\f368"; } + +.fa.fa-question-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-question-circle-o:before { + content: "\f059"; } + +.fa.fa-volume-control-phone:before { + content: "\f2a0"; } + +.fa.fa-asl-interpreting:before { + content: "\f2a3"; } + +.fa.fa-deafness:before { + content: "\f2a4"; } + +.fa.fa-hard-of-hearing:before { + content: "\f2a4"; } + +.fa.fa-glide { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-glide-g { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-signing:before { + content: "\f2a7"; } + +.fa.fa-viadeo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-viadeo-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-viadeo-square:before { + content: "\f2aa"; } + +.fa.fa-snapchat { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-snapchat-ghost { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-snapchat-ghost:before { + content: "\f2ab"; } + +.fa.fa-snapchat-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-snapchat-square:before { + content: "\f2ad"; } + +.fa.fa-pied-piper { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-first-order { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yoast { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; 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calc(var(--fa-li-width, 2em) * -1); + position: absolute; + text-align: center; + width: var(--fa-li-width, 2em); + line-height: inherit; } + +.fa-border { + border-color: var(--fa-border-color, #eee); + border-radius: var(--fa-border-radius, 0.1em); + border-style: var(--fa-border-style, solid); + border-width: var(--fa-border-width, 0.08em); + padding: var(--fa-border-padding, 0.2em 0.25em 0.15em); } + +.fa-pull-left { + float: left; + margin-right: var(--fa-pull-margin, 0.3em); } + +.fa-pull-right { + float: right; + margin-left: var(--fa-pull-margin, 0.3em); } + +.fa-beat { + -webkit-animation-name: fa-beat; + animation-name: fa-beat; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); + animation-timing-function: var(--fa-animation-timing, ease-in-out); } + +.fa-bounce { + -webkit-animation-name: fa-bounce; + animation-name: fa-bounce; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); } + +.fa-fade { + -webkit-animation-name: fa-fade; + animation-name: fa-fade; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } + +.fa-beat-fade { + -webkit-animation-name: fa-beat-fade; + animation-name: fa-beat-fade; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } + +.fa-flip { + -webkit-animation-name: fa-flip; + animation-name: fa-flip; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); + animation-timing-function: var(--fa-animation-timing, ease-in-out); } + +.fa-shake { + -webkit-animation-name: fa-shake; + animation-name: fa-shake; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, linear); + animation-timing-function: var(--fa-animation-timing, linear); } + +.fa-spin { + -webkit-animation-name: fa-spin; + animation-name: fa-spin; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 2s); + animation-duration: var(--fa-animation-duration, 2s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, linear); + animation-timing-function: var(--fa-animation-timing, linear); } + +.fa-spin-reverse { + --fa-animation-direction: reverse; } + +.fa-pulse, +.fa-spin-pulse { + -webkit-animation-name: fa-spin; + animation-name: fa-spin; + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, steps(8)); + animation-timing-function: var(--fa-animation-timing, steps(8)); } + +@media (prefers-reduced-motion: reduce) { + .fa-beat, + .fa-bounce, + .fa-fade, + .fa-beat-fade, + .fa-flip, + .fa-pulse, + .fa-shake, + .fa-spin, + .fa-spin-pulse { + -webkit-animation-delay: -1ms; + animation-delay: -1ms; + -webkit-animation-duration: 1ms; + animation-duration: 1ms; + -webkit-animation-iteration-count: 1; + animation-iteration-count: 1; + -webkit-transition-delay: 0s; + transition-delay: 0s; + -webkit-transition-duration: 0s; + transition-duration: 0s; } } + +@-webkit-keyframes fa-beat { + 0%, 90% { + -webkit-transform: scale(1); + transform: scale(1); } + 45% { + -webkit-transform: scale(var(--fa-beat-scale, 1.25)); + transform: scale(var(--fa-beat-scale, 1.25)); } } + +@keyframes fa-beat { + 0%, 90% { + -webkit-transform: scale(1); + transform: scale(1); } + 45% { + -webkit-transform: scale(var(--fa-beat-scale, 1.25)); + transform: scale(var(--fa-beat-scale, 1.25)); } } + +@-webkit-keyframes fa-bounce { + 0% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 10% { + -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); + transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } + 30% { + -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); + transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } + 50% { + -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); + transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } + 57% { + -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); + transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } + 64% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 100% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } } + +@keyframes fa-bounce { + 0% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 10% { + -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); + transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } + 30% { + -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); + transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } + 50% { + -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); + transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } + 57% { + -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); + transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } + 64% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 100% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } } + +@-webkit-keyframes fa-fade { + 50% { + opacity: var(--fa-fade-opacity, 0.4); } } + +@keyframes fa-fade { + 50% { + opacity: var(--fa-fade-opacity, 0.4); } } + +@-webkit-keyframes fa-beat-fade { + 0%, 100% { + opacity: var(--fa-beat-fade-opacity, 0.4); + -webkit-transform: scale(1); + transform: scale(1); } + 50% { + opacity: 1; + -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); + transform: scale(var(--fa-beat-fade-scale, 1.125)); } } + +@keyframes fa-beat-fade { + 0%, 100% { + opacity: var(--fa-beat-fade-opacity, 0.4); + -webkit-transform: scale(1); + transform: scale(1); } + 50% { + opacity: 1; + -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); + transform: scale(var(--fa-beat-fade-scale, 1.125)); } } + +@-webkit-keyframes fa-flip { + 50% { + -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); + transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } + +@keyframes fa-flip { + 50% { + -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); + transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } + +@-webkit-keyframes fa-shake { + 0% { + -webkit-transform: rotate(-15deg); + transform: rotate(-15deg); } + 4% { + -webkit-transform: rotate(15deg); + transform: rotate(15deg); } + 8%, 24% { + -webkit-transform: rotate(-18deg); + transform: rotate(-18deg); } + 12%, 28% { + -webkit-transform: rotate(18deg); + transform: rotate(18deg); } + 16% { + -webkit-transform: rotate(-22deg); + transform: rotate(-22deg); } + 20% { + -webkit-transform: rotate(22deg); + transform: rotate(22deg); } + 32% { + -webkit-transform: rotate(-12deg); + transform: rotate(-12deg); } + 36% { + -webkit-transform: rotate(12deg); + transform: rotate(12deg); } + 40%, 100% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } } + +@keyframes fa-shake { + 0% { + -webkit-transform: rotate(-15deg); + transform: rotate(-15deg); } + 4% { + -webkit-transform: rotate(15deg); + transform: rotate(15deg); } + 8%, 24% { + -webkit-transform: rotate(-18deg); + transform: rotate(-18deg); } + 12%, 28% { + -webkit-transform: rotate(18deg); + transform: rotate(18deg); } + 16% { + -webkit-transform: rotate(-22deg); + transform: rotate(-22deg); } + 20% { + -webkit-transform: rotate(22deg); + transform: rotate(22deg); } + 32% { + -webkit-transform: rotate(-12deg); + transform: rotate(-12deg); } + 36% { + -webkit-transform: rotate(12deg); + transform: rotate(12deg); } + 40%, 100% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } } + +@-webkit-keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } + 100% { + -webkit-transform: rotate(360deg); + transform: rotate(360deg); } } + +@keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } + 100% { + -webkit-transform: rotate(360deg); + transform: rotate(360deg); } } + +.fa-rotate-90 { + -webkit-transform: rotate(90deg); + transform: rotate(90deg); } + +.fa-rotate-180 { + -webkit-transform: rotate(180deg); + transform: rotate(180deg); } + +.fa-rotate-270 { + -webkit-transform: rotate(270deg); + transform: rotate(270deg); } + +.fa-flip-horizontal { + -webkit-transform: scale(-1, 1); + transform: scale(-1, 1); } + +.fa-flip-vertical { + -webkit-transform: scale(1, -1); + transform: scale(1, -1); } + +.fa-flip-both, +.fa-flip-horizontal.fa-flip-vertical { + -webkit-transform: scale(-1, -1); + transform: scale(-1, -1); } + +.fa-rotate-by { + -webkit-transform: rotate(var(--fa-rotate-angle, 0)); + transform: rotate(var(--fa-rotate-angle, 0)); } + +.fa-stack { + display: inline-block; + height: 2em; + line-height: 2em; + position: relative; + vertical-align: middle; + width: 2.5em; } + +.fa-stack-1x, +.fa-stack-2x { + left: 0; + position: absolute; + text-align: center; + width: 100%; + z-index: var(--fa-stack-z-index, auto); } + +.fa-stack-1x { + line-height: inherit; } + +.fa-stack-2x { + font-size: 2em; } + +.fa-inverse { + color: var(--fa-inverse, #fff); } + +/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen +readers do not read off random characters that represent icons */ + +.fa-0::before { + content: "\30"; } + +.fa-1::before { + content: "\31"; } + +.fa-2::before { + content: "\32"; } + +.fa-3::before { + content: "\33"; } + +.fa-4::before { + content: "\34"; } + +.fa-5::before { + content: "\35"; } + +.fa-6::before { + content: "\36"; } + +.fa-7::before { + content: "\37"; } + +.fa-8::before { + content: "\38"; } + +.fa-9::before { + content: "\39"; } + +.fa-fill-drip::before { + content: "\f576"; } + +.fa-arrows-to-circle::before { + content: "\e4bd"; } + +.fa-circle-chevron-right::before { + content: "\f138"; } + +.fa-chevron-circle-right::before { + content: "\f138"; } + +.fa-at::before { + content: "\40"; } + +.fa-trash-can::before { + content: "\f2ed"; } + +.fa-trash-alt::before { + content: "\f2ed"; } + +.fa-text-height::before { + content: "\f034"; } + +.fa-user-xmark::before { + content: "\f235"; } + +.fa-user-times::before { + content: "\f235"; } + +.fa-stethoscope::before { + content: "\f0f1"; } + +.fa-message::before { + content: "\f27a"; } + +.fa-comment-alt::before { + content: "\f27a"; } + +.fa-info::before { + content: "\f129"; } + +.fa-down-left-and-up-right-to-center::before { + content: "\f422"; } + +.fa-compress-alt::before { + content: "\f422"; } + +.fa-explosion::before { + content: "\e4e9"; } + +.fa-file-lines::before { + content: "\f15c"; } + +.fa-file-alt::before { + content: "\f15c"; } + +.fa-file-text::before { + content: "\f15c"; } + +.fa-wave-square::before { + content: "\f83e"; } + +.fa-ring::before { + content: "\f70b"; } + +.fa-building-un::before { + content: "\e4d9"; } + +.fa-dice-three::before { + content: "\f527"; } + +.fa-calendar-days::before { + content: "\f073"; } + +.fa-calendar-alt::before { + content: "\f073"; } + +.fa-anchor-circle-check::before { + content: "\e4aa"; } + +.fa-building-circle-arrow-right::before { + content: "\e4d1"; } + +.fa-volleyball::before { + content: "\f45f"; } + +.fa-volleyball-ball::before { + content: "\f45f"; } + +.fa-arrows-up-to-line::before { + content: "\e4c2"; } + +.fa-sort-down::before { + content: "\f0dd"; } + +.fa-sort-desc::before { + content: "\f0dd"; } + +.fa-circle-minus::before { + content: "\f056"; } + +.fa-minus-circle::before { + content: "\f056"; } + +.fa-door-open::before { + content: "\f52b"; } + +.fa-right-from-bracket::before { + content: "\f2f5"; } + +.fa-sign-out-alt::before { + content: "\f2f5"; } + +.fa-atom::before { + content: "\f5d2"; } + +.fa-soap::before { + content: "\e06e"; } + +.fa-icons::before { + content: "\f86d"; } + +.fa-heart-music-camera-bolt::before { + content: "\f86d"; } + +.fa-microphone-lines-slash::before { + content: "\f539"; } + +.fa-microphone-alt-slash::before { + content: "\f539"; } + +.fa-bridge-circle-check::before { + content: "\e4c9"; } + +.fa-pump-medical::before { + content: "\e06a"; } + +.fa-fingerprint::before { + content: "\f577"; } + +.fa-hand-point-right::before { + content: "\f0a4"; } + +.fa-magnifying-glass-location::before { + content: "\f689"; } + +.fa-search-location::before { + content: "\f689"; } + +.fa-forward-step::before { + content: "\f051"; } + +.fa-step-forward::before { + content: "\f051"; } + +.fa-face-smile-beam::before { + content: "\f5b8"; } + +.fa-smile-beam::before { + content: "\f5b8"; } + +.fa-flag-checkered::before { + content: "\f11e"; } + +.fa-football::before { + content: "\f44e"; } + +.fa-football-ball::before { + content: "\f44e"; } + +.fa-school-circle-exclamation::before { + content: "\e56c"; } + +.fa-crop::before { + content: "\f125"; } + +.fa-angles-down::before { + content: "\f103"; } + +.fa-angle-double-down::before { + content: "\f103"; } + +.fa-users-rectangle::before { + content: "\e594"; } + +.fa-people-roof::before { + content: "\e537"; } + +.fa-people-line::before { + content: "\e534"; } + +.fa-beer-mug-empty::before { + content: "\f0fc"; } + +.fa-beer::before { + content: "\f0fc"; } + +.fa-diagram-predecessor::before { + content: "\e477"; } + +.fa-arrow-up-long::before { + content: "\f176"; } + +.fa-long-arrow-up::before { + content: "\f176"; } + +.fa-fire-flame-simple::before { + content: "\f46a"; } + +.fa-burn::before { + content: "\f46a"; } + +.fa-person::before { + content: "\f183"; } + +.fa-male::before { + content: "\f183"; } + +.fa-laptop::before { + content: "\f109"; } + +.fa-file-csv::before { + content: "\f6dd"; } + +.fa-menorah::before { + content: "\f676"; } + +.fa-truck-plane::before { + content: "\e58f"; } + +.fa-record-vinyl::before { + content: "\f8d9"; } + +.fa-face-grin-stars::before { + content: "\f587"; } + +.fa-grin-stars::before { + content: "\f587"; } + +.fa-bong::before { + content: "\f55c"; } + +.fa-spaghetti-monster-flying::before { + content: "\f67b"; } + +.fa-pastafarianism::before { + content: "\f67b"; } + +.fa-arrow-down-up-across-line::before { + content: "\e4af"; } + +.fa-spoon::before { + content: "\f2e5"; } + +.fa-utensil-spoon::before { + content: "\f2e5"; } + +.fa-jar-wheat::before { + content: "\e517"; } + +.fa-envelopes-bulk::before { + content: "\f674"; } + +.fa-mail-bulk::before { + content: "\f674"; } + +.fa-file-circle-exclamation::before { + content: "\e4eb"; } + +.fa-circle-h::before { + content: "\f47e"; } + +.fa-hospital-symbol::before { + content: "\f47e"; } + +.fa-pager::before { + content: "\f815"; } + +.fa-address-book::before { + content: "\f2b9"; } + +.fa-contact-book::before { + content: "\f2b9"; } + +.fa-strikethrough::before { + content: "\f0cc"; } + +.fa-k::before { + content: "\4b"; } + +.fa-landmark-flag::before { + content: "\e51c"; } + +.fa-pencil::before { + content: "\f303"; } + +.fa-pencil-alt::before { + content: "\f303"; } + +.fa-backward::before { + content: "\f04a"; } + +.fa-caret-right::before { + content: "\f0da"; } + +.fa-comments::before { + content: "\f086"; } + +.fa-paste::before { + content: "\f0ea"; } + +.fa-file-clipboard::before { + content: "\f0ea"; } + +.fa-code-pull-request::before { + content: "\e13c"; } + +.fa-clipboard-list::before { + content: "\f46d"; } + +.fa-truck-ramp-box::before { + content: "\f4de"; } + +.fa-truck-loading::before { + content: "\f4de"; } + +.fa-user-check::before { + content: "\f4fc"; } + +.fa-vial-virus::before { + content: "\e597"; } + +.fa-sheet-plastic::before { + content: "\e571"; } + +.fa-blog::before { + content: "\f781"; } + +.fa-user-ninja::before { + content: "\f504"; } + +.fa-person-arrow-up-from-line::before { + content: "\e539"; } + +.fa-scroll-torah::before { + content: "\f6a0"; } + +.fa-torah::before { + content: "\f6a0"; } + +.fa-broom-ball::before { + content: "\f458"; } + +.fa-quidditch::before { + content: "\f458"; } + +.fa-quidditch-broom-ball::before { + content: "\f458"; } + +.fa-toggle-off::before { + content: "\f204"; } + +.fa-box-archive::before { + content: "\f187"; } + +.fa-archive::before { + content: "\f187"; } + +.fa-person-drowning::before { + content: "\e545"; } + +.fa-arrow-down-9-1::before { + content: "\f886"; } + +.fa-sort-numeric-desc::before { + content: "\f886"; } + +.fa-sort-numeric-down-alt::before { + content: "\f886"; } + +.fa-face-grin-tongue-squint::before { + content: "\f58a"; } + +.fa-grin-tongue-squint::before { + content: "\f58a"; } + +.fa-spray-can::before { + content: "\f5bd"; } + +.fa-truck-monster::before { + content: "\f63b"; } + +.fa-w::before { + content: "\57"; } + +.fa-earth-africa::before { + content: "\f57c"; } + +.fa-globe-africa::before { + content: "\f57c"; } + +.fa-rainbow::before { + content: "\f75b"; } + +.fa-circle-notch::before { + 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content: "\e23d"; } + +.fa-magnifying-glass::before { + content: "\f002"; } + +.fa-search::before { + content: "\f002"; } + +.fa-table-tennis-paddle-ball::before { + content: "\f45d"; } + +.fa-ping-pong-paddle-ball::before { + content: "\f45d"; } + +.fa-table-tennis::before { + content: "\f45d"; } + +.fa-person-dots-from-line::before { + content: "\f470"; } + +.fa-diagnoses::before { + content: "\f470"; } + +.fa-trash-can-arrow-up::before { + content: "\f82a"; } + +.fa-trash-restore-alt::before { + content: "\f82a"; } + +.fa-naira-sign::before { + content: "\e1f6"; } + +.fa-cart-arrow-down::before { + content: "\f218"; } + +.fa-walkie-talkie::before { + content: "\f8ef"; } + +.fa-file-pen::before { + content: "\f31c"; } + +.fa-file-edit::before { + content: "\f31c"; } + +.fa-receipt::before { + content: "\f543"; } + +.fa-square-pen::before { + content: "\f14b"; } + +.fa-pen-square::before { + content: "\f14b"; } + +.fa-pencil-square::before { + content: "\f14b"; } + 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"\f48e"; } + +.fa-cloud-sun::before { + content: "\f6c4"; } + +.fa-stopwatch-20::before { + content: "\e06f"; } + +.fa-square-full::before { + content: "\f45c"; } + +.fa-magnet::before { + content: "\f076"; } + +.fa-jar::before { + content: "\e516"; } + +.fa-note-sticky::before { + content: "\f249"; } + +.fa-sticky-note::before { + content: "\f249"; } + +.fa-bug-slash::before { + content: "\e490"; } + +.fa-arrow-up-from-water-pump::before { + content: "\e4b6"; } + +.fa-bone::before { + content: "\f5d7"; } + +.fa-user-injured::before { + content: "\f728"; } + +.fa-face-sad-tear::before { + content: "\f5b4"; } + +.fa-sad-tear::before { + content: "\f5b4"; } + +.fa-plane::before { + content: "\f072"; } + +.fa-tent-arrows-down::before { + content: "\e581"; } + +.fa-exclamation::before { + content: "\21"; } + +.fa-arrows-spin::before { + content: "\e4bb"; } + +.fa-print::before { + content: "\f02f"; } + +.fa-turkish-lira-sign::before { + content: "\e2bb"; } + +.fa-try::before { + content: "\e2bb"; } + +.fa-turkish-lira::before { + content: "\e2bb"; } + +.fa-dollar-sign::before { + content: "\24"; } + +.fa-dollar::before { + content: "\24"; } + +.fa-usd::before { + content: "\24"; } + +.fa-x::before { + content: "\58"; } + +.fa-magnifying-glass-dollar::before { + content: "\f688"; } + +.fa-search-dollar::before { + content: "\f688"; } + +.fa-users-gear::before { + content: "\f509"; } + +.fa-users-cog::before { + content: "\f509"; } + +.fa-person-military-pointing::before { + content: "\e54a"; } + +.fa-building-columns::before { + content: "\f19c"; } + +.fa-bank::before { + content: "\f19c"; } + +.fa-institution::before { + content: "\f19c"; } + +.fa-museum::before { + content: "\f19c"; } + +.fa-university::before { + content: "\f19c"; } + +.fa-umbrella::before { + content: "\f0e9"; } + +.fa-trowel::before { + content: "\e589"; } + +.fa-d::before { + content: "\44"; } + +.fa-stapler::before { + content: "\e5af"; } + +.fa-masks-theater::before { + content: "\f630"; } + 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content: "\f3ca"; } + +.fa-qq:before { + content: "\f1d6"; } + +.fa-orcid:before { + content: "\f8d2"; } + +.fa-java:before { + content: "\f4e4"; } + +.fa-invision:before { + content: "\f7b0"; } + +.fa-creative-commons-pd-alt:before { + content: "\f4ed"; } + +.fa-centercode:before { + content: "\f380"; } + +.fa-glide-g:before { + content: "\f2a6"; } + +.fa-drupal:before { + content: "\f1a9"; } + +.fa-jxl:before { + content: "\e67b"; } + +.fa-hire-a-helper:before { + content: "\f3b0"; } + +.fa-creative-commons-by:before { + content: "\f4e7"; } + +.fa-unity:before { + content: "\e049"; } + +.fa-whmcs:before { + content: "\f40d"; } + +.fa-rocketchat:before { + content: "\f3e8"; } + +.fa-vk:before { + content: "\f189"; } + +.fa-untappd:before { + content: "\f405"; } + +.fa-mailchimp:before { + content: "\f59e"; } + +.fa-css3-alt:before { + content: "\f38b"; } + +.fa-square-reddit:before { + content: "\f1a2"; } + +.fa-reddit-square:before { + content: "\f1a2"; } + +.fa-vimeo-v:before { + content: "\f27d"; } + +.fa-contao:before { + content: "\f26d"; } + +.fa-square-font-awesome:before { + content: "\e5ad"; } + +.fa-deskpro:before { + content: "\f38f"; } + +.fa-brave:before { + content: "\e63c"; } + +.fa-sistrix:before { + content: "\f3ee"; } + +.fa-square-instagram:before { + content: "\e055"; } + +.fa-instagram-square:before { + content: "\e055"; } + +.fa-battle-net:before { + content: "\f835"; } + +.fa-the-red-yeti:before { + content: "\f69d"; } + +.fa-square-hacker-news:before { + content: "\f3af"; } + +.fa-hacker-news-square:before { + content: "\f3af"; } + +.fa-edge:before { + content: "\f282"; } + +.fa-threads:before { + content: "\e618"; } + +.fa-napster:before { + content: "\f3d2"; } + +.fa-square-snapchat:before { + content: "\f2ad"; } + +.fa-snapchat-square:before { + content: "\f2ad"; } + +.fa-google-plus-g:before { + content: "\f0d5"; } + +.fa-artstation:before { + content: "\f77a"; } + +.fa-markdown:before { + content: "\f60f"; } + +.fa-sourcetree:before { + content: "\f7d3"; } + +.fa-google-plus:before { + content: "\f2b3"; } + +.fa-diaspora:before { + content: "\f791"; } + +.fa-foursquare:before { + content: "\f180"; } + +.fa-stack-overflow:before { + content: "\f16c"; } + +.fa-github-alt:before { + content: "\f113"; } + +.fa-phoenix-squadron:before { + content: "\f511"; } + +.fa-pagelines:before { + content: "\f18c"; } + +.fa-algolia:before { + content: "\f36c"; } + +.fa-red-river:before { + content: "\f3e3"; } + +.fa-creative-commons-sa:before { + content: "\f4ef"; } + +.fa-safari:before { + content: "\f267"; } + +.fa-google:before { + content: "\f1a0"; } + +.fa-square-font-awesome-stroke:before { + content: "\f35c"; } + +.fa-font-awesome-alt:before { + content: "\f35c"; } + +.fa-atlassian:before { + content: "\f77b"; } + +.fa-linkedin-in:before { + content: "\f0e1"; } + +.fa-digital-ocean:before { + content: "\f391"; } + +.fa-nimblr:before { + content: "\f5a8"; } + +.fa-chromecast:before { + content: "\f838"; } + 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+.fa.fa-bar-chart-o:before { + content: "\e0e3"; } + +.fa.fa-twitter-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-twitter-square:before { + content: "\f081"; } + +.fa.fa-facebook-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-facebook-square:before { + content: "\f082"; } + +.fa.fa-gears:before { + content: "\f085"; } + +.fa.fa-thumbs-o-up { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-thumbs-o-up:before { + content: "\f164"; } + +.fa.fa-thumbs-o-down { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-thumbs-o-down:before { + content: "\f165"; } + +.fa.fa-heart-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-heart-o:before { + content: "\f004"; } + +.fa.fa-sign-out:before { + content: "\f2f5"; } + +.fa.fa-linkedin-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-linkedin-square:before { + content: "\f08c"; } + +.fa.fa-thumb-tack:before { + content: "\f08d"; } + +.fa.fa-external-link:before { + content: "\f35d"; } + +.fa.fa-sign-in:before { + content: "\f2f6"; } + +.fa.fa-github-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-github-square:before { + content: "\f092"; } + +.fa.fa-lemon-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-lemon-o:before { + content: "\f094"; } + +.fa.fa-square-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-square-o:before { + content: "\f0c8"; } + +.fa.fa-bookmark-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-bookmark-o:before { + content: "\f02e"; } + +.fa.fa-twitter { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-facebook { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-facebook:before { + content: "\f39e"; } + +.fa.fa-facebook-f { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-facebook-f:before { + content: "\f39e"; } + +.fa.fa-github { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-credit-card { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-feed:before { + content: "\f09e"; } + +.fa.fa-hdd-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hdd-o:before { + content: "\f0a0"; } + +.fa.fa-hand-o-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-o-right:before { + content: "\f0a4"; } + +.fa.fa-hand-o-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-o-left:before { + content: "\f0a5"; } + +.fa.fa-hand-o-up { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-o-up:before { + content: "\f0a6"; } + +.fa.fa-hand-o-down { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-o-down:before { + content: "\f0a7"; } + +.fa.fa-globe:before { + content: "\f57d"; } + +.fa.fa-tasks:before { + content: "\f828"; } + +.fa.fa-arrows-alt:before { + content: "\f31e"; } + +.fa.fa-group:before { + content: "\f0c0"; } + +.fa.fa-chain:before { + content: "\f0c1"; } + +.fa.fa-cut:before { + content: "\f0c4"; } + +.fa.fa-files-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-files-o:before { + content: "\f0c5"; } + +.fa.fa-floppy-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-floppy-o:before { + content: "\f0c7"; } + +.fa.fa-save { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-save:before { + content: "\f0c7"; } + +.fa.fa-navicon:before { + content: "\f0c9"; } + +.fa.fa-reorder:before { + content: "\f0c9"; } + +.fa.fa-magic:before { + content: "\e2ca"; } + +.fa.fa-pinterest { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pinterest-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pinterest-square:before { + content: "\f0d3"; } + +.fa.fa-google-plus-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-plus-square:before { + content: "\f0d4"; } + +.fa.fa-google-plus { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-plus:before { + content: "\f0d5"; } + +.fa.fa-money:before { + content: "\f3d1"; } + +.fa.fa-unsorted:before { + content: "\f0dc"; } + +.fa.fa-sort-desc:before { + content: "\f0dd"; } + +.fa.fa-sort-asc:before { + content: "\f0de"; } + +.fa.fa-linkedin { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-linkedin:before { + content: "\f0e1"; } + +.fa.fa-rotate-left:before { + content: "\f0e2"; } + +.fa.fa-legal:before { + content: "\f0e3"; } + +.fa.fa-tachometer:before { + content: "\f625"; } + +.fa.fa-dashboard:before { + content: "\f625"; } + +.fa.fa-comment-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-comment-o:before { + content: "\f075"; } + +.fa.fa-comments-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-comments-o:before { + content: "\f086"; } + +.fa.fa-flash:before { + content: "\f0e7"; } + +.fa.fa-clipboard:before { + content: "\f0ea"; } + +.fa.fa-lightbulb-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-lightbulb-o:before { + content: "\f0eb"; } + +.fa.fa-exchange:before { + content: "\f362"; } + +.fa.fa-cloud-download:before { + content: "\f0ed"; } + +.fa.fa-cloud-upload:before { + content: "\f0ee"; } + +.fa.fa-bell-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-bell-o:before { + content: "\f0f3"; } + +.fa.fa-cutlery:before { + content: "\f2e7"; } + +.fa.fa-file-text-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-text-o:before { + content: "\f15c"; } + +.fa.fa-building-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-building-o:before { + content: "\f1ad"; } + +.fa.fa-hospital-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hospital-o:before { + content: "\f0f8"; } + +.fa.fa-tablet:before { + content: "\f3fa"; } + +.fa.fa-mobile:before { + content: "\f3cd"; } + +.fa.fa-mobile-phone:before { + content: "\f3cd"; } + +.fa.fa-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-circle-o:before { + content: "\f111"; } + +.fa.fa-mail-reply:before { + content: "\f3e5"; } + +.fa.fa-github-alt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-folder-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-folder-o:before { + content: "\f07b"; } + +.fa.fa-folder-open-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-folder-open-o:before { + content: "\f07c"; } + +.fa.fa-smile-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-smile-o:before { + content: "\f118"; } + +.fa.fa-frown-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-frown-o:before { + content: "\f119"; } + +.fa.fa-meh-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-meh-o:before { + content: "\f11a"; } + +.fa.fa-keyboard-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-keyboard-o:before { + content: "\f11c"; } + +.fa.fa-flag-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-flag-o:before { + content: "\f024"; } + +.fa.fa-mail-reply-all:before { + content: "\f122"; } + +.fa.fa-star-half-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-star-half-o:before { + content: "\f5c0"; } + +.fa.fa-star-half-empty { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-star-half-empty:before { + content: "\f5c0"; } + +.fa.fa-star-half-full { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-star-half-full:before { + content: "\f5c0"; } + +.fa.fa-code-fork:before { + content: "\f126"; } + +.fa.fa-chain-broken:before { + content: "\f127"; } + +.fa.fa-unlink:before { + content: "\f127"; } + +.fa.fa-calendar-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-o:before { + content: "\f133"; } + +.fa.fa-maxcdn { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-html5 { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-css3 { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-unlock-alt:before { + content: "\f09c"; } + +.fa.fa-minus-square-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-minus-square-o:before { + content: "\f146"; } + +.fa.fa-level-up:before { + content: "\f3bf"; } + +.fa.fa-level-down:before { + content: "\f3be"; } + +.fa.fa-pencil-square:before { + content: "\f14b"; } + +.fa.fa-external-link-square:before { + content: "\f360"; } + +.fa.fa-compass { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-down { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-down:before { + content: "\f150"; } + +.fa.fa-toggle-down { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-down:before { + content: "\f150"; } + +.fa.fa-caret-square-o-up { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-up:before { + content: "\f151"; } + +.fa.fa-toggle-up { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-up:before { + content: "\f151"; } + +.fa.fa-caret-square-o-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-right:before { + content: "\f152"; } + +.fa.fa-toggle-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-right:before { + content: "\f152"; } + +.fa.fa-eur:before { + content: "\f153"; } + +.fa.fa-euro:before { + content: "\f153"; } + +.fa.fa-gbp:before { + content: "\f154"; } + +.fa.fa-usd:before { + content: "\24"; } + +.fa.fa-dollar:before { + content: "\24"; } + +.fa.fa-inr:before { + content: "\e1bc"; } + +.fa.fa-rupee:before { + content: "\e1bc"; } + +.fa.fa-jpy:before { + content: "\f157"; } + +.fa.fa-cny:before { + content: "\f157"; } + +.fa.fa-rmb:before { + content: "\f157"; } + +.fa.fa-yen:before { + content: "\f157"; } + +.fa.fa-rub:before { + content: "\f158"; } + +.fa.fa-ruble:before { + content: "\f158"; } + +.fa.fa-rouble:before { + content: "\f158"; } + +.fa.fa-krw:before { + content: "\f159"; } + +.fa.fa-won:before { + content: "\f159"; } + +.fa.fa-btc { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitcoin { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitcoin:before { + content: "\f15a"; } + +.fa.fa-file-text:before { + content: "\f15c"; } + +.fa.fa-sort-alpha-asc:before { + content: "\f15d"; } + +.fa.fa-sort-alpha-desc:before { + content: "\f881"; } + +.fa.fa-sort-amount-asc:before { + content: "\f884"; } + +.fa.fa-sort-amount-desc:before { + content: "\f160"; } + +.fa.fa-sort-numeric-asc:before { + content: "\f162"; } + +.fa.fa-sort-numeric-desc:before { + content: "\f886"; } + +.fa.fa-youtube-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-youtube-square:before { + content: "\f431"; } + +.fa.fa-youtube { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-xing { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-xing-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-xing-square:before { + content: "\f169"; } + +.fa.fa-youtube-play { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-youtube-play:before { + content: "\f167"; } + +.fa.fa-dropbox { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-stack-overflow { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-instagram { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-flickr { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-adn { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitbucket { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitbucket-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitbucket-square:before { + content: "\f171"; } + +.fa.fa-tumblr { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-tumblr-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-tumblr-square:before { + content: "\f174"; } + +.fa.fa-long-arrow-down:before { + content: "\f309"; } + +.fa.fa-long-arrow-up:before { + content: "\f30c"; } + +.fa.fa-long-arrow-left:before { + content: "\f30a"; } + +.fa.fa-long-arrow-right:before { + content: "\f30b"; } + +.fa.fa-apple { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-windows { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-android { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-linux { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-dribbble { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-skype { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-foursquare { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-trello { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gratipay { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gittip { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gittip:before { + content: "\f184"; } + +.fa.fa-sun-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-sun-o:before { + content: "\f185"; } + +.fa.fa-moon-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-moon-o:before { + content: "\f186"; } + +.fa.fa-vk { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-weibo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-renren { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pagelines { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-stack-exchange { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-arrow-circle-o-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-arrow-circle-o-right:before { + content: "\f35a"; } + +.fa.fa-arrow-circle-o-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-arrow-circle-o-left:before { + content: "\f359"; } + +.fa.fa-caret-square-o-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-left:before { + content: "\f191"; } + +.fa.fa-toggle-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-left:before { + content: "\f191"; } + +.fa.fa-dot-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-dot-circle-o:before { + content: "\f192"; } + +.fa.fa-vimeo-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-vimeo-square:before { + content: "\f194"; } + +.fa.fa-try:before { + content: "\e2bb"; } + +.fa.fa-turkish-lira:before { + content: "\e2bb"; } + +.fa.fa-plus-square-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-plus-square-o:before { + content: "\f0fe"; } + +.fa.fa-slack { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wordpress { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-openid { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-institution:before { + content: "\f19c"; } + +.fa.fa-bank:before { + content: "\f19c"; } + +.fa.fa-mortar-board:before { + content: "\f19d"; } + +.fa.fa-yahoo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit-square:before { + content: "\f1a2"; } + +.fa.fa-stumbleupon-circle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-stumbleupon { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-delicious { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-digg { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pied-piper-pp { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pied-piper-alt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-drupal { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-joomla { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-behance { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-behance-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-behance-square:before { + content: "\f1b5"; } + +.fa.fa-steam { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-steam-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-steam-square:before { + content: "\f1b7"; } + +.fa.fa-automobile:before { + content: "\f1b9"; } + +.fa.fa-cab:before { + content: "\f1ba"; } + +.fa.fa-spotify { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-deviantart { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-soundcloud { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-file-pdf-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-pdf-o:before { + content: "\f1c1"; } + +.fa.fa-file-word-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-word-o:before { + content: "\f1c2"; } + +.fa.fa-file-excel-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-excel-o:before { + content: "\f1c3"; } + +.fa.fa-file-powerpoint-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-powerpoint-o:before { + content: "\f1c4"; } + +.fa.fa-file-image-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-image-o:before { + content: "\f1c5"; } + +.fa.fa-file-photo-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-photo-o:before { + content: "\f1c5"; } + +.fa.fa-file-picture-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-picture-o:before { + content: "\f1c5"; } + +.fa.fa-file-archive-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-archive-o:before { + content: "\f1c6"; } + +.fa.fa-file-zip-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-zip-o:before { + content: "\f1c6"; } + +.fa.fa-file-audio-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-audio-o:before { + content: "\f1c7"; } + +.fa.fa-file-sound-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-sound-o:before { + content: "\f1c7"; } + +.fa.fa-file-video-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-video-o:before { + content: "\f1c8"; } + +.fa.fa-file-movie-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-movie-o:before { + content: "\f1c8"; } + +.fa.fa-file-code-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-code-o:before { + content: "\f1c9"; } + +.fa.fa-vine { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-codepen { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-jsfiddle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-life-bouy:before { + content: "\f1cd"; } + +.fa.fa-life-buoy:before { + content: "\f1cd"; } + +.fa.fa-life-saver:before { + content: "\f1cd"; } + +.fa.fa-support:before { + content: "\f1cd"; } + +.fa.fa-circle-o-notch:before { + content: "\f1ce"; } + +.fa.fa-rebel { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ra { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ra:before { + content: "\f1d0"; } + +.fa.fa-resistance { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-resistance:before { + content: "\f1d0"; } + +.fa.fa-empire { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ge { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ge:before { + content: "\f1d1"; } + +.fa.fa-git-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-git-square:before { + content: "\f1d2"; } + +.fa.fa-git { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-hacker-news { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-y-combinator-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-y-combinator-square:before { + content: "\f1d4"; } + +.fa.fa-yc-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yc-square:before { + content: "\f1d4"; } + +.fa.fa-tencent-weibo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-qq { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-weixin { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wechat { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wechat:before { + content: "\f1d7"; } + +.fa.fa-send:before { + content: "\f1d8"; } + +.fa.fa-paper-plane-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-paper-plane-o:before { + content: "\f1d8"; } + +.fa.fa-send-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-send-o:before { + content: "\f1d8"; } + +.fa.fa-circle-thin { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-circle-thin:before { + content: "\f111"; } + +.fa.fa-header:before { + content: "\f1dc"; } + +.fa.fa-futbol-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-futbol-o:before { + content: "\f1e3"; } + +.fa.fa-soccer-ball-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-soccer-ball-o:before { + content: "\f1e3"; } + +.fa.fa-slideshare { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-twitch { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yelp { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-newspaper-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-newspaper-o:before { + content: "\f1ea"; } + +.fa.fa-paypal { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-wallet { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-visa { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-mastercard { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-discover { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-amex { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-paypal { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-stripe { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bell-slash-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-bell-slash-o:before { + content: "\f1f6"; } + +.fa.fa-trash:before { + content: "\f2ed"; } + +.fa.fa-copyright { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-eyedropper:before { + content: "\f1fb"; } + +.fa.fa-area-chart:before { + content: "\f1fe"; } + +.fa.fa-pie-chart:before { + content: "\f200"; } + +.fa.fa-line-chart:before { + content: "\f201"; } + +.fa.fa-lastfm { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-lastfm-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-lastfm-square:before { + content: "\f203"; } + +.fa.fa-ioxhost { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-angellist { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-cc:before { + content: "\f20a"; } + +.fa.fa-ils:before { + content: "\f20b"; } + +.fa.fa-shekel:before { + content: "\f20b"; } + +.fa.fa-sheqel:before { + content: "\f20b"; } + +.fa.fa-buysellads { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-connectdevelop { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-dashcube { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-forumbee { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-leanpub { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-sellsy { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-shirtsinbulk { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-simplybuilt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-skyatlas { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-diamond { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-diamond:before { + content: "\f3a5"; } + +.fa.fa-transgender:before { + content: "\f224"; } + +.fa.fa-intersex:before { + content: "\f224"; } + +.fa.fa-transgender-alt:before { + content: "\f225"; } + +.fa.fa-facebook-official { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-facebook-official:before { + content: "\f09a"; } + +.fa.fa-pinterest-p { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-whatsapp { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-hotel:before { + content: "\f236"; } + +.fa.fa-viacoin { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-medium { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-y-combinator { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yc { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yc:before { + content: "\f23b"; } + +.fa.fa-optin-monster { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-opencart { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-expeditedssl { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-battery-4:before { + content: "\f240"; } + +.fa.fa-battery:before { + content: "\f240"; } + +.fa.fa-battery-3:before { + content: "\f241"; } + +.fa.fa-battery-2:before { + content: "\f242"; } + +.fa.fa-battery-1:before { + content: "\f243"; } + +.fa.fa-battery-0:before { + content: "\f244"; } + +.fa.fa-object-group { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-object-ungroup { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-sticky-note-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-sticky-note-o:before { + content: "\f249"; } + +.fa.fa-cc-jcb { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-diners-club { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-clone { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hourglass-o:before { + content: "\f254"; } + +.fa.fa-hourglass-1:before { + content: "\f251"; } + +.fa.fa-hourglass-2:before { + content: "\f252"; } + +.fa.fa-hourglass-3:before { + content: "\f253"; } + +.fa.fa-hand-rock-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-rock-o:before { + content: "\f255"; } + +.fa.fa-hand-grab-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-grab-o:before { + content: "\f255"; } + +.fa.fa-hand-paper-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-paper-o:before { + content: "\f256"; } + +.fa.fa-hand-stop-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-stop-o:before { + content: "\f256"; } + +.fa.fa-hand-scissors-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-scissors-o:before { + content: "\f257"; } + +.fa.fa-hand-lizard-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-lizard-o:before { + content: "\f258"; } + +.fa.fa-hand-spock-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-spock-o:before { + content: "\f259"; } + +.fa.fa-hand-pointer-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-pointer-o:before { + content: "\f25a"; } + +.fa.fa-hand-peace-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-peace-o:before { + content: "\f25b"; } + +.fa.fa-registered { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-creative-commons { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gg { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gg-circle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-odnoklassniki { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-odnoklassniki-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-odnoklassniki-square:before { + content: "\f264"; } + +.fa.fa-get-pocket { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wikipedia-w { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-safari { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-chrome { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-firefox { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-opera { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-internet-explorer { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-television:before { + content: "\f26c"; } + +.fa.fa-contao { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-500px { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-amazon { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-calendar-plus-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-plus-o:before { + content: "\f271"; } + +.fa.fa-calendar-minus-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-minus-o:before { + content: "\f272"; } + +.fa.fa-calendar-times-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-times-o:before { + content: "\f273"; } + +.fa.fa-calendar-check-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-check-o:before { + content: "\f274"; } + +.fa.fa-map-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-map-o:before { + content: "\f279"; } + +.fa.fa-commenting:before { + content: "\f4ad"; } + +.fa.fa-commenting-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-commenting-o:before { + content: "\f4ad"; } + +.fa.fa-houzz { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-vimeo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-vimeo:before { + content: "\f27d"; } + +.fa.fa-black-tie { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-fonticons { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit-alien { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-edge { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-credit-card-alt:before { + content: "\f09d"; } + +.fa.fa-codiepie { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-modx { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-fort-awesome { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-usb { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-product-hunt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-mixcloud { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-scribd { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pause-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-pause-circle-o:before { + content: "\f28b"; } + +.fa.fa-stop-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-stop-circle-o:before { + content: "\f28d"; } + +.fa.fa-bluetooth { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bluetooth-b { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gitlab { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wpbeginner { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wpforms { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-envira { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wheelchair-alt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wheelchair-alt:before { + content: "\f368"; } + +.fa.fa-question-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-question-circle-o:before { + content: "\f059"; } + +.fa.fa-volume-control-phone:before { + content: "\f2a0"; } + +.fa.fa-asl-interpreting:before { + content: "\f2a3"; } + +.fa.fa-deafness:before { + content: "\f2a4"; } + +.fa.fa-hard-of-hearing:before { + content: "\f2a4"; } + +.fa.fa-glide { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-glide-g { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-signing:before { + content: "\f2a7"; } + +.fa.fa-viadeo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-viadeo-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-viadeo-square:before { + content: "\f2aa"; } + +.fa.fa-snapchat { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-snapchat-ghost { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-snapchat-ghost:before { + content: "\f2ab"; } + +.fa.fa-snapchat-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-snapchat-square:before { + content: "\f2ad"; } + +.fa.fa-pied-piper { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-first-order { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yoast { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; 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Hide your header until you need it + * Copyright (c) 2017 Nick Williams - http://wicky.nillia.ms/headroom.js + * License: MIT + */ + +!function(a){a&&(a.fn.headroom=function(b){return this.each(function(){var c=a(this),d=c.data("headroom"),e="object"==typeof b&&b;e=a.extend(!0,{},Headroom.options,e),d||(d=new Headroom(this,e),d.init(),c.data("headroom",d)),"string"==typeof b&&(d[b](),"destroy"===b&&c.removeData("headroom"))})},a("[data-headroom]").each(function(){var b=a(this);b.headroom(b.data())}))}(window.Zepto||window.jQuery); \ No newline at end of file diff --git a/docs/deps/jquery-3.6.0/jquery-3.6.0.js b/docs/deps/jquery-3.6.0/jquery-3.6.0.js new file mode 100644 index 0000000..fc6c299 --- /dev/null +++ b/docs/deps/jquery-3.6.0/jquery-3.6.0.js @@ -0,0 +1,10881 @@ +/*! + * jQuery JavaScript Library v3.6.0 + * https://jquery.com/ + * + * Includes Sizzle.js + * https://sizzlejs.com/ + * + * Copyright OpenJS Foundation and other contributors + * Released under the MIT license + * https://jquery.org/license + * + * Date: 2021-03-02T17:08Z + */ +( function( global, factory ) { + + "use strict"; + + if ( typeof module === "object" && typeof module.exports === "object" ) { + + // For CommonJS and CommonJS-like environments where a proper `window` + // is present, execute the factory and get jQuery. + // For environments that do not have a `window` with a `document` + // (such as Node.js), expose a factory as module.exports. + // This accentuates the need for the creation of a real `window`. + // e.g. var jQuery = require("jquery")(window); + // See ticket #14549 for more info. + module.exports = global.document ? + factory( global, true ) : + function( w ) { + if ( !w.document ) { + throw new Error( "jQuery requires a window with a document" ); + } + return factory( w ); + }; + } else { + factory( global ); + } + +// Pass this if window is not defined yet +} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { + +// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 +// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode +// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common +// enough that all such attempts are guarded in a try block. +"use strict"; + +var arr = []; + +var getProto = Object.getPrototypeOf; + +var slice = arr.slice; + +var flat = arr.flat ? function( array ) { + return arr.flat.call( array ); +} : function( array ) { + return arr.concat.apply( [], array ); +}; + + +var push = arr.push; + +var indexOf = arr.indexOf; + +var class2type = {}; + +var toString = class2type.toString; + +var hasOwn = class2type.hasOwnProperty; + +var fnToString = hasOwn.toString; + +var ObjectFunctionString = fnToString.call( Object ); + +var support = {}; + +var isFunction = function isFunction( obj ) { + + // Support: Chrome <=57, Firefox <=52 + // In some browsers, typeof returns "function" for HTML elements + // (i.e., `typeof document.createElement( "object" ) === "function"`). + // We don't want to classify *any* DOM node as a function. + // Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5 + // Plus for old WebKit, typeof returns "function" for HTML collections + // (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756) + return typeof obj === "function" && typeof obj.nodeType !== "number" && + typeof obj.item !== "function"; + }; + + +var isWindow = function isWindow( obj ) { + return obj != null && obj === obj.window; + }; + + +var document = window.document; + + + + var preservedScriptAttributes = { + type: true, + src: true, + nonce: true, + noModule: true + }; + + function DOMEval( code, node, doc ) { + doc = doc || document; + + var i, val, + script = doc.createElement( "script" ); + + script.text = code; + if ( node ) { + for ( i in preservedScriptAttributes ) { + + // Support: Firefox 64+, Edge 18+ + // Some browsers don't support the "nonce" property on scripts. + // On the other hand, just using `getAttribute` is not enough as + // the `nonce` attribute is reset to an empty string whenever it + // becomes browsing-context connected. + // See https://github.com/whatwg/html/issues/2369 + // See https://html.spec.whatwg.org/#nonce-attributes + // The `node.getAttribute` check was added for the sake of + // `jQuery.globalEval` so that it can fake a nonce-containing node + // via an object. + val = node[ i ] || node.getAttribute && node.getAttribute( i ); + if ( val ) { + script.setAttribute( i, val ); + } + } + } + doc.head.appendChild( script ).parentNode.removeChild( script ); + } + + +function toType( obj ) { + if ( obj == null ) { + return obj + ""; + } + + // Support: Android <=2.3 only (functionish RegExp) + return typeof obj === "object" || typeof obj === "function" ? + class2type[ toString.call( obj ) ] || "object" : + typeof obj; +} +/* global Symbol */ +// Defining this global in .eslintrc.json would create a danger of using the global +// unguarded in another place, it seems safer to define global only for this module + + + +var + version = "3.6.0", + + // Define a local copy of jQuery + jQuery = function( selector, context ) { + + // The jQuery object is actually just the init constructor 'enhanced' + // Need init if jQuery is called (just allow error to be thrown if not included) + return new jQuery.fn.init( selector, context ); + }; + +jQuery.fn = jQuery.prototype = { + + // The current version of jQuery being used + jquery: version, + + constructor: jQuery, + + // The default length of a jQuery object is 0 + length: 0, + + toArray: function() { + return slice.call( this ); + }, + + // Get the Nth element in the matched element set OR + // Get the whole matched element set as a clean array + get: function( num ) { + + // Return all the elements in a clean array + if ( num == null ) { + return slice.call( this ); + } + + // Return just the one element from the set + return num < 0 ? this[ num + this.length ] : this[ num ]; + }, + + // Take an array of elements and push it onto the stack + // (returning the new matched element set) + pushStack: function( elems ) { + + // Build a new jQuery matched element set + var ret = jQuery.merge( this.constructor(), elems ); + + // Add the old object onto the stack (as a reference) + ret.prevObject = this; + + // Return the newly-formed element set + return ret; + }, + + // Execute a callback for every element in the matched set. + each: function( callback ) { + return jQuery.each( this, callback ); + }, + + map: function( callback ) { + return this.pushStack( jQuery.map( this, function( elem, i ) { + return callback.call( elem, i, elem ); + } ) ); + }, + + slice: function() { + return this.pushStack( slice.apply( this, arguments ) ); + }, + + first: function() { + return this.eq( 0 ); + }, + + last: function() { + return this.eq( -1 ); + }, + + even: function() { + return this.pushStack( jQuery.grep( this, function( _elem, i ) { + return ( i + 1 ) % 2; + } ) ); + }, + + odd: function() { + return this.pushStack( jQuery.grep( this, function( _elem, i ) { + return i % 2; + } ) ); + }, + + eq: function( i ) { + var len = this.length, + j = +i + ( i < 0 ? len : 0 ); + return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); + }, + + end: function() { + return this.prevObject || this.constructor(); + }, + + // For internal use only. + // Behaves like an Array's method, not like a jQuery method. + push: push, + sort: arr.sort, + splice: arr.splice +}; + +jQuery.extend = jQuery.fn.extend = function() { + var options, name, src, copy, copyIsArray, clone, + target = arguments[ 0 ] || {}, + i = 1, + length = arguments.length, + deep = false; + + // Handle a deep copy situation + if ( typeof target === "boolean" ) { + deep = target; + + // Skip the boolean and the target + target = arguments[ i ] || {}; + i++; + } + + // Handle case when target is a string or something (possible in deep copy) + if ( typeof target !== "object" && !isFunction( target ) ) { + target = {}; + } + + // Extend jQuery itself if only one argument is passed + if ( i === length ) { + target = this; + i--; + } + + for ( ; i < length; i++ ) { + + // Only deal with non-null/undefined values + if ( ( options = arguments[ i ] ) != null ) { + + // Extend the base object + for ( name in options ) { + copy = options[ name ]; + + // Prevent Object.prototype pollution + // Prevent never-ending loop + if ( name === "__proto__" || target === copy ) { + continue; + } + + // Recurse if we're merging plain objects or arrays + if ( deep && copy && ( jQuery.isPlainObject( copy ) || + ( copyIsArray = Array.isArray( copy ) ) ) ) { + src = target[ name ]; + + // Ensure proper type for the source value + if ( copyIsArray && !Array.isArray( src ) ) { + clone = []; + } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) { + clone = {}; + } else { + clone = src; + } + copyIsArray = false; + + // Never move original objects, clone them + target[ name ] = jQuery.extend( deep, clone, copy ); + + // Don't bring in undefined values + } else if ( copy !== undefined ) { + target[ name ] = copy; + } + } + } + } + + // Return the modified object + return target; +}; + +jQuery.extend( { + + // Unique for each copy of jQuery on the page + expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), + + // Assume jQuery is ready without the ready module + isReady: true, + + error: function( msg ) { + throw new Error( msg ); + }, + + noop: function() {}, + + isPlainObject: function( obj ) { + var proto, Ctor; + + // Detect obvious negatives + // Use toString instead of jQuery.type to catch host objects + if ( !obj || toString.call( obj ) !== "[object Object]" ) { + return false; + } + + proto = getProto( obj ); + + // Objects with no prototype (e.g., `Object.create( null )`) are plain + if ( !proto ) { + return true; + } + + // Objects with prototype are plain iff they were constructed by a global Object function + Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; + return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; + }, + + isEmptyObject: function( obj ) { + var name; + + for ( name in obj ) { + return false; + } + return true; + }, + + // Evaluates a script in a provided context; falls back to the global one + // if not specified. + globalEval: function( code, options, doc ) { + DOMEval( code, { nonce: options && options.nonce }, doc ); + }, + + each: function( obj, callback ) { + var length, i = 0; + + if ( isArrayLike( obj ) ) { + length = obj.length; + for ( ; i < length; i++ ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } else { + for ( i in obj ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } + + return obj; + }, + + // results is for internal usage only + makeArray: function( arr, results ) { + var ret = results || []; + + if ( arr != null ) { + if ( isArrayLike( Object( arr ) ) ) { + jQuery.merge( ret, + typeof arr === "string" ? + [ arr ] : arr + ); + } else { + push.call( ret, arr ); + } + } + + return ret; + }, + + inArray: function( elem, arr, i ) { + return arr == null ? -1 : indexOf.call( arr, elem, i ); + }, + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + merge: function( first, second ) { + var len = +second.length, + j = 0, + i = first.length; + + for ( ; j < len; j++ ) { + first[ i++ ] = second[ j ]; + } + + first.length = i; + + return first; + }, + + grep: function( elems, callback, invert ) { + var callbackInverse, + matches = [], + i = 0, + length = elems.length, + callbackExpect = !invert; + + // Go through the array, only saving the items + // that pass the validator function + for ( ; i < length; i++ ) { + callbackInverse = !callback( elems[ i ], i ); + if ( callbackInverse !== callbackExpect ) { + matches.push( elems[ i ] ); + } + } + + return matches; + }, + + // arg is for internal usage only + map: function( elems, callback, arg ) { + var length, value, + i = 0, + ret = []; + + // Go through the array, translating each of the items to their new values + if ( isArrayLike( elems ) ) { + length = elems.length; + for ( ; i < length; i++ ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + + // Go through every key on the object, + } else { + for ( i in elems ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + } + + // Flatten any nested arrays + return flat( ret ); + }, + + // A global GUID counter for objects + guid: 1, + + // jQuery.support is not used in Core but other projects attach their + // properties to it so it needs to exist. + support: support +} ); + +if ( typeof Symbol === "function" ) { + jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; +} + +// Populate the class2type map +jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), + function( _i, name ) { + class2type[ "[object " + name + "]" ] = name.toLowerCase(); + } ); + +function isArrayLike( obj ) { + + // Support: real iOS 8.2 only (not reproducible in simulator) + // `in` check used to prevent JIT error (gh-2145) + // hasOwn isn't used here due to false negatives + // regarding Nodelist length in IE + var length = !!obj && "length" in obj && obj.length, + type = toType( obj ); + + if ( isFunction( obj ) || isWindow( obj ) ) { + return false; + } + + return type === "array" || length === 0 || + typeof length === "number" && length > 0 && ( length - 1 ) in obj; +} +var Sizzle = +/*! + * Sizzle CSS Selector Engine v2.3.6 + * https://sizzlejs.com/ + * + * Copyright JS Foundation and other contributors + * Released under the MIT license + * https://js.foundation/ + * + * Date: 2021-02-16 + */ +( function( window ) { +var i, + support, + Expr, + getText, + isXML, + tokenize, + compile, + select, + outermostContext, + sortInput, + hasDuplicate, + + // Local document vars + setDocument, + document, + docElem, + documentIsHTML, + rbuggyQSA, + rbuggyMatches, + matches, + contains, + + // Instance-specific data + expando = "sizzle" + 1 * new Date(), + preferredDoc = window.document, + dirruns = 0, + done = 0, + classCache = createCache(), + tokenCache = createCache(), + compilerCache = createCache(), + nonnativeSelectorCache = createCache(), + sortOrder = function( a, b ) { + if ( a === b ) { + hasDuplicate = true; + } + return 0; + }, + + // Instance methods + hasOwn = ( {} ).hasOwnProperty, + arr = [], + pop = arr.pop, + pushNative = arr.push, + push = arr.push, + slice = arr.slice, + + // Use a stripped-down indexOf as it's faster than native + // https://jsperf.com/thor-indexof-vs-for/5 + indexOf = function( list, elem ) { + var i = 0, + len = list.length; + for ( ; i < len; i++ ) { + if ( list[ i ] === elem ) { + return i; + } + } + return -1; + }, + + booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" + + "ismap|loop|multiple|open|readonly|required|scoped", + + // Regular expressions + + // http://www.w3.org/TR/css3-selectors/#whitespace + whitespace = "[\\x20\\t\\r\\n\\f]", + + // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram + identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace + + "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+", + + // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors + attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + + + // Operator (capture 2) + "*([*^$|!~]?=)" + whitespace + + + // "Attribute values must be CSS identifiers [capture 5] + // or strings [capture 3 or capture 4]" + "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + + whitespace + "*\\]", + + pseudos = ":(" + identifier + ")(?:\\((" + + + // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: + // 1. quoted (capture 3; capture 4 or capture 5) + "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + + + // 2. simple (capture 6) + "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + + + // 3. anything else (capture 2) + ".*" + + ")\\)|)", + + // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter + rwhitespace = new RegExp( whitespace + "+", "g" ), + rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + + whitespace + "+$", "g" ), + + rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), + rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + + "*" ), + rdescend = new RegExp( whitespace + "|>" ), + + rpseudo = new RegExp( pseudos ), + ridentifier = new RegExp( "^" + identifier + "$" ), + + matchExpr = { + "ID": new RegExp( "^#(" + identifier + ")" ), + "CLASS": new RegExp( "^\\.(" + identifier + ")" ), + "TAG": new RegExp( "^(" + identifier + "|[*])" ), + "ATTR": new RegExp( "^" + attributes ), + "PSEUDO": new RegExp( "^" + pseudos ), + "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + + whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + + whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), + "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), + + // For use in libraries implementing .is() + // We use this for POS matching in `select` + "needsContext": new RegExp( "^" + whitespace + + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace + + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) + }, + + rhtml = /HTML$/i, + rinputs = /^(?:input|select|textarea|button)$/i, + rheader = /^h\d$/i, + + rnative = /^[^{]+\{\s*\[native \w/, + + // Easily-parseable/retrievable ID or TAG or CLASS selectors + rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, + + rsibling = /[+~]/, + + // CSS escapes + // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters + runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ), + funescape = function( escape, nonHex ) { + var high = "0x" + escape.slice( 1 ) - 0x10000; + + return nonHex ? + + // Strip the backslash prefix from a non-hex escape sequence + nonHex : + + // Replace a hexadecimal escape sequence with the encoded Unicode code point + // Support: IE <=11+ + // For values outside the Basic Multilingual Plane (BMP), manually construct a + // surrogate pair + high < 0 ? + String.fromCharCode( high + 0x10000 ) : + String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); + }, + + // CSS string/identifier serialization + // https://drafts.csswg.org/cssom/#common-serializing-idioms + rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, + fcssescape = function( ch, asCodePoint ) { + if ( asCodePoint ) { + + // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER + if ( ch === "\0" ) { + return "\uFFFD"; + } + + // Control characters and (dependent upon position) numbers get escaped as code points + return ch.slice( 0, -1 ) + "\\" + + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; + } + + // Other potentially-special ASCII characters get backslash-escaped + return "\\" + ch; + }, + + // Used for iframes + // See setDocument() + // Removing the function wrapper causes a "Permission Denied" + // error in IE + unloadHandler = function() { + setDocument(); + }, + + inDisabledFieldset = addCombinator( + function( elem ) { + return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset"; + }, + { dir: "parentNode", next: "legend" } + ); + +// Optimize for push.apply( _, NodeList ) +try { + push.apply( + ( arr = slice.call( preferredDoc.childNodes ) ), + preferredDoc.childNodes + ); + + // Support: Android<4.0 + // Detect silently failing push.apply + // eslint-disable-next-line no-unused-expressions + arr[ preferredDoc.childNodes.length ].nodeType; +} catch ( e ) { + push = { apply: arr.length ? + + // Leverage slice if possible + function( target, els ) { + pushNative.apply( target, slice.call( els ) ); + } : + + // Support: IE<9 + // Otherwise append directly + function( target, els ) { + var j = target.length, + i = 0; + + // Can't trust NodeList.length + while ( ( target[ j++ ] = els[ i++ ] ) ) {} + target.length = j - 1; + } + }; +} + +function Sizzle( selector, context, results, seed ) { + var m, i, elem, nid, match, groups, newSelector, + newContext = context && context.ownerDocument, + + // nodeType defaults to 9, since context defaults to document + nodeType = context ? context.nodeType : 9; + + results = results || []; + + // Return early from calls with invalid selector or context + if ( typeof selector !== "string" || !selector || + nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { + + return results; + } + + // Try to shortcut find operations (as opposed to filters) in HTML documents + if ( !seed ) { + setDocument( context ); + context = context || document; + + if ( documentIsHTML ) { + + // If the selector is sufficiently simple, try using a "get*By*" DOM method + // (excepting DocumentFragment context, where the methods don't exist) + if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) { + + // ID selector + if ( ( m = match[ 1 ] ) ) { + + // Document context + if ( nodeType === 9 ) { + if ( ( elem = context.getElementById( m ) ) ) { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( elem.id === m ) { + results.push( elem ); + return results; + } + } else { + return results; + } + + // Element context + } else { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( newContext && ( elem = newContext.getElementById( m ) ) && + contains( context, elem ) && + elem.id === m ) { + + results.push( elem ); + return results; + } + } + + // Type selector + } else if ( match[ 2 ] ) { + push.apply( results, context.getElementsByTagName( selector ) ); + return results; + + // Class selector + } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName && + context.getElementsByClassName ) { + + push.apply( results, context.getElementsByClassName( m ) ); + return results; + } + } + + // Take advantage of querySelectorAll + if ( support.qsa && + !nonnativeSelectorCache[ selector + " " ] && + ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) && + + // Support: IE 8 only + // Exclude object elements + ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) { + + newSelector = selector; + newContext = context; + + // qSA considers elements outside a scoping root when evaluating child or + // descendant combinators, which is not what we want. + // In such cases, we work around the behavior by prefixing every selector in the + // list with an ID selector referencing the scope context. + // The technique has to be used as well when a leading combinator is used + // as such selectors are not recognized by querySelectorAll. + // Thanks to Andrew Dupont for this technique. + if ( nodeType === 1 && + ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) { + + // Expand context for sibling selectors + newContext = rsibling.test( selector ) && testContext( context.parentNode ) || + context; + + // We can use :scope instead of the ID hack if the browser + // supports it & if we're not changing the context. + if ( newContext !== context || !support.scope ) { + + // Capture the context ID, setting it first if necessary + if ( ( nid = context.getAttribute( "id" ) ) ) { + nid = nid.replace( rcssescape, fcssescape ); + } else { + context.setAttribute( "id", ( nid = expando ) ); + } + } + + // Prefix every selector in the list + groups = tokenize( selector ); + i = groups.length; + while ( i-- ) { + groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " + + toSelector( groups[ i ] ); + } + newSelector = groups.join( "," ); + } + + try { + push.apply( results, + newContext.querySelectorAll( newSelector ) + ); + return results; + } catch ( qsaError ) { + nonnativeSelectorCache( selector, true ); + } finally { + if ( nid === expando ) { + context.removeAttribute( "id" ); + } + } + } + } + } + + // All others + return select( selector.replace( rtrim, "$1" ), context, results, seed ); +} + +/** + * Create key-value caches of limited size + * @returns {function(string, object)} Returns the Object data after storing it on itself with + * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) + * deleting the oldest entry + */ +function createCache() { + var keys = []; + + function cache( key, value ) { + + // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) + if ( keys.push( key + " " ) > Expr.cacheLength ) { + + // Only keep the most recent entries + delete cache[ keys.shift() ]; + } + return ( cache[ key + " " ] = value ); + } + return cache; +} + +/** + * Mark a function for special use by Sizzle + * @param {Function} fn The function to mark + */ +function markFunction( fn ) { + fn[ expando ] = true; + return fn; +} + +/** + * Support testing using an element + * @param {Function} fn Passed the created element and returns a boolean result + */ +function assert( fn ) { + var el = document.createElement( "fieldset" ); + + try { + return !!fn( el ); + } catch ( e ) { + return false; + } finally { + + // Remove from its parent by default + if ( el.parentNode ) { + el.parentNode.removeChild( el ); + } + + // release memory in IE + el = null; + } +} + +/** + * Adds the same handler for all of the specified attrs + * @param {String} attrs Pipe-separated list of attributes + * @param {Function} handler The method that will be applied + */ +function addHandle( attrs, handler ) { + var arr = attrs.split( "|" ), + i = arr.length; + + while ( i-- ) { + Expr.attrHandle[ arr[ i ] ] = handler; + } +} + +/** + * Checks document order of two siblings + * @param {Element} a + * @param {Element} b + * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b + */ +function siblingCheck( a, b ) { + var cur = b && a, + diff = cur && a.nodeType === 1 && b.nodeType === 1 && + a.sourceIndex - b.sourceIndex; + + // Use IE sourceIndex if available on both nodes + if ( diff ) { + return diff; + } + + // Check if b follows a + if ( cur ) { + while ( ( cur = cur.nextSibling ) ) { + if ( cur === b ) { + return -1; + } + } + } + + return a ? 1 : -1; +} + +/** + * Returns a function to use in pseudos for input types + * @param {String} type + */ +function createInputPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for buttons + * @param {String} type + */ +function createButtonPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return ( name === "input" || name === "button" ) && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for :enabled/:disabled + * @param {Boolean} disabled true for :disabled; false for :enabled + */ +function createDisabledPseudo( disabled ) { + + // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable + return function( elem ) { + + // Only certain elements can match :enabled or :disabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled + if ( "form" in elem ) { + + // Check for inherited disabledness on relevant non-disabled elements: + // * listed form-associated elements in a disabled fieldset + // https://html.spec.whatwg.org/multipage/forms.html#category-listed + // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled + // * option elements in a disabled optgroup + // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled + // All such elements have a "form" property. + if ( elem.parentNode && elem.disabled === false ) { + + // Option elements defer to a parent optgroup if present + if ( "label" in elem ) { + if ( "label" in elem.parentNode ) { + return elem.parentNode.disabled === disabled; + } else { + return elem.disabled === disabled; + } + } + + // Support: IE 6 - 11 + // Use the isDisabled shortcut property to check for disabled fieldset ancestors + return elem.isDisabled === disabled || + + // Where there is no isDisabled, check manually + /* jshint -W018 */ + elem.isDisabled !== !disabled && + inDisabledFieldset( elem ) === disabled; + } + + return elem.disabled === disabled; + + // Try to winnow out elements that can't be disabled before trusting the disabled property. + // Some victims get caught in our net (label, legend, menu, track), but it shouldn't + // even exist on them, let alone have a boolean value. + } else if ( "label" in elem ) { + return elem.disabled === disabled; + } + + // Remaining elements are neither :enabled nor :disabled + return false; + }; +} + +/** + * Returns a function to use in pseudos for positionals + * @param {Function} fn + */ +function createPositionalPseudo( fn ) { + return markFunction( function( argument ) { + argument = +argument; + return markFunction( function( seed, matches ) { + var j, + matchIndexes = fn( [], seed.length, argument ), + i = matchIndexes.length; + + // Match elements found at the specified indexes + while ( i-- ) { + if ( seed[ ( j = matchIndexes[ i ] ) ] ) { + seed[ j ] = !( matches[ j ] = seed[ j ] ); + } + } + } ); + } ); +} + +/** + * Checks a node for validity as a Sizzle context + * @param {Element|Object=} context + * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value + */ +function testContext( context ) { + return context && typeof context.getElementsByTagName !== "undefined" && context; +} + +// Expose support vars for convenience +support = Sizzle.support = {}; + +/** + * Detects XML nodes + * @param {Element|Object} elem An element or a document + * @returns {Boolean} True iff elem is a non-HTML XML node + */ +isXML = Sizzle.isXML = function( elem ) { + var namespace = elem && elem.namespaceURI, + docElem = elem && ( elem.ownerDocument || elem ).documentElement; + + // Support: IE <=8 + // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes + // https://bugs.jquery.com/ticket/4833 + return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" ); +}; + +/** + * Sets document-related variables once based on the current document + * @param {Element|Object} [doc] An element or document object to use to set the document + * @returns {Object} Returns the current document + */ +setDocument = Sizzle.setDocument = function( node ) { + var hasCompare, subWindow, + doc = node ? node.ownerDocument || node : preferredDoc; + + // Return early if doc is invalid or already selected + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) { + return document; + } + + // Update global variables + document = doc; + docElem = document.documentElement; + documentIsHTML = !isXML( document ); + + // Support: IE 9 - 11+, Edge 12 - 18+ + // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( preferredDoc != document && + ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) { + + // Support: IE 11, Edge + if ( subWindow.addEventListener ) { + subWindow.addEventListener( "unload", unloadHandler, false ); + + // Support: IE 9 - 10 only + } else if ( subWindow.attachEvent ) { + subWindow.attachEvent( "onunload", unloadHandler ); + } + } + + // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only, + // Safari 4 - 5 only, Opera <=11.6 - 12.x only + // IE/Edge & older browsers don't support the :scope pseudo-class. + // Support: Safari 6.0 only + // Safari 6.0 supports :scope but it's an alias of :root there. + support.scope = assert( function( el ) { + docElem.appendChild( el ).appendChild( document.createElement( "div" ) ); + return typeof el.querySelectorAll !== "undefined" && + !el.querySelectorAll( ":scope fieldset div" ).length; + } ); + + /* Attributes + ---------------------------------------------------------------------- */ + + // Support: IE<8 + // Verify that getAttribute really returns attributes and not properties + // (excepting IE8 booleans) + support.attributes = assert( function( el ) { + el.className = "i"; + return !el.getAttribute( "className" ); + } ); + + /* getElement(s)By* + ---------------------------------------------------------------------- */ + + // Check if getElementsByTagName("*") returns only elements + support.getElementsByTagName = assert( function( el ) { + el.appendChild( document.createComment( "" ) ); + return !el.getElementsByTagName( "*" ).length; + } ); + + // Support: IE<9 + support.getElementsByClassName = rnative.test( document.getElementsByClassName ); + + // Support: IE<10 + // Check if getElementById returns elements by name + // The broken getElementById methods don't pick up programmatically-set names, + // so use a roundabout getElementsByName test + support.getById = assert( function( el ) { + docElem.appendChild( el ).id = expando; + return !document.getElementsByName || !document.getElementsByName( expando ).length; + } ); + + // ID filter and find + if ( support.getById ) { + Expr.filter[ "ID" ] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + return elem.getAttribute( "id" ) === attrId; + }; + }; + Expr.find[ "ID" ] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var elem = context.getElementById( id ); + return elem ? [ elem ] : []; + } + }; + } else { + Expr.filter[ "ID" ] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + var node = typeof elem.getAttributeNode !== "undefined" && + elem.getAttributeNode( "id" ); + return node && node.value === attrId; + }; + }; + + // Support: IE 6 - 7 only + // getElementById is not reliable as a find shortcut + Expr.find[ "ID" ] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var node, i, elems, + elem = context.getElementById( id ); + + if ( elem ) { + + // Verify the id attribute + node = elem.getAttributeNode( "id" ); + if ( node && node.value === id ) { + return [ elem ]; + } + + // Fall back on getElementsByName + elems = context.getElementsByName( id ); + i = 0; + while ( ( elem = elems[ i++ ] ) ) { + node = elem.getAttributeNode( "id" ); + if ( node && node.value === id ) { + return [ elem ]; + } + } + } + + return []; + } + }; + } + + // Tag + Expr.find[ "TAG" ] = support.getElementsByTagName ? + function( tag, context ) { + if ( typeof context.getElementsByTagName !== "undefined" ) { + return context.getElementsByTagName( tag ); + + // DocumentFragment nodes don't have gEBTN + } else if ( support.qsa ) { + return context.querySelectorAll( tag ); + } + } : + + function( tag, context ) { + var elem, + tmp = [], + i = 0, + + // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too + results = context.getElementsByTagName( tag ); + + // Filter out possible comments + if ( tag === "*" ) { + while ( ( elem = results[ i++ ] ) ) { + if ( elem.nodeType === 1 ) { + tmp.push( elem ); + } + } + + return tmp; + } + return results; + }; + + // Class + Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) { + if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { + return context.getElementsByClassName( className ); + } + }; + + /* QSA/matchesSelector + ---------------------------------------------------------------------- */ + + // QSA and matchesSelector support + + // matchesSelector(:active) reports false when true (IE9/Opera 11.5) + rbuggyMatches = []; + + // qSa(:focus) reports false when true (Chrome 21) + // We allow this because of a bug in IE8/9 that throws an error + // whenever `document.activeElement` is accessed on an iframe + // So, we allow :focus to pass through QSA all the time to avoid the IE error + // See https://bugs.jquery.com/ticket/13378 + rbuggyQSA = []; + + if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) { + + // Build QSA regex + // Regex strategy adopted from Diego Perini + assert( function( el ) { + + var input; + + // Select is set to empty string on purpose + // This is to test IE's treatment of not explicitly + // setting a boolean content attribute, + // since its presence should be enough + // https://bugs.jquery.com/ticket/12359 + docElem.appendChild( el ).innerHTML = "" + + ""; + + // Support: IE8, Opera 11-12.16 + // Nothing should be selected when empty strings follow ^= or $= or *= + // The test attribute must be unknown in Opera but "safe" for WinRT + // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section + if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) { + rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); + } + + // Support: IE8 + // Boolean attributes and "value" are not treated correctly + if ( !el.querySelectorAll( "[selected]" ).length ) { + rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); + } + + // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ + if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { + rbuggyQSA.push( "~=" ); + } + + // Support: IE 11+, Edge 15 - 18+ + // IE 11/Edge don't find elements on a `[name='']` query in some cases. + // Adding a temporary attribute to the document before the selection works + // around the issue. + // Interestingly, IE 10 & older don't seem to have the issue. + input = document.createElement( "input" ); + input.setAttribute( "name", "" ); + el.appendChild( input ); + if ( !el.querySelectorAll( "[name='']" ).length ) { + rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" + + whitespace + "*(?:''|\"\")" ); + } + + // Webkit/Opera - :checked should return selected option elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + // IE8 throws error here and will not see later tests + if ( !el.querySelectorAll( ":checked" ).length ) { + rbuggyQSA.push( ":checked" ); + } + + // Support: Safari 8+, iOS 8+ + // https://bugs.webkit.org/show_bug.cgi?id=136851 + // In-page `selector#id sibling-combinator selector` fails + if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { + rbuggyQSA.push( ".#.+[+~]" ); + } + + // Support: Firefox <=3.6 - 5 only + // Old Firefox doesn't throw on a badly-escaped identifier. + el.querySelectorAll( "\\\f" ); + rbuggyQSA.push( "[\\r\\n\\f]" ); + } ); + + assert( function( el ) { + el.innerHTML = "" + + ""; + + // Support: Windows 8 Native Apps + // The type and name attributes are restricted during .innerHTML assignment + var input = document.createElement( "input" ); + input.setAttribute( "type", "hidden" ); + el.appendChild( input ).setAttribute( "name", "D" ); + + // Support: IE8 + // Enforce case-sensitivity of name attribute + if ( el.querySelectorAll( "[name=d]" ).length ) { + rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); + } + + // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) + // IE8 throws error here and will not see later tests + if ( el.querySelectorAll( ":enabled" ).length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Support: IE9-11+ + // IE's :disabled selector does not pick up the children of disabled fieldsets + docElem.appendChild( el ).disabled = true; + if ( el.querySelectorAll( ":disabled" ).length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Support: Opera 10 - 11 only + // Opera 10-11 does not throw on post-comma invalid pseudos + el.querySelectorAll( "*,:x" ); + rbuggyQSA.push( ",.*:" ); + } ); + } + + if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches || + docElem.webkitMatchesSelector || + docElem.mozMatchesSelector || + docElem.oMatchesSelector || + docElem.msMatchesSelector ) ) ) ) { + + assert( function( el ) { + + // Check to see if it's possible to do matchesSelector + // on a disconnected node (IE 9) + support.disconnectedMatch = matches.call( el, "*" ); + + // This should fail with an exception + // Gecko does not error, returns false instead + matches.call( el, "[s!='']:x" ); + rbuggyMatches.push( "!=", pseudos ); + } ); + } + + rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) ); + rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) ); + + /* Contains + ---------------------------------------------------------------------- */ + hasCompare = rnative.test( docElem.compareDocumentPosition ); + + // Element contains another + // Purposefully self-exclusive + // As in, an element does not contain itself + contains = hasCompare || rnative.test( docElem.contains ) ? + function( a, b ) { + var adown = a.nodeType === 9 ? a.documentElement : a, + bup = b && b.parentNode; + return a === bup || !!( bup && bup.nodeType === 1 && ( + adown.contains ? + adown.contains( bup ) : + a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 + ) ); + } : + function( a, b ) { + if ( b ) { + while ( ( b = b.parentNode ) ) { + if ( b === a ) { + return true; + } + } + } + return false; + }; + + /* Sorting + ---------------------------------------------------------------------- */ + + // Document order sorting + sortOrder = hasCompare ? + function( a, b ) { + + // Flag for duplicate removal + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + // Sort on method existence if only one input has compareDocumentPosition + var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; + if ( compare ) { + return compare; + } + + // Calculate position if both inputs belong to the same document + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ? + a.compareDocumentPosition( b ) : + + // Otherwise we know they are disconnected + 1; + + // Disconnected nodes + if ( compare & 1 || + ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) { + + // Choose the first element that is related to our preferred document + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( a == document || a.ownerDocument == preferredDoc && + contains( preferredDoc, a ) ) { + return -1; + } + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( b == document || b.ownerDocument == preferredDoc && + contains( preferredDoc, b ) ) { + return 1; + } + + // Maintain original order + return sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + } + + return compare & 4 ? -1 : 1; + } : + function( a, b ) { + + // Exit early if the nodes are identical + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + var cur, + i = 0, + aup = a.parentNode, + bup = b.parentNode, + ap = [ a ], + bp = [ b ]; + + // Parentless nodes are either documents or disconnected + if ( !aup || !bup ) { + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + /* eslint-disable eqeqeq */ + return a == document ? -1 : + b == document ? 1 : + /* eslint-enable eqeqeq */ + aup ? -1 : + bup ? 1 : + sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + + // If the nodes are siblings, we can do a quick check + } else if ( aup === bup ) { + return siblingCheck( a, b ); + } + + // Otherwise we need full lists of their ancestors for comparison + cur = a; + while ( ( cur = cur.parentNode ) ) { + ap.unshift( cur ); + } + cur = b; + while ( ( cur = cur.parentNode ) ) { + bp.unshift( cur ); + } + + // Walk down the tree looking for a discrepancy + while ( ap[ i ] === bp[ i ] ) { + i++; + } + + return i ? + + // Do a sibling check if the nodes have a common ancestor + siblingCheck( ap[ i ], bp[ i ] ) : + + // Otherwise nodes in our document sort first + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + /* eslint-disable eqeqeq */ + ap[ i ] == preferredDoc ? -1 : + bp[ i ] == preferredDoc ? 1 : + /* eslint-enable eqeqeq */ + 0; + }; + + return document; +}; + +Sizzle.matches = function( expr, elements ) { + return Sizzle( expr, null, null, elements ); +}; + +Sizzle.matchesSelector = function( elem, expr ) { + setDocument( elem ); + + if ( support.matchesSelector && documentIsHTML && + !nonnativeSelectorCache[ expr + " " ] && + ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && + ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { + + try { + var ret = matches.call( elem, expr ); + + // IE 9's matchesSelector returns false on disconnected nodes + if ( ret || support.disconnectedMatch || + + // As well, disconnected nodes are said to be in a document + // fragment in IE 9 + elem.document && elem.document.nodeType !== 11 ) { + return ret; + } + } catch ( e ) { + nonnativeSelectorCache( expr, true ); + } + } + + return Sizzle( expr, document, null, [ elem ] ).length > 0; +}; + +Sizzle.contains = function( context, elem ) { + + // Set document vars if needed + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( ( context.ownerDocument || context ) != document ) { + setDocument( context ); + } + return contains( context, elem ); +}; + +Sizzle.attr = function( elem, name ) { + + // Set document vars if needed + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( ( elem.ownerDocument || elem ) != document ) { + setDocument( elem ); + } + + var fn = Expr.attrHandle[ name.toLowerCase() ], + + // Don't get fooled by Object.prototype properties (jQuery #13807) + val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? + fn( elem, name, !documentIsHTML ) : + undefined; + + return val !== undefined ? + val : + support.attributes || !documentIsHTML ? + elem.getAttribute( name ) : + ( val = elem.getAttributeNode( name ) ) && val.specified ? + val.value : + null; +}; + +Sizzle.escape = function( sel ) { + return ( sel + "" ).replace( rcssescape, fcssescape ); +}; + +Sizzle.error = function( msg ) { + throw new Error( "Syntax error, unrecognized expression: " + msg ); +}; + +/** + * Document sorting and removing duplicates + * @param {ArrayLike} results + */ +Sizzle.uniqueSort = function( results ) { + var elem, + duplicates = [], + j = 0, + i = 0; + + // Unless we *know* we can detect duplicates, assume their presence + hasDuplicate = !support.detectDuplicates; + sortInput = !support.sortStable && results.slice( 0 ); + results.sort( sortOrder ); + + if ( hasDuplicate ) { + while ( ( elem = results[ i++ ] ) ) { + if ( elem === results[ i ] ) { + j = duplicates.push( i ); + } + } + while ( j-- ) { + results.splice( duplicates[ j ], 1 ); + } + } + + // Clear input after sorting to release objects + // See https://github.com/jquery/sizzle/pull/225 + sortInput = null; + + return results; +}; + +/** + * Utility function for retrieving the text value of an array of DOM nodes + * @param {Array|Element} elem + */ +getText = Sizzle.getText = function( elem ) { + var node, + ret = "", + i = 0, + nodeType = elem.nodeType; + + if ( !nodeType ) { + + // If no nodeType, this is expected to be an array + while ( ( node = elem[ i++ ] ) ) { + + // Do not traverse comment nodes + ret += getText( node ); + } + } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { + + // Use textContent for elements + // innerText usage removed for consistency of new lines (jQuery #11153) + if ( typeof elem.textContent === "string" ) { + return elem.textContent; + } else { + + // Traverse its children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + ret += getText( elem ); + } + } + } else if ( nodeType === 3 || nodeType === 4 ) { + return elem.nodeValue; + } + + // Do not include comment or processing instruction nodes + + return ret; +}; + +Expr = Sizzle.selectors = { + + // Can be adjusted by the user + cacheLength: 50, + + createPseudo: markFunction, + + match: matchExpr, + + attrHandle: {}, + + find: {}, + + relative: { + ">": { dir: "parentNode", first: true }, + " ": { dir: "parentNode" }, + "+": { dir: "previousSibling", first: true }, + "~": { dir: "previousSibling" } + }, + + preFilter: { + "ATTR": function( match ) { + match[ 1 ] = match[ 1 ].replace( runescape, funescape ); + + // Move the given value to match[3] whether quoted or unquoted + match[ 3 ] = ( match[ 3 ] || match[ 4 ] || + match[ 5 ] || "" ).replace( runescape, funescape ); + + if ( match[ 2 ] === "~=" ) { + match[ 3 ] = " " + match[ 3 ] + " "; + } + + return match.slice( 0, 4 ); + }, + + "CHILD": function( match ) { + + /* matches from matchExpr["CHILD"] + 1 type (only|nth|...) + 2 what (child|of-type) + 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) + 4 xn-component of xn+y argument ([+-]?\d*n|) + 5 sign of xn-component + 6 x of xn-component + 7 sign of y-component + 8 y of y-component + */ + match[ 1 ] = match[ 1 ].toLowerCase(); + + if ( match[ 1 ].slice( 0, 3 ) === "nth" ) { + + // nth-* requires argument + if ( !match[ 3 ] ) { + Sizzle.error( match[ 0 ] ); + } + + // numeric x and y parameters for Expr.filter.CHILD + // remember that false/true cast respectively to 0/1 + match[ 4 ] = +( match[ 4 ] ? + match[ 5 ] + ( match[ 6 ] || 1 ) : + 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) ); + match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" ); + + // other types prohibit arguments + } else if ( match[ 3 ] ) { + Sizzle.error( match[ 0 ] ); + } + + return match; + }, + + "PSEUDO": function( match ) { + var excess, + unquoted = !match[ 6 ] && match[ 2 ]; + + if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) { + return null; + } + + // Accept quoted arguments as-is + if ( match[ 3 ] ) { + match[ 2 ] = match[ 4 ] || match[ 5 ] || ""; + + // Strip excess characters from unquoted arguments + } else if ( unquoted && rpseudo.test( unquoted ) && + + // Get excess from tokenize (recursively) + ( excess = tokenize( unquoted, true ) ) && + + // advance to the next closing parenthesis + ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) { + + // excess is a negative index + match[ 0 ] = match[ 0 ].slice( 0, excess ); + match[ 2 ] = unquoted.slice( 0, excess ); + } + + // Return only captures needed by the pseudo filter method (type and argument) + return match.slice( 0, 3 ); + } + }, + + filter: { + + "TAG": function( nodeNameSelector ) { + var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); + return nodeNameSelector === "*" ? + function() { + return true; + } : + function( elem ) { + return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; + }; + }, + + "CLASS": function( className ) { + var pattern = classCache[ className + " " ]; + + return pattern || + ( pattern = new RegExp( "(^|" + whitespace + + ")" + className + "(" + whitespace + "|$)" ) ) && classCache( + className, function( elem ) { + return pattern.test( + typeof elem.className === "string" && elem.className || + typeof elem.getAttribute !== "undefined" && + elem.getAttribute( "class" ) || + "" + ); + } ); + }, + + "ATTR": function( name, operator, check ) { + return function( elem ) { + var result = Sizzle.attr( elem, name ); + + if ( result == null ) { + return operator === "!="; + } + if ( !operator ) { + return true; + } + + result += ""; + + /* eslint-disable max-len */ + + return operator === "=" ? result === check : + operator === "!=" ? result !== check : + operator === "^=" ? check && result.indexOf( check ) === 0 : + operator === "*=" ? check && result.indexOf( check ) > -1 : + operator === "$=" ? check && result.slice( -check.length ) === check : + operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : + operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : + false; + /* eslint-enable max-len */ + + }; + }, + + "CHILD": function( type, what, _argument, first, last ) { + var simple = type.slice( 0, 3 ) !== "nth", + forward = type.slice( -4 ) !== "last", + ofType = what === "of-type"; + + return first === 1 && last === 0 ? + + // Shortcut for :nth-*(n) + function( elem ) { + return !!elem.parentNode; + } : + + function( elem, _context, xml ) { + var cache, uniqueCache, outerCache, node, nodeIndex, start, + dir = simple !== forward ? "nextSibling" : "previousSibling", + parent = elem.parentNode, + name = ofType && elem.nodeName.toLowerCase(), + useCache = !xml && !ofType, + diff = false; + + if ( parent ) { + + // :(first|last|only)-(child|of-type) + if ( simple ) { + while ( dir ) { + node = elem; + while ( ( node = node[ dir ] ) ) { + if ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) { + + return false; + } + } + + // Reverse direction for :only-* (if we haven't yet done so) + start = dir = type === "only" && !start && "nextSibling"; + } + return true; + } + + start = [ forward ? parent.firstChild : parent.lastChild ]; + + // non-xml :nth-child(...) stores cache data on `parent` + if ( forward && useCache ) { + + // Seek `elem` from a previously-cached index + + // ...in a gzip-friendly way + node = parent; + outerCache = node[ expando ] || ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex && cache[ 2 ]; + node = nodeIndex && parent.childNodes[ nodeIndex ]; + + while ( ( node = ++nodeIndex && node && node[ dir ] || + + // Fallback to seeking `elem` from the start + ( diff = nodeIndex = 0 ) || start.pop() ) ) { + + // When found, cache indexes on `parent` and break + if ( node.nodeType === 1 && ++diff && node === elem ) { + uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; + break; + } + } + + } else { + + // Use previously-cached element index if available + if ( useCache ) { + + // ...in a gzip-friendly way + node = elem; + outerCache = node[ expando ] || ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex; + } + + // xml :nth-child(...) + // or :nth-last-child(...) or :nth(-last)?-of-type(...) + if ( diff === false ) { + + // Use the same loop as above to seek `elem` from the start + while ( ( node = ++nodeIndex && node && node[ dir ] || + ( diff = nodeIndex = 0 ) || start.pop() ) ) { + + if ( ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) && + ++diff ) { + + // Cache the index of each encountered element + if ( useCache ) { + outerCache = node[ expando ] || + ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + uniqueCache[ type ] = [ dirruns, diff ]; + } + + if ( node === elem ) { + break; + } + } + } + } + } + + // Incorporate the offset, then check against cycle size + diff -= last; + return diff === first || ( diff % first === 0 && diff / first >= 0 ); + } + }; + }, + + "PSEUDO": function( pseudo, argument ) { + + // pseudo-class names are case-insensitive + // http://www.w3.org/TR/selectors/#pseudo-classes + // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters + // Remember that setFilters inherits from pseudos + var args, + fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || + Sizzle.error( "unsupported pseudo: " + pseudo ); + + // The user may use createPseudo to indicate that + // arguments are needed to create the filter function + // just as Sizzle does + if ( fn[ expando ] ) { + return fn( argument ); + } + + // But maintain support for old signatures + if ( fn.length > 1 ) { + args = [ pseudo, pseudo, "", argument ]; + return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? + markFunction( function( seed, matches ) { + var idx, + matched = fn( seed, argument ), + i = matched.length; + while ( i-- ) { + idx = indexOf( seed, matched[ i ] ); + seed[ idx ] = !( matches[ idx ] = matched[ i ] ); + } + } ) : + function( elem ) { + return fn( elem, 0, args ); + }; + } + + return fn; + } + }, + + pseudos: { + + // Potentially complex pseudos + "not": markFunction( function( selector ) { + + // Trim the selector passed to compile + // to avoid treating leading and trailing + // spaces as combinators + var input = [], + results = [], + matcher = compile( selector.replace( rtrim, "$1" ) ); + + return matcher[ expando ] ? + markFunction( function( seed, matches, _context, xml ) { + var elem, + unmatched = matcher( seed, null, xml, [] ), + i = seed.length; + + // Match elements unmatched by `matcher` + while ( i-- ) { + if ( ( elem = unmatched[ i ] ) ) { + seed[ i ] = !( matches[ i ] = elem ); + } + } + } ) : + function( elem, _context, xml ) { + input[ 0 ] = elem; + matcher( input, null, xml, results ); + + // Don't keep the element (issue #299) + input[ 0 ] = null; + return !results.pop(); + }; + } ), + + "has": markFunction( function( selector ) { + return function( elem ) { + return Sizzle( selector, elem ).length > 0; + }; + } ), + + "contains": markFunction( function( text ) { + text = text.replace( runescape, funescape ); + return function( elem ) { + return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1; + }; + } ), + + // "Whether an element is represented by a :lang() selector + // is based solely on the element's language value + // being equal to the identifier C, + // or beginning with the identifier C immediately followed by "-". + // The matching of C against the element's language value is performed case-insensitively. + // The identifier C does not have to be a valid language name." + // http://www.w3.org/TR/selectors/#lang-pseudo + "lang": markFunction( function( lang ) { + + // lang value must be a valid identifier + if ( !ridentifier.test( lang || "" ) ) { + Sizzle.error( "unsupported lang: " + lang ); + } + lang = lang.replace( runescape, funescape ).toLowerCase(); + return function( elem ) { + var elemLang; + do { + if ( ( elemLang = documentIsHTML ? + elem.lang : + elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) { + + elemLang = elemLang.toLowerCase(); + return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; + } + } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 ); + return false; + }; + } ), + + // Miscellaneous + "target": function( elem ) { + var hash = window.location && window.location.hash; + return hash && hash.slice( 1 ) === elem.id; + }, + + "root": function( elem ) { + return elem === docElem; + }, + + "focus": function( elem ) { + return elem === document.activeElement && + ( !document.hasFocus || document.hasFocus() ) && + !!( elem.type || elem.href || ~elem.tabIndex ); + }, + + // Boolean properties + "enabled": createDisabledPseudo( false ), + "disabled": createDisabledPseudo( true ), + + "checked": function( elem ) { + + // In CSS3, :checked should return both checked and selected elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + var nodeName = elem.nodeName.toLowerCase(); + return ( nodeName === "input" && !!elem.checked ) || + ( nodeName === "option" && !!elem.selected ); + }, + + "selected": function( elem ) { + + // Accessing this property makes selected-by-default + // options in Safari work properly + if ( elem.parentNode ) { + // eslint-disable-next-line no-unused-expressions + elem.parentNode.selectedIndex; + } + + return elem.selected === true; + }, + + // Contents + "empty": function( elem ) { + + // http://www.w3.org/TR/selectors/#empty-pseudo + // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), + // but not by others (comment: 8; processing instruction: 7; etc.) + // nodeType < 6 works because attributes (2) do not appear as children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + if ( elem.nodeType < 6 ) { + return false; + } + } + return true; + }, + + "parent": function( elem ) { + return !Expr.pseudos[ "empty" ]( elem ); + }, + + // Element/input types + "header": function( elem ) { + return rheader.test( elem.nodeName ); + }, + + "input": function( elem ) { + return rinputs.test( elem.nodeName ); + }, + + "button": function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === "button" || name === "button"; + }, + + "text": function( elem ) { + var attr; + return elem.nodeName.toLowerCase() === "input" && + elem.type === "text" && + + // Support: IE<8 + // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" + ( ( attr = elem.getAttribute( "type" ) ) == null || + attr.toLowerCase() === "text" ); + }, + + // Position-in-collection + "first": createPositionalPseudo( function() { + return [ 0 ]; + } ), + + "last": createPositionalPseudo( function( _matchIndexes, length ) { + return [ length - 1 ]; + } ), + + "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) { + return [ argument < 0 ? argument + length : argument ]; + } ), + + "even": createPositionalPseudo( function( matchIndexes, length ) { + var i = 0; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "odd": createPositionalPseudo( function( matchIndexes, length ) { + var i = 1; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "lt": createPositionalPseudo( function( matchIndexes, length, argument ) { + var i = argument < 0 ? + argument + length : + argument > length ? + length : + argument; + for ( ; --i >= 0; ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "gt": createPositionalPseudo( function( matchIndexes, length, argument ) { + var i = argument < 0 ? argument + length : argument; + for ( ; ++i < length; ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ) + } +}; + +Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ]; + +// Add button/input type pseudos +for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { + Expr.pseudos[ i ] = createInputPseudo( i ); +} +for ( i in { submit: true, reset: true } ) { + Expr.pseudos[ i ] = createButtonPseudo( i ); +} + +// Easy API for creating new setFilters +function setFilters() {} +setFilters.prototype = Expr.filters = Expr.pseudos; +Expr.setFilters = new setFilters(); + +tokenize = Sizzle.tokenize = function( selector, parseOnly ) { + var matched, match, tokens, type, + soFar, groups, preFilters, + cached = tokenCache[ selector + " " ]; + + if ( cached ) { + return parseOnly ? 0 : cached.slice( 0 ); + } + + soFar = selector; + groups = []; + preFilters = Expr.preFilter; + + while ( soFar ) { + + // Comma and first run + if ( !matched || ( match = rcomma.exec( soFar ) ) ) { + if ( match ) { + + // Don't consume trailing commas as valid + soFar = soFar.slice( match[ 0 ].length ) || soFar; + } + groups.push( ( tokens = [] ) ); + } + + matched = false; + + // Combinators + if ( ( match = rcombinators.exec( soFar ) ) ) { + matched = match.shift(); + tokens.push( { + value: matched, + + // Cast descendant combinators to space + type: match[ 0 ].replace( rtrim, " " ) + } ); + soFar = soFar.slice( matched.length ); + } + + // Filters + for ( type in Expr.filter ) { + if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] || + ( match = preFilters[ type ]( match ) ) ) ) { + matched = match.shift(); + tokens.push( { + value: matched, + type: type, + matches: match + } ); + soFar = soFar.slice( matched.length ); + } + } + + if ( !matched ) { + break; + } + } + + // Return the length of the invalid excess + // if we're just parsing + // Otherwise, throw an error or return tokens + return parseOnly ? + soFar.length : + soFar ? + Sizzle.error( selector ) : + + // Cache the tokens + tokenCache( selector, groups ).slice( 0 ); +}; + +function toSelector( tokens ) { + var i = 0, + len = tokens.length, + selector = ""; + for ( ; i < len; i++ ) { + selector += tokens[ i ].value; + } + return selector; +} + +function addCombinator( matcher, combinator, base ) { + var dir = combinator.dir, + skip = combinator.next, + key = skip || dir, + checkNonElements = base && key === "parentNode", + doneName = done++; + + return combinator.first ? + + // Check against closest ancestor/preceding element + function( elem, context, xml ) { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + return matcher( elem, context, xml ); + } + } + return false; + } : + + // Check against all ancestor/preceding elements + function( elem, context, xml ) { + var oldCache, uniqueCache, outerCache, + newCache = [ dirruns, doneName ]; + + // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching + if ( xml ) { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + if ( matcher( elem, context, xml ) ) { + return true; + } + } + } + } else { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + outerCache = elem[ expando ] || ( elem[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ elem.uniqueID ] || + ( outerCache[ elem.uniqueID ] = {} ); + + if ( skip && skip === elem.nodeName.toLowerCase() ) { + elem = elem[ dir ] || elem; + } else if ( ( oldCache = uniqueCache[ key ] ) && + oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { + + // Assign to newCache so results back-propagate to previous elements + return ( newCache[ 2 ] = oldCache[ 2 ] ); + } else { + + // Reuse newcache so results back-propagate to previous elements + uniqueCache[ key ] = newCache; + + // A match means we're done; a fail means we have to keep checking + if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) { + return true; + } + } + } + } + } + return false; + }; +} + +function elementMatcher( matchers ) { + return matchers.length > 1 ? + function( elem, context, xml ) { + var i = matchers.length; + while ( i-- ) { + if ( !matchers[ i ]( elem, context, xml ) ) { + return false; + } + } + return true; + } : + matchers[ 0 ]; +} + +function multipleContexts( selector, contexts, results ) { + var i = 0, + len = contexts.length; + for ( ; i < len; i++ ) { + Sizzle( selector, contexts[ i ], results ); + } + return results; +} + +function condense( unmatched, map, filter, context, xml ) { + var elem, + newUnmatched = [], + i = 0, + len = unmatched.length, + mapped = map != null; + + for ( ; i < len; i++ ) { + if ( ( elem = unmatched[ i ] ) ) { + if ( !filter || filter( elem, context, xml ) ) { + newUnmatched.push( elem ); + if ( mapped ) { + map.push( i ); + } + } + } + } + + return newUnmatched; +} + +function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { + if ( postFilter && !postFilter[ expando ] ) { + postFilter = setMatcher( postFilter ); + } + if ( postFinder && !postFinder[ expando ] ) { + postFinder = setMatcher( postFinder, postSelector ); + } + return markFunction( function( seed, results, context, xml ) { + var temp, i, elem, + preMap = [], + postMap = [], + preexisting = results.length, + + // Get initial elements from seed or context + elems = seed || multipleContexts( + selector || "*", + context.nodeType ? [ context ] : context, + [] + ), + + // Prefilter to get matcher input, preserving a map for seed-results synchronization + matcherIn = preFilter && ( seed || !selector ) ? + condense( elems, preMap, preFilter, context, xml ) : + elems, + + matcherOut = matcher ? + + // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, + postFinder || ( seed ? preFilter : preexisting || postFilter ) ? + + // ...intermediate processing is necessary + [] : + + // ...otherwise use results directly + results : + matcherIn; + + // Find primary matches + if ( matcher ) { + matcher( matcherIn, matcherOut, context, xml ); + } + + // Apply postFilter + if ( postFilter ) { + temp = condense( matcherOut, postMap ); + postFilter( temp, [], context, xml ); + + // Un-match failing elements by moving them back to matcherIn + i = temp.length; + while ( i-- ) { + if ( ( elem = temp[ i ] ) ) { + matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem ); + } + } + } + + if ( seed ) { + if ( postFinder || preFilter ) { + if ( postFinder ) { + + // Get the final matcherOut by condensing this intermediate into postFinder contexts + temp = []; + i = matcherOut.length; + while ( i-- ) { + if ( ( elem = matcherOut[ i ] ) ) { + + // Restore matcherIn since elem is not yet a final match + temp.push( ( matcherIn[ i ] = elem ) ); + } + } + postFinder( null, ( matcherOut = [] ), temp, xml ); + } + + // Move matched elements from seed to results to keep them synchronized + i = matcherOut.length; + while ( i-- ) { + if ( ( elem = matcherOut[ i ] ) && + ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) { + + seed[ temp ] = !( results[ temp ] = elem ); + } + } + } + + // Add elements to results, through postFinder if defined + } else { + matcherOut = condense( + matcherOut === results ? + matcherOut.splice( preexisting, matcherOut.length ) : + matcherOut + ); + if ( postFinder ) { + postFinder( null, results, matcherOut, xml ); + } else { + push.apply( results, matcherOut ); + } + } + } ); +} + +function matcherFromTokens( tokens ) { + var checkContext, matcher, j, + len = tokens.length, + leadingRelative = Expr.relative[ tokens[ 0 ].type ], + implicitRelative = leadingRelative || Expr.relative[ " " ], + i = leadingRelative ? 1 : 0, + + // The foundational matcher ensures that elements are reachable from top-level context(s) + matchContext = addCombinator( function( elem ) { + return elem === checkContext; + }, implicitRelative, true ), + matchAnyContext = addCombinator( function( elem ) { + return indexOf( checkContext, elem ) > -1; + }, implicitRelative, true ), + matchers = [ function( elem, context, xml ) { + var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( + ( checkContext = context ).nodeType ? + matchContext( elem, context, xml ) : + matchAnyContext( elem, context, xml ) ); + + // Avoid hanging onto element (issue #299) + checkContext = null; + return ret; + } ]; + + for ( ; i < len; i++ ) { + if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) { + matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ]; + } else { + matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches ); + + // Return special upon seeing a positional matcher + if ( matcher[ expando ] ) { + + // Find the next relative operator (if any) for proper handling + j = ++i; + for ( ; j < len; j++ ) { + if ( Expr.relative[ tokens[ j ].type ] ) { + break; + } + } + return setMatcher( + i > 1 && elementMatcher( matchers ), + i > 1 && toSelector( + + // If the preceding token was a descendant combinator, insert an implicit any-element `*` + tokens + .slice( 0, i - 1 ) + .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } ) + ).replace( rtrim, "$1" ), + matcher, + i < j && matcherFromTokens( tokens.slice( i, j ) ), + j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ), + j < len && toSelector( tokens ) + ); + } + matchers.push( matcher ); + } + } + + return elementMatcher( matchers ); +} + +function matcherFromGroupMatchers( elementMatchers, setMatchers ) { + var bySet = setMatchers.length > 0, + byElement = elementMatchers.length > 0, + superMatcher = function( seed, context, xml, results, outermost ) { + var elem, j, matcher, + matchedCount = 0, + i = "0", + unmatched = seed && [], + setMatched = [], + contextBackup = outermostContext, + + // We must always have either seed elements or outermost context + elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ), + + // Use integer dirruns iff this is the outermost matcher + dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ), + len = elems.length; + + if ( outermost ) { + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + outermostContext = context == document || context || outermost; + } + + // Add elements passing elementMatchers directly to results + // Support: IE<9, Safari + // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id + for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) { + if ( byElement && elem ) { + j = 0; + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( !context && elem.ownerDocument != document ) { + setDocument( elem ); + xml = !documentIsHTML; + } + while ( ( matcher = elementMatchers[ j++ ] ) ) { + if ( matcher( elem, context || document, xml ) ) { + results.push( elem ); + break; + } + } + if ( outermost ) { + dirruns = dirrunsUnique; + } + } + + // Track unmatched elements for set filters + if ( bySet ) { + + // They will have gone through all possible matchers + if ( ( elem = !matcher && elem ) ) { + matchedCount--; + } + + // Lengthen the array for every element, matched or not + if ( seed ) { + unmatched.push( elem ); + } + } + } + + // `i` is now the count of elements visited above, and adding it to `matchedCount` + // makes the latter nonnegative. + matchedCount += i; + + // Apply set filters to unmatched elements + // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` + // equals `i`), unless we didn't visit _any_ elements in the above loop because we have + // no element matchers and no seed. + // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that + // case, which will result in a "00" `matchedCount` that differs from `i` but is also + // numerically zero. + if ( bySet && i !== matchedCount ) { + j = 0; + while ( ( matcher = setMatchers[ j++ ] ) ) { + matcher( unmatched, setMatched, context, xml ); + } + + if ( seed ) { + + // Reintegrate element matches to eliminate the need for sorting + if ( matchedCount > 0 ) { + while ( i-- ) { + if ( !( unmatched[ i ] || setMatched[ i ] ) ) { + setMatched[ i ] = pop.call( results ); + } + } + } + + // Discard index placeholder values to get only actual matches + setMatched = condense( setMatched ); + } + + // Add matches to results + push.apply( results, setMatched ); + + // Seedless set matches succeeding multiple successful matchers stipulate sorting + if ( outermost && !seed && setMatched.length > 0 && + ( matchedCount + setMatchers.length ) > 1 ) { + + Sizzle.uniqueSort( results ); + } + } + + // Override manipulation of globals by nested matchers + if ( outermost ) { + dirruns = dirrunsUnique; + outermostContext = contextBackup; + } + + return unmatched; + }; + + return bySet ? + markFunction( superMatcher ) : + superMatcher; +} + +compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { + var i, + setMatchers = [], + elementMatchers = [], + cached = compilerCache[ selector + " " ]; + + if ( !cached ) { + + // Generate a function of recursive functions that can be used to check each element + if ( !match ) { + match = tokenize( selector ); + } + i = match.length; + while ( i-- ) { + cached = matcherFromTokens( match[ i ] ); + if ( cached[ expando ] ) { + setMatchers.push( cached ); + } else { + elementMatchers.push( cached ); + } + } + + // Cache the compiled function + cached = compilerCache( + selector, + matcherFromGroupMatchers( elementMatchers, setMatchers ) + ); + + // Save selector and tokenization + cached.selector = selector; + } + return cached; +}; + +/** + * A low-level selection function that works with Sizzle's compiled + * selector functions + * @param {String|Function} selector A selector or a pre-compiled + * selector function built with Sizzle.compile + * @param {Element} context + * @param {Array} [results] + * @param {Array} [seed] A set of elements to match against + */ +select = Sizzle.select = function( selector, context, results, seed ) { + var i, tokens, token, type, find, + compiled = typeof selector === "function" && selector, + match = !seed && tokenize( ( selector = compiled.selector || selector ) ); + + results = results || []; + + // Try to minimize operations if there is only one selector in the list and no seed + // (the latter of which guarantees us context) + if ( match.length === 1 ) { + + // Reduce context if the leading compound selector is an ID + tokens = match[ 0 ] = match[ 0 ].slice( 0 ); + if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" && + context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) { + + context = ( Expr.find[ "ID" ]( token.matches[ 0 ] + .replace( runescape, funescape ), context ) || [] )[ 0 ]; + if ( !context ) { + return results; + + // Precompiled matchers will still verify ancestry, so step up a level + } else if ( compiled ) { + context = context.parentNode; + } + + selector = selector.slice( tokens.shift().value.length ); + } + + // Fetch a seed set for right-to-left matching + i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length; + while ( i-- ) { + token = tokens[ i ]; + + // Abort if we hit a combinator + if ( Expr.relative[ ( type = token.type ) ] ) { + break; + } + if ( ( find = Expr.find[ type ] ) ) { + + // Search, expanding context for leading sibling combinators + if ( ( seed = find( + token.matches[ 0 ].replace( runescape, funescape ), + rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) || + context + ) ) ) { + + // If seed is empty or no tokens remain, we can return early + tokens.splice( i, 1 ); + selector = seed.length && toSelector( tokens ); + if ( !selector ) { + push.apply( results, seed ); + return results; + } + + break; + } + } + } + } + + // Compile and execute a filtering function if one is not provided + // Provide `match` to avoid retokenization if we modified the selector above + ( compiled || compile( selector, match ) )( + seed, + context, + !documentIsHTML, + results, + !context || rsibling.test( selector ) && testContext( context.parentNode ) || context + ); + return results; +}; + +// One-time assignments + +// Sort stability +support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando; + +// Support: Chrome 14-35+ +// Always assume duplicates if they aren't passed to the comparison function +support.detectDuplicates = !!hasDuplicate; + +// Initialize against the default document +setDocument(); + +// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) +// Detached nodes confoundingly follow *each other* +support.sortDetached = assert( function( el ) { + + // Should return 1, but returns 4 (following) + return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1; +} ); + +// Support: IE<8 +// Prevent attribute/property "interpolation" +// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx +if ( !assert( function( el ) { + el.innerHTML = ""; + return el.firstChild.getAttribute( "href" ) === "#"; +} ) ) { + addHandle( "type|href|height|width", function( elem, name, isXML ) { + if ( !isXML ) { + return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); + } + } ); +} + +// Support: IE<9 +// Use defaultValue in place of getAttribute("value") +if ( !support.attributes || !assert( function( el ) { + el.innerHTML = ""; + el.firstChild.setAttribute( "value", "" ); + return el.firstChild.getAttribute( "value" ) === ""; +} ) ) { + addHandle( "value", function( elem, _name, isXML ) { + if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { + return elem.defaultValue; + } + } ); +} + +// Support: IE<9 +// Use getAttributeNode to fetch booleans when getAttribute lies +if ( !assert( function( el ) { + return el.getAttribute( "disabled" ) == null; +} ) ) { + addHandle( booleans, function( elem, name, isXML ) { + var val; + if ( !isXML ) { + return elem[ name ] === true ? name.toLowerCase() : + ( val = elem.getAttributeNode( name ) ) && val.specified ? + val.value : + null; + } + } ); +} + +return Sizzle; + +} )( window ); + + + +jQuery.find = Sizzle; +jQuery.expr = Sizzle.selectors; + +// Deprecated +jQuery.expr[ ":" ] = jQuery.expr.pseudos; +jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; +jQuery.text = Sizzle.getText; +jQuery.isXMLDoc = Sizzle.isXML; +jQuery.contains = Sizzle.contains; +jQuery.escapeSelector = Sizzle.escape; + + + + +var dir = function( elem, dir, until ) { + var matched = [], + truncate = until !== undefined; + + while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { + if ( elem.nodeType === 1 ) { + if ( truncate && jQuery( elem ).is( until ) ) { + break; + } + matched.push( elem ); + } + } + return matched; +}; + + +var siblings = function( n, elem ) { + var matched = []; + + for ( ; n; n = n.nextSibling ) { + if ( n.nodeType === 1 && n !== elem ) { + matched.push( n ); + } + } + + return matched; +}; + + +var rneedsContext = jQuery.expr.match.needsContext; + + + +function nodeName( elem, name ) { + + return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); + +} +var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); + + + +// Implement the identical functionality for filter and not +function winnow( elements, qualifier, not ) { + if ( isFunction( qualifier ) ) { + return jQuery.grep( elements, function( elem, i ) { + return !!qualifier.call( elem, i, elem ) !== not; + } ); + } + + // Single element + if ( qualifier.nodeType ) { + return jQuery.grep( elements, function( elem ) { + return ( elem === qualifier ) !== not; + } ); + } + + // Arraylike of elements (jQuery, arguments, Array) + if ( typeof qualifier !== "string" ) { + return jQuery.grep( elements, function( elem ) { + return ( indexOf.call( qualifier, elem ) > -1 ) !== not; + } ); + } + + // Filtered directly for both simple and complex selectors + return jQuery.filter( qualifier, elements, not ); +} + +jQuery.filter = function( expr, elems, not ) { + var elem = elems[ 0 ]; + + if ( not ) { + expr = ":not(" + expr + ")"; + } + + if ( elems.length === 1 && elem.nodeType === 1 ) { + return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; + } + + return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { + return elem.nodeType === 1; + } ) ); +}; + +jQuery.fn.extend( { + find: function( selector ) { + var i, ret, + len = this.length, + self = this; + + if ( typeof selector !== "string" ) { + return this.pushStack( jQuery( selector ).filter( function() { + for ( i = 0; i < len; i++ ) { + if ( jQuery.contains( self[ i ], this ) ) { + return true; + } + } + } ) ); + } + + ret = this.pushStack( [] ); + + for ( i = 0; i < len; i++ ) { + jQuery.find( selector, self[ i ], ret ); + } + + return len > 1 ? jQuery.uniqueSort( ret ) : ret; + }, + filter: function( selector ) { + return this.pushStack( winnow( this, selector || [], false ) ); + }, + not: function( selector ) { + return this.pushStack( winnow( this, selector || [], true ) ); + }, + is: function( selector ) { + return !!winnow( + this, + + // If this is a positional/relative selector, check membership in the returned set + // so $("p:first").is("p:last") won't return true for a doc with two "p". + typeof selector === "string" && rneedsContext.test( selector ) ? + jQuery( selector ) : + selector || [], + false + ).length; + } +} ); + + +// Initialize a jQuery object + + +// A central reference to the root jQuery(document) +var rootjQuery, + + // A simple way to check for HTML strings + // Prioritize #id over to avoid XSS via location.hash (#9521) + // Strict HTML recognition (#11290: must start with <) + // Shortcut simple #id case for speed + rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, + + init = jQuery.fn.init = function( selector, context, root ) { + var match, elem; + + // HANDLE: $(""), $(null), $(undefined), $(false) + if ( !selector ) { + return this; + } + + // Method init() accepts an alternate rootjQuery + // so migrate can support jQuery.sub (gh-2101) + root = root || rootjQuery; + + // Handle HTML strings + if ( typeof selector === "string" ) { + if ( selector[ 0 ] === "<" && + selector[ selector.length - 1 ] === ">" && + selector.length >= 3 ) { + + // Assume that strings that start and end with <> are HTML and skip the regex check + match = [ null, selector, null ]; + + } else { + match = rquickExpr.exec( selector ); + } + + // Match html or make sure no context is specified for #id + if ( match && ( match[ 1 ] || !context ) ) { + + // HANDLE: $(html) -> $(array) + if ( match[ 1 ] ) { + context = context instanceof jQuery ? context[ 0 ] : context; + + // Option to run scripts is true for back-compat + // Intentionally let the error be thrown if parseHTML is not present + jQuery.merge( this, jQuery.parseHTML( + match[ 1 ], + context && context.nodeType ? context.ownerDocument || context : document, + true + ) ); + + // HANDLE: $(html, props) + if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { + for ( match in context ) { + + // Properties of context are called as methods if possible + if ( isFunction( this[ match ] ) ) { + this[ match ]( context[ match ] ); + + // ...and otherwise set as attributes + } else { + this.attr( match, context[ match ] ); + } + } + } + + return this; + + // HANDLE: $(#id) + } else { + elem = document.getElementById( match[ 2 ] ); + + if ( elem ) { + + // Inject the element directly into the jQuery object + this[ 0 ] = elem; + this.length = 1; + } + return this; + } + + // HANDLE: $(expr, $(...)) + } else if ( !context || context.jquery ) { + return ( context || root ).find( selector ); + + // HANDLE: $(expr, context) + // (which is just equivalent to: $(context).find(expr) + } else { + return this.constructor( context ).find( selector ); + } + + // HANDLE: $(DOMElement) + } else if ( selector.nodeType ) { + this[ 0 ] = selector; + this.length = 1; + return this; + + // HANDLE: $(function) + // Shortcut for document ready + } else if ( isFunction( selector ) ) { + return root.ready !== undefined ? + root.ready( selector ) : + + // Execute immediately if ready is not present + selector( jQuery ); + } + + return jQuery.makeArray( selector, this ); + }; + +// Give the init function the jQuery prototype for later instantiation +init.prototype = jQuery.fn; + +// Initialize central reference +rootjQuery = jQuery( document ); + + +var rparentsprev = /^(?:parents|prev(?:Until|All))/, + + // Methods guaranteed to produce a unique set when starting from a unique set + guaranteedUnique = { + children: true, + contents: true, + next: true, + prev: true + }; + +jQuery.fn.extend( { + has: function( target ) { + var targets = jQuery( target, this ), + l = targets.length; + + return this.filter( function() { + var i = 0; + for ( ; i < l; i++ ) { + if ( jQuery.contains( this, targets[ i ] ) ) { + return true; + } + } + } ); + }, + + closest: function( selectors, context ) { + var cur, + i = 0, + l = this.length, + matched = [], + targets = typeof selectors !== "string" && jQuery( selectors ); + + // Positional selectors never match, since there's no _selection_ context + if ( !rneedsContext.test( selectors ) ) { + for ( ; i < l; i++ ) { + for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { + + // Always skip document fragments + if ( cur.nodeType < 11 && ( targets ? + targets.index( cur ) > -1 : + + // Don't pass non-elements to Sizzle + cur.nodeType === 1 && + jQuery.find.matchesSelector( cur, selectors ) ) ) { + + matched.push( cur ); + break; + } + } + } + } + + return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); + }, + + // Determine the position of an element within the set + index: function( elem ) { + + // No argument, return index in parent + if ( !elem ) { + return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; + } + + // Index in selector + if ( typeof elem === "string" ) { + return indexOf.call( jQuery( elem ), this[ 0 ] ); + } + + // Locate the position of the desired element + return indexOf.call( this, + + // If it receives a jQuery object, the first element is used + elem.jquery ? elem[ 0 ] : elem + ); + }, + + add: function( selector, context ) { + return this.pushStack( + jQuery.uniqueSort( + jQuery.merge( this.get(), jQuery( selector, context ) ) + ) + ); + }, + + addBack: function( selector ) { + return this.add( selector == null ? + this.prevObject : this.prevObject.filter( selector ) + ); + } +} ); + +function sibling( cur, dir ) { + while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} + return cur; +} + +jQuery.each( { + parent: function( elem ) { + var parent = elem.parentNode; + return parent && parent.nodeType !== 11 ? parent : null; + }, + parents: function( elem ) { + return dir( elem, "parentNode" ); + }, + parentsUntil: function( elem, _i, until ) { + return dir( elem, "parentNode", until ); + }, + next: function( elem ) { + return sibling( elem, "nextSibling" ); + }, + prev: function( elem ) { + return sibling( elem, "previousSibling" ); + }, + nextAll: function( elem ) { + return dir( elem, "nextSibling" ); + }, + prevAll: function( elem ) { + return dir( elem, "previousSibling" ); + }, + nextUntil: function( elem, _i, until ) { + return dir( elem, "nextSibling", until ); + }, + prevUntil: function( elem, _i, until ) { + return dir( elem, "previousSibling", until ); + }, + siblings: function( elem ) { + return siblings( ( elem.parentNode || {} ).firstChild, elem ); + }, + children: function( elem ) { + return siblings( elem.firstChild ); + }, + contents: function( elem ) { + if ( elem.contentDocument != null && + + // Support: IE 11+ + // elements with no `data` attribute has an object + // `contentDocument` with a `null` prototype. + getProto( elem.contentDocument ) ) { + + return elem.contentDocument; + } + + // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only + // Treat the template element as a regular one in browsers that + // don't support it. + if ( nodeName( elem, "template" ) ) { + elem = elem.content || elem; + } + + return jQuery.merge( [], elem.childNodes ); + } +}, function( name, fn ) { + jQuery.fn[ name ] = function( until, selector ) { + var matched = jQuery.map( this, fn, until ); + + if ( name.slice( -5 ) !== "Until" ) { + selector = until; + } + + if ( selector && typeof selector === "string" ) { + matched = jQuery.filter( selector, matched ); + } + + if ( this.length > 1 ) { + + // Remove duplicates + if ( !guaranteedUnique[ name ] ) { + jQuery.uniqueSort( matched ); + } + + // Reverse order for parents* and prev-derivatives + if ( rparentsprev.test( name ) ) { + matched.reverse(); + } + } + + return this.pushStack( matched ); + }; +} ); +var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); + + + +// Convert String-formatted options into Object-formatted ones +function createOptions( options ) { + var object = {}; + jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { + object[ flag ] = true; + } ); + return object; +} + +/* + * Create a callback list using the following parameters: + * + * options: an optional list of space-separated options that will change how + * the callback list behaves or a more traditional option object + * + * By default a callback list will act like an event callback list and can be + * "fired" multiple times. + * + * Possible options: + * + * once: will ensure the callback list can only be fired once (like a Deferred) + * + * memory: will keep track of previous values and will call any callback added + * after the list has been fired right away with the latest "memorized" + * values (like a Deferred) + * + * unique: will ensure a callback can only be added once (no duplicate in the list) + * + * stopOnFalse: interrupt callings when a callback returns false + * + */ +jQuery.Callbacks = function( options ) { + + // Convert options from String-formatted to Object-formatted if needed + // (we check in cache first) + options = typeof options === "string" ? + createOptions( options ) : + jQuery.extend( {}, options ); + + var // Flag to know if list is currently firing + firing, + + // Last fire value for non-forgettable lists + memory, + + // Flag to know if list was already fired + fired, + + // Flag to prevent firing + locked, + + // Actual callback list + list = [], + + // Queue of execution data for repeatable lists + queue = [], + + // Index of currently firing callback (modified by add/remove as needed) + firingIndex = -1, + + // Fire callbacks + fire = function() { + + // Enforce single-firing + locked = locked || options.once; + + // Execute callbacks for all pending executions, + // respecting firingIndex overrides and runtime changes + fired = firing = true; + for ( ; queue.length; firingIndex = -1 ) { + memory = queue.shift(); + while ( ++firingIndex < list.length ) { + + // Run callback and check for early termination + if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && + options.stopOnFalse ) { + + // Jump to end and forget the data so .add doesn't re-fire + firingIndex = list.length; + memory = false; + } + } + } + + // Forget the data if we're done with it + if ( !options.memory ) { + memory = false; + } + + firing = false; + + // Clean up if we're done firing for good + if ( locked ) { + + // Keep an empty list if we have data for future add calls + if ( memory ) { + list = []; + + // Otherwise, this object is spent + } else { + list = ""; + } + } + }, + + // Actual Callbacks object + self = { + + // Add a callback or a collection of callbacks to the list + add: function() { + if ( list ) { + + // If we have memory from a past run, we should fire after adding + if ( memory && !firing ) { + firingIndex = list.length - 1; + queue.push( memory ); + } + + ( function add( args ) { + jQuery.each( args, function( _, arg ) { + if ( isFunction( arg ) ) { + if ( !options.unique || !self.has( arg ) ) { + list.push( arg ); + } + } else if ( arg && arg.length && toType( arg ) !== "string" ) { + + // Inspect recursively + add( arg ); + } + } ); + } )( arguments ); + + if ( memory && !firing ) { + fire(); + } + } + return this; + }, + + // Remove a callback from the list + remove: function() { + jQuery.each( arguments, function( _, arg ) { + var index; + while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { + list.splice( index, 1 ); + + // Handle firing indexes + if ( index <= firingIndex ) { + firingIndex--; + } + } + } ); + return this; + }, + + // Check if a given callback is in the list. + // If no argument is given, return whether or not list has callbacks attached. + has: function( fn ) { + return fn ? + jQuery.inArray( fn, list ) > -1 : + list.length > 0; + }, + + // Remove all callbacks from the list + empty: function() { + if ( list ) { + list = []; + } + return this; + }, + + // Disable .fire and .add + // Abort any current/pending executions + // Clear all callbacks and values + disable: function() { + locked = queue = []; + list = memory = ""; + return this; + }, + disabled: function() { + return !list; + }, + + // Disable .fire + // Also disable .add unless we have memory (since it would have no effect) + // Abort any pending executions + lock: function() { + locked = queue = []; + if ( !memory && !firing ) { + list = memory = ""; + } + return this; + }, + locked: function() { + return !!locked; + }, + + // Call all callbacks with the given context and arguments + fireWith: function( context, args ) { + if ( !locked ) { + args = args || []; + args = [ context, args.slice ? args.slice() : args ]; + queue.push( args ); + if ( !firing ) { + fire(); + } + } + return this; + }, + + // Call all the callbacks with the given arguments + fire: function() { + self.fireWith( this, arguments ); + return this; + }, + + // To know if the callbacks have already been called at least once + fired: function() { + return !!fired; + } + }; + + return self; +}; + + +function Identity( v ) { + return v; +} +function Thrower( ex ) { + throw ex; +} + +function adoptValue( value, resolve, reject, noValue ) { + var method; + + try { + + // Check for promise aspect first to privilege synchronous behavior + if ( value && isFunction( ( method = value.promise ) ) ) { + method.call( value ).done( resolve ).fail( reject ); + + // Other thenables + } else if ( value && isFunction( ( method = value.then ) ) ) { + method.call( value, resolve, reject ); + + // Other non-thenables + } else { + + // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: + // * false: [ value ].slice( 0 ) => resolve( value ) + // * true: [ value ].slice( 1 ) => resolve() + resolve.apply( undefined, [ value ].slice( noValue ) ); + } + + // For Promises/A+, convert exceptions into rejections + // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in + // Deferred#then to conditionally suppress rejection. + } catch ( value ) { + + // Support: Android 4.0 only + // Strict mode functions invoked without .call/.apply get global-object context + reject.apply( undefined, [ value ] ); + } +} + +jQuery.extend( { + + Deferred: function( func ) { + var tuples = [ + + // action, add listener, callbacks, + // ... .then handlers, argument index, [final state] + [ "notify", "progress", jQuery.Callbacks( "memory" ), + jQuery.Callbacks( "memory" ), 2 ], + [ "resolve", "done", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 0, "resolved" ], + [ "reject", "fail", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 1, "rejected" ] + ], + state = "pending", + promise = { + state: function() { + return state; + }, + always: function() { + deferred.done( arguments ).fail( arguments ); + return this; + }, + "catch": function( fn ) { + return promise.then( null, fn ); + }, + + // Keep pipe for back-compat + pipe: function( /* fnDone, fnFail, fnProgress */ ) { + var fns = arguments; + + return jQuery.Deferred( function( newDefer ) { + jQuery.each( tuples, function( _i, tuple ) { + + // Map tuples (progress, done, fail) to arguments (done, fail, progress) + var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; + + // deferred.progress(function() { bind to newDefer or newDefer.notify }) + // deferred.done(function() { bind to newDefer or newDefer.resolve }) + // deferred.fail(function() { bind to newDefer or newDefer.reject }) + deferred[ tuple[ 1 ] ]( function() { + var returned = fn && fn.apply( this, arguments ); + if ( returned && isFunction( returned.promise ) ) { + returned.promise() + .progress( newDefer.notify ) + .done( newDefer.resolve ) + .fail( newDefer.reject ); + } else { + newDefer[ tuple[ 0 ] + "With" ]( + this, + fn ? [ returned ] : arguments + ); + } + } ); + } ); + fns = null; + } ).promise(); + }, + then: function( onFulfilled, onRejected, onProgress ) { + var maxDepth = 0; + function resolve( depth, deferred, handler, special ) { + return function() { + var that = this, + args = arguments, + mightThrow = function() { + var returned, then; + + // Support: Promises/A+ section 2.3.3.3.3 + // https://promisesaplus.com/#point-59 + // Ignore double-resolution attempts + if ( depth < maxDepth ) { + return; + } + + returned = handler.apply( that, args ); + + // Support: Promises/A+ section 2.3.1 + // https://promisesaplus.com/#point-48 + if ( returned === deferred.promise() ) { + throw new TypeError( "Thenable self-resolution" ); + } + + // Support: Promises/A+ sections 2.3.3.1, 3.5 + // https://promisesaplus.com/#point-54 + // https://promisesaplus.com/#point-75 + // Retrieve `then` only once + then = returned && + + // Support: Promises/A+ section 2.3.4 + // https://promisesaplus.com/#point-64 + // Only check objects and functions for thenability + ( typeof returned === "object" || + typeof returned === "function" ) && + returned.then; + + // Handle a returned thenable + if ( isFunction( then ) ) { + + // Special processors (notify) just wait for resolution + if ( special ) { + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ) + ); + + // Normal processors (resolve) also hook into progress + } else { + + // ...and disregard older resolution values + maxDepth++; + + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ), + resolve( maxDepth, deferred, Identity, + deferred.notifyWith ) + ); + } + + // Handle all other returned values + } else { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Identity ) { + that = undefined; + args = [ returned ]; + } + + // Process the value(s) + // Default process is resolve + ( special || deferred.resolveWith )( that, args ); + } + }, + + // Only normal processors (resolve) catch and reject exceptions + process = special ? + mightThrow : + function() { + try { + mightThrow(); + } catch ( e ) { + + if ( jQuery.Deferred.exceptionHook ) { + jQuery.Deferred.exceptionHook( e, + process.stackTrace ); + } + + // Support: Promises/A+ section 2.3.3.3.4.1 + // https://promisesaplus.com/#point-61 + // Ignore post-resolution exceptions + if ( depth + 1 >= maxDepth ) { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Thrower ) { + that = undefined; + args = [ e ]; + } + + deferred.rejectWith( that, args ); + } + } + }; + + // Support: Promises/A+ section 2.3.3.3.1 + // https://promisesaplus.com/#point-57 + // Re-resolve promises immediately to dodge false rejection from + // subsequent errors + if ( depth ) { + process(); + } else { + + // Call an optional hook to record the stack, in case of exception + // since it's otherwise lost when execution goes async + if ( jQuery.Deferred.getStackHook ) { + process.stackTrace = jQuery.Deferred.getStackHook(); + } + window.setTimeout( process ); + } + }; + } + + return jQuery.Deferred( function( newDefer ) { + + // progress_handlers.add( ... ) + tuples[ 0 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onProgress ) ? + onProgress : + Identity, + newDefer.notifyWith + ) + ); + + // fulfilled_handlers.add( ... ) + tuples[ 1 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onFulfilled ) ? + onFulfilled : + Identity + ) + ); + + // rejected_handlers.add( ... ) + tuples[ 2 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onRejected ) ? + onRejected : + Thrower + ) + ); + } ).promise(); + }, + + // Get a promise for this deferred + // If obj is provided, the promise aspect is added to the object + promise: function( obj ) { + return obj != null ? jQuery.extend( obj, promise ) : promise; + } + }, + deferred = {}; + + // Add list-specific methods + jQuery.each( tuples, function( i, tuple ) { + var list = tuple[ 2 ], + stateString = tuple[ 5 ]; + + // promise.progress = list.add + // promise.done = list.add + // promise.fail = list.add + promise[ tuple[ 1 ] ] = list.add; + + // Handle state + if ( stateString ) { + list.add( + function() { + + // state = "resolved" (i.e., fulfilled) + // state = "rejected" + state = stateString; + }, + + // rejected_callbacks.disable + // fulfilled_callbacks.disable + tuples[ 3 - i ][ 2 ].disable, + + // rejected_handlers.disable + // fulfilled_handlers.disable + tuples[ 3 - i ][ 3 ].disable, + + // progress_callbacks.lock + tuples[ 0 ][ 2 ].lock, + + // progress_handlers.lock + tuples[ 0 ][ 3 ].lock + ); + } + + // progress_handlers.fire + // fulfilled_handlers.fire + // rejected_handlers.fire + list.add( tuple[ 3 ].fire ); + + // deferred.notify = function() { deferred.notifyWith(...) } + // deferred.resolve = function() { deferred.resolveWith(...) } + // deferred.reject = function() { deferred.rejectWith(...) } + deferred[ tuple[ 0 ] ] = function() { + deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); + return this; + }; + + // deferred.notifyWith = list.fireWith + // deferred.resolveWith = list.fireWith + // deferred.rejectWith = list.fireWith + deferred[ tuple[ 0 ] + "With" ] = list.fireWith; + } ); + + // Make the deferred a promise + promise.promise( deferred ); + + // Call given func if any + if ( func ) { + func.call( deferred, deferred ); + } + + // All done! + return deferred; + }, + + // Deferred helper + when: function( singleValue ) { + var + + // count of uncompleted subordinates + remaining = arguments.length, + + // count of unprocessed arguments + i = remaining, + + // subordinate fulfillment data + resolveContexts = Array( i ), + resolveValues = slice.call( arguments ), + + // the primary Deferred + primary = jQuery.Deferred(), + + // subordinate callback factory + updateFunc = function( i ) { + return function( value ) { + resolveContexts[ i ] = this; + resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; + if ( !( --remaining ) ) { + primary.resolveWith( resolveContexts, resolveValues ); + } + }; + }; + + // Single- and empty arguments are adopted like Promise.resolve + if ( remaining <= 1 ) { + adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject, + !remaining ); + + // Use .then() to unwrap secondary thenables (cf. gh-3000) + if ( primary.state() === "pending" || + isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { + + return primary.then(); + } + } + + // Multiple arguments are aggregated like Promise.all array elements + while ( i-- ) { + adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject ); + } + + return primary.promise(); + } +} ); + + +// These usually indicate a programmer mistake during development, +// warn about them ASAP rather than swallowing them by default. +var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; + +jQuery.Deferred.exceptionHook = function( error, stack ) { + + // Support: IE 8 - 9 only + // Console exists when dev tools are open, which can happen at any time + if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { + window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); + } +}; + + + + +jQuery.readyException = function( error ) { + window.setTimeout( function() { + throw error; + } ); +}; + + + + +// The deferred used on DOM ready +var readyList = jQuery.Deferred(); + +jQuery.fn.ready = function( fn ) { + + readyList + .then( fn ) + + // Wrap jQuery.readyException in a function so that the lookup + // happens at the time of error handling instead of callback + // registration. + .catch( function( error ) { + jQuery.readyException( error ); + } ); + + return this; +}; + +jQuery.extend( { + + // Is the DOM ready to be used? Set to true once it occurs. + isReady: false, + + // A counter to track how many items to wait for before + // the ready event fires. See #6781 + readyWait: 1, + + // Handle when the DOM is ready + ready: function( wait ) { + + // Abort if there are pending holds or we're already ready + if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { + return; + } + + // Remember that the DOM is ready + jQuery.isReady = true; + + // If a normal DOM Ready event fired, decrement, and wait if need be + if ( wait !== true && --jQuery.readyWait > 0 ) { + return; + } + + // If there are functions bound, to execute + readyList.resolveWith( document, [ jQuery ] ); + } +} ); + +jQuery.ready.then = readyList.then; + +// The ready event handler and self cleanup method +function completed() { + document.removeEventListener( "DOMContentLoaded", completed ); + window.removeEventListener( "load", completed ); + jQuery.ready(); +} + +// Catch cases where $(document).ready() is called +// after the browser event has already occurred. +// Support: IE <=9 - 10 only +// Older IE sometimes signals "interactive" too soon +if ( document.readyState === "complete" || + ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { + + // Handle it asynchronously to allow scripts the opportunity to delay ready + window.setTimeout( jQuery.ready ); + +} else { + + // Use the handy event callback + document.addEventListener( "DOMContentLoaded", completed ); + + // A fallback to window.onload, that will always work + window.addEventListener( "load", completed ); +} + + + + +// Multifunctional method to get and set values of a collection +// The value/s can optionally be executed if it's a function +var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { + var i = 0, + len = elems.length, + bulk = key == null; + + // Sets many values + if ( toType( key ) === "object" ) { + chainable = true; + for ( i in key ) { + access( elems, fn, i, key[ i ], true, emptyGet, raw ); + } + + // Sets one value + } else if ( value !== undefined ) { + chainable = true; + + if ( !isFunction( value ) ) { + raw = true; + } + + if ( bulk ) { + + // Bulk operations run against the entire set + if ( raw ) { + fn.call( elems, value ); + fn = null; + + // ...except when executing function values + } else { + bulk = fn; + fn = function( elem, _key, value ) { + return bulk.call( jQuery( elem ), value ); + }; + } + } + + if ( fn ) { + for ( ; i < len; i++ ) { + fn( + elems[ i ], key, raw ? + value : + value.call( elems[ i ], i, fn( elems[ i ], key ) ) + ); + } + } + } + + if ( chainable ) { + return elems; + } + + // Gets + if ( bulk ) { + return fn.call( elems ); + } + + return len ? fn( elems[ 0 ], key ) : emptyGet; +}; + + +// Matches dashed string for camelizing +var rmsPrefix = /^-ms-/, + rdashAlpha = /-([a-z])/g; + +// Used by camelCase as callback to replace() +function fcamelCase( _all, letter ) { + return letter.toUpperCase(); +} + +// Convert dashed to camelCase; used by the css and data modules +// Support: IE <=9 - 11, Edge 12 - 15 +// Microsoft forgot to hump their vendor prefix (#9572) +function camelCase( string ) { + return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); +} +var acceptData = function( owner ) { + + // Accepts only: + // - Node + // - Node.ELEMENT_NODE + // - Node.DOCUMENT_NODE + // - Object + // - Any + return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); +}; + + + + +function Data() { + this.expando = jQuery.expando + Data.uid++; +} + +Data.uid = 1; + +Data.prototype = { + + cache: function( owner ) { + + // Check if the owner object already has a cache + var value = owner[ this.expando ]; + + // If not, create one + if ( !value ) { + value = {}; + + // We can accept data for non-element nodes in modern browsers, + // but we should not, see #8335. + // Always return an empty object. + if ( acceptData( owner ) ) { + + // If it is a node unlikely to be stringify-ed or looped over + // use plain assignment + if ( owner.nodeType ) { + owner[ this.expando ] = value; + + // Otherwise secure it in a non-enumerable property + // configurable must be true to allow the property to be + // deleted when data is removed + } else { + Object.defineProperty( owner, this.expando, { + value: value, + configurable: true + } ); + } + } + } + + return value; + }, + set: function( owner, data, value ) { + var prop, + cache = this.cache( owner ); + + // Handle: [ owner, key, value ] args + // Always use camelCase key (gh-2257) + if ( typeof data === "string" ) { + cache[ camelCase( data ) ] = value; + + // Handle: [ owner, { properties } ] args + } else { + + // Copy the properties one-by-one to the cache object + for ( prop in data ) { + cache[ camelCase( prop ) ] = data[ prop ]; + } + } + return cache; + }, + get: function( owner, key ) { + return key === undefined ? + this.cache( owner ) : + + // Always use camelCase key (gh-2257) + owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ]; + }, + access: function( owner, key, value ) { + + // In cases where either: + // + // 1. No key was specified + // 2. A string key was specified, but no value provided + // + // Take the "read" path and allow the get method to determine + // which value to return, respectively either: + // + // 1. The entire cache object + // 2. The data stored at the key + // + if ( key === undefined || + ( ( key && typeof key === "string" ) && value === undefined ) ) { + + return this.get( owner, key ); + } + + // When the key is not a string, or both a key and value + // are specified, set or extend (existing objects) with either: + // + // 1. An object of properties + // 2. A key and value + // + this.set( owner, key, value ); + + // Since the "set" path can have two possible entry points + // return the expected data based on which path was taken[*] + return value !== undefined ? value : key; + }, + remove: function( owner, key ) { + var i, + cache = owner[ this.expando ]; + + if ( cache === undefined ) { + return; + } + + if ( key !== undefined ) { + + // Support array or space separated string of keys + if ( Array.isArray( key ) ) { + + // If key is an array of keys... + // We always set camelCase keys, so remove that. + key = key.map( camelCase ); + } else { + key = camelCase( key ); + + // If a key with the spaces exists, use it. + // Otherwise, create an array by matching non-whitespace + key = key in cache ? + [ key ] : + ( key.match( rnothtmlwhite ) || [] ); + } + + i = key.length; + + while ( i-- ) { + delete cache[ key[ i ] ]; + } + } + + // Remove the expando if there's no more data + if ( key === undefined || jQuery.isEmptyObject( cache ) ) { + + // Support: Chrome <=35 - 45 + // Webkit & Blink performance suffers when deleting properties + // from DOM nodes, so set to undefined instead + // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) + if ( owner.nodeType ) { + owner[ this.expando ] = undefined; + } else { + delete owner[ this.expando ]; + } + } + }, + hasData: function( owner ) { + var cache = owner[ this.expando ]; + return cache !== undefined && !jQuery.isEmptyObject( cache ); + } +}; +var dataPriv = new Data(); + +var dataUser = new Data(); + + + +// Implementation Summary +// +// 1. Enforce API surface and semantic compatibility with 1.9.x branch +// 2. Improve the module's maintainability by reducing the storage +// paths to a single mechanism. +// 3. Use the same single mechanism to support "private" and "user" data. +// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) +// 5. Avoid exposing implementation details on user objects (eg. expando properties) +// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 + +var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, + rmultiDash = /[A-Z]/g; + +function getData( data ) { + if ( data === "true" ) { + return true; + } + + if ( data === "false" ) { + return false; + } + + if ( data === "null" ) { + return null; + } + + // Only convert to a number if it doesn't change the string + if ( data === +data + "" ) { + return +data; + } + + if ( rbrace.test( data ) ) { + return JSON.parse( data ); + } + + return data; +} + +function dataAttr( elem, key, data ) { + var name; + + // If nothing was found internally, try to fetch any + // data from the HTML5 data-* attribute + if ( data === undefined && elem.nodeType === 1 ) { + name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); + data = elem.getAttribute( name ); + + if ( typeof data === "string" ) { + try { + data = getData( data ); + } catch ( e ) {} + + // Make sure we set the data so it isn't changed later + dataUser.set( elem, key, data ); + } else { + data = undefined; + } + } + return data; +} + +jQuery.extend( { + hasData: function( elem ) { + return dataUser.hasData( elem ) || dataPriv.hasData( elem ); + }, + + data: function( elem, name, data ) { + return dataUser.access( elem, name, data ); + }, + + removeData: function( elem, name ) { + dataUser.remove( elem, name ); + }, + + // TODO: Now that all calls to _data and _removeData have been replaced + // with direct calls to dataPriv methods, these can be deprecated. + _data: function( elem, name, data ) { + return dataPriv.access( elem, name, data ); + }, + + _removeData: function( elem, name ) { + dataPriv.remove( elem, name ); + } +} ); + +jQuery.fn.extend( { + data: function( key, value ) { + var i, name, data, + elem = this[ 0 ], + attrs = elem && elem.attributes; + + // Gets all values + if ( key === undefined ) { + if ( this.length ) { + data = dataUser.get( elem ); + + if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { + i = attrs.length; + while ( i-- ) { + + // Support: IE 11 only + // The attrs elements can be null (#14894) + if ( attrs[ i ] ) { + name = attrs[ i ].name; + if ( name.indexOf( "data-" ) === 0 ) { + name = camelCase( name.slice( 5 ) ); + dataAttr( elem, name, data[ name ] ); + } + } + } + dataPriv.set( elem, "hasDataAttrs", true ); + } + } + + return data; + } + + // Sets multiple values + if ( typeof key === "object" ) { + return this.each( function() { + dataUser.set( this, key ); + } ); + } + + return access( this, function( value ) { + var data; + + // The calling jQuery object (element matches) is not empty + // (and therefore has an element appears at this[ 0 ]) and the + // `value` parameter was not undefined. An empty jQuery object + // will result in `undefined` for elem = this[ 0 ] which will + // throw an exception if an attempt to read a data cache is made. + if ( elem && value === undefined ) { + + // Attempt to get data from the cache + // The key will always be camelCased in Data + data = dataUser.get( elem, key ); + if ( data !== undefined ) { + return data; + } + + // Attempt to "discover" the data in + // HTML5 custom data-* attrs + data = dataAttr( elem, key ); + if ( data !== undefined ) { + return data; + } + + // We tried really hard, but the data doesn't exist. + return; + } + + // Set the data... + this.each( function() { + + // We always store the camelCased key + dataUser.set( this, key, value ); + } ); + }, null, value, arguments.length > 1, null, true ); + }, + + removeData: function( key ) { + return this.each( function() { + dataUser.remove( this, key ); + } ); + } +} ); + + +jQuery.extend( { + queue: function( elem, type, data ) { + var queue; + + if ( elem ) { + type = ( type || "fx" ) + "queue"; + queue = dataPriv.get( elem, type ); + + // Speed up dequeue by getting out quickly if this is just a lookup + if ( data ) { + if ( !queue || Array.isArray( data ) ) { + queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); + } else { + queue.push( data ); + } + } + return queue || []; + } + }, + + dequeue: function( elem, type ) { + type = type || "fx"; + + var queue = jQuery.queue( elem, type ), + startLength = queue.length, + fn = queue.shift(), + hooks = jQuery._queueHooks( elem, type ), + next = function() { + jQuery.dequeue( elem, type ); + }; + + // If the fx queue is dequeued, always remove the progress sentinel + if ( fn === "inprogress" ) { + fn = queue.shift(); + startLength--; + } + + if ( fn ) { + + // Add a progress sentinel to prevent the fx queue from being + // automatically dequeued + if ( type === "fx" ) { + queue.unshift( "inprogress" ); + } + + // Clear up the last queue stop function + delete hooks.stop; + fn.call( elem, next, hooks ); + } + + if ( !startLength && hooks ) { + hooks.empty.fire(); + } + }, + + // Not public - generate a queueHooks object, or return the current one + _queueHooks: function( elem, type ) { + var key = type + "queueHooks"; + return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { + empty: jQuery.Callbacks( "once memory" ).add( function() { + dataPriv.remove( elem, [ type + "queue", key ] ); + } ) + } ); + } +} ); + +jQuery.fn.extend( { + queue: function( type, data ) { + var setter = 2; + + if ( typeof type !== "string" ) { + data = type; + type = "fx"; + setter--; + } + + if ( arguments.length < setter ) { + return jQuery.queue( this[ 0 ], type ); + } + + return data === undefined ? + this : + this.each( function() { + var queue = jQuery.queue( this, type, data ); + + // Ensure a hooks for this queue + jQuery._queueHooks( this, type ); + + if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { + jQuery.dequeue( this, type ); + } + } ); + }, + dequeue: function( type ) { + return this.each( function() { + jQuery.dequeue( this, type ); + } ); + }, + clearQueue: function( type ) { + return this.queue( type || "fx", [] ); + }, + + // Get a promise resolved when queues of a certain type + // are emptied (fx is the type by default) + promise: function( type, obj ) { + var tmp, + count = 1, + defer = jQuery.Deferred(), + elements = this, + i = this.length, + resolve = function() { + if ( !( --count ) ) { + defer.resolveWith( elements, [ elements ] ); + } + }; + + if ( typeof type !== "string" ) { + obj = type; + type = undefined; + } + type = type || "fx"; + + while ( i-- ) { + tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); + if ( tmp && tmp.empty ) { + count++; + tmp.empty.add( resolve ); + } + } + resolve(); + return defer.promise( obj ); + } +} ); +var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; + +var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); + + +var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; + +var documentElement = document.documentElement; + + + + var isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ); + }, + composed = { composed: true }; + + // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only + // Check attachment across shadow DOM boundaries when possible (gh-3504) + // Support: iOS 10.0-10.2 only + // Early iOS 10 versions support `attachShadow` but not `getRootNode`, + // leading to errors. We need to check for `getRootNode`. + if ( documentElement.getRootNode ) { + isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ) || + elem.getRootNode( composed ) === elem.ownerDocument; + }; + } +var isHiddenWithinTree = function( elem, el ) { + + // isHiddenWithinTree might be called from jQuery#filter function; + // in that case, element will be second argument + elem = el || elem; + + // Inline style trumps all + return elem.style.display === "none" || + elem.style.display === "" && + + // Otherwise, check computed style + // Support: Firefox <=43 - 45 + // Disconnected elements can have computed display: none, so first confirm that elem is + // in the document. + isAttached( elem ) && + + jQuery.css( elem, "display" ) === "none"; + }; + + + +function adjustCSS( elem, prop, valueParts, tween ) { + var adjusted, scale, + maxIterations = 20, + currentValue = tween ? + function() { + return tween.cur(); + } : + function() { + return jQuery.css( elem, prop, "" ); + }, + initial = currentValue(), + unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), + + // Starting value computation is required for potential unit mismatches + initialInUnit = elem.nodeType && + ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && + rcssNum.exec( jQuery.css( elem, prop ) ); + + if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { + + // Support: Firefox <=54 + // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144) + initial = initial / 2; + + // Trust units reported by jQuery.css + unit = unit || initialInUnit[ 3 ]; + + // Iteratively approximate from a nonzero starting point + initialInUnit = +initial || 1; + + while ( maxIterations-- ) { + + // Evaluate and update our best guess (doubling guesses that zero out). + // Finish if the scale equals or crosses 1 (making the old*new product non-positive). + jQuery.style( elem, prop, initialInUnit + unit ); + if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) { + maxIterations = 0; + } + initialInUnit = initialInUnit / scale; + + } + + initialInUnit = initialInUnit * 2; + jQuery.style( elem, prop, initialInUnit + unit ); + + // Make sure we update the tween properties later on + valueParts = valueParts || []; + } + + if ( valueParts ) { + initialInUnit = +initialInUnit || +initial || 0; + + // Apply relative offset (+=/-=) if specified + adjusted = valueParts[ 1 ] ? + initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : + +valueParts[ 2 ]; + if ( tween ) { + tween.unit = unit; + tween.start = initialInUnit; + tween.end = adjusted; + } + } + return adjusted; +} + + +var defaultDisplayMap = {}; + +function getDefaultDisplay( elem ) { + var temp, + doc = elem.ownerDocument, + nodeName = elem.nodeName, + display = defaultDisplayMap[ nodeName ]; + + if ( display ) { + return display; + } + + temp = doc.body.appendChild( doc.createElement( nodeName ) ); + display = jQuery.css( temp, "display" ); + + temp.parentNode.removeChild( temp ); + + if ( display === "none" ) { + display = "block"; + } + defaultDisplayMap[ nodeName ] = display; + + return display; +} + +function showHide( elements, show ) { + var display, elem, + values = [], + index = 0, + length = elements.length; + + // Determine new display value for elements that need to change + for ( ; index < length; index++ ) { + elem = elements[ index ]; + if ( !elem.style ) { + continue; + } + + display = elem.style.display; + if ( show ) { + + // Since we force visibility upon cascade-hidden elements, an immediate (and slow) + // check is required in this first loop unless we have a nonempty display value (either + // inline or about-to-be-restored) + if ( display === "none" ) { + values[ index ] = dataPriv.get( elem, "display" ) || null; + if ( !values[ index ] ) { + elem.style.display = ""; + } + } + if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { + values[ index ] = getDefaultDisplay( elem ); + } + } else { + if ( display !== "none" ) { + values[ index ] = "none"; + + // Remember what we're overwriting + dataPriv.set( elem, "display", display ); + } + } + } + + // Set the display of the elements in a second loop to avoid constant reflow + for ( index = 0; index < length; index++ ) { + if ( values[ index ] != null ) { + elements[ index ].style.display = values[ index ]; + } + } + + return elements; +} + +jQuery.fn.extend( { + show: function() { + return showHide( this, true ); + }, + hide: function() { + return showHide( this ); + }, + toggle: function( state ) { + if ( typeof state === "boolean" ) { + return state ? this.show() : this.hide(); + } + + return this.each( function() { + if ( isHiddenWithinTree( this ) ) { + jQuery( this ).show(); + } else { + jQuery( this ).hide(); + } + } ); + } +} ); +var rcheckableType = ( /^(?:checkbox|radio)$/i ); + +var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i ); + +var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i ); + + + +( function() { + var fragment = document.createDocumentFragment(), + div = fragment.appendChild( document.createElement( "div" ) ), + input = document.createElement( "input" ); + + // Support: Android 4.0 - 4.3 only + // Check state lost if the name is set (#11217) + // Support: Windows Web Apps (WWA) + // `name` and `type` must use .setAttribute for WWA (#14901) + input.setAttribute( "type", "radio" ); + input.setAttribute( "checked", "checked" ); + input.setAttribute( "name", "t" ); + + div.appendChild( input ); + + // Support: Android <=4.1 only + // Older WebKit doesn't clone checked state correctly in fragments + support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; + + // Support: IE <=11 only + // Make sure textarea (and checkbox) defaultValue is properly cloned + div.innerHTML = ""; + support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; + + // Support: IE <=9 only + // IE <=9 replaces "; + support.option = !!div.lastChild; +} )(); + + +// We have to close these tags to support XHTML (#13200) +var wrapMap = { + + // XHTML parsers do not magically insert elements in the + // same way that tag soup parsers do. So we cannot shorten + // this by omitting or other required elements. + thead: [ 1, "", "
    " ], + col: [ 2, "", "
    " ], + tr: [ 2, "", "
    " ], + td: [ 3, "", "
    " ], + + _default: [ 0, "", "" ] +}; + +wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; +wrapMap.th = wrapMap.td; + +// Support: IE <=9 only +if ( !support.option ) { + wrapMap.optgroup = wrapMap.option = [ 1, "" ]; +} + + +function getAll( context, tag ) { + + // Support: IE <=9 - 11 only + // Use typeof to avoid zero-argument method invocation on host objects (#15151) + var ret; + + if ( typeof context.getElementsByTagName !== "undefined" ) { + ret = context.getElementsByTagName( tag || "*" ); + + } else if ( typeof context.querySelectorAll !== "undefined" ) { + ret = context.querySelectorAll( tag || "*" ); + + } else { + ret = []; + } + + if ( tag === undefined || tag && nodeName( context, tag ) ) { + return jQuery.merge( [ context ], ret ); + } + + return ret; +} + + +// Mark scripts as having already been evaluated +function setGlobalEval( elems, refElements ) { + var i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + dataPriv.set( + elems[ i ], + "globalEval", + !refElements || dataPriv.get( refElements[ i ], "globalEval" ) + ); + } +} + + +var rhtml = /<|&#?\w+;/; + +function buildFragment( elems, context, scripts, selection, ignored ) { + var elem, tmp, tag, wrap, attached, j, + fragment = context.createDocumentFragment(), + nodes = [], + i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + elem = elems[ i ]; + + if ( elem || elem === 0 ) { + + // Add nodes directly + if ( toType( elem ) === "object" ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); + + // Convert non-html into a text node + } else if ( !rhtml.test( elem ) ) { + nodes.push( context.createTextNode( elem ) ); + + // Convert html into DOM nodes + } else { + tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); + + // Deserialize a standard representation + tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); + wrap = wrapMap[ tag ] || wrapMap._default; + tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; + + // Descend through wrappers to the right content + j = wrap[ 0 ]; + while ( j-- ) { + tmp = tmp.lastChild; + } + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, tmp.childNodes ); + + // Remember the top-level container + tmp = fragment.firstChild; + + // Ensure the created nodes are orphaned (#12392) + tmp.textContent = ""; + } + } + } + + // Remove wrapper from fragment + fragment.textContent = ""; + + i = 0; + while ( ( elem = nodes[ i++ ] ) ) { + + // Skip elements already in the context collection (trac-4087) + if ( selection && jQuery.inArray( elem, selection ) > -1 ) { + if ( ignored ) { + ignored.push( elem ); + } + continue; + } + + attached = isAttached( elem ); + + // Append to fragment + tmp = getAll( fragment.appendChild( elem ), "script" ); + + // Preserve script evaluation history + if ( attached ) { + setGlobalEval( tmp ); + } + + // Capture executables + if ( scripts ) { + j = 0; + while ( ( elem = tmp[ j++ ] ) ) { + if ( rscriptType.test( elem.type || "" ) ) { + scripts.push( elem ); + } + } + } + } + + return fragment; +} + + +var rtypenamespace = /^([^.]*)(?:\.(.+)|)/; + +function returnTrue() { + return true; +} + +function returnFalse() { + return false; +} + +// Support: IE <=9 - 11+ +// focus() and blur() are asynchronous, except when they are no-op. +// So expect focus to be synchronous when the element is already active, +// and blur to be synchronous when the element is not already active. +// (focus and blur are always synchronous in other supported browsers, +// this just defines when we can count on it). +function expectSync( elem, type ) { + return ( elem === safeActiveElement() ) === ( type === "focus" ); +} + +// Support: IE <=9 only +// Accessing document.activeElement can throw unexpectedly +// https://bugs.jquery.com/ticket/13393 +function safeActiveElement() { + try { + return document.activeElement; + } catch ( err ) { } +} + +function on( elem, types, selector, data, fn, one ) { + var origFn, type; + + // Types can be a map of types/handlers + if ( typeof types === "object" ) { + + // ( types-Object, selector, data ) + if ( typeof selector !== "string" ) { + + // ( types-Object, data ) + data = data || selector; + selector = undefined; + } + for ( type in types ) { + on( elem, type, selector, data, types[ type ], one ); + } + return elem; + } + + if ( data == null && fn == null ) { + + // ( types, fn ) + fn = selector; + data = selector = undefined; + } else if ( fn == null ) { + if ( typeof selector === "string" ) { + + // ( types, selector, fn ) + fn = data; + data = undefined; + } else { + + // ( types, data, fn ) + fn = data; + data = selector; + selector = undefined; + } + } + if ( fn === false ) { + fn = returnFalse; + } else if ( !fn ) { + return elem; + } + + if ( one === 1 ) { + origFn = fn; + fn = function( event ) { + + // Can use an empty set, since event contains the info + jQuery().off( event ); + return origFn.apply( this, arguments ); + }; + + // Use same guid so caller can remove using origFn + fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); + } + return elem.each( function() { + jQuery.event.add( this, types, fn, data, selector ); + } ); +} + +/* + * Helper functions for managing events -- not part of the public interface. + * Props to Dean Edwards' addEvent library for many of the ideas. + */ +jQuery.event = { + + global: {}, + + add: function( elem, types, handler, data, selector ) { + + var handleObjIn, eventHandle, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.get( elem ); + + // Only attach events to objects that accept data + if ( !acceptData( elem ) ) { + return; + } + + // Caller can pass in an object of custom data in lieu of the handler + if ( handler.handler ) { + handleObjIn = handler; + handler = handleObjIn.handler; + selector = handleObjIn.selector; + } + + // Ensure that invalid selectors throw exceptions at attach time + // Evaluate against documentElement in case elem is a non-element node (e.g., document) + if ( selector ) { + jQuery.find.matchesSelector( documentElement, selector ); + } + + // Make sure that the handler has a unique ID, used to find/remove it later + if ( !handler.guid ) { + handler.guid = jQuery.guid++; + } + + // Init the element's event structure and main handler, if this is the first + if ( !( events = elemData.events ) ) { + events = elemData.events = Object.create( null ); + } + if ( !( eventHandle = elemData.handle ) ) { + eventHandle = elemData.handle = function( e ) { + + // Discard the second event of a jQuery.event.trigger() and + // when an event is called after a page has unloaded + return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? + jQuery.event.dispatch.apply( elem, arguments ) : undefined; + }; + } + + // Handle multiple events separated by a space + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // There *must* be a type, no attaching namespace-only handlers + if ( !type ) { + continue; + } + + // If event changes its type, use the special event handlers for the changed type + special = jQuery.event.special[ type ] || {}; + + // If selector defined, determine special event api type, otherwise given type + type = ( selector ? special.delegateType : special.bindType ) || type; + + // Update special based on newly reset type + special = jQuery.event.special[ type ] || {}; + + // handleObj is passed to all event handlers + handleObj = jQuery.extend( { + type: type, + origType: origType, + data: data, + handler: handler, + guid: handler.guid, + selector: selector, + needsContext: selector && jQuery.expr.match.needsContext.test( selector ), + namespace: namespaces.join( "." ) + }, handleObjIn ); + + // Init the event handler queue if we're the first + if ( !( handlers = events[ type ] ) ) { + handlers = events[ type ] = []; + handlers.delegateCount = 0; + + // Only use addEventListener if the special events handler returns false + if ( !special.setup || + special.setup.call( elem, data, namespaces, eventHandle ) === false ) { + + if ( elem.addEventListener ) { + elem.addEventListener( type, eventHandle ); + } + } + } + + if ( special.add ) { + special.add.call( elem, handleObj ); + + if ( !handleObj.handler.guid ) { + handleObj.handler.guid = handler.guid; + } + } + + // Add to the element's handler list, delegates in front + if ( selector ) { + handlers.splice( handlers.delegateCount++, 0, handleObj ); + } else { + handlers.push( handleObj ); + } + + // Keep track of which events have ever been used, for event optimization + jQuery.event.global[ type ] = true; + } + + }, + + // Detach an event or set of events from an element + remove: function( elem, types, handler, selector, mappedTypes ) { + + var j, origCount, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); + + if ( !elemData || !( events = elemData.events ) ) { + return; + } + + // Once for each type.namespace in types; type may be omitted + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // Unbind all events (on this namespace, if provided) for the element + if ( !type ) { + for ( type in events ) { + jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); + } + continue; + } + + special = jQuery.event.special[ type ] || {}; + type = ( selector ? special.delegateType : special.bindType ) || type; + handlers = events[ type ] || []; + tmp = tmp[ 2 ] && + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); + + // Remove matching events + origCount = j = handlers.length; + while ( j-- ) { + handleObj = handlers[ j ]; + + if ( ( mappedTypes || origType === handleObj.origType ) && + ( !handler || handler.guid === handleObj.guid ) && + ( !tmp || tmp.test( handleObj.namespace ) ) && + ( !selector || selector === handleObj.selector || + selector === "**" && handleObj.selector ) ) { + handlers.splice( j, 1 ); + + if ( handleObj.selector ) { + handlers.delegateCount--; + } + if ( special.remove ) { + special.remove.call( elem, handleObj ); + } + } + } + + // Remove generic event handler if we removed something and no more handlers exist + // (avoids potential for endless recursion during removal of special event handlers) + if ( origCount && !handlers.length ) { + if ( !special.teardown || + special.teardown.call( elem, namespaces, elemData.handle ) === false ) { + + jQuery.removeEvent( elem, type, elemData.handle ); + } + + delete events[ type ]; + } + } + + // Remove data and the expando if it's no longer used + if ( jQuery.isEmptyObject( events ) ) { + dataPriv.remove( elem, "handle events" ); + } + }, + + dispatch: function( nativeEvent ) { + + var i, j, ret, matched, handleObj, handlerQueue, + args = new Array( arguments.length ), + + // Make a writable jQuery.Event from the native event object + event = jQuery.event.fix( nativeEvent ), + + handlers = ( + dataPriv.get( this, "events" ) || Object.create( null ) + )[ event.type ] || [], + special = jQuery.event.special[ event.type ] || {}; + + // Use the fix-ed jQuery.Event rather than the (read-only) native event + args[ 0 ] = event; + + for ( i = 1; i < arguments.length; i++ ) { + args[ i ] = arguments[ i ]; + } + + event.delegateTarget = this; + + // Call the preDispatch hook for the mapped type, and let it bail if desired + if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { + return; + } + + // Determine handlers + handlerQueue = jQuery.event.handlers.call( this, event, handlers ); + + // Run delegates first; they may want to stop propagation beneath us + i = 0; + while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { + event.currentTarget = matched.elem; + + j = 0; + while ( ( handleObj = matched.handlers[ j++ ] ) && + !event.isImmediatePropagationStopped() ) { + + // If the event is namespaced, then each handler is only invoked if it is + // specially universal or its namespaces are a superset of the event's. + if ( !event.rnamespace || handleObj.namespace === false || + event.rnamespace.test( handleObj.namespace ) ) { + + event.handleObj = handleObj; + event.data = handleObj.data; + + ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || + handleObj.handler ).apply( matched.elem, args ); + + if ( ret !== undefined ) { + if ( ( event.result = ret ) === false ) { + event.preventDefault(); + event.stopPropagation(); + } + } + } + } + } + + // Call the postDispatch hook for the mapped type + if ( special.postDispatch ) { + special.postDispatch.call( this, event ); + } + + return event.result; + }, + + handlers: function( event, handlers ) { + var i, handleObj, sel, matchedHandlers, matchedSelectors, + handlerQueue = [], + delegateCount = handlers.delegateCount, + cur = event.target; + + // Find delegate handlers + if ( delegateCount && + + // Support: IE <=9 + // Black-hole SVG instance trees (trac-13180) + cur.nodeType && + + // Support: Firefox <=42 + // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) + // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click + // Support: IE 11 only + // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) + !( event.type === "click" && event.button >= 1 ) ) { + + for ( ; cur !== this; cur = cur.parentNode || this ) { + + // Don't check non-elements (#13208) + // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) + if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { + matchedHandlers = []; + matchedSelectors = {}; + for ( i = 0; i < delegateCount; i++ ) { + handleObj = handlers[ i ]; + + // Don't conflict with Object.prototype properties (#13203) + sel = handleObj.selector + " "; + + if ( matchedSelectors[ sel ] === undefined ) { + matchedSelectors[ sel ] = handleObj.needsContext ? + jQuery( sel, this ).index( cur ) > -1 : + jQuery.find( sel, this, null, [ cur ] ).length; + } + if ( matchedSelectors[ sel ] ) { + matchedHandlers.push( handleObj ); + } + } + if ( matchedHandlers.length ) { + handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); + } + } + } + } + + // Add the remaining (directly-bound) handlers + cur = this; + if ( delegateCount < handlers.length ) { + handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); + } + + return handlerQueue; + }, + + addProp: function( name, hook ) { + Object.defineProperty( jQuery.Event.prototype, name, { + enumerable: true, + configurable: true, + + get: isFunction( hook ) ? + function() { + if ( this.originalEvent ) { + return hook( this.originalEvent ); + } + } : + function() { + if ( this.originalEvent ) { + return this.originalEvent[ name ]; + } + }, + + set: function( value ) { + Object.defineProperty( this, name, { + enumerable: true, + configurable: true, + writable: true, + value: value + } ); + } + } ); + }, + + fix: function( originalEvent ) { + return originalEvent[ jQuery.expando ] ? + originalEvent : + new jQuery.Event( originalEvent ); + }, + + special: { + load: { + + // Prevent triggered image.load events from bubbling to window.load + noBubble: true + }, + click: { + + // Utilize native event to ensure correct state for checkable inputs + setup: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Claim the first handler + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + // dataPriv.set( el, "click", ... ) + leverageNative( el, "click", returnTrue ); + } + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Force setup before triggering a click + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + leverageNative( el, "click" ); + } + + // Return non-false to allow normal event-path propagation + return true; + }, + + // For cross-browser consistency, suppress native .click() on links + // Also prevent it if we're currently inside a leveraged native-event stack + _default: function( event ) { + var target = event.target; + return rcheckableType.test( target.type ) && + target.click && nodeName( target, "input" ) && + dataPriv.get( target, "click" ) || + nodeName( target, "a" ); + } + }, + + beforeunload: { + postDispatch: function( event ) { + + // Support: Firefox 20+ + // Firefox doesn't alert if the returnValue field is not set. + if ( event.result !== undefined && event.originalEvent ) { + event.originalEvent.returnValue = event.result; + } + } + } + } +}; + +// Ensure the presence of an event listener that handles manually-triggered +// synthetic events by interrupting progress until reinvoked in response to +// *native* events that it fires directly, ensuring that state changes have +// already occurred before other listeners are invoked. +function leverageNative( el, type, expectSync ) { + + // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add + if ( !expectSync ) { + if ( dataPriv.get( el, type ) === undefined ) { + jQuery.event.add( el, type, returnTrue ); + } + return; + } + + // Register the controller as a special universal handler for all event namespaces + dataPriv.set( el, type, false ); + jQuery.event.add( el, type, { + namespace: false, + handler: function( event ) { + var notAsync, result, + saved = dataPriv.get( this, type ); + + if ( ( event.isTrigger & 1 ) && this[ type ] ) { + + // Interrupt processing of the outer synthetic .trigger()ed event + // Saved data should be false in such cases, but might be a leftover capture object + // from an async native handler (gh-4350) + if ( !saved.length ) { + + // Store arguments for use when handling the inner native event + // There will always be at least one argument (an event object), so this array + // will not be confused with a leftover capture object. + saved = slice.call( arguments ); + dataPriv.set( this, type, saved ); + + // Trigger the native event and capture its result + // Support: IE <=9 - 11+ + // focus() and blur() are asynchronous + notAsync = expectSync( this, type ); + this[ type ](); + result = dataPriv.get( this, type ); + if ( saved !== result || notAsync ) { + dataPriv.set( this, type, false ); + } else { + result = {}; + } + if ( saved !== result ) { + + // Cancel the outer synthetic event + event.stopImmediatePropagation(); + event.preventDefault(); + + // Support: Chrome 86+ + // In Chrome, if an element having a focusout handler is blurred by + // clicking outside of it, it invokes the handler synchronously. If + // that handler calls `.remove()` on the element, the data is cleared, + // leaving `result` undefined. We need to guard against this. + return result && result.value; + } + + // If this is an inner synthetic event for an event with a bubbling surrogate + // (focus or blur), assume that the surrogate already propagated from triggering the + // native event and prevent that from happening again here. + // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the + // bubbling surrogate propagates *after* the non-bubbling base), but that seems + // less bad than duplication. + } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) { + event.stopPropagation(); + } + + // If this is a native event triggered above, everything is now in order + // Fire an inner synthetic event with the original arguments + } else if ( saved.length ) { + + // ...and capture the result + dataPriv.set( this, type, { + value: jQuery.event.trigger( + + // Support: IE <=9 - 11+ + // Extend with the prototype to reset the above stopImmediatePropagation() + jQuery.extend( saved[ 0 ], jQuery.Event.prototype ), + saved.slice( 1 ), + this + ) + } ); + + // Abort handling of the native event + event.stopImmediatePropagation(); + } + } + } ); +} + +jQuery.removeEvent = function( elem, type, handle ) { + + // This "if" is needed for plain objects + if ( elem.removeEventListener ) { + elem.removeEventListener( type, handle ); + } +}; + +jQuery.Event = function( src, props ) { + + // Allow instantiation without the 'new' keyword + if ( !( this instanceof jQuery.Event ) ) { + return new jQuery.Event( src, props ); + } + + // Event object + if ( src && src.type ) { + this.originalEvent = src; + this.type = src.type; + + // Events bubbling up the document may have been marked as prevented + // by a handler lower down the tree; reflect the correct value. + this.isDefaultPrevented = src.defaultPrevented || + src.defaultPrevented === undefined && + + // Support: Android <=2.3 only + src.returnValue === false ? + returnTrue : + returnFalse; + + // Create target properties + // Support: Safari <=6 - 7 only + // Target should not be a text node (#504, #13143) + this.target = ( src.target && src.target.nodeType === 3 ) ? + src.target.parentNode : + src.target; + + this.currentTarget = src.currentTarget; + this.relatedTarget = src.relatedTarget; + + // Event type + } else { + this.type = src; + } + + // Put explicitly provided properties onto the event object + if ( props ) { + jQuery.extend( this, props ); + } + + // Create a timestamp if incoming event doesn't have one + this.timeStamp = src && src.timeStamp || Date.now(); + + // Mark it as fixed + this[ jQuery.expando ] = true; +}; + +// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding +// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html +jQuery.Event.prototype = { + constructor: jQuery.Event, + isDefaultPrevented: returnFalse, + isPropagationStopped: returnFalse, + isImmediatePropagationStopped: returnFalse, + isSimulated: false, + + preventDefault: function() { + var e = this.originalEvent; + + this.isDefaultPrevented = returnTrue; + + if ( e && !this.isSimulated ) { + e.preventDefault(); + } + }, + stopPropagation: function() { + var e = this.originalEvent; + + this.isPropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopPropagation(); + } + }, + stopImmediatePropagation: function() { + var e = this.originalEvent; + + this.isImmediatePropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopImmediatePropagation(); + } + + this.stopPropagation(); + } +}; + +// Includes all common event props including KeyEvent and MouseEvent specific props +jQuery.each( { + altKey: true, + bubbles: true, + cancelable: true, + changedTouches: true, + ctrlKey: true, + detail: true, + eventPhase: true, + metaKey: true, + pageX: true, + pageY: true, + shiftKey: true, + view: true, + "char": true, + code: true, + charCode: true, + key: true, + keyCode: true, + button: true, + buttons: true, + clientX: true, + clientY: true, + offsetX: true, + offsetY: true, + pointerId: true, + pointerType: true, + screenX: true, + screenY: true, + targetTouches: true, + toElement: true, + touches: true, + which: true +}, jQuery.event.addProp ); + +jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) { + jQuery.event.special[ type ] = { + + // Utilize native event if possible so blur/focus sequence is correct + setup: function() { + + // Claim the first handler + // dataPriv.set( this, "focus", ... ) + // dataPriv.set( this, "blur", ... ) + leverageNative( this, type, expectSync ); + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function() { + + // Force setup before trigger + leverageNative( this, type ); + + // Return non-false to allow normal event-path propagation + return true; + }, + + // Suppress native focus or blur as it's already being fired + // in leverageNative. + _default: function() { + return true; + }, + + delegateType: delegateType + }; +} ); + +// Create mouseenter/leave events using mouseover/out and event-time checks +// so that event delegation works in jQuery. +// Do the same for pointerenter/pointerleave and pointerover/pointerout +// +// Support: Safari 7 only +// Safari sends mouseenter too often; see: +// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 +// for the description of the bug (it existed in older Chrome versions as well). +jQuery.each( { + mouseenter: "mouseover", + mouseleave: "mouseout", + pointerenter: "pointerover", + pointerleave: "pointerout" +}, function( orig, fix ) { + jQuery.event.special[ orig ] = { + delegateType: fix, + bindType: fix, + + handle: function( event ) { + var ret, + target = this, + related = event.relatedTarget, + handleObj = event.handleObj; + + // For mouseenter/leave call the handler if related is outside the target. + // NB: No relatedTarget if the mouse left/entered the browser window + if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { + event.type = handleObj.origType; + ret = handleObj.handler.apply( this, arguments ); + event.type = fix; + } + return ret; + } + }; +} ); + +jQuery.fn.extend( { + + on: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn ); + }, + one: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn, 1 ); + }, + off: function( types, selector, fn ) { + var handleObj, type; + if ( types && types.preventDefault && types.handleObj ) { + + // ( event ) dispatched jQuery.Event + handleObj = types.handleObj; + jQuery( types.delegateTarget ).off( + handleObj.namespace ? + handleObj.origType + "." + handleObj.namespace : + handleObj.origType, + handleObj.selector, + handleObj.handler + ); + return this; + } + if ( typeof types === "object" ) { + + // ( types-object [, selector] ) + for ( type in types ) { + this.off( type, selector, types[ type ] ); + } + return this; + } + if ( selector === false || typeof selector === "function" ) { + + // ( types [, fn] ) + fn = selector; + selector = undefined; + } + if ( fn === false ) { + fn = returnFalse; + } + return this.each( function() { + jQuery.event.remove( this, types, fn, selector ); + } ); + } +} ); + + +var + + // Support: IE <=10 - 11, Edge 12 - 13 only + // In IE/Edge using regex groups here causes severe slowdowns. + // See https://connect.microsoft.com/IE/feedback/details/1736512/ + rnoInnerhtml = /\s*$/g; + +// Prefer a tbody over its parent table for containing new rows +function manipulationTarget( elem, content ) { + if ( nodeName( elem, "table" ) && + nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { + + return jQuery( elem ).children( "tbody" )[ 0 ] || elem; + } + + return elem; +} + +// Replace/restore the type attribute of script elements for safe DOM manipulation +function disableScript( elem ) { + elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; + return elem; +} +function restoreScript( elem ) { + if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) { + elem.type = elem.type.slice( 5 ); + } else { + elem.removeAttribute( "type" ); + } + + return elem; +} + +function cloneCopyEvent( src, dest ) { + var i, l, type, pdataOld, udataOld, udataCur, events; + + if ( dest.nodeType !== 1 ) { + return; + } + + // 1. Copy private data: events, handlers, etc. + if ( dataPriv.hasData( src ) ) { + pdataOld = dataPriv.get( src ); + events = pdataOld.events; + + if ( events ) { + dataPriv.remove( dest, "handle events" ); + + for ( type in events ) { + for ( i = 0, l = events[ type ].length; i < l; i++ ) { + jQuery.event.add( dest, type, events[ type ][ i ] ); + } + } + } + } + + // 2. Copy user data + if ( dataUser.hasData( src ) ) { + udataOld = dataUser.access( src ); + udataCur = jQuery.extend( {}, udataOld ); + + dataUser.set( dest, udataCur ); + } +} + +// Fix IE bugs, see support tests +function fixInput( src, dest ) { + var nodeName = dest.nodeName.toLowerCase(); + + // Fails to persist the checked state of a cloned checkbox or radio button. + if ( nodeName === "input" && rcheckableType.test( src.type ) ) { + dest.checked = src.checked; + + // Fails to return the selected option to the default selected state when cloning options + } else if ( nodeName === "input" || nodeName === "textarea" ) { + dest.defaultValue = src.defaultValue; + } +} + +function domManip( collection, args, callback, ignored ) { + + // Flatten any nested arrays + args = flat( args ); + + var fragment, first, scripts, hasScripts, node, doc, + i = 0, + l = collection.length, + iNoClone = l - 1, + value = args[ 0 ], + valueIsFunction = isFunction( value ); + + // We can't cloneNode fragments that contain checked, in WebKit + if ( valueIsFunction || + ( l > 1 && typeof value === "string" && + !support.checkClone && rchecked.test( value ) ) ) { + return collection.each( function( index ) { + var self = collection.eq( index ); + if ( valueIsFunction ) { + args[ 0 ] = value.call( this, index, self.html() ); + } + domManip( self, args, callback, ignored ); + } ); + } + + if ( l ) { + fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); + first = fragment.firstChild; + + if ( fragment.childNodes.length === 1 ) { + fragment = first; + } + + // Require either new content or an interest in ignored elements to invoke the callback + if ( first || ignored ) { + scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); + hasScripts = scripts.length; + + // Use the original fragment for the last item + // instead of the first because it can end up + // being emptied incorrectly in certain situations (#8070). + for ( ; i < l; i++ ) { + node = fragment; + + if ( i !== iNoClone ) { + node = jQuery.clone( node, true, true ); + + // Keep references to cloned scripts for later restoration + if ( hasScripts ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( scripts, getAll( node, "script" ) ); + } + } + + callback.call( collection[ i ], node, i ); + } + + if ( hasScripts ) { + doc = scripts[ scripts.length - 1 ].ownerDocument; + + // Reenable scripts + jQuery.map( scripts, restoreScript ); + + // Evaluate executable scripts on first document insertion + for ( i = 0; i < hasScripts; i++ ) { + node = scripts[ i ]; + if ( rscriptType.test( node.type || "" ) && + !dataPriv.access( node, "globalEval" ) && + jQuery.contains( doc, node ) ) { + + if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) { + + // Optional AJAX dependency, but won't run scripts if not present + if ( jQuery._evalUrl && !node.noModule ) { + jQuery._evalUrl( node.src, { + nonce: node.nonce || node.getAttribute( "nonce" ) + }, doc ); + } + } else { + DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc ); + } + } + } + } + } + } + + return collection; +} + +function remove( elem, selector, keepData ) { + var node, + nodes = selector ? jQuery.filter( selector, elem ) : elem, + i = 0; + + for ( ; ( node = nodes[ i ] ) != null; i++ ) { + if ( !keepData && node.nodeType === 1 ) { + jQuery.cleanData( getAll( node ) ); + } + + if ( node.parentNode ) { + if ( keepData && isAttached( node ) ) { + setGlobalEval( getAll( node, "script" ) ); + } + node.parentNode.removeChild( node ); + } + } + + return elem; +} + +jQuery.extend( { + htmlPrefilter: function( html ) { + return html; + }, + + clone: function( elem, dataAndEvents, deepDataAndEvents ) { + var i, l, srcElements, destElements, + clone = elem.cloneNode( true ), + inPage = isAttached( elem ); + + // Fix IE cloning issues + if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && + !jQuery.isXMLDoc( elem ) ) { + + // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 + destElements = getAll( clone ); + srcElements = getAll( elem ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + fixInput( srcElements[ i ], destElements[ i ] ); + } + } + + // Copy the events from the original to the clone + if ( dataAndEvents ) { + if ( deepDataAndEvents ) { + srcElements = srcElements || getAll( elem ); + destElements = destElements || getAll( clone ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + cloneCopyEvent( srcElements[ i ], destElements[ i ] ); + } + } else { + cloneCopyEvent( elem, clone ); + } + } + + // Preserve script evaluation history + destElements = getAll( clone, "script" ); + if ( destElements.length > 0 ) { + setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); + } + + // Return the cloned set + return clone; + }, + + cleanData: function( elems ) { + var data, elem, type, + special = jQuery.event.special, + i = 0; + + for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { + if ( acceptData( elem ) ) { + if ( ( data = elem[ dataPriv.expando ] ) ) { + if ( data.events ) { + for ( type in data.events ) { + if ( special[ type ] ) { + jQuery.event.remove( elem, type ); + + // This is a shortcut to avoid jQuery.event.remove's overhead + } else { + jQuery.removeEvent( elem, type, data.handle ); + } + } + } + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataPriv.expando ] = undefined; + } + if ( elem[ dataUser.expando ] ) { + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataUser.expando ] = undefined; + } + } + } + } +} ); + +jQuery.fn.extend( { + detach: function( selector ) { + return remove( this, selector, true ); + }, + + remove: function( selector ) { + return remove( this, selector ); + }, + + text: function( value ) { + return access( this, function( value ) { + return value === undefined ? + jQuery.text( this ) : + this.empty().each( function() { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + this.textContent = value; + } + } ); + }, null, value, arguments.length ); + }, + + append: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.appendChild( elem ); + } + } ); + }, + + prepend: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.insertBefore( elem, target.firstChild ); + } + } ); + }, + + before: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this ); + } + } ); + }, + + after: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this.nextSibling ); + } + } ); + }, + + empty: function() { + var elem, + i = 0; + + for ( ; ( elem = this[ i ] ) != null; i++ ) { + if ( elem.nodeType === 1 ) { + + // Prevent memory leaks + jQuery.cleanData( getAll( elem, false ) ); + + // Remove any remaining nodes + elem.textContent = ""; + } + } + + return this; + }, + + clone: function( dataAndEvents, deepDataAndEvents ) { + dataAndEvents = dataAndEvents == null ? false : dataAndEvents; + deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; + + return this.map( function() { + return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); + } ); + }, + + html: function( value ) { + return access( this, function( value ) { + var elem = this[ 0 ] || {}, + i = 0, + l = this.length; + + if ( value === undefined && elem.nodeType === 1 ) { + return elem.innerHTML; + } + + // See if we can take a shortcut and just use innerHTML + if ( typeof value === "string" && !rnoInnerhtml.test( value ) && + !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { + + value = jQuery.htmlPrefilter( value ); + + try { + for ( ; i < l; i++ ) { + elem = this[ i ] || {}; + + // Remove element nodes and prevent memory leaks + if ( elem.nodeType === 1 ) { + jQuery.cleanData( getAll( elem, false ) ); + elem.innerHTML = value; + } + } + + elem = 0; + + // If using innerHTML throws an exception, use the fallback method + } catch ( e ) {} + } + + if ( elem ) { + this.empty().append( value ); + } + }, null, value, arguments.length ); + }, + + replaceWith: function() { + var ignored = []; + + // Make the changes, replacing each non-ignored context element with the new content + return domManip( this, arguments, function( elem ) { + var parent = this.parentNode; + + if ( jQuery.inArray( this, ignored ) < 0 ) { + jQuery.cleanData( getAll( this ) ); + if ( parent ) { + parent.replaceChild( elem, this ); + } + } + + // Force callback invocation + }, ignored ); + } +} ); + +jQuery.each( { + appendTo: "append", + prependTo: "prepend", + insertBefore: "before", + insertAfter: "after", + replaceAll: "replaceWith" +}, function( name, original ) { + jQuery.fn[ name ] = function( selector ) { + var elems, + ret = [], + insert = jQuery( selector ), + last = insert.length - 1, + i = 0; + + for ( ; i <= last; i++ ) { + elems = i === last ? this : this.clone( true ); + jQuery( insert[ i ] )[ original ]( elems ); + + // Support: Android <=4.0 only, PhantomJS 1 only + // .get() because push.apply(_, arraylike) throws on ancient WebKit + push.apply( ret, elems.get() ); + } + + return this.pushStack( ret ); + }; +} ); +var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); + +var getStyles = function( elem ) { + + // Support: IE <=11 only, Firefox <=30 (#15098, #14150) + // IE throws on elements created in popups + // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" + var view = elem.ownerDocument.defaultView; + + if ( !view || !view.opener ) { + view = window; + } + + return view.getComputedStyle( elem ); + }; + +var swap = function( elem, options, callback ) { + var ret, name, + old = {}; + + // Remember the old values, and insert the new ones + for ( name in options ) { + old[ name ] = elem.style[ name ]; + elem.style[ name ] = options[ name ]; + } + + ret = callback.call( elem ); + + // Revert the old values + for ( name in options ) { + elem.style[ name ] = old[ name ]; + } + + return ret; +}; + + +var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); + + + +( function() { + + // Executing both pixelPosition & boxSizingReliable tests require only one layout + // so they're executed at the same time to save the second computation. + function computeStyleTests() { + + // This is a singleton, we need to execute it only once + if ( !div ) { + return; + } + + container.style.cssText = "position:absolute;left:-11111px;width:60px;" + + "margin-top:1px;padding:0;border:0"; + div.style.cssText = + "position:relative;display:block;box-sizing:border-box;overflow:scroll;" + + "margin:auto;border:1px;padding:1px;" + + "width:60%;top:1%"; + documentElement.appendChild( container ).appendChild( div ); + + var divStyle = window.getComputedStyle( div ); + pixelPositionVal = divStyle.top !== "1%"; + + // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 + reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12; + + // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3 + // Some styles come back with percentage values, even though they shouldn't + div.style.right = "60%"; + pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36; + + // Support: IE 9 - 11 only + // Detect misreporting of content dimensions for box-sizing:border-box elements + boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36; + + // Support: IE 9 only + // Detect overflow:scroll screwiness (gh-3699) + // Support: Chrome <=64 + // Don't get tricked when zoom affects offsetWidth (gh-4029) + div.style.position = "absolute"; + scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12; + + documentElement.removeChild( container ); + + // Nullify the div so it wouldn't be stored in the memory and + // it will also be a sign that checks already performed + div = null; + } + + function roundPixelMeasures( measure ) { + return Math.round( parseFloat( measure ) ); + } + + var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal, + reliableTrDimensionsVal, reliableMarginLeftVal, + container = document.createElement( "div" ), + div = document.createElement( "div" ); + + // Finish early in limited (non-browser) environments + if ( !div.style ) { + return; + } + + // Support: IE <=9 - 11 only + // Style of cloned element affects source element cloned (#8908) + div.style.backgroundClip = "content-box"; + div.cloneNode( true ).style.backgroundClip = ""; + support.clearCloneStyle = div.style.backgroundClip === "content-box"; + + jQuery.extend( support, { + boxSizingReliable: function() { + computeStyleTests(); + return boxSizingReliableVal; + }, + pixelBoxStyles: function() { + computeStyleTests(); + return pixelBoxStylesVal; + }, + pixelPosition: function() { + computeStyleTests(); + return pixelPositionVal; + }, + reliableMarginLeft: function() { + computeStyleTests(); + return reliableMarginLeftVal; + }, + scrollboxSize: function() { + computeStyleTests(); + return scrollboxSizeVal; + }, + + // Support: IE 9 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Behavior in IE 9 is more subtle than in newer versions & it passes + // some versions of this test; make sure not to make it pass there! + // + // Support: Firefox 70+ + // Only Firefox includes border widths + // in computed dimensions. (gh-4529) + reliableTrDimensions: function() { + var table, tr, trChild, trStyle; + if ( reliableTrDimensionsVal == null ) { + table = document.createElement( "table" ); + tr = document.createElement( "tr" ); + trChild = document.createElement( "div" ); + + table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate"; + tr.style.cssText = "border:1px solid"; + + // Support: Chrome 86+ + // Height set through cssText does not get applied. + // Computed height then comes back as 0. + tr.style.height = "1px"; + trChild.style.height = "9px"; + + // Support: Android 8 Chrome 86+ + // In our bodyBackground.html iframe, + // display for all div elements is set to "inline", + // which causes a problem only in Android 8 Chrome 86. + // Ensuring the div is display: block + // gets around this issue. + trChild.style.display = "block"; + + documentElement + .appendChild( table ) + .appendChild( tr ) + .appendChild( trChild ); + + trStyle = window.getComputedStyle( tr ); + reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) + + parseInt( trStyle.borderTopWidth, 10 ) + + parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight; + + documentElement.removeChild( table ); + } + return reliableTrDimensionsVal; + } + } ); +} )(); + + +function curCSS( elem, name, computed ) { + var width, minWidth, maxWidth, ret, + + // Support: Firefox 51+ + // Retrieving style before computed somehow + // fixes an issue with getting wrong values + // on detached elements + style = elem.style; + + computed = computed || getStyles( elem ); + + // getPropertyValue is needed for: + // .css('filter') (IE 9 only, #12537) + // .css('--customProperty) (#3144) + if ( computed ) { + ret = computed.getPropertyValue( name ) || computed[ name ]; + + if ( ret === "" && !isAttached( elem ) ) { + ret = jQuery.style( elem, name ); + } + + // A tribute to the "awesome hack by Dean Edwards" + // Android Browser returns percentage for some values, + // but width seems to be reliably pixels. + // This is against the CSSOM draft spec: + // https://drafts.csswg.org/cssom/#resolved-values + if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) { + + // Remember the original values + width = style.width; + minWidth = style.minWidth; + maxWidth = style.maxWidth; + + // Put in the new values to get a computed value out + style.minWidth = style.maxWidth = style.width = ret; + ret = computed.width; + + // Revert the changed values + style.width = width; + style.minWidth = minWidth; + style.maxWidth = maxWidth; + } + } + + return ret !== undefined ? + + // Support: IE <=9 - 11 only + // IE returns zIndex value as an integer. + ret + "" : + ret; +} + + +function addGetHookIf( conditionFn, hookFn ) { + + // Define the hook, we'll check on the first run if it's really needed. + return { + get: function() { + if ( conditionFn() ) { + + // Hook not needed (or it's not possible to use it due + // to missing dependency), remove it. + delete this.get; + return; + } + + // Hook needed; redefine it so that the support test is not executed again. + return ( this.get = hookFn ).apply( this, arguments ); + } + }; +} + + +var cssPrefixes = [ "Webkit", "Moz", "ms" ], + emptyStyle = document.createElement( "div" ).style, + vendorProps = {}; + +// Return a vendor-prefixed property or undefined +function vendorPropName( name ) { + + // Check for vendor prefixed names + var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), + i = cssPrefixes.length; + + while ( i-- ) { + name = cssPrefixes[ i ] + capName; + if ( name in emptyStyle ) { + return name; + } + } +} + +// Return a potentially-mapped jQuery.cssProps or vendor prefixed property +function finalPropName( name ) { + var final = jQuery.cssProps[ name ] || vendorProps[ name ]; + + if ( final ) { + return final; + } + if ( name in emptyStyle ) { + return name; + } + return vendorProps[ name ] = vendorPropName( name ) || name; +} + + +var + + // Swappable if display is none or starts with table + // except "table", "table-cell", or "table-caption" + // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display + rdisplayswap = /^(none|table(?!-c[ea]).+)/, + rcustomProp = /^--/, + cssShow = { position: "absolute", visibility: "hidden", display: "block" }, + cssNormalTransform = { + letterSpacing: "0", + fontWeight: "400" + }; + +function setPositiveNumber( _elem, value, subtract ) { + + // Any relative (+/-) values have already been + // normalized at this point + var matches = rcssNum.exec( value ); + return matches ? + + // Guard against undefined "subtract", e.g., when used as in cssHooks + Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : + value; +} + +function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) { + var i = dimension === "width" ? 1 : 0, + extra = 0, + delta = 0; + + // Adjustment may not be necessary + if ( box === ( isBorderBox ? "border" : "content" ) ) { + return 0; + } + + for ( ; i < 4; i += 2 ) { + + // Both box models exclude margin + if ( box === "margin" ) { + delta += jQuery.css( elem, box + cssExpand[ i ], true, styles ); + } + + // If we get here with a content-box, we're seeking "padding" or "border" or "margin" + if ( !isBorderBox ) { + + // Add padding + delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + + // For "border" or "margin", add border + if ( box !== "padding" ) { + delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + + // But still keep track of it otherwise + } else { + extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + + // If we get here with a border-box (content + padding + border), we're seeking "content" or + // "padding" or "margin" + } else { + + // For "content", subtract padding + if ( box === "content" ) { + delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + } + + // For "content" or "padding", subtract border + if ( box !== "margin" ) { + delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + } + } + + // Account for positive content-box scroll gutter when requested by providing computedVal + if ( !isBorderBox && computedVal >= 0 ) { + + // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border + // Assuming integer scroll gutter, subtract the rest and round down + delta += Math.max( 0, Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + computedVal - + delta - + extra - + 0.5 + + // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter + // Use an explicit zero to avoid NaN (gh-3964) + ) ) || 0; + } + + return delta; +} + +function getWidthOrHeight( elem, dimension, extra ) { + + // Start with computed style + var styles = getStyles( elem ), + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322). + // Fake content-box until we know it's needed to know the true value. + boxSizingNeeded = !support.boxSizingReliable() || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + valueIsBorderBox = isBorderBox, + + val = curCSS( elem, dimension, styles ), + offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ); + + // Support: Firefox <=54 + // Return a confounding non-pixel value or feign ignorance, as appropriate. + if ( rnumnonpx.test( val ) ) { + if ( !extra ) { + return val; + } + val = "auto"; + } + + + // Support: IE 9 - 11 only + // Use offsetWidth/offsetHeight for when box sizing is unreliable. + // In those cases, the computed value can be trusted to be border-box. + if ( ( !support.boxSizingReliable() && isBorderBox || + + // Support: IE 10 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Interestingly, in some cases IE 9 doesn't suffer from this issue. + !support.reliableTrDimensions() && nodeName( elem, "tr" ) || + + // Fall back to offsetWidth/offsetHeight when value is "auto" + // This happens for inline elements with no explicit setting (gh-3571) + val === "auto" || + + // Support: Android <=4.1 - 4.3 only + // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602) + !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) && + + // Make sure the element is visible & connected + elem.getClientRects().length ) { + + isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; + + // Where available, offsetWidth/offsetHeight approximate border box dimensions. + // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the + // retrieved value as a content box dimension. + valueIsBorderBox = offsetProp in elem; + if ( valueIsBorderBox ) { + val = elem[ offsetProp ]; + } + } + + // Normalize "" and auto + val = parseFloat( val ) || 0; + + // Adjust for the element's box model + return ( val + + boxModelAdjustment( + elem, + dimension, + extra || ( isBorderBox ? "border" : "content" ), + valueIsBorderBox, + styles, + + // Provide the current computed size to request scroll gutter calculation (gh-3589) + val + ) + ) + "px"; +} + +jQuery.extend( { + + // Add in style property hooks for overriding the default + // behavior of getting and setting a style property + cssHooks: { + opacity: { + get: function( elem, computed ) { + if ( computed ) { + + // We should always get a number back from opacity + var ret = curCSS( elem, "opacity" ); + return ret === "" ? "1" : ret; + } + } + } + }, + + // Don't automatically add "px" to these possibly-unitless properties + cssNumber: { + "animationIterationCount": true, + "columnCount": true, + "fillOpacity": true, + "flexGrow": true, + "flexShrink": true, + "fontWeight": true, + "gridArea": true, + "gridColumn": true, + "gridColumnEnd": true, + "gridColumnStart": true, + "gridRow": true, + "gridRowEnd": true, + "gridRowStart": true, + "lineHeight": true, + "opacity": true, + "order": true, + "orphans": true, + "widows": true, + "zIndex": true, + "zoom": true + }, + + // Add in properties whose names you wish to fix before + // setting or getting the value + cssProps: {}, + + // Get and set the style property on a DOM Node + style: function( elem, name, value, extra ) { + + // Don't set styles on text and comment nodes + if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { + return; + } + + // Make sure that we're working with the right name + var ret, type, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ), + style = elem.style; + + // Make sure that we're working with the right name. We don't + // want to query the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Gets hook for the prefixed version, then unprefixed version + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // Check if we're setting a value + if ( value !== undefined ) { + type = typeof value; + + // Convert "+=" or "-=" to relative numbers (#7345) + if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { + value = adjustCSS( elem, name, ret ); + + // Fixes bug #9237 + type = "number"; + } + + // Make sure that null and NaN values aren't set (#7116) + if ( value == null || value !== value ) { + return; + } + + // If a number was passed in, add the unit (except for certain CSS properties) + // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append + // "px" to a few hardcoded values. + if ( type === "number" && !isCustomProp ) { + value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); + } + + // background-* props affect original clone's values + if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { + style[ name ] = "inherit"; + } + + // If a hook was provided, use that value, otherwise just set the specified value + if ( !hooks || !( "set" in hooks ) || + ( value = hooks.set( elem, value, extra ) ) !== undefined ) { + + if ( isCustomProp ) { + style.setProperty( name, value ); + } else { + style[ name ] = value; + } + } + + } else { + + // If a hook was provided get the non-computed value from there + if ( hooks && "get" in hooks && + ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { + + return ret; + } + + // Otherwise just get the value from the style object + return style[ name ]; + } + }, + + css: function( elem, name, extra, styles ) { + var val, num, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ); + + // Make sure that we're working with the right name. We don't + // want to modify the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Try prefixed name followed by the unprefixed name + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // If a hook was provided get the computed value from there + if ( hooks && "get" in hooks ) { + val = hooks.get( elem, true, extra ); + } + + // Otherwise, if a way to get the computed value exists, use that + if ( val === undefined ) { + val = curCSS( elem, name, styles ); + } + + // Convert "normal" to computed value + if ( val === "normal" && name in cssNormalTransform ) { + val = cssNormalTransform[ name ]; + } + + // Make numeric if forced or a qualifier was provided and val looks numeric + if ( extra === "" || extra ) { + num = parseFloat( val ); + return extra === true || isFinite( num ) ? num || 0 : val; + } + + return val; + } +} ); + +jQuery.each( [ "height", "width" ], function( _i, dimension ) { + jQuery.cssHooks[ dimension ] = { + get: function( elem, computed, extra ) { + if ( computed ) { + + // Certain elements can have dimension info if we invisibly show them + // but it must have a current display style that would benefit + return rdisplayswap.test( jQuery.css( elem, "display" ) ) && + + // Support: Safari 8+ + // Table columns in Safari have non-zero offsetWidth & zero + // getBoundingClientRect().width unless display is changed. + // Support: IE <=11 only + // Running getBoundingClientRect on a disconnected node + // in IE throws an error. + ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? + swap( elem, cssShow, function() { + return getWidthOrHeight( elem, dimension, extra ); + } ) : + getWidthOrHeight( elem, dimension, extra ); + } + }, + + set: function( elem, value, extra ) { + var matches, + styles = getStyles( elem ), + + // Only read styles.position if the test has a chance to fail + // to avoid forcing a reflow. + scrollboxSizeBuggy = !support.scrollboxSize() && + styles.position === "absolute", + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991) + boxSizingNeeded = scrollboxSizeBuggy || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + subtract = extra ? + boxModelAdjustment( + elem, + dimension, + extra, + isBorderBox, + styles + ) : + 0; + + // Account for unreliable border-box dimensions by comparing offset* to computed and + // faking a content-box to get border and padding (gh-3699) + if ( isBorderBox && scrollboxSizeBuggy ) { + subtract -= Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + parseFloat( styles[ dimension ] ) - + boxModelAdjustment( elem, dimension, "border", false, styles ) - + 0.5 + ); + } + + // Convert to pixels if value adjustment is needed + if ( subtract && ( matches = rcssNum.exec( value ) ) && + ( matches[ 3 ] || "px" ) !== "px" ) { + + elem.style[ dimension ] = value; + value = jQuery.css( elem, dimension ); + } + + return setPositiveNumber( elem, value, subtract ); + } + }; +} ); + +jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, + function( elem, computed ) { + if ( computed ) { + return ( parseFloat( curCSS( elem, "marginLeft" ) ) || + elem.getBoundingClientRect().left - + swap( elem, { marginLeft: 0 }, function() { + return elem.getBoundingClientRect().left; + } ) + ) + "px"; + } + } +); + +// These hooks are used by animate to expand properties +jQuery.each( { + margin: "", + padding: "", + border: "Width" +}, function( prefix, suffix ) { + jQuery.cssHooks[ prefix + suffix ] = { + expand: function( value ) { + var i = 0, + expanded = {}, + + // Assumes a single number if not a string + parts = typeof value === "string" ? value.split( " " ) : [ value ]; + + for ( ; i < 4; i++ ) { + expanded[ prefix + cssExpand[ i ] + suffix ] = + parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; + } + + return expanded; + } + }; + + if ( prefix !== "margin" ) { + jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; + } +} ); + +jQuery.fn.extend( { + css: function( name, value ) { + return access( this, function( elem, name, value ) { + var styles, len, + map = {}, + i = 0; + + if ( Array.isArray( name ) ) { + styles = getStyles( elem ); + len = name.length; + + for ( ; i < len; i++ ) { + map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); + } + + return map; + } + + return value !== undefined ? + jQuery.style( elem, name, value ) : + jQuery.css( elem, name ); + }, name, value, arguments.length > 1 ); + } +} ); + + +function Tween( elem, options, prop, end, easing ) { + return new Tween.prototype.init( elem, options, prop, end, easing ); +} +jQuery.Tween = Tween; + +Tween.prototype = { + constructor: Tween, + init: function( elem, options, prop, end, easing, unit ) { + this.elem = elem; + this.prop = prop; + this.easing = easing || jQuery.easing._default; + this.options = options; + this.start = this.now = this.cur(); + this.end = end; + this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); + }, + cur: function() { + var hooks = Tween.propHooks[ this.prop ]; + + return hooks && hooks.get ? + hooks.get( this ) : + Tween.propHooks._default.get( this ); + }, + run: function( percent ) { + var eased, + hooks = Tween.propHooks[ this.prop ]; + + if ( this.options.duration ) { + this.pos = eased = jQuery.easing[ this.easing ]( + percent, this.options.duration * percent, 0, 1, this.options.duration + ); + } else { + this.pos = eased = percent; + } + this.now = ( this.end - this.start ) * eased + this.start; + + if ( this.options.step ) { + this.options.step.call( this.elem, this.now, this ); + } + + if ( hooks && hooks.set ) { + hooks.set( this ); + } else { + Tween.propHooks._default.set( this ); + } + return this; + } +}; + +Tween.prototype.init.prototype = Tween.prototype; + +Tween.propHooks = { + _default: { + get: function( tween ) { + var result; + + // Use a property on the element directly when it is not a DOM element, + // or when there is no matching style property that exists. + if ( tween.elem.nodeType !== 1 || + tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { + return tween.elem[ tween.prop ]; + } + + // Passing an empty string as a 3rd parameter to .css will automatically + // attempt a parseFloat and fallback to a string if the parse fails. + // Simple values such as "10px" are parsed to Float; + // complex values such as "rotate(1rad)" are returned as-is. + result = jQuery.css( tween.elem, tween.prop, "" ); + + // Empty strings, null, undefined and "auto" are converted to 0. + return !result || result === "auto" ? 0 : result; + }, + set: function( tween ) { + + // Use step hook for back compat. + // Use cssHook if its there. + // Use .style if available and use plain properties where available. + if ( jQuery.fx.step[ tween.prop ] ) { + jQuery.fx.step[ tween.prop ]( tween ); + } else if ( tween.elem.nodeType === 1 && ( + jQuery.cssHooks[ tween.prop ] || + tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) { + jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); + } else { + tween.elem[ tween.prop ] = tween.now; + } + } + } +}; + +// Support: IE <=9 only +// Panic based approach to setting things on disconnected nodes +Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { + set: function( tween ) { + if ( tween.elem.nodeType && tween.elem.parentNode ) { + tween.elem[ tween.prop ] = tween.now; + } + } +}; + +jQuery.easing = { + linear: function( p ) { + return p; + }, + swing: function( p ) { + return 0.5 - Math.cos( p * Math.PI ) / 2; + }, + _default: "swing" +}; + +jQuery.fx = Tween.prototype.init; + +// Back compat <1.8 extension point +jQuery.fx.step = {}; + + + + +var + fxNow, inProgress, + rfxtypes = /^(?:toggle|show|hide)$/, + rrun = /queueHooks$/; + +function schedule() { + if ( inProgress ) { + if ( document.hidden === false && window.requestAnimationFrame ) { + window.requestAnimationFrame( schedule ); + } else { + window.setTimeout( schedule, jQuery.fx.interval ); + } + + jQuery.fx.tick(); + } +} + +// Animations created synchronously will run synchronously +function createFxNow() { + window.setTimeout( function() { + fxNow = undefined; + } ); + return ( fxNow = Date.now() ); +} + +// Generate parameters to create a standard animation +function genFx( type, includeWidth ) { + var which, + i = 0, + attrs = { height: type }; + + // If we include width, step value is 1 to do all cssExpand values, + // otherwise step value is 2 to skip over Left and Right + includeWidth = includeWidth ? 1 : 0; + for ( ; i < 4; i += 2 - includeWidth ) { + which = cssExpand[ i ]; + attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; + } + + if ( includeWidth ) { + attrs.opacity = attrs.width = type; + } + + return attrs; +} + +function createTween( value, prop, animation ) { + var tween, + collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), + index = 0, + length = collection.length; + for ( ; index < length; index++ ) { + if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { + + // We're done with this property + return tween; + } + } +} + +function defaultPrefilter( elem, props, opts ) { + var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, + isBox = "width" in props || "height" in props, + anim = this, + orig = {}, + style = elem.style, + hidden = elem.nodeType && isHiddenWithinTree( elem ), + dataShow = dataPriv.get( elem, "fxshow" ); + + // Queue-skipping animations hijack the fx hooks + if ( !opts.queue ) { + hooks = jQuery._queueHooks( elem, "fx" ); + if ( hooks.unqueued == null ) { + hooks.unqueued = 0; + oldfire = hooks.empty.fire; + hooks.empty.fire = function() { + if ( !hooks.unqueued ) { + oldfire(); + } + }; + } + hooks.unqueued++; + + anim.always( function() { + + // Ensure the complete handler is called before this completes + anim.always( function() { + hooks.unqueued--; + if ( !jQuery.queue( elem, "fx" ).length ) { + hooks.empty.fire(); + } + } ); + } ); + } + + // Detect show/hide animations + for ( prop in props ) { + value = props[ prop ]; + if ( rfxtypes.test( value ) ) { + delete props[ prop ]; + toggle = toggle || value === "toggle"; + if ( value === ( hidden ? "hide" : "show" ) ) { + + // Pretend to be hidden if this is a "show" and + // there is still data from a stopped show/hide + if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { + hidden = true; + + // Ignore all other no-op show/hide data + } else { + continue; + } + } + orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); + } + } + + // Bail out if this is a no-op like .hide().hide() + propTween = !jQuery.isEmptyObject( props ); + if ( !propTween && jQuery.isEmptyObject( orig ) ) { + return; + } + + // Restrict "overflow" and "display" styles during box animations + if ( isBox && elem.nodeType === 1 ) { + + // Support: IE <=9 - 11, Edge 12 - 15 + // Record all 3 overflow attributes because IE does not infer the shorthand + // from identically-valued overflowX and overflowY and Edge just mirrors + // the overflowX value there. + opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; + + // Identify a display type, preferring old show/hide data over the CSS cascade + restoreDisplay = dataShow && dataShow.display; + if ( restoreDisplay == null ) { + restoreDisplay = dataPriv.get( elem, "display" ); + } + display = jQuery.css( elem, "display" ); + if ( display === "none" ) { + if ( restoreDisplay ) { + display = restoreDisplay; + } else { + + // Get nonempty value(s) by temporarily forcing visibility + showHide( [ elem ], true ); + restoreDisplay = elem.style.display || restoreDisplay; + display = jQuery.css( elem, "display" ); + showHide( [ elem ] ); + } + } + + // Animate inline elements as inline-block + if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { + if ( jQuery.css( elem, "float" ) === "none" ) { + + // Restore the original display value at the end of pure show/hide animations + if ( !propTween ) { + anim.done( function() { + style.display = restoreDisplay; + } ); + if ( restoreDisplay == null ) { + display = style.display; + restoreDisplay = display === "none" ? "" : display; + } + } + style.display = "inline-block"; + } + } + } + + if ( opts.overflow ) { + style.overflow = "hidden"; + anim.always( function() { + style.overflow = opts.overflow[ 0 ]; + style.overflowX = opts.overflow[ 1 ]; + style.overflowY = opts.overflow[ 2 ]; + } ); + } + + // Implement show/hide animations + propTween = false; + for ( prop in orig ) { + + // General show/hide setup for this element animation + if ( !propTween ) { + if ( dataShow ) { + if ( "hidden" in dataShow ) { + hidden = dataShow.hidden; + } + } else { + dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); + } + + // Store hidden/visible for toggle so `.stop().toggle()` "reverses" + if ( toggle ) { + dataShow.hidden = !hidden; + } + + // Show elements before animating them + if ( hidden ) { + showHide( [ elem ], true ); + } + + /* eslint-disable no-loop-func */ + + anim.done( function() { + + /* eslint-enable no-loop-func */ + + // The final step of a "hide" animation is actually hiding the element + if ( !hidden ) { + showHide( [ elem ] ); + } + dataPriv.remove( elem, "fxshow" ); + for ( prop in orig ) { + jQuery.style( elem, prop, orig[ prop ] ); + } + } ); + } + + // Per-property setup + propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); + if ( !( prop in dataShow ) ) { + dataShow[ prop ] = propTween.start; + if ( hidden ) { + propTween.end = propTween.start; + propTween.start = 0; + } + } + } +} + +function propFilter( props, specialEasing ) { + var index, name, easing, value, hooks; + + // camelCase, specialEasing and expand cssHook pass + for ( index in props ) { + name = camelCase( index ); + easing = specialEasing[ name ]; + value = props[ index ]; + if ( Array.isArray( value ) ) { + easing = value[ 1 ]; + value = props[ index ] = value[ 0 ]; + } + + if ( index !== name ) { + props[ name ] = value; + delete props[ index ]; + } + + hooks = jQuery.cssHooks[ name ]; + if ( hooks && "expand" in hooks ) { + value = hooks.expand( value ); + delete props[ name ]; + + // Not quite $.extend, this won't overwrite existing keys. + // Reusing 'index' because we have the correct "name" + for ( index in value ) { + if ( !( index in props ) ) { + props[ index ] = value[ index ]; + specialEasing[ index ] = easing; + } + } + } else { + specialEasing[ name ] = easing; + } + } +} + +function Animation( elem, properties, options ) { + var result, + stopped, + index = 0, + length = Animation.prefilters.length, + deferred = jQuery.Deferred().always( function() { + + // Don't match elem in the :animated selector + delete tick.elem; + } ), + tick = function() { + if ( stopped ) { + return false; + } + var currentTime = fxNow || createFxNow(), + remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), + + // Support: Android 2.3 only + // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) + temp = remaining / animation.duration || 0, + percent = 1 - temp, + index = 0, + length = animation.tweens.length; + + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( percent ); + } + + deferred.notifyWith( elem, [ animation, percent, remaining ] ); + + // If there's more to do, yield + if ( percent < 1 && length ) { + return remaining; + } + + // If this was an empty animation, synthesize a final progress notification + if ( !length ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + } + + // Resolve the animation and report its conclusion + deferred.resolveWith( elem, [ animation ] ); + return false; + }, + animation = deferred.promise( { + elem: elem, + props: jQuery.extend( {}, properties ), + opts: jQuery.extend( true, { + specialEasing: {}, + easing: jQuery.easing._default + }, options ), + originalProperties: properties, + originalOptions: options, + startTime: fxNow || createFxNow(), + duration: options.duration, + tweens: [], + createTween: function( prop, end ) { + var tween = jQuery.Tween( elem, animation.opts, prop, end, + animation.opts.specialEasing[ prop ] || animation.opts.easing ); + animation.tweens.push( tween ); + return tween; + }, + stop: function( gotoEnd ) { + var index = 0, + + // If we are going to the end, we want to run all the tweens + // otherwise we skip this part + length = gotoEnd ? animation.tweens.length : 0; + if ( stopped ) { + return this; + } + stopped = true; + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( 1 ); + } + + // Resolve when we played the last frame; otherwise, reject + if ( gotoEnd ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + deferred.resolveWith( elem, [ animation, gotoEnd ] ); + } else { + deferred.rejectWith( elem, [ animation, gotoEnd ] ); + } + return this; + } + } ), + props = animation.props; + + propFilter( props, animation.opts.specialEasing ); + + for ( ; index < length; index++ ) { + result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); + if ( result ) { + if ( isFunction( result.stop ) ) { + jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = + result.stop.bind( result ); + } + return result; + } + } + + jQuery.map( props, createTween, animation ); + + if ( isFunction( animation.opts.start ) ) { + animation.opts.start.call( elem, animation ); + } + + // Attach callbacks from options + animation + .progress( animation.opts.progress ) + .done( animation.opts.done, animation.opts.complete ) + .fail( animation.opts.fail ) + .always( animation.opts.always ); + + jQuery.fx.timer( + jQuery.extend( tick, { + elem: elem, + anim: animation, + queue: animation.opts.queue + } ) + ); + + return animation; +} + +jQuery.Animation = jQuery.extend( Animation, { + + tweeners: { + "*": [ function( prop, value ) { + var tween = this.createTween( prop, value ); + adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); + return tween; + } ] + }, + + tweener: function( props, callback ) { + if ( isFunction( props ) ) { + callback = props; + props = [ "*" ]; + } else { + props = props.match( rnothtmlwhite ); + } + + var prop, + index = 0, + length = props.length; + + for ( ; index < length; index++ ) { + prop = props[ index ]; + Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; + Animation.tweeners[ prop ].unshift( callback ); + } + }, + + prefilters: [ defaultPrefilter ], + + prefilter: function( callback, prepend ) { + if ( prepend ) { + Animation.prefilters.unshift( callback ); + } else { + Animation.prefilters.push( callback ); + } + } +} ); + +jQuery.speed = function( speed, easing, fn ) { + var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { + complete: fn || !fn && easing || + isFunction( speed ) && speed, + duration: speed, + easing: fn && easing || easing && !isFunction( easing ) && easing + }; + + // Go to the end state if fx are off + if ( jQuery.fx.off ) { + opt.duration = 0; + + } else { + if ( typeof opt.duration !== "number" ) { + if ( opt.duration in jQuery.fx.speeds ) { + opt.duration = jQuery.fx.speeds[ opt.duration ]; + + } else { + opt.duration = jQuery.fx.speeds._default; + } + } + } + + // Normalize opt.queue - true/undefined/null -> "fx" + if ( opt.queue == null || opt.queue === true ) { + opt.queue = "fx"; + } + + // Queueing + opt.old = opt.complete; + + opt.complete = function() { + if ( isFunction( opt.old ) ) { + opt.old.call( this ); + } + + if ( opt.queue ) { + jQuery.dequeue( this, opt.queue ); + } + }; + + return opt; +}; + +jQuery.fn.extend( { + fadeTo: function( speed, to, easing, callback ) { + + // Show any hidden elements after setting opacity to 0 + return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() + + // Animate to the value specified + .end().animate( { opacity: to }, speed, easing, callback ); + }, + animate: function( prop, speed, easing, callback ) { + var empty = jQuery.isEmptyObject( prop ), + optall = jQuery.speed( speed, easing, callback ), + doAnimation = function() { + + // Operate on a copy of prop so per-property easing won't be lost + var anim = Animation( this, jQuery.extend( {}, prop ), optall ); + + // Empty animations, or finishing resolves immediately + if ( empty || dataPriv.get( this, "finish" ) ) { + anim.stop( true ); + } + }; + + doAnimation.finish = doAnimation; + + return empty || optall.queue === false ? + this.each( doAnimation ) : + this.queue( optall.queue, doAnimation ); + }, + stop: function( type, clearQueue, gotoEnd ) { + var stopQueue = function( hooks ) { + var stop = hooks.stop; + delete hooks.stop; + stop( gotoEnd ); + }; + + if ( typeof type !== "string" ) { + gotoEnd = clearQueue; + clearQueue = type; + type = undefined; + } + if ( clearQueue ) { + this.queue( type || "fx", [] ); + } + + return this.each( function() { + var dequeue = true, + index = type != null && type + "queueHooks", + timers = jQuery.timers, + data = dataPriv.get( this ); + + if ( index ) { + if ( data[ index ] && data[ index ].stop ) { + stopQueue( data[ index ] ); + } + } else { + for ( index in data ) { + if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { + stopQueue( data[ index ] ); + } + } + } + + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && + ( type == null || timers[ index ].queue === type ) ) { + + timers[ index ].anim.stop( gotoEnd ); + dequeue = false; + timers.splice( index, 1 ); + } + } + + // Start the next in the queue if the last step wasn't forced. + // Timers currently will call their complete callbacks, which + // will dequeue but only if they were gotoEnd. + if ( dequeue || !gotoEnd ) { + jQuery.dequeue( this, type ); + } + } ); + }, + finish: function( type ) { + if ( type !== false ) { + type = type || "fx"; + } + return this.each( function() { + var index, + data = dataPriv.get( this ), + queue = data[ type + "queue" ], + hooks = data[ type + "queueHooks" ], + timers = jQuery.timers, + length = queue ? queue.length : 0; + + // Enable finishing flag on private data + data.finish = true; + + // Empty the queue first + jQuery.queue( this, type, [] ); + + if ( hooks && hooks.stop ) { + hooks.stop.call( this, true ); + } + + // Look for any active animations, and finish them + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && timers[ index ].queue === type ) { + timers[ index ].anim.stop( true ); + timers.splice( index, 1 ); + } + } + + // Look for any animations in the old queue and finish them + for ( index = 0; index < length; index++ ) { + if ( queue[ index ] && queue[ index ].finish ) { + queue[ index ].finish.call( this ); + } + } + + // Turn off finishing flag + delete data.finish; + } ); + } +} ); + +jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) { + var cssFn = jQuery.fn[ name ]; + jQuery.fn[ name ] = function( speed, easing, callback ) { + return speed == null || typeof speed === "boolean" ? + cssFn.apply( this, arguments ) : + this.animate( genFx( name, true ), speed, easing, callback ); + }; +} ); + +// Generate shortcuts for custom animations +jQuery.each( { + slideDown: genFx( "show" ), + slideUp: genFx( "hide" ), + slideToggle: genFx( "toggle" ), + fadeIn: { opacity: "show" }, + fadeOut: { opacity: "hide" }, + fadeToggle: { opacity: "toggle" } +}, function( name, props ) { + jQuery.fn[ name ] = function( speed, easing, callback ) { + return this.animate( props, speed, easing, callback ); + }; +} ); + +jQuery.timers = []; +jQuery.fx.tick = function() { + var timer, + i = 0, + timers = jQuery.timers; + + fxNow = Date.now(); + + for ( ; i < timers.length; i++ ) { + timer = timers[ i ]; + + // Run the timer and safely remove it when done (allowing for external removal) + if ( !timer() && timers[ i ] === timer ) { + timers.splice( i--, 1 ); + } + } + + if ( !timers.length ) { + jQuery.fx.stop(); + } + fxNow = undefined; +}; + +jQuery.fx.timer = function( timer ) { + jQuery.timers.push( timer ); + jQuery.fx.start(); +}; + +jQuery.fx.interval = 13; +jQuery.fx.start = function() { + if ( inProgress ) { + return; + } + + inProgress = true; + schedule(); +}; + +jQuery.fx.stop = function() { + inProgress = null; +}; + +jQuery.fx.speeds = { + slow: 600, + fast: 200, + + // Default speed + _default: 400 +}; + + +// Based off of the plugin by Clint Helfers, with permission. +// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ +jQuery.fn.delay = function( time, type ) { + time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; + type = type || "fx"; + + return this.queue( type, function( next, hooks ) { + var timeout = window.setTimeout( next, time ); + hooks.stop = function() { + window.clearTimeout( timeout ); + }; + } ); +}; + + +( function() { + var input = document.createElement( "input" ), + select = document.createElement( "select" ), + opt = select.appendChild( document.createElement( "option" ) ); + + input.type = "checkbox"; + + // Support: Android <=4.3 only + // Default value for a checkbox should be "on" + support.checkOn = input.value !== ""; + + // Support: IE <=11 only + // Must access selectedIndex to make default options select + support.optSelected = opt.selected; + + // Support: IE <=11 only + // An input loses its value after becoming a radio + input = document.createElement( "input" ); + input.value = "t"; + input.type = "radio"; + support.radioValue = input.value === "t"; +} )(); + + +var boolHook, + attrHandle = jQuery.expr.attrHandle; + +jQuery.fn.extend( { + attr: function( name, value ) { + return access( this, jQuery.attr, name, value, arguments.length > 1 ); + }, + + removeAttr: function( name ) { + return this.each( function() { + jQuery.removeAttr( this, name ); + } ); + } +} ); + +jQuery.extend( { + attr: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set attributes on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + // Fallback to prop when attributes are not supported + if ( typeof elem.getAttribute === "undefined" ) { + return jQuery.prop( elem, name, value ); + } + + // Attribute hooks are determined by the lowercase version + // Grab necessary hook if one is defined + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + hooks = jQuery.attrHooks[ name.toLowerCase() ] || + ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); + } + + if ( value !== undefined ) { + if ( value === null ) { + jQuery.removeAttr( elem, name ); + return; + } + + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + elem.setAttribute( name, value + "" ); + return value; + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + ret = jQuery.find.attr( elem, name ); + + // Non-existent attributes return null, we normalize to undefined + return ret == null ? undefined : ret; + }, + + attrHooks: { + type: { + set: function( elem, value ) { + if ( !support.radioValue && value === "radio" && + nodeName( elem, "input" ) ) { + var val = elem.value; + elem.setAttribute( "type", value ); + if ( val ) { + elem.value = val; + } + return value; + } + } + } + }, + + removeAttr: function( elem, value ) { + var name, + i = 0, + + // Attribute names can contain non-HTML whitespace characters + // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 + attrNames = value && value.match( rnothtmlwhite ); + + if ( attrNames && elem.nodeType === 1 ) { + while ( ( name = attrNames[ i++ ] ) ) { + elem.removeAttribute( name ); + } + } + } +} ); + +// Hooks for boolean attributes +boolHook = { + set: function( elem, value, name ) { + if ( value === false ) { + + // Remove boolean attributes when set to false + jQuery.removeAttr( elem, name ); + } else { + elem.setAttribute( name, name ); + } + return name; + } +}; + +jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) { + var getter = attrHandle[ name ] || jQuery.find.attr; + + attrHandle[ name ] = function( elem, name, isXML ) { + var ret, handle, + lowercaseName = name.toLowerCase(); + + if ( !isXML ) { + + // Avoid an infinite loop by temporarily removing this function from the getter + handle = attrHandle[ lowercaseName ]; + attrHandle[ lowercaseName ] = ret; + ret = getter( elem, name, isXML ) != null ? + lowercaseName : + null; + attrHandle[ lowercaseName ] = handle; + } + return ret; + }; +} ); + + + + +var rfocusable = /^(?:input|select|textarea|button)$/i, + rclickable = /^(?:a|area)$/i; + +jQuery.fn.extend( { + prop: function( name, value ) { + return access( this, jQuery.prop, name, value, arguments.length > 1 ); + }, + + removeProp: function( name ) { + return this.each( function() { + delete this[ jQuery.propFix[ name ] || name ]; + } ); + } +} ); + +jQuery.extend( { + prop: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set properties on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + + // Fix name and attach hooks + name = jQuery.propFix[ name ] || name; + hooks = jQuery.propHooks[ name ]; + } + + if ( value !== undefined ) { + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + return ( elem[ name ] = value ); + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + return elem[ name ]; + }, + + propHooks: { + tabIndex: { + get: function( elem ) { + + // Support: IE <=9 - 11 only + // elem.tabIndex doesn't always return the + // correct value when it hasn't been explicitly set + // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ + // Use proper attribute retrieval(#12072) + var tabindex = jQuery.find.attr( elem, "tabindex" ); + + if ( tabindex ) { + return parseInt( tabindex, 10 ); + } + + if ( + rfocusable.test( elem.nodeName ) || + rclickable.test( elem.nodeName ) && + elem.href + ) { + return 0; + } + + return -1; + } + } + }, + + propFix: { + "for": "htmlFor", + "class": "className" + } +} ); + +// Support: IE <=11 only +// Accessing the selectedIndex property +// forces the browser to respect setting selected +// on the option +// The getter ensures a default option is selected +// when in an optgroup +// eslint rule "no-unused-expressions" is disabled for this code +// since it considers such accessions noop +if ( !support.optSelected ) { + jQuery.propHooks.selected = { + get: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent && parent.parentNode ) { + parent.parentNode.selectedIndex; + } + return null; + }, + set: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent ) { + parent.selectedIndex; + + if ( parent.parentNode ) { + parent.parentNode.selectedIndex; + } + } + } + }; +} + +jQuery.each( [ + "tabIndex", + "readOnly", + "maxLength", + "cellSpacing", + "cellPadding", + "rowSpan", + "colSpan", + "useMap", + "frameBorder", + "contentEditable" +], function() { + jQuery.propFix[ this.toLowerCase() ] = this; +} ); + + + + + // Strip and collapse whitespace according to HTML spec + // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace + function stripAndCollapse( value ) { + var tokens = value.match( rnothtmlwhite ) || []; + return tokens.join( " " ); + } + + +function getClass( elem ) { + return elem.getAttribute && elem.getAttribute( "class" ) || ""; +} + +function classesToArray( value ) { + if ( Array.isArray( value ) ) { + return value; + } + if ( typeof value === "string" ) { + return value.match( rnothtmlwhite ) || []; + } + return []; +} + +jQuery.fn.extend( { + addClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + if ( cur.indexOf( " " + clazz + " " ) < 0 ) { + cur += clazz + " "; + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + removeClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + if ( !arguments.length ) { + return this.attr( "class", "" ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + + // This expression is here for better compressibility (see addClass) + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + + // Remove *all* instances + while ( cur.indexOf( " " + clazz + " " ) > -1 ) { + cur = cur.replace( " " + clazz + " ", " " ); + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + toggleClass: function( value, stateVal ) { + var type = typeof value, + isValidValue = type === "string" || Array.isArray( value ); + + if ( typeof stateVal === "boolean" && isValidValue ) { + return stateVal ? this.addClass( value ) : this.removeClass( value ); + } + + if ( isFunction( value ) ) { + return this.each( function( i ) { + jQuery( this ).toggleClass( + value.call( this, i, getClass( this ), stateVal ), + stateVal + ); + } ); + } + + return this.each( function() { + var className, i, self, classNames; + + if ( isValidValue ) { + + // Toggle individual class names + i = 0; + self = jQuery( this ); + classNames = classesToArray( value ); + + while ( ( className = classNames[ i++ ] ) ) { + + // Check each className given, space separated list + if ( self.hasClass( className ) ) { + self.removeClass( className ); + } else { + self.addClass( className ); + } + } + + // Toggle whole class name + } else if ( value === undefined || type === "boolean" ) { + className = getClass( this ); + if ( className ) { + + // Store className if set + dataPriv.set( this, "__className__", className ); + } + + // If the element has a class name or if we're passed `false`, + // then remove the whole classname (if there was one, the above saved it). + // Otherwise bring back whatever was previously saved (if anything), + // falling back to the empty string if nothing was stored. + if ( this.setAttribute ) { + this.setAttribute( "class", + className || value === false ? + "" : + dataPriv.get( this, "__className__" ) || "" + ); + } + } + } ); + }, + + hasClass: function( selector ) { + var className, elem, + i = 0; + + className = " " + selector + " "; + while ( ( elem = this[ i++ ] ) ) { + if ( elem.nodeType === 1 && + ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { + return true; + } + } + + return false; + } +} ); + + + + +var rreturn = /\r/g; + +jQuery.fn.extend( { + val: function( value ) { + var hooks, ret, valueIsFunction, + elem = this[ 0 ]; + + if ( !arguments.length ) { + if ( elem ) { + hooks = jQuery.valHooks[ elem.type ] || + jQuery.valHooks[ elem.nodeName.toLowerCase() ]; + + if ( hooks && + "get" in hooks && + ( ret = hooks.get( elem, "value" ) ) !== undefined + ) { + return ret; + } + + ret = elem.value; + + // Handle most common string cases + if ( typeof ret === "string" ) { + return ret.replace( rreturn, "" ); + } + + // Handle cases where value is null/undef or number + return ret == null ? "" : ret; + } + + return; + } + + valueIsFunction = isFunction( value ); + + return this.each( function( i ) { + var val; + + if ( this.nodeType !== 1 ) { + return; + } + + if ( valueIsFunction ) { + val = value.call( this, i, jQuery( this ).val() ); + } else { + val = value; + } + + // Treat null/undefined as ""; convert numbers to string + if ( val == null ) { + val = ""; + + } else if ( typeof val === "number" ) { + val += ""; + + } else if ( Array.isArray( val ) ) { + val = jQuery.map( val, function( value ) { + return value == null ? "" : value + ""; + } ); + } + + hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; + + // If set returns undefined, fall back to normal setting + if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { + this.value = val; + } + } ); + } +} ); + +jQuery.extend( { + valHooks: { + option: { + get: function( elem ) { + + var val = jQuery.find.attr( elem, "value" ); + return val != null ? + val : + + // Support: IE <=10 - 11 only + // option.text throws exceptions (#14686, #14858) + // Strip and collapse whitespace + // https://html.spec.whatwg.org/#strip-and-collapse-whitespace + stripAndCollapse( jQuery.text( elem ) ); + } + }, + select: { + get: function( elem ) { + var value, option, i, + options = elem.options, + index = elem.selectedIndex, + one = elem.type === "select-one", + values = one ? null : [], + max = one ? index + 1 : options.length; + + if ( index < 0 ) { + i = max; + + } else { + i = one ? index : 0; + } + + // Loop through all the selected options + for ( ; i < max; i++ ) { + option = options[ i ]; + + // Support: IE <=9 only + // IE8-9 doesn't update selected after form reset (#2551) + if ( ( option.selected || i === index ) && + + // Don't return options that are disabled or in a disabled optgroup + !option.disabled && + ( !option.parentNode.disabled || + !nodeName( option.parentNode, "optgroup" ) ) ) { + + // Get the specific value for the option + value = jQuery( option ).val(); + + // We don't need an array for one selects + if ( one ) { + return value; + } + + // Multi-Selects return an array + values.push( value ); + } + } + + return values; + }, + + set: function( elem, value ) { + var optionSet, option, + options = elem.options, + values = jQuery.makeArray( value ), + i = options.length; + + while ( i-- ) { + option = options[ i ]; + + /* eslint-disable no-cond-assign */ + + if ( option.selected = + jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 + ) { + optionSet = true; + } + + /* eslint-enable no-cond-assign */ + } + + // Force browsers to behave consistently when non-matching value is set + if ( !optionSet ) { + elem.selectedIndex = -1; + } + return values; + } + } + } +} ); + +// Radios and checkboxes getter/setter +jQuery.each( [ "radio", "checkbox" ], function() { + jQuery.valHooks[ this ] = { + set: function( elem, value ) { + if ( Array.isArray( value ) ) { + return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); + } + } + }; + if ( !support.checkOn ) { + jQuery.valHooks[ this ].get = function( elem ) { + return elem.getAttribute( "value" ) === null ? "on" : elem.value; + }; + } +} ); + + + + +// Return jQuery for attributes-only inclusion + + +support.focusin = "onfocusin" in window; + + +var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/, + stopPropagationCallback = function( e ) { + e.stopPropagation(); + }; + +jQuery.extend( jQuery.event, { + + trigger: function( event, data, elem, onlyHandlers ) { + + var i, cur, tmp, bubbleType, ontype, handle, special, lastElement, + eventPath = [ elem || document ], + type = hasOwn.call( event, "type" ) ? event.type : event, + namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; + + cur = lastElement = tmp = elem = elem || document; + + // Don't do events on text and comment nodes + if ( elem.nodeType === 3 || elem.nodeType === 8 ) { + return; + } + + // focus/blur morphs to focusin/out; ensure we're not firing them right now + if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { + return; + } + + if ( type.indexOf( "." ) > -1 ) { + + // Namespaced trigger; create a regexp to match event type in handle() + namespaces = type.split( "." ); + type = namespaces.shift(); + namespaces.sort(); + } + ontype = type.indexOf( ":" ) < 0 && "on" + type; + + // Caller can pass in a jQuery.Event object, Object, or just an event type string + event = event[ jQuery.expando ] ? + event : + new jQuery.Event( type, typeof event === "object" && event ); + + // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) + event.isTrigger = onlyHandlers ? 2 : 3; + event.namespace = namespaces.join( "." ); + event.rnamespace = event.namespace ? + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : + null; + + // Clean up the event in case it is being reused + event.result = undefined; + if ( !event.target ) { + event.target = elem; + } + + // Clone any incoming data and prepend the event, creating the handler arg list + data = data == null ? + [ event ] : + jQuery.makeArray( data, [ event ] ); + + // Allow special events to draw outside the lines + special = jQuery.event.special[ type ] || {}; + if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { + return; + } + + // Determine event propagation path in advance, per W3C events spec (#9951) + // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) + if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) { + + bubbleType = special.delegateType || type; + if ( !rfocusMorph.test( bubbleType + type ) ) { + cur = cur.parentNode; + } + for ( ; cur; cur = cur.parentNode ) { + eventPath.push( cur ); + tmp = cur; + } + + // Only add window if we got to document (e.g., not plain obj or detached DOM) + if ( tmp === ( elem.ownerDocument || document ) ) { + eventPath.push( tmp.defaultView || tmp.parentWindow || window ); + } + } + + // Fire handlers on the event path + i = 0; + while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { + lastElement = cur; + event.type = i > 1 ? + bubbleType : + special.bindType || type; + + // jQuery handler + handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] && + dataPriv.get( cur, "handle" ); + if ( handle ) { + handle.apply( cur, data ); + } + + // Native handler + handle = ontype && cur[ ontype ]; + if ( handle && handle.apply && acceptData( cur ) ) { + event.result = handle.apply( cur, data ); + if ( event.result === false ) { + event.preventDefault(); + } + } + } + event.type = type; + + // If nobody prevented the default action, do it now + if ( !onlyHandlers && !event.isDefaultPrevented() ) { + + if ( ( !special._default || + special._default.apply( eventPath.pop(), data ) === false ) && + acceptData( elem ) ) { + + // Call a native DOM method on the target with the same name as the event. + // Don't do default actions on window, that's where global variables be (#6170) + if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) { + + // Don't re-trigger an onFOO event when we call its FOO() method + tmp = elem[ ontype ]; + + if ( tmp ) { + elem[ ontype ] = null; + } + + // Prevent re-triggering of the same event, since we already bubbled it above + jQuery.event.triggered = type; + + if ( event.isPropagationStopped() ) { + lastElement.addEventListener( type, stopPropagationCallback ); + } + + elem[ type ](); + + if ( event.isPropagationStopped() ) { + lastElement.removeEventListener( type, stopPropagationCallback ); + } + + jQuery.event.triggered = undefined; + + if ( tmp ) { + elem[ ontype ] = tmp; + } + } + } + } + + return event.result; + }, + + // Piggyback on a donor event to simulate a different one + // Used only for `focus(in | out)` events + simulate: function( type, elem, event ) { + var e = jQuery.extend( + new jQuery.Event(), + event, + { + type: type, + isSimulated: true + } + ); + + jQuery.event.trigger( e, null, elem ); + } + +} ); + +jQuery.fn.extend( { + + trigger: function( type, data ) { + return this.each( function() { + jQuery.event.trigger( type, data, this ); + } ); + }, + triggerHandler: function( type, data ) { + var elem = this[ 0 ]; + if ( elem ) { + return jQuery.event.trigger( type, data, elem, true ); + } + } +} ); + + +// Support: Firefox <=44 +// Firefox doesn't have focus(in | out) events +// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 +// +// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 +// focus(in | out) events fire after focus & blur events, +// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order +// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 +if ( !support.focusin ) { + jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { + + // Attach a single capturing handler on the document while someone wants focusin/focusout + var handler = function( event ) { + jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); + }; + + jQuery.event.special[ fix ] = { + setup: function() { + + // Handle: regular nodes (via `this.ownerDocument`), window + // (via `this.document`) & document (via `this`). + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ); + + if ( !attaches ) { + doc.addEventListener( orig, handler, true ); + } + dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); + }, + teardown: function() { + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ) - 1; + + if ( !attaches ) { + doc.removeEventListener( orig, handler, true ); + dataPriv.remove( doc, fix ); + + } else { + dataPriv.access( doc, fix, attaches ); + } + } + }; + } ); +} +var location = window.location; + +var nonce = { guid: Date.now() }; + +var rquery = ( /\?/ ); + + + +// Cross-browser xml parsing +jQuery.parseXML = function( data ) { + var xml, parserErrorElem; + if ( !data || typeof data !== "string" ) { + return null; + } + + // Support: IE 9 - 11 only + // IE throws on parseFromString with invalid input. + try { + xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); + } catch ( e ) {} + + parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ]; + if ( !xml || parserErrorElem ) { + jQuery.error( "Invalid XML: " + ( + parserErrorElem ? + jQuery.map( parserErrorElem.childNodes, function( el ) { + return el.textContent; + } ).join( "\n" ) : + data + ) ); + } + return xml; +}; + + +var + rbracket = /\[\]$/, + rCRLF = /\r?\n/g, + rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, + rsubmittable = /^(?:input|select|textarea|keygen)/i; + +function buildParams( prefix, obj, traditional, add ) { + var name; + + if ( Array.isArray( obj ) ) { + + // Serialize array item. + jQuery.each( obj, function( i, v ) { + if ( traditional || rbracket.test( prefix ) ) { + + // Treat each array item as a scalar. + add( prefix, v ); + + } else { + + // Item is non-scalar (array or object), encode its numeric index. + buildParams( + prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", + v, + traditional, + add + ); + } + } ); + + } else if ( !traditional && toType( obj ) === "object" ) { + + // Serialize object item. + for ( name in obj ) { + buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); + } + + } else { + + // Serialize scalar item. + add( prefix, obj ); + } +} + +// Serialize an array of form elements or a set of +// key/values into a query string +jQuery.param = function( a, traditional ) { + var prefix, + s = [], + add = function( key, valueOrFunction ) { + + // If value is a function, invoke it and use its return value + var value = isFunction( valueOrFunction ) ? + valueOrFunction() : + valueOrFunction; + + s[ s.length ] = encodeURIComponent( key ) + "=" + + encodeURIComponent( value == null ? "" : value ); + }; + + if ( a == null ) { + return ""; + } + + // If an array was passed in, assume that it is an array of form elements. + if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { + + // Serialize the form elements + jQuery.each( a, function() { + add( this.name, this.value ); + } ); + + } else { + + // If traditional, encode the "old" way (the way 1.3.2 or older + // did it), otherwise encode params recursively. + for ( prefix in a ) { + buildParams( prefix, a[ prefix ], traditional, add ); + } + } + + // Return the resulting serialization + return s.join( "&" ); +}; + +jQuery.fn.extend( { + serialize: function() { + return jQuery.param( this.serializeArray() ); + }, + serializeArray: function() { + return this.map( function() { + + // Can add propHook for "elements" to filter or add form elements + var elements = jQuery.prop( this, "elements" ); + return elements ? jQuery.makeArray( elements ) : this; + } ).filter( function() { + var type = this.type; + + // Use .is( ":disabled" ) so that fieldset[disabled] works + return this.name && !jQuery( this ).is( ":disabled" ) && + rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && + ( this.checked || !rcheckableType.test( type ) ); + } ).map( function( _i, elem ) { + var val = jQuery( this ).val(); + + if ( val == null ) { + return null; + } + + if ( Array.isArray( val ) ) { + return jQuery.map( val, function( val ) { + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ); + } + + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ).get(); + } +} ); + + +var + r20 = /%20/g, + rhash = /#.*$/, + rantiCache = /([?&])_=[^&]*/, + rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, + + // #7653, #8125, #8152: local protocol detection + rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, + rnoContent = /^(?:GET|HEAD)$/, + rprotocol = /^\/\//, + + /* Prefilters + * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) + * 2) These are called: + * - BEFORE asking for a transport + * - AFTER param serialization (s.data is a string if s.processData is true) + * 3) key is the dataType + * 4) the catchall symbol "*" can be used + * 5) execution will start with transport dataType and THEN continue down to "*" if needed + */ + prefilters = {}, + + /* Transports bindings + * 1) key is the dataType + * 2) the catchall symbol "*" can be used + * 3) selection will start with transport dataType and THEN go to "*" if needed + */ + transports = {}, + + // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression + allTypes = "*/".concat( "*" ), + + // Anchor tag for parsing the document origin + originAnchor = document.createElement( "a" ); + +originAnchor.href = location.href; + +// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport +function addToPrefiltersOrTransports( structure ) { + + // dataTypeExpression is optional and defaults to "*" + return function( dataTypeExpression, func ) { + + if ( typeof dataTypeExpression !== "string" ) { + func = dataTypeExpression; + dataTypeExpression = "*"; + } + + var dataType, + i = 0, + dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; + + if ( isFunction( func ) ) { + + // For each dataType in the dataTypeExpression + while ( ( dataType = dataTypes[ i++ ] ) ) { + + // Prepend if requested + if ( dataType[ 0 ] === "+" ) { + dataType = dataType.slice( 1 ) || "*"; + ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); + + // Otherwise append + } else { + ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); + } + } + } + }; +} + +// Base inspection function for prefilters and transports +function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { + + var inspected = {}, + seekingTransport = ( structure === transports ); + + function inspect( dataType ) { + var selected; + inspected[ dataType ] = true; + jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { + var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); + if ( typeof dataTypeOrTransport === "string" && + !seekingTransport && !inspected[ dataTypeOrTransport ] ) { + + options.dataTypes.unshift( dataTypeOrTransport ); + inspect( dataTypeOrTransport ); + return false; + } else if ( seekingTransport ) { + return !( selected = dataTypeOrTransport ); + } + } ); + return selected; + } + + return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); +} + +// A special extend for ajax options +// that takes "flat" options (not to be deep extended) +// Fixes #9887 +function ajaxExtend( target, src ) { + var key, deep, + flatOptions = jQuery.ajaxSettings.flatOptions || {}; + + for ( key in src ) { + if ( src[ key ] !== undefined ) { + ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; + } + } + if ( deep ) { + jQuery.extend( true, target, deep ); + } + + return target; +} + +/* Handles responses to an ajax request: + * - finds the right dataType (mediates between content-type and expected dataType) + * - returns the corresponding response + */ +function ajaxHandleResponses( s, jqXHR, responses ) { + + var ct, type, finalDataType, firstDataType, + contents = s.contents, + dataTypes = s.dataTypes; + + // Remove auto dataType and get content-type in the process + while ( dataTypes[ 0 ] === "*" ) { + dataTypes.shift(); + if ( ct === undefined ) { + ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); + } + } + + // Check if we're dealing with a known content-type + if ( ct ) { + for ( type in contents ) { + if ( contents[ type ] && contents[ type ].test( ct ) ) { + dataTypes.unshift( type ); + break; + } + } + } + + // Check to see if we have a response for the expected dataType + if ( dataTypes[ 0 ] in responses ) { + finalDataType = dataTypes[ 0 ]; + } else { + + // Try convertible dataTypes + for ( type in responses ) { + if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { + finalDataType = type; + break; + } + if ( !firstDataType ) { + firstDataType = type; + } + } + + // Or just use first one + finalDataType = finalDataType || firstDataType; + } + + // If we found a dataType + // We add the dataType to the list if needed + // and return the corresponding response + if ( finalDataType ) { + if ( finalDataType !== dataTypes[ 0 ] ) { + dataTypes.unshift( finalDataType ); + } + return responses[ finalDataType ]; + } +} + +/* Chain conversions given the request and the original response + * Also sets the responseXXX fields on the jqXHR instance + */ +function ajaxConvert( s, response, jqXHR, isSuccess ) { + var conv2, current, conv, tmp, prev, + converters = {}, + + // Work with a copy of dataTypes in case we need to modify it for conversion + dataTypes = s.dataTypes.slice(); + + // Create converters map with lowercased keys + if ( dataTypes[ 1 ] ) { + for ( conv in s.converters ) { + converters[ conv.toLowerCase() ] = s.converters[ conv ]; + } + } + + current = dataTypes.shift(); + + // Convert to each sequential dataType + while ( current ) { + + if ( s.responseFields[ current ] ) { + jqXHR[ s.responseFields[ current ] ] = response; + } + + // Apply the dataFilter if provided + if ( !prev && isSuccess && s.dataFilter ) { + response = s.dataFilter( response, s.dataType ); + } + + prev = current; + current = dataTypes.shift(); + + if ( current ) { + + // There's only work to do if current dataType is non-auto + if ( current === "*" ) { + + current = prev; + + // Convert response if prev dataType is non-auto and differs from current + } else if ( prev !== "*" && prev !== current ) { + + // Seek a direct converter + conv = converters[ prev + " " + current ] || converters[ "* " + current ]; + + // If none found, seek a pair + if ( !conv ) { + for ( conv2 in converters ) { + + // If conv2 outputs current + tmp = conv2.split( " " ); + if ( tmp[ 1 ] === current ) { + + // If prev can be converted to accepted input + conv = converters[ prev + " " + tmp[ 0 ] ] || + converters[ "* " + tmp[ 0 ] ]; + if ( conv ) { + + // Condense equivalence converters + if ( conv === true ) { + conv = converters[ conv2 ]; + + // Otherwise, insert the intermediate dataType + } else if ( converters[ conv2 ] !== true ) { + current = tmp[ 0 ]; + dataTypes.unshift( tmp[ 1 ] ); + } + break; + } + } + } + } + + // Apply converter (if not an equivalence) + if ( conv !== true ) { + + // Unless errors are allowed to bubble, catch and return them + if ( conv && s.throws ) { + response = conv( response ); + } else { + try { + response = conv( response ); + } catch ( e ) { + return { + state: "parsererror", + error: conv ? e : "No conversion from " + prev + " to " + current + }; + } + } + } + } + } + } + + return { state: "success", data: response }; +} + +jQuery.extend( { + + // Counter for holding the number of active queries + active: 0, + + // Last-Modified header cache for next request + lastModified: {}, + etag: {}, + + ajaxSettings: { + url: location.href, + type: "GET", + isLocal: rlocalProtocol.test( location.protocol ), + global: true, + processData: true, + async: true, + contentType: "application/x-www-form-urlencoded; charset=UTF-8", + + /* + timeout: 0, + data: null, + dataType: null, + username: null, + password: null, + cache: null, + throws: false, + traditional: false, + headers: {}, + */ + + accepts: { + "*": allTypes, + text: "text/plain", + html: "text/html", + xml: "application/xml, text/xml", + json: "application/json, text/javascript" + }, + + contents: { + xml: /\bxml\b/, + html: /\bhtml/, + json: /\bjson\b/ + }, + + responseFields: { + xml: "responseXML", + text: "responseText", + json: "responseJSON" + }, + + // Data converters + // Keys separate source (or catchall "*") and destination types with a single space + converters: { + + // Convert anything to text + "* text": String, + + // Text to html (true = no transformation) + "text html": true, + + // Evaluate text as a json expression + "text json": JSON.parse, + + // Parse text as xml + "text xml": jQuery.parseXML + }, + + // For options that shouldn't be deep extended: + // you can add your own custom options here if + // and when you create one that shouldn't be + // deep extended (see ajaxExtend) + flatOptions: { + url: true, + context: true + } + }, + + // Creates a full fledged settings object into target + // with both ajaxSettings and settings fields. + // If target is omitted, writes into ajaxSettings. + ajaxSetup: function( target, settings ) { + return settings ? + + // Building a settings object + ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : + + // Extending ajaxSettings + ajaxExtend( jQuery.ajaxSettings, target ); + }, + + ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), + ajaxTransport: addToPrefiltersOrTransports( transports ), + + // Main method + ajax: function( url, options ) { + + // If url is an object, simulate pre-1.5 signature + if ( typeof url === "object" ) { + options = url; + url = undefined; + } + + // Force options to be an object + options = options || {}; + + var transport, + + // URL without anti-cache param + cacheURL, + + // Response headers + responseHeadersString, + responseHeaders, + + // timeout handle + timeoutTimer, + + // Url cleanup var + urlAnchor, + + // Request state (becomes false upon send and true upon completion) + completed, + + // To know if global events are to be dispatched + fireGlobals, + + // Loop variable + i, + + // uncached part of the url + uncached, + + // Create the final options object + s = jQuery.ajaxSetup( {}, options ), + + // Callbacks context + callbackContext = s.context || s, + + // Context for global events is callbackContext if it is a DOM node or jQuery collection + globalEventContext = s.context && + ( callbackContext.nodeType || callbackContext.jquery ) ? + jQuery( callbackContext ) : + jQuery.event, + + // Deferreds + deferred = jQuery.Deferred(), + completeDeferred = jQuery.Callbacks( "once memory" ), + + // Status-dependent callbacks + statusCode = s.statusCode || {}, + + // Headers (they are sent all at once) + requestHeaders = {}, + requestHeadersNames = {}, + + // Default abort message + strAbort = "canceled", + + // Fake xhr + jqXHR = { + readyState: 0, + + // Builds headers hashtable if needed + getResponseHeader: function( key ) { + var match; + if ( completed ) { + if ( !responseHeaders ) { + responseHeaders = {}; + while ( ( match = rheaders.exec( responseHeadersString ) ) ) { + responseHeaders[ match[ 1 ].toLowerCase() + " " ] = + ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] ) + .concat( match[ 2 ] ); + } + } + match = responseHeaders[ key.toLowerCase() + " " ]; + } + return match == null ? null : match.join( ", " ); + }, + + // Raw string + getAllResponseHeaders: function() { + return completed ? responseHeadersString : null; + }, + + // Caches the header + setRequestHeader: function( name, value ) { + if ( completed == null ) { + name = requestHeadersNames[ name.toLowerCase() ] = + requestHeadersNames[ name.toLowerCase() ] || name; + requestHeaders[ name ] = value; + } + return this; + }, + + // Overrides response content-type header + overrideMimeType: function( type ) { + if ( completed == null ) { + s.mimeType = type; + } + return this; + }, + + // Status-dependent callbacks + statusCode: function( map ) { + var code; + if ( map ) { + if ( completed ) { + + // Execute the appropriate callbacks + jqXHR.always( map[ jqXHR.status ] ); + } else { + + // Lazy-add the new callbacks in a way that preserves old ones + for ( code in map ) { + statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; + } + } + } + return this; + }, + + // Cancel the request + abort: function( statusText ) { + var finalText = statusText || strAbort; + if ( transport ) { + transport.abort( finalText ); + } + done( 0, finalText ); + return this; + } + }; + + // Attach deferreds + deferred.promise( jqXHR ); + + // Add protocol if not provided (prefilters might expect it) + // Handle falsy url in the settings object (#10093: consistency with old signature) + // We also use the url parameter if available + s.url = ( ( url || s.url || location.href ) + "" ) + .replace( rprotocol, location.protocol + "//" ); + + // Alias method option to type as per ticket #12004 + s.type = options.method || options.type || s.method || s.type; + + // Extract dataTypes list + s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; + + // A cross-domain request is in order when the origin doesn't match the current origin. + if ( s.crossDomain == null ) { + urlAnchor = document.createElement( "a" ); + + // Support: IE <=8 - 11, Edge 12 - 15 + // IE throws exception on accessing the href property if url is malformed, + // e.g. http://example.com:80x/ + try { + urlAnchor.href = s.url; + + // Support: IE <=8 - 11 only + // Anchor's host property isn't correctly set when s.url is relative + urlAnchor.href = urlAnchor.href; + s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== + urlAnchor.protocol + "//" + urlAnchor.host; + } catch ( e ) { + + // If there is an error parsing the URL, assume it is crossDomain, + // it can be rejected by the transport if it is invalid + s.crossDomain = true; + } + } + + // Convert data if not already a string + if ( s.data && s.processData && typeof s.data !== "string" ) { + s.data = jQuery.param( s.data, s.traditional ); + } + + // Apply prefilters + inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); + + // If request was aborted inside a prefilter, stop there + if ( completed ) { + return jqXHR; + } + + // We can fire global events as of now if asked to + // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) + fireGlobals = jQuery.event && s.global; + + // Watch for a new set of requests + if ( fireGlobals && jQuery.active++ === 0 ) { + jQuery.event.trigger( "ajaxStart" ); + } + + // Uppercase the type + s.type = s.type.toUpperCase(); + + // Determine if request has content + s.hasContent = !rnoContent.test( s.type ); + + // Save the URL in case we're toying with the If-Modified-Since + // and/or If-None-Match header later on + // Remove hash to simplify url manipulation + cacheURL = s.url.replace( rhash, "" ); + + // More options handling for requests with no content + if ( !s.hasContent ) { + + // Remember the hash so we can put it back + uncached = s.url.slice( cacheURL.length ); + + // If data is available and should be processed, append data to url + if ( s.data && ( s.processData || typeof s.data === "string" ) ) { + cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; + + // #9682: remove data so that it's not used in an eventual retry + delete s.data; + } + + // Add or update anti-cache param if needed + if ( s.cache === false ) { + cacheURL = cacheURL.replace( rantiCache, "$1" ); + uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) + + uncached; + } + + // Put hash and anti-cache on the URL that will be requested (gh-1732) + s.url = cacheURL + uncached; + + // Change '%20' to '+' if this is encoded form body content (gh-2658) + } else if ( s.data && s.processData && + ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { + s.data = s.data.replace( r20, "+" ); + } + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + if ( jQuery.lastModified[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); + } + if ( jQuery.etag[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); + } + } + + // Set the correct header, if data is being sent + if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { + jqXHR.setRequestHeader( "Content-Type", s.contentType ); + } + + // Set the Accepts header for the server, depending on the dataType + jqXHR.setRequestHeader( + "Accept", + s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? + s.accepts[ s.dataTypes[ 0 ] ] + + ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : + s.accepts[ "*" ] + ); + + // Check for headers option + for ( i in s.headers ) { + jqXHR.setRequestHeader( i, s.headers[ i ] ); + } + + // Allow custom headers/mimetypes and early abort + if ( s.beforeSend && + ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { + + // Abort if not done already and return + return jqXHR.abort(); + } + + // Aborting is no longer a cancellation + strAbort = "abort"; + + // Install callbacks on deferreds + completeDeferred.add( s.complete ); + jqXHR.done( s.success ); + jqXHR.fail( s.error ); + + // Get transport + transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); + + // If no transport, we auto-abort + if ( !transport ) { + done( -1, "No Transport" ); + } else { + jqXHR.readyState = 1; + + // Send global event + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); + } + + // If request was aborted inside ajaxSend, stop there + if ( completed ) { + return jqXHR; + } + + // Timeout + if ( s.async && s.timeout > 0 ) { + timeoutTimer = window.setTimeout( function() { + jqXHR.abort( "timeout" ); + }, s.timeout ); + } + + try { + completed = false; + transport.send( requestHeaders, done ); + } catch ( e ) { + + // Rethrow post-completion exceptions + if ( completed ) { + throw e; + } + + // Propagate others as results + done( -1, e ); + } + } + + // Callback for when everything is done + function done( status, nativeStatusText, responses, headers ) { + var isSuccess, success, error, response, modified, + statusText = nativeStatusText; + + // Ignore repeat invocations + if ( completed ) { + return; + } + + completed = true; + + // Clear timeout if it exists + if ( timeoutTimer ) { + window.clearTimeout( timeoutTimer ); + } + + // Dereference transport for early garbage collection + // (no matter how long the jqXHR object will be used) + transport = undefined; + + // Cache response headers + responseHeadersString = headers || ""; + + // Set readyState + jqXHR.readyState = status > 0 ? 4 : 0; + + // Determine if successful + isSuccess = status >= 200 && status < 300 || status === 304; + + // Get response data + if ( responses ) { + response = ajaxHandleResponses( s, jqXHR, responses ); + } + + // Use a noop converter for missing script but not if jsonp + if ( !isSuccess && + jQuery.inArray( "script", s.dataTypes ) > -1 && + jQuery.inArray( "json", s.dataTypes ) < 0 ) { + s.converters[ "text script" ] = function() {}; + } + + // Convert no matter what (that way responseXXX fields are always set) + response = ajaxConvert( s, response, jqXHR, isSuccess ); + + // If successful, handle type chaining + if ( isSuccess ) { + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + modified = jqXHR.getResponseHeader( "Last-Modified" ); + if ( modified ) { + jQuery.lastModified[ cacheURL ] = modified; + } + modified = jqXHR.getResponseHeader( "etag" ); + if ( modified ) { + jQuery.etag[ cacheURL ] = modified; + } + } + + // if no content + if ( status === 204 || s.type === "HEAD" ) { + statusText = "nocontent"; + + // if not modified + } else if ( status === 304 ) { + statusText = "notmodified"; + + // If we have data, let's convert it + } else { + statusText = response.state; + success = response.data; + error = response.error; + isSuccess = !error; + } + } else { + + // Extract error from statusText and normalize for non-aborts + error = statusText; + if ( status || !statusText ) { + statusText = "error"; + if ( status < 0 ) { + status = 0; + } + } + } + + // Set data for the fake xhr object + jqXHR.status = status; + jqXHR.statusText = ( nativeStatusText || statusText ) + ""; + + // Success/Error + if ( isSuccess ) { + deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); + } else { + deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); + } + + // Status-dependent callbacks + jqXHR.statusCode( statusCode ); + statusCode = undefined; + + if ( fireGlobals ) { + globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", + [ jqXHR, s, isSuccess ? success : error ] ); + } + + // Complete + completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); + + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); + + // Handle the global AJAX counter + if ( !( --jQuery.active ) ) { + jQuery.event.trigger( "ajaxStop" ); + } + } + } + + return jqXHR; + }, + + getJSON: function( url, data, callback ) { + return jQuery.get( url, data, callback, "json" ); + }, + + getScript: function( url, callback ) { + return jQuery.get( url, undefined, callback, "script" ); + } +} ); + +jQuery.each( [ "get", "post" ], function( _i, method ) { + jQuery[ method ] = function( url, data, callback, type ) { + + // Shift arguments if data argument was omitted + if ( isFunction( data ) ) { + type = type || callback; + callback = data; + data = undefined; + } + + // The url can be an options object (which then must have .url) + return jQuery.ajax( jQuery.extend( { + url: url, + type: method, + dataType: type, + data: data, + success: callback + }, jQuery.isPlainObject( url ) && url ) ); + }; +} ); + +jQuery.ajaxPrefilter( function( s ) { + var i; + for ( i in s.headers ) { + if ( i.toLowerCase() === "content-type" ) { + s.contentType = s.headers[ i ] || ""; + } + } +} ); + + +jQuery._evalUrl = function( url, options, doc ) { + return jQuery.ajax( { + url: url, + + // Make this explicit, since user can override this through ajaxSetup (#11264) + type: "GET", + dataType: "script", + cache: true, + async: false, + global: false, + + // Only evaluate the response if it is successful (gh-4126) + // dataFilter is not invoked for failure responses, so using it instead + // of the default converter is kludgy but it works. + converters: { + "text script": function() {} + }, + dataFilter: function( response ) { + jQuery.globalEval( response, options, doc ); + } + } ); +}; + + +jQuery.fn.extend( { + wrapAll: function( html ) { + var wrap; + + if ( this[ 0 ] ) { + if ( isFunction( html ) ) { + html = html.call( this[ 0 ] ); + } + + // The elements to wrap the target around + wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); + + if ( this[ 0 ].parentNode ) { + wrap.insertBefore( this[ 0 ] ); + } + + wrap.map( function() { + var elem = this; + + while ( elem.firstElementChild ) { + elem = elem.firstElementChild; + } + + return elem; + } ).append( this ); + } + + return this; + }, + + wrapInner: function( html ) { + if ( isFunction( html ) ) { + return this.each( function( i ) { + jQuery( this ).wrapInner( html.call( this, i ) ); + } ); + } + + return this.each( function() { + var self = jQuery( this ), + contents = self.contents(); + + if ( contents.length ) { + contents.wrapAll( html ); + + } else { + self.append( html ); + } + } ); + }, + + wrap: function( html ) { + var htmlIsFunction = isFunction( html ); + + return this.each( function( i ) { + jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html ); + } ); + }, + + unwrap: function( selector ) { + this.parent( selector ).not( "body" ).each( function() { + jQuery( this ).replaceWith( this.childNodes ); + } ); + return this; + } +} ); + + +jQuery.expr.pseudos.hidden = function( elem ) { + return !jQuery.expr.pseudos.visible( elem ); +}; +jQuery.expr.pseudos.visible = function( elem ) { + return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length ); +}; + + + + +jQuery.ajaxSettings.xhr = function() { + try { + return new window.XMLHttpRequest(); + } catch ( e ) {} +}; + +var xhrSuccessStatus = { + + // File protocol always yields status code 0, assume 200 + 0: 200, + + // Support: IE <=9 only + // #1450: sometimes IE returns 1223 when it should be 204 + 1223: 204 + }, + xhrSupported = jQuery.ajaxSettings.xhr(); + +support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported ); +support.ajax = xhrSupported = !!xhrSupported; + +jQuery.ajaxTransport( function( options ) { + var callback, errorCallback; + + // Cross domain only allowed if supported through XMLHttpRequest + if ( support.cors || xhrSupported && !options.crossDomain ) { + return { + send: function( headers, complete ) { + var i, + xhr = options.xhr(); + + xhr.open( + options.type, + options.url, + options.async, + options.username, + options.password + ); + + // Apply custom fields if provided + if ( options.xhrFields ) { + for ( i in options.xhrFields ) { + xhr[ i ] = options.xhrFields[ i ]; + } + } + + // Override mime type if needed + if ( options.mimeType && xhr.overrideMimeType ) { + xhr.overrideMimeType( options.mimeType ); + } + + // X-Requested-With header + // For cross-domain requests, seeing as conditions for a preflight are + // akin to a jigsaw puzzle, we simply never set it to be sure. + // (it can always be set on a per-request basis or even using ajaxSetup) + // For same-domain requests, won't change header if already provided. + if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) { + headers[ "X-Requested-With" ] = "XMLHttpRequest"; + } + + // Set headers + for ( i in headers ) { + xhr.setRequestHeader( i, headers[ i ] ); + } + + // Callback + callback = function( type ) { + return function() { + if ( callback ) { + callback = errorCallback = xhr.onload = + xhr.onerror = xhr.onabort = xhr.ontimeout = + xhr.onreadystatechange = null; + + if ( type === "abort" ) { + xhr.abort(); + } else if ( type === "error" ) { + + // Support: IE <=9 only + // On a manual native abort, IE9 throws + // errors on any property access that is not readyState + if ( typeof xhr.status !== "number" ) { + complete( 0, "error" ); + } else { + complete( + + // File: protocol always yields status 0; see #8605, #14207 + xhr.status, + xhr.statusText + ); + } + } else { + complete( + xhrSuccessStatus[ xhr.status ] || xhr.status, + xhr.statusText, + + // Support: IE <=9 only + // IE9 has no XHR2 but throws on binary (trac-11426) + // For XHR2 non-text, let the caller handle it (gh-2498) + ( xhr.responseType || "text" ) !== "text" || + typeof xhr.responseText !== "string" ? + { binary: xhr.response } : + { text: xhr.responseText }, + xhr.getAllResponseHeaders() + ); + } + } + }; + }; + + // Listen to events + xhr.onload = callback(); + errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" ); + + // Support: IE 9 only + // Use onreadystatechange to replace onabort + // to handle uncaught aborts + if ( xhr.onabort !== undefined ) { + xhr.onabort = errorCallback; + } else { + xhr.onreadystatechange = function() { + + // Check readyState before timeout as it changes + if ( xhr.readyState === 4 ) { + + // Allow onerror to be called first, + // but that will not handle a native abort + // Also, save errorCallback to a variable + // as xhr.onerror cannot be accessed + window.setTimeout( function() { + if ( callback ) { + errorCallback(); + } + } ); + } + }; + } + + // Create the abort callback + callback = callback( "abort" ); + + try { + + // Do send the request (this may raise an exception) + xhr.send( options.hasContent && options.data || null ); + } catch ( e ) { + + // #14683: Only rethrow if this hasn't been notified as an error yet + if ( callback ) { + throw e; + } + } + }, + + abort: function() { + if ( callback ) { + callback(); + } + } + }; + } +} ); + + + + +// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432) +jQuery.ajaxPrefilter( function( s ) { + if ( s.crossDomain ) { + s.contents.script = false; + } +} ); + +// Install script dataType +jQuery.ajaxSetup( { + accepts: { + script: "text/javascript, application/javascript, " + + "application/ecmascript, application/x-ecmascript" + }, + contents: { + script: /\b(?:java|ecma)script\b/ + }, + converters: { + "text script": function( text ) { + jQuery.globalEval( text ); + return text; + } + } +} ); + +// Handle cache's special case and crossDomain +jQuery.ajaxPrefilter( "script", function( s ) { + if ( s.cache === undefined ) { + s.cache = false; + } + if ( s.crossDomain ) { + s.type = "GET"; + } +} ); + +// Bind script tag hack transport +jQuery.ajaxTransport( "script", function( s ) { + + // This transport only deals with cross domain or forced-by-attrs requests + if ( s.crossDomain || s.scriptAttrs ) { + var script, callback; + return { + send: function( _, complete ) { + script = jQuery( " + + + + + + + + + + + Skip to contents + + +
    +
    +
    +

    dsims +

    +
    +
    +

    Distance Sampling Simulations +

    +

    CRAN (RStudio Mirror) Downloads CRAN Version Codecov test coverage

    +

    dsims is a package for simulating distance sampling surveys to allow users to optimise survey design for studies with particular properties.

    +
    +
    +

    Using dsims + +

    +

    There is currently three vignette within the dsims package to help you get started using dsims:

    +
      +
    • GettingStarted: Getting Started with dsims available from the navigation bar at top of the page
      • +
    • Transition from DSsim to dsims: under Articles on the navigation bar
      • +
    • Grouped strata: Combining abundance estimates across strata constructed for design purposes; under Articles on the navigation bar
      • +
    +
    +
    +

    Getting dsims + +

    +

    The easiest way to get dsims is to install it from CRAN within R-studio or the R interface. We endeavour to make all new functionality available on CRAN in a timely manor. However, if you wish to download the development version with the latest updates immediately you can do this using Hadley Wickham’s devtools package:

    +
      install.packages("devtools")
    +

    then install dsims from github:

    +
      library(devtools)
+  install_github("DistanceDevelopment/dsims", build_vignettes = TRUE)
    +
    +

    Troubleshooting tip +

    +

    During installation of packages, you may get the message “These packages have more recent versions available. It is recommended to update all of them. Which would you like to update?” and then a list of packages. We recommend you typically choose the option “CRAN packages only”. Note you may then get the message that some packages cannot be installed because they are already loaded. In this case, a solution may be to note which packages these are, to open an R console (rather than R Studio) and to use the Packages | Update packages menu option (or the update.packages function) to update these packages.

    +
    +
    + +
    +
    + + +
    +

    We improve our site and software support by using Microsoft Clarity to see
    how you use our website. By using our site, you agree that we and Microsoft
    can collect and use this data. Clarity is GDPR compliant.

    +
    + +
    +

    If you wish to donate to development and maintenance, please email us.

    +
    + +
    +
    + + + + + + + diff --git a/docs/katex-auto.js b/docs/katex-auto.js new file mode 100644 index 0000000..20651d9 --- /dev/null +++ b/docs/katex-auto.js @@ -0,0 +1,14 @@ +// https://github.com/jgm/pandoc/blob/29fa97ab96b8e2d62d48326e1b949a71dc41f47a/src/Text/Pandoc/Writers/HTML.hs#L332-L345 +document.addEventListener("DOMContentLoaded", function () { + var mathElements = document.getElementsByClassName("math"); + var macros = []; + for (var i = 0; i < mathElements.length; i++) { + var texText = mathElements[i].firstChild; + if (mathElements[i].tagName == "SPAN") { + katex.render(texText.data, mathElements[i], { + displayMode: mathElements[i].classList.contains("display"), + throwOnError: false, + macros: macros, + fleqn: false + }); + }}}); diff --git a/docs/lightswitch.js b/docs/lightswitch.js new file mode 100644 index 0000000..9467125 --- /dev/null +++ b/docs/lightswitch.js @@ -0,0 +1,85 @@ + +/*! + * Color mode toggler for Bootstrap's docs (https://getbootstrap.com/) + * Copyright 2011-2023 The Bootstrap Authors + * Licensed under the Creative Commons Attribution 3.0 Unported License. + * Updates for {pkgdown} by the {bslib} authors, also licensed under CC-BY-3.0. + */ + +const getStoredTheme = () => localStorage.getItem('theme') +const setStoredTheme = theme => localStorage.setItem('theme', theme) + +const getPreferredTheme = () => { + const storedTheme = getStoredTheme() + if (storedTheme) { + return storedTheme + } + + return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light' +} + +const setTheme = theme => { + if (theme === 'auto') { + document.documentElement.setAttribute('data-bs-theme', (window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light')) + } else { + document.documentElement.setAttribute('data-bs-theme', theme) + } +} + +function bsSetupThemeToggle () { + 'use strict' + + const showActiveTheme = (theme, focus = false) => { + var activeLabel, activeIcon; + + document.querySelectorAll('[data-bs-theme-value]').forEach(element => { + const buttonTheme = element.getAttribute('data-bs-theme-value') + const isActive = buttonTheme == theme + + element.classList.toggle('active', isActive) + element.setAttribute('aria-pressed', isActive) + + if (isActive) { + activeLabel = element.textContent; + activeIcon = element.querySelector('span').classList.value; + } + }) + + const themeSwitcher = document.querySelector('#dropdown-lightswitch') + if (!themeSwitcher) { + return + } + + themeSwitcher.setAttribute('aria-label', activeLabel) + themeSwitcher.querySelector('span').classList.value = activeIcon; + + if (focus) { + themeSwitcher.focus() + } + } + + window.matchMedia('(prefers-color-scheme: dark)').addEventListener('change', () => { + const storedTheme = getStoredTheme() + if (storedTheme !== 'light' && storedTheme !== 'dark') { + setTheme(getPreferredTheme()) + } + }) + + window.addEventListener('DOMContentLoaded', () => { + showActiveTheme(getPreferredTheme()) + + document + .querySelectorAll('[data-bs-theme-value]') + .forEach(toggle => { + toggle.addEventListener('click', () => { + const theme = toggle.getAttribute('data-bs-theme-value') + setTheme(theme) + setStoredTheme(theme) + showActiveTheme(theme, true) + }) + }) + }) +} + +setTheme(getPreferredTheme()); +bsSetupThemeToggle(); diff --git a/docs/link.svg b/docs/link.svg new file mode 100644 index 0000000..88ad827 --- /dev/null +++ b/docs/link.svg @@ -0,0 +1,12 @@ + + + + + + diff --git a/docs/news/index.html b/docs/news/index.html new file mode 100644 index 0000000..6872c03 --- /dev/null +++ b/docs/news/index.html @@ -0,0 +1,152 @@ + +Changelog • dsims + Skip to contents + + +
    +
    +
    + +

    Changelog

    + Source: NEWS.md +
    + +
    +

    dsims 1.0.4

    CRAN release: 2023-11-29

    +

    Bug Fixes

    +
    • Fixed a bug when generating the simulation summary which meant that only the first value of mean.k and n.miss.dists was repeated rather than including all values in the summary tables. Issue #84
      • +
    • The make.simulation function now throws an error if the P2 ER variance estimator is used with line transects designs (rather than after the simulation has completed). Issue #61
      • +
    +
    +

    dsims 1.0.3

    +

    Bug Fixes

    +
    • Simulations were crashing if there were zero detections - now fixed and warnings displayed instead. Issue #77
      • +
    • Errors were also occurring when there were no individuals generated in a stratum, now fixed. Issue #80
      • +
    • Detections are no longer permitted across stratum boundaries - this was causing errors due to NA area values in the data. This is inline with expected protocols on surveys. Issue #81
      • +
    • Remove dependence on sp and rgeos. Issue #42
      • +
    +
    +

    dsims 1.0.2

    +

    Bug Fixes

    +
    • Fixed transparency issue with detection distance histograms when saving to wmf (generated a warning in Distance for Windows)
      • +
    • Only print summary table for individuals if animals occur as individuals (and not as clusters)
      • +
    • Updated references to examples
      • +
    • Fixed grouped strata bugs
      • +
    • Can now read transect shapefiles in from file and will convert all to one strata if global region used. This allows regional simulations from stratified designs in distance for windows.
      • +
    +
    +

    dsims 1.0.1

    CRAN release: 2022-08-30

    +

    New Features

    +
    • Added save.sim.results function so that simulation results can be written to .txt files. This is mainly useful for Distance for Windows users as R users would probably prefer to just save the whole simulation object to file.
      • +
    • Can write the simulation progress to file - this allows the simulation progress to be displayed when simulations are being run from Distance for Windows using dsims.
      • +
    • Add segmented trackline design as an option in simulation summary (currently these design can only be generated inside Distance for Windows for use in simulations).
      • +

    Bug Fixes

    +
    • Partial fix to the bug relating to grouping strata at the analysis stage. Strata grouping should now work when detections are of individuals. Still needs to be fixed for when clusters are present.
      • +
    +
    +

    dsims 1.0.0

    CRAN release: 2022-08-09

    +

    New Features

    +
    • Reading transects from file - this functionality is primarily envisioned for use from within Distance for Windows.
      • +

    Enhancements

    +
    • New routine which will generate covariate values from a zero-truncated Poisson distribution for non integer values.
      • +
    • There is now no lower limit on the number of detections in simulations as this was introducing bias. There is now a warning system in place. Very low numbers of detections may cause issues fitting. There must be more detections than there are parameters in the model for the model to have a chance of fitting successfully. Note that distance sampling good practice recommends minimum of 60-80 detections for estimating the detection function for line transects and more for points.
      • +
    • Improved histogram.N.ests function will now plot either a histogram of estimates of individuals or clusters. It also provides the use.max.reps argument so that the plot can be consistent with the option selected for the simulation summary.
      • +

    Bug Fixes

    +
    • Fixed simulations where cluster size was included - there was a formatting change in mrds output tables.
      • +
    • Added a check for repeat model definitions.
      • +
    • Add code to deal with equal model criteria values.
      • +
    • Fixed bug when no simulation repetitions had been successful
      • +
    • AICc method fixed
      • +
    • Warning indexes from parallel runs are now fixed
      • +
    +
    +

    dsims 0.2.2 / 0.2.3

    CRAN release: 2022-03-31

    +

    Bug Fixes

    +
    • Minor modifications to stay CRAN compliant.
      • +
    +
    +

    dsims 0.2.1

    CRAN release: 2022-03-17

    +

    New Features

    +
    • Now interfaces with new syntax in Distance >= 1.0.5 (it will remain backwards compatible with older versions of Distance for this release)
      • +

    Bug Fixes

    +
    • Plus sampling simulations now issue a warning and modify to minus sampling - these should not have run in previous versions.
      • +
    • Fixed default simulation truncation distance to 50 in the analyses (will fix dssd to be consistent with this in release 0.3.2)
      • +
    • Fixed the recording of warning / error indexing in parallel simulations
      • +
    +
    +

    dsims 0.2.0

    CRAN release: 2021-09-01

    +

    New Features

    +
    • Delta selection criteria is now recorded as the difference in information criteria between the top 2 best fitting models as determined by the information criteria.]
      • +
    • The iteration numbers generating warnings or errors are now stored and displayed so user can choose what to do with these results.
      • +

    Bug Fixes

    +
    • Fixed missing RMSE values
      • +
    • Fix strata re-ordering for cluster size
      • +
    • Models with -Inf information criteria no longer selected
      • +
    • Models with dht = NULL are no longer selected
      • +
    • Models which predict detection values < 1 no longer cause errors and are correctly excluded.
      • +
    • Detectibility parameters for continuous covariates are now checked and validated.
      • +
    • Fix situation where all reps are to be excluded due to problematic model fitting.
      • +
    • There was a bug in the underlying code on windows machines that meant that segmented lines were not being clipped properly. The dependencies of sf have not been updated and the issue fixed. Please update sf if you run into missing segment transects.
      • +
    +
    +

    dsims 0.1.0

    +

    Enhancements

    +
    • Introducing the new Distance Sampling Simulation package. # dsims is our latest simulation package which interfaces with dssd so designs can be generated within R, thus making the simulation process a lot easier. # dsims also makes use of ggplot to produce cleaner looking graphics.
      • +
    • Region and Design: # dsims can make use of the region creation and all the designs currently in dssd.
      • +
    • Density: # dsims can generate density objects from constant values for each strata, from fitted mgcv gam objects with x and y as explantory covariates and from formulas of x and y.
      • +
    • Density: Density grids are stored as sf polygons with their associated x, y central coordinates and density value
      • +
    • Population Description: Populations can either be created with fixed population sizes or based on the densities in the density grid.
      • +
    • Population Description: Both discrete and continuous individual level covariates can be included in the population
      • +
    • Detectablity: The detectability of the population can be described by either half normal, hazard rate or uniform detection shapes. Parameters can vary by stratum
      • +
    • Detectablity: Covariate parameters can be included to modify the scale parameter for each individual based on their covariate values.
      • +
    • Analyses:A number of detection function analyses can be incorporated in a simulation and the model with the lowest criterion (AIC / AICc / BIC) will be selected.
      • +
    • Analyses:Defining analyses is based on the arguments which are passed to our Distance R library.
      • +
    • Simulations: Simulations can be run in serial or parallel and their progress is output.
      • +
    • Simulations: The function run.survey can be used to create a single instance of a survey and check the simulation setup.
      • +
    +
    + + +
    +

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    +
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    If you wish to donate to development and maintenance, please email us.

    +
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    > ${s.what}`; + } else { + return `${s.dir} >
    ${s.title}
    > ${s.previous_headings} > ${s.what}`; + } + }, + }, + }, + ]).on('autocomplete:selected', function(event, s) { + window.location.href = s.path + "?q=" + q + "#" + s.id; + }); + }); +})(window.jQuery || window.$) + +document.addEventListener('keydown', function(event) { + // Check if the pressed key is '/' + if (event.key === '/') { + event.preventDefault(); // Prevent any default action associated with the '/' key + document.getElementById('search-input').focus(); // Set focus to the search input + } +}); diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml new file mode 100644 index 0000000..8af6c46 --- /dev/null +++ b/docs/pkgdown.yml @@ -0,0 +1,8 @@ +pandoc: '3.5' +pkgdown: 2.1.1 +pkgdown_sha: ~ +articles: + dsims-examples: dsims-examples.html + dsims_grouped_strata: dsims_grouped_strata.html + GettingStarted: GettingStarted.html +last_built: 2024-11-18T17:13Z diff --git a/docs/reference/DS.Analysis-class.html b/docs/reference/DS.Analysis-class.html new file mode 100644 index 0000000..777a7d6 --- /dev/null +++ b/docs/reference/DS.Analysis-class.html @@ -0,0 +1,134 @@ + +Class "DS.Analysis" — DS.Analysis-class • dsims + Skip to contents + + +
    +
    +
    + +

    Class "DS.Analysis"

    + Source: R/DS.Analysis.R +
    DS.Analysis-class.Rd
    +
    + +
    +

    Class "DDF.Analysis" is an S4 class describing a basic + detection function model to be fitted to distance sampling data.

    +
    + + +
    +

    Slots

    + + +
    dfmodel
    +

    Object of class "formula"; describing the +detection function model.

    + + +
    key
    +

    key function to use; "hn" gives half-normal (default), "hr" +gives hazard-rate and "unif" gives uniform. Note that if uniform key +is used, covariates cannot be included in the model.

    + + +
    adjustment
    +

    a list containing adjustment parameters: adjustment - +either "cos" (recommended), "herm" or "poly", order - the orders of +the adjustment terms to fit, scale - the scale by which the distances +in the adjustment terms are divided. See details.

    + + +
    truncation
    +

    Object of class "list"; Specifies +the truncation distance for the analyses.

    + + +
    cutpoints
    +

    Object of class "character"; gives the +cutpoints of the bins for binned data analysis.

    + + +
    er.var
    +

    specifies which encounter rate variance estimator to use.

    + + +
    control.opts
    +

    A list to specify various options including +monotonicity, method, initial.values.

    + + +
    group.strata
    +

    Dataframe with two columns ("design.id" and +"analysis.id"). The former gives the strata names as defined in the +design (i.e. the region object) the second specifies how they should +be grouped (into less strata) for the analyses

    + + +
    criteria
    +

    Object of class "character"; describes +which model selection criteria to use ("AIC","AICc","BIC").

    + + +
    +
    +

    Methods

    + + +
    run.analysis
    +

    signature=c(object = "DS.Analysis", + data = data.frame): runs the analysis described in the object on the + data provided.

    + + +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/Density-class.html b/docs/reference/Density-class.html new file mode 100644 index 0000000..af7d09f --- /dev/null +++ b/docs/reference/Density-class.html @@ -0,0 +1,110 @@ + +Class "Density" — Density-class • dsims + Skip to contents + + +
    +
    +
    + +

    Class "Density"

    + Source: R/Density.R +
    Density-class.Rd
    +
    + +
    +

    Class "Density" is an S4 class containing a list of grids which +describe the density of individuals / clusters of a population. The list +contains one grid (data.frame) for each strata.

    +
    + + +
    +

    Slots

    + + +
    region.name
    +

    Object of class "character"; the region name.

    + + +
    strata.name
    +

    Object of class "character"; the strata names

    + + +
    density.surface
    +

    Object of class "list"; list of data.frames +with the columns x, y and density. There must be one data.frame for each +strata.

    + + +
    x.space
    +

    Object of class "numeric"; The spacing between +gridpoints described in the density data.frames in the x-direction.

    + + +
    y.space
    +

    Object of class "numeric"; The spacing between +gridpoints described in the density data.frames in the y-direction.

    + + +
    units
    +

    Object of class "numeric"; The units of the grid +points.

    + + +
    +
    +

    See also

    +

    make.density

    +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/Density.Summary-class.html b/docs/reference/Density.Summary-class.html new file mode 100644 index 0000000..d6d6595 --- /dev/null +++ b/docs/reference/Density.Summary-class.html @@ -0,0 +1,83 @@ + +Class "Density.Summary" — Density.Summary-class • dsims + Skip to contents + + +
    +
    +
    + +

    Class "Density.Summary"

    + Source: R/Density.Summary.R +
    Density.Summary-class.Rd
    +
    + +
    +

    Class "Density.Summary" is an S4 class containing a +summary of the density grids for each strata.

    +
    + + +
    +

    Slots

    + + +
    summary
    +

    a summary of the average abundances and densities for +each strata.

    + + +
    +
    +

    See also

    +

    make.density

    +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/Detectability-class.html b/docs/reference/Detectability-class.html new file mode 100644 index 0000000..e18bb45 --- /dev/null +++ b/docs/reference/Detectability-class.html @@ -0,0 +1,101 @@ + +S4 Class "Detectability" — Detectability-class • dsims + Skip to contents + + +
    +
    +
    + +

    S4 Class "Detectability"

    + Source: R/Detectability.R +
    Detectability-class.Rd
    +
    + +
    +

    S4 Class "Detectability"

    +
    + + +
    +

    Slots

    + + +
    key.function
    +

    Object of class "character"; a code +specifying the detection function form ("hn" = half normal, "hr" = +hazard rate.)

    + + +
    scale.param
    +

    Object of class "numeric"; The scale +parameter for the detection function.

    + + +
    shape.param
    +

    Object of class "numeric"; The shape +parameter for the detection function.

    + + +
    cov.param
    +

    Object of class "numeric"; The parameter +values associated with the covariates. Not yet implemented

    + + +
    truncation
    +

    Object of class "numeric"; The maximum +distance at which objects may be detected.

    + + +
    +
    +

    See also

    +

    make.detectability

    +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/Population-class.html b/docs/reference/Population-class.html new file mode 100644 index 0000000..9a6ab86 --- /dev/null +++ b/docs/reference/Population-class.html @@ -0,0 +1,116 @@ + +Class "Population" — Population-class • dsims + Skip to contents + + +
    +
    +
    + +

    Class "Population"

    + Source: R/Population.R +
    Population-class.Rd
    +
    + +
    +

    Contains an instance of a population including a description of their detectability +in the form of an object of class Detectability.

    +
    + + +
    +

    Slots

    + + +
    region.name
    +

    Object of class "character"; the name of the region +object.

    + + +
    strata.names
    +

    Object of class "character"; the names of the +strata.

    + + +
    N
    +

    Object of class "numeric"; the number of individuals/clusters.

    + + +
    D
    +

    Object of class "numeric"; the density of individuals/clusters.

    + + +
    population
    +

    Object of class "data.frame"; the locations of +individuals/clusters and any population covariates.

    + + +
    detectability
    +

    Object of class "Detectability"; describes how +easily the individuals/clusters can be detected.

    + + +
    +
    +

    Methods

    + + +
    plot
    +

    signature=(object = "Line.Transect"): plots the locations + of the individuals/clusters.

    + + +
    +
    +

    See also

    +

    make.population.description, make.detectability

    +
    + +
    + + +
    +

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    +
    + +
    +

    If you wish to donate to development and maintenance, please email us.

    +
    + +
    + + + + + + + diff --git a/docs/reference/Population.Description-class.html b/docs/reference/Population.Description-class.html new file mode 100644 index 0000000..2aaddf6 --- /dev/null +++ b/docs/reference/Population.Description-class.html @@ -0,0 +1,130 @@ + +Class "Population.Description" — Population.Description-class • dsims + Skip to contents + + +
    +
    +
    + +

    Class "Population.Description"

    + Source: R/Population.Description.R +
    Population.Description-class.Rd
    +
    + +
    +

    Class "Population.Description" is an S4 class containing a +description of the population. It provides methods to generate an +example population.

    +
    + + +
    +

    Slots

    + + +
    N
    +

    Object of class "numeric"; number of individuals +in the population (optional).

    + + +
    density
    +

    Object of class "Density"; describes the +population density

    + + +
    region.name
    +

    Object of class "character"; name of +the region in which the population exists.

    + + +
    strata.names
    +

    Character vector giving the strata names for the study region.

    + + +
    covariates
    +

    Named list with one named entry per individual level covariate. +Cluster sizes can be defined here. Each list entry will either be a data.frame +containing 2 columns, the first the level (level) and the second the probability

    + + +
    size
    +

    logical value indicating whether the population occurs in +clusters. +(prob). The cluster size entry in the list must be named 'size'.

    + + +
    gen.by.N
    +

    Object of class "logical"; If TRUE +N is fixed otherwise it is generated from a Poisson distribution.

    + + +
    +
    +

    Methods

    + + +
    get.N
    +

    signature=(object = "Population.Description"): + returns the value of N

    + +
    generate.population
    +

    signature=(object = "Population.Description"): generates a single realisation of the population.

    + + +
    +
    +

    See also

    +

    make.population.description

    +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/Rplot001.png b/docs/reference/Rplot001.png new file mode 100644 index 0000000..7c9c9cd Binary files /dev/null and b/docs/reference/Rplot001.png differ diff --git a/docs/reference/Rplot002.png b/docs/reference/Rplot002.png new file mode 100644 index 0000000..3147494 Binary files /dev/null and b/docs/reference/Rplot002.png differ diff --git a/docs/reference/Simulation-class.html b/docs/reference/Simulation-class.html new file mode 100644 index 0000000..3efad75 --- /dev/null +++ b/docs/reference/Simulation-class.html @@ -0,0 +1,222 @@ + +Class "Simulation" — Simulation-class • dsims + Skip to contents + + +
    +
    +
    + +

    Class "Simulation"

    + Source: R/Simulation.R +
    Simulation-class.Rd
    +
    + +
    +

    Class "Simulation" is an S4 class containing descriptions of the +region, population, survey design and analyses the user wishes to investigate. +Once the simulation has been run the N.D.Estimates will contain multiple +estimates of abundance and density obtained by repeatedly generating +populations, simulating the survey and completing the analyses.

    +
    + + +
    +

    Slots

    + + +
    reps
    +

    Object of class "numeric"; the number of +times the simulation should be repeated.

    + + +
    single.transect.set
    +

    Object of class "logical"; if +TRUE the same set of transects are used in each repetition.

    + + +
    design
    +

    Object of class "Survey.Design"; the +survey design.

    + + +
    population.description
    +

    Object of class "Population.Description"; +the population.description.

    + + +
    detectability
    +

    Object of class "Detectability"; a +description of the detectability of the population.

    + + +
    ds.analysis
    +

    Object of class "DS.Analysis"

    + + +
    add.options
    +

    a list to expand simulation options in the future.

    + + +
    ddf.param.ests
    +

    Object of class "array"; stores the +parameters associated with the detection function.

    + + +
    results
    +

    A "list" with elements 'individuals' (and +optionally 'clusters' and 'expected.size') as well as 'Detection'.

    +

    The 'individuals' and 'clusters' elements are a list of three +3-dimensional arrays. The first is a summary array containing +values for 'Area' (strata area), 'CoveredArea' (the area +covered in the strata by the survey), Effort' (the line length +or number of points surveyed), 'n' (the number of sightings), +'n.miss.dists' (the number of missing distances - only applicable +to mixed detector types and not yet implemented in dsims), 'k' +(the number of transects), 'ER' (encounter rate), 'se.ER' +(standard error of the encounter rate), 'cv.ER' (coefficient of +variation of the encounter rate). A value is provided for each +of these for each strata as well as the region as a whole and +for each simulation repetition as well as storing the mean and +standard deviation of these values across simulation repetitions.

    +

    The second array 'N' is the abundance estimates table. It contains +values for the 'Estimate' (estimated abundance based on data from +iteration i), 'se' (standard error associated with the estimate), +'cv' (coefficient of variation of estimate), 'lcl' (lower 95% +confidence interval value), 'ucl' (upper 95% confidence interval +value), 'df' the degrees of freedom associated with the estimate. +A value is provided for each of these for each strata as well as +the region as a whole and for each simulation repetition as well +as storing the mean and standard deviation of these values across +simulation repetitions.

    +

    The third array 'D' is the density estimates table. It contains +values for the 'Estimate' (estimated density based on data from +iteration i), 'se' (standard error associated with the estimate), +'cv' (coefficient of variation of estimate), 'lcl' (lower 95% +confidence interval value), 'ucl' (upper 95% confidence interval +value), 'df' the degrees of freedom associated with the estimate. +A value is provided for each of these for each strata as well as +the region as a whole and for each simulation repetition as well +as storing the mean and standard deviation of these values across +simulation repetitions.

    + +

    When animals occur in clusters the expected.size element of the +results list contains a 3-dimensional array. It gives values +for 'Expected.S' (expected cluster size), 'se.Expected.S' +(the standard error of the expected cluster size), +'cv.Expected.S' (the coefficient of variation for the expected +cluster size). Values are given for each analysis strata as +well as a value for the survey region as a whole and across +each simulation repetition as well as overall means and standard +deviations across repetitions.

    + +

    The Detection element of the results list is a 3-dimensional +array with values for 'True.Pa' (the proportion of animals in +the covered region which were detected), 'Pa' (the estimated +proportion of animals detected in the covered region), 'ESW' +(the estimated strip width), 'f(0)' (The estimated value of +the detection function pdf at distance 0), 'SelectedModel' +(the index of the model which had the best fit to the dataset +for the repetition), 'DeltaCriteria' (the difference in +information criteria between the best and second best fitting +models where two or more models were fitted and converged), +'SuccessfulModels' (the number of models which successfully +converged). Currently detection functions are pooled across +all strata so there is only one global value for each +simulated dataset as well as a mean value and standard +deviation where appropriate.

    + + +
    warnings
    +

    A "list" to store warnings and error messages encountered +during runtime.

    + + +
    +
    +

    Methods

    + + +
    summary
    +

    signature=(object = "Simulation"): produces + a summary of the simulation and its results.

    + +
    generate.population
    +

    signature = (object = + "Simulation"): generates a single instance of a population.

    + +
    generate.transects
    +

    signature = (object = + "Simulation"): generates a single set of transects.

    + +
    run.survey
    +

    signature = (object = + "Simulation"): carries out the simulation process as far as generating + the distance data and returns an object containing the population, + transects and data.

    + +
    run.simulation
    +

    signature = (simulation = "Simulation"): runs + the whole simulation for the specified number of repetitions.

    + + +
    +
    +

    See also

    +

    make.simulation

    +
    + +
    + + +
    +

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    +
    + +
    +

    If you wish to donate to development and maintenance, please email us.

    +
    + +
    + + + + + + + diff --git a/docs/reference/Simulation.Summary-class.html b/docs/reference/Simulation.Summary-class.html new file mode 100644 index 0000000..ea1f635 --- /dev/null +++ b/docs/reference/Simulation.Summary-class.html @@ -0,0 +1,84 @@ + +Class "Simulation.Summary" — Simulation.Summary-class • dsims + Skip to contents + + +
    +
    +
    + +

    Class "Simulation.Summary"

    + Source: R/Simulation.Summary.R +
    Simulation.Summary-class.Rd
    +
    + +
    +

    Class "Simulation.Summary" is an S4 class containing a summary of +the simulation results. This is returned when summary(Simulation) +is called. If it is not assigned to a variable the object will be +displayed via the show method.

    +
    + + +
    +

    Methods

    + + +
    show
    +

    signature=(object = "Simulation.Summary"): prints + the contents of the object in a user friendly format.

    + + +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/Survey-class.html b/docs/reference/Survey-class.html new file mode 100644 index 0000000..1cb0502 --- /dev/null +++ b/docs/reference/Survey-class.html @@ -0,0 +1,75 @@ + +Virtual Class "Survey" — Survey-class • dsims + Skip to contents + + +
    +
    +
    + +

    Virtual Class "Survey"

    + Source: R/Survey.R +
    Survey-class.Rd
    +
    + +
    +

    Class "Survey" is an S4 class containing an instance of a population.

    +
    + + +
    +

    Slots

    + + +
    population
    +

    Object of class "Population"; an instance of +a population.

    + + +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/Survey.LT-class.html b/docs/reference/Survey.LT-class.html new file mode 100644 index 0000000..e6b3015 --- /dev/null +++ b/docs/reference/Survey.LT-class.html @@ -0,0 +1,88 @@ + +Class "Survey.LT" extends class "Survey" — Survey.LT-class • dsims + Skip to contents + + +
    +
    +
    + +

    Class "Survey.LT" extends class "Survey"

    + Source: R/Survey.LT.R +
    Survey.LT-class.Rd
    +
    + +
    +

    Class "Survey.LT" is an S4 class containing a population +and a set of transects.

    +
    + + +
    +

    Slots

    + + +
    transect
    +

    Object of class "Line.Transect"; the +line transects.

    + + +
    perpendicular.truncation
    +

    Object of class "numeric"; the +maximum distance from the transect at which animals may be detected.

    + + +
    +
    +

    See also

    +

    make.design

    +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/Survey.PT-class.html b/docs/reference/Survey.PT-class.html new file mode 100644 index 0000000..ccde07d --- /dev/null +++ b/docs/reference/Survey.PT-class.html @@ -0,0 +1,88 @@ + +Class "Survey.PT" extends class "Survey" — Survey.PT-class • dsims + Skip to contents + + +
    +
    +
    + +

    Class "Survey.PT" extends class "Survey"

    + Source: R/Survey.PT.R +
    Survey.PT-class.Rd
    +
    + +
    +

    Class "Survey.PT" is an S4 class containing a population +and a set of transects.

    +
    + + +
    +

    Slots

    + + +
    transect
    +

    Object of class "Point.Transect"; the +point transects.

    + + +
    radial.truncation
    +

    Object of class "numeric"; the +maximum distance from the transect at which animals may be detected.

    + + +
    +
    +

    See also

    +

    make.design

    +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/add.hotspot-methods.html b/docs/reference/add.hotspot-methods.html new file mode 100644 index 0000000..d32dcad --- /dev/null +++ b/docs/reference/add.hotspot-methods.html @@ -0,0 +1,104 @@ + +S4 generic method to add a hotspot to the density grid — add.hotspot • dsims + Skip to contents + + +
    +
    +
    + +

    S4 generic method to add a hotspot to the density grid

    + Source: R/generic.functions.R, R/Density.R +
    add.hotspot-methods.Rd
    +
    + +
    +

    Uses a Gaussian decay around a central location to add a hotspot to the +density grid.

    +
    + +
    +

    Usage

    + 
    add.hotspot(object, centre, sigma, amplitude)
+
+# S4 method for class 'Density'
+add.hotspot(object, centre, sigma, amplitude)
    +
    + +
    +

    Arguments

    + + +
    object
    +

    a Density-class object

    + + +
    centre
    +

    an x,y-coordinate giving the centre of the hotspot

    + + +
    sigma
    +

    a value giving the scale parameter for a gaussian decay

    + + +
    amplitude
    +

    the height of the hotspot at its centre

    + +
    +
    +

    Value

    +

    the updated Density-class object

    +
    +
    +

    See also

    +

    make.density

    +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/analyse.data-methods.html b/docs/reference/analyse.data-methods.html new file mode 100644 index 0000000..128e76d --- /dev/null +++ b/docs/reference/analyse.data-methods.html @@ -0,0 +1,126 @@ + +S4 generic method to run analyses — analyse.data • dsims + Skip to contents + + +
    +
    +
    + +

    S4 generic method to run analyses

    + Source: R/generic.functions.R, R/DS.Analysis.R +
    analyse.data-methods.Rd
    +
    + +
    +

    This method carries out an analysis of distance sampling data. This method +is provided to allow the user to perform diagnostics of the analyses used +in the simulation. The data argument can be obtained by a call to +simulate.survey(object, dht.table = TRUE). Note if the first object +supplied is of class DS.Analysis then the second argument must be of class +DDf.Data. The data argument may be of either class for an object argument +of class Simulation.

    +
    + +
    +

    Usage

    + 
    analyse.data(analysis, data.obj, ...)
+
+# S4 method for class 'DS.Analysis,Survey'
+analyse.data(analysis, data.obj, warnings = NULL, ...)
+
+# S4 method for class 'DS.Analysis,data.frame'
+analyse.data(analysis, data.obj, warnings = NULL, transect = "line", ...)
    +
    + +
    +

    Arguments

    + + +
    analysis
    +

    an object of class DS.Analysis

    + + +
    data.obj
    +

    an object of class Survey or a dataframe

    + + +
    ...
    +

    optional arguments (currently not used)

    + + +
    warnings
    +

    a list of warnings and how many times they arose

    + + +
    transect
    +

    character value either "line" or "point" specifying type of +transect used in survey

    + +
    +
    +

    Value

    +

    a list containing an S3 ddf object and optionally an S3 dht object relating to the model with the minimum criteria.

    +

    either returns a list of the best model, warnings and the number of successfully +fitted models (if warnings is supplied as a list) otherwise displays warnings as it goes +and returns the best fitting ds model.

    +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/description.summary.html b/docs/reference/description.summary.html new file mode 100644 index 0000000..0acd54b --- /dev/null +++ b/docs/reference/description.summary.html @@ -0,0 +1,78 @@ + +Provides a description of the summary object/output — description.summary • dsims + Skip to contents + + +
    +
    +
    + +

    Provides a description of the summary object/output

    + Source: R/description.summary.R +
    description.summary.Rd
    +
    + +
    +

    Prints a list of the terms used in the simulation summary.

    +
    + +
    +

    Usage

    + 
    description.summary()
    +
    + +
    +

    Value

    +

    no return, displays an explanation of the simulation summary

    +
    +
    +

    Author

    +

    Laura Marshall

    +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/dsims-package.html b/docs/reference/dsims-package.html new file mode 100644 index 0000000..6a28682 --- /dev/null +++ b/docs/reference/dsims-package.html @@ -0,0 +1,93 @@ + +Distance Sampling Simulations 'dsims' — dsims-package • dsims + Skip to contents + + +
    +
    +
    + +

    Distance Sampling Simulations 'dsims'

    + Source: R/dsims-package.R +
    dsims-package.Rd
    +
    + +
    +

    Runs simulations of distance sampling surveys to help users optimise +their survey designs for their particular study.

    +
    + + +
    +

    Details

    +

    The full process involves defining the study region, a description of the +population of interest (including its distribution within the study region), +a survey design, a detection process and one or more models to fit to the +resulting data. The simulation engine will then use this information to +generate both a population and a set of transects and simulate the detection +process. The resulting data will be analysed and the estimates stored. +By repeating this many times we can test the accuracy and precision of +our estimates from various survey designs given our particular population +of interest.

    +

    This package interfaces with the survey design package 'dssd' to create the survey +regions, designs and generate the survey transects. While the 'DSsim' simulation +package relied on survey transects already being contained in shapefiles within +the supplied directory, dsims will generate the survey transects directly in R.

    +

    The main functions in this package are: make.density, make.population.description, make.detectability, make.ds.analysis, make.simulation, run.survey and run.simulation. See also make.region and make.design in the dssd package for examples of how to define study regions and designs.

    +

    Further information on distance sampling methods and example code is available at http://distancesampling.org/R/.

    +

    Also see our website for vignettes / example code at http://examples.distancesampling.org.

    +

    For help with distance sampling and this package, there is a Google Group https://groups.google.com/forum/#!forum/distance-sampling.

    +
    +
    +

    Author

    +

    Laura Marshall <lhm@st-and.ac.uk>

    +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/generate.population-methods.html b/docs/reference/generate.population-methods.html new file mode 100644 index 0000000..467623f --- /dev/null +++ b/docs/reference/generate.population-methods.html @@ -0,0 +1,112 @@ + +S4 generic method to generate an instance of a population — generate.population • dsims + Skip to contents + + +
    +
    +
    + +

    S4 generic method to generate an instance of a population

    + Source: R/generic.functions.R, R/Population.Description.R, R/Simulation.R +
    generate.population-methods.Rd
    +
    + +
    +

    Uses the population description and detectability details to generate an +instance of the population. Note that if the first argument supplied is +of class Population.Description rather than class Simulation then +detectability and region must also be supplied.

    +
    + +
    +

    Usage

    + 
    generate.population(object, ...)
+
+# S4 method for class 'Population.Description'
+generate.population(object, detectability = NULL, region = NULL)
+
+# S4 method for class 'Simulation'
+generate.population(object, ...)
    +
    + +
    +

    Arguments

    + + +
    object
    +

    an object of class Simulation or Population.Description

    + + +
    ...
    +

    when this is called on an object of class Population.Description +the additional arguments detectability and region.obj should also be supplied

    + + +
    detectability
    +

    object of class Detectability (optional - only +required if object is of class Population.Description)

    + + +
    region
    +

    the region object for the population (optional - only +required if object is of class Population.Description)

    + +
    +
    +

    Value

    +

    Population-class object

    +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/generate.transects.Simulation-methods.html b/docs/reference/generate.transects.Simulation-methods.html new file mode 100644 index 0000000..d759ce4 --- /dev/null +++ b/docs/reference/generate.transects.Simulation-methods.html @@ -0,0 +1,91 @@ + +generate.transects — generate.transects,Simulation-method • dsims + Skip to contents + + +
    +
    +
    + +

    generate.transects

    + Source: R/Simulation.R +
    generate.transects.Simulation-methods.Rd
    +
    + +
    +

    Generates a set of transects based on the design provided.

    +
    + +
    +

    Usage

    + 
    # S4 method for class 'Simulation'
+generate.transects(object, quiet = FALSE, ...)
    +
    + +
    +

    Arguments

    + + +
    object
    +

    object of class Simulation

    + + +
    quiet
    +

    if TRUE silences some warnings

    + + +
    ...
    +

    not implemented

    + +
    +
    +

    Value

    +

    an object of class Transect from dssd package

    +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/get.N-methods.html b/docs/reference/get.N-methods.html new file mode 100644 index 0000000..4a8cf21 --- /dev/null +++ b/docs/reference/get.N-methods.html @@ -0,0 +1,85 @@ + +S4 generic method to return N — get.N • dsims + Skip to contents + + +
    +
    +
    + +

    S4 generic method to return N

    + Source: R/Population.Description.R +
    get.N-methods.Rd
    +
    + +
    +

    Returns the population size

    +
    + +
    +

    Usage

    + 
    get.N(object)
+
+# S4 method for class 'Population.Description'
+get.N(object)
    +
    + +
    +

    Arguments

    + + +
    object
    +

    an object of class Population.Description

    + +
    +
    +

    Value

    +

    numeric value of the population size

    +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/get.densities-methods.html b/docs/reference/get.densities-methods.html new file mode 100644 index 0000000..b0a517c --- /dev/null +++ b/docs/reference/get.densities-methods.html @@ -0,0 +1,93 @@ + +Method to get density values — get.densities • dsims + Skip to contents + + +
    +
    +
    + +

    Method to get density values

    + Source: R/Density.R +
    get.densities-methods.Rd
    +
    + +
    +

    This method extracts the density values from a density object. It will +optionally also return the x and y centre points for the density grid +cells.

    +
    + +
    +

    Usage

    + 
    get.densities(density, coords = FALSE)
    +
    + +
    +

    Arguments

    + + +
    density
    +

    object of class Density

    + + +
    coords
    +

    if TRUE also returns x, y coordinates

    + +
    +
    +

    Value

    +

    either returns a numeric vector of density values or a dataframe +with columns x, y and density.

    +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/histogram.N.ests-methods.html b/docs/reference/histogram.N.ests-methods.html new file mode 100644 index 0000000..6cf1249 --- /dev/null +++ b/docs/reference/histogram.N.ests-methods.html @@ -0,0 +1,95 @@ + +histogram.N.ests — histogram.N.ests • dsims + Skip to contents + + +
    +
    +
    + +

    histogram.N.ests

    + Source: R/Simulation.R +
    histogram.N.ests-methods.Rd
    +
    + +
    +

    Plots a histogram of the estimates abundances

    +
    + +
    +

    Usage

    + 
    histogram.N.ests(x, use.max.reps = FALSE, N.ests = "individuals", ...)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    object of class Simulation

    + + +
    use.max.reps
    +

    by default this is FALSE meaning that only simulation repetitions where all models converged for that data set are included. By setting this to TRUE any repetition where one or more models converged will be included in the summary results.

    + + +
    N.ests
    +

    character indicating whether to plot estimates of abundance of 'individuals', +'clusters' or 'both'. By default this is individuals.

    + + +
    ...
    +

    optional parameters to pass to the generic hist function in graphics

    + +
    +
    +

    Value

    +

    No return value, displays a histogram of the abundance estimates

    +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/index.html b/docs/reference/index.html new file mode 100644 index 0000000..5dcbcf3 --- /dev/null +++ b/docs/reference/index.html @@ -0,0 +1,303 @@ + +Package index • dsims + Skip to contents + + +
    +
    +
    + +

    Package index

    +
    + +
    +

    All functions

    + + + + +
    + + + + +
    + + add.hotspot() + +
    +
    S4 generic method to add a hotspot to the density grid
    +
    + + analyse.data() + +
    +
    S4 generic method to run analyses
    +
    + + Density-class + +
    +
    Class "Density"
    +
    + + Density.Summary-class + +
    +
    Class "Density.Summary"
    +
    + + description.summary() + +
    +
    Provides a description of the summary object/output
    +
    + + Detectability-class + +
    +
    S4 Class "Detectability"
    +
    + + DS.Analysis-class + +
    +
    Class "DS.Analysis"
    +
    + + dsims-package dsims + +
    +
    Distance Sampling Simulations 'dsims'
    +
    + + generate.population() + +
    +
    S4 generic method to generate an instance of a population
    +
    + + generate.transects(<Simulation>) + +
    +
    generate.transects
    +
    + + get.densities() + +
    +
    Method to get density values
    +
    + + get.N() + +
    +
    S4 generic method to return N
    +
    + + histogram.N.ests() + +
    +
    histogram.N.ests
    +
    + + make.density() + +
    +
    Creates a Density object
    +
    + + make.detectability() + +
    +
    Creates a Detectability object
    +
    + + make.ds.analysis() + +
    +
    Creates an Analysis object
    +
    + + make.population.description() + +
    +
    Creates a Population.Description object
    +
    + + make.simulation() + +
    +
    Creates a Simulation object
    +
    + + plot(<Survey>,<Region>) plot(<Survey>,<ANY>) + +
    +
    plot
    +
    + + plot(<Density>,<ANY>) plot(<Density>,<Region>) + +
    +
    Plot
    +
    + + plot(<Detectability>,<ANY>) plot(<Detectability>,<Population.Description>) + +
    +
    Plot
    +
    + + plot(<Population>,<ANY>) plot(<Population>,<Region>) + +
    +
    Plot
    +
    + + Population-class + +
    +
    Class "Population"
    +
    + + Population.Description-class + +
    +
    Class "Population.Description"
    +
    + + run.simulation() + +
    +
    Method to run a simulation
    +
    + + run.survey() + +
    +
    S4 generic method to simulate a survey
    +
    + + rztpois() + +
    +
    Randomly generates values from a zero-truncated Poisson distribution
    +
    + + save.sim.results() + +
    +
    save.sim.results
    +
    + + set.densities() + +
    +
    Method to set density values
    +
    + + show(<Density.Summary>) + +
    +
    show
    +
    + + show(<Simulation>) + +
    +
    show
    +
    + + show(<Simulation.Summary>) + +
    +
    show
    +
    + + Simulation-class + +
    +
    Class "Simulation"
    +
    + + Simulation.Summary-class + +
    +
    Class "Simulation.Summary"
    +
    + + summary(<Density>) + +
    +
    summary
    +
    + + summary(<Simulation>) + +
    +
    summary
    +
    + + Survey-class + +
    +
    Virtual Class "Survey"
    +
    + + Survey.LT-class + +
    +
    Class "Survey.LT" extends class "Survey"
    +
    + + Survey.PT-class + +
    +
    Class "Survey.PT" extends class "Survey"
    +
    +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/make.density-1.png b/docs/reference/make.density-1.png new file mode 100644 index 0000000..75d4417 Binary files /dev/null and b/docs/reference/make.density-1.png differ diff --git a/docs/reference/make.density.html b/docs/reference/make.density.html new file mode 100644 index 0000000..f2440ae --- /dev/null +++ b/docs/reference/make.density.html @@ -0,0 +1,168 @@ + +Creates a Density object — make.density • dsims + Skip to contents + + +
    +
    +
    + +

    Creates a Density object

    + Source: R/ClassConstructors.R +
    make.density.Rd
    +
    + +
    +

    Creates a density grid across the study area describing the distribution +of animals.

    +
    + +
    +

    Usage

    + 
    make.density(
+  region = make.region(),
+  x.space = 20,
+  y.space = NULL,
+  constant = numeric(0),
+  fitted.model = NULL,
+  density.formula = NULL,
+  density.surface = list()
+)
    +
    + +
    +

    Arguments

    + + +
    region
    +

    the Region object in which the density grid will be created

    + + +
    x.space
    +

    the intervals in the grid in the x direction

    + + +
    y.space
    +

    the intervals in the grid in the y direction

    + + +
    constant
    +

    a value describing a constant density across the surface. If not supplied a default value of 1 is used for all strata.

    + + +
    fitted.model
    +

    gam object created using mgcv with only x and y as explanatory covariates.

    + + +
    density.formula
    +

    a formula of x and/or y describing the +density surface.

    + + +
    density.surface
    +

    Object of class list; an sf grid recording +the density grid polygons, density values within those polygons and the +central x and y coordinates.

    + +
    +
    +

    Value

    +

    Density-class object

    +
    +
    +

    Details

    +

    There are multiple ways to create the density grid. The most straight forward +is to create a grid with constant values (to which high and low areas can later +be added) or pass in a fitted mgcv gam. The gam model should only be fitted +with x and y as explanatory variables. If you plan on trying multiple +animal distributions by adding high and low areas to a constant surface it is +recommended to make a copy of the initial flat density grid object as the first +step in grid generation is computationally intensive and can take a little while +to complete, especially if you have a fine density grid.

    +
    +
    +

    See also

    +

    make.region

    +
    +
    +

    Author

    +

    Laura Marshall

    +
    + +
    +

    Examples

    + 
    # A simple density surface with a constant value of 1 can be created within a rectangular
+# Create a region from shapefile
+shapefile.name <- system.file("extdata", "StAndrew.shp", package = "dssd")
+region <- make.region(region.name = "St Andrews bay",
+                      shape = shapefile.name)
+
+# Create a density object
+density <- make.density(region = region,
+                       x.space = 1000,
+                       constant = 1)
+
+# Add some ares of higher / lower density
+density <- add.hotspot(object = density,
+                       centre = c(-170000, 6255000),
+                       sigma = 10000,
+                       amplitude = 4)
+density <- add.hotspot(object = density,
+                       centre = c(-150000, 6240000),
+                       sigma = 10000,
+                       amplitude = -0.9)
+
+# Plot the density
+plot(density, region)
+
+
+
    +
    +
    + + +
    +

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    +
    + +
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    +
    + +
    + + + + + + + diff --git a/docs/reference/make.detectability-1.png b/docs/reference/make.detectability-1.png new file mode 100644 index 0000000..6fdc176 Binary files /dev/null and b/docs/reference/make.detectability-1.png differ diff --git a/docs/reference/make.detectability.html b/docs/reference/make.detectability.html new file mode 100644 index 0000000..83d236e --- /dev/null +++ b/docs/reference/make.detectability.html @@ -0,0 +1,167 @@ + +Creates a Detectability object — make.detectability • dsims + Skip to contents + + +
    +
    +
    + +

    Creates a Detectability object

    + Source: R/ClassConstructors.R +
    make.detectability.Rd
    +
    + +
    +

    The detectability of the population is described by the values in this +class.

    +
    + +
    +

    Usage

    + 
    make.detectability(
+  key.function = "hn",
+  scale.param = 25,
+  shape.param = numeric(0),
+  cov.param = list(),
+  truncation = 50
+)
    +
    + +
    +

    Arguments

    + + +
    key.function
    +

    specifies shape of the detection function (either +half-normal "hn", hazard rate "hr" or uniform "uf")

    + + +
    scale.param
    +

    numeric vector with either a single value to be applied globally or a value for each strata. These should be supplied on the natural scale.

    + + +
    shape.param
    +

    numeric vector with either a single value to be applied globally or a value for each strata. These should be supplied on the natural scale.

    + + +
    cov.param
    +

    Named list with one named entry per individual level covariate. Covariate parameter values should be defined on the log scale (rather than the natural scale), this is the same scale as provided in the ddf output in mrds and also in the MCDS output in Distance. Cluster sizes parameter values can be defined here. Each list entry will either be a data.frame containing 2 or 3 columns: level, param and where desired strata. If the region has multiple strata but this column is omitted then the values will be assumed to apply globally. The cluster size entry in the list must be named 'size'. Alternatively the list element may a numeric vector with either a single value to be applied globally or a value for each strata.

    + + +
    truncation
    +

    the maximum perpendicular (or radial) distance at which +objects may be detected from a line (or point) transect.

    + +
    +
    +

    Value

    +

    Detectability-class object

    +
    +
    +

    See also

    +

    make.simulation make.population.description make.density

    +
    +
    +

    Author

    +

    Laura Marshall

    +
    + +
    +

    Examples

    + 
    # Multi-strata example (make sf shape)
+s1 = matrix(c(0,0,0,2,1,2,1,0,0,0),ncol=2, byrow=TRUE)
+s2 = matrix(c(1,0,1,2,2,2,2,0,1,0),ncol=2, byrow=TRUE)
+pol1 = sf::st_polygon(list(s1))
+pol2 = sf::st_polygon(list(s2))
+sfc <- sf::st_sfc(pol1,pol2)
+strata.names <- c("low", "high")
+sf.pol <- sf::st_sf(strata = strata.names, geom = sfc)
+
+region <- make.region(region.name = "Multi-strata Eg",
+                      strata.name = strata.names,
+                      shape = sf.pol)
+
+density <- make.density(region = region,
+                        x.space = 0.22,
+                        constant = c(20,50))
+
+covs <- list()
+covs$size <- list(list(distribution = "poisson", lambda = 25),
+                  list(distribution = "poisson", lambda = 15))
+covs$sex <- data.frame(level = rep(c("male", "female"),2),
+                      prob = c(0.5, 0.5, 0.6, 0.4),
+                      strata = c(rep("low",2),rep("high",2)))
+
+# Define the population description (this time using the density to determine
+# the population size)
+popdesc <- make.population.description(region = region,
+                                       density = density,
+                                       covariates = covs,
+                                       fixed.N = FALSE)
+
+cov.param <- list()
+cov.param$size <- c(log(1.02),log(1.005))
+cov.param$sex <- data.frame(level = c("male", "female", "male", "female"),
+                            param = c(log(1.5), 0, log(1.7), log(1.2)),
+                            strata = c("low","low","high","high"))
+
+# define the detecability
+detect <- make.detectability(key.function = "hn",
+                             scale.param = 0.08,
+                             cov.param = cov.param,
+                             truncation = 0.2)
+
+plot(detect, popdesc)
+
+
+
    +
    +
    + + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/make.ds.analysis.html b/docs/reference/make.ds.analysis.html new file mode 100644 index 0000000..912a070 --- /dev/null +++ b/docs/reference/make.ds.analysis.html @@ -0,0 +1,186 @@ + +Creates an Analysis object — make.ds.analysis • dsims + Skip to contents + + +
    +
    +
    + +

    Creates an Analysis object

    + Source: R/ClassConstructors.R +
    make.ds.analysis.Rd
    +
    + +
    +

    This method creates an Analysis objects which describes a one or more +models to fit to the distance data. The simulation will fit each of these +models to the data generated in the simulation and select the model with +the minimum criteria value.

    +
    + +
    +

    Usage

    + 
    make.ds.analysis(
+  dfmodel = list(~1),
+  key = "hn",
+  truncation = numeric(0),
+  cutpoints = numeric(0),
+  er.var = "R2",
+  control.opts = list(),
+  group.strata = data.frame(),
+  criteria = "AIC"
+)
    +
    + +
    +

    Arguments

    + + +
    dfmodel
    +

    list of distance sampling model formula specifying the detection function +(see ?Distance::ds for further details)

    + + +
    key
    +

    key function to use; "hn" gives half-normal (default) and "hr" gives +hazard-rate.

    + + +
    truncation
    +

    absolute truncation distance in simulation units matching the +region units.

    + + +
    cutpoints
    +

    supply a vector of cutpoints if you wish the simulation to perform +binned analyses.

    + + +
    er.var
    +

    encounter rate variance estimator to use when abundance estimates are +required. Defaults to "R2" for line transects and "P3" for point transects. See +mrds::varn for more information / options.

    + + +
    control.opts
    +

    A list of control options: method - optimisation method,

    + + +
    group.strata
    +

    Dataframe with two columns ("design.id" and "analysis.id"). The +former gives the strata names as defined in the design (i.e. the region object) the +second specifies how they should be grouped (into less strata) for the analyses. See +details for more information.

    + + +
    criteria
    +

    character model selection criteria (AIC, AICc, BIC)

    + +
    +
    +

    Value

    +

    DS.Analysis-class object

    +
    +
    +

    Details

    +

    It is possible to group strata at the analysis stage using the group.strata +argument. For example, for design purposes it may have been sensible to +divide strata into substrata. This can help make more convex shapes and +therefore zigzag designs more efficient or perhaps it helped to keep +transects angled parallel to density gradients across the study area. +Despite these (purely design relevant) substrata we may still wish to +calculate estimates of density / abundance etc. for each stratum. The +table below gives an example of the data.frame which can be used to do +this. Imagine a study region with an onshore strata and an offshore +strata. The onshore strata has been divided in two at the design stage +to keep transects perpendicular to the coast. We now want to analyse +this as just two strata the onshore and offshore.

    +
    design.idanalysis.id
    ——————–
    onshoreNonshore
    onshoreSonshore
    offshoreoffshore
    +
    +

    See also

    +

    ds make.simulation

    +
    +
    +

    Author

    +

    Laura Marshall

    +
    + +
    +

    Examples

    + 
    
+# Model selection considering both a half-normal and a hazard-rate model
+# using AIC criteria and truncating 5% of the data
+ds.analyses <- make.ds.analysis(dfmodel = ~1,
+                                key = c("hn", "hr"),
+                                truncation = 500,
+                                criteria = "AIC")
+
+# Model selection considering both a half-normal with no covariates and with size
+# as a covariate using AIC criteria and truncating at 500
+ds.analyses <- make.ds.analysis(dfmodel = list(~1, ~size),
+                                key = "hn",
+                                truncation = 500,
+                                criteria = "AIC")
+
+# Model selection considering both a half-normal with no covariates and with size
+# as a covariate and a hazard rate, using AIC criteria and truncating at 500
+ds.analyses <- make.ds.analysis(dfmodel = list(~1, ~size, ~1),
+                                key = c("hn", "hn", "hr"),
+                                truncation = 500,
+                                criteria = "AIC")
+
+
    +
    +
    + + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/make.population.description-1.png b/docs/reference/make.population.description-1.png new file mode 100644 index 0000000..abad91c Binary files /dev/null and b/docs/reference/make.population.description-1.png differ diff --git a/docs/reference/make.population.description-2.png b/docs/reference/make.population.description-2.png new file mode 100644 index 0000000..b170944 Binary files /dev/null and b/docs/reference/make.population.description-2.png differ diff --git a/docs/reference/make.population.description.html b/docs/reference/make.population.description.html new file mode 100644 index 0000000..860ff5f --- /dev/null +++ b/docs/reference/make.population.description.html @@ -0,0 +1,196 @@ + +Creates a Population.Description object — make.population.description • dsims + Skip to contents + + +
    +
    +
    + +

    Creates a Population.Description object

    + Source: R/ClassConstructors.R +
    make.population.description.Rd
    +
    + +
    +

    Creates an object which describes a population. The values in this object +will be used to create instances of the population.

    +
    + +
    +

    Usage

    + 
    make.population.description(
+  region = make.region(),
+  density = make.density(),
+  covariates = list(),
+  N = numeric(0),
+  fixed.N = TRUE
+)
    +
    + +
    +

    Arguments

    + + +
    region
    +

    the Region object in which this population exists (see make.region).

    + + +
    density
    +

    the Density object describing the distribution of the individuals / clusters (see make.density).

    + + +
    covariates
    +

    Named list with one named entry per individual-level covariate. Cluster sizes can be defined here, it must be named 'size'. The distribution of covariate values can either be defined by specifying a particular distribution and its parameters or as a discrete distribution in a dataframe. Dataframes should have columns level and prob (and optionally strata) specifying the covariates levels, probabilities and strata if they are strata specific. Distributions can be defined as lists with named entries distribution and the relevant parameters as specified in details. A list of distributions can be provided with one for each strata.

    + + +
    N
    +

    the number of individuals / clusters in a population with one value per +strata. Total population size is 1000 by default.

    + + +
    fixed.N
    +

    a logical value. If TRUE the population is generated from the value(s) +of N otherwise it is generated from the values in the density grid.

    + +
    +
    +

    Value

    +

    Population.Description-class

    +
    +
    +

    Details

    +

    Individual-level covariate values can be defined as one of the following distributions: 'normal', 'poisson', 'ztruncpois' or 'lognormal'. The distribution name and the associated parameters as defined in the table below must be provided in a named list. Either one list can be provided for the entire study area or multiple lists grouped together as a list with one per strata.

    +
    DistributionParameters
    normalmeansd
    poissonlambda
    ztruncpoismean
    lognormalmeanlogsdlog
    +
    +

    See also

    +

    make.simulation make.detectability make.density

    +
    +
    +

    Author

    +

    Laura Marshall

    +
    + +
    +

    Examples

    + 
    # Create a basic rectangular study area
+region <- make.region()
+
+# Make a density grid (large spacing for speed)
+density <- make.density(region = region,
+                        x.space = 200,
+                        y.space = 100,
+                        constant = 1)
+density <- add.hotspot(density, centre = c(1000, 100), sigma = 250, amplitude = 10)
+
+# Define some covariate values for out population
+covs <- list()
+covs$size <- list(distribution = "ztruncpois", mean = 5)
+
+# Define the population description
+popdsc <- make.population.description(region = region,
+                                      density = density,
+                                      covariates = covs,
+                                      N = 200)
+
+# define the detecability
+detect <- make.detectability(key.function = "hn", scale.param = 25, truncation = 50)
+
+# generate an example population
+pop <- generate.population(popdsc, region = region, detectability = detect)
+
+plot(pop, region)
+
+
+# Multi-strata example (make sf shape)
+s1 = matrix(c(0,0,0,2,1,2,1,0,0,0),ncol=2, byrow=TRUE)
+s2 = matrix(c(1,0,1,2,2,2,2,0,1,0),ncol=2, byrow=TRUE)
+pol1 = sf::st_polygon(list(s1))
+pol2 = sf::st_polygon(list(s2))
+sfc <- sf::st_sfc(pol1,pol2)
+strata.names <- c("low", "high")
+sf.pol <- sf::st_sf(strata = strata.names, geom = sfc)
+
+region <- make.region(region.name = "Multi-strata Eg",
+                      strata.name = strata.names,
+                      shape = sf.pol)
+# \donttest{
+density <- make.density(region = region,
+                        x.space = 0.22,
+                        constant = c(10,80))
+
+covs <- list()
+covs$size <- list(list(distribution = "poisson", lambda = 25),
+                  list(distribution = "poisson", lambda = 15))
+covs$sex <- data.frame(level = rep(c("male", "female"),2),
+                      prob = c(0.5, 0.5, 0.6, 0.4),
+                      strata = c(rep("low",2),rep("high",2)))
+
+# Define the population description (this time using the density to determine
+# the population size)
+popdesc <- make.population.description(region = region,
+                                       density = density,
+                                       covariates = covs,
+                                       fixed.N = FALSE)
+
+# define the detecability (see make.detectability to alter detection function
+# for different covariate values)
+detect <- make.detectability(key.function = "hn", scale.param = 25, truncation = 50)
+
+# generate an example population
+pop <- generate.population(popdesc, region = region, detectability = detect)
+
+plot(pop, region)
+
+# }
+
+
    +
    +
    + + +
    +

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    +
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    +
    + +
    + + + + + + + diff --git a/docs/reference/make.simulation-1.png b/docs/reference/make.simulation-1.png new file mode 100644 index 0000000..c5f4611 Binary files /dev/null and b/docs/reference/make.simulation-1.png differ diff --git a/docs/reference/make.simulation.html b/docs/reference/make.simulation.html new file mode 100644 index 0000000..3954973 --- /dev/null +++ b/docs/reference/make.simulation.html @@ -0,0 +1,327 @@ + +Creates a Simulation object — make.simulation • dsims + Skip to contents + + +
    +
    +
    + +

    Creates a Simulation object

    + Source: R/ClassConstructors.R +
    make.simulation.Rd
    +
    + +
    +

    This creates a simulation with all the information necessary for dsims +to generate a population, create transects, simulate the survey process +and fit detection functions and estimate density / abundance. This function can be +used by itself based on default values to create a simple line transect example, see +Examples below. To create more complex simulations it is advisable to define the +different parts of the simulation individually before grouping them together. See +the Arguments for links to the functions which make the definitions for the +individual simulation components. For a more in depth example please refer to the +'GettingStarted' vignette.

    +
    + +
    +

    Usage

    + 
    make.simulation(
+  reps = 10,
+  design = make.design(),
+  population.description = make.population.description(),
+  detectability = make.detectability(),
+  ds.analysis = make.ds.analysis()
+)
    +
    + +
    +

    Arguments

    + + +
    reps
    +

    number of times the simulation should be repeated

    + + +
    design
    +

    an object of class Survey.Design created by a call to +make.design

    + + +
    population.description
    +

    an object of class Population.Description +created by a call to make.population.description

    + + +
    detectability
    +

    and object of class Detectability created by a call to +make.detectability

    + + +
    ds.analysis
    +

    an objects of class DS.Analysis created by +a call to make.ds.analysis

    + +
    +
    +

    Value

    +

    Simulation-class object

    +
    +
    +

    Details

    +

    The make.simulation function is now set up so that by + default (with the exception of specifying point transects rather than + line) it can run a simple simulation example. See examples.

    +
    +
    +

    See also

    +

    make.region make.density make.population.description make.detectability make.ds.analysis make.design

    +
    +
    +

    Author

    +

    Laura Marshall

    +
    + +
    +

    Examples

    + 
    # Create a basic rectangular study area
+region <- make.region()
+
+# Make a density grid (large spacing for speed)
+density <- make.density(region = region,
+                        x.space = 300,
+                        y.space = 100,
+                        constant = 1)
+density <- add.hotspot(density, centre = c(1000, 100), sigma = 250, amplitude = 10)
+
+# Define the population description
+popdsc <- make.population.description(region = region,
+                                      density = density,
+                                      N = 200)
+
+# Define the detecability
+detect <- make.detectability(key.function = "hn",
+                             scale.param = 25,
+                             truncation = 50)
+
+# Define the design
+design <- make.design(region = region,
+                      transect.type = "line",
+                      design = "systematic",
+                      samplers = 20,
+                      design.angle = 0,
+                      truncation = 50)
+
+# Define the analyses
+ds.analyses <- make.ds.analysis(dfmodel = ~1,
+                                key = "hn",
+                                truncation = 50,
+                                criteria = "AIC")
+
+# Put all the components together in the simulation (note no. of replicates
+# reps = 1 is only for a single test run and should be 999 or more to be able
+# to draw inference.)
+simulation <- make.simulation(reps = 1,
+                              design = design,
+                              population.description = popdsc,
+                              detectability = detect,
+                              ds.analysis = ds.analyses)
+
+# run an example survey to check the setup
+survey <- run.survey(simulation)
+plot(survey, region)
+
+
+# Run the simulation
+# Warning: if you have increased the number of replications then it can take a
+# long time to run!
+simulation <- run.simulation(simulation)
+#> 
1 out of 1 reps     

+summary(simulation)
+#> 
+#> GLOSSARY
+#> --------
+#> 
+#> Summary of Simulation Output
+#> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#> 
+#> Region          : the region name.
+#> No. Repetitions : the number of times the simulation was repeated.
+#> No. Excluded Repetitions : the number of times the simulation failed
+#>                   (too few sightings, model fitting failure etc.)
+#> 
+#> Summary for Individuals
+#> ~~~~~~~~~~~~~~~~~~~~~~~
+#> 
+#> Summary Statistics:
+#>    mean.Cover.Area : mean covered across simulation.
+#>    mean.Effort     : mean effort across simulation.
+#>    mean.n          : mean number of observed objects across
+#>                      simulation.
+#>    mean.n.miss.dist: mean number of observed objects where no distance
+#>                     was recorded (only displayed if value > 0).
+#>    no.zero.n       : number of surveys in simulation where
+#>                      nothing was detected (only displayed if value > 0).
+#>    mean.ER         : mean encounter rate across simulation.
+#>    mean.se.ER      : mean standard error of the encounter rates
+#>                      across simulation.
+#>    sd.mean.ER      : standard deviation of the encounter rates
+#>                      across simulation.
+#> 
+#> Estimates of Abundance:
+#>    Truth            : true population size, (or mean of true
+#>                       population sizes across simulation for Poisson N.
+#>    mean.Estimate    : mean estimate of abundance across simulation.
+#>    percent.bias     : the percentage of bias in the estimates.
+#>    RMSE             : root mean squared error/no. successful reps
+#>    CI.coverage.prob : proportion of times the 95% confidence interval
+#>                       contained the true value.
+#>    mean.se          : the mean standard error of the estimates of
+#>                       abundance
+#>    sd.of.means      : the standard deviation of the estimates
+#> 
+#> Estimates of Density:
+#>    Truth            : true average density.
+#>    mean.Estimate    : mean estimate of density across simulation.
+#>    percent.bias     : the percentage of bias in the estimates.
+#>    RMSE             : root mean squared error/no. successful reps
+#>    CI.coverage.prob : proportion of times the 95% confidence interval
+#>                       contained the true value.
+#>    mean.se          : the mean standard error of the estimates.
+#>    sd.of.means      : the standard deviation of the estimates.
+#> 
+#> Detection Function Values
+#> ~~~~~~~~~~~~~~~~~~~~~~~~~
+#> 
+#>  mean.observed.Pa : mean proportion of individuals/clusters observed in
+#>                     the covered region.
+#>  mean.estimte.Pa  : mean estimate of the proportion of individuals/
+#>                     clusters observed in the covered region.
+#>  sd.estimate.Pa   : standard deviation of the mean estimates of the
+#>                     proportion of individuals/clusters observed in the
+#>                     covered region.
+#>  mean.ESW         : mean estimated strip width.
+#>  sd.ESW           : standard deviation of the mean estimated strip widths.
+#> 
+#> 
+#> Region:  region
+#> No. Repetitions:  1
+#> No. Excluded Repetitions:  0
+#> Using only repetitions where all models converged.
+#> 
+#> Design:  Systematic parallel line design
+#>    design.type :  Systematic parallel line design
+#>    bounding.shape :  rectangle
+#>    samplers :  20
+#>    design.angle :  0
+#>    edge.protocol :  minus
+#> 
+#> Population Detectability Summary:
+#>     key.function  =  hn
+#>     scale.param  =  25
+#>     truncation  =  50
+#> 
+#> Analysis Summary:
+#>    Candidate Models:
+#>       Model 1: key function 'hn', formula '~1', was selected 1 time(s).
+#>    criteria  =  AIC
+#>    variance.estimator  =  R2
+#>    truncation  =  50
+#> 
+#> Summary for Individuals
+#> 
+#> Summary Statistics
+#> 
+#>   mean.Cover.Area mean.Effort mean.n mean.k mean.ER  mean.se.ER sd.mean.ER
+#> 1           1e+06       10000    116     20  0.0116 0.002630189         NA
+#> 
+#>      ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#> Estimates of Abundance (N)
+#> 
+#>   Truth mean.Estimate percent.bias  RMSE CI.coverage.prob mean.se sd.of.means
+#> 1   200        180.93        -9.54 19.07                1    43.3          NA
+#> 
+#>      ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#> Estimates of Density (D)
+#> 
+#>   Truth mean.Estimate percent.bias         RMSE CI.coverage.prob      mean.se
+#> 1 2e-04  0.0001809257    -9.537164 1.907433e-05                1 4.329508e-05
+#>   sd.of.means
+#> 1          NA
+#> 
+#>      ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#>      ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#> 
+#> Detection Function Values
+#> 
+#>   mean.observed.Pa mean.estimate.Pa sd.estimate.Pa mean.ESW sd.ESW
+#> 1             0.58             0.64             NA    32.06     NA
+
+# For a more in depth example please look at
+vignette("GettingStarted", 'dsims')
+#> Warning: vignette 'GettingStarted' not found
+
+
    +
    +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/plot-methods.html b/docs/reference/plot-methods.html new file mode 100644 index 0000000..12b867c --- /dev/null +++ b/docs/reference/plot-methods.html @@ -0,0 +1,114 @@ + +plot — plot,Survey,Region-method • dsims + Skip to contents + + +
    +
    +
    + +

    plot

    + Source: R/Survey.R +
    plot-methods.Rd
    +
    + +
    +

    Produces four plots of the survey: 1) Plots the transects inside the survey +region, 2) plots the population, 3) plots the transects, population and +detections 4) plots a histogram of the detection distances. Note that only +plots 3 & 4 are generated without the survey region if Region is omitted.

    +
    + +
    +

    Usage

    + 
    # S4 method for class 'Survey,Region'
+plot(x, y, type = "all", ...)
+
+# S4 method for class 'Survey,ANY'
+plot(x, y = NULL, type = "all", ...)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    object of class Survey

    + + +
    y
    +

    object of class Region or NULL

    + + +
    type
    +

    character specifies which plots you would like, defaults to "all". +Other options include "transects", "population", "survey" and "distances". These +will plot only the transects, only the population locations, both the transects +and population with detections indicated or a histogram of the detection distances, +respectively. Note that the final plots is only available if there were +one or more detections. Only "survey" and "distances" available if the y +Region argument is not supplied.

    + + +
    ...
    +

    additional plotting parameters

    + +
    +
    +

    Value

    +

    Generate 4 plots showing the survey population, transects (including covered areas), detections and a histogram of the detection distances. Plots include the survey region. Also invisibly returns a list of ggplot objects if the user would like to customise the plots.

    +

    Generate 2 plots showing the survey population, transects (including covered areas), detections and a histogram of the detection distances. Plots do not include survey region. Also invisibly returns a list of ggplot objects if the user would like to customise the plots.

    +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/plot.Density-methods.html b/docs/reference/plot.Density-methods.html new file mode 100644 index 0000000..2cd9fd4 --- /dev/null +++ b/docs/reference/plot.Density-methods.html @@ -0,0 +1,112 @@ + +Plot — plot,Density,ANY-method • dsims + Skip to contents + + +
    +
    +
    + +

    Plot

    + Source: R/Density.R +
    plot.Density-methods.Rd
    +
    + +
    +

    Plots an S4 object of class 'Density'

    +

    Plots an S4 object of class 'Density'

    +
    + +
    +

    Usage

    + 
    # S4 method for class 'Density,ANY'
+plot(x, y, strata = "all", title = "", scale = 1)
+
+# S4 method for class 'Density,Region'
+plot(x, y, strata = "all", title = "", scale = 1, line.col = gray(0.2))
    +
    + +
    +

    Arguments

    + + +
    x
    +

    object of class Density

    + + +
    y
    +

    object of class Region

    + + +
    strata
    +

    the strata name or number to be plotted. By default +all strata will be plotted.

    + + +
    title
    +

    plot title

    + + +
    scale
    +

    used to scale the x and y values in the plot (warning may give +unstable results when a projection is defined for the study area!)

    + + +
    line.col
    +

    sets the line colour for the shapefile

    + +
    +
    +

    Value

    +

    ggplot object

    +

    ggplot object

    +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/plot.Detectability-methods.html b/docs/reference/plot.Detectability-methods.html new file mode 100644 index 0000000..196327c --- /dev/null +++ b/docs/reference/plot.Detectability-methods.html @@ -0,0 +1,135 @@ + +Plot — plot,Detectability,ANY-method • dsims + Skip to contents + + +
    +
    +
    + +

    Plot

    + Source: R/Detectability.R +
    plot.Detectability-methods.Rd
    +
    + +
    +

    Plots an S4 object of class 'Detectability'

    +
    + +
    +

    Usage

    + 
    # S4 method for class 'Detectability,ANY'
+plot(
+  x,
+  y,
+  add = FALSE,
+  plot.units = character(0),
+  region.col = NULL,
+  gap.col = NULL,
+  main = "",
+  ...
+)
+
+# S4 method for class 'Detectability,Population.Description'
+plot(
+  x,
+  y,
+  add = FALSE,
+  plot.units = character(0),
+  region.col = NULL,
+  gap.col = NULL,
+  main = "",
+  ...
+)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    object of class Detectability

    + + +
    y
    +

    object of class Population.Description

    + + +
    add
    +

    logical indicating whether it should be added to +existing plot

    + + +
    plot.units
    +

    allows for units to be converted between m +and km

    + + +
    region.col
    +

    fill colour for the region

    + + +
    gap.col
    +

    fill colour for the gaps

    + + +
    main
    +

    character plot title

    + + +
    ...
    +

    other general plot parameters

    + +
    +
    +

    Value

    +

    No return value, gives a warning to the user

    +

    No return value, plotting function

    +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/plot.Population-methods.html b/docs/reference/plot.Population-methods.html new file mode 100644 index 0000000..67ff494 --- /dev/null +++ b/docs/reference/plot.Population-methods.html @@ -0,0 +1,103 @@ + +Plot — plot,Population,ANY-method • dsims + Skip to contents + + +
    +
    +
    + +

    Plot

    + Source: R/Population.R +
    plot.Population-methods.Rd
    +
    + +
    +

    Unused, will give a warning that the region must also be supplied.

    +

    Plots an S4 object of class 'Population'. Requires that the +associated region has already been plotted. This function adds +the locations of the individuals/clusters in the population.

    +
    + +
    +

    Usage

    + 
    # S4 method for class 'Population,ANY'
+plot(x, y, ...)
+
+# S4 method for class 'Population,Region'
+plot(x, y, ...)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    object of class Population

    + + +
    y
    +

    object of class Region

    + + +
    ...
    +

    other general plot parameters

    + +
    +
    +

    Value

    +

    ggplot object

    +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/run.simulation-methods.html b/docs/reference/run.simulation-methods.html new file mode 100644 index 0000000..b06c950 --- /dev/null +++ b/docs/reference/run.simulation-methods.html @@ -0,0 +1,130 @@ + +Method to run a simulation — run.simulation • dsims + Skip to contents + + +
    +
    +
    + +

    Method to run a simulation

    + Source: R/run.simulation.R +
    run.simulation-methods.Rd
    +
    + +
    +

    Runs the simulation and returns the simulation object with results. If +running in parallel and max.cores is not specified it will default to using +one less than the number of cores / threads on your machine. For example +code see make.simulation

    +
    + +
    +

    Usage

    + 
    run.simulation(
+  simulation,
+  run.parallel = FALSE,
+  max.cores = NA,
+  counter = TRUE,
+  transect.path = character(0),
+  progress.file = character(0)
+)
    +
    + +
    +

    Arguments

    + + +
    simulation
    +

    Simulation-class object

    + + +
    run.parallel
    +

    logical option to use multiple processors

    + + +
    max.cores
    +

    integer maximum number of cores to use, if not specified then +one less than the number available will be used.

    + + +
    counter
    +

    logical indicates if you would like to see the progress counter.

    + + +
    transect.path
    +

    character gives the pathway to a folder of shapefiles or +the path to a single shapefile (.shp file) which give the transects which should +be used for the simulations. If a folder of transects a new shapefile will be +used for each repetition. If a path specifying a single shapefile then the same +transects will be used for each repetition.

    + + +
    progress.file
    +

    character path with filename to output progress to file +for Distance for Windows progress counter. Not to be used when running directly +in R.

    + +
    +
    +

    Value

    +

    the Simulation-class object which now includes +the results

    +
    +
    +

    See also

    +

    make.simulation

    +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/run.survey-methods.html b/docs/reference/run.survey-methods.html new file mode 100644 index 0000000..1a732c3 --- /dev/null +++ b/docs/reference/run.survey-methods.html @@ -0,0 +1,123 @@ + +S4 generic method to simulate a survey — run.survey • dsims + Skip to contents + + +
    +
    +
    + +

    S4 generic method to simulate a survey

    + Source: R/generic.functions.R, R/Simulation.R, R/Survey.LT.R, and 1 more +
    run.survey-methods.Rd
    +
    + +
    +

    Simulates the process by which individuals or clusters are detected. If +a simulation is passed in then it will generate a population, set of +transects and simulate the detection process. If a survey is passed in +it will simply simulate the detection process. See +make.simulation for example usage.

    +
    + +
    +

    Usage

    + 
    run.survey(object, ...)
+
+# S4 method for class 'Simulation'
+run.survey(object, filename = character(0))
+
+# S4 method for class 'Survey.LT'
+run.survey(object, region = NULL)
+
+# S4 method for class 'Survey.PT'
+run.survey(object, region = NULL)
    +
    + +
    +

    Arguments

    + + +
    object
    +

    an object of class Simulation

    + + +
    ...
    +

    allows extra arguments

    + + +
    filename
    +

    optional argument specifying a path to a shapefile if +the transects are to be loaded from file.

    + + +
    region
    +

    an object of class Region.

    + +
    +
    +

    Value

    +

    An object which inherits from a Survey-class object. +This will be a Survey.LT-class object in the case of a +simulation with a line transect design and a Survey.PT-class +if the simulation has a point transect design.

    +
    +
    +

    See also

    +

    make.simulation

    +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/rztpois.html b/docs/reference/rztpois.html new file mode 100644 index 0000000..86d6c42 --- /dev/null +++ b/docs/reference/rztpois.html @@ -0,0 +1,101 @@ + +Randomly generates values from a zero-truncated Poisson distribution — rztpois • dsims + Skip to contents + + +
    +
    +
    + +

    Randomly generates values from a zero-truncated Poisson distribution

    + Source: R/rztpois.R +
    rztpois.Rd
    +
    + +
    +

    Generates values from a zero-truncated Poisson distribution with mean +equal to that specified. It uses an optimisation routine to check which +value of lambda will give values with the requested mean.

    +
    + +
    +

    Usage

    + 
    rztpois(n, mean = NA)
    +
    + +
    +

    Arguments

    + + +
    n
    +

    number of values to randomly generate

    + + +
    mean
    +

    mean of the generated values

    + +
    +
    +

    Value

    +

    returns a randomly generated value from a zero-truncated Poisson +distribution.

    +
    +
    +

    Note

    +

    Internal function not intended to be called by user.

    +
    +
    +

    Author

    +

    Len Thomas

    +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/save.sim.results-methods.html b/docs/reference/save.sim.results-methods.html new file mode 100644 index 0000000..cf75293 --- /dev/null +++ b/docs/reference/save.sim.results-methods.html @@ -0,0 +1,94 @@ + +save.sim.results — save.sim.results • dsims + Skip to contents + + +
    +
    +
    + +

    save.sim.results

    + Source: R/save.sim.results.R +
    save.sim.results-methods.Rd
    +
    + +
    +

    Saves the simulation results from each replicate to file. It will save up to 3 txt files, one for the abundance estimation for individuals, one for the abundance estimation of clusters (where applicable) and one for detectability estimates and model selection information.

    +
    + +
    +

    Usage

    + 
    save.sim.results(simulation, filepath = character(0), sim.ID = numeric(0))
    +
    + +
    +

    Arguments

    + + +
    simulation
    +

    object of class Simulation which has been run.

    + + +
    filepath
    +

    optionally a path to the directory where you would like the files saved, otherwise it will save it to the working directory.

    + + +
    sim.ID
    +

    optionally you can add a simulation ID to the filename

    + +
    +
    +

    Value

    +

    invisibly returns the original simulation object

    +
    +
    +

    Author

    +

    L. Marshall

    +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/set.densities-methods.html b/docs/reference/set.densities-methods.html new file mode 100644 index 0000000..d667990 --- /dev/null +++ b/docs/reference/set.densities-methods.html @@ -0,0 +1,87 @@ + +Method to set density values — set.densities • dsims + Skip to contents + + +
    +
    +
    + +

    Method to set density values

    + Source: R/Density.R +
    set.densities-methods.Rd
    +
    + +
    +

    This method sets the density values in a density object.

    +
    + +
    +

    Usage

    + 
    set.densities(density, densities)
    +
    + +
    +

    Arguments

    + + +
    density
    +

    object of class Density

    + + +
    densities
    +

    a numeric vector of density values to update the +density grid with.

    + +
    +
    +

    Value

    +

    returns the Density object with updated density values

    +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/show.Density.Summary-methods.html b/docs/reference/show.Density.Summary-methods.html new file mode 100644 index 0000000..9966c1a --- /dev/null +++ b/docs/reference/show.Density.Summary-methods.html @@ -0,0 +1,83 @@ + +show — show,Density.Summary-method • dsims + Skip to contents + + +
    +
    +
    + +

    show

    + Source: R/Density.Summary.R +
    show.Density.Summary-methods.Rd
    +
    + +
    +

    displays the density summary table

    +
    + +
    +

    Usage

    + 
    # S4 method for class 'Density.Summary'
+show(object)
    +
    + +
    +

    Arguments

    + + +
    object
    +

    object of class Density.Summary

    + +
    +
    +

    Value

    +

    No return value, displays the density summary

    +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/show.Simulation-methods.html b/docs/reference/show.Simulation-methods.html new file mode 100644 index 0000000..1ef6a86 --- /dev/null +++ b/docs/reference/show.Simulation-methods.html @@ -0,0 +1,83 @@ + +show — show,Simulation-method • dsims + Skip to contents + + +
    +
    +
    + +

    show

    + Source: R/Simulation.R +
    show.Simulation-methods.Rd
    +
    + +
    +

    Not currently implemented

    +
    + +
    +

    Usage

    + 
    # S4 method for class 'Simulation'
+show(object)
    +
    + +
    +

    Arguments

    + + +
    object
    +

    object of class Simulation

    + +
    +
    +

    Value

    +

    No return value, displays a summary of the simulation

    +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/show.Simulation.Summary-methods.html b/docs/reference/show.Simulation.Summary-methods.html new file mode 100644 index 0000000..98a7140 --- /dev/null +++ b/docs/reference/show.Simulation.Summary-methods.html @@ -0,0 +1,84 @@ + +show — show,Simulation.Summary-method • dsims + Skip to contents + + +
    +
    +
    + +

    show

    + Source: R/Simulation.Summary.R +
    show.Simulation.Summary-methods.Rd
    +
    + +
    +

    Displays the simulation summary

    +
    + +
    +

    Usage

    + 
    # S4 method for class 'Simulation.Summary'
+show(object)
    +
    + +
    +

    Arguments

    + + +
    object
    +

    object of class Simulation.Summary

    + +
    +
    +

    Value

    +

    No return value, displays information in Simulation.Summary +object

    +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/summary.Density-methods.html b/docs/reference/summary.Density-methods.html new file mode 100644 index 0000000..66d99f4 --- /dev/null +++ b/docs/reference/summary.Density-methods.html @@ -0,0 +1,87 @@ + +summary — summary,Density-method • dsims + Skip to contents + + +
    +
    +
    + +

    summary

    + Source: R/Density.R +
    summary.Density-methods.Rd
    +
    + +
    +

    Provides a summary table of the density object.

    +
    + +
    +

    Usage

    + 
    # S4 method for class 'Density'
+summary(object, ...)
    +
    + +
    +

    Arguments

    + + +
    object
    +

    object of class Simulation

    + + +
    ...
    +

    not implemented

    + +
    +
    +

    Value

    +

    a Density.Summary-class object

    +
    + +
    + + +
    +

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    +
    + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/reference/summary.Simulation-methods.html b/docs/reference/summary.Simulation-methods.html new file mode 100644 index 0000000..148a753 --- /dev/null +++ b/docs/reference/summary.Simulation-methods.html @@ -0,0 +1,96 @@ + +summary — summary,Simulation-method • dsims + Skip to contents + + +
    +
    +
    + +

    summary

    + Source: R/Simulation.R +
    summary.Simulation-methods.Rd
    +
    + +
    +

    Provides a summary of the simulation results.

    +
    + +
    +

    Usage

    + 
    # S4 method for class 'Simulation'
+summary(object, description.summary = TRUE, use.max.reps = FALSE, ...)
    +
    + +
    +

    Arguments

    + + +
    object
    +

    object of class Simulation

    + + +
    description.summary
    +

    logical indicating whether an +explanation of the summary should be displayed

    + + +
    use.max.reps
    +

    by default this is FALSE meaning that only simulation repetitions where all models converged for that data set are included. By setting this to TRUE any repetition where one or more models converged will be included in the summary results.

    + + +
    ...
    +

    no additional arguments currently implemented

    + +
    +
    +

    Value

    +

    Object of class Simulation.Summary

    +
    + +
    + + +
    +

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    +
    + +
    + + + + + + + diff --git a/docs/search.json b/docs/search.json new file mode 100644 index 0000000..64ac811 --- /dev/null +++ b/docs/search.json @@ -0,0 +1 @@ +[{"path":"/articles/dsims-examples.html","id":"preamble","dir":"Articles","previous_headings":"","what":"Preamble","title":"Transition from `DSsim` to `dsims`","text":"first version simulation engine package called DSsim (Marshall, 2019); improving GIS capabilities R later released second efficient simulation package, dsims (Marshall, 2022a). vignette originally written DSsim use now demonstrate dsims also example users DSsim showing transition dsims. two packages largely function names loading together advised, vignette therefore leave DSsim code comments comparison dsims code. Please note normal comments follow single # DSsim code follows double ##. also noted dsims now uses distance sampling survey design package, dssd (Marshall, 2022b), generate transects based design shapefiles containing transects longer need created advance. goal transition dsims DSsim find need sections including section running simulations. latter sections go run series additional simulations investigating pooling robustness covariate parameter estimation respect truncation distance. completely new distance sampling simulations alternative place start Getting Started vignette inside dsims package. vignette uses dsims compare systematic parallel design zigzag design assess accuracy/precision trade . view open R installing dsims, enter following code:","code":"vignette(\"GettingStarted\", package = \"dsims\") ## Warning: vignette 'GettingStarted' not found"},{"path":"/articles/dsims-examples.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Transition from `DSsim` to `dsims`","text":"Distance sampling process study area surveyed estimate size population within . can thought extension plot sampling. However, plot sampling assumes objects within plots detected, distance sampling relaxes assumption. Distance sampling makes assumptions distribution objects respect transects satisfy assumptions transects (points lines) must randomly located within study region. Note purposes distance sampling object can either individual cluster individuals. next step distance sampling record distances detected object transect detected fit detection function. function can estimate many objects missed hence total number covered area. example, Figure 1 shows histograms distances might collected line transect survey, fitted detection function. lines placed random within study region expect average number object occur given distance transect. Therefore drop number detection increasing distance line can attributed failure detect objects. can therefore estimate detection function probability seeing object within covered region chosen truncation distance area curve (shaded grey) divided area rectangle. Figure 1: example detection function. histogram shows example distances recorded line transect. smooth curve detection function. grey shaded area represents number detected objects diagonal hash region represents number objects covered region detected. R package dsims allows users simulate point line transect surveys, test range design analysis decisions specific population interest. simulate surveys user must make assumptions population interest detection process giving rise observed distances. Simulations can repeated range assumptions user can confident chosen design perform well despite uncertainty.","code":""},{"path":"/articles/dsims-examples.html","id":"introduction-to-dsims","dir":"Articles","previous_headings":"Introduction","what":"Introduction to dsims","title":"Transition from `DSsim` to `dsims`","text":"dsims takes information user study region, population detection process uses generate distance sampling data. dsims can asked fit detection functions data produce estimates density, abundance associated uncertainty. dsims splits process three stages. Firstly, generates instance population set survey transects. Secondly, simulates distance sampling survey using assumed detection function(s) provided user. Lastly, dsims analyses data survey. Figure 2 illustrates simulation process highlights information must provided user. Distance sampling simulations can useful researchers wish optimise survey design specific study regions species interest order try achieve accurate / precise estimates populations. Setting running simulations optimise design small cost comparison associated actually completing survey! Figure 2: Illustrates simulation process. Blue rectangles indicate information supplied user. Green rectangles objects created dsims simulation process. Orange diamonds indicate processes carried dsims. dsims written using S4 object orientated system R. S4 system formal rigorous style object orientated programming commonly implemented S3. process defining simulation involves specification many variables relating survey region, population, survey design finally analysis. design dsims based around descriptions contained class formal S4 class definition procedure ensures objects created correct format simulation. objects created dsims instances S4 classes, user wishes access information within symbol used slightly different. access named parts S3 objects “$” symbol used, S4 objects “@” symbol must used. following code demonstrates .","code":"# load simulation package ## library(DSsim) library(dsims) # Make a default region object ## eg.region <- make.region() eg.region <- make.region() # Let's check the structure of the object we have created str(eg.region) ## Formal class 'Region' [package \"dssd\"] with 5 slots ## ..@ region.name: chr \"region\" ## ..@ strata.name: chr \"region\" ## ..@ units : chr(0) ## ..@ area : num 1e+06 ## ..@ region :Classes 'sf' and 'data.frame': 1 obs. of 2 variables: ## .. ..$ region : chr \"study_ar\" ## .. ..$ geometry:sfc_POLYGON of length 1; first list element: List of 1 ## .. .. ..$ : num [1:5, 1:2] 0 0 2000 2000 0 0 500 500 0 0 ## .. .. ..- attr(*, \"class\")= chr [1:3] \"XY\" \"POLYGON\" \"sfg\" ## .. ..- attr(*, \"sf_column\")= chr \"geometry\" ## .. ..- attr(*, \"agr\")= Factor w/ 3 levels \"constant\",\"aggregate\",..: NA ## .. .. ..- attr(*, \"names\")= chr \"region\" # If we wanted to extract the area of the region we would use eg.region@area ## [1] 1e+06"},{"path":"/articles/dsims-examples.html","id":"example-simulation-study-which-truncation-distance","dir":"Articles","previous_headings":"Introduction","what":"Example Simulation Study: Which Truncation Distance?","title":"Transition from `DSsim` to `dsims`","text":"usual distance sampling studies truncate data distance transect. observations far away transect lesser importance fitting detection function also sparse observations large distances high influence model selection possibly increase variability estimated abundance / density. Buckland et al. (2001) suggest truncating data probability detection around 0.15 general rule thumb. However, distance sampling data often costly obtain discarding data points can feel counter intuitive. vignette investigate truncation distance distance sampling analyses. series three simulations outlined . Firstly, vignette investigate data generated assuming simple half normal detection function every object probability detection specific distance transect. Figure 3 shows simple half normal detection function three possible truncation distances \\(1*\\sigma\\), \\(2*\\sigma\\) \\(3*\\sigma\\) \\(\\sigma\\) scale parameter half normal detection function. truncation distance \\(2*\\sigma\\) gives probability detection 0.135 close 0.15 rule thumb. Figure 3: Half-normal detection function showing 3 proposed truncation distances \\(1*\\sigma\\), \\(2*\\sigma\\) \\(3*\\sigma\\). truncation distance twice sigma gives probability detection 0.135 close 0.15 rule thumb. first set simulations assume simple half normal detection function, reality individual objects clusters objects likely varying probability detected based certain characteristics. Perhaps behaviour males make easier detect. also easy see larger clusters individuals might easier spot large distances small clusters. also investigate effects truncation distance individual level covariates affect probability detection. Figure 4 shows covariates may affect detectability. use simulated distance data one covariate (sex) investigate effects truncation assume able measure covariate affecting detectability assume can therefore include relevant covariate detection function model. Figure 4: Half-normal detection function varies based cluster size animal sex.","code":""},{"path":"/articles/dsims-examples.html","id":"model-uncertainty-and-pooling-robustness","dir":"Articles","previous_headings":"Introduction","what":"Model Uncertainty and Pooling Robustness","title":"Transition from `DSsim` to `dsims`","text":"simulate data, provide detection function generate detections, therefore know underlying true detection function. collecting data field, information, rely form model selection. One method model selection compare information criterion, dsims allows user select either AIC, AICc BIC model selection criteria. simulations use AIC allow dsims select half-normal hazard rate model first two sets simulations. addition, probability detection affected covariates may single underlying detection function combination detection functions giving rise observed data. situation can either model detectability function covariates rely concept called pooling robustness. Pooling robustness refers fact distance sampling techniques robust pooling multiple detection functions one. means necessarily need include covariates affect detectability detection function accurately estimate density / abundance. vignette examine concept pooling robustness see affected truncation distance.","code":""},{"path":"/articles/dsims-examples.html","id":"methods","dir":"Articles","previous_headings":"","what":"Methods","title":"Transition from `DSsim` to `dsims`","text":"vignette guide steps create run series simulations investigate effects varying truncation distance data generated simple half-normal detection function detection function detectability affected covariate.","code":""},{"path":"/articles/dsims-examples.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Transition from `DSsim` to `dsims`","text":"First load dsims library.","code":"## library(DSsim) library(dsims)"},{"path":"/articles/dsims-examples.html","id":"simulation-components","dir":"Articles","previous_headings":"","what":"Simulation Components","title":"Transition from `DSsim` to `dsims`","text":"detailed Introduction dsims simulation comprises number components. dsims designed components defined individually grouped together simulation. helps keep process clear also allows reuse simulation components different simulations. function names create simulation component simulation takes form make..","code":""},{"path":"/articles/dsims-examples.html","id":"region","dir":"Articles","previous_headings":"Simulation Components","what":"Region","title":"Transition from `DSsim` to `dsims`","text":"simulations use rectangular study region 5 km 20 km. Survey regions can defined units units must throughout components simulation. shapefile used create survey region, information units taken .prj file. define coordinates m. simple study region (Figure 5) vertices can simply provide coordinates. change DSsim now need turn coordinates sf polygon shape prior creating region. step documented . Note standard shapefiles outer polygon coordinates given clockwise direction, sf uses counter clockwise external polygons clockwise holes within polygons. details creating multi-part multi-strata sf objects can found end multi-strata dssd vignette. also note units longer plotting option. plot functions modified use ggplot2 additional plotting options desired ggplot object can captured modified. Figure 5: study region.","code":"## # Create a polgon ## poly1 <- data.frame(x = c(0,0,20000,20000,0), y = c(0,5000,5000,0,0)) ## ## # Create an empty list ## # Store the polygon inside a list in the first element of the coords list referring to strata 1. ## coords <- list() ## coords[[1]] <- list(poly1) # Create an sf polgon library(sf) # Put the coordinates of the polygon in a matrix poly1 = matrix(c(0,0, 20000,0, 20000,5000, 0,5000, 0,0),ncol=2, byrow=TRUE) # Turn them into an sf polygon pl1 = st_polygon(list(poly1)) ## # Create the survey region ## region <- make.region(region.name = \"study area\", ## units = \"m\", ## coords = coords) ## # The plot function allows plotting in km or m. ## plot(region, plot.units = \"km\") # Create the survey region region <- make.region(region.name = \"study area\", units = \"m\", shape = pl1) # The plot function allows plotting in km or m. plot(region)"},{"path":"/articles/dsims-examples.html","id":"population","dir":"Articles","previous_headings":"Simulation Components","what":"Population","title":"Transition from `DSsim` to `dsims`","text":"now define population within study region. Firstly, must describe distribution population defining density surface. simulations assume uniform distribution animals throughout study region. dsims generate sf grid describing density surface us provide x (optionally y) spacing constant density value surface. y spacing omitted assumed equal x spacing. example value constant important generate animals based fixed population size rather using exact values density grid. two argument name changes make.density function: region.obj now region density.gam now fitted model. buffer argument longer needed now option supply formula density based x y using density.formula. Figure 6: density surface. aside, wished add areas higher lower density density surface using add.hotspot function dsims. function adds hot low spots based Gaussian decay function. provide central coordinates sigma value tell dsims location shape hot/low spot. amplitude argument gives value hot low spot centre combined existing density surface addition. code used dsims identical used DSsim repeated code chunk . Figure 7: non-uniform density surface. can now define aspects population. simple case (covariates) need define fixed population size provide region density grid created . fixed population size 200 selected value sufficient give around 100 detections per simulated survey large cause simulations run slowly. minimum recommended number detections fitting detection function 60 (Buckland et al., 2001). minor argument names changes function: region.obj now region density.obj now density. simulations involving covariates need define individuals allocated covariate values. dsims allows user either define discrete distribution alternatively provide distribution (Normal, Poisson, Zero-truncated Poisson Lognormal) associated parameters. simulation use sex covariate assume 50% population female 50% male. example sex covariate defined exactly way DSsim. Note defining covariates using distributions format changed slightly. example included . dsims format simplified covariate distribution list provided stratum now just list named elements ‘distribution’ distribution parameters. Please refer help parameters defined distribution examples.","code":"## # Create the density surface ## density <- make.density(region.obj = region, ## x.space = 100, ## constant = 1) ## ## # Plot the density surface ## plot(density, style = \"blocks\") ## plot(region, add = TRUE) density <- make.density(region = region, x.space = 100, constant = 1) # Plot the density surface plot(density, region) # Add a hotspot to the density surface, centre located at x = 15000, y = 4000 with # a Gaussian decay parameter sigma = 1500. The value at the centre point will now # be 1 (the current value of the density surface defined above) + 0.5 = 1.5 eg.density <- add.hotspot(density, centre = c(15000,4000), sigma = 1500, amplitude = 0.5) # Add a lowspot to this new density surface (eg.density) eg.density <- add.hotspot(eg.density, centre = c(10000,3000), sigma = 1000, amplitude = -0.25) # Plot the density surface plot(eg.density, region) ## # Create the population description, with a population size N = 200 ## pop.desc <- make.population.description(region.obj = region, ## density.obj = density, ## N = 200, ## fixed.N = TRUE) # Create the population description, with a population size N = 200 pop.desc <- make.population.description(region = region, density = density, N = 200, fixed.N = TRUE) # Create the covariate list covariate.list <- list() # The population will be 50% males and 50% females covariate.list$sex <- list(data.frame(level = c(\"female\", \"male\"), prob = c(0.5,0.5))) ## # Create the population description, with a population size N = 200 ## pop.desc.cov <- make.population.description(region = region, ## density = density, ## covariates = covariate.list, ## N = 200) # Create the population description, with a population size N = 200 pop.desc.cov <- make.population.description(region = region, density = density, covariates = covariate.list, N = 200) ## covariate.list <- list() ## covariate.list$size <- list(list(\"poisson\", list(lambda = 35))) covariate.list <- list() covariate.list$size <- list(list(distribution = \"poisson\", lambda = 35))"},{"path":"/articles/dsims-examples.html","id":"detectability","dir":"Articles","previous_headings":"Simulation Components","what":"Detectability","title":"Transition from `DSsim` to `dsims`","text":"Detectability refers detection function functions feed simulation generate observations. simple case can set animals probability detection given distance transect. define half-normal detection function scale parameter \\(\\sigma = 200\\) data generation truncation distance 1000. truncation distance defined aid simulation efficiency means detections can occur beyond value. can plot function check defined correctly. defined survey region m scale parameter truncation distance also assumed metres. scale parameter 200 selected average gives around 100 detections truncation distance 1000m chosen population size 200. Defining detectability dsims uses identical code DSsim code repeated . Figure 8: detection functions males females. covariates population may choose vary scale parameter detection function based covariate values. dsims assumes scale parameter function covariates follows: \\[ \\sigma = exp(\\beta_0+\\sum_{j=1}^{q}\\beta_{j}z_{ij}) \\] \\(\\beta_0\\) log scale parameter supplied make.detectability, \\(\\beta_j\\)’s covariate parameters supplied log scale \\(z_{ij}\\) ith value jth covariate. formula taken Buckland et al. (2004). covariate values selected males higher probability detection females. values selected example give sample size around 150 observations 1000m truncation value population 200. Defining detectability dsims uses identical code DSsim code repeated . Figure 9: detection functions males females.","code":"# Make a simple half normal detection function with a scale parameter of 200 detect.hn <- make.detectability(key.function = \"hn\", scale.param = 200, truncation = 1000) # We can now visualise these detection functions plot(detect.hn, pop.desc) # Create the covariate parameter list cov.params <- list() # Note the covariate parameters are supplied on the log scale cov.params$sex = data.frame(level = c(\"female\", \"male\"), param = c(0, 1.5)) detect.cov <- make.detectability(key.function = \"hn\" , scale.param = 120, cov.param = cov.params, truncation = 1000) # This setup gives a scale parameter of around 120 for the females and 540 for # the males. We can calculate the sigma for the males using the formula above: # exp(log(scale.param) + sex.male) exp(log(120) + 1.5) ## [1] 537.8027 # We can now visualise these detection functions plot(detect.cov, pop.desc.cov)"},{"path":"/articles/dsims-examples.html","id":"design","dir":"Articles","previous_headings":"Simulation Components","what":"Design","title":"Transition from `DSsim` to `dsims`","text":"design section simulations dsims part differs significantly DSsim. DSsim generated basic designs anything complex needed generated externally loaded shapefiles. dsims uses dssd survey design package R specify designs generate transects . example use systematic parallel line transect design. recommended minimum number transects 10 20 (Buckland et al., 2001) set spacing lines 1000 m give 20 transects per survey. basic designs arguments make.design function changed slightly: region.obj now region design.details now design. Note, now important define truncation distance design, allows design coverage assessed. dssd also now provides comprehensive set arguments defining designs. investigate , please see Getting Started dssd vignette Multiple Strata dssd vignette. design objects now contain survey region need supply separate argument generating transects. like plot covered areas covered.area argument can set TRUE plot function, example covered areas may obvious truncation distance transect spacing. Figure 10: Example survey transects.","code":"## # Define the design ## design <- make.design(region.obj = region, ## transect.type = \"line\", ## design.details = c(\"parallel\", \"systematic\"), ## spacing = 1000) # Define the design design <- make.design(region = region, transect.type = \"line\", design = \"systematic\", spacing = 1000, truncation = 1000) ## transects <- generate.transects(design, region = region) ## plot(region) ## plot(transects, col = 4, lwd = 2) transects <- generate.transects(design) plot(region, transects)"},{"path":"/articles/dsims-examples.html","id":"analysis","dir":"Articles","previous_headings":"Simulation Components","what":"Analysis","title":"Transition from `DSsim` to `dsims`","text":"final stage simulation analyse distance sampling data generated. discussed , collecting data field know true underlying detection function therefore incorporate model uncertainty. can ask simulation fit two models, half-normal hazard rate, data select best model based minimum AIC. fairly substantial change syntax used define detection function models analyses well function name . syntax DSsim based mrds felt user friendly syntax used Distance R package (Miller, Rexstad, Thomas, Marshall, & Laake, 2019). therefore made code dsims simililar defining models Distance. code set truncation distance 600 later vary value investigate effects truncation distance simulation results. Note truncation distance can set value, exceed truncation value defined detectability design observations occur beyond values. addition, field may possible identify covariates affect detectability may wish fit detection function incorporates . case, following model appropriate:","code":"## ddf.analyses <- make.ddf.analysis.list(dsmodel = list(~cds(key = \"hn\", formula = ~1), ## ~cds(key = \"hr\", formula = ~1)), ## method = \"ds\", ## truncation = 600) ## criteria = \"AIC\", ddf.analyses <- make.ds.analysis(dfmodel = list(~1, ~1), key = c(\"hn\", \"hr\"), criteria = \"AIC\", truncation = 600) ## ddf.analyses.cov <- make.ddf.analysis.list(dsmodel = list(~mcds(key = \"hn\", formula = ~sex)), ## method = \"ds\", ## truncation = 600) ddf.analyses.cov <- make.ds.analysis(dfmodel = list(~sex), key = c(\"hn\"), truncation = 600)"},{"path":"/articles/dsims-examples.html","id":"simulations","dir":"Articles","previous_headings":"","what":"Simulations","title":"Transition from `DSsim` to `dsims`","text":"simulation created grouping components together. create two simulations , first simple case involve difference detectability animals, second include difference detectability due sex. Initially, include analyses allow selection half-normal hazard rate model, later modify run third set simulations fit detection function sex included covariate. created simulation objects, good idea check everything intended. function run.survey simulates single survey generates set transects population simulates survey process create distance sampling data set. can plotted (Figures 11 12). Figure 11: Example survey. Top left - example set transects. Top right - example population. Bottom left - detections transects. Bottom right - histogram distances observations transect detected. now create second simulation object simulations covariates. can re-use design component add new population description detectability include sex covariate. include non-covariate analyses final set simulations change fit covariate detection function model. Figure 12: Example survey. Top left - example set transects. Top right - example population. Bottom left - detections transects. Bottom right - histogram distances observations transect detected. check second simulation correctly generating covariate values population can examine first detections simulated distance data.","code":"## sim <- make.simulation(reps = 999, ## region.obj = region, ## design.obj = design, ## detectability.obj = detect.hn, ## ddf.analyses.list = ddf.analyses) ## population.description.obj = pop.desc, ## # Produce simulation setup plots ## check.sim.setup(sim) sim <- make.simulation(reps = 999, design = design, population.description = pop.desc, detectability = detect.hn, ds.analysis = ddf.analyses) # Produce survey and plot it survey <- run.survey(sim) plot(survey, region) ## sim.cov <- make.simulation(reps = 999, ## region.obj = region, ## design.obj = design, ## population.description.obj = pop.desc.cov, ## detectability.obj = detect.cov, ## ddf.analyses.list = ddf.analyses) ## # Produce simulation setup plots ## check.sim.setup(sim.cov) sim.cov <- make.simulation(reps = 999, design = design, population.description = pop.desc.cov, detectability = detect.cov, ds.analysis = ddf.analyses) # Produce survey and plot it survey.cov <- run.survey(sim.cov) plot(survey.cov, region) head(survey.cov@dist.data) ## object individual obs.Region.Label Sample.Label distance x y ## 2 2 2 study area 1 439.6622 680.6209 833.2067 ## 15 15 13 study area 1 127.7175 113.2412 3562.4237 ## 16 16 14 study area 1 385.4299 626.3886 2696.8466 ## 36 36 35 study area 1 699.3118 940.2705 4044.2287 ## 47 47 45 study area 1 521.5709 762.5297 411.8589 ## 87 87 89 study area 1 349.0618 590.0205 710.0814 ## sex Region.Label Effort Area ## 2 male study area 5000 1e+08 ## 15 male study area 5000 1e+08 ## 16 male study area 5000 1e+08 ## 36 male study area 5000 1e+08 ## 47 male study area 5000 1e+08 ## 87 male study area 5000 1e+08"},{"path":"/articles/dsims-examples.html","id":"running-simulations","dir":"Articles","previous_headings":"","what":"Running Simulations","title":"Transition from `DSsim` to `dsims`","text":"run simulations syntax changed slightly run DSsim run.simulations dsims object argument now simulation. simulations can still run parallel using run.parallel maximum cores set using max.cores counter argument retained. transect.path argument run.simulation function dsims can optionally supply folder filename wish load pre-generated shapefiles (DSsim specified design). option expected widely used incorporated allow simulations Distance Windows run using dsims. demonstrate run basic simulation example. see code incorporated multiple simulations run within loops following sections. Note advisable first run simulation iterations (<10) give indication run without issues setting hundreds / thousands repetitions. run simulation can view results using summary function provides glossary explain output. histogram estimates abundance can also viewed, Figure 13. Figure 13: Histogram abundance estimates simulation. goal simply learn syntax switching DSsim dsims can finish . remainder vignette loops simulations test altering truncation distance affects pooling robustness covariate parameter estimation. now dsims code shown.","code":"## sim <- run(object = sim) sim <- run.simulation(simulation = sim, run.parallel = TRUE) # Display a summary of the simulation summary(sim) # Display a histogram of the estimates of abundance histogram.N.ests(sim) ## ## GLOSSARY ## -------- ## ## Summary of Simulation Output ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## ## Region : the region name. ## No. Repetitions : the number of times the simulation was repeated. ## No. Excluded Repetitions : the number of times the simulation failed ## (too few sightings, model fitting failure etc.) ## ## Summary for Individuals ## ~~~~~~~~~~~~~~~~~~~~~~~ ## ## Summary Statistics: ## mean.Cover.Area : mean covered across simulation. ## mean.Effort : mean effort across simulation. ## mean.n : mean number of observed objects across ## simulation. ## mean.n.miss.dist: mean number of observed objects where no distance ## was recorded (only displayed if value > 0). ## no.zero.n : number of surveys in simulation where ## nothing was detected (only displayed if value > 0). ## mean.ER : mean encounter rate across simulation. ## mean.se.ER : mean standard error of the encounter rates ## across simulation. ## sd.mean.ER : standard deviation of the encounter rates ## across simulation. ## ## Estimates of Abundance: ## Truth : true population size, (or mean of true ## population sizes across simulation for Poisson N. ## mean.Estimate : mean estimate of abundance across simulation. ## percent.bias : the percentage of bias in the estimates. ## RMSE : root mean squared error/no. successful reps ## CI.coverage.prob : proportion of times the 95% confidence interval ## contained the true value. ## mean.se : the mean standard error of the estimates of ## abundance ## sd.of.means : the standard deviation of the estimates ## ## Estimates of Density: ## Truth : true average density. ## mean.Estimate : mean estimate of density across simulation. ## percent.bias : the percentage of bias in the estimates. ## RMSE : root mean squared error/no. successful reps ## CI.coverage.prob : proportion of times the 95% confidence interval ## contained the true value. ## mean.se : the mean standard error of the estimates. ## sd.of.means : the standard deviation of the estimates. ## ## Detection Function Values ## ~~~~~~~~~~~~~~~~~~~~~~~~~ ## ## mean.observed.Pa : mean proportion of individuals/clusters observed in ## the covered region. ## mean.estimte.Pa : mean estimate of the proportion of individuals/ ## clusters observed in the covered region. ## sd.estimate.Pa : standard deviation of the mean estimates of the ## proportion of individuals/clusters observed in the ## covered region. ## mean.ESW : mean estimated strip width. ## sd.ESW : standard deviation of the mean estimated strip widths. ## ## ## Region: study area ## No. Repetitions: 999 ## No. Excluded Repetitions: 0 ## Using only repetitions where all models converged. ## ## Design: Systematic parallel line design ## design.type : Systematic parallel line design ## bounding.shape : rectangle ## spacing : 1000 ## design.angle : 0 ## edge.protocol : minus ## ## Population Detectability Summary: ## key.function = hn ## scale.param = 200 ## truncation = 1000 ## ## Analysis Summary: ## Candidate Models: ## Model 1: key function 'hn', formula '~1', was selected 798 time(s). ## Model 2: key function 'hr', formula '~1', was selected 201 time(s). ## criteria = AIC ## variance.estimator = R2 ## truncation = 600 ## ## Summary for Individuals ## ## Summary Statistics ## ## mean.Cover.Area mean.Effort mean.n mean.k mean.ER mean.se.ER ## 1 1.2e+08 1e+05 99.11311 20 0.0009911311 9.838211e-05 ## sd.mean.ER ## 1 7.436991e-05 ## ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Estimates of Abundance (N) ## ## Truth mean.Estimate percent.bias RMSE CI.coverage.prob mean.se sd.of.means ## 1 200 198.33 -0.84 26.17 0.94 25.36 26.13 ## ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Estimates of Density (D) ## ## Truth mean.Estimate percent.bias RMSE CI.coverage.prob mean.se ## 1 2e-06 1.983281e-06 -0.8359462 2.617061e-07 0.9379379 2.535869e-07 ## sd.of.means ## 1 2.613023e-07 ## ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## ## Detection Function Values ## ## mean.observed.Pa mean.estimate.Pa sd.estimate.Pa mean.ESW sd.ESW ## 1 0.42 0.42 0.05 252.75 27.29"},{"path":"/articles/dsims-examples.html","id":"running-multiple-simulations-to-investigate-truncation","dir":"Articles","previous_headings":"","what":"Running Multiple Simulations to investigate Truncation","title":"Transition from `DSsim` to `dsims`","text":"investigate effects varying truncation distance analysis simply need run one simulation, one truncation distance. following code shows iterated number different truncation distances stored simulation results simulation summaries lists. first set simulations detectability change individual level covariates. now move investigate happens sex covariate affects detectability. First, need select suitable candidate truncation distances; plot example data. Figure 12 shows data generated population size 2500, increase population size increase number detections make shape resulting data less variable. histogram five candidate truncation distances selected shown red vertical lines. selected truncation distances represent range values probability detection starting 0.6 shortest truncation distance. Figure 14: Histogram data covariate simulation increased population size 2500. detection function shows best fit data (code allowed select half normal hazard rate based minimum AIC). red lines indicate manually selected candidate truncation distances. can now feed candidate truncation distances covariate simulations way simple half normal simulation store results summaries lists. Note now still fitting half-normal hazard rate intercept models therefore testing pooling robustness. Finally, may also wish fit covariate model used generate data rather non covariate half-normal hazard rate models. allow us investigate effects truncation fact aware “measure” covariate knew affecting detectability.","code":"# Truncation distances to iterate over truncation <- c(200, 400, 600) # Storage space for results results.list <- list() summary.list <- list() # We will now run the simulation for each of the analysis truncation distances. for(tdist in seq(along= truncation)){ # Screen display to indicate how far through the simulations we are cat(\"\\n Running for truncation = \", truncation[tdist], fill = T) # Update analysis with new truncation distance new.ds.analyses <- make.ds.analysis(dfmodel = list(~1, ~1), key = c(\"hn\", \"hr\"), criteria = \"AIC\", truncation = truncation[tdist]) # Update simulation to include new analysis component # We can use the @ symbol to change the contents of a slot or alternatively we could have # re-created the simulation with the new analyses using make.simulation(). sim@ds.analysis <- new.ds.analyses # Run simulation and store the results in the appropriate list element results.list[[tdist]] <- run.simulation(sim, run.parallel = TRUE) # Store simulation summary in another list in the appropriate list element # As we are storing the summary we do not need the description.summary displayed summary.list[[tdist]] <- summary(results.list[[tdist]], description.summary = FALSE) } # Add names to the summary and results list so we know which truncation distance they # relate to names(results.list) <- paste(\"t\", truncation, sep = \"\") names(summary.list) <- paste(\"t\", truncation, sep = \"\") # Truncation distances to iterate over truncation <- c(200, 400, 600, 800, 1000) # Storage space for results cov.results.list <- list() cov.summary.list <- list() for(tdist in seq(along= truncation)){ # Screen display to indicate how far through the simulations we are cat(\"\\n Running for truncation = \", truncation[tdist], fill = T) # Update analysis truncation distance new.ds.analyses <- make.ds.analysis(dfmodel = list(~1, ~1), key = c(\"hn\", \"hr\"), criteria = \"AIC\", truncation = truncation[tdist]) # Update simulation sim.cov@ds.analysis <- new.ds.analyses # Run Simulation cov.results.list[[tdist]] <- run.simulation(sim.cov, run.parallel = TRUE) # Store simulation summaries cov.summary.list[[tdist]] <- summary(cov.results.list[[tdist]], description.summary = FALSE) } # Add names to the summary and results list names(cov.results.list) <- paste(\"t\", truncation, sep = \"\") names(cov.summary.list) <- paste(\"t\", truncation, sep = \"\") # Now include the ddf.analyses.cov in the simulation sim.cov <- make.simulation(reps = 999, design = design, population.description = pop.desc.cov, detectability = detect.cov, ds.analysis = ddf.analyses.cov) # Truncation distances to iterate over truncation <- c(200, 400, 600, 800, 1000) # Storage space for results covmod.results.list <- list() covmod.summary.list <- list() for(tdist in seq(along= truncation)){ # Screen display to indicate how far through the simulations we are cat(\"\\n Running for truncation = \", truncation[tdist], fill = T) # Update analysis truncation distance so that detecability is now modelled as a function of sex new.ds.analyses <- make.ds.analysis(dfmodel = list(~sex), key = c(\"hn\"), truncation = truncation[tdist]) # Update simulation sim.cov@ds.analysis <- new.ds.analyses # Run Simulation covmod.results.list[[tdist]] <- run.simulation(sim.cov, run.parallel = TRUE) # Store simulation summaries covmod.summary.list[[tdist]] <- summary(covmod.results.list[[tdist]], description.summary = FALSE) } # Add names to the summary and results list names(covmod.results.list) <- paste(\"t\", truncation, sep = \"\") names(covmod.summary.list) <- paste(\"t\", truncation, sep = \"\")"},{"path":"/articles/dsims-examples.html","id":"running-simulations-to-check-detection-function-parameter-estimates","dir":"Articles","previous_headings":"","what":"Running Simulations to Check Detection Function Parameter Estimates","title":"Transition from `DSsim` to `dsims`","text":"simulations concentrate question accurately precisely can estimate abundance density population. However, may also interested learning individual level covariates affect detectability. require different slightly advanced setup. dsims currently store detection function parameter estimates therefore need manually, however dsims provide functionality fairly straight forward. create simulation need get dsims give us survey data can run analyses obtain parameter estimates. Please note extraction parameter estimates ddf model specific model, adapting code need check ddf documentation mrds understand parameters different models.","code":"sim.cov <- make.simulation(reps = 999, design = design, population.description = pop.desc.cov, detectability = detect.cov, ds.analysis = ddf.analyses.cov) # Truncation distances to iterate over truncation <- c(200, 400, 600, 800, 1000) reps <- sim.cov@reps # To store values of interest sigma.est <- male.param <- array(NA, dim = c(length(truncation), reps), dimnames = list(truncation, 1:reps)) # Iterate over truncation distances for(tdist in 2:5){#seq(along = truncation)){ # Screen display to indicate how far through the simulations we are cat(\"\\n Running for truncation = \", truncation[tdist], fill = T) # Update truncation distance new.ds.analyses <- make.ds.analysis(dfmodel = list(~sex), key = c(\"hn\"), truncation = truncation[tdist]) # Update simulation sim.cov@ds.analysis <- new.ds.analyses # Simulation repetitions for(i in 1:reps){ cat(\"\\r\", i, \" out of \", reps, \" reps \\r\") # Simulates the survey process simulated.data <- run.survey(sim.cov) # Run analyses results <- analyse.data(new.ds.analyses, simulated.data) # Obtain detection function model ddf.results <- results$ddf # Store values of interest try(sigma.est[tdist,i] <- ddf.results$par[1]) try(male.param[tdist,i] <- ddf.results$par[2]) } }"},{"path":"/articles/dsims-examples.html","id":"results","dir":"Articles","previous_headings":"","what":"Results","title":"Transition from `DSsim` to `dsims`","text":"simulations take substantial amount time run saved results summaries; can downloaded dsims_truncation_results.zip. Running one simulations 999 repetitions one truncation distance takes 11 minutes i7-2600K 3.40GHz processor running parallel across 7 threads. running parallel maximum number cores (threads) permitted one less number machine, default number used unless max.cores specifies lower number. downloaded unzipped sub folder called results results summaries can loaded follows: objects loaded workspace include results.list, summary.list, cov.results.list, cov.summary.list, covmod.results.list, covmod.summary.list, sigma_est male_param. results.list list 3 simulation objects simple half normal simulations truncation distances 200, 400 600. summary.list list 3 simulation summaries associated simulations results.list. cov.results.list list 5 simulation objects covariate simulations detectability affected sex sex included covariate detection function models. simulations relate truncation distances 200, 400, 600, 800 1000. cov.summary.list list 5 simulation summaries associated simulations cov.results.list. covmod.results.list list 5 simulation objects covariate simulations detectability affected sex analyses including covariate sex detection function model. simulations relate truncation distances 200, 400, 600, 800 1000. covmod.summary.list list 5 simulation summaries associated simulations covmod.results.list. sigma_est male_param contains parameter estimates simulation set covmod.summary.list. sigma_est 2D array containing parameter estimates sigma five truncation distances male_param contains parameter estimates male sex parameter truncation distance.","code":"# Running simulations in parallel run.simulation(sim.cov, run.parallel = TRUE, max.cores = 7) # Simulations using a simple half normal detection function load(\"results/results_list.ROBJ\") load(\"results/summary_list.ROBJ\") # Covartiate simulations load(\"results/results_cov_list.ROBJ\") load(\"results/summary_cov_list.ROBJ\") # Covariate simulations with covariate model load(\"results/covmod_results_list.ROBJ\") load(\"results/covmod_summary_list.ROBJ\") load(\"results/sigma_est.ROBJ\") load(\"results/male_param.ROBJ\") # To view the full summary for the simple half normal simulation with a truncation distance of 200: summary.list$t200 # To view the full summary for the covariate simulation with a truncation distance of 600: cov.summary.list$t600"},{"path":"/articles/dsims-examples.html","id":"extracting-result-statistics","dir":"Articles","previous_headings":"Results","what":"Extracting Result Statistics","title":"Transition from `DSsim` to `dsims`","text":"investigate truncation distance affects results need produce tables comparison. section details can done using knitr. section provided interested users can just skip next section results tables actually presented. code applicable study regions one strata, need modified deal multiple strata.","code":"library(knitr) N <- unlist(lapply(summary.list, function(x){x@individuals$N$mean.Estimate})) n <- unlist(lapply(summary.list, function(x){x@individuals$summary$mean.n})) se <- unlist(lapply(summary.list, function(x){x@individuals$N$mean.se})) sd.N <- unlist(lapply(summary.list, function(x){x@individuals$N$sd.of.means})) bias <- unlist(lapply(summary.list, function(x){x@individuals$N$percent.bias})) RMSE <- unlist(lapply(summary.list, function(x){x@individuals$N$RMSE})) cov <- unlist(lapply(summary.list, function(x){x@individuals$N$CI.coverage.prob})) sim.data <- data.frame(trunc = c(200,400,600), n = round(n), N = round(N), se = round(se,2), sd.N = round(sd.N,2), bias = round(bias,2), RMSE = round(RMSE,2), cov = round(cov*100,1)) kable(sim.data, col.names = c(\"$Truncation$\", \"$mean\\\\ n$\", \"$mean\\\\ \\\\hat{N}$\", \"$mean\\\\ se$\", \"$SD(\\\\hat{N})$\", \"$\\\\% Bias$\", \"$RMSE$\", \"$\\\\%\\\\ CI\\\\ Coverage$\"), row.names = FALSE, align = c('c', 'c', 'c', 'c', 'c', 'c', 'c', 'c'), caption = \"Simulation Results for the simple half normal detection probability: The truncation distance, mean number of detections, mean estimated population size (N), mean standard error of $\\\\hat{N}$, the standard deviation of $\\\\hat{N}$, percentage bias, root mean squared error, percentage of times the true value of N was captured in the confidence intervals.\", table.placement=\"!h\", format = \"html\")"},{"path":[]},{"path":"/articles/dsims-examples.html","id":"simple-half-normal-simulations","dir":"Articles","previous_headings":"Simulation Results","what":"Simple Half-Normal Simulations","title":"Transition from `DSsim` to `dsims`","text":"simulations data generated based single half-normal detection function truncation distance used analysis stage made little difference estimates abundance. perhaps small decrease coverage 95% confidence intervals truncation distance increased. truncation distance 400 600 didn’t quite capture truth 95% time, Table 1. root mean squared error (RMSE) values suggested away transect distances truncated closer abundance estimates truth, although bias appeared minimal three scenarios. Precision looked improve larger truncation distances. Table 1: Simulation Results simple half normal detection probability. truncation distance, mean number detections, mean estimated population size (N), mean standard error \\(\\hat{N}\\), standard deviation \\(\\hat{N}\\), percentage bias, root mean squared error, percentage times true value N captured 95% confidence intervals.","code":""},{"path":"/articles/dsims-examples.html","id":"covariate-simulation-testing-pooling-robustness","dir":"Articles","previous_headings":"Simulation Results","what":"Covariate Simulation Testing Pooling Robustness","title":"Transition from `DSsim` to `dsims`","text":"simulations test whether can rely assumption pooling robustness situation. deliberately provided model used generate data candidate model analysis stage. can see setup, pooled two quite distinct detection functions, bias abundance estimates truncation distance larger, Table 2. results also show 95% confidence intervals capture true abundance substantially less 95% time use large truncation distances. underestimation variability, Table 2 shows large truncation values mean se (mean estimated standard errors) lower standard deviation estimates abundance. analyses correctly estimating variability expected values similar. addition, RMSE suggests larger truncation distance away truth abundance estimates become, significant jump 800 1000m. Table 2: Simulation Results covariate detection probability, detectability affected sex candidate models (half-normal hazard rate) contain covariates. truncation distance, mean number detections, mean estimated population size (N), mean standard error \\(\\hat{N}\\), standard deviation \\(\\hat{N}\\), percentage bias, root mean squared error, percentage times true value N captured 95% confidence intervals.","code":""},{"path":"/articles/dsims-examples.html","id":"covariate-simulation-with-covariate-model","dir":"Articles","previous_headings":"Simulation Results","what":"Covariate Simulation with Covariate Model","title":"Transition from `DSsim` to `dsims`","text":"Finally ran simulations fitted model used generate data. simulations truncation distance little influence accuracy estimates abundance, exception small amount bias smallest truncation distance, Table 3. RMSE values suggest larger truncation distances better job estimating abundance significant improvement coming step 200m truncation 400m truncation. 95% confidence intervals captured true abundance least 95% time truncation distances. simulations, variability always estimated mean estimated standard errors always higher standard deviation estimates. estimates abundance greatly affected truncation distance simulations, said parameter estimates. Figure 15, suggests parameter estimation accurate reliable maximum truncation distance. unstable parameter estimates smallest truncation distance leading sometimes large estimates sigma bimodal distribution sex.male explain slight bias abundance estimates truncation distance seen Table 2. hoped practise strange behaviour might associated poor fit data identified estimates rejected based extensive model selection criteria. Table 3: Simulation Results covariate detection probability, detectability affected sex modelled detection function. truncation distance, mean number detections, mean estimated population size (N), mean standard error \\(\\hat{N}\\), standard deviation \\(\\hat{N}\\), percentage bias, root mean squared error, percentage times true value N captured 95% confidence intervals. Figure 15: Histograms parameter estimates sigma sex.male three five truncation distances investigated. Red lines indicate truth.","code":""},{"path":"/articles/dsims-examples.html","id":"discussion","dir":"Articles","previous_headings":"","what":"Discussion","title":"Transition from `DSsim` to `dsims`","text":"simulations pushed concept pooling robustness limit two detection functions males females distinct one another. increased potential spiked data simulations, number detections falls away quickly small distances can make fitting detection function unreliable (fact numerous warnings running simulations scenario). recommendation performing distance sampling surveys review data frequently field collected. detect spiked data field methods adapted achieve wider shoulder detection function. practise help ensure pooling robustness holds. model selection () applied simulations done purely basis AIC. practise AIC value one number diagnostic techniques researchers rely select appropriate detection function model. likely, especially due potential spiked data, models simulations good fits data selected researcher. model selection manual researcher may chosen include adjustment terms half-normal hazard rate models may improved model fit associated estimates abundance relying pooling robustness. simulations suggest small cost precision researcher truncating data. fact, truncation may beneficial large differences underlying detection functions due covariate included detection function models. suspect multiple detection functions pooled together tails observed combined detection function represent detection functions already dropped extremely low probabilities detection closer transect. general rule distance sampling shape detection function close transect importance going tail. indeed detections made large distances, included, can undesired large influence detection function parameters. generally accepted rule thumb truncate data probability detection around 0.15. Conversely, researcher hopes identify covariates affect detectability obtain reliable parameter estimates minimal () truncation appears preferable. effects truncation distance estimated abundance precision interesting, especially comparison estimated observed variability. allow simulations fit half-normal hazard rate models detectability affected sex covariate, truncation distance increases estimated variability (mean se) stays roughly observed variability \\((SD(\\hat{N}))\\) increases. larger truncation distances variability estimated abundance underestimated confidence interval coverage low. However, fitting covariate model estimated variance higher observed variance suggesting model estimating variability truncation distances confidence interval coverage high.","code":""},{"path":"/articles/dsims-examples.html","id":"conclusions","dir":"Articles","previous_headings":"","what":"Conclusions","title":"Transition from `DSsim` to `dsims`","text":"Truncation can help ensure concept pooling robustness holds differences detection functions individuals population covariates affecting detectability modelled. estimates abundance accurate precise covariate affecting detectability included detection function model. Larger truncation distances truncation preferable trying accurately obtain parameters covariates affect detectability.","code":""},{"path":[]},{"path":"/articles/dsims_grouped_strata.html","id":"getting-started","dir":"Articles","previous_headings":"","what":"Getting started","title":"Grouping strata during simulation","text":"Ensure administrator privileges computer install necessary R packages.","code":""},{"path":"/articles/dsims_grouped_strata.html","id":"running-the-simulation-and-viewing-the-results-for-yourself","dir":"Articles","previous_headings":"Getting started","what":"Running the simulation and viewing the results for yourself","title":"Grouping strata during simulation","text":"advisable download .Rmd file like replicate simulations . addition, results simulations provided allow compile .Rmd document. results included zip archive results.zip. Uncompressing contents folder called results within folder .Rmd file give required structure run code .Rmd file. end file sim.results.ROBJ within results folder.","code":""},{"path":[]},{"path":"/articles/dsims_grouped_strata.html","id":"creating-a-region-object","dir":"Articles","previous_headings":"Creating a grouped strata simulation","what":"Creating a region object","title":"Grouping strata during simulation","text":"First, create region object using shapefile stored within package directory. shapefile provided contains marine study area coast Ireland. region already projected metres dssd detect shapefile .prj file. study region also divided six strata provide names code identify (“North”, “NW”, “West Upper”, “West Lower”, “SW”, “South”). Care taken check order strata expected checking plot study region. division study area six strata design purposes, allows us specify design angles stratum individually. However, analysis purposes interested estimates two distinct areas study region, consist three northern strata grouped together three southern strata grouped together.","code":"# Find the full file path to the shapefile on the users machine shapefile.path <- system.file(\"extdata\", \"AreaRProjStrata.shp\", package = \"dssd\") # Create the region object region <- make.region(region.name = \"study area\", strata.name = c(\"North\", \"NW\", \"West Upper\", \"West Lower\", \"SW\", \"South\"), shape = shapefile.path) # Plot the survey region plot(region)"},{"path":"/articles/dsims_grouped_strata.html","id":"creating-a-design-object","dir":"Articles","previous_headings":"Creating a grouped strata simulation","what":"Creating a design object","title":"Grouping strata during simulation","text":"mentioned , two sub regions interest study area, like estimates density / abundance (northern three strata southern three strata). Let’s start constructing design though divided study region two strata. expect animals southern strata implement non-uniform coverage design allocating effort per unit area (.e. higher coverage) strata northern strata. Let’s assume effort calculations suggested sufficient resources survey parallel lines spacing 16,000m northern strata spacing 8,000m southern strata. Note shapefile units metres, simulation measurements must also provided metres. supply single design angle three northern strata one three southern strata, let’s set 135 70 degrees, respectively. also specify minus sampling expect observe animals beyond 1,500m. generate set transects design assess desirable design qualities. optimal design aim maximise number samplers (many short lines better fewer long lines) place parallel density gradients. case long thin study region , want lay transects across short dimension region (.e. perpendicular coast). also often case marine species distributed relation coast (usually particular depth preference) laying transects perpendicular coast align parallel density gradient thereby reduce variability encounter rate transects resulting precise estimates. can see first design, given complexity region, choosing single design angle northern southern groups strata going achieve goal. particularly problematic southern strata selecting design angle give lines perpendicular coast one area gives lines parallel coast another. now make use fact six strata select appropriate design angles aim orientating transects perpendicular coast. can see image dividing northern southern regions interest substrata allows us better orientate lines maximise number samplers place perpendicular coast. stratification purely design purposes (modify design angle moved along coast) still treat three substrata one come analyse data. However, important note can kept uniform coverage across substrata. However, design allow us simply group 6 strata analysis stage. northern strata lower coverage southern strata full dataset representative southern strata northern must therefore ensure differences detectability modelled.","code":"# Define a design based on only two strata design <- make.design(region = region, transect.type = \"line\", design = \"systematic\", spacing = c(rep(16000, 3), rep(8000, 3)), design.angle = c(135, 135, 135, 70, 70, 70), edge.protocol = \"minus\", truncation = 1500) # Generate and plot a single set of transects survey <- generate.transects(design) plot(region, survey) # Define the design design <- make.design(region = region, transect.type = \"line\", design = \"systematic\", spacing = c(rep(16000, 3), rep(8000, 3)), design.angle = c(160, 135, 80, 135, 50, 150), edge.protocol = \"minus\", truncation = 1500) # Create a single set of transects to check survey <- generate.transects(design) plot(region, survey)"},{"path":"/articles/dsims_grouped_strata.html","id":"creating-a-density-object","dir":"Articles","previous_headings":"Creating a grouped strata simulation","what":"Creating a density object","title":"Grouping strata during simulation","text":"create density surface represent distribution animals abundant south also prefers coastal waters. order get idea place hostpots can first check range coordinates projected scale. Note plot region gives scale lat lon despite region projected. can access information requesting bounding box sf object stored within dssd region. can now create density grid spacing 2,500m dimensions add two hotspots simulate potentially realistic distribution animals prefer stick closely coast. Adding hotspots largely done trial error know range x-y coordinate values. measurement values must provided metres. later use fixed population size simulations, need worry exact values provide density grid relate one another. example, area density cell value twice another density cell , average, end twice many animals population generated.","code":"# Get the bounding box of the sf object within the region sf::st_bbox(region@region) # Make a density grid with values of 1 across the region my.density <- make.density(region = region, x.space = 2500, y.space = 2500, constant = 1) # Add a hotspot at coordinates (0, 1900000) my.density <- add.hotspot(my.density, centre = c(0, 1900000), sigma = 70000, amplitude = 10) # Add a hotspot at coordinates (80000, 210000) my.density <- add.hotspot(my.density, centre = c(80000, 2100000), sigma = 1e+05, amplitude = 5) # Plot this example density surface plot(my.density, region)"},{"path":"/articles/dsims_grouped_strata.html","id":"population-size","dir":"Articles","previous_headings":"Creating a grouped strata simulation > Creating a density object","what":"Population size","title":"Grouping strata during simulation","text":"base simulation total population size 2,500 animals. make.population command requires us specify many individuals per stratum, calculate using density summary. can see table used exact densities density grid generate lot animals (see ave.N column)! However, mentioned , simulation use density surface guide relative density across region. Therefore, use value decide many animals allocate strata scaling . point, also create individual level covariate indicate whether animals northern group strata southern group strata. enable us later model differences detectability northern southern sub populations. Ignoring differences lead bias estimates abundance northern southern strata also total estimates due non-uniform coverage design. now include information population description set fixed population size argument true.","code":"# View the density summary summary(my.density) ## strata area ave.N ave.D ## 1 North 4176461143 [m^2] 8731625315 2.090676 ## 2 NW 8180996497 [m^2] 25656220203 3.136075 ## 3 West Upper 6316380968 [m^2] 17438152704 2.760782 ## 4 West Lower 8188111047 [m^2] 41315196625 5.045754 ## 5 SW 2654685511 [m^2] 13585880563 5.117699 ## 6 South 9291229356 [m^2] 48534037861 5.223640 # Extract average N values ave.N.vals <- summary(my.density)@summary$ave.N # Scale average N vals to sum to 2500 N.per.stratum <- round(2500 * ave.N.vals/sum(ave.N.vals)) # View the allocation per stratum N.per.stratum ## [1] 141 413 281 665 219 781 # Check the total sums to 2500 (sometimes rounding may cause slight variation) sum(N.per.stratum) ## [1] 2500 # Create the population description covs <- list() # Adds a strata group entry allocating 'North' to all animals in the North, NW # and West Upper strata and allocating 'South' to all animals in the West # Lower, SW and South strata. covs$strata.group <- data.frame(level = c(rep(\"North\", 3), rep(\"South\", 3)), prob = rep(1, 6), strata = c(\"North\", \"NW\", \"West Upper\", \"West Lower\", \"SW\", \"South\")) # Create the population description pop.description <- make.population.description(region = region, density = my.density, covariates = covs, N = N.per.stratum, fixed.N = TRUE)"},{"path":"/articles/dsims_grouped_strata.html","id":"true-detection-function","dir":"Articles","previous_headings":"Creating a grouped strata simulation > Creating a density object","what":"True detection function","title":"Grouping strata during simulation","text":"simulate using half-normal detection function change \\(\\sigma\\) (scale.param) depending stratum use truncation distance 1500m. changing detection functions across strata can demonstrate pooling robustness applies. Pooling robustness refers property distance sampling allows us obtain unbiased abundance estimates single ‘pooled’ detection function fitted across number sub populations, even detectability may vary greatly, (Rexstad, Buckland, Marshall, & Borchers, 2023). Pooling robustness applies data representative sample across population generating estimates. example, data three northern sub-strata can pooled data three southern sub- strata can pooled coverage . pool detections strata / sub-strata coverage varies (without accounting non-uniform coverage) resulting detection function representative strata higher coverage.","code":"# Create the detectability detect <- make.detectability(key.function = \"hn\", scale.param = c(950, 850, 750, 650, 550, 450), truncation = 1500) # Plot the detectability plot(detect, pop.description)"},{"path":"/articles/dsims_grouped_strata.html","id":"creating-the-analyses-object","dir":"Articles","previous_headings":"Creating a grouped strata simulation","what":"Creating the analyses object","title":"Grouping strata during simulation","text":"simulation engine currently fits one global detection function simulated dataset. scenario constructed, know pooling robustness apply across study region whole different levels coverage northern southern stratum groups. Given fit separate detection functions, must allow model able vary detection function across two groups strata. achieve can include strata.group covariate (included population description) model, allow different scale parameter estimated northern three strata southern three. Note simply included Region.Label covariate detection function model, however, within simulation stage fitting detection function strata included dataset resulted scale parameter estimated 6 strata individually. analysis stage also need define strata grouped order obtain estimates regions interest. dataframe created code tells simulation group strata. now define analyses. simulating detections range difference detection functions, incorporate model uncertainty allowing simulation select half normal hazard rate model. models include strata.group covariate use AIC criterion model selection.","code":"# Create a dataframe describing how the strata will be grouped group.strata <- data.frame(design.id = c(\"North\", \"NW\", \"West Upper\", \"West Lower\", \"SW\", \"South\"), analysis.id = c(rep(\"North\", 3), rep(\"South\", 3))) # View the dataframe print(group.strata) ## design.id analysis.id ## 1 North North ## 2 NW North ## 3 West Upper North ## 4 West Lower South ## 5 SW South ## 6 South South # Define the analyses - both the hn and hr models use the ~strata.group formula ds.analyses <- make.ds.analysis(dfmodel = list(~strata.group, ~strata.group), key = c(\"hn\", \"hr\"), truncation = 1500, group.strata = group.strata, criteria = \"AIC\")"},{"path":"/articles/dsims_grouped_strata.html","id":"running-the-simulation","dir":"Articles","previous_headings":"Creating a grouped strata simulation","what":"Running the simulation","title":"Grouping strata during simulation","text":"running simulation group components simulation object define number repetitions. example simulate 1000 surveys simulation definition. Note first time run simulation limit number repetitions check everything works expected. useful way check simulation setup generate single example survey, may take moment complete. previous plots lead believe properly parameterised simulation, time run . run small number repetitions take minute two complete, running 1000 repetitions take considerably longer simulation already run results can loaded instead. loaded simulation results, can view . view full summary use summary(simulation), store simulation summary look specific tables within . Firstly, view summary table. Notice requested results northern southern strata instead six substrata. summary table indicates around 98 detections made average northern strata 275 southern strata. important check sufficient detections strata differences detectability can accurately modelled detection function. Next view table giving abundance estimates. small amount negative bias strata total estimate abundance. However, coverage confidence intervals (0.95) 0.92 southern strata total estimate. Sometimes reduced confidence interval coverage can due variance estimated case mean.se (mean estimated standard error) sd..means (truth - observed standard error estimates) close suggesting variance estimated accurately.","code":"# Create the simulation simulation <- make.simulation(reps = 1000, design = design, population.description = pop.description, detectability = detect, ds.analysis = ds.analyses) # Simulate the data generation for a single survey eg.survey <- run.survey(simulation) # Plot the example survey plot(eg.survey, region) # Run the simulation in parallel simulation <- run.simulation(simulation, run.parallel = TRUE) # Load the simulation object which has already been run load(\"files/sim.results.ROBJ\") # Create a summary (silently without the description) sim.summary <- summary(simulation, description.summary = FALSE) # Display the summary table sim.summary@individuals$summary ## mean.Cover.Area mean.Effort mean.n mean.k mean.ER mean.se.ER ## North 3498729895 1166243 98.490 27.912 8.445558e-05 9.150347e-06 ## South 7551517574 2517173 274.998 71.931 1.092477e-04 7.244126e-06 ## Total 11050247469 3683416 373.488 99.843 9.731804e-05 5.812492e-06 ## sd.mean.ER ## North 7.782846e-06 ## South 5.842293e-06 ## Total 4.780433e-06 # Display the table of abundance estimates round(sim.summary@individuals$N, 3) ## Truth mean.Estimate percent.bias RMSE CI.coverage.prob mean.se ## North 835 819.344 -1.875 111.016 0.956 115.122 ## South 1665 1604.102 -3.658 145.372 0.924 132.674 ## Total 2500 2423.446 -3.062 193.676 0.915 176.878 ## sd.of.means ## North 109.962 ## South 132.068 ## Total 177.993"},{"path":"/articles/dsims_grouped_strata.html","id":"discussion","dir":"Articles","previous_headings":"Creating a grouped strata simulation","what":"Discussion","title":"Grouping strata during simulation","text":"Even though detectability animals varied across six sub-strata, estimates northern southern groups sub-strata combined low bias. result due pooling robustness applying across groups sub strata (coverage group). Meanwhile, difference detectability northern southern groups modelled explicitly using strata.group covariate models. simulation repeated, covariate omitted, expect see bias abundance estimated northern southern regions well overall estimate. due differences coverage three northen sub-strata three southern sub-strata fitted detection function representative southern region (due higher coverage) northern region. setup analysis simulation little complex due restrictions simulation package (.e. needing include stratum covariate population description). analysing distance sampling data field, similar scenario, able either fit separate detection functions data different regions interest create kind stratum variable want, giving analysis options.","code":""},{"path":[]},{"path":"/articles/GettingStarted.html","id":"distance-sampling-simulations","dir":"Articles","previous_headings":"","what":"Distance Sampling Simulations","title":"Getting Started with dsims","text":"vignette introduces basic procedure setting running distance sampling simulation using ‘dsims’ (Laura Marshall 2023a). ‘dsims’ package uses distance sampling survey design package ‘dssd’ (Laura Marshall 2023b) define design generate surveys (sets transects). details defining designs please refer ‘dssd’ vignettes. ‘dsims’ designed largely similar ‘DSsim’ package (L. Marshall 2020) terms work flow, functions arguments. main differences terms use lie definition designs can now generated R using ‘dssd’ package (packages automatically linked) definition analyses. Analyses now defined using terminology based ‘Distance’ package (Miller et al. 2019). addition, underlying functionality now makes use ‘sf’ package (Pebesma Baston 2021). Distance Sampling techniques provide design based estimates density abundance populations. accuracy estimates relies valid survey design. general rules thumb can help guide design choices, simulations emulating specific set survey characteristics can often help us achieve efficient robust designs individual studies. example, simulations can help us investigate effort allocation can affect estimates effects efficient design less uniform coverage probability. Due individual nature study, specific set characteristics, simulation can powerful tool evaluating survey design.","code":""},{"path":"/articles/GettingStarted.html","id":"setting-up-the-region","dir":"Articles","previous_headings":"","what":"Setting up the Region","title":"Getting Started with dsims","text":"use St Andrews bay area example study region simulations. single strata study region projected metres. first load ‘dsims’ package, also automatically load ‘dssd’ package. shapefile projection recorded (associated .prj file) tell ‘dsims’ units metres. Figure 1: study region.","code":"library(dsims) ## Loading required package: dssd # Find the file path to the example shapefile in dssd shapefile.name <- system.file(\"extdata\", \"StAndrew.shp\", package = \"dssd\") # Create the survey region object region <- make.region(region.name = \"St Andrews bay\", shape = shapefile.name, units = \"m\") plot(region)"},{"path":"/articles/GettingStarted.html","id":"defining-the-study-population","dir":"Articles","previous_headings":"","what":"Defining the study population","title":"Getting Started with dsims","text":"define study population require number intermediate steps. describe turn .","code":""},{"path":"/articles/GettingStarted.html","id":"population-density-grid","dir":"Articles","previous_headings":"Defining the study population","what":"Population Density Grid","title":"Getting Started with dsims","text":"first step defining study population set density grid. One way first create flat surface add hot low spots represent think might areas higher lower density animals. assume 300 groups St Andrews bay study area (fairly large number!) give us average density 3.04-07 groups per square metre. simulation, use fixed population size, need worry absolute values density surface. Instead, can simpler work larger values aware defining relative density surface. create surface density twice another area relationship maintained (much smaller absolute values) later generate population. purposes simulation likely want test range plausible animal distributions (knew exactly many going find given location probably wouldn’t study!). testing non-uniform coverage designs advisable try worst case scenarios, .e. set density area higher lower coverage differ majority survey region. give idea degree potential bias introduced. example, equal spaced zigzag design, generated convex hull areas differing coverage likely top bottom survey region. density grid areas shown lower animal density rest survey region, likely scenario study region constructed order catch range population interest. Figure 2: density map representing plausible distributions animals within study region. situations may need rely constructing density distribution scratch. Now demonstrate use gam construct density surface. data area use density grid created example dataset. fit gam data use create new density object. need restrict predicted values greater zero, use log link Gaussian error distribution. can also useful trick want turn something created using method, can look bit lumpy bumpy, smoother distribution surface. gam fitted must use smooth x y fit model predictor covariates present density surface. Figure 3: density map representing plausible distributions animals within study region.","code":"# We first create a flat density grid density <- make.density(region = region, x.space = 500, constant = 1) # Now we can add some high and low points to give some spatial variability density <- add.hotspot(object = density, centre = c(-170000, 6255000), sigma = 8000, amplitude = 4) density <- add.hotspot(object = density, centre = c(-160000, 6275000), sigma = 6000, amplitude = 4) density <- add.hotspot(object = density, centre = c(-155000, 6260000), sigma = 3000, amplitude = 2) density <- add.hotspot(object = density, centre = c(-150000, 6240000), sigma = 10000, amplitude = -0.9) density <- add.hotspot(object = density, centre = c(-155000, 6285000), sigma = 10000, amplitude = -1) # I will choose to plot in km rather than m (scale = 0.001) plot(density, region, scale = 0.001) # First extract the data above - this is simple in this case as we only have a single strata # Multi-strata regions will involve combining the density grids for each strata into a # single dataset. density.data <- density@density.surface[[1]] head(density.data) ## Simple feature collection with 6 features and 4 fields ## Geometry type: POLYGON ## Dimension: XY ## Bounding box: xmin: -157572.4 ymin: 6241463 xmax: -154890.4 ymax: 6241543 ## CRS: NA ## strata density x y geometry ## 34 St Andrews bay 0.6128054 -157640.4 6241293 POLYGON ((-157390.4 6241543... ## 35 St Andrews bay 0.5614958 -157140.4 6241293 POLYGON ((-157390.4 6241543... ## 36 St Andrews bay 0.5125986 -156640.4 6241293 POLYGON ((-156890.4 6241543... ## 37 St Andrews bay 0.4662975 -156140.4 6241293 POLYGON ((-156390.4 6241543... ## 38 St Andrews bay 0.4227525 -155640.4 6241293 POLYGON ((-155890.4 6241543... ## 39 St Andrews bay 0.3821010 -155140.4 6241293 POLYGON ((-155390.4 6241543... # Fit a simple gam to the data library(mgcv) ## Loading required package: nlme ## This is mgcv 1.9-1. For overview type 'help(\"mgcv-package\")'. fit.gam <- gam(density ~ s(x,y), data = density.data, family = gaussian(link=\"log\")) # Use the gam object to create a density object gam.density <- make.density(region = region, x.space = 500, fitted.model = fit.gam) plot(gam.density, region, scale = 0.001)"},{"path":"/articles/GettingStarted.html","id":"other-population-parameters","dir":"Articles","previous_headings":"Defining the study population","what":"Other Population Parameters","title":"Getting Started with dsims","text":"created plausible animal density distribution can go define population parameters. constructing population description. assume animals occur small clusters first create covariate list define distribution cluster size (must named “size”) zero-truncated Poisson distribution mean equal 3. familiar ‘DSsim’ please note simplified format defining population covariates. population value define population size. clusters population, N refer number clusters rather individuals. set number clusters 100. leave fixed.N argument default TRUE say like generate population based population size rather density surface.","code":"# Create a covariate list describing the distribution of cluster sizes covariates <- list(size = list(distribution = \"ztruncpois\", mean = 3)) # Define the population description pop.desc <- make.population.description(region = region, density = gam.density, covariates = covariates, N = 300, fixed.N = TRUE)"},{"path":"/articles/GettingStarted.html","id":"coverage-grid","dir":"Articles","previous_headings":"","what":"Coverage Grid","title":"Getting Started with dsims","text":"good practice create coverage grid study area assess coverage probability varies spatially across study area specified designs. designs may non-uniform coverage, advise coverage probability assessed prior running simulations. However, step essential running simulations omit refer ‘dssd’ vignettes details.","code":""},{"path":"/articles/GettingStarted.html","id":"defining-the-design","dir":"Articles","previous_headings":"","what":"Defining the Design","title":"Getting Started with dsims","text":"‘dsims’ working together ‘dssd’ provides number point line transect designs. details defining designs can found ‘dssd’ help vignettes. also provide examples online https://distancedevelopment.github.io/distancesamplingcom2/resources/vignettes.html . simulations compare two line transect designs, systematically spaced parallel lines equal spaced zigzag lines. zigzag design generated within convex hull try minimise -effort transit time ends transects. design angles design selected transects run roughly perpendicular coast. way two designs defined means 90 degrees parallel line design 0 zigzag design. designs assumed minus sampling protocol truncation distance set 750m transect. spacings design selected give trackline lengths around 450 km (assessed running coverage simulations designs using ‘run.coverage’, see help ‘dssd’). trackline lengths can thought indicator cost survey give total travel time (effort) beginning first transect end last transect.","code":"parallel.design <- make.design(region = region, design = \"systematic\", spacing = 2500, edge.protocol = \"minus\", design.angle = 90, truncation = 750) zigzag.design <- make.design(region = region, design = \"eszigzag\", spacing = 2233, edge.protocol = \"minus\", design.angle = 0, bounding.shape = \"convex.hull\", truncation = 750)"},{"path":"/articles/GettingStarted.html","id":"generating-a-set-of-transects","dir":"Articles","previous_headings":"Defining the Design","what":"Generating a Set of Transects","title":"Getting Started with dsims","text":"always good idea run quick check design expected generating set transects plotting . Figure 4: example set transects generated systematic parallel line design plotted within study region. Figure 5: example set transects generated systematic parallel line design plotted within study region.","code":"p.survey <- generate.transects(parallel.design) plot(region, p.survey) z.survey <- generate.transects(zigzag.design) plot(region, z.survey)"},{"path":"/articles/GettingStarted.html","id":"defining-detectability","dir":"Articles","previous_headings":"","what":"Defining Detectability","title":"Getting Started with dsims","text":"defined population interest design use survey population now need provide information detectable individuals clusters . example assume larger clusters detectable. Take care defining covariate parameters covariate names match population description. setting basic scale parameter along side covariate parameters values need aware covariate parameter values incorporated. covariate parameter values provided adjust value scale parameter log scale. scale parameter individual (\\(\\sigma_j\\)) can calculated : \\[\\sigma_j = exp(log(\\sigma_0)+\\sum_{=1}^{k}\\beta_ix_{ij})\\] \\(j\\) individual, \\(\\sigma_0\\) base line scale parameter (passed argument ‘scale.param’ natural scale), \\(\\beta_i\\)’s covariate parameters passed log scale covariate \\(\\) \\(x_{ij}\\) values covariate values covariate \\(\\) individual \\(j\\). assume half normal detection function scale parameter 300. set truncation distance design 750 m. set covariate slope coefficient log scale log(1.08) = 0.077. can check detection functions look like different covariate values plotting . plot example detection functions need provide population description well detectability. Figure 6: Plot detection function mean group size (solid line) 2.5 97.5 percentile values group size (dashed lines) population. can also calculate average detection function mean cluster size 3 defined population description: \\[\\sigma_{size = 3} = exp(log(300)+log(1.05)*3) = 347.3 \\]","code":"# Define the covariate parameters on the log scale cov.param <- list(size = log(1.08)) # Create the detectability description detect <- make.detectability(key.function = \"hn\", scale.param = 300, cov.param = cov.param, truncation = 750) # Plot the simulation detection functions plot(detect, pop.desc)"},{"path":"/articles/GettingStarted.html","id":"defining-analyses","dir":"Articles","previous_headings":"","what":"Defining Analyses","title":"Getting Started with dsims","text":"final component simulation analysis set analyses wish fit simulated data. define number models allow automatic model selection based minimum AIC value. models included half-normal covariates, hazard rate covariates half-normal cluster size covariate. leave truncation value 750 previously defined (must \\(\\le\\) truncation values used previously). use default error variance estimator “R2”. See ?mrds::varn descriptions various empirical variance estimators encounter rate.","code":"analyses <- make.ds.analysis(dfmodel = list(~1, ~1, ~size), key = c(\"hn\", \"hr\", \"hn\"), truncation = 750, er.var = \"R2\", criteria = \"AIC\")"},{"path":"/articles/GettingStarted.html","id":"putting-the-simulation-together","dir":"Articles","previous_headings":"","what":"Putting the Simulation Together","title":"Getting Started with dsims","text":"Now simulation components defined can create simulation objects. create one systematic parallel line design one equal spaced zigzag design. created simulation recommend check see simulated survey might look like. Figure 7: Example survey systematic parallel design. Panels showing: top left - transects, top right - population, bottom left - transects, population survey detections (cyan dots), bottom right - histogram detection distances Figure 8: Example survey equal spaced zigzag design. Panels showing: top left - transects, top right - population, bottom left - transects, population survey detections (cyan dots), bottom right - histogram detection distances","code":"sim.parallel <- make.simulation(reps = 999, design = parallel.design, population.description = pop.desc, detectability = detect, ds.analysis = analyses) sim.zigzag <- make.simulation(reps = 999, design = zigzag.design, population.description = pop.desc, detectability = detect, ds.analysis = analyses) # Generate a single instance of a survey: a population, set of transects # and the resulting distance data eg.parallel.survey <- run.survey(sim.parallel) # Plot it to view a summary plot(eg.parallel.survey, region) # Generate a single instance of a survey: a population, set of transects # and the resulting distance data eg.zigzag.survey <- run.survey(sim.zigzag) # Plot it to view a summary plot(eg.zigzag.survey, region)"},{"path":"/articles/GettingStarted.html","id":"running-the-simulation","dir":"Articles","previous_headings":"","what":"Running the Simulation","title":"Getting Started with dsims","text":"simulations can run follows. Note take time run!","code":"# Running the simulations sim.parallel <- run.simulation(sim.parallel) sim.zigzag <- run.simulation(sim.zigzag)"},{"path":"/articles/GettingStarted.html","id":"simulation-results","dir":"Articles","previous_headings":"","what":"Simulation Results","title":"Getting Started with dsims","text":"simulations run can view summary results. Viewing summary simulation first summarise simulation setup simulation run provide summary results. glossary also provided aid interpretation results. Note run produce slightly different results due random component generation populations sets survey transects. Firstly, systematic parallel lines design can see low bias 1.85% estimated abundance/density individuals. bias even lower 0.16% estimated abundance/density clusters. Also can see analyses done good job estimating mean cluster size, 1.72% bias. can also see 95% confidence intervals calculated abundance/density estimates fact capturing true value around 97% time (CI.coverage.prob). can also note observed standard deviation estimates mean bit lower mean se, meaning realising lower variance estimate. often seen systematic designs default variance estimator assumes completely random allocation transect locations, systematic designs usually lower variance. Reassuringly, results expected systematic parallel line design. expect low bias, definition, parallel line designs produce uniform coverage probability. areas design might produce uniform coverage around boundary minor edge effects due minus sampling. can now check results zigzag design. zigzag designs generated inside convex hull can much efficient parallel line designs (less -effort transit) possibility non-uniform coverage. coverage can assessed running run.coverage give much indication likely effects survey results. degree non-uniform coverage may affect survey results determined variability coverage also combines density animals region. Note run one density scenario , non-uniform coverage probability advisable test effects range plausible animal distributions. assumed distribution animals, looks like effects non-uniform coverage going minimal effects estimates abundance / density. individuals bias around 2.5% clusters 0.65%. Similar parallel line design, confidence intervals also giving coverage 97%. can note improved efficiency design increased effort line length corresponding covered area thus giving us bit better precision systematic parallel line design. Histograms estimates abundance simulation replicates can also viewed check possible effects extreme values skewed distributions. Figure 9: Left - histogram estimates abundance clusters systematic parallel design. Right - histogram estimates abundance clusters zigzag design. can see Figure 9 couple high estimates generated >500 parallel line zigzag designs. probably represent data sets difficult fit model (perhaps chance spiked data set). estimates centered around truth occasional high estimates may increased mean value slightly associated small amount positive bias.","code":"summary(sim.parallel) ## ## GLOSSARY ## -------- ## ## Summary of Simulation Output ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## ## Region : the region name. ## No. Repetitions : the number of times the simulation was repeated. ## No. Excluded Repetitions : the number of times the simulation failed ## (too few sightings, model fitting failure etc.) ## ## Summary for Individuals ## ~~~~~~~~~~~~~~~~~~~~~~~ ## ## Summary Statistics: ## mean.Cover.Area : mean covered across simulation. ## mean.Effort : mean effort across simulation. ## mean.n : mean number of observed objects across ## simulation. ## mean.n.miss.dist: mean number of observed objects where no distance ## was recorded (only displayed if value > 0). ## no.zero.n : number of surveys in simulation where ## nothing was detected (only displayed if value > 0). ## mean.ER : mean encounter rate across simulation. ## mean.se.ER : mean standard error of the encounter rates ## across simulation. ## sd.mean.ER : standard deviation of the encounter rates ## across simulation. ## ## Estimates of Abundance: ## Truth : true population size, (or mean of true ## population sizes across simulation for Poisson N. ## mean.Estimate : mean estimate of abundance across simulation. ## percent.bias : the percentage of bias in the estimates. ## RMSE : root mean squared error/no. successful reps ## CI.coverage.prob : proportion of times the 95% confidence interval ## contained the true value. ## mean.se : the mean standard error of the estimates of ## abundance ## sd.of.means : the standard deviation of the estimates ## ## Estimates of Density: ## Truth : true average density. ## mean.Estimate : mean estimate of density across simulation. ## percent.bias : the percentage of bias in the estimates. ## RMSE : root mean squared error/no. successful reps ## CI.coverage.prob : proportion of times the 95% confidence interval ## contained the true value. ## mean.se : the mean standard error of the estimates. ## sd.of.means : the standard deviation of the estimates. ## ## Detection Function Values ## ~~~~~~~~~~~~~~~~~~~~~~~~~ ## ## mean.observed.Pa : mean proportion of individuals/clusters observed in ## the covered region. ## mean.estimte.Pa : mean estimate of the proportion of individuals/ ## clusters observed in the covered region. ## sd.estimate.Pa : standard deviation of the mean estimates of the ## proportion of individuals/clusters observed in the ## covered region. ## mean.ESW : mean estimated strip width. ## sd.ESW : standard deviation of the mean estimated strip widths. ## ## ## Region: St Andrews bay ## No. Repetitions: 999 ## No. Excluded Repetitions: 0 ## Using only repetitions where all models converged. ## ## Design: Systematic parallel line design ## design.type : Systematic parallel line design ## bounding.shape : rectangle ## spacing : 2500 ## design.angle : 90 ## edge.protocol : minus ## ## Individual Level Covariate Summary: ## size:ztruncpois , mean = 3 ## Population Detectability Summary: ## key.function = hn ## scale.param = 300 ## truncation = 750 ## ## Covariate Detectability Summary (params on log scale): ## size parameters: ## Strata St Andrews bay ## 0.07696104 ## ## Analysis Summary: ## Candidate Models: ## Model 1: key function 'hn', formula '~1', was selected 474 time(s). ## Model 2: key function 'hr', formula '~1', was selected 201 time(s). ## Model 3: key function 'hn', formula '~size', was selected 324 time(s). ## criteria = AIC ## variance.estimator = R2 ## truncation = 750 ## ## Summary for Individuals ## ## Estimates of Abundance (N) ## ## Truth mean.Estimate percent.bias RMSE CI.coverage.prob mean.se sd.of.means ## 1 900 916.67 1.85 149.89 0.97 155.99 149.04 ## ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Estimates of Density (D) ## ## Truth mean.Estimate percent.bias RMSE CI.coverage.prob ## 1 9.113923e-07 9.282781e-07 1.852743 1.517923e-07 0.968969 ## mean.se sd.of.means ## 1 1.579674e-07 1.509258e-07 ## ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## ## Summary for Clusters ## ## Summary Statistics ## ## mean.Cover.Area mean.Effort mean.n mean.k mean.ER mean.se.ER ## 1 592153913 394769.3 106.7317 15.82883 0.0002704223 3.569565e-05 ## sd.mean.ER ## 1 2.137828e-05 ## ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Estimates of Abundance (N) ## ## Truth mean.Estimate percent.bias RMSE CI.coverage.prob mean.se sd.of.means ## 1 300 300.49 0.16 45.02 0.97 49.01 45.04 ## ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Estimates of Density (D) ## ## Truth mean.Estimate percent.bias RMSE CI.coverage.prob ## 1 3.037974e-07 3.042914e-07 0.1626056 4.55915e-08 0.970971 ## mean.se sd.of.means ## 1 4.962823e-08 4.561166e-08 ## ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Estimates of Expected Cluster Size ## ## Truth mean.Expected.S percent.bias mean.se.ExpS sd.mean.ExpS ## 1 3 3.05 1.72 0.16 0.2 ## ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## ## Detection Function Values ## ## mean.observed.Pa mean.estimate.Pa sd.estimate.Pa mean.ESW sd.ESW ## 1 0.6 0.6 0.07 451.01 54.08 summary(sim.zigzag) ## ## GLOSSARY ## -------- ## ## Summary of Simulation Output ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## ## Region : the region name. ## No. Repetitions : the number of times the simulation was repeated. ## No. Excluded Repetitions : the number of times the simulation failed ## (too few sightings, model fitting failure etc.) ## ## Summary for Individuals ## ~~~~~~~~~~~~~~~~~~~~~~~ ## ## Summary Statistics: ## mean.Cover.Area : mean covered across simulation. ## mean.Effort : mean effort across simulation. ## mean.n : mean number of observed objects across ## simulation. ## mean.n.miss.dist: mean number of observed objects where no distance ## was recorded (only displayed if value > 0). ## no.zero.n : number of surveys in simulation where ## nothing was detected (only displayed if value > 0). ## mean.ER : mean encounter rate across simulation. ## mean.se.ER : mean standard error of the encounter rates ## across simulation. ## sd.mean.ER : standard deviation of the encounter rates ## across simulation. ## ## Estimates of Abundance: ## Truth : true population size, (or mean of true ## population sizes across simulation for Poisson N. ## mean.Estimate : mean estimate of abundance across simulation. ## percent.bias : the percentage of bias in the estimates. ## RMSE : root mean squared error/no. successful reps ## CI.coverage.prob : proportion of times the 95% confidence interval ## contained the true value. ## mean.se : the mean standard error of the estimates of ## abundance ## sd.of.means : the standard deviation of the estimates ## ## Estimates of Density: ## Truth : true average density. ## mean.Estimate : mean estimate of density across simulation. ## percent.bias : the percentage of bias in the estimates. ## RMSE : root mean squared error/no. successful reps ## CI.coverage.prob : proportion of times the 95% confidence interval ## contained the true value. ## mean.se : the mean standard error of the estimates. ## sd.of.means : the standard deviation of the estimates. ## ## Detection Function Values ## ~~~~~~~~~~~~~~~~~~~~~~~~~ ## ## mean.observed.Pa : mean proportion of individuals/clusters observed in ## the covered region. ## mean.estimte.Pa : mean estimate of the proportion of individuals/ ## clusters observed in the covered region. ## sd.estimate.Pa : standard deviation of the mean estimates of the ## proportion of individuals/clusters observed in the ## covered region. ## mean.ESW : mean estimated strip width. ## sd.ESW : standard deviation of the mean estimated strip widths. ## ## ## Region: St Andrews bay ## No. Repetitions: 999 ## No. Excluded Repetitions: 0 ## Using only repetitions where all models converged. ## ## Design: Equal spaced zigzag line design ## design.type : Equal spaced zigzag line design ## bounding.shape : convex.hull ## spacing : 2233 ## design.angle : 0 ## edge.protocol : minus ## ## Individual Level Covariate Summary: ## size:ztruncpois , mean = 3 ## Population Detectability Summary: ## key.function = hn ## scale.param = 300 ## truncation = 750 ## ## Covariate Detectability Summary (params on log scale): ## size parameters: ## Strata St Andrews bay ## 0.07696104 ## ## Analysis Summary: ## Candidate Models: ## Model 1: key function 'hn', formula '~1', was selected 476 time(s). ## Model 2: key function 'hr', formula '~1', was selected 178 time(s). ## Model 3: key function 'hn', formula '~size', was selected 345 time(s). ## criteria = AIC ## variance.estimator = R2 ## truncation = 750 ## ## Summary for Individuals ## ## Estimates of Abundance (N) ## ## Truth mean.Estimate percent.bias RMSE CI.coverage.prob mean.se sd.of.means ## 1 900 922.42 2.49 134.29 0.97 145.28 132.47 ## ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Estimates of Density (D) ## ## Truth mean.Estimate percent.bias RMSE CI.coverage.prob ## 1 9.113923e-07 9.340926e-07 2.490729 1.359901e-07 0.971972 ## mean.se sd.of.means ## 1 1.471139e-07 1.341493e-07 ## ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## ## Summary for Clusters ## ## Summary Statistics ## ## mean.Cover.Area mean.Effort mean.n mean.k mean.ER mean.se.ER ## 1 663209990 442140 120.3654 18.47948 0.0002722232 3.346453e-05 ## sd.mean.ER ## 1 2.143639e-05 ## ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Estimates of Abundance (N) ## ## Truth mean.Estimate percent.bias RMSE CI.coverage.prob mean.se sd.of.means ## 1 300 301.94 0.65 41.32 0.97 45.58 41.29 ## ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Estimates of Density (D) ## ## Truth mean.Estimate percent.bias RMSE CI.coverage.prob ## 1 3.037974e-07 3.057631e-07 0.6470286 4.18412e-08 0.973974 ## mean.se sd.of.means ## 1 4.616181e-08 4.181593e-08 ## ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Estimates of Expected Cluster Size ## ## Truth mean.Expected.S percent.bias mean.se.ExpS sd.mean.ExpS ## 1 3 3.06 1.97 0.15 0.2 ## ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## ## Detection Function Values ## ## mean.observed.Pa mean.estimate.Pa sd.estimate.Pa mean.ESW sd.ESW ## 1 0.6 0.6 0.07 450.54 49.27 oldparams <- par(mfrow = c(1,2)) histogram.N.ests(sim.parallel) histogram.N.ests(sim.zigzag) par(oldparams)"},{"path":"/articles/GettingStarted.html","id":"simulation-conclusions","dir":"Articles","previous_headings":"","what":"Simulation Conclusions","title":"Getting Started with dsims","text":"simulation assumptions appears zigzag design cost us little accuracy allow us gain precision. noted cost accuracy vary depending distribution animals survey region.","code":""},{"path":[]},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Laura Marshall. Author, maintainer. Thomas Len. Contributor.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Marshall L (2024). dsims: Distance Sampling Simulations. R package version 1.0.4, https://github.com/DistanceDevelopment/dsims.","code":"@Manual{, title = {dsims: Distance Sampling Simulations}, author = {Laura Marshall}, year = {2024}, note = {R package version 1.0.4}, url = {https://github.com/DistanceDevelopment/dsims}, }"},{"path":[]},{"path":"/index.html","id":"distance-sampling-simulations","dir":"","previous_headings":"","what":"Distance Sampling Simulations","title":"Distance Sampling Simulations","text":"dsims package simulating distance sampling surveys allow users optimise survey design studies particular properties.","code":""},{"path":"/index.html","id":"using-dsims","dir":"","previous_headings":"","what":"Using dsims","title":"Distance Sampling Simulations","text":"currently three vignette within dsims package help get started using dsims: GettingStarted: Getting Started dsims available navigation bar top page Transition DSsim dsims: Articles navigation bar Grouped strata: Combining abundance estimates across strata constructed design purposes; Articles navigation bar","code":""},{"path":"/index.html","id":"getting-dsims","dir":"","previous_headings":"","what":"Getting dsims","title":"Distance Sampling Simulations","text":"easiest way get dsims install CRAN within R-studio R interface. endeavour make new functionality available CRAN timely manor. However, wish download development version latest updates immediately can using Hadley Wickham’s devtools package: install dsims github:","code":"install.packages(\"devtools\") library(devtools) install_github(\"DistanceDevelopment/dsims\", build_vignettes = TRUE)"},{"path":"/index.html","id":"troubleshooting-tip","dir":"","previous_headings":"","what":"Troubleshooting tip","title":"Distance Sampling Simulations","text":"installation packages, may get message “packages recent versions available. recommended update . like update?” list packages. recommend typically choose option “CRAN packages ”. Note may get message packages installed already loaded. case, solution may note packages , open R console (rather R Studio) use Packages | Update packages menu option (update.packages function) update packages.","code":""},{"path":"/reference/add.hotspot-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"S4 generic method to add a hotspot to the density grid — add.hotspot","title":"S4 generic method to add a hotspot to the density grid — add.hotspot","text":"Uses Gaussian decay around central location add hotspot density grid.","code":""},{"path":"/reference/add.hotspot-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"S4 generic method to add a hotspot to the density grid — add.hotspot","text":"","code":"add.hotspot(object, centre, sigma, amplitude) # S4 method for class 'Density' add.hotspot(object, centre, sigma, amplitude)"},{"path":"/reference/add.hotspot-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"S4 generic method to add a hotspot to the density grid — add.hotspot","text":"object Density-class object centre x,y-coordinate giving centre hotspot sigma value giving scale parameter gaussian decay amplitude height hotspot centre","code":""},{"path":"/reference/add.hotspot-methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"S4 generic method to add a hotspot to the density grid — add.hotspot","text":"updated Density-class object","code":""},{"path":[]},{"path":"/reference/analyse.data-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"S4 generic method to run analyses — analyse.data","title":"S4 generic method to run analyses — analyse.data","text":"method carries analysis distance sampling data. method provided allow user perform diagnostics analyses used simulation. data argument can obtained call simulate.survey(object, dht.table = TRUE). Note first object supplied class DS.Analysis second argument must class DDf.Data. data argument may either class object argument class Simulation.","code":""},{"path":"/reference/analyse.data-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"S4 generic method to run analyses — analyse.data","text":"","code":"analyse.data(analysis, data.obj, ...) # S4 method for class 'DS.Analysis,Survey' analyse.data(analysis, data.obj, warnings = NULL, ...) # S4 method for class 'DS.Analysis,data.frame' analyse.data(analysis, data.obj, warnings = NULL, transect = \"line\", ...)"},{"path":"/reference/analyse.data-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"S4 generic method to run analyses — analyse.data","text":"analysis object class DS.Analysis data.obj object class Survey dataframe ... optional arguments (currently used) warnings list warnings many times arose transect character value either \"line\" \"point\" specifying type transect used survey","code":""},{"path":"/reference/analyse.data-methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"S4 generic method to run analyses — analyse.data","text":"list containing S3 ddf object optionally S3 dht object relating model minimum criteria. either returns list best model, warnings number successfully fitted models (warnings supplied list) otherwise displays warnings goes returns best fitting ds model.","code":""},{"path":"/reference/Density-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Class ","title":"Class ","text":"Class \"Density\" S4 class containing list grids describe density individuals / clusters population. list contains one grid (data.frame) strata.","code":""},{"path":"/reference/Density-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Class ","text":"region.name Object class \"character\"; region name. strata.name Object class \"character\"; strata names density.surface Object class \"list\"; list data.frames columns x, y density. must one data.frame strata. x.space Object class \"numeric\"; spacing gridpoints described density data.frames x-direction. y.space Object class \"numeric\"; spacing gridpoints described density data.frames y-direction. units Object class \"numeric\"; units grid points.","code":""},{"path":[]},{"path":"/reference/Density.Summary-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Class ","title":"Class ","text":"Class \"Density.Summary\" S4 class containing summary density grids strata.","code":""},{"path":"/reference/Density.Summary-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Class ","text":"summary summary average abundances densities strata.","code":""},{"path":[]},{"path":"/reference/description.summary.html","id":null,"dir":"Reference","previous_headings":"","what":"Provides a description of the summary object/output — description.summary","title":"Provides a description of the summary object/output — description.summary","text":"Prints list terms used simulation summary.","code":""},{"path":"/reference/description.summary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Provides a description of the summary object/output — description.summary","text":"","code":"description.summary()"},{"path":"/reference/description.summary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Provides a description of the summary object/output — description.summary","text":"return, displays explanation simulation summary","code":""},{"path":"/reference/description.summary.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Provides a description of the summary object/output — description.summary","text":"Laura Marshall","code":""},{"path":"/reference/Detectability-class.html","id":null,"dir":"Reference","previous_headings":"","what":"S4 Class ","title":"S4 Class ","text":"S4 Class \"Detectability\"","code":""},{"path":"/reference/Detectability-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"S4 Class ","text":"key.function Object class \"character\"; code specifying detection function form (\"hn\" = half normal, \"hr\" = hazard rate.) scale.param Object class \"numeric\"; scale parameter detection function. shape.param Object class \"numeric\"; shape parameter detection function. cov.param Object class \"numeric\"; parameter values associated covariates. yet implemented truncation Object class \"numeric\"; maximum distance objects may detected.","code":""},{"path":[]},{"path":"/reference/DS.Analysis-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Class ","title":"Class ","text":"Class \"DDF.Analysis\" S4 class describing basic detection function model fitted distance sampling data.","code":""},{"path":"/reference/DS.Analysis-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Class ","text":"dfmodel Object class \"formula\"; describing detection function model. key key function use; \"hn\" gives half-normal (default), \"hr\" gives hazard-rate \"unif\" gives uniform. Note uniform key used, covariates included model. adjustment list containing adjustment parameters: adjustment - either \"cos\" (recommended), \"herm\" \"poly\", order - orders adjustment terms fit, scale - scale distances adjustment terms divided. See details. truncation Object class \"list\"; Specifies truncation distance analyses. cutpoints Object class \"character\"; gives cutpoints bins binned data analysis. er.var specifies encounter rate variance estimator use. control.opts list specify various options including monotonicity, method, initial.values. group.strata Dataframe two columns (\"design.id\" \"analysis.id\"). former gives strata names defined design (.e. region object) second specifies grouped (less strata) analyses criteria Object class \"character\"; describes model selection criteria use (\"AIC\",\"AICc\",\"BIC\").","code":""},{"path":"/reference/DS.Analysis-class.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Class ","text":"run.analysis signature=c(object = \"DS.Analysis\", data = data.frame): runs analysis described object data provided.","code":""},{"path":"/reference/dsims-package.html","id":null,"dir":"Reference","previous_headings":"","what":"Distance Sampling Simulations 'dsims' — dsims-package","title":"Distance Sampling Simulations 'dsims' — dsims-package","text":"Runs simulations distance sampling surveys help users optimise survey designs particular study.","code":""},{"path":"/reference/dsims-package.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Distance Sampling Simulations 'dsims' — dsims-package","text":"full process involves defining study region, description population interest (including distribution within study region), survey design, detection process one models fit resulting data. simulation engine use information generate population set transects simulate detection process. resulting data analysed estimates stored. repeating many times can test accuracy precision estimates various survey designs given particular population interest. package interfaces survey design package 'dssd' create survey regions, designs generate survey transects. 'DSsim' simulation package relied survey transects already contained shapefiles within supplied directory, dsims generate survey transects directly R. main functions package : make.density, make.population.description, make.detectability, make.ds.analysis, make.simulation, run.survey run.simulation. See also make.region make.design dssd package examples define study regions designs. information distance sampling methods example code available http://distancesampling.org/R/. Also see website vignettes / example code http://examples.distancesampling.org. help distance sampling package, Google Group https://groups.google.com/forum/#!forum/distance-sampling.","code":""},{"path":"/reference/dsims-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Distance Sampling Simulations 'dsims' — dsims-package","text":"Laura Marshall ","code":""},{"path":"/reference/generate.population-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"S4 generic method to generate an instance of a population — generate.population","title":"S4 generic method to generate an instance of a population — generate.population","text":"Uses population description detectability details generate instance population. Note first argument supplied class Population.Description rather class Simulation detectability region must also supplied.","code":""},{"path":"/reference/generate.population-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"S4 generic method to generate an instance of a population — generate.population","text":"","code":"generate.population(object, ...) # S4 method for class 'Population.Description' generate.population(object, detectability = NULL, region = NULL) # S4 method for class 'Simulation' generate.population(object, ...)"},{"path":"/reference/generate.population-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"S4 generic method to generate an instance of a population — generate.population","text":"object object class Simulation Population.Description ... called object class Population.Description additional arguments detectability region.obj also supplied detectability object class Detectability (optional - required object class Population.Description) region region object population (optional - required object class Population.Description)","code":""},{"path":"/reference/generate.population-methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"S4 generic method to generate an instance of a population — generate.population","text":"Population-class object","code":""},{"path":"/reference/generate.transects.Simulation-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"generate.transects — generate.transects,Simulation-method","title":"generate.transects — generate.transects,Simulation-method","text":"Generates set transects based design provided.","code":""},{"path":"/reference/generate.transects.Simulation-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"generate.transects — generate.transects,Simulation-method","text":"","code":"# S4 method for class 'Simulation' generate.transects(object, quiet = FALSE, ...)"},{"path":"/reference/generate.transects.Simulation-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"generate.transects — generate.transects,Simulation-method","text":"object object class Simulation quiet TRUE silences warnings ... implemented","code":""},{"path":"/reference/generate.transects.Simulation-methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"generate.transects — generate.transects,Simulation-method","text":"object class Transect dssd package","code":""},{"path":"/reference/get.densities-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"Method to get density values — get.densities","title":"Method to get density values — get.densities","text":"method extracts density values density object. optionally also return x y centre points density grid cells.","code":""},{"path":"/reference/get.densities-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Method to get density values — get.densities","text":"","code":"get.densities(density, coords = FALSE)"},{"path":"/reference/get.densities-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Method to get density values — get.densities","text":"density object class Density coords TRUE also returns x, y coordinates","code":""},{"path":"/reference/get.densities-methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Method to get density values — get.densities","text":"either returns numeric vector density values dataframe columns x, y density.","code":""},{"path":"/reference/get.N-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"S4 generic method to return N — get.N","title":"S4 generic method to return N — get.N","text":"Returns population size","code":""},{"path":"/reference/get.N-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"S4 generic method to return N — get.N","text":"","code":"get.N(object) # S4 method for class 'Population.Description' get.N(object)"},{"path":"/reference/get.N-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"S4 generic method to return N — get.N","text":"object object class Population.Description","code":""},{"path":"/reference/get.N-methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"S4 generic method to return N — get.N","text":"numeric value population size","code":""},{"path":"/reference/histogram.N.ests-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"histogram.N.ests — histogram.N.ests","title":"histogram.N.ests — histogram.N.ests","text":"Plots histogram estimates abundances","code":""},{"path":"/reference/histogram.N.ests-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"histogram.N.ests — histogram.N.ests","text":"","code":"histogram.N.ests(x, use.max.reps = FALSE, N.ests = \"individuals\", ...)"},{"path":"/reference/histogram.N.ests-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"histogram.N.ests — histogram.N.ests","text":"x object class Simulation use.max.reps default FALSE meaning simulation repetitions models converged data set included. setting TRUE repetition one models converged included summary results. N.ests character indicating whether plot estimates abundance 'individuals', 'clusters' ''. default individuals. ... optional parameters pass generic hist function graphics","code":""},{"path":"/reference/histogram.N.ests-methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"histogram.N.ests — histogram.N.ests","text":"return value, displays histogram abundance estimates","code":""},{"path":"/reference/make.density.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a Density object — make.density","title":"Creates a Density object — make.density","text":"Creates density grid across study area describing distribution animals.","code":""},{"path":"/reference/make.density.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a Density object — make.density","text":"","code":"make.density( region = make.region(), x.space = 20, y.space = NULL, constant = numeric(0), fitted.model = NULL, density.formula = NULL, density.surface = list() )"},{"path":"/reference/make.density.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a Density object — make.density","text":"region Region object density grid created x.space intervals grid x direction y.space intervals grid y direction constant value describing constant density across surface. supplied default value 1 used strata. fitted.model gam object created using mgcv x y explanatory covariates. density.formula formula x /y describing density surface. density.surface Object class list; sf grid recording density grid polygons, density values within polygons central x y coordinates.","code":""},{"path":"/reference/make.density.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a Density object — make.density","text":"Density-class object","code":""},{"path":"/reference/make.density.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Creates a Density object — make.density","text":"multiple ways create density grid. straight forward create grid constant values (high low areas can later added) pass fitted mgcv gam. gam model fitted x y explanatory variables. plan trying multiple animal distributions adding high low areas constant surface recommended make copy initial flat density grid object first step grid generation computationally intensive can take little complete, especially fine density grid.","code":""},{"path":[]},{"path":"/reference/make.density.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Creates a Density object — make.density","text":"Laura Marshall","code":""},{"path":"/reference/make.density.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a Density object — make.density","text":"","code":"# A simple density surface with a constant value of 1 can be created within a rectangular # Create a region from shapefile shapefile.name <- system.file(\"extdata\", \"StAndrew.shp\", package = \"dssd\") region <- make.region(region.name = \"St Andrews bay\", shape = shapefile.name) # Create a density object density <- make.density(region = region, x.space = 1000, constant = 1) # Add some ares of higher / lower density density <- add.hotspot(object = density, centre = c(-170000, 6255000), sigma = 10000, amplitude = 4) density <- add.hotspot(object = density, centre = c(-150000, 6240000), sigma = 10000, amplitude = -0.9) # Plot the density plot(density, region)"},{"path":"/reference/make.detectability.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a Detectability object — make.detectability","title":"Creates a Detectability object — make.detectability","text":"detectability population described values class.","code":""},{"path":"/reference/make.detectability.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a Detectability object — make.detectability","text":"","code":"make.detectability( key.function = \"hn\", scale.param = 25, shape.param = numeric(0), cov.param = list(), truncation = 50 )"},{"path":"/reference/make.detectability.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a Detectability object — make.detectability","text":"key.function specifies shape detection function (either half-normal \"hn\", hazard rate \"hr\" uniform \"uf\") scale.param numeric vector either single value applied globally value strata. supplied natural scale. shape.param numeric vector either single value applied globally value strata. supplied natural scale. cov.param Named list one named entry per individual level covariate. Covariate parameter values defined log scale (rather natural scale), scale provided ddf output mrds also MCDS output Distance. Cluster sizes parameter values can defined . list entry either data.frame containing 2 3 columns: level, param desired strata. region multiple strata column omitted values assumed apply globally. cluster size entry list must named 'size'. Alternatively list element may numeric vector either single value applied globally value strata. truncation maximum perpendicular (radial) distance objects may detected line (point) transect.","code":""},{"path":"/reference/make.detectability.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a Detectability object — make.detectability","text":"Detectability-class object","code":""},{"path":[]},{"path":"/reference/make.detectability.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Creates a Detectability object — make.detectability","text":"Laura Marshall","code":""},{"path":"/reference/make.detectability.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a Detectability object — make.detectability","text":"","code":"# Multi-strata example (make sf shape) s1 = matrix(c(0,0,0,2,1,2,1,0,0,0),ncol=2, byrow=TRUE) s2 = matrix(c(1,0,1,2,2,2,2,0,1,0),ncol=2, byrow=TRUE) pol1 = sf::st_polygon(list(s1)) pol2 = sf::st_polygon(list(s2)) sfc <- sf::st_sfc(pol1,pol2) strata.names <- c(\"low\", \"high\") sf.pol <- sf::st_sf(strata = strata.names, geom = sfc) region <- make.region(region.name = \"Multi-strata Eg\", strata.name = strata.names, shape = sf.pol) density <- make.density(region = region, x.space = 0.22, constant = c(20,50)) covs <- list() covs$size <- list(list(distribution = \"poisson\", lambda = 25), list(distribution = \"poisson\", lambda = 15)) covs$sex <- data.frame(level = rep(c(\"male\", \"female\"),2), prob = c(0.5, 0.5, 0.6, 0.4), strata = c(rep(\"low\",2),rep(\"high\",2))) # Define the population description (this time using the density to determine # the population size) popdesc <- make.population.description(region = region, density = density, covariates = covs, fixed.N = FALSE) cov.param <- list() cov.param$size <- c(log(1.02),log(1.005)) cov.param$sex <- data.frame(level = c(\"male\", \"female\", \"male\", \"female\"), param = c(log(1.5), 0, log(1.7), log(1.2)), strata = c(\"low\",\"low\",\"high\",\"high\")) # define the detecability detect <- make.detectability(key.function = \"hn\", scale.param = 0.08, cov.param = cov.param, truncation = 0.2) plot(detect, popdesc)"},{"path":"/reference/make.ds.analysis.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates an Analysis object — make.ds.analysis","title":"Creates an Analysis object — make.ds.analysis","text":"method creates Analysis objects describes one models fit distance data. simulation fit models data generated simulation select model minimum criteria value.","code":""},{"path":"/reference/make.ds.analysis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates an Analysis object — make.ds.analysis","text":"","code":"make.ds.analysis( dfmodel = list(~1), key = \"hn\", truncation = numeric(0), cutpoints = numeric(0), er.var = \"R2\", control.opts = list(), group.strata = data.frame(), criteria = \"AIC\" )"},{"path":"/reference/make.ds.analysis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates an Analysis object — make.ds.analysis","text":"dfmodel list distance sampling model formula specifying detection function (see ?Distance::ds details) key key function use; \"hn\" gives half-normal (default) \"hr\" gives hazard-rate. truncation absolute truncation distance simulation units matching region units. cutpoints supply vector cutpoints wish simulation perform binned analyses. er.var encounter rate variance estimator use abundance estimates required. Defaults \"R2\" line transects \"P3\" point transects. See mrds::varn information / options. control.opts list control options: method - optimisation method, group.strata Dataframe two columns (\"design.id\" \"analysis.id\"). former gives strata names defined design (.e. region object) second specifies grouped (less strata) analyses. See details information. criteria character model selection criteria (AIC, AICc, BIC)","code":""},{"path":"/reference/make.ds.analysis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates an Analysis object — make.ds.analysis","text":"DS.Analysis-class object","code":""},{"path":"/reference/make.ds.analysis.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Creates an Analysis object — make.ds.analysis","text":"possible group strata analysis stage using group.strata argument. example, design purposes may sensible divide strata substrata. can help make convex shapes therefore zigzag designs efficient perhaps helped keep transects angled parallel density gradients across study area. Despite (purely design relevant) substrata may still wish calculate estimates density / abundance etc. stratum. table gives example data.frame can used . Imagine study region onshore strata offshore strata. onshore strata divided two design stage keep transects perpendicular coast. now want analyse just two strata onshore offshore.","code":""},{"path":[]},{"path":"/reference/make.ds.analysis.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Creates an Analysis object — make.ds.analysis","text":"Laura Marshall","code":""},{"path":"/reference/make.ds.analysis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates an Analysis object — make.ds.analysis","text":"","code":"# Model selection considering both a half-normal and a hazard-rate model # using AIC criteria and truncating 5% of the data ds.analyses <- make.ds.analysis(dfmodel = ~1, key = c(\"hn\", \"hr\"), truncation = 500, criteria = \"AIC\") # Model selection considering both a half-normal with no covariates and with size # as a covariate using AIC criteria and truncating at 500 ds.analyses <- make.ds.analysis(dfmodel = list(~1, ~size), key = \"hn\", truncation = 500, criteria = \"AIC\") # Model selection considering both a half-normal with no covariates and with size # as a covariate and a hazard rate, using AIC criteria and truncating at 500 ds.analyses <- make.ds.analysis(dfmodel = list(~1, ~size, ~1), key = c(\"hn\", \"hn\", \"hr\"), truncation = 500, criteria = \"AIC\")"},{"path":"/reference/make.population.description.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a Population.Description object — make.population.description","title":"Creates a Population.Description object — make.population.description","text":"Creates object describes population. values object used create instances population.","code":""},{"path":"/reference/make.population.description.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a Population.Description object — make.population.description","text":"","code":"make.population.description( region = make.region(), density = make.density(), covariates = list(), N = numeric(0), fixed.N = TRUE )"},{"path":"/reference/make.population.description.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a Population.Description object — make.population.description","text":"region Region object population exists (see make.region). density Density object describing distribution individuals / clusters (see make.density). covariates Named list one named entry per individual-level covariate. Cluster sizes can defined , must named 'size'. distribution covariate values can either defined specifying particular distribution parameters discrete distribution dataframe. Dataframes columns level prob (optionally strata) specifying covariates levels, probabilities strata strata specific. Distributions can defined lists named entries distribution relevant parameters specified details. list distributions can provided one strata. N number individuals / clusters population one value per strata. Total population size 1000 default. fixed.N logical value. TRUE population generated value(s) N otherwise generated values density grid.","code":""},{"path":"/reference/make.population.description.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a Population.Description object — make.population.description","text":"Population.Description-class","code":""},{"path":"/reference/make.population.description.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Creates a Population.Description object — make.population.description","text":"Individual-level covariate values can defined one following distributions: 'normal', 'poisson', 'ztruncpois' 'lognormal'. distribution name associated parameters defined table must provided named list. Either one list can provided entire study area multiple lists grouped together list one per strata.","code":""},{"path":[]},{"path":"/reference/make.population.description.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Creates a Population.Description object — make.population.description","text":"Laura Marshall","code":""},{"path":"/reference/make.population.description.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a Population.Description object — make.population.description","text":"","code":"# Create a basic rectangular study area region <- make.region() # Make a density grid (large spacing for speed) density <- make.density(region = region, x.space = 200, y.space = 100, constant = 1) density <- add.hotspot(density, centre = c(1000, 100), sigma = 250, amplitude = 10) # Define some covariate values for out population covs <- list() covs$size <- list(distribution = \"ztruncpois\", mean = 5) # Define the population description popdsc <- make.population.description(region = region, density = density, covariates = covs, N = 200) # define the detecability detect <- make.detectability(key.function = \"hn\", scale.param = 25, truncation = 50) # generate an example population pop <- generate.population(popdsc, region = region, detectability = detect) plot(pop, region) # Multi-strata example (make sf shape) s1 = matrix(c(0,0,0,2,1,2,1,0,0,0),ncol=2, byrow=TRUE) s2 = matrix(c(1,0,1,2,2,2,2,0,1,0),ncol=2, byrow=TRUE) pol1 = sf::st_polygon(list(s1)) pol2 = sf::st_polygon(list(s2)) sfc <- sf::st_sfc(pol1,pol2) strata.names <- c(\"low\", \"high\") sf.pol <- sf::st_sf(strata = strata.names, geom = sfc) region <- make.region(region.name = \"Multi-strata Eg\", strata.name = strata.names, shape = sf.pol) # \\donttest{ density <- make.density(region = region, x.space = 0.22, constant = c(10,80)) covs <- list() covs$size <- list(list(distribution = \"poisson\", lambda = 25), list(distribution = \"poisson\", lambda = 15)) covs$sex <- data.frame(level = rep(c(\"male\", \"female\"),2), prob = c(0.5, 0.5, 0.6, 0.4), strata = c(rep(\"low\",2),rep(\"high\",2))) # Define the population description (this time using the density to determine # the population size) popdesc <- make.population.description(region = region, density = density, covariates = covs, fixed.N = FALSE) # define the detecability (see make.detectability to alter detection function # for different covariate values) detect <- make.detectability(key.function = \"hn\", scale.param = 25, truncation = 50) # generate an example population pop <- generate.population(popdesc, region = region, detectability = detect) plot(pop, region) # }"},{"path":"/reference/make.simulation.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a Simulation object — make.simulation","title":"Creates a Simulation object — make.simulation","text":"creates simulation information necessary dsims generate population, create transects, simulate survey process fit detection functions estimate density / abundance. function can used based default values create simple line transect example, see Examples . create complex simulations advisable define different parts simulation individually grouping together. See Arguments links functions make definitions individual simulation components. depth example please refer 'GettingStarted' vignette.","code":""},{"path":"/reference/make.simulation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a Simulation object — make.simulation","text":"","code":"make.simulation( reps = 10, design = make.design(), population.description = make.population.description(), detectability = make.detectability(), ds.analysis = make.ds.analysis() )"},{"path":"/reference/make.simulation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a Simulation object — make.simulation","text":"reps number times simulation repeated design object class Survey.Design created call make.design population.description object class Population.Description created call make.population.description detectability object class Detectability created call make.detectability ds.analysis objects class DS.Analysis created call make.ds.analysis","code":""},{"path":"/reference/make.simulation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a Simulation object — make.simulation","text":"Simulation-class object","code":""},{"path":"/reference/make.simulation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Creates a Simulation object — make.simulation","text":"make.simulation function now set default (exception specifying point transects rather line) can run simple simulation example. See examples.","code":""},{"path":[]},{"path":"/reference/make.simulation.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Creates a Simulation object — make.simulation","text":"Laura Marshall","code":""},{"path":"/reference/make.simulation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Creates a Simulation object — make.simulation","text":"","code":"# Create a basic rectangular study area region <- make.region() # Make a density grid (large spacing for speed) density <- make.density(region = region, x.space = 300, y.space = 100, constant = 1) density <- add.hotspot(density, centre = c(1000, 100), sigma = 250, amplitude = 10) # Define the population description popdsc <- make.population.description(region = region, density = density, N = 200) # Define the detecability detect <- make.detectability(key.function = \"hn\", scale.param = 25, truncation = 50) # Define the design design <- make.design(region = region, transect.type = \"line\", design = \"systematic\", samplers = 20, design.angle = 0, truncation = 50) # Define the analyses ds.analyses <- make.ds.analysis(dfmodel = ~1, key = \"hn\", truncation = 50, criteria = \"AIC\") # Put all the components together in the simulation (note no. of replicates # reps = 1 is only for a single test run and should be 999 or more to be able # to draw inference.) simulation <- make.simulation(reps = 1, design = design, population.description = popdsc, detectability = detect, ds.analysis = ds.analyses) # run an example survey to check the setup survey <- run.survey(simulation) plot(survey, region) # Run the simulation # Warning: if you have increased the number of replications then it can take a # long time to run! simulation <- run.simulation(simulation) #> 1 out of 1 reps summary(simulation) #> #> GLOSSARY #> -------- #> #> Summary of Simulation Output #> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #> #> Region : the region name. #> No. Repetitions : the number of times the simulation was repeated. #> No. Excluded Repetitions : the number of times the simulation failed #> (too few sightings, model fitting failure etc.) #> #> Summary for Individuals #> ~~~~~~~~~~~~~~~~~~~~~~~ #> #> Summary Statistics: #> mean.Cover.Area : mean covered across simulation. #> mean.Effort : mean effort across simulation. #> mean.n : mean number of observed objects across #> simulation. #> mean.n.miss.dist: mean number of observed objects where no distance #> was recorded (only displayed if value > 0). #> no.zero.n : number of surveys in simulation where #> nothing was detected (only displayed if value > 0). #> mean.ER : mean encounter rate across simulation. #> mean.se.ER : mean standard error of the encounter rates #> across simulation. #> sd.mean.ER : standard deviation of the encounter rates #> across simulation. #> #> Estimates of Abundance: #> Truth : true population size, (or mean of true #> population sizes across simulation for Poisson N. #> mean.Estimate : mean estimate of abundance across simulation. #> percent.bias : the percentage of bias in the estimates. #> RMSE : root mean squared error/no. successful reps #> CI.coverage.prob : proportion of times the 95% confidence interval #> contained the true value. #> mean.se : the mean standard error of the estimates of #> abundance #> sd.of.means : the standard deviation of the estimates #> #> Estimates of Density: #> Truth : true average density. #> mean.Estimate : mean estimate of density across simulation. #> percent.bias : the percentage of bias in the estimates. #> RMSE : root mean squared error/no. successful reps #> CI.coverage.prob : proportion of times the 95% confidence interval #> contained the true value. #> mean.se : the mean standard error of the estimates. #> sd.of.means : the standard deviation of the estimates. #> #> Detection Function Values #> ~~~~~~~~~~~~~~~~~~~~~~~~~ #> #> mean.observed.Pa : mean proportion of individuals/clusters observed in #> the covered region. #> mean.estimte.Pa : mean estimate of the proportion of individuals/ #> clusters observed in the covered region. #> sd.estimate.Pa : standard deviation of the mean estimates of the #> proportion of individuals/clusters observed in the #> covered region. #> mean.ESW : mean estimated strip width. #> sd.ESW : standard deviation of the mean estimated strip widths. #> #> #> Region: region #> No. Repetitions: 1 #> No. Excluded Repetitions: 0 #> Using only repetitions where all models converged. #> #> Design: Systematic parallel line design #> design.type : Systematic parallel line design #> bounding.shape : rectangle #> samplers : 20 #> design.angle : 0 #> edge.protocol : minus #> #> Population Detectability Summary: #> key.function = hn #> scale.param = 25 #> truncation = 50 #> #> Analysis Summary: #> Candidate Models: #> Model 1: key function 'hn', formula '~1', was selected 1 time(s). #> criteria = AIC #> variance.estimator = R2 #> truncation = 50 #> #> Summary for Individuals #> #> Summary Statistics #> #> mean.Cover.Area mean.Effort mean.n mean.k mean.ER mean.se.ER sd.mean.ER #> 1 1e+06 10000 116 20 0.0116 0.002630189 NA #> #> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #> Estimates of Abundance (N) #> #> Truth mean.Estimate percent.bias RMSE CI.coverage.prob mean.se sd.of.means #> 1 200 180.93 -9.54 19.07 1 43.3 NA #> #> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #> Estimates of Density (D) #> #> Truth mean.Estimate percent.bias RMSE CI.coverage.prob mean.se #> 1 2e-04 0.0001809257 -9.537164 1.907433e-05 1 4.329508e-05 #> sd.of.means #> 1 NA #> #> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #> #> Detection Function Values #> #> mean.observed.Pa mean.estimate.Pa sd.estimate.Pa mean.ESW sd.ESW #> 1 0.58 0.64 NA 32.06 NA # For a more in depth example please look at vignette(\"GettingStarted\", 'dsims') #> Warning: vignette 'GettingStarted' not found"},{"path":"/reference/plot-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"plot — plot,Survey,Region-method","title":"plot — plot,Survey,Region-method","text":"Produces four plots survey: 1) Plots transects inside survey region, 2) plots population, 3) plots transects, population detections 4) plots histogram detection distances. Note plots 3 & 4 generated without survey region Region omitted.","code":""},{"path":"/reference/plot-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot — plot,Survey,Region-method","text":"","code":"# S4 method for class 'Survey,Region' plot(x, y, type = \"all\", ...) # S4 method for class 'Survey,ANY' plot(x, y = NULL, type = \"all\", ...)"},{"path":"/reference/plot-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot — plot,Survey,Region-method","text":"x object class Survey y object class Region NULL type character specifies plots like, defaults \"\". options include \"transects\", \"population\", \"survey\" \"distances\". plot transects, population locations, transects population detections indicated histogram detection distances, respectively. Note final plots available one detections. \"survey\" \"distances\" available y Region argument supplied. ... additional plotting parameters","code":""},{"path":"/reference/plot-methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"plot — plot,Survey,Region-method","text":"Generate 4 plots showing survey population, transects (including covered areas), detections histogram detection distances. Plots include survey region. Also invisibly returns list ggplot objects user like customise plots. Generate 2 plots showing survey population, transects (including covered areas), detections histogram detection distances. Plots include survey region. Also invisibly returns list ggplot objects user like customise plots.","code":""},{"path":"/reference/plot.Density-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot — plot,Density,ANY-method","title":"Plot — plot,Density,ANY-method","text":"Plots S4 object class 'Density' Plots S4 object class 'Density'","code":""},{"path":"/reference/plot.Density-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot — plot,Density,ANY-method","text":"","code":"# S4 method for class 'Density,ANY' plot(x, y, strata = \"all\", title = \"\", scale = 1) # S4 method for class 'Density,Region' plot(x, y, strata = \"all\", title = \"\", scale = 1, line.col = gray(0.2))"},{"path":"/reference/plot.Density-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot — plot,Density,ANY-method","text":"x object class Density y object class Region strata strata name number plotted. default strata plotted. title plot title scale used scale x y values plot (warning may give unstable results projection defined study area!) line.col sets line colour shapefile","code":""},{"path":"/reference/plot.Density-methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot — plot,Density,ANY-method","text":"ggplot object ggplot object","code":""},{"path":"/reference/plot.Detectability-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot — plot,Detectability,ANY-method","title":"Plot — plot,Detectability,ANY-method","text":"Plots S4 object class 'Detectability'","code":""},{"path":"/reference/plot.Detectability-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot — plot,Detectability,ANY-method","text":"","code":"# S4 method for class 'Detectability,ANY' plot( x, y, add = FALSE, plot.units = character(0), region.col = NULL, gap.col = NULL, main = \"\", ... ) # S4 method for class 'Detectability,Population.Description' plot( x, y, add = FALSE, plot.units = character(0), region.col = NULL, gap.col = NULL, main = \"\", ... )"},{"path":"/reference/plot.Detectability-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot — plot,Detectability,ANY-method","text":"x object class Detectability y object class Population.Description add logical indicating whether added existing plot plot.units allows units converted m km region.col fill colour region gap.col fill colour gaps main character plot title ... general plot parameters","code":""},{"path":"/reference/plot.Detectability-methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot — plot,Detectability,ANY-method","text":"return value, gives warning user return value, plotting function","code":""},{"path":"/reference/plot.Population-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot — plot,Population,ANY-method","title":"Plot — plot,Population,ANY-method","text":"Unused, give warning region must also supplied. Plots S4 object class 'Population'. Requires associated region already plotted. function adds locations individuals/clusters population.","code":""},{"path":"/reference/plot.Population-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot — plot,Population,ANY-method","text":"","code":"# S4 method for class 'Population,ANY' plot(x, y, ...) # S4 method for class 'Population,Region' plot(x, y, ...)"},{"path":"/reference/plot.Population-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot — plot,Population,ANY-method","text":"x object class Population y object class Region ... general plot parameters","code":""},{"path":"/reference/plot.Population-methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot — plot,Population,ANY-method","text":"ggplot object","code":""},{"path":"/reference/Population-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Class ","title":"Class ","text":"Contains instance population including description detectability form object class Detectability.","code":""},{"path":"/reference/Population-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Class ","text":"region.name Object class \"character\"; name region object. strata.names Object class \"character\"; names strata. N Object class \"numeric\"; number individuals/clusters. D Object class \"numeric\"; density individuals/clusters. population Object class \"data.frame\"; locations individuals/clusters population covariates. detectability Object class \"Detectability\"; describes easily individuals/clusters can detected.","code":""},{"path":"/reference/Population-class.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Class ","text":"plot signature=(object = \"Line.Transect\"): plots locations individuals/clusters.","code":""},{"path":[]},{"path":"/reference/Population.Description-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Class ","title":"Class ","text":"Class \"Population.Description\" S4 class containing description population. provides methods generate example population.","code":""},{"path":"/reference/Population.Description-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Class ","text":"N Object class \"numeric\"; number individuals population (optional). density Object class \"Density\"; describes population density region.name Object class \"character\"; name region population exists. strata.names Character vector giving strata names study region. covariates Named list one named entry per individual level covariate. Cluster sizes can defined . list entry either data.frame containing 2 columns, first level (level) second probability size logical value indicating whether population occurs clusters. (prob). cluster size entry list must named 'size'. gen..N Object class \"logical\"; TRUE N fixed otherwise generated Poisson distribution.","code":""},{"path":"/reference/Population.Description-class.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Class ","text":"get.N signature=(object = \"Population.Description\"): returns value N generate.population signature=(object = \"Population.Description\"): generates single realisation population.","code":""},{"path":[]},{"path":"/reference/run.simulation-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"Method to run a simulation — run.simulation","title":"Method to run a simulation — run.simulation","text":"Runs simulation returns simulation object results. running parallel max.cores specified default using one less number cores / threads machine. example code see make.simulation","code":""},{"path":"/reference/run.simulation-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Method to run a simulation — run.simulation","text":"","code":"run.simulation( simulation, run.parallel = FALSE, max.cores = NA, counter = TRUE, transect.path = character(0), progress.file = character(0) )"},{"path":"/reference/run.simulation-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Method to run a simulation — run.simulation","text":"simulation Simulation-class object run.parallel logical option use multiple processors max.cores integer maximum number cores use, specified one less number available used. counter logical indicates like see progress counter. transect.path character gives pathway folder shapefiles path single shapefile (.shp file) give transects used simulations. folder transects new shapefile used repetition. path specifying single shapefile transects used repetition. progress.file character path filename output progress file Distance Windows progress counter. used running directly R.","code":""},{"path":"/reference/run.simulation-methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Method to run a simulation — run.simulation","text":"Simulation-class object now includes results","code":""},{"path":[]},{"path":"/reference/run.survey-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"S4 generic method to simulate a survey — run.survey","title":"S4 generic method to simulate a survey — run.survey","text":"Simulates process individuals clusters detected. simulation passed generate population, set transects simulate detection process. survey passed simply simulate detection process. See make.simulation example usage.","code":""},{"path":"/reference/run.survey-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"S4 generic method to simulate a survey — run.survey","text":"","code":"run.survey(object, ...) # S4 method for class 'Simulation' run.survey(object, filename = character(0)) # S4 method for class 'Survey.LT' run.survey(object, region = NULL) # S4 method for class 'Survey.PT' run.survey(object, region = NULL)"},{"path":"/reference/run.survey-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"S4 generic method to simulate a survey — run.survey","text":"object object class Simulation ... allows extra arguments filename optional argument specifying path shapefile transects loaded file. region object class Region.","code":""},{"path":"/reference/run.survey-methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"S4 generic method to simulate a survey — run.survey","text":"object inherits Survey-class object. Survey.LT-class object case simulation line transect design Survey.PT-class simulation point transect design.","code":""},{"path":[]},{"path":"/reference/rztpois.html","id":null,"dir":"Reference","previous_headings":"","what":"Randomly generates values from a zero-truncated Poisson distribution — rztpois","title":"Randomly generates values from a zero-truncated Poisson distribution — rztpois","text":"Generates values zero-truncated Poisson distribution mean equal specified. uses optimisation routine check value lambda give values requested mean.","code":""},{"path":"/reference/rztpois.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Randomly generates values from a zero-truncated Poisson distribution — rztpois","text":"","code":"rztpois(n, mean = NA)"},{"path":"/reference/rztpois.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Randomly generates values from a zero-truncated Poisson distribution — rztpois","text":"n number values randomly generate mean mean generated values","code":""},{"path":"/reference/rztpois.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Randomly generates values from a zero-truncated Poisson distribution — rztpois","text":"returns randomly generated value zero-truncated Poisson distribution.","code":""},{"path":"/reference/rztpois.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Randomly generates values from a zero-truncated Poisson distribution — rztpois","text":"Internal function intended called user.","code":""},{"path":"/reference/rztpois.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Randomly generates values from a zero-truncated Poisson distribution — rztpois","text":"Len Thomas","code":""},{"path":"/reference/save.sim.results-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"save.sim.results — save.sim.results","title":"save.sim.results — save.sim.results","text":"Saves simulation results replicate file. save 3 txt files, one abundance estimation individuals, one abundance estimation clusters (applicable) one detectability estimates model selection information.","code":""},{"path":"/reference/save.sim.results-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"save.sim.results — save.sim.results","text":"","code":"save.sim.results(simulation, filepath = character(0), sim.ID = numeric(0))"},{"path":"/reference/save.sim.results-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"save.sim.results — save.sim.results","text":"simulation object class Simulation run. filepath optionally path directory like files saved, otherwise save working directory. sim.ID optionally can add simulation ID filename","code":""},{"path":"/reference/save.sim.results-methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"save.sim.results — save.sim.results","text":"invisibly returns original simulation object","code":""},{"path":"/reference/save.sim.results-methods.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"save.sim.results — save.sim.results","text":"L. Marshall","code":""},{"path":"/reference/set.densities-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"Method to set density values — set.densities","title":"Method to set density values — set.densities","text":"method sets density values density object.","code":""},{"path":"/reference/set.densities-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Method to set density values — set.densities","text":"","code":"set.densities(density, densities)"},{"path":"/reference/set.densities-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Method to set density values — set.densities","text":"density object class Density densities numeric vector density values update density grid .","code":""},{"path":"/reference/set.densities-methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Method to set density values — set.densities","text":"returns Density object updated density values","code":""},{"path":"/reference/show.Density.Summary-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"show — show,Density.Summary-method","title":"show — show,Density.Summary-method","text":"displays density summary table","code":""},{"path":"/reference/show.Density.Summary-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"show — show,Density.Summary-method","text":"","code":"# S4 method for class 'Density.Summary' show(object)"},{"path":"/reference/show.Density.Summary-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"show — show,Density.Summary-method","text":"object object class Density.Summary","code":""},{"path":"/reference/show.Density.Summary-methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"show — show,Density.Summary-method","text":"return value, displays density summary","code":""},{"path":"/reference/show.Simulation-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"show — show,Simulation-method","title":"show — show,Simulation-method","text":"currently implemented","code":""},{"path":"/reference/show.Simulation-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"show — show,Simulation-method","text":"","code":"# S4 method for class 'Simulation' show(object)"},{"path":"/reference/show.Simulation-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"show — show,Simulation-method","text":"object object class Simulation","code":""},{"path":"/reference/show.Simulation-methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"show — show,Simulation-method","text":"return value, displays summary simulation","code":""},{"path":"/reference/show.Simulation.Summary-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"show — show,Simulation.Summary-method","title":"show — show,Simulation.Summary-method","text":"Displays simulation summary","code":""},{"path":"/reference/show.Simulation.Summary-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"show — show,Simulation.Summary-method","text":"","code":"# S4 method for class 'Simulation.Summary' show(object)"},{"path":"/reference/show.Simulation.Summary-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"show — show,Simulation.Summary-method","text":"object object class Simulation.Summary","code":""},{"path":"/reference/show.Simulation.Summary-methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"show — show,Simulation.Summary-method","text":"return value, displays information Simulation.Summary object","code":""},{"path":"/reference/Simulation-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Class ","title":"Class ","text":"Class \"Simulation\" S4 class containing descriptions region, population, survey design analyses user wishes investigate. simulation run N.D.Estimates contain multiple estimates abundance density obtained repeatedly generating populations, simulating survey completing analyses.","code":""},{"path":"/reference/Simulation-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Class ","text":"reps Object class \"numeric\"; number times simulation repeated. single.transect.set Object class \"logical\"; TRUE set transects used repetition. design Object class \"Survey.Design\"; survey design. population.description Object class \"Population.Description\"; population.description. detectability Object class \"Detectability\"; description detectability population. ds.analysis Object class \"DS.Analysis\" add.options list expand simulation options future. ddf.param.ests Object class \"array\"; stores parameters associated detection function. results \"list\" elements 'individuals' (optionally 'clusters' 'expected.size') well 'Detection'. 'individuals' 'clusters' elements list three 3-dimensional arrays. first summary array containing values 'Area' (strata area), 'CoveredArea' (area covered strata survey), Effort' (line length number points surveyed), 'n' (number sightings), 'n.miss.dists' (number missing distances - applicable mixed detector types yet implemented dsims), 'k' (number transects), 'ER' (encounter rate), 'se.ER' (standard error encounter rate), 'cv.ER' (coefficient variation encounter rate). value provided strata well region whole simulation repetition well storing mean standard deviation values across simulation repetitions. second array 'N' abundance estimates table. contains values 'Estimate' (estimated abundance based data iteration ), 'se' (standard error associated estimate), 'cv' (coefficient variation estimate), 'lcl' (lower 95% confidence interval value), 'ucl' (upper 95% confidence interval value), 'df' degrees freedom associated estimate. value provided strata well region whole simulation repetition well storing mean standard deviation values across simulation repetitions. third array 'D' density estimates table. contains values 'Estimate' (estimated density based data iteration ), 'se' (standard error associated estimate), 'cv' (coefficient variation estimate), 'lcl' (lower 95% confidence interval value), 'ucl' (upper 95% confidence interval value), 'df' degrees freedom associated estimate. value provided strata well region whole simulation repetition well storing mean standard deviation values across simulation repetitions. animals occur clusters expected.size element results list contains 3-dimensional array. gives values 'Expected.S' (expected cluster size), 'se.Expected.S' (standard error expected cluster size), 'cv.Expected.S' (coefficient variation expected cluster size). Values given analysis strata well value survey region whole across simulation repetition well overall means standard deviations across repetitions. Detection element results list 3-dimensional array values 'True.Pa' (proportion animals covered region detected), 'Pa' (estimated proportion animals detected covered region), 'ESW' (estimated strip width), 'f(0)' (estimated value detection function pdf distance 0), 'SelectedModel' (index model best fit dataset repetition), 'DeltaCriteria' (difference information criteria best second best fitting models two models fitted converged), 'SuccessfulModels' (number models successfully converged). Currently detection functions pooled across strata one global value simulated dataset well mean value standard deviation appropriate. warnings \"list\" store warnings error messages encountered runtime.","code":""},{"path":"/reference/Simulation-class.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Class ","text":"summary signature=(object = \"Simulation\"): produces summary simulation results. generate.population signature = (object = \"Simulation\"): generates single instance population. generate.transects signature = (object = \"Simulation\"): generates single set transects. run.survey signature = (object = \"Simulation\"): carries simulation process far generating distance data returns object containing population, transects data. run.simulation signature = (simulation = \"Simulation\"): runs whole simulation specified number repetitions.","code":""},{"path":[]},{"path":"/reference/Simulation.Summary-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Class ","title":"Class ","text":"Class \"Simulation.Summary\" S4 class containing summary simulation results. returned summary(Simulation) called. assigned variable object displayed via show method.","code":""},{"path":"/reference/Simulation.Summary-class.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Class ","text":"show signature=(object = \"Simulation.Summary\"): prints contents object user friendly format.","code":""},{"path":"/reference/summary.Density-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"summary — summary,Density-method","title":"summary — summary,Density-method","text":"Provides summary table density object.","code":""},{"path":"/reference/summary.Density-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"summary — summary,Density-method","text":"","code":"# S4 method for class 'Density' summary(object, ...)"},{"path":"/reference/summary.Density-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"summary — summary,Density-method","text":"object object class Simulation ... implemented","code":""},{"path":"/reference/summary.Density-methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"summary — summary,Density-method","text":"Density.Summary-class object","code":""},{"path":"/reference/summary.Simulation-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"summary — summary,Simulation-method","title":"summary — summary,Simulation-method","text":"Provides summary simulation results.","code":""},{"path":"/reference/summary.Simulation-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"summary — summary,Simulation-method","text":"","code":"# S4 method for class 'Simulation' summary(object, description.summary = TRUE, use.max.reps = FALSE, ...)"},{"path":"/reference/summary.Simulation-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"summary — summary,Simulation-method","text":"object object class Simulation description.summary logical indicating whether explanation summary displayed use.max.reps default FALSE meaning simulation repetitions models converged data set included. setting TRUE repetition one models converged included summary results. ... additional arguments currently implemented","code":""},{"path":"/reference/summary.Simulation-methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"summary — summary,Simulation-method","text":"Object class Simulation.Summary","code":""},{"path":"/reference/Survey-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Virtual Class ","title":"Virtual Class ","text":"Class \"Survey\" S4 class containing instance population.","code":""},{"path":"/reference/Survey-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Virtual Class ","text":"population Object class \"Population\"; instance population.","code":""},{"path":"/reference/Survey.LT-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Class ","title":"Class ","text":"Class \"Survey.LT\" S4 class containing population set transects.","code":""},{"path":"/reference/Survey.LT-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Class ","text":"transect Object class \"Line.Transect\"; line transects. perpendicular.truncation Object class \"numeric\"; maximum distance transect animals may detected.","code":""},{"path":[]},{"path":"/reference/Survey.PT-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Class ","title":"Class ","text":"Class \"Survey.PT\" S4 class containing population set transects.","code":""},{"path":"/reference/Survey.PT-class.html","id":"slots","dir":"Reference","previous_headings":"","what":"Slots","title":"Class ","text":"transect Object class \"Point.Transect\"; point transects. radial.truncation Object class \"numeric\"; maximum distance transect animals may detected.","code":""},{"path":[]},{"path":"/news/index.html","id":"dsims-104","dir":"Changelog","previous_headings":"","what":"dsims 1.0.4","title":"dsims 1.0.4","text":"CRAN release: 2023-11-29 Bug Fixes Fixed bug generating simulation summary meant first value mean.k n.miss.dists repeated rather including values summary tables. Issue #84 make.simulation function now throws error P2 ER variance estimator used line transects designs (rather simulation completed). Issue #61","code":""},{"path":"/news/index.html","id":"dsims-103","dir":"Changelog","previous_headings":"","what":"dsims 1.0.3","title":"dsims 1.0.3","text":"Bug Fixes Simulations crashing zero detections - now fixed warnings displayed instead. Issue #77 Errors also occurring individuals generated stratum, now fixed. Issue #80 Detections longer permitted across stratum boundaries - causing errors due NA area values data. inline expected protocols surveys. Issue #81 Remove dependence sp rgeos. Issue #42","code":""},{"path":"/news/index.html","id":"dsims-102","dir":"Changelog","previous_headings":"","what":"dsims 1.0.2","title":"dsims 1.0.2","text":"Bug Fixes Fixed transparency issue detection distance histograms saving wmf (generated warning Distance Windows) print summary table individuals animals occur individuals (clusters) Updated references examples Fixed grouped strata bugs Can now read transect shapefiles file convert one strata global region used. allows regional simulations stratified designs distance windows.","code":""},{"path":"/news/index.html","id":"dsims-101","dir":"Changelog","previous_headings":"","what":"dsims 1.0.1","title":"dsims 1.0.1","text":"CRAN release: 2022-08-30 New Features Added save.sim.results function simulation results can written .txt files. mainly useful Distance Windows users R users probably prefer just save whole simulation object file. Can write simulation progress file - allows simulation progress displayed simulations run Distance Windows using dsims. Add segmented trackline design option simulation summary (currently design can generated inside Distance Windows use simulations). Bug Fixes Partial fix bug relating grouping strata analysis stage. Strata grouping now work detections individuals. Still needs fixed clusters present.","code":""},{"path":"/news/index.html","id":"dsims-100","dir":"Changelog","previous_headings":"","what":"dsims 1.0.0","title":"dsims 1.0.0","text":"CRAN release: 2022-08-09 New Features Reading transects file - functionality primarily envisioned use within Distance Windows. Enhancements New routine generate covariate values zero-truncated Poisson distribution non integer values. now lower limit number detections simulations introducing bias. now warning system place. low numbers detections may cause issues fitting. must detections parameters model model chance fitting successfully. Note distance sampling good practice recommends minimum 60-80 detections estimating detection function line transects points. Improved histogram.N.ests function now plot either histogram estimates individuals clusters. also provides use.max.reps argument plot can consistent option selected simulation summary. Bug Fixes Fixed simulations cluster size included - formatting change mrds output tables. Added check repeat model definitions. Add code deal equal model criteria values. Fixed bug simulation repetitions successful AICc method fixed Warning indexes parallel runs now fixed","code":""},{"path":"/news/index.html","id":"dsims-022--023","dir":"Changelog","previous_headings":"","what":"dsims 0.2.2 / 0.2.3","title":"dsims 0.2.2 / 0.2.3","text":"CRAN release: 2022-03-31 Bug Fixes Minor modifications stay CRAN compliant.","code":""},{"path":"/news/index.html","id":"dsims-021","dir":"Changelog","previous_headings":"","what":"dsims 0.2.1","title":"dsims 0.2.1","text":"CRAN release: 2022-03-17 New Features Now interfaces new syntax Distance >= 1.0.5 (remain backwards compatible older versions Distance release) Bug Fixes Plus sampling simulations now issue warning modify minus sampling - run previous versions. Fixed default simulation truncation distance 50 analyses (fix dssd consistent release 0.3.2) Fixed recording warning / error indexing parallel simulations","code":""},{"path":"/news/index.html","id":"dsims-020","dir":"Changelog","previous_headings":"","what":"dsims 0.2.0","title":"dsims 0.2.0","text":"CRAN release: 2021-09-01 New Features Delta selection criteria now recorded difference information criteria top 2 best fitting models determined information criteria.] iteration numbers generating warnings errors now stored displayed user can choose results. Bug Fixes Fixed missing RMSE values Fix strata re-ordering cluster size Models -Inf information criteria longer selected Models dht = NULL longer selected Models predict detection values < 1 longer cause errors correctly excluded. Detectibility parameters continuous covariates now checked validated. Fix situation reps excluded due problematic model fitting. bug underlying code windows machines meant segmented lines clipped properly. dependencies sf updated issue fixed. Please update sf run missing segment transects.","code":""},{"path":"/news/index.html","id":"dsims-010","dir":"Changelog","previous_headings":"","what":"dsims 0.1.0","title":"dsims 0.1.0","text":"Enhancements Introducing new Distance Sampling Simulation package. # dsims latest simulation package interfaces dssd designs can generated within R, thus making simulation process lot easier. # dsims also makes use ggplot produce cleaner looking graphics. Region Design: # dsims can make use region creation designs currently dssd. Density: # dsims can generate density objects constant values strata, fitted mgcv gam objects x y explantory covariates formulas x y. Density: Density grids stored sf polygons associated x, y central coordinates density value Population Description: Populations can either created fixed population sizes based densities density grid. Population Description: discrete continuous individual level covariates can included population Detectablity: detectability population can described either half normal, hazard rate uniform detection shapes. Parameters can vary stratum Detectablity: Covariate parameters can included modify scale parameter individual based covariate values. Analyses:number detection function analyses can incorporated simulation model lowest criterion (AIC / AICc / BIC) selected. Analyses:Defining analyses based arguments passed Distance R library. Simulations: Simulations can run serial parallel progress output. Simulations: function run.survey can used create single instance survey check simulation setup.","code":""}] diff --git a/docs/sitemap.xml b/docs/sitemap.xml new file mode 100644 index 0000000..121bb68 --- /dev/null +++ b/docs/sitemap.xml @@ -0,0 +1,51 @@ + +/404.html +/articles/dsims-examples.html +/articles/dsims_grouped_strata.html +/articles/GettingStarted.html +/articles/index.html +/authors.html +/index.html +/news/index.html +/reference/add.hotspot-methods.html +/reference/analyse.data-methods.html +/reference/Density-class.html +/reference/Density.Summary-class.html +/reference/description.summary.html +/reference/Detectability-class.html +/reference/DS.Analysis-class.html +/reference/dsims-package.html +/reference/generate.population-methods.html +/reference/generate.transects.Simulation-methods.html +/reference/get.densities-methods.html +/reference/get.N-methods.html +/reference/histogram.N.ests-methods.html +/reference/index.html +/reference/make.density.html +/reference/make.detectability.html +/reference/make.ds.analysis.html +/reference/make.population.description.html +/reference/make.simulation.html +/reference/plot-methods.html +/reference/plot.Density-methods.html +/reference/plot.Detectability-methods.html +/reference/plot.Population-methods.html +/reference/Population-class.html +/reference/Population.Description-class.html +/reference/run.simulation-methods.html +/reference/run.survey-methods.html +/reference/rztpois.html +/reference/save.sim.results-methods.html +/reference/set.densities-methods.html +/reference/show.Density.Summary-methods.html +/reference/show.Simulation-methods.html +/reference/show.Simulation.Summary-methods.html +/reference/Simulation-class.html +/reference/Simulation.Summary-class.html +/reference/summary.Density-methods.html +/reference/summary.Simulation-methods.html +/reference/Survey-class.html +/reference/Survey.LT-class.html +/reference/Survey.PT-class.html + + diff --git a/man/dsims-package.Rd b/man/dsims-package.Rd index 165d334..6ce2a28 100644 --- a/man/dsims-package.Rd +++ b/man/dsims-package.Rd @@ -1,6 +1,5 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/dsims-package.R -\docType{package} \name{dsims-package} \alias{dsims-package} \alias{dsims} @@ -35,5 +34,5 @@ For help with distance sampling and this package, there is a Google Group \url{h } \author{ Laura Marshall +"_PACKAGE" } -\keyword{package} diff --git a/tests/testthat/test-check_IndividualCovariates.R b/tests/testthat/test-check_IndividualCovariates.R index 3493496..45c329e 100644 --- a/tests/testthat/test-check_IndividualCovariates.R +++ b/tests/testthat/test-check_IndividualCovariates.R @@ -229,3 +229,55 @@ test_that("Size biased testing", { }) + + +test_that("Factor level covariates", { + + # Multi-strata example (make sf shape) + s1 = matrix(c(0,0,0,2,1,2,1,0,0,0),ncol=2, byrow=TRUE) + s2 = matrix(c(1,0,1,2,2,2,2,0,1,0),ncol=2, byrow=TRUE) + pol1 = sf::st_polygon(list(s1)) + pol2 = sf::st_polygon(list(s2)) + sfc <- sf::st_sfc(pol1,pol2) + strata.names <- c("low", "high") + + mytrunc <- 0.2 + sf.pol <- sf::st_sf(strata = strata.names, geom = sfc) + + region <- make.region(region.name = "Multi-strata Eg", + strata.name = strata.names, + shape = sf.pol) + + cov.param <- list() + cov.param$size <- c(log(1.02),log(1.005)) + cov.param$sex <- data.frame(level = c("male", "female", "male", "female"), + param = c(log(1.5), 0, log(1.7), log(1.2)), + strata = c("low","low","high","high")) + + # define the detecability + detect <- make.detectability(key.function = "hn", + scale.param = 0.08, + cov.param = cov.param, + truncation = mytrunc) + + individual <- 1:5 + x <- c(0.1919572, 0.3876822, 0.3894108, 0.2305219, 0.5572704) + y <- c(0.1811401, 0.2098292, 0.1137614, 0.1911631, 0.1512140) + Region.Label <- c(rep("low",3),rep("high",2)) + size <- c(19, 17, 25, 26, 30) + sex <- c("male", "male", "female", "female", "male") + + pop.data <- data.frame(individual, x, y, Region.Label, size, sex) + + check.scale.params <- calculate.scale.param(pop.data, detect, region) + + ind1.scale <- exp(log(0.08) + log(1.02)*size[1] + log(1.5)) + ind2.scale <- exp(log(0.08) + log(1.02)*size[2] + log(1.5)) + ind3.scale <- exp(log(0.08) + log(1.02)*size[3]) + ind4.scale <- exp(log(0.08) + log(1.005)*size[4] + log(1.2)) + ind5.scale <- exp(log(0.08) + log(1.005)*size[5] + log(1.7)) + check.scale <- c(ind1.scale, ind2.scale, ind3.scale, ind4.scale, ind5.scale) + + expect_equivalent(check.scale.params$scale.param, check.scale) + +}) diff --git a/vignettes/GettingStarted.R b/vignettes/GettingStarted.R deleted file mode 100644 index 0de06c0..0000000 --- a/vignettes/GettingStarted.R +++ /dev/null @@ -1,177 +0,0 @@ -## ----region, fig.align='center', fig.cap="Figure 1: The study region.", fig.width=3.8, fig.height=4---- -library(dsims) -# Find the file path to the example shapefile in dssd -shapefile.name <- system.file("extdata", "StAndrew.shp", package = "dssd") -# Create the survey region object -region <- make.region(region.name = "St Andrews bay", - shape = shapefile.name, - units = "m") -plot(region) - -## ----density, fig.align='center', fig.cap="Figure 2: A density map representing a plausible distributions of animals within the study region.", fig.width=4, fig.height=4---- - -# We first create a flat density grid -density <- make.density(region = region, - x.space = 500, - constant = 1) - -# Now we can add some high and low points to give some spatial variability -density <- add.hotspot(object = density, - centre = c(-170000, 6255000), - sigma = 8000, - amplitude = 4) - -density <- add.hotspot(object = density, - centre = c(-160000, 6275000), - sigma = 6000, - amplitude = 4) - -density <- add.hotspot(object = density, - centre = c(-155000, 6260000), - sigma = 3000, - amplitude = 2) - -density <- add.hotspot(object = density, - centre = c(-150000, 6240000), - sigma = 10000, - amplitude = -0.9) - -density <- add.hotspot(object = density, - centre = c(-155000, 6285000), - sigma = 10000, - amplitude = -1) - -# I will choose to plot in km rather than m (scale = 0.001) -plot(density, region, scale = 0.001) - - - - -## ----densitygam, fig.align='center', fig.cap="Figure 3: A density map representing a plausible distributions of animals within the study region.", fig.width=4, fig.height=4---- - -# First extract the data above - this is simple in this case as we only have a single strata -# Multi-strata regions will involve combining the density grids for each strata into a -# single dataset. -density.data <- density@density.surface[[1]] -head(density.data) - -# Fit a simple gam to the data -library(mgcv) -fit.gam <- gam(density ~ s(x,y), data = density.data, family = gaussian(link="log")) - -# Use the gam object to create a density object -gam.density <- make.density(region = region, - x.space = 500, - fitted.model = fit.gam) - -plot(gam.density, region, scale = 0.001) - -## ----popdesc------------------------------------------------------------------ - -# Create a covariate list describing the distribution of cluster sizes -covariates <- list(size = list(distribution = "ztruncpois", mean = 3)) - -# Define the population description -pop.desc <- make.population.description(region = region, - density = gam.density, - covariates = covariates, - N = 300, - fixed.N = TRUE) - - -## ----designs------------------------------------------------------------------ -parallel.design <- make.design(region = region, - design = "systematic", - spacing = 2500, - edge.protocol = "minus", - design.angle = 90, - truncation = 750) - -zigzag.design <- make.design(region = region, - design = "eszigzag", - spacing = 2233, - edge.protocol = "minus", - design.angle = 0, - bounding.shape = "convex.hull", - truncation = 750) - - -## ----seed, echo=FALSE--------------------------------------------------------- -set.seed(476) - -## ----paralleltransects, fig.align='center', fig.cap="Figure 4: An example set of transects generated from the systematic parallel line design plotted within the study region.", fig.width=3.8, fig.height=4, fig.align='center'---- -p.survey <- generate.transects(parallel.design) -plot(region, p.survey) - -## ----zigzagtransects, fig.align='center', fig.cap="Figure 5: An example set of transects generated from the systematic parallel line design plotted within the study region.", fig.width=3.8, fig.height=4, fig.align='center'---- -z.survey <- generate.transects(zigzag.design) -plot(region, z.survey) - -## ----detect, fig.align='center', fig.cap="Figure 6: Plot of the detection function for the mean group size (solid line) and for the 2.5 and 97.5 percentile values of group size (dashed lines) for this population. ", fig.width=6, fig.height=4---- - -# Define the covariate parameters on the log scale -cov.param <- list(size = log(1.08)) - -# Create the detectability description -detect <- make.detectability(key.function = "hn", - scale.param = 300, - cov.param = cov.param, - truncation = 750) - -# Plot the simulation detection functions -plot(detect, pop.desc) - - -## ----analysis----------------------------------------------------------------- - -analyses <- make.ds.analysis(dfmodel = list(~1, ~1, ~size), - key = c("hn", "hr", "hn"), - truncation = 750, - er.var = "R2", - criteria = "AIC") - - -## ----simulation--------------------------------------------------------------- - -sim.parallel <- make.simulation(reps = 999, - design = parallel.design, - population.description = pop.desc, - detectability = detect, - ds.analysis = analyses) - -sim.zigzag <- make.simulation(reps = 999, - design = zigzag.design, - population.description = pop.desc, - detectability = detect, - ds.analysis = analyses) - - -## ----parallel.survey, fig.align='center', fig.cap="Figure 7: Example survey from systematic parallel design. Panels showing: top left - transects, top right - population, bottom left - transects, population and survey detections (cyan dots), bottom right - histogram of detection distances", fig.width=6, fig.height=6---- -# Generate a single instance of a survey: a population, set of transects -# and the resulting distance data -eg.parallel.survey <- run.survey(sim.parallel) - -# Plot it to view a summary -plot(eg.parallel.survey, region) - - -## ----zigzag.survey, fig.align='center', fig.cap="Figure 8: Example survey from equal spaced zigzag design. Panels showing: top left - transects, top right - population, bottom left - transects, population and survey detections (cyan dots), bottom right - histogram of detection distances", fig.width=6, fig.height=6---- -# Generate a single instance of a survey: a population, set of transects -# and the resulting distance data -eg.zigzag.survey <- run.survey(sim.zigzag) - -# Plot it to view a summary -plot(eg.zigzag.survey, region) - - -## ----runsim, eval=FALSE------------------------------------------------------- -# -# # Running the simulations -# sim.parallel <- run.simulation(sim.parallel) -# sim.zigzag <- run.simulation(sim.zigzag) -# - -## ----simresults, echo=FALSE--------------------------------------------------- -load("files/sim.parallel.ROBJ") -load("files/sim.zigzag.ROBJ") - diff --git a/vignettes/GettingStarted.Rmd b/vignettes/GettingStarted.Rmd index 9cf371c..77755e2 100644 --- a/vignettes/GettingStarted.Rmd +++ b/vignettes/GettingStarted.Rmd @@ -1,8 +1,21 @@ --- title: "Getting Started with dsims" -author: "L Marshall" -date: "`r Sys.Date()`" -output: rmarkdown::html_vignette +description: | + Assessing behaviour of a survey before going into the field +author: + - name: L. Marshall + url: http://distancesampling.org + affiliation: CREEM, Univ of St Andrews + affiliation_url: https://creem.st-andrews.ac.uk +date: "`r format(Sys.time(), '%B %Y')`" +output: + bookdown::html_document2: + number_sections: false + toc: true + toc_depth: 2 + base_format: rmarkdown::html_vignette +pkgdown: + as_is: true bibliography: refs.bib vignette: > %\VignetteIndexEntry{Getting Started with dsims} @@ -10,11 +23,8 @@ vignette: > %\VignetteEncoding{UTF-8} --- - ## Distance Sampling Simulations -Additional vignettes can be found in the distance sampling examples page of our website: http://examples.distancesampling.org - This vignette introduces the basic procedure for setting up and running a distance sampling simulation using 'dsims' [@dsims-pkg]. The 'dsims' package uses the distance sampling survey design package 'dssd' [@dssd-pkg] to define the design and generate the surveys (sets of transects). For further details on defining designs please refer to the 'dssd' vignettes. 'dsims' was designed to be largely similar to the 'DSsim' package [@DSsim-pkg] in terms of work flow, functions and arguments. The main differences in terms of its use lie in the definition of the designs which can now be generated in R using the 'dssd' package (these packages are automatically linked) and the definition of analyses. Analyses are now defined using terminology based on the 'Distance' package [@Distance-pkg]. In addition, the underlying functionality now makes use of the 'sf' package [@sf-pkg]. Distance Sampling techniques provide design based estimates of density and abundance for populations. The accuracy of these estimates relies on valid survey design. While general rules of thumb can help guide our design choices, simulations emulating a specific set of survey characteristics can often help us achieve more efficient and robust designs for individual studies. For example, simulations can help us investigate how effort allocation can affect our estimates or the effects of a more efficient design which has less uniform coverage probability. Due to the individual nature of each study, each with their specific set of characteristics, simulation can be a powerful tool in evaluating survey design. @@ -23,7 +33,7 @@ Distance Sampling techniques provide design based estimates of density and abund We will use the St Andrews bay area as an example study region for these simulations. This is a single strata study region which has been projected into metres. We will first load the 'dsims' package, this will also automatically load the 'dssd' package. As this shapefile does not have a projection recorded (in an associated .prj file) we tell 'dsims' that the units are metres. -```{r region, fig.align='center', fig.cap="Figure 1: The study region.", fig.width=3.8, fig.height=4} +```{r region, fig.align='center', fig.cap="The study region.", fig.dim=c(7,5)} library(dsims) # Find the file path to the example shapefile in dssd shapefile.name <- system.file("extdata", "StAndrew.shp", package = "dssd") @@ -49,7 +59,7 @@ For the purposes of simulation you will likely want to test over a range of plau In this example, for the equal spaced zigzag design, as it is generated in a convex hull the areas with differing coverage are likely to be at the very top and very bottom of the survey region. In the density grid below these areas are shown to have lower animal density than the rest of the survey region, a likely scenario when a study region has been constructed in order to catch the range of a population of interest. -```{r density, fig.align='center', fig.cap="Figure 2: A density map representing a plausible distributions of animals within the study region.", fig.width=4, fig.height=4} +```{r density, fig.align='center', fig.cap="A density map representing a plausible distributions of animals within the study region.", fig.dim=c(7,5)} # We first create a flat density grid density <- make.density(region = region, @@ -89,9 +99,9 @@ plot(density, region, scale = 0.001) ``` -In some situations you may not need to rely on constructing a density distribution from scratch. Now we will demonstrate how to use a gam to construct the density surface. As I do not have data for this area I will use the density grid I created above as an example dataset. I will fit a gam to this data and then use this to create a new density object. As I need to restrict the predicted values to be greater than zero, I will use a log link with the gaussian error distribution. This can also be a useful trick if you want to turn something created using the above method, which can look a bit lumpy and bumpy, into a smoother distribution surface. The gam fitted must only use a smooth over x and y to fit the model as no other predictor covariates will be present in the density surface. +In some situations you may not need to rely on constructing a density distribution from scratch. Now we will demonstrate how to use a gam to construct the density surface. As I do not have data for this area I will use the density grid I created above as an example dataset. I will fit a gam to this data and then use this to create a new density object. As I need to restrict the predicted values to be greater than zero, I will use a log link with the Gaussian error distribution. This can also be a useful trick if you want to turn something created using the above method, which can look a bit lumpy and bumpy, into a smoother distribution surface. The gam fitted must only use a smooth over x and y to fit the model as no other predictor covariates will be present in the density surface. -```{r densitygam, fig.align='center', fig.cap="Figure 3: A density map representing a plausible distributions of animals within the study region.", fig.width=4, fig.height=4} +```{r densitygam, fig.align='center', fig.cap="A density map representing a plausible distributions of animals within the study region.", fig.dim=c(7,5)} # First extract the data above - this is simple in this case as we only have a single strata # Multi-strata regions will involve combining the density grids for each strata into a @@ -130,7 +140,6 @@ pop.desc <- make.population.description(region = region, covariates = covariates, N = 300, fixed.N = TRUE) - ``` ## Coverage Grid @@ -139,7 +148,7 @@ It is good practice to create a coverage grid over your study area to assess how ## Defining the Design -'dsims' working together with 'dssd' provides a number of point and line transect designs. Further details on defining designs can be found in the 'dssd' help and vignettes. We also provide examples online at \url{https://examples.distancesampling.org/}. +'dsims' working together with 'dssd' provides a number of point and line transect designs. Further details on defining designs can be found in the 'dssd' help and vignettes. We also provide examples online at https://distancedevelopment.github.io/distancesamplingcom2/resources/vignettes.html . For these simulations we will compare two line transect designs, systematically spaced parallel lines and equal spaced zigzag lines. The zigzag design will be generated within a convex hull to try to minimise the off-effort transit time between the ends of transects. @@ -160,7 +169,6 @@ zigzag.design <- make.design(region = region, design.angle = 0, bounding.shape = "convex.hull", truncation = 750) - ``` @@ -172,12 +180,12 @@ It is always a good idea to run a quick check that your design is as expected by set.seed(476) ``` -```{r paralleltransects, fig.align='center', fig.cap="Figure 4: An example set of transects generated from the systematic parallel line design plotted within the study region.", fig.width=3.8, fig.height=4, fig.align='center'} +```{r paralleltransects, fig.align='center', fig.cap="An example set of transects generated from the systematic parallel line design plotted within the study region.", fig.dim=c(7,5), fig.align='center'} p.survey <- generate.transects(parallel.design) plot(region, p.survey) ``` -```{r zigzagtransects, fig.align='center', fig.cap="Figure 5: An example set of transects generated from the systematic parallel line design plotted within the study region.", fig.width=3.8, fig.height=4, fig.align='center'} +```{r zigzagtransects, fig.align='center', fig.cap="An example set of transects generated from the systematic parallel line design plotted within the study region.", fig.dim=c(7,5), fig.align='center'} z.survey <- generate.transects(zigzag.design) plot(region, z.survey) ``` @@ -193,7 +201,7 @@ where $j$ is the individual, $\sigma_0$ is the base line scale parameter (passed We will assume a half normal detection function with a scale parameter of 300. We will set the truncation distance to be the same as the design at 750 m. and set the covariate slope coefficient on the log scale to log(1.08) = 0.077. We can check what our detection functions will look like for the different covariate values by plotting them. To plot the example detection functions we need to provide the population description as well as detectability. -```{r detect, fig.align='center', fig.cap="Figure 6: Plot of the detection function for the mean group size (solid line) and for the 2.5 and 97.5 percentile values of group size (dashed lines) for this population. ", fig.width=6, fig.height=4} +```{r detect, fig.align='center', fig.cap="Plot of the detection function for the mean group size (solid line) and for the 2.5 and 97.5 percentile values of group size (dashed lines) for this population.", fig.dim=c(7,5)} # Define the covariate parameters on the log scale cov.param <- list(size = log(1.08)) @@ -206,7 +214,6 @@ detect <- make.detectability(key.function = "hn", # Plot the simulation detection functions plot(detect, pop.desc) - ``` We can also calculate the average detection function for our mean cluster size of 3 as defined in our population description: @@ -216,7 +223,7 @@ $$\sigma_{size = 3} = exp(log(300)+log(1.05)*3) = 347.3 $$ ## Defining Analyses -The final component to a simulation is the analysis or set of analyses you wish to fit to the simulated data. We will define a number of models and allow automatic model selection based on the minimum AIC value. The models included below are a half-normal with no covariates, a hazard rate with no covariates and a half-normal with cluster size as a covariate. We will leave the truncation value at 750 as previously defined (it must be <= to the truncation values used previously). We will use the default error variance estimator "R2". See '?mrds::varn' for descriptions of the various empirical variance estimators for encounter rate. +The final component to a simulation is the analysis or set of analyses you wish to fit to the simulated data. We will define a number of models and allow automatic model selection based on the minimum AIC value. The models included below are a half-normal with no covariates, a hazard rate with no covariates and a half-normal with cluster size as a covariate. We will leave the truncation value at 750 as previously defined (it must be $\le$ to the truncation values used previously). We will use the default error variance estimator "R2". See `?mrds::varn` for descriptions of the various empirical variance estimators for encounter rate. ```{r analysis} @@ -225,7 +232,6 @@ analyses <- make.ds.analysis(dfmodel = list(~1, ~1, ~size), truncation = 750, er.var = "R2", criteria = "AIC") - ``` ## Putting the Simulation Together @@ -245,30 +251,27 @@ sim.zigzag <- make.simulation(reps = 999, population.description = pop.desc, detectability = detect, ds.analysis = analyses) - ``` Once you have created a simulation we recommend you check to see what a simulated survey might look like. -```{r parallel.survey, fig.align='center', fig.cap="Figure 7: Example survey from systematic parallel design. Panels showing: top left - transects, top right - population, bottom left - transects, population and survey detections (cyan dots), bottom right - histogram of detection distances", fig.width=6, fig.height=6} +```{r parallelsurvey, fig.align='center', fig.cap="Example survey from systematic parallel design. Panels showing: top left - transects, top right - population, bottom left - transects, population and survey detections (cyan dots), bottom right - histogram of detection distances", fig.dim=c(7,6)} # Generate a single instance of a survey: a population, set of transects # and the resulting distance data eg.parallel.survey <- run.survey(sim.parallel) # Plot it to view a summary plot(eg.parallel.survey, region) - ``` -```{r zigzag.survey, fig.align='center', fig.cap="Figure 8: Example survey from equal spaced zigzag design. Panels showing: top left - transects, top right - population, bottom left - transects, population and survey detections (cyan dots), bottom right - histogram of detection distances", fig.width=6, fig.height=6} +```{r zigzagsurvey, fig.align='center', fig.cap="Example survey from equal spaced zigzag design. Panels showing: top left - transects, top right - population, bottom left - transects, population and survey detections (cyan dots), bottom right - histogram of detection distances", fig.dim=c(7,6)} # Generate a single instance of a survey: a population, set of transects # and the resulting distance data eg.zigzag.survey <- run.survey(sim.zigzag) # Plot it to view a summary plot(eg.zigzag.survey, region) - ``` @@ -277,11 +280,9 @@ plot(eg.zigzag.survey, region) The simulations can be run as follows. Note that these will take some time to run! ```{r runsim, eval=FALSE} - # Running the simulations sim.parallel <- run.simulation(sim.parallel) sim.zigzag <- run.simulation(sim.zigzag) - ``` ## Simulation Results @@ -293,9 +294,9 @@ load("files/sim.zigzag.ROBJ") Once the simulations have run we can view a summary of the results. Viewing a summary of a simulation will first summarise the simulation setup and then if the simulation has been run provide a summary of the results. A glossary is also provided to aid interpretation of the results. Note that each run will produce slightly different results due to the random component of the generation of both the populations and the sets of survey transects. -Firstly, for the systematic parallel lines design we can see that there is very low bias 1.85% for the estimated abundance / density of individuals. The bias is even lower at only 0.16% for the estimated abundance / density of clusters. Also we can see that the analyses have done a good job at estimating the mean cluster size, there is only 1.72% bias. +Firstly, for the systematic parallel lines design we can see that there is very low bias 1.85% for the estimated abundance/density of individuals. The bias is even lower at only 0.16% for the estimated abundance/density of clusters. Also we can see that the analyses have done a good job at estimating the mean cluster size, there is only 1.72% bias. -We can also see that the 95% confidence intervals calculated for the abundance / density estimates are in fact capturing the true value around 97% of the time (CI.coverage.prob). We can also note that the observed standard deviation of the estimates of the mean is a bit lower than the mean se, meaning we are realising a lower variance than we would estimate. This is often seen with systematic designs as the default variance estimator assumes a completely random allocation of transect locations, systematic designs usually have lower variance. +We can also see that the 95% confidence intervals calculated for the abundance/density estimates are in fact capturing the true value around 97% of the time (CI.coverage.prob). We can also note that the observed standard deviation of the estimates of the mean is a bit lower than the mean se, meaning we are realising a lower variance than we would estimate. This is often seen with systematic designs as the default variance estimator assumes a completely random allocation of transect locations, systematic designs usually have lower variance. Reassuringly, these results are as expected for the systematic parallel line design. We expect low bias, as by definition, parallel line designs produce a very uniform coverage probability. The only areas where this design might not produce uniform coverage is around the boundary where there could be minor edge effects due to the minus sampling. @@ -303,7 +304,7 @@ Reassuringly, these results are as expected for the systematic parallel line des summary(sim.parallel) ``` -We can now check the results for the zigzag design. While zigzag designs generated inside a convex hull can be much more efficient than parallel line designs (less off-effort transit) there is the possibility of non-uniform coverage. The coverage can be assessed by running 'run.coverage' but by itself this does not give much of an indication of the likely effects on the survey results. The degree to which non-uniform coverage may affect survey results is determined not only by the variability in coverage but also in how that combines with the density of animals in the region. Note that while we have run only one density scenario here, if you have non-uniform coverage probability it is advisable to test the effects under a range of plausible animal distributions. +We can now check the results for the zigzag design. While zigzag designs generated inside a convex hull can be much more efficient than parallel line designs (less off-effort transit) there is the possibility of non-uniform coverage. The coverage can be assessed by running `run.coverage` but by itself this does not give much of an indication of the likely effects on the survey results. The degree to which non-uniform coverage may affect survey results is determined not only by the variability in coverage but also in how that combines with the density of animals in the region. Note that while we have run only one density scenario here, if you have non-uniform coverage probability it is advisable to test the effects under a range of plausible animal distributions. Under this assumed distribution of animals, it looks like any effects of non-uniform coverage are going to have minimal effects on the estimates of abundance / density. For individuals the bias is around 2.5% and for clusters it is 0.65%. Similar to the parallel line design, the confidence intervals are also giving a coverage of 97%. @@ -315,17 +316,17 @@ summary(sim.zigzag) Histograms of the estimates of abundance from each of the simulation replicates can also be viewed to check for the possible effects of extreme values or skewed distributions. -```{r hist.results, fig.align='center', fig.cap="Figure 9: Left - histogram of estimates of abundance of clusters for systematic parallel design. Right - histogram of estimates of abundance of clusters for zigzag design.", fig.width=6, fig.height=4} +```{r histresults, fig.align='center', fig.cap="Left - histogram of estimates of abundance of clusters for systematic parallel design. Right - histogram of estimates of abundance of clusters for zigzag design.", fig.dim=c(7,5)} oldparams <- par(mfrow = c(1,2)) histogram.N.ests(sim.parallel) histogram.N.ests(sim.zigzag) par(oldparams) ``` -We can see in Figure 9 that there were a couple of high estimates generated >500 for both the parallel line and zigzag designs. These probably represent data sets that were difficult to fit a model too (perhaps a chance spiked data set). Most of the estimates are centered around truth but these occasional high estimates may have increased the mean value slightly and could be associated with the small amount of positive bias. +We can see in Figure \@ref(fig:histresults) that there were a couple of high estimates generated >500 for both the parallel line and zigzag designs. These probably represent data sets that were difficult to fit a model too (perhaps a chance spiked data set). Most of the estimates are centered around truth but these occasional high estimates may have increased the mean value slightly and could be associated with the small amount of positive bias. ## Simulation Conclusions Under these simulation assumptions it appears that the zigzag design will cost us a little in accuracy but allow us to gain some precision. It should be noted that the cost in accuracy will vary depending on the distribution of animals in the survey region. -## Bibliography +## References diff --git a/vignettes/SimulationDiagram.png b/vignettes/SimulationDiagram.png new file mode 100644 index 0000000..4245628 Binary files /dev/null and b/vignettes/SimulationDiagram.png differ diff --git a/vignettes/apa.csl b/vignettes/apa.csl new file mode 100644 index 0000000..3f2ccc8 --- /dev/null +++ b/vignettes/apa.csl @@ -0,0 +1,1539 @@ + + diff --git a/vignettes/dsims-examples.Rmd b/vignettes/dsims-examples.Rmd new file mode 100644 index 0000000..75fb199 --- /dev/null +++ b/vignettes/dsims-examples.Rmd @@ -0,0 +1,937 @@ +--- +title: "Transition from `DSsim` to `dsims`" +description: | + Learning the distinction between the former simulation engine and the current simulation engine by using dsims to investigate truncation distances with individual level covariates +author: + - name: L. Marshall + url: http://distancesampling.org + affiliation: CREEM, Univ of St Andrews + affiliation_url: https://creem.st-andrews.ac.uk +date: "`r format(Sys.time(), '%B %Y')`" +output: + bookdown::html_document2: + number_sections: false + toc: true + toc_depth: 2 + base_format: rmarkdown::html_vignette +pkgdown: + as_is: true +bibliography: refs-transition.bib +csl: apa.csl +vignette: > + %\VignetteIndexEntry{Transition from `DSsim` to `dsims`} + %\VignetteEngine{knitr::rmarkdown} + \usepackage[utf8]{inputenc} +--- + +## Preamble + +The first version of the simulation engine was a package called DSsim [@dssimpkg]; with improving GIS capabilities in R we have later released a second more efficient simulation package, dsims [@dsimspkg]. This vignette was originally written for DSsim and so we use it now to not only demonstrate dsims but also as an example for users of DSsim showing how to transition to dsims. As the two packages have largely the same function names loading them together is not advised, this vignette will therefore leave the DSsim code in as comments for comparison with the dsims code. Please note that normal comments follow a single # and DSsim code follows double ##. It should also be noted that dsims now uses the distance sampling survey design package, dssd [@dssdpkg], to generate the transects based on the design so that shapefiles containing the transects no longer need to be created in advance. + +If your goal is to transition to dsims from DSsim then you will find all you need in the sections up to and including the section on running simulations. The latter sections go on to run a series of additional simulations investigating pooling robustness and covariate parameter estimation with respect to truncation distance. If you are completely new to distance sampling simulations then an alternative place to start is the Getting Started vignette inside the dsims package. This vignette uses dsims to compare a systematic parallel design with a zigzag design to assess the accuracy/precision trade off. To view this open R and after installing dsims, enter the following code: + +```{r startVignette, echo=TRUE} +vignette("GettingStarted", package = "dsims") +``` + +## Introduction + +Distance sampling is a process in which a study area is surveyed to estimate the size of the population within it. It can be thought of as an extension to plot sampling. However, while plot sampling assumes that all objects within the plots are detected, distance sampling relaxes this assumption. To do this Distance sampling makes an assumptions about the distribution of objects with respect to the transects and to satisfy these assumptions the transects (the points or lines) must be randomly located within the study region. Note that for the purposes of distance sampling an object can either be an individual or a cluster or individuals. + +The next step in distance sampling is then to record the distances from each detected object to the transect it was detected from and fit a detection function. From this function we can estimate how many objects were missed and hence the total number in the covered area. For example, Figure \@ref(fig:detectionfunction) shows histograms of distances that might be collected on a line transect survey, with a fitted detection function. If the lines have been placed at random within the study region then we would expect on average the same number of object to occur at any given distance from the transect. Therefore the drop in number of detection with increasing distance from the line can be attributed to a failure to detect all objects. We can therefore estimate from this detection function that the probability of seeing an object within the covered region out to a chosen truncation distance is the area under the curve (shaded grey) divided by the area of the rectangle. + +```{r detectionfunction, warning=FALSE, message=FALSE, echo=FALSE, fig.width = 6, fig.cap="An example detection function. The histogram shows example distances recorded from a line transect. The smooth curve is the detection function. The grey shaded area represents the number of detected objects and the diagonal hash region represents the number of objects in the covered region that were not detected."} +x <- seq(0, 70, length = 200) +scale <- 25 +y <- exp(-x^2/(2*scale^2)) + +plot(x,y, type = "l", xlab = "Distance", ylab = "Probability of Detection", main = "Example Detection Function") + +coords.x <- c(0,x,70,0) +coords.y <- c(0,y,0,0) +polygon(coords.x, coords.y, col = "grey") + +coords.x <- c(0,70,x[200:1]) +coords.y <- c(1,1,y[200:1]) +polygon(coords.x, coords.y, density = 10, angle = 45) + +norm.vals <- abs(rnorm(1000,0,25)) +temp <- hist(norm.vals, plot = FALSE) +temp$density <- temp$density/temp$density[1] +plot(temp, freq = FALSE, add = TRUE) +``` + + +The R package dsims allows users to simulate both point and line transect surveys, and test out a range of design and analysis decisions specific to their population of interest. To simulate surveys the user must make some assumptions about the population of interest and the detection process giving rise to the observed distances. Simulations can be repeated over a range of assumptions so that the user can be confident that their chosen design will perform well despite any uncertainty. + +### Introduction to dsims + +dsims takes information from the user on the study region, population and detection process and uses it to generate distance sampling data. dsims can then be asked to fit detection functions to this data and produce estimates of density, abundance and the associated uncertainty. dsims splits this process into three stages. Firstly, it generates an instance of a population and a set of survey transects. Secondly, it simulates the distance sampling survey using the assumed detection function(s) provided by the user. Lastly, dsims analyses the data from the survey. Figure \@ref(fig:flowchart) illustrates the simulation process and highlights the information which must be provided by the user. + +Distance sampling simulations can be very useful to researchers who wish to optimise their survey design for their specific study regions and species of interest in order to try and achieve the most accurate / precise estimates for their populations. Setting up and running such simulations to optimise a design is a very small cost in comparison to those associated with actually completing the survey! + +```{r flowchart, echo=FALSE, fig.cap="Illustrates the simulation process. Blue rectangles indicate information supplied by the user. Green rectangles are objects created by dsims in the simulation process. Orange diamonds indicate the processes carried out by dsims."} +knitr::include_graphics("SimulationDiagram.png") +``` + +dsims is written using the S4 object orientated system in R. The S4 system is a more formal and rigorous style of object orientated programming than the more commonly implemented S3. The process of defining a simulation involves the specification of many variables relating to the survey region, population, survey design and finally the analysis. The design of dsims is based around each of these descriptions being contained in its own class and the formal S4 class definition procedure ensures that the objects created are of the correct format for the simulation. As the objects created by dsims are instances of S4 classes, if the user wishes to access information within them the symbol used is slightly different. To access named parts of S3 objects the "$" symbol would be used, while for S4 objects the "@" symbol must be used. The following code demonstrates this. + +```{r S4eg, warning=FALSE, message=FALSE, echo=TRUE} +# load simulation package +## library(DSsim) +library(dsims) + +# Make a default region object +## eg.region <- make.region() +eg.region <- make.region() + +# Let's check the structure of the object we have created +str(eg.region) +# If we wanted to extract the area of the region we would use +eg.region@area +``` + +### Example Simulation Study: Which Truncation Distance? + +It is usual in distance sampling studies to truncate the data at some distance from the transect. This is because the observations far away from the transect are of lesser importance when fitting the detection function and also these sparse observations at large distances could have high influence on model selection and possibly increase variability in estimated abundance / density. + +Buckland et al. [-@Buckland2001vm] suggest truncating the data where the probability of detection is around 0.15 as a general rule of thumb. However, distance sampling data is often costly to obtain and discarding some of the data points can feel counter intuitive. In this vignette we investigate truncation distance in distance sampling analyses. We will do this through a series of three simulations outlined below. + +Firstly, this vignette will investigate data generated assuming a simple half normal detection function where every object has the same probability of detection at a specific distance from the transect. Figure \@ref(fig:truncdists1) shows a simple half normal detection function with three possible truncation distances at $1*\sigma$, $2*\sigma$ and $3*\sigma$ where $\sigma$ is the scale parameter of the half normal detection function. The truncation distance at $2*\sigma$ gives a probability of detection of 0.135 so close to the 0.15 rule of thumb. + +```{r truncdists1, warning=FALSE, message=FALSE, echo=FALSE, fig.width = 6, fig.cap="Half-normal detection function showing 3 proposed truncation distances at $1*\\sigma$, $2*\\sigma$ and $3*\\sigma$. The truncation distance at twice sigma gives a probability of detection of 0.135 so close to the 0.15 rule of thumb."} + +x <- seq(0, 80, length = 200) +scale <- 25 +y <- exp(-x^2/(2*scale^2)) + +plot(x,y, type = "l", xlab = "Distance", ylab = "Probability of Detection", main = "Half-Normal Detection Function (sigma = 25)", lwd = 3) + +# Add lines for truncation distances +x.lines <- c(25,50,75) +y.lines <- exp(-x.lines^2/(2*scale^2)) + +for(i in seq(along = x.lines)){ + lines(c(x.lines[i],x.lines[i]), c(0,y.lines[i]), col = 2, lwd = 3) +} + +``` + +While the first set of simulations assume a simple half normal detection function, in reality individual objects or clusters of objects will likely have varying probability of being detected based on certain characteristics. Perhaps the behaviour of males will make them easier to detect. It is also easy to see that larger clusters of individuals might be easier to spot at large distances than small clusters. We will also investigate the effects of truncation distance when individual level covariates affect the probability of detection. Figure \@ref(fig:truncdists2) shows how covariates may affect detectability. We will use simulated distance data with one covariate (sex) to investigate both the effects of truncation when we assume that we were not able to measure the covariate affecting detectability and when we assume that we can and therefore will include the relevant covariate in the detection function model. + + +```{r truncdists2, warning=FALSE, message=FALSE, echo=FALSE, fig.width = 7.2, fig.cap="Half-normal detection function which varies based on cluster size and animal sex."} + +## covariate.list <- list() +## covariate.list$size <- list(list("poisson", list(lambda = 35))) +## covariate.list$sex <- list(data.frame(level = c("male", "female"), +## prob = c(0.5,0.5))) +## # Create covariate description +## pop.desc <- make.population.description(region = eg.region, +## covariates = covariate.list) +## +## # define covariate parameters +## cov.params <- list(size = c(0.015), +## sex = data.frame(level = c("male", "female"), +## param = c(0.9, -0.1))) +## +## detect <- make.detectability(scale.param = 10, +## cov.param = cov.params, +## truncation = 60) +## +## plot(detect, pop.desc) + +covariate.list <- list() +covariate.list$size <- list(list(distribution = "poisson", lambda = 35)) +covariate.list$sex <- list(data.frame(level = c("male", "female"), + prob = c(0.5,0.5))) +# Create covariate description +pop.desc <- make.population.description(region = eg.region, + covariates = covariate.list) + +# define covariate parameters +cov.params <- list(size = c(0.015), + sex = data.frame(level = c("male", "female"), + param = c(0.9, -0.1))) + +detect <- make.detectability(scale.param = 10, + cov.param = cov.params, + truncation = 60) + +plot(detect, pop.desc) + +``` + +### Model Uncertainty and Pooling Robustness + +When we simulate data, we have to provide the detection function to generate detections, and we therefore know the underlying true detection function. When collecting data in the field, we will not have this information, and so we will have to rely on some form of model selection. One method of model selection is to compare information criterion, dsims allows the user to select either AIC, AICc or BIC as the model selection criteria. For these simulations we will use AIC and allow dsims to select between a half-normal and a hazard rate model in the first two sets of simulations. + +In addition, if the probability of detection is affected by covariates then we may not only have a single underlying detection function but a combination of detection functions giving rise to our observed data. In this situation we can either model detectability as a function of these covariates or rely on a concept called pooling robustness. Pooling robustness refers to the fact that distance sampling techniques are robust to the pooling of multiple detection functions into one. This means that we do not necessarily need to include all the covariates which affect detectability in the detection function to accurately estimate density / abundance. This vignette will examine the concept of pooling robustness to see if it is affected by truncation distance. + +## Methods + +This vignette will guide you through the steps to create and run a series of simulations to investigate the effects of varying truncation distance on both data generated from a simple half-normal detection function and from a detection function where detectability is affected by a covariate. + +## Setup + +First we load the dsims library. + +```{r setup, warning=FALSE, message=FALSE} +## library(DSsim) +library(dsims) +``` + +## Simulation Components + +As detailed in [Introduction to dsims](#introduction-to-dsims) a simulation comprises of a number of components. dsims is designed so that each of these components is defined individually before they are grouped together into a simulation. This helps keep the process clear and also allows reuse of simulation components between different simulations. Each of the function names to create a simulation component or simulation takes the form *make.\*. + +### Region + +These simulations will use a rectangular study region of 5 km by 20 km. Survey regions can be defined in any units but all units must be the same throughout the components of the simulation. If a shapefile is used to create the survey region, then information on the units will be taken from the .prj file. Here we will define the coordinates in m. As this is a simple study region (Figure \@ref(fig:region)) with few vertices we can simply provide the coordinates. A change from DSsim is that you now need to turn the coordinates into an sf polygon shape prior to creating the region. This step is documented below. Note that while standard shapefiles have their outer polygon coordinates given in a clockwise direction, sf uses counter clockwise for external polygons and clockwise for holes within polygons. Further details on creating multi-part or multi-strata sf objects can be found at the end of the [multi-strata dssd vignette](https://examples.distancesampling.org/dssd-multi-strata/MultiStrataVignette-distill.html). + +You will also note that units are no longer a plotting option. The plot functions have been modified to use ggplot2 and if additional plotting options are desired the ggplot object can be captured and modified. + +```{r region, warning=FALSE, message=FALSE, fig.width=5, fig.cap="The study region."} +## # Create a polgon +## poly1 <- data.frame(x = c(0,0,20000,20000,0), y = c(0,5000,5000,0,0)) +## +## # Create an empty list +## # Store the polygon inside a list in the first element of the coords list referring to strata 1. +## coords <- list() +## coords[[1]] <- list(poly1) + +# Create an sf polgon +library(sf) +# Put the coordinates of the polygon in a matrix +poly1 = matrix(c(0,0, 20000,0, 20000,5000, 0,5000, 0,0),ncol=2, byrow=TRUE) +# Turn them into an sf polygon +pl1 = st_polygon(list(poly1)) + +## # Create the survey region +## region <- make.region(region.name = "study area", +## units = "m", +## coords = coords) +## # The plot function allows plotting in km or m. +## plot(region, plot.units = "km") + +# Create the survey region +region <- make.region(region.name = "study area", + units = "m", + shape = pl1) +# The plot function allows plotting in km or m. +plot(region) + +``` + +### Population + +We will now define our population within our study region. Firstly, we must describe the distribution of the population by defining a density surface. For these simulations we will assume a uniform distribution of animals throughout the study region. dsims will generate an sf grid describing the density surface for us if we provide the x (and optionally the y) spacing and a constant density value for the surface. If the y spacing is omitted it will be assumed to be equal to the x spacing. In this example the value of the constant is not important as we will generate animals based on a fixed population size rather than using the exact values in the density grid. + +There are two argument name changes in the make.density function: *region.obj* is now *region* and *density.gam* is now *fitted model*. The *buffer* argument is no longer needed and there is now an option to supply a formula for density based on x and y using *density.formula*. + +```{r density, warning=FALSE, message=FALSE, fig.width=5, fig.cap="The density surface."} +## # Create the density surface +## density <- make.density(region.obj = region, +## x.space = 100, +## constant = 1) +## +## # Plot the density surface +## plot(density, style = "blocks") +## plot(region, add = TRUE) + +density <- make.density(region = region, + x.space = 100, + constant = 1) + +# Plot the density surface +plot(density, region) +``` + +As an aside, if we wished to add areas of higher or lower density to our density surface we could do this using the *add.hotspot* function in dsims. This function adds these hot or low spots based on a Gaussian decay function. We have to provide the central coordinates and a sigma value to tell dsims about the location and shape of the hot/low spot. The amplitude argument gives the value of the hot or low spot at its centre and is combined with the existing density surface through addition. + +The code used to do this in dsims is identical to that used with DSsim and so is not repeated in the code chunk below. + +```{r density2, warning=FALSE, message=FALSE, fig.width=5, fig.cap="The non-uniform density surface."} +# Add a hotspot to the density surface, centre located at x = 15000, y = 4000 with +# a Gaussian decay parameter sigma = 1500. The value at the centre point will now +# be 1 (the current value of the density surface defined above) + 0.5 = 1.5 +eg.density <- add.hotspot(density, centre = c(15000,4000), sigma = 1500, amplitude = 0.5) +# Add a lowspot to this new density surface (eg.density) +eg.density <- add.hotspot(eg.density, centre = c(10000,3000), sigma = 1000, amplitude = -0.25) +# Plot the density surface +plot(eg.density, region) +``` + +We can now define other aspects of the population. For the simple case (with no covariates) we only need to define a fixed population size and provide the region and density grid we created above. This fixed population size of 200 was selected as a value sufficient to give around 100 detections per simulated survey while not so large as to cause the simulations to run more slowly. The minimum recommended number of detections for fitting a detection function to is 60 [@Buckland2001vm]. + +There are only minor argument names changes to this function: *region.obj* is now *region* and *density.obj* is now *density*. + +```{r popdesc1, warning=FALSE, message=FALSE} +## # Create the population description, with a population size N = 200 +## pop.desc <- make.population.description(region.obj = region, +## density.obj = density, +## N = 200, +## fixed.N = TRUE) + +# Create the population description, with a population size N = 200 +pop.desc <- make.population.description(region = region, + density = density, + N = 200, + fixed.N = TRUE) +``` + +For our simulations involving covariates we need to define how individuals will be allocated these covariate values. dsims allows the user to either define their own discrete distribution or alternatively provide a distribution (Normal, Poisson, Zero-truncated Poisson or Lognormal) with associated parameters. For these simulation we will use sex as a covariate and assume that 50% of the population are female and 50% are male. + +In this example the sex covariate is defined in exactly the same way as in DSsim. + +```{r popdesc2, warning=FALSE, message=FALSE} +# Create the covariate list +covariate.list <- list() +# The population will be 50% males and 50% females +covariate.list$sex <- list(data.frame(level = c("female", "male"), + prob = c(0.5,0.5))) + + +## # Create the population description, with a population size N = 200 +## pop.desc.cov <- make.population.description(region = region, +## density = density, +## covariates = covariate.list, +## N = 200) + +# Create the population description, with a population size N = 200 +pop.desc.cov <- make.population.description(region = region, + density = density, + covariates = covariate.list, + N = 200) +``` + +Note that when defining covariates using distributions the format has changed slightly. An example is included below. In dsims the format has been simplified in that the covariate distribution list provided for each stratum is now just a list with named elements 'distribution' and the distribution parameters. Please refer to the help for which parameters should be defined for each distribution and further examples. + +```{r popdesc3, warning=FALSE, message=FALSE, echo = TRUE, eval = FALSE} +## covariate.list <- list() +## covariate.list$size <- list(list("poisson", list(lambda = 35))) + +covariate.list <- list() +covariate.list$size <- list(list(distribution = "poisson", lambda = 35)) +``` + +### Detectability + +Detectability refers to the detection function or functions we feed into the simulation to generate the observations. In the simple case we can set all animals to have the same probability of detection given their distance from the transect. Here we define a half-normal detection function with scale parameter $\sigma = 200$ and data generation truncation distance of 1000. The truncation distance defined here is to aid simulation efficiency and means that no detections can occur beyond this value. We can then plot this function to check we have defined it correctly. As we defined our survey region in m the scale parameter and truncation distance will also be assumed to be in metres. + +The scale parameter of 200 was selected as on average it gives around 100 detections out to a truncation distance of 1000m with our chosen population size of 200. + +Defining detectability in dsims uses identical code to that in DSsim and so the code is not repeated here. + +```{r detect1, warning=FALSE, message=FALSE, fig.width=4, fig.cap="The detection functions for males and females."} +# Make a simple half normal detection function with a scale parameter of 200 +detect.hn <- make.detectability(key.function = "hn", + scale.param = 200, + truncation = 1000) +# We can now visualise these detection functions +plot(detect.hn, pop.desc) +``` + +When we have covariates in the population we may choose to vary the scale parameter of the detection function based on the covariate values. dsims assumes that the scale parameter is a function of the covariates as follows: + +$$ \sigma = exp(\beta_0+\sum_{j=1}^{q}\beta_{j}z_{ij}) $$ + +where $\beta_0$ is the log of the scale parameter supplied to *make.detectability*, the $\beta_j$'s are the covariate parameters supplied on the log scale and $z_{ij}$ is the ith value of the jth covariate. This formula was taken from @Buckland2004ts. + +The covariate values were selected so that males had a higher probability of detection than females. The values selected in this example give a sample size of around 150 observations out to the 1000m truncation value for our population of 200. + +Defining detectability in dsims uses identical code to that in DSsim and so the code is not repeated here. + +```{r detect2, warning=FALSE, message=FALSE, fig.width=4, fig.cap="The detection functions for males and females."} +# Create the covariate parameter list +cov.params <- list() +# Note the covariate parameters are supplied on the log scale +cov.params$sex = data.frame(level = c("female", "male"), + param = c(0, 1.5)) + +detect.cov <- make.detectability(key.function = "hn" , + scale.param = 120, + cov.param = cov.params, + truncation = 1000) + +# This setup gives a scale parameter of around 120 for the females and 540 for +# the males. We can calculate the sigma for the males using the formula above: +# exp(log(scale.param) + sex.male) +exp(log(120) + 1.5) +# We can now visualise these detection functions +plot(detect.cov, pop.desc.cov) +``` + +### Design + +The design section of the simulations in dsims is the part which differs most significantly from DSsim. DSsim only generated very basic designs and anything more complex needed to be generated externally and loaded as shapefiles. dsims uses the dssd survey design package in R to specify designs and generate transects from them. + +For this example we will use a systematic parallel line transect design. As the recommended minimum number of transects is between 10 and 20 [@Buckland2001vm] we have set the spacing between the lines to be 1000 m to give 20 transects per survey. + +For basic designs the arguments to the make.design function have only changed slightly: *region.obj* is now *region* and *design.details* is now *design*. Note, it is now important to define a truncation distance for the design, this allows design coverage to be assessed. dssd also now provides a more comprehensive set of arguments for defining designs. To investigate these further, please see our [Getting Started with dssd vignette](https://examples.distancesampling.org/dssd-getting-started/GettingStarted-distill.html) and our [Multiple Strata in dssd vignette](https://examples.distancesampling.org/dssd-multi-strata/MultiStrataVignette-distill.html). + +```{r design, warning=FALSE, message=FALSE} + +## # Define the design +## design <- make.design(region.obj = region, +## transect.type = "line", +## design.details = c("parallel", "systematic"), +## spacing = 1000) + +# Define the design +design <- make.design(region = region, + transect.type = "line", + design = "systematic", + spacing = 1000, + truncation = 1000) +``` + +The design objects now contain the survey region and so there is no need to supply this as a separate argument when generating transects. If you would like to plot the covered areas then the *covered.area* argument can be set to TRUE in the *plot* function, in this example the covered areas may not be obvious as the truncation distance is the same as the transect spacing. + +```{r transects, warning=FALSE, message=FALSE, echo = TRUE, fig.width=4, fig.cap="Example survey transects."} +## transects <- generate.transects(design, region = region) +## plot(region) +## plot(transects, col = 4, lwd = 2) + +transects <- generate.transects(design) +plot(region, transects) +``` + +### Analysis + +The final stage of the simulation is to analyse the distance sampling data that has been generated. As discussed above, when collecting data in the field we would not know the true underlying detection function and will therefore incorporate model uncertainty. We can ask the simulation to fit two models, a half-normal and a hazard rate, to the data and select the best model based on the minimum AIC. + +There is a fairly substantial change to the syntax used to define the detection function models for the analyses as well as the function name itself. The syntax for DSsim was based on mrds which we felt was not as user friendly as the syntax used by the Distance R package [@Distancepkg]. We have therefore made the code in dsims more simililar to defining models for Distance. + +```{r analyses1} +## ddf.analyses <- make.ddf.analysis.list(dsmodel = list(~cds(key = "hn", formula = ~1), +## ~cds(key = "hr", formula = ~1)), +## method = "ds", +## truncation = 600) +## criteria = "AIC", + +ddf.analyses <- make.ds.analysis(dfmodel = list(~1, ~1), + key = c("hn", "hr"), + criteria = "AIC", + truncation = 600) +``` + +In this code we have set the truncation distance to 600 but later we will vary this value to investigate the effects of truncation distance on our simulation results. Note that while the truncation distance can be set to any value, it should not exceed the truncation value defined in the detectability or design as no observations will occur beyond these values. + +In addition, in the field it may be possible to identify the covariates that affect detectability so we may wish to fit a detection function that incorporates this. In this case, the following model would be appropriate: + +```{r analyses2} +## ddf.analyses.cov <- make.ddf.analysis.list(dsmodel = list(~mcds(key = "hn", formula = ~sex)), +## method = "ds", +## truncation = 600) + +ddf.analyses.cov <- make.ds.analysis(dfmodel = list(~sex), + key = c("hn"), + truncation = 600) +``` + +## Simulations + +The simulation is created by grouping all these components together. We will create two simulations here, the first simple case will involve no difference in detectability between animals, the second will include the difference in detectability due to sex. Initially, we will only include the analyses which allow selection between a half-normal and hazard rate model, later we modify this to run a third set of simulations where we fit a detection function with sex included as a covariate. + +Once we have created the simulation objects, it is a good idea to check that everything is as you intended. The function *run.survey* simulates a single survey and generates a set of transects and a population and then simulates the survey process to create a distance sampling data set. These can then be plotted (Figures \@ref(fig:checksim) and \@ref(fig:checksim2)). + +```{r set.seed, echo = FALSE, eval = TRUE} +set.seed(474) +``` + +```{r checksim, fig.height=5.5, fig.width=7.2, fig.cap="Example survey. Top left - an example set of transects. Top right - an example population. Bottom left - the detections from the transects. Bottom right - A histogram of the distances from these observations to the transect it was detected."} +## sim <- make.simulation(reps = 999, +## region.obj = region, +## design.obj = design, +## detectability.obj = detect.hn, +## ddf.analyses.list = ddf.analyses) +## population.description.obj = pop.desc, +## # Produce simulation setup plots +## check.sim.setup(sim) + +sim <- make.simulation(reps = 999, + design = design, + population.description = pop.desc, + detectability = detect.hn, + ds.analysis = ddf.analyses) +# Produce survey and plot it +survey <- run.survey(sim) +plot(survey, region) +``` + +We will now create a second simulation object for the simulations with covariates. We can re-use the design component and then add in the new population description and detectability to include the sex covariate. Here we include the same non-covariate analyses but for the final set of simulations we will change this to fit the covariate detection function model. + +```{r checksim2, fig.height=5.5, fig.width=7.2, fig.cap="Example survey. Top left - an example set of transects. Top right - an example population. Bottom left - the detections from the transects. Bottom right - A histogram of the distances from these observations to the transect it was detected."} +## sim.cov <- make.simulation(reps = 999, +## region.obj = region, +## design.obj = design, +## population.description.obj = pop.desc.cov, +## detectability.obj = detect.cov, +## ddf.analyses.list = ddf.analyses) +## # Produce simulation setup plots +## check.sim.setup(sim.cov) + +sim.cov <- make.simulation(reps = 999, + design = design, + population.description = pop.desc.cov, + detectability = detect.cov, + ds.analysis = ddf.analyses) +# Produce survey and plot it +survey.cov <- run.survey(sim.cov) +plot(survey.cov, region) +``` + +To check that our second simulation is correctly generating covariate values for our population we can examine the first few detections in the simulated distance data. + +```{r checksim2_detects, echo = TRUE, eval = TRUE} +head(survey.cov@dist.data) +``` + +## Running Simulations + +To run simulations the syntax has changed slightly from *run* in DSsim to *run.simulations* in dsims and the *object* argument is now *simulation*. The simulations can still be run in parallel using *run.parallel* with the maximum cores set using *max.cores* and the *counter* argument is retained. The *transect.path* argument of the *run.simulation* function in dsims is where you can optionally supply a folder or filename if you wish to load pre-generated shapefiles (in DSsim this was specified in the design). This option is not expected to be widely used and was incorporated to allow simulations in Distance for Windows to be run using dsims. + +Here we demonstrate how to run the basic simulation as an example. You will see the code to do this incorporated into the multiple simulations run within for loops in the following sections. Note that it is advisable to first run your simulation with a few iterations (<10) to give an indication that it should run without issues before setting it off on hundreds / thousands repetitions. Once you have run a simulation you can view the results using the summary function which provides a glossary to explain the output. A histogram of the estimates of abundance can also be viewed, Figure \@ref(fig:simhist). + +```{r runsims, eval = FALSE, echo = TRUE} +## sim <- run(object = sim) + +sim <- run.simulation(simulation = sim, run.parallel = TRUE) +# Display a summary of the simulation +summary(sim) +# Display a histogram of the estimates of abundance +histogram.N.ests(sim) +``` + +```{r simsum, eval = TRUE, echo = FALSE} +# load the simulation objects +load('results/sim.ROBJ') +# Display a summary of the simulation +summary(sim) +``` + +```{r simhist, eval = TRUE, echo = FALSE, fig.width=5, fig.cap="Histogram of abundance estimates from the simulation."} +# Display a histogram of the estimates of abundance +histogram.N.ests(sim) +``` + +If your goal was to simply learn the syntax for switching from DSsim to dsims then you can finish here. The remainder of this vignette loops through further simulations to test how altering truncation distance affects pooling robustness and covariate parameter estimation. From now on only dsims code will be shown. + +## Running Multiple Simulations to investigate Truncation + +To investigate the effects of varying the truncation distance during analysis we do not simply need to run one simulation, but one for each truncation distance. The following code shows how we iterated over a number of different truncation distances and stored the simulation with its results and the simulation summaries as lists. In this first set of simulations detectability does not change with individual level covariates. + +```{r run.sim1, eval = FALSE} +# Truncation distances to iterate over +truncation <- c(200, 400, 600) +# Storage space for results +results.list <- list() +summary.list <- list() + +# We will now run the simulation for each of the analysis truncation distances. +for(tdist in seq(along= truncation)){ + # Screen display to indicate how far through the simulations we are + cat("\n Running for truncation = ", truncation[tdist], fill = T) + # Update analysis with new truncation distance + new.ds.analyses <- make.ds.analysis(dfmodel = list(~1, ~1), + key = c("hn", "hr"), + criteria = "AIC", + truncation = truncation[tdist]) + # Update simulation to include new analysis component + # We can use the @ symbol to change the contents of a slot or alternatively we could have + # re-created the simulation with the new analyses using make.simulation(). + sim@ds.analysis <- new.ds.analyses + # Run simulation and store the results in the appropriate list element + results.list[[tdist]] <- run.simulation(sim, run.parallel = TRUE) + # Store simulation summary in another list in the appropriate list element + # As we are storing the summary we do not need the description.summary displayed + summary.list[[tdist]] <- summary(results.list[[tdist]], description.summary = FALSE) +} + +# Add names to the summary and results list so we know which truncation distance they +# relate to +names(results.list) <- paste("t", truncation, sep = "") +names(summary.list) <- paste("t", truncation, sep = "") +``` + +We will now move on to investigate what happens when the sex covariate affects detectability. First, we need to select suitable candidate truncation distances; to do this we will plot some example data. Figure \@ref(fig:checksim2) shows data generated from a population size of 2500, this increase in population size will increase the number of detections and make the shape of the resulting data less variable. From this histogram five candidate truncation distances were selected and are shown by the red vertical lines. These were selected so that the truncation distances represent a range of values for the probability of detection starting at about 0.6 for the shortest truncation distance. + +```{r covtruncation, eval = TRUE, echo = FALSE, fig.width=4, fig.cap="Histogram of data from covariate simulation with an increased population size of 2500. The detection function shows the best fit to the data (the code was allowed to select between a half normal and hazard rate based on minimum AIC). The red lines indicate the manually selected candidate truncation distances."} +#{r covtruncation, eval = TRUE, echo = FALSE, fig.path = "images/", fig.keep = #'last', fig.show = 'asis', dev = 'png', fig.width=4, fig.cap="Figure 13: Histogram #of data from covariate simulation with an increased population size of 5000. The #detection function shows the best fit to the data (the code was allowed to select #between a half normal and hazard rate based on minimum AIC). The red lines indicate #the selected candidate truncation distances."} +# This code was used to generate the image above. It takes a long time to +# run so saving the image seemed preferable to running for each recompile +set.seed(2320) +temp <- sim.cov +temp@population.description@N <- 2500 +eg.survey <- run.survey(temp) +ddf.dat <- eg.survey@dist.data +n <- nrow(ddf.dat) +plot.title <- paste("Detection Distances (n=", n,")", sep = "") +temp2 <- hist(ddf.dat$distance, breaks = 20, plot = FALSE) +temp2$density <- temp2$density/temp2$density[1] + +#png(filename = "CovTruncation.png", width = 4.5, height = 3, units = "in", res = 200) + +plot(temp2, freq = FALSE, xlab = "Distance (m)", ylab = "Probability of Detection", main = "Detection Distances") + +ddf.result.hn <- mrds::ddf(dsmodel = ~cds(key = "hn"), data = ddf.dat, meta.data = list(width = 1000)) +ddf.result.hr <- mrds::ddf(dsmodel = ~cds(key = "hr"), data = ddf.dat, meta.data = list(width = 1000)) + +min.AIC <- min(ddf.result.hn$criterion, ddf.result.hr$criterion) +index <- which(c(ddf.result.hn$criterion, ddf.result.hr$criterion) == min.AIC) + +x.vals <- seq(0, temp@detectability@truncation, length = 200) +trunc.vals <- c(200, 400, 600, 800, 1000) +if(index ==1){ + scale <- exp(ddf.result.hn$par) + y.vals <- exp(-x.vals^2/(2*scale^2)) + trunc.y <- exp(-trunc.vals^2/(2*scale^2)) +}else{ + scale <- exp(ddf.result.hr$par[2]) + shape <- exp(ddf.result.hr$par[1]) + y.vals <- 1-exp(-(x.vals/scale)^-shape) + trunc.y <- 1-exp(-(trunc.vals/scale)^-shape) + rm(shape) +} + +lines(x.vals, y.vals, lwd = 3) + +for(i in seq(along = trunc.vals)){ + lines(x = rep(trunc.vals[i],2), y = c(0,trunc.y[i]), lwd = 3, col = 2) +} + +rm(temp, temp2, ddf.result.hn, ddf.result.hr, eg.survey, n, ddf.dat, plot.title, min.AIC, trunc.vals, scale, y.vals, trunc.y, x.vals, i) + +#dev.off() +``` + +We can now feed these candidate truncation distances into our covariate simulations in the same way as we did for the simple half normal simulation and again store the results and summaries as lists. Note that for now we are still fitting the half-normal and hazard rate intercept only models and are therefore testing pooling robustness. + +```{r check.sim4, eval = FALSE} +# Truncation distances to iterate over +truncation <- c(200, 400, 600, 800, 1000) +# Storage space for results +cov.results.list <- list() +cov.summary.list <- list() + +for(tdist in seq(along= truncation)){ + # Screen display to indicate how far through the simulations we are + cat("\n Running for truncation = ", truncation[tdist], fill = T) + # Update analysis truncation distance + new.ds.analyses <- make.ds.analysis(dfmodel = list(~1, ~1), + key = c("hn", "hr"), + criteria = "AIC", + truncation = truncation[tdist]) + # Update simulation + sim.cov@ds.analysis <- new.ds.analyses + # Run Simulation + cov.results.list[[tdist]] <- run.simulation(sim.cov, run.parallel = TRUE) + # Store simulation summaries + cov.summary.list[[tdist]] <- summary(cov.results.list[[tdist]], description.summary = FALSE) +} +# Add names to the summary and results list +names(cov.results.list) <- paste("t", truncation, sep = "") +names(cov.summary.list) <- paste("t", truncation, sep = "") +``` + +Finally, we may also wish to fit the covariate model we used to generate the data rather than the non covariate half-normal and hazard rate models. This will allow us to investigate the effects of truncation if in fact we were aware of and could "measure" the covariate that we knew to be affecting detectability. + +```{r covsimulation, eval = FALSE} +# Now include the ddf.analyses.cov in the simulation +sim.cov <- make.simulation(reps = 999, + design = design, + population.description = pop.desc.cov, + detectability = detect.cov, + ds.analysis = ddf.analyses.cov) + +# Truncation distances to iterate over +truncation <- c(200, 400, 600, 800, 1000) + +# Storage space for results +covmod.results.list <- list() +covmod.summary.list <- list() + +for(tdist in seq(along= truncation)){ + # Screen display to indicate how far through the simulations we are + cat("\n Running for truncation = ", truncation[tdist], fill = T) + # Update analysis truncation distance so that detecability is now modelled as a function of sex + new.ds.analyses <- make.ds.analysis(dfmodel = list(~sex), + key = c("hn"), + truncation = truncation[tdist]) + # Update simulation + sim.cov@ds.analysis <- new.ds.analyses + # Run Simulation + covmod.results.list[[tdist]] <- run.simulation(sim.cov, run.parallel = TRUE) + # Store simulation summaries + covmod.summary.list[[tdist]] <- summary(covmod.results.list[[tdist]], description.summary = FALSE) +} +# Add names to the summary and results list +names(covmod.results.list) <- paste("t", truncation, sep = "") +names(covmod.summary.list) <- paste("t", truncation, sep = "") +``` + +## Running Simulations to Check Detection Function Parameter Estimates + +The above simulations concentrate on the question of how accurately and precisely we can estimate the abundance and density of a population. However, we may also be interested in learning how individual level covariates affect detectability. To do this we require a different and slightly more advanced setup. dsims does not currently store the detection function parameter estimates therefore we need to do this manually, however dsims does provide functionality so that doing this is fairly straight forward. As before we create our simulation but then we need to get dsims to give us the survey data so that we can run the analyses and obtain the parameter estimates. Please note that the extraction of the parameter estimates from the ddf model is specific to this model, if you are adapting this code you will need to check the ddf documentation in mrds to understand the parameters for different models. + +```{r covsimulation2, eval = FALSE} +sim.cov <- make.simulation(reps = 999, + design = design, + population.description = pop.desc.cov, + detectability = detect.cov, + ds.analysis = ddf.analyses.cov) + +# Truncation distances to iterate over +truncation <- c(200, 400, 600, 800, 1000) +reps <- sim.cov@reps + +# To store values of interest +sigma.est <- male.param <- array(NA, + dim = c(length(truncation), reps), + dimnames = list(truncation, 1:reps)) + +# Iterate over truncation distances +for(tdist in 2:5){#seq(along = truncation)){ + # Screen display to indicate how far through the simulations we are + cat("\n Running for truncation = ", truncation[tdist], fill = T) + # Update truncation distance + new.ds.analyses <- make.ds.analysis(dfmodel = list(~sex), + key = c("hn"), + truncation = truncation[tdist]) + # Update simulation + sim.cov@ds.analysis <- new.ds.analyses + # Simulation repetitions + for(i in 1:reps){ + cat("\r", i, " out of ", reps, " reps \r") + # Simulates the survey process + simulated.data <- run.survey(sim.cov) + # Run analyses + results <- analyse.data(new.ds.analyses, simulated.data) + # Obtain detection function model + ddf.results <- results$ddf + # Store values of interest + try(sigma.est[tdist,i] <- ddf.results$par[1]) + try(male.param[tdist,i] <- ddf.results$par[2]) + } +} +``` + +## Results + +As these simulations take a substantial amount of time to run we have saved the results and summaries; these can be downloaded as [dsims_truncation_results.zip](dsims_truncation_results.zip). Running one of these simulations with 999 repetitions for one truncation distance takes about 11 minutes on an i7-2600K 3.40GHz processor when running in parallel across 7 threads. When running in parallel the maximum number of cores (or threads) permitted is one less than the number on the machine, this is the default number used unless max.cores specifies a lower number. + +```{r runparallel, eval = FALSE, echo = TRUE} +# Running simulations in parallel +run.simulation(sim.cov, run.parallel = TRUE, max.cores = 7) +``` + +Once they have been downloaded and unzipped into a sub folder called results the results and summaries can be loaded as follows: + +```{r loadresults, eval = TRUE, echo = TRUE} + +# Simulations using a simple half normal detection function +load("results/results_list.ROBJ") +load("results/summary_list.ROBJ") + +# Covartiate simulations +load("results/results_cov_list.ROBJ") +load("results/summary_cov_list.ROBJ") + +# Covariate simulations with covariate model +load("results/covmod_results_list.ROBJ") +load("results/covmod_summary_list.ROBJ") +load("results/sigma_est.ROBJ") +load("results/male_param.ROBJ") +``` + +The objects this has loaded into the workspace include *results.list*, *summary.list*, *cov.results.list*, *cov.summary.list*, *covmod.results.list*, *covmod.summary.list*, *sigma_est* and *male_param*. *results.list* is a list of 3 simulation objects for the simple half normal simulations with truncation distances of 200, 400 and 600. *summary.list* is a list of the 3 simulation summaries associated with simulations in *results.list*. *cov.results.list* is a list of 5 simulation objects for the covariate simulations where detectability is affected by sex but sex is not included as a covariate in the detection function models. These simulations relate to truncation distances of 200, 400, 600, 800 and 1000. *cov.summary.list* is a list of the 5 simulation summaries associated with simulations in *cov.results.list*. *covmod.results.list* is a list of 5 simulation objects for the covariate simulations where detectability is affected by sex and with the analyses including the covariate sex in the detection function model. These simulations relate to truncation distances of 200, 400, 600, 800 and 1000. *covmod.summary.list* is a list of the 5 simulation summaries associated with simulations in *covmod.results.list*. *sigma_est* and *male_param* contains the parameter estimates from the same simulation set up as *covmod.summary.list*. *sigma_est* is a 2D array containing parameter estimates for sigma for the five truncation distances and *male_param* contains the parameter estimates for the male sex parameter for each truncation distance. + +```{r displaysummary, eval = FALSE, echo = TRUE} + +# To view the full summary for the simple half normal simulation with a truncation distance of 200: +summary.list$t200 + +# To view the full summary for the covariate simulation with a truncation distance of 600: +cov.summary.list$t600 + +``` + +### Extracting Result Statistics + +To investigate how truncation distance affects the results we need to produce tables for comparison. This section details how this can be done using knitr. This section is provided for those interested but users can just skip to the next section where the results tables are actually presented. This code is only applicable to study regions which only have one strata, it would need to be modified to deal with multiple strata. + +```{r maketables, eval = FALSE, echo = TRUE} + +library(knitr) + +N <- unlist(lapply(summary.list, function(x){x@individuals$N$mean.Estimate})) +n <- unlist(lapply(summary.list, function(x){x@individuals$summary$mean.n})) +se <- unlist(lapply(summary.list, function(x){x@individuals$N$mean.se})) +sd.N <- unlist(lapply(summary.list, function(x){x@individuals$N$sd.of.means})) +bias <- unlist(lapply(summary.list, function(x){x@individuals$N$percent.bias})) +RMSE <- unlist(lapply(summary.list, function(x){x@individuals$N$RMSE})) +cov <- unlist(lapply(summary.list, function(x){x@individuals$N$CI.coverage.prob})) + +sim.data <- data.frame(trunc = c(200,400,600), + n = round(n), + N = round(N), + se = round(se,2), + sd.N = round(sd.N,2), + bias = round(bias,2), + RMSE = round(RMSE,2), + cov = round(cov*100,1)) + +kable(sim.data, + col.names = c("$Truncation$", "$mean\\ n$", "$mean\\ \\hat{N}$", "$mean\\ se$", "$SD(\\hat{N})$", "$\\% Bias$", "$RMSE$", "$\\%\\ CI\\ Coverage$"), + row.names = FALSE, + align = c('c', 'c', 'c', 'c', 'c', 'c', 'c', 'c'), + caption = "Simulation Results for the simple half normal detection probability: The truncation distance, mean number of detections, mean estimated population size (N), mean standard error of $\\hat{N}$, the standard deviation of $\\hat{N}$, percentage bias, root mean squared error, percentage of times the true value of N was captured in the confidence intervals.", + table.placement="!h", + format = "html") + +``` + +## Simulation Results + +### Simple Half-Normal Simulations + +For the simulations where the data were generated based on a single half-normal detection function the truncation distance used at the analysis stage made little difference to the estimates of abundance. There was perhaps some small decrease in coverage of the 95% confidence intervals as truncation distance was increased. A truncation distance of 400 or 600 didn't quite capture truth 95% of the time, Table 1. The root mean squared error (RMSE) values suggested that the further away from the transect the distances were truncated the closer the abundance estimates were to truth, although bias appeared minimal for all three scenarios. Precision looked to improve with larger truncation distances. + +```{r maketables1, eval = TRUE, echo = FALSE} + +library(knitr) + +N <- unlist(lapply(summary.list, function(x){x@individuals$N$mean.Estimate})) +n <- unlist(lapply(summary.list, function(x){x@individuals$summary$mean.n})) +se <- unlist(lapply(summary.list, function(x){x@individuals$N$mean.se})) +sd.N <- unlist(lapply(summary.list, function(x){x@individuals$N$sd.of.means})) +bias <- unlist(lapply(summary.list, function(x){x@individuals$N$percent.bias})) +RMSE <- unlist(lapply(summary.list, function(x){x@individuals$N$RMSE})) +cov <- unlist(lapply(summary.list, function(x){x@individuals$N$CI.coverage.prob})) + +sim.data <- data.frame(trunc = c(200,400,600), + n = round(n), + N = round(N), + se = round(se,2), + sd.N = round(sd.N,2), + bias = round(bias,2), + RMSE = round(RMSE,2), + cov = round(cov*100,1)) + +kable(sim.data, + col.names = c("$Truncation$", "$mean\\ n$", "$mean\\ \\hat{N}$", "$mean\\ se$", "$SD(\\hat{N})$", "$\\% Bias$", "$RMSE$", "$\\%\\ CI\\ Coverage$"), + row.names = FALSE, + align = c('c', 'c', 'c', 'c', 'c', 'c', 'c', 'c'), + caption = "Simulation Results for the simple half normal detection probability. The truncation distance, mean number of detections, mean estimated population size (N), mean standard error of $\\hat{N}$, the standard deviation of $\\hat{N}$, percentage bias, root mean squared error, percentage of times the true value of N was captured in the 95% confidence intervals.", + table.placement="!h", + format = "html") + +``` + + +### Covariate Simulation Testing Pooling Robustness + +These simulations test whether or not we can rely on our assumption of pooling robustness in this situation. We have deliberately not provided the model used to generate the data as a candidate model in the analysis stage. We can see that for this setup, when we have pooled two quite distinct detection functions, there is some bias in the abundance estimates when the truncation distance is larger, Table 2. These results also show that our 95% confidence intervals capture the true abundance substantially less than 95% of the time when we use large truncation distances. This could be down to an underestimation of the variability, Table 2 shows that for large truncation values the mean se (mean of the estimated standard errors) is lower than the standard deviation of the estimates of abundance. If the analyses were correctly estimating the variability we would expected these values to be similar. In addition, the RMSE suggests that the larger the truncation distance the further away from truth the abundance estimates become, with the most significant jump between 800 and 1000m. + +```{r maketables2, eval= TRUE, echo = FALSE} + +N <- unlist(lapply(cov.summary.list, function(x){x@individuals$N$mean.Estimate})) +n <- unlist(lapply(cov.summary.list, function(x){x@individuals$summary$mean.n})) +se <- unlist(lapply(cov.summary.list, function(x){x@individuals$N$mean.se})) +sd.N <- unlist(lapply(cov.summary.list, function(x){x@individuals$N$sd.of.means})) +bias <- unlist(lapply(cov.summary.list, function(x){x@individuals$N$percent.bias})) +RMSE <- unlist(lapply(cov.summary.list, function(x){x@individuals$N$RMSE})) +cov <- unlist(lapply(cov.summary.list, function(x){x@individuals$N$CI.coverage.prob})) + +sim.data <- data.frame(trunc = c(200,400,600,800,1000), + n = round(n), + N = round(N), + se = round(se,2), + sd.N = round(sd.N,2), + bias = round(bias,2), + RMSE = round(RMSE,2), + cov = round(cov*100,1)) + +kable(sim.data, + col.names = c("$Truncation$", "$mean\\ n$", "$mean\\ \\hat{N}$", "$mean\\ se$", "$SD(\\hat{N})$", "$\\% Bias$", "$RMSE$", "$\\%\\ CI\\ Coverage$"), + row.names = FALSE, + align = c('c', 'c', 'c', 'c', 'c', 'c', 'c', 'c'), + caption = "Simulation Results for the covariate detection probability, where detectability is affected by sex but the candidate models (half-normal and hazard rate) do not contain covariates. The truncation distance, mean number of detections, mean estimated population size (N), mean standard error of $\\hat{N}$, the standard deviation of $\\hat{N}$, percentage bias, root mean squared error, percentage of times the true value of N was captured in the 95% confidence intervals.", + table.placement="!h", + format = "html") + +``` + +### Covariate Simulation with Covariate Model + +Finally we ran simulations and fitted the model we used to generate the data. In these simulations truncation distance had little influence on the accuracy of the estimates of abundance, with the exception of a small amount of bias for the smallest truncation distance, Table 3. The RMSE values suggest that the larger truncation distances did a better job at estimating abundance with the most significant improvement coming with the step from 200m truncation to 400m truncation. The 95% confidence intervals captured the true abundance at least 95% of the time for all truncation distances. In these simulations, the variability was always over estimated with the mean of the estimated standard errors always being higher than the standard deviation of the estimates. + +While the estimates of abundance are not greatly affected by truncation distance for these simulations, the same cannot be said for the parameter estimates. Figure \@ref(fig:makeplots), suggests that parameter estimation is most accurate and reliable at maximum truncation distance. The unstable parameter estimates for the smallest truncation distance leading to sometimes very large estimates of sigma and a bimodal distribution for sex.male could explain the slight bias in abundance estimates for this truncation distance seen in Table 2. It is hoped that in practise this strange behaviour might be associated with a poor fit to the data and would be identified and such estimates rejected based on more extensive model selection criteria. + +```{r maketables3, eval= TRUE, echo = FALSE} + +N <- unlist(lapply(covmod.summary.list, function(x){x@individuals$N$mean.Estimate})) +n <- unlist(lapply(covmod.summary.list, function(x){x@individuals$summary$mean.n})) +se <- unlist(lapply(covmod.summary.list, function(x){x@individuals$N$mean.se})) +sd.N <- unlist(lapply(covmod.summary.list, function(x){x@individuals$N$sd.of.means})) +bias <- unlist(lapply(covmod.summary.list, function(x){x@individuals$N$percent.bias})) +RMSE <- unlist(lapply(covmod.summary.list, function(x){x@individuals$N$RMSE})) +cov <- unlist(lapply(covmod.summary.list, function(x){x@individuals$N$CI.coverage.prob})) + +sim.data <- data.frame(trunc = c(200,400,600,800,1000), + n = round(n), + N = round(N), + se = round(se,2), + sd.N = round(sd.N,2), + bias = round(bias,2), + RMSE = round(RMSE,2), + cov = round(cov*100,1)) + +kable(sim.data, + col.names = c("$Truncation$", "$mean\\ n$", "$mean\\ \\hat{N}$", "$mean\\ se$", "$SD(\\hat{N})$", "$\\% Bias$", "$RMSE$", "$\\%\\ CI\\ Coverage$"), + row.names = FALSE, + align = c('c', 'c', 'c', 'c', 'c', 'c', 'c', 'c'), + caption = "Simulation Results for the covariate detection probability, where detectability is affected by sex and this is modelled in the detection function. The truncation distance, mean number of detections, mean estimated population size (N), mean standard error of $\\hat{N}$, the standard deviation of the $\\hat{N}$, percentage bias, root mean squared error, percentage of times the true value of N was captured in the 95% confidence intervals.", + table.placement="!h", + format = "html") + +``` + +```{r makeplots, eval= TRUE, echo = FALSE, layout="l-body-outset", fig.cap="Histograms of the parameter estimates for sigma and sex.male for three of the five truncation distances investigated. Red lines indicate truth."} +truncation <- c("200" , "600" , "1000" ) +oldpar <- par(mfrow = c(2,length(truncation))) + +for(i in seq(along = truncation)){ + # histogram of the sigma estimates + hist(exp(sigma.est[truncation[i],]), xlab = "Estimate of sigma", main = paste("Truncation = ",truncation[i], sep = "")) + abline(v = sim.cov@detectability@scale.param, col = 2, lwd = 2) + +} +for(i in seq(along = truncation)){ +# histogram of the male cov estimates + hist(male.param[truncation[i],], xlab = "Estimate of sex.male", main = "") + abline(v = sim.cov@detectability@cov.param$sex$param[sim.cov@detectability@cov.param$sex$level == "male"], col = 2, lwd = 2) +} +par(oldpar) +``` + +## Discussion + +In these simulations we have pushed the concept of pooling robustness to the limit in that our two detection functions for males and females were very distinct from one another. This would have increased the potential for spiked data in our simulations, that is when the number of detections falls away quickly at small distances and can make fitting the detection function unreliable (and in fact there were numerous warnings when running some of the simulations about such a scenario). The recommendation when performing distance sampling surveys is to review your data frequently in the field as it is being collected. If you detect spiked data then field methods should be adapted to achieve a wider shoulder in the detection function. This practise will help ensure that pooling robustness holds. + +The model selection (if any) applied in these simulations was done purely on the basis of AIC. In practise the AIC value is one of a number of diagnostic techniques researchers rely on to select an appropriate detection function model. It is likely, especially due to the potential for spiked data, that some of the models in these simulations were not good fits to the data and would not have been selected by a researcher. If model selection would have been manual then a researcher may have chosen to include adjustment terms in the half-normal or hazard rate models which may have improved the model fit and associated estimates of abundance when relying on pooling robustness. + +These simulations do suggest that there is only a small cost in precision to the researcher in truncating the data. In fact, truncation may be beneficial if there are large differences in the underlying detection functions due to a covariate which have not been included in the detection function models. We suspect that this is because when there are multiple detection functions pooled together the tails of the observed combined detection function only represent some of these detection functions while other have already dropped to extremely low probabilities of detection closer to the transect. It is a general rule in distance sampling that the shape of the detection function close to the transect is of more importance that what is going on in the tail. And indeed detections made at large distances, if included, can have an undesired large influence on detection function parameters. The generally accepted rule of thumb is to truncate data where the probability of detection is around 0.15. + +Conversely, if the researcher hopes to identify which covariates affect detectability and obtain reliable parameter estimates then minimal (if any) truncation appears to be preferable. + +The effects of truncation distance on estimated abundance precision are interesting, especially the comparison between our estimated and observed variability. When we only allow the simulations to fit the half-normal and hazard rate models but detectability if affected by the sex covariate, as truncation distance increases the estimated variability (mean se) stays roughly the same while the observed variability $(SD(\hat{N}))$ increases. So at larger truncation distances the variability in our estimated abundance is underestimated and our confidence interval coverage is low. However, when fitting the covariate model our estimated variance is higher than our observed variance suggesting that for this model we are over estimating variability for all truncation distances and our confidence interval coverage is high. + +## Conclusions + +* Truncation can help ensure the concept of pooling robustness holds when there are differences in the detection functions of the individuals in the population and the covariates affecting detectability are not modelled. +* The estimates of abundance are more accurate and precise when the covariate affecting detectability is included in the detection function model. +* Larger truncation distances or no truncation is preferable when trying to accurately obtain the parameters for the covariates that affect detectability. + +## References \ No newline at end of file diff --git a/vignettes/dsims_grouped_strata.Rmd b/vignettes/dsims_grouped_strata.Rmd new file mode 100644 index 0000000..7198e00 --- /dev/null +++ b/vignettes/dsims_grouped_strata.Rmd @@ -0,0 +1,320 @@ +--- +title: "Grouping strata during simulation" +description: | + Estimation when combining strata for logistical or design reasons. +author: + - name: L. Marshall + url: http://distancesampling.org + affiliation: CREEM, Univ of St Andrews + affiliation_url: https://creem.st-andrews.ac.uk +date: "`r format(Sys.time(), '%B %Y')`" +output: + bookdown::html_document2: + number_sections: false + toc: true + toc_depth: 2 + base_format: rmarkdown::html_vignette +pkgdown: + as_is: true +bibliography: refs-grouped.bib +csl: apa.csl +vignette: > + %\VignetteIndexEntry{Grouping strata during simulation} + %\VignetteEngine{knitr::rmarkdown} + \usepackage[utf8]{inputenc} +--- + +```{r include=FALSE} +knitr::opts_chunk$set(eval=TRUE, echo=TRUE, message=FALSE, warnings=FALSE) +``` + +```{r loadpack, warning=FALSE, message=FALSE} +library(knitr) +library(dsims) +``` + +```{r, echo=FALSE,} +myecho <- TRUE +myeval <- TRUE +opts_chunk$set( + tidy=TRUE # display NOT code as typed +) +``` + +These example simulations demonstrate the option to group strata at the analysis stage during a simulation. There are different reasons why we may wish to divide our study region into strata, or perhaps strata into sub strata, but sometimes we might need to create strata purely to optimise the design. For example, if we have a narrow study region that follows a coastline and we wish to keep our lines perpendicular to the coast then we may need to divide the region into strata and use different design angles in each stratum. Assuming we keep the coverage constant across these strata, the data can then be grouped at the analysis stage. We will illustrate an example of grouping strata at the analysis stage below. + +# Getting started + +Ensure you have administrator privileges on your computer and install the necessary R packages. + +```{r packages_temp, echo=FALSE, eval=FALSE} +needed.packages <- c("dsims") +myrepo <- "http://cran.rstudio.com" +install.packages(needed.packages, repos=myrepo) +# Both libraries will be loaded by just loading dsims +library(dsims) +``` + +## Running the simulation and viewing the results for yourself + +It is advisable to download the [.Rmd](dsims_grouped_strata.Rmd) file if you would like to replicate the simulations for yourself. In addition, results from these simulations are provided to allow you to compile the .Rmd document. The results are included in a zip archive [results.zip](results.zip). Uncompressing the contents into a folder called results within the same folder as the .Rmd file should give you the required structure to run the code in the .Rmd file. You should end up with the file *sim.results.ROBJ* within the results folder. + +# Creating a grouped strata simulation + +## Creating a region object + +First, we create the region object using a shapefile stored within the package directory. The shapefile provided contains a marine study area off the coast of Ireland. This region has already been projected into metres and `dssd` will detect that from the shapefile .prj file. The study region has also been divided into six strata and we will provide names in the code below to identify them ("North", "NW", "West Upper", "West Lower", "SW", "South"). Care should be taken to check that the order of the strata is as expected by checking a plot of the study region. + +The division of the study area into six strata was for design purposes, this allows us to specify design angles for each stratum individually. However, for analysis purposes we are interested in estimates for only two distinct areas in this study region, these will consist of the three northern strata grouped together and the three southern strata grouped together. + +```{r makereg, echo=myecho, eval=myeval, fig.dim=c(7,5)} +# Find the full file path to the shapefile on the users machine +shapefile.path <- system.file("extdata", "AreaRProjStrata.shp", package = "dssd") + +# Create the region object +region <- make.region(region.name = "study area", + strata.name = c("North", "NW", "West Upper", + "West Lower", "SW", "South"), + shape = shapefile.path) + +# Plot the survey region +plot(region) +``` + +## Creating a design object + +As mentioned above, we have two sub regions of interest in this study area, for which we would like estimates of density / abundance (the northern three strata and the southern three strata). Let's start by constructing our design as though we had only divided our study region into two strata. We expect more animals in the southern strata so we will implement a non-uniform coverage design by allocating more effort per unit area (i.e. higher coverage) to this strata than the northern strata. + +Let's assume that our effort calculations have suggested that we have sufficient resources to survey parallel lines with a spacing of 16,000m in the northern strata and a spacing of 8,000m in the southern strata. Note that as our shapefile units are metres, all our simulation measurements must also be provided in metres. We will supply a single design angle for the three northern strata and one for the three southern strata, let's set these to be 135 and 70 degrees, respectively. We will also specify that we will be doing minus sampling and do not expect to observe animals beyond 1,500m. + +We will generate a set of transects from this design and assess them for desirable design qualities. + +```{r design_one, echo=myecho, eval=myeval, fig.dim=c(7,5)} +# Define a design based on only two strata +design <- make.design(region = region, + transect.type = "line", + design = "systematic", + spacing = c(rep(16000,3),rep(8000,3)), + design.angle = c(135, 135, 135, 70, 70, 70), + edge.protocol = "minus", + truncation = 1500) + +# Generate and plot a single set of transects +survey <- generate.transects(design) +plot(region, survey) +``` + +An optimal design will aim to both maximise the number of samplers (many short lines are better than fewer long lines) and place them parallel to any density gradients. In the case of a long thin study region such as this, we want to lay the transects across the short dimension of the region (i.e. perpendicular to the coast). It is also often the case that marine species are distributed in relation to the coast (usually having a particular depth preference) so again laying the transects perpendicular to the coast should align them parallel to any density gradient and thereby reduce variability in encounter rate between transects resulting in more precise estimates. + +We can see from this first design, given the complexity of the region, choosing a single design angle for the northern and southern groups of strata is not going to achieve this goal. This is particularly problematic in the southern strata where selecting a design angle to give lines perpendicular to the coast in one area gives lines that are parallel to the coast in another. We now make use of the fact that we have six strata and select appropriate design angles in each with the aim of orientating the transects so they are perpendicular to the coast. + +```{r design_two, echo=myecho, eval=myeval, fig.dim=c(7,5)} +# Define the design +design <- make.design(region = region, + transect.type = "line", + design = "systematic", + spacing = c(rep(16000,3),rep(8000,3)), + design.angle = c(160, 135, 80, 135, 50, 150), + edge.protocol = "minus", + truncation = 1500) + + +# Create a single set of transects to check +survey <- generate.transects(design) +plot(region, survey) +``` + +We can see from the image above that further dividing the northern and southern regions of interest into substrata allows us to better orientate our lines to both maximise the number of samplers and place them perpendicular to the coast. + +As this further stratification was purely for design purposes (so we could modify the design angle as we moved along the coast) we would still treat each of the three substrata as one when we come to analyse the data. However, it is important to note that we can only do this because we have kept a uniform coverage across the substrata. However, the above design would not allow us to simply group all 6 strata at the analysis stage. As the northern strata have lower coverage than the southern strata the full dataset will be more representative of the southern strata than the northern and we must therefore ensure that any differences in detectability are modelled. + +## Creating a density object + +We will create a density surface to represent a distribution of animals which is more abundant in the south and also prefers coastal waters. + +In order to get an idea of where to place the hostpots we can first check the range of the coordinates on the projected scale. Note that the plot of the region gives the scale in lat and lon despite the region being projected. We can access this information by requesting the bounding box of the sf object stored within the `dssd` region. + +```{r range, echo=myecho, eval=FALSE} +# Get the bounding box of the sf object within the region +sf::st_bbox(region@region) +``` +We can now create a density grid with a spacing of 2,500m in both dimensions and add two hotspots to simulate a potentially realistic distribution of animals which prefer to stick closely to the coast. Adding hotspots is largely done by trial and error once we know the range of the x-y coordinate values. Again all measurement values must be provided in metres. As we will later use a fixed population size in the simulations, we do not need to worry about the exact values we provide in the density grid only how they relate to one another. For example, an area with a density cell with a value twice that of another density cell will, on average, end up with twice as many animals when the population is generated. + +```{r density, echo=myecho, eval=myeval, fig.dim=c(7,5)} +# Make a density grid with values of 1 across the region +my.density <- make.density(region = region, + x.space = 2500, + y.space = 2500, + constant = 1) + +# Add a hotspot at coordinates (0, 1900000) +my.density <- add.hotspot(my.density, + centre = c(0, 1900000), + sigma = 70000, + amplitude = 10) + +# Add a hotspot at coordinates (80000, 210000) +my.density <- add.hotspot(my.density, + centre = c(80000, 2100000), + sigma = 100000, + amplitude = 5) + +# Plot this example density surface +plot(my.density, region) +``` + +### Population size + +We will base our simulation on a total population size of 2,500 animals. As the make.population command requires us to specify how many individuals per stratum, we will have to calculate this using the density summary. + +```{r density_summary, echo=myecho, eval=myeval} +# View the density summary +summary(my.density) +``` +We can see from the table that if we used the exact densities in the density grid we would generate a lot of animals (see ave.N column)! However, as mentioned above, the simulation will only use this density surface as a guide to relative density across the region. Therefore, we will use these value to decide how many animals to allocate to each strata by scaling them. + +```{r Nperstratum, echo=myecho, eval=myeval} +# Extract average N values +ave.N.vals <- summary(my.density)@summary$ave.N +# Scale average N vals to sum to 2500 +N.per.stratum <- round(2500*ave.N.vals/sum(ave.N.vals)) + +# View the allocation per stratum +N.per.stratum +# Check the total sums to 2500 (sometimes rounding may cause slight variation) +sum(N.per.stratum) + +``` +At this point, we will also create an individual level covariate to indicate whether the animals are in the northern group of strata or the southern group of strata. We will do this to enable us to later model any differences in detectability between the northern and southern sub populations. Ignoring any differences would not only lead bias in our estimates of abundance for the northern and southern strata but also in our total estimates due to the non-uniform coverage design. + +```{r covs, echo=myecho, eval=myeval} +# Create the population description +covs <- list() +# Adds a strata group entry allocating "North" to all animals in the +# North, NW and West Upper strata and allocating "South" to all animals +# in the West Lower, SW and South strata. +covs$strata.group <- data.frame(level = c(rep("North",3), rep("South",3)), + prob = rep(1,6), + strata = c('North', 'NW', 'West Upper', 'West Lower', 'SW', 'South')) +``` + +We will now include the above information in our population description and set the fixed population size argument to be true. + +```{r abund, echo=myecho, eval=myeval} +# Create the population description +pop.description <- make.population.description(region = region, + density = my.density, + covariates = covs, + N = N.per.stratum, + fixed.N = TRUE) +``` + +### True detection function + +We will simulate using a half-normal detection function but change $\sigma$ (scale.param) depending on stratum and use a truncation distance of 1500m. By changing the detection functions across strata we can demonstrate when pooling robustness applies. Pooling robustness refers to a property in distance sampling which allows us to obtain unbiased abundance estimates from a single 'pooled' detection function fitted across a number of sub populations, even when detectability may vary greatly, [@Rexstad2013]. Pooling robustness applies when our data are a representative sample across the population for which we are generating estimates. In this example, the data in our three northern sub-strata can be pooled and the data in our three southern sub- strata can be pooled as these have the same coverage as each other. We cannot pool detections from any strata / sub-strata where coverage varies (without accounting for the non-uniform coverage) as the resulting detection function will be more representative of the strata with higher coverage. + +```{r truedetect, echo=myecho, eval=myeval, fig.dim=c(7,5)} +# Create the detectability +detect <- make.detectability(key.function = "hn", + scale.param = c(950,850,750,650,550,450), + truncation = 1500) + +# Plot the detectability +plot(detect, pop.description) +``` + + +## Creating the analyses object + +The simulation engine currently only fits one global detection function to each simulated dataset. In the scenario we have constructed, we know that pooling robustness does not apply across the study region as a whole as we have different levels of coverage between the northern and southern stratum groups. Given we cannot fit separate detection functions, we must allow our model to be able to vary the detection function across the two groups of strata. To achieve this we can include the `strata.group` covariate (which we included in the population description) in the model, this will allow a different scale parameter to be estimated for the northern three strata than for the southern three. + +Note that we could have simply included `Region.Label` as a covariate in the detection function model, however, within the simulation at the stage of fitting the detection function all strata are included in the dataset and this would have resulted in a scale parameter being estimated for all 6 strata individually. + +It is at the analysis stage that we also need to define how the strata will be grouped in order to obtain estimates for our regions of interest. The dataframe created in the code below tells the simulation how to group the strata. + + +```{r group.strata, echo=myecho, eval=myeval} +# Create a dataframe describing how the strata will be grouped +group.strata <- data.frame(design.id = c('North', 'NW', 'West Upper', 'West Lower', 'SW', 'South'), + analysis.id = c(rep("North",3), rep("South",3))) + +# View the dataframe +print(group.strata) +``` +We will now define the analyses. As we are simulating detections from a range of difference detection functions, we will incorporate some model uncertainty by allowing the simulation to select between a half normal and a hazard rate model. Both these models will include the strata.group covariate and we will use the AIC as the criterion for model selection. + +```{r candidate.detfns, echo=myecho, eval=myeval} +# Define the analyses - both the hn and hr models use the +# ~strata.group formula +ds.analyses <- make.ds.analysis(dfmodel = list(~strata.group, ~strata.group), + key = c("hn", "hr"), + truncation = 1500, + group.strata = group.strata, + criteria = "AIC") +``` + +## Running the simulation + +Before running the simulation we group all the components into a simulation object and define the number of repetitions. For this example we will simulate 1000 surveys from our simulation definition. Note that the first time you run a simulation you should limit the number of repetitions to only a few to check everything works as expected. + +```{r simobj, echo=myecho, eval=myeval} +# Create the simulation +simulation <- make.simulation(reps = 1000, + design = design, + population.description = pop.description, + detectability = detect, + ds.analysis = ds.analyses) +``` + +A useful way to check the simulation setup is to generate a single example survey, this may take a moment to complete. + +```{r egsurvey, echo=myecho, eval=myeval, warning=FALSE, fig.dim=c(7,5)} +# Simulate the data generation for a single survey +eg.survey <- run.survey(simulation) + +# Plot the example survey +plot(eg.survey, region) +``` + + +If the previous plots lead you to believe you have properly parameterised your simulation, it is time to run it. If you run it for a small number of repetitions it should only take a minute or two to complete, running for a 1000 repetitions will take considerably longer and so this simulation has already been run and the results can be loaded instead. + +```{r runsim, echo=myecho, eval=FALSE, warning=FALSE} +# Run the simulation in parallel +simulation <- run.simulation(simulation, run.parallel = TRUE) +``` + +```{r savesim, echo=FALSE, eval=FALSE, warning=FALSE} +# Save the simulation results +save(simulation, file = "files/sim.results.ROBJ") +``` + +```{r loadsim, echo=myecho, eval=myeval} +# Load the simulation object which has already been run +load("files/sim.results.ROBJ") +``` + +After you have loaded the simulation with the results, you can view them. To view the full summary use `summary(simulation)`, below we will store the simulation summary and look at specific tables within it. Firstly, we will view the summary table. Notice that as requested we have results for only a northern and a southern strata instead of all six of the substrata. The summary table indicates that around 98 detections were made on average in the northern strata and 275 in the southern strata. It is important to check that there are sufficient detections in each strata so that any differences in detectability can be accurately modelled in the detection function. + +```{r summary, echo=myecho, eval=myeval} +# Create a summary (silently without the description) +sim.summary <- summary(simulation, description.summary = FALSE) + +# Display the summary table +sim.summary@individuals$summary +``` + +Next we will view the table giving the abundance estimates. There is only a small amount of negative bias for both strata and in the total estimate of abundance. However, the coverage of the confidence intervals (which should be 0.95) is only 0.92 for the southern strata and the total estimate. Sometimes reduced confidence interval coverage can be due to the variance being under estimated but in this case the mean.se (mean of the estimated standard error) and the sd.of.means (truth - observed standard error of the estimates) are very close suggesting the variance has been estimated accurately. + +```{r estimates, echo=myecho, eval=myeval} +# Display the table of abundance estimates +round(sim.summary@individuals$N,3) +``` + +## Discussion + +Even though the detectability of animals was varied across each of the six sub-strata, the estimates for the northern and southern groups of sub-strata combined had very low bias. This result was due to pooling robustness applying across both these groups of sub strata (coverage was the same in each group). Meanwhile, the difference in detectability between the northern and southern groups was modelled explicitly using the strata.group covariate in each of the models. If the simulation was repeated, but this covariate was omitted, we would expect to see bias in the abundance estimated for the northern and southern regions as well as the overall estimate. This would be due to the differences in coverage between the three northen sub-strata and the three southern sub-strata and the fitted detection function being more representative of the southern region (due to the higher coverage) than the northern region. + +The setup for the analysis in this simulation is a little complex due to the restrictions of the simulation package (i.e. needing to include the stratum covariate in the population description). When analysing your own distance sampling data from the field, if you have a similar scenario, you will be able to either fit separate detection functions to the data from the different regions of interest or create any kind of stratum variable you want, giving you more analysis options. + +## References diff --git a/vignettes/files/sim.results.ROBJ b/vignettes/files/sim.results.ROBJ new file mode 100644 index 0000000..f0ed14a Binary files /dev/null and b/vignettes/files/sim.results.ROBJ differ diff --git a/vignettes/refs-grouped.bib b/vignettes/refs-grouped.bib new file mode 100644 index 0000000..452c2f4 --- /dev/null +++ b/vignettes/refs-grouped.bib @@ -0,0 +1,81 @@ +@book{Buckland2001vm, + author = {Buckland, S.T. and Anderson, D.R. and Burnham, K.P. and Borchers, D.L. and Thomas, L.}, + title = {Introduction to Distance Sampling}, + publisher = {Oxford University Press, Oxford, UK}, + year = {2001}, + series = {Estimating Abundance of Biological Populations} +} + +@book{Buckland2004ts, + author = {Buckland, S.T. and Anderson, D.R. and Burnham, K.P. and Laake, J.L. and Borchers, D.L. and Thomas, L.}, + title = {Advanced Distance Sampling}, + publisher = {Oxford University Press}, + year = {2004} +} + +@article{Thomas2010cf, + author = {Thomas, L. and Buckland, S.T. and Rexstad, E.A. and Laake, J.L. and Strindberg, S. and Hedley, S.L. and Bishop, J.R.B. and Marques, T.A. and Burnham, K.P.}, + title = {Distance software: design and analysis of distance sampling surveys for estimating population size}, + journal = {Journal of Applied Ecology}, + year = {2010}, + volume = {47}, + number = {1}, + pages = {5--14}, + month = {feb}, + url = { https://doi.org/10.1111/j.1365-2664.2009.01737.x} +} + +@article{Rexstad2013, + author = {Rexstad, E. and Buckland, S. and Marshall, L. and Borchers, D.}, + title = {Pooling robustness in distance sampling: Avoiding bias when there is unmodelled heterogeneity.}, + journal = {Ecology and Evolution}, + year = {2023}, + volume = {13}, + number = {1}, + pages = {e9684}, + month = {jan}, + url = { https://onlinelibrary.wiley.com/doi/10.1002/ece3.9684} +} + + +@book{Buckland2015b, + author = {S.T. Buckland and E.A. Rexstad and T. A. Marques and C.S. Oedekoven}, + title = {Distance Sampling: Methods and Applications}, + publisher = {Springer}, + year = {2015}, + url = {https://www.springer.com/gp/book/9783319192185} +} + +@incollection{Strin2004, + author = {Strindberg,S. and Buckland, S. T. and Thomas, L.}, + title = {Design of distance sampling surveys and Geographic Information Systems}, + booktitle = {Advanced Distance Sampling}, + publisher = {Oxford University Press}, + year = {2004}, + pages = {190-228} +} + +@Manual{dssdpkg, + title = {dssd: Distance Sampling Survey Design}, + author = {Marshall, L.}, + year = {2022}, + url = {https://CRAN.R-project.org/package=dssd}, + note = {R package version 0.3.4}, +} + + +@Manual{dsimspkg, + title = {dsims: Distance Sampling Simulations}, + author = {Marshall, L.}, + year = {2022}, + url = {https://CRAN.R-project.org/package=dsims}, + note = {R package version 1.0.1}, +} + +@Manual{devtoolspkg, + title = {devtools: Tools to Make Developing R Packages Easier}, + author = {Hadley Wickham and Jim Hester and Winston Chang and Jennifer Bryan}, + year = {2022}, + note = {R package version 2.4.5}, + url = {https://CRAN.R-project.org/package=devtools}, + } diff --git a/vignettes/refs-transition.bib b/vignettes/refs-transition.bib new file mode 100644 index 0000000..0e9a7f2 --- /dev/null +++ b/vignettes/refs-transition.bib @@ -0,0 +1,87 @@ +@book{Buckland2001vm, + author = {Buckland, S.T. and Anderson, D.R. and Burnham, K.P. and Borchers, D.L. and Thomas, L.}, + title = {Introduction to Distance Sampling}, + publisher = {Oxford University Press, Oxford, UK}, + year = {2001}, + series = {Estimating Abundance of Biological Populations} +} + +@book{Buckland2004ts, + author = {Buckland, S.T. and Anderson, D.R. and Burnham, K.P. and Laake, J.L. and Borchers, D.L. and Thomas, L.}, + title = {Advanced Distance Sampling}, + publisher = {Oxford University Press}, + year = {2004} +} + +@article{Thomas2010cf, + author = {Thomas, L. and Buckland, S.T. and Rexstad, E.A. and Laake, J.L. and Strindberg, S. and Hedley, S.L. and Bishop, J.R.B. and Marques, T.A. and Burnham, K.P.}, + title = {Distance software: design and analysis of distance sampling surveys for estimating population size}, + journal = {Journal of Applied Ecology}, + year = {2010}, + volume = {47}, + number = {1}, + pages = {5--14}, + month = {feb} +} + +@book{Buckland2015b, + author = {S.T. Buckland and E.A. Rexstad and T. A. Marques and C.S. Oedekoven}, + title = {Distance Sampling: Methods and Applications}, + publisher = {Springer}, + year = {2015} +} + +@incollection{Strin2004, + author = {Strindberg,S. and Buckland, S. T. and Thomas, L.}, + title = {Design of distance sampling surveys and Geographic Information Systems}, + booktitle = {Advanced Distance Sampling}, + publisher = {Oxford University Press}, + year = {2004}, + pages = {190-228} +} + +@Manual{dssdpkg, + title = {dssd: Distance Sampling Survey Design}, + author = {Marshall, L.}, + year = {2022}, + url = {https://CRAN.R-project.org/package=dssd}, + note = {R package version 0.3.3}, +} + +@Manual{dssimpkg, + title = {DSsim: Distance Sampling Simulations}, + author = {Marshall, L.}, + year = {2019}, + url = {https://CRAN.R-project.org/package=DSsim}, + note = {R package version 1.1.4}, +} + +@Manual{dsimspkg, + title = {dsims: Distance Sampling Simulations}, + author = {Marshall, L.}, + year = {2022}, + url = {https://CRAN.R-project.org/package=dsims}, + note = {R package version 1.0.0}, +} + +@Article{Distancepkg, + title = {Distance Sampling in {R}}, + author = {David L. Miller and Eric Rexstad and Len Thomas and Laura Marshall and Jeffrey L. Laake}, + journal = {Journal of Statistical Software}, + year = {2019}, + volume = {89}, + number = {1}, + pages = {1--28}, + doi = {10.18637/jss.v089.i01}, +} + +@Manual{mrdspkg, +title = {mrds: Mark-Recapture Distance Sampling}, +author = {Laake, J. and Borchers, D. and Thomas, L. and Miller, D. and Bishop, J.}, +year = {2022}, +url = {https://CRAN.R-project.org/package=mrds}, +note = {R package version 2.2.6}, +} + + + diff --git a/vignettes/refs.bib b/vignettes/refs.bib index 26984c8..c9c0d75 100644 --- a/vignettes/refs.bib +++ b/vignettes/refs.bib @@ -24,7 +24,6 @@ @article{Thomas:2010cf month = feb } - @Manual{sf-pkg, title = {sf: Simple Features for R}, author = {Pebesma, E., Bivand, R., Racine, E., Sumner, M., Cook, I., Keitt, T., Lovelace, R., Wickham, H., Ooms, J., Muller, K., Pedersen, T.L. and Baston, D.}, @@ -33,7 +32,6 @@ @Manual{sf-pkg note = {R package version 0.9-7}, } - @Manual{DSsim-pkg, title = {DSsim: Distance Sampling Simulations}, author = {Marshall, L.}, @@ -42,26 +40,29 @@ @Manual{DSsim-pkg note = {R package version 1.1.5}, } -@Manual{Distance-pkg, -title = {Distance: Distance Sampling Detection Function and Abundance Estimation}, -author = {Miller, D.L.}, -year = {2020}, -url = {https://CRAN.R-project.org/package=Distance}, -note = {R package version 1.0.2}, +@Article{Distance-pkg, + title = {Distance Sampling in {R}}, + author = {David L. Miller and Eric Rexstad and Len Thomas and Laura Marshall and Jeffrey L. Laake}, + journal = {Journal of Statistical Software}, + year = {2019}, + volume = {89}, + number = {1}, + pages = {1--28}, + doi = {10.18637/jss.v089.i01}, } @Manual{dssd-pkg, -title = {dssd: Distance Sampling Survey Design}, -author = {Marshall, L.}, -year = {2020}, -url = {https://CRAN.R-project.org/package=dssd}, -note = {R package version 0.2.1}, + title = {dssd: Distance Sampling Survey Design}, + author = {Laura Marshall}, + year = {2023}, + note = {R package version 1.0.2}, + url = {https://CRAN.R-project.org/package=dssd}, } @Manual{dsims-pkg, -title = {dsims: Distance Sampling Simulations}, -author = {Marshall, L.}, -year = {2021}, -url = {https://CRAN.R-project.org/package=dsims}, -note = {R package version 0.0.1}, + title = {dsims: Distance Sampling Simulations}, + author = {Laura Marshall}, + year = {2023}, + note = {R package version 1.0.4}, + url = {https://CRAN.R-project.org/package=dsims}, } \ No newline at end of file diff --git a/vignettes/results/covmod_results_list.ROBJ b/vignettes/results/covmod_results_list.ROBJ new file mode 100644 index 0000000..4919178 Binary files /dev/null and b/vignettes/results/covmod_results_list.ROBJ differ diff --git a/vignettes/results/covmod_summary_list.ROBJ b/vignettes/results/covmod_summary_list.ROBJ new file mode 100644 index 0000000..580d1ae Binary files /dev/null and b/vignettes/results/covmod_summary_list.ROBJ differ diff --git a/vignettes/results/male_param.ROBJ b/vignettes/results/male_param.ROBJ new file mode 100644 index 0000000..ad3c438 Binary files /dev/null and b/vignettes/results/male_param.ROBJ differ diff --git a/vignettes/results/results_cov_list.ROBJ b/vignettes/results/results_cov_list.ROBJ new file mode 100644 index 0000000..837ab11 Binary files /dev/null and b/vignettes/results/results_cov_list.ROBJ differ diff --git a/vignettes/results/results_list.ROBJ b/vignettes/results/results_list.ROBJ new file mode 100644 index 0000000..89d8e50 Binary files /dev/null and b/vignettes/results/results_list.ROBJ differ diff --git a/vignettes/results/sigma_est.ROBJ b/vignettes/results/sigma_est.ROBJ new file mode 100644 index 0000000..82e5bb7 Binary files /dev/null and b/vignettes/results/sigma_est.ROBJ differ diff --git a/vignettes/results/sim.ROBJ b/vignettes/results/sim.ROBJ new file mode 100644 index 0000000..913ee44 Binary files /dev/null and b/vignettes/results/sim.ROBJ differ diff --git a/vignettes/results/sim_cov.ROBJ b/vignettes/results/sim_cov.ROBJ new file mode 100644 index 0000000..a2b768f Binary files /dev/null and b/vignettes/results/sim_cov.ROBJ differ diff --git a/vignettes/results/summary_cov_list.ROBJ b/vignettes/results/summary_cov_list.ROBJ new file mode 100644 index 0000000..6fa4456 Binary files /dev/null and b/vignettes/results/summary_cov_list.ROBJ differ diff --git a/vignettes/results/summary_list.ROBJ b/vignettes/results/summary_list.ROBJ new file mode 100644 index 0000000..d1ad139 Binary files /dev/null and b/vignettes/results/summary_list.ROBJ differ