diff --git a/.Rbuildignore b/.Rbuildignore
index 53aaa071..5af5293d 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -6,10 +6,11 @@
 ^\.httr-oauth$
 ^doc$
 ^Meta$
-google-analytics.js
-droptoken.rds
 ^cran-comments\.md$
 ^CRAN-RELEASE$
 ^_pkgdown\.yml$
 ^docs$
 ^pkgdown$
+^codecov\.yml$
+^vignettes/spectragryph.*
+^vignettes/advanced.*
diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml
index f8220209..4b6f8a42 100644
--- a/.github/workflows/R-CMD-check.yaml
+++ b/.github/workflows/R-CMD-check.yaml
@@ -1,4 +1,4 @@
-# Workflow derived from https://github.com/r-lib/actions/tree/master/examples
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
 # Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
 on:
   push:
@@ -18,7 +18,7 @@ jobs:
       fail-fast: false
       matrix:
         config:
-          - {os: macOS-latest,   r: 'release'}
+          - {os: macos-latest,   r: 'release'}
           - {os: windows-latest, r: 'release'}
           - {os: ubuntu-latest,   r: 'devel', http-user-agent: 'release'}
           - {os: ubuntu-latest,   r: 'release'}
@@ -29,30 +29,31 @@ jobs:
       R_KEEP_PKG_SOURCE: yes
 
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
 
-      - uses: r-lib/actions/setup-pandoc@v1
+      - uses: r-lib/actions/setup-pandoc@v2
 
-      - uses: r-lib/actions/setup-r@v1
+      - name: Install XQuartz on macOS
+        if: runner.os == 'macOS'
+        run: brew install xquartz --cask
+
+      - uses: r-lib/actions/setup-r@v2
         with:
           r-version: ${{ matrix.config.r }}
           http-user-agent: ${{ matrix.config.http-user-agent }}
           use-public-rspm: true
 
-      - uses: r-lib/actions/setup-r-dependencies@v1
+      - uses: r-lib/actions/setup-r-dependencies@v2
         with:
-          extra-packages: rcmdcheck
-
-      - uses: r-lib/actions/check-r-package@v1
+          extra-packages: any::rcmdcheck
+          needs: check
 
-      - name: Show testthat output
-        if: always()
-        run: find check -name 'testthat.Rout*' -exec cat '{}' \; || true
+      - name: Populate .Renviron with GitHub Secrets
+        run: |
+          echo AWS_ACCESS_KEY_ID="${{ secrets.AWS_ACCESS_KEY_ID }}" >> ~/.Renviron
+          echo AWS_SECRET_ACCESS_KEY="${{ secrets.AWS_SECRET_ACCESS_KEY }}" >> ~/.Renviron
         shell: bash
 
-      - name: Upload check results
-        if: failure()
-        uses: actions/upload-artifact@main
+      - uses: r-lib/actions/check-r-package@v2
         with:
-          name: ${{ runner.os }}-r${{ matrix.config.r }}-results
-          path: check
+          upload-snapshots: true
diff --git a/.github/workflows/test-coverage.yaml b/.github/workflows/test-coverage.yaml
new file mode 100644
index 00000000..6cf3ff8f
--- /dev/null
+++ b/.github/workflows/test-coverage.yaml
@@ -0,0 +1,50 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+on:
+  push:
+    branches: [main, release]
+  pull_request:
+    branches: [main, release]
+
+name: test-coverage
+
+jobs:
+  test-coverage:
+    runs-on: ubuntu-latest
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+
+    steps:
+      - uses: actions/checkout@v3
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::covr
+          needs: coverage
+
+      - name: Test coverage
+        run: |
+          covr::codecov(
+            quiet = FALSE,
+            clean = FALSE,
+            install_path = file.path(Sys.getenv("RUNNER_TEMP"), "package")
+          )
+        shell: Rscript {0}
+
+      - name: Show testthat output
+        if: always()
+        run: |
+          ## --------------------------------------------------------------------
+          find ${{ runner.temp }}/package -name 'testthat.Rout*' -exec cat '{}' \; || true
+        shell: bash
+
+      - name: Upload test results
+        if: failure()
+        uses: actions/upload-artifact@v3
+        with:
+          name: coverage-test-failures
+          path: ${{ runner.temp }}/package
diff --git a/.gitignore b/.gitignore
index b9533613..1650843a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,9 +2,6 @@
 .Rhistory
 .Rapp.history
 
-# Desktop drive files
-.ini
-
 # Session Data files
 .RData
 
@@ -27,8 +24,11 @@
 # produced PDF vignettes and docs
 vignettes/*.pdf
 inst/doc
-doc
-Meta
+doc/
+Meta/
+
+# Shiny app
+inst/OpenSpecy-shiny-main/
 
 # OAuth2 token, see https://github.com/hadley/httr/releases/tag/v0.3
 .httr-oauth
@@ -48,7 +48,7 @@ Meta
 docs/
 
 # System files
-*.ini
+.ini
 *.swp
 .DS_Store
-docs
+delete.bat
diff --git a/DESCRIPTION b/DESCRIPTION
index 4600f4fe..d13ebc54 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,15 +1,19 @@
 Package: OpenSpecy
 Type: Package
 Title: Analyze, Process, Identify, and Share Raman and (FT)IR Spectra
-Version: 0.9.5
-Date: 2022-07-06
-Authors@R: c(person("Win", "Cowger", role = c("cre", "aut"),
+Version: 1.0.0
+Date: 2023-08-29
+Authors@R: c(person("Win", "Cowger", role = c("cre", "aut", "dtc"),
       email = "wincowger@gmail.com",
       comment = c(ORCID = "0000-0001-9226-3104")),
     person("Zacharias", "Steinmetz", role = c("aut"),
-      email = "steinmetz-z@uni-landau.de",
+      email = "z.steinmetz@rptu.de",
       comment = c(ORCID = "0000-0001-6675-5033")),
-    person("Andrew", "Gray", role = c("ctb"),
+    person("Nick", "Leong", role = c("aut"),
+      comment = c(ORCID = "0009-0008-3313-4132")),
+    person("Andrea","Faltynkova", role = c("aut", "dtc"),
+      comment = c(ORCID = "0000-0003-2523-3137")),
+    person("Andrew B", "Gray", role = c("ctb"),
       comment = c(ORCID = "0000-0003-2252-7367")), 
     person("Hannah", "Hapich", role = c("ctb"),
       comment = c(ORCID = "0000-0003-0000-6632")),
@@ -23,18 +27,28 @@ Authors@R: c(person("Win", "Cowger", role = c("cre", "aut"),
       comment = c(ORCID = "0000-0002-7624-711X")),
     person("Sebastian", "Primpke", role = c("ctb", "dtc"),
       comment = c(ORCID = "0000-0001-7633-8524")),
-    person("Orestis", "Herodotou", role = c("ctb", "dtc")))
+    person("Orestis", "Herodotou", role = c("ctb")),
+    person("Mary C", "Norris", role = c("ctb")),
+    person("Christine M", "Knauss", role = c("ctb"), 
+      comment = c(ORCID = "0000-0003-4404-8922")), 
+    person("Aleksandra","Karapetrova", role = c("ctb", "dtc", "rev"),
+      comment = c(ORCID = "0000-0002-9856-1644")),
+    person("Vesna","Teofilovic", role = c("ctb"),
+      comment = c(ORCID = "0000-0002-3557-1482")),
+    person("Laura A. T.","Markley", role = c("ctb"),
+      comment = c(ORCID = "0000-0003-0620-8366")),
+    person("Shreyas","Patankar", role = c("ctb", "dtc")))
 Description: Raman and (FT)IR spectral analysis tool for plastic particles and
     other environmental samples (Cowger et al. 2021,
-    <doi:10.1021/acs.analchem.1c00123>). Supported features include reading
-    spectral data files (.asp, .csv, .jdx, .spc, .spa, .0), Savitzky-Golay
-    smoothing of spectral intensities with smooth_intens(), correcting
-    background noise with subtr_bg() in accordance with Zhao et al. (2007)
-    <doi:10.1366/000370207782597003>, and identifying spectra using an onboard
-    reference library (Cowger et al. 2020, <doi:10.1177/0003702820929064>).
-    Analyzed spectra can be shared with the Open Specy community. A Shiny app is
-    available via run_app() or online at
-    <https://openanalysis.org/openspecy/>.
+    <doi:10.1021/acs.analchem.1c00123>). With read_any(), Open Specy provides a
+    single function for reading individual, batch, or map spectral data files
+    like .asp, .csv, .jdx, .spc, .spa, .0, and .zip. process_spec() simplifies
+    preprocessing spectra, including smoothing, baseline correction,
+    range restriction and flattening, intensity conversions, wavenumber
+    alignment, and min-max normalization. Spectra can be identified in batch
+    using an onboard reference library (Cowger et al. 2020,
+    <doi:10.1177/0003702820929064>) using match_spec(). A Shiny app is available
+    via run_app() or online at <https://openanalysis.org/openspecy/>.
 URL: https://github.com/wincowgerDEV/OpenSpecy-package/,
     http://wincowger.com/OpenSpecy-package/
 BugReports: https://github.com/wincowgerDEV/OpenSpecy-package/issues/
@@ -44,32 +58,37 @@ LazyLoad: true
 LazyData: true
 VignetteBuilder: knitr
 Depends:
-    R (>= 4.0.0)
+    R (>= 4.1.0)
 Imports:
-    dplyr,
-    rlang,
+    methods,
+    data.table,
+    jsonlite,
+    yaml,
     osfr,
+    caTools,
     hyperSpec,
-    hexView,
+    imager,
+    plotly,
     digest,
     signal,
+    glmnet,
     shiny
 Suggests:
     knitr,
     rmarkdown,
-    testthat (>= 3.0.0),
+    testthat (>= 3.1.9),
     config,
+    qs,
     shinyjs,
-    shinythemes,
-    shinyBS,
     shinyWidgets,
+    bs4Dash,
+    dplyr,
     ggplot2,
-    plotly,
-    data.table,
     DT,
     curl,
-    rdrop2, 
+    aws.s3, 
     mongolite,
     loggit
 RoxygenNote: 7.2.3
+Roxygen: list(markdown = TRUE)
 Config/testthat/edition: 3
diff --git a/NAMESPACE b/NAMESPACE
index b9b657ac..e62d5632 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,73 +1,175 @@
 # Generated by roxygen2: do not edit by hand
 
-S3method(adj_intens,data.frame)
+S3method(adj_intens,OpenSpecy)
 S3method(adj_intens,default)
-S3method(adj_intens,formula)
-S3method(match_spec,data.frame)
+S3method(ai_classify,OpenSpecy)
+S3method(ai_classify,default)
+S3method(as_OpenSpecy,OpenSpecy)
+S3method(as_OpenSpecy,data.frame)
+S3method(as_OpenSpecy,default)
+S3method(as_OpenSpecy,hyperSpec)
+S3method(as_OpenSpecy,list)
+S3method(c_spec,OpenSpecy)
+S3method(c_spec,default)
+S3method(c_spec,list)
+S3method(collapse_spec,OpenSpecy)
+S3method(collapse_spec,default)
+S3method(conform_spec,OpenSpecy)
+S3method(conform_spec,default)
+S3method(cor_spec,OpenSpecy)
+S3method(cor_spec,default)
+S3method(def_features,OpenSpecy)
+S3method(def_features,default)
+S3method(filter_spec,OpenSpecy)
+S3method(filter_spec,default)
+S3method(flatten_range,OpenSpecy)
+S3method(flatten_range,default)
+S3method(get_metadata,OpenSpecy)
+S3method(get_metadata,default)
+S3method(head,OpenSpecy)
+S3method(heatmap_spec,OpenSpecy)
+S3method(heatmap_spec,default)
+S3method(interactive_plot,OpenSpecy)
+S3method(interactive_plot,default)
+S3method(lines,OpenSpecy)
+S3method(match_spec,OpenSpecy)
 S3method(match_spec,default)
-S3method(match_spec,formula)
-S3method(share_spec,data.frame)
+S3method(plot,OpenSpecy)
+S3method(plotly_spec,OpenSpecy)
+S3method(plotly_spec,default)
+S3method(print,OpenSpecy)
+S3method(process_spec,OpenSpecy)
+S3method(process_spec,default)
+S3method(restrict_range,OpenSpecy)
+S3method(restrict_range,default)
+S3method(sample_spec,OpenSpecy)
+S3method(sample_spec,default)
+S3method(share_spec,OpenSpecy)
 S3method(share_spec,default)
-S3method(smooth_intens,data.frame)
+S3method(sig_noise,OpenSpecy)
+S3method(sig_noise,default)
+S3method(smooth_intens,OpenSpecy)
 S3method(smooth_intens,default)
-S3method(smooth_intens,formula)
-S3method(subtr_bg,data.frame)
-S3method(subtr_bg,default)
-S3method(subtr_bg,formula)
+S3method(spec_res,OpenSpecy)
+S3method(spec_res,default)
+S3method(subtr_baseline,OpenSpecy)
+S3method(subtr_baseline,default)
+S3method(summary,OpenSpecy)
+S3method(write_spec,OpenSpecy)
+S3method(write_spec,default)
+export(OpenSpecy)
 export(adj_intens)
 export(adj_neg)
+export(adj_res)
+export(ai_classify)
+export(as_OpenSpecy)
+export(as_hyperSpec)
+export(c_spec)
+export(check_OpenSpecy)
 export(check_lib)
-export(find_spec)
+export(collapse_spec)
+export(conform_res)
+export(conform_spec)
+export(cor_spec)
+export(def_features)
+export(filter_spec)
+export(flatten_range)
+export(gen_grid)
 export(get_lib)
+export(get_metadata)
+export(heatmap_spec)
 export(human_ts)
+export(ident_spec)
+export(interactive_plot)
+export(is_OpenSpecy)
+export(is_empty_vector)
 export(load_lib)
 export(make_rel)
 export(match_spec)
-export(read_0)
+export(max_cor_named)
+export(mean_replace)
+export(plotly_spec)
+export(process_spec)
+export(read_any)
 export(read_asp)
+export(read_envi)
 export(read_extdata)
 export(read_jdx)
+export(read_opus)
+export(read_opus_raw)
 export(read_spa)
 export(read_spc)
+export(read_spec)
 export(read_text)
+export(read_zip)
+export(restrict_range)
+export(rm_lib)
 export(run_app)
+export(sample_spec)
 export(share_spec)
+export(sig_noise)
 export(smooth_intens)
 export(spec_res)
-export(subtr_bg)
+export(subtr_baseline)
+export(write_spec)
+importFrom(caTools,read.ENVI)
+importFrom(data.table,":=")
+importFrom(data.table,.SD)
+importFrom(data.table,as.data.table)
+importFrom(data.table,data.table)
+importFrom(data.table,dcast)
+importFrom(data.table,fifelse)
+importFrom(data.table,fread)
+importFrom(data.table,frollapply)
+importFrom(data.table,is.data.table)
+importFrom(data.table,melt)
+importFrom(data.table,rbindlist)
+importFrom(data.table,setDT)
+importFrom(data.table,setorder)
+importFrom(data.table,transpose)
 importFrom(digest,digest)
-importFrom(dplyr,"%>%")
-importFrom(dplyr,arrange)
-importFrom(dplyr,desc)
-importFrom(dplyr,group_by)
-importFrom(dplyr,inner_join)
-importFrom(dplyr,mutate)
-importFrom(dplyr,select)
-importFrom(dplyr,summarize)
-importFrom(dplyr,top_n)
-importFrom(dplyr,ungroup)
-importFrom(hexView,blockString)
-importFrom(hexView,readRaw)
+importFrom(glmnet,predict.glmnet)
+importFrom(grDevices,chull)
+importFrom(graphics,matlines)
+importFrom(graphics,matplot)
 importFrom(hyperSpec,read.jdx)
 importFrom(hyperSpec,read.spc)
+importFrom(imager,as.cimg)
+importFrom(imager,label)
+importFrom(jsonlite,read_json)
+importFrom(jsonlite,write_json)
+importFrom(methods,new)
 importFrom(osfr,osf_download)
 importFrom(osfr,osf_ls_files)
 importFrom(osfr,osf_retrieve_node)
-importFrom(rlang,.data)
-importFrom(shiny,runApp)
+importFrom(plotly,add_markers)
+importFrom(plotly,add_trace)
+importFrom(plotly,layout)
+importFrom(plotly,plot_ly)
+importFrom(plotly,subplot)
+importFrom(shiny,runGitHub)
 importFrom(shiny,shinyOptions)
 importFrom(signal,filter)
 importFrom(signal,sgolay)
 importFrom(stats,approx)
 importFrom(stats,cor)
+importFrom(stats,dist)
 importFrom(stats,lm)
+importFrom(stats,median)
 importFrom(stats,model.frame)
 importFrom(stats,poly)
+importFrom(stats,predict)
 importFrom(stats,sd)
+importFrom(stats,setNames)
 importFrom(stats,terms)
+importFrom(utils,head)
 importFrom(utils,installed.packages)
+importFrom(utils,modifyList)
 importFrom(utils,packageVersion)
 importFrom(utils,read.csv)
 importFrom(utils,read.table)
 importFrom(utils,sessionInfo)
+importFrom(utils,unzip)
 importFrom(utils,write.csv)
+importFrom(yaml,read_yaml)
+importFrom(yaml,write_yaml)
diff --git a/NEWS.md b/NEWS.md
index d1652ac1..12f37b71 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,23 @@
+# OpenSpecy 1.0.0
+
+## New Features
+
+- Complete package, app, and SOP overhaul!
+- The Shiny app has been outsourced to an own GitHub repository:
+  https://github.com/wincowgerDEV/OpenSpecy-shiny
+- Spectra are now stored in dedicated `OpenSpecy` objects, which can be managed
+  with a set of new functions including `c_spec()` for concatenating spectra
+- Various functions have been renamed and improved, for instance, to facilitate
+  reading (and writing) spectral files
+- New functions include `def_features()` to identify microplastics in spectral
+  maps and `ai_classify()` to use AI for matching/identifying spectra
+
+## Minor Improvements
+
+- Added pkgdown documentation
+- Added code coverage tests
+
+
 # OpenSpecy 0.9.5
 
 ## Bug Fixes
diff --git a/R/adj_intens.R b/R/adj_intens.R
index 4bf1d067..a1ded912 100644
--- a/R/adj_intens.R
+++ b/R/adj_intens.R
@@ -1,12 +1,12 @@
-#' @title Adjust spectral intensities to absorbance units
+#' @rdname adj_intens
+#' @title Adjust spectral intensities to absorbance units.
 #'
 #' @description
 #' Converts reflectance or transmittance intensity units to absorbance units.
 #'
 #' @details
 #' Many of the Open Specy functions will assume that the spectrum is in
-#' absorbance units. For example, see \code{\link{match_spec}()} and
-#' \code{\link{subtr_bg}()}.
+#' absorbance units. For example, see \code{\link{subtr_baseline}()}.
 #' To run those functions properly, you will need to first convert any spectra
 #' from transmittance or reflectance to absorbance using this function.
 #' The transmittance adjustment uses the \eqn{log10(1 / T)}
@@ -16,19 +16,15 @@
 #' is a percent from 1-100 and first correct the intensity by dividing by 100
 #' so that it fits the form expected by the equation.
 #'
-#' @param x a numeric vector containing the spectral wavenumbers; alternatively
-#' a data frame containing spectral data as \code{"wavenumber"} and
-#' \code{"intensity"} can be supplied.
-#' @param y a numeric vector containing the spectral intensities.
-#' @param formula an object of class '\code{\link[stats]{formula}}' of the form
-#' \code{intensity ~ wavenumber}.
-#' @param data a data frame containing the variables in \code{formula}.
+#' @param x a list object of class \code{OpenSpecy}.
 #' @param type a character string specifying whether the input spectrum is
 #' in absorbance units (\code{"none"}, default) or needs additional conversion
 #' from \code{"reflectance"} or \code{"transmittance"} data.
 #' @param make_rel logical; if \code{TRUE} spectra are automatically normalized
 #' with \code{\link{make_rel}()}.
-#' @param \ldots further arguments passed to the submethods.
+#' @param \ldots further arguments passed to submethods; this is
+#' to \code{\link{adj_neg}()} for \code{adj_intens()} and
+#' to \code{\link{conform_res}()} for \code{conform_intens()}.
 #'
 #' @return
 #' \code{adj_intens()} returns a data frame containing two columns
@@ -43,11 +39,9 @@
 #' Win Cowger, Zacharias Steinmetz
 #'
 #' @seealso
-#' \code{\link{subtr_bg}()} for spectral background correction;
-#' \code{\link{match_spec}()} matches spectra with the Open Specy or other
-#' reference libraries
+#' \code{\link{subtr_baseline}()} for spectral background correction.
 #'
-#' @importFrom dplyr %>%
+#' @importFrom data.table .SD
 #' @export
 adj_intens <- function(x, ...) {
   UseMethod("adj_intens")
@@ -56,39 +50,23 @@ adj_intens <- function(x, ...) {
 #' @rdname adj_intens
 #'
 #' @export
-adj_intens.formula <- function(formula, data = NULL, ...) {
-  if (missing(formula) || (length(formula) != 3L) ||
-      (length(attr(terms(formula[-2L]), "term.labels")) != 1L))
-    stop("'formula' missing or incorrect")
-
-  mf <- model.frame(formula, data)
-  lst <- as.list(mf)
-  names(lst) <- c("y", "x")
-
-  do.call("adj_intens", c(lst, list(...)))
+adj_intens.default <- function(x, ...) {
+  stop("object 'x' needs to be of class 'OpenSpecy'", call. = F)
 }
 
 #' @rdname adj_intens
 #'
 #' @export
-adj_intens.data.frame <- function(x, ...) {
-  if (!all(c("wavenumber", "intensity") %in% names(x)))
-    stop("'data' must contain 2 columns named 'wavenumber' and 'intensity'")
-
-  do.call("adj_intens", list(x$wavenumber, x$intensity, ...))
-}
+adj_intens.OpenSpecy <- function(x, type = "none", make_rel = TRUE, ...) {
+  spec <- x$spectra
 
-#' @rdname adj_intens
-#'
-#' @export
-adj_intens.default <- function(x, y, type = "none", make_rel = TRUE,
-                               ...) {
-  yadj <- switch(type,
-                 "reflectance" = (1 - y/100)^2 / (2 * y/100),
-                 "transmittance" = log10(1/adj_neg(y)),
-                 "none" = adj_neg(y)
+  adj <- switch(type,
+                "reflectance" = (1 - spec/100)^2 / (2 * spec/100),
+                "transmittance" = log10(1/adj_neg(spec, ...)),
+                "none" = adj_neg(spec, ...)
   )
-  if (make_rel) yout <- make_rel(yadj) else yout <- yadj
 
-  data.frame(wavenumber = x, intensity = yout)
+  if (make_rel) x$spectra <- adj[, lapply(.SD, make_rel)] else x$spectra <- adj
+
+  return(x)
 }
diff --git a/R/adj_range.R b/R/adj_range.R
new file mode 100644
index 00000000..9621c9e3
--- /dev/null
+++ b/R/adj_range.R
@@ -0,0 +1,121 @@
+#' @rdname adj_range
+#' @title Range restriction and flattening for spectra
+#'
+#' @description
+#' \code{restrict_range()} restricts wavenumber ranges to user specified values.
+#' Multiple ranges can be specified by inputting a series of max and min
+#' values in order.
+#' \code{flatten_range()} will flatten ranges of the spectra that should have no
+#' peaks.
+#' Multiple ranges can be specified by inputting the series of max and min
+#' values in order.
+#'
+#' @param x an \code{OpenSpecy} object.
+#' @param min a vector of minimum values for the range to be flattened.
+#' @param max a vector of maximum values for the range to be flattened.
+#' @param make_rel logical; should the output intensities be normalized to the
+#' range \[0, 1\] using `make_rel()` function?
+#' @param \ldots additional arguments passed to subfunctions; currently not
+#' in use.
+#'
+#' @return
+#' An \code{OpenSpecy} object with the spectral intensities within specified
+#' ranges restricted or flattened.
+#'
+#' @examples
+#' test_noise <- as_OpenSpecy(x = seq(400,4000, by = 10),
+#'                            spectra = data.frame(intensity = rnorm(361)))
+#' plot(test_noise)
+#'
+#' restrict_range(test_noise, min = 1000, max = 2000)
+#'
+#' flattened_intensities <- flatten_range(test_noise, min = c(1000, 2000),
+#'                                        max = c(1500, 2500))
+#' plot(flattened_intensities)
+#'
+#' @author
+#' Win Cowger, Zacharias Steinmetz
+#'
+#' @seealso
+#' \code{\link{conform_spec}()} for conforming wavenumbers to be matched with
+#' a reference library;
+#' \code{\link{adj_intens}()} for log transformation functions;
+#' \code{\link[base]{min}()} and \code{\link[base]{round}()}
+#'
+#' @importFrom data.table as.data.table .SD
+#' @export
+restrict_range <- function(x, ...) {
+  UseMethod("restrict_range")
+}
+
+#' @rdname adj_range
+#'
+#' @export
+restrict_range.default <- function(x, ...) {
+  stop("object 'x' needs to be of class 'OpenSpecy'")
+}
+
+#' @rdname adj_range
+#'
+#' @export
+restrict_range.OpenSpecy <- function(x, min, max, make_rel = TRUE,
+                                     ...) {
+  test <- as.data.table(lapply(1:length(min), function(y){
+    x$wavenumber >= min[y] & x$wavenumber <= max[y]})
+  )
+
+  vals <- rowSums(test) > 0
+  filt <- x$spectra[vals,]
+  x$wavenumber <- x$wavenumber[vals]
+
+  if (make_rel) x$spectra <- filt[, lapply(.SD, make_rel)] else x$spectra <- filt
+
+  return(x)
+}
+
+#' @rdname adj_range
+#'
+#' @export
+flatten_range <- function(x, ...) {
+  UseMethod("flatten_range")
+}
+
+#' @rdname adj_range
+#'
+#' @export
+flatten_range.default <- function(x, ...) {
+  stop("object 'x' needs to be of class 'OpenSpecy'")
+}
+
+#' @rdname adj_range
+#'
+#' @export
+flatten_range.OpenSpecy <- function(x, min = 2200, max = 2400, make_rel = TRUE,
+                                    ...) {
+  if(length(min) != length(max)) {
+    stop("min and max need to be the same length", call. = F)
+  }
+  if(any(vapply(1:length(min), function(y) {
+    min[y] > max[y]
+  }, FUN.VALUE = logical(1)))) {
+    stop("all min values must be lower than corresponding max", call. = F)
+  }
+  flat <- x$spectra[, lapply(.SD, .flatten_range, x = x$wavenumber,
+                             min = min, max = max)]
+
+  if (make_rel) x$spectra <- flat[, lapply(.SD, make_rel)] else x$spectra <- flat
+
+  return(x)
+}
+
+.flatten_range <- function(y, x, min, max) {
+  if(all(min > max(x)) ||  all(max < min(x)))
+    stop("'min' or 'max' out of range")
+
+  for(i in 1:length(min)) {
+    y[x >= min[i] & x <= max[i]] <-
+      mean(c(y[min(which(x >= min[i]))],
+             y[max(which(x <= max[i]))]))
+  }
+  return(y)
+}
diff --git a/R/as_OpenSpecy.R b/R/as_OpenSpecy.R
new file mode 100644
index 00000000..542648c3
--- /dev/null
+++ b/R/as_OpenSpecy.R
@@ -0,0 +1,368 @@
+#' @rdname as_OpenSpecy
+#'
+#' @title Create \code{OpenSpecy} objects
+#'
+#' @description
+#' Functions to check if an object is an OpenSpecy, or coerce it if
+#' possible.
+#'
+#' @param x depending on the method, a list with all OpenSpecy parameters,
+#' a vector with the wavenumbers for all spectra, or a data.frame with a full
+#' spectrum in the classic Open Specy format.
+#' @param spectra spectral intensities formatted as a data.table with one column
+#' per spectrum.
+#' @param metadata metadata for each spectrum with one row per spectrum,
+#' see details.
+#' @param coords spatial coordinates for the spectra.
+#' @param session_id logical. Whether to add a session ID to the metadata.
+#' The session ID is based on current session info so metadata of the same
+#' spectra will not return equal if session info changes. Sometimes that is
+#' desirable.
+#' @param colnames names of the wavenumber column and spectra column, makes
+#' assumptions based on column names or placement if \code{NULL}.
+#' @param n number of spectra to generate the spatial coordinate grid with.
+#' @param \ldots additional arguments passed to submethods.
+#'
+#' @details
+#' \code{as_OpenSpecy()} converts spectral datasets to a three part list;
+#' the first with a vector of the wavenumbers of the spectra,
+#' the second with a \code{data.table} of all spectral intensities ordered as
+#' columns,
+#' the third item is another \code{data.table} with any metadata the user
+#' provides or is harvested from the files themselves.
+#'
+#' The \code{metadata} argument may contain a named list with the following
+#' details (\code{*} = minimum recommended):
+#'
+#' \tabular{ll}{
+#' \code{file_name*}: \tab The file name, defaults to
+#' \code{\link[base]{basename}()} if not specified\cr
+#' \code{user_name*}: \tab User name, e.g. "Win Cowger"\cr
+#' \code{contact_info}: \tab Contact information, e.g. "1-513-673-8956,
+#' wincowger@@gmail.com"\cr
+#' \code{organization}: \tab Affiliation, e.g. "University of California,
+#' Riverside"\cr
+#' \code{citation}: \tab Data citation, e.g. "Primpke, S., Wirth, M., Lorenz,
+#' C., & Gerdts, G. (2018). Reference database design for the automated analysis
+#' of microplastic samples based on Fourier transform infrared (FTIR)
+#' spectroscopy. \emph{Analytical and Bioanalytical Chemistry}.
+#' \doi{10.1007/s00216-018-1156-x}"\cr
+#' \code{spectrum_type*}: \tab Raman or FTIR\cr
+#' \code{spectrum_identity*}: \tab Material/polymer analyzed, e.g.
+#' "Polystyrene"\cr
+#' \code{material_form}: \tab Form of the material analyzed, e.g. textile fiber,
+#' rubber band, sphere, granule \cr
+#' \code{material_phase}: \tab Phase of the material analyzed (liquid, gas,
+#' solid) \cr
+#' \code{material_producer}: \tab Producer of the material analyzed,
+#' e.g. Dow \cr
+#' \code{material_purity}: \tab Purity of the material analyzed, e.g. 99.98%
+#' \cr
+#' \code{material_quality}: \tab Quality of the material analyzed, e.g.
+#' consumer product, manufacturer material, analytical standard,
+#' environmental sample \cr
+#' \code{material_color}: \tab Color of the material analyzed,
+#' e.g. blue, #0000ff, (0, 0, 255) \cr
+#' \code{material_other}: \tab Other material description, e.g. 5 µm diameter
+#' fibers, 1 mm spherical particles \cr
+#' \code{cas_number}: \tab CAS number, e.g. 9003-53-6 \cr
+#' \code{instrument_used}: \tab Instrument used, e.g. Horiba LabRam \cr
+#' \code{instrument_accessories}: \tab Instrument accessories, e.g.
+#' Focal Plane Array, CCD\cr
+#' \code{instrument_mode}: \tab Instrument modes/settings, e.g.
+#' transmission, reflectance \cr
+#' \code{intensity_units*}: \tab Units of the intensity values for the spectrum, options
+#' transmittance, reflectance, absorbance \cr
+#' \code{spectral_resolution}: \tab Spectral resolution, e.g. 4/cm \cr
+#' \code{laser_light_used}: \tab Wavelength of the laser/light used, e.g.
+#' 785 nm \cr
+#' \code{number_of_accumulations}: \tab Number of accumulations, e.g 5 \cr
+#' \code{total_acquisition_time_s}: \tab Total acquisition time (s), e.g. 10 s
+#' \cr
+#' \code{data_processing_procedure}: \tab Data processing procedure,
+#' e.g. spikefilter, baseline correction, none \cr
+#' \code{level_of_confidence_in_identification}: \tab Level of confidence in
+#' identification, e.g. 99% \cr
+#' \code{other_info}: \tab Other information \cr
+#' \code{license}: \tab The license of the shared spectrum; defaults to
+#' \code{"CC BY-NC"} (see
+#' \url{https://creativecommons.org/licenses/by-nc/4.0/} for details). Any other
+#' creative commons license is allowed, for example, CC0 or CC BY \cr
+#' \code{session_id}: \tab A unique user and session identifier; populated
+#' automatically with \code{paste(digest(Sys.info()), digest(sessionInfo()),
+#' sep = "/")}\cr
+#' \code{file_id}: \tab A unique file identifier; populated automatically
+#' with \code{digest(object[c("wavenumber", "spectra")])}\cr
+#' }
+#'
+#' @return
+#' \code{as_OpenSpecy()} and \code{OpenSpecy()} returns three part lists
+#' described in details.
+#' \code{is_OpenSpecy()} returns \code{TRUE} if the object is an OpenSpecy and
+#' \code{FALSE} if not.
+#' \code{gen_grid()} returns a \code{data.table} with \code{x} and \code{y}
+#' coordinates to use for generating a spatial grid for the spectra if one is
+#' not specified in the data.
+#'
+#' @examples
+#' data("raman_hdpe")
+#'
+#' # Inspect the spectra
+#' raman_hdpe # See how OpenSpecy objects print.
+#' raman_hdpe$wavenumber # Look at just the wavenumbers of the spectra.
+#' raman_hdpe$spectra # Look at just the spectral intensities data.table.
+#' raman_hdpe$metadata # Look at just the metadata of the spectra.
+#'
+#' # Creating a list and transforming to OpenSpecy
+#' as_OpenSpecy(list(wavenumber = raman_hdpe$wavenumber,
+#'                   spectra = raman_hdpe$spectra,
+#'                   metadata = raman_hdpe$metadata[,-c("x", "y")]))
+#'
+#' # If you try to produce an OpenSpecy using an OpenSpecy it will just return
+#' # the same object.
+#' as_OpenSpecy(raman_hdpe)
+#'
+#' # Creating an OpenSpecy from a data.frame
+#' as_OpenSpecy(x = data.frame(wavenumber = raman_hdpe$wavenumber,
+#'                             spectra = raman_hdpe$spectra$intensity))
+#'
+#' # Test that the spectrum is formatted as an OpenSpecy object.
+#' is_OpenSpecy(raman_hdpe)  #should be TRUE
+#' is_OpenSpecy(raman_hdpe$spectra) #should be FALSE
+#'
+#' @author
+#' Zacharias Steinmetz, Win Cowger
+#'
+#' @seealso
+#' \code{\link{read_spec}()} for reading \code{OpenSpecy} objects.
+#'
+#' @importFrom data.table as.data.table
+#' @export
+as_OpenSpecy <- function(x, ...) {
+  UseMethod("as_OpenSpecy")
+}
+
+#' @rdname as_OpenSpecy
+#'
+#' @export
+as_OpenSpecy.OpenSpecy <- function(x, session_id = FALSE, ...) {
+  if(session_id)
+    x$metadata$session_id <- paste(digest(Sys.info()),
+                                   digest(sessionInfo()),
+                                   sep = "/")
+  if(!c("file_id") %in% names(x$metadata))
+    x$metadata$file_id = digest(x[c("wavenumber", "spectra")])
+
+  return(x)
+}
+
+#' @rdname as_OpenSpecy
+#'
+#' @export
+as_OpenSpecy.list <- function(x, ...) {
+  do.call("as_OpenSpecy", unname(x))
+}
+
+#' @rdname as_OpenSpecy
+#'
+#' @export
+as_OpenSpecy.hyperSpec <- function(x, ...) {
+  do.call("as_OpenSpecy", list(x = x@wavelength,
+                               spectra = as.data.table(t(x$spc)), ...))
+}
+
+#' @rdname as_OpenSpecy
+#'
+#' @export
+as_OpenSpecy.data.frame <- function(x, colnames = list(wavenumber = NULL,
+                                                       spectra = NULL), ...) {
+  x <- as.data.table(x)
+
+  # Try to find spectral data
+  if (is.null(colnames$wavenumber)) {
+    if (any(grepl("wav", ignore.case = T, names(x)))) {
+      if (length(grep("wav", ignore.case = T, names(x))) > 1L)
+        warning("Ambiguous column names: taking 'wavenumber' data from the",
+                " first column; use 'colnames' to supply user-defined columns",
+                call. = F)
+      wavenumber <- x[[grep("wav", ignore.case = T, names(x))[1L]]]
+      wn <- names(x)[grep("wav", ignore.case = T, names(x))]
+    } else {
+      warning("Ambiguous column names: taking 'wavenumber' data from the",
+              " first column; use 'colnames' to supply user-defined columns",
+              call. = F)
+      wavenumber <- x[[1L]]
+      wn <- names(x)[1L]
+    }
+  } else {
+    wavenumber <- x[[colnames$wavenumber]]
+    wn <- colnames$wavenumber
+  }
+
+  if (is.null(colnames$spectra)) {
+    if (any(grep("(transmit.*)|(reflect.*)|(abs.*)|(intens.*)", ignore.case = T,
+                 names(x)))) {
+      spectra <- x[, grepl("(transmit.*)|(reflect.*)|(abs.*)|(intens.*)",
+                           ignore.case = T, names(x)), with = F]
+    } else {
+      warning("Ambiguous column names: taking 'spectra' data from all but the",
+              " 'wavenumber' column; use 'colnames' to supply user-defined",
+              " columns", call. = F)
+      spectra <- x[, -wn, with = F]
+    }
+  } else {
+    spectra <- x[, colnames$spectra, with = F]
+  }
+
+  do.call("as_OpenSpecy",
+          list(x = wavenumber, spectra = spectra, ...))
+}
+
+#' @rdname as_OpenSpecy
+#'
+#' @export
+as_OpenSpecy.default <- function(x, spectra,
+                                 metadata = list(
+                                   file_name = NULL,
+                                   user_name = NULL,
+                                   contact_info = NULL,
+                                   organization = NULL,
+                                   citation = NULL,
+                                   spectrum_type = NULL,
+                                   spectrum_identity = NULL,
+                                   material_form = NULL,
+                                   material_phase = NULL,
+                                   material_producer = NULL,
+                                   material_purity = NULL,
+                                   material_quality = NULL,
+                                   material_color = NULL,
+                                   material_other = NULL,
+                                   cas_number = NULL,
+                                   instrument_used = NULL,
+                                   instrument_accessories = NULL,
+                                   instrument_mode = NULL,
+                                   intensity_units = NULL,
+                                   spectral_resolution = NULL,
+                                   laser_light_used = NULL,
+                                   number_of_accumulations = NULL,
+                                   total_acquisition_time_s = NULL,
+                                   data_processing_procedure = NULL,
+                                   level_of_confidence_in_identification = NULL,
+                                   other_info = NULL,
+                                   license = "CC BY-NC"),
+                                 coords = "gen_grid",
+                                 session_id = FALSE,
+                                 ...) {
+  if (!is.numeric(x) || !is.vector(x))
+    stop("'x' must be numeric vector", call. = F)
+  if (!inherits(spectra, c("data.frame", "matrix")))
+    stop("'spectra' must inherit from data.frame or matrix", call. = F)
+  if (!sapply(spectra, is.numeric)[1L] && !sapply(spectra, is.complex)[1L] &&
+      !sapply(spectra, is.logical)[1L])
+    stop("at least the first column of 'spectra' must be numeric or logical",
+         call. = F)
+  if(length(unique(names(spectra))) != ncol(spectra))
+    stop("column names in 'spectra' must be unique", call. = F)
+  if (length(x) != nrow(spectra))
+    stop("'x' and 'spectra' must be of equal length", call. = F)
+
+  obj <- structure(list(), class = c("OpenSpecy", "list"))
+
+  obj$wavenumber <- x[order(x)]
+
+  obj$spectra <- as.data.table(spectra)[order(x)]
+
+  if (inherits(coords, "character")) {
+    obj$metadata <- do.call(coords, list(ncol(obj$spectra)))
+  } else if (inherits(coords, c("data.frame", "list")) &&
+             all(is.element(c("x", "y"), names(coords)))) {
+    obj$metadata <- as.data.table(coords)
+  } else if(is.null(coords)){
+    obj$metadata <- data.table()
+  }
+  else {
+    stop("inconsistent input for 'coords'", call. = F)
+  }
+  if (!is.null(metadata)) {
+    if (inherits(metadata, c("data.frame", "list"))) {
+      obj$metadata <- cbind(obj$metadata, as.data.table(metadata))
+      if(session_id)
+        obj$metadata$session_id <- paste(digest(Sys.info()),
+                                         digest(sessionInfo()),
+                                         sep = "/")
+      if(!c("file_id") %in% names(obj$metadata))
+        obj$metadata$file_id = digest(obj[c("wavenumber", "spectra")])
+      if(!c("col_id") %in% names(obj$metadata))
+          obj$metadata$col_id = names(obj$spectra)
+    } else {
+      stop("inconsistent input for 'metadata'", call. = F)
+    }
+  }
+
+  return(obj)
+}
+
+#' @rdname as_OpenSpecy
+#'
+#' @export
+is_OpenSpecy <- function(x) {
+  inherits(x, "OpenSpecy")
+}
+
+#' @rdname as_OpenSpecy
+#'
+#' @importFrom data.table is.data.table
+#' @export
+check_OpenSpecy <- function(x) {
+  if(!(is_OpenSpecy(x)))
+    stop("object 'x' is not of class 'OpenSpecy'", call. = F)
+  if(!identical(names(x), c("wavenumber", "spectra", "metadata")))
+    stop("names of the object components are incorrect", call. = F)
+
+  if(!(cw <- is.vector(x$wavenumber)))
+    warning("Wavenumber is not a vector", call. = F)
+  if(!(cs <- is.data.table(x$spectra)))
+    message("Spectra are not of class 'data.table'")
+  if(!(cm <- is.data.table(x$metadata)))
+    message("Metadata are not a 'data.table'")
+  if(!(cr <- ncol(x$spectra) == nrow(x$metadata)))
+    warning("Number of columns in spectra is not equal to number of rows ",
+            "in metadata", call. = F)
+  if(!(cl <- length(x$wavenumber) == nrow(x$spectra)))
+    warning("Length of wavenumber is not equal to number of rows in spectra",
+            call. = F)
+  if(!(cu <- length(unique(names(x$spectra))) == ncol(x$spectra)))
+    warning("Column names in spectra are not unique", call. = F)
+  if(!(cv <- length(unique(names(x$metadata))) == ncol(x$metadata)))
+    message("Column names in metadata are not unique")
+  if(!(co <- identical(order(x$wavenumber), 1:length(x$wavenumber)) |
+       identical(order(x$wavenumber), length(x$wavenumber):1)))
+    message("This is technically an 'OpenSpecy' object but wavenumbers ",
+            "should be a continuous sequence for all OpenSpecy functions to ",
+            "run smoothly")
+
+  chk <- all(cw, cs, cm, cr, cl, cu, cv, co)
+
+  return(chk)
+}
+
+#' @rdname as_OpenSpecy
+#'
+#' @export
+OpenSpecy <- function(x, ...) {
+  if (is_OpenSpecy(x)) {
+    return(x)
+  } else {
+    do.call("as_OpenSpecy", list(x, ...))
+  }
+}
+
+#' @rdname as_OpenSpecy
+#'
+#' @export
+gen_grid <- function(n) {
+  base <- sqrt(n)
+
+  expand.grid(x = 1:ceiling(base), y = 1:ceiling(base))[1:n,] |>
+    as.data.table()
+}
diff --git a/R/conform_spec.R b/R/conform_spec.R
new file mode 100644
index 00000000..04762c48
--- /dev/null
+++ b/R/conform_spec.R
@@ -0,0 +1,86 @@
+#' @rdname conform_spec
+#' @title Conform spectra to a standard wavenumber series
+#'
+#' @description
+#' Spectra can be conformed to a standard suite of wavenumbers to be compared
+#' with a reference library or to be merged to other spectra.
+#'
+#' @param x a list object of class \code{OpenSpecy}.
+#' @param range a vector of new wavenumber values, can be just supplied as a
+#' min and max value.
+#' @param res spectral resolution adjusted to or \code{NULL} if the raw range
+#' should be used.
+#' @param type the type of wavenumber adjustment to make. \code{"interp"}
+#' results in linear interpolation while \code{"roll"} conducts a nearest
+#' rolling join of the wavenumbers.
+#' @param \ldots further arguments passed to \code{\link[stats]{approx}()}
+#'
+#' @return
+#' \code{adj_intens()} returns a data frame containing two columns
+#' named \code{"wavenumber"} and \code{"intensity"}
+#'
+#' @examples
+#' data("raman_hdpe")
+#' conform_spec(raman_hdpe, c(1000, 2000))
+#'
+#' @author
+#' Win Cowger, Zacharias Steinmetz
+#'
+#' @seealso
+#' \code{\link{restrict_range}()} and  \code{\link{flatten_range}()} for
+#' adjusting wavenumber ranges;
+#' \code{\link{subtr_baseline}()} for spectral background correction
+#'
+#' @importFrom data.table .SD
+#' @export
+conform_spec <- function(x, ...) {
+  UseMethod("conform_spec")
+}
+
+#' @rdname conform_spec
+#'
+#' @export
+conform_spec.default <- function(x, ...) {
+  stop("object 'x' needs to be of class 'OpenSpecy'", call. = F)
+}
+
+#' @rdname conform_spec
+#'
+#' @export
+conform_spec.OpenSpecy <- function(x, range = NULL, res = 5, type = "interp",
+                                   ...) {
+  if(!any(type %in% c("interp", "roll")))
+    stop("type must be either interp or roll")
+
+  if(is.null(range)) range <- x$wavenumber
+
+  if(!is.null(res)) {
+    range <- c(max(min(range), min(x$wavenumber)),
+               min(max(range), max(x$wavenumber)))
+
+    wn <- conform_res(range, res = res)
+  } else {
+    wn <- range
+  }
+
+  if(type == "interp")
+    spec <- x$spectra[, lapply(.SD, .conform_intens, x = x$wavenumber,
+                               xout = wn, ...)]
+
+  if(type == "roll") {
+    join <- data.table("wavenumber" = wn)
+    # Rolling join option
+    spec <- x$spectra[,"wavenumber" := x$wavenumber]
+    spec <- spec[join, roll = "nearest", on = "wavenumber"]
+    spec <- spec[,-"wavenumber"]
+  }
+
+  x$wavenumber <- wn
+  x$spectra <- spec
+
+  return(x)
+}
+
+.conform_intens <- function(...) {
+  approx(...)$y
+}
diff --git a/R/data_norm.R b/R/data_norm.R
index 29ef6dbd..a6ea0cf1 100644
--- a/R/data_norm.R
+++ b/R/data_norm.R
@@ -1,33 +1,45 @@
 #' @rdname data_norm
-#'
-#' @title Normalization of spectral data
+#' @title Normalization and conversion of spectral data
 #'
 #' @description
-#' \code{adj_neg()} converts numeric values \code{x} < 1 into values
-#' >= 1, keeping absolute differences between values by shifting intensity
-#' values with the value of the smallest number.
-#' \code{make_rel()} converts values \code{x} into relative values between
+#' \code{adj_res()} and \code{conform_res()} are helper functions to align
+#' wavenumbers in terms of their spectral resolution.
+#' \code{adj_neg()} converts numeric intensities \code{y} < 1 into values >= 1,
+#' keeping absolute differences between intensity values by shifting each value
+#' by the minimum intensity.
+#' \code{make_rel()} converts intensities \code{y} into relative values between
 #' 0 and 1 using the standard normalization equation.
 #' If \code{na.rm} is \code{TRUE}, missing values are removed before the
 #' computation proceeds.
 #'
 #' @details
-#' \code{adj_neg()} is used in Open Specy to avoid errors that could
-#' arise from log transforming spectra when using
-#' \code{\link{adj_intens}()} and other functions.
-#' \code{make_rel()} is used in Open Specy to retain the relative
-#' height proportions between spectra while avoiding the large numbers that can
-#' result from some spectral instruments.
+#' \code{adj_res()} and \code{conform_res()} are used in Open Specy to
+#' facilitate comparisons of spectra with different resolutions.
+#' \code{adj_neg()} is used to avoid errors that could arise from log
+#' transforming spectra when using \code{\link{adj_intens}()} and other
+#' functions.
+#' \code{make_rel()} is used to retain the relative height proportions between
+#' spectra while avoiding the large numbers that can result from some spectral
+#' instruments.
 #'
 #' @param x a numeric vector or an \R object which is coercible to one by
-#' \code{as.vector(x, "numeric")}; \code{x} should be \code{intensity} data.
+#' \code{as.vector(x, "numeric")}; \code{x} should contain the spectral
+#' wavenumbers.
+#' @param y a numeric vector containing the spectral intensities.
+#' @param res spectral resolution supplied to \code{fun}.
+#' @param fun the function to be applied to each element of \code{x}; defaults
+#' to \code{\link[base]{round}()} to round to a specific resolution \code{res}.
 #' @param na.rm logical. Should missing values be removed?
 #'
 #' @return
+#' \code{adj_res()} and \code{conform_res()} return a numeric vector with
+#' resolution-conformed wavenumbers.
 #' \code{adj_neg()} and \code{make_rel()} return numeric vectors
-#' with the normalized data.
+#' with the normalized intensity data.
 #'
 #' @examples
+#' adj_res(seq(500, 4000, 4), 5)
+#' conform_res(seq(500, 4000, 4))
 #' adj_neg(c(-1000, -1, 0, 1, 10))
 #' make_rel(c(-1000, -1, 0, 1, 10))
 #'
@@ -35,22 +47,72 @@
 #' Win Cowger, Zacharias Steinmetz
 #'
 #' @seealso
-#' \code{\link[base]{min}()} for the calculation of minima;
-#' \code{\link{adj_intens}()} for log transformation functions
+#' \code{\link[base]{min}()} and \code{\link[base]{round}()};
+#' \code{\link{adj_intens}()} for log transformation functions;
+#' \code{\link{conform_spec}()} for conforming wavenumbers of an
+#' \code{OpenSpecy} object to be matched with a reference library
+#'
+#' @export
+adj_res <- function(x, res = 1, fun = round) {
+  fun(x / res) * res
+}
+
+#' @rdname data_norm
 #'
-#' @importFrom dplyr %>%
 #' @export
-adj_neg <- function(x, na.rm = FALSE) {
-  if (min(x, na.rm = na.rm) < 1) {
-    x + min(x, na.rm = na.rm) %>% abs() + 1
+conform_res <- function(x, res = 5) {
+  seq(adj_res(min(x), res, ceiling), adj_res(max(x), res, floor), by = res)
+}
+
+#' @rdname data_norm
+#'
+#' @export
+adj_neg <- function(y, na.rm = FALSE) {
+  if (min(y, na.rm = na.rm) < 1) {
+    y + min(y, na.rm = na.rm) |> abs() + 1
   } else {
-    x
+    y
   }
 }
 
 #' @rdname data_norm
 #'
 #' @export
-make_rel <- function(x, na.rm = FALSE) {
-  (x - min(x, na.rm = na.rm)) / (max(x, na.rm = na.rm) - min(x, na.rm = na.rm))
+make_rel <- function(y, na.rm = FALSE) {
+  r <- range(y, na.rm = na.rm)
+
+  (y - r[1]) / (r[2] - r[1])
+}
+
+#' @rdname data_norm
+#' @importFrom data.table fifelse
+#'
+#' @export
+mean_replace <- function(y, na.rm = TRUE) {
+  m <- mean(y, na.rm = na.rm)
+
+  fifelse(is.na(y), m, y)
+}
+
+#' @rdname data_norm
+#'
+#' @export
+is_empty_vector <- function(x) {
+  # Check if the vector is NULL or has zero length
+  if (is.null(x) || length(x) == 0) {
+    return(TRUE)
+  }
+
+  # Check if all values are NA or NaN (for numeric vectors)
+  if (is.numeric(x)) {
+    return(all(is.na(x) | is.nan(x)))
+  }
+
+  # Check if all values are NA or empty strings (for character vectors)
+  if (is.character(x)) {
+    return(all(is.na(x) | x == ""))
+  }
+
+  # Check if all values are NA (for other types of vectors)
+  return(all(is.na(x)))
 }
diff --git a/R/def_features.R b/R/def_features.R
new file mode 100644
index 00000000..a587f5e3
--- /dev/null
+++ b/R/def_features.R
@@ -0,0 +1,189 @@
+#' @rdname def_features
+#' @title Define features
+#'
+#' @description
+#' Functions for analyzing features, like particles, fragments, or fibers, in
+#' spectral map oriented \code{OpenSpecy} object.
+#'
+#' @details
+#' `def_features()` accepts an \code{OpenSpecy} object and a logical or
+#' character vector describing which pixels correspond to particles.
+#' `collapse_spec()` takes an \code{OpenSpecy} object with particle-specific
+#' metadata (from `def_features()`) and collapses the spectra to median
+#' intensities for each unique particle.
+#' It also updates the metadata with centroid coordinates, while preserving the
+#' feature information on area and Feret max.
+#'
+#' @return
+#' An \code{OpenSpecy} object appended with metadata about the features or
+#' collapsed for the features.
+#'
+#' @examples
+#' # Logical input
+#' map <- read_extdata("CA_tiny_map.zip") |> read_any()
+#' map$metadata$features <- map$metadata$x == 0
+#' identified_map <- def_features(map, map$metadata$features)
+#' test_collapsed <- collapse_spec(identified_map)
+#'
+#' # Character input
+#' map <- read_extdata("CA_tiny_map.zip") |> read_any()
+#' map$metadata$features <- ifelse(map$metadata$x == 1, "particle",
+#'                                 "not_particle")
+#' identified_map <- def_features(map, map$metadata$features)
+#' test_collapsed <- collapse_spec(identified_map)
+#'
+#' @param x an \code{OpenSpecy} object
+#' @param features a logical vector or character vector describing which of the
+#' spectra are of features (\code{TRUE}) and which are not (\code{FALSE}).
+#' If a character vector is provided, it should represent the different feature
+#' types present in the spectra.
+#' @param \ldots additional arguments passed to subfunctions.
+#'
+#' @author
+#' Win Cowger, Zacharias Steinmetz
+#'
+#' @importFrom data.table data.table as.data.table setDT rbindlist transpose .SD :=
+#' @export
+collapse_spec <- function(x, ...) {
+  UseMethod("collapse_spec")
+}
+
+#' @rdname def_features
+#'
+#' @export
+collapse_spec.default <- function(x, ...) {
+  stop("object 'x' needs to be of class 'OpenSpecy'")
+}
+
+#' @rdname def_features
+#'
+#' @export
+collapse_spec.OpenSpecy <- function(x, ...) {
+  # Calculate the median spectra for each unique feature_id
+  ts <- transpose(x$spectra)
+  ts$id <- x$metadata$feature_id
+  x$spectra <- ts[, lapply(.SD, median, na.rm = T), by = "id"] |>
+    transpose(make.names = "id")
+
+  x$metadata <- x$metadata |>
+    unique(by = c("feature_id", "area", "feret_max", "centroid_y",
+                  "centroid_x"))
+
+  return(x)
+}
+
+#' @rdname def_features
+#'
+#' @export
+def_features <- function(x, ...) {
+  UseMethod("def_features")
+}
+
+#' @rdname def_features
+#'
+#' @export
+def_features.default <- function(x, ...) {
+  stop("object 'x' needs to be of class 'OpenSpecy'")
+}
+
+#' @rdname def_features
+#'
+#' @importFrom imager label as.cimg
+#' @importFrom data.table as.data.table setDT rbindlist data.table
+#' @export
+def_features.OpenSpecy <- function(x, features, ...) {
+  if(is.logical(features)) {
+    if(all(features) | all(!features))
+      stop("features cannot be all TRUE or FALSE because that would indicate ",
+           "that there are no distinct features", call. = F)
+
+    features_df <- .def_features(x, features)
+  } else if(is.character(features)) {
+    if(length(unique(features)) == 1)
+      stop("features cannot all have a single name because that would ",
+           "indicate that there are no distinct features", call. = F)
+
+    features_df <- rbindlist(lapply(unique(features),
+                                    function(y) .def_features(x, features == y))
+    )
+  } else {
+    stop("features needs to be a character or logical vector", call. = F)
+  }
+
+  obj <- x
+  x <- y <- feature_id <- NULL # workaround for data.table non-standard
+                               # evaluation
+  md <- features_df[setDT(obj$metadata), on = c("x", "y")]
+  md[, feature_id := ifelse(is.na(feature_id), "-88", feature_id)]
+  md[, "centroid_x" := mean(x), by = "feature_id"]
+  md[, "centroid_y" := mean(y), by = "feature_id"]
+
+  obj$metadata <- md
+
+  return(obj)
+}
+
+#' @importFrom grDevices chull
+#' @importFrom stats dist
+.def_features <- function(x, binary, name = NULL) {
+  # Label connected components in the binary image
+  binary_matrix <- matrix(binary, ncol = max(x$metadata$y) + 1, byrow = T)
+  labeled_image <- imager::label(imager::as.cimg(binary_matrix),
+                                 high_connectivity = T)
+
+  # Create a dataframe with feature IDs for each true pixel
+  feature_points_dt <- data.table(x = x$metadata$x,
+                                  y = x$metadata$y,
+                                  feature_id = ifelse(binary_matrix,
+                                                      labeled_image, -88) |>
+                                    t() |> as.vector() |> as.character())
+
+  # Apply the logic to clean components
+  cleaned_components <- ifelse(binary_matrix, labeled_image, -88)
+
+  # Calculate the convex hull for each feature
+  convex_hulls <- lapply(
+    split(
+      as.data.frame(which(cleaned_components >= 0, arr.ind = TRUE)),
+      cleaned_components[cleaned_components >= 0]
+    ),
+    function(coords) {coords[unique(chull(coords[,2], coords[,1])),]
+    })
+
+  # Calculate area, Feret max, and feature IDs for each feature
+  features_dt <- rbindlist(lapply(seq_along(convex_hulls), function(i) {
+    hull <- convex_hulls[[i]]
+    id <- names(convex_hulls)[i]
+
+    # Calculate Feret dimensions
+    dist_matrix <- as.matrix(dist(hull))
+    feret_max <- max(dist_matrix) + 1
+
+    perimeter <- 0
+    cols = 1:nrow(hull)
+    rows = c(2:nrow(hull), 1)
+    for (j in 1:length(cols)) {
+      # Fetch the distance from the distance matrix
+      perimeter <- perimeter + dist_matrix[rows[j], cols[j]]
+    }
+
+    # Area
+    area <- sum(cleaned_components == as.integer(id))
+
+    feret_min = area/feret_max #Can probably calculate this better.
+
+    data.table(feature_id = id,
+               area = area,
+               perimeter = perimeter,
+               feret_min = feret_min,
+               feret_max = feret_max
+    )
+  }), fill = T)
+
+  # Join with the coordinates from the binary image
+  if (!is.null(name)) {
+    features_dt$feature_id <- paste0(name, "_", features_dt$feature_id)
+  }
+
+  feature_points_dt[features_dt, on = "feature_id"]
+}
diff --git a/R/gen_OpenSpecy.R b/R/gen_OpenSpecy.R
new file mode 100644
index 00000000..25b9f267
--- /dev/null
+++ b/R/gen_OpenSpecy.R
@@ -0,0 +1,106 @@
+#' @title Generic Open Specy Methods
+#' @rdname gen_OpenSpecy
+#'
+#' @description
+#' Methods to visualize \code{OpenSpecy} objects.
+#'
+#' @details
+#' \code{head()} shows the first few lines of an \code{OpenSpecy} object.
+#' \code{print()} prints the contents of an \code{OpenSpecy} object.
+#' \code{summary()} produces a result summary of an \code{OpenSpecy} object.
+#' \code{plot()} produces a \code{\link[graphics]{matplot}()} of an OpenSpecy
+#' object; \code{lines()} adds new spectra to it.
+#'
+#' @param x an \code{OpenSpecy} object.
+#' @param object an \code{OpenSpecy} object.
+#' @param \ldots further arguments passed to the respective default method.
+#'
+#' @return
+#' \code{head()}, \code{print()}, and \code{summary()} return a textual
+#' representation of an \code{OpenSpecy} object.
+#' \code{plot()} and \code{lines()} return a plot.
+#'
+#' @examples
+#' data("raman_hdpe")
+#'
+#' # Printing the OpenSpecy object
+#' print(raman_hdpe)
+#'
+#' # Displaying the first few lines of the OpenSpecy object
+#' head(raman_hdpe)
+#'
+#' # Plotting the spectra
+#' plot(raman_hdpe)
+#'
+#' @author
+#' Zacharias Steinmetz, Win Cowger
+#'
+#' @seealso
+#' \code{\link[utils]{head}()}, \code{\link[base]{print}()},
+#' \code{\link[base]{summary}()}, \code{\link[graphics]{matplot}()}, and
+#' \code{\link[graphics]{matlines}()}
+#'
+#' @importFrom utils head
+#' @importFrom graphics matplot matlines
+#' @export
+head.OpenSpecy <- function(x, ...) {
+  cbind(wavenumber = x$wavenumber, x$spectra) |> head(...)
+}
+
+#' @rdname gen_OpenSpecy
+#'
+#' @method print OpenSpecy
+#' @export
+print.OpenSpecy <- function(x, ...) {
+  cbind(wavenumber = x$wavenumber, x$spectra) |> print(...)
+  cat("\n$metadata\n")
+  print(x$metadata)
+}
+
+#' @rdname gen_OpenSpecy
+#'
+#' @method plot OpenSpecy
+#' @export
+plot.OpenSpecy <- function(x, ...) {
+  matplot(x$wavenumber, x$spectra, type = "l",
+  xlab = "wavenumber",
+  ylab = "absorbance intensity",
+  xlim = rev(range(x$wavenumber)),
+  ...)
+}
+
+#' @rdname gen_OpenSpecy
+#'
+#' @method lines OpenSpecy
+#' @export
+lines.OpenSpecy <- function(x, ...) {
+  matlines(x$wavenumber, x$spectra, type = "l",
+          ...)
+}
+
+#' @rdname gen_OpenSpecy
+#'
+#' @method summary OpenSpecy
+#' @export
+summary.OpenSpecy <- function(object, ...) {
+  cat("$wavenumber\n")
+  wl <- length(object$wavenumber)
+  wr <- range(object$wavenumber)
+  res <- spec_res(object)
+  array(c(wl, wr, res), c(1,4), list("", c("Length", "Min.", "Max.",
+                                           "Res."))) |>
+    print()
+
+  cat("\n$spectra\n")
+  sl <- length(object$spectra)
+  sr <- range(object$spectra)
+  array(c(sl, sr), c(1,3), list("", c("Number", "Min. Intensity",
+                                      "Max. Intensity"))) |>
+    print()
+
+  cat("\n$metadata\n")
+  xr <- range(object$metadata$x)
+  yr <- range(object$metadata$y)
+  t(array(c(xr, yr), c(2,2), list(c("Min.", "Max."), c("x", "y")))) |> print()
+  names(object$metadata) |> print()
+}
diff --git a/R/human_ts.R b/R/human_ts.R
index c133b3c0..6792a07a 100644
--- a/R/human_ts.R
+++ b/R/human_ts.R
@@ -16,13 +16,12 @@
 #' human_ts()
 #'
 #' @author
-#' Win Cowger
+#' Win Cowger, Zacharias Steinmetz
 #'
 #' @seealso
 #' \code{\link[base]{format.Date}} for date conversion functions
 #'
-#' @importFrom dplyr %>%
 #' @export
 human_ts <- function() {
-  Sys.time() %>% format("%Y%m%d-%H%M%OS")
+  Sys.time() |> format("%Y%m%d-%H%M%OS")
 }
diff --git a/R/interactive_plots.R b/R/interactive_plots.R
new file mode 100644
index 00000000..5f437bf6
--- /dev/null
+++ b/R/interactive_plots.R
@@ -0,0 +1,239 @@
+#' @rdname interactive_plots
+#' @title Interactive plots for OpenSpecy objects
+#'
+#' @description
+#' These functions generate heatmaps, spectral plots, and interactive plots for
+#' OpenSpecy data.
+#'
+#' @param x an \code{OpenSpecy} object containing metadata and spectral data for
+#' the first group.
+#' @param x2 an optional second \code{OpenSpecy} object containing metadata and
+#' spectral data for the second group.
+#' @param z optional numeric vector specifying the intensity values for the
+#' heatmap. If not provided, the function will use the intensity values from the
+#' \code{OpenSpecy} object.
+#' @param sn optional numeric value specifying the signal-to-noise ratio
+#' threshold. If provided along with \code{min_sn}, regions with SNR below the
+#' threshold will be excluded from the heatmap.
+#' @param cor optional numeric value specifying the correlation threshold. If
+#' provided along with \code{min_cor}, regions with correlation below the
+#' threshold will be excluded from the heatmap.
+#' @param min_sn optional numeric value specifying the minimum signal-to-noise
+#' ratio for inclusion in the heatmap. Regions with SNR below this threshold
+#' will be excluded.
+#' @param min_cor optional numeric value specifying the minimum correlation for
+#' inclusion in the heatmap. Regions with correlation below this threshold
+#' will be excluded.
+#' @param select optional index of the selected spectrum to highlight on the
+#' heatmap.
+#' @param line list; \code{line} parameter for \code{x}; passed to
+#' \code{\link[plotly]{add_trace}()}.
+#' @param line2 list; \code{line} parameter for \code{x2}; passed to
+#' @param font list; passed to \code{\link[plotly]{layout}()}.
+#' @param plot_bgcolor color value; passed to \code{\link[plotly]{layout}()}.
+#' @param paper_bgcolor color value; passed to \code{\link[plotly]{layout}()}.
+#' @param colorscale colorscale passed to \code{\link[plotly]{add_trace}()}.
+#' @param \ldots further arguments passed to \code{\link[plotly]{plot_ly}()}.
+#'
+#' @return
+#' A plotly heatmap object displaying the OpenSpecy data. A subplot
+#' containing the heatmap and spectra plot. A plotly object displaying the
+#' spectra from the \code{OpenSpecy} object(s).
+#'
+#' @examples
+#' data("raman_hdpe")
+#' tiny_map <- read_extdata("CA_tiny_map.zip") |> read_zip()
+#' plotly_spec(raman_hdpe)
+#'
+#' correlation <- cor_spec(
+#'   conform_spec(raman_hdpe, range = raman_hdpe$wavenumber, res = 5),
+#'   conform_spec(tiny_map, tiny_map$wavenumbers, res = 5)
+#' )
+#' heatmap_spec(tiny_map, z = tiny_map$metadata$y)
+#'
+#' sample_spec(tiny_map, size = 12) |>
+#'   interactive_plot(select = 2, x2 = raman_hdpe)
+#'
+#' @author
+#' Win Cowger, Zacharias Steinmetz
+#'
+#' @importFrom plotly plot_ly add_trace add_markers subplot layout
+#' @importFrom data.table melt
+#'
+#' @export
+plotly_spec <- function(x, ...) {
+  UseMethod("plotly_spec")
+}
+
+#' @rdname interactive_plots
+#'
+#' @export
+plotly_spec.default <- function(x, ...) {
+  stop("object 'x' needs to be of class 'OpenSpecy'")
+}
+
+#' @rdname interactive_plots
+#'
+#' @export
+plotly_spec.OpenSpecy <- function(x, x2 = NULL,
+                                  line = list(color = 'rgb(255, 255, 255)'),
+                                  line2 = list(dash = "dot", 
+                                               color = "rgb(255,0,0)"),
+                                  font = list(color = '#FFFFFF'),
+                                  plot_bgcolor = 'rgb(17, 0, 73)',
+                                  paper_bgcolor = 'rgb(0, 0, 0)',
+                                  ...) {
+  dt <- cbind(wavenumber = x$wavenumber, x$spectra) |>
+    melt(id.vars = "wavenumber", variable.name = "id", value.name = "intensity")
+
+  p <- plot_ly(dt, type = "scatter", mode = "lines", ...) |>
+    add_trace(x = ~wavenumber, y = ~make_rel(intensity, na.rm = T),
+              color = ~id, line = line,
+              name = "Your Spectra", showlegend = F) |>
+    layout(xaxis = list(title = "wavenumber [cm<sup>-1</sup>]",
+                        autorange = "reversed"),
+           yaxis = list(title = "intensity [-]"),
+           plot_bgcolor = plot_bgcolor,
+           paper_bgcolor = paper_bgcolor,
+           legend = list(orientation = 'h', y = 1.1), font = font)
+
+  # Add dummy trace for Your Spectra
+  p <- p |>
+    add_trace(x = NULL, y = NULL,
+              line = line, name = "Your Spectra", showlegend = T)
+
+  if (!is.null(x2)) {
+    dt2 <- cbind(wavenumber = x2$wavenumber, x2$spectra) |>
+      melt(id.vars = "wavenumber", variable.name = "id", value.name = "intensity")
+
+    p <- p |>
+      add_trace(data = dt2, x = ~wavenumber, y = ~make_rel(intensity, na.rm = T),
+                color = ~id, type = "scatter", mode = "lines",
+                name = "Library Spectra",
+                line = line2, showlegend = F)
+
+    # Add dummy trace for Library Spectra
+    p <- p |>
+      add_trace(x = NULL, y = NULL,
+                line = line2,
+                name = "Library Spectra", showlegend = T)
+  }
+
+  return(p)
+}
+
+#' @rdname interactive_plots
+#'
+#' @export
+heatmap_spec <- function(x, ...) {
+  UseMethod("heatmap_spec")
+}
+
+#' @rdname interactive_plots
+#'
+#' @export
+heatmap_spec.default <- function(x, ...) {
+  stop("object 'x' needs to be of class 'OpenSpecy'")
+}
+
+#' @rdname interactive_plots
+#'
+#' @export
+heatmap_spec.OpenSpecy <- function(x,
+                                   z = NULL, sn = NULL, cor = NULL,
+                                   min_sn = NULL, min_cor = NULL, select = NULL,
+                                   font = list(color = '#FFFFFF'),
+                                   plot_bgcolor = 'rgba(17, 0, 73, 0)',
+                                   paper_bgcolor = 'rgb(0, 0, 0)',
+                                   colorscale = 'Viridis',
+                                   ...) {
+  if(!is.null(z))
+    plot_z <- z # default
+  else if(!is.null(cor))
+    plot_z <- cor
+  else if(!is.null(sn))
+    plot_z <- sn
+  else stop("z, cor, or sn need to be specified to plot the z axis", call. = F)
+
+  if (!is.null(sn) && !is.null(min_sn))
+    plot_z <- ifelse(sn > min_sn, plot_z, NA)
+
+  if (!is.null(cor) && !is.null(min_cor))
+    plot_z <- ifelse(cor > min_cor, plot_z, NA)
+
+  p <- plot_ly(...) |>
+    add_trace(x = x$metadata$x, y = x$metadata$y, z = if(!is.numeric(plot_z)){as.numeric(as.factor(plot_z))} else{plot_z},
+              colorscale = colorscale, type = "heatmap", hoverinfo = 'text',
+              showscale = F,
+              text = ~paste("row: ", 1:nrow(x$metadata),
+                            "<br>x: ", x$metadata$x,", y: ", x$metadata$y,
+                            ", z: ", plot_z,
+                            if(!is.null(sn)) paste("<br>snr: ", signif(sn, 2)) else "",
+                            if(!is.null(cor)) paste("<br>cor: ", signif(cor, 2)) else ""
+                            )) |>
+    layout(
+      xaxis = list(title = 'x', zeroline = F, showgrid = F),
+      yaxis = list(title = 'y', scaleanchor = "x", scaleratio = 1,
+                   zeroline = F, showgrid = F),
+      plot_bgcolor = plot_bgcolor, paper_bgcolor = paper_bgcolor,
+      showlegend = FALSE, font = font)
+
+  if(!is.null(select)){
+    p <-  p |> add_markers(x = x$metadata$x[select], y = x$metadata$y[select],
+                           name = "Selected Spectrum")
+  }
+
+  return(p)
+}
+
+#' @rdname interactive_plots
+#'
+#' @export
+interactive_plot <- function(x, ...) {
+  UseMethod("interactive_plot")
+}
+
+#' @rdname interactive_plots
+#'
+#' @export
+interactive_plot.default <- function(x, ...) {
+  stop("object 'x' needs to be of class 'OpenSpecy'")
+}
+
+#' @rdname interactive_plots
+#'
+#' @export
+interactive_plot.OpenSpecy <- function(x, x2 = NULL, select = NULL,
+                                       line = list(color = 'rgb(255, 255, 255)'),
+                                       line2 = list(dash = "dot", 
+                                                    color = "rgb(255,0,0)"),
+                                       font = list(color = '#FFFFFF'),
+                                       plot_bgcolor = 'rgba(17, 0, 73, 0)',
+                                       paper_bgcolor = 'rgb(0, 0, 0)',
+                                       colorscale = 'Viridis',
+                                       ...) {
+  # Generate the heatmap
+  heat_map <- heatmap_spec(x, z = x$metadata$y, select = select,
+                           font = font, plot_bgcolor = plot_bgcolor,
+                           paper_bgcolor = paper_bgcolor,
+                           colorscale = colorscale)
+  
+  x3 <- filter_spec(x, logic = select)
+
+  # Generate the spectral plot
+  spectra_plot <- plotly_spec(x3, x2 = x2,
+                              line = line, line2 = line2, font = font,
+                              plot_bgcolor = plot_bgcolor,
+                              paper_bgcolor = paper_bgcolor)
+
+
+  # Add margin to heatmap for separation
+  heat_map <- heat_map |> layout(autosize = TRUE, margin = list(b = 100))
+
+  # Combine both plots using subplot
+  plot_grid <- subplot(heat_map, spectra_plot, nrows = 2, heights = c(0.6, 0.4),
+                       margin = 0.1)
+
+  # Show the interactive plot
+  return(plot_grid)
+}
diff --git a/R/io_spec.R b/R/io_spec.R
new file mode 100644
index 00000000..b25bb5ac
--- /dev/null
+++ b/R/io_spec.R
@@ -0,0 +1,149 @@
+#' @rdname io_spec
+#' @title Read and write spectral data
+#'
+#' @description
+#' Functions for reading and writing spectral data to and from OpenSpecy format.
+#' \code{OpenSpecy} objects are lists with components `wavenumber`, `spectra`,
+#' and `metadata`. Currently supported formats are .y(a)ml, .json, or .rds.
+#'
+#' @param x an object of class \code{\link{OpenSpecy}}.
+#' @param file file path to be read from or written to.
+#' @param share defaults to \code{NULL}; needed to share spectra with the
+#' Open Specy community; see \code{\link{share_spec}()} for details.
+#' @param method optional; function to be used as a custom reader or writer.
+#' Defaults to the appropriate function based on the file extension.
+#' @param digits number of significant digits to use when formatting numeric
+#' values; defaults to \code{\link[base]{getOption}("digits")}.
+#' @param \ldots further arguments passed to the submethods.
+#'
+#' @details
+#' Due to floating point number errors there may be some differences in the
+#' precision of the numbers returned if using multiple devices for .json and
+#' .yaml files but the numbers should be nearly identical.
+#' \code{\link[base]{readRDS}()} should return the exact same object every time.
+#'
+#' @return
+#' \code{read_spec()} reads data formatted as an \code{OpenSpecy} object and
+#' returns a list object of class \code{\link{OpenSpecy}} containing spectral
+#' data.
+#' \code{write_spec()} writes a file for an object of class
+#' \code{\link{OpenSpecy}} containing spectral data.
+#' \code{as_hyperspec()} converts an \code{OpenSpecy} object to a
+#' \code{\link[hyperSpec]{hyperSpec-class}} object.
+#'
+#' @examples
+#' read_extdata("raman_hdpe.yml") |> read_spec()
+#' read_extdata("raman_hdpe.json") |> read_spec()
+#' read_extdata("raman_hdpe.rds") |> read_spec()
+#'
+#' \dontrun{
+#' data(raman_hdpe)
+#' write_spec(raman_hdpe, "raman_hdpe.yml")
+#' write_spec(raman_hdpe, "raman_hdpe.json")
+#' write_spec(raman_hdpe, "raman_hdpe.rds")
+#'
+#' # Convert an OpenSpecy object to a hyperSpec object
+#' hyper <- as_hyperSpec(raman_hdpe)
+#' }
+#'
+#' @author
+#' Zacharias Steinmetz, Win Cowger
+#'
+#' @seealso
+#' \code{\link{OpenSpecy}()};
+#' \code{\link{read_text}()}, \code{\link{read_asp}()}, \code{\link{read_spa}()},
+#' \code{\link{read_spc}()}, and \code{\link{read_jdx}()} for text files, .asp,
+#' .spa, .spa, .spc, and .jdx formats, respectively;
+#' \code{\link{read_zip}()} and \code{\link{read_any}()} for wrapper functions;
+#' \code{\link[base]{saveRDS}()}; \code{\link[base]{readRDS}()};
+#' \code{\link[yaml]{write_yaml}()}; \code{\link[yaml]{read_yaml}()};
+#' \code{\link[jsonlite]{write_json}()}; \code{\link[jsonlite]{read_json}()};
+#'
+#' @importFrom yaml write_yaml read_yaml
+#' @importFrom jsonlite write_json read_json
+#' @importFrom data.table as.data.table
+#'
+#' @export
+write_spec <- function(x, ...) {
+  UseMethod("write_spec")
+}
+
+#' @rdname io_spec
+#'
+#' @export
+write_spec.default <- function(x, ...) {
+  stop("object 'x' needs to be of class 'OpenSpecy'", call. = F)
+}
+
+#' @rdname io_spec
+#'
+#' @export
+write_spec.OpenSpecy <- function(x, file, method = NULL,
+                                 digits = getOption("digits"),
+                                 ...) {
+  if (is.null(method)) {
+    if (grepl("(\\.yaml$)|(\\.yml$)", file, ignore.case = T)) {
+      write_yaml(x, file = file, precision = digits, ...)
+    } else if (grepl("\\.json$", file, ignore.case = T)) {
+      write_json(x, path = file, dataframe = "columns", digits = digits, ...)
+    } else if (grepl("\\.rds$", file, ignore.case = T)) {
+      saveRDS(x, file = file, ...)
+    } else {
+      stop("unknown file type: specify a method to write custom formats or ",
+           "provide one of the supported .yml, .json, or .rds formats as ",
+           "file extension", call. = F)
+    }
+  } else {
+    do.call(method, list(x, file, ...))
+  }
+}
+
+#' @rdname io_spec
+#'
+#' @export
+read_spec <- function(file, share = NULL, method = NULL, ...) {
+  if (is.null(method)) {
+    if (grepl("(\\.yaml$)|(\\.yml$)", file, ignore.case = T)) {
+      yml <- read_yaml(file = file, ...)
+
+      os <- as_OpenSpecy(yml$wavenumber,
+                         spectra = as.data.table(yml$spectra),
+                         metadata = data.table(as.data.table(yml$metadata),
+                                               file_name = basename(file)),
+                         coords = NULL)
+    } else if (grepl("\\.json$", file, ignore.case = T)) {
+      jsn <- read_json(file, simplifyVector = T, ...)
+
+      os <- as_OpenSpecy(jsn$wavenumber,
+                         spectra = as.data.table(jsn$spectra),
+                         metadata = data.table(as.data.table(jsn$metadata),
+                                               file_name = basename(file)),
+                         coords = NULL)
+    } else if (grepl("\\.rds$", file, ignore.case = T)) {
+      os <- readRDS(file, ...)
+      os$metadata$file_name <- basename(file)
+    } else {
+      stop("unknown file type: specify a method to read custom formats or ",
+           "provide files of one of the supported file types .yml, .json, .rds",
+           call. = F)
+    }
+  } else {
+    io <- do.call(method, list(file, ...))
+
+    os <- OpenSpecy(io, coords = NULL)
+    os$metadata$file_name <- basename(file)
+  }
+
+  if (!is.null(share)) share_spec(os, file = file, share = share)
+
+  return(os)
+}
+
+#' @rdname io_spec
+#' @importFrom methods new
+#'
+#' @export
+as_hyperSpec <- function(x) {
+    new("hyperSpec", spc = as.matrix(transpose(x$spectra)),
+        wavelength = x$wavenumber)
+}
diff --git a/R/manage_lib.R b/R/manage_lib.R
index aea62e61..0199f92e 100644
--- a/R/manage_lib.R
+++ b/R/manage_lib.R
@@ -1,5 +1,4 @@
 #' @rdname manage_lib
-#'
 #' @title Manage spectral libraries
 #'
 #' @description
@@ -16,12 +15,13 @@
 #' (\doi{10.17605/OSF.IO/X7DPZ}).
 #' \code{load_lib()} will load the library into the global environment for use
 #' with the Open Specy functions.
+#' \code{rm_lib()} removes the libraries from your computer.
 #'
-#' @param which a character string specifying which library to use,
-#' \code{"raman"} or \code{"ftir"}.
-#' @param types library types to check/retrieve; defaults to
-#' \code{c("metadata", "library", "peaks")}.
-#' @param node the OSF node to be retrieved; should be \code{"x7dpz"}.
+#' @param type library type to check/retrieve; defaults to
+#' \code{c("derivative", "nobaseline", "raw", "mediod", "model")} which reads
+#' everything.
+#' @param node the OSF node to be retrieved; should be \code{"x7dpz"} unless you
+#' maintain your own OSF node with spectral libraries.
 #' @param path where to save or look for local library files; defaults to
 #' \code{"system"} pointing to
 #' \code{system.file("extdata", package = "OpenSpecy")}.
@@ -41,22 +41,19 @@
 #'
 #' @return
 #' \code{check_lib()} and \code{get_lib()} return messages only;
-#' \code{load_lib()} returns a list object containing the respective spectral
-#' reference library.
+#' \code{load_lib()} returns an \code{OpenSpecy} object containing the
+#' respective spectral reference library.
 #'
 #' @examples
 #' \dontrun{
-#' check_lib(which = c("ftir", "raman"))
-#' get_lib(which = c("ftir", "raman"))
+#' check_lib("derivative")
+#' get_lib("derivative")
 #'
-#' spec_lib <- load_lib(which = c("ftir", "raman"))
+#' spec_lib <- load_lib("derivative")
 #' }
 #'
 #' @author
-#' Zacharias Steinmetz
-#'
-#' @seealso
-#' \code{\link{match_spec}()}
+#' Zacharias Steinmetz, Win Cowger
 #'
 #' @references
 #' Cowger W, Gray A, Christiansen SH, Christiansen SH, Christiansen SH,
@@ -67,19 +64,16 @@
 #'
 #' Cowger, W (2021). “Library data.” \emph{OSF}. \doi{10.17605/OSF.IO/X7DPZ}.
 #'
-#' @importFrom dplyr %>%
-#'
 #' @export
-check_lib <- function(which = c("ftir", "raman"),
-                      types = c("metadata", "library", "peaks"),
-                      path = "system",
-                      condition = "warning") {
+check_lib <- function(type = c("derivative", "nobaseline", "raw", "mediod",
+                               "model"),
+                      path = "system", condition = "warning") {
 
   lp <- ifelse(path == "system",
                system.file("extdata", package = "OpenSpecy"),
                path)
 
-  sapply(which, .chkf, types = types, path = lp, condition = condition)
+  .chkf(type, path = lp, condition = condition)
 
   invisible()
 }
@@ -90,24 +84,22 @@ check_lib <- function(which = c("ftir", "raman"),
 #' @importFrom osfr osf_retrieve_node osf_ls_files osf_download
 #'
 #' @export
-get_lib <- function(which = c("ftir", "raman"),
-                    types = c("metadata", "library", "peaks"),
-                    path = "system",
-                    node = "x7dpz", conflicts = "overwrite", ...) {
+get_lib <- function(type = c("derivative", "nobaseline", "raw", "mediod",
+                             "model"),
+                    path = "system", node = "x7dpz", conflicts = "overwrite",
+                    ...) {
   lp <- ifelse(path == "system",
                system.file("extdata", package = "OpenSpecy"),
                path)
 
-  osf <- osf_retrieve_node(node) %>%
+  osf <- osf_retrieve_node(node) |>
     osf_ls_files(pattern = ".rds", n_max = Inf)
 
   message("Fetching Open Specy reference libraries from OSF ...")
-  for (w in which) {
-    osf %>% subset(grepl(
-      paste0("^", w, "_(", paste(types, collapse = "|"), ").rds"),
-      osf$name)) %>%
+    osf |> subset(grepl(
+      paste0("(", paste(type, collapse = "|"), ").rds"),
+      osf$name)) |>
       osf_download(path = lp, conflicts = conflicts, progress = TRUE, ...)
-  }
 
   message("Use 'load_lib()' to load the library")
 }
@@ -115,51 +107,54 @@ get_lib <- function(which = c("ftir", "raman"),
 #' @rdname manage_lib
 #'
 #' @export
-load_lib <- function(which = c("ftir", "raman"),
-                     types = c("metadata", "library", "peaks"),
-                     path = "system") {
+load_lib <- function(type, path = "system") {
   lp <- ifelse(path == "system",
                system.file("extdata", package = "OpenSpecy"),
                path)
 
-  chk <- lapply(which, .chkf, types = types, path = lp, condition = "stop")
+  chk <- .chkf(type, path = lp, condition = "stop")
 
-  res <- lapply(chk, function(x) {
-    fls <- file.path(lp, paste0(x[[2L]], "_", names(x[[1L]]), ".rds"))
+  fp <- file.path(lp, paste0(type, ".rds"))
+  rds <- readRDS(fp)
 
-    rrds <- lapply(fls, readRDS)
-    names(rrds) <- names(x[[1L]])
+  return(rds)
+}
 
-    rrds
-  })
+#' @rdname manage_lib
+#'
+#' @export
+rm_lib <- function(type = c("derivative", "nobaseline", "raw", "mediod",
+                            "model"),
+                   path = "system") {
+  lp <- ifelse(path == "system",
+               system.file("extdata", package = "OpenSpecy"),
+               path)
+
+  fp <- file.path(lp, paste0(type, ".rds"))
+  file.remove(fp)
 
-  names(res) <- which
-  return(res)
+  invisible()
 }
 
 # Auxiliary function for library checks
-.chkf <- function(which, types = c("metadata", "library", "peaks"),
-                  path = "system",
-                  condition = "warning") {
-  fn <- paste0(which, "_", types, ".rds")
+.chkf <- function(type, path = "system", condition = "warning") {
+  fn <- paste0(type, ".rds")
 
-  lp <- ifelse(path == "system",
-               system.file("extdata", package = "OpenSpecy"),
+  lp <- ifelse(path == "system", system.file("extdata", package = "OpenSpecy"),
                path)
 
-  chk <- file.path(lp, fn) %>% file.exists()
-  names(chk) <- types
-
-  out <- switch (which,
-                 "ftir" = "FTIR",
-                 "raman" = "Raman"
-  )
-
-  if (!all(chk)) do.call(condition, list(out, " library missing or incomplete; ",
-                                         "use 'get_lib()' to download a current version",
-                                         call. =  ifelse(condition %in%
-                                                           c("message",
-                                                             "packageStartupMessage"),
-                                                         "", FALSE)))
-  list(chk, which, out)
+  chk <- file.path(lp, fn) |> file.exists()
+
+  names(chk) <- type
+
+  out <- paste(type[!chk], collapse = ", ")
+
+  if (!all(chk))
+    do.call(condition, list("Library missing or incomplete: ", out, "; ",
+                            "use 'get_lib()' to download a current version",
+                            call. =  ifelse(condition %in%
+                                              c("message",
+                                                "packageStartupMessage"),
+                                            "", FALSE)))
+  chk
 }
diff --git a/R/manage_spec.R b/R/manage_spec.R
new file mode 100644
index 00000000..b5b645e1
--- /dev/null
+++ b/R/manage_spec.R
@@ -0,0 +1,128 @@
+#' @rdname manage_spec
+#' @title Manage spectral objects
+#'
+#' @description
+#' \code{c_spec()} concatenates \code{OpenSpecy} objects.
+#' \code{sample_spec()} samples spectra from an \code{OpenSpecy} object.
+#'
+#' @param x a list of \code{OpenSpecy} objects.
+#' @param range a numeric providing your own wavenumber ranges or character
+#' argument called \code{"common"} to let \code{c_spec()} find the common
+#' wavenumber range of the supplied spectra. \code{NULL} will interpret the
+#' spectra having all the same wavenumber range.
+#' @param res defaults to \code{NULL}, the resolution you want the output
+#' wavenumbers to be.
+#' @param \ldots further arguments passed to submethods.
+#'
+#' @return
+#' \code{c_spec()} and \code{sample_spec()} return \code{OpenSpecy} objects.
+#'
+#' @examples
+#' # Concatenating spectra
+#' spectra <- lapply(c(read_extdata("raman_hdpe.csv"),
+#'                     read_extdata("ftir_ldpe_soil.asp")), read_any)
+#' common <- c_spec(spectra, range = "common", res = 5)
+#' range <- c_spec(spectra, range = c(1000, 2000), res = 5)
+#'
+#' # Sampling spectra
+#' tiny_map <- read_any(read_extdata("CA_tiny_map.zip"))
+#' sampled <- sample_spec(tiny_map, size = 3)
+#'
+#' @author
+#' Zacharias Steinmetz, Win Cowger
+#'
+#' @seealso
+#' \code{\link[OpenSpecy]{conform_spec}()} for conforming wavenumbers
+#'
+#' @importFrom data.table data.table as.data.table fread rbindlist
+#'
+#' @export
+c_spec <- function(x, ...) {
+  UseMethod("c_spec")
+}
+
+#' @rdname manage_spec
+#'
+#' @export
+c_spec.default <- function(x, ...) {
+  stop("object 'x' needs to be a list of 'OpenSpecy' objects")
+}
+
+#' @rdname manage_spec
+#'
+#' @export
+c_spec.OpenSpecy <- function(x, ...) {
+  warning("object 'x' needs to be a list of 'OpenSpecy' objects; ",
+          "nothing to concatenate, returning 'x'")
+
+  return(x)
+}
+
+#' @rdname manage_spec
+#'
+#' @export
+c_spec.list <- function(x, range = NULL, res = 5, ...) {
+  if(!all(vapply(x, function(y) {inherits(y, "OpenSpecy")}, FUN.VALUE = T)))
+    stop("object 'x' needs to be a list of 'OpenSpecy' objects", call. = F)
+
+  if(!is.null(range)) {
+    if(is.numeric(range)) {
+      wn <- range
+    }
+    else if(!is.null(range) && range == "common") {
+      pmin <- vapply(x, function(y) min(y$wavenumber), FUN.VALUE = numeric(1))
+      pmax <- vapply(x, function(y) max(y$wavenumber), FUN.VALUE = numeric(1))
+
+      if(any(max(pmin) > pmax) | any(min(pmax) < pmin))
+        stop("data points need to overlap in their ranges", call. = F)
+
+      wn <- c(max(pmin), min(pmax))
+    }
+
+    x <- lapply(x, conform_spec, range = wn, res = res)
+  }
+
+  unlisted <- unlist(x, recursive = F)
+  list <- tapply(unlisted, names(unlisted), unname)
+
+  if(length(unique(vapply(list$wavenumber, length, FUN.VALUE = numeric(1)))) > 1
+     & is.null(range)) {
+    stop("wavenumbers need to be identical between spectra; specify how; use ",
+         "'range' to specify how wavenumbers should be merged", call. = F)
+  }
+
+  as_OpenSpecy(x = list$wavenumber[[1]],
+               spectra = as.data.table(list$spectra),
+               metadata = rbindlist(list$metadata, fill = T)[,-c("x","y")]
+  )
+}
+
+
+#' @rdname manage_spec
+#'
+#' @export
+sample_spec <- function(x, ...) {
+  UseMethod("sample_spec")
+}
+
+#' @rdname manage_spec
+#'
+#' @export
+sample_spec.default <- function(x, ...) {
+  stop("object 'x' needs to be of class 'OpenSpecy'")
+}
+
+#' @rdname manage_spec
+#'
+#' @export
+sample_spec.OpenSpecy <- function(x, ...) {
+  # replace = false is mandatory currently because we don't have a way to
+  # rename and recoordinate duplicates.
+  cols <- sample(1:ncol(x$spectra), ...)
+
+  as_OpenSpecy(
+    x = x$wavenumber,
+    spectra = x$spectra[, cols, with = F],
+    metadata = x$metadata[cols, ]
+  )
+}
diff --git a/R/match_spec.R b/R/match_spec.R
index c035acb2..86eefcd0 100644
--- a/R/match_spec.R
+++ b/R/match_spec.R
@@ -1,62 +1,71 @@
-#' @title Match spectra with reference library
+#' @rdname match_spec
+#' @title Identify and filter spectra
 #'
 #' @description
-#' \code{match_spec()} will compare a spectrum to a spectral library formatted
-#' with the Open Specy standard and report the best match using the Pearson
-#' correlation coefficient.
-#' \code{find_spec()} makes it easy to retrieve single spectra and metadata
-#' from the Open Specy reference library.
-#'
-#' @details
-#' This routine will match the spectrum you want to identify to the wavenumbers
-#' present in the spectral library. Once the spectra are aligned, it computes
-#' the Pearson correlation coefficient between the spectrum you want to
-#' identify and all spectra in the library (see \code{\link[stats]{cor}}).
-#' The function returns a table with the Pearson correlation coefficient values
-#' and all metadata for the top spectral matches.
-#' If using the Open Specy library, all intensity values are in absorbance, so
-#' your spectra should also be in absorbance units. If you need to convert your
-#' spectrum, use \code{\link{adj_intens}()}.
-#'
-#' @param x a numeric vector containing the spectral wavenumbers; alternatively
-#' a data frame containing spectral data as \code{"wavenumber"} and
-#' \code{"intensity"} can be supplied.
-#' @param y a numeric vector containing the spectral intensities.
-#' @param formula an object of class '\code{\link[stats]{formula}}' of the form
-#' \code{intensity ~ wavenumber}.
-#' @param data a data frame containing the variables in \code{formula}.
-#' @param library reference library you want to compare against.
-#' @param which a character string specifying which library to match,
-#' \code{"raman"} or \code{"ftir"}.
-#' @param type a character string specifying whether the \code{"full"} spectrum
-#' should be matched or spectrum \code{"peaks"} only. \code{"metadata"} is
-#' needed to browser spectra with \code{find_spec()}.
-#' @param range this should be all possible wavenumber values from your spectral
-#' library.
-#' @param top_n number of top matches that you want to be returned.
-#' @param subset logical expression indicating elements or rows to search for;
-#' see \code{\link[base]{subset}()} for details.
-#' @param cols columns to retrieve from the Open Specy reference library;
-#' columns containing no or missing values are automatically removed.
-#' @param \ldots further arguments passed to the submethods.
+#' \code{match_spec()} joins two \code{OpenSpecy} objects and their metadata
+#' based on similarity.
+#' \code{cor_spec()} correlates two \code{OpenSpecy} objects, typically one with
+#' knowns and one with unknowns.
+#' \code{ident_spec()} retrieves the top match values from a correlation matrix
+#' and formats them with metadata.
+#' \code{get_metadata()} retrieves metadata from OpenSpecy objects.
+#' \code{max_cor_named()} formats the top correlation values from a correlation
+#' matrix as a named vector.
+#' \code{filter_spec()} filters an Open Specy object.
 #'
-#' @examples
-#' \dontrun{
-#' data("raman_hdpe")
+#' @param x an \code{OpenSpecy} object, typically with unknowns.
+#' @param library an \code{OpenSpecy} or \code{glmnet} object representing the
+#' reference library of spectra or model to use in identification.
+#' @param na.rm logical; indicating whether missing values should be removed
+#' when calculating correlations. Default is \code{TRUE}.
+#' @param top_n integer; specifying the number of top matches to return.
+#' If \code{NULL} (default), all matches will be returned.
+#' @param cor_matrix a correlation matrix for object and library,
+#' can be returned by \code{cor_spec()}
+#' @param add_library_metadata name of a column in the library metadata to be
+#' joined; \code{NULL} if you don't want to join.
+#' @param add_object_metadata name of a column in the object metadata to be
+#' joined; \code{NULL} if you don't want to join.
+#' @param rm_empty logical; whether to remove empty columns in the metadata.
+#' @param logic a logical or numeric vector describing which spectra to keep.
+#' @param fill an \code{OpenSpecy} object with a single spectrum to be used to
+#' fill missing values for alignment with the AI classification.
+#' @param \ldots additional arguments passed \code{\link[stats]{cor}()}.
 #'
-#' get_lib("raman")
-#' spec_lib <- load_lib("raman")
+#' @return
+#' \code{match_spec()} and \code{ident_spec()} will return
+#' a \code{\link[data.table]{data.table-class}()} containing correlations
+#' between spectra and the library.
+#' The table has three columns: \code{object_id}, \code{library_id}, and
+#' \code{match_val}.
+#' Each row represents a unique pairwise correlation between a spectrum in the
+#' object and a spectrum in the library.
+#' If \code{top_n} is specified, only the top \code{top_n} matches for each
+#' object spectrum will be returned.
+#' If \code{add_library_metadata} is \code{is.character}, the library metadata
+#' will be added to the output.
+#' If \code{add_object_metadata} is \code{is.character}, the object metadata
+#' will be added to the output.
+#' \code{filter_spec()} returns an \code{OpenSpecy} object.
+#' \code{cor_spec()} returns a correlation matrix.
+#' \code{get_metadata()} returns a \code{\link[data.table]{data.table-class}()}
+#' with the metadata for columns which have information.
 #'
-#' match_spec(raman_proc, library = spec_lib, which = "raman")
+#' @examples
+#' data("test_lib")
+#' unknown <- read_extdata("ftir_ldpe_soil.asp") |>
+#'   read_any() |>
+#'   conform_spec(range = test_lib$wavenumber,
+#'                res = spec_res(test_lib)) |>
+#'   process_spec()
+#' matches <- cor_spec(unknown, test_lib)
 #'
-#' find_spec(sample_name == 5381, library = spec_lib, which = "raman")
-#' }
+#' test_lib_extract <- filter_spec(test_lib,
+#'   logic = grepl("polycarbonate", test_lib$metadata$polymer_class,
+#'                 ignore.case = TRUE)
+#' )
 #'
-#' @return
-#' \code{match_spec()} returns a data frame with the \code{top_n} material
-#' matches, their Pearson's r value, and the organization they were provided by.
-#' \code{find_spec()} returns a data frame with the spectral raw data or
-#' metadata of a specific reference spectrum.
+#' matches2 <- cor_spec(unknown, library = test_lib_extract)
 #'
 #' @author
 #' Win Cowger, Zacharias Steinmetz
@@ -67,11 +76,51 @@
 #' \code{\link{load_lib}()} loads the Open Specy reference library into an \R
 #' object of choice
 #'
-#' @importFrom rlang .data
-#' @importFrom stats approx cor
-#' @importFrom dplyr %>% inner_join mutate group_by ungroup summarize select arrange desc top_n
+#' @importFrom stats cor predict
+#' @importFrom glmnet predict.glmnet
+#' @importFrom data.table data.table setorder fifelse .SD as.data.table rbindlist
+#' @export
+cor_spec <- function(x, ...) {
+  UseMethod("cor_spec")
+}
+
+#' @rdname match_spec
+#'
+#' @export
+cor_spec.default <- function(x, ...) {
+  stop("object 'x' needs to be of class 'OpenSpecy'")
+}
+
+#' @rdname match_spec
 #'
 #' @export
+cor_spec.OpenSpecy <- function(x, library, na.rm = T, ...) {
+  if(sum(x$wavenumber %in% library$wavenumber) < 3)
+    stop("there are less than 3 matching wavenumbers in the objects you are ",
+         "trying to correlate; this won't work for correlation analysis. ",
+         "Consider first conforming the spectra to the same wavenumbers.",
+         call. = F)
+
+  if(!all(x$wavenumber %in% library$wavenumber))
+    warning(paste0("some wavenumbers in 'x' are not in the library and the ",
+                   "function is not using these in the identification routine: ",
+                   paste(x$wavenumber[!x$wavenumber %in% library$wavenumber],
+                         collapse = " ")),
+            call. = F)
+
+  lib <- library$spectra[library$wavenumber %in% x$wavenumber, ]
+  lib <- lib[, lapply(.SD, make_rel, na.rm = na.rm)]
+  lib <- lib[, lapply(.SD, mean_replace)]
+
+  spec <- x$spectra[x$wavenumber %in% library$wavenumber,]
+  spec <- spec[,lapply(.SD, make_rel, na.rm = na.rm)]
+  spec <- spec[,lapply(.SD, mean_replace)]
+
+  cor(lib, spec, ...)
+}
+
+#' @rdname match_spec
+#' @export
 match_spec <- function(x, ...) {
   UseMethod("match_spec")
 }
@@ -79,94 +128,194 @@ match_spec <- function(x, ...) {
 #' @rdname match_spec
 #'
 #' @export
-match_spec.formula <- function(formula, data = NULL, ...) {
-  if (missing(formula) || (length(formula) != 3L) ||
-      (length(attr(terms(formula[-2L]), "term.labels")) != 1L))
-    stop("'formula' missing or incorrect")
+match_spec.default <- function(x, ...) {
+  stop("object 'x' needs to be of class 'OpenSpecy'")
+}
 
-  mf <- model.frame(formula, data)
-  lst <- as.list(mf)
-  names(lst) <- c("y", "x")
+#' @rdname match_spec
+#'
+#' @export
+match_spec.OpenSpecy <- function(x, library, na.rm = T, top_n = NULL,
+                                 add_library_metadata = NULL,
+                                 add_object_metadata = NULL, fill = NULL, ...) {
+  if(is_OpenSpecy(library)) {
+    cor_spec(x, library =  library) |>
+      ident_spec(x, library = library, top_n = top_n,
+                 add_library_metadata = add_library_metadata,
+                 add_object_metadata = add_object_metadata)
+  } else {
+    ai_classify(x, library, fill)
+  }
+}
 
-  do.call("match_spec", c(lst, list(...)))
+#' @rdname match_spec
+#'
+#' @export
+ident_spec <- function(cor_matrix, x, library, top_n = NULL,
+                       add_library_metadata = NULL,
+                       add_object_metadata = NULL, ...){
+  if(is.numeric(top_n) && top_n > ncol(library$spectra)){
+    top_n = NULL
+    message("'top_n' was larger than the number of spectra in the library; ",
+            "returning all matches")
+  }
+
+  out <- data.table(object_id = colnames(x$spectra),
+                    library_id = rep(colnames(library$spectra),
+                                     each = ncol(x$spectra)),
+                    match_val = c(cor_matrix))
+
+  if (is.character(add_library_metadata))
+    out <- merge(out, library$metadata,
+                 by.x = "library_id", by.y = add_library_metadata, all.x = T)
+  if (is.character(add_object_metadata))
+    out <- merge(out, x$metadata,
+                 by.x = "object_id", by.y = add_object_metadata, all.x = T)
+  if (is.numeric(top_n)) {
+    setorder(out, -"match_val")
+    out <- out[, head(.SD, top_n), by = "object_id"]
+  }
+
+  return(out)
 }
 
 #' @rdname match_spec
 #'
 #' @export
-match_spec.data.frame <- function(x, ...) {
-  if (!all(c("wavenumber", "intensity") %in% names(x)))
-    stop("'data' must contain 2 columns named 'wavenumber' and 'intensity'")
+get_metadata <- function(x, ...) {
+  UseMethod("get_metadata")
+}
 
-  do.call("match_spec", list(x$wavenumber, x$intensity, ...))
+#' @rdname match_spec
+#'
+#' @export
+get_metadata.default <- function(x, ...) {
+  stop("object 'x' needs to be of class 'OpenSpecy'")
 }
 
 #' @rdname match_spec
 #'
 #' @export
-match_spec.default <- function(x, y, library, which = NULL, type = "full",
-                               range = seq(0, 6000, 0.1), top_n = 100, ...) {
-  if(type == "full") type <- "library"
+get_metadata.OpenSpecy <- function(x, logic, rm_empty = TRUE, ...) {
+  if(is.character(logic))
+    logic <- which(names(x$spectra) %in% logic)
 
-  lib <- library[[which]][[type]]
-  meta <- library[[which]][["metadata"]]
+  res <- x$metadata[logic, ]
 
-  wls <- range[range >= min(x) & range <= max(x)]
-  aprx <- approx(x, y, xout = wls, rule = 2, method = "linear", ties = mean) %>%
-    data.frame()
-  names(aprx) <- c("wavenumber", "baseline_remove")
+  if(rm_empty)
+    res <- res[, !sapply(res, is_empty_vector), with = F]
 
-  m <- lib %>%
-    inner_join(aprx, by = "wavenumber") %>%
-    group_by(.data$group, .data$sample_name) %>%
-    mutate(baseline_remove = .data$baseline_remove -
-             min(.data$baseline_remove)) %>%
-    ungroup() %>%
-    mutate(baseline_remove = make_rel(.data$baseline_remove)) %>%
-    group_by(.data$sample_name) %>%
-    summarize(rsq = cor(.data$intensity, .data$baseline_remove)) %>%
-    top_n(top_n, .data$rsq) %>%
-    inner_join(select(meta, -.data$rsq), by = "sample_name") %>%
-    select(.data$sample_name, .data$spectrum_identity, .data$rsq,
-           .data$organization) %>%
-    arrange(desc(.data$rsq)) %>%
-    mutate(rsq = round(.data$rsq, 2))
+  return(res)
+}
 
-  message("Visit openspecy.org to share your spectrum with the Open Specy ",
-          "community and improve the spectral library.")
-  return(m)
+#' @rdname match_spec
+#'
+#' @export
+max_cor_named <- function(cor_matrix, na.rm = T) {
+  # Find the indices of maximum correlations
+  max_cor_indices <- apply(cor_matrix, 2, function(x) which.max(x))
+
+  # Use indices to get max correlation values
+  max_cor_values <- vapply(1:length(max_cor_indices), function(idx) {
+    cor_matrix[max_cor_indices[idx],idx]}, FUN.VALUE = numeric(1))
+
+  # Use indices to get the corresponding names
+  names(max_cor_values) <- rownames(cor_matrix)[max_cor_indices]
+
+  return(max_cor_values)
+}
+
+#' @rdname match_spec
+#'
+#' @export
+filter_spec <- function(x, ...) {
+  UseMethod("filter_spec")
+}
+
+#' @rdname match_spec
+#'
+#' @export
+filter_spec.default <- function(x, ...) {
+  stop("object 'x' needs to be of class 'OpenSpecy'")
+}
+
+#' @rdname match_spec
+#'
+#' @export
+filter_spec.OpenSpecy <- function(x, logic, ...) {
+  if(is.character(logic)){
+    logic = which(names(x$spectra) %in% logic)
+  }
+  x$spectra <- x$spectra[, logic, with = F]
+  x$metadata <- x$metadata[logic,]
+
+  return(x)
+}
+
+#' @rdname match_spec
+#'
+#' @export
+ai_classify <- function(x, ...) {
+  UseMethod("ai_classify")
 }
 
 #' @rdname match_spec
 #'
 #' @export
-find_spec <- function(subset, library, which = NULL, type = "metadata",
-                      cols = c("spectrum_identity", "organization",
-                               "contact_info", "spectrum_type",
-                               "instrument_used", "instrument_accessories",
-                               "instrument_mode", "laser_light_used",
-                               "total_acquisition_time_s",
-                               "number_of_accumulations",
-                               "level_of_confidence_in_identification",
-                               "cas_number", "material_producer",
-                               "material_purity", "material_form",
-                               "material_quality", "spectral_resolution",
-                               "data_processing_procedure",
-                               "other_information", "sample_name",
-                               "wavenumber", "intensity", "group"),
-                      ...) {
+ai_classify.default <- function(x, ...) {
+  stop("object 'x' needs to be of class 'OpenSpecy'")
+}
+
+#' @rdname match_spec
+#'
+#' @export
+ai_classify.OpenSpecy <- function(x, library, fill = NULL, ...) {
+  if(!is.null(fill)) {
+    filled <- .fill_spec(x, fill)
+  } else {
+    filled <- x
+  }
+  filled$spectra$wavenumber <- filled$wavenumber
+  proc <- transpose(filled$spectra, make.names = "wavenumber") |>
+    as.matrix()
+
+  pred <- predict(library$model,
+                  newx = proc,
+                  min(library$model$lambda),
+                  type = "response") |>
+    as.data.table()
+  names(pred)[1:3] <- c("x", "y", "z")
+  pred$x <- as.integer(pred$x)
+  pred$y <- as.integer(pred$y)
+  pred <- merge(pred, data.table(x = 1:dim(proc)[1]), all.y = T)
+
+  value <- NULL # workaround for data.table non-standard evaluation
+  filt <- pred[, .SD[value == max(value, na.rm = T) | is.na(value)], by = "x"]
+
+  res <- merge(filt, library$dimension_conversion, all.x = T,
+               by.x = "y", by.y = "factor_num")
+  setorder(res, "x")
+
+  return(res)
+}
+
+.fill_spec <- function(x, fill) {
+  blank_dt <- x$spectra[1,]
+
+  blank_dt[1,] <- NA
 
-  if(type == "full") type <- "library"
+  test <- rbindlist(lapply(1:length(fill$wavenumber), function(x) {
+    blank_dt
+  }
+  ))[, lapply(.SD, function(x) {
+    unlist(fill$spectra)
+  })]
 
-  df <- data.frame(library[[which]][[type]])
-  e <- substitute(subset)
+  test[match(x$wavenumber, fill$wavenumber),] <- x$spectra
 
-  if (!is.call(e)) stop("subset needs to be a logical expression")
+  x$spectra <- test
 
-  r <- eval(e, df, parent.frame())
-  c <- cols[cols %in% names(df)]
-  out <- df[r, c]
+  x$wavenumber <- fill$wavenumber
 
-  out[,colSums(is.na(out)) < nrow(out) &
-        colSums(out == "") < nrow(out)]
+  x
 }
diff --git a/R/process_spec.R b/R/process_spec.R
new file mode 100644
index 00000000..2d31a6ff
--- /dev/null
+++ b/R/process_spec.R
@@ -0,0 +1,131 @@
+#' @rdname process_spec
+#' @title Preprocess Spectra
+#'
+#' @description
+#' \code{process_spec()} is a monolithic wrapper function for all spectral
+#' preprocessing steps.
+#'
+#' @param x an \code{OpenSpecy} object.
+#' @param active logical; indicating whether to perform preprocessing.
+#' If \code{TRUE}, the preprocessing steps will be applied.
+#' If \code{FALSE}, the original data will be returned.
+#' @param adj_intens logical; describing whether to adjust the intensity units.
+#' @param adj_intens_args named list of arguments passed to
+#' \code{\link{smooth_intens}()}.
+#' @param conform_spec logical; whether to conform the spectra to a new
+#' wavenumber range and resolution.
+#' @param conform_spec_args named list of arguments passed to
+#' \code{\link{conform_spec}()}.
+#' @param restrict_range logical; indicating whether to restrict the wavenumber
+#' range of the spectra.
+#' @param restrict_range_args named list of arguments passed to
+#' \code{\link{restrict_range}()}.
+#' @param flatten_range logical; indicating whether to flatten the range around
+#' the carbon dioxide region.
+#' @param flatten_range_args named list of arguments passed to
+#' \code{\link{flatten_range}()}.
+#' @param smooth_intens logical; indicating whether to apply a smoothing filter
+#' to the spectra.
+#' @param smooth_intens_args named list of arguments passed to
+#' \code{\link{smooth_intens}()}.
+#' @param subtr_baseline logical; indicating whether to subtract the baseline
+#' from the spectra.
+#' @param subtr_baseline_args named list of arguments passed to
+#' \code{\link{subtr_baseline}()}.
+#' @param make_rel logical; if \code{TRUE} spectra are automatically normalized
+#' with \code{\link{make_rel}()}.
+#' @param \ldots further arguments passed to subfunctions.
+#'
+#' @return
+#' \code{process_spec()} returns an \code{OpenSpecy} object with preprocessed
+#' spectra based on the specified parameters.
+#'
+#' @examples
+#' data("raman_hdpe")
+#' plot(raman_hdpe)
+#'
+#' # Process spectra with range restriction and baseline subtraction
+#' process_spec(raman_hdpe,
+#'              restrict_range = TRUE,
+#'              restrict_range_args = list(min = 500, max = 3000),
+#'              subtr_baseline = TRUE,
+#'              subtr_baseline_args = list(type = "polynomial",
+#'                                         polynomial = 8)) |>
+#'   lines(col = "darkred")
+#'
+#' # Process spectra with smoothing and derivative
+#' process_spec(raman_hdpe,
+#'              smooth_intens = TRUE,
+#'              smooth_intens_args = list(
+#'                polynomial = 3,
+#'                window = 11,
+#'                derivative = 1
+#'                )
+#'              ) |>
+#'   lines(col = "darkgreen")
+#'
+#' @export
+process_spec <- function(x, ...) {
+  UseMethod("process_spec")
+}
+
+#' @rdname process_spec
+#'
+#' @export
+process_spec.default <- function(x, ...) {
+  stop("object 'x' needs to be of class 'OpenSpecy'")
+}
+
+#' @rdname process_spec
+#'
+#' @export
+process_spec.OpenSpecy <- function(x, active = TRUE,
+                                   adj_intens = FALSE,
+                                   adj_intens_args = list(
+                                     type = "none"
+                                     ),
+                                   conform_spec = TRUE,
+                                   conform_spec_args = list(
+                                     range = NULL, res = 5, type = "interp"
+                                     ),
+                                   restrict_range = FALSE,
+                                   restrict_range_args = list(
+                                     min = 0, max = 6000
+                                     ),
+                                   flatten_range = FALSE,
+                                   flatten_range_args = list(
+                                     min = 2200, max = 2420
+                                     ),
+                                   subtr_baseline = FALSE,
+                                   subtr_baseline_args = list(
+                                     type = "polynomial", degree = 8,
+                                     raw = FALSE, baseline = NULL),
+                                   smooth_intens = TRUE,
+                                   smooth_intens_args = list(
+                                     polynomial = 3, window = 11,
+                                     derivative = 1, abs = TRUE),
+                                   make_rel = TRUE,
+                                   ...) {
+  if(active) {
+    if(adj_intens)
+      x <- do.call("adj_intens", c(list(x, make_rel = F), adj_intens_args))
+    if(conform_spec)
+      x <- do.call("conform_spec", c(list(x), conform_spec_args))
+    if(restrict_range)
+      x <- do.call("restrict_range", c(list(x, make_rel = F),
+                                       restrict_range_args))
+    if(subtr_baseline)
+      x <- do.call("subtr_baseline", c(list(x, make_rel = F),
+                                       subtr_baseline_args))
+    if(flatten_range)
+      x <- do.call("flatten_range", c(list(x, make_rel = F),
+                                      flatten_range_args))
+    if(smooth_intens)
+      x <- do.call("smooth_intens", c(list(x, make_rel = F),
+                                      smooth_intens_args))
+    if(make_rel)
+      x$spectra <- x$spectra[, lapply(.SD, make_rel)]
+  }
+
+  return(x)
+}
diff --git a/R/raman_hdpe.R b/R/raman_hdpe.R
index 60843897..d379a34a 100644
--- a/R/raman_hdpe.R
+++ b/R/raman_hdpe.R
@@ -1,23 +1,27 @@
 #' @title Sample Raman spectrum
 #'
 #' @description
-#' Raman spectrum of high-density polyethylene (HDPE).
+#' Raman spectrum of high-density polyethylene (HDPE) provided by
+#' Horiba Scientific.
 #'
 #' @format
-#' A data table containing 964 rows and 2 columns:
+#' An threepart list of class \code{\link{OpenSpecy}} containing:
 #' \tabular{ll}{
-#' \code{wavenumber}: \tab spectral wavenumber [1/cm] \cr
-#' \code{intensity}: \tab absorbance values [-] \cr
+#' \code{wavenumber}: \tab spectral wavenumbers \[1/cm\] (vector of 964 rows) \cr
+#' \code{spectra}: \tab absorbance values [-]
+#'   (a \code{\link[data.table]{data.table}} with 964 rows and 1 column) \cr
+#' \code{metadata}: \tab spectral metadata \cr
 #' }
 #'
 #' @examples
-#' data("raman_hdpe")
+#' data(raman_hdpe)
+#' print(raman_hdpe)
 #'
 #' @author
-#' Win Cowger
+#' Zacharias Steinmetz, Win Cowger
 #'
 #' @references
-#' Cowger W, Gray A, Christiansen SH, Christiansen SH, Christiansen SH,
+#' Cowger W, Steinmetz Z, Gray A, Christiansen SH, Christiansen SH, Christiansen SH,
 #' De Frond H, Deshpande AD, Hemabessiere L, Lee E, Mill L, et al. (2020).
 #' “Critical Review of Processing and Classification Techniques for Images and
 #' Spectra in Microplastic Research.” \emph{Applied Spectroscopy},
diff --git a/R/read_envi.R b/R/read_envi.R
new file mode 100644
index 00000000..f93bd553
--- /dev/null
+++ b/R/read_envi.R
@@ -0,0 +1,150 @@
+#' @title Read ENVI data
+#'
+#' @description
+#' This function allows ENVI data import.
+#'
+#' @param file name of the binary file.
+#' @param header name of the ASCII header file. If `NULL`, the name of the
+#' header file is guessed by looking for a second file with the same basename as
+#' `file` but with .hdr extension.
+#' @param share defaults to \code{NULL}; needed to share spectra with the
+#' Open Specy community; see \code{\link{share_spec}()} for details.
+#' @param metadata a named list of the metadata; see
+#' \code{\link{as_OpenSpecy}()} for details.
+#' @param \ldots further arguments passed to the submethods.
+#'
+#' @details
+#' ENVI data usually consists of two files, an ASCII header and a binary data
+#' file. The header contains all information necessary for correctly reading
+#' the binary file via \code{\link[caTools]{read.ENVI}()}.
+#'
+#' @return
+#' An `OpenSpecy` object.
+#'
+#' @author Zacharias Steinmetz, Claudia Beleites
+#'
+#' @seealso
+#' \code{\link{read_spec}()} for reading .y(a)ml, .json, or .rds (OpenSpecy)
+#' files;
+#' \code{\link{read_text}()}, \code{\link{read_asp}()}, \code{\link{read_spa}()},
+#' \code{\link{read_spc}()}, and \code{\link{read_jdx}()} for text files, .asp,
+#' .spa, .spa, .spc, and .jdx formats, respectively;
+#' \code{\link{read_opus}()} for reading .0 (OPUS) files;
+#' \code{\link{read_zip}()} and \code{\link{read_any}()} for wrapper functions;
+#' \code{\link[caTools]{read.ENVI}()}
+#'
+#' @importFrom utils modifyList
+#' @importFrom data.table as.data.table dcast
+#' @importFrom caTools read.ENVI
+#' @export
+read_envi <- function(file, header = NULL, share = NULL,
+                      metadata = list(
+                        file_name = basename(file),
+                        user_name = NULL,
+                        contact_info = NULL,
+                        organization = NULL,
+                        citation = NULL,
+                        spectrum_type = NULL,
+                        spectrum_identity = NULL,
+                        material_form = NULL,
+                        material_phase = NULL,
+                        material_producer = NULL,
+                        material_purity = NULL,
+                        material_quality = NULL,
+                        material_color = NULL,
+                        material_other = NULL,
+                        cas_number = NULL,
+                        instrument_used = NULL,
+                        instrument_accessories = NULL,
+                        instrument_mode = NULL,
+                        spectral_resolution = NULL,
+                        laser_light_used = NULL,
+                        number_of_accumulations = NULL,
+                        total_acquisition_time_s = NULL,
+                        data_processing_procedure = NULL,
+                        level_of_confidence_in_identification = NULL,
+                        other_info = NULL,
+                        license = "CC BY-NC"),
+                      ...) {
+  if(is.null(header))
+    header <- sub(pattern = "(.*)\\..*$", replacement = "\\1", file) |>
+      paste0(".hdr")
+
+  hdr <- .read_envi_header(header)
+  arr <- read.ENVI(file, header)
+  dt <- as.data.table(arr)
+  md <- hdr[names(hdr) != "wavelength"]
+  names(dt) <- c("x", "y", "z", "value")
+  dt[, 1:2] <- dt[, 1:2] -1
+
+  os <- as_OpenSpecy(x = hdr$wavelength,
+                     spectra = dcast(dt, z ~ x + y)[, -1],
+                     metadata = c(metadata, md),
+                     coords = dt[, 1:2] |> unique(),
+                     session_id = T)
+
+  if (!is.null(share)) share_spec(os, file = file, share = share)
+
+  return(os)
+}
+
+.read_envi_header <- function(headerfile, ...) {
+  tr <- file.path(headerfile) |> file(...)
+  hdr <- tr |> readLines()
+  close(tr)
+
+  if(!grepl("ENVI", hdr[1])) {
+    stop("envi header not found", call. = F)
+  } else {
+    hdr <- hdr[-1]
+  }
+
+  hdr <- gsub("\\{([^}]*)\\}", "\\1", hdr)
+
+  l <- grep("\\{", hdr)
+  r <- grep("\\}", hdr)
+
+  if (length(l) != length(r) || any(r <= l))
+    stop("header data does not match", call. = F)
+
+  hdr[l] <- sub("\\{", "", hdr[l])
+  hdr[r] <- sub("\\}", "", hdr[r])
+
+  for (i in rev(seq_along(l))) {
+    hdr <- c(
+      hdr[seq_len(l[i] - 1)],
+      paste(hdr[l[i]:r[i]], collapse = " "),
+      hdr[-seq_len(r[i])]
+    )
+  }
+
+  hdr <- sapply(hdr, .split_line, "=", USE.NAMES = FALSE)
+  names(hdr) <- tolower(names(hdr))
+
+  tmp <- names(hdr) %in% c("samples", "lines", "bands", "data type",
+                           "header offset")
+  hdr[tmp] <- lapply(hdr[tmp], as.numeric)
+
+  hdr$wavelength <- strsplit(hdr$wavelength, "[,;[:blank:]]+") |> unlist() |>
+    as.numeric()
+
+  return(hdr)
+}
+
+.split_line <- function(x, sep, trim.blank = TRUE) {
+  tmp <- regexpr(sep, x)
+
+  key <- substr(x, 1, tmp - 1)
+  val <- substr(x, tmp + 1, nchar(x))
+
+  if (trim.blank) {
+    blank.pattern <- "^[[:blank:]]*([^[:blank:]]+.*[^[:blank:]]+)[[:blank:]]*$"
+    key <- sub(blank.pattern, "\\1", key)
+    val <- sub(blank.pattern, "\\1", val)
+  }
+
+  val <- as.list(val)
+  names(val) <- key
+
+  return(val)
+}
diff --git a/R/read_ext.R b/R/read_ext.R
new file mode 100644
index 00000000..b4e41353
--- /dev/null
+++ b/R/read_ext.R
@@ -0,0 +1,338 @@
+#' @rdname read_ext
+#'
+#' @title Read spectral data
+#'
+#' @description
+#' Functions for reading spectral data from external file types.
+#' Currently supported reading formats are .csv and other text files, .asp,
+#' .spa, .spc, and .jdx.
+#' Additionally, .0 (OPUS) and .dat (ENVI) files are supported via
+#' \code{\link{read_opus}()} and \code{\link{read_envi}()}, respectively.
+#' \code{\link{read_zip}()} takes any of the files listed above.
+#' Note that proprietary file formats like .0, .asp, and .spa are poorly
+#' supported but will likely still work in most cases.
+#'
+#' @param file file to be read from or written to.
+#' @param colnames character vector of \code{length = 2} indicating the column
+#' names for the wavenumber and intensity; if \code{NULL} columns are guessed.
+#' @param method submethod to be used for reading text files; defaults to
+#' \code{\link[data.table]{fread}()} but \code{\link[utils]{read.csv}()} works
+#' as well.
+#' @param share defaults to \code{NULL}; needed to share spectra with the
+#' Open Specy community; see \code{\link{share_spec}()} for details.
+#' @param metadata a named list of the metadata; see
+#' \code{\link{as_OpenSpecy}()} for details.
+#' @param \ldots further arguments passed to the submethods.
+#'
+#' @details
+#' \code{read_spc()} and \code{read_jdx()} are wrappers around the
+#' functions provided by the \link[hyperSpec:hyperSpec-package]{hyperSpec}.
+#' Other functions have been adapted various online sources.
+#' Metadata is harvested if possible.
+#' There are many unique iterations of spectral file formats so there may be
+#' bugs in the file conversion. Please contact us if you identify any.
+#'
+#' @return
+#' All \code{read_*()} functions return data frames containing two columns
+#' named \code{"wavenumber"} and \code{"intensity"}.
+#'
+#' @examples
+#' read_extdata("raman_hdpe.csv") |> read_text()
+#' read_extdata("raman_atacamit.spc") |> read_spc()
+#' read_extdata("ftir_ldpe_soil.asp") |> read_asp()
+#' read_extdata("testdata_zipped.zip") |> read_zip()
+#'
+#' @author
+#' Zacharias Steinmetz, Win Cowger
+#'
+#' @seealso
+#' \code{\link{read_spec}()} for reading .y(a)ml, .json, or .rds (OpenSpecy)
+#' files;
+#' \code{\link{read_opus}()} for reading .0 (OPUS) files;
+#' \code{\link{read_envi}()} for reading .dat (ENVI) files;
+#' \code{\link{read_zip}()} and \code{\link{read_any}()} for wrapper functions;
+#' \code{\link[hyperSpec]{read.jdx}()}; \code{\link[hyperSpec]{read.spc}()}
+#'
+#' @importFrom data.table data.table as.data.table fread
+#' @export
+read_text <- function(file, colnames = NULL, method = "fread",
+                      share = NULL,
+                      metadata = list(
+                        file_name = basename(file),
+                        user_name = NULL,
+                        contact_info = NULL,
+                        organization = NULL,
+                        citation = NULL,
+                        spectrum_type = NULL,
+                        spectrum_identity = NULL,
+                        material_form = NULL,
+                        material_phase = NULL,
+                        material_producer = NULL,
+                        material_purity = NULL,
+                        material_quality = NULL,
+                        material_color = NULL,
+                        material_other = NULL,
+                        cas_number = NULL,
+                        instrument_used = NULL,
+                        instrument_accessories = NULL,
+                        instrument_mode = NULL,
+                        spectral_resolution = NULL,
+                        laser_light_used = NULL,
+                        number_of_accumulations = NULL,
+                        total_acquisition_time_s = NULL,
+                        data_processing_procedure = NULL,
+                        level_of_confidence_in_identification = NULL,
+                        other_info = NULL,
+                        license = "CC BY-NC"),
+                      ...) {
+  dt <- do.call(method, list(file, ...)) |> as.data.table()
+
+  if (all(grepl("^X[0-9]*", names(dt)))) stop("missing header: ",
+                                              "use 'header = FALSE' or an ",
+                                              "alternative read method",
+                                              call. = F)
+
+  os <- as_OpenSpecy(dt, colnames = colnames, metadata = metadata,
+                     session_id = T)
+
+  if (!is.null(share)) share_spec(os, file = file, share = share)
+
+  return(os)
+}
+
+#' @rdname read_ext
+#'
+#' @export
+read_asp <- function(file, share = NULL,
+                     metadata = list(
+                       file_name = basename(file),
+                       user_name = NULL,
+                       contact_info = NULL,
+                       organization = NULL,
+                       citation = NULL,
+                       spectrum_type = NULL,
+                       spectrum_identity = NULL,
+                       material_form = NULL,
+                       material_phase = NULL,
+                       material_producer = NULL,
+                       material_purity = NULL,
+                       material_quality = NULL,
+                       material_color = NULL,
+                       material_other = NULL,
+                       cas_number = NULL,
+                       instrument_used = NULL,
+                       instrument_accessories = NULL,
+                       instrument_mode = NULL,
+                       spectral_resolution = NULL,
+                       laser_light_used = NULL,
+                       number_of_accumulations = NULL,
+                       total_acquisition_time_s = NULL,
+                       data_processing_procedure = NULL,
+                       level_of_confidence_in_identification = NULL,
+                       other_info = NULL,
+                       license = "CC BY-NC"),
+                     ...) {
+  if (!grepl("\\.asp$", ignore.case = T, file))
+    stop("file type should be 'asp'", call. = F)
+
+  tr <- file.path(file) |> file(...)
+  lns <- tr |> readLines() |> as.numeric()
+  close(tr)
+
+  y <- lns[-c(1:6)]
+  x <- seq(lns[2], lns[3], length.out = lns[1])
+
+  os <- as_OpenSpecy(x, data.table(intensity = y), metadata = metadata,
+                     session_id = T)
+
+  if (!is.null(share)) share_spec(os, file = file, share = share)
+
+  return(os)
+}
+
+#' @rdname read_ext
+#'
+#' @importFrom utils read.table
+#' @export
+read_spa <- function(file, share = NULL,
+                     metadata = list(
+                       file_name = basename(file),
+                       user_name = NULL,
+                       contact_info = NULL,
+                       organization = NULL,
+                       citation = NULL,
+                       spectrum_type = NULL,
+                       spectrum_identity = NULL,
+                       material_form = NULL,
+                       material_phase = NULL,
+                       material_producer = NULL,
+                       material_purity = NULL,
+                       material_quality = NULL,
+                       material_color = NULL,
+                       material_other = NULL,
+                       cas_number = NULL,
+                       instrument_used = NULL,
+                       instrument_accessories = NULL,
+                       instrument_mode = NULL,
+                       spectral_resolution = NULL,
+                       laser_light_used = NULL,
+                       number_of_accumulations = NULL,
+                       total_acquisition_time_s = NULL,
+                       data_processing_procedure = NULL,
+                       level_of_confidence_in_identification = NULL,
+                       other_info = NULL,
+                       license = "CC BY-NC"),
+                     ...) {
+  if (!grepl("\\.spa$", ignore.case = T, file))
+    stop("file type should be 'spa'", call. = F)
+
+  trb <- file.path(file) |> file(open = "rb", ...)
+
+  seek(trb, 576, origin = "start")
+  spr <- readBin(trb, "numeric", n = 2, size = 4)
+
+  if (!all(spr >= 0 & spr <= 15000 & spr[1] > spr[2]))
+    stop("unknown spectral range", call. = F)
+
+  # Read the start offset
+  seek(trb, 386, origin = "start")
+  startOffset <- readBin(trb, "int", n = 1, size = 2)
+  # Read the length
+  seek(trb, 390, origin = "start")
+  readLength <- readBin(trb, "int", n = 1, size = 2)
+
+  # seek to the start
+  seek(trb, startOffset, origin = "start")
+
+  # we'll read four byte chunks
+  floatCount <- readLength / 4
+
+  # read all our floats
+  floatData <- c(readBin(trb, "double", floatCount, size = 4))
+
+  close(trb)
+
+  x <- seq(spr[1], spr[2], length = length(floatData))
+  y <- floatData
+
+  os <- as_OpenSpecy(x, data.table(intensity = y), metadata = metadata,
+                     session_id = T)
+
+  if (!is.null(share)) share_spec(os, file = file, share = share)
+
+  return(os)
+}
+
+
+#' @rdname read_ext
+#'
+#' @importFrom hyperSpec read.spc
+#' @export
+read_spc <- function(file, share = NULL,
+                     metadata = list(
+                       file_name = basename(file),
+                       user_name = NULL,
+                       contact_info = NULL,
+                       organization = NULL,
+                       citation = NULL,
+                       spectrum_type = NULL,
+                       spectrum_identity = NULL,
+                       material_form = NULL,
+                       material_phase = NULL,
+                       material_producer = NULL,
+                       material_purity = NULL,
+                       material_quality = NULL,
+                       material_color = NULL,
+                       material_other = NULL,
+                       cas_number = NULL,
+                       instrument_used = NULL,
+                       instrument_accessories = NULL,
+                       instrument_mode = NULL,
+                       spectral_resolution = NULL,
+                       laser_light_used = NULL,
+                       number_of_accumulations = NULL,
+                       total_acquisition_time_s = NULL,
+                       data_processing_procedure = NULL,
+                       level_of_confidence_in_identification = NULL,
+                       other_info = NULL,
+                       license = "CC BY-NC"),
+                     ...) {
+  spc <- read.spc(file)
+
+  x <- spc@wavelength
+  y <- as.numeric(unname(spc@data$spc[1,]))
+
+  os <- as_OpenSpecy(x, data.table(intensity = y), metadata = metadata,
+                     session_id = T)
+
+  if (!is.null(share)) share_spec(os, file = file, share = share)
+
+  return(os)
+}
+
+#' @rdname read_ext
+#'
+#' @importFrom hyperSpec read.jdx
+#' @export
+read_jdx <- function(file, share = NULL,
+                     metadata = list(
+                       file_name = basename(file),
+                       user_name = NULL,
+                       contact_info = NULL,
+                       organization = NULL,
+                       citation = NULL,
+                       spectrum_type = NULL,
+                       spectrum_identity = NULL,
+                       material_form = NULL,
+                       material_phase = NULL,
+                       material_producer = NULL,
+                       material_purity = NULL,
+                       material_quality = NULL,
+                       material_color = NULL,
+                       material_other = NULL,
+                       cas_number = NULL,
+                       instrument_used = NULL,
+                       instrument_accessories = NULL,
+                       instrument_mode = NULL,
+                       spectral_resolution = NULL,
+                       laser_light_used = NULL,
+                       number_of_accumulations = NULL,
+                       total_acquisition_time_s = NULL,
+                       data_processing_procedure = NULL,
+                       level_of_confidence_in_identification = NULL,
+                       other_info = NULL,
+                       license = "CC BY-NC"),
+                     ...) {
+  jdx <- read.jdx(file, ...)
+
+  x <- jdx@wavelength
+  y <- as.numeric(unname(jdx@data$spc[1,]))
+
+  lns <- readLines(file)
+  test <- lns[grepl("##", lns)|grepl("[:alpha:]", lns)]
+  vals <- ifelse(grepl("##",test), gsub("##.{1,}=", "", test),
+                   gsub(".{1,}:", "", test))
+  names <- ifelse(grepl("##",test), gsub("##", "", gsub("=.{1,}", "", test)),
+                  gsub(":.{1,}", "", test))
+  df_metadata <- as.data.table(t(vals))
+  colnames(df_metadata) <- names
+
+  os <- as_OpenSpecy(x, data.table(intensity = y), metadata = df_metadata,
+                     session_id = T)
+
+  if (!is.null(share)) share_spec(os, file = file, share = share)
+
+  return(os)
+}
+
+#' @rdname read_ext
+#'
+#' @export
+read_extdata <- function(file = NULL) {
+  if (is.null(file)) {
+    dir(system.file("extdata", package = "OpenSpecy"))
+  }
+  else {
+    system.file("extdata", file, package = "OpenSpecy", mustWork = TRUE)
+  }
+}
diff --git a/R/read_multi.R b/R/read_multi.R
new file mode 100644
index 00000000..912f4184
--- /dev/null
+++ b/R/read_multi.R
@@ -0,0 +1,80 @@
+#' @rdname read_multi
+#' @title Read spectral data from multiple files
+#'
+#' @description
+#' Wrapper functions for reading files in batch.
+#'
+#' @details
+#' \code{read_any()} provides a single function to quickly read in any of the
+#' supported formats, it assumes that the file extension will tell it how to
+#' process the spectra.
+#' \code{read_zip()} provides functionality for reading in spectral map files
+#' with ENVI file format or as individual files in a zip folder. If individual
+#' files, spectra are concatenated.
+#'
+#' @param file file to be read from or written to.
+#' @param \ldots further arguments passed to the submethods.
+#'
+#' @return
+#' All \code{read_*()} functions return \code{OpenSpecy} objects
+#'
+#' @examples
+#' read_extdata("raman_hdpe.csv") |> read_any()
+#' read_extdata("ftir_ldpe_soil.asp") |> read_any()
+#' read_extdata("ftir_ps.0") |> read_any()
+#' read_extdata("testdata_zipped.zip") |> read_zip()
+#' read_extdata("CA_tiny_map.zip") |> read_zip()
+#'
+#' @author
+#' Zacharias Steinmetz, Win Cowger
+#'
+#' @seealso
+#' \code{\link{read_spec}()}
+#'
+#' @export
+read_any <- function(file, ...) {
+  if (grepl("(\\.csv$)|(\\.txt$)", ignore.case = T, file)) {
+    os <- read_text(file = file, ...)
+  } else if (grepl("\\.[0-999]$", ignore.case = T, file)) {
+    os <- read_opus(file = file, ...)
+  } else if (grepl("(\\.asp$)|(\\.spa$)|(\\.spc$)|(\\.jdx$)",
+                   ignore.case = T, file)) {
+    ex <- strsplit(basename(file), split="\\.")[[1]][-1]
+    os <- do.call(paste0("read_", tolower(ex)), list(file = file, ...))
+  } else if (grepl("(\\.zip$)", ignore.case = T, file)) {
+    os <- read_zip(file = file, ...)
+  } else {
+    os <- read_spec(file = file, ...)
+  }
+
+  return(os)
+}
+
+#' @rdname read_multi
+#'
+#' @importFrom utils unzip
+#' @importFrom data.table transpose
+#' @export
+read_zip <- function(file, ...) {
+  flst <- unzip(zipfile = file, list = T)
+
+  tmp <- file.path(tempdir(), "OpenSpecy-unzip")
+  dir.create(tmp, showWarnings = F)
+
+  unzip(file, exdir = tmp)
+
+  if(nrow(flst) == 2 & any(grepl("\\.dat$", ignore.case = T, flst$Name)) &
+      any(grepl("\\.hdr$", ignore.case = T, flst$Name))) {
+    dat <- flst$Name[grepl("\\.dat$", ignore.case = T, flst$Name)]
+    hdr <- flst$Name[grepl("\\.hdr$", ignore.case = T, flst$Name)]
+
+    os <- read_envi(file.path(tmp, dat), file.path(tmp, hdr), ...)
+  } else {
+    lst <- lapply(file.path(tmp, flst$Name), read_any, ...)
+
+    os <- c_spec(lst)
+  }
+
+  unlink(tmp, recursive = T)
+  return(os)
+}
diff --git a/R/read_opus.R b/R/read_opus.R
new file mode 100644
index 00000000..d8a4bcf2
--- /dev/null
+++ b/R/read_opus.R
@@ -0,0 +1,189 @@
+#' @title Read spectral data from Bruker OPUS binary files
+#'
+#' @description
+#' Read file(s) acquired with a Bruker Vertex FTIR Instrument. This function
+#' is basically a fork of \code{opus_read()} from
+#' \url{https://github.com/pierreroudier/opusreader}.
+#'
+#' @param file character vector with path to file(s).
+#' @param share defaults to \code{NULL}; needed to share spectra with the
+#' Open Specy community; see \code{\link{share_spec}()} for details.
+#' @param metadata a named list of the metadata; see
+#' \code{\link{as_OpenSpecy}()} for details.
+#' @param type character vector of spectra types to extract from OPUS binary
+#' file. Default is `"spec"`, which will extract the final spectra, e.g.
+#' expressed in absorbance (named `AB` in Bruker OPUS programs). Possible
+#' additional values for the character vector supplied to `type` are
+#' `"spec_no_atm_comp"` (spectrum of the sample without compensation for
+#' atmospheric gases, water vapor and/or carbon dioxide),
+#' `"sc_sample"` (single channel spectrum of the sample measurement), `"sc_ref"`
+#' (single channel spectrum of the reference measurement),
+#' `"ig_sample"` (interferogram of the sample measurement) and `"ig_ref"`
+#' (interferogram of the reference measurement).
+#' @param digits Integer that specifies the number of decimal places used to
+#' round the wavenumbers (values of x-variables).
+#' @param atm_comp_minus4offset Logical whether spectra after atmospheric
+#' compensation are read with an offset of -4 bytes from Bruker OPUS files;
+#' default is `FALSE`.
+#'
+#' @details
+#' The type of spectra returned by the function when using
+#' `type = "spec"` depends on the setting of the Bruker instrument: typically,
+#' it can be either absorbance or reflectance.
+#'
+#' The type of spectra to extract from the file can also use Bruker's OPUS
+#' software naming conventions, as follows:
+#'
+#' - `ScSm` corresponds to `sc_sample`
+#' - `ScRf` corresponds to `sc_ref`
+#' - `IgSm` corresponds to `ig_sample`
+#' - `IgRf` corresponds to `ig_ref`
+#'
+#' @return
+#' An \code{OpenSpecy} object.
+#'
+#' @examples
+#' read_extdata("ftir_ps.0") |> read_opus()
+#'
+#' @author Philipp Baumann, Zacharias Steinmetz, Win Cowger
+#'
+#' @seealso
+#' \code{\link{read_spec}()} for reading .y(a)ml, .json, or .rds (OpenSpecy)
+#' files;
+#' \code{\link{read_text}()}, \code{\link{read_asp}()}, \code{\link{read_spa}()},
+#' \code{\link{read_spc}()}, and \code{\link{read_jdx}()} for text files, .asp,
+#' .spa, .spa, .spc, and .jdx formats, respectively;
+#' \code{\link{read_text}()} for reading .dat (ENVI) files;
+#' \code{\link{read_zip}()} and \code{\link{read_any}()} for wrapper functions;
+#' \code{\link{read_opus_raw}()};
+#'
+#' @importFrom stats approx
+#' @importFrom data.table as.data.table data.table
+#' @export
+read_opus <- function(file, share = NULL,
+                      metadata = list(
+                        file_name = basename(file),
+                        user_name = NULL,
+                        contact_info = NULL,
+                        organization = NULL,
+                        citation = NULL,
+                        spectrum_type = NULL,
+                        spectrum_identity = NULL,
+                        material_form = NULL,
+                        material_phase = NULL,
+                        material_producer = NULL,
+                        material_purity = NULL,
+                        material_quality = NULL,
+                        material_color = NULL,
+                        material_other = NULL,
+                        cas_number = NULL,
+                        instrument_used = NULL,
+                        instrument_accessories = NULL,
+                        instrument_mode = NULL,
+                        spectral_resolution = NULL,
+                        laser_light_used = NULL,
+                        number_of_accumulations = NULL,
+                        total_acquisition_time_s = NULL,
+                        data_processing_procedure = NULL,
+                        level_of_confidence_in_identification = NULL,
+                        other_info = NULL,
+                        license = "CC BY-NC"),
+                      type = "spec", digits = 1L,
+                      atm_comp_minus4offset = FALSE) {
+  if (!all(grepl("\\.[0-999]$", ignore.case = T, file)))
+    stop("file type should be '0'", call. = F)
+
+  res <- lapply(
+    file,
+    function(fn) {
+
+      if (!file.exists(fn)) stop(paste0("file '", fn, "' does not exist"),
+                                 call. = F)
+
+      # Get raw vector
+      rw <- readBin(fn, "raw", n = file.size(fn))
+      out <- read_opus_raw(rw, type = type,
+                           atm_comp_minus4offset = atm_comp_minus4offset)
+
+      return(out)
+    })
+
+  # If there was only one file to read, we un-nest the list one level
+  if (length(file) == 1) {
+    res <- res[[1]]
+    res$spec <- as.data.table(c(res$spec))
+  } else {
+    if (length(type) > 1) {
+      stop("simple output is currently only implemented for one value of the ",
+           "`type` option.\nA workaround this limitation is to use the ",
+           "`lapply` function, e.g.:\n\nlapply(c('spec', 'sc_ref'), ",
+           "function(x) read_opus(file, type = x))", call. = F)
+    }
+
+    # Fetch wavenumbers
+    wns <- lapply(res, function(x) round(x$wavenumbers, digits = digits))
+
+    # Arbitrarily take the first rounded WN as the reference one
+    wn_ref <- wns[[1]]
+
+    # Check the wavenumbers have all the same length
+    if (length(unique(sapply(wns, length))) > 1) {
+      warning("Spectra don't all have the same number of wavenumbers; ",
+              "interpolation will be used to combine them in a matrix",
+              call. = F)
+    }
+
+    specs <- lapply(
+      res,
+      function(x) {
+
+        id <- switch(type,
+                     spec = "spec",
+                     spec_no_atm_comp = "spec_no_atm_comp",
+                     sc_sample = "sc_sample",
+                     sc_ref = "sc_ref",
+                     ig_sample = "ig_sample",
+                     ig_ref = "ig_ref"
+        )
+
+        # Grab correct wavenumbers for interpolation
+        wn <- switch(type,
+                     spec = x$wavenumbers,
+                     spec_no_atm_comp = x$wavenumbers,
+                     sc_sample = x$wavenumbers_sc_sample,
+                     sc_ref = x$wavenumbers_sc_ref,
+                     ig_sample = x$wavenumbers_sc_sample,
+                     ig_ref = x$wavenumbers_sc_ref
+        )
+
+        # Linear interpolation to get spectra at rounded wavenumber
+        s <- approx(
+          x = wn,
+          y = x[[id]],
+          xout = wn_ref,
+          method = "linear"
+        )$y
+        names(s) <- as.character(wn_ref)
+
+        return(s)
+      })
+
+    md <- do.call(rbind, lapply(res, function(x) x$metadata))
+
+    res <- list(
+      wavenumbers = wns[[1]],
+      spec = as.data.table(specs),
+      metadata = as.data.table(md)
+    )
+
+  }
+
+  os <- as_OpenSpecy(x = res$wavenumbers,
+                     spectra = res$spec,
+                     metadata = cbind(as.data.table(metadata), res$metadata),
+                     session_id = T)
+
+  if (!is.null(share)) share_spec(os, file = file, share = share)
+
+  return(os)
+}
diff --git a/R/read_opus_raw.R b/R/read_opus_raw.R
new file mode 100644
index 00000000..53df1e7e
--- /dev/null
+++ b/R/read_opus_raw.R
@@ -0,0 +1,1027 @@
+#' @title Read a Bruker OPUS spectrum binary raw string
+#'
+#' @description
+#' Read single binary acquired with an Bruker Vertex FTIR Instrument
+#'
+#' @param rw a raw vector
+#' @param type character vector of spectra types to extract from OPUS binary
+#' file. Default is `"spec"`, which will extract the final spectra, e.g.
+#' expressed in absorbance (named `AB` in Bruker OPUS programs). Possible
+#' additional values for the character vector supplied to `type` are
+#' `"spec_no_atm_comp"` (spectrum of the sample without compensation for
+#' atmospheric gases, water vapor and/or carbon dioxide),
+#' `"sc_sample"` (single channel spectrum of the sample measurement),
+#' `"sc_ref"` (single channel spectrum of the reference measurement),
+#' `"ig_sample"` (interferogram of the sample measurement) and `"ig_ref"`
+#' (interferogram of the reference measurement).
+#' @param atm_comp_minus4offset logical; whether spectra after atmospheric
+#' compensation are read with an offset of -4 bytes from Bruker OPUS
+#' files. Default is `FALSE`.
+#'
+#' @details
+#' The type of spectra returned by the function when using
+#' `type = "spec"` depends on the setting of the Bruker instrument: typically,
+#' it can be either absorbance or reflectance.
+#'
+#' The type of spectra to extract from the file can also use Bruker's OPUS
+#' software naming conventions, as follows:
+#'
+#' - `ScSm` corresponds to `sc_sample`
+#' - `ScRf` corresponds to `sc_ref`
+#' - `IgSm` corresponds to `ig_sample`
+#' - `IgRf` corresponds to `ig_ref`
+#'
+#' @return
+#' A list of 10 elements:
+#'
+#' \describe{
+#'   \item{`metadata`}{a `data.frame` containing metadata from the OPUS file.}
+#'   \item{`spec`}{if `"spec"` was requested in the `type` option, a matrix of
+#'   the spectrum of the sample (otherwise set to `NULL`).}
+#'   \item{`spec_no_atm_comp`}{if `"spec_no_atm_comp"` was requested in the
+#'   `type` option, a matrix of the spectrum of the sample without atmospheric
+#'   compensation (otherwise set to `NULL`).}
+#'   \item{`sc_sample`}{if `"sc_sample"` was requested in the `type` option, a
+#'   matrix of the single channel spectrum of the sample (otherwise set to
+#'   `NULL`).}
+#'   \item{`sc_ref`}{if `"sc_ref"` was requested in the `type` option, a matrix
+#'   of the single channel spectrum of the reference (otherwise set to `NULL`).}
+#'   \item{`ig_sample`}{if `"ig_sample"` was requested in the `type` option, a
+#'   matrix of the interferogram of the sample (otherwise set to `NULL`).}
+#'   \item{`ig_ref`}{if `"ig_ref"` was requested in the `type` option, a matrix
+#'   of the interferogram of the reference (otherwise set to `NULL`).}
+#'   \item{`wavenumbers`}{if `"spec"` or `"spec_no_atm_comp"` was requested in
+#'   the `type` option, a numeric vector of the wavenumbers of the spectrum of
+#'   the sample (otherwise set to `NULL`).}
+#'   \item{`wavenumbers_sc_sample`}{if `"sc_sample"` was requested in the `type`
+#'   option, a numeric vector of the wavenumbers of the single channel spectrum
+#'   of the sample (otherwise set to `NULL`).}
+#'   \item{`wavenumbers_sc_ref`}{if `"sc_ref"` was requested in the `type`
+#'   option, a numeric vector of the wavenumbers of the single channel spectrum
+#'   of the reference (otherwise set to `NULL`).}
+#' }
+#'
+#' @author Philipp Baumann and Pierre Roudier
+#'
+#' @seealso
+#' \code{\link{read_opus}()}
+#'
+#' @importFrom stats setNames
+#' @export
+read_opus_raw <- function(rw, type = "spec", atm_comp_minus4offset = FALSE) {
+  # Silently support and convert the default Bruker values
+  type <- switch (type,
+    "spc" = "spec", # for backwards compatibility
+    "spc_nocomp" = "spec_no_atm_comp", # for backwards compatibility
+    "ScSm" = "sc_sample",
+    "ScRf" = "sc_ref",
+    "IgSm" = "ig_sample",
+    "IgRf" = "ig_ref",
+    type
+  )
+
+  # Sanity check on `type`
+  if (!all(type %in% c("spec", "spec_no_atm_comp", "sc_sample", "sc_ref",
+                       "ig_sample", "ig_ref"))) {
+    stop("Invalid value for the `type` option.", call. = F)
+  }
+
+  # Avoid `R CMD check` NOTE: no visible binding for global variable ...
+  x <- y <- i <- npt <- NULL
+
+  # do not stop if one OPUS has erroneous parsing for any reason
+  try({
+
+  # Read byte positions for selected 3 letter strings that flag important
+  # spectral information -------------------------------------------------------
+
+  # Get positions of "END" strings
+  end <- grepRaw("END", rw, all = TRUE) + 11
+  # Get all positions of "NPT" (number of points) string
+  npt_all <- grepRaw("NPT", rw, all = TRUE) + 3
+  # Get frequency of first (FXV) and last point (LXV) positions
+  fxv_all <- grepRaw("FXV", rw, all = TRUE) + 7
+  lxv_all <- grepRaw("LXV", rw, all = TRUE) + 7
+
+  # For some files, the number of positions where "FXV" and "LXV" occur
+  # are not equal, e.g. for the file in
+  # data/soilspec_esal_bin/BF_mo_01_soil_cal.0 ; As a consequence, the
+  # fist and last point numbers (e.g. wavenumber or points for interferograms)
+  # are not correctly read. This results in an error when trying to calculate
+  # the wavenumbers; The below code is a quick and dirty fix to remove
+  # FXV values that don't have LXV values and vice versa
+  # (difference between "LXV" and "FXV" for a spectral data block
+  # should be 16) --------------------------------------------------------------
+  if (length(fxv_all) > length(lxv_all)) {
+
+    # We detect the `fxv_all` values to remove by finding those that do NOT have
+    # a 16 bit difference with the `lxv_all` values
+    idx_extra <- which(!(fxv_all + 16) %in% lxv_all)
+    fxv_all <- fxv_all[-idx_extra]
+  }
+
+  if (length(lxv_all) > length(fxv_all)) {
+
+    # We detect the `lxv_all` values to remove by finding those that do NOT have
+    # a 16 bit difference with the `fxv_all` values
+    idx_extra <- which(!(lxv_all - 16) %in% fxv_all)
+    lxv_all <- lxv_all[-idx_extra]
+  }
+
+  # Reduce size of npt_all -----------------------------------------------------
+  # Some files have an extra "NPT" string without FXV, LXV, and spectral block
+  if (length(npt_all) != length(fxv_all)) {
+
+    diff_npt_fxv <- lapply(npt_all, function(x) fxv_all - x)
+
+    min_bigger0_smallerequal40 <- lapply(
+      diff_npt_fxv,
+      function(x) {
+        which_min_bigger0 <- x == min(x[x > 0])
+        which_smallerequal40 <- x <= 40
+        which_min_bigger0 & which_smallerequal40
+      }
+    )
+
+    which_npt_valid <- vapply(
+      min_bigger0_smallerequal40,
+      FUN = function(x) any(x == TRUE),
+      FUN.VALUE = logical(1)
+    )
+
+    npt_all <- npt_all[which_npt_valid]
+  }
+
+  # ----------------------------------------------------------------------------
+
+  ## Read basic spectral information ===========================================
+  con <- rawConnection(rw)
+
+  # Read all number of points (NPT) at once
+  NPT <- vapply(
+    npt_all,
+    FUN = function(npt) {
+      seek(con, npt, origin = "start", rw = "read")
+      readBin(con, what = "integer", n = 12, size = 4)[2]
+    },
+    FUN.VALUE = numeric(1)
+  )
+
+  # Specific error for file: <"data/soilspec_eth_bin/CI_tb_05_soil_cal.2">
+  # "Invalid number of bytes" when trying to read spectra
+  # -> Reason: NPT at position 1 is 995236000 !!!
+  # Omit this entry in NPT and corresponding byte position in npt_all
+  # Quick fix ------------------------------------------------------------------
+  npt_all <- npt_all[NPT < 40000]
+  NPT <- NPT[NPT < 40000]
+
+  # ----------------------------------------------------------------------------
+
+  # Figure out how many spectral blocks exist and select final spectra
+  # positions; end_spc is vector of offsets where spectra start
+  if (length(end) == 1) {
+    end_spc <- end
+  } else {
+    end_spc <- end[diff(end) > 4 * min(NPT)]
+  }
+
+  ## Find final spectra information block positions
+  ## that belong to spectra data ===============================================
+
+  # Save positions that contain possible spectra data block
+  # standard parameters
+  spc_param_list <- list(
+    'npt' = npt_all,
+    'fxv' = fxv_all,
+    'lxv' = lxv_all
+  )
+
+  ## Return list of final parameters corresponding to data blocks that contain
+  ## spectra, elements are npt (number of points),
+  ## fxv (frequency of first point) and lxv (frequency of last point);
+  ## returned values represent byte positions in the file where spectra
+  ## parameters are stored. ----------------------------------------------------
+  return_spc_param <- function(end_spc, spc_param_list) {
+
+    # Difference between any NPT position vector elements end_spc element
+    # (end_spc[i] is a scalar, constant value at iteration i)
+    diff_l <- lapply(end_spc, function(x) npt_all - x)
+    # Test of any vector in list contains -164 (returns list of vectors
+    # TRUE or FALSE)
+    isminus164 <- lapply(diff_l, function(x) x == -164)
+
+    # Find minimum positive difference within each list
+    if (length(diff_l) == 1) {sel_min <- list(TRUE)} else {
+      sel_min <- lapply(diff_l,
+                        function(x) {
+                          if (any(x > 0)) {
+                            x == min(x[x > 0])
+                          } else {x == -164}
+                        })
+    }
+
+    # Set FALSE repeated vector in sel_min element where TRUE positions are
+    # duplicated
+    which_elem_dupl <- which(duplicated(vapply(sel_min, FUN = which,
+                                               FUN.VALUE = numeric(1))))
+
+    if (length(which_elem_dupl) > 1) {
+      sel_min[which_elem_dupl] <- NULL
+      # Reduce end_spc with duplicated elements
+      end_spc <- end_spc[- which_elem_dupl]
+    }
+
+    # Select minimum difference NPT position for each END position
+    npt_min <- Map(
+      function(x, y) x[y],
+      rep(list(npt_all), length(end_spc)),
+      sel_min
+    )
+
+    npt_min <- Filter(length, npt_min)
+
+    # Select spectra parameters that immediately follow END positions before
+    # corresponding spectra
+    param_min <- setNames(
+      lapply(
+        seq_along(spc_param_list),
+        function(i) {
+          Map(function(x, y) x[y],
+              rep(list(spc_param_list[[i]]), length(end_spc)),
+                  sel_min)
+        }
+      ),
+      names(spc_param_list)
+    )
+
+    # Test if any difference in list is -164
+    if (any(unlist(isminus164) == TRUE)) {
+      # Find all list element that contain TRUE in logical vector
+      minus164 <- lapply(isminus164, function(x) Find(isTRUE, x))
+      # Return element position of last TRUE in list
+      where <- function(f, x) {
+        vapply(x, f, logical(1))
+      }
+      last_minus164 <- Position(isTRUE, where(isTRUE, minus164),
+                                right = TRUE)
+      # Replace positions in parameter list are at positions of last
+      # -164 difference between end_spc element and NPT position
+      param_min <- setNames(
+        lapply(
+          seq_along(spc_param_list),
+          function(i) {
+            param_min[[i]][[last_minus164]] <-
+              spc_param_list[[i]][isminus164[[last_minus164]]]
+              param_min[[i]]
+          }
+        ),
+        names(spc_param_list)
+      )
+    }
+    # Return list of final parameters corresponding to data blocks that
+    # contain spectra
+    param_spc <- lapply(param_min, unlist)
+    param_spc$end_spc <- end_spc
+    param_spc
+  }
+
+  # Save spectra parameter list
+  param_spc <- return_spc_param(end_spc, spc_param_list)
+
+  # Create individual vectors containing spectra parameters
+  npt_spc <- param_spc[["npt"]]
+  fxv_spc <- param_spc[["fxv"]]
+  lxv_spc <- param_spc[["lxv"]]
+  end_spc <- param_spc[["end_spc"]]
+
+  # Read number of points corresponding to spectra in file ---------------------
+  NPT_spc <- vapply(
+    seq_along(npt_spc),
+    FUN = function(i) {
+      seek(con, npt_spc[i], origin = "start", rw = "read")
+      readBin(con, what = "integer", n = 12, size = 4)[2]
+    },
+    FUN.VALUE = numeric(1)
+  )
+
+  # Delete NPT with negative signs
+  NPT_spc <- NPT_spc[NPT_spc > 0]
+
+  ## Read all spectra ==========================================================
+
+  spc <- Map(
+    function(end, NPT) {
+      seek(con, end - 4, origin = "start", rw = "read")
+      readBin(con, what = "numeric", n = NPT, size = 4, endian = "little")
+    },
+    end_spc,
+    NPT_spc
+  )
+
+  # Read FXV and LXV and calculate wavenumbers  --------------------------------
+
+  FXV_spc <- vapply(
+    fxv_spc,
+    FUN = function(fxv_spc) {
+      seek(con, fxv_spc, origin = "start", rw = "read")
+      readBin(con, what = "numeric", n = 16, size = 8)[1]
+    },
+    FUN.VALUE = numeric(1)
+  )
+
+  LXV_spc <- vapply(
+    lxv_spc,
+    FUN = function(lxv_spc) {
+      seek(con, lxv_spc, origin = "start", rw = "read")
+      readBin(con, what = "numeric", n = 16, size = 8)[1]
+    },
+    FUN.VALUE = numeric(1)
+  )
+
+  # Calculate wavenumbers
+  wavenumbers <- lapply(
+    seq_along(FXV_spc),
+    function(i) {
+      rev(seq(LXV_spc[i], FXV_spc[i], (FXV_spc[i] - LXV_spc[i]) /
+                (NPT_spc[i] - 1)))
+    }
+  )
+
+  ## Assigning list of initially read spectra depending on block type ==========
+
+  # Assign an index name to the spectra and parameters for reading
+  names(end_spc) <- paste0("idx", 1:length(end_spc))
+  names(spc) <- paste0("idx", 1:length(spc))
+  names(NPT_spc) <- paste0("idx", 1:length(NPT_spc))
+  names(FXV_spc) <- paste0("idx", 1:length(FXV_spc))
+  names(wavenumbers) <- paste0("idx", 1:length(wavenumbers))
+
+  # Check if elements in FXV_spc (frequency of first point) are equal to 0;
+  # these are interferogram spectra
+  which_Ig <- FXV_spc[which(FXV_spc == 0)]
+  Ig_assigned <- if (length(which_Ig) == 0) {
+    NULL
+  } else if (length(which_Ig) == 1) {
+    list(
+      spc_idx = names(which_Ig),
+      spc_code = "ig_sample"
+    )
+  } else if (length(which_Ig) == 3) {
+    list(
+      spc_idx = names(which_Ig)[c(1, 3)],
+      spc_code = c("ig_sample", "ig_ref")
+    )
+  } else {
+    list(
+      spc_idx = names(which_Ig),
+      spc_code = c("ig_sample", "ig_ref")
+    )
+  }
+
+  na_assigned <- list(
+    spc_idx = NULL,
+    spc_code = NULL
+  )
+  if (length(which_Ig) == 3) {
+    # Assign duplicated interferogram spectrum to 'not available' assigned
+    na_assigned <- list(
+      spc_idx = names(which_Ig)[2],
+      spc_code = NA
+    )
+  }
+
+  # Remove NA assigned spectra in spc list -------------------------------------
+  if (!is.null(na_assigned$spc_idx)) {
+    spc[na_assigned$spc_idx] <- NULL
+    # Remove wavenumbers with NA assigned spectra in spc list
+    wavenumbers[na_assigned$spc_idx] <- NULL
+  }
+
+  # Assign single channel spectra if present in file ---------------------------
+
+  # Return idx (index names) of all remaining spectra that are not
+  # interferograms
+  notIg <- names(spc)[
+    !names(spc) %in% c(Ig_assigned$spc_idx, na_assigned$spc_idx)
+  ]
+
+  # Check if the MIR range was measured
+  wavenumbers_mir <- lapply(
+    names(wavenumbers[notIg]),
+    function(i) {
+      spc[[i]][wavenumbers[notIg][[i]] < 2392 &
+      wavenumbers[notIg][[i]] > 2358]
+    })
+
+  is_mir <- any(
+    vapply(
+      wavenumbers_mir,
+      FUN = function(x) {length(x) != 0},
+      FUN.VALUE = logical(1)
+    )
+  )
+
+  if (isTRUE(is_mir)) {
+    # Calculate peak ratio for absorbance at around 2392 cm^(-1)
+    # and 2358 cm^(-1)
+    peak_ratio <- lapply(
+      lapply(names(wavenumbers[notIg]),
+             function(i) {
+               spc[[i]][wavenumbers[notIg][[i]] < 2392 &
+                        wavenumbers[notIg][[i]] > 2358]
+             }),
+      function(j) j[[1]] / j[[length(j)]]
+    )
+    names(peak_ratio) <- names(spc[notIg])
+    # Single channel (Sc) assignment list
+    which_Sc <- names(which(peak_ratio > 2))
+  } else {
+    peak_ratio <- lapply(
+      lapply(
+        names(wavenumbers[notIg]),
+        function(i) {
+          spc[[i]][wavenumbers[notIg][[i]] < 5340 &
+                     wavenumbers[notIg][[i]] > 5318]
+        }
+      ),
+      function(j) {
+        j[[1]] / j[[length(j)]]
+      }
+    )
+    names(peak_ratio) <- names(spc[notIg])
+    # Single channel (Sc) assignment list
+    which_Sc <- names(which(peak_ratio < 0.9))
+  }
+
+  # browser()
+
+  # Check for single channel, exclude spectral blocks already assigned to
+  # interferograms
+  Sc_assigned <- if (length(which_Sc) == 0) {
+    NULL
+  } else if (length(which_Sc) == 1) {
+    list(
+      spc_idx = which_Sc,
+      spc_code = "sc_sample"
+    )
+  } else {
+    list(
+      spc_idx = which_Sc,
+      spc_code = c("sc_sample", "sc_ref")
+    )
+  }
+
+  # Assign corrected and uncorrected (if present) ------------------------------
+  # AB spectra list
+  which_AB <- names(spc)[
+    !names(spc) %in% c(
+      Ig_assigned[["spc_idx"]],
+      na_assigned[["spc_idx"]],
+      Sc_assigned[["spc_idx"]]
+      )
+    ]
+
+  AB_assigned <- if (length(which_AB) == 1) {
+    list(
+      spc_idx = which_AB,
+      spc_code = "spec"
+    )
+  } else {
+    list(
+      spc_idx = which_AB,
+      spc_code = c("spec_no_atm_comp", "spec")
+    )
+  }
+
+  # Read result spectrum with new offset (no `-4`) when atmospheric
+  # compensation was done by the OPUS software; replace the spectrum position
+  # with index name idx that corresponds to final spectrum after atmospheric
+  # compensation; OPUS files from particular spectrometers/OPUS software
+  # versions do still need the same offset end_spc[[spc_idx]] - 4 as the other
+  # spectra types; new argument atm_comp_minus4offset (default FALSE) is a
+  # quick fix to read files with different offsets after atmospheric
+  # compensation ---------------------------------------------------------------
+  if (length(which_AB) == 2 && !atm_comp_minus4offset) {
+    seek(con, end_spc[which_AB[length(which_AB)]], origin = "start",
+         rw = "read")
+    spc[[which_AB[length(which_AB)]]] <- readBin(
+      con,
+      what = "numeric",
+      # n = NPT_spc[which_AB[length(which_AB)]] * 4,
+      n = NPT_spc[which_AB[length(which_AB)]],
+      size = 4,
+      endian = "little"
+    )
+
+  }
+
+  # Assign spectra type for final spectra in element names of spc list ---------
+  # Combine spectral assignments lists
+  list_assigned <- list(
+    'Ig' = Ig_assigned,
+    'Sc' = Sc_assigned,
+    'AB' = AB_assigned
+  )
+
+  # Transpose spectra assignment list, first remove NULL elements in list
+  # https://stackoverflow.com/questions/54970592/how-to-transpose-a-list-of-vectors
+  .base_transpose <- function(l) do.call(Map, c(f = list, l))
+  # to avoid bringing purrr::transpose
+
+  list_assigned_t <- .base_transpose(
+    Filter(Negate(function(x) is.null(unlist(x))), list_assigned)
+  )
+
+  # Save spectra index (spc_idx) and spectra code (spc_code)
+  # in character vector
+  spc_idx <- unlist(list_assigned_t[["spc_idx"]])
+  spc_code <- unlist(list_assigned_t[["spc_code"]])
+
+  # Order spc_idx from 1 to n spectra (n = length of end_spc)
+  order_spc <- as.numeric(
+    sub(".*idx", "", unlist(list_assigned_t[["spc_idx"]]))
+  )
+
+  spc_type <- spc_code[order(order_spc)]
+
+  # Set spectrum type as element names of spectra list (spc)
+  names(spc) <- spc_type
+
+  # Set spectrum type in wavenumbers list
+  names(wavenumbers) <- spc_type
+
+  # Read with new offset when first value of
+  # ScSm  single channel sample spectrumspectrum is 0 and replace previous ---
+  if (any(names(spc) %in% "sc_sample" & spc[["sc_sample"]][1] == 0)) {
+    seek(
+      con,
+      end_spc[Sc_assigned$spc_idx[Sc_assigned$spc_code == "sc_sample"]],
+      origin = "start",
+      rw = "read"
+    )
+    spc[["sc_sample"]] <- readBin(
+      con,
+      what = "numeric",
+      # n = NPT_spc[Sc_assigned$spc_idx[Sc_assigned$spc_code == "ScSm"]] * 4,
+      n = NPT_spc[Sc_assigned$spc_idx[Sc_assigned$spc_code == "sc_sample"]],
+      size = 4,
+      endian = "little"
+    )
+  }
+
+  ## Get additional parameters from OPUS binary file ===========================
+
+  # Instrument parameters ------------------------------------------------------
+
+  ins <- grepRaw("INS", rw, all = TRUE) # Instrument type
+  seek(con, ins[length(ins)] + 7, origin = "start", rw = "read")
+  INS <- readBin(
+    con,
+    what = "character",
+    n = 10,
+    size = 1,
+    endian = "little"
+  )[1]
+
+  lwn <- grepRaw("LWN", rw, all = TRUE)[1] + 7 # Laser wavenumber
+  seek(con, lwn, origin = "start", rw = "read")
+  LWN <- readBin(
+    con,
+    what = "numeric",
+    n = 8,
+    size = 8
+  )[1]
+
+  tsc <- grepRaw("TSC", rw, all = TRUE) + 7 # Scanner temperature
+  TSC_all <- lapply(tsc, function(tsc) {
+    seek(con, tsc, origin = "start", rw = "read")
+    readBin(
+      con,
+      what = "numeric",
+      n = 16,
+      size = 8
+    )[[1]]
+  })
+
+  # Read relative humidity of the interferometer during measurement
+  hum_rel <- grepRaw("HUM", rw, all = TRUE) + 7
+  HUM_rel <- lapply(
+    hum_rel,
+    function(hum_rel) {
+      # can include sample and background humidity
+      seek(con, hum_rel, origin = "start", rw = "read")
+      readBin(
+        con,
+        what = "integer",
+        n = 16,
+        size = 8
+      )[[1]]
+    })
+  HUM_rel <- HUM_rel[[1]]
+
+  # Read absolute humidity of the interferometer during measurement
+  hum_abs <- grepRaw("HUA", rw, all = TRUE) + 7
+  HUM_abs <- lapply(
+    hum_abs,
+    function(hum_abs) {
+      # can include sample and background humidity
+      seek(con, hum_abs, origin = "start", rw = "read")
+      readBin(
+        con,
+        what = "numeric",
+        n = 16,
+        size = 8
+      )[[1]]
+    })
+  HUM_abs <- unlist(HUM_abs)
+  if (is.null(HUM_abs)) HUM_abs <- NA
+
+  # Optics parameters ----------------------------------------------------------
+
+  src <- grepRaw("SRC", rw, all = TRUE) # Source: MIR or NIR
+  seek(con, src[length(src)] + 4, origin = "start", rw = "read")
+  SRC <- readBin(
+    con,
+    what = "character",
+    n = 3,
+    size = 1,
+    endian = "little"
+  )[[1]][1]
+
+  # instrument range
+  instr_range <- paste(INS, SRC, sep = "-")
+  instr_range <- unlist(strsplit(instr_range, "'"))[1]
+
+  bms <- grepRaw("BMS", rw, all = TRUE) # Beamsplitter
+  seek(con, bms[length(bms)] + 4, origin = "start", rw = "read")
+  BMS <- readBin(
+    con,
+    what = "character",
+    n = 3,
+    size = 1,
+    endian = "little"
+  )[[1]][1]
+  BMS <- unlist(strsplit(BMS, "'", useBytes = TRUE))[1]
+
+  # Fourier transform parameters -----------------------------------------------
+  zff <- grepRaw("ZFF", rw, all = TRUE)[1] + 5 # Zero filling factor (numeric)
+  seek(con, zff, origin = "start", rw = "read")
+  ZFF <- readBin(
+    con,
+    what = "integer",
+    n = 4,
+    size = 2,
+    endian = "little"
+  )[1]
+
+  # (Additional) Standard parameters -------------------------------------------
+
+  csf_all <- grepRaw("CSF", rw, all = TRUE) + 7 # y-scaling factor
+  # Read only CSF byte positions that correspond to final spectra
+  CSF <- lapply(
+    csf_all[npt_all %in% npt_spc],
+    function(csf) {
+      seek(con, csf, origin = "start", rw = "read")
+      readBin(
+        con,
+        what = "numeric",
+        n = 8,
+        size = 8,
+        endian = "little"
+      )[1]
+    })
+
+  mxy_all <- grepRaw("MXY", rw, all = TRUE) + 7 # Y-maximum
+  MXY <- unlist(
+    lapply(
+      mxy_all[npt_all %in% npt_spc],
+      function(mxy) {
+        seek(con, mxy, origin = "start", rw = "read")
+        readBin(
+          con,
+          what = "numeric",
+          n = 8,
+          size = 8
+        )[1]
+      }
+    )
+  )
+
+  mny <- grepRaw("MNY", rw, all = TRUE) + 7 # Y-minimum
+
+  dxu_all <- grepRaw("DXU", rw, all = TRUE) + 7 # X units
+  DXU <- lapply(
+    dxu_all,
+    function(dxu) {
+      seek(con, dxu, origin = "start", rw = "read")
+      readBin(
+        con,
+        what = "character",
+        n = 3,
+        size = 1,
+        endian = "little"
+      )[1]
+    }
+  )
+
+  # Y units -> PR: there is no DYU present in file -> PB: yes, but there is in
+  # others
+  dyu_all <- grepRaw("DYU", rw, all = TRUE) + 7
+  dat <- grepRaw("DAT", rw, all = TRUE) + 7 # Date
+
+  tim <- grepRaw("TIM", rw, all = TRUE) + 7 # Time
+
+  time <- unlist(
+    lapply(
+      tim,
+      function(tim) {
+        seek(con, tim, origin = "start", rw = "read")
+        readBin(
+          con,
+          what = "character",
+          n = 22,
+          size = 1,
+          endian = "little"
+        )[1]
+      }
+    )
+  )
+
+  # Time needs to have a valid encoding; replace entries that have invalid
+  # encoding with NA
+  time_invalid <- !vapply(time, validEnc, FUN.VALUE = logical(1))
+  time[time_invalid] <- NA
+
+  # Only select "DAT" string positions that are immediately before time
+  dat_sel <- vapply(
+    seq_along(tim),
+    FUN = function(i) {
+      diff_sel <- dat - tim[i]
+      res <- dat[which(diff_sel <= 32 & diff_sel >= -20)]
+
+      # If no valid position can be extracted we flag value as NA
+      if (length(res) == 0) res <- NA
+
+      res
+    },
+    FUN.VALUE = numeric(1)
+  )
+
+  date <- lapply(
+    dat_sel,
+    function(dat){
+
+      # If not date string was extracted we just return NA
+      if (is.na(dat)) {
+        res <- NA
+      } else {
+        seek(con, dat, origin = "start", rw = "read")
+        res <- readBin(
+          con,
+          what = "character",
+          n = 10,
+          size = 1,
+          endian = "little"
+        )[1]
+      }
+
+      res
+
+    }
+  )
+
+  date_time <- unique(paste(date, time))
+
+  # Convert date_time from character to class POSIXct (calendar date and time)
+  date_time <- as.POSIXct(date_time, format = "%d/%m/%Y %H:%M:%S")
+  # , tz = "GMT+1") # tz is argument for time zone
+
+  # Scale all spectra with y-scaling factor if any of spectra types present
+  # in file are not 1 ----------------------------------------------------------
+  # Set names of CSF elements equal to spectra list element names
+  names(CSF) <- names(spc)
+
+  if (any(unlist(CSF) != 1)) {
+    # Return all elements in CSF that have scaling value not equal to 1
+    CSF_toscale <- Filter(function(x) x != 1, CSF)
+    # Apply scaling for spectra with CSF value not equal to 1;
+    # Map() returns list
+    spc_scaled <- Map(function(CSF, spc) CSF * spc, unlist(CSF_toscale),
+                      spc[names(CSF_toscale)])
+
+    # Replace all spc list elements that have CSF not equal 1 with
+    # scaled values
+    spc <- replace(x = spc, list = names(CSF_toscale), values = spc_scaled)
+  }
+
+  # Data acquisition parameters ------------------------------------------------
+
+  plf <- grepRaw("PLF", rw, all = TRUE) + 4 # Result spectrum
+  PLF_all <- lapply(
+    plf,
+    function(plf) {
+      seek(con, plf, origin = "start", rw = "read")
+      res <- readBin(
+        con,
+        what = "character",
+        n = 2,
+        size = 1,
+        endian = "little"
+      )[1]
+      unlist(strsplit(res, ",", useBytes = TRUE))[1]
+    }
+  )
+
+  # Select only result spectra abbreviations that have valid encoding and
+  # are more than 0 characters long; some OPUS files had invalid encoding
+  # for this entry (first element)
+  PLF_invalid <- !vapply(PLF_all, validEnc, FUN.VALUE = logical(1))
+  PLF_all[PLF_invalid] <- NA
+  PLF <- unlist(PLF_all[lapply(PLF_all, nchar) > 0])
+  PLF <- unique(unlist(strsplit(PLF, "'")))
+
+  res <- grepRaw("RES", rw, all = TRUE)[1] + 5 # Resolution (wavenumber)
+  seek(con, res, origin = "start", rw = "read")
+  RES <- readBin(
+    con,
+    what = "integer",
+    n = 4,
+    size = 2,
+    endian = "little"
+  )[1]
+
+  ## Create sample metadata objects ============================================
+
+  snm <- grepRaw("SNM", rw, all = TRUE)[1] + 7
+  seek(con, snm, origin = "start", rw = "read")
+  SNM <- readBin(
+    con,
+    what = "character",
+    n = 30,
+    size = 1,
+    endian = "little"
+  )[1]
+
+  # Close connection
+  close(con)
+
+  # == sample ID ==
+
+  sample_name <- unlist(strsplit(SNM, ";", useBytes = TRUE))[1]
+  sample_id <- sample_name
+  # PB: 2021-09-01: todo: add `rep_no` via `file_id` or `stream_id`
+  file_name_nopath <- NA # PB: 2021-08-19: to fix
+
+  # Create unique_id using file_name and time
+  ymdhms_id <- max(date_time, na.rm = TRUE)
+  unique_id <- paste0(sample_id, "_", ymdhms_id)
+
+  ## Convert all spectra in list spc into a matrix of 1 row ====================
+  spc_m <- lapply(spc, function(x) matrix(x, ncol = length(x), byrow = FALSE))
+
+  # Add dimnames (wavenumbers for columns and unique_id for rows
+  spc_m <- setNames(
+    lapply(
+      seq_along(spc_m),
+      function(i) {
+        colnames(spc_m[[i]]) <- round(wavenumbers[[i]], 1)
+        rownames(spc_m[[i]]) <- unique_id
+        spc_m[[i]]
+      }
+    ),
+    names(spc_m)
+  )
+
+  # Save all relevant metadata
+  metadata <- data.frame(
+    unique_id = unique_id,
+    # Removing "file_id" which does not make sense on RAW streams
+    # file_id = file_name_nopath, # pb (20170514): changed `scan_id` to `file_id`
+    sample_id = sample_id,
+    # rep_no = as.numeric(rep_no), # pb 2021-09-01: find workaround to re-add
+    date_time_sm = max(date_time, na.rm = TRUE),
+    date_time_rf = min(date_time, na.rm = TRUE),
+    sample_name = SNM,
+    instr_name_range = instr_range,
+    resolution_wn = RES,
+    # Result spectrum; e.g. "AB" = Absorbance
+    # result_spc = ifelse(length(unique(PLF)) == 1, unique(PLF), unique(PLF)[2]),
+    # // pb: 2019-11-19: allow NULL value for PLF
+    result_spc = if (length(unique(PLF)) == 1) {
+        unique(PLF)
+      } else if (length(unique(PLF)) > 1) {
+        unique(PLF)[2]
+      } else {
+        NA
+      },
+    beamspl = BMS,
+    laser_wn = LWN,
+    # `spc_in_file`: character vector of spectra found in OPUS file
+    spc_in_file = paste(unlist(list_assigned_t[["spc_code"]]),
+                        collapse = ";", sep = ";"),
+    zero_filling = ZFF, # Zero filling factor for fourier transformation
+    # Temperature of scanner during sample measurement
+    temp_scanner_sm = TSC_all[[length(TSC_all)]], # select last element
+    # Temperature of scanner during reference measurement;
+    # if there is only one element in TSC_all, temperature during reference
+    # measurement is not saved
+    temp_scanner_rf = ifelse(length(TSC_all) == 1, NA, TSC_all[[1]]),
+    # Relative humidity
+    hum_rel_sm = HUM_rel[[length(HUM_rel)]], # sample measurement
+    hum_rel_rf = ifelse(length(HUM_rel) == 1, NA, HUM_rel[[1]]), # reference
+    # measurement
+    # Absolute humidity; sample measurement (sm); reference measurment (rf);
+    # note: for Vertex 70 instrument HUA is not present, in this case,
+    # HUM_abs is a list without elements
+    hum_abs_sm = ifelse(length(HUM_abs) != 0, HUM_abs[[length(HUM_abs)]], NA),
+    hum_abs_rf = ifelse(
+      length(HUM_abs) == 1 | length(HUM_abs) == 0,
+      NA,
+      HUM_abs[[1]]
+    ), # reference measurement
+    stringsAsFactors = FALSE
+  )
+
+  ## Allocate and return data from spectra in output list (out) ================
+
+  out <- list(
+    # Metadata
+    'metadata' = metadata,
+    'spec' = if("spec" %in% type) {
+        if ("spec" %in% names(spc_m)) {
+          spc_m[["spec"]]
+        } else {
+          warning("No spectra found", call. = F)
+          NULL
+        }
+      } else {
+        NULL
+      },
+    'spec_no_atm_comp' = if ("spec_no_atm_comp" %in% type) {
+        if ("spec_no_atm_comp" %in% names(spc_m)) {
+          spc_m[["spec_no_atm_comp"]]
+        } else {
+          warning("No 'spec_no_atm_comp' spectra found", call. = F)
+          NULL
+        }
+    } else {
+      NULL
+    },
+    'sc_sample' = if ("sc_sample" %in% type) {
+        if ("sc_sample" %in% names(spc_m)) {
+          spc_m[["sc_sample"]]
+        } else {
+          warning("No 'sc_sample' spectra found", call. = F)
+          NULL
+        }
+    } else {
+      NULL
+    },
+    'sc_ref' = if ("sc_ref" %in% type) {
+        if ("sc_ref" %in% names(spc_m)) {
+          spc_m[["sc_ref"]]
+        } else {
+          warning("No 'sc_ref' spectra found", call. = F)
+          NULL
+        }
+    } else {
+      NULL
+    },
+    'ig_sample' = if ("ig_sample" %in% type) {
+        if ("ig_sample" %in% names(spc_m)) {
+          spc_m[["ig_sample"]]
+        } else {
+          warning("No 'ig_sample' spectra found", call. = F)
+          NULL
+        }
+    } else {
+      NULL
+    },
+    'ig_ref' = if ("ig_ref" %in% type) {
+        if("ig_ref" %in% names(spc_m)) {
+          spc_m[["ig_ref"]]
+        } else {
+          warning("No 'ig_ref' spectra found", call. = F)
+          NULL
+        }
+    } else {
+      NULL
+    },
+    # Wavenumbers of final AB spectra
+    wavenumbers = wavenumbers[["spec"]],
+    wavenumbers_sc_sample = if ("sc_sample" %in% type) {
+      wavenumbers[["sc_sample"]]
+    } else {
+      NULL
+    },
+    wavenumbers_sc_ref = if ("sc_ref" %in% type) {
+      wavenumbers[["sc_ref"]]
+    } else {
+      NULL
+    }
+  )
+
+  # Return spectra data and metadata contained as elements in list out
+  return(out)
+  }) # closes try() function
+}
diff --git a/R/read_spec.R b/R/read_spec.R
deleted file mode 100644
index 0a254bb6..00000000
--- a/R/read_spec.R
+++ /dev/null
@@ -1,279 +0,0 @@
-#' @rdname read_spec
-#'
-#' @title Read spectral data
-#'
-#' @description
-#' Functions for reading spectral data types including .asp, .jdx,
-#' .spc, .spa, .0, and .csv.
-#'
-#' @details
-#' \code{read_spc()} and \code{read_jdx()} are just a wrapper around the
-#' functions provided by the \link[hyperSpec:hyperSpec-package]{hyperSpec}
-#' package.
-#' Other functions have been adapted various online sources.
-#' All functions convert datasets to a 2 column table with one column labeled
-#' "wavenumber" and the other "intensity". There are many unique iterations of
-#' spectral file formats so there may be bugs in the file conversion.
-#' Please contact us if you identify any.
-#'
-#' @param file file to be read from.
-#' @param cols character vector of \code{length = 2} indicating the colum names
-#' for the wavenumber and intensity; if \code{NULL} columns are guessed.
-#' @param method submethod to be used for reading text files; defaults to
-#' \link[utils]{read.csv} but \link[data.table]{fread} works as well.
-#' @param share defaults to \code{NULL}; needed to share spectra with the
-#' Open Specy community; see \code{\link{share_spec}()} for details.
-#' @param id a unique user and/or session ID; defaults to
-#' \code{paste(digest(Sys.info()), digest(sessionInfo()), sep = "/")}.
-#' @param \ldots further arguments passed to the submethods.
-#'
-#' @return
-#' All \code{read_*()} functions return data frames containing two columns
-#' named \code{"wavenumber"} and \code{"intensity"}.
-#'
-#' @examples
-#' read_text(read_extdata("raman_hdpe.csv"))
-#' read_asp(read_extdata("ftir_ldpe_soil.asp"))
-#' read_0(read_extdata("ftir_ps.0"))
-#'
-#' @author
-#' Zacharias Steinmetz, Win Cowger
-#'
-#' @seealso
-#' \code{\link[hyperSpec]{read.jdx}()}; \code{\link[hyperSpec]{read.spc}()};
-#' \code{\link[hexView]{readRaw}()}; \code{\link{share_spec}()}
-#'
-#' @importFrom dplyr %>%
-#' @export
-read_text <- function(file = ".", cols = NULL, method = "read.csv",
-                      share = NULL, id = paste(digest(Sys.info()),
-                                               digest(sessionInfo()),
-                                               sep = "/"),
-                      ...) {
-  df <- do.call(method, list(file, ...)) %>%
-    data.frame()
-
-  if (all(grepl("^X[0-9]*", names(df)))) stop("missing header: ",
-                                              "use 'header = FALSE' or an ",
-                                              "alternative read method")
-
-  # Try to guess column names
-  if (is.null(cols)) {
-    if (all(grepl("^V[0-9]*", names(df)))) {
-      cols <- 1:2
-      warning("missing header: guessing 'wavenumber' and 'intensity' data ",
-              "from the first two columns; use 'cols' to supply user-defined ",
-              "columns")
-    } else {
-      cols <- c(names(df)[grep("(wav*)", ignore.case = T, names(df))][1L],
-                names(df)[grep("(transmit*)|(reflect*)|(abs*)|(intens*)",
-                               ignore.case = T, names(df))][1L])
-    }
-  }
-  if (any(is.na(cols))) stop("undefined columns selected; columns should be ",
-                             "named 'wavenumber' and 'intensity'")
-  if (cols[1] == cols[2]) stop("inconsistent input format")
-
-  df <- df[cols]
-
-  # Check if columns are numeric
-  if (!all(sapply(df, is.numeric))) stop("input not numeric")
-
-  names(df) <- c("wavenumber", "intensity")
-
-  if (!is.null(share)) share_spec(df, file = file, share = share, id = id)
-
-  return(df)
-}
-
-#' @rdname read_spec
-#'
-#' @export
-read_asp <- function(file = ".", share = NULL, id = paste(digest(Sys.info()),
-                                                          digest(sessionInfo()),
-                                                          sep = "/"),
-                     ...) {
-  if (!grepl("\\.asp$", ignore.case = T, file))
-    stop("file type should be 'asp'")
-
-  tr <- file.path(file) %>% file(...)
-  lns <- tr %>% readLines() %>% as.numeric()
-  close(tr)
-
-  y <- lns[-c(1:6)]
-  x <- seq(lns[2], lns[3], length.out = lns[1])
-
-  df <- data.frame(wavenumber = x, intensity = y)
-
-  if (!is.null(share)) share_spec(df, file = file, share = share, id = id)
-
-  return(df)
-}
-
-#' @rdname read_spec
-#'
-#' @importFrom utils read.table
-#' @export
-read_spa <- function(file = ".", share = NULL, id = paste(digest(Sys.info()),
-                                                          digest(sessionInfo()),
-                                                          sep = "/"),
-                     ...) {
-  if (!grepl("\\.spa$", ignore.case = T, file))
-    stop("file type should be 'spa'")
-
-  trb <- file.path(file) %>% file(open = "rb", ...)
-
-  seek(trb, 576, origin = "start")
-  spr <- readBin(trb, "numeric", n = 2, size = 4)
-
-  if (!all(spr >= 0 & spr <= 15000 & spr[1] > spr[2]))
-    stop("unknown spectral range")
-
-  # Read the start offset
-  seek(trb, 386, origin = "start")
-  startOffset <- readBin(trb, "int", n = 1, size = 2)
-  # Read the length
-  seek(trb, 390, origin = "start")
-  readLength <- readBin(trb, "int", n = 1, size = 2)
-
-  # seek to the start
-  seek(trb, startOffset, origin = "start")
-
-  # we'll read four byte chunks
-  floatCount <- readLength / 4
-
-  # read all our floats
-  floatData <- c(readBin(trb, "double", floatCount, size = 4))
-
-  close(trb)
-
-  df <- data.frame(wavenumber = seq(spr[1], spr[2], length = length(floatData)),
-                   intensity = floatData)
-
-  if (!is.null(share)) share_spec(df, file = file, share = share, id = id)
-
-  return(df)
-}
-
-#' @rdname read_spec
-#'
-#' @importFrom hyperSpec read.jdx
-#' @export
-read_jdx <- function(file = ".", share = NULL, id = paste(digest(Sys.info()),
-                                                          digest(sessionInfo()),
-                                                          sep = "/"),
-                     ...) {
-  jdx <- read.jdx(file, ...)
-
-  df <- data.frame(wavenumber = jdx@wavelength,
-                   intensity = as.numeric(unname(jdx@data$spc[1,])))
-
-  if (!is.null(share)) share_spec(df, file = file, share = share, id = id)
-
-  return(df)
-}
-
-#' @rdname read_spec
-#'
-#' @importFrom hyperSpec read.spc
-#' @export
-read_spc <- function(file = ".", share = NULL, id = paste(digest(Sys.info()),
-                                                          digest(sessionInfo()),
-                                                          sep = "/"),
-                     ...) {
-  spc <- read.spc(file)
-
-  df <- data.frame(wavenumber = spc@wavelength,
-                   intensity = as.numeric(unname(spc@data$spc[1,])))
-
-  if (!is.null(share)) share_spec(df, file = file, share = share, id = id)
-
-  return(df)
-}
-
-#' @rdname read_spec
-#'
-#' @importFrom hexView readRaw blockString
-#' @export
-read_0 <- function(file = ".", share = NULL, id = paste(digest(Sys.info()),
-                                                        digest(sessionInfo()),
-                                                        sep = "/"),
-                   ...) {
-  if (!grepl("\\.[0-999]$", ignore.case = T, file))
-    stop("file type should be '0'")
-
-  pa <- readRaw(file, offset = 0, nbytes = file.info(file)$size, human = "char",
-                size = 1, endian = "little")
-  pr <- pa$fileRaw
-
-  # Get positions where the following parameters are found in the file
-  codes <- c("ZFF", "RES", "SNM", "DAT", "LWN", "FXV", "LXV", "NPT", "MXY",
-             "MNY", "END", "TIM")
-
-  z <- grepRaw(codes[1], pr, all = TRUE)[1] + 5
-  re <- grepRaw(codes[2], pr, all = TRUE)[1] + 5
-  snm <- grepRaw(codes[3], pr, all = TRUE)[1] + 7
-  dat <- grepRaw(codes[4], pr, all = TRUE)[1] + 7
-  lwn <- grepRaw(codes[5], pr, all = TRUE)[1] + 7
-  fx <- grepRaw(codes[6], pr, all = TRUE)[3] + 7
-  lx <- grepRaw(codes[7], pr, all = TRUE)[3] + 7
-  npt0 <- grepRaw(codes[8], pr, all = TRUE)[2] + 3
-  npt1 <- grepRaw(codes[8], pr, all = TRUE)[3] + 7
-  mxy <- grepRaw(codes[9], pr, all = TRUE)[1] + 7
-  mny <- grepRaw(codes[10], pr, all = TRUE)[3] + 7
-  end <- grepRaw(codes[11], pr, all = TRUE) + 11
-  tim <- grepRaw(codes[12], pr, all = TRUE) + 11
-
-  ## Calculate end and start of each block
-  offs <- sapply(5:10, function(x){end[x]})
-  byts <- diff(offs)
-  ZFF <- readRaw(file, offset = z, nbytes = 4, human = "int", size = 2)[[5]][1]
-  RES <- readRaw(file, offset = re, nbytes = 4, human = "int", size = 2)[[5]][1]
-  snm.lab.material <- blockString(readRaw(file, offset = snm, nbytes = 22,
-                                          human = "char", size = 1,
-                                          endian = "little"))
-
-  ## Get number of data points for each spectra data block
-  NPT0 <- readRaw(file, offset = npt0, nbytes = 12, human = "int", size = 4)[[5]][2]
-  NPT1 <- readRaw(file, offset = npt1, nbytes = 4, human = "int", size = 4)[[5]][1]
-  fxv <- readRaw(file, offset = fx, nbytes = 16, human = "real", size = 8)[[5]][1] ## fxv = frequency of first point
-  lxv <- readRaw(file, offset = lx, nbytes = 16, human = "real", size = 8)[[5]][1] ## lxv = frequency of last point
-  x <- rev(seq(lxv, fxv, (fxv - lxv) / (NPT1 - 1)))
-
-  ## Read all through all the data blocks inside the OPUS file:
-  nbytes1 <- NPT0 * 4 ## initial parameters
-  smxa <- c()
-  smna <- c()
-  nbytes.f <- NPT1 * 4
-  if(offs[1] < 2000) {
-    offs.f <- offs[3]
-  }
-
-  if(offs[1] > 20000) {
-    offs.f <- offs[2]
-  }
-
-  # Selected spectra block
-  opus.p <- readRaw(file, width = NULL, offset = offs.f - 4,
-                    nbytes = nbytes.f, human = "real", size = 4,
-                    endian = "little")
-  y <- opus.p[[5]]
-
-  df <- data.frame(wavenumber = x, intensity = y)
-
-  if (!is.null(share)) share_spec(df, file = file, share = share, id = id)
-
-  return(df)
-}
-
-#' @rdname read_spec
-#'
-#' @export
-read_extdata <- function(file = NULL) {
-  if (is.null(file)) {
-    dir(system.file("extdata", package = "OpenSpecy"))
-  }
-  else {
-    system.file("extdata", file, package = "OpenSpecy", mustWork = TRUE)
-  }
-}
diff --git a/R/run_app.R b/R/run_app.R
index 312d2b19..56874c8e 100644
--- a/R/run_app.R
+++ b/R/run_app.R
@@ -7,17 +7,17 @@
 #' After running this function the Open Specy GUI should open in a separate
 #' window or in your computer browser.
 #'
-#' @param app_dir the app to run; defaults to \code{"system"} pointing to
-#' \code{system.file("shiny", package = "OpenSpecy")}.
-#' @param path where to look for the local library files; defaults to
-#' \code{"system"} pointing to
-#' \code{system.file("extdata", package = "OpenSpecy")}.
+#' @param path to store the downloaded app files; defaults to \code{"system"}
+#' pointing to \code{system.file(package = "OpenSpecy")}.
 #' @param log logical; enables/disables logging to \code{\link[base]{tempdir}()}
+#' @param ref git reference; could be a commit, tag, or branch name. Defaults to
+#' "main". Only change this in case of errors.
+#' @param test_mode logical; for internal testing only.
 #' @param \dots arguments passed to \code{\link[shiny]{runApp}()}.
 #'
 #' @return
 #' This function normally does not return any value, see
-#' \code{\link[shiny]{runApp}()}.
+#' \code{\link[shiny]{runGitHub}()}.
 #'
 #' @examples
 #' \dontrun{
@@ -28,34 +28,29 @@
 #' Zacharias Steinmetz
 #'
 #' @seealso
-#' \code{\link[shiny]{runApp}()}
+#' \code{\link[shiny]{runGitHub}()}
 #'
-#' @importFrom shiny runApp shinyOptions
+#' @importFrom shiny runGitHub shinyOptions
 #' @importFrom utils installed.packages
 #' @export
-run_app <- function(app_dir = "system", path = "system", log = TRUE, ...) {
-  if (is.null(app_dir) || app_dir == "")
-    stop("Could not find app directory. Try reinstalling OpenSpecy.",
-         call. = FALSE)
+run_app <- function(path = "system", log = TRUE, ref = "main",
+                    test_mode = FALSE, ...) {
+  pkg <- c("config", "qs", "shinyjs", "shinyWidgets", "bs4Dash",
+           "dplyr", "ggplot2", "DT", "curl", "aws.s3", "mongolite", "loggit")
 
-  pkg <- c("config", "shinyjs", "shinythemes", "shinyBS", "shinyWidgets",
-           "plotly", "data.table", "DT", "curl", "rdrop2", "mongolite",
-           "loggit")
-  mpkg <- pkg[!(pkg %in% installed.packages()[ , "Package"])]
+  miss <- pkg[!(pkg %in% installed.packages()[ , "Package"])]
 
-  if(length(mpkg)) stop("run_app() requires the following packages: ",
-                        paste(paste0("'", mpkg, "'"), collapse = ", "),
+  if(length(miss)) stop("run_app() requires the following packages: ",
+                        paste(paste0("'", miss, "'"), collapse = ", "),
                         call. = F)
 
-  ad <- ifelse(app_dir == "system",
-               system.file("shiny", package = "OpenSpecy"),
-               app_dir)
-  wd <- ifelse(path == "system",
-               system.file("extdata", package = "OpenSpecy"),
+  dd <- ifelse(path == "system",
+               system.file(package = "OpenSpecy"),
                path)
 
   Sys.setenv(R_CONFIG_ACTIVE = "run_app")
 
-  shinyOptions(library_path = wd, log = log)
-  runApp(ad, ...)
+  shinyOptions(log = log)
+  if(!test_mode)
+    runGitHub("OpenSpecy-shiny", "wincowgerDEV", destdir = dd, ref = ref, ...)
 }
diff --git a/R/share_spec.R b/R/share_spec.R
index b0de01d8..82f7534c 100644
--- a/R/share_spec.R
+++ b/R/share_spec.R
@@ -4,79 +4,20 @@
 #' This helper function shares spectral data and metadata with the Open Specy
 #' community.
 #'
-#' \strong{Please note} that \code{share_spec()} only provides basic sharing
+#' \strong{Please note:} that \code{share_spec()} only provides basic sharing
 #' functionality if used interactively. This means that files are only formatted
-#' and saved for sharing but are not send automatically. This only works with
+#' and saved for sharing but are not sent automatically. This only works with
 #' hosted instances of Open Specy.
 #'
-#' @details
-#' The \code{metadata} argument may contain a named vector with the following
-#' details (\code{*} = mandatory):
-#'
-#' \tabular{ll}{
-#' \code{user_name*}: \tab User name, e.g. "Win Cowger"\cr
-#' \code{contact_info}: \tab Contact information, e.g. "1-513-673-8956,
-#' wincowger@@gmail.com"\cr
-#' \code{organization}: \tab Affiliation, e.g. "University of California,
-#' Riverside"\cr
-#' \code{citation}: \tab Data citation, e.g. "Primpke, S., Wirth, M., Lorenz,
-#' C., & Gerdts, G. (2018). Reference database design for the automated analysis
-#' of microplastic samples based on Fourier transform infrared (FTIR)
-#' spectroscopy. \emph{Analytical and Bioanalytical Chemistry}.
-#' \doi{10.1007/s00216-018-1156-x}"\cr
-#' \code{spectrum_type*}: \tab Raman or FTIR\cr
-#' \code{spectrum_identity*}: \tab Material/polymer analyzed, e.g.
-#' "Polystyrene"\cr
-#' \code{material_form}: \tab Form of the material analyzed, e.g. textile fiber,
-#' rubber band, sphere, granule \cr
-#' \code{material_phase}: \tab Phase of the material analyzed (liquid, gas,
-#' solid) \cr
-#' \code{material_producer}: \tab Producer of the material analyzed,
-#' e.g. Dow \cr
-#' \code{material_purity}: \tab Purity of the material analyzed, e.g. 99.98\%
-#' \cr
-#' \code{material_quality}: \tab Quality of the material analyzed, e.g.
-#' consumer product, manufacturer material, analytical standard,
-#' environmental sample \cr
-#' \code{material_color}: \tab Color of the material analyzed,
-#' e.g. blue, #0000ff, (0, 0, 255) \cr
-#' \code{material_other}: \tab Other material description, e.g. 5 µm diameter
-#' fibers, 1 mm spherical particles \cr
-#' \code{cas_number}: \tab CAS number, e.g. 9003-53-6 \cr
-#' \code{instrument_used}: \tab Instrument used, e.g. Horiba LabRam \cr
-#' \code{instrument_accessories}: \tab Instrument accessories, e.g.
-#' Focal Plane Array, CCD\cr
-#' \code{instrument_mode}: \tab Instrument modes/settings, e.g.
-#' transmission, reflectance \cr
-#' \code{spectral_resolution}: \tab Spectral resolution, e.g. 4/cm \cr
-#' \code{laser_light_used}: \tab Wavelength of the laser/light used, e.g.
-#' 785 nm \cr
-#' \code{number_of_accumulations}: \tab Number of accumulations, e.g 5 \cr
-#' \code{total_acquisition_time_s}: \tab Total acquisition time (s), e.g. 10 s
-#' \cr
-#' \code{data_processing_procedure}: \tab Data processing procedure,
-#' e.g. spikefilter, baseline correction, none \cr
-#' \code{level_of_confidence_in_identification}: \tab Level of confidence in
-#' identification, e.g. 99\% \cr
-#' \code{other_info}: \tab Other information \cr
-#' \code{license}: \tab The license of the shared spectrum; defaults to
-#' \code{"CC BY-NC"} (see
-#' \url{https://creativecommons.org/licenses/by-nc/4.0/} for details). Any other
-#' creative commons license is allowed, for example, CC0 or CC BY \cr
-#' }
-#'
-#' @param data a data frame containing the spectral data; columns should be
-#' named \code{"wavenumber"} and \code{"intensity"}.
-#' @param metadata a named vector of the metadata to share; see details below.
+#' @param x a list object of class \code{OpenSpecy}.
 #' @param file file to share (optional).
 #' @param share accepts any local directory to save the spectrum for later
-#' sharing via e-mail to \email{wincowger@@gmail.com};
-#' \code{"system"} (default) uses the Open Specy package directory at
-#' \code{system.file("extdata", package = "OpenSpecy")};
-#' if a correct API token exists, \code{"dropbox"} shares the spectrum with the
-#' cloud.
-#' @param id a unique user and/or session ID; defaults to
-#' \code{paste(digest(Sys.info()), digest(sessionInfo()), sep = "/")}.
+#' sharing via email to \email{wincowger@gmail.com}; \code{"system"} (default)
+#' uses the Open Specy package directory at \code{system.file("extdata",
+#' package = "OpenSpecy")}; if a correct API token exists, \code{"cloud"}
+#' shares the spectrum with the cloud.
+#' @param credentials a named list of credentials for cloud sharing; required if
+#' \code{share = "cloud"}).
 #' @param \ldots further arguments passed to the submethods.
 #'
 #' @return
@@ -86,11 +27,12 @@
 #' \dontrun{
 #' data("raman_hdpe")
 #' share_spec(raman_hdpe,
-#'            metadata = c(user_name = "Win Cowger",
-#'                         spectrum_type = "FTIR",
-#'                         spectrum_identity = "PE",
-#'                         license = "CC BY-NC"),
-#'            share = tempdir())
+#'            metadata = list(
+#'              user_name = "Win Cowger",
+#'              spectrum_type = "FTIR",
+#'              spectrum_identity = "PE",
+#'              license = "CC BY-NC"
+#'            ))
 #' }
 #'
 #' @author
@@ -100,107 +42,85 @@
 #' \code{\link{read_text}()};
 #' \code{\link[digest]{digest}()}; \code{\link[utils]{sessionInfo}()}
 #'
-#' @importFrom dplyr %>%
 #' @importFrom digest digest
 #' @importFrom utils write.csv sessionInfo
 #'
 #' @export
-share_spec <- function(data, ...) {
+share_spec <- function(x, ...) {
   UseMethod("share_spec")
 }
 
 #' @rdname share_spec
 #'
 #' @export
-share_spec.default <- function(data, ...) {
-  stop("object needs to be of class 'data.frame'")
+share_spec.default <- function(x, ...) {
+  stop("object 'x' needs to be of class 'OpenSpecy'", call. = F)
 }
 
 #' @rdname share_spec
 #'
 #' @export
-share_spec.data.frame <- function(data,
-                                  metadata = c(user_name = "",
-                                               contact_info = "",
-                                               organization = "",
-                                               citation = "",
-                                               spectrum_type = "",
-                                               spectrum_identity = "",
-                                               material_form = "",
-                                               material_phase = "",
-                                               material_producer = "",
-                                               material_purity = "",
-                                               material_quality = "",
-                                               material_color = "",
-                                               material_other = "",
-                                               cas_number = "",
-                                               instrument_used = "",
-                                               instrument_accessories = "",
-                                               instrument_mode = "",
-                                               spectral_resolution = "",
-                                               laser_light_used = "",
-                                               number_of_accumulations = "",
-                                               total_acquisition_time_s = "",
-                                               data_processing_procedure = "",
-                                               level_of_confidence_in_identification = "",
-                                               other_info = "",
-                                               license = "CC BY-NC"),
-                                  file = NULL,
-                                  share = "system",
-                                  id = paste(digest(Sys.info()),
-                                             digest(sessionInfo()), sep = "/"),
-                                  ...) {
-  if (is.null(names(metadata))) stop("'metadata' needs to be a named vector")
-  if (any(is.na(metadata[c("user_name", "spectrum_type", "spectrum_identity")])) |
-      metadata["user_name"] == "" | metadata["spectrum_type"] == "" |
-      metadata["spectrum_identity"] == "") {
-    mex <- FALSE
-    warning("fields 'user_name', 'spectrum_type', and 'spectrum_identity' ",
-            "must not be empty if you like to share your metadata", call. = F)
-  } else {
-    mex <- TRUE
-  }
+share_spec.OpenSpecy <- function(x, file = NULL, share = "system",
+                                 credentials = NULL, ...) {
+  md <- x$metadata
+  if(any(!c("user_name", "spectrum_type", "spectrum_identity") %in%
+         names(md)) |
+     is.null(md$user_name) | is.null(md$spectrum_type) |
+     is.null(md$spectrum_identity))
+    warning("Fields 'user_name', 'spectrum_type', and 'spectrum_identity' ",
+            "should not be empty if you like to share your metadata", call. = F)
 
-  fid <- digest(data)
-  mdata <- data.frame(variable = names(metadata), input = metadata,
-                      row.names = NULL)
+  if(is.null(md$session_id))
+    stop("'session_id' must not be empty if you like to share your metadata;",
+         "run 'as_OpenSpecy(x, session_id = T)' to add one", call. = F)
 
   if (share == "system") {
     fp <- file.path(system.file("extdata", package = "OpenSpecy"),
-                    "user_spectra", id)
-  } else if (share == "dropbox") {
-
-    pkg <- "rdrop2"
+                    "user_spectra", md$session_id) |> unique()
+  } else if (share == "cloud") {
+    pkg <- "aws.s3"
     mpkg <- pkg[!(pkg %in% installed.packages()[ , "Package"])]
-    if (length(mpkg)) stop("share = 'dropbox' requires package 'rdrop2'")
+    if(length(mpkg)) stop("share = 'cloud' requires package 'aws.s3'",
+                           call. = F)
+
+    if(is.null(credentials))
+      stop("'credentials' required to share with the cloud", call. = F)
+
+    if(!is.list(credentials) || !(all(c("s3_key", "s3_secret", "s3_region",
+                                        "s3_bucket") %in% names(credentials))))
+      stop("'credentials' needs to be a named list containing the following ",
+           "items: 's3_key', 's3_secret', 's3_region', 's3_bucket'", call. = F)
 
-    fp <- file.path(tempdir(), id)
+    fp <- file.path(tempdir(), md$session_id) |> unique()
   } else {
-    fp <- file.path(share, id)
+    fp <- file.path(share, md$session_id) |> unique()
   }
   dir.create(fp, recursive = T, showWarnings = F)
 
-  fd <- file.path(fp, paste0(fid, ".csv"))
-  fm <- file.path(fp, paste0(fid, "_form", ".csv"))
+  fd <- file.path(fp, paste0(md$file_id, ".yml")) |> unique()
 
-  write.csv(data, fd, row.names = FALSE, quote = TRUE)
+  write_spec(x, fd)
 
-  if (mex) write.csv(mdata, fm, row.names = FALSE, quote = TRUE)
   if (!is.null(file)) {
     ex <- strsplit(basename(file), split="\\.")[[1]]
-    file.copy(file, file.path(fp, paste0(fid, ".", ex[-1])))
+    file.copy(file, file.path(fp, paste0(md$file_id, ".", ex[-1])))
   }
 
-  if (share == "dropbox") {
-    for (lf in list.files(fp, pattern = fid, full.names = T)) {
-      rdrop2::drop_upload(lf, path = paste0("data/users/", id), ...)
+  if (share == "cloud") {
+    for (lf in list.files(fp, pattern = md$file_id, full.names = T)) {
+      aws.s3::put_object(
+        file = lf,
+        bucket = credentials$s3_bucket,
+        key = credentials$s3_key, secret = credentials$s3_secret,
+        region = credentials$s3_region
+      )
     }
   }
 
-  message("thank you for your willigness to share your data; ",
+  message("Thank you for your willigness to share your data; ",
           "your data has been saved to\n    ",
           fp, "\n",
-          "if you run Open Specy locally, you may consider e-mailing your ",
+          "If you run Open Specy locally, you may consider e-mailing your ",
           "files to\n    ",
           "Win Cowger <wincowger@gmail.com>")
 }
diff --git a/R/sig_noise.R b/R/sig_noise.R
new file mode 100644
index 00000000..aae90245
--- /dev/null
+++ b/R/sig_noise.R
@@ -0,0 +1,77 @@
+#' @rdname sig_noise
+#' @title Calculate signal and noise metrics for OpenSpecy objects
+#'
+#' @description
+#' This function calculates common signal and noise metrics for \code{OpenSpecy}
+#' objects.
+#'
+#' @param x an \code{OpenSpecy} object.
+#' @param metric character; specifying the desired metric to calculate.
+#' Options include \code{"sig"} (mean intensity), \code{"noise"} (standard
+#' deviation of intensity), \code{"sig_times_noise"} (absolute value of
+#' signal times noise), \code{"sig_over_noise"} (absolute value of signal /
+#' noise), or \code{"tot_sig"} (total signal = signal * number of data
+#' points).
+#' @param na.rm logical; indicating whether missing values should be removed
+#' when calculating signal and noise. Default is \code{TRUE}.
+#' @param \ldots further arguments passed to subfunctions; currently not used.
+#'
+#' @return
+#' A numeric vector containing the calculated metric for each spectrum in the
+#' \code{OpenSpecy} object.
+#'
+#' @examples
+#' data("raman_hdpe")
+#'
+#' sig_noise(raman_hdpe, metric = "sig")
+#' sig_noise(raman_hdpe, metric = "noise")
+#' sig_noise(raman_hdpe, metric = "sig_times_noise")
+#'
+#' @importFrom stats median
+#' @importFrom data.table frollapply
+#'
+#' @export
+sig_noise <- function(x, ...) {
+  UseMethod("sig_noise")
+}
+
+#' @rdname sig_noise
+#'
+#' @export
+sig_noise.default <- function(x, ...) {
+  stop("object 'x' needs to be of class 'OpenSpecy'", call. = F)
+}
+
+#' @rdname sig_noise
+#'
+#' @export
+sig_noise.OpenSpecy <- function(x, metric = "sig_over_noise",
+                                na.rm = TRUE, ...) {
+  vapply(x$spectra, function(y) {
+    if(length(y[!is.na(y)]) < 20) {
+      warning("Need at least 20 intensity values to calculate the signal or ",
+              "noise values accurately; returning NA", call. = F)
+      return(NA)
+    }
+
+    if(metric == "run_sig_over_noise") {
+      max <- frollapply(y[!is.na(y)], 20, max)
+      max[(length(max) - 19):length(max)] <- NA
+      signal <- max(max, na.rm = T)#/mean(x, na.rm = T)
+      noise <- median(max[max != 0], na.rm = T)
+    }
+    else {
+      signal = mean(y, na.rm = na.rm)
+      noise = sd(y, na.rm = na.rm)
+    }
+
+    if(metric == "sig") return(signal)
+    if(metric == "noise") return(noise)
+    if(metric == "sig_times_noise") return(abs(signal * noise))
+
+    if(metric %in% c("sig_over_noise", "run_sig_over_noise"))
+      return(abs(signal/noise))
+    if(metric == "tot_sig") return(sum(y))
+    if(metric == "log_tot_sig") return(sum(exp(y)))
+  }, FUN.VALUE = numeric(1))
+}
diff --git a/R/smooth_intens.R b/R/smooth_intens.R
index f4db3b04..c52326c3 100644
--- a/R/smooth_intens.R
+++ b/R/smooth_intens.R
@@ -1,3 +1,4 @@
+#' @rdname smooth_intens
 #' @title Smooth spectral intensities
 #'
 #' @description
@@ -11,22 +12,20 @@
 #' A typical good smooth can be achieved with 11 data point window and a 3rd or
 #' 4th order polynomial.
 #'
-#' @param x a numeric vector containing the spectral wavenumbers; alternatively
-#' a data frame containing spectral data as \code{"wavenumber"} and
-#' \code{"intensity"} can be supplied.
-#' @param y a numeric vector containing the spectral intensities.
-#' @param formula an object of class '\code{\link[stats]{formula}}' of the form
-#' \code{intensity ~ wavenumber}.
-#' @param data a data frame containing the variables in \code{formula}.
-#' @param p polynomial order for the filter
-#' @param n number of data points in the window, filter length (must be odd).
+#' @param x an object of class \code{OpenSpecy}.
+#' @param polynomial polynomial order for the filter
+#' @param window number of data points in the window, filter length (must be
+#' odd).
+#' @param derivative the derivative order if you want to calculate the
+#' derivative. Zero (default) is no derivative.
+#' @param abs logical; whether you want to calculate the absolute value of the
+#' resulting output.
 #' @param make_rel logical; if \code{TRUE} spectra are automatically normalized
 #' with \code{\link{make_rel}()}.
 #' @param \ldots further arguments passed to \code{\link[signal]{sgolay}()}.
 #'
 #' @return
-#' \code{smooth_intens()} returns a data frame containing two columns named
-#' \code{"wavenumber"} and \code{"intensity"}.
+#' \code{smooth_intens()} returns an \code{OpenSpecy} object.
 #'
 #' @examples
 #' data("raman_hdpe")
@@ -44,7 +43,7 @@
 #' Simplified Least Squares Procedures.” \emph{Analytical Chemistry},
 #' \strong{36}(8), 1627--1639.
 #'
-#' @importFrom dplyr %>%
+#' @importFrom data.table .SD
 #' @export
 smooth_intens <- function(x, ...) {
   UseMethod("smooth_intens")
@@ -53,36 +52,28 @@ smooth_intens <- function(x, ...) {
 #' @rdname smooth_intens
 #'
 #' @export
-smooth_intens.formula <- function(formula, data = NULL, ...) {
-  if (missing(formula) || (length(formula) != 3L) ||
-      (length(attr(terms(formula[-2L]), "term.labels")) != 1L))
-    stop("'formula' missing or incorrect")
-
-  mf <- model.frame(formula, data)
-  lst <- as.list(mf)
-  names(lst) <- c("y", "x")
-
-  do.call("smooth_intens", c(lst, list(...)))
+smooth_intens.default <- function(x, ...) {
+  stop("object 'x' needs to be of class 'OpenSpecy'")
 }
 
 #' @rdname smooth_intens
 #'
 #' @export
-smooth_intens.data.frame <- function(x, ...) {
-  if (!all(c("wavenumber", "intensity") %in% names(x)))
-    stop("'data' must contain 2 columns named 'wavenumber' and 'intensity'")
+smooth_intens.OpenSpecy <- function(x, polynomial = 3, window = 11,
+                                    derivative = 0, abs = FALSE,
+                                    make_rel = TRUE, ...) {
+  filt <- x$spectra[, lapply(.SD, .sgfilt, p = polynomial, n = window,
+                             m = derivative, abs = abs, ...)]
+
+  if(make_rel) x$spectra <- filt[, lapply(.SD, make_rel)] else x$spectra <- filt
 
-  do.call("smooth_intens", list(x$wavenumber, x$intensity, ...))
+  return(x)
 }
 
-#' @rdname smooth_intens
 #' @importFrom signal filter sgolay
-#' @export
-smooth_intens.default <- function(x, y, p = 3, n = 11, make_rel = TRUE,
-                                  ...) {
-  yflt <- signal::filter(filt = sgolay(p = p, n = n, ...), x = y)
-
-  if (make_rel) yout <- make_rel(yflt) else yout <- yflt
+.sgfilt <- function(y, p, n, m, abs = F, ...) {
+  out <- signal::filter(filt = sgolay(p = p, n = n, m = m, ...), x = y)
+  if(abs) out <- abs(out)
 
-  data.frame(wavenumber = x, intensity = yout)
+  return(out)
 }
diff --git a/R/spec_res.R b/R/spec_res.R
index 74fe6642..44575609 100644
--- a/R/spec_res.R
+++ b/R/spec_res.R
@@ -1,3 +1,4 @@
+#' @rdname spec_res
 #' @title Spectral resolution
 #'
 #' @description
@@ -9,22 +10,37 @@
 #' frequency difference between two lines in a spectrum that can still be
 #' distinguished.
 #'
-#' @param x a numeric vector or an \R object which is coercible to one by
-#' \code{as.vector(x, "numeric")}; \code{x} should be \code{wavenumber} data.
+#' @param x a numeric vector with \code{wavenumber} data or an \code{OpenSpecy}
+#' object.
+#' @param \ldots further arguments passed to subfunctions; currently not used.
 #'
 #' @return
 #' \code{spec_res()} returns a single numeric value.
 #'
 #' @examples
 #' data("raman_hdpe")
-#' spec_res(raman_hdpe$wavenumber)
+#'
+#' spec_res(raman_hdpe)
 #'
 #' @author
 #' Win Cowger, Zacharias Steinmetz
 #'
-#' @importFrom dplyr %>%
 #' @export
-spec_res <- function(x) {
-  (max(x) - min(x)) / length(x)
+spec_res <- function(x, ...) {
+  UseMethod("spec_res")
+}
+
+#' @rdname spec_res
+#'
+#' @export
+spec_res.default <- function(x, ...) {
+  freq <- diff(x) |> table()
+  as.numeric(names(freq)[which.max(freq)])
 }
 
+#' @rdname spec_res
+#'
+#' @export
+spec_res.OpenSpecy <- function(x, ...) {
+  do.call("spec_res", list(x = x$wavenumber))
+}
diff --git a/R/subtr_bg.R b/R/subtr_baseline.R
similarity index 58%
rename from R/subtr_bg.R
rename to R/subtr_baseline.R
index 8551ce9c..613a2bbe 100644
--- a/R/subtr_bg.R
+++ b/R/subtr_baseline.R
@@ -1,36 +1,36 @@
+#' @rdname subtr_baseline
 #' @title Automated background subtraction for spectral data
 #'
 #' @description
 #' This baseline correction routine iteratively finds the baseline of a spectrum
-#' using a polynomial fitting.
+#' using a polynomial fitting or accepts a manual baseline.
 #'
 #' @details
 #' This is a translation of Michael Stephen Chen's MATLAB code written for the
 #' \code{imodpolyfit} routine from Zhao et al. 2007.
 #'
-#' @param x a numeric vector containing the spectral wavenumbers; alternatively
-#' a data frame containing spectral data as \code{"wavenumber"} and
-#' \code{"intensity"} can be supplied.
-#' @param y a numeric vector containing the spectral intensities.
-#' @param formula an object of class '\code{\link[stats]{formula}}' of the form
-#' \code{intensity ~ wavenumber}.
-#' @param data a data frame containing the variables in \code{formula}.
+#' @param x a list object of class \code{OpenSpecy}.
+#' @param type one of \code{"polynomial"} or \code{"manual"} depending on
+#' whether you want spectra to be corrected with a manual baseline or with
+#' polynomial baseline fitting.
 #' @param degree the degree of the polynomial. Must be less than the number of
 #' unique points when raw is \code{FALSE}. Typically a good fit can be
 #' found with a 8th order polynomial.
 #' @param raw if \code{TRUE}, use raw and not orthogonal polynomials.
+#' @param baseline an \code{OpenSpecy} object containing the baseline data to be
+#' subtracted.
 #' @param make_rel logical; if \code{TRUE} spectra are automatically normalized
 #' with \code{\link{make_rel}()}.
 #' @param \ldots further arguments passed to \code{\link[stats]{poly}()}.
 #'
 #' @return
-#' \code{subtr_bg()} returns a data frame containing two columns named
+#' \code{subtr_baseline()} returns a data frame containing two columns named
 #' \code{"wavenumber"} and \code{"intensity"}.
 #'
 #' @examples
 #' data("raman_hdpe")
 #'
-#' subtr_bg(raman_hdpe)
+#' subtr_baseline(raman_hdpe)
 #'
 #' @author
 #' Win Cowger, Zacharias Steinmetz
@@ -49,44 +49,44 @@
 #' \emph{Applied Spectroscopy}, \strong{61}(11), 1225–1232.
 #' \doi{10.1366/000370207782597003}.
 #'
-#' @importFrom dplyr %>%
 #' @importFrom stats terms model.frame sd lm poly approx
+#' @importFrom data.table .SD
 #' @export
-subtr_bg <- function(x, ...) {
-  UseMethod("subtr_bg")
+subtr_baseline <- function(x, ...) {
+  UseMethod("subtr_baseline")
 }
 
-#' @rdname subtr_bg
+#' @rdname subtr_baseline
 #'
 #' @export
-subtr_bg.formula <- function(formula, data = NULL, ...) {
-  if (missing(formula) || (length(formula) != 3L) ||
-      (length(attr(terms(formula[-2L]), "term.labels")) != 1L))
-    stop("'formula' missing or incorrect")
-
-  mf <- model.frame(formula, data)
-  lst <- as.list(mf)
-  names(lst) <- c("y", "x")
-
-  do.call("subtr_bg", c(lst, list(...)))
+subtr_baseline.default <- function(x, ...) {
+  stop("object 'x' needs to be of class 'OpenSpecy'", call. = F)
 }
 
-#' @rdname subtr_bg
+#' @rdname subtr_baseline
 #'
 #' @export
-subtr_bg.data.frame <- function(x, ...) {
-  if (!all(c("wavenumber", "intensity") %in% names(x)))
-    stop("'data' must contain 2 columns named 'wavenumber' and 'intensity'")
-
-  do.call("subtr_bg", list(x$wavenumber, x$intensity, ...))
+subtr_baseline.OpenSpecy <- function(x, type = "polynomial",
+                                     degree = 8, raw = FALSE,
+                                     baseline, make_rel = TRUE, ...) {
+  if(type == "polynomial") {
+    sbg <- x$spectra[, lapply(.SD, .subtr_bl_poly, x = x$wavenumber,
+                              degree = degree, raw = raw, ...)]
+  } else if(type == "manual") {
+    if(!is_OpenSpecy(baseline))
+      stop("'baseline' needs to be of class 'OpenSpecy'", call. = F)
+    sbg <- x$spectra[, lapply(.SD, .subtr_bl_manual, x = x$wavenumber,
+                              bl_y = baseline$spectra[[1]],
+                              bl_x = baseline$wavenumber)]
+  } else stop("'type' must be either 'polynomial' or 'manual'", call. = F)
+
+  if (make_rel) x$spectra <- sbg[, lapply(.SD, make_rel)] else x$spectra <- sbg
+
+  return(x)
 }
 
-#' @rdname subtr_bg
-#'
-#' @export
-subtr_bg.default <- function(x, y, degree = 8, raw = FALSE,
-                             make_rel = TRUE, ...) {
-  xin <- x
+.subtr_bl_poly <- function(y, x, degree, raw, ...) {
+  xout <- x
   yin <- y
   dev_prev <- 0 # standard deviation residuals for the last iteration of polyfit;
   # set initially to 0
@@ -132,18 +132,19 @@ subtr_bg.default <- function(x, y, degree = 8, raw = FALSE,
     # Approximate the intensity back to the original wavelengths, allows below
     # the peak to be interpolated
     if(criteria_met) {
-      ysbg <- approx(x, y, xout = xin, rule = 2, method = "linear",
-                     ties = mean)[2] %>%
-        unlist() %>%
+      ysbg <- approx(x, y, xout = xout, rule = 2, method = "linear",
+                     ties = mean)[2] |>
+        unlist() |>
         unname()
 
-      if (make_rel) yout <- make_rel(yin - ysbg) else
-        yout <- yin - ysbg
-
-      return(data.frame(wavenumber = xin, intensity = yout))
+      return(yin - ysbg)
     }
 
     # Update previous residual metric
     dev_prev <- dev_curr
   }
 }
+
+.subtr_bl_manual <- function(y, x, bl_y, bl_x, ...) {
+  y - approx(bl_x, bl_y, xout = x, rule = 2, method = "linear", ties = mean)$y
+}
diff --git a/R/test_lib.R b/R/test_lib.R
index ecbae00c..79c1d2f9 100644
--- a/R/test_lib.R
+++ b/R/test_lib.R
@@ -1,22 +1,17 @@
 #' @title Test reference library
 #'
 #' @description
-#' Reference library of 34 Raman spectra used for internal testing.
+#' Reference library with 29 FTIR and 28 Raman spectra used for examples and
+#' internal testing.
 #'
 #' @format
-#' A list named \code{"test"} with two elements:
-#'
-#' \tabular{ll}{
-#' \code{metadata}: \tab metadata of 34 Raman spectra \cr
-#' \code{library}: \tab all reference spectra, \code{sample_name} serves as
-#' identifier \cr
-#' }
+#' An \code{OpenSpecy} object; \code{sample_name} is the class of the spectra.
 #'
 #' @examples
 #' data("test_lib")
 #'
 #' @author
-#' Jennifer Lynch
+#' Win Cowger
 #'
 #' @docType data
 #' @keywords data
diff --git a/README.md b/README.md
index ae10cf15..aba59499 100644
--- a/README.md
+++ b/README.md
@@ -1,30 +1,30 @@
-# Open Specy
+# Open Specy 1.0
 
 Analyze, Process, Identify, and Share Raman and (FT)IR Spectra
 
 <!-- badges: start -->
 [![CRAN version](https://www.r-pkg.org/badges/version/OpenSpecy)](https://CRAN.R-project.org/package=OpenSpecy) 
 [![Project Status](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
-[![R-CMD-check](https://github.com/wincowgerDEV/OpenSpecy-package/workflows/R-CMD-check/badge.svg)](https://github.com/wincowgerDEV/OpenSpecy-package/actions)
+[![R-CMD-check](https://github.com/wincowgerDEV/OpenSpecy-package/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/wincowgerDEV/OpenSpecy-package/actions/workflows/R-CMD-check.yaml)
+[![Codecov test coverage](https://codecov.io/gh/wincowgerDEV/OpenSpecy-package/branch/main/graph/badge.svg)](https://app.codecov.io/gh/wincowgerDEV/OpenSpecy-package?branch=main)
 [![License: CC BY 4.0](https://img.shields.io/badge/license-CC%20BY%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)
 [![DOI](https://img.shields.io/badge/DOI-10.1021/acs.analchem.1c00123-blue.svg)](https://doi.org/10.1021/acs.analchem.1c00123)
 [![Website](https://img.shields.io/badge/web-openspecy.org-white)](https://wincowger.shinyapps.io/OpenSpecy/)
-[![Twitter Follow](https://img.shields.io/twitter/follow/OpenSpecy)](https://twitter.com/OpenSpecy)
-[![Gitter](https://badges.gitter.im/Open-Specy/community.svg)](https://gitter.im/Open-Specy/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
+[![Gitter](https://badges.gitter.im/Open-Specy/community.svg)](https://app.gitter.im/#/room/#Open-Specy_community:gitter.im)
 <!-- badges: end -->
 
-Raman and (FT)IR spectral analysis tool for plastic particles and
-other environmental samples (Cowger et al. 2021, doi: 
+Raman and (FT)IR spectral analysis tool for plastic particles and other 
+environmental samples (Cowger et al. 2021, doi: 
 [10.1021/acs.analchem.1c00123](https://doi.org/10.1021/acs.analchem.1c00123)).
-Supported features include reading spectral data files (.asp, .csv, .jdx, .spc,
-.spa, .0), Savitzky-Golay smoothing of spectral intensities with
-`smooth_intens()`, correcting background noise with `subtr_bg()` in accordance
-with Zhao et al. (2007, doi: 
-[10.1366/000370207782597003](https://doi.org/10.1366/000370207782597003)), and
-identifying spectra using an onboard reference library (Cowger et al. 2020, doi: [10.1177/0003702820929064](https://doi.org/10.1177/0003702820929064)).
-Analyzed spectra can be shared with the Open Specy community. A Shiny app is
-available via `run_app()` or online at
-[https://openanalysis.org/openspecy/](https://openanalysis.org/openspecy/).
+With `read_any()`, Open Specy provides a single function for reading individual,
+batch, or map spectral data files like .asp, .csv, .jdx, .spc, .spa, .0, and
+.zip. `process_spec()` simplifies preprocessing spectra, including smoothing,
+baseline correction, range restriction and flattening, intensity conversions,
+wavenumber alignment, and min-max normalization. 
+Spectra can be identified in batch using an onboard reference library
+(Cowger et al. 2020, doi: [10.1177/0003702820929064](https://doi.org/10.1177/0003702820929064))
+using `match_spec()`. A Shiny app is available via `run_app()`
+or online at [https://openanalysis.org/openspecy/](https://openanalysis.org/openspecy/).
 
 ## Installation
 
@@ -46,7 +46,7 @@ into your R console (requires **devtools**):
 
 ```r
 if (!require(devtools)) install.packages("devtools")
-devtools::install_github("wincowgerDEV/OpenSpecy")
+devtools::install_github("wincowgerDEV/OpenSpecy-package")
 ```
 
 ## Getting started
@@ -55,45 +55,42 @@ library(OpenSpecy)
 run_app()
 ```
 
-See
-[package vignette](https://htmlpreview.github.io/?https://github.com/wincowgerDEV/OpenSpecy-package/blob/main/vignettes/sop.html)
-for a detailed standard operating procedure.
+## See [package vignette](http://wincowger.com/OpenSpecy-package/articles/sop.html) for a detailed standard operating procedure.
 
-## Workflow
+## Simple workflow for single spectral identification
 
 ```r
-library(dplyr)
-
 # Fetch current spectral library from https://osf.io/x7dpz/
-get_lib()
+get_lib("derivative")
 
 # Load library into global environment
-spec_lib <- load_lib()
+spec_lib <- load_lib("derivative")
 
 # Read sample spectrum
-raman_hdpe <- read_text(read_extdata("raman_hdpe.csv"))
+raman_hdpe <- read_extdata("raman_hdpe.csv") |> 
+  read_any()
+
+# Look at the spectrum
+plotly_spec(raman_hdpe)
 
-# Share your spectrum with the Open Spey community
-share_spec(raman_hdpe,
-           metadata = c(user_name = "Win Cowger",
-                        contact_info = "wincowger@gmail.com",
-                        spectrum_type = "Raman",
-                        spectrum_identity = "HDPE")
-           )
+# Process the spectra and conform it to the library format
+raman_proc <- raman_hdpe |>
+  process_spec(conform_spec_args = list(range = spec_lib$wavenumbers), 
+               smooth_intens = T, make_rel = T)
 
-# Adjust spectral intensity
-raman_adj <- raman_hdpe %>%
-  adj_intens()
+# Compare raw and processed spectra
+plotly_spec(raman_hdpe, raman_proc)
 
-# Smooth and background-correct spectrum
-raman_proc <- raman_adj %>% 
-  smooth_intens() %>% 
-  subtr_bg()
+top_matches <- match_spec(raman_proc, library = spec_lib, na.rm = T, top_n = 5,
+                          add_library_metadata = "sample_name",
+                          add_object_metadata = "col_id")
 
-# Match spectrum with library and retrieve meta data
-match_spec(raman_proc, library = spec_lib, which = "raman")
+#Print the top 5 results with relevant metadata
+top_matches[, c("object_id", "library_id", "match_val", "SpectrumType",
+                "SpectrumIdentity")]
 
-find_spec(sample_name == 5381, library = spec_lib, which = "raman")
+#Get all metadata for the matches
+get_metadata(spec_lib, logic = top_matches$library_id)
 ```
 
 ## Citations
@@ -104,6 +101,6 @@ Needs an Open Source Community: Open Specy to the Rescue!”
 *Analytical Chemistry*, **93**(21), 7543–7548. doi:
 [10.1021/acs.analchem.1c00123](https://doi.org/10.1021/acs.analchem.1c00123).
 
-Cowger W, Steinmetz Z (2021). “OpenSpecy: Analyze, Process,
-Identify, and Share Raman and (FT)IR Spectra.” *R package*, **0.9.5**.
+Cowger W, Steinmetz Z (2023). “OpenSpecy: Analyze, Process,
+Identify, and Share Raman and (FT)IR Spectra.” *R package*, **1.0.0**.
 [https://github.com/wincowgerDEV/OpenSpecy-package](https://github.com/wincowgerDEV/OpenSpecy-package).
diff --git a/codecov.yml b/codecov.yml
new file mode 100644
index 00000000..04c55859
--- /dev/null
+++ b/codecov.yml
@@ -0,0 +1,14 @@
+comment: false
+
+coverage:
+  status:
+    project:
+      default:
+        target: auto
+        threshold: 1%
+        informational: true
+    patch:
+      default:
+        target: auto
+        threshold: 1%
+        informational: true
diff --git a/cran-comments.md b/cran-comments.md
index da0b5c40..72c6bdb7 100644
--- a/cran-comments.md
+++ b/cran-comments.md
@@ -1,6 +1,6 @@
 ## Test environments
 
-* manjaro linux 5.18.6-1 (local), R-4.2.1
+* manjaro linux 6.3.13-2 (local), R-4.3.1
 * macOS latest (via GitHub Actions), R-release
 * ubuntu latest (via GitHub Actions), R-devel
 * ubuntu latest (via GitHub Actions), R-release
@@ -10,12 +10,22 @@
 
 ## R CMD check results
 
-0 errors | 0 warnings | 1 note
+0 errors | 0 warnings | 2 notes
 
 
 ## Comments
 
-DOI links do not pass remote checks although they resolve fine using a web
-browser
+    Possibly misspelled words in DESCRIPTION:
+      preprocessing (46:5)
+      wavenumber (47:62)
 
-For further details, see NEWS file
+Both words are spelled correctly.
+
+    installed size is  6.5Mb
+    sub-directories of 1Mb or more:
+      doc   4.9Mb
+
+We tried to reduce the vignette size as much as possible but would like to keep
+the current number of screenshots for better comprehensibility.
+
+For further details, see NEWS.md
diff --git a/data/raman_hdpe.RData b/data/raman_hdpe.RData
index aa19bb8e..75d53abd 100644
Binary files a/data/raman_hdpe.RData and b/data/raman_hdpe.RData differ
diff --git a/data/test_lib.RData b/data/test_lib.RData
index 566e9148..9cd423ec 100644
Binary files a/data/test_lib.RData and b/data/test_lib.RData differ
diff --git a/inst/extdata/CA_tiny_map.zip b/inst/extdata/CA_tiny_map.zip
new file mode 100644
index 00000000..508be0f5
Binary files /dev/null and b/inst/extdata/CA_tiny_map.zip differ
diff --git a/inst/extdata/ftir_ps_interior.1 b/inst/extdata/ftir_ps_interior.1
deleted file mode 100644
index 8c43cb5a..00000000
Binary files a/inst/extdata/ftir_ps_interior.1 and /dev/null differ
diff --git a/inst/extdata/raman_hdpe.csv b/inst/extdata/raman_hdpe.csv
index 6ab5960c..0ed9137f 100644
--- a/inst/extdata/raman_hdpe.csv
+++ b/inst/extdata/raman_hdpe.csv
@@ -1,1096 +1,965 @@
-Wavelength,Absorbance
-150.92,3264.21
-154.057,3347.43
-157.193,3358.72
-160.329,3346.65
-163.463,3418.66
-166.596,3411.73
-169.728,3331.23
-172.859,3408.39
-175.988,3375.29
-179.115,3427.94
-182.243,3365.69
-185.37,3404.79
-188.493,3388.36
-191.618,3431.65
-194.742,3401.5
-197.862,3447.33
-200.983,3430.75
-204.102,3374.73
-207.221,3411.18
-210.337,3464.12
-213.455,3463.18
-216.569,3449.68
-219.684,3404.23
-222.796,3474.66
-225.907,3476.48
-229.019,3498.27
-232.128,3477.13
-235.236,3467.18
-238.343,3452.98
-241.451,3370.01
-244.556,3399.67
-247.659,3431.37
-250.762,3342.03
-253.864,3352.13
-256.964,3371.29
-260.064,3397.34
-263.162,3354.89
-266.26,3316.97
-269.356,3360.31
-272.452,3457.76
-275.544,3379.16
-278.638,3413.9
-281.73,3419.73
-284.821,3452.37
-287.909,3420.14
-290.999,3382.45
-294.087,3470.43
-297.172,3473.75
-300.258,3471.46
-303.341,3393.9
-306.425,3478.48
-309.506,3455.51
-312.588,3416.15
-315.667,3493.94
-318.745,3419.68
-321.824,3489.42
-324.9,3547.65
-327.975,3556.18
-331.051,3560.12
-334.123,3620.58
-337.195,3591.92
-340.266,3667.08
-343.336,3651.43
-346.404,3703.35
-349.472,3766.32
-352.539,3771.1
-355.604,3821.1
-358.669,3862.19
-361.732,3842.41
-364.795,3783.53
-367.857,3838.93
-370.917,3743.65
-373.975,3787.94
-377.033,3716.15
-380.091,3656.18
-383.145,3740.77
-386.2,3717.66
-389.255,3713.7
-392.306,3734.33
-395.359,3785.11
-398.408,3799.8
-401.458,3728.63
-404.506,3726.81
-407.552,3780.11
-410.599,3765.81
-413.644,3814.53
-416.687,3757.41
-419.729,3848.85
-422.773,3773.93
-425.813,3787.15
-428.852,3770.89
-431.89,3785.42
-434.928,3814.7
-437.964,3778.64
-440.999,3795.95
-444.033,3777.13
-447.067,3730.75
-450.099,3752.22
-453.13,3706.41
-456.158,3740.69
-459.188,3703.46
-462.216,3751.39
-465.243,3765.53
-468.267,3778.31
-471.292,3728
-474.315,3704.36
-477.338,3883.48
-480.358,4154.71
-483.379,4201.9
-486.398,4150.49
-489.417,4157.42
-492.433,3752.87
-495.448,3644.24
-498.464,3665.89
-501.478,3748.05
-504.49,3673.19
-507.501,3653.01
-510.511,3703.71
-513.521,3708.62
-516.529,3662.01
-519.536,3621.28
-522.543,3700.87
-525.548,3675.56
-528.552,3675.23
-531.555,3671.57
-534.558,3694.77
-537.559,3687.15
-540.557,3686.72
-543.557,3726.62
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diff --git a/inst/extdata/raman_hdpe.json b/inst/extdata/raman_hdpe.json
new file mode 100644
index 00000000..3b86314d
--- /dev/null
+++ b/inst/extdata/raman_hdpe.json
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diff --git a/inst/extdata/raman_hdpe.rds b/inst/extdata/raman_hdpe.rds
new file mode 100644
index 00000000..21ae7a84
Binary files /dev/null and b/inst/extdata/raman_hdpe.rds differ
diff --git a/inst/extdata/raman_hdpe.yml b/inst/extdata/raman_hdpe.yml
new file mode 100644
index 00000000..5d04a7d2
--- /dev/null
+++ b/inst/extdata/raman_hdpe.yml
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+metadata:
+  x: 1
+  'y': 1
+  user_name: Win Cowger
+  spectrum_type: Raman
+  spectrum_identity: HDPE
+  organization: Horiba Scientific
+  license: CC BY-NC
+  session_id: 5728ddde4f649fd71f6f487fc5ad8d80/dc85257201307a131e71d9ec24aaccbf
+  file_id: cb06ce2846b119d932fb6696479a445b
diff --git a/inst/extdata/testdata_zipped.zip b/inst/extdata/testdata_zipped.zip
new file mode 100644
index 00000000..d91314f2
Binary files /dev/null and b/inst/extdata/testdata_zipped.zip differ
diff --git a/inst/shiny/.gitignore b/inst/shiny/.gitignore
deleted file mode 100644
index 407ab6a4..00000000
--- a/inst/shiny/.gitignore
+++ /dev/null
@@ -1,13 +0,0 @@
-raman_*.rds
-ftir_*.rds
-test_*.rds
-augmented_*.rds
-augmented_*.rds
-subset_*.RDS
-test_*.RDS
-google-analytics.js
-droptoken.rds
-rsconnect/
-.httr-oauth
-.db_url
-loggit.log
diff --git a/inst/shiny/config.yml b/inst/shiny/config.yml
deleted file mode 100644
index 501dadb0..00000000
--- a/inst/shiny/config.yml
+++ /dev/null
@@ -1,14 +0,0 @@
-default:
-  library_path: 'system'
-  share: 'system'
-  log: TRUE
-
-run_app:
-  library_path: !expr shiny::getShinyOption("library_path")
-  share: !expr file.path(shiny::getShinyOption("library_path"), "user_spectra")
-  log: !expr shiny::getShinyOption("log")
-
-shinyapps:
-  library_path: 'data/library'
-  share: 'dropbox'
-  log: TRUE
diff --git a/inst/shiny/data/namekey.RData b/inst/shiny/data/namekey.RData
deleted file mode 100644
index c2f0b315..00000000
Binary files a/inst/shiny/data/namekey.RData and /dev/null differ
diff --git a/inst/shiny/server.R b/inst/shiny/server.R
deleted file mode 100644
index 6ecdd87a..00000000
--- a/inst/shiny/server.R
+++ /dev/null
@@ -1,643 +0,0 @@
-#' Shiny app server function
-#'
-#' @param input provided by shiny
-#' @param output provided by shiny
-#'
-#'
-# Check for Auth Tokens and setup, you can change these to test the triggering
-# of functions without removing the files.
-droptoken <- file.exists("data/droptoken.rds")
-db <- file.exists(".db_url") #reminder, this will break if you login to a new wifi network even with the token.
-translate <- file.exists("www/googletranslate.html")
-
-# Libraries ----
-library(shiny)
-library(shinyjs)
-library(dplyr)
-library(plotly)
-# library(viridis)
-library(data.table)
-library(DT)
-library(digest)
-library(curl)
-library(config)
-library(mongolite)
-library(loggit)
-if(droptoken) library(rdrop2)
-
-#devtools::install_github("wincowgerDEV/OpenSpecy")
-library(OpenSpecy)
-
-#library(future)
-#library(bslib)
-
-# Global config ----
-conf <- config::get() #Add config = "shinyapps" for ec2 server
-
-# Logging ----
-if(conf$log) {
-  if(db) {
-    database <- mongo(url = readLines(".db_url"))
-  } else {
-    set_logfile(file.path(tempdir(), "OpenSpecy.log"))
-  }
-}
-
-# Load all data ----
-load_data <- function() {
-
-  testdata <- raman_hdpe
-
-  tweets <- c("https://twitter.com/EnviroMichaela/status/1471622640183959555",
-              #"https://twitter.com/OpenSpecy/status/1472361269093023744",
-              "https://twitter.com/DSemensatto/status/1461038613903380484",
-              "https://twitter.com/SETAC_plastics/status/1460738878101356544",
-              "https://twitter.com/AliciaMateos_/status/1460197329760313344",
-              "https://twitter.com/Irreverent_KUP/status/1454418069036568578",
-              "https://twitter.com/PeterPuskic/status/1454267818166210561",
-              "https://twitter.com/JannesJegminat/status/1427257468384681985",
-              "https://twitter.com/pnwmicroplastic/status/1415730821730734080",
-              #"https://twitter.com/OpenSpecy/status/1408391168745000961",
-              "https://twitter.com/ToMExApp/status/1399859256615079936",
-              "https://twitter.com/kat_lasdin/status/1399576094622175241",
-              "https://twitter.com/an_chem/status/1397621113421803521",
-              "https://twitter.com/WarrierAnish/status/1395245636967014401",
-              "https://twitter.com/EnviroMichaela/status/1395199312645300233",
-              "https://twitter.com/SocAppSpec/status/1392883693027430400",
-              #"https://twitter.com/zsteinmetz_/status/1387677422028480512",
-              #"https://twitter.com/OpenSpecy/status/1382820319635775488",
-              #"https://twitter.com/zsteinmetz_/status/1377222029250822146",
-              #"https://twitter.com/OpenSpecy/status/1318214558549372928",
-              "https://twitter.com/YokotaLimnoLab/status/1311069417892184065") %>%
-    sample(2)
-
-  goals <- tibble(
-    Status =      c("Revolutionizing",
-                    "Thriving",
-                    "Maintaining",
-                    "Supporting",
-                    "Saving"),
-    Description = c("A paid team that is pushing Open Specy closer to the ultimate goal of 100% accurate spectral identification and deep spectral diagnostics with a single click",
-                    "A single paid staff person working to update and build the community and the tool",
-                    "Maintenance costs and minor ad-hoc updates and bug fixes",
-                    "Keeping the app online and essential maintenance",
-                    "Long term storage only"),
-    'Annual Need'  = c(">100,000$",
-                       "10,000–100,000$",
-                       "1,000–10,000$",
-                       "100–1,000$",
-                       "<100$")
-  )
-  # Check if spectral library is present and load
-  test_lib <- class(tryCatch(check_lib(path = conf$library_path),
-                             warning = function(w) {w}))
-
-  if(any(test_lib == "warning")) get_lib(path = conf$library_path)
-
-  spec_lib <- load_lib(path = conf$library_path)
-
-  if(droptoken) {
-    drop_auth(rdstoken = "data/droptoken.rds")
-  }
-
-  # Name keys for human readable column names
-  load("data/namekey.RData")
-
-  # Inject variables into the parent environment
-  invisible(list2env(as.list(environment()), parent.frame()))
-}
-
-# This is the actual server functions, all functions before this point are not
-# reactive
-server <- shinyServer(function(input, output, session) {
-  #For theming
-  #bs_themer()
-  session_id <- digest(runif(10))
-
-  # Loading overlay
-  load_data()
-  hide(id = "loading_overlay", anim = TRUE, animType = "fade")
-  show("app_content")
-
-# For desktop version of the app.
-#  if (!interactive()) {
-#    session$onSessionEnded(function() {
-#      stopApp()
-#      q("no")
-#    })
-#  }
-
-  #brks <- seq(5, 320000, 1000)
-  clrs <- colorRampPalette(c("white", "#6baed6"))(5 + 1)
-
-  output$event_goals <- DT::renderDataTable({
-    datatable(goals,
-              options = list(
-                             dom = "t",
-                             ordering = FALSE,
-                             paging = FALSE,
-                             searching = FALSE
-                             #sDom  = '<"top">lrt<"bottom">ip',
-
-                             ),
-              caption = "Progress (current status selected)",
-              style = "bootstrap",
-              class = 'row-border',
-              escape = FALSE,
-              rownames = FALSE,
-              #formatStyle(c("Annual Need"), backgroundColor = styleColorBar(color = clrs)),
-              selection = list(mode = "single", selected = c(2)))
-  })
-
-  #Reading Data and Startup ----
-  # Sharing ID
-  id <- reactive({
-    if (!is.null(input$fingerprint)) {
-      paste(input$fingerprint, session_id, sep = "/")
-    } else {
-      paste(digest(Sys.info()), digest(sessionInfo()), sep = "/")
-    }
-  })
-
-  # Save the metadata and data submitted upon pressing the button
-  observeEvent(input$submit, {
-    if (input$share_decision & !is.null(data()) & curl::has_internet()) {
-      withProgress(message = "Sharing Metadata",
-                   value = 3/3, {
-        sout <- tryCatch(share_spec(
-          data = preprocessed_data(),
-          metadata = sapply(names(namekey)[c(1:24,32)], function(x) input[[x]]),
-          share = conf$share,
-          id = id()),
-          warning = function(w) {w}, error = function(e) {e})
-
-        if (inherits(sout, "simpleWarning") | inherits(sout, "simpleError"))
-          mess <- sout$message
-
-        if (is.null(sout)) {
-          show_alert(
-            title = "Thank you for sharing your data!",
-            text = "Your data will soon be available at https://osf.io/stmv4/",
-            type = "success"
-          )
-        } else {
-          show_alert(
-            title = "Something went wrong :-(",
-            text = paste0("All mandatory data added? R says: '", mess, "'. ",
-                          "Try again."),
-            type = "warning"
-          )
-        }
-      })
-    }
-  })
-
-  # Read in data when uploaded based on the file type
-  preprocessed_data <- reactive({
-    req(input$file1)
-    file <- input$file1
-    filename <- as.character(file$datapath)
-
-    if (!grepl("(\\.csv$)|(\\.asp$)|(\\.spa$)|(\\.spc$)|(\\.jdx$)|(\\.[0-9]$)",
-              ignore.case = T, filename)) {
-      show_alert(
-        title = "Data type not supported!",
-        text = paste0("Uploaded data type is not currently supported; please
-                      check tooltips and 'About' tab for details."),
-        type = "warning")
-      return(NULL)
-      }
-
-    if (input$share_decision & curl::has_internet()) {
-      share <- conf$share
-      progm <- "Sharing Spectrum to Community Library"
-      } else {
-        share <- NULL
-        progm <- "Reading Spectrum"
-      }
-
-    withProgress(message = progm, value = 3/3, {
-      if(grepl("\\.csv$", ignore.case = T, filename)) {
-        rout <- tryCatch(read_text(filename, method = "fread",
-                                   share = share,
-                                   id = id()),
-                         error = function(e) {e})
-      }
-      else if(grepl("\\.[0-9]$", ignore.case = T, filename)) {
-        rout <- tryCatch(read_0(filename, share = share, id = id()),
-                         error = function(e) {e})
-      }
-      else {
-        ex <- strsplit(basename(filename), split="\\.")[[1]]
-
-        rout <- tryCatch(do.call(paste0("read_", tolower(ex[-1])),
-                                 list(filename, share = share, id = id())),
-                         error = function(e) {e})
-      }
-
-      if (inherits(rout, "simpleError")) {
-        reset("file1")
-        show_alert(
-          title = "Something went wrong :-(",
-          text = paste0("R says: '", rout$message, "'. ",
-                        "If you uploaded a text/csv file, make sure that the ",
-                        "columns are numeric and named 'wavenumber' and ",
-                        "'intensity'."),
-          type = "error"
-        )
-        return(NULL)
-      } else {
-        rout
-      }
-    })
-  })
-
-  # Corrects spectral intensity units using the user specified correction
-  data <- reactive({
-    req(preprocessed_data())
-    adj_intens(preprocessed_data(), type = input$intensity_corr)
-    })
-
-  #Preprocess Spectra ----
-  # All cleaning of the data happens here. Smoothing and Baseline removing
-  baseline_data <- reactive({
-    req(data())
-
-    testdata <- data() %>% dplyr::filter(wavenumber > input$MinRange &
-                                           wavenumber < input$MaxRange)
-    test <-  nrow(testdata) < 3
-    if (test) {
-      data() %>%
-        mutate(intensity = if(input$smooth_decision) {
-          smooth_intens(.$wavenumber, .$intensity, p = input$smoother)$intensity
-        } else .$intensity) %>%
-        mutate(intensity = if(input$baseline_decision) {
-          subtr_bg(.$wavenumber, .$intensity, degree = input$baseline)$intensity
-          } else .$intensity)
-    } else {
-      data() %>%
-        dplyr::filter(
-          if(input$range_decision) {wavenumber > input$MinRange &
-              wavenumber < input$MaxRange} else {
-                wavenumber == wavenumber}) %>%
-        mutate(intensity = if(input$smooth_decision) {
-          smooth_intens(.$wavenumber, .$intensity, p = input$smoother)$intensity
-        } else .$intensity) %>%
-        mutate(intensity = if(input$baseline_decision & input$baseline_selection == "Polynomial") {
-          subtr_bg(.$wavenumber, .$intensity, degree = input$baseline)$intensity
-          }
-          else if(input$baseline_decision & input$baseline_selection == "Manual" & !is.null(trace$data)){
-            make_rel(.$intensity - approx(trace$data$wavenumber, trace$data$intensity, xout = .$wavenumber, rule = 2, method = "linear", ties = mean)$y)
-          } else .$intensity)
-    }
-  })
-
-  # Create file view and preprocess view
-  output$MyPlot <- renderPlotly({
-    plot_ly(data(), type = 'scatter', mode = 'lines') %>%
-      add_trace(x = ~wavenumber, y = ~intensity, name = 'Uploaded Spectrum',
-                line = list(color = 'rgba(240,236,19, 0.8)')) %>%
-      layout(yaxis = list(title = "absorbance intensity [-]"),
-             xaxis = list(title = "wavenumber [cm<sup>-1</sup>]",
-                          autorange = "reversed"),
-             plot_bgcolor = 'rgb(17,0,73)',
-             paper_bgcolor = 'rgba(0,0,0,0.5)',
-             font = list(color = '#FFFFFF'))
-  })
-
-  output$MyPlotB <- renderPlotly({
-    plot_ly(type = 'scatter', mode = 'lines', source = "B") %>%
-      add_trace(data = baseline_data(), x = ~wavenumber, y = ~intensity,
-                name = 'Processed Spectrum',
-                line = list(color = 'rgb(240,19,207)')) %>%
-      add_trace(data = data(), x = ~wavenumber, y = ~intensity,
-                name = 'Uploaded Spectrum',
-                line = list(color = 'rgba(240,236,19,0.8)')) %>%
-      # Dark blue rgb(63,96,130)
-      # https://www.rapidtables.com/web/color/RGB_Color.html https://www.color-hex.com/color-names.html
-      layout(yaxis = list(title = "absorbance intensity [-]"),
-             xaxis = list(title = "wavenumber [cm<sup>-1</sup>]",
-                          autorange = "reversed"),
-             plot_bgcolor = 'rgb(17,0,73)',
-             paper_bgcolor = 'rgba(0,0,0,0.5)',
-             font = list(color = '#FFFFFF')) %>%
-      config(modeBarButtonsToAdd = list("drawopenpath", "eraseshape" ))
-  })
-
-trace <- reactiveValues(data = NULL)
-
-observeEvent(input$go, {
-  pathinfo <- event_data(event = "plotly_relayout", source = "B")$shapes$path
-  if (is.null(pathinfo)) trace$data <- NULL
-  else {
-   nodes <- unlist(strsplit(
-             gsub("(L)|(M)", "_",
-                  paste(unlist(pathinfo), collapse = "")),
-             "(,)|(_)"))
-   nodes = nodes[-1]
-   df <- as.data.frame(matrix(nodes, ncol = 2, byrow = T))
-   names(df) <- c("wavenumber", "intensity")
-   trace$data <- df
-  }
-})
-
-observeEvent(input$reset, {
-  #js$resetClick()
-  #runjs("Shiny.setInputValue('plotly_selected-B', null);")
-  trace$data <- NULL
-})
-
-#  output$text <- renderPrint({
-#   trace$data#
-#    })
-
-  # Choose which spectrum to use
-  DataR <- reactive({
-    if(input$Data == "uploaded") {
-      data()
-    }
-    else if(input$Data == "processed") {
-      baseline_data()
-    }
-  })
-
-  # Identify Spectra function ----
-  # Joins their spectrum to the internal database and computes correlation.
-  MatchSpectra <- reactive ({
-    req(input$tabs == "tab3")
-    input
-    withProgress(message = 'Analyzing Spectrum', value = 1/3, {
-
-      incProgress(1/3, detail = "Finding Match")
-
-      Lib <- match_spec(DataR(),
-                        library = spec_lib, which = input$Spectra,
-                        type = input$Library, top_n = 100)
-
-      incProgress(1/3, detail = "Making Plot")
-
-    })
-    return(Lib)
-  })
-
-  # Create the data tables
-  output$event <- DT::renderDataTable({
-    datatable(MatchSpectra() %>%
-                dplyr::rename("Material" = spectrum_identity) %>%
-                dplyr::select(-sample_name) %>%
-                dplyr::rename("Pearson's r" = rsq,
-                              "Organization" = organization),
-              options = list(searchHighlight = TRUE,
-                             sDom  = '<"top">lrt<"bottom">ip',
-                             lengthChange = FALSE, pageLength = 5),
-              filter = "top", caption = "Selectable Matches",
-              style = "bootstrap",
-              selection = list(mode = "single", selected = c(1)))
-  })
-
-
-  output$eventmetadata <- DT::renderDataTable({
-    # Default to first row if not yet clicked
-    id_select <- ifelse(is.null(input$event_rows_selected),
-                        1,
-                        MatchSpectra()[[input$event_rows_selected,
-                                        "sample_name"]])
-    # Get data from find_spec
-    current_meta <- find_spec(sample_name == id_select, spec_lib,
-                              which = input$Spectra)
-    names(current_meta) <- namekey[names(current_meta)]
-
-    datatable(current_meta,
-              escape = FALSE, rownames = F,
-              options = list(dom = 't', bSort = F, lengthChange = FALSE,
-                             rownames = FALSE, info = FALSE),
-              style = 'bootstrap', caption = "Selection Metadata",
-              selection = list(mode = 'none'))
-  })
-
-  # Display matches based on table selection ----
-  output$MyPlotC <- renderPlotly({
-    if(!length(input$event_rows_selected)) {
-      plot_ly(DataR()) %>%
-        add_lines(x = ~wavenumber, y = ~intensity,
-                  line = list(color = 'rgba(255,255,255,0.8)')) %>%
-        layout(yaxis = list(title = "absorbance intensity [-]"),
-               xaxis = list(title = "wavenumber [cm<sup>-1</sup>]",
-                            autorange = "reversed"),
-               plot_bgcolor='rgb(17,0, 73)',
-               paper_bgcolor= 'rgba(0,0,0,0.5)',
-               font = list(color = '#FFFFFF'))
-    }
-    else if(length(input$event_rows_selected)) {
-      # Default to first row if not yet clicked
-      id_select <- ifelse(is.null(input$event_rows_selected),
-                          1,
-                          MatchSpectra()[[input$event_rows_selected,
-                                          "sample_name"]])
-      # Get data from find_spec
-      current_spectrum <- find_spec(sample_name == id_select,
-                                    spec_lib, which = input$Spectra,
-                                    type = input$Library)
-
-      TopTens <- current_spectrum %>%
-        inner_join(MatchSpectra()[input$event_rows_selected,,drop = FALSE],
-                   by = "sample_name") %>%
-        select(wavenumber, intensity, spectrum_identity)
-
-      OGData <- DataR() %>%
-        select(wavenumber, intensity) %>%
-        mutate(spectrum_identity = "Spectrum to Analyze")
-
-      plot_ly(TopTens, x = ~wavenumber, y = ~Intensity) %>%
-        add_lines(data = TopTens, x = ~wavenumber, y = ~intensity,
-                  color = ~factor(spectrum_identity), colors = "#FF0000") %>%
-        # viridisLite::plasma(7, begin = 0.2, end = 0.8)
-        add_lines(data = OGData, x = ~wavenumber, y = ~intensity,
-                  line = list(color = "rgba(255,255,255,0.8)"),
-                  name = "Spectrum to Analyze") %>%
-        layout(yaxis = list(title = "absorbance intensity [-]"),
-               xaxis = list(title = "wavenumber [cm<sup>-1</sup>]",
-                            autorange = "reversed"),
-               plot_bgcolor = "rgb(17,0, 73)",
-               paper_bgcolor = 'rgba(0,0,0,0.5)',
-               font = list(color = "#FFFFFF"))
-    }})
-
-  # Data Download options
-  output$downloadData5 <- downloadHandler(
-    filename = function() {"ftir_library.csv"},
-    content = function(file) {fwrite(spec_lib[["ftir"]][["library"]], file)}
-  )
-
-  output$downloadData6 <- downloadHandler(
-    filename = function() {"raman_library.csv"},
-    content = function(file) {fwrite(spec_lib[["raman"]][["library"]], file)}
-  )
-
-  output$downloadData4 <- downloadHandler(
-    filename = function() {"raman_metadata.csv"},
-    content = function(file) {fwrite(spec_lib[["raman"]][["metadata"]], file)}
-  )
-
-  output$downloadData3 <- downloadHandler(
-    filename = function() {"ftir_metadata.csv"},
-    content = function(file) {fwrite(spec_lib[["ftir"]][["metadata"]], file)}
-  )
-
-  output$download_testdata <- downloadHandler(
-    filename = function() {"testdata.csv"},
-    content = function(file) {fwrite(testdata, file)}
-  )
-
-  ## Download own data ----
-  output$downloadData <- downloadHandler(
-    filename = function() {paste('data-', human_ts(), '.csv', sep='')},
-    content = function(file) {fwrite(baseline_data(), file)}
-  )
-
-  ## Sharing data ----
-  # Hide functions which shouldn't exist when there is no internet or
-  # when the API token doesn't exist
-  observe({
-    if((conf$share == "dropbox" & droptoken) | curl::has_internet()) {
-      show("share_decision")
-      show("share_meta")
-    }
-    else {
-      hide("share_decision")
-      hide("share_meta")
-    }
-  })
-
-  observe({
-    if (input$share_decision) {
-      show("share_meta")
-      } else {
-        hide("share_meta")
-        sapply(names(namekey)[c(1:24,32)], function(x) hide(x))
-        hide("submit")
-      }
-  })
-
-
-  observe({
-    if (input$baseline_selection == "Polynomial") {
-      show("baseline")
-      hide("go")
-      hide("reset")
-    } else {
-      hide("baseline")
-      show("go")
-      show("reset")
-    }
-  })
-
-
-  observe({
-    if (is.null(preprocessed_data())) {
-      show("placeholder1")
-      show("placeholder2")
-      show("placeholder3")
-    } else {
-      hide("placeholder1")
-      hide("placeholder2")
-      hide("placeholder3")
-    }
-  })
-
-  #This toggles the hidden metadata input layers.
-  observeEvent(input$share_meta, {
-    sapply(names(namekey)[c(1:24,32)], function(x) toggle(x))
-    toggle("submit")
-  })
-
-  output$translate <- renderUI({
-    if(translate & curl::has_internet()) {
-      includeHTML("www/googletranslate.html")
-    }
-  })
-
-  render_tweet <- function(x){renderUI({
-    div(class = "inline-block",
-        style = "display:inline-block; margin-left:4px;",
-        tags$blockquote(class = "twitter-tweet", `data-theme` = "dark",
-                        style = "width: 600px; display:inline-block;" ,
-                        tags$a(href = x)),
-        tags$script('twttr.widgets.load(document.getElementById("tweet"));')
-    )
-  })
-  }
-
-  output$tweet1 <- renderUI({
-    render_tweet(tweets[1])
-  })
-
-  output$tweet2 <- renderUI({
-    render_tweet(tweets[2])
-  })
-
-
-  # Log events ----
-
-  observeEvent(input$go, {
-    if(conf$log) {
-      if(db) {
-        database$insert(data.frame(user_name = input$fingerprint,
-                                   session_name = session_id,
-                                   wavenumber = trace$data$wavenumber,
-                                   intensity = trace$data$intensity,
-                                   data_id = digest::digest(preprocessed_data(),
-                                                            algo = "md5"),
-                                   ipid = input$ipid,
-                                   time = human_ts()))
-        }
-    }
-  })
-
-  observe({
-    req(input$file1)
-    req(input$share_decision)
-    if(conf$log) {
-      if(db) {
-        database$insert(data.frame(user_name = input$fingerprint,
-                                   session_name = session_id,
-                                   intensity_adj = input$intensity_corr,
-                                   smoother = input$smoother,
-                                   smooth_decision = input$smooth_decision,
-                                   baseline = input$baseline,
-                                   baseline_decision = input$baseline_decision,
-                                   max_range = input$MinRange,
-                                   min_range = input$MaxRange,
-                                   range_decision = input$range_decision,
-                                   data_id = digest::digest(preprocessed_data(),
-                                                            algo = "md5"),
-                                   spectra_type = input$Spectra,
-                                   analyze_type = input$Data,
-                                   region_type = input$Library,
-                                   ipid = input$ipid,
-                                   time = human_ts()))
-      } else {
-        loggit("INFO", "trigger",
-               user_name = input$fingerprint,
-               session_name = session_id,
-               intensity_adj = input$intensity_corr,
-               smoother = input$smoother,
-               smooth_decision = input$smooth_decision,
-               baseline = input$baseline,
-               baseline_decision = input$baseline_decision,
-               max_range = input$MinRange,
-               min_range = input$MaxRange,
-               range_decision = input$range_decision,
-               data_id = digest::digest(preprocessed_data(), algo = "md5"),
-               spectra_type = input$Spectra,
-               analyze_type = input$Data,
-               region_type = input$Library,
-               ipid = input$ipid,
-               time = human_ts())
-      }
-    }
-
-  })
-
-})
-
diff --git a/inst/shiny/ui.R b/inst/shiny/ui.R
deleted file mode 100644
index 112e0851..00000000
--- a/inst/shiny/ui.R
+++ /dev/null
@@ -1,892 +0,0 @@
-#' Shiny app server object
-#'
-#' @importFrom graphics hist
-#' @import shiny
-#'
-# Libraries ----
-library(shiny)
-library(shinyjs)
-library(shinythemes)
-library(shinyWidgets)
-library(shinyBS)
-library(dplyr)
-library(plotly)
-library(DT)
-
-# Name keys for human readable column names ----
-load("data/namekey.RData")
-
-version <- paste0("Open Specy v", packageVersion("OpenSpecy"))
-citation <- HTML(
-  "Cowger W, Steinmetz Z, Gray A, Munno K, Lynch J, Hapich H, Primpke S, De
-  Frond H, Rochman C, Herodotou O (2021). “Microplastic Spectral
-  Classification Needs an Open Source Community: Open Specy to the Rescue!”
-  <i>Analytical Chemistry</i>, <b>93</b>(21), 7543–7548. doi:
-  <a href='https://doi.org/10.1021/acs.analchem.1c00123'>10.1021/acs.analchem.1c00123</a>."
-)
-
-# Functions ----
-labelMandatory <- function(label) {
-  tagList(
-    label,
-    span("*", class = "mandatory_star")
-  )
-}
-
-inputUserid <- function(inputId, value="") {
-  #   print(paste(inputId, "=", value))
-  tagList(
-    singleton(tags$head(tags$script(src = "js/md5.js",
-                                    type="text/javascript"))),
-    singleton(tags$head(tags$script(src = "js/shinyBindings.js",
-                                    type="text/javascript"))),
-    tags$body(onload="setvalues()"),
-    tags$input(id = inputId, class = "userid", value=as.character(value),
-               type = "text", style = "display:none;")
-  )
-}
-
-inputIp <- function(inputId, value=""){
-  tagList(
-    singleton(tags$head(tags$script(src = "js/md5.js",
-                                    type="text/javascript"))),
-    singleton(tags$head(tags$script(src = "js/shinyBindings.js",
-                                    type="text/javascript"))),
-    tags$body(onload="setvalues()"),
-    tags$input(id = inputId, class = "ipaddr", value=as.character(value),
-               type = "text", style = "display:none;")
-  )
-}
-
-css <- HTML(
-  "body {
-    color: #fff;
-  }
-  .nav-tabs > li[class=active] > a,
-  .nav-tabs > li[class=active] > a:focus,
-  .nav-tabs > li[class=active] > a:hover
-  {
-    background-color: #000;
-  }"
-)
-
-# CSS for star
-appCSS <-
-  ".mandatory_star { color: red; }
-    #loading_overlay {
-      position: absolute;
-      margin-top: 10%;
-      background: #000000;
-      opacity: 0.9;
-      z-index: 100;
-      left: 0;
-      right: 0;
-      height: 100%;
-      text-align: center;
-      color: #f7f7f9;
-    }"
-
-containerfunction <- function(...) {
-  div(
-    style = "padding:5rem",
-    div(class = "jumbotron jumbotron-fluid",
-        style = "border:solid #f7f7f9;background-color:rgba(0, 0, 0, 0.5)",
-        align = "justify", ... ))
-}
-
-plotcontainerfunction <- function(...) {
-  div(
-    #style = "padding:0.1rem",
-    div(class = "jumbotron jumbotron-fluid",
-        style = "border:solid #f7f7f9;background-color:rgba(0, 0, 0, 0.5);padding:1rem",
-        align = "justify",
-        ...)
-  )
-}
-
-columnformat <- function() {
-  # 'background-color:rgba(0, 0, 0, 0.5);
-  # padding-bottom: 2rem'
-}
-
-bodyformat <- function() {
-  # 'background-color:rgba(0, 0, 0, 0.5);
-  # padding-bottom: 2rem'
-}
-
-#linefunction <- function(...){
-#  hr(style = "color:#f7f7f9", ...)
-#}
-
-# UI ----
-ui <- fluidPage(
-  
-  # Script for all pages ----
-  # Required for any of the shinyjs functions.
-  shinyjs::useShinyjs(),
-  #extendShinyjs(text = "shinyjs.resetClick = function() { Shiny.onInputChange('.clientValue-plotly_click-A', 'null'); }", functions = "resetClick"),
-  inputIp("ipid"),
-  inputUserid("fingerprint"),
-  # tags$head(uiOutput("name_get")),singleton(tags$head()),
-  
-  tags$head(tags$style(css),
-            tags$script(async = NA, src = "https://platform.twitter.com/widgets.js"),
-            tags$script(async = T, src = "https://buttons.github.io/buttons.js"),
-            tags$style(HTML("
-                    .shiny-output-error-validation {
-                    color: green; font-size: 300%;
-                    }
-                    ")),
-            tags$link(rel = "icon", type = "image/png", href = "favicon.png")
-            #This is for the error messages.
-  ),
-  #theme = bs_theme(fg = "#F9FBFA", bootswatch = "cyborg", bg = "#060606"),
-  theme = shinytheme("cyborg"),
-  # Change this for other themes
-  setBackgroundImage("jumbotron.png"),
-  
-  shinyjs::inlineCSS(appCSS),
-  
-  # Startup ----
-  
-  #Title Panel ----
-  titlePanel(
-    
-    fluidRow(
-      column(10, align = "left", img(src = "logo.png", width = 300, height = 75)),
-      column(2, align = "right",
-             div(style = "width: 90%;
-                            padding: 15px;
-                            font-size: 14pt;
-                            border-radius: 0;
-                            outline: none;
-                            border: none;
-                            text-align:left !important;",
-                 uiOutput("translate")
-                 # Google Translate
-             )
-      )
-    ), windowTitle = "Open Specy"
-  ),
-  # About Tab ----
-  tabsetPanel(id = "tabs",
-              tabPanel("About", value = "tab0",
-                       #Popovers ----
-                       
-                       bsPopover(
-                         id = "download_testdata",
-                         title = "Sample Data Help",
-                         content = "This is a sample spectrum that can be uploaded to the tool for testing it out and understanding how the csv files should be formatted.",
-                         placement = "bottom",
-                         trigger = "hover"
-                       ),
-                       bsPopover(
-                         id = "share_meta",
-                         title = "Metadata Help",
-                         content = "We share any uploaded spectra and metadata with the spectroscopy community if you fill out the metadata here and select share. Uploaded spectra and metadata will appear here: https://osf.io/rjg3c",
-                         placement = "bottom",
-                         trigger = "hover"
-                       ),
-                       bsPopover(id = "downloadData",
-                                 title = "Download Help",
-                                 content = "Some users may wish to save a copy of their processed spectrum. This button downloads the processed spectrum as a csv file.",
-                                 placement = "bottom",
-                                 trigger = "hover"),
-                       bsPopover(
-                         id = "smooth_decision",
-                         title = "Smoothing Help",
-                         content = "This smoother can enhance the signal to noise ratio of the data and uses a Savitzky-Golay filter with 12 running data points and the polynomial specified.",
-                         placement = "bottom",
-                         trigger = "hover"
-                       ),
-                       bsPopover(
-                         id = "baseline_decision",
-                         title = "Baseline Correction Help",
-                         content = "This baseline correction routine has two options for baseline correction, 1) the polynomial imodpolyfit procedure to itteratively find the baseline of the spectrum using a polynomial fit to the entire region of the spectra. 2) manual lines can be drawn using the line tool on the plot and the correct button will use the lines to subtract the baseline.",
-                         placement = "bottom",
-                         trigger = "hover"
-                       ),
-                       bsPopover(
-                         id = "range_decision",
-                         title = "Spectral Range Help",
-                         content = "Restricting the spectral range can remove regions of spectrum where no peaks exist and improve matching",
-                         placement = "bottom",
-                         trigger = "hover"
-                       ),
-                       bsPopover(
-                         id = "smooth_tools",
-                         title = "Smoothing Help",
-                         content = "Toggle advanced smoothing options",
-                         placement = "bottom",
-                         trigger = "hover"
-                       ),
-                       bsPopover(
-                         id = "baseline_tools",
-                         title = "Baseline Correction Help",
-                         content = "Toggle advanced options for baseline corrections",
-                         placement = "bottom",
-                         trigger = "hover"
-                       ),
-                       bsPopover(
-                         id = "range_tools",
-                         title = "Spectral Range Help",
-                         content = "Toggle advanced range selection",
-                         placement = "bottom",
-                         trigger = "hover"
-                       ),
-                       bsPopover(
-                         id = "Spectra",
-                         title = "Spectrum Type Help",
-                         content = "This selection will determine whether the FTIR or Raman matching library is used. Choose the spectrum type that was uploaded.",
-                         placement = "bottom",
-                         trigger = "hover"
-                       ),
-                       bsPopover(
-                         id = "Data",
-                         title = "Spectrum to Analyze Help",
-                         content = "This selection will determine whether the uploaded (not processed) spectrum or the spectrum processed using the processing tab is used in the spectrum match.",
-                         placement = "bottom",
-                         trigger = "hover"
-                       ),
-                       bsPopover(
-                         id = "Library",
-                         title = "Region to Match Help",
-                         content = "This selection will determine whether the library you are matching to consists of the full spectrum or only spectrum peaks.",
-                         placement = "bottom",
-                         trigger = "hover"
-                       ),
-                       bsPopover(
-                         id = "share_decision",
-                         title = "Share Help",
-                         content = "If you like, we share your uploaded spectra and settings with the spectroscopy community. By default, all data will be licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Uploaded spectra will appear here: https://osf.io/rjg3c",
-                         placement = "bottom",
-                         trigger = "hover"
-                       ),
-                       bsPopover(
-                         id = "file1",
-                         title = "Upload Help",
-                         content = "Upload Raman or FTIR spectrum files as a csv, jdx, spc, or spa. A csv file is preferred. If a csv, the file must contain one column labeled wavenumber in units of (1/cm) and another column labeled intensity in absorbance units. If jdx, spc, spa, or 0 the file should be a single absorbance spectrum with wavenumber in (1/cm). These files will not always work perfectly because they are tricky to read so double check them in another software. Hit the Test Data button to download a sample Raman spectrum.",
-                         placement = "bottom",
-                         trigger = "click"
-                       ),
-                       bsPopover(
-                         id = "intensity_corr",
-                         title = "Intensity Correction Help",
-                         content = "If the uploaded spectrum is not in absorbance units, use this input to specify the units to convert from.The transmittance adjustment uses the log10(1/T) calculation which does not correct for system and particle characteristics. The reflectance adjustment uses the Kubelka-Munk equation (1-R)2/(2*R). We assume that the reflectance is formatted as a percent from 1-100 and first correct the intensity by dividing by 100 so that it fits the form expected by the equation. If none is selected, Open Specy assumes that the uploaded data is an absorbance spectrum.",
-                         placement = "bottom",
-                         trigger = "hover"
-                       ),
-                       containerfunction(
-                         h2("Welcome"),
-                         fluidRow(
-                           column(9,
-                                  p(class = "lead", "Join the hundreds of
-                               researchers from around the world who are part of
-                               the Open Specy community by
-                               analyzing, sharing, processing, and identifying
-                               their Raman and IR spectra."),
-                               p(class = "lead",
-                                 HTML("<span style='position: relative; top:.6ex;'><a
-                                      href='https://twitter.com/OpenSpecy?ref_src=twsrc%5Etfw'
-                                      class='twitter-follow-button' data-size='large' data-dnt='true'
-                                      data-show-count='false'>
-                                      Follow @OpenSpecy</a></span>
-                                      on Twitter")
-                               ),
-                               p(class = "lead",
-                                 HTML("<span style='position: relative; top:.8ex;'><a
-                                    class='github-button' href='https://github.com/wincowgerDEV/OpenSpecy-package/subscription'
-                                    data-color-scheme='no-preference: dark; light: dark; dark: dark;'
-                                    data-size='large' aria-label='Watch wincowgerDEV/OpenSpecy'>Watch</a></span>
-                                    us develop Open Specy on GitHub, file an
-                                    <span style='position: relative; top:.8ex;'><a
-                                    class='github-button'
-                                    href='https://github.com/wincowgerDEV/OpenSpecy-package/issues'
-                                    data-color-scheme='no-preference: dark; light: dark; dark: dark;'
-                                    data-icon='octicon-issue-opened' data-size='large'
-                                    aria-label='Issue wincowgerDEV/OpenSpecy on GitHub'>Issue</a></span>,
-                                    or request a feature")
-                               ),
-                               p(class = "lead",
-                                 HTML("Or just e-mail <a href='mailto:wincowger@gmail.com?subject=Open Specy mailing list'>
-                                          wincowger@gmail.com</a>
-                                          to be added to the Open Specy mailing list"),
-                               ),
-                               br(),
-                               h3("Citation"),
-                               p(class = "lead", citation),
-                               br(),br(),
-                               p(class = "lead", "Open Specy is free and open
-                               source thanks to our partners:")),
-                           column(3, img(src = "dancing.jpg", width = "100%")
-                           )
-                         ),
-                         fluidRow(
-                           column(6,
-                                  h3("Monetary Partners"),
-                                  panel(style = "align: centre",
-                                        div(class = "jumbotron",
-                                            style = "padding:0rem 1rem 0rem;
-                               border:solid #f7f7f9;
-                               background-color:rgb(205, 127, 50, 0.5)",
-                               h3("Thriving (10,000–100,000$)"),
-                               img(src = "https://mooreplasticresearch.org/wp-content/uploads/2021/06/HorizontalLogo-FullName-1.png", style = "padding:1rem", height = 100),
-                               h4("Mcpike Zima Charitable Foundation")
-                                        ),
-                               div(class = "jumbotron",
-                                   style = "padding:0rem 1rem 0rem;
-                               border:solid #f7f7f9;
-                               background-color:rgb(3, 252, 15, 0.5)",
-                               h3("Maintaining (1,000–10,000$)"),
-                               img(src = "https://upload.wikimedia.org/wikipedia/commons/thumb/a/aa/UC_Riverside_logo.svg/1024px-UC_Riverside_logo.svg.png", style = "padding:1rem", height = 50),
-                               img(src = "https://upload.wikimedia.org/wikipedia/commons/7/7e/NSF_logo.png", style = "padding:1rem", height = 50),
-                               img(src = "https://www.awi.de/typo3conf/ext/sms_boilerplate/Resources/Public/Images/AWI/awi_logo.svg", style = "padding:1rem",  height = 50),
-                               img(src = "https://www.hpu.edu/_global/images/header-logo.png", style = "padding:1rem",  height = 50),
-                               img(src = "https://www.nist.gov/libraries/nist-component-library/dist/img/logo/nist_logo_sidestack_rev.svg", style = "padding:1rem",  height = 50),
-                               img(src = "https://www.utoronto.ca/sites/all/themes/uoft_stark/img/U-of-T-logo.svg", style = "padding:1rem",  height = 50),
-                               img(src = "https://www.uni-koblenz-landau.de/logo.png", style = "padding:1rem",  height = 50),
-                               img(src = "https://upload.wikimedia.org/wikipedia/commons/thumb/5/50/Thermo_Fisher_Scientific_logo.svg/2560px-Thermo_Fisher_Scientific_logo.svg.png", style = "padding:1rem", height = 50)
-                               ),
-                               div(class = "jumbotron",
-                                   style = "padding:0rem 1rem 0rem;
-                               border:solid #f7f7f9;
-                               background-color:rgb(0, 0, 255, 0.5)",
-                               h3("Supporting (100–1,000$)"),
-                               h5( "Jennifer Gadd")
-                               ),
-                               div(class = "jumbotron",
-                                   style = "padding:0rem 1rem 0rem;
-                               border:solid #f7f7f9;
-                               background-color:rgb(128, 0, 128, 0.5)",
-                               h3("Saving (<100$)"),
-                               h6("Susanne Brander (Oregon State University), Jeremy Conkle (TEXAS  A&M  UNIVERSITY  CORPUS  CHRISTI)")
-                               )
-                                  )
-                           ),
-                           column(6,
-                                  h3("In-Kind Partners"),
-                                  panel(style = "align: centre",
-                                        div(class = "jumbotron",
-                                            style = "padding:0rem 1rem 0rem;
-                                    border:solid #f7f7f9;
-                                    background-color:rgb(205, 127, 50, 0.5)",
-                                    h3("Thriving (10,000–100,000$)"),
-                                    h4("Win Cowger, Zacharias Steinmetz")
-                                        ),
-                                    div(class = "jumbotron",
-                                        style = "padding:0rem 1rem 0rem;
-                                    border:solid #f7f7f9;
-                                    background-color:rgb(3, 252, 15, 0.5)",
-                                    h3("Maintaining (1,000–10,000$)"),
-                                    h5("Sebastian Primpke, Andrew Gray, Chelsea Rochman, Orestis Herodotu, Hannah De Frond, Keenan Munno, Hannah Hapich, Jennifer Lynch")
-                                    ),
-                                    div(class = "jumbotron",
-                                        style = "padding:0rem 1rem 0rem;
-                                    border:solid #f7f7f9;
-                                    background-color:rgb(0, 0, 255, 0.5)",
-                                    h3("Supporting (100–1,000$)"),
-                                    h6( "Shreyas Patankar, Andrea Faltynkova, Alexandre Dehaut, Gabriel Erni Cassola, Aline Carvalho")
-                                    )
-                                  ))
-                         )
-                       ),
-                       containerfunction(
-                         h2("Testimonials"),
-                         fluidRow(
-                           column(6, uiOutput("tweet1")),
-                           column(6, uiOutput("tweet2"))
-                         )
-                       ),
-                       
-                       containerfunction(
-                         h2("Quick Video Tutorial"),
-                         HTML("<iframe width='560' height='315' src='https://www.youtube-nocookie.com/embed/w55WGtV2Dz4' title='YouTube video player' frameborder='0' allow='accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture' allowfullscreen></iframe>")
-                       ),
-                       containerfunction(
-                         h2("Instructions"),
-                         fluidRow(
-                           column(6,
-                                  HTML("<iframe width='560' height='315' src='https://www.youtube-nocookie.com/embed/JjhCdhjdcRY' title='YouTube video player' frameborder='0' allow='accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture' allowfullscreen></iframe>")
-                           ),
-                           column(6,
-                                  p(class = "lead", "In Brief: To use the tool upload a csv, asp, jdx, spc, or spa file to the upload file tab.
-                                  If csv, one column should be named 'wavenumber' (in units of 1/cm) and another named 'intensity'.
-                                  You can smooth your data using an SG filter, baseline correct your data using the polynomial order of iModPolyFit, and restrict the wavelength range for the match.
-                                  The result will be compared to an internal Raman or FTIR spectra library. The strongest 1000 matches along with your
-                                  uploaded or processed data will be presented in an interactive plot and table. For more details click the button below
-                                  or watch the detailed instructional video."),
-                                  a("Detailed Standard Operating Procedure",
-                                    onclick = "window.open('https://cran.r-project.org/web/packages/OpenSpecy/vignettes/sop.html', '_blank')",
-                                    class="btn btn-primary btn-lg")
-                           )
-                         )
-                       ),
-                       
-                       containerfunction(
-                         h2("Download Open Data"),
-                         p(class = "lead", "Reference spectra was sourced from open access resources
-                                online, peer reviewed publications, and corporate donations. In the future,
-                                spectra that is uploaded to the tool will be incorporated to the reference
-                                library to make it even better."),
-                         div(
-                           downloadButton("downloadData6", "Raman Reference Library", style = "background-color: #2a9fd6;"),
-                           downloadButton("downloadData5", "FTIR Reference Library", style = "background-color: #2a9fd6;"),
-                           downloadButton("downloadData4", "Raman Reference Library Metadata", style = "background-color: #2a9fd6;"),
-                           downloadButton("downloadData3", "FTIR Reference Library Metadata", style = "background-color: #2a9fd6;")
-                         )
-                       ),
-                       
-                       containerfunction(
-                         h2("Contribute Spectra"),
-                         p(class = "lead", "To share spectra upload a file to the upload file tab.
-                             If you selected Share a copy of your spectra will be sent to the Community
-                             Data Warehouse on Open Science Framework. To add additional metadata,
-                             fill in the avaliable metadata fields and click -Share Data-. The
-                             spectra file that you uploaded along with your responses will be copied
-                             to the a -With Metadata- subfolder at the link below. All shared data holds
-                             a Creative Commons Attribution License 4.0."),
-                         div(
-                           a("Community Data Warehouse",
-                             onclick = "window.open('https://osf.io/rjg3c/', '_blank')",
-                             class="btn btn-primary btn-lg")
-                         )
-                       ),
-                       
-                       containerfunction(
-                         h2("Tool Validation"),
-                         p(class = "lead", "All parameters in this tool are tested to validate that
-                                the tool is functioning as best as possible and determine the best default
-                                parameters to use. Our current validation proceedure includes correcting
-                                duplicated entries in the reference libraries, checking for spectra in
-                                metadata that isn't in the spectral library, and ensuring the the default
-                                parameters provide over 80% accuracy in the first match."
-                         ),
-                         div(
-                           a("Detailed Validation Procedure",
-                             onclick = "window.open('https://docs.google.com/document/d/1Zd2GY4bWIwegGeE4JpX8O0S5l_IYju0sLDl1ddTTMxU/edit?usp=sharing', '_blank')",
-                             class="btn btn-primary btn-lg")
-                         )
-                       ),
-                       
-                       containerfunction(
-                         h2("Useful Links"),
-                         a(href = "https://simple-plastics.eu/", "Free FTIR Software: siMPle microplastic IR spectral identification software", class = "lead"),
-                         p(),
-                         a(href = "https://www.thermofisher.com/us/en/home/industrial/spectroscopy-elemental-isotope-analysis/spectroscopy-elemental-isotope-analysis-learning-center/molecular-spectroscopy-information.html", "Free Spectroscopy Learning Academy from ThermoFisher", class = "lead"),
-                         p(),
-                         a(href = "https://micro.magnet.fsu.edu/primer/", "Free Optical Microscopy Learning Resource from Florida State University", class = "lead"),
-                         p(),
-                         a(href = "https://www.effemm2.de/spectragryph/index.html", "Free desktop application for spectral analysis and links to reference databases.", class = "lead")
-                       ),
-                       
-                       containerfunction(
-                         h2("Terms And Conditions"),
-                         pre(includeText("www/TOS.txt"))
-                       ),
-                       
-                       containerfunction(
-                         h2("Privacy Policy"),
-                         pre(includeText("www/privacy_policy.txt"))
-                       ),
-              ),
-              
-              #Upload File Tab ----
-              tabPanel("Upload File", value = "tab1",
-                       titlePanel(h4("Upload, View, and Share Spectra")),
-                       br(),
-                       fluidRow(
-                         column(3, style = columnformat(),
-                                tags$label("Choose .csv (preferred), .asp, .jdx, .spc, .spa, or .0 File"),
-                                
-                                prettySwitch("share_decision",
-                                             label = "Share Your Data?",
-                                             inline = T,
-                                             value = T,
-                                             status = "success",
-                                             fill = T),
-                                fileInput("file1", NULL,
-                                          placeholder = ".csv, .asp, .jdx, .spc, .spa, .0",
-                                          accept=c("text/csv",
-                                                   "text/comma-separated-values,text/plain",
-                                                   ".csv", ".asp", ".spc", ".jdx", ".spa", ".0")),
-                                
-                                
-                                radioButtons("intensity_corr", "Intensity Adjustment",
-                                             c("None" = "none",
-                                               "Transmittance" = "transmittance", "Reflectance" = "reflectance")),
-                                tags$br(),
-                                
-                                tags$div(downloadButton("download_testdata",
-                                                        "Sample File",
-                                                        style = "background-color: #2a9fd6;")),
-                                
-                                
-                                tags$br(),
-                                
-                                actionButton("share_meta", "Metadata Input", style = "background-color: #2a9fd6;"),
-                                
-                                
-                                hidden(
-                                  textInput(names(namekey)[1],
-                                            labelMandatory(namekey[1]),
-                                            placeholder = "e.g. Win Cowger"),
-                                  textInput(names(namekey)[2],
-                                            label = namekey[2],
-                                            placeholder = "e.g. 1-513-673-8956, wincowger@gmail.com"),
-                                  textInput(names(namekey)[3],
-                                            label = namekey[3],
-                                            placeholder = "e.g. University of California, Riverside"),
-                                  textInput(names(namekey)[4],
-                                            label = namekey[4],
-                                            placeholder = "e.g. Primpke, S., Wirth, M., Lorenz, C., & Gerdts, G. (2018). Reference database design for the automated analysis of microplastic samples based on Fourier transform infrared (FTIR) spectroscopy. Analytical and Bioanalytical Chemistry. doi: 10.1007/s00216-018-1156-x"),
-                                  textInput(names(namekey)[5],
-                                            labelMandatory(namekey[5]),
-                                            placeholder = "Raman or FTIR"),
-                                  textInput(names(namekey)[6],
-                                            labelMandatory(namekey[6]),
-                                            placeholder = "e.g. polystyrene"),
-                                  textInput(names(namekey)[7],
-                                            label = namekey[7],
-                                            placeholder = "e.g. textile fiber, rubber band, sphere, granule"),
-                                  textInput(names(namekey)[8],
-                                            label = namekey[8],
-                                            placeholder = "liquid, gas, solid"),
-                                  textInput(names(namekey)[9],
-                                            label = namekey[9],
-                                            placeholder = "e.g. Dow" ),
-                                  textInput(names(namekey)[10],
-                                            label = namekey[10],
-                                            placeholder = "e.g. 99.98%"),
-                                  textInput(names(namekey)[11],
-                                            label = namekey[11],
-                                            placeholder = "consumer product, manufacturer material, analytical standard, environmental sample"),
-                                  textInput(names(namekey)[12],
-                                            label = namekey[12],
-                                            placeholder = "e.g. blue, #0000ff, (0, 0, 255)"),
-                                  textInput(names(namekey)[13],
-                                            label = namekey[13],
-                                            placeholder = "e.g. 5 µm diameter fibers, 1 mm spherical particles"),
-                                  textInput(names(namekey)[14],
-                                            label = namekey[14],
-                                            placeholder = "9003-53-6"),
-                                  textInput(names(namekey)[15],
-                                            label = namekey[15],
-                                            placeholder = "Horiba LabRam"),
-                                  textInput(names(namekey)[16],
-                                            label = namekey[16],
-                                            placeholder = "Focal Plane Array, CCD"),
-                                  textInput(names(namekey)[17],
-                                            label = namekey[17],
-                                            placeholder = "transmission, reflectance"),
-                                  textInput(names(namekey)[18],
-                                            label = namekey[18],
-                                            placeholder = "e.g. 4/cm"),
-                                  textInput(names(namekey)[19],
-                                            label = namekey[19],
-                                            placeholder = "e.g. 785 nm" ),
-                                  textInput(names(namekey)[20],
-                                            label = namekey[20],
-                                            placeholder = "e.g. 5"),
-                                  textInput(names(namekey)[21],
-                                            label = namekey[21],
-                                            placeholder = "10 s"),
-                                  textInput(names(namekey)[22],
-                                            label = namekey[22],
-                                            placeholder = "spikefilter, baseline correction, none"),
-                                  textInput(names(namekey)[23],
-                                            label = namekey[23],
-                                            placeholder = "e.g. 99%"),
-                                  textInput(names(namekey)[24], label = "Other information"),
-                                  selectInput(names(namekey)[32],
-                                              label = namekey[32],
-                                              selected = "CC BY-NC",
-                                              choices = c("CC0", "CC BY",
-                                                          "CC BY-SA",
-                                                          "CC BY-NC",
-                                                          "CC BY-ND",
-                                                          "CC BY-NC-SA",
-                                                          "CC BY-NC-ND")),
-                                  
-                                  tags$br(),
-                                  actionButton("submit", "Share Metadata", class = "btn-primary")
-                                )
-                         ),
-                         
-                         
-                         column(9,
-                                plotcontainerfunction(h4(id = "placeholder1", "Upload some data to get started..."), plotlyOutput("MyPlot")),
-                                style = bodyformat()
-                                
-                         ),
-                       ),
-                       hr(),
-                       fluidRow(
-                         column(3),
-                         column(6, align = "center",
-                                tags$p(citation),
-                                tags$p(version)
-                         ),
-                         column(3)
-                         
-                       )),
-              
-              
-              #Preprocess Spectrum Tab ----
-              tabPanel("Preprocess Spectrum", value = "tab2",
-                       titlePanel(h4("Smooth, Baseline Correct, and Download Processed Spectra")),
-                       br(),
-                       fluidRow(
-                         column(3, style = columnformat(),
-                                fluidRow(
-                                  column(12,
-                                         downloadButton("downloadData", "Download (recommended)",
-                                                        style = "background-color: #2a9fd6;")
-                                         
-                                  )
-                                ),
-                                br(),
-                                fluidRow(
-                                  column(9, ""),
-                                  column(3, align = "center", tags$label("Options"))
-                                ),
-                                br(),
-                                fluidRow(
-                                  column(9,
-                                         prettySwitch(inputId = "smooth_decision",
-                                                      label = "Smoothing",
-                                                      inline = T,
-                                                      value = T,
-                                                      status = "success",
-                                                      fill = T)
-                                  ),
-                                  column(3, align = "center",
-                                         prettyToggle("smooth_tools",
-                                                      icon_on = icon("eye"),
-                                                      icon_off = icon("eye-slash"), 
-                                                      label_on = NULL, label_off = NULL,
-                                                      status_on = "success",
-                                                      status_off = "default",
-                                                      outline = TRUE,
-                                                      plain = TRUE,
-                                                      bigger = T),
-                                  )
-                                ),
-                                
-                                fluidRow(
-                                  column(9,
-                                         prettySwitch("baseline_decision",
-                                                      label = "Baseline Correction",
-                                                      inline = T,
-                                                      value = T,
-                                                      status = "success",
-                                                      fill = T),
-                                         
-                                  ),
-                                  column(3, align = "center",
-                                         prettyToggle("baseline_tools",
-                                                      icon_on = icon("eye"),
-                                                      icon_off = icon("eye-slash"), 
-                                                      label_on = NULL, label_off = NULL,
-                                                      status_on = "success",
-                                                      status_off = "default",
-                                                      outline = TRUE,
-                                                      plain = TRUE,
-                                                      bigger = T),
-                                  )
-                                ),
-                                fluidRow(
-                                  column(9,
-                                         prettySwitch("range_decision",
-                                                      label = "Range Selection",
-                                                      inline = T,
-                                                      value = T,
-                                                      status = "success",
-                                                      fill = T)
-                                  ),
-                                  column(3, align = "center",
-                                         prettyToggle("range_tools",
-                                                      icon_on = icon("eye"),
-                                                      icon_off = icon("eye-slash"), 
-                                                      label_on = NULL, label_off = NULL,
-                                                      status_on = "success",
-                                                      status_off = "default",
-                                                      outline = TRUE,
-                                                      plain = TRUE,
-                                                      bigger = T),
-                                  )
-                                ),
-                                br(),
-                                fluidRow(column(12,
-                                                conditionalPanel("input.smooth_tools == true & input.smooth_decision == true",
-                                                                 plotcontainerfunction(sliderInput("smoother", "Smoothing Polynomial", min = 0, max = 7, value = 3)
-                                                                 )),
-                                                conditionalPanel("input.baseline_tools == true & input.baseline_decision == true",
-                                                                 plotcontainerfunction(
-                                                                   selectInput(inputId = "baseline_selection", label = "Baseline Correction Technique", choices = c("Polynomial", "Manual")),
-                                                                   sliderInput("baseline", "Baseline Correction Polynomial", min = 1, max = 20, value = 8),
-                                                                   fluidRow(
-                                                                     column(6,
-                                                                            actionButton("go", "Correct With Trace"),
-                                                                     ),
-                                                                     column(6,
-                                                                            actionButton("reset", "Reset"),
-                                                                     )
-                                                                   )
-                                                                 )
-                                                                 
-                                                ),
-                                                conditionalPanel("input.range_tools == true & input.range_decision == true",
-                                                                 plotcontainerfunction(
-                                                                   numericInput(
-                                                                     "MaxRange",
-                                                                     "Maximum Spectral Range",
-                                                                     value = 6000,
-                                                                     min = NA,
-                                                                     max = NA,
-                                                                     step = NA,
-                                                                     width = NULL
-                                                                   ),
-                                                                   numericInput(
-                                                                     "MinRange",
-                                                                     "Minimum Spectral Range",
-                                                                     value = 0,
-                                                                     min = NA,
-                                                                     max = NA,
-                                                                     step = NA,
-                                                                     width = NULL
-                                                                   )
-                                                                 )
-                                                )
-                                )
-                                )
-                         ),
-                         
-                         
-                         column(9,
-                                plotcontainerfunction(h4(id = "placeholder2", "Upload some data to get started..."), plotlyOutput("MyPlotB")),
-                                #verbatimTextOutput(outputId = "text"),
-                                style = bodyformat()
-                                
-                         )),
-                       hr(),
-                       fluidRow(
-                         column(3),
-                         column(6, align = "center",
-                                tags$p(citation),
-                                tags$p(version)
-                         ),
-                         column(3)
-                         
-                       )),
-              
-              #Match Spectrum Tab ----
-              tabPanel("Identify Spectrum",value = "tab3",
-                       titlePanel(h4("Identify Spectrum Using the Reference Library")),
-                       br(),
-                       fluidRow(
-                         column(3, style = columnformat(),
-                                fluidRow(
-                                  column(4,
-                                         radioButtons("Spectra", "Type",
-                                                      c("Raman" = "raman",
-                                                        "FTIR" = "ftir"))
-                                  ),
-                                  column(4,
-                                         radioButtons("Data", "Analysis",
-                                                      c("Processed" = "processed",
-                                                        "Uploaded" = "uploaded"
-                                                      ))
-                                  ),
-                                  column(4,
-                                         radioButtons("Library", "Region",
-                                                      c("Full" = "full",
-                                                        "Peaks" = "peaks"))
-                                         
-                                  )
-                                ),
-                                fluidRow(style = "padding:1rem",
-                                         DT::dataTableOutput("event")
-                                )
-                         ),
-                         
-                         column(9,
-                                plotcontainerfunction(h4(id = "placeholder3", "Upload some data to get started..."), plotlyOutput("MyPlotC"),
-                                                      DT::dataTableOutput("eventmetadata")),
-                                style = bodyformat()
-                                
-                         )
-                       ),
-                       
-                       
-                       hr(),
-                       fluidRow(
-                         column(3),
-                         column(6, align = "center",
-                                tags$p(citation),
-                                tags$p(version)
-                         ),
-                         column(3)
-                         
-                       )),
-              
-              
-              #Partner With Us tab ----
-              tabPanel("Partner With Us",
-                       titlePanel(h4("Help us reach our goal to revolutionize spectroscopy.")),
-                       br(),
-                       fluidRow(
-                         column(1),
-                         column(3,
-                                plotcontainerfunction(
-                                  tags$h3("Donate Cash"),
-                                  icon = icon("shopping-cart"),
-                                  img(src = "https://p.turbosquid.com/ts-thumb/rX/Wm1eqB/t5/currencysymbolsgoldensetc4dmodel000/jpg/1613802168/300x300/sharp_fit_q85/a31625492ce9c8009ab3e4281ad752006e1163ec/currencysymbolsgoldensetc4dmodel000.jpg", style = "padding:1rem; background-color:rgba(255,255,255, 0.9)", width = "100%"),
-                                  actionButton(inputId = "ab1", label = "Donate", style="padding:4px; background-color: #2a9fd6; font-size:200%", width = "100%",
-                                               icon = icon("donate"),
-                                               onclick = "window.open('https://www.paypal.com/cgi-bin/webscr?cmd=_donations&business=wincowger@gmail.com&lc=US&item_name=Donation+to+Open+Specy&no_note=0&cn=&currency_code=USD&bn=PP-DonationsBF:btn_donateCC_LG.gif:NonHosted', '_blank')")
-                                )),
-                         column(3,
-                                plotcontainerfunction(tags$h3("Buy From Swag Store"),
-                                                      img(src = "https://image.spreadshirtmedia.com/image-server/v1/products/T813A823PA3132PT17X42Y46D1038541132FS4033/views/1,width=650,height=650,appearanceId=823/updated-logo-for-open-specy-designed-by-alex-mcgoran.jpg", style = "padding:1rem; background-color:rgba(255,255,255, 0.9)", width = "100%"),
-                                                      actionButton(inputId = "ab2", label = "Shop", style="padding:4px; background-color: #2a9fd6; font-size:200%", width = "100%",
-                                                                   icon = icon("shopping-cart"),
-                                                                   onclick ="window.open('https://shop.spreadshirt.com/openspecy/all', '_blank')")
-                                )),
-                         column(3,
-                                plotcontainerfunction(
-                                  h2("Contribute time"),
-                                  #p(class = "lead", "We are looking for coders, moderators, spectroscopy experts, microplastic researchers, industry, government, and others to join the Open Specy team. Please contact Win at wincowger@gmail.com"),
-                                  img(src = "https://health.sunnybrook.ca/wp-content/uploads/2020/02/healthy-hands-810x424.jpg", style = "padding:1rem; background-color:rgba(255,255,255, 0.9)", width = "100%"),
-                                  actionButton(inputId = "ab3", label = "Guidelines", style="padding:4px; background-color: #2a9fd6; font-size:200%", width = "100%",
-                                               icon = icon("clock"),
-                                               onclick ="window.open('https://docs.google.com/document/d/1SaFgAYKsLbMSYdJClR5s42TyGmPRWihLQcf5zun_yfo/edit?usp=sharing', '_blank')")
-                                )
-                         ),
-                         column(2)
-                       ),
-                       fluidRow(
-                         column(1),
-                         column(9,
-                                div(style = "font-size:150%",
-                                    DT::dataTableOutput("event_goals"),
-                                    br()
-                                ),
-                         ),
-                         column(2)
-                       ),
-                       hr(),
-                       fluidRow(
-                         column(3),
-                         column(6, align = "center",
-                                tags$p(citation),
-                                tags$p(version)
-                         ),
-                         column(3)
-                         
-                       ))
-  )
-)
-
-
-#Ideas
-# see https://stackoverflow.com/questions/36412407/shiny-add-link-to-another-tabpanel-in-another-tabpanel/36426258
diff --git a/inst/shiny/www/TOS.txt b/inst/shiny/www/TOS.txt
deleted file mode 100644
index 3c12f711..00000000
--- a/inst/shiny/www/TOS.txt
+++ /dev/null
@@ -1,49 +0,0 @@
-1. Who May Use the Services
-You may use the Services only if you agree to form a binding contract with Open Specy by accepting these Terms. If you are accepting these Terms and using the Services on behalf of a company, organization, government, or other legal entity, you represent and warrant that you are authorized to do so and have the authority to bind such entity to these Terms, in which case the words “you” and “your” as used in these Terms shall refer to such entity.
-
-2. Content on the Services
-You are responsible for your use of the Services and for any Content you provide, including compliance with applicable laws, rules, and regulations. You should only provide Content that you are comfortable sharing with others.
-
-Any use or reliance on any Content or materials posted via the Services or obtained by you through the Services is at your own risk. We do not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Content or communications posted via the Services. You understand that by using the Services, you may be exposed to Content that might be offensive, harmful, inaccurate or otherwise inappropriate, or in some cases, postings that have been mislabeled or are otherwise deceptive. All Content is the sole responsibility of the person who originated such Content. We may not monitor or control the Content posted via the Services and, we cannot take responsibility for such Content.
-
-We reserve the right to remove Content that violates the User Agreement, including for example, copyright or trademark violations or other intellectual property misappropriation, impersonation, unlawful conduct, or harassment. Information regarding specific policies and the process for reporting or appealing violations can be found by emailing Win at wincowger@gmail.com.
-
-If you believe that your Content has been copied in a way that constitutes copyright infringement, please report this by emailing Win at wincowger@gmail.com.
-
-3. Your Rights and Grant of Rights in the Content
-You retain your rights to any Content you submit, post or display on or through the Services. What’s yours is yours — you own your Content (and your incorporated audio, photos and videos are considered part of the Content).
-
-By submitting, posting or displaying Content on or through the Services, you grant us a worldwide, non-exclusive, royalty-free license (with the right to sublicense) to use, copy, reproduce, process, adapt, modify, publish, transmit, display and distribute such Content in any and all media or distribution methods now known or later developed (for clarity, these rights include, for example, curating, transforming, and translating). This license authorizes us to make your Content available to the rest of the world and to let others do the same. You agree that this license includes the right for Open Specy to provide, promote, and improve the Services and to make Content submitted to or through the Services available to other companies, organizations or individuals for the syndication, broadcast, distribution, Retweet, promotion or publication of such Content on other media and services, subject to our terms and conditions for such Content use. Such additional uses by Open Specy, or other companies, organizations or individuals, is made with no compensation paid to you with respect to the Content that you submit, post, transmit or otherwise make available through the Services as the use of the Services by you is hereby agreed as being sufficient compensation for the Content and grant of rights herein.
-
-You understand that we may modify or adapt your Content as it is distributed, syndicated, published, or broadcast by us and our partners and/or make changes to your Content in order to adapt the Content to different media.
-
-You represent and warrant that you have, or have obtained, all rights, licenses, consents, permissions, power and/or authority necessary to grant the rights granted herein for any Content that you submit, post or display on or through the Services. You agree that such Content will not contain material subject to copyright or other proprietary rights, unless you have necessary permission or are otherwise legally entitled to post the material and to grant Open Specy the license described above.
-
- 
-4. Using the Services
-
-Our Services evolve constantly. As such, the Services may change from time to time, at our discretion. We may stop (permanently or temporarily) providing the Services or any features within the Services to you or to users generally. We also retain the right to create limits on use and storage at our sole discretion at any time. We may also remove or refuse to distribute any Content on the Services, limit distribution or visibility of any Content on the service.
-
-In consideration for Open Specy granting you access to and use of the Services, you agree that Open Specy and its third-party providers and partners may place advertising on the Services or in connection with the display of Content or information from the Services whether submitted by you or others. You also agree not to misuse our Services, for example, by interfering with them. You may not do any of the following while accessing or using the Services: (i) access, tamper with, or use non-public areas of the Services, Open Specy’s computer systems, or the technical delivery systems of Open Specy’s providers; (ii) probe, scan, or test the vulnerability of any system or network or breach or circumvent any security or authentication measures; (iii) access or search or attempt to access or search the Services by any means (automated or otherwise) other than through our currently available, published interfaces that are provided by Open Specy (and only pursuant to the applicable terms and conditions), unless you have been specifically allowed to do so in a separate agreement with Open Specy; (iv) forge any TCP/IP packet header or any part of the header information in any email or posting, or in any way use the Services to send altered, deceptive or false source-identifying information; or (v) interfere with, or disrupt, (or attempt to do so), the access of any user, host or network, including, without limitation, sending a virus, overloading, flooding, spamming, mail-bombing the Services, or by scripting the creation of Content in such a manner as to interfere with or create an undue burden on the Services. We also reserve the right to access, read, preserve, and disclose any information as we reasonably believe is necessary to (i) satisfy any applicable law, regulation, legal process or governmental request, (ii) enforce the Terms, including investigation of potential violations hereof, (iii) detect, prevent, or otherwise address fraud, security or technical issues, (iv) respond to user support requests, or (v) protect the rights, property or safety of Open Specy, its users and the public. Open Specy does not disclose personally-identifying information to third parties except in accordance with our Privacy Policy.
-
-Your Account
-You may need to create an account to use some of our Services. You are responsible for safeguarding your account, so use a strong password and limit its use to this account. We cannot and will not be liable for any loss or damage arising from your failure to comply with the above.
-You can control most communications from the Services. We may need to provide you with certain communications, such as service announcements and administrative messages. These communications are considered part of the Services and your account, and you may not be able to opt-out from receiving them. 
- 
-Your License to Use the Services
-Open Specy gives you a personal, worldwide, royalty-free, non-assignable and non-exclusive license to use the software provided to you as part of the Services. This license has the sole purpose of enabling you to use and enjoy the benefit of the Services as provided by Open Specy, in the manner permitted by these Terms.
-The Services are protected by copyright, trademark, and other laws of both the United States and other countries. Nothing in the Terms gives you a right to use the Open Specy name or any of the Open Specy trademarks, logos, domain names, other distinctive brand features, and other proprietary rights. All right, title, and interest in and to the Services (excluding Content provided by users) are and will remain the exclusive property of Open Specy and its licensors. Any feedback, comments, or suggestions you may provide regarding Open Specy, or the Services is entirely voluntary and we will be free to use such feedback, comments or suggestions as we see fit and without any obligation to you.
- 
-Ending These Terms
-You may end your legal agreement with Open Specy at any time by deactivating your accounts and discontinuing your use of the Services. 
- 
-5. Limitations of Liability
-By using the Services you agree that Open Specy, its parents, affiliates, related companies, officers, directors, employees, agents representatives, partners and licensors, liability is limited to the maximum extent permissible in your country of residence.
- 
-6. General
-We may revise these Terms from time to time. The changes will not be retroactive, and the most current version of the Terms, will govern our relationship with you. By continuing to access or use the Services after those revisions become effective, you agree to be bound by the revised Terms.
-
-In the event that any provision of these Terms is held to be invalid or unenforceable, then that provision will be limited or eliminated to the minimum extent necessary, and the remaining provisions of these Terms will remain in full force and effect. Open Specy’s failure to enforce any right or provision of these Terms will not be deemed a waiver of such right or provision.
-
-These Terms are an agreement between you and Open Specy. If you have any questions about these Terms, please contact us.
-
diff --git a/inst/shiny/www/dancing.jpg b/inst/shiny/www/dancing.jpg
deleted file mode 100644
index 261d28d5..00000000
Binary files a/inst/shiny/www/dancing.jpg and /dev/null differ
diff --git a/inst/shiny/www/favicon.png b/inst/shiny/www/favicon.png
deleted file mode 100644
index b422c90b..00000000
Binary files a/inst/shiny/www/favicon.png and /dev/null differ
diff --git a/inst/shiny/www/googletranslate.html b/inst/shiny/www/googletranslate.html
deleted file mode 100644
index 7792e618..00000000
--- a/inst/shiny/www/googletranslate.html
+++ /dev/null
@@ -1,13 +0,0 @@
-<div id="google_translate_element"></div><script>
-
-function googleTranslateElementInit() {
-
-new google.translate.TranslateElement({
-
-pageLanguage: 'en'
-
-}, 'google_translate_element');
-
-}
-
-</script><script src="//translate.google.com/translate_a/element.js?cb=googleTranslateElementInit"></script>
\ No newline at end of file
diff --git a/inst/shiny/www/js/jqfp.js b/inst/shiny/www/js/jqfp.js
deleted file mode 100644
index fd9e5d49..00000000
--- a/inst/shiny/www/js/jqfp.js
+++ /dev/null
@@ -1,79 +0,0 @@
-// Browser fingerprinting is a technique to "mark" anonymous users using JS
-// (or other things).  To build an "identity" of sorts the browser is queried
-// for a list of its plugins, the screen size and several other things, then
-// hashes them.  The idea is that these bits of information produce an unique
-// "fingerprint" of sorts; the more elaborate the list of data points is, the
-// more unique this fingerprint becomes.  And you wouldn't even need to set a
-// cookie to recognize this user when she visits again.
-//
-// For more information on this topic consult
-// [Ars Technica](http://arstechnica.com/tech-policy/news/2010/05/how-your-web-browser-rats-you-out-online.ars)
-// or the [EFF](http://panopticlick.eff.org/).  There is a lot of potential
-// for undesirable shenanigans, and I strictly oppose using this technique for
-// marketing and ad-related tracking purposes.
-//
-// Anyways, I needed a really simple fingerprinting library, so I wrote a
-// quick and dirty jQuery plugin.  This is by no means a complete and
-// watertight implementation -- it is merely the scratch for a particular itch
-// I was having.  YMMV.
-//
-// This library was written by Carlo Zottmann, carlo@municode.de, has its home
-// on [Github](http://github.com/carlo/jquery-browser-fingerprint) and is
-// WTF-licensed (see LICENSE.txt).
-
-( function($) {
-
-  // Calling `jQuery.fingerprint()` will return an MD5 hash, i.e. said
-  // fingerprint.
-
-  $.fingerprint = function() {
-
-    // This function, `_raw()`, uses several browser details which are
-    // available to JS here to build a string, namely...
-    //
-    // * the user agent
-    // * screen size
-    // * color depth
-    // * the timezone offset
-    // * sessionStorage support
-    // * localStorage support
-    // * the list of all installed plugins (we're using their names,
-    //    descriptions, mime types and file name extensions here)
-    function _raw() {
-      // That string is the return value.
-      return [
-        navigator.userAgent,
-        [ screen.height, screen.width, screen.colorDepth ].join("x"),
-        ( new Date() ).getTimezoneOffset(),
-        !!window.sessionStorage,
-        !!window.localStorage,
-        $.map( navigator.plugins, function(p) {
-          return [
-            p.name,
-            p.description,
-            $.map( p, function(mt) {
-              return [ mt.type, mt.suffixes ].join("~");
-            }).join(",")
-          ].join("::");
-        }).join(";")
-      ].join("###");
-    }
-
-    // `_md5()` computes a MD5 hash using [md5-js](http://github.com/wbond/md5-js/).
-    function _md5() {
-      if ( typeof window.md5 === "function" ) {
-        // The return value is the hashed fingerprint string.
-        return md5( _raw() );
-      }
-      else {
-        // If `window.md5()` isn't available, an error is thrown.
-        throw "md5 unavailable, please get it from http://github.com/wbond/md5-js/";
-      }
-    }
-
-    // And, since I'm lazy, calling `$.fingerprint()` will return the hash
-    // right away, without the need for any other calls.
-    return _md5();
-  }
-
-})(jQuery);
diff --git a/inst/shiny/www/js/md5.js b/inst/shiny/www/js/md5.js
deleted file mode 100644
index bebcdacc..00000000
--- a/inst/shiny/www/js/md5.js
+++ /dev/null
@@ -1,180 +0,0 @@
-/*!
- * Joseph Myer's md5() algorithm wrapped in a self-invoked function to prevent
- * global namespace polution, modified to hash unicode characters as UTF-8.
- *  
- * Copyright 1999-2010, Joseph Myers, Paul Johnston, Greg Holt, Will Bond <will@wbond.net>
- * http://www.myersdaily.org/joseph/javascript/md5-text.html
- * http://pajhome.org.uk/crypt/md5
- * 
- * Released under the BSD license
- * http://www.opensource.org/licenses/bsd-license
- */
-(function() {
-	function md5cycle(x, k) {
-		var a = x[0], b = x[1], c = x[2], d = x[3];
-
-		a = ff(a, b, c, d, k[0], 7, -680876936);
-		d = ff(d, a, b, c, k[1], 12, -389564586);
-		c = ff(c, d, a, b, k[2], 17, 606105819);
-		b = ff(b, c, d, a, k[3], 22, -1044525330);
-		a = ff(a, b, c, d, k[4], 7, -176418897);
-		d = ff(d, a, b, c, k[5], 12, 1200080426);
-		c = ff(c, d, a, b, k[6], 17, -1473231341);
-		b = ff(b, c, d, a, k[7], 22, -45705983);
-		a = ff(a, b, c, d, k[8], 7, 1770035416);
-		d = ff(d, a, b, c, k[9], 12, -1958414417);
-		c = ff(c, d, a, b, k[10], 17, -42063);
-		b = ff(b, c, d, a, k[11], 22, -1990404162);
-		a = ff(a, b, c, d, k[12], 7, 1804603682);
-		d = ff(d, a, b, c, k[13], 12, -40341101);
-		c = ff(c, d, a, b, k[14], 17, -1502002290);
-		b = ff(b, c, d, a, k[15], 22, 1236535329);
-
-		a = gg(a, b, c, d, k[1], 5, -165796510);
-		d = gg(d, a, b, c, k[6], 9, -1069501632);
-		c = gg(c, d, a, b, k[11], 14, 643717713);
-		b = gg(b, c, d, a, k[0], 20, -373897302);
-		a = gg(a, b, c, d, k[5], 5, -701558691);
-		d = gg(d, a, b, c, k[10], 9, 38016083);
-		c = gg(c, d, a, b, k[15], 14, -660478335);
-		b = gg(b, c, d, a, k[4], 20, -405537848);
-		a = gg(a, b, c, d, k[9], 5, 568446438);
-		d = gg(d, a, b, c, k[14], 9, -1019803690);
-		c = gg(c, d, a, b, k[3], 14, -187363961);
-		b = gg(b, c, d, a, k[8], 20, 1163531501);
-		a = gg(a, b, c, d, k[13], 5, -1444681467);
-		d = gg(d, a, b, c, k[2], 9, -51403784);
-		c = gg(c, d, a, b, k[7], 14, 1735328473);
-		b = gg(b, c, d, a, k[12], 20, -1926607734);
-
-		a = hh(a, b, c, d, k[5], 4, -378558);
-		d = hh(d, a, b, c, k[8], 11, -2022574463);
-		c = hh(c, d, a, b, k[11], 16, 1839030562);
-		b = hh(b, c, d, a, k[14], 23, -35309556);
-		a = hh(a, b, c, d, k[1], 4, -1530992060);
-		d = hh(d, a, b, c, k[4], 11, 1272893353);
-		c = hh(c, d, a, b, k[7], 16, -155497632);
-		b = hh(b, c, d, a, k[10], 23, -1094730640);
-		a = hh(a, b, c, d, k[13], 4, 681279174);
-		d = hh(d, a, b, c, k[0], 11, -358537222);
-		c = hh(c, d, a, b, k[3], 16, -722521979);
-		b = hh(b, c, d, a, k[6], 23, 76029189);
-		a = hh(a, b, c, d, k[9], 4, -640364487);
-		d = hh(d, a, b, c, k[12], 11, -421815835);
-		c = hh(c, d, a, b, k[15], 16, 530742520);
-		b = hh(b, c, d, a, k[2], 23, -995338651);
-
-		a = ii(a, b, c, d, k[0], 6, -198630844);
-		d = ii(d, a, b, c, k[7], 10, 1126891415);
-		c = ii(c, d, a, b, k[14], 15, -1416354905);
-		b = ii(b, c, d, a, k[5], 21, -57434055);
-		a = ii(a, b, c, d, k[12], 6, 1700485571);
-		d = ii(d, a, b, c, k[3], 10, -1894986606);
-		c = ii(c, d, a, b, k[10], 15, -1051523);
-		b = ii(b, c, d, a, k[1], 21, -2054922799);
-		a = ii(a, b, c, d, k[8], 6, 1873313359);
-		d = ii(d, a, b, c, k[15], 10, -30611744);
-		c = ii(c, d, a, b, k[6], 15, -1560198380);
-		b = ii(b, c, d, a, k[13], 21, 1309151649);
-		a = ii(a, b, c, d, k[4], 6, -145523070);
-		d = ii(d, a, b, c, k[11], 10, -1120210379);
-		c = ii(c, d, a, b, k[2], 15, 718787259);
-		b = ii(b, c, d, a, k[9], 21, -343485551);
-
-		x[0] = add32(a, x[0]);
-		x[1] = add32(b, x[1]);
-		x[2] = add32(c, x[2]);
-		x[3] = add32(d, x[3]);
-	}
-
-	function cmn(q, a, b, x, s, t) {
-		a = add32(add32(a, q), add32(x, t));
-		return add32((a << s) | (a >>> (32 - s)), b);
-	}
-
-	function ff(a, b, c, d, x, s, t) {
-		return cmn((b & c) | ((~b) & d), a, b, x, s, t);
-	}
-
-	function gg(a, b, c, d, x, s, t) {
-		return cmn((b & d) | (c & (~d)), a, b, x, s, t);
-	}
-
-	function hh(a, b, c, d, x, s, t) {
-		return cmn(b ^ c ^ d, a, b, x, s, t);
-	}
-
-	function ii(a, b, c, d, x, s, t) {
-		return cmn(c ^ (b | (~d)), a, b, x, s, t);
-	}
-
-	function md51(s) {
-		// Converts the string to UTF-8 "bytes" when necessary
-		if (/[\x80-\xFF]/.test(s)) {
-			s = unescape(encodeURI(s));
-		}
-		txt = '';
-		var n = s.length, state = [1732584193, -271733879, -1732584194, 271733878], i;
-		for (i = 64; i <= s.length; i += 64) {
-			md5cycle(state, md5blk(s.substring(i - 64, i)));
-		}
-		s = s.substring(i - 64);
-		var tail = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0];
-		for (i = 0; i < s.length; i++)
-		tail[i >> 2] |= s.charCodeAt(i) << ((i % 4) << 3);
-		tail[i >> 2] |= 0x80 << ((i % 4) << 3);
-		if (i > 55) {
-			md5cycle(state, tail);
-			for (i = 0; i < 16; i++) tail[i] = 0;
-		}
-		tail[14] = n * 8;
-		md5cycle(state, tail);
-		return state;
-	}
-
-	function md5blk(s) { /* I figured global was faster.   */
-		var md5blks = [], i; /* Andy King said do it this way. */
-		for (i = 0; i < 64; i += 4) {
-			md5blks[i >> 2] = s.charCodeAt(i) +
-			                  (s.charCodeAt(i + 1) << 8) +
-			                  (s.charCodeAt(i + 2) << 16) +
-			                  (s.charCodeAt(i + 3) << 24);
-		}
-		return md5blks;
-	}
-
-	var hex_chr = '0123456789abcdef'.split('');
-
-	function rhex(n) {
-		var s = '', j = 0;
-		for (; j < 4; j++)
-		s += hex_chr[(n >> (j * 8 + 4)) & 0x0F] +
-		     hex_chr[(n >> (j * 8)) & 0x0F];
-		return s;
-	}
-
-	function hex(x) {
-		for (var i = 0; i < x.length; i++)
-		x[i] = rhex(x[i]);
-		return x.join('');
-	}
-
-	md5 = function (s) {
-		return hex(md51(s));
-	}
-
-	/* this function is much faster, so if possible we use it. Some IEs are the
-	only ones I know of that need the idiotic second function, generated by an
-	if clause.  */
-	function add32(a, b) {
-		return (a + b) & 0xFFFFFFFF;
-	}
-
-	if (md5('hello') != '5d41402abc4b2a76b9719d911017c592') {
-		function add32(x, y) {
-			var lsw = (x & 0xFFFF) + (y & 0xFFFF),
-			    msw = (x >> 16) + (y >> 16) + (lsw >> 16);
-			return (msw << 16) | (lsw & 0xFFFF);
-		}
-	}
-})();
\ No newline at end of file
diff --git a/inst/shiny/www/js/shinyBindings.js b/inst/shiny/www/js/shinyBindings.js
deleted file mode 100644
index ae1a5b67..00000000
--- a/inst/shiny/www/js/shinyBindings.js
+++ /dev/null
@@ -1,163 +0,0 @@
-  // Calling `jQuery.fingerprint()` will return an MD5 hash, i.e. said
-  // fingerprint.
-
-  $.fingerprint = function() {
-
-    // This function, `_raw()`, uses several browser details which are
-    // available to JS here to build a string, namely...
-    //
-    // * the user agent
-    // * screen size
-    // * color depth
-    // * the timezone offset
-    // * sessionStorage support
-    // * localStorage support
-    // * the list of all installed plugins (we're using their names,
-    //    descriptions, mime types and file name extensions here)
-    function _raw() {
-      // That string is the return value.
-      return [
-        navigator.userAgent,getip(),
-        [ screen.height, screen.width, screen.colorDepth ].join("x"),
-        ( new Date() ).getTimezoneOffset(),
-        !!window.sessionStorage,
-        !!window.localStorage,
-        $.map( navigator.plugins, function(p) {
-          return [
-            p.name,
-            p.description,
-            $.map( p, function(mt) {
-              return [ mt.type, mt.suffixes ].join("~");
-            }).join(",")
-          ].join("::");
-        }).join(";")
-      ].join("###");
-    }
-
-    // `_md5()` computes a MD5 hash using [md5-js](http://github.com/wbond/md5-js/).
-    function _md5() {
-      if ( typeof window.md5 === "function" ) {
-        // The return value is the hashed fingerprint string.
-        return md5( _raw() );
-      }
-      else {
-        // If `window.md5()` isn't available, an error is thrown.
-        throw "md5 unavailable, please get it from http://github.com/wbond/md5-js/";
-      }
-    }
-
-    // And, since I'm lazy, calling `$.fingerprint()` will return the hash
-    // right away, without the need for any other calls.
-    return _md5();
-  }
-
-  
-  /*
-  var outputUserid = new Shiny.OutputBinding();
-  $.extend(outputUserid, {
-    find: function(scope) {
-      return $.find('.userid');
-    },
-    renderError: function(el,error) {
-      console.log("Foe");
-    },
-    renderValue: function(el,data) {
-      updateView(data);
-      console.log("Friend");
-      
-    }
-  });
-  Shiny.outputBindings.register(outputUserid);
-  */
-  
-  var inputUseridBinding = new Shiny.InputBinding();
-  $.extend(inputUseridBinding, {
-    find: function(scope) {
-      return $.find('.userid');
-    },
-    getValue: function(el) {
-      return $(el).val();
-    },
-    setValue: function(el, values) {
-      $(el).attr("value", $.fingerprint());
-      $(el).trigger("change");
-    },
-    subscribe: function(el, callback) {
-      $(el).on("change.inputUseridBinding", function(e) {
-        callback();
-      });
-    },
-    unsubscribe: function(el) {
-      $(el).off(".inputUseridBinding");
-    }
-  });
-  Shiny.inputBindings.register(inputUseridBinding);
-  
-  //setuid();
-
-//A unique ID generated from the fingerprint of
-// several browser characteristics.
-shiny_uid=$.fingerprint();
-
-
-/*
- * Set the uid fingerprint into the DOM elements that need to know about it.
- * Do not call before the form loads, or the selectors won't find anything.
- */
-function setuid() {
-  var fph = $('.userid');
-  fph.attr("value", shiny_uid);
-  fph.trigger("change");
-}
-
-function setvalues(){
-  getip();
-  setuid();
-}
-/*
- * Set the uid fingerprint into the DOM elements that need to know about it.
- * Do not call before the form loads, or the selectors won't find anything.
- */
-
-var inputIpBinding = new Shiny.InputBinding();
-$.extend(inputIpBinding, {
-  find: function(scope) {
-    return $.find('.ipaddr');
-  },
-  getValue: function(el) {
-    return $(el).val();
-  },
-  setValue: function(el, values) {
-    $(el).attr("value", getip())
-    $(el).trigger("change");
-  },
-  subscribe: function(el, callback) {
-    $(el).on("change.inputIpBinding", function(e) {
-      callback();
-    });
-  },
-  unsubscribe: function(el) {
-    $(el).off(".inputIpBinding");
-  }
-});
-Shiny.inputBindings.register(inputIpBinding);
-
-
-function getip() {
-ip = null;
-$.getJSON("https://jsonip.com?callback=?",
-  function(data){
-       ip = data.ip;
-       callback(ip);
-       $(".ipaddr").attr("value", ip);
-       $(".ipaddr").trigger("change");
- //return ip address correctly
-  });
-//alert(ip); //undefined or null
-}
-
-function callback(tempip)
-{
-ip=tempip;
-// alert(ip); //undefined or null
-}
diff --git a/inst/shiny/www/jumbotron.png b/inst/shiny/www/jumbotron.png
deleted file mode 100644
index 5602bdeb..00000000
Binary files a/inst/shiny/www/jumbotron.png and /dev/null differ
diff --git a/inst/shiny/www/logo.png b/inst/shiny/www/logo.png
deleted file mode 100644
index 71dec32c..00000000
Binary files a/inst/shiny/www/logo.png and /dev/null differ
diff --git a/inst/shiny/www/privacy_policy.txt b/inst/shiny/www/privacy_policy.txt
deleted file mode 100644
index 59a7202d..00000000
--- a/inst/shiny/www/privacy_policy.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-Log Data
-We receive information when you view content on or otherwise interact with our services, which we refer to as “Log Data,” even if you have not created an account. For example, when you visit our websites, sign into our services, interact with our email notifications, or visit a third-party service that includes Open Specy content, we may receive information. This Log Data includes information such as your IP address, browser type, operating system, the referring web page, pages visited, location, your mobile carrier, device information (including device and application IDs), search terms (including those not submitted as queries), and cookie information. We also receive Log Data when you click on, view, or interact with links and buttons on our services, including when you install another application through Open Specy. We use Log Data to operate our services and ensure their secure, reliable, and robust performance. For example, we use Log Data to protect the security of accounts and to determine what content is popular on our services. We also use this data to improve the functionality of the tools on the site, the content we show you, including ads and to improve the effectiveness of our own marketing.
-
-Non-Personal Information
-We share or disclose non-personal data, such as aggregated information like the total number of times people visited the site, the number of people who clicked on a particular link, some inferred interests, or reports to advertisers about how many people saw or clicked on their ads.
\ No newline at end of file
diff --git a/man/OpenSpecy-package.Rd b/man/OpenSpecy-package.Rd
index dfd6f8c0..03bcc458 100644
--- a/man/OpenSpecy-package.Rd
+++ b/man/OpenSpecy-package.Rd
@@ -7,7 +7,7 @@
 \alias{openspecy}
 \title{OpenSpecy: Analyze, Process, Identify, and Share Raman and (FT)IR Spectra}
 \description{
-Raman and (FT)IR spectral analysis tool for plastic particles and other environmental samples (Cowger et al. 2021, \doi{10.1021/acs.analchem.1c00123}). Supported features include reading spectral data files (.asp, .csv, .jdx, .spc, .spa, .0), Savitzky-Golay smoothing of spectral intensities with smooth_intens(), correcting background noise with subtr_bg() in accordance with Zhao et al. (2007) \doi{10.1366/000370207782597003}, and identifying spectra using an onboard reference library (Cowger et al. 2020, \doi{10.1177/0003702820929064}). Analyzed spectra can be shared with the Open Specy community. A Shiny app is available via run_app() or online at \url{https://openanalysis.org/openspecy/}.
+Raman and (FT)IR spectral analysis tool for plastic particles and other environmental samples (Cowger et al. 2021, \doi{10.1021/acs.analchem.1c00123}). With read_any(), Open Specy provides a single function for reading individual, batch, or map spectral data files like .asp, .csv, .jdx, .spc, .spa, .0, and .zip. process_spec() simplifies preprocessing spectra, including smoothing, baseline correction, range restriction and flattening, intensity conversions, wavenumber alignment, and min-max normalization. Spectra can be identified in batch using an onboard reference library (Cowger et al. 2020, \doi{10.1177/0003702820929064}) using match_spec(). A Shiny app is available via run_app() or online at \url{https://openanalysis.org/openspecy/}.
 }
 \references{
 Chabuka BK, Kalivas JH (2020). “Application of a Hybrid Fusion Classification
@@ -53,23 +53,31 @@ Useful links:
 
 }
 \author{
-\strong{Maintainer}: Win Cowger \email{wincowger@gmail.com} (\href{https://orcid.org/0000-0001-9226-3104}{ORCID})
+\strong{Maintainer}: Win Cowger \email{wincowger@gmail.com} (\href{https://orcid.org/0000-0001-9226-3104}{ORCID}) [data contributor]
 
 Authors:
 \itemize{
-  \item Zacharias Steinmetz \email{steinmetz-z@uni-landau.de} (\href{https://orcid.org/0000-0001-6675-5033}{ORCID})
+  \item Zacharias Steinmetz \email{z.steinmetz@rptu.de} (\href{https://orcid.org/0000-0001-6675-5033}{ORCID})
+  \item Nick Leong (\href{https://orcid.org/0009-0008-3313-4132}{ORCID})
+  \item Andrea Faltynkova (\href{https://orcid.org/0000-0003-2523-3137}{ORCID}) [data contributor]
 }
 
 Other contributors:
 \itemize{
-  \item Andrew Gray (\href{https://orcid.org/0000-0003-2252-7367}{ORCID}) [contributor]
+  \item Andrew B Gray (\href{https://orcid.org/0000-0003-2252-7367}{ORCID}) [contributor]
   \item Hannah Hapich (\href{https://orcid.org/0000-0003-0000-6632}{ORCID}) [contributor]
   \item Jennifer Lynch (\href{https://orcid.org/0000-0003-3572-8782}{ORCID}) [contributor, data contributor]
   \item Hannah De Frond (\href{https://orcid.org/0000-0003-1199-0727}{ORCID}) [contributor, data contributor]
   \item Keenan Munno (\href{https://orcid.org/0000-0003-2916-5944}{ORCID}) [contributor, data contributor]
   \item Chelsea Rochman (\href{https://orcid.org/0000-0002-7624-711X}{ORCID}) [contributor, data contributor]
   \item Sebastian Primpke (\href{https://orcid.org/0000-0001-7633-8524}{ORCID}) [contributor, data contributor]
-  \item Orestis Herodotou [contributor, data contributor]
+  \item Orestis Herodotou [contributor]
+  \item Mary C Norris [contributor]
+  \item Christine M Knauss (\href{https://orcid.org/0000-0003-4404-8922}{ORCID}) [contributor]
+  \item Aleksandra Karapetrova (\href{https://orcid.org/0000-0002-9856-1644}{ORCID}) [contributor, data contributor, reviewer]
+  \item Vesna Teofilovic (\href{https://orcid.org/0000-0002-3557-1482}{ORCID}) [contributor]
+  \item Laura A. T. Markley (\href{https://orcid.org/0000-0003-0620-8366}{ORCID}) [contributor]
+  \item Shreyas Patankar [contributor, data contributor]
 }
 
 }
diff --git a/man/adj_intens.Rd b/man/adj_intens.Rd
index cc4caf88..5007a61c 100644
--- a/man/adj_intens.Rd
+++ b/man/adj_intens.Rd
@@ -2,30 +2,18 @@
 % Please edit documentation in R/adj_intens.R
 \name{adj_intens}
 \alias{adj_intens}
-\alias{adj_intens.formula}
-\alias{adj_intens.data.frame}
 \alias{adj_intens.default}
-\title{Adjust spectral intensities to absorbance units}
+\alias{adj_intens.OpenSpecy}
+\title{Adjust spectral intensities to absorbance units.}
 \usage{
 adj_intens(x, ...)
 
-\method{adj_intens}{formula}(formula, data = NULL, ...)
+\method{adj_intens}{default}(x, ...)
 
-\method{adj_intens}{data.frame}(x, ...)
-
-\method{adj_intens}{default}(x, y, type = "none", make_rel = TRUE, ...)
+\method{adj_intens}{OpenSpecy}(x, type = "none", make_rel = TRUE, ...)
 }
 \arguments{
-\item{x}{a numeric vector containing the spectral wavenumbers; alternatively
-a data frame containing spectral data as \code{"wavenumber"} and
-\code{"intensity"} can be supplied.}
-
-\item{formula}{an object of class '\code{\link[stats]{formula}}' of the form
-\code{intensity ~ wavenumber}.}
-
-\item{data}{a data frame containing the variables in \code{formula}.}
-
-\item{y}{a numeric vector containing the spectral intensities.}
+\item{x}{a list object of class \code{OpenSpecy}.}
 
 \item{type}{a character string specifying whether the input spectrum is
 in absorbance units (\code{"none"}, default) or needs additional conversion
@@ -34,7 +22,9 @@ from \code{"reflectance"} or \code{"transmittance"} data.}
 \item{make_rel}{logical; if \code{TRUE} spectra are automatically normalized
 with \code{\link{make_rel}()}.}
 
-\item{\ldots}{further arguments passed to the submethods.}
+\item{\ldots}{further arguments passed to submethods; this is
+to \code{\link{adj_neg}()} for \code{adj_intens()} and
+to \code{\link{conform_res}()} for \code{conform_intens()}.}
 }
 \value{
 \code{adj_intens()} returns a data frame containing two columns
@@ -45,8 +35,7 @@ Converts reflectance or transmittance intensity units to absorbance units.
 }
 \details{
 Many of the Open Specy functions will assume that the spectrum is in
-absorbance units. For example, see \code{\link{match_spec}()} and
-\code{\link{subtr_bg}()}.
+absorbance units. For example, see \code{\link{subtr_baseline}()}.
 To run those functions properly, you will need to first convert any spectra
 from transmittance or reflectance to absorbance using this function.
 The transmittance adjustment uses the \eqn{log10(1 / T)}
@@ -63,9 +52,7 @@ adj_intens(raman_hdpe)
 
 }
 \seealso{
-\code{\link{subtr_bg}()} for spectral background correction;
-\code{\link{match_spec}()} matches spectra with the Open Specy or other
-reference libraries
+\code{\link{subtr_baseline}()} for spectral background correction.
 }
 \author{
 Win Cowger, Zacharias Steinmetz
diff --git a/man/adj_range.Rd b/man/adj_range.Rd
new file mode 100644
index 00000000..7d7eecb1
--- /dev/null
+++ b/man/adj_range.Rd
@@ -0,0 +1,70 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/adj_range.R
+\name{restrict_range}
+\alias{restrict_range}
+\alias{restrict_range.default}
+\alias{restrict_range.OpenSpecy}
+\alias{flatten_range}
+\alias{flatten_range.default}
+\alias{flatten_range.OpenSpecy}
+\title{Range restriction and flattening for spectra}
+\usage{
+restrict_range(x, ...)
+
+\method{restrict_range}{default}(x, ...)
+
+\method{restrict_range}{OpenSpecy}(x, min, max, make_rel = TRUE, ...)
+
+flatten_range(x, ...)
+
+\method{flatten_range}{default}(x, ...)
+
+\method{flatten_range}{OpenSpecy}(x, min = 2200, max = 2400, make_rel = TRUE, ...)
+}
+\arguments{
+\item{x}{an \code{OpenSpecy} object.}
+
+\item{min}{a vector of minimum values for the range to be flattened.}
+
+\item{max}{a vector of maximum values for the range to be flattened.}
+
+\item{make_rel}{logical; should the output intensities be normalized to the
+range [0, 1] using \code{make_rel()} function?}
+
+\item{\ldots}{additional arguments passed to subfunctions; currently not
+in use.}
+}
+\value{
+An \code{OpenSpecy} object with the spectral intensities within specified
+ranges restricted or flattened.
+}
+\description{
+\code{restrict_range()} restricts wavenumber ranges to user specified values.
+Multiple ranges can be specified by inputting a series of max and min
+values in order.
+\code{flatten_range()} will flatten ranges of the spectra that should have no
+peaks.
+Multiple ranges can be specified by inputting the series of max and min
+values in order.
+}
+\examples{
+test_noise <- as_OpenSpecy(x = seq(400,4000, by = 10),
+                           spectra = data.frame(intensity = rnorm(361)))
+plot(test_noise)
+
+restrict_range(test_noise, min = 1000, max = 2000)
+
+flattened_intensities <- flatten_range(test_noise, min = c(1000, 2000),
+                                       max = c(1500, 2500))
+plot(flattened_intensities)
+
+}
+\seealso{
+\code{\link{conform_spec}()} for conforming wavenumbers to be matched with
+a reference library;
+\code{\link{adj_intens}()} for log transformation functions;
+\code{\link[base]{min}()} and \code{\link[base]{round}()}
+}
+\author{
+Win Cowger, Zacharias Steinmetz
+}
diff --git a/man/as_OpenSpecy.Rd b/man/as_OpenSpecy.Rd
new file mode 100644
index 00000000..e70cee70
--- /dev/null
+++ b/man/as_OpenSpecy.Rd
@@ -0,0 +1,194 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/as_OpenSpecy.R
+\name{as_OpenSpecy}
+\alias{as_OpenSpecy}
+\alias{as_OpenSpecy.OpenSpecy}
+\alias{as_OpenSpecy.list}
+\alias{as_OpenSpecy.hyperSpec}
+\alias{as_OpenSpecy.data.frame}
+\alias{as_OpenSpecy.default}
+\alias{is_OpenSpecy}
+\alias{check_OpenSpecy}
+\alias{OpenSpecy}
+\alias{gen_grid}
+\title{Create \code{OpenSpecy} objects}
+\usage{
+as_OpenSpecy(x, ...)
+
+\method{as_OpenSpecy}{OpenSpecy}(x, session_id = FALSE, ...)
+
+\method{as_OpenSpecy}{list}(x, ...)
+
+\method{as_OpenSpecy}{hyperSpec}(x, ...)
+
+\method{as_OpenSpecy}{data.frame}(x, colnames = list(wavenumber = NULL, spectra = NULL), ...)
+
+\method{as_OpenSpecy}{default}(
+  x,
+  spectra,
+  metadata = list(file_name = NULL, user_name = NULL, contact_info = NULL, organization =
+    NULL, citation = NULL, spectrum_type = NULL, spectrum_identity = NULL, material_form
+    = NULL, material_phase = NULL, material_producer = NULL, material_purity = NULL,
+    material_quality = NULL, material_color = NULL, material_other = NULL, cas_number =
+    NULL, instrument_used = NULL, instrument_accessories = NULL, instrument_mode = NULL,
+    intensity_units = NULL, spectral_resolution = NULL, laser_light_used = NULL,
+    number_of_accumulations = NULL, 
+     total_acquisition_time_s = NULL,
+    data_processing_procedure = NULL, level_of_confidence_in_identification = NULL,
+    other_info = NULL, license = "CC BY-NC"),
+  coords = "gen_grid",
+  session_id = FALSE,
+  ...
+)
+
+is_OpenSpecy(x)
+
+check_OpenSpecy(x)
+
+OpenSpecy(x, ...)
+
+gen_grid(n)
+}
+\arguments{
+\item{x}{depending on the method, a list with all OpenSpecy parameters,
+a vector with the wavenumbers for all spectra, or a data.frame with a full
+spectrum in the classic Open Specy format.}
+
+\item{session_id}{logical. Whether to add a session ID to the metadata.
+The session ID is based on current session info so metadata of the same
+spectra will not return equal if session info changes. Sometimes that is
+desirable.}
+
+\item{colnames}{names of the wavenumber column and spectra column, makes
+assumptions based on column names or placement if \code{NULL}.}
+
+\item{spectra}{spectral intensities formatted as a data.table with one column
+per spectrum.}
+
+\item{metadata}{metadata for each spectrum with one row per spectrum,
+see details.}
+
+\item{coords}{spatial coordinates for the spectra.}
+
+\item{n}{number of spectra to generate the spatial coordinate grid with.}
+
+\item{\ldots}{additional arguments passed to submethods.}
+}
+\value{
+\code{as_OpenSpecy()} and \code{OpenSpecy()} returns three part lists
+described in details.
+\code{is_OpenSpecy()} returns \code{TRUE} if the object is an OpenSpecy and
+\code{FALSE} if not.
+\code{gen_grid()} returns a \code{data.table} with \code{x} and \code{y}
+coordinates to use for generating a spatial grid for the spectra if one is
+not specified in the data.
+}
+\description{
+Functions to check if an object is an OpenSpecy, or coerce it if
+possible.
+}
+\details{
+\code{as_OpenSpecy()} converts spectral datasets to a three part list;
+the first with a vector of the wavenumbers of the spectra,
+the second with a \code{data.table} of all spectral intensities ordered as
+columns,
+the third item is another \code{data.table} with any metadata the user
+provides or is harvested from the files themselves.
+
+The \code{metadata} argument may contain a named list with the following
+details (\code{*} = minimum recommended):
+
+\tabular{ll}{
+\code{file_name*}: \tab The file name, defaults to
+\code{\link[base]{basename}()} if not specified\cr
+\code{user_name*}: \tab User name, e.g. "Win Cowger"\cr
+\code{contact_info}: \tab Contact information, e.g. "1-513-673-8956,
+wincowger@gmail.com"\cr
+\code{organization}: \tab Affiliation, e.g. "University of California,
+Riverside"\cr
+\code{citation}: \tab Data citation, e.g. "Primpke, S., Wirth, M., Lorenz,
+C., & Gerdts, G. (2018). Reference database design for the automated analysis
+of microplastic samples based on Fourier transform infrared (FTIR)
+spectroscopy. \emph{Analytical and Bioanalytical Chemistry}.
+\doi{10.1007/s00216-018-1156-x}"\cr
+\code{spectrum_type*}: \tab Raman or FTIR\cr
+\code{spectrum_identity*}: \tab Material/polymer analyzed, e.g.
+"Polystyrene"\cr
+\code{material_form}: \tab Form of the material analyzed, e.g. textile fiber,
+rubber band, sphere, granule \cr
+\code{material_phase}: \tab Phase of the material analyzed (liquid, gas,
+solid) \cr
+\code{material_producer}: \tab Producer of the material analyzed,
+e.g. Dow \cr
+\code{material_purity}: \tab Purity of the material analyzed, e.g. 99.98\%
+\cr
+\code{material_quality}: \tab Quality of the material analyzed, e.g.
+consumer product, manufacturer material, analytical standard,
+environmental sample \cr
+\code{material_color}: \tab Color of the material analyzed,
+e.g. blue, #0000ff, (0, 0, 255) \cr
+\code{material_other}: \tab Other material description, e.g. 5 µm diameter
+fibers, 1 mm spherical particles \cr
+\code{cas_number}: \tab CAS number, e.g. 9003-53-6 \cr
+\code{instrument_used}: \tab Instrument used, e.g. Horiba LabRam \cr
+\code{instrument_accessories}: \tab Instrument accessories, e.g.
+Focal Plane Array, CCD\cr
+\code{instrument_mode}: \tab Instrument modes/settings, e.g.
+transmission, reflectance \cr
+\code{intensity_units*}: \tab Units of the intensity values for the spectrum, options
+transmittance, reflectance, absorbance \cr
+\code{spectral_resolution}: \tab Spectral resolution, e.g. 4/cm \cr
+\code{laser_light_used}: \tab Wavelength of the laser/light used, e.g.
+785 nm \cr
+\code{number_of_accumulations}: \tab Number of accumulations, e.g 5 \cr
+\code{total_acquisition_time_s}: \tab Total acquisition time (s), e.g. 10 s
+\cr
+\code{data_processing_procedure}: \tab Data processing procedure,
+e.g. spikefilter, baseline correction, none \cr
+\code{level_of_confidence_in_identification}: \tab Level of confidence in
+identification, e.g. 99\% \cr
+\code{other_info}: \tab Other information \cr
+\code{license}: \tab The license of the shared spectrum; defaults to
+\code{"CC BY-NC"} (see
+\url{https://creativecommons.org/licenses/by-nc/4.0/} for details). Any other
+creative commons license is allowed, for example, CC0 or CC BY \cr
+\code{session_id}: \tab A unique user and session identifier; populated
+automatically with \code{paste(digest(Sys.info()), digest(sessionInfo()),
+sep = "/")}\cr
+\code{file_id}: \tab A unique file identifier; populated automatically
+with \code{digest(object[c("wavenumber", "spectra")])}\cr
+}
+}
+\examples{
+data("raman_hdpe")
+
+# Inspect the spectra
+raman_hdpe # See how OpenSpecy objects print.
+raman_hdpe$wavenumber # Look at just the wavenumbers of the spectra.
+raman_hdpe$spectra # Look at just the spectral intensities data.table.
+raman_hdpe$metadata # Look at just the metadata of the spectra.
+
+# Creating a list and transforming to OpenSpecy
+as_OpenSpecy(list(wavenumber = raman_hdpe$wavenumber,
+                  spectra = raman_hdpe$spectra,
+                  metadata = raman_hdpe$metadata[,-c("x", "y")]))
+
+# If you try to produce an OpenSpecy using an OpenSpecy it will just return
+# the same object.
+as_OpenSpecy(raman_hdpe)
+
+# Creating an OpenSpecy from a data.frame
+as_OpenSpecy(x = data.frame(wavenumber = raman_hdpe$wavenumber,
+                            spectra = raman_hdpe$spectra$intensity))
+
+# Test that the spectrum is formatted as an OpenSpecy object.
+is_OpenSpecy(raman_hdpe)  #should be TRUE
+is_OpenSpecy(raman_hdpe$spectra) #should be FALSE
+
+}
+\seealso{
+\code{\link{read_spec}()} for reading \code{OpenSpecy} objects.
+}
+\author{
+Zacharias Steinmetz, Win Cowger
+}
diff --git a/man/conform_spec.Rd b/man/conform_spec.Rd
new file mode 100644
index 00000000..ab8b7f7d
--- /dev/null
+++ b/man/conform_spec.Rd
@@ -0,0 +1,50 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/conform_spec.R
+\name{conform_spec}
+\alias{conform_spec}
+\alias{conform_spec.default}
+\alias{conform_spec.OpenSpecy}
+\title{Conform spectra to a standard wavenumber series}
+\usage{
+conform_spec(x, ...)
+
+\method{conform_spec}{default}(x, ...)
+
+\method{conform_spec}{OpenSpecy}(x, range = NULL, res = 5, type = "interp", ...)
+}
+\arguments{
+\item{x}{a list object of class \code{OpenSpecy}.}
+
+\item{range}{a vector of new wavenumber values, can be just supplied as a
+min and max value.}
+
+\item{res}{spectral resolution adjusted to or \code{NULL} if the raw range
+should be used.}
+
+\item{type}{the type of wavenumber adjustment to make. \code{"interp"}
+results in linear interpolation while \code{"roll"} conducts a nearest
+rolling join of the wavenumbers.}
+
+\item{\ldots}{further arguments passed to \code{\link[stats]{approx}()}}
+}
+\value{
+\code{adj_intens()} returns a data frame containing two columns
+named \code{"wavenumber"} and \code{"intensity"}
+}
+\description{
+Spectra can be conformed to a standard suite of wavenumbers to be compared
+with a reference library or to be merged to other spectra.
+}
+\examples{
+data("raman_hdpe")
+conform_spec(raman_hdpe, c(1000, 2000))
+
+}
+\seealso{
+\code{\link{restrict_range}()} and  \code{\link{flatten_range}()} for
+adjusting wavenumber ranges;
+\code{\link{subtr_baseline}()} for spectral background correction
+}
+\author{
+Win Cowger, Zacharias Steinmetz
+}
diff --git a/man/data_norm.Rd b/man/data_norm.Rd
index a57054d6..117a1dde 100644
--- a/man/data_norm.Rd
+++ b/man/data_norm.Rd
@@ -1,49 +1,79 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/data_norm.R
-\name{adj_neg}
+\name{adj_res}
+\alias{adj_res}
+\alias{conform_res}
 \alias{adj_neg}
 \alias{make_rel}
-\title{Normalization of spectral data}
+\alias{mean_replace}
+\alias{is_empty_vector}
+\title{Normalization and conversion of spectral data}
 \usage{
-adj_neg(x, na.rm = FALSE)
+adj_res(x, res = 1, fun = round)
 
-make_rel(x, na.rm = FALSE)
+conform_res(x, res = 5)
+
+adj_neg(y, na.rm = FALSE)
+
+make_rel(y, na.rm = FALSE)
+
+mean_replace(y, na.rm = TRUE)
+
+is_empty_vector(x)
 }
 \arguments{
 \item{x}{a numeric vector or an \R object which is coercible to one by
-\code{as.vector(x, "numeric")}; \code{x} should be \code{intensity} data.}
+\code{as.vector(x, "numeric")}; \code{x} should contain the spectral
+wavenumbers.}
+
+\item{res}{spectral resolution supplied to \code{fun}.}
+
+\item{fun}{the function to be applied to each element of \code{x}; defaults
+to \code{\link[base]{round}()} to round to a specific resolution \code{res}.}
+
+\item{y}{a numeric vector containing the spectral intensities.}
 
 \item{na.rm}{logical. Should missing values be removed?}
 }
 \value{
+\code{adj_res()} and \code{conform_res()} return a numeric vector with
+resolution-conformed wavenumbers.
 \code{adj_neg()} and \code{make_rel()} return numeric vectors
-with the normalized data.
+with the normalized intensity data.
 }
 \description{
-\code{adj_neg()} converts numeric values \code{x} < 1 into values
->= 1, keeping absolute differences between values by shifting intensity
-values with the value of the smallest number.
-\code{make_rel()} converts values \code{x} into relative values between
+\code{adj_res()} and \code{conform_res()} are helper functions to align
+wavenumbers in terms of their spectral resolution.
+\code{adj_neg()} converts numeric intensities \code{y} < 1 into values >= 1,
+keeping absolute differences between intensity values by shifting each value
+by the minimum intensity.
+\code{make_rel()} converts intensities \code{y} into relative values between
 0 and 1 using the standard normalization equation.
 If \code{na.rm} is \code{TRUE}, missing values are removed before the
 computation proceeds.
 }
 \details{
-\code{adj_neg()} is used in Open Specy to avoid errors that could
-arise from log transforming spectra when using
-\code{\link{adj_intens}()} and other functions.
-\code{make_rel()} is used in Open Specy to retain the relative
-height proportions between spectra while avoiding the large numbers that can
-result from some spectral instruments.
+\code{adj_res()} and \code{conform_res()} are used in Open Specy to
+facilitate comparisons of spectra with different resolutions.
+\code{adj_neg()} is used to avoid errors that could arise from log
+transforming spectra when using \code{\link{adj_intens}()} and other
+functions.
+\code{make_rel()} is used to retain the relative height proportions between
+spectra while avoiding the large numbers that can result from some spectral
+instruments.
 }
 \examples{
+adj_res(seq(500, 4000, 4), 5)
+conform_res(seq(500, 4000, 4))
 adj_neg(c(-1000, -1, 0, 1, 10))
 make_rel(c(-1000, -1, 0, 1, 10))
 
 }
 \seealso{
-\code{\link[base]{min}()} for the calculation of minima;
-\code{\link{adj_intens}()} for log transformation functions
+\code{\link[base]{min}()} and \code{\link[base]{round}()};
+\code{\link{adj_intens}()} for log transformation functions;
+\code{\link{conform_spec}()} for conforming wavenumbers of an
+\code{OpenSpecy} object to be matched with a reference library
 }
 \author{
 Win Cowger, Zacharias Steinmetz
diff --git a/man/def_features.Rd b/man/def_features.Rd
new file mode 100644
index 00000000..9bd9291a
--- /dev/null
+++ b/man/def_features.Rd
@@ -0,0 +1,68 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/def_features.R
+\name{collapse_spec}
+\alias{collapse_spec}
+\alias{collapse_spec.default}
+\alias{collapse_spec.OpenSpecy}
+\alias{def_features}
+\alias{def_features.default}
+\alias{def_features.OpenSpecy}
+\title{Define features}
+\usage{
+collapse_spec(x, ...)
+
+\method{collapse_spec}{default}(x, ...)
+
+\method{collapse_spec}{OpenSpecy}(x, ...)
+
+def_features(x, ...)
+
+\method{def_features}{default}(x, ...)
+
+\method{def_features}{OpenSpecy}(x, features, ...)
+}
+\arguments{
+\item{x}{an \code{OpenSpecy} object}
+
+\item{features}{a logical vector or character vector describing which of the
+spectra are of features (\code{TRUE}) and which are not (\code{FALSE}).
+If a character vector is provided, it should represent the different feature
+types present in the spectra.}
+
+\item{\ldots}{additional arguments passed to subfunctions.}
+}
+\value{
+An \code{OpenSpecy} object appended with metadata about the features or
+collapsed for the features.
+}
+\description{
+Functions for analyzing features, like particles, fragments, or fibers, in
+spectral map oriented \code{OpenSpecy} object.
+}
+\details{
+\code{def_features()} accepts an \code{OpenSpecy} object and a logical or
+character vector describing which pixels correspond to particles.
+\code{collapse_spec()} takes an \code{OpenSpecy} object with particle-specific
+metadata (from \code{def_features()}) and collapses the spectra to median
+intensities for each unique particle.
+It also updates the metadata with centroid coordinates, while preserving the
+feature information on area and Feret max.
+}
+\examples{
+# Logical input
+map <- read_extdata("CA_tiny_map.zip") |> read_any()
+map$metadata$features <- map$metadata$x == 0
+identified_map <- def_features(map, map$metadata$features)
+test_collapsed <- collapse_spec(identified_map)
+
+# Character input
+map <- read_extdata("CA_tiny_map.zip") |> read_any()
+map$metadata$features <- ifelse(map$metadata$x == 1, "particle",
+                                "not_particle")
+identified_map <- def_features(map, map$metadata$features)
+test_collapsed <- collapse_spec(identified_map)
+
+}
+\author{
+Win Cowger, Zacharias Steinmetz
+}
diff --git a/man/gen_OpenSpecy.Rd b/man/gen_OpenSpecy.Rd
new file mode 100644
index 00000000..bca31257
--- /dev/null
+++ b/man/gen_OpenSpecy.Rd
@@ -0,0 +1,63 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/gen_OpenSpecy.R
+\name{head.OpenSpecy}
+\alias{head.OpenSpecy}
+\alias{print.OpenSpecy}
+\alias{plot.OpenSpecy}
+\alias{lines.OpenSpecy}
+\alias{summary.OpenSpecy}
+\title{Generic Open Specy Methods}
+\usage{
+\method{head}{OpenSpecy}(x, ...)
+
+\method{print}{OpenSpecy}(x, ...)
+
+\method{plot}{OpenSpecy}(x, ...)
+
+\method{lines}{OpenSpecy}(x, ...)
+
+\method{summary}{OpenSpecy}(object, ...)
+}
+\arguments{
+\item{x}{an \code{OpenSpecy} object.}
+
+\item{object}{an \code{OpenSpecy} object.}
+
+\item{\ldots}{further arguments passed to the respective default method.}
+}
+\value{
+\code{head()}, \code{print()}, and \code{summary()} return a textual
+representation of an \code{OpenSpecy} object.
+\code{plot()} and \code{lines()} return a plot.
+}
+\description{
+Methods to visualize \code{OpenSpecy} objects.
+}
+\details{
+\code{head()} shows the first few lines of an \code{OpenSpecy} object.
+\code{print()} prints the contents of an \code{OpenSpecy} object.
+\code{summary()} produces a result summary of an \code{OpenSpecy} object.
+\code{plot()} produces a \code{\link[graphics]{matplot}()} of an OpenSpecy
+object; \code{lines()} adds new spectra to it.
+}
+\examples{
+data("raman_hdpe")
+
+# Printing the OpenSpecy object
+print(raman_hdpe)
+
+# Displaying the first few lines of the OpenSpecy object
+head(raman_hdpe)
+
+# Plotting the spectra
+plot(raman_hdpe)
+
+}
+\seealso{
+\code{\link[utils]{head}()}, \code{\link[base]{print}()},
+\code{\link[base]{summary}()}, \code{\link[graphics]{matplot}()}, and
+\code{\link[graphics]{matlines}()}
+}
+\author{
+Zacharias Steinmetz, Win Cowger
+}
diff --git a/man/human_ts.Rd b/man/human_ts.Rd
index 830ddf1a..a09dfd04 100644
--- a/man/human_ts.Rd
+++ b/man/human_ts.Rd
@@ -26,5 +26,5 @@ human_ts()
 \code{\link[base]{format.Date}} for date conversion functions
 }
 \author{
-Win Cowger
+Win Cowger, Zacharias Steinmetz
 }
diff --git a/man/interactive_plots.Rd b/man/interactive_plots.Rd
new file mode 100644
index 00000000..42398219
--- /dev/null
+++ b/man/interactive_plots.Rd
@@ -0,0 +1,137 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/interactive_plots.R
+\name{plotly_spec}
+\alias{plotly_spec}
+\alias{plotly_spec.default}
+\alias{plotly_spec.OpenSpecy}
+\alias{heatmap_spec}
+\alias{heatmap_spec.default}
+\alias{heatmap_spec.OpenSpecy}
+\alias{interactive_plot}
+\alias{interactive_plot.default}
+\alias{interactive_plot.OpenSpecy}
+\title{Interactive plots for OpenSpecy objects}
+\usage{
+plotly_spec(x, ...)
+
+\method{plotly_spec}{default}(x, ...)
+
+\method{plotly_spec}{OpenSpecy}(
+  x,
+  x2 = NULL,
+  line = list(color = "rgb(255, 255, 255)"),
+  line2 = list(dash = "dot", color = "rgb(255,0,0)"),
+  font = list(color = "#FFFFFF"),
+  plot_bgcolor = "rgb(17, 0, 73)",
+  paper_bgcolor = "rgb(0, 0, 0)",
+  ...
+)
+
+heatmap_spec(x, ...)
+
+\method{heatmap_spec}{default}(x, ...)
+
+\method{heatmap_spec}{OpenSpecy}(
+  x,
+  z = NULL,
+  sn = NULL,
+  cor = NULL,
+  min_sn = NULL,
+  min_cor = NULL,
+  select = NULL,
+  font = list(color = "#FFFFFF"),
+  plot_bgcolor = "rgba(17, 0, 73, 0)",
+  paper_bgcolor = "rgb(0, 0, 0)",
+  colorscale = "Viridis",
+  ...
+)
+
+interactive_plot(x, ...)
+
+\method{interactive_plot}{default}(x, ...)
+
+\method{interactive_plot}{OpenSpecy}(
+  x,
+  x2 = NULL,
+  select = NULL,
+  line = list(color = "rgb(255, 255, 255)"),
+  line2 = list(dash = "dot", color = "rgb(255,0,0)"),
+  font = list(color = "#FFFFFF"),
+  plot_bgcolor = "rgba(17, 0, 73, 0)",
+  paper_bgcolor = "rgb(0, 0, 0)",
+  colorscale = "Viridis",
+  ...
+)
+}
+\arguments{
+\item{x}{an \code{OpenSpecy} object containing metadata and spectral data for
+the first group.}
+
+\item{x2}{an optional second \code{OpenSpecy} object containing metadata and
+spectral data for the second group.}
+
+\item{line}{list; \code{line} parameter for \code{x}; passed to
+\code{\link[plotly]{add_trace}()}.}
+
+\item{line2}{list; \code{line} parameter for \code{x2}; passed to}
+
+\item{font}{list; passed to \code{\link[plotly]{layout}()}.}
+
+\item{plot_bgcolor}{color value; passed to \code{\link[plotly]{layout}()}.}
+
+\item{paper_bgcolor}{color value; passed to \code{\link[plotly]{layout}()}.}
+
+\item{z}{optional numeric vector specifying the intensity values for the
+heatmap. If not provided, the function will use the intensity values from the
+\code{OpenSpecy} object.}
+
+\item{sn}{optional numeric value specifying the signal-to-noise ratio
+threshold. If provided along with \code{min_sn}, regions with SNR below the
+threshold will be excluded from the heatmap.}
+
+\item{cor}{optional numeric value specifying the correlation threshold. If
+provided along with \code{min_cor}, regions with correlation below the
+threshold will be excluded from the heatmap.}
+
+\item{min_sn}{optional numeric value specifying the minimum signal-to-noise
+ratio for inclusion in the heatmap. Regions with SNR below this threshold
+will be excluded.}
+
+\item{min_cor}{optional numeric value specifying the minimum correlation for
+inclusion in the heatmap. Regions with correlation below this threshold
+will be excluded.}
+
+\item{select}{optional index of the selected spectrum to highlight on the
+heatmap.}
+
+\item{colorscale}{colorscale passed to \code{\link[plotly]{add_trace}()}.}
+
+\item{\ldots}{further arguments passed to \code{\link[plotly]{plot_ly}()}.}
+}
+\value{
+A plotly heatmap object displaying the OpenSpecy data. A subplot
+containing the heatmap and spectra plot. A plotly object displaying the
+spectra from the \code{OpenSpecy} object(s).
+}
+\description{
+These functions generate heatmaps, spectral plots, and interactive plots for
+OpenSpecy data.
+}
+\examples{
+data("raman_hdpe")
+tiny_map <- read_extdata("CA_tiny_map.zip") |> read_zip()
+plotly_spec(raman_hdpe)
+
+correlation <- cor_spec(
+  conform_spec(raman_hdpe, range = raman_hdpe$wavenumber, res = 5),
+  conform_spec(tiny_map, tiny_map$wavenumbers, res = 5)
+)
+heatmap_spec(tiny_map, z = tiny_map$metadata$y)
+
+sample_spec(tiny_map, size = 12) |>
+  interactive_plot(select = 2, x2 = raman_hdpe)
+
+}
+\author{
+Win Cowger, Zacharias Steinmetz
+}
diff --git a/man/io_spec.Rd b/man/io_spec.Rd
new file mode 100644
index 00000000..1802fac8
--- /dev/null
+++ b/man/io_spec.Rd
@@ -0,0 +1,85 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/io_spec.R
+\name{write_spec}
+\alias{write_spec}
+\alias{write_spec.default}
+\alias{write_spec.OpenSpecy}
+\alias{read_spec}
+\alias{as_hyperSpec}
+\title{Read and write spectral data}
+\usage{
+write_spec(x, ...)
+
+\method{write_spec}{default}(x, ...)
+
+\method{write_spec}{OpenSpecy}(x, file, method = NULL, digits = getOption("digits"), ...)
+
+read_spec(file, share = NULL, method = NULL, ...)
+
+as_hyperSpec(x)
+}
+\arguments{
+\item{x}{an object of class \code{\link{OpenSpecy}}.}
+
+\item{file}{file path to be read from or written to.}
+
+\item{method}{optional; function to be used as a custom reader or writer.
+Defaults to the appropriate function based on the file extension.}
+
+\item{digits}{number of significant digits to use when formatting numeric
+values; defaults to \code{\link[base]{getOption}("digits")}.}
+
+\item{share}{defaults to \code{NULL}; needed to share spectra with the
+Open Specy community; see \code{\link{share_spec}()} for details.}
+
+\item{\ldots}{further arguments passed to the submethods.}
+}
+\value{
+\code{read_spec()} reads data formatted as an \code{OpenSpecy} object and
+returns a list object of class \code{\link{OpenSpecy}} containing spectral
+data.
+\code{write_spec()} writes a file for an object of class
+\code{\link{OpenSpecy}} containing spectral data.
+\code{as_hyperspec()} converts an \code{OpenSpecy} object to a
+\code{\link[hyperSpec]{hyperSpec-class}} object.
+}
+\description{
+Functions for reading and writing spectral data to and from OpenSpecy format.
+\code{OpenSpecy} objects are lists with components \code{wavenumber}, \code{spectra},
+and \code{metadata}. Currently supported formats are .y(a)ml, .json, or .rds.
+}
+\details{
+Due to floating point number errors there may be some differences in the
+precision of the numbers returned if using multiple devices for .json and
+.yaml files but the numbers should be nearly identical.
+\code{\link[base]{readRDS}()} should return the exact same object every time.
+}
+\examples{
+read_extdata("raman_hdpe.yml") |> read_spec()
+read_extdata("raman_hdpe.json") |> read_spec()
+read_extdata("raman_hdpe.rds") |> read_spec()
+
+\dontrun{
+data(raman_hdpe)
+write_spec(raman_hdpe, "raman_hdpe.yml")
+write_spec(raman_hdpe, "raman_hdpe.json")
+write_spec(raman_hdpe, "raman_hdpe.rds")
+
+# Convert an OpenSpecy object to a hyperSpec object
+hyper <- as_hyperSpec(raman_hdpe)
+}
+
+}
+\seealso{
+\code{\link{OpenSpecy}()};
+\code{\link{read_text}()}, \code{\link{read_asp}()}, \code{\link{read_spa}()},
+\code{\link{read_spc}()}, and \code{\link{read_jdx}()} for text files, .asp,
+.spa, .spa, .spc, and .jdx formats, respectively;
+\code{\link{read_zip}()} and \code{\link{read_any}()} for wrapper functions;
+\code{\link[base]{saveRDS}()}; \code{\link[base]{readRDS}()};
+\code{\link[yaml]{write_yaml}()}; \code{\link[yaml]{read_yaml}()};
+\code{\link[jsonlite]{write_json}()}; \code{\link[jsonlite]{read_json}()};
+}
+\author{
+Zacharias Steinmetz, Win Cowger
+}
diff --git a/man/manage_lib.Rd b/man/manage_lib.Rd
index c920d968..1246548a 100644
--- a/man/manage_lib.Rd
+++ b/man/manage_lib.Rd
@@ -4,36 +4,34 @@
 \alias{check_lib}
 \alias{get_lib}
 \alias{load_lib}
+\alias{rm_lib}
 \title{Manage spectral libraries}
 \usage{
 check_lib(
-  which = c("ftir", "raman"),
-  types = c("metadata", "library", "peaks"),
+  type = c("derivative", "nobaseline", "raw", "mediod", "model"),
   path = "system",
   condition = "warning"
 )
 
 get_lib(
-  which = c("ftir", "raman"),
-  types = c("metadata", "library", "peaks"),
+  type = c("derivative", "nobaseline", "raw", "mediod", "model"),
   path = "system",
   node = "x7dpz",
   conflicts = "overwrite",
   ...
 )
 
-load_lib(
-  which = c("ftir", "raman"),
-  types = c("metadata", "library", "peaks"),
+load_lib(type, path = "system")
+
+rm_lib(
+  type = c("derivative", "nobaseline", "raw", "mediod", "model"),
   path = "system"
 )
 }
 \arguments{
-\item{which}{a character string specifying which library to use,
-\code{"raman"} or \code{"ftir"}.}
-
-\item{types}{library types to check/retrieve; defaults to
-\code{c("metadata", "library", "peaks")}.}
+\item{type}{library type to check/retrieve; defaults to
+\code{c("derivative", "nobaseline", "raw", "mediod", "model")} which reads
+everything.}
 
 \item{path}{where to save or look for local library files; defaults to
 \code{"system"} pointing to
@@ -42,25 +40,26 @@ load_lib(
 \item{condition}{determines if \code{check_lib()} should warn
 (\code{"warning"}, the default) or throw and error (\code{"error"}).}
 
-\item{node}{the OSF node to be retrieved; should be \code{"x7dpz"}.}
+\item{node}{the OSF node to be retrieved; should be \code{"x7dpz"} unless you
+maintain your own OSF node with spectral libraries.}
 
 \item{conflicts}{determines what happens when a file with the same name
 exists at the specified destination. Can be one of the following (see
 \code{\link[osfr]{osf_download}()} for details):
 \itemize{
-  \item{"error"}{ throw an error and abort the file transfer operation.}
-  \item{"skip"}{ skip the conflicting file(s) and continue transferring the
-  remaining files.}
-  \item{"overwrite" (default)}{ replace the existing file with the
-  transferred copy.}
+\item{"error"}{ throw an error and abort the file transfer operation.}
+\item{"skip"}{ skip the conflicting file(s) and continue transferring the
+remaining files.}
+\item{"overwrite" (default)}{ replace the existing file with the
+transferred copy.}
 }}
 
 \item{\ldots}{further arguments passed to \code{\link[osfr]{osf_download}()}.}
 }
 \value{
 \code{check_lib()} and \code{get_lib()} return messages only;
-\code{load_lib()} returns a list object containing the respective spectral
-reference library.
+\code{load_lib()} returns an \code{OpenSpecy} object containing the
+respective spectral reference library.
 }
 \description{
 These functions will import the spectral libraries from Open Specy if they
@@ -76,13 +75,14 @@ already exists on the users computer.
 (\doi{10.17605/OSF.IO/X7DPZ}).
 \code{load_lib()} will load the library into the global environment for use
 with the Open Specy functions.
+\code{rm_lib()} removes the libraries from your computer.
 }
 \examples{
 \dontrun{
-check_lib(which = c("ftir", "raman"))
-get_lib(which = c("ftir", "raman"))
+check_lib("derivative")
+get_lib("derivative")
 
-spec_lib <- load_lib(which = c("ftir", "raman"))
+spec_lib <- load_lib("derivative")
 }
 
 }
@@ -95,9 +95,6 @@ Spectra in Microplastic Research.” \emph{Applied Spectroscopy},
 
 Cowger, W (2021). “Library data.” \emph{OSF}. \doi{10.17605/OSF.IO/X7DPZ}.
 }
-\seealso{
-\code{\link{match_spec}()}
-}
 \author{
-Zacharias Steinmetz
+Zacharias Steinmetz, Win Cowger
 }
diff --git a/man/manage_spec.Rd b/man/manage_spec.Rd
new file mode 100644
index 00000000..50bd2612
--- /dev/null
+++ b/man/manage_spec.Rd
@@ -0,0 +1,64 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/manage_spec.R
+\name{c_spec}
+\alias{c_spec}
+\alias{c_spec.default}
+\alias{c_spec.OpenSpecy}
+\alias{c_spec.list}
+\alias{sample_spec}
+\alias{sample_spec.default}
+\alias{sample_spec.OpenSpecy}
+\title{Manage spectral objects}
+\usage{
+c_spec(x, ...)
+
+\method{c_spec}{default}(x, ...)
+
+\method{c_spec}{OpenSpecy}(x, ...)
+
+\method{c_spec}{list}(x, range = NULL, res = 5, ...)
+
+sample_spec(x, ...)
+
+\method{sample_spec}{default}(x, ...)
+
+\method{sample_spec}{OpenSpecy}(x, ...)
+}
+\arguments{
+\item{x}{a list of \code{OpenSpecy} objects.}
+
+\item{range}{a numeric providing your own wavenumber ranges or character
+argument called \code{"common"} to let \code{c_spec()} find the common
+wavenumber range of the supplied spectra. \code{NULL} will interpret the
+spectra having all the same wavenumber range.}
+
+\item{res}{defaults to \code{NULL}, the resolution you want the output
+wavenumbers to be.}
+
+\item{\ldots}{further arguments passed to submethods.}
+}
+\value{
+\code{c_spec()} and \code{sample_spec()} return \code{OpenSpecy} objects.
+}
+\description{
+\code{c_spec()} concatenates \code{OpenSpecy} objects.
+\code{sample_spec()} samples spectra from an \code{OpenSpecy} object.
+}
+\examples{
+# Concatenating spectra
+spectra <- lapply(c(read_extdata("raman_hdpe.csv"),
+                    read_extdata("ftir_ldpe_soil.asp")), read_any)
+common <- c_spec(spectra, range = "common", res = 5)
+range <- c_spec(spectra, range = c(1000, 2000), res = 5)
+
+# Sampling spectra
+tiny_map <- read_any(read_extdata("CA_tiny_map.zip"))
+sampled <- sample_spec(tiny_map, size = 3)
+
+}
+\seealso{
+\code{\link[OpenSpecy]{conform_spec}()} for conforming wavenumbers
+}
+\author{
+Zacharias Steinmetz, Win Cowger
+}
diff --git a/man/match_spec.Rd b/man/match_spec.Rd
index a81cf7bc..f90eb855 100644
--- a/man/match_spec.Rd
+++ b/man/match_spec.Rd
@@ -1,114 +1,152 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/match_spec.R
-\name{match_spec}
+\name{cor_spec}
+\alias{cor_spec}
+\alias{cor_spec.default}
+\alias{cor_spec.OpenSpecy}
 \alias{match_spec}
-\alias{match_spec.formula}
-\alias{match_spec.data.frame}
 \alias{match_spec.default}
-\alias{find_spec}
-\title{Match spectra with reference library}
+\alias{match_spec.OpenSpecy}
+\alias{ident_spec}
+\alias{get_metadata}
+\alias{get_metadata.default}
+\alias{get_metadata.OpenSpecy}
+\alias{max_cor_named}
+\alias{filter_spec}
+\alias{filter_spec.default}
+\alias{filter_spec.OpenSpecy}
+\alias{ai_classify}
+\alias{ai_classify.default}
+\alias{ai_classify.OpenSpecy}
+\title{Identify and filter spectra}
 \usage{
-match_spec(x, ...)
+cor_spec(x, ...)
+
+\method{cor_spec}{default}(x, ...)
+
+\method{cor_spec}{OpenSpecy}(x, library, na.rm = T, ...)
 
-\method{match_spec}{formula}(formula, data = NULL, ...)
+match_spec(x, ...)
 
-\method{match_spec}{data.frame}(x, ...)
+\method{match_spec}{default}(x, ...)
 
-\method{match_spec}{default}(
+\method{match_spec}{OpenSpecy}(
   x,
-  y,
   library,
-  which = NULL,
-  type = "full",
-  range = seq(0, 6000, 0.1),
-  top_n = 100,
+  na.rm = T,
+  top_n = NULL,
+  add_library_metadata = NULL,
+  add_object_metadata = NULL,
+  fill = NULL,
   ...
 )
 
-find_spec(
-  subset,
+ident_spec(
+  cor_matrix,
+  x,
   library,
-  which = NULL,
-  type = "metadata",
-  cols = c("spectrum_identity", "organization", "contact_info", "spectrum_type",
-    "instrument_used", "instrument_accessories", "instrument_mode", "laser_light_used",
-    "total_acquisition_time_s", "number_of_accumulations",
-    "level_of_confidence_in_identification", "cas_number", "material_producer",
-    "material_purity", "material_form", "material_quality", "spectral_resolution",
-    "data_processing_procedure", "other_information", "sample_name", "wavenumber",
-    "intensity", "group"),
+  top_n = NULL,
+  add_library_metadata = NULL,
+  add_object_metadata = NULL,
   ...
 )
+
+get_metadata(x, ...)
+
+\method{get_metadata}{default}(x, ...)
+
+\method{get_metadata}{OpenSpecy}(x, logic, rm_empty = TRUE, ...)
+
+max_cor_named(cor_matrix, na.rm = T)
+
+filter_spec(x, ...)
+
+\method{filter_spec}{default}(x, ...)
+
+\method{filter_spec}{OpenSpecy}(x, logic, ...)
+
+ai_classify(x, ...)
+
+\method{ai_classify}{default}(x, ...)
+
+\method{ai_classify}{OpenSpecy}(x, library, fill = NULL, ...)
 }
 \arguments{
-\item{x}{a numeric vector containing the spectral wavenumbers; alternatively
-a data frame containing spectral data as \code{"wavenumber"} and
-\code{"intensity"} can be supplied.}
-
-\item{formula}{an object of class '\code{\link[stats]{formula}}' of the form
-\code{intensity ~ wavenumber}.}
+\item{x}{an \code{OpenSpecy} object, typically with unknowns.}
 
-\item{data}{a data frame containing the variables in \code{formula}.}
+\item{library}{an \code{OpenSpecy} or \code{glmnet} object representing the
+reference library of spectra or model to use in identification.}
 
-\item{y}{a numeric vector containing the spectral intensities.}
+\item{na.rm}{logical; indicating whether missing values should be removed
+when calculating correlations. Default is \code{TRUE}.}
 
-\item{library}{reference library you want to compare against.}
+\item{top_n}{integer; specifying the number of top matches to return.
+If \code{NULL} (default), all matches will be returned.}
 
-\item{which}{a character string specifying which library to match,
-\code{"raman"} or \code{"ftir"}.}
+\item{add_library_metadata}{name of a column in the library metadata to be
+joined; \code{NULL} if you don't want to join.}
 
-\item{type}{a character string specifying whether the \code{"full"} spectrum
-should be matched or spectrum \code{"peaks"} only. \code{"metadata"} is
-needed to browser spectra with \code{find_spec()}.}
+\item{add_object_metadata}{name of a column in the object metadata to be
+joined; \code{NULL} if you don't want to join.}
 
-\item{range}{this should be all possible wavenumber values from your spectral
-library.}
+\item{fill}{an \code{OpenSpecy} object with a single spectrum to be used to
+fill missing values for alignment with the AI classification.}
 
-\item{top_n}{number of top matches that you want to be returned.}
+\item{cor_matrix}{a correlation matrix for object and library,
+can be returned by \code{cor_spec()}}
 
-\item{subset}{logical expression indicating elements or rows to search for;
-see \code{\link[base]{subset}()} for details.}
+\item{logic}{a logical or numeric vector describing which spectra to keep.}
 
-\item{cols}{columns to retrieve from the Open Specy reference library;
-columns containing no or missing values are automatically removed.}
+\item{rm_empty}{logical; whether to remove empty columns in the metadata.}
 
-\item{\ldots}{further arguments passed to the submethods.}
+\item{\ldots}{additional arguments passed \code{\link[stats]{cor}()}.}
 }
 \value{
-\code{match_spec()} returns a data frame with the \code{top_n} material
-matches, their Pearson's r value, and the organization they were provided by.
-\code{find_spec()} returns a data frame with the spectral raw data or
-metadata of a specific reference spectrum.
+\code{match_spec()} and \code{ident_spec()} will return
+a \code{\link[data.table]{data.table-class}()} containing correlations
+between spectra and the library.
+The table has three columns: \code{object_id}, \code{library_id}, and
+\code{match_val}.
+Each row represents a unique pairwise correlation between a spectrum in the
+object and a spectrum in the library.
+If \code{top_n} is specified, only the top \code{top_n} matches for each
+object spectrum will be returned.
+If \code{add_library_metadata} is \code{is.character}, the library metadata
+will be added to the output.
+If \code{add_object_metadata} is \code{is.character}, the object metadata
+will be added to the output.
+\code{filter_spec()} returns an \code{OpenSpecy} object.
+\code{cor_spec()} returns a correlation matrix.
+\code{get_metadata()} returns a \code{\link[data.table]{data.table-class}()}
+with the metadata for columns which have information.
 }
 \description{
-\code{match_spec()} will compare a spectrum to a spectral library formatted
-with the Open Specy standard and report the best match using the Pearson
-correlation coefficient.
-\code{find_spec()} makes it easy to retrieve single spectra and metadata
-from the Open Specy reference library.
-}
-\details{
-This routine will match the spectrum you want to identify to the wavenumbers
-present in the spectral library. Once the spectra are aligned, it computes
-the Pearson correlation coefficient between the spectrum you want to
-identify and all spectra in the library (see \code{\link[stats]{cor}}).
-The function returns a table with the Pearson correlation coefficient values
-and all metadata for the top spectral matches.
-If using the Open Specy library, all intensity values are in absorbance, so
-your spectra should also be in absorbance units. If you need to convert your
-spectrum, use \code{\link{adj_intens}()}.
+\code{match_spec()} joins two \code{OpenSpecy} objects and their metadata
+based on similarity.
+\code{cor_spec()} correlates two \code{OpenSpecy} objects, typically one with
+knowns and one with unknowns.
+\code{ident_spec()} retrieves the top match values from a correlation matrix
+and formats them with metadata.
+\code{get_metadata()} retrieves metadata from OpenSpecy objects.
+\code{max_cor_named()} formats the top correlation values from a correlation
+matrix as a named vector.
+\code{filter_spec()} filters an Open Specy object.
 }
 \examples{
-\dontrun{
-data("raman_hdpe")
-
-get_lib("raman")
-spec_lib <- load_lib("raman")
-
-match_spec(raman_proc, library = spec_lib, which = "raman")
+data("test_lib")
+unknown <- read_extdata("ftir_ldpe_soil.asp") |>
+  read_any() |>
+  conform_spec(range = test_lib$wavenumber,
+               res = spec_res(test_lib)) |>
+  process_spec()
+matches <- cor_spec(unknown, test_lib)
+
+test_lib_extract <- filter_spec(test_lib,
+  logic = grepl("polycarbonate", test_lib$metadata$polymer_class,
+                ignore.case = TRUE)
+)
 
-find_spec(sample_name == 5381, library = spec_lib, which = "raman")
-}
+matches2 <- cor_spec(unknown, library = test_lib_extract)
 
 }
 \seealso{
diff --git a/man/process_spec.Rd b/man/process_spec.Rd
new file mode 100644
index 00000000..383f8c0d
--- /dev/null
+++ b/man/process_spec.Rd
@@ -0,0 +1,112 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/process_spec.R
+\name{process_spec}
+\alias{process_spec}
+\alias{process_spec.default}
+\alias{process_spec.OpenSpecy}
+\title{Preprocess Spectra}
+\usage{
+process_spec(x, ...)
+
+\method{process_spec}{default}(x, ...)
+
+\method{process_spec}{OpenSpecy}(
+  x,
+  active = TRUE,
+  adj_intens = FALSE,
+  adj_intens_args = list(type = "none"),
+  conform_spec = TRUE,
+  conform_spec_args = list(range = NULL, res = 5, type = "interp"),
+  restrict_range = FALSE,
+  restrict_range_args = list(min = 0, max = 6000),
+  flatten_range = FALSE,
+  flatten_range_args = list(min = 2200, max = 2420),
+  subtr_baseline = FALSE,
+  subtr_baseline_args = list(type = "polynomial", degree = 8, raw = FALSE, baseline =
+    NULL),
+  smooth_intens = TRUE,
+  smooth_intens_args = list(polynomial = 3, window = 11, derivative = 1, abs = TRUE),
+  make_rel = TRUE,
+  ...
+)
+}
+\arguments{
+\item{x}{an \code{OpenSpecy} object.}
+
+\item{active}{logical; indicating whether to perform preprocessing.
+If \code{TRUE}, the preprocessing steps will be applied.
+If \code{FALSE}, the original data will be returned.}
+
+\item{adj_intens}{logical; describing whether to adjust the intensity units.}
+
+\item{adj_intens_args}{named list of arguments passed to
+\code{\link{smooth_intens}()}.}
+
+\item{conform_spec}{logical; whether to conform the spectra to a new
+wavenumber range and resolution.}
+
+\item{conform_spec_args}{named list of arguments passed to
+\code{\link{conform_spec}()}.}
+
+\item{restrict_range}{logical; indicating whether to restrict the wavenumber
+range of the spectra.}
+
+\item{restrict_range_args}{named list of arguments passed to
+\code{\link{restrict_range}()}.}
+
+\item{flatten_range}{logical; indicating whether to flatten the range around
+the carbon dioxide region.}
+
+\item{flatten_range_args}{named list of arguments passed to
+\code{\link{flatten_range}()}.}
+
+\item{subtr_baseline}{logical; indicating whether to subtract the baseline
+from the spectra.}
+
+\item{subtr_baseline_args}{named list of arguments passed to
+\code{\link{subtr_baseline}()}.}
+
+\item{smooth_intens}{logical; indicating whether to apply a smoothing filter
+to the spectra.}
+
+\item{smooth_intens_args}{named list of arguments passed to
+\code{\link{smooth_intens}()}.}
+
+\item{make_rel}{logical; if \code{TRUE} spectra are automatically normalized
+with \code{\link{make_rel}()}.}
+
+\item{\ldots}{further arguments passed to subfunctions.}
+}
+\value{
+\code{process_spec()} returns an \code{OpenSpecy} object with preprocessed
+spectra based on the specified parameters.
+}
+\description{
+\code{process_spec()} is a monolithic wrapper function for all spectral
+preprocessing steps.
+}
+\examples{
+data("raman_hdpe")
+plot(raman_hdpe)
+
+# Process spectra with range restriction and baseline subtraction
+process_spec(raman_hdpe,
+             restrict_range = TRUE,
+             restrict_range_args = list(min = 500, max = 3000),
+             subtr_baseline = TRUE,
+             subtr_baseline_args = list(type = "polynomial",
+                                        polynomial = 8)) |>
+  lines(col = "darkred")
+
+# Process spectra with smoothing and derivative
+process_spec(raman_hdpe,
+             smooth_intens = TRUE,
+             smooth_intens_args = list(
+               polynomial = 3,
+               window = 11,
+               derivative = 1
+               )
+             ) |>
+  lines(col = "darkgreen")
+
+}
diff --git a/man/raman_hdpe.Rd b/man/raman_hdpe.Rd
index e2e3b319..f323515e 100644
--- a/man/raman_hdpe.Rd
+++ b/man/raman_hdpe.Rd
@@ -5,27 +5,31 @@
 \alias{raman_hdpe}
 \title{Sample Raman spectrum}
 \format{
-A data table containing 964 rows and 2 columns:
+An threepart list of class \code{\link{OpenSpecy}} containing:
 \tabular{ll}{
-\code{wavenumber}: \tab spectral wavenumber [1/cm] \cr
-\code{intensity}: \tab absorbance values [-] \cr
+\code{wavenumber}: \tab spectral wavenumbers [1/cm] (vector of 964 rows) \cr
+\code{spectra}: \tab absorbance values \link{-}
+(a \code{\link[data.table]{data.table}} with 964 rows and 1 column) \cr
+\code{metadata}: \tab spectral metadata \cr
 }
 }
 \description{
-Raman spectrum of high-density polyethylene (HDPE).
+Raman spectrum of high-density polyethylene (HDPE) provided by
+Horiba Scientific.
 }
 \examples{
-data("raman_hdpe")
+data(raman_hdpe)
+print(raman_hdpe)
 
 }
 \references{
-Cowger W, Gray A, Christiansen SH, Christiansen SH, Christiansen SH,
+Cowger W, Steinmetz Z, Gray A, Christiansen SH, Christiansen SH, Christiansen SH,
 De Frond H, Deshpande AD, Hemabessiere L, Lee E, Mill L, et al. (2020).
 “Critical Review of Processing and Classification Techniques for Images and
 Spectra in Microplastic Research.” \emph{Applied Spectroscopy},
 \strong{74}(9), 989–1010. \doi{10.1177/0003702820929064}.
 }
 \author{
-Win Cowger
+Zacharias Steinmetz, Win Cowger
 }
 \keyword{data}
diff --git a/man/read_envi.Rd b/man/read_envi.Rd
new file mode 100644
index 00000000..c0c07254
--- /dev/null
+++ b/man/read_envi.Rd
@@ -0,0 +1,63 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/read_envi.R
+\name{read_envi}
+\alias{read_envi}
+\title{Read ENVI data}
+\usage{
+read_envi(
+  file,
+  header = NULL,
+  share = NULL,
+  metadata = list(file_name = basename(file), user_name = NULL, contact_info = NULL,
+    organization = NULL, citation = NULL, spectrum_type = NULL, spectrum_identity = NULL,
+    material_form = NULL, material_phase = NULL, material_producer = NULL,
+    material_purity = NULL, material_quality = NULL, material_color = NULL,
+    material_other = NULL, cas_number = NULL, instrument_used = NULL,
+    instrument_accessories = NULL, instrument_mode = NULL, spectral_resolution = NULL,
+    laser_light_used = NULL, number_of_accumulations = NULL, 
+    
+    total_acquisition_time_s = NULL, data_processing_procedure = NULL,
+    level_of_confidence_in_identification = NULL, other_info = NULL, license =
+    "CC BY-NC"),
+  ...
+)
+}
+\arguments{
+\item{file}{name of the binary file.}
+
+\item{header}{name of the ASCII header file. If \code{NULL}, the name of the
+header file is guessed by looking for a second file with the same basename as
+\code{file} but with .hdr extension.}
+
+\item{share}{defaults to \code{NULL}; needed to share spectra with the
+Open Specy community; see \code{\link{share_spec}()} for details.}
+
+\item{metadata}{a named list of the metadata; see
+\code{\link{as_OpenSpecy}()} for details.}
+
+\item{\ldots}{further arguments passed to the submethods.}
+}
+\value{
+An \code{OpenSpecy} object.
+}
+\description{
+This function allows ENVI data import.
+}
+\details{
+ENVI data usually consists of two files, an ASCII header and a binary data
+file. The header contains all information necessary for correctly reading
+the binary file via \code{\link[caTools]{read.ENVI}()}.
+}
+\seealso{
+\code{\link{read_spec}()} for reading .y(a)ml, .json, or .rds (OpenSpecy)
+files;
+\code{\link{read_text}()}, \code{\link{read_asp}()}, \code{\link{read_spa}()},
+\code{\link{read_spc}()}, and \code{\link{read_jdx}()} for text files, .asp,
+.spa, .spa, .spc, and .jdx formats, respectively;
+\code{\link{read_opus}()} for reading .0 (OPUS) files;
+\code{\link{read_zip}()} and \code{\link{read_any}()} for wrapper functions;
+\code{\link[caTools]{read.ENVI}()}
+}
+\author{
+Zacharias Steinmetz, Claudia Beleites
+}
diff --git a/man/read_ext.Rd b/man/read_ext.Rd
new file mode 100644
index 00000000..60601518
--- /dev/null
+++ b/man/read_ext.Rd
@@ -0,0 +1,158 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/read_ext.R
+\name{read_text}
+\alias{read_text}
+\alias{read_asp}
+\alias{read_spa}
+\alias{read_spc}
+\alias{read_jdx}
+\alias{read_extdata}
+\title{Read spectral data}
+\usage{
+read_text(
+  file,
+  colnames = NULL,
+  method = "fread",
+  share = NULL,
+  metadata = list(file_name = basename(file), user_name = NULL, contact_info = NULL,
+    organization = NULL, citation = NULL, spectrum_type = NULL, spectrum_identity = NULL,
+    material_form = NULL, material_phase = NULL, material_producer = NULL,
+    material_purity = NULL, material_quality = NULL, material_color = NULL,
+    material_other = NULL, cas_number = NULL, instrument_used = NULL,
+    instrument_accessories = NULL, instrument_mode = NULL, spectral_resolution = NULL,
+    laser_light_used = NULL, number_of_accumulations = NULL, 
+    
+    total_acquisition_time_s = NULL, data_processing_procedure = NULL,
+    level_of_confidence_in_identification = NULL, other_info = NULL, license =
+    "CC BY-NC"),
+  ...
+)
+
+read_asp(
+  file,
+  share = NULL,
+  metadata = list(file_name = basename(file), user_name = NULL, contact_info = NULL,
+    organization = NULL, citation = NULL, spectrum_type = NULL, spectrum_identity = NULL,
+    material_form = NULL, material_phase = NULL, material_producer = NULL,
+    material_purity = NULL, material_quality = NULL, material_color = NULL,
+    material_other = NULL, cas_number = NULL, instrument_used = NULL,
+    instrument_accessories = NULL, instrument_mode = NULL, spectral_resolution = NULL,
+    laser_light_used = NULL, number_of_accumulations = NULL, 
+    
+    total_acquisition_time_s = NULL, data_processing_procedure = NULL,
+    level_of_confidence_in_identification = NULL, other_info = NULL, license =
+    "CC BY-NC"),
+  ...
+)
+
+read_spa(
+  file,
+  share = NULL,
+  metadata = list(file_name = basename(file), user_name = NULL, contact_info = NULL,
+    organization = NULL, citation = NULL, spectrum_type = NULL, spectrum_identity = NULL,
+    material_form = NULL, material_phase = NULL, material_producer = NULL,
+    material_purity = NULL, material_quality = NULL, material_color = NULL,
+    material_other = NULL, cas_number = NULL, instrument_used = NULL,
+    instrument_accessories = NULL, instrument_mode = NULL, spectral_resolution = NULL,
+    laser_light_used = NULL, number_of_accumulations = NULL, 
+    
+    total_acquisition_time_s = NULL, data_processing_procedure = NULL,
+    level_of_confidence_in_identification = NULL, other_info = NULL, license =
+    "CC BY-NC"),
+  ...
+)
+
+read_spc(
+  file,
+  share = NULL,
+  metadata = list(file_name = basename(file), user_name = NULL, contact_info = NULL,
+    organization = NULL, citation = NULL, spectrum_type = NULL, spectrum_identity = NULL,
+    material_form = NULL, material_phase = NULL, material_producer = NULL,
+    material_purity = NULL, material_quality = NULL, material_color = NULL,
+    material_other = NULL, cas_number = NULL, instrument_used = NULL,
+    instrument_accessories = NULL, instrument_mode = NULL, spectral_resolution = NULL,
+    laser_light_used = NULL, number_of_accumulations = NULL, 
+    
+    total_acquisition_time_s = NULL, data_processing_procedure = NULL,
+    level_of_confidence_in_identification = NULL, other_info = NULL, license =
+    "CC BY-NC"),
+  ...
+)
+
+read_jdx(
+  file,
+  share = NULL,
+  metadata = list(file_name = basename(file), user_name = NULL, contact_info = NULL,
+    organization = NULL, citation = NULL, spectrum_type = NULL, spectrum_identity = NULL,
+    material_form = NULL, material_phase = NULL, material_producer = NULL,
+    material_purity = NULL, material_quality = NULL, material_color = NULL,
+    material_other = NULL, cas_number = NULL, instrument_used = NULL,
+    instrument_accessories = NULL, instrument_mode = NULL, spectral_resolution = NULL,
+    laser_light_used = NULL, number_of_accumulations = NULL, 
+    
+    total_acquisition_time_s = NULL, data_processing_procedure = NULL,
+    level_of_confidence_in_identification = NULL, other_info = NULL, license =
+    "CC BY-NC"),
+  ...
+)
+
+read_extdata(file = NULL)
+}
+\arguments{
+\item{file}{file to be read from or written to.}
+
+\item{colnames}{character vector of \code{length = 2} indicating the column
+names for the wavenumber and intensity; if \code{NULL} columns are guessed.}
+
+\item{method}{submethod to be used for reading text files; defaults to
+\code{\link[data.table]{fread}()} but \code{\link[utils]{read.csv}()} works
+as well.}
+
+\item{share}{defaults to \code{NULL}; needed to share spectra with the
+Open Specy community; see \code{\link{share_spec}()} for details.}
+
+\item{metadata}{a named list of the metadata; see
+\code{\link{as_OpenSpecy}()} for details.}
+
+\item{\ldots}{further arguments passed to the submethods.}
+}
+\value{
+All \code{read_*()} functions return data frames containing two columns
+named \code{"wavenumber"} and \code{"intensity"}.
+}
+\description{
+Functions for reading spectral data from external file types.
+Currently supported reading formats are .csv and other text files, .asp,
+.spa, .spc, and .jdx.
+Additionally, .0 (OPUS) and .dat (ENVI) files are supported via
+\code{\link{read_opus}()} and \code{\link{read_envi}()}, respectively.
+\code{\link{read_zip}()} takes any of the files listed above.
+Note that proprietary file formats like .0, .asp, and .spa are poorly
+supported but will likely still work in most cases.
+}
+\details{
+\code{read_spc()} and \code{read_jdx()} are wrappers around the
+functions provided by the \link[hyperSpec:hyperSpec-package]{hyperSpec}.
+Other functions have been adapted various online sources.
+Metadata is harvested if possible.
+There are many unique iterations of spectral file formats so there may be
+bugs in the file conversion. Please contact us if you identify any.
+}
+\examples{
+read_extdata("raman_hdpe.csv") |> read_text()
+read_extdata("raman_atacamit.spc") |> read_spc()
+read_extdata("ftir_ldpe_soil.asp") |> read_asp()
+read_extdata("testdata_zipped.zip") |> read_zip()
+
+}
+\seealso{
+\code{\link{read_spec}()} for reading .y(a)ml, .json, or .rds (OpenSpecy)
+files;
+\code{\link{read_opus}()} for reading .0 (OPUS) files;
+\code{\link{read_envi}()} for reading .dat (ENVI) files;
+\code{\link{read_zip}()} and \code{\link{read_any}()} for wrapper functions;
+\code{\link[hyperSpec]{read.jdx}()}; \code{\link[hyperSpec]{read.spc}()}
+}
+\author{
+Zacharias Steinmetz, Win Cowger
+}
diff --git a/man/read_multi.Rd b/man/read_multi.Rd
new file mode 100644
index 00000000..094ce56e
--- /dev/null
+++ b/man/read_multi.Rd
@@ -0,0 +1,44 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/read_multi.R
+\name{read_any}
+\alias{read_any}
+\alias{read_zip}
+\title{Read spectral data from multiple files}
+\usage{
+read_any(file, ...)
+
+read_zip(file, ...)
+}
+\arguments{
+\item{file}{file to be read from or written to.}
+
+\item{\ldots}{further arguments passed to the submethods.}
+}
+\value{
+All \code{read_*()} functions return \code{OpenSpecy} objects
+}
+\description{
+Wrapper functions for reading files in batch.
+}
+\details{
+\code{read_any()} provides a single function to quickly read in any of the
+supported formats, it assumes that the file extension will tell it how to
+process the spectra.
+\code{read_zip()} provides functionality for reading in spectral map files
+with ENVI file format or as individual files in a zip folder. If individual
+files, spectra are concatenated.
+}
+\examples{
+read_extdata("raman_hdpe.csv") |> read_any()
+read_extdata("ftir_ldpe_soil.asp") |> read_any()
+read_extdata("ftir_ps.0") |> read_any()
+read_extdata("testdata_zipped.zip") |> read_zip()
+read_extdata("CA_tiny_map.zip") |> read_zip()
+
+}
+\seealso{
+\code{\link{read_spec}()}
+}
+\author{
+Zacharias Steinmetz, Win Cowger
+}
diff --git a/man/read_opus.Rd b/man/read_opus.Rd
new file mode 100644
index 00000000..f2338354
--- /dev/null
+++ b/man/read_opus.Rd
@@ -0,0 +1,91 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/read_opus.R
+\name{read_opus}
+\alias{read_opus}
+\title{Read spectral data from Bruker OPUS binary files}
+\usage{
+read_opus(
+  file,
+  share = NULL,
+  metadata = list(file_name = basename(file), user_name = NULL, contact_info = NULL,
+    organization = NULL, citation = NULL, spectrum_type = NULL, spectrum_identity = NULL,
+    material_form = NULL, material_phase = NULL, material_producer = NULL,
+    material_purity = NULL, material_quality = NULL, material_color = NULL,
+    material_other = NULL, cas_number = NULL, instrument_used = NULL,
+    instrument_accessories = NULL, instrument_mode = NULL, spectral_resolution = NULL,
+    laser_light_used = NULL, number_of_accumulations = NULL, 
+    
+    total_acquisition_time_s = NULL, data_processing_procedure = NULL,
+    level_of_confidence_in_identification = NULL, other_info = NULL, license =
+    "CC BY-NC"),
+  type = "spec",
+  digits = 1L,
+  atm_comp_minus4offset = FALSE
+)
+}
+\arguments{
+\item{file}{character vector with path to file(s).}
+
+\item{share}{defaults to \code{NULL}; needed to share spectra with the
+Open Specy community; see \code{\link{share_spec}()} for details.}
+
+\item{metadata}{a named list of the metadata; see
+\code{\link{as_OpenSpecy}()} for details.}
+
+\item{type}{character vector of spectra types to extract from OPUS binary
+file. Default is \code{"spec"}, which will extract the final spectra, e.g.
+expressed in absorbance (named \code{AB} in Bruker OPUS programs). Possible
+additional values for the character vector supplied to \code{type} are
+\code{"spec_no_atm_comp"} (spectrum of the sample without compensation for
+atmospheric gases, water vapor and/or carbon dioxide),
+\code{"sc_sample"} (single channel spectrum of the sample measurement), \code{"sc_ref"}
+(single channel spectrum of the reference measurement),
+\code{"ig_sample"} (interferogram of the sample measurement) and \code{"ig_ref"}
+(interferogram of the reference measurement).}
+
+\item{digits}{Integer that specifies the number of decimal places used to
+round the wavenumbers (values of x-variables).}
+
+\item{atm_comp_minus4offset}{Logical whether spectra after atmospheric
+compensation are read with an offset of -4 bytes from Bruker OPUS files;
+default is \code{FALSE}.}
+}
+\value{
+An \code{OpenSpecy} object.
+}
+\description{
+Read file(s) acquired with a Bruker Vertex FTIR Instrument. This function
+is basically a fork of \code{opus_read()} from
+\url{https://github.com/pierreroudier/opusreader}.
+}
+\details{
+The type of spectra returned by the function when using
+\code{type = "spec"} depends on the setting of the Bruker instrument: typically,
+it can be either absorbance or reflectance.
+
+The type of spectra to extract from the file can also use Bruker's OPUS
+software naming conventions, as follows:
+\itemize{
+\item \code{ScSm} corresponds to \code{sc_sample}
+\item \code{ScRf} corresponds to \code{sc_ref}
+\item \code{IgSm} corresponds to \code{ig_sample}
+\item \code{IgRf} corresponds to \code{ig_ref}
+}
+}
+\examples{
+read_extdata("ftir_ps.0") |> read_opus()
+
+}
+\seealso{
+\code{\link{read_spec}()} for reading .y(a)ml, .json, or .rds (OpenSpecy)
+files;
+\code{\link{read_text}()}, \code{\link{read_asp}()}, \code{\link{read_spa}()},
+\code{\link{read_spc}()}, and \code{\link{read_jdx}()} for text files, .asp,
+.spa, .spa, .spc, and .jdx formats, respectively;
+\code{\link{read_text}()} for reading .dat (ENVI) files;
+\code{\link{read_zip}()} and \code{\link{read_any}()} for wrapper functions;
+\code{\link{read_opus_raw}()};
+}
+\author{
+Philipp Baumann, Zacharias Steinmetz, Win Cowger
+}
diff --git a/man/read_opus_raw.Rd b/man/read_opus_raw.Rd
new file mode 100644
index 00000000..d8bdf18f
--- /dev/null
+++ b/man/read_opus_raw.Rd
@@ -0,0 +1,79 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/read_opus_raw.R
+\name{read_opus_raw}
+\alias{read_opus_raw}
+\title{Read a Bruker OPUS spectrum binary raw string}
+\usage{
+read_opus_raw(rw, type = "spec", atm_comp_minus4offset = FALSE)
+}
+\arguments{
+\item{rw}{a raw vector}
+
+\item{type}{character vector of spectra types to extract from OPUS binary
+file. Default is \code{"spec"}, which will extract the final spectra, e.g.
+expressed in absorbance (named \code{AB} in Bruker OPUS programs). Possible
+additional values for the character vector supplied to \code{type} are
+\code{"spec_no_atm_comp"} (spectrum of the sample without compensation for
+atmospheric gases, water vapor and/or carbon dioxide),
+\code{"sc_sample"} (single channel spectrum of the sample measurement),
+\code{"sc_ref"} (single channel spectrum of the reference measurement),
+\code{"ig_sample"} (interferogram of the sample measurement) and \code{"ig_ref"}
+(interferogram of the reference measurement).}
+
+\item{atm_comp_minus4offset}{logical; whether spectra after atmospheric
+compensation are read with an offset of -4 bytes from Bruker OPUS
+files. Default is \code{FALSE}.}
+}
+\value{
+A list of 10 elements:
+
+\describe{
+\item{\code{metadata}}{a \code{data.frame} containing metadata from the OPUS file.}
+\item{\code{spec}}{if \code{"spec"} was requested in the \code{type} option, a matrix of
+the spectrum of the sample (otherwise set to \code{NULL}).}
+\item{\code{spec_no_atm_comp}}{if \code{"spec_no_atm_comp"} was requested in the
+\code{type} option, a matrix of the spectrum of the sample without atmospheric
+compensation (otherwise set to \code{NULL}).}
+\item{\code{sc_sample}}{if \code{"sc_sample"} was requested in the \code{type} option, a
+matrix of the single channel spectrum of the sample (otherwise set to
+\code{NULL}).}
+\item{\code{sc_ref}}{if \code{"sc_ref"} was requested in the \code{type} option, a matrix
+of the single channel spectrum of the reference (otherwise set to \code{NULL}).}
+\item{\code{ig_sample}}{if \code{"ig_sample"} was requested in the \code{type} option, a
+matrix of the interferogram of the sample (otherwise set to \code{NULL}).}
+\item{\code{ig_ref}}{if \code{"ig_ref"} was requested in the \code{type} option, a matrix
+of the interferogram of the reference (otherwise set to \code{NULL}).}
+\item{\code{wavenumbers}}{if \code{"spec"} or \code{"spec_no_atm_comp"} was requested in
+the \code{type} option, a numeric vector of the wavenumbers of the spectrum of
+the sample (otherwise set to \code{NULL}).}
+\item{\code{wavenumbers_sc_sample}}{if \code{"sc_sample"} was requested in the \code{type}
+option, a numeric vector of the wavenumbers of the single channel spectrum
+of the sample (otherwise set to \code{NULL}).}
+\item{\code{wavenumbers_sc_ref}}{if \code{"sc_ref"} was requested in the \code{type}
+option, a numeric vector of the wavenumbers of the single channel spectrum
+of the reference (otherwise set to \code{NULL}).}
+}
+}
+\description{
+Read single binary acquired with an Bruker Vertex FTIR Instrument
+}
+\details{
+The type of spectra returned by the function when using
+\code{type = "spec"} depends on the setting of the Bruker instrument: typically,
+it can be either absorbance or reflectance.
+
+The type of spectra to extract from the file can also use Bruker's OPUS
+software naming conventions, as follows:
+\itemize{
+\item \code{ScSm} corresponds to \code{sc_sample}
+\item \code{ScRf} corresponds to \code{sc_ref}
+\item \code{IgSm} corresponds to \code{ig_sample}
+\item \code{IgRf} corresponds to \code{ig_ref}
+}
+}
+\seealso{
+\code{\link{read_opus}()}
+}
+\author{
+Philipp Baumann and Pierre Roudier
+}
diff --git a/man/read_spec.Rd b/man/read_spec.Rd
deleted file mode 100644
index 3d040c4e..00000000
--- a/man/read_spec.Rd
+++ /dev/null
@@ -1,106 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/read_spec.R
-\name{read_text}
-\alias{read_text}
-\alias{read_asp}
-\alias{read_spa}
-\alias{read_jdx}
-\alias{read_spc}
-\alias{read_0}
-\alias{read_extdata}
-\title{Read spectral data}
-\usage{
-read_text(
-  file = ".",
-  cols = NULL,
-  method = "read.csv",
-  share = NULL,
-  id = paste(digest(Sys.info()), digest(sessionInfo()), sep = "/"),
-  ...
-)
-
-read_asp(
-  file = ".",
-  share = NULL,
-  id = paste(digest(Sys.info()), digest(sessionInfo()), sep = "/"),
-  ...
-)
-
-read_spa(
-  file = ".",
-  share = NULL,
-  id = paste(digest(Sys.info()), digest(sessionInfo()), sep = "/"),
-  ...
-)
-
-read_jdx(
-  file = ".",
-  share = NULL,
-  id = paste(digest(Sys.info()), digest(sessionInfo()), sep = "/"),
-  ...
-)
-
-read_spc(
-  file = ".",
-  share = NULL,
-  id = paste(digest(Sys.info()), digest(sessionInfo()), sep = "/"),
-  ...
-)
-
-read_0(
-  file = ".",
-  share = NULL,
-  id = paste(digest(Sys.info()), digest(sessionInfo()), sep = "/"),
-  ...
-)
-
-read_extdata(file = NULL)
-}
-\arguments{
-\item{file}{file to be read from.}
-
-\item{cols}{character vector of \code{length = 2} indicating the colum names
-for the wavenumber and intensity; if \code{NULL} columns are guessed.}
-
-\item{method}{submethod to be used for reading text files; defaults to
-\link[utils]{read.csv} but \link[data.table]{fread} works as well.}
-
-\item{share}{defaults to \code{NULL}; needed to share spectra with the
-Open Specy community; see \code{\link{share_spec}()} for details.}
-
-\item{id}{a unique user and/or session ID; defaults to
-\code{paste(digest(Sys.info()), digest(sessionInfo()), sep = "/")}.}
-
-\item{\ldots}{further arguments passed to the submethods.}
-}
-\value{
-All \code{read_*()} functions return data frames containing two columns
-named \code{"wavenumber"} and \code{"intensity"}.
-}
-\description{
-Functions for reading spectral data types including .asp, .jdx,
-.spc, .spa, .0, and .csv.
-}
-\details{
-\code{read_spc()} and \code{read_jdx()} are just a wrapper around the
-functions provided by the \link[hyperSpec:hyperSpec-package]{hyperSpec}
-package.
-Other functions have been adapted various online sources.
-All functions convert datasets to a 2 column table with one column labeled
-"wavenumber" and the other "intensity". There are many unique iterations of
-spectral file formats so there may be bugs in the file conversion.
-Please contact us if you identify any.
-}
-\examples{
-read_text(read_extdata("raman_hdpe.csv"))
-read_asp(read_extdata("ftir_ldpe_soil.asp"))
-read_0(read_extdata("ftir_ps.0"))
-
-}
-\seealso{
-\code{\link[hyperSpec]{read.jdx}()}; \code{\link[hyperSpec]{read.spc}()};
-\code{\link[hexView]{readRaw}()}; \code{\link{share_spec}()}
-}
-\author{
-Zacharias Steinmetz, Win Cowger
-}
diff --git a/man/run_app.Rd b/man/run_app.Rd
index 9943ce5a..5d333e1b 100644
--- a/man/run_app.Rd
+++ b/man/run_app.Rd
@@ -4,23 +4,24 @@
 \alias{run_app}
 \title{Run Open Specy app}
 \usage{
-run_app(app_dir = "system", path = "system", log = TRUE, ...)
+run_app(path = "system", log = TRUE, ref = "main", test_mode = FALSE, ...)
 }
 \arguments{
-\item{app_dir}{the app to run; defaults to \code{"system"} pointing to
-\code{system.file("shiny", package = "OpenSpecy")}.}
-
-\item{path}{where to look for the local library files; defaults to
-\code{"system"} pointing to
-\code{system.file("extdata", package = "OpenSpecy")}.}
+\item{path}{to store the downloaded app files; defaults to \code{"system"}
+pointing to \code{system.file(package = "OpenSpecy")}.}
 
 \item{log}{logical; enables/disables logging to \code{\link[base]{tempdir}()}}
 
+\item{ref}{git reference; could be a commit, tag, or branch name. Defaults to
+"main". Only change this in case of errors.}
+
+\item{test_mode}{logical; for internal testing only.}
+
 \item{\dots}{arguments passed to \code{\link[shiny]{runApp}()}.}
 }
 \value{
 This function normally does not return any value, see
-\code{\link[shiny]{runApp}()}.
+\code{\link[shiny]{runGitHub}()}.
 }
 \description{
 This wrapper function starts the graphical user interface of Open Specy.
@@ -36,7 +37,7 @@ run_app()
 
 }
 \seealso{
-\code{\link[shiny]{runApp}()}
+\code{\link[shiny]{runGitHub}()}
 }
 \author{
 Zacharias Steinmetz
diff --git a/man/share_spec.Rd b/man/share_spec.Rd
index 0938acba..796ac64f 100644
--- a/man/share_spec.Rd
+++ b/man/share_spec.Rd
@@ -3,47 +3,28 @@
 \name{share_spec}
 \alias{share_spec}
 \alias{share_spec.default}
-\alias{share_spec.data.frame}
+\alias{share_spec.OpenSpecy}
 \title{Share data with the Open Specy community}
 \usage{
-share_spec(data, ...)
+share_spec(x, ...)
 
-\method{share_spec}{default}(data, ...)
+\method{share_spec}{default}(x, ...)
 
-\method{share_spec}{data.frame}(
-  data,
-  metadata = c(user_name = "", contact_info = "", organization = "", citation = "",
-    spectrum_type = "", spectrum_identity = "", material_form = "", material_phase = "",
-    material_producer = "", material_purity = "", material_quality = "", material_color =
-    "", material_other = "", cas_number = "", instrument_used = "",
-    instrument_accessories = "", instrument_mode = "", spectral_resolution = "",
-    laser_light_used = "", number_of_accumulations = "", total_acquisition_time_s = "",
-    data_processing_procedure = "", level_of_confidence_in_identification = "", 
-    
-    other_info = "", license = "CC BY-NC"),
-  file = NULL,
-  share = "system",
-  id = paste(digest(Sys.info()), digest(sessionInfo()), sep = "/"),
-  ...
-)
+\method{share_spec}{OpenSpecy}(x, file = NULL, share = "system", credentials = NULL, ...)
 }
 \arguments{
-\item{data}{a data frame containing the spectral data; columns should be
-named \code{"wavenumber"} and \code{"intensity"}.}
-
-\item{metadata}{a named vector of the metadata to share; see details below.}
+\item{x}{a list object of class \code{OpenSpecy}.}
 
 \item{file}{file to share (optional).}
 
 \item{share}{accepts any local directory to save the spectrum for later
-sharing via e-mail to \email{wincowger@gmail.com};
-\code{"system"} (default) uses the Open Specy package directory at
-\code{system.file("extdata", package = "OpenSpecy")};
-if a correct API token exists, \code{"dropbox"} shares the spectrum with the
-cloud.}
+sharing via email to \email{wincowger@gmail.com}; \code{"system"} (default)
+uses the Open Specy package directory at \code{system.file("extdata",
+package = "OpenSpecy")}; if a correct API token exists, \code{"cloud"}
+shares the spectrum with the cloud.}
 
-\item{id}{a unique user and/or session ID; defaults to
-\code{paste(digest(Sys.info()), digest(sessionInfo()), sep = "/")}.}
+\item{credentials}{a named list of credentials for cloud sharing; required if
+\code{share = "cloud"}).}
 
 \item{\ldots}{further arguments passed to the submethods.}
 }
@@ -54,76 +35,21 @@ cloud.}
 This helper function shares spectral data and metadata with the Open Specy
 community.
 
-\strong{Please note} that \code{share_spec()} only provides basic sharing
+\strong{Please note:} that \code{share_spec()} only provides basic sharing
 functionality if used interactively. This means that files are only formatted
-and saved for sharing but are not send automatically. This only works with
+and saved for sharing but are not sent automatically. This only works with
 hosted instances of Open Specy.
 }
-\details{
-The \code{metadata} argument may contain a named vector with the following
-details (\code{*} = mandatory):
-
-\tabular{ll}{
-\code{user_name*}: \tab User name, e.g. "Win Cowger"\cr
-\code{contact_info}: \tab Contact information, e.g. "1-513-673-8956,
-wincowger@gmail.com"\cr
-\code{organization}: \tab Affiliation, e.g. "University of California,
-Riverside"\cr
-\code{citation}: \tab Data citation, e.g. "Primpke, S., Wirth, M., Lorenz,
-C., & Gerdts, G. (2018). Reference database design for the automated analysis
-of microplastic samples based on Fourier transform infrared (FTIR)
-spectroscopy. \emph{Analytical and Bioanalytical Chemistry}.
-\doi{10.1007/s00216-018-1156-x}"\cr
-\code{spectrum_type*}: \tab Raman or FTIR\cr
-\code{spectrum_identity*}: \tab Material/polymer analyzed, e.g.
-"Polystyrene"\cr
-\code{material_form}: \tab Form of the material analyzed, e.g. textile fiber,
-rubber band, sphere, granule \cr
-\code{material_phase}: \tab Phase of the material analyzed (liquid, gas,
-solid) \cr
-\code{material_producer}: \tab Producer of the material analyzed,
-e.g. Dow \cr
-\code{material_purity}: \tab Purity of the material analyzed, e.g. 99.98\%
-\cr
-\code{material_quality}: \tab Quality of the material analyzed, e.g.
-consumer product, manufacturer material, analytical standard,
-environmental sample \cr
-\code{material_color}: \tab Color of the material analyzed,
-e.g. blue, #0000ff, (0, 0, 255) \cr
-\code{material_other}: \tab Other material description, e.g. 5 µm diameter
-fibers, 1 mm spherical particles \cr
-\code{cas_number}: \tab CAS number, e.g. 9003-53-6 \cr
-\code{instrument_used}: \tab Instrument used, e.g. Horiba LabRam \cr
-\code{instrument_accessories}: \tab Instrument accessories, e.g.
-Focal Plane Array, CCD\cr
-\code{instrument_mode}: \tab Instrument modes/settings, e.g.
-transmission, reflectance \cr
-\code{spectral_resolution}: \tab Spectral resolution, e.g. 4/cm \cr
-\code{laser_light_used}: \tab Wavelength of the laser/light used, e.g.
-785 nm \cr
-\code{number_of_accumulations}: \tab Number of accumulations, e.g 5 \cr
-\code{total_acquisition_time_s}: \tab Total acquisition time (s), e.g. 10 s
-\cr
-\code{data_processing_procedure}: \tab Data processing procedure,
-e.g. spikefilter, baseline correction, none \cr
-\code{level_of_confidence_in_identification}: \tab Level of confidence in
-identification, e.g. 99\% \cr
-\code{other_info}: \tab Other information \cr
-\code{license}: \tab The license of the shared spectrum; defaults to
-\code{"CC BY-NC"} (see
-\url{https://creativecommons.org/licenses/by-nc/4.0/} for details). Any other
-creative commons license is allowed, for example, CC0 or CC BY \cr
-}
-}
 \examples{
 \dontrun{
 data("raman_hdpe")
 share_spec(raman_hdpe,
-           metadata = c(user_name = "Win Cowger",
-                        spectrum_type = "FTIR",
-                        spectrum_identity = "PE",
-                        license = "CC BY-NC"),
-           share = tempdir())
+           metadata = list(
+             user_name = "Win Cowger",
+             spectrum_type = "FTIR",
+             spectrum_identity = "PE",
+             license = "CC BY-NC"
+           ))
 }
 
 }
diff --git a/man/sig_noise.Rd b/man/sig_noise.Rd
new file mode 100644
index 00000000..3688cba2
--- /dev/null
+++ b/man/sig_noise.Rd
@@ -0,0 +1,45 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/sig_noise.R
+\name{sig_noise}
+\alias{sig_noise}
+\alias{sig_noise.default}
+\alias{sig_noise.OpenSpecy}
+\title{Calculate signal and noise metrics for OpenSpecy objects}
+\usage{
+sig_noise(x, ...)
+
+\method{sig_noise}{default}(x, ...)
+
+\method{sig_noise}{OpenSpecy}(x, metric = "sig_over_noise", na.rm = TRUE, ...)
+}
+\arguments{
+\item{x}{an \code{OpenSpecy} object.}
+
+\item{metric}{character; specifying the desired metric to calculate.
+Options include \code{"sig"} (mean intensity), \code{"noise"} (standard
+deviation of intensity), \code{"sig_times_noise"} (absolute value of
+signal times noise), \code{"sig_over_noise"} (absolute value of signal /
+noise), or \code{"tot_sig"} (total signal = signal * number of data
+points).}
+
+\item{na.rm}{logical; indicating whether missing values should be removed
+when calculating signal and noise. Default is \code{TRUE}.}
+
+\item{\ldots}{further arguments passed to subfunctions; currently not used.}
+}
+\value{
+A numeric vector containing the calculated metric for each spectrum in the
+\code{OpenSpecy} object.
+}
+\description{
+This function calculates common signal and noise metrics for \code{OpenSpecy}
+objects.
+}
+\examples{
+data("raman_hdpe")
+
+sig_noise(raman_hdpe, metric = "sig")
+sig_noise(raman_hdpe, metric = "noise")
+sig_noise(raman_hdpe, metric = "sig_times_noise")
+
+}
diff --git a/man/smooth_intens.Rd b/man/smooth_intens.Rd
index 005eaf25..afed0c95 100644
--- a/man/smooth_intens.Rd
+++ b/man/smooth_intens.Rd
@@ -2,34 +2,37 @@
 % Please edit documentation in R/smooth_intens.R
 \name{smooth_intens}
 \alias{smooth_intens}
-\alias{smooth_intens.formula}
-\alias{smooth_intens.data.frame}
 \alias{smooth_intens.default}
+\alias{smooth_intens.OpenSpecy}
 \title{Smooth spectral intensities}
 \usage{
 smooth_intens(x, ...)
 
-\method{smooth_intens}{formula}(formula, data = NULL, ...)
+\method{smooth_intens}{default}(x, ...)
 
-\method{smooth_intens}{data.frame}(x, ...)
-
-\method{smooth_intens}{default}(x, y, p = 3, n = 11, make_rel = TRUE, ...)
+\method{smooth_intens}{OpenSpecy}(
+  x,
+  polynomial = 3,
+  window = 11,
+  derivative = 0,
+  abs = FALSE,
+  make_rel = TRUE,
+  ...
+)
 }
 \arguments{
-\item{x}{a numeric vector containing the spectral wavenumbers; alternatively
-a data frame containing spectral data as \code{"wavenumber"} and
-\code{"intensity"} can be supplied.}
-
-\item{formula}{an object of class '\code{\link[stats]{formula}}' of the form
-\code{intensity ~ wavenumber}.}
+\item{x}{an object of class \code{OpenSpecy}.}
 
-\item{data}{a data frame containing the variables in \code{formula}.}
+\item{polynomial}{polynomial order for the filter}
 
-\item{y}{a numeric vector containing the spectral intensities.}
+\item{window}{number of data points in the window, filter length (must be
+odd).}
 
-\item{p}{polynomial order for the filter}
+\item{derivative}{the derivative order if you want to calculate the
+derivative. Zero (default) is no derivative.}
 
-\item{n}{number of data points in the window, filter length (must be odd).}
+\item{abs}{logical; whether you want to calculate the absolute value of the
+resulting output.}
 
 \item{make_rel}{logical; if \code{TRUE} spectra are automatically normalized
 with \code{\link{make_rel}()}.}
@@ -37,8 +40,7 @@ with \code{\link{make_rel}()}.}
 \item{\ldots}{further arguments passed to \code{\link[signal]{sgolay}()}.}
 }
 \value{
-\code{smooth_intens()} returns a data frame containing two columns named
-\code{"wavenumber"} and \code{"intensity"}.
+\code{smooth_intens()} returns an \code{OpenSpecy} object.
 }
 \description{
 This smoother can enhance the signal to noise ratio of the data and uses a
diff --git a/man/spec_res.Rd b/man/spec_res.Rd
index fc733ccd..b71a7341 100644
--- a/man/spec_res.Rd
+++ b/man/spec_res.Rd
@@ -2,13 +2,21 @@
 % Please edit documentation in R/spec_res.R
 \name{spec_res}
 \alias{spec_res}
+\alias{spec_res.default}
+\alias{spec_res.OpenSpecy}
 \title{Spectral resolution}
 \usage{
-spec_res(x)
+spec_res(x, ...)
+
+\method{spec_res}{default}(x, ...)
+
+\method{spec_res}{OpenSpecy}(x, ...)
 }
 \arguments{
-\item{x}{a numeric vector or an \R object which is coercible to one by
-\code{as.vector(x, "numeric")}; \code{x} should be \code{wavenumber} data.}
+\item{x}{a numeric vector with \code{wavenumber} data or an \code{OpenSpecy}
+object.}
+
+\item{\ldots}{further arguments passed to subfunctions; currently not used.}
 }
 \value{
 \code{spec_res()} returns a single numeric value.
@@ -24,7 +32,8 @@ distinguished.
 }
 \examples{
 data("raman_hdpe")
-spec_res(raman_hdpe$wavenumber)
+
+spec_res(raman_hdpe)
 
 }
 \author{
diff --git a/man/subtr_bg.Rd b/man/subtr_baseline.Rd
similarity index 61%
rename from man/subtr_bg.Rd
rename to man/subtr_baseline.Rd
index 3359acdf..9387d5f7 100644
--- a/man/subtr_bg.Rd
+++ b/man/subtr_baseline.Rd
@@ -1,31 +1,31 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/subtr_bg.R
-\name{subtr_bg}
-\alias{subtr_bg}
-\alias{subtr_bg.formula}
-\alias{subtr_bg.data.frame}
-\alias{subtr_bg.default}
+% Please edit documentation in R/subtr_baseline.R
+\name{subtr_baseline}
+\alias{subtr_baseline}
+\alias{subtr_baseline.default}
+\alias{subtr_baseline.OpenSpecy}
 \title{Automated background subtraction for spectral data}
 \usage{
-subtr_bg(x, ...)
+subtr_baseline(x, ...)
 
-\method{subtr_bg}{formula}(formula, data = NULL, ...)
+\method{subtr_baseline}{default}(x, ...)
 
-\method{subtr_bg}{data.frame}(x, ...)
-
-\method{subtr_bg}{default}(x, y, degree = 8, raw = FALSE, make_rel = TRUE, ...)
+\method{subtr_baseline}{OpenSpecy}(
+  x,
+  type = "polynomial",
+  degree = 8,
+  raw = FALSE,
+  baseline,
+  make_rel = TRUE,
+  ...
+)
 }
 \arguments{
-\item{x}{a numeric vector containing the spectral wavenumbers; alternatively
-a data frame containing spectral data as \code{"wavenumber"} and
-\code{"intensity"} can be supplied.}
-
-\item{formula}{an object of class '\code{\link[stats]{formula}}' of the form
-\code{intensity ~ wavenumber}.}
+\item{x}{a list object of class \code{OpenSpecy}.}
 
-\item{data}{a data frame containing the variables in \code{formula}.}
-
-\item{y}{a numeric vector containing the spectral intensities.}
+\item{type}{one of \code{"polynomial"} or \code{"manual"} depending on
+whether you want spectra to be corrected with a manual baseline or with
+polynomial baseline fitting.}
 
 \item{degree}{the degree of the polynomial. Must be less than the number of
 unique points when raw is \code{FALSE}. Typically a good fit can be
@@ -33,18 +33,21 @@ found with a 8th order polynomial.}
 
 \item{raw}{if \code{TRUE}, use raw and not orthogonal polynomials.}
 
+\item{baseline}{an \code{OpenSpecy} object containing the baseline data to be
+subtracted.}
+
 \item{make_rel}{logical; if \code{TRUE} spectra are automatically normalized
 with \code{\link{make_rel}()}.}
 
 \item{\ldots}{further arguments passed to \code{\link[stats]{poly}()}.}
 }
 \value{
-\code{subtr_bg()} returns a data frame containing two columns named
+\code{subtr_baseline()} returns a data frame containing two columns named
 \code{"wavenumber"} and \code{"intensity"}.
 }
 \description{
 This baseline correction routine iteratively finds the baseline of a spectrum
-using a polynomial fitting.
+using a polynomial fitting or accepts a manual baseline.
 }
 \details{
 This is a translation of Michael Stephen Chen's MATLAB code written for the
@@ -53,7 +56,7 @@ This is a translation of Michael Stephen Chen's MATLAB code written for the
 \examples{
 data("raman_hdpe")
 
-subtr_bg(raman_hdpe)
+subtr_baseline(raman_hdpe)
 
 }
 \references{
diff --git a/man/test_lib.Rd b/man/test_lib.Rd
index 2f75bd8b..58027c47 100644
--- a/man/test_lib.Rd
+++ b/man/test_lib.Rd
@@ -5,22 +5,17 @@
 \alias{test_lib}
 \title{Test reference library}
 \format{
-A list named \code{"test"} with two elements:
-
-\tabular{ll}{
-\code{metadata}: \tab metadata of 34 Raman spectra \cr
-\code{library}: \tab all reference spectra, \code{sample_name} serves as
-identifier \cr
-}
+An \code{OpenSpecy} object; \code{sample_name} is the class of the spectra.
 }
 \description{
-Reference library of 34 Raman spectra used for internal testing.
+Reference library with 29 FTIR and 28 Raman spectra used for examples and
+internal testing.
 }
 \examples{
 data("test_lib")
 
 }
 \author{
-Jennifer Lynch
+Win Cowger
 }
 \keyword{data}
diff --git a/tests/testthat/test-adj_intens.R b/tests/testthat/test-adj_intens.R
index 25301868..39ed63e4 100644
--- a/tests/testthat/test-adj_intens.R
+++ b/tests/testthat/test-adj_intens.R
@@ -1,16 +1,20 @@
 data("raman_hdpe")
+raman_hdpe$spectra$intensity2 <- raman_hdpe$spectra$intensity * 2
 
 test_that("adj_intens() works as expected", {
   expect_silent(adj <- adj_intens(raman_hdpe))
-  expect_identical(adj, adj_intens(raman_hdpe$wavenumber,
-                                   raman_hdpe$intensity))
-  expect_identical(adj, adj_intens(intensity ~ wavenumber, raman_hdpe))
-  expect_equal(as.numeric(
-    cor(raman_hdpe[2], adj_intens(raman_hdpe)[2])
-  ), 1, ignore_attr = F)
-  expect_s3_class(adj, "data.frame")
-  expect_equal(names(adj), c("wavenumber", "intensity"))
-  expect_equal(nrow(adj), nrow(raman_hdpe))
-  expect_equal(adj[1], raman_hdpe[1])
-  expect_equal(range(adj[2]), c(0, 1))
+  expect_silent(adj_intens(raman_hdpe, type = "reflectance"))
+  expect_silent(adj_intens(raman_hdpe, type = "transmittance"))
+  expect_equal(
+    cor(raman_hdpe$spectra$intensity, adj$spectra$intensity), 1,
+    ignore_attr = F)
+  expect_equal(
+    cor(raman_hdpe$spectra$intensity, adj$spectra$intensity2), 1,
+    ignore_attr = F)
+
+  expect_s3_class(adj, "OpenSpecy")
+
+  expect_equal(nrow(adj$spectra), nrow(raman_hdpe$spectra))
+  expect_equal(adj$wavenumber, raman_hdpe$wavenumber)
+  expect_equal(adj$spectra |> range(), c(0, 1))
 })
diff --git a/tests/testthat/test-adj_range.R b/tests/testthat/test-adj_range.R
new file mode 100644
index 00000000..6e09a892
--- /dev/null
+++ b/tests/testthat/test-adj_range.R
@@ -0,0 +1,58 @@
+library(data.table)
+
+test_that("restrict_range() provides correct range", {
+  test_noise <- as_OpenSpecy(x = seq(400,4000, by = 10),
+                             spectra = data.table(intensity = rnorm(361)))
+  single_range <- restrict_range(test_noise, min = 1000,
+                                 max = 2000) |>
+    expect_silent()
+
+  double_range <- restrict_range(test_noise, min = c(1000, 2000),
+                                 max = c(1500, 2500)) |>
+    expect_silent()
+
+  is_OpenSpecy(single_range) |> expect_true()
+  expect_identical(single_range$wavenumber, seq(1000,2000, by = 10))
+  expect_identical(double_range$wavenumber, c(seq(1000,1500, by = 10),
+                                              seq(2000,2500, by = 10)))
+})
+
+test_that("flatten_range() function test", {
+  sam <- as_OpenSpecy(x = 1:10, spectra = data.table(V1 = 1:10))
+  flat_sam <- flatten_range(sam, min = c(4, 7), max = c(5, 10),
+                             make_rel = F) |>
+    expect_silent()
+
+  expect_equal(flat_sam$spectra$V1[4:5], c(4.5, 4.5))
+  expect_equal(flat_sam$spectra$V1[7:10], c(8.5, 8.5, 8.5, 8.5))
+
+  data("raman_hdpe")
+  flat_hdpe <- flatten_range(raman_hdpe, min = c(500, 1000),
+                             max = c(700, 1500)) |>
+    expect_silent()
+  expect_equal(flat_hdpe$spectra$intensity[1:50],
+               make_rel(raman_hdpe$spectra$intensity)[1:50])
+  expect_equal(flat_hdpe$spectra$intensity[60:100] |> unique() |> round(6),
+               0.036709)
+
+  tiny_map <- read_extdata("CA_tiny_map.zip") |> read_zip()
+  flat_map <- flatten_range(tiny_map, min = c(1000, 2000),
+                            max = c(1200, 2400), make_rel = F) |>
+    expect_silent()
+
+  expect_false(all.equal(flat_map$spectra, tiny_map$spectra) |> isTRUE())
+  expect_equal(flat_map$spectra[1:20], tiny_map$spectra[1:20])
+
+  expect_equal(flat_map$spectra[40:60, 1:5] |> unique() |> round(4)
+               |> as.numeric(),
+               c(-0.8694, -1.246, -0.8304, -1.1909, -0.7857))
+})
+
+test_that("flatten_range() error handling", {
+  test <- as_OpenSpecy(x = 1:10, spectra = data.table(V1 = 1:10))
+
+  expect_error(flatten_range(test))
+  expect_error(flatten_range(test, min = c(1000),
+                             max = c(2000, 3000)))
+  expect_error(flatten_range(test, min = c(2000), max = c(1000)))
+})
diff --git a/tests/testthat/test-as_OpenSpecy.R b/tests/testthat/test-as_OpenSpecy.R
new file mode 100644
index 00000000..61369532
--- /dev/null
+++ b/tests/testthat/test-as_OpenSpecy.R
@@ -0,0 +1,101 @@
+library(data.table)
+
+df <- read_extdata("raman_hdpe.csv") |> read.csv()
+
+test_that("as_OpenSpecy() generates OpenSpecy objects", {
+  expect_silent(as_OpenSpecy(df))
+
+  expect_silent(osf <- as_OpenSpecy(df))
+  expect_silent(ost <- data.table(df) |> as_OpenSpecy())
+  expect_silent(osl <- list(df$wavenumber, df[2]) |> as_OpenSpecy())
+  expect_silent(OpenSpecy(osf))
+
+  expect_s3_class(osf, "OpenSpecy")
+  expect_s3_class(ost, "OpenSpecy")
+  expect_s3_class(osl, "OpenSpecy")
+
+  expect_equal(names(osf), c("wavenumber", "spectra", "metadata"))
+  expect_equal(names(ost), c("wavenumber", "spectra", "metadata"))
+  expect_equal(names(osl), c("wavenumber", "spectra", "metadata"))
+
+  expect_equal(OpenSpecy(df), OpenSpecy(osf))
+  expect_equal(ost$spectra, osf$spectra)
+  expect_equal(ost$wavenumber, osf$wavenumber)
+  expect_equal(ost$metadata, osf$metadata)
+
+  expect_true(is_OpenSpecy(osf))
+  expect_true(is_OpenSpecy(ost))
+  expect_true(is_OpenSpecy(osl))
+  expect_false(is_OpenSpecy(df))
+})
+
+test_that("as_OpenSpecy() handles errors correctly", {
+  expect_silent(as_OpenSpecy(df$wavenumber, as.data.frame(df$intensity)))
+  expect_error(as_OpenSpecy(df$wavenumber, df$intensity))
+  expect_error(as_OpenSpecy(df$wavenumber))
+  expect_error(as_OpenSpecy(df$wavenumber, as.data.frame(df$intensity[-1])))
+  expect_error(as_OpenSpecy(df$wavenumber,
+                            data.table(intensity = df$intensity,
+                                       intensity = df$intensity)))
+
+  expect_warning(as_OpenSpecy(data.frame(x = df$wavenumber,
+                                         abs = df$intensity)))
+  expect_warning(as_OpenSpecy(data.frame(wav = df$wavenumber,
+                                         y = df$intensity)))
+
+  expect_error(as_OpenSpecy(df$wavenumber, as.data.frame(df$intensity),
+                            coords = ""))
+  expect_error(as_OpenSpecy(df$wavenumber, as.data.frame(df$intensity),
+                            coords = df))
+  expect_error(as_OpenSpecy(df$wavenumber, as.data.frame(df$intensity),
+                            metadata = ""))
+})
+
+test_that("check_OpenSpecy() work as expected", {
+  os <- as_OpenSpecy(df)
+  check_OpenSpecy(os) |> expect_true()
+
+  check_OpenSpecy(df) |> expect_error()
+
+  osv <- osn <- oss <- ost <- osl <- os
+
+  osv$wavenumber <- list(osv$wavenumber)
+  check_OpenSpecy(osv) |> expect_false() |> expect_warning()
+
+  names(osn) <- 1:3
+  check_OpenSpecy(osn) |> expect_error()
+
+  oss$wavenumber <- sample(oss$wavenumber)
+  check_OpenSpecy(oss) |> expect_false() |> expect_message()
+
+  class(ost$metadata) <- class(ost$spectra) <- "data.frame"
+  check_OpenSpecy(ost) |> expect_false() |> expect_message() |> expect_message()
+
+  osl$metadata <- rbind(osl$metadata, osl$metadata)
+  osl$spectra <- osl$spectra[-1]
+
+  check_OpenSpecy(osl) |> expect_false() |> expect_warning() |> expect_warning()
+})
+
+test_that("'OpenSpecy' objects are read correctly", {
+  os <- as_OpenSpecy(df)
+
+  expect_equal(range(os$wavenumber) |> round(2), c(301.04, 3198.12))
+  expect_equal(range(os$spectra) |> round(2), c(26, 816))
+  expect_length(os$wavenumber, 964)
+  expect_equal(os$metadata$x, 1)
+  expect_equal(os$metadata$y, 1)
+  expect_equal(os$metadata$license, "CC BY-NC")
+})
+
+test_that("'OpenSpecy' objects are transcribed to and from 'hyperSpec' objects", {
+  os <- as_OpenSpecy(df)
+  hyper <- as_hyperSpec(os)
+  expect_s4_class(hyper, "hyperSpec")
+
+  openhyper <- as_OpenSpecy(hyper)
+
+  expect_true(is_OpenSpecy(openhyper))
+  expect_equal(openhyper$wavenumber, hyper@wavelength)
+  expect_equal(unlist(openhyper$spectra$V1),unname(t(hyper$spc)[,1]))
+})
diff --git a/tests/testthat/test-conform_spec.R b/tests/testthat/test-conform_spec.R
new file mode 100644
index 00000000..0cc8b172
--- /dev/null
+++ b/tests/testthat/test-conform_spec.R
@@ -0,0 +1,49 @@
+library(data.table)
+
+data("raman_hdpe")
+
+test_that("conform_spec() throws an error for non-OpenSpecy objects", {
+  # Create a non-OpenSpecy object
+  non_OpenSpecy <- data.frame(wavenumber = c(1000, 1100, 1200),
+                              intensity = c(0.1, 0.2, 0.3))
+
+  conform_spec(non_OpenSpecy, c(1000, 2000)) |>
+    expect_error()
+})
+
+test_that("conform_spec() handles errors correctly", {
+  conform_spec(raman_hdpe, range = c(min(raman_hdpe$wavenumber + 10),
+                                     max(raman_hdpe$wavenumber))) |>
+    expect_silent()
+  conform_spec(raman_hdpe, range = c(0,5000)) |>
+    expect_silent()
+  expect_equal(conform_spec(raman_hdpe, c(1000, 2000))$wavenumber,
+               seq(1000, 2000, by = 5))
+  conform_spec(raman_hdpe, c(min(raman_hdpe$wavenumber + 10))) |>
+    expect_error()
+  conform_spec(raman_hdpe, c(min(raman_hdpe$wavenumber + 10),
+                             max(raman_hdpe$wavenumber)), res = 0) |>
+    expect_error()
+})
+
+test_that("conform_spec() conforms wavenumbers correctly", {
+  sam <- as_OpenSpecy(wn <- seq(1000, 2000, 5),
+                      data.table(intensity = rnorm(length(wn))))
+  new_wavenumbers <- c(1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900)
+  conf_new <- conform_spec(sam, new_wavenumbers) |>
+    expect_silent()
+  conf_roll <- conform_spec(sam, new_wavenumbers,res = NULL, type = "roll") |>
+    expect_silent()
+
+  expect_equal(length(conf_new$wavenumber), length(conf_new$spectra[[1]]))
+  expect_equal(range(conf_new$wavenumber), range(new_wavenumbers))
+
+  expect_identical(conf_roll$wavenumber, new_wavenumbers)
+
+  expect_s3_class(conf_new, "OpenSpecy")
+  expect_s3_class(conf_roll, "OpenSpecy")
+
+  conform_spec(raman_hdpe)$spectra$intensity[c(63, 143, 283, 325, 402)] |>
+    round(2) |>
+    expect_equal(c(78.84, 65.00, 105.73, 109.41, 116.00))
+})
diff --git a/tests/testthat/test-data_norm.R b/tests/testthat/test-data_norm.R
index 73226aac..fd88d10b 100644
--- a/tests/testthat/test-data_norm.R
+++ b/tests/testthat/test-data_norm.R
@@ -10,3 +10,22 @@ test_that("make_rel() give correct output", {
   expect_equal(round(make_rel(c(965.86, 82.75, -458.28, -0.98, -62.94)), 4),
                c(1.0000, 0.3799, 0.0000, 0.3211, 0.2776))
 })
+
+test_that("adj_res() function test", {
+    expect_equal(adj_res(seq(500, 4000, 4), 5), round(seq(500, 4000, 4)/5)*5)
+})
+
+test_that("conform_res() function test", {
+    x <- seq(500, 4000, 4)
+    expect_equal(conform_res(x, 5), seq(floor(min(x)/5)*5, ceiling(max(x)/5)*5, by=5))
+})
+
+test_that("adj_neg() function test", {
+    expect_equal(adj_neg(c(-1000, -1, 0, 1, 10)), c(1, 1000, 1001, 1002, 1011))
+    expect_equal(adj_neg(c(1, 2, 3)), c(1, 2, 3))
+})
+
+test_that("make_rel() function test", {
+    expect_equal(make_rel(c(1, 2, 3)), c(0, 0.5, 1))
+    expect_equal(make_rel(c(10, 20, 30)), c(0, 0.5, 1))
+})
diff --git a/tests/testthat/test-def_features.R b/tests/testthat/test-def_features.R
new file mode 100644
index 00000000..646c43b8
--- /dev/null
+++ b/tests/testthat/test-def_features.R
@@ -0,0 +1,70 @@
+map <- read_extdata("CA_tiny_map.zip") |> read_any()
+
+test_that("features are identified when given logical", {
+  map$metadata$particles <- map$metadata$y == 0
+  id_map <- def_features(map, map$metadata$particles)
+  expect_length(unique(id_map$metadata$feature_id), 2)
+  expect_equal(max(id_map$metadata$area, na.rm = T), 13)
+  expect_equal(max(id_map$metadata$feret_max, na.rm = T), 13)
+  expect_equal(max(id_map$metadata$feret_min, na.rm = T), 1)
+  expect_equal(max(id_map$metadata$perimeter, na.rm = T), 24)
+})
+
+test_that("particles are identified when given character", {
+  map$metadata$particles <- ifelse(map$metadata$y == 1, "particle", "not_particle")
+  id_map <- def_features(map, map$metadata$particles)
+  expect_length(unique(id_map$metadata$feature_id), 3)
+  expect_equal(max(id_map$metadata$area, na.rm = T), 182)
+  expect_equal(round(max(id_map$metadata$feret_max, na.rm = T)), 19)
+})
+
+test_that("an error is thrown for invalid feature input", {
+  def_features(map, map$metadata) |> expect_error()
+})
+
+test_that("check that particles are identified with all TRUE or FALSE logical vectors", {
+  # All TRUE case
+  map$metadata$particles <- rep(TRUE, nrow(map$metadata))
+  def_features(map, map$metadata$particles) |> expect_error()
+
+  # All FALSE case
+  map$metadata$particles <- rep("test_FALSE", nrow(map$metadata))
+  def_features(map, map$metadata$particles) |> expect_error()
+})
+
+test_that("the original spectrum remains unmodified and metadata is amended", {
+  map <- read_extdata("CA_tiny_map.zip") |> read_any()
+
+  id_map <- def_features(map,ifelse(map$metadata$x == 1,
+                                    "particle", "not_particle"))
+
+  expect_equal(id_map$wavenumber, map$wavenumber)
+  expect_equal(id_map$spectra, map$spectra)
+  expect_contains(id_map$metadata, map$metadata)
+
+  expect_contains(names(id_map$metadata),
+                  c("feature_id", "area", "feret_max", "centroid_y",
+                    "centroid_x"))
+})
+
+test_that("collapse particles returns expected values", {
+  particles <- ifelse(map$metadata$y == 1, "particleA", "particleB")
+  id_map <- def_features(map, particles)
+  test_collapsed <- collapse_spec(id_map)
+
+  expect_equal(test_collapsed$metadata |> nrow(), 3)
+  expect_equal(test_collapsed$metadata$feret_max |> round(2), c(13, 13, 18.69))
+  expect_equal(test_collapsed$metadata$centroid_x |> unique(), 6)
+
+  particles <- map$metadata$y == 1
+  id_map <- def_features(map, particles)
+  test_collapsed <- collapse_spec(id_map)
+
+  expect_equal(test_collapsed$metadata |> nrow(), 2)
+  expect_equal(test_collapsed$metadata$feret_max |> round(2), c(NA, 13))
+  expect_equal(test_collapsed$metadata$centroid_x |> unique(), 6)
+
+  expect_contains(names(test_collapsed$metadata),
+                  c("feature_id", "area", "feret_max", "centroid_y",
+                    "centroid_x"))
+})
diff --git a/tests/testthat/test-gen_OpenSpecy.R b/tests/testthat/test-gen_OpenSpecy.R
new file mode 100644
index 00000000..766dceaa
--- /dev/null
+++ b/tests/testthat/test-gen_OpenSpecy.R
@@ -0,0 +1,22 @@
+library(data.table)
+data("raman_hdpe")
+
+test_that("print() and summary() work as expected", {
+  print(raman_hdpe) |> expect_output(
+    paste0("wavenumber.*intensity.*308.221.*",
+           "48.*metadata.*spectrum_identity.*HDPE"))
+  summary(raman_hdpe) |> expect_output("Length.*964.*spectra.*26")
+})
+
+test_that("head returns the first few lines of the OpenSpecy object", {
+  head <- head(raman_hdpe)
+
+  nrow(head) |> expect_equal(6)
+  head |> expect_equal(head(data.table(wavenumber = raman_hdpe$wavenumber,
+                                       raman_hdpe$spectra)))
+})
+
+test_that("ploting works without errors", {
+  plot(raman_hdpe) |> expect_silent()
+})
+
diff --git a/tests/testthat/test-interactive_plots.R b/tests/testthat/test-interactive_plots.R
new file mode 100644
index 00000000..d1c1df78
--- /dev/null
+++ b/tests/testthat/test-interactive_plots.R
@@ -0,0 +1,20 @@
+map <- read_zip(read_extdata("CA_tiny_map.zip"))
+data("raman_hdpe")
+
+test_that("heatmap_spec() generates 'plotly' object", {
+  heatmap_spec(map, z = map$metadata$y) |>
+    expect_silent() |>
+    expect_s3_class("plotly")
+})
+
+test_that("plotly_spec() generates 'plotly' object", {
+  plotly_spec(raman_hdpe, select = 1) |>
+    expect_silent() |>
+    expect_s3_class("plotly")
+})
+
+test_that("interactive_plot() generates 'plotly' object", {
+  interactive_plot(map, x2 = raman_hdpe, select = 2) |>
+    suppressWarnings() |>
+    expect_s3_class("plotly")
+})
diff --git a/tests/testthat/test-io_spec.R b/tests/testthat/test-io_spec.R
new file mode 100644
index 00000000..dbc972fb
--- /dev/null
+++ b/tests/testthat/test-io_spec.R
@@ -0,0 +1,77 @@
+# Loading test data
+data(raman_hdpe)
+
+# Create temp dir for testthat
+tmp <- file.path(tempdir(), "OpenSpecy-testthat")
+dir.create(tmp, showWarnings = F)
+
+library(data.table)
+
+test_that("extdata files are present", {
+  ed <- read_extdata()
+  any(grepl("\\.yml$", ed)) |> expect_true()
+  any(grepl("\\.json$", ed)) |> expect_true()
+  any(grepl("\\.rds$", ed)) |> expect_true()
+})
+
+test_that("write_spec() works without errors", {
+  write_spec(raman_hdpe, file.path(tmp, "test.yml")) |> expect_silent()
+  write_spec(raman_hdpe, file.path(tmp, "test.json")) |> expect_silent()
+  write_spec(raman_hdpe, file.path(tmp, "test.rds")) |> expect_silent()
+
+  write_spec(as.data.frame(raman_hdpe), file.path(tmp, "test.yml")) |>
+    expect_error()
+  write_spec(raman_hdpe, file.path(tmp, "test.csv")) |> expect_error()
+})
+
+test_that("read_spec() gives expected output", {
+  yml <- read_extdata("raman_hdpe.yml") |> read_spec() |> expect_silent()
+  jsn <- read_extdata("raman_hdpe.json") |> read_spec() |> expect_silent()
+  rds <- read_extdata("raman_hdpe.rds") |> read_spec() |> expect_silent()
+
+  read_spec(read_extdata("raman_hdpe.csv")) |> expect_error()
+
+  read_extdata("raman_hdpe.yml") |> read_spec(share = tmp) |> expect_message()
+  read_extdata("raman_hdpe.json") |> read_spec(share = tmp) |> expect_message()
+  read_extdata("raman_hdpe.rds") |> read_spec(share = tmp) |> expect_message()
+
+  expect_s3_class(yml, "OpenSpecy")
+  expect_s3_class(jsn, "OpenSpecy")
+  expect_s3_class(rds, "OpenSpecy")
+
+  jsn$metadata$file_name <- yml$metadata$file_name <-
+    rds$metadata$file_name <- NULL
+  expect_equal(jsn, yml)
+  expect_equal(rds, raman_hdpe)
+  expect_equal(jsn[1:2], raman_hdpe[1:2])
+  expect_equal(yml[1:2], raman_hdpe[1:2])
+
+  read_spec(read_extdata("raman_hdpe.yml"), share = tmp) |> expect_message()
+})
+
+test_that("read_spec() and write_spec() work nicely together", {
+  yml <- read_spec(file.path(tmp, "test.yml")) |> expect_silent()
+  jsn <- read_spec(file.path(tmp, "test.json")) |> expect_silent()
+  rds <- read_spec(file.path(tmp, "test.rds")) |> expect_silent()
+
+  jsn$metadata$file_name <- yml$metadata$file_name <-
+    rds$metadata$file_name <- NULL
+  expect_equal(jsn, yml)
+  expect_equal(rds, raman_hdpe)
+  expect_equal(jsn[1:2], raman_hdpe[1:2])
+  expect_equal(yml[1:2], raman_hdpe[1:2])
+})
+
+test_that("as_hyperspec function", {
+  hyperspec_object <- as_hyperSpec(raman_hdpe)
+
+  # Verify the class of the output
+  expect_s4_class(hyperspec_object, "hyperSpec")
+
+  # Verify the equality of the content
+  expect_equal(hyperspec_object@wavelength, raman_hdpe$wavenumber)
+  expect_equal(c(hyperspec_object$spc), raman_hdpe$spectra$intensity)
+})
+
+# Tidy up
+unlink(tmp, recursive = T)
diff --git a/tests/testthat/test-manage_lib.R b/tests/testthat/test-manage_lib.R
index 2a6898af..fc57f016 100644
--- a/tests/testthat/test-manage_lib.R
+++ b/tests/testthat/test-manage_lib.R
@@ -1,5 +1,3 @@
-data("test_lib")
-
 # Create temp dir for testthat
 tmp <- file.path(tempdir(), "OpenSpecy-testthat")
 dir.create(tmp, showWarnings = F)
@@ -9,37 +7,57 @@ test_that("stop if OSF not reachable", {
   skip_if_not(is.null(curl::nslookup("api.osf.io", error = F)),
               message = "OSF is online")
 
-  expect_error(get_lib(which = "test", type = c("metadata", "library"),
-                       path = tmp))
+  get_lib(type = "test", path = tmp) |>
+    expect_error()
 })
 
 test_that("get_lib() downloads test library", {
   skip_on_cran()
   skip_if_offline(host = "api.osf.io")
 
-  expect_message(
-    expect_output(get_lib(which = "test", type = c("metadata", "library"),
-                          path = tmp))
-  )
+  get_lib(type = "test", path = tmp) |>
+    expect_output() |> expect_message()
+  get_lib(type = "model", path = tmp) |>
+    expect_output() |> expect_message()
 })
 
 test_that("check_lib() finds test library", {
   skip_on_cran()
   skip_if_offline(host = "api.osf.io")
 
-  expect_silent(check_lib(which = "test", type = c("metadata", "library"),
-                          path = tmp))
-  expect_warning(check_lib(which = "test", type = c("peaks"),
-                           path = tmp))
+  check_lib(type = "test", path = tmp) |> expect_silent()
+  check_lib(type = c("raw", "derivative", "nobaseline"), path = tmp) |>
+    expect_warning()
+})
+
+test_that("get_lib() downloads complete library", {
+  skip_on_cran()
+  skip_if_offline(host = "api.osf.io")
+  skip_if_not(testthat:::on_ci(), "Not on CI")
+
+  get_lib(type = c("derivative", "nobaseline"), path = tmp) |>
+    expect_output() |> expect_message()
+})
+
+test_that("check_lib() finds complete library", {
+  skip_on_cran()
+  skip_if_offline(host = "api.osf.io")
+  skip_if_not(testthat:::on_ci(), "Not on CI")
+
+  check_lib(type = c("derivative", "nobaseline"), path = tmp) |>
+    expect_silent()
+  check_lib(type = "raw", path = tmp) |>
+    expect_warning()
 })
 
 test_that("load_lib() works as expected", {
   skip_on_cran()
   skip_if_offline(host = "api.osf.io")
 
-  expect_silent(tl <- load_lib(which = "test", type = c("metadata", "library"),
-                               path = tmp))
+  tl <- load_lib(type = "test", path = tmp) |>
+    expect_silent()
   expect_type(tl, "list")
+  expect_s3_class(tl, "OpenSpecy")
   expect_identical(tl, test_lib, ignore_attr = T)
 })
 
diff --git a/tests/testthat/test-manage_spec.R b/tests/testthat/test-manage_spec.R
new file mode 100644
index 00000000..ade9e807
--- /dev/null
+++ b/tests/testthat/test-manage_spec.R
@@ -0,0 +1,42 @@
+library(data.table)
+data("raman_hdpe")
+
+test_that("merging identical files without range specification", {
+  specs <- lapply(c(read_extdata("raman_hdpe.yml"),
+                    read_extdata("raman_hdpe.yml")), read_spec)
+  same <- c_spec(specs) |> expect_silent()
+
+  expect_equal(same$wavenumber, raman_hdpe$wavenumber)
+  expect_equal(same$spectra$intensity, raman_hdpe$spectra$intensity)
+})
+
+specs <- lapply(c(read_extdata("raman_hdpe.yml"),
+                  read_extdata("ftir_ldpe_soil.asp")), read_any)
+
+test_that("merging different files with common range", {
+  diff <- c_spec(specs, range = "common", res = 5) |>
+    expect_silent()
+
+  diff$wavenumber[1:2] |> expect_equal(c(655, 660))
+  diff$spectra$intensity[1:2] |> round(2) |> expect_equal(c(53.87, 59.00))
+  diff$spectra$intensity.1[1:2] |> round(2) |> expect_equal(c(0.03, 0.03))
+})
+
+test_that("merging different files with specified range", {
+  spec <- c_spec(specs, range = c(1000, 2000), res = 5) |>
+    expect_silent()
+
+  spec$wavenumber |> expect_equal(seq(1000, 2000, 5))
+  spec$spectra$intensity |>
+    expect_equal(
+      conform_spec(raman_hdpe, c(1000, 2000), res = 5)$spectra$intensity
+      )
+})
+
+test_that("sample_spec() returns a subset of the spectra", {
+  tiny_map <- read_any(read_extdata("CA_tiny_map.zip"))
+  sampled <- sample_spec(tiny_map, size = 5)
+  expect_s3_class(sampled, "OpenSpecy")
+  expect_equal(ncol(sampled$spectra), 5)
+})
+
diff --git a/tests/testthat/test-match_spec.R b/tests/testthat/test-match_spec.R
index 8859a465..c90c3a9f 100644
--- a/tests/testthat/test-match_spec.R
+++ b/tests/testthat/test-match_spec.R
@@ -1,25 +1,115 @@
-data("raman_hdpe")
+# Create temp dir for testthat
+tmp <- file.path(tempdir(), "OpenSpecy-testthat")
+dir.create(tmp, showWarnings = F)
+
+# Create test data for cor_spec function
 data("test_lib")
 
-test_that("match_spec() gives expected results", {
-  expect_message(
-    ms <- match_spec(raman_hdpe, test_lib, which = "test")
+unknown <- read_extdata("ftir_ldpe_soil.asp") |> read_any() |>
+  conform_spec(range = test_lib$wavenumber, res = spec_res(test_lib)) |>
+  process_spec(smooth_intens = T, make_rel = T)
+
+# Create a subset of test_lib for filtering
+test_lib_extract <- filter_spec(
+  test_lib, logic = test_lib$metadata$polymer_class == "polycarbonates"
+  )
+
+# Match_spec function with AI
+test_that("match_spec returns correct structure with AI", {
+  skip_on_cran()
+  skip_if_offline(host = "api.osf.io")
+
+  get_lib("model", path = tmp)
+  lib <- load_lib(type = "model", path = tmp)
+
+  set.seed(47)
+  rn <- runif(n = length(unique(lib$variables_in)))
+  fill <- as_OpenSpecy(as.numeric(unique(lib$variables_in)),
+                       spectra = data.frame(rn))
+  matches <- match_spec(x = unknown, library = lib, na.rm = T, fill = fill) |>
+    expect_silent()
+  nrow(matches) |> expect_equal(1)
+  names(matches) |> expect_contains(c("x", "y", "z", "value", "name"))
+  round(matches$value, 2) |> expect_equal(0.52)
+  grepl("polyamide", matches$name) |> expect_true()
+})
+
+# Match_spec function
+test_that("match_spec returns correct structure", {
+  matches <- match_spec(x = unknown, library = test_lib, na.rm = T, top_n = 5,
+                        add_library_metadata = "sample_name",
+                        add_object_metadata = "col_id") |>
+    expect_silent()
+
+  nrow(matches) |> expect_equal(5)
+  names(matches) |> expect_contains(c("object_id", "library_id", "match_val"))
+  round(matches$match_val, 2) |> expect_equal(c(0.67, 0.57, 0.55, 0.50, 0.43))
+  tolower(matches$polymer) |> expect_equal(
+    c("poly(ethylene)", "polystyrene", "poly(vinyl chloride)",
+      "poly(dimethylsiloxane) (pdms)", NA)
   )
-  expect_error(match_spec(raman_hdpe, test_lib))
-  expect_length(ms, 4)
-  h1 <- c(ms[1,1:3])
-  expect_equal(h1$sample_name, 5373)
-  expect_equal(h1$spectrum_identity, "HDPE")
-  expect_equal(h1$rsq, 0.91)
 })
 
-test_that("find_spec() works as expected", {
-  expect_silent(
-    fs <- find_spec(sample_name == 5373, test_lib, which = "test")
+# Write the tests for cor_spec function
+test_that("cor_spec returns a data.table with correct columns", {
+  matches <- cor_spec(unknown, library = test_lib) |>
+    expect_silent()
+
+  unknown2 <- unknown
+  unknown2$wavenumber[1:3] <- unknown2$wavenumber[1:3] + 1
+
+  matches2 <- cor_spec(unknown2, library =  test_lib) |>
+    expect_warning()
+  inherits(matches, "matrix") |> expect_true()
+  expect_identical(dim(matches), c(ncol(test_lib$spectra),
+                                   ncol(unknown$spectra)))
+
+  top_matches <- max_cor_named(cor_matrix = matches, na.rm = T) |>
+    expect_silent()
+
+  expect_length(top_matches, 1)
+
+  ncol(filter_spec(test_lib, logic = names(top_matches))$spectra) |>
+    expect_equal(1)
+
+  test_lib$metadata$test <- NA
+
+  test_metadata <- get_metadata(test_lib, logic = names(top_matches),
+                                rm_empty = T) |>
+    expect_silent()
+
+  expect_equal(nrow(test_metadata), 1)
+  full_test <- ident_spec(matches, unknown, library = test_lib, top_n = 5,
+                          add_library_metadata = "sample_name") |>
+    expect_silent()
+
+  nrow(full_test) |> expect_equal(5)
+  names(full_test) |> expect_contains(c("object_id", "library_id", "match_val"))
+  round(full_test$match_val, 2) |> expect_equal(c(0.67, 0.57, 0.55, 0.50, 0.43))
+  tolower(full_test$polymer) |> expect_equal(
+    c("poly(ethylene)", "polystyrene", "poly(vinyl chloride)",
+      "poly(dimethylsiloxane) (pdms)", NA)
   )
-  expect_error(find_spec(sample_name == 5373, test_lib))
-  expect_length(fs, 4)
-  h2 <- c(fs[1,c(1,3,4)])
-  expect_equal(h2$sample_name, 5373)
-  expect_equal(h2$spectrum_identity, "HDPE")
 })
+
+# Write the tests for filter_spec function
+test_that("filter_spec returns OpenSpecy object with filtered spectra", {
+  os_filtered <- filter_spec(test_lib, logic = rep(F, ncol(test_lib$spectra))) |>
+    expect_silent()
+  expect_equal(ncol(os_filtered$spectra), 0)
+  expect_equal(nrow(os_filtered$metadata), 0)
+})
+
+# Write the tests for filter_spec function
+test_that("filter_spec returns OpenSpecy object with filtered spectra", {
+  logic <- rep(F,ncol(test_lib$spectra))
+  logic[1] <- TRUE
+
+  os_filtered <- filter_spec(test_lib, logic = logic) |>
+    expect_silent()
+  expect_equal(ncol(os_filtered$spectra), 1)
+  expect_equal(nrow(os_filtered$metadata), 1)
+})
+
+# Tidy up
+unlink(tmp, recursive = T)
diff --git a/tests/testthat/test-process_spec.R b/tests/testthat/test-process_spec.R
new file mode 100644
index 00000000..d2dc6c39
--- /dev/null
+++ b/tests/testthat/test-process_spec.R
@@ -0,0 +1,24 @@
+test_that("process_spec() returns expected values", {
+  tiny_map <- read_extdata("CA_tiny_map.zip") |> read_any()
+
+  conf <- process_spec(tiny_map) |> expect_silent()
+  expect_equal(conf, conform_spec(tiny_map, range = NULL, res = 5) |>
+                 smooth_intens(polynomial = 3,
+                               window = 11,
+                               derivative = 1,
+                               abs = T,
+                               make_rel = T))
+
+
+  proc <- process_spec(raman_hdpe,
+                       smooth_intens = TRUE,
+                       smooth_intens_args = list(
+                         polynomial = 3,
+                         window = 11,
+                         derivative = 1
+                       )
+  ) |> expect_silent()
+
+  expect_equal(proc, conform_spec(raman_hdpe) |>
+                 smooth_intens(derivative = 1, make_rel = T))
+})
diff --git a/tests/testthat/test-read_envi.R b/tests/testthat/test-read_envi.R
new file mode 100644
index 00000000..9f54bf92
--- /dev/null
+++ b/tests/testthat/test-read_envi.R
@@ -0,0 +1,29 @@
+# Create temp dir for testthat
+tmp <- file.path(tempdir(), "OpenSpecy-testthat")
+dir.create(tmp, showWarnings = F)
+
+test_that("ENVI files are read", {
+  tiny_map <- read_extdata("CA_tiny_map.zip") |> read_zip() |>
+    expect_silent()
+  read_extdata("CA_tiny_map.zip") |> read_zip(share = tmp) |>
+    expect_message() |> expect_warning()
+
+  expect_s3_class(tiny_map, "OpenSpecy")
+
+  expect_equal(ncol(tiny_map$spectra), 208)
+  expect_length(tiny_map$wavenumber, 427)
+
+  range(tiny_map$wavenumber) |> round(1) |>
+    expect_equal(c(717.4, 4003.7))
+  range(tiny_map$spectra) |> round(2) |>
+    expect_equal(c(-1.32, 1.17))
+  tiny_map$spectra[c(1,427), c(1,45)] |> round(2) |> unlist() |> as.numeric() |>
+    expect_equal(c(-0.86, -0.88, -0.62, -0.64))
+
+  names(tiny_map$metadata) |>
+    expect_contains(c("x", "y", "file_name", "file_id", "description",
+                      "pixel size"))
+})
+
+# Tidy up
+unlink(tmp, recursive = T)
diff --git a/tests/testthat/test-read_ext.R b/tests/testthat/test-read_ext.R
new file mode 100644
index 00000000..6547a3f6
--- /dev/null
+++ b/tests/testthat/test-read_ext.R
@@ -0,0 +1,111 @@
+# Create temp dir for testthat
+tmp <- file.path(tempdir(), "OpenSpecy-testthat")
+dir.create(tmp, showWarnings = F)
+
+library(data.table)
+
+data("raman_hdpe")
+
+test_that("extdata files are present", {
+  ed <- read_extdata()
+  expect_true(any(grepl("\\.asp$", ed)))
+  expect_true(any(grepl("\\.csv$", ed)))
+  expect_true(any(grepl("\\.0$", ed)))
+  expect_true(any(grepl("\\.jdx$", ed)))
+  expect_true(any(grepl("\\.spa$", ed)))
+  expect_true(any(grepl("\\.spc$", ed)))
+})
+
+test_that("read_text() gives expected output", {
+  csv <- read_extdata("raman_hdpe.csv") |> read_text() |>
+    expect_silent()
+  read_extdata("raman_hdpe.csv") |> read_text(share = tmp) |>
+    expect_message() |> expect_warning()
+
+  dtf <- read_extdata("raman_hdpe.csv") |> read_text(method = "fread") |>
+    expect_silent()
+  read_extdata("ftir_pva_without_header.csv") |> read_text() |>
+    expect_warning() |> expect_warning()
+  read_extdata("ftir_pva_without_header.csv") |> read_text(header = F) |>
+    expect_warning() |> expect_warning()
+  read_extdata("ftir_pva_without_header.csv") |> read_text(method = "fread") |>
+    expect_warning() |> expect_warning()
+
+  expect_s3_class(csv, "OpenSpecy")
+  expect_equal(names(csv), c("wavenumber", "spectra", "metadata"))
+  expect_equal(csv$wavenumber, raman_hdpe$wavenumber)
+  expect_equal(csv$spectra, raman_hdpe$spectra)
+  expect_equal(csv, dtf)
+})
+
+test_that("read_asp() gives expected output", {
+  asp <- read_extdata("ftir_ldpe_soil.asp") |> read_asp() |>
+    expect_silent()
+  read_extdata("raman_hdpe.csv") |> read_asp() |>
+    expect_error()
+  read_extdata("ftir_ldpe_soil.asp") |> read_asp(share = tmp) |>
+    expect_message() |> expect_warning()
+
+  expect_s3_class(asp, "OpenSpecy")
+  expect_equal(names(asp), c("wavenumber", "spectra", "metadata"))
+  expect_length(asp$wavenumber, 1798)
+  range(asp$wavenumber) |> round(1) |>
+    expect_equal(c(650.4, 3999.4))
+  range(asp$spectra) |> round(4) |>
+    expect_equal(c(0.0010, 0.5182))
+})
+
+test_that("read_spa() gives expected output", {
+  spa <- read_extdata("ftir_polyethylene_reflectance_adjustment_not_working.spa") |>
+    read_spa() |> expect_silent()
+  read_extdata("raman_hdpe.csv") |> read_spa() |>
+    expect_error()
+  read_extdata("ftir_polyethylene_reflectance_adjustment_not_working.spa") |>
+    read_spa(share = tmp) |>
+    expect_message() |> expect_warning()
+
+  expect_s3_class(spa, "OpenSpecy")
+  expect_equal(names(spa), c("wavenumber", "spectra", "metadata"))
+  expect_length(spa$wavenumber, 1738)
+  range(spa$wavenumber) |> round(1) |>
+    expect_equal(c(649.9, 3999.8))
+  range(spa$spectra) |> round(2) |>
+    expect_equal(c(61.51, 102.88))
+})
+
+test_that("read_jdx() gives expected output", {
+  suppressWarnings(jdx <- read_jdx(read_extdata("fitr_nitrocellulose.jdx"),
+                                   encoding = "latin1")) |>
+    capture_messages() |>
+    expect_match("JDX file inconsistency.*")
+  read_extdata("raman_hdpe.csv") |> read_jdx() |>
+    expect_error()
+
+  expect_s3_class(jdx, "OpenSpecy")
+  expect_equal(names(jdx), c("wavenumber", "spectra", "metadata"))
+  expect_length(jdx$wavenumber, 7154)
+  range(jdx$wavenumber) |> round(1) |>
+    expect_equal(c(599.9, 7499.0))
+  range(jdx$spectra) |> round(4) |>
+    expect_equal(c(0.0106, 0.6989))
+})
+
+test_that("read_spc() gives expected output", {
+  spc <- read_extdata("raman_atacamit.spc") |> read_spc() |>
+    expect_silent()
+  read_extdata("raman_hdpe.csv") |> read_spc() |>
+    expect_error()
+  read_extdata("raman_atacamit.spc") |> read_spc(share = tmp) |>
+    expect_message() |> expect_warning()
+
+  expect_s3_class(spc, "OpenSpecy")
+  expect_equal(names(spc), c("wavenumber", "spectra", "metadata"))
+  expect_length(spc$wavenumber, 559)
+  range(spc$wavenumber) |> round(1) |>
+    expect_equal(c(117.8, 1050.0))
+  range(spc$spectra) |> round(2) |>
+    expect_equal(c(0.08, 585.51))
+})
+
+# Tidy up
+unlink(tmp, recursive = T)
diff --git a/tests/testthat/test-read_multi.R b/tests/testthat/test-read_multi.R
new file mode 100644
index 00000000..f080b632
--- /dev/null
+++ b/tests/testthat/test-read_multi.R
@@ -0,0 +1,22 @@
+# Create temp dir for testthat
+tmp <- file.path(tempdir(), "OpenSpecy-testthat")
+dir.create(tmp, showWarnings = F)
+
+data("raman_hdpe")
+
+test_that("reading in multi files doesn't throw error", {
+  expect_silent(multi <- read_extdata("testdata_zipped.zip") |> read_any())
+  expect_s3_class(multi, "OpenSpecy")
+
+  read_extdata("testdata_zipped.zip") |> read_any(share = tmp) |>
+    expect_message() |> expect_message() |> expect_message() |>
+    expect_warning() |> expect_warning() |> expect_warning()
+
+  expect_equal(multi$wavenumber, raman_hdpe$wavenumber)
+  expect_equal(multi$spectra$intensity, raman_hdpe$spectra$intensity)
+  expect_equal(multi$spectra$intensity.1, raman_hdpe$spectra$intensity)
+  expect_equal(multi$spectra$intensity.2, raman_hdpe$spectra$intensity)
+})
+
+# Tidy up
+unlink(tmp, recursive = T)
diff --git a/tests/testthat/test-read_opus.R b/tests/testthat/test-read_opus.R
new file mode 100644
index 00000000..23dba994
--- /dev/null
+++ b/tests/testthat/test-read_opus.R
@@ -0,0 +1,40 @@
+# Create temp dir for testthat
+tmp <- file.path(tempdir(), "OpenSpecy-testthat")
+dir.create(tmp, showWarnings = F)
+
+test_that("opus files are read correctly", {
+  single <- read_extdata("ftir_ps.0") |> read_opus() |>
+    expect_silent()
+  multi <- c(read_extdata("ftir_ps.0"), read_extdata("ftir_ps.0")) |>
+    read_opus() |> expect_silent()
+  read_extdata("raman_hdpe.csv") |> read_opus() |>
+    expect_error()
+  read_extdata("ftir_ps.0") |> read_opus(share = tmp) |> expect_message() |>
+    expect_warning()
+
+  expect_s3_class(single, "OpenSpecy")
+  expect_s3_class(multi, "OpenSpecy")
+
+  expect_equal(names(single), c("wavenumber", "spectra", "metadata"))
+  expect_equal(names(multi), c("wavenumber", "spectra", "metadata"))
+  expect_length(single$wavenumber, 2126)
+  range(single$wavenumber) |> round(1) |>
+    expect_equal(c(399.2, 4497.5))
+  range(single$spectra) |> round(4) |>
+    expect_equal(c(0.0130, 0.6112))
+  expect_equal(multi$wavenumber, single$wavenumber |> round(1))
+  range(multi$spectra, na.rm = T) |> round(4) |>
+    expect_equal(c(0.0130, 0.6103))
+
+  names(single$metadata) |>
+    expect_contains(c("x", "y", "unique_id", "sample_id", "date_time_sm",
+                      "date_time_rf", "sample_name", "instr_name_range",
+                      "resolution_wn", "result_spc", "beamspl", "laser_wn",
+                      "spc_in_file", "zero_filling", "temp_scanner_sm",
+                      "temp_scanner_rf", "hum_rel_sm", "hum_rel_rf",
+                      "hum_abs_sm", "hum_abs_rf", "file_id"))
+  expect_identical(names(multi$metadata), names(single$metadata))
+})
+
+# Tidy up
+unlink(tmp, recursive = T)
diff --git a/tests/testthat/test-read_spec.R b/tests/testthat/test-read_spec.R
deleted file mode 100644
index 0aa2b175..00000000
--- a/tests/testthat/test-read_spec.R
+++ /dev/null
@@ -1,95 +0,0 @@
-# Create temp dir for testthat
-tmp <- file.path(tempdir(), "OpenSpecy-testthat")
-dir.create(tmp, showWarnings = F)
-
-library(data.table)
-
-test_that("extdata files are present", {
-  ed <- read_extdata()
-  expect_true(any(grepl("\\.asp$", ed)))
-  expect_true(any(grepl("\\.csv$", ed)))
-  expect_true(any(grepl("\\.0$", ed)))
-  expect_true(any(grepl("\\.jdx$", ed)))
-  expect_true(any(grepl("\\.spa$", ed)))
-  expect_true(any(grepl("\\.spc$", ed)))
-})
-
-test_that("read_text() gives expected output", {
-  expect_silent(csv <- read_text(read_extdata("raman_hdpe.csv")))
-  expect_warning(
-    expect_message(read_text(read_extdata("raman_hdpe.csv"), share = tmp))
-  )
-  expect_silent(
-    dtf <- read_text(read_extdata("raman_hdpe.csv"), method = "fread")
-  )
-  expect_error(read_text(read_extdata("ftir_pva_without_header.csv")))
-  expect_warning(read_text(read_extdata("ftir_pva_without_header.csv"),
-                           header = F))
-  expect_warning(
-    read_text(read_extdata("ftir_pva_without_header.csv"), method = "fread")
-  )
-  expect_s3_class(csv, "data.frame")
-  expect_equal(names(csv), c("wavenumber", "intensity"))
-  expect_equal(nrow(csv), 1095)
-  expect_equal(round(range(csv[1]), 1), c(150.9, 2998.5))
-  expect_equal(round(range(csv[2]), 1), c(3264.2, 41238.9))
-  expect_equal(csv, dtf)
-})
-
-test_that("read_asp() gives expected output", {
-  expect_silent(asp <- read_asp(read_extdata("ftir_ldpe_soil.asp")))
-  expect_error(read_asp(read_extdata("raman_hdpe.csv")))
-  expect_s3_class(asp, "data.frame")
-  expect_equal(names(asp), c("wavenumber", "intensity"))
-  expect_equal(nrow(asp), 1798)
-  expect_equal(round(range(asp[1]), 1), c(650.4, 3999.4))
-  expect_equal(round(range(asp[2]), 4), c(0.0010, 0.5182))
-})
-
-test_that("read_spa() gives expected output", {
-  expect_silent(spa <- read_spa(read_extdata("ftir_polyethylene_reflectance_adjustment_not_working.spa")))
-  expect_error(read_spa(read_extdata("raman_hdpe.csv")))
-  expect_s3_class(spa, "data.frame")
-  expect_equal(names(spa), c("wavenumber", "intensity"))
-  expect_equal(nrow(spa), 1738)
-  expect_equal(round(range(spa[1]), 1), c(649.9, 3999.8))
-  expect_equal(round(range(spa[2]), 2), c(61.51, 102.88))
-})
-
-test_that("read_jdx() gives expected output", {
-  expect_match(capture_messages(
-    suppressWarnings(jdx <- read_jdx(read_extdata("fitr_nitrocellulose.jdx"),
-                                     encoding = "latin1"))
-  ), "JDX file inconsistency.*"
-  )
-  expect_error(read_jdx(read_extdata("throws_error_raman_1000002.jdx")))
-  expect_error(read_jdx(read_extdata("raman_hdpe.csv")))
-  expect_s3_class(jdx, "data.frame")
-  expect_equal(names(jdx), c("wavenumber", "intensity"))
-  expect_equal(nrow(jdx), 7154)
-  expect_equal(round(range(jdx[1]), 1), c(599.9, 7499.0))
-  expect_equal(round(range(jdx[2]), 4), c(0.0106, 0.6989))
-})
-
-test_that("read_spc() gives expected output", {
-  expect_silent(spc <- read_spc(read_extdata("raman_atacamit.spc")))
-  expect_error(read_spc(read_extdata("raman_hdpe.csv")))
-  expect_s3_class(spc, "data.frame")
-  expect_equal(names(spc), c("wavenumber", "intensity"))
-  expect_equal(nrow(spc), 559)
-  expect_equal(round(range(spc[1]), 1), c(117.8, 1050.0))
-  expect_equal(round(range(spc[2]), 2), c(0.08, 585.51))
-})
-
-test_that("read_0() gives expected output", {
-  expect_silent(f0 <- read_0(read_extdata("ftir_ps.0")))
-  expect_error(read_0(read_extdata("raman_hdpe.csv")))
-  expect_s3_class(f0, "data.frame")
-  expect_equal(names(f0), c("wavenumber", "intensity"))
-  expect_equal(nrow(f0), 2126)
-  expect_equal(round(range(f0[1]), 1), c(399.2, 4497.5))
-  expect_equal(round(range(f0[2]), 4), c(0.0130, 0.6112))
-})
-
-# Tidy up
-unlink(tmp, recursive = T)
diff --git a/tests/testthat/test-run_app.R b/tests/testthat/test-run_app.R
new file mode 100644
index 00000000..3abe4935
--- /dev/null
+++ b/tests/testthat/test-run_app.R
@@ -0,0 +1,11 @@
+# Create temp dir for testthat
+tmp <- file.path(tempdir(), "OpenSpecy-testthat")
+dir.create(tmp, showWarnings = F)
+
+test_that("run_app() wrapper doesn't produce errors", {
+  run_app(path = tmp, test_mode = T) |>
+    expect_silent()
+})
+
+# Tidy up
+unlink(tmp, recursive = T)
diff --git a/tests/testthat/test-share_spec.R b/tests/testthat/test-share_spec.R
index a83c32a6..a0f560ad 100644
--- a/tests/testthat/test-share_spec.R
+++ b/tests/testthat/test-share_spec.R
@@ -1,22 +1,41 @@
-data("raman_hdpe")
-
 # Create temp dir for testthat
 tmp <- file.path(tempdir(), "OpenSpecy-testthat")
 dir.create(tmp, showWarnings = F)
 
-test_that("share_text() gives expected output", {
-  expect_message(share_spec(raman_hdpe,
-                            metadata = c(user_name = "Win Cowger",
-                                         spectrum_type = "FTIR",
-                                         spectrum_identity = "PE",
-                                         license = "CC BY-NC"),
-                            share = tmp
-  ))
-  expect_warning(
-    expect_message(share_spec(raman_hdpe, share = tmp))
-  )
-  expect_error(share_spec(raman_hdpe, metadata = c("a", "b", "c"),
-                          share = tmp))
+data("raman_hdpe")
+
+wo_meta <- raman_hdpe
+wo_meta$metadata$spectrum_identity <- NULL
+
+test_that("share_text() works locally", {
+  share_spec(raman_hdpe, file = read_extdata("raman_hdpe.csv"), share = tmp) |>
+    expect_message()
+  read_extdata("testdata_zipped.zip") |> read_zip() |>
+    share_spec(share = tmp) |>
+    expect_message() |> expect_warning()
+
+  file.path(tmp, raman_hdpe$metadata$session_id,
+            paste0(raman_hdpe$metadata$file_id, ".yml")) |>
+    file.exists() |>
+    expect_true()
+
+  share_spec(wo_meta, share = tmp) |>
+    expect_message() |>
+    expect_warning()
+})
+
+test_that("share_text() uploads to the cloud", {
+  skip_on_cran()
+  skip_if_not(testthat:::on_ci(), "Not on CI")
+  skip_if(Sys.getenv("AWS_ACCESS_KEY_ID") == "" ||
+            Sys.getenv("AWS_SECRET_ACCESS_KEY") == "", "No credentials")
+
+  share_spec(raman_hdpe, file = read_extdata("raman_hdpe.csv"), share = "cloud",
+             credentials = list(
+               s3_key = Sys.getenv("AWS_ACCESS_KEY_ID"),
+               s3_secret = Sys.getenv("AWS_SECRET_ACCESS_KEY"),
+               s3_region = "us-east-2", s3_bucket = "openspecy")) |>
+    expect_message()
 })
 
 # Tidy up
diff --git a/tests/testthat/test-sig_noise.R b/tests/testthat/test-sig_noise.R
new file mode 100644
index 00000000..f8074a96
--- /dev/null
+++ b/tests/testthat/test-sig_noise.R
@@ -0,0 +1,19 @@
+# Load your OpenSpecy object
+data("raman_hdpe")
+
+test_that("sig_noise returns correct values", {
+  sig_noise(raman_hdpe, metric = "sig") |> round(2) |> unname() |>
+    expect_equal(101.17)
+
+  sig_noise(raman_hdpe, metric = "noise") |> round(2) |> unname() |>
+    expect_equal(61.01)
+
+  sig_noise(raman_hdpe, metric = "sig_times_noise") |> round(2) |> unname() |>
+    expect_equal(6172.1)
+
+  sig_noise(raman_hdpe, metric = "sig_over_noise") |> round(2) |> unname() |>
+    expect_equal(1.66)
+
+  sig_noise(raman_hdpe, metric = "tot_sig") |> round(2) |> unname() |>
+    expect_equal(97527)
+})
diff --git a/tests/testthat/test-smooth_intens.R b/tests/testthat/test-smooth_intens.R
index 8f23eee6..954f2167 100644
--- a/tests/testthat/test-smooth_intens.R
+++ b/tests/testthat/test-smooth_intens.R
@@ -1,16 +1,13 @@
 data("raman_hdpe")
 
 test_that("smooth_intens() works as expected", {
-  expect_silent(smt <- smooth_intens(raman_hdpe, p = 3))
-  expect_identical(smt, smooth_intens(raman_hdpe$wavenumber,
-                                      raman_hdpe$intensity))
-  expect_identical(smt, smooth_intens(intensity ~ wavenumber, raman_hdpe))
-  expect_equal(as.numeric(
-    round(cor(smt[2], smooth_intens(raman_hdpe, p = 1)[2]), 4)
-  ), 0.9756, ignore_attr = F)
-  expect_s3_class(smt, "data.frame")
-  expect_equal(names(smt), c("wavenumber", "intensity"))
-  expect_equal(nrow(smt), nrow(raman_hdpe))
-  expect_equal(smt[1], raman_hdpe[1])
-  expect_equal(range(smt[2]), c(0, 1))
+  smt <- smooth_intens(raman_hdpe, polynomial = 3) |> expect_silent()
+
+  cor(smt$spectra$intensity,
+      smooth_intens(raman_hdpe, polynomial = 1)$spectra$intensity) |> round(4) |>
+    expect_equal(0.9756, ignore_attr = F)
+  expect_s3_class(smt, "OpenSpecy")
+  expect_equal(nrow(smt$spectra), nrow(raman_hdpe$spectra))
+  expect_equal(smt$wavenumber, raman_hdpe$wavenumber)
+  expect_equal(range(smt$spectra), c(0, 1))
 })
diff --git a/tests/testthat/test-spec_res.R b/tests/testthat/test-spec_res.R
index 20fc9b3c..9e8f62e6 100644
--- a/tests/testthat/test-spec_res.R
+++ b/tests/testthat/test-spec_res.R
@@ -1,5 +1,5 @@
 data("raman_hdpe")
 
 test_that("spec_res() gives correct output", {
-  expect_equal(round(spec_res(raman_hdpe$wavenumber), 3), 3.005)
+  spec_res(raman_hdpe$wavenumber) |> round(2) |> expect_equal(2.54)
 })
diff --git a/tests/testthat/test-subtr_baseline.R b/tests/testthat/test-subtr_baseline.R
new file mode 100644
index 00000000..52c6b542
--- /dev/null
+++ b/tests/testthat/test-subtr_baseline.R
@@ -0,0 +1,29 @@
+data("raman_hdpe")
+
+test_that("polynomial subtr_baseline() works as expected", {
+  poly <- subtr_baseline(raman_hdpe, degree = 8) |> expect_silent()
+
+  cor(poly$spectra$intensity,
+      subtr_baseline(raman_hdpe, degree = 1)$spectra$intensity) |> round(4) |>
+    expect_equal(0.9763, ignore_attr = F)
+  expect_s3_class(poly, "OpenSpecy")
+  expect_equal(nrow(poly$spectra), nrow(raman_hdpe$spectra))
+  expect_equal(poly$wavenumber, raman_hdpe$wavenumber)
+  expect_equal(range(poly$spectra), c(0, 1))
+})
+
+test_that("manual subtr_baseline() works as expected", {
+  subtr_baseline(raman_hdpe, type = "manual") |> expect_error()
+
+  bl <- raman_hdpe
+  bl$spectra$intensity <- bl$spectra$intensity / 2
+
+  man <- subtr_baseline(raman_hdpe, type = "manual", baseline = bl) |>
+    expect_silent()
+
+  cor(raman_hdpe$spectra$intensity, man$spectra$intensity) |>
+    expect_equal(1, ignore_attr = F)
+  expect_equal(nrow(man$spectra), nrow(raman_hdpe$spectra))
+  expect_equal(man$wavenumber, raman_hdpe$wavenumber)
+  expect_equal(range(man$spectra), c(0, 1))
+})
diff --git a/tests/testthat/test-subtr_bg.R b/tests/testthat/test-subtr_bg.R
deleted file mode 100644
index 972804dc..00000000
--- a/tests/testthat/test-subtr_bg.R
+++ /dev/null
@@ -1,16 +0,0 @@
-data("raman_hdpe")
-
-test_that("subtr_bg() works as expected", {
-  expect_silent(sb <- subtr_bg(raman_hdpe, degree = 8))
-  expect_identical(sb, subtr_bg(raman_hdpe$wavenumber,
-                                raman_hdpe$intensity))
-  expect_identical(sb, subtr_bg(intensity ~ wavenumber, raman_hdpe))
-  expect_equal(as.numeric(
-    round(cor(sb[2], subtr_bg(raman_hdpe, degree = 1)[2]), 4)
-  ), 0.9763, ignore_attr = F)
-  expect_s3_class(sb, "data.frame")
-  expect_equal(names(sb), c("wavenumber", "intensity"))
-  expect_equal(nrow(sb), nrow(raman_hdpe))
-  expect_equal(sb[1], raman_hdpe[1])
-  expect_equal(range(sb[2]), c(0, 1))
-})
diff --git a/tests/testthat/test-workflows.R b/tests/testthat/test-workflows.R
new file mode 100644
index 00000000..d4456292
--- /dev/null
+++ b/tests/testthat/test-workflows.R
@@ -0,0 +1,74 @@
+# Create temp dir for testthat
+tmp <- file.path(tempdir(), "OpenSpecy-testthat")
+dir.create(tmp, showWarnings = F)
+
+test_that("Raman batch analysis with test library", {
+  skip_on_cran()
+  skip_if_offline(host = "api.osf.io")
+
+  batch <- read_extdata("testdata_zipped.zip") |> read_any() |>
+    expect_silent()
+  is_OpenSpecy(batch) |> expect_true()
+  plot(batch) |> expect_silent()
+  plotly_spec(batch) |> expect_silent()
+
+  get_lib(type = "test", path = tmp) |> expect_no_error()
+  check_lib(type = "test", path = tmp) |> expect_silent()
+  lib <- load_lib(type = "test", path = tmp) |> expect_silent()
+
+  filter_spec(lib, lib$metadata$SpectrumType == "Raman") |> expect_silent()
+  batch2 <- conform_spec(batch, lib$wavenumber, res = spec_res(lib$wavenumber)) |>
+    expect_silent()
+
+  plotly_spec(batch2) |> expect_silent()
+
+  sig_noise(batch2, metric = "run_sig_over_noise") |>
+    expect_silent()
+  batch3 <- process_spec(batch2, subtr_baseline = T) |> expect_silent()
+  plotly_spec(x = batch3, x2 = batch) |> expect_silent()
+
+  matches <- cor_spec(batch3, library = lib) |> expect_silent()
+  test_max_cor <- max_cor_named(matches) |> expect_silent()
+  sig_noise(batch3, metric = "run_sig_over_noise") |>
+    expect_silent()
+})
+
+test_that("Raman batch analysis with complete library", {
+  skip_on_cran()
+  skip_if_offline(host = "api.osf.io")
+  skip_if_not(testthat:::on_ci(), "Not on CI")
+
+  batch <- read_extdata(file = "testdata_zipped.zip") |> read_any() |>
+    expect_silent()
+  is_OpenSpecy(batch) |> expect_true()
+  plot(batch) |> expect_silent()
+  plotly_spec(batch) |> expect_silent()
+
+  get_lib(type = "nobaseline", path = tmp) |> expect_no_error()
+  check_lib(type = "nobaseline", path = tmp) |> expect_silent()
+  lib <- load_lib(type = "nobaseline", path = tmp) |> expect_silent()
+
+  filter_spec(lib, lib$metadata$SpectrumType == "Raman") |> expect_silent()
+  batch2 <- conform_spec(batch, range = lib$wavenumber,
+                         res = spec_res(lib$wavenumber)) |>
+    expect_silent()
+
+  plotly_spec(batch2) |> expect_silent()
+
+  test_sn2 <- sig_noise(batch2, metric = "run_sig_over_noise") |>
+    expect_silent()
+  batch3 <- process_spec(batch2, subtr_baseline = T) |> expect_silent()
+  plotly_spec(x = batch3, x2 = batch) |> expect_silent()
+
+  matches <- cor_spec(batch3, library = lib) |> expect_silent()
+  test_max_cor <- max_cor_named(matches) |> expect_silent()
+  test_sn <- sig_noise(batch3, metric = "run_sig_over_noise") |>
+    expect_silent()
+
+  heatmap_spec(batch3, sn = test_sn, cor = test_max_cor, min_sn = 4,
+               min_cor = 0.7, select = 2, source = "heatplot") |>
+    expect_silent()
+})
+
+# Tidy up
+unlink(tmp, recursive = T)
diff --git a/tests/validation/test-augmented.R b/tests/validation/test-augmented.R
index c4a7f0ad..476ac74a 100644
--- a/tests/validation/test-augmented.R
+++ b/tests/validation/test-augmented.R
@@ -53,13 +53,13 @@ augmented_ftir <- augment_full(spec_lib$ftir$library %>%
 raman_proc <- augmented_raman %>%
     group_by(sample_name) %>%
     smooth_intens(p = 3) #%>%
-    subtr_bg(degree = 8) %>%
+    subtr_baseline(degree = 8) %>%
     mutate()
 
 ftir_proc <- augmented_ftir %>%
     group_by(sample_name) %>%
     smooth_intens() %>%
-    subtr_bg()
+    subtr_baseline()
 
 spectrum = raman_subset[100]
 
@@ -72,7 +72,7 @@ ggplot() +
     geom_line(data = augmented_raman %>%
                     filter(sample_name == spectrum) %>%
                     smooth_intens(p = 3) %>%
-                    subtr_bg(degree = 8),
+                    subtr_baseline(degree = 8),
                 aes(x = wavenumber, y = intensity))
 
 
@@ -80,7 +80,7 @@ ggplot() +
 augmented_raman %>%
     filter(sample_name == spectrum) %>%
     smooth_intens(p = 3) %>%
-    subtr_bg(degree = 8) %>%
+    subtr_baseline(degree = 8) %>%
     match_spec(library = spec_lib, which = "raman")
 
 spectrum
@@ -95,7 +95,7 @@ for(spectrum in raman_subset){
     topmatch <- augmented_raman %>%
         filter(sample_name == spectrum) %>%
         smooth_intens(p = 3) %>%
-        subtr_bg(degree = 8) %>%
+        subtr_baseline(degree = 8) %>%
         match_spec(library = spec_lib, which = "raman", top_n = 1) %>%
         select(spectrum_identity) %>%
         unlist()
@@ -117,7 +117,7 @@ for(spectrum in ftir_subset){
     topmatch <- augmented_ftir %>%
         filter(sample_name == spectrum) %>%
         smooth_intens(p = 3) %>%
-        subtr_bg(degree = 8) %>%
+        subtr_baseline(degree = 8) %>%
         match_spec(library = spec_lib, which = "ftir", top_n = 1) %>%
         select(spectrum_identity) %>%
         unlist()
diff --git a/vignettes/advanced.Rmd b/vignettes/advanced.Rmd
new file mode 100644
index 00000000..6509573f
--- /dev/null
+++ b/vignettes/advanced.Rmd
@@ -0,0 +1,193 @@
+---
+title: "Spectroscopy Advanced"
+author: >
+  Jessica Meyers, Jeremy Conkle, Win Cowger, Zacharias Steinmetz,
+  Andrew Gray, Chelsea Rochman, Sebastian Primpke, Jennifer Lynch,
+  Hannah Hapich, Hannah De Frond, Keenan Munno, Bridget O’Donnell
+date: "`r Sys.Date()`"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Spectroscopy Advanced}
+  %\VignetteEncoding{UTF-8}
+  %\VignetteEngine{knitr::rmarkdown}
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  warning = FALSE
+)
+```
+
+# How to Interpret the Reported Matches
+
+There are several important things to consider when interpreting a spectral
+match including the library source, the Pearson's r, and other metrics.
+
+# The Library Source
+
+When you click on a spectrum, all of the metadata that we have in Open Specy
+about that source will be displayed in a metadata window below to the matches
+table. Each library has different methodologies used to develop it. It is useful
+to read up on the library sources from the literature that they came from. E.g.
+Chabuka et al. 2020 focuses on weathered plastics, so matching to it may suggest
+that your spectrum is of a weathered polymer. Primpke et al. 2018 only has a
+spectral range up to 2000, so some polymers may be difficult to differentiate
+with it. Make sure to cite the libraries that you use during your search when
+you publish your results. The authors were kind enough to make their data open
+access so that it could be used in Open Specy and we should return the favor by
+citing them.
+
+# Pearson's R
+
+Correlation values are used to identify the closest matches available in the
+current Open Specy spectral libraries to improve material identification and
+reduce sample processing times. Pearson's r values range from 0 - 1 with 0 being
+a completely different spectrum and 1 being an exact match. Some general
+guidelines that we have observed from using Open Specy. If no matches are \>
+\~0.3 the material may require additional processing or may not exist in the
+Open Specy library. Correlation values are not the only metric you should use to
+assess your spectra's match to a material in the library, matches need to make
+sense.
+
+# Things to Consider beyond Correlation
+
+Peak position and height similarities are more important than correlation and
+need to be assessed manually. Peak position correlates with specific bond types.
+Peak height correlates to the concentration of a compound. Therefore, peak
+height and peak position should match as closely as possible to the matched
+spectrum. When there are peaks that exist in the spectra you are trying to
+interpret that do not exist in the match, there may be additional materials to
+identify. In this case, restrict the preprocessing range to just the
+unidentified peak and try to identify it as an additional component (see also
+[https://www.compoundchem.com/2015/02/05/irspectroscopy/](https://www.compoundchem.com/2015/02/05/irspectroscopy/)).
+
+Also, check the match metadata to see if the match makes sense. Example: A
+single fiber cannot be a "cotton blend" since there would be no other fibers to
+make up the rest of the blend. Example: Cellophane does not degrade into fibers,
+so a match for a fiber to cellophane wouldn't make sense. Example: You are
+analyzing a particle at room temperature, but the matched material is liquid at
+room temperature. The material may be a component of the particle but it cannot
+be the whole particle.
+
+# How Specific Do You Need to be in the Material Type of the Match?
+
+You can choose to be specific about how you classify a substance (e.g.
+polyester, cellophane) or more general (e.g. synthetic, semi-synthetic, natural,
+etc.). The choice depends on your research question. Using more general groups
+can speed up analysis time but will decrease the information you have for
+interpretation. To identify materials more generally, you can often clump the
+identities provided by Open Specy to suit your needs. For example, matches to
+"polyester" and "polypropylene" could be clumped to the category "plastic".
+
+# How to Differentiate Between Similar Spectra?
+
+One common challenge is differentiating between LDPE and HDPE. But, even with a
+low resolution instrument (MacroRAM, 2 cm^-1^ pixel^-1^), you can still see some
+differences. From a wide view, these low, medium, and high density PE samples
+all look relatively similar (figures courtesy of Bridget O\'Donnell, Horiba
+Scientific):
+
+```{r, fig.align="center", echo=FALSE}
+knitr::include_graphics("advanced/horiba-1.png")
+```
+
+But, a closer look at the 1450 cm^-1^ band reveals clear differences:
+
+```{r, fig.align="center", echo=FALSE}
+knitr::include_graphics("advanced/horiba-2.png")
+```
+
+When you overlay them, you start to see differences in other spectral regions
+too:
+
+```{r, fig.align="center", echo=FALSE}
+knitr::include_graphics("advanced/horiba-3.png")
+```
+
+So, the question is, how do we deal with samples that are very similar with only
+subtle differences? Usually, researchers will use MVA techniques after they've
+collected multiple reference spectra of known samples (LDPE and HDPE in this
+case). They can then develop models and apply them to distinguish between
+different types of PE. With a reference database like Open Specy, this is
+complicated by the fact that researchers are measuring samples on different
+instruments with correspondingly different spectral responses and spectral
+resolutions. That makes it even more difficult to accurately match definitively
+to LDPE and HDPE as opposed to generic 'PE'.
+
+One possibility is to place more emphasis (from a computational perspective) on
+the bands that show the most difference (the triplet at 1450 cm^-1^) by
+restricting the range used to match in Open Specy.
+
+The other, much simpler option is to just match any PE hit to generic 'PE' and
+not specifically HDPE or LDPE.
+
+Another challenge is in differentiating between types of nylons. But, Raman has
+a pretty easy time distinguishing nylons. These spectra were recorded of a
+series of nylons and the differences are much more distinguishable compared to
+the PE results above (nylon 6, 6-6, 6-9, 6-10, and 6-12 top to bottom):
+
+```{r, fig.align="center", echo=FALSE}
+knitr::include_graphics("advanced/horiba-4.png")
+```
+
+The differences are even more pronounced when you overlay the
+spectra:
+
+```{r, fig.align="center", echo=FALSE}
+knitr::include_graphics("advanced/horiba-5.png")
+```
+
+# What to Do When Matches Aren't Making Sense
+
+1.  Double check that the baseline correction and smoothing parameters
+    result in the best preprocessing of the data. 
+2.  Try reprocessing your spectrum, but limit it to specific peak
+    regions with a higher signal to noise ratio.
+3.  Restrict the spectral range to include or exclude questionable
+    peaks or peaks that were not present in the previous matches.
+4.  Restrict the spectral range to exclude things like CO~2~ (2200 cm^-1^)
+    or H~2~O (\~1600 cm^-1^) in spikes in the IR spectrum.
+5.  If nothing above works to determine a quality match, you may need
+    to measure the spectrum of your material again or use another
+    spectral analysis tool.
+    
+# References
+
+Chabuka BK, Kalivas JH (2020). “Application of a Hybrid Fusion Classification
+Process for Identification of Microplastics Based on Fourier Transform Infrared
+Spectroscopy.” *Applied Spectroscopy*, **74**(9), 1167–1183. doi:
+[10.1177/0003702820923993](https://doi.org/10.1177/0003702820923993).
+
+Cowger W, Gray A, Christiansen SH, Christiansen SH, Christiansen SH, De Frond H,
+Deshpande AD, Hemabessiere L, Lee E, Mill L, et al. (2020). “Critical Review of
+Processing and Classification Techniques for Images and Spectra in Microplastic
+Research.” *Applied Spectroscopy*, **74**(9), 989–1010. doi:
+[10.1177/0003702820929064](https://doi.org/10.1177/0003702820929064).
+
+Cowger W, Steinmetz Z, Gray A, Munno K, Lynch J, Hapich H, Primpke S,
+De Frond H, Rochman C, Herodotou O (2021). “Microplastic Spectral Classification
+Needs an Open Source Community: Open Specy to the Rescue!”
+*Analytical Chemistry*, **93**(21), 7543–7548. doi:
+[10.1021/acs.analchem.1c00123](https://doi.org/10.1021/acs.analchem.1c00123).
+
+Primpke S, Wirth M, Lorenz C, Gerdts G (2018). “Reference Database Design for
+the Automated Analysis of Microplastic Samples Based on Fourier Transform
+Infrared (FTIR) Spectroscopy.” *Analytical and Bioanalytical Chemistry*,
+**410**(21), 5131–5141. doi: 
+[10.1007/s00216-018-1156-x](https://doi.org/10.1007/s00216-018-1156-x).
+
+Renner G, Schmidt TC, Schram J (2017). “A New Chemometric Approach for Automatic
+Identification of Microplastics from Environmental Compartments Based on FT-IR
+Spectroscopy.” *Analytical Chemistry*, **89**(22), 12045–12053. doi: 
+[10.1021/acs.analchem.7b02472](https://doi.org/10.1021/acs.analchem.7b02472).
+
+Savitzky A, Golay MJ (1964). “Smoothing and Differentiation of Data by
+Simplified Least Squares Procedures.” *Analytical Chemistry*, **36**(8),
+1627–1639.
+
+Zhao J, Lui H, McLean DI, Zeng H (2007). “Automated Autofluorescence Background
+Subtraction Algorithm for Biomedical Raman Spectroscopy.”
+*Applied Spectroscopy*, **61**(11), 1225–1232. doi:
+[10.1366/000370207782597003](https://doi.org/10.1366/000370207782597003).
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diff --git a/vignettes/sop.Rmd b/vignettes/sop.Rmd
index d924bfca..04d4c4da 100644
--- a/vignettes/sop.Rmd
+++ b/vignettes/sop.Rmd
@@ -1,5 +1,5 @@
 ---
-title: "Standard Operating Procedure"
+title: "Open Specy Tutorial"
 author: >
   Jessica Meyers, Jeremy Conkle, Win Cowger, Zacharias Steinmetz,
   Andrew Gray, Chelsea Rochman, Sebastian Primpke, Jennifer Lynch,
@@ -7,7 +7,7 @@ author: >
 date: "`r Sys.Date()`"
 output: rmarkdown::html_vignette
 vignette: >
-  %\VignetteIndexEntry{Standard Operating Procedure}
+  %\VignetteIndexEntry{Open Specy Tutorial}
   %\VignetteEncoding{UTF-8}
   %\VignetteEngine{knitr::rmarkdown}
 ---
@@ -15,697 +15,660 @@ vignette: >
 ```{r, include = FALSE}
 knitr::opts_chunk$set(
   collapse = TRUE,
-  comment = "#>"
+  comment = "#>",
+  warning = FALSE
 )
 ```
 
-Open Specy Raman and (FT)IR spectral analysis tool for plastic particles and
-other environmental samples. Supported features include reading spectral data
-files (.asp, .csv, .jdx, .spc, .spa, .0), smoothing spectral intensities
-with `smooth_intens()`, correcting background with 
-`subtr_bg()`, and identifying spectra using an onboard reference
-library. Analyzed spectra can be shared with the Open Specy community.
-A Shiny app is available via `run_app()` or online at
-[https://openanalysis.org/openspecy/](https://openanalysis.org/openspecy/).
+# Document Overview 
 
-This document outlines a common workflow for
-using Open Specy and highlights some topics that users are often
-requesting a tutorial on. If the document is followed sequentially from
-beginning to end, the user will have a better understanding of every
-procedure involved in using Open Specy as a tool for interpreting
-spectra. It takes approximately 45 minutes to read through and follow
-along with this standard operating procedure the first time. Afterward,
-knowledgeable users should be able to thoroughly analyze individual
-spectra at an average speed of 1 min^-1^.
+This document outlines a common workflow for using the Open Specy package and
+app and highlights some topics that users are often requesting a tutorial on. If
+the document is followed sequentially from beginning to end, the user will have
+a better understanding of every procedure involved in using the Open Specy R
+package and app as a tool for interpreting spectra. It takes approximately 45
+minutes to read through and follow along with this standard operating procedure
+the first time. Afterward, knowledgeable users should be able to thoroughly
+analyze spectra at an average speed of 1 min^-1^ or faster with the new batch
+and automated procedures.
 
-# Getting started
+# R Package and App
+
+The Open Specy R package is the backbone of the Shiny app. The choice is yours
+as to which you start with, we use both on a regular basis. The tutorial will
+talk through the R functions and app features step by step together.
+
+## Installation
+
+After installing the R package, you just need to read in the library.
 
 ```{r setup}
 library(OpenSpecy)
 ```
 
-# Viewing and Sharing Spectra
+## Running the App
 
 To get started with the Open Specy user interface, access
 [https://openanalysis.org/openspecy/](https://openanalysis.org/openspecy/)
-or start the Shiny GUI directly from R typing
+or start the Shiny GUI directly from your own computer in R. 
 
 ```{r eval=FALSE}
 run_app()
 ```
 
-Then click the **Upload File** tab at the top of the page.
+## Reading Data
 
-```{r, fig.align="center", out.width="98%", echo=FALSE}
-knitr::include_graphics("images/mainpage.jpg")
-```
+If you have your own data, click **Browse** at the top left hand corner of the
+Analyze Spectra tab.
 
-Accessibility is extremely important to us and we are making strives to improve
-the accessibility of Open Specy for all spectroscopists. Please reach out if you
-have ideas for improvement.
-
-We added a Google translate plugin to all pages in the app so that you can
-easily translate the app. We know that not all languages will be fully supported
-but we will continue to try and improve the translations.  
-
-```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("images/googletranslate.jpg")
+```{r, fig.align="center", out.width="98%", echo=FALSE}
+knitr::include_graphics("sop/mainpage.jpg")
 ```
 
-## Download a test dataset
+Before uploading, indicate if you would like to share the uploaded data or not
+using the slider. If selected, any data uploaded to the tool will automatically
+be shared under [CC-BY 4.0
+license](https://creativecommons.org/licenses/by/4.0/) and will be available for
+researchers and other ventures to use to improve spectral analysis, build
+machine learning tools, etc. Some users may choose not to share if they need to
+keep their data private. If switched off, none of the uploaded data will be
+stored or shared in Open Specy.
 
 ```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("images/samplefile.jpg")
+knitr::include_graphics("sop/uploadfile.jpg")
 ```
 
-If you don't have your own data to use right away, that is ok. You can
-download test data to try out the tool by clicking on the test data
-button. A .csv file of HDPE Raman spectrum will download on your
-computer. This file can also be used as a template for formatting .csv
-data into an Open Specy accepted format. The following line of code does the
-same:
+Open Specy allows for upload of native Open Specy .y(a)ml, .json, or .rds files.
+In addition, .csv, .asp, .jdx, .0, .spa, .spc, and .zip files can be imported.
+.zip  files can either contain multiple files with individual spectra in them of
+the non-zip formats or it can contain a .hdr and .dat file that form an ENVI
+file for a spectral map. Open Specy and .csv files should always load correctly
+but the other file types are still in development, though most of the time these
+files work perfectly. If uploading a .csv file, label the column with the
+wavenumbers `wavenumber` and name the column with the intensities `intensity`.
+Wavenumber units must be cm^-1^. Any other columns are not used by the software.
+Always keep a copy of the original file before alteration to preserve metadata
+and raw data for your records.
 
-```{r eval=FALSE}
-data("raman_hdpe")
+```{r sample_data, echo=FALSE, out.width="50%"}
+knitr::kable(head(raman_hdpe), caption = "Sample data `raman_hdpe`")
 ```
 
-## Choose whether to share your uploaded data or not
+It is best practice to cross check files in the proprietary software they came
+from and Open Specy before use in Open Specy. Due to the complexity of some
+proprietary file types, we haven't been able to make them fully compatible yet.
+If your file is not working, please contact the administrator and share the file
+so that we can work on integrating it.
 
-```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("images/uploadfile.jpg")
-```
-
-Before uploading, indicate if you would like to share the
-uploaded data or not using the slider. If selected,
-any data uploaded to the tool will automatically be shared under [CC-BY 4.0 license](https://creativecommons.org/licenses/by/4.0/)
-and will be available for
-researchers and other ventures to use to improve spectral analysis,
-build machine learning tools, etc. Some users may choose not to share if
-they need to keep their data private. If switched off, none of
-the uploaded data will be stored or shared in Open Specy.
-
-## Upload/Read Data
-
-Open Specy allows for upload of .csv, .asp, .jdx, .0, .spc, and
-.spa files. .csv files should always load correctly but the other file
-types are still in beta development, though most of the time these files
-work perfectly. It is best practice to cross check files in the
-proprietary software they came from and Open Specy before use in Open
-Specy. Due to the complexity of these file types, we haven't been able
-to make them fully compatible yet. If your file is not working, please
-contact the administrator and share the file so that we can get it to
-work.
-
-For the most consistent results, files should be converted to .csv
-format before uploading to Open Specy. The specific steps to converting
-your instrument's native files to .csv can be found in its software
-manual or you can check out
+The specific steps to converting your instrument's native files to .csv can be
+found in its software manual or you can check out
 [Spectragryph](https://www.effemm2.de/spectragryph/), which supports many
 spectral file conversions (see Mini Tutorial section: File conversion in
 Spectragryph to Open Specy accepted format).
 
-If uploading a .csv file, label the column with the wavenumbers
-`wavenumber` and name the column with the intensities `intensity`.
+The following line of code will read in your data when using the package and
+interprets which reading function to use based on the file extension.
 
-```{r sample_data, echo=FALSE, out.width="50%"}
-knitr::kable(head(raman_hdpe), caption = "Sample data `raman_hdpe`")
+```{r, eval=FALSE}
+read_any("path/to/your/data")
 ```
 
-Wavenumber units must be cm^-1^. Any other columns are not used by the
-software. Always keep a copy of the original file before alteration to preserve
-metadata and raw data for your records.
-
-To upload data, click **Browse** and choose one of your
-files to upload, or drag and drop your file into the gray box. At this
-time you can only upload one file at a time. 
-
-Upon upload and throughout the analysis, intensity values are min-max normalized
-(Equation 1).
-
-$$\frac{x - \mathrm{min}(x)}{\mathrm{min}(x) - \mathrm{max}(x)}$$
-<p style="text-align: center;">Equation 1: Max-Min Normalization</p>
-
-The following R functions from the Open Specy package will also read in spectral
-data accordingly:
+These file type specific functions will also read in spectral
+data accordingly if you have a particular format in mind.
 
 ```{r, eval=FALSE}
 read_text(".csv")
 read_asp(".asp")
-read_0(".0")
+read_opus(".0")
 ```
 
-## Viewing Spectra Plot
+If you don't have your own data, you can use a test dataset.
 
-After spectral data are uploaded, it will appear in the main window. This plot
-is selectable, zoomable, and provides information on hover. You can also
-save a .png file of the plot view using the camera icon at the top right when
-you hover over the plot. This plot will change the view based on updates
-from the **Intensity Adjustment** selection.
+```{r, fig.align="center", echo=FALSE}
+knitr::include_graphics("sop/samplefile.jpg")
+```
 
-## Intensity Adjustment
+A .csv file of an HDPE Raman spectrum will download on your computer. This file
+can also be used as a template for formatting .csv data into an Open Specy
+accepted format.
 
-```{r, fig.align='center', echo=FALSE}
-knitr::include_graphics("images/intensityadjustment.jpg")
+The following line of code does the same to load the example file into R:
+
+```{r}
+data("raman_hdpe")
 ```
 
-Open Specy assumes that intensity units are in absorbance units but
-Open Specy can adjust reflectance or transmittance spectra to absorbance
-units using this selection in the upload file tab. The transmittance
-adjustment uses the $\log_{10} 1/T$ calculation which does not correct for
-system or particle characteristics. The reflectance adjustment
-use the Kubelka-Munk equation $\frac{(1-R)^2}{2R}$. If none is selected, Open
-Specy assumes that the uploaded data is an absorbance spectrum and does not
-apply an adjustment.
+We also have many onboard files that you can call to test different formats:
 
-This is the respective R code:
+```{r}
+spectral_map <- read_extdata("CA_tiny_map.zip") |> 
+  read_any() # preserves some metadata
+asp_example <- read_extdata("ftir_ldpe_soil.asp") |> 
+  read_any() 
+ps_example <- read_extdata("ftir_ps.0") |> 
+  read_any() # preserves some metadata
+spc_example <- read_extdata("raman_atacamit.spc") |> 
+  read_any()
+csv_example <- read_extdata("raman_hdpe.csv") |> 
+  read_any()
+json_example <- read_extdata("raman_hdpe.json") |> 
+  read_any() # read in exactly as an OpenSpecy object
+rds_example <- read_extdata("raman_hdpe.rds") |> 
+  read_any() # read in exactly as an OpenSpecy object
+yml_example <- read_extdata("raman_hdpe.yml") |> 
+  read_any() # read in exactly as an OpenSpecy object
+batch_example <- read_extdata("testdata_zipped.zip") |> 
+  read_any()
+```
+
+You will notice now that the R package reads in files into an object with class
+`OpenSpecy`. This is a class we created for high throughput spectral analysis
+which now also preserves spectral metadata. You can even create these from
+scratch if you'd like:
 
 ```{r}
-library(dplyr)
+scratch_OpenSpecy <- as_OpenSpecy(x = seq(1000,2000, by = 5), 
+                                  spectra =  data.frame(runif(n = 201)), 
+                                  metadata = list(file_name = "fake_noise")) 
+```
 
-raman_adj <- raman_hdpe %>%
-  adj_intens()
+We have some generic functions built for inspecting the spectra:
 
-head(raman_adj)
+```{r}
+print(scratch_OpenSpecy) # shows the raw object
 ```
 
-## Share metadata on known spectra
+```{r}
+summary(scratch_OpenSpecy) # summarizes the contents of the spectra
+```
 
-```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("images/metadatainput.jpg")
+```{r}
+head(scratch_OpenSpecy) # shows the top wavenumbers and intensities
 ```
 
-To share metadata about your spectrum, click the metadata input button.
-When sharing data, please provide as much metadata as you can. Metadata
-helps make shared data as useful as possible. Metadata inputs each have
-examples provided in the input. The examples disappear when the box is
-clicked and will not be saved if nothing is input by the user.
-Mandatory inputs are marked with a red asterisk. If these inputs are not
-filled, the data will be considered uninterpretable and will be
-discarded. Inputs left blank will be left blank in the metadata sheet
-and interpreted as "unknown" or "not applicable". To share metadata,
-click the share data button at the bottom of the metadata inputs.
+## Visualization
+
+### Spectra
 
-When the user clicks the **Share Data** button their current uploaded data
-and metadata is sent to an
-[open-access online repository](https://osf.io/rjg3c/).
+In the app after spectral data are uploaded, it will appear in the main window.
+This plot is selectable, zoomable, and provides information on hover. You can
+also save a .png file of the plot view using the camera icon at the top right
+when you hover over the plot. 
 
 ```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("images/sharemetadata.jpg")
+knitr::include_graphics("sop/spectra_vis.jpg")
 ```
 
-All inputs from the metadata (described below) are input to a
-metadata sheet. The metadata sheet is
-given the same unique name as the data, but it ends with "\_form". The
-exact same data is saved as would be downloaded using the download data
-button (described below). All high quality uploaded data with metadata
-will eventually be reviewed by spectroscopy experts and added to the
-internal library if it passes review. If multiple files are going to be
-uploaded with metadata, the cells will also stay filled with the last
-input after the share data button is clicked. Just upload the next
-dataset and change the metadata inputs that are different. If more than
-50 files will be shared at once, you can contact the website
-administrator to get a bulk upload sheet for more rapid upload.
+In R, we have two ways to visualize your spectra, one is quick and efficient and
+the other is interactive. Here is an example of quick and efficient plotting.
 
-Type
+```{r, fig.align="center", fig.width=6}
+plot(scratch_OpenSpecy) # quick and efficient
+```
 
-```{r eval=FALSE}
-share_spec(raman_hdpe,
-           metadata = c(user_name = "Win Cowger",
-                        contact_info = "wincowger@gmail.com",
-                        spectrum_type = "Raman",
-                        spectrum_identity = "HDPE")
-           )
+This is an example of an interactive plot. 
+
+```{r, fig.align="center", out.width="98%"}
+plotly_spec(scratch_OpenSpecy) # this will min-max normalize your data even
+                               # if it isn't already but are not changing your
+                               # underlying data
 ```
 
-to share your spectral data from the R console.
+With interactive plots you can also plot two different datasets simultaneously
+to compare. 
 
-# Preprocessing
+```{r, fig.align="center", out.width="98%"}
+plotly_spec(scratch_OpenSpecy, rds_example) # interactive comparison
+```
 
-After uploading data, you can preprocess the data using baseline
-correction, smoothing, and range selection and save your preprocessed
-data. Go to the **Preprocess Spectrum** tab to select
-your parameters for processing the spectrum.
+### Maps
 
-## Preprocess Spectra Plot
+Spectral maps can also be visualized as overlaid spectra but in addition the
+spatial information can be plotted as a heatmap. A similar plot should appear in
+the app if you upload multiple spectra or a spectral map. It is important to
+note that when multiple spectra are uploaded in batch they are prescribed `x`
+and `y` coordinates, this can be helpful for visualizing summary statistics and
+navigating vast amounts of data but shouldn't be confused with data which
+actually has spatial coordinates. Hovering over the map will reveal information
+about the signal and noise and correlation values. Clicking the map will provide
+the selected spectrum underneath. In the app, the colors of the heatmap are
+either the signal and noise or the spectral identifications depending on whether
+the identification is turned on or not. Pixels are black if the spectra does not
+pass the signal-noise threshold and/or the correlation threshold.
 
 ```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("images/preprocessplot.png")
+knitr::include_graphics("sop/map_vis.jpg")
+```
+
+The same plot can be created in R but the user can control what values form the
+colors of the heatmap by specifying `z`.
+
+```{r, fig.align="center", out.width="98%"}
+heatmap_spec(spectral_map,
+             z = spectral_map$metadata$x) # will show more advanced example 
+                                          # later in tutorial
+```
+
+An interactive plot of the heatmap and spectra overlayed can be generated with
+the `interactive_plot()` function. A user can hover over the heatmap to identify
+the next row id they are interested in and update the value of select to see
+that spectrum.
+
+```{r, fig.align="center", out.width="98%"}
+interactive_plot(spectral_map, select = 100, z = spectral_map$metadata$x)
 ```
 
-The preprocess spectra plot shows the uploaded spectra in comparison to
-the processed spectra that has been processed using the processing
-inputs on the page. It will automatically update with any new slider
-inputs. This allows the user to tune the inputs to optimize the signal
-to noise ratio. The goal with preprocessing is to make peak regions have
-high intensities and non-peak regions should have low intensities.
+## Processing
 
-## Preprocessing Tools
+After uploading data, you can preprocess the data using intensity adjustment,
+baseline subtraction, smoothing, flattening, and range selection and save your
+preprocessed data. Once the process button is selected the default processing
+will initiate. This is an absolute derivative transformation, it is kind of
+magic, it does something similar to smoothing, baseline subtraction, and
+intensity correction simultaneously and really quickly. By clicking
+preprocessing you should see the spectrum update with the processed spectra.
 
 ```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("images/smoothing.jpg")
+knitr::include_graphics("sop/processing.jpg")
 ```
 
-When the slider is green for the tool type, that means that that tool is being
-used to preprocess the spectrum. If the slider is clicked blank, the cog button
-to the right will disappear to indicate that the tool is no longer being used. 
+To view the raw data again, just deselect preprocessing. Toggling preprocessing
+on and off can help you to make sure that the spectra are processed correctly.
 
-```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("images/deselection.jpg")
+The `process_spec()` function is a monolithic function for all
+processing procedures which is optimized by default to result in high signal to
+noise in most cases, same as the app.
+
+```{r}
+processed <- process_spec(raman_hdpe)
+summary(processed)
+summary(raman_hdpe)
 ```
 
-If the cog button is clicked, any functions associated with that tool will be
-displayed and can be manipulated to process the spectrum. 
+You can compare the processed and unprocessed data in an overlay plot:
 
-## Smoothing Polynomial
+```{r eval=FALSE}
+plotly_spec(raman_hdpe, processed)
+```
+
+We want people to use the `process_spec()` function for most processing
+operations, all processing functions can be tuned using its parameters. However,
+we recognize that nesting of functions and order of operations can be useful for
+users to control so you can also use individual functions for each operation if
+you'd like.
+
+#### Threshold Signal-Noise
+
+Considering whether you have enough signal to analyze spectra is important.
+Classical spectroscopy would recommend your highest peak to be at least 10 times
+the baseline of your processed spectra before you begin analysis. If your
+spectra is below that threshold, you may want to consider recollecting it. In
+practice, we are rarely able to collect spectra of that good quality and more
+often use 5. The Signal Over Noise technique searches your spectra for high and
+low regions and conducts division on them to derive the signal to noise ratio.
+Signal Times Noise multiplies the mean signal by the standard deviation of the
+signal and Total Signal sums the intensities. The latter can be really useful
+for thresholding spectral maps to identify particles which we will discuss
+later.
 
 ```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("images/smoothingpoly.jpg")
-```
-
-The first step of the Open Specy preprocessing routing is spectral
-smoothing. The goal of this function is to increase the signal to noise
-ratio (S/N) without distorting the shape or relative size of the peaks.
-The value on the slider is the polynomial order of the
-[Savitzky-Golay (SG) filter](https://en.wikipedia.org/wiki/Savitzky%E2%80%93Golay_filter). 
-The SG filter is fit to a moving window of 11 data points where the center
-point in the window is replaced with the polynomial estimate. The
-number of data points in the window is not user adjustable. Higher
-numbers lead to more wiggly fits and thus less smooth, lower numbers
-lead to more smooth fits, a 7th order polynomial will make the spectrum
-have almost no smoothing. If smoothing is set to 0 then no smoothing is
-conducted on the spectrum. When smoothing is done well, peak shapes and
-relative heights should not change. Typically a 3rd order
-polynomial (3 on the slider) works to increase the signal to
-noise without distortion, but if the spectrum is noisy, decrease
-polynomial order and if it is already smooth, increase the polynomial
-order to the maximum (7). Examples of smoothing below:
-
-```{r smoothing, fig.cap = "Sample `raman_hdpe` spectrum with different smoothing polynomials (p) from Cowger et al. (2020).", fig.width=6, echo=FALSE}
-library(ggplot2)
-data("raman_hdpe")
+knitr::include_graphics("sop/snr_threshold.jpg")
+```
 
-compare_smoothing <- rbind(
-  cbind(smoothing = "none", adj_intens(raman_hdpe)),
-  cbind(smoothing = "p = 1", smooth_intens(raman_hdpe, p = 1)),
-  cbind(smoothing = "p = 4", smooth_intens(raman_hdpe, p = 4))
-)
+If analyzing spectra in batch, we recommend looking at the heatmap and
+optimizing the percent of spectra that are above your signal to noise threshold
+to determine the correct settings instead of looking through spectra
+individually. Good Signal tells you what percent of your data are above your
+signal threshold.
 
-ggplot(compare_smoothing, aes(wavenumber, intensity)) +
-  geom_line(aes(color = smoothing)) +
-  theme_minimal()
+```{r, fig.align="center", echo=FALSE}
+knitr::include_graphics("sop/signal_settings.jpg")
 ```
 
-The different degrees of smoothing were achieved with the following R commands:
+#### Intensity Adjustment
 
-```{r, eval=FALSE}
-smooth_intens(raman_hdpe, p = 1)
-smooth_intens(raman_hdpe, p = 4)
+```{r, fig.align='center', echo=FALSE}
+knitr::include_graphics("sop/intensityadjustment.jpg")
 ```
 
-The intensity-adjusted sample spectrum `raman_adj` is smoothed accordingly:
+Open Specy assumes that intensity units are in absorbance units but Open Specy
+can adjust reflectance or transmittance spectra to absorbance units using this
+selection. The transmittance adjustment uses the $\log_{10} 1/T$ calculation
+which does not correct for system or particle characteristics. The reflectance
+adjustment use the Kubelka-Munk equation $\frac{(1-R)^2}{2R}$. If none is
+selected, Open Specy assumes that the uploaded data is an absorbance spectrum
+and does not apply an adjustment.
+
+This is the respective R code for a scenario where the spectra doesn't need
+intensity adjustment:
+
+```{r, fig.align="center", fig.width=6}
+raman_adj <- raman_hdpe |> 
+  adj_intens(type = "none")
+
+plot(raman_adj)
+```
+
+#### Conforming
+
+Conforming spectra is essential before comparing to a reference library and can
+be useful for summarizing data when you don't need it to be highly resolved
+spectrally. In the app, conforming happens behind the scenes without any user
+input to make sure that the spectra the user uploads and the library spectra
+will be compatible. In code, we set the default spectral resolution to 5 because
+this tends to be pretty good for a lot of applications and is in between 4 and 8
+which are commonly used wavenumber resolutions. There are several ways that this
+is function can be specified.
 
 ```{r}
-raman_smooth <- raman_adj %>% 
-  smooth_intens()
+conform_spec(raman_hdpe) |> # default convert res to 5 wavenumbers.
+  summary()
+
+# Force one spectrum to have the exact same wavenumbers as another
+conform_spec(asp_example, range = ps_example$wavenumber, res = NULL) |> 
+  summary()
 
-head(raman_smooth)
+# Specify the wavenumber resolution and use a rolling join instead of linear
+# approximation (faster for large datasets). 
+conform_spec(spectral_map, range = ps_example$wavenumber, res = 10,
+             type = "roll") |> 
+  summary()
 ```
 
-## Baseline Correction Polynomial
+#### Smoothing
 
 ```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("images/baselinecorrectionpoly.jpg")
-```
-
-The second step of Open Specy's preprocessing routine is baseline
-correction. The goal of baseline correction is to get all non-peak
-regions of the spectra to zero absorbance. The higher the polynomial order,
-the more wiggly the fit to the baseline. If the baseline
-is not very wiggly, a more wiggly fit could remove peaks which is not
-desired. The baseline correction algorithm used in Open Specy is called
-"iModPolyfit" (Zhao et al. 2007). This algorithm iteratively fits polynomial
-equations of the
-specified order to the whole spectrum. During the first fit iteration,
-peak regions will often be above the baseline fit. The data in the peak
-region is removed from the fit to make sure that the baseline is less
-likely to fit to the peaks. The iterative fitting terminates once the
-difference between the new and previous fit is small. An example of a
-good baseline fit below.
-
-```{r subtraction, fig.cap = "Sample `raman_hdpe` spectrum with different degrees of background subtraction (Cowger et al., 2020).", fig.width=6, echo=FALSE}
+knitr::include_graphics("sop/smoothingpoly.jpg")
+```
+
+The first step of the Open Specy preprocessing routing is spectral smoothing.
+The goal of this function is to increase the signal to noise ratio (S/N) without
+distorting the shape or relative size of the peaks. The value on the slider is
+the polynomial order of the [Savitzky-Golay (SG)
+filter](https://en.wikipedia.org/wiki/Savitzky%E2%80%93Golay_filter). Higher
+numbers lead to more wiggly fits and thus less smooth, lower numbers lead to
+more smooth fits. The SG filter is fit to a moving window of 11 data points by
+default where the center point in the window is replaced with the polynomial
+estimate. Larger windows will produce smoother fits. The derivative order is set
+to 1 by default which transforms the spectra to their first derivative. A zero
+order derivative will have no derivative transformation. When smoothing is done
+well, peak shapes and relative heights should not change. The absolute value is
+primarily useful for first derivative spectra where the absolute value results
+in an absorbance-like spectrum which is why we set it as the default.
+
+Examples of smoothing with the R package:
+
+```{r smooth_intens, fig.cap = "Sample `raman_hdpe` spectrum with different smoothing polynomials from Cowger et al. (2020).", fig.width=6, fig.align="center"}
 library(ggplot2)
 data("raman_hdpe")
 
-compare_subtraction <- rbind(
-  cbind(fitting = "none", adj_intens(raman_hdpe)),
-  cbind(fitting = "degree = 2", subtr_bg(raman_hdpe, degree = 2)),
-  cbind(fitting = "degree = 8", subtr_bg(raman_hdpe, degree = 8))
-)
+none <- adj_intens(raman_hdpe, make_rel = T)
+p1 <- smooth_intens(raman_hdpe, polynomial = 1)
+p4 <- smooth_intens(raman_hdpe, polynomial = 4)
 
-ggplot(compare_subtraction, aes(wavenumber, intensity)) +
-  geom_line(aes(color = fitting)) +
-  theme_minimal()
+compare_derivative <- c_spec(list(none, p1, p4))
+    
+plot(compare_derivative)
 ```
 
-The smoothed sample spectrum `raman_smooth` is background-corrected as follows:
+Derivative transformation can happen with the same function. You'll notice a new
+function we are using `c_spec()` which is used to combine spectral objects into
+one OpenSpecy object.
 
-```{r}
-raman_bgc <- raman_smooth %>% 
-  subtr_bg()
+```{r compare_derivative, fig.cap = "Sample `raman_hdpe` spectrum with different derivatives from Cowger et al. (2020).", fig.width=6, fig.align="center"}
+
+none <- adj_intens(raman_hdpe, make_rel = T)
+d1 <- smooth_intens(raman_hdpe, derivative = 1, abs = T)
+d2 <- smooth_intens(raman_hdpe, derivative = 2)
 
-head(raman_bgc)
+compare_derivative <- c_spec(list(none, d1, d2))
+    
+plot(compare_derivative)
 ```
 
-## Spectral Range
+#### Baseline Correction
 
 ```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("images/rangeselection.jpg")
-```
+knitr::include_graphics("sop/baselinecorrectionpoly.jpg")
+```
+
+The second step of Open Specy's preprocessing routine is baseline correction.
+The goal of baseline correction is to get all non-peak regions of the spectra to
+zero absorbance. The higher the polynomial order, the more wiggly the fit to the
+baseline. If the baseline is not very wiggly, a more wiggly fit could remove
+peaks which is not desired. The baseline correction algorithm used in Open Specy
+is called "iModPolyfit" (Zhao et al. 2007). This algorithm iteratively fits
+polynomial equations of the specified order to the whole spectrum. During the
+first fit iteration, peak regions will often be above the baseline fit. The data
+in the peak region is removed from the fit to make sure that the baseline is
+less likely to fit to the peaks. The iterative fitting terminates once the
+difference between the new and previous fit is small. An example of a good
+baseline fit below. For those who have been with OpenSpecy a while, you will
+notice the app no longer supports manual baseline correction, it was a hard
+choice but it just didn't make sense to keep it now that we are moving toward
+high throughput automated methods. It does still exist in the R function though.
+
+```{r subtr_baseline, fig.cap = "Sample `raman_hdpe` spectrum with different degrees of background subtraction (Cowger et al., 2020).", fig.width=6, fig.align="center"}
+data("raman_hdpe")
+
+none <- adj_intens(raman_hdpe)
+d2 <- subtr_baseline(raman_hdpe, degree = 2)
+d8 <- subtr_baseline(raman_hdpe, degree = 8)
+
+compare_subtraction <- c_spec(list(none, d2, d8))
 
-The final step of preprocessing is restricting the spectral range.
-Sometimes the instrument operates with high noise at the ends of the
-spectrum and sometimes the baseline fit can produce distortions at the
-ends of the spectrum, both can be removed using this routine. You should
-look into the signal to noise ratio of your specific
-instrument by wavelength to determine what wavelength ranges to use.
-Distortions due to baseline fit can be assessed from looking at the
-preprocess spectra plot. Additionally, you can restrict the range
-to examine a single peak or a subset of peaks of interests. This
-function allows users to isolate peaks of interest for matching, while
-removing noise and influence from less relevant spectral data.
+plot(compare_subtraction)
+```
 
-## Download Data
+#### Range Selection
 
 ```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("images/download.jpg")
+knitr::include_graphics("sop/rangeselection.jpg")
 ```
 
-After you have the preprocessing parameters set, we recommend that you
-download the preprocessed data for your records. The download data
-button will append the uploaded data to three columns created by the
-preprocessing parameters. "Wavelength" and "Absorbance" are columns from
-the data uploaded by the user. "NormalizedIntensity" is the max-min
-normalized value (Equation 1) of the "Absorbance". "Smoothed" is the
-Savitzky-Golay filter specified by the slider explained above.
-"BaselineRemoved" is the smoothed and baseline corrected value that is
-visible on the center plot.
+Sometimes an instrument operates with high noise at the ends of the spectrum
+and, a baseline fit produces distortions, or there are regions of interest for
+analysis. Range selection accomplishes those goals. You should look into the
+signal to noise ratio of your specific instrument by wavelength to determine
+what wavelength ranges to use. Distortions due to baseline fit can be assessed
+from looking at the preprocess spectra. Additionally, you can restrict the range
+to examine a single peak or a subset of peaks of interests. The maximum and
+minimum wavenumbers will specify the range to keep in the app. In the R
+function, multiple ranges can be specified simultaneously.
+
+```{r restrict_range, fig.cap = "Sample `raman_hdpe` spectrum with different degrees of background subtraction (Cowger et al., 2020).", fig.width=6, fig.align="center"}
+data("raman_hdpe")
 
-# Matching
+none <- adj_intens(raman_hdpe)
+r1 <- restrict_range(raman_hdpe, min = 1000, max = 2000)
+r2 <- restrict_range(raman_hdpe, min = c(1000, 1800), max = c(1200, 2000))
 
-After uploading data and preprocessing it (if desired) you can now
-identify the spectrum. To identify the spectrum go to the **Match
-Spectrum** tab.
+compare_ranges <- c_spec(list(none, r1, r2), range = "common")
+# Common argument crops the ranges to the most common range between the spectra
+# when joining. 
 
-You will see your spectrum and the top matches, but before looking at
-matches, you need to check the three selectable
-parameters below.
+plot(compare_ranges)
+```
 
-## Spectrum Type
+#### Flattening Ranges
 
 ```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("images/spectrumtype.jpg")
+knitr::include_graphics("sop/flattten_example.jpg")
 ```
 
-The spectra type input on the "Match spectra" tab specifies the type of
-spectra (Raman or FTIR) that the user has uploaded and wants to match
-to. This input will tell the website whether to use the FTIR library or
-the Raman library to make the match.
+Sometimes there are peaks that really shouldn't be in your spectra and can
+distort your interpretation of the spectra but you don't necessarily want to
+remove the regions from the analysis because you believe those regions should be
+flat instead of having a peak. One way to deal with this is to replace the peak
+values with the mean of the values around the peak. This is the purpose of the
+`flatten_range` function. By default it is set to flatten the CO2 region for
+FTIR spectra because that region often needs to be flattened when atmouspheric
+artefacts occur in spectra. Like `restrict_range`, the R function can accept
+multiple ranges.
 
-## Spectrum To Analyze
+```{r flatten_range, fig.cap = "Sample `raman_hdpe` spectrum with different degrees of background subtraction (Cowger et al., 2020).", fig.width=6, fig.align="center"}
+single <- filter_spec(spectral_map, 120) # Function to filter spectra by index
+                                         # number or name or a logical vector. 
+none <- adj_intens(single)
+f1 <- flatten_range(single)
+f2 <- flatten_range(single, min = c(1000, 2500), max = c(1200, 3000))
 
-```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("images/spectrumtoanalyze.jpg")
+compare_flats <- c_spec(list(none, f1, f2))
+
+plot(compare_flats)
 ```
 
-The spectra to analyze input specifies if the tool will match the
-**Uploaded** spectra (unaltered by the inputs on the **Preprocess Spectra**
-tab) or the **Processed Spectra** (manipulated by the inputs in the
-Preprocess Spectra Tab).
+#### Min-Max Normalization
 
-## Region To Match
+Min-Max normalization is used throughout the app but is not one of the options
+the user can specify. Often we regard spectral intensities as arbitrary and
+min-max normalization allows us to view spectra on the same scale without
+drastically distorting their shapes or relative peak intensities. In the app, it
+is primarily used for plotting and as a processing step before correlation
+analysis. In the package, most of the processing functions will min-max
+transform your spectra relative by default if you do not specify otherwise. This
+plot shows the two spectra on such different scales that one looks like a
+straight line.
 
-```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("images/regiontomatch.jpg")
-```
+```{r adj_intens, fig.cap = "Sample `raman_hdpe` spectrum with different degrees of background subtraction (Cowger et al., 2020).", fig.width=6, fig.align="center"}
+none <- adj_intens(raman_hdpe, make_rel = F)
+relative <- adj_intens(raman_hdpe, make_rel = T)
 
-The region to match input specifies if the "Full Spectrum" will match
-the entire range of the spectra (including non peak regions) in the
-reference database. This is the most intuitive match. Or should the
-**Peaks Only** match just the peak regions in the reference database. This
-is an advanced feature proposed in Renner et al. (2017).
-This can be a less intuitive approach but in cases where
-there are few peaks and high baseline interference, it could be the best
-option. In cases where non-peak regions are important for the
-interpretation of the match, this is not the best approach.
+compare_rel <- c_spec(list(none, relative))
 
-## Match Table
+plot(compare_rel)
+```
+
+## Identifying Spectra 
 
 ```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("images/matches.jpg")
+knitr::include_graphics("sop/identification.jpg")
 ```
 
-The selectable table shows the top material matches returned by the tool,
-their Pearson's r value, and the organization they were provided by.
-When rows are selected their spectra are added to the match plot.
-The spectrum being matched and reference library are determined by
-the previously mentioned parameters. During the matching process, one
-final cleaning step happens using a simple minimum subtraction
-algorithm (Equation 2) which in many cases will allow unprocessed
-spectra to remove subtle baseline, but will not harm the spectra which
-has no baseline. Then, these aligned data are tested for correlation
-using the Pearson's r. The Pearson's r is used as a match quality
-indicator and the spectra from the top 1000 best matches are returned
-from the library. You can restrict the libraries which are displayed
-in the table by clicking the box that says **All** under the Organization column.
+After uploading data and preprocessing it (if desired) you can now identify the
+spectrum. To identify the spectrum go to the **Identification** box. Pro tip: if
+you select *Identification** without uploading data to the app, you'll be able
+to explore the library by itself.
 
-Similarly you can restrict the range of Pearson\'s r values or search
-for specific material types.
+### Reading Libraries
 
-$$\mathrm{for~each}~peak~group^{1,n}: x - \mathrm{min}(x)$$
-<p style="text-align: center;">Equation 2: Minimum Subtraction</p>
+These options define the strategy for identification. The ID Library will inform
+which library is used. Both (default) will search both FTIR and Raman libraries.
+Deriv will search against a derivative transformed library. No Baseline will
+search against a baseline corrected library. This should be in line with how you
+choose to process your spectra. Cor options use a simple Pearson correlation
+search algorithm. AI is currently experimental and uses either a multinomial
+model or correlation on mediod spectra from the library. Correlation
+thresholding will set the minimum value from matching to use as a 'positive
+identification'
 
-The same table can be returned using the Open Specy library commands in
-the R console.
+In the R package, you'll download and load the libraries into your working
+environment.
 
-```{r eval=FALSE}
-# Fetch current spectral library from https://osf.io/x7dpz/
-get_lib()
-# Load library into global environment
-spec_lib <- load_lib()
-# Match spectrum with library and retrieve meta data
-match_spec(raman_bgc, library = spec_lib, which = "raman")
+```{r eval=F}
+get_lib() #will load all libraries unless specified otherwise. 
+lib <- load_lib(type = "derivative")
 ```
 
-## Selection Metadata
+### Matches
 
-```{r, fig.align="center", out.width="98%", echo=FALSE}
-knitr::include_graphics("images/selectionmetadata.jpg")
+```{r, fig.align="center", echo=FALSE}
+knitr::include_graphics("sop/matches.jpg")
 ```
 
-Whatever match is selected from the match table may have additional
-metadata about it. That metadata will be displayed below the plot.
-Some of this metadata may assist you in interpreting the spectra. For
-example, if the spectra has metadata which says it is a liquid and you
-are analyzing a solid particle, that spectrum may not be the best match.
-
-The R command for manual metadata selection using `sample_name == 5381` as
-example is:
+Top matches in the app can be assessed by clicking the cog in the right hand
+corner of the Spectra box. This will open a side window with the matches sorted
+from most to least similar. Clicking on rows in the table will add the selected
+match to the spectra viewer. Using the table's filter options, you can restrict
+the range of Pearson\'s r values or search for specific material types.
 
-```{r, eval=FALSE}
-find_spec(sample_name == 5381, library = spec_lib, which = "raman")
-```
+The same table can be returned using the OpenSpecy package commands in the R
+console.
 
-## Match Plot
+```{r}
+data("test_lib")
 
-```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("images/matchplot.png")
-```
-
-This plot is dynamically updated by selecting matches from the match
-table. The red spectrum is the spectrum that you selected from the
-reference library and the white spectrum is the spectrum that you are
-trying to identify. Whenever a new dataset is uploaded, the plot and
-data table in this tab will be updated. These plots can be saved as a
-.png by clicking the camera button at the top of the plot.
-
-## How to interpret the reported matches
-
-There are several important things to consider when interpreting a
-spectral match including the library source, the Pearson's r, and other
-metrics.
-
-### The library source
-
-When you click on a spectrum, all of the metadata that we have in Open
-Specy about that source will be displayed in a metadata window below to
-the matches table. Each library has different methodologies used to
-develop it. It is useful to read up on the library sources from the
-literature that they came from. E.g. Chabuka et
-al. 2020 focuses on weathered plastics, so matching to it
-may suggest that your spectrum is of a weathered polymer. Primpke
-et al. 2018 only has a spectral range up to 2000, so some polymers may
-be difficult to differentiate with it. Make sure to cite the libraries
-that you use during your search when you publish your results.
-The authors were kind enough to make their data open access so that it
-could be used in Open Specy and we should return the favor by citing
-them.
-
-### Pearson's r
-
-Correlation values are used to identify the closest matches available
-in the current Open Specy spectral libraries to improve material
-identification and reduce sample processing times. Pearson's r values
-range from 0 - 1 with 0 being a completely different spectrum and 1
-being an exact match. Some general guidelines that we have observed from
-using Open Specy. If no matches are \> \~0.3 the material
-may require additional processing or may not exist in the Open Specy
-library. Correlation values are not the only metric you should
-use to assess your spectra's match to a material in the library, matches
-need to make sense.
-
-### Things to consider beyond correlation
-
-Peak position and height similarities are more important than
-correlation and need to be assessed manually. Peak position
-correlates with specific bond types. Peak height correlates to
-the concentration of a compound. Therefore, peak height and peak
-position should match as closely as possible to the matched spectrum.
-When there are peaks that exist in the spectra you are trying to
-interpret that do not exist in the match, there may be additional
-materials to identify. In this case, restrict the preprocessing range to
-just the unidentified peak and try to identify it as an additional
-component (see also
-[https://www.compoundchem.com/2015/02/05/irspectroscopy/](https://www.compoundchem.com/2015/02/05/irspectroscopy/)).
-
-Also, check the match metadata to see if the match makes sense.
-Example: A single fiber cannot be a "cotton blend"
-since there would be no other fibers to make up the rest of the blend.
-Example: Cellophane does not degrade into fibers, so
-a match for a fiber to cellophane wouldn't make
-sense. Example: You are analyzing a particle
-at room temperature, but the matched material is liquid at room
-temperature. The material may be a component of the particle but it
-cannot be the whole particle.
-
-### How specific do you need to be in the material type of the match?
-
-You can choose to be specific about how you classify a substance (e.g.
-polyester, cellophane) or more general (e.g. synthetic, semi-synthetic,
-natural, etc.). The choice depends on your research question. Using more
-general groups can speed up analysis time but will decrease the
-information you have for interpretation. To identify materials more
-generally, you can often clump the identities provided by Open Specy to
-suit your needs. For example, matches to "polyester" and "polypropylene"
-could be clumped to the category "plastic".
-
-### How to differentiate between similar spectra?
-
-One common challenge is differentiating between LDPE and HDPE. But,
-even with a low resolution instrument (MacroRAM, 2 cm^-1^ pixel^-1^),
-you can still see some differences. From a wide view,
-these low, medium, and high density PE samples all look relatively
-similar (figures courtesy of Bridget O\'Donnell, Horiba Scientific):
+processed <- process_spec(ps_example, 
+                        conform_spec_args = list(range = test_lib$wavenumber, 
+                                                 res = NULL))
 
-```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("images/horiba-1.png")
+test <- match_spec(processed, test_lib, add_object_metadata = "col_id",
+                   add_library_metadata = "sample_name")
 ```
 
-But, a closer look at the 1450 cm^-1^ band reveals
-clear differences:
+### Selection Metadata
 
-```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("images/horiba-2.png")
+```{r, fig.align="center", out.width="98%", echo=FALSE}
+knitr::include_graphics("sop/selectionmetadata.jpg")
 ```
 
-When you overlay them, you start to see differences in other spectral
-regions too:
+Whatever match is selected from the match table may have additional metadata
+about it. That metadata will be displayed below the plot. Some of this metadata
+may assist you in interpreting the spectra. For example, if the spectra has
+metadata which says it is a liquid and you are analyzing a solid particle, that
+spectrum may not be the best match.
 
-```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("images/horiba-3.png")
-```
-
-So, the question is, how do we deal with samples that are very similar
-with only subtle differences? Usually, researchers will use MVA
-techniques after they've collected multiple reference spectra of known
-samples (LDPE and HDPE in this case). They can then develop models and
-apply them to distinguish between different types of PE. With a
-reference database like Open Specy, this is complicated by the fact that
-researchers are measuring samples on different instruments with
-correspondingly different spectral responses and spectral resolutions.
-That makes it even more difficult to accurately match definitively to
-LDPE and HDPE as opposed to generic 'PE'.
-
-One possibility is to place more emphasis (from a computational
-perspective) on the bands that show the most difference (the triplet at
-1450 cm^-1^) by restricting the range used to match in
-Open Specy. 
-
-The other, much simpler option is to just match any PE hit to generic
-'PE' and not specifically HDPE or LDPE.
-
-Another challenge is in differentiating between types of nylons. But,
-Raman has a pretty easy time distinguishing nylons. These spectra were
-recorded of a series of nylons and the differences are much more
-distinguishable compared to the PE results above (nylon 6, 6-6, 6-9,
-6-10, and 6-12 top to
-bottom):
+The R command for manual metadata selection using `sample_name == 5381` as
+example is:
 
-```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("images/horiba-4.png")
+```{r, eval=FALSE}
+find_spec(sample_name == 5381, library = spec_lib, which = "raman")
 ```
 
-The differences are even more pronounced when you overlay the
-spectra:
+### Match Plot
 
 ```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("images/horiba-5.png")
+knitr::include_graphics("sop/matchplot.png")
 ```
 
-### What to do when matches aren't making sense
+This plot is dynamically updated by selecting matches from the match table. The
+red spectrum is the spectrum that you selected from the reference library and
+the white spectrum is the spectrum that you are trying to identify. Whenever a
+new dataset is uploaded, the plot and data table in this tab will be updated.
+These plots can be saved as a .png by clicking the camera button at the top of
+the plot.
 
-1.  Double check that the baseline correction and smoothing parameters
-    result in the best preprocessing of the data. 
-2.  Try reprocessing your spectrum, but limit it to specific peak
-    regions with a higher signal to noise ratio.
-3.  Restrict the spectral range to include or exclude questionable
-    peaks or peaks that were not present in the previous matches.
-4.  Restrict the spectral range to exclude things like CO~2~ (2200 cm^-1^)
-    or H~2~O (\~1600 cm^-1^) in spikes in the IR spectrum.
-5.  If nothing above works to determine a quality match, you may need
-    to measure the spectrum of your material again or use another
-    spectral analysis tool.
+### Sharing Reference Data
 
-# Mini Tutorials
+If you have reference data or AI models that you think would be useful for
+sharing with the spectroscopy community through OpenSpecy please contact the
+website administrator to discuss options for collaborating.
 
-## File Conversion in SpectraGryph to Open Specy Accepted Format
+## Characterizing Particles
 
-1. Download Spectragryph from
-[https://www.effemm2.de/spectragryph/down.html](https://www.effemm2.de/spectragryph/down.html)
+### Thresholding
 
-2. Open Spectragryph and upload your file by dragging and dropping it
-into the console.
+### Collapse Spectra
 
-```{r, fig.align="center", out.width="98%", echo=FALSE}
-knitr::include_graphics("images/spectragryph-1.png")
-```
+## Additional App Features
 
-3. Click File, Save/export data, save data as, and save it as an spc
-file. ¸
+Accessibility is extremely important to us and we are making strives to improve
+the accessibility of Open Specy for all spectroscopists. Please reach out if you
+have ideas for improvement.
 
-```{r, fig.align="center", out.width="98%", echo=FALSE}
-knitr::include_graphics("images/spectragryph-2.png")
-```
+We added a Google translate plugin to all pages in the app so that you can
+easily translate the app. We know that not all languages will be fully supported
+but we will continue to try and improve the translations.
 
-4. Then upload that .spc file to Open Specy.
+### Download Data
 
 ```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("images/uploadfile.jpg")
+knitr::include_graphics("sop/download.jpg")
 ```
 
-## Conceptual diagram of data flow through Open Specy
+After you have the preprocessing parameters set, we recommend that you download
+the preprocessed data for your records. The download data button will append the
+uploaded data to three columns created by the preprocessing parameters.
+"Wavelength" and "Absorbance" are columns from the data uploaded by the user.
+"NormalizedIntensity" is the max-min normalized value (Equation 1) of the
+"Absorbance". "Smoothed" is the Savitzky-Golay filter specified by the slider
+explained above. "BaselineRemoved" is the smoothed and baseline corrected value
+that is visible on the center plot.
+
+## Conceptual diagram of Open Specy
 
 ```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("images/flowchart.png")
+knitr::include_graphics("sop/flowchart.png")
 ```
 
 # References
diff --git a/vignettes/sop.html b/vignettes/sop.html
index aeb2af7b..2b89eca7 100644
--- a/vignettes/sop.html
+++ b/vignettes/sop.html
@@ -11,9 +11,9 @@
 <meta name="viewport" content="width=device-width, initial-scale=1" />
 
 
-<meta name="date" content="2022-07-06" />
+<meta name="date" content="2023-08-30" />
 
-<title>Standard Operating Procedure</title>
+<title>Open Specy Tutorial</title>
 
 <script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
 // be compatible with the behavior of Pandoc < 2.8).
@@ -28,25 +28,1951 @@
   }
 });
 </script>
+<script>(function() {
+  // If window.HTMLWidgets is already defined, then use it; otherwise create a
+  // new object. This allows preceding code to set options that affect the
+  // initialization process (though none currently exist).
+  window.HTMLWidgets = window.HTMLWidgets || {};
 
-<style type="text/css">
-  code{white-space: pre-wrap;}
-  span.smallcaps{font-variant: small-caps;}
-  span.underline{text-decoration: underline;}
-  div.column{display: inline-block; vertical-align: top; width: 50%;}
-  div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
-  ul.task-list{list-style: none;}
-    </style>
+  // See if we're running in a viewer pane. If not, we're in a web browser.
+  var viewerMode = window.HTMLWidgets.viewerMode =
+      /\bviewer_pane=1\b/.test(window.location);
 
+  // See if we're running in Shiny mode. If not, it's a static document.
+  // Note that static widgets can appear in both Shiny and static modes, but
+  // obviously, Shiny widgets can only appear in Shiny apps/documents.
+  var shinyMode = window.HTMLWidgets.shinyMode =
+      typeof(window.Shiny) !== "undefined" && !!window.Shiny.outputBindings;
 
+  // We can't count on jQuery being available, so we implement our own
+  // version if necessary.
+  function querySelectorAll(scope, selector) {
+    if (typeof(jQuery) !== "undefined" && scope instanceof jQuery) {
+      return scope.find(selector);
+    }
+    if (scope.querySelectorAll) {
+      return scope.querySelectorAll(selector);
+    }
+  }
 
-<style type="text/css">
-  code {
-    white-space: pre;
+  function asArray(value) {
+    if (value === null)
+      return [];
+    if ($.isArray(value))
+      return value;
+    return [value];
+  }
+
+  // Implement jQuery's extend
+  function extend(target /*, ... */) {
+    if (arguments.length == 1) {
+      return target;
+    }
+    for (var i = 1; i < arguments.length; i++) {
+      var source = arguments[i];
+      for (var prop in source) {
+        if (source.hasOwnProperty(prop)) {
+          target[prop] = source[prop];
+        }
+      }
+    }
+    return target;
+  }
+
+  // IE8 doesn't support Array.forEach.
+  function forEach(values, callback, thisArg) {
+    if (values.forEach) {
+      values.forEach(callback, thisArg);
+    } else {
+      for (var i = 0; i < values.length; i++) {
+        callback.call(thisArg, values[i], i, values);
+      }
+    }
+  }
+
+  // Replaces the specified method with the return value of funcSource.
+  //
+  // Note that funcSource should not BE the new method, it should be a function
+  // that RETURNS the new method. funcSource receives a single argument that is
+  // the overridden method, it can be called from the new method. The overridden
+  // method can be called like a regular function, it has the target permanently
+  // bound to it so "this" will work correctly.
+  function overrideMethod(target, methodName, funcSource) {
+    var superFunc = target[methodName] || function() {};
+    var superFuncBound = function() {
+      return superFunc.apply(target, arguments);
+    };
+    target[methodName] = funcSource(superFuncBound);
+  }
+
+  // Add a method to delegator that, when invoked, calls
+  // delegatee.methodName. If there is no such method on
+  // the delegatee, but there was one on delegator before
+  // delegateMethod was called, then the original version
+  // is invoked instead.
+  // For example:
+  //
+  // var a = {
+  //   method1: function() { console.log('a1'); }
+  //   method2: function() { console.log('a2'); }
+  // };
+  // var b = {
+  //   method1: function() { console.log('b1'); }
+  // };
+  // delegateMethod(a, b, "method1");
+  // delegateMethod(a, b, "method2");
+  // a.method1();
+  // a.method2();
+  //
+  // The output would be "b1", "a2".
+  function delegateMethod(delegator, delegatee, methodName) {
+    var inherited = delegator[methodName];
+    delegator[methodName] = function() {
+      var target = delegatee;
+      var method = delegatee[methodName];
+
+      // The method doesn't exist on the delegatee. Instead,
+      // call the method on the delegator, if it exists.
+      if (!method) {
+        target = delegator;
+        method = inherited;
+      }
+
+      if (method) {
+        return method.apply(target, arguments);
+      }
+    };
+  }
+
+  // Implement a vague facsimilie of jQuery's data method
+  function elementData(el, name, value) {
+    if (arguments.length == 2) {
+      return el["htmlwidget_data_" + name];
+    } else if (arguments.length == 3) {
+      el["htmlwidget_data_" + name] = value;
+      return el;
+    } else {
+      throw new Error("Wrong number of arguments for elementData: " +
+        arguments.length);
+    }
+  }
+
+  // http://stackoverflow.com/questions/3446170/escape-string-for-use-in-javascript-regex
+  function escapeRegExp(str) {
+    return str.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g, "\\$&");
+  }
+
+  function hasClass(el, className) {
+    var re = new RegExp("\\b" + escapeRegExp(className) + "\\b");
+    return re.test(el.className);
+  }
+
+  // elements - array (or array-like object) of HTML elements
+  // className - class name to test for
+  // include - if true, only return elements with given className;
+  //   if false, only return elements *without* given className
+  function filterByClass(elements, className, include) {
+    var results = [];
+    for (var i = 0; i < elements.length; i++) {
+      if (hasClass(elements[i], className) == include)
+        results.push(elements[i]);
+    }
+    return results;
+  }
+
+  function on(obj, eventName, func) {
+    if (obj.addEventListener) {
+      obj.addEventListener(eventName, func, false);
+    } else if (obj.attachEvent) {
+      obj.attachEvent(eventName, func);
+    }
+  }
+
+  function off(obj, eventName, func) {
+    if (obj.removeEventListener)
+      obj.removeEventListener(eventName, func, false);
+    else if (obj.detachEvent) {
+      obj.detachEvent(eventName, func);
+    }
+  }
+
+  // Translate array of values to top/right/bottom/left, as usual with
+  // the "padding" CSS property
+  // https://developer.mozilla.org/en-US/docs/Web/CSS/padding
+  function unpackPadding(value) {
+    if (typeof(value) === "number")
+      value = [value];
+    if (value.length === 1) {
+      return {top: value[0], right: value[0], bottom: value[0], left: value[0]};
+    }
+    if (value.length === 2) {
+      return {top: value[0], right: value[1], bottom: value[0], left: value[1]};
+    }
+    if (value.length === 3) {
+      return {top: value[0], right: value[1], bottom: value[2], left: value[1]};
+    }
+    if (value.length === 4) {
+      return {top: value[0], right: value[1], bottom: value[2], left: value[3]};
+    }
+  }
+
+  // Convert an unpacked padding object to a CSS value
+  function paddingToCss(paddingObj) {
+    return paddingObj.top + "px " + paddingObj.right + "px " + paddingObj.bottom + "px " + paddingObj.left + "px";
+  }
+
+  // Makes a number suitable for CSS
+  function px(x) {
+    if (typeof(x) === "number")
+      return x + "px";
+    else
+      return x;
+  }
+
+  // Retrieves runtime widget sizing information for an element.
+  // The return value is either null, or an object with fill, padding,
+  // defaultWidth, defaultHeight fields.
+  function sizingPolicy(el) {
+    var sizingEl = document.querySelector("script[data-for='" + el.id + "'][type='application/htmlwidget-sizing']");
+    if (!sizingEl)
+      return null;
+    var sp = JSON.parse(sizingEl.textContent || sizingEl.text || "{}");
+    if (viewerMode) {
+      return sp.viewer;
+    } else {
+      return sp.browser;
+    }
+  }
+
+  // @param tasks Array of strings (or falsy value, in which case no-op).
+  //   Each element must be a valid JavaScript expression that yields a
+  //   function. Or, can be an array of objects with "code" and "data"
+  //   properties; in this case, the "code" property should be a string
+  //   of JS that's an expr that yields a function, and "data" should be
+  //   an object that will be added as an additional argument when that
+  //   function is called.
+  // @param target The object that will be "this" for each function
+  //   execution.
+  // @param args Array of arguments to be passed to the functions. (The
+  //   same arguments will be passed to all functions.)
+  function evalAndRun(tasks, target, args) {
+    if (tasks) {
+      forEach(tasks, function(task) {
+        var theseArgs = args;
+        if (typeof(task) === "object") {
+          theseArgs = theseArgs.concat([task.data]);
+          task = task.code;
+        }
+        var taskFunc = tryEval(task);
+        if (typeof(taskFunc) !== "function") {
+          throw new Error("Task must be a function! Source:\n" + task);
+        }
+        taskFunc.apply(target, theseArgs);
+      });
+    }
   }
-  .sourceCode {
-    overflow: visible;
+
+  // Attempt eval() both with and without enclosing in parentheses.
+  // Note that enclosing coerces a function declaration into
+  // an expression that eval() can parse
+  // (otherwise, a SyntaxError is thrown)
+  function tryEval(code) {
+    var result = null;
+    try {
+      result = eval("(" + code + ")");
+    } catch(error) {
+      if (!(error instanceof SyntaxError)) {
+        throw error;
+      }
+      try {
+        result = eval(code);
+      } catch(e) {
+        if (e instanceof SyntaxError) {
+          throw error;
+        } else {
+          throw e;
+        }
+      }
+    }
+    return result;
   }
+
+  function initSizing(el) {
+    var sizing = sizingPolicy(el);
+    if (!sizing)
+      return;
+
+    var cel = document.getElementById("htmlwidget_container");
+    if (!cel)
+      return;
+
+    if (typeof(sizing.padding) !== "undefined") {
+      document.body.style.margin = "0";
+      document.body.style.padding = paddingToCss(unpackPadding(sizing.padding));
+    }
+
+    if (sizing.fill) {
+      document.body.style.overflow = "hidden";
+      document.body.style.width = "100%";
+      document.body.style.height = "100%";
+      document.documentElement.style.width = "100%";
+      document.documentElement.style.height = "100%";
+      cel.style.position = "absolute";
+      var pad = unpackPadding(sizing.padding);
+      cel.style.top = pad.top + "px";
+      cel.style.right = pad.right + "px";
+      cel.style.bottom = pad.bottom + "px";
+      cel.style.left = pad.left + "px";
+      el.style.width = "100%";
+      el.style.height = "100%";
+
+      return {
+        getWidth: function() { return cel.getBoundingClientRect().width; },
+        getHeight: function() { return cel.getBoundingClientRect().height; }
+      };
+
+    } else {
+      el.style.width = px(sizing.width);
+      el.style.height = px(sizing.height);
+
+      return {
+        getWidth: function() { return cel.getBoundingClientRect().width; },
+        getHeight: function() { return cel.getBoundingClientRect().height; }
+      };
+    }
+  }
+
+  // Default implementations for methods
+  var defaults = {
+    find: function(scope) {
+      return querySelectorAll(scope, "." + this.name);
+    },
+    renderError: function(el, err) {
+      var $el = $(el);
+
+      this.clearError(el);
+
+      // Add all these error classes, as Shiny does
+      var errClass = "shiny-output-error";
+      if (err.type !== null) {
+        // use the classes of the error condition as CSS class names
+        errClass = errClass + " " + $.map(asArray(err.type), function(type) {
+          return errClass + "-" + type;
+        }).join(" ");
+      }
+      errClass = errClass + " htmlwidgets-error";
+
+      // Is el inline or block? If inline or inline-block, just display:none it
+      // and add an inline error.
+      var display = $el.css("display");
+      $el.data("restore-display-mode", display);
+
+      if (display === "inline" || display === "inline-block") {
+        $el.hide();
+        if (err.message !== "") {
+          var errorSpan = $("<span>").addClass(errClass);
+          errorSpan.text(err.message);
+          $el.after(errorSpan);
+        }
+      } else if (display === "block") {
+        // If block, add an error just after the el, set visibility:none on the
+        // el, and position the error to be on top of the el.
+        // Mark it with a unique ID and CSS class so we can remove it later.
+        $el.css("visibility", "hidden");
+        if (err.message !== "") {
+          var errorDiv = $("<div>").addClass(errClass).css("position", "absolute")
+            .css("top", el.offsetTop)
+            .css("left", el.offsetLeft)
+            // setting width can push out the page size, forcing otherwise
+            // unnecessary scrollbars to appear and making it impossible for
+            // the element to shrink; so use max-width instead
+            .css("maxWidth", el.offsetWidth)
+            .css("height", el.offsetHeight);
+          errorDiv.text(err.message);
+          $el.after(errorDiv);
+
+          // Really dumb way to keep the size/position of the error in sync with
+          // the parent element as the window is resized or whatever.
+          var intId = setInterval(function() {
+            if (!errorDiv[0].parentElement) {
+              clearInterval(intId);
+              return;
+            }
+            errorDiv
+              .css("top", el.offsetTop)
+              .css("left", el.offsetLeft)
+              .css("maxWidth", el.offsetWidth)
+              .css("height", el.offsetHeight);
+          }, 500);
+        }
+      }
+    },
+    clearError: function(el) {
+      var $el = $(el);
+      var display = $el.data("restore-display-mode");
+      $el.data("restore-display-mode", null);
+
+      if (display === "inline" || display === "inline-block") {
+        if (display)
+          $el.css("display", display);
+        $(el.nextSibling).filter(".htmlwidgets-error").remove();
+      } else if (display === "block"){
+        $el.css("visibility", "inherit");
+        $(el.nextSibling).filter(".htmlwidgets-error").remove();
+      }
+    },
+    sizing: {}
+  };
+
+  // Called by widget bindings to register a new type of widget. The definition
+  // object can contain the following properties:
+  // - name (required) - A string indicating the binding name, which will be
+  //   used by default as the CSS classname to look for.
+  // - initialize (optional) - A function(el) that will be called once per
+  //   widget element; if a value is returned, it will be passed as the third
+  //   value to renderValue.
+  // - renderValue (required) - A function(el, data, initValue) that will be
+  //   called with data. Static contexts will cause this to be called once per
+  //   element; Shiny apps will cause this to be called multiple times per
+  //   element, as the data changes.
+  window.HTMLWidgets.widget = function(definition) {
+    if (!definition.name) {
+      throw new Error("Widget must have a name");
+    }
+    if (!definition.type) {
+      throw new Error("Widget must have a type");
+    }
+    // Currently we only support output widgets
+    if (definition.type !== "output") {
+      throw new Error("Unrecognized widget type '" + definition.type + "'");
+    }
+    // TODO: Verify that .name is a valid CSS classname
+
+    // Support new-style instance-bound definitions. Old-style class-bound
+    // definitions have one widget "object" per widget per type/class of
+    // widget; the renderValue and resize methods on such widget objects
+    // take el and instance arguments, because the widget object can't
+    // store them. New-style instance-bound definitions have one widget
+    // object per widget instance; the definition that's passed in doesn't
+    // provide renderValue or resize methods at all, just the single method
+    //   factory(el, width, height)
+    // which returns an object that has renderValue(x) and resize(w, h).
+    // This enables a far more natural programming style for the widget
+    // author, who can store per-instance state using either OO-style
+    // instance fields or functional-style closure variables (I guess this
+    // is in contrast to what can only be called C-style pseudo-OO which is
+    // what we required before).
+    if (definition.factory) {
+      definition = createLegacyDefinitionAdapter(definition);
+    }
+
+    if (!definition.renderValue) {
+      throw new Error("Widget must have a renderValue function");
+    }
+
+    // For static rendering (non-Shiny), use a simple widget registration
+    // scheme. We also use this scheme for Shiny apps/documents that also
+    // contain static widgets.
+    window.HTMLWidgets.widgets = window.HTMLWidgets.widgets || [];
+    // Merge defaults into the definition; don't mutate the original definition.
+    var staticBinding = extend({}, defaults, definition);
+    overrideMethod(staticBinding, "find", function(superfunc) {
+      return function(scope) {
+        var results = superfunc(scope);
+        // Filter out Shiny outputs, we only want the static kind
+        return filterByClass(results, "html-widget-output", false);
+      };
+    });
+    window.HTMLWidgets.widgets.push(staticBinding);
+
+    if (shinyMode) {
+      // Shiny is running. Register the definition with an output binding.
+      // The definition itself will not be the output binding, instead
+      // we will make an output binding object that delegates to the
+      // definition. This is because we foolishly used the same method
+      // name (renderValue) for htmlwidgets definition and Shiny bindings
+      // but they actually have quite different semantics (the Shiny
+      // bindings receive data that includes lots of metadata that it
+      // strips off before calling htmlwidgets renderValue). We can't
+      // just ignore the difference because in some widgets it's helpful
+      // to call this.renderValue() from inside of resize(), and if
+      // we're not delegating, then that call will go to the Shiny
+      // version instead of the htmlwidgets version.
+
+      // Merge defaults with definition, without mutating either.
+      var bindingDef = extend({}, defaults, definition);
+
+      // This object will be our actual Shiny binding.
+      var shinyBinding = new Shiny.OutputBinding();
+
+      // With a few exceptions, we'll want to simply use the bindingDef's
+      // version of methods if they are available, otherwise fall back to
+      // Shiny's defaults. NOTE: If Shiny's output bindings gain additional
+      // methods in the future, and we want them to be overrideable by
+      // HTMLWidget binding definitions, then we'll need to add them to this
+      // list.
+      delegateMethod(shinyBinding, bindingDef, "getId");
+      delegateMethod(shinyBinding, bindingDef, "onValueChange");
+      delegateMethod(shinyBinding, bindingDef, "onValueError");
+      delegateMethod(shinyBinding, bindingDef, "renderError");
+      delegateMethod(shinyBinding, bindingDef, "clearError");
+      delegateMethod(shinyBinding, bindingDef, "showProgress");
+
+      // The find, renderValue, and resize are handled differently, because we
+      // want to actually decorate the behavior of the bindingDef methods.
+
+      shinyBinding.find = function(scope) {
+        var results = bindingDef.find(scope);
+
+        // Only return elements that are Shiny outputs, not static ones
+        var dynamicResults = results.filter(".html-widget-output");
+
+        // It's possible that whatever caused Shiny to think there might be
+        // new dynamic outputs, also caused there to be new static outputs.
+        // Since there might be lots of different htmlwidgets bindings, we
+        // schedule execution for later--no need to staticRender multiple
+        // times.
+        if (results.length !== dynamicResults.length)
+          scheduleStaticRender();
+
+        return dynamicResults;
+      };
+
+      // Wrap renderValue to handle initialization, which unfortunately isn't
+      // supported natively by Shiny at the time of this writing.
+
+      shinyBinding.renderValue = function(el, data) {
+        Shiny.renderDependencies(data.deps);
+        // Resolve strings marked as javascript literals to objects
+        if (!(data.evals instanceof Array)) data.evals = [data.evals];
+        for (var i = 0; data.evals && i < data.evals.length; i++) {
+          window.HTMLWidgets.evaluateStringMember(data.x, data.evals[i]);
+        }
+        if (!bindingDef.renderOnNullValue) {
+          if (data.x === null) {
+            el.style.visibility = "hidden";
+            return;
+          } else {
+            el.style.visibility = "inherit";
+          }
+        }
+        if (!elementData(el, "initialized")) {
+          initSizing(el);
+
+          elementData(el, "initialized", true);
+          if (bindingDef.initialize) {
+            var rect = el.getBoundingClientRect();
+            var result = bindingDef.initialize(el, rect.width, rect.height);
+            elementData(el, "init_result", result);
+          }
+        }
+        bindingDef.renderValue(el, data.x, elementData(el, "init_result"));
+        evalAndRun(data.jsHooks.render, elementData(el, "init_result"), [el, data.x]);
+      };
+
+      // Only override resize if bindingDef implements it
+      if (bindingDef.resize) {
+        shinyBinding.resize = function(el, width, height) {
+          // Shiny can call resize before initialize/renderValue have been
+          // called, which doesn't make sense for widgets.
+          if (elementData(el, "initialized")) {
+            bindingDef.resize(el, width, height, elementData(el, "init_result"));
+          }
+        };
+      }
+
+      Shiny.outputBindings.register(shinyBinding, bindingDef.name);
+    }
+  };
+
+  var scheduleStaticRenderTimerId = null;
+  function scheduleStaticRender() {
+    if (!scheduleStaticRenderTimerId) {
+      scheduleStaticRenderTimerId = setTimeout(function() {
+        scheduleStaticRenderTimerId = null;
+        window.HTMLWidgets.staticRender();
+      }, 1);
+    }
+  }
+
+  // Render static widgets after the document finishes loading
+  // Statically render all elements that are of this widget's class
+  window.HTMLWidgets.staticRender = function() {
+    var bindings = window.HTMLWidgets.widgets || [];
+    forEach(bindings, function(binding) {
+      var matches = binding.find(document.documentElement);
+      forEach(matches, function(el) {
+        var sizeObj = initSizing(el, binding);
+
+        var getSize = function(el) {
+          if (sizeObj) {
+            return {w: sizeObj.getWidth(), h: sizeObj.getHeight()}
+          } else {
+            var rect = el.getBoundingClientRect();
+            return {w: rect.width, h: rect.height}
+          }
+        };
+
+        if (hasClass(el, "html-widget-static-bound"))
+          return;
+        el.className = el.className + " html-widget-static-bound";
+
+        var initResult;
+        if (binding.initialize) {
+          var size = getSize(el);
+          initResult = binding.initialize(el, size.w, size.h);
+          elementData(el, "init_result", initResult);
+        }
+
+        if (binding.resize) {
+          var lastSize = getSize(el);
+          var resizeHandler = function(e) {
+            var size = getSize(el);
+            if (size.w === 0 && size.h === 0)
+              return;
+            if (size.w === lastSize.w && size.h === lastSize.h)
+              return;
+            lastSize = size;
+            binding.resize(el, size.w, size.h, initResult);
+          };
+
+          on(window, "resize", resizeHandler);
+
+          // This is needed for cases where we're running in a Shiny
+          // app, but the widget itself is not a Shiny output, but
+          // rather a simple static widget. One example of this is
+          // an rmarkdown document that has runtime:shiny and widget
+          // that isn't in a render function. Shiny only knows to
+          // call resize handlers for Shiny outputs, not for static
+          // widgets, so we do it ourselves.
+          if (window.jQuery) {
+            window.jQuery(document).on(
+              "shown.htmlwidgets shown.bs.tab.htmlwidgets shown.bs.collapse.htmlwidgets",
+              resizeHandler
+            );
+            window.jQuery(document).on(
+              "hidden.htmlwidgets hidden.bs.tab.htmlwidgets hidden.bs.collapse.htmlwidgets",
+              resizeHandler
+            );
+          }
+
+          // This is needed for the specific case of ioslides, which
+          // flips slides between display:none and display:block.
+          // Ideally we would not have to have ioslide-specific code
+          // here, but rather have ioslides raise a generic event,
+          // but the rmarkdown package just went to CRAN so the
+          // window to getting that fixed may be long.
+          if (window.addEventListener) {
+            // It's OK to limit this to window.addEventListener
+            // browsers because ioslides itself only supports
+            // such browsers.
+            on(document, "slideenter", resizeHandler);
+            on(document, "slideleave", resizeHandler);
+          }
+        }
+
+        var scriptData = document.querySelector("script[data-for='" + el.id + "'][type='application/json']");
+        if (scriptData) {
+          var data = JSON.parse(scriptData.textContent || scriptData.text);
+          // Resolve strings marked as javascript literals to objects
+          if (!(data.evals instanceof Array)) data.evals = [data.evals];
+          for (var k = 0; data.evals && k < data.evals.length; k++) {
+            window.HTMLWidgets.evaluateStringMember(data.x, data.evals[k]);
+          }
+          binding.renderValue(el, data.x, initResult);
+          evalAndRun(data.jsHooks.render, initResult, [el, data.x]);
+        }
+      });
+    });
+
+    invokePostRenderHandlers();
+  }
+
+
+  function has_jQuery3() {
+    if (!window.jQuery) {
+      return false;
+    }
+    var $version = window.jQuery.fn.jquery;
+    var $major_version = parseInt($version.split(".")[0]);
+    return $major_version >= 3;
+  }
+
+  /*
+  / Shiny 1.4 bumped jQuery from 1.x to 3.x which means jQuery's
+  / on-ready handler (i.e., $(fn)) is now asyncronous (i.e., it now
+  / really means $(setTimeout(fn)).
+  / https://jquery.com/upgrade-guide/3.0/#breaking-change-document-ready-handlers-are-now-asynchronous
+  /
+  / Since Shiny uses $() to schedule initShiny, shiny>=1.4 calls initShiny
+  / one tick later than it did before, which means staticRender() is
+  / called renderValue() earlier than (advanced) widget authors might be expecting.
+  / https://github.com/rstudio/shiny/issues/2630
+  /
+  / For a concrete example, leaflet has some methods (e.g., updateBounds)
+  / which reference Shiny methods registered in initShiny (e.g., setInputValue).
+  / Since leaflet is privy to this life-cycle, it knows to use setTimeout() to
+  / delay execution of those methods (until Shiny methods are ready)
+  / https://github.com/rstudio/leaflet/blob/18ec981/javascript/src/index.js#L266-L268
+  /
+  / Ideally widget authors wouldn't need to use this setTimeout() hack that
+  / leaflet uses to call Shiny methods on a staticRender(). In the long run,
+  / the logic initShiny should be broken up so that method registration happens
+  / right away, but binding happens later.
+  */
+  function maybeStaticRenderLater() {
+    if (shinyMode && has_jQuery3()) {
+      window.jQuery(window.HTMLWidgets.staticRender);
+    } else {
+      window.HTMLWidgets.staticRender();
+    }
+  }
+
+  if (document.addEventListener) {
+    document.addEventListener("DOMContentLoaded", function() {
+      document.removeEventListener("DOMContentLoaded", arguments.callee, false);
+      maybeStaticRenderLater();
+    }, false);
+  } else if (document.attachEvent) {
+    document.attachEvent("onreadystatechange", function() {
+      if (document.readyState === "complete") {
+        document.detachEvent("onreadystatechange", arguments.callee);
+        maybeStaticRenderLater();
+      }
+    });
+  }
+
+
+  window.HTMLWidgets.getAttachmentUrl = function(depname, key) {
+    // If no key, default to the first item
+    if (typeof(key) === "undefined")
+      key = 1;
+
+    var link = document.getElementById(depname + "-" + key + "-attachment");
+    if (!link) {
+      throw new Error("Attachment " + depname + "/" + key + " not found in document");
+    }
+    return link.getAttribute("href");
+  };
+
+  window.HTMLWidgets.dataframeToD3 = function(df) {
+    var names = [];
+    var length;
+    for (var name in df) {
+        if (df.hasOwnProperty(name))
+            names.push(name);
+        if (typeof(df[name]) !== "object" || typeof(df[name].length) === "undefined") {
+            throw new Error("All fields must be arrays");
+        } else if (typeof(length) !== "undefined" && length !== df[name].length) {
+            throw new Error("All fields must be arrays of the same length");
+        }
+        length = df[name].length;
+    }
+    var results = [];
+    var item;
+    for (var row = 0; row < length; row++) {
+        item = {};
+        for (var col = 0; col < names.length; col++) {
+            item[names[col]] = df[names[col]][row];
+        }
+        results.push(item);
+    }
+    return results;
+  };
+
+  window.HTMLWidgets.transposeArray2D = function(array) {
+      if (array.length === 0) return array;
+      var newArray = array[0].map(function(col, i) {
+          return array.map(function(row) {
+              return row[i]
+          })
+      });
+      return newArray;
+  };
+  // Split value at splitChar, but allow splitChar to be escaped
+  // using escapeChar. Any other characters escaped by escapeChar
+  // will be included as usual (including escapeChar itself).
+  function splitWithEscape(value, splitChar, escapeChar) {
+    var results = [];
+    var escapeMode = false;
+    var currentResult = "";
+    for (var pos = 0; pos < value.length; pos++) {
+      if (!escapeMode) {
+        if (value[pos] === splitChar) {
+          results.push(currentResult);
+          currentResult = "";
+        } else if (value[pos] === escapeChar) {
+          escapeMode = true;
+        } else {
+          currentResult += value[pos];
+        }
+      } else {
+        currentResult += value[pos];
+        escapeMode = false;
+      }
+    }
+    if (currentResult !== "") {
+      results.push(currentResult);
+    }
+    return results;
+  }
+  // Function authored by Yihui/JJ Allaire
+  window.HTMLWidgets.evaluateStringMember = function(o, member) {
+    var parts = splitWithEscape(member, '.', '\\');
+    for (var i = 0, l = parts.length; i < l; i++) {
+      var part = parts[i];
+      // part may be a character or 'numeric' member name
+      if (o !== null && typeof o === "object" && part in o) {
+        if (i == (l - 1)) { // if we are at the end of the line then evalulate
+          if (typeof o[part] === "string")
+            o[part] = tryEval(o[part]);
+        } else { // otherwise continue to next embedded object
+          o = o[part];
+        }
+      }
+    }
+  };
+
+  // Retrieve the HTMLWidget instance (i.e. the return value of an
+  // HTMLWidget binding's initialize() or factory() function)
+  // associated with an element, or null if none.
+  window.HTMLWidgets.getInstance = function(el) {
+    return elementData(el, "init_result");
+  };
+
+  // Finds the first element in the scope that matches the selector,
+  // and returns the HTMLWidget instance (i.e. the return value of
+  // an HTMLWidget binding's initialize() or factory() function)
+  // associated with that element, if any. If no element matches the
+  // selector, or the first matching element has no HTMLWidget
+  // instance associated with it, then null is returned.
+  //
+  // The scope argument is optional, and defaults to window.document.
+  window.HTMLWidgets.find = function(scope, selector) {
+    if (arguments.length == 1) {
+      selector = scope;
+      scope = document;
+    }
+
+    var el = scope.querySelector(selector);
+    if (el === null) {
+      return null;
+    } else {
+      return window.HTMLWidgets.getInstance(el);
+    }
+  };
+
+  // Finds all elements in the scope that match the selector, and
+  // returns the HTMLWidget instances (i.e. the return values of
+  // an HTMLWidget binding's initialize() or factory() function)
+  // associated with the elements, in an array. If elements that
+  // match the selector don't have an associated HTMLWidget
+  // instance, the returned array will contain nulls.
+  //
+  // The scope argument is optional, and defaults to window.document.
+  window.HTMLWidgets.findAll = function(scope, selector) {
+    if (arguments.length == 1) {
+      selector = scope;
+      scope = document;
+    }
+
+    var nodes = scope.querySelectorAll(selector);
+    var results = [];
+    for (var i = 0; i < nodes.length; i++) {
+      results.push(window.HTMLWidgets.getInstance(nodes[i]));
+    }
+    return results;
+  };
+
+  var postRenderHandlers = [];
+  function invokePostRenderHandlers() {
+    while (postRenderHandlers.length) {
+      var handler = postRenderHandlers.shift();
+      if (handler) {
+        handler();
+      }
+    }
+  }
+
+  // Register the given callback function to be invoked after the
+  // next time static widgets are rendered.
+  window.HTMLWidgets.addPostRenderHandler = function(callback) {
+    postRenderHandlers.push(callback);
+  };
+
+  // Takes a new-style instance-bound definition, and returns an
+  // old-style class-bound definition. This saves us from having
+  // to rewrite all the logic in this file to accomodate both
+  // types of definitions.
+  function createLegacyDefinitionAdapter(defn) {
+    var result = {
+      name: defn.name,
+      type: defn.type,
+      initialize: function(el, width, height) {
+        return defn.factory(el, width, height);
+      },
+      renderValue: function(el, x, instance) {
+        return instance.renderValue(x);
+      },
+      resize: function(el, width, height, instance) {
+        return instance.resize(width, height);
+      }
+    };
+
+    if (defn.find)
+      result.find = defn.find;
+    if (defn.renderError)
+      result.renderError = defn.renderError;
+    if (defn.clearError)
+      result.clearError = defn.clearError;
+
+    return result;
+  }
+})();
+</script>
+<script>
+HTMLWidgets.widget({
+  name: "plotly",
+  type: "output",
+
+  initialize: function(el, width, height) {
+    return {};
+  },
+
+  resize: function(el, width, height, instance) {
+    if (instance.autosize) {
+      var width = instance.width || width;
+      var height = instance.height || height;
+      Plotly.relayout(el.id, {width: width, height: height});
+    }
+  },  
+  
+  renderValue: function(el, x, instance) {
+    
+    // Plotly.relayout() mutates the plot input object, so make sure to 
+    // keep a reference to the user-supplied width/height *before*
+    // we call Plotly.plot();
+    var lay = x.layout || {};
+    instance.width = lay.width;
+    instance.height = lay.height;
+    instance.autosize = lay.autosize || true;
+    
+    /* 
+    / 'inform the world' about highlighting options this is so other
+    / crosstalk libraries have a chance to respond to special settings 
+    / such as persistent selection. 
+    / AFAIK, leaflet is the only library with such intergration
+    / https://github.com/rstudio/leaflet/pull/346/files#diff-ad0c2d51ce5fdf8c90c7395b102f4265R154
+    */
+    var ctConfig = crosstalk.var('plotlyCrosstalkOpts').set(x.highlight);
+      
+    if (typeof(window) !== "undefined") {
+      // make sure plots don't get created outside the network (for on-prem)
+      window.PLOTLYENV = window.PLOTLYENV || {};
+      window.PLOTLYENV.BASE_URL = x.base_url;
+      
+      // Enable persistent selection when shift key is down
+      // https://stackoverflow.com/questions/1828613/check-if-a-key-is-down
+      var persistOnShift = function(e) {
+        if (!e) window.event;
+        if (e.shiftKey) { 
+          x.highlight.persistent = true; 
+          x.highlight.persistentShift = true;
+        } else {
+          x.highlight.persistent = false; 
+          x.highlight.persistentShift = false;
+        }
+      };
+      
+      // Only relevant if we haven't forced persistent mode at command line
+      if (!x.highlight.persistent) {
+        window.onmousemove = persistOnShift;
+      }
+    }
+
+    var graphDiv = document.getElementById(el.id);
+    
+    // TODO: move the control panel injection strategy inside here...
+    HTMLWidgets.addPostRenderHandler(function() {
+      
+      // lower the z-index of the modebar to prevent it from highjacking hover
+      // (TODO: do this via CSS?)
+      // https://github.com/ropensci/plotly/issues/956
+      // https://www.w3schools.com/jsref/prop_style_zindex.asp
+      var modebars = document.querySelectorAll(".js-plotly-plot .plotly .modebar");
+      for (var i = 0; i < modebars.length; i++) {
+        modebars[i].style.zIndex = 1;
+      }
+    });
+      
+      // inject a "control panel" holding selectize/dynamic color widget(s)
+    if ((x.selectize || x.highlight.dynamic) && !instance.plotly) {
+      var flex = document.createElement("div");
+      flex.class = "plotly-crosstalk-control-panel";
+      flex.style = "display: flex; flex-wrap: wrap";
+      
+      // inject the colourpicker HTML container into the flexbox
+      if (x.highlight.dynamic) {
+        var pickerDiv = document.createElement("div");
+        
+        var pickerInput = document.createElement("input");
+        pickerInput.id = el.id + "-colourpicker";
+        pickerInput.placeholder = "asdasd";
+        
+        var pickerLabel = document.createElement("label");
+        pickerLabel.for = pickerInput.id;
+        pickerLabel.innerHTML = "Brush color&nbsp;&nbsp;";
+        
+        pickerDiv.appendChild(pickerLabel);
+        pickerDiv.appendChild(pickerInput);
+        flex.appendChild(pickerDiv);
+      }
+      
+      // inject selectize HTML containers (one for every crosstalk group)
+      if (x.selectize) {
+        var ids = Object.keys(x.selectize);
+        
+        for (var i = 0; i < ids.length; i++) {
+          var container = document.createElement("div");
+          container.id = ids[i];
+          container.style = "width: 80%; height: 10%";
+          container.class = "form-group crosstalk-input-plotly-highlight";
+          
+          var label = document.createElement("label");
+          label.for = ids[i];
+          label.innerHTML = x.selectize[ids[i]].group;
+          label.class = "control-label";
+          
+          var selectDiv = document.createElement("div");
+          var select = document.createElement("select");
+          select.multiple = true;
+          
+          selectDiv.appendChild(select);
+          container.appendChild(label);
+          container.appendChild(selectDiv);
+          flex.appendChild(container);
+        }
+      }
+      
+      // finally, insert the flexbox inside the htmlwidget container,
+      // but before the plotly graph div
+      graphDiv.parentElement.insertBefore(flex, graphDiv);
+      
+      if (x.highlight.dynamic) {
+        var picker = $("#" + pickerInput.id);
+        var colors = x.highlight.color || [];
+        // TODO: let users specify options?
+        var opts = {
+          value: colors[0],
+          showColour: "both",
+          palette: "limited",
+          allowedCols: colors.join(" "),
+          width: "20%",
+          height: "10%"
+        };
+        picker.colourpicker({changeDelay: 0});
+        picker.colourpicker("settings", opts);
+        picker.colourpicker("value", opts.value);
+        // inform crosstalk about a change in the current selection colour
+        var grps = x.highlight.ctGroups || [];
+        for (var i = 0; i < grps.length; i++) {
+          crosstalk.group(grps[i]).var('plotlySelectionColour')
+            .set(picker.colourpicker('value'));
+        }
+        picker.on("change", function() {
+          for (var i = 0; i < grps.length; i++) {
+            crosstalk.group(grps[i]).var('plotlySelectionColour')
+              .set(picker.colourpicker('value'));
+          }
+        });
+      }
+    }
+    
+    // if no plot exists yet, create one with a particular configuration
+    if (!instance.plotly) {
+      
+      var plot = Plotly.newPlot(graphDiv, x);
+      instance.plotly = true;
+      
+    } else if (x.layout.transition) {
+      
+      var plot = Plotly.react(graphDiv, x);
+    
+    } else {
+      
+      // this is essentially equivalent to Plotly.newPlot(), but avoids creating 
+      // a new webgl context
+      // https://github.com/plotly/plotly.js/blob/2b24f9def901831e61282076cf3f835598d56f0e/src/plot_api/plot_api.js#L531-L532
+
+      // TODO: restore crosstalk selections?
+      Plotly.purge(graphDiv);
+      // TODO: why is this necessary to get crosstalk working?
+      graphDiv.data = undefined;
+      graphDiv.layout = undefined;
+      var plot = Plotly.newPlot(graphDiv, x);
+    }
+    
+    // Trigger plotly.js calls defined via `plotlyProxy()`
+    plot.then(function() {
+      if (HTMLWidgets.shinyMode) {
+        Shiny.addCustomMessageHandler("plotly-calls", function(msg) {
+          var gd = document.getElementById(msg.id);
+          if (!gd) {
+            throw new Error("Couldn't find plotly graph with id: " + msg.id);
+          }
+          // This isn't an official plotly.js method, but it's the only current way to 
+          // change just the configuration of a plot 
+          // https://community.plot.ly/t/update-config-function/9057
+          if (msg.method == "reconfig") {
+            Plotly.react(gd, gd.data, gd.layout, msg.args);
+            return;
+          }
+          if (!Plotly[msg.method]) {
+            throw new Error("Unknown method " + msg.method);
+          }
+          var args = [gd].concat(msg.args);
+          Plotly[msg.method].apply(null, args);
+        });
+      }
+      
+      // plotly's mapbox API doesn't currently support setting bounding boxes
+      // https://www.mapbox.com/mapbox-gl-js/example/fitbounds/
+      // so we do this manually...
+      // TODO: make sure this triggers on a redraw and relayout as well as on initial draw
+      var mapboxIDs = graphDiv._fullLayout._subplots.mapbox || [];
+      for (var i = 0; i < mapboxIDs.length; i++) {
+        var id = mapboxIDs[i];
+        var mapOpts = x.layout[id] || {};
+        var args = mapOpts._fitBounds || {};
+        if (!args) {
+          continue;
+        }
+        var mapObj = graphDiv._fullLayout[id]._subplot.map;
+        mapObj.fitBounds(args.bounds, args.options);
+      }
+      
+    });
+    
+    // Attach attributes (e.g., "key", "z") to plotly event data
+    function eventDataWithKey(eventData) {
+      if (eventData === undefined || !eventData.hasOwnProperty("points")) {
+        return null;
+      }
+      return eventData.points.map(function(pt) {
+        var obj = {
+          curveNumber: pt.curveNumber, 
+          pointNumber: pt.pointNumber, 
+          x: pt.x,
+          y: pt.y
+        };
+        
+        // If 'z' is reported with the event data, then use it!
+        if (pt.hasOwnProperty("z")) {
+          obj.z = pt.z;
+        }
+        
+        if (pt.hasOwnProperty("customdata")) {
+          obj.customdata = pt.customdata;
+        }
+        
+        /* 
+          TL;DR: (I think) we have to select the graph div (again) to attach keys...
+          
+          Why? Remember that crosstalk will dynamically add/delete traces 
+          (see traceManager.prototype.updateSelection() below)
+          For this reason, we can't simply grab keys from x.data (like we did previously)
+          Moreover, we can't use _fullData, since that doesn't include 
+          unofficial attributes. It's true that click/hover events fire with 
+          pt.data, but drag events don't...
+        */
+        var gd = document.getElementById(el.id);
+        var trace = gd.data[pt.curveNumber];
+        
+        if (!trace._isSimpleKey) {
+          var attrsToAttach = ["key"];
+        } else {
+          // simple keys fire the whole key
+          obj.key = trace.key;
+          var attrsToAttach = [];
+        }
+        
+        for (var i = 0; i < attrsToAttach.length; i++) {
+          var attr = trace[attrsToAttach[i]];
+          if (Array.isArray(attr)) {
+            if (typeof pt.pointNumber === "number") {
+              obj[attrsToAttach[i]] = attr[pt.pointNumber];
+            } else if (Array.isArray(pt.pointNumber)) {
+              obj[attrsToAttach[i]] = attr[pt.pointNumber[0]][pt.pointNumber[1]];
+            } else if (Array.isArray(pt.pointNumbers)) {
+              obj[attrsToAttach[i]] = pt.pointNumbers.map(function(idx) { return attr[idx]; });
+            }
+          }
+        }
+        return obj;
+      });
+    }
+    
+    
+    var legendEventData = function(d) {
+      // if legendgroup is not relevant just return the trace
+      var trace = d.data[d.curveNumber];
+      if (!trace.legendgroup) return trace;
+      
+      // if legendgroup was specified, return all traces that match the group
+      var legendgrps = d.data.map(function(trace){ return trace.legendgroup; });
+      var traces = [];
+      for (i = 0; i < legendgrps.length; i++) {
+        if (legendgrps[i] == trace.legendgroup) {
+          traces.push(d.data[i]);
+        }
+      }
+      
+      return traces;
+    };
+
+    
+    // send user input event data to shiny
+    if (HTMLWidgets.shinyMode && Shiny.setInputValue) {
+      
+      // Some events clear other input values
+      // TODO: always register these?
+      var eventClearMap = {
+        plotly_deselect: ["plotly_selected", "plotly_selecting", "plotly_brushed", "plotly_brushing", "plotly_click"],
+        plotly_unhover: ["plotly_hover"],
+        plotly_doubleclick: ["plotly_click"]
+      };
+    
+      Object.keys(eventClearMap).map(function(evt) {
+        graphDiv.on(evt, function() {
+          var inputsToClear = eventClearMap[evt];
+          inputsToClear.map(function(input) {
+            Shiny.setInputValue(input + "-" + x.source, null, {priority: "event"});
+          });
+        });
+      });
+      
+      var eventDataFunctionMap = {
+        plotly_click: eventDataWithKey,
+        plotly_sunburstclick: eventDataWithKey,
+        plotly_hover: eventDataWithKey,
+        plotly_unhover: eventDataWithKey,
+        // If 'plotly_selected' has already been fired, and you click
+        // on the plot afterwards, this event fires `undefined`?!?
+        // That might be considered a plotly.js bug, but it doesn't make 
+        // sense for this input change to occur if `d` is falsy because,
+        // even in the empty selection case, `d` is truthy (an object),
+        // and the 'plotly_deselect' event will reset this input
+        plotly_selected: function(d) { if (d) { return eventDataWithKey(d); } },
+        plotly_selecting: function(d) { if (d) { return eventDataWithKey(d); } },
+        plotly_brushed: function(d) {
+          if (d) { return d.range ? d.range : d.lassoPoints; }
+        },
+        plotly_brushing: function(d) {
+          if (d) { return d.range ? d.range : d.lassoPoints; }
+        },
+        plotly_legendclick: legendEventData,
+        plotly_legenddoubleclick: legendEventData,
+        plotly_clickannotation: function(d) { return d.fullAnnotation }
+      };
+      
+      var registerShinyValue = function(event) {
+        var eventDataPreProcessor = eventDataFunctionMap[event] || function(d) { return d ? d : el.id };
+        // some events are unique to the R package
+        var plotlyJSevent = (event == "plotly_brushed") ? "plotly_selected" : (event == "plotly_brushing") ? "plotly_selecting" : event;
+        // register the event
+        graphDiv.on(plotlyJSevent, function(d) {
+          Shiny.setInputValue(
+            event + "-" + x.source,
+            JSON.stringify(eventDataPreProcessor(d)),
+            {priority: "event"}
+          );
+        });
+      }
+    
+      var shinyEvents = x.shinyEvents || [];
+      shinyEvents.map(registerShinyValue);
+    }
+    
+    // Given an array of {curveNumber: x, pointNumber: y} objects,
+    // return a hash of {
+    //   set1: {value: [key1, key2, ...], _isSimpleKey: false}, 
+    //   set2: {value: [key3, key4, ...], _isSimpleKey: false}
+    // }
+    function pointsToKeys(points) {
+      var keysBySet = {};
+      for (var i = 0; i < points.length; i++) {
+        
+        var trace = graphDiv.data[points[i].curveNumber];
+        if (!trace.key || !trace.set) {
+          continue;
+        }
+        
+        // set defaults for this keySet
+        // note that we don't track the nested property (yet) since we always 
+        // emit the union -- http://cpsievert.github.io/talks/20161212b/#21
+        keysBySet[trace.set] = keysBySet[trace.set] || {
+          value: [],
+          _isSimpleKey: trace._isSimpleKey
+        };
+        
+        // Use pointNumber by default, but aggregated traces should emit pointNumbers
+        var ptNum = points[i].pointNumber;
+        var hasPtNum = typeof ptNum === "number";
+        var ptNum = hasPtNum ? ptNum : points[i].pointNumbers;
+        
+        // selecting a point of a "simple" trace means: select the 
+        // entire key attached to this trace, which is useful for,
+        // say clicking on a fitted line to select corresponding observations 
+        var key = trace._isSimpleKey ? trace.key : Array.isArray(ptNum) ? ptNum.map(function(idx) { return trace.key[idx]; }) : trace.key[ptNum];
+        // http://stackoverflow.com/questions/10865025/merge-flatten-an-array-of-arrays-in-javascript
+        var keyFlat = trace._isNestedKey ? [].concat.apply([], key) : key;
+        
+        // TODO: better to only add new values?
+        keysBySet[trace.set].value = keysBySet[trace.set].value.concat(keyFlat);
+      }
+      
+      return keysBySet;
+    }
+    
+    
+    x.highlight.color = x.highlight.color || [];
+    // make sure highlight color is an array
+    if (!Array.isArray(x.highlight.color)) {
+      x.highlight.color = [x.highlight.color];
+    }
+
+    var traceManager = new TraceManager(graphDiv, x.highlight);
+
+    // Gather all *unique* sets.
+    var allSets = [];
+    for (var curveIdx = 0; curveIdx < x.data.length; curveIdx++) {
+      var newSet = x.data[curveIdx].set;
+      if (newSet) {
+        if (allSets.indexOf(newSet) === -1) {
+          allSets.push(newSet);
+        }
+      }
+    }
+
+    // register event listeners for all sets
+    for (var i = 0; i < allSets.length; i++) {
+      
+      var set = allSets[i];
+      var selection = new crosstalk.SelectionHandle(set);
+      var filter = new crosstalk.FilterHandle(set);
+      
+      var filterChange = function(e) {
+        removeBrush(el);
+        traceManager.updateFilter(set, e.value);
+      };
+      filter.on("change", filterChange);
+      
+      
+      var selectionChange = function(e) {
+        
+        // Workaround for 'plotly_selected' now firing previously selected
+        // points (in addition to new ones) when holding shift key. In our case,
+        // we just want the new keys 
+        if (x.highlight.on === "plotly_selected" && x.highlight.persistentShift) {
+          // https://stackoverflow.com/questions/1187518/how-to-get-the-difference-between-two-arrays-in-javascript
+          Array.prototype.diff = function(a) {
+              return this.filter(function(i) {return a.indexOf(i) < 0;});
+          };
+          e.value = e.value.diff(e.oldValue);
+        }
+        
+        // array of "event objects" tracking the selection history
+        // this is used to avoid adding redundant selections
+        var selectionHistory = crosstalk.var("plotlySelectionHistory").get() || [];
+        
+        // Construct an event object "defining" the current event. 
+        var event = {
+          receiverID: traceManager.gd.id,
+          plotlySelectionColour: crosstalk.group(set).var("plotlySelectionColour").get()
+        };
+        event[set] = e.value;
+        // TODO: is there a smarter way to check object equality?
+        if (selectionHistory.length > 0) {
+          var ev = JSON.stringify(event);
+          for (var i = 0; i < selectionHistory.length; i++) {
+            var sel = JSON.stringify(selectionHistory[i]);
+            if (sel == ev) {
+              return;
+            }
+          }
+        }
+        
+        // accumulate history for persistent selection
+        if (!x.highlight.persistent) {
+          selectionHistory = [event];
+        } else {
+          selectionHistory.push(event);
+        }
+        crosstalk.var("plotlySelectionHistory").set(selectionHistory);
+        
+        // do the actual updating of traces, frames, and the selectize widget
+        traceManager.updateSelection(set, e.value);
+        // https://github.com/selectize/selectize.js/blob/master/docs/api.md#methods_items
+        if (x.selectize) {
+          if (!x.highlight.persistent || e.value === null) {
+            selectize.clear(true);
+          }
+          selectize.addItems(e.value, true);
+          selectize.close();
+        }
+      }
+      selection.on("change", selectionChange);
+      
+      // Set a crosstalk variable selection value, triggering an update
+      var turnOn = function(e) {
+        if (e) {
+          var selectedKeys = pointsToKeys(e.points);
+          // Keys are group names, values are array of selected keys from group.
+          for (var set in selectedKeys) {
+            if (selectedKeys.hasOwnProperty(set)) {
+              selection.set(selectedKeys[set].value, {sender: el});
+            }
+          }
+        }
+      };
+      if (x.highlight.debounce > 0) {
+        turnOn = debounce(turnOn, x.highlight.debounce);
+      }
+      graphDiv.on(x.highlight.on, turnOn);
+      
+      graphDiv.on(x.highlight.off, function turnOff(e) {
+        // remove any visual clues
+        removeBrush(el);
+        // remove any selection history
+        crosstalk.var("plotlySelectionHistory").set(null);
+        // trigger the actual removal of selection traces
+        selection.set(null, {sender: el});
+      });
+          
+      // register a callback for selectize so that there is bi-directional
+      // communication between the widget and direct manipulation events
+      if (x.selectize) {
+        var selectizeID = Object.keys(x.selectize)[i];
+        var options = x.selectize[selectizeID];
+        var first = [{value: "", label: "(All)"}];
+        var opts = $.extend({
+            options: first.concat(options.items),
+            searchField: "label",
+            valueField: "value",
+            labelField: "label",
+            maxItems: 50
+          }, 
+          options
+        );
+        var select = $("#" + selectizeID).find("select")[0];
+        var selectize = $(select).selectize(opts)[0].selectize;
+        // NOTE: this callback is triggered when *directly* altering 
+        // dropdown items
+        selectize.on("change", function() {
+          var currentItems = traceManager.groupSelections[set] || [];
+          if (!x.highlight.persistent) {
+            removeBrush(el);
+            for (var i = 0; i < currentItems.length; i++) {
+              selectize.removeItem(currentItems[i], true);
+            }
+          }
+          var newItems = selectize.items.filter(function(idx) { 
+            return currentItems.indexOf(idx) < 0;
+          });
+          if (newItems.length > 0) {
+            traceManager.updateSelection(set, newItems);
+          } else {
+            // Item has been removed...
+            // TODO: this logic won't work for dynamically changing palette 
+            traceManager.updateSelection(set, null);
+            traceManager.updateSelection(set, selectize.items);
+          }
+        });
+      }
+    } // end of selectionChange
+    
+  } // end of renderValue
+}); // end of widget definition
+
+/**
+ * @param graphDiv The Plotly graph div
+ * @param highlight An object with options for updating selection(s)
+ */
+function TraceManager(graphDiv, highlight) {
+  // The Plotly graph div
+  this.gd = graphDiv;
+
+  // Preserve the original data.
+  // TODO: try using Lib.extendFlat() as done in  
+  // https://github.com/plotly/plotly.js/pull/1136 
+  this.origData = JSON.parse(JSON.stringify(graphDiv.data));
+  
+  // avoid doing this over and over
+  this.origOpacity = [];
+  for (var i = 0; i < this.origData.length; i++) {
+    this.origOpacity[i] = this.origData[i].opacity === 0 ? 0 : (this.origData[i].opacity || 1);
+  }
+
+  // key: group name, value: null or array of keys representing the
+  // most recently received selection for that group.
+  this.groupSelections = {};
+  
+  // selection parameters (e.g., transient versus persistent selection)
+  this.highlight = highlight;
+}
+
+TraceManager.prototype.close = function() {
+  // TODO: Unhook all event handlers
+};
+
+TraceManager.prototype.updateFilter = function(group, keys) {
+
+  if (typeof(keys) === "undefined" || keys === null) {
+    
+    this.gd.data = JSON.parse(JSON.stringify(this.origData));
+    
+  } else {
+  
+    var traces = [];
+    for (var i = 0; i < this.origData.length; i++) {
+      var trace = this.origData[i];
+      if (!trace.key || trace.set !== group) {
+        continue;
+      }
+      var matchFunc = getMatchFunc(trace);
+      var matches = matchFunc(trace.key, keys);
+      
+      if (matches.length > 0) {
+        if (!trace._isSimpleKey) {
+          // subsetArrayAttrs doesn't mutate trace (it makes a modified clone)
+          trace = subsetArrayAttrs(trace, matches);
+        }
+        traces.push(trace);
+      }
+    }
+    this.gd.data = traces;
+  }
+  
+  Plotly.redraw(this.gd);
+  
+  // NOTE: we purposely do _not_ restore selection(s), since on filter,
+  // axis likely will update, changing the pixel -> data mapping, leading 
+  // to a likely mismatch in the brush outline and highlighted marks
+  
+};
+
+TraceManager.prototype.updateSelection = function(group, keys) {
+  
+  if (keys !== null && !Array.isArray(keys)) {
+    throw new Error("Invalid keys argument; null or array expected");
+  }
+  
+  // if selection has been cleared, or if this is transient
+  // selection, delete the "selection traces"
+  var nNewTraces = this.gd.data.length - this.origData.length;
+  if (keys === null || !this.highlight.persistent && nNewTraces > 0) {
+    var tracesToRemove = [];
+    for (var i = 0; i < this.gd.data.length; i++) {
+      if (this.gd.data[i]._isCrosstalkTrace) tracesToRemove.push(i);
+    }
+    Plotly.deleteTraces(this.gd, tracesToRemove);
+    this.groupSelections[group] = keys;
+  } else {
+    // add to the groupSelection, rather than overwriting it
+    // TODO: can this be removed?
+    this.groupSelections[group] = this.groupSelections[group] || [];
+    for (var i = 0; i < keys.length; i++) {
+      var k = keys[i];
+      if (this.groupSelections[group].indexOf(k) < 0) {
+        this.groupSelections[group].push(k);
+      }
+    }
+  }
+  
+  if (keys === null) {
+    
+    Plotly.restyle(this.gd, {"opacity": this.origOpacity});
+    
+  } else if (keys.length >= 1) {
+    
+    // placeholder for new "selection traces"
+    var traces = [];
+    // this variable is set in R/highlight.R
+    var selectionColour = crosstalk.group(group).var("plotlySelectionColour").get() || 
+      this.highlight.color[0];
+
+    for (var i = 0; i < this.origData.length; i++) {
+      // TODO: try using Lib.extendFlat() as done in  
+      // https://github.com/plotly/plotly.js/pull/1136 
+      var trace = JSON.parse(JSON.stringify(this.gd.data[i]));
+      if (!trace.key || trace.set !== group) {
+        continue;
+      }
+      // Get sorted array of matching indices in trace.key
+      var matchFunc = getMatchFunc(trace);
+      var matches = matchFunc(trace.key, keys);
+      
+      if (matches.length > 0) {
+        // If this is a "simple" key, that means select the entire trace
+        if (!trace._isSimpleKey) {
+          trace = subsetArrayAttrs(trace, matches);
+        }
+        // reach into the full trace object so we can properly reflect the 
+        // selection attributes in every view
+        var d = this.gd._fullData[i];
+        
+        /* 
+        / Recursively inherit selection attributes from various sources, 
+        / in order of preference:
+        /  (1) official plotly.js selected attribute
+        /  (2) highlight(selected = attrs_selected(...))
+        */
+        // TODO: it would be neat to have a dropdown to dynamically specify these!
+        $.extend(true, trace, this.highlight.selected);
+        
+        // if it is defined, override color with the "dynamic brush color""
+        if (d.marker) {
+          trace.marker = trace.marker || {};
+          trace.marker.color =  selectionColour || trace.marker.color || d.marker.color;
+        }
+        if (d.line) {
+          trace.line = trace.line || {};
+          trace.line.color =  selectionColour || trace.line.color || d.line.color;
+        }
+        if (d.textfont) {
+          trace.textfont = trace.textfont || {};
+          trace.textfont.color =  selectionColour || trace.textfont.color || d.textfont.color;
+        }
+        if (d.fillcolor) {
+          // TODO: should selectionColour inherit alpha from the existing fillcolor?
+          trace.fillcolor = selectionColour || trace.fillcolor || d.fillcolor;
+        }
+        // attach a sensible name/legendgroup
+        trace.name = trace.name || keys.join("<br />");
+        trace.legendgroup = trace.legendgroup || keys.join("<br />");
+        
+        // keep track of mapping between this new trace and the trace it targets
+        // (necessary for updating frames to reflect the selection traces)
+        trace._originalIndex = i;
+        trace._newIndex = this.gd._fullData.length + traces.length;
+        trace._isCrosstalkTrace = true;
+        traces.push(trace);
+      }
+    }
+    
+    if (traces.length > 0) {
+      
+      Plotly.addTraces(this.gd, traces).then(function(gd) {
+        // incrementally add selection traces to frames
+        // (this is heavily inspired by Plotly.Plots.modifyFrames() 
+        // in src/plots/plots.js)
+        var _hash = gd._transitionData._frameHash;
+        var _frames = gd._transitionData._frames || [];
+        
+        for (var i = 0; i < _frames.length; i++) {
+          
+          // add to _frames[i].traces *if* this frame references selected trace(s)
+          var newIndices = [];
+          for (var j = 0; j < traces.length; j++) {
+            var tr = traces[j];
+            if (_frames[i].traces.indexOf(tr._originalIndex) > -1) {
+              newIndices.push(tr._newIndex);
+              _frames[i].traces.push(tr._newIndex);
+            }
+          }
+          
+          // nothing to do...
+          if (newIndices.length === 0) {
+            continue;
+          }
+          
+          var ctr = 0;
+          var nFrameTraces = _frames[i].data.length;
+          
+          for (var j = 0; j < nFrameTraces; j++) {
+            var frameTrace = _frames[i].data[j];
+            if (!frameTrace.key || frameTrace.set !== group) {
+              continue;
+            }
+            
+            var matchFunc = getMatchFunc(frameTrace);
+            var matches = matchFunc(frameTrace.key, keys);
+            
+            if (matches.length > 0) {
+              if (!trace._isSimpleKey) {
+                frameTrace = subsetArrayAttrs(frameTrace, matches);
+              }
+              var d = gd._fullData[newIndices[ctr]];
+              if (d.marker) {
+                frameTrace.marker = d.marker;
+              }
+              if (d.line) {
+                frameTrace.line = d.line;
+              }
+              if (d.textfont) {
+                frameTrace.textfont = d.textfont;
+              }
+              ctr = ctr + 1;
+              _frames[i].data.push(frameTrace);
+            }
+          }
+          
+          // update gd._transitionData._frameHash
+          _hash[_frames[i].name] = _frames[i];
+        }
+      
+      });
+      
+      // dim traces that have a set matching the set of selection sets
+      var tracesToDim = [],
+          opacities = [],
+          sets = Object.keys(this.groupSelections),
+          n = this.origData.length;
+          
+      for (var i = 0; i < n; i++) {
+        var opacity = this.origOpacity[i] || 1;
+        // have we already dimmed this trace? Or is this even worth doing?
+        if (opacity !== this.gd._fullData[i].opacity || this.highlight.opacityDim === 1) {
+          continue;
+        }
+        // is this set an element of the set of selection sets?
+        var matches = findMatches(sets, [this.gd.data[i].set]);
+        if (matches.length) {
+          tracesToDim.push(i);
+          opacities.push(opacity * this.highlight.opacityDim);
+        }
+      }
+      
+      if (tracesToDim.length > 0) {
+        Plotly.restyle(this.gd, {"opacity": opacities}, tracesToDim);
+        // turn off the selected/unselected API
+        Plotly.restyle(this.gd, {"selectedpoints": null});
+      }
+      
+    }
+    
+  }
+};
+
+/* 
+Note: in all of these match functions, we assume needleSet (i.e. the selected keys)
+is a 1D (or flat) array. The real difference is the meaning of haystack.
+findMatches() does the usual thing you'd expect for 
+linked brushing on a scatterplot matrix. findSimpleMatches() returns a match iff 
+haystack is a subset of the needleSet. findNestedMatches() returns 
+*/
+
+function getMatchFunc(trace) {
+  return (trace._isNestedKey) ? findNestedMatches : 
+    (trace._isSimpleKey) ? findSimpleMatches : findMatches;
+}
+
+// find matches for "flat" keys
+function findMatches(haystack, needleSet) {
+  var matches = [];
+  haystack.forEach(function(obj, i) {
+    if (obj === null || needleSet.indexOf(obj) >= 0) {
+      matches.push(i);
+    }
+  });
+  return matches;
+}
+
+// find matches for "simple" keys
+function findSimpleMatches(haystack, needleSet) {
+  var match = haystack.every(function(val) {
+    return val === null || needleSet.indexOf(val) >= 0;
+  });
+  // yes, this doesn't make much sense other than conforming 
+  // to the output type of the other match functions
+  return (match) ? [0] : []
+}
+
+// find matches for a "nested" haystack (2D arrays)
+function findNestedMatches(haystack, needleSet) {
+  var matches = [];
+  for (var i = 0; i < haystack.length; i++) {
+    var hay = haystack[i];
+    var match = hay.every(function(val) { 
+      return val === null || needleSet.indexOf(val) >= 0; 
+    });
+    if (match) {
+      matches.push(i);
+    }
+  }
+  return matches;
+}
+
+function isPlainObject(obj) {
+  return (
+    Object.prototype.toString.call(obj) === '[object Object]' &&
+    Object.getPrototypeOf(obj) === Object.prototype
+  );
+}
+
+function subsetArrayAttrs(obj, indices) {
+  var newObj = {};
+  Object.keys(obj).forEach(function(k) {
+    var val = obj[k];
+
+    if (k.charAt(0) === "_") {
+      newObj[k] = val;
+    } else if (k === "transforms" && Array.isArray(val)) {
+      newObj[k] = val.map(function(transform) {
+        return subsetArrayAttrs(transform, indices);
+      });
+    } else if (k === "colorscale" && Array.isArray(val)) {
+      newObj[k] = val;
+    } else if (isPlainObject(val)) {
+      newObj[k] = subsetArrayAttrs(val, indices);
+    } else if (Array.isArray(val)) {
+      newObj[k] = subsetArray(val, indices);
+    } else {
+      newObj[k] = val;
+    }
+  });
+  return newObj;
+}
+
+function subsetArray(arr, indices) {
+  var result = [];
+  for (var i = 0; i < indices.length; i++) {
+    result.push(arr[indices[i]]);
+  }
+  return result;
+}
+
+// Convenience function for removing plotly's brush 
+function removeBrush(el) {
+  var outlines = el.querySelectorAll(".select-outline");
+  for (var i = 0; i < outlines.length; i++) {
+    outlines[i].remove();
+  }
+}
+
+
+// https://davidwalsh.name/javascript-debounce-function
+
+// Returns a function, that, as long as it continues to be invoked, will not
+// be triggered. The function will be called after it stops being called for
+// N milliseconds. If `immediate` is passed, trigger the function on the
+// leading edge, instead of the trailing.
+function debounce(func, wait, immediate) {
+	var timeout;
+	return function() {
+		var context = this, args = arguments;
+		var later = function() {
+			timeout = null;
+			if (!immediate) func.apply(context, args);
+		};
+		var callNow = immediate && !timeout;
+		clearTimeout(timeout);
+		timeout = setTimeout(later, wait);
+		if (callNow) func.apply(context, args);
+	};
+};
+</script>
+<script>(function(global){"use strict";var undefined=void 0;var MAX_ARRAY_LENGTH=1e5;function Type(v){switch(typeof v){case"undefined":return"undefined";case"boolean":return"boolean";case"number":return"number";case"string":return"string";default:return v===null?"null":"object"}}function Class(v){return Object.prototype.toString.call(v).replace(/^\[object *|\]$/g,"")}function IsCallable(o){return typeof o==="function"}function ToObject(v){if(v===null||v===undefined)throw TypeError();return Object(v)}function ToInt32(v){return v>>0}function ToUint32(v){return v>>>0}var LN2=Math.LN2,abs=Math.abs,floor=Math.floor,log=Math.log,max=Math.max,min=Math.min,pow=Math.pow,round=Math.round;(function(){var orig=Object.defineProperty;var dom_only=!function(){try{return Object.defineProperty({},"x",{})}catch(_){return false}}();if(!orig||dom_only){Object.defineProperty=function(o,prop,desc){if(orig)try{return orig(o,prop,desc)}catch(_){}if(o!==Object(o))throw TypeError("Object.defineProperty called 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n=C.setTimeout(e,r);t.stop=function(){C.clearTimeout(n)}})},rt=E.createElement("input"),it=E.createElement("select").appendChild(E.createElement("option")),rt.type="checkbox",y.checkOn=""!==rt.value,y.optSelected=it.selected,(rt=E.createElement("input")).value="t",rt.type="radio",y.radioValue="t"===rt.value;var pt,dt=S.expr.attrHandle;S.fn.extend({attr:function(e,t){return $(this,S.attr,e,t,1<arguments.length)},removeAttr:function(e){return this.each(function(){S.removeAttr(this,e)})}}),S.extend({attr:function(e,t,n){var r,i,o=e.nodeType;if(3!==o&&8!==o&&2!==o)return"undefined"==typeof e.getAttribute?S.prop(e,t,n):(1===o&&S.isXMLDoc(e)||(i=S.attrHooks[t.toLowerCase()]||(S.expr.match.bool.test(t)?pt:void 0)),void 0!==n?null===n?void S.removeAttr(e,t):i&&"set"in i&&void 0!==(r=i.set(e,n,t))?r:(e.setAttribute(t,n+""),n):i&&"get"in i&&null!==(r=i.get(e,t))?r:null==(r=S.find.attr(e,t))?void 0:r)},attrHooks:{type:{set:function(e,t){if(!y.radioValue&&"radio"===t&&A(e,"input")){var n=e.value;return e.setAttribute("type",t),n&&(e.value=n),t}}}},removeAttr:function(e,t){var n,r=0,i=t&&t.match(P);if(i&&1===e.nodeType)while(n=i[r++])e.removeAttribute(n)}}),pt={set:function(e,t,n){return!1===t?S.removeAttr(e,n):e.setAttribute(n,n),n}},S.each(S.expr.match.bool.source.match(/\w+/g),function(e,t){var a=dt[t]||S.find.attr;dt[t]=function(e,t,n){var r,i,o=t.toLowerCase();return n||(i=dt[o],dt[o]=r,r=null!=a(e,t,n)?o:null,dt[o]=i),r}});var ht=/^(?:input|select|textarea|button)$/i,gt=/^(?:a|area)$/i;function vt(e){return(e.match(P)||[]).join(" ")}function yt(e){return e.getAttribute&&e.getAttribute("class")||""}function mt(e){return Array.isArray(e)?e:"string"==typeof e&&e.match(P)||[]}S.fn.extend({prop:function(e,t){return $(this,S.prop,e,t,1<arguments.length)},removeProp:function(e){return this.each(function(){delete this[S.propFix[e]||e]})}}),S.extend({prop:function(e,t,n){var r,i,o=e.nodeType;if(3!==o&&8!==o&&2!==o)return 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i=e.geoCentroid({type:"MultiPoint",coordinates:[t,r,n]}),a=[-i[0],-i[1]],o=e.geoRotation(a),s=at(ot(o(t)),ot(o(r)),ot(o(n)));s.invert=H(s);var l=e.geoProjection(s).rotate(a),c=l.center;return delete l.rotate,l.center=function(t){return arguments.length?c(o(t)):o.invert(c())},l.clipAngle(90)}function lt(t,e){var r=L(1-m(e));return[2/T*t*r,T*(1-r)]}function ct(t){var e=g(t);function r(t,r){return[t,(t?t/m(t):1)*(m(r)*o(t)-e*o(r))]}return r.invert=e?function(t,r){t&&(r*=m(t)/t);var n=o(t);return[t,2*a(L(n*n+e*e-r*r)-n,e-r)]}:function(t,e){return[t,S(t?e*g(t)/t:e)]},r}lt.invert=function(t,e){var r=(r=e/T-1)*r;return[r>0?t*L(y/r)/2:0,S(1-r)]};var ut=L(3);function ft(t,e){return[ut*t*(2*o(2*e/3)-1)/T,ut*T*m(e/3)]}function ht(t){var e=o(t);function r(t,r){return[t*e,m(r)/e]}return r.invert=function(t,r){return[t/e,S(r*e)]},r}function pt(t){var e=o(t);function r(t,r){return[t*e,(1+e)*g(r/2)]}return r.invert=function(t,r){return[t/e,2*i(r/(1+e))]},r}function dt(t,e){var r=L(8/(3*y));return[r*t*(1-n(e)/y),r*e]}function mt(t,e){var r=L(4-3*m(n(e)));return[2/L(6*y)*t*r,d(e)*L(2*y/3)*(2-r)]}function gt(t,e){var r=L(y*(4+y));return[2/r*t*(1+L(1-4*e*e/(y*y))),4/r*e]}function vt(t,e){var r=(2+x)*m(e);e/=2;for(var i=0,a=1/0;i<10&&n(a)>v;i++){var s=o(e);e-=a=(e+m(e)*(s+2)-r)/(2*s*(1+s))}return[2/L(y*(4+y))*t*(1+o(e)),2*L(y/(4+y))*m(e)]}function yt(t,e){return[t*(1+o(e))/L(2+y),2*e/L(2+y)]}function xt(t,e){for(var r=(1+x)*m(e),i=0,a=1/0;i<10&&n(a)>v;i++)e-=a=(e+m(e)-r)/(1+o(e));return r=L(2+y),[t*(1+o(e))/r,2*e/r]}ft.invert=function(t,e){var r=3*S(e/(ut*T));return[T*t/(ut*(2*o(2*r/3)-1)),r]},dt.invert=function(t,e){var r=L(8/(3*y)),i=e/r;return[t/(r*(1-n(i)/y)),i]},mt.invert=function(t,e){var r=2-n(e)/L(2*y/3);return[t*L(6*y)/(2*r),d(e)*S((4-r*r)/3)]},gt.invert=function(t,e){var r=L(y*(4+y))/2;return[t*r/(1+L(1-e*e*(4+y)/(4*y))),e*r/2]},vt.invert=function(t,e){var r=e*L((4+y)/y)/2,n=S(r),i=o(n);return[t/(2/L(y*(4+y))*(1+i)),S((n+r*(i+2))/(2+x))]},yt.invert=function(t,e){var r=L(2+y),n=e*r/2;return[r*t/(1+o(n)),n]},xt.invert=function(t,e){var r=1+x,n=L(r/2);return[2*t*n/(1+o(e*=n)),S((e+m(e))/r)]};var bt=3+2*w;function _t(t,e){var r=m(t/=2),n=o(t),a=L(o(e)),s=o(e/=2),l=m(e)/(s+w*n*a),u=L(2/(1+l*l)),f=L((w*s+(n+r)*a)/(w*s+(n-r)*a));return[bt*(u*(f-1/f)-2*c(f)),bt*(u*l*(f+1/f)-2*i(l))]}_t.invert=function(t,e){if(!(r=D.invert(t/1.2,1.065*e)))return null;var r,a=r[0],s=r[1],l=20;t/=bt,e/=bt;do{var h=a/2,p=s/2,d=m(h),g=o(h),y=m(p),b=o(p),T=o(s),k=L(T),A=y/(b+w*g*k),M=A*A,S=L(2/(1+M)),E=(w*b+(g+d)*k)/(w*b+(g-d)*k),C=L(E),P=C-1/C,I=C+1/C,O=S*P-2*c(C)-t,z=S*A*I-2*i(A)-e,R=y&&_*k*d*M/y,F=(w*g*b+k)/(2*(b+w*g*k)*(b+w*g*k)*k),B=-.5*A*S*S*S,N=B*R,j=B*F,U=(U=2*b+w*k*(g-d))*U*C,V=(w*g*b*k+T)/U,H=-w*d*y/(k*U),q=P*N-2*V/C+S*(V+V/E),G=P*j-2*H/C+S*(H+H/E),Y=A*I*N-2*R/(1+M)+S*I*R+S*A*(V-V/E),W=A*I*j-2*F/(1+M)+S*I*F+S*A*(H-H/E),X=G*Y-W*q;if(!X)break;var 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b=g*g,_=t+b*(e+b*(r+b*i)),w=g*(a-1+b*(s-c+b*l)),T=_*_+w*w,k=2*w,A=T/k,M=A*A,E=S(_/A)/y,C=d*E,P=_*_,I=(2*e+b*(4*r+6*b*i))*g,O=a+b*(3*s+5*b*l),z=(2*(_*I+w*(O-1))*k-T*(2*(O-1)))/(k*k),D=o(C),R=m(C),F=A*D,B=A*R,N=d/y*(1/L(1-P/M))*(I*A-_*z)/M,j=B-u,U=g*(1+b*c)+A-F-f,V=z*R+F*N,H=F*E,q=1+z-(z*D-B*N),G=B*E,Y=V*G-q*H;if(!Y)break;d-=h=(U*V-j*q)/Y,g-=p=(j*G-U*H)/Y}while((n(h)>v||n(p)>v)&&--x>0);return[d,g]},u}Tt.invert=function(t,e){var r=e/(1+wt);return[t&&t/(wt*L(1-r*r)),2*i(r)]},kt.invert=function(t,e){var r=i(e/T),n=o(r),a=2*r;return[t*T/2/(o(a)*n*n),a]};var It=Pt(2.8284,-1.6988,.75432,-.18071,1.76003,-.38914,.042555);var Ot=Pt(2.583819,-.835827,.170354,-.038094,1.543313,-.411435,.082742);var zt=Pt(5/6*y,-.62636,-.0344,0,1.3493,-.05524,0,.045);function Dt(t,e){var r=t*t,n=e*e;return[t*(1-.162388*n)*(.87-952426e-9*r*r),e*(1+n/12)]}Dt.invert=function(t,e){var r,i=t,a=e,o=50;do{var s=a*a;a-=r=(a*(1+s/12)-e)/(1+s/4)}while(n(r)>v&&--o>0);o=50,t/=1-.162388*s;do{var 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t[0]=1-(p+m),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+m),t[6]=d+g,t[7]=0,t[8]=h+v,t[9]=d-g,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],198:[function(t,e,r){e.exports=function(t,e){return t[0]=e[0],t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=e[1],t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=e[2],t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],199:[function(t,e,r){e.exports=function(t,e){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=e[0],t[13]=e[1],t[14]=e[2],t[15]=1,t}},{}],200:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=n,t[6]=r,t[7]=0,t[8]=0,t[9]=-r,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],201:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return 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n(t);f=m-_,h=g-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*m+o*g+s*v),t[13]=-(l*m+c*g+u*v),t[14]=-(f*m+h*g+p*v),t[15]=1,t}},{"./identity":204}],208:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],m=e[12],g=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*m,t[1]=x*i+b*l+_*h+w*g,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*m,t[5]=x*i+b*l+_*h+w*g,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*m,t[9]=x*i+b*l+_*h+w*g,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*m,t[13]=x*i+b*l+_*h+w*g,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],209:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},{}],210:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],211:[function(t,e,r){e.exports=function(t,e,r,n){var i=Math.tan(e.upDegrees*Math.PI/180),a=Math.tan(e.downDegrees*Math.PI/180),o=Math.tan(e.leftDegrees*Math.PI/180),s=Math.tan(e.rightDegrees*Math.PI/180),l=2/(o+s),c=2/(i+a);return t[0]=l,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=c,t[6]=0,t[7]=0,t[8]=-(o-s)*l*.5,t[9]=(i-a)*c*.5,t[10]=n/(r-n),t[11]=-1,t[12]=0,t[13]=0,t[14]=n*r/(r-n),t[15]=0,t}},{}],212:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);if(Math.abs(P)<1e-6)return null;E*=P=1/P,L*=P,C*=P,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],m=e[8],g=e[9],v=e[10],y=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,M=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+m*_,t[1]=l*x+h*b+g*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+m*k,t[5]=l*w+h*T+g*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*A+f*M+m*S,t[9]=l*A+h*M+g*S,t[10]=c*A+p*M+v*S,t[11]=u*A+d*M+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],213:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],214:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],215:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],216:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],217:[function(t,e,r){e.exports=function(t){return"mat4("+t[0]+", "+t[1]+", "+t[2]+", "+t[3]+", "+t[4]+", "+t[5]+", "+t[6]+", "+t[7]+", "+t[8]+", "+t[9]+", "+t[10]+", "+t[11]+", "+t[12]+", "+t[13]+", "+t[14]+", "+t[15]+")"}},{}],218:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,m=r[0],g=r[1],v=r[2];e===t?(t[12]=e[0]*m+e[4]*g+e[8]*v+e[12],t[13]=e[1]*m+e[5]*g+e[9]*v+e[13],t[14]=e[2]*m+e[6]*g+e[10]*v+e[14],t[15]=e[3]*m+e[7]*g+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*m+s*g+f*v+e[12],t[13]=i*m+l*g+h*v+e[13],t[14]=a*m+c*g+p*v+e[14],t[15]=o*m+u*g+d*v+e[15]);return t}},{}],219:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],220:[function(t,e,r){"use strict";var n=t("css-font"),i=t("pick-by-alias"),a=t("regl"),o=t("gl-util/context"),s=t("es6-weak-map"),l=t("color-normalize"),c=t("font-atlas"),u=t("typedarray-pool"),f=t("parse-rect"),h=t("is-plain-obj"),p=t("parse-unit"),d=t("to-px"),m=t("detect-kerning"),g=t("object-assign"),v=t("font-measure"),y=t("flatten-vertex-data"),x=t("bit-twiddle").nextPow2,b=new s,_=!1;if(document.body){var w=document.body.appendChild(document.createElement("div"));w.style.font="italic small-caps bold condensed 16px/2 cursive",getComputedStyle(w).fontStretch&&(_=!0),document.body.removeChild(w)}var T=function(t){!function(t){return"function"==typeof t&&t._gl&&t.prop&&t.texture&&t.buffer}(t)?this.gl=o(t):(t={regl:t},this.gl=t.regl._gl),this.shader=b.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=t.regl||a({gl:this.gl}),this.charBuffer=this.regl.buffer({type:"uint8",usage:"stream"}),this.sizeBuffer=this.regl.buffer({type:"float",usage:"stream"}),this.shader||(this.shader=this.createShader(),b.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(h(t)?t:{})};T.prototype.createShader=function(){var t=this.regl,e=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst alpha",dstAlpha:"one"}},stencil:{enable:!1},depth:{enable:!1},count:t.prop("count"),offset:t.prop("offset"),attributes:{charOffset:{offset:4,stride:8,buffer:t.this("sizeBuffer")},width:{offset:0,stride:8,buffer:t.this("sizeBuffer")},char:t.this("charBuffer"),position:t.this("position")},uniforms:{atlasSize:function(t,e){return[e.atlas.width,e.atlas.height]},atlasDim:function(t,e){return[e.atlas.cols,e.atlas.rows]},atlas:function(t,e){return e.atlas.texture},charStep:function(t,e){return e.atlas.step},em:function(t,e){return e.atlas.em},color:t.prop("color"),opacity:t.prop("opacity"),viewport:t.this("viewportArray"),scale:t.this("scale"),align:t.prop("align"),baseline:t.prop("baseline"),translate:t.this("translate"),positionOffset:t.prop("positionOffset")},primitive:"points",viewport:t.this("viewport"),vert:"\n\t\t\tprecision highp float;\n\t\t\tattribute float width, charOffset, char;\n\t\t\tattribute vec2 position;\n\t\t\tuniform float fontSize, charStep, em, align, baseline;\n\t\t\tuniform vec4 viewport;\n\t\t\tuniform vec4 color;\n\t\t\tuniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;\n\t\t\tvarying vec2 charCoord, charId;\n\t\t\tvarying float charWidth;\n\t\t\tvarying vec4 fontColor;\n\t\t\tvoid main () {\n\t\t\t\tvec2 offset = floor(em * (vec2(align + charOffset, baseline)\n\t\t\t\t\t+ vec2(positionOffset.x, -positionOffset.y)))\n\t\t\t\t\t/ (viewport.zw * scale.xy);\n\n\t\t\t\tvec2 position = (position + translate) * scale;\n\t\t\t\tposition += offset * scale;\n\n\t\t\t\tcharCoord = position * viewport.zw + viewport.xy;\n\n\t\t\t\tgl_Position = vec4(position * 2. - 1., 0, 1);\n\n\t\t\t\tgl_PointSize = charStep;\n\n\t\t\t\tcharId.x = mod(char, atlasDim.x);\n\t\t\t\tcharId.y = floor(char / atlasDim.x);\n\n\t\t\t\tcharWidth = width * em;\n\n\t\t\t\tfontColor = color / 255.;\n\t\t\t}",frag:"\n\t\t\tprecision highp float;\n\t\t\tuniform float fontSize, charStep, opacity;\n\t\t\tuniform vec2 atlasSize;\n\t\t\tuniform vec4 viewport;\n\t\t\tuniform sampler2D atlas;\n\t\t\tvarying vec4 fontColor;\n\t\t\tvarying vec2 charCoord, charId;\n\t\t\tvarying float charWidth;\n\n\t\t\tfloat lightness(vec4 color) {\n\t\t\t\treturn color.r * 0.299 + color.g * 0.587 + color.b * 0.114;\n\t\t\t}\n\n\t\t\tvoid main () {\n\t\t\t\tvec2 uv = gl_FragCoord.xy - charCoord + charStep * .5;\n\t\t\t\tfloat halfCharStep = floor(charStep * .5 + .5);\n\n\t\t\t\t// invert y and shift by 1px (FF expecially needs that)\n\t\t\t\tuv.y = charStep - uv.y;\n\n\t\t\t\t// ignore points outside of character bounding box\n\t\t\t\tfloat halfCharWidth = ceil(charWidth * .5);\n\t\t\t\tif (floor(uv.x) > halfCharStep + halfCharWidth ||\n\t\t\t\t\tfloor(uv.x) < halfCharStep - halfCharWidth) return;\n\n\t\t\t\tuv += charId * charStep;\n\t\t\t\tuv = uv / atlasSize;\n\n\t\t\t\tvec4 color = fontColor;\n\t\t\t\tvec4 mask = texture2D(atlas, uv);\n\n\t\t\t\tfloat maskY = lightness(mask);\n\t\t\t\t// float colorY = lightness(color);\n\t\t\t\tcolor.a *= maskY;\n\t\t\t\tcolor.a *= opacity;\n\n\t\t\t\t// color.a += .1;\n\n\t\t\t\t// antialiasing, see yiq color space y-channel formula\n\t\t\t\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\n\n\t\t\t\tgl_FragColor = color;\n\t\t\t}"});return{regl:t,draw:e,atlas:{}}},T.prototype.update=function(t){var e=this;if("string"==typeof t)t={text:t};else if(!t)return;null!=(t=i(t,{position:"position positions coord coords coordinates",font:"font fontFace fontface typeface cssFont css-font family fontFamily",fontSize:"fontSize fontsize size font-size",text:"text texts chars characters value values symbols",align:"align alignment textAlign textbaseline",baseline:"baseline textBaseline textbaseline",direction:"dir direction textDirection",color:"color colour fill fill-color fillColor textColor textcolor",kerning:"kerning kern",range:"range dataBox",viewport:"vp viewport viewBox viewbox viewPort",opacity:"opacity alpha transparency visible visibility opaque",offset:"offset positionOffset padding shift indent indentation"},!0)).opacity&&(Array.isArray(t.opacity)?this.opacity=t.opacity.map((function(t){return parseFloat(t)})):this.opacity=parseFloat(t.opacity)),null!=t.viewport&&(this.viewport=f(t.viewport),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null==this.viewport&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null!=t.kerning&&(this.kerning=t.kerning),null!=t.offset&&("number"==typeof t.offset&&(t.offset=[t.offset,0]),this.positionOffset=y(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),this.font.length||t.font||(t.font=T.baseFontSize+"px sans-serif");var r,a=!1,o=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach((function(t,r){if("string"==typeof t)try{t=n.parse(t)}catch(e){t=n.parse(T.baseFontSize+"px "+t)}else t=n.parse(n.stringify(t));var i=n.stringify({size:T.baseFontSize,family:t.family,stretch:_?t.stretch:void 0,variant:t.variant,weight:t.weight,style:t.style}),s=p(t.size),l=Math.round(s[0]*d(s[1]));if(l!==e.fontSize[r]&&(o=!0,e.fontSize[r]=l),!(e.font[r]&&i==e.font[r].baseString||(a=!0,e.font[r]=T.fonts[i],e.font[r]))){var c=t.family.join(", "),u=[t.style];t.style!=t.variant&&u.push(t.variant),t.variant!=t.weight&&u.push(t.weight),_&&t.weight!=t.stretch&&u.push(t.stretch),e.font[r]={baseString:i,family:c,weight:t.weight,stretch:t.stretch,style:t.style,variant:t.variant,width:{},kerning:{},metrics:v(c,{origin:"top",fontSize:T.baseFontSize,fontStyle:u.join(" ")})},T.fonts[i]=e.font[r]}})),(a||o)&&this.font.forEach((function(r,i){var a=n.stringify({size:e.fontSize[i],family:r.family,stretch:_?r.stretch:void 0,variant:r.variant,weight:r.weight,style:r.style});if(e.fontAtlas[i]=e.shader.atlas[a],!e.fontAtlas[i]){var o=r.metrics;e.shader.atlas[a]=e.fontAtlas[i]={fontString:a,step:2*Math.ceil(e.fontSize[i]*o.bottom*.5),em:e.fontSize[i],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:e.regl.texture()}}null==t.text&&(t.text=e.text)})),"string"==typeof t.text&&t.position&&t.position.length>2){for(var s=Array(.5*t.position.length),h=0;h<s.length;h++)s[h]=t.text;t.text=s}if(null!=t.text||a){if(this.textOffsets=[0],Array.isArray(t.text)){this.count=t.text[0].length,this.counts=[this.count];for(var b=1;b<t.text.length;b++)this.textOffsets[b]=this.textOffsets[b-1]+t.text[b-1].length,this.count+=t.text[b].length,this.counts.push(t.text[b].length);this.text=t.text.join("")}else this.text=t.text,this.count=this.text.length,this.counts=[this.count];r=[],this.font.forEach((function(t,n){T.atlasContext.font=t.baseString;for(var i=e.fontAtlas[n],a=0;a<e.text.length;a++){var o=e.text.charAt(a);if(null==i.ids[o]&&(i.ids[o]=i.chars.length,i.chars.push(o),r.push(o)),null==t.width[o]&&(t.width[o]=T.atlasContext.measureText(o).width/T.baseFontSize,e.kerning)){var s=[];for(var l in t.width)s.push(l+o,o+l);g(t.kerning,m(t.family,{pairs:s}))}}}))}if(t.position)if(t.position.length>2){for(var w=!t.position[0].length,k=u.mallocFloat(2*this.count),A=0,M=0;A<this.counts.length;A++){var S=this.counts[A];if(w)for(var E=0;E<S;E++)k[M++]=t.position[2*A],k[M++]=t.position[2*A+1];else for(var L=0;L<S;L++)k[M++]=t.position[A][0],k[M++]=t.position[A][1]}this.position.call?this.position({type:"float",data:k}):this.position=this.regl.buffer({type:"float",data:k}),u.freeFloat(k)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text||a){var C=u.mallocUint8(this.count),P=u.mallocFloat(2*this.count);this.textWidth=[];for(var I=0,O=0;I<this.counts.length;I++){for(var z=this.counts[I],D=this.font[I]||this.font[0],R=this.fontAtlas[I]||this.fontAtlas[0],F=0;F<z;F++){var B=this.text.charAt(O),N=this.text.charAt(O-1);if(C[O]=R.ids[B],P[2*O]=D.width[B],F){var j=P[2*O-2],U=P[2*O],V=P[2*O-1]+.5*j+.5*U;if(this.kerning){var H=D.kerning[N+B];H&&(V+=.001*H)}P[2*O+1]=V}else P[2*O+1]=.5*P[2*O];O++}this.textWidth.push(P.length?.5*P[2*O-2]+P[2*O-1]:0)}t.align||(t.align=this.align),this.charBuffer({data:C,type:"uint8",usage:"stream"}),this.sizeBuffer({data:P,type:"float",usage:"stream"}),u.freeUint8(C),u.freeFloat(P),r.length&&this.font.forEach((function(t,r){var n=e.fontAtlas[r],i=n.step,a=Math.floor(T.maxAtlasSize/i),o=Math.min(a,n.chars.length),s=Math.ceil(n.chars.length/o),l=x(o*i),u=x(s*i);n.width=l,n.height=u,n.rows=s,n.cols=o,n.em&&n.texture({data:c({canvas:T.atlasCanvas,font:n.fontString,chars:n.chars,shape:[l,u],step:[i,i]})})}))}if(t.align&&(this.align=t.align,this.alignOffset=this.textWidth.map((function(t,r){var n=Array.isArray(e.align)?e.align.length>1?e.align[r]:e.align[0]:e.align;if("number"==typeof n)return n;switch(n){case"right":case"end":return-t;case"center":case"centre":case"middle":return.5*-t}return 0}))),null==this.baseline&&null==t.baseline&&(t.baseline=0),null!=t.baseline&&(this.baseline=t.baseline,Array.isArray(this.baseline)||(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map((function(t,r){var n=(e.font[r]||e.font[0]).metrics,i=0;return i+=.5*n.bottom,i+="number"==typeof t?t-n.baseline:-n[t],i*=-1}))),null!=t.color)if(t.color||(t.color="transparent"),"string"!=typeof t.color&&isNaN(t.color)){var q;if("number"==typeof t.color[0]&&t.color.length>this.counts.length){var G=t.color.length;q=u.mallocUint8(G);for(var Y=(t.color.subarray||t.color.slice).bind(t.color),W=0;W<G;W+=4)q.set(l(Y(W,W+4),"uint8"),W)}else{var X=t.color.length;q=u.mallocUint8(4*X);for(var Z=0;Z<X;Z++)q.set(l(t.color[Z]||0,"uint8"),4*Z)}this.color=q}else this.color=l(t.color,"uint8");if(t.position||t.text||t.color||t.baseline||t.align||t.font||t.offset||t.opacity)if(this.color.length>4||this.baselineOffset.length>1||this.align&&this.align.length>1||this.fontAtlas.length>1||this.positionOffset.length>2){var J=Math.max(.5*this.position.length||0,.25*this.color.length||0,this.baselineOffset.length||0,this.alignOffset.length||0,this.font.length||0,this.opacity.length||0,.5*this.positionOffset.length||0);this.batch=Array(J);for(var K=0;K<this.batch.length;K++)this.batch[K]={count:this.counts.length>1?this.counts[K]:this.counts[0],offset:this.textOffsets.length>1?this.textOffsets[K]:this.textOffsets[0],color:this.color?this.color.length<=4?this.color:this.color.subarray(4*K,4*K+4):[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[K]:this.opacity,baseline:null!=this.baselineOffset[K]?this.baselineOffset[K]:this.baselineOffset[0],align:this.align?null!=this.alignOffset[K]?this.alignOffset[K]:this.alignOffset[0]:0,atlas:this.fontAtlas[K]||this.fontAtlas[0],positionOffset:this.positionOffset.length>2?this.positionOffset.subarray(2*K,2*K+2):this.positionOffset}}else this.count?this.batch=[{count:this.count,offset:0,color:this.color||[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[0]:this.opacity,baseline:this.baselineOffset[0],align:this.alignOffset?this.alignOffset[0]:0,atlas:this.fontAtlas[0],positionOffset:this.positionOffset}]:this.batch=[]},T.prototype.destroy=function(){},T.prototype.kerning=!0,T.prototype.position={constant:new Float32Array(2)},T.prototype.translate=null,T.prototype.scale=null,T.prototype.font=null,T.prototype.text="",T.prototype.positionOffset=[0,0],T.prototype.opacity=1,T.prototype.color=new Uint8Array([0,0,0,255]),T.prototype.alignOffset=[0,0],T.maxAtlasSize=1024,T.atlasCanvas=document.createElement("canvas"),T.atlasContext=T.atlasCanvas.getContext("2d",{alpha:!1}),T.baseFontSize=64,T.fonts={},e.exports=T},{"bit-twiddle":77,"color-normalize":84,"css-font":94,"detect-kerning":119,"es6-weak-map":177,"flatten-vertex-data":186,"font-atlas":187,"font-measure":188,"gl-util/context":221,"is-plain-obj":231,"object-assign":242,"parse-rect":244,"parse-unit":246,"pick-by-alias":248,regl:278,"to-px":309,"typedarray-pool":322}],221:[function(t,e,r){(function(r){(function(){"use strict";var n=t("pick-by-alias");function i(t){if(t.container)if(t.container==document.body)document.body.style.width||(t.canvas.width=t.width||t.pixelRatio*r.innerWidth),document.body.style.height||(t.canvas.height=t.height||t.pixelRatio*r.innerHeight);else{var e=t.container.getBoundingClientRect();t.canvas.width=t.width||e.right-e.left,t.canvas.height=t.height||e.bottom-e.top}}function 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0===s?jr(e.default,r.default):n(e,r,s,i,a)}}}(this._parameters,this._specification))};function Qr(t){var e=t.key,r=t.value,n=t.valueSpec||{},i=t.objectElementValidators||{},a=t.style,o=t.styleSpec,s=[],l=Fr(r);if("object"!==l)return[new Ct(e,r,"object expected, "+l+" found")];for(var c in r){var u=c.split(".")[0],f=n[u]||n["*"],h=void 0;if(i[u])h=i[u];else if(n[u])h=kn;else if(i["*"])h=i["*"];else{if(!n["*"]){s.push(new Ct(e,r[c],'unknown property "'+c+'"'));continue}h=kn}s=s.concat(h({key:(e?e+".":e)+c,value:r[c],valueSpec:f,style:a,styleSpec:o,object:r,objectKey:c},r))}for(var p in n)i[p]||n[p].required&&void 0===n[p].default&&void 0===r[p]&&s.push(new Ct(e,r,'missing required property "'+p+'"'));return s}function $r(t){var e=t.value,r=t.valueSpec,n=t.style,i=t.styleSpec,a=t.key,o=t.arrayElementValidator||kn;if("array"!==Fr(e))return[new Ct(a,e,"array expected, "+Fr(e)+" found")];if(r.length&&e.length!==r.length)return[new Ct(a,e,"array length "+r.length+" expected, length 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0===t.value.property,l=!s,c="array"===Fr(t.value.stops)&&"array"===Fr(t.value.stops[0])&&"object"===Fr(t.value.stops[0][0]),u=Qr({key:t.key,value:t.value,valueSpec:t.styleSpec.function,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{stops:function(t){if("identity"===a)return[new Ct(t.key,t.value,'identity function may not have a "stops" property')];var e=[],r=t.value;e=e.concat($r({key:t.key,value:r,valueSpec:t.valueSpec,style:t.style,styleSpec:t.styleSpec,arrayElementValidator:f})),"array"===Fr(r)&&0===r.length&&e.push(new Ct(t.key,r,"array must have at least one stop"));return e},default:function(t){return kn({key:t.key,value:t.value,valueSpec:i,style:t.style,styleSpec:t.styleSpec})}}});return"identity"===a&&s&&u.push(new Ct(t.key,t.value,'missing required property "property"')),"identity"===a||t.value.stops||u.push(new Ct(t.key,t.value,'missing required property "stops"')),"exponential"===a&&t.valueSpec.expression&&!Rr(t.valueSpec)&&u.push(new Ct(t.key,t.value,"exponential functions not supported")),t.styleSpec.$version>=8&&(l&&!zr(t.valueSpec)?u.push(new Ct(t.key,t.value,"property functions not supported")):s&&!Dr(t.valueSpec)&&u.push(new Ct(t.key,t.value,"zoom functions not supported"))),"categorical"!==a&&!c||void 0!==t.value.property||u.push(new Ct(t.key,t.value,'"property" property is required')),u;function f(t){var e=[],a=t.value,s=t.key;if("array"!==Fr(a))return[new Ct(s,a,"array expected, "+Fr(a)+" found")];if(2!==a.length)return[new Ct(s,a,"array length 2 expected, length "+a.length+" found")];if(c){if("object"!==Fr(a[0]))return[new Ct(s,a,"object expected, "+Fr(a[0])+" found")];if(void 0===a[0].zoom)return[new Ct(s,a,"object stop key must have zoom")];if(void 0===a[0].value)return[new Ct(s,a,"object stop key must have value")];if(n&&n>Ot(a[0].zoom))return[new Ct(s,a[0].zoom,"stop zoom values must appear in ascending order")];Ot(a[0].zoom)!==n&&(n=Ot(a[0].zoom),r=void 0,o={}),e=e.concat(Qr({key:s+"[0]",value:a[0],valueSpec:{zoom:{}},style:t.style,styleSpec:t.styleSpec,objectElementValidators:{zoom:tn,value:h}}))}else e=e.concat(h({key:s+"[0]",value:a[0],valueSpec:{},style:t.style,styleSpec:t.styleSpec},a));return Yr(zt(a[1]))?e.concat([new Ct(s+"[1]",a[1],"expressions are not allowed in function stops.")]):e.concat(kn({key:s+"[1]",value:a[1],valueSpec:i,style:t.style,styleSpec:t.styleSpec}))}function h(t,n){var s=Fr(t.value),l=Ot(t.value),c=null!==t.value?t.value:n;if(e){if(s!==e)return[new Ct(t.key,c,s+" stop domain type must match previous stop domain type "+e)]}else e=s;if("number"!==s&&"string"!==s&&"boolean"!==s)return[new Ct(t.key,c,"stop domain value must be a number, string, or boolean")];if("number"!==s&&"categorical"!==a){var u="number expected, "+s+" found";return zr(i)&&void 0===a&&(u+='\nIf you intended to use a categorical function, specify `"type": "categorical"`.'),[new Ct(t.key,c,u)]}return"categorical"!==a||"number"!==s||isFinite(l)&&Math.floor(l)===l?"categorical"!==a&&"number"===s&&void 0!==r&&l<r?[new Ct(t.key,c,"stop domain values must appear in ascending order")]:(r=l,"categorical"===a&&l in o?[new Ct(t.key,c,"stop domain values must be unique")]:(o[l]=!0,[])):[new Ct(t.key,c,"integer expected, found "+l)]}}function rn(t){var e=("property"===t.expressionContext?Jr:Wr)(zt(t.value),t.valueSpec);if("error"===e.result)return e.value.map((function(e){return new Ct(""+t.key+e.key,t.value,e.message)}));var r=e.value.expression||e.value._styleExpression.expression;if("property"===t.expressionContext&&"text-font"===t.propertyKey&&!r.outputDefined())return[new Ct(t.key,t.value,'Invalid data expression for "'+t.propertyKey+'". Output values must be contained as literals within the expression.')];if("property"===t.expressionContext&&"layout"===t.propertyType&&!Be(r))return[new Ct(t.key,t.value,'"feature-state" data expressions are not supported with layout properties.')];if("filter"===t.expressionContext&&!Be(r))return[new Ct(t.key,t.value,'"feature-state" data expressions are not supported with filters.')];if(t.expressionContext&&0===t.expressionContext.indexOf("cluster")){if(!Ne(r,["zoom","feature-state"]))return[new Ct(t.key,t.value,'"zoom" and "feature-state" expressions are not supported with cluster properties.')];if("cluster-initial"===t.expressionContext&&!Fe(r))return[new Ct(t.key,t.value,"Feature data expressions are not supported with initial expression part of cluster properties.")]}return[]}function nn(t){var e=t.key,r=t.value,n=t.valueSpec,i=[];return Array.isArray(n.values)?-1===n.values.indexOf(Ot(r))&&i.push(new Ct(e,r,"expected one of ["+n.values.join(", ")+"], "+JSON.stringify(r)+" found")):-1===Object.keys(n.values).indexOf(Ot(r))&&i.push(new Ct(e,r,"expected one of ["+Object.keys(n.values).join(", ")+"], "+JSON.stringify(r)+" found")),i}function an(t){if(!0===t||!1===t)return!0;if(!Array.isArray(t)||0===t.length)return!1;switch(t[0]){case"has":return t.length>=2&&"$id"!==t[1]&&"$type"!==t[1];case"in":return t.length>=3&&("string"!=typeof t[1]||Array.isArray(t[2]));case"!in":case"!has":case"none":return!1;case"==":case"!=":case">":case">=":case"<":case"<=":return 3!==t.length||Array.isArray(t[1])||Array.isArray(t[2]);case"any":case"all":for(var e=0,r=t.slice(1);e<r.length;e+=1){var n=r[e];if(!an(n)&&"boolean"!=typeof n)return!1}return!0;default:return!0}}Kr.deserialize=function(t){return new Kr(t._parameters,t._specification)},Kr.serialize=function(t){return{_parameters:t._parameters,_specification:t._specification}};var on={type:"boolean",default:!1,transition:!1,"property-type":"data-driven",expression:{interpolated:!1,parameters:["zoom","feature"]}};function sn(t){if(null==t)return{filter:function(){return!0},needGeometry:!1};an(t)||(t=cn(t));var e=Wr(t,on);if("error"===e.result)throw new Error(e.value.map((function(t){return t.key+": "+t.message})).join(", "));return{filter:function(t,r,n){return e.value.evaluate(t,r,{},n)},needGeometry:function t(e){if(!Array.isArray(e))return!1;if("within"===e[0])return!0;for(var r=1;r<e.length;r++)if(t(e[r]))return!0;return!1}(t)}}function ln(t,e){return t<e?-1:t>e?1:0}function cn(t){if(!t)return!0;var e,r=t[0];return t.length<=1?"any"!==r:"=="===r?un(t[1],t[2],"=="):"!="===r?pn(un(t[1],t[2],"==")):"<"===r||">"===r||"<="===r||">="===r?un(t[1],t[2],r):"any"===r?(e=t.slice(1),["any"].concat(e.map(cn))):"all"===r?["all"].concat(t.slice(1).map(cn)):"none"===r?["all"].concat(t.slice(1).map(cn).map(pn)):"in"===r?fn(t[1],t.slice(2)):"!in"===r?pn(fn(t[1],t.slice(2))):"has"===r?hn(t[1]):"!has"===r?pn(hn(t[1])):"within"!==r||t}function un(t,e,r){switch(t){case"$type":return["filter-type-"+r,e];case"$id":return["filter-id-"+r,e];default:return["filter-"+r,t,e]}}function fn(t,e){if(0===e.length)return!1;switch(t){case"$type":return["filter-type-in",["literal",e]];case"$id":return["filter-id-in",["literal",e]];default:return e.length>200&&!e.some((function(t){return typeof t!=typeof e[0]}))?["filter-in-large",t,["literal",e.sort(ln)]]:["filter-in-small",t,["literal",e]]}}function hn(t){switch(t){case"$type":return!0;case"$id":return["filter-has-id"];default:return["filter-has",t]}}function pn(t){return["!",t]}function dn(t){return an(zt(t.value))?rn(It({},t,{expressionContext:"filter",valueSpec:{value:"boolean"}})):function t(e){var r=e.value,n=e.key;if("array"!==Fr(r))return[new Ct(n,r,"array expected, "+Fr(r)+" found")];var i,a=e.styleSpec,o=[];if(r.length<1)return[new Ct(n,r,"filter array must have at least 1 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Ct(n,r,'filter array for "'+r[0]+'" operator must have 2 elements')):"string"!==i&&o.push(new Ct(n+"[1]",r[1],"string expected, "+i+" found"));break;case"within":i=Fr(r[1]),2!==r.length?o.push(new Ct(n,r,'filter array for "'+r[0]+'" operator must have 2 elements')):"object"!==i&&o.push(new Ct(n+"[1]",r[1],"object expected, "+i+" found"))}return o}(t)}function mn(t,e){var r=t.key,n=t.style,i=t.styleSpec,a=t.value,o=t.objectKey,s=i[e+"_"+t.layerType];if(!s)return[];var l=o.match(/^(.*)-transition$/);if("paint"===e&&l&&s[l[1]]&&s[l[1]].transition)return kn({key:r,value:a,valueSpec:i.transition,style:n,styleSpec:i});var c,u=t.valueSpec||s[o];if(!u)return[new Ct(r,a,'unknown property "'+o+'"')];if("string"===Fr(a)&&zr(u)&&!u.tokens&&(c=/^{([^}]+)}$/.exec(a)))return[new Ct(r,a,'"'+o+'" does not support interpolation syntax\nUse an identity property function instead: `{ "type": "identity", "property": '+JSON.stringify(c[1])+" }`.")];var f=[];return"symbol"===t.layerType&&("text-field"===o&&n&&!n.glyphs&&f.push(new Ct(r,a,'use of "text-field" requires a style "glyphs" property')),"text-font"===o&&Br(zt(a))&&"identity"===Ot(a.type)&&f.push(new Ct(r,a,'"text-font" does not support identity functions'))),f.concat(kn({key:t.key,value:a,valueSpec:u,style:n,styleSpec:i,expressionContext:"property",propertyType:e,propertyKey:o}))}function gn(t){return mn(t,"paint")}function vn(t){return mn(t,"layout")}function yn(t){var e=[],r=t.value,n=t.key,i=t.style,a=t.styleSpec;r.type||r.ref||e.push(new Ct(n,r,'either "type" or "ref" is required'));var o,s=Ot(r.type),l=Ot(r.ref);if(r.id)for(var c=Ot(r.id),u=0;u<t.arrayIndex;u++){var f=i.layers[u];Ot(f.id)===c&&e.push(new Ct(n,r.id,'duplicate layer id "'+r.id+'", previously used at line '+f.id.__line__))}if("ref"in r)["type","source","source-layer","filter","layout"].forEach((function(t){t in r&&e.push(new Ct(n,r[t],'"'+t+'" is prohibited for ref 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e.push(new Ct(n,r,'missing required property "source"'));return e=e.concat(Qr({key:n,value:r,valueSpec:a.layer,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(){return[]},type:function(){return kn({key:n+".type",value:r.type,valueSpec:a.layer.type,style:t.style,styleSpec:t.styleSpec,object:r,objectKey:"type"})},filter:dn,layout:function(t){return Qr({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(t){return vn(It({layerType:s},t))}}})},paint:function(t){return Qr({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(t){return gn(It({layerType:s},t))}}})}}}))}function xn(t){var e=t.value,r=t.key,n=Fr(e);return"string"!==n?[new Ct(r,e,"string expected, "+n+" found")]:[]}var bn={promoteId:function(t){var e=t.key,r=t.value;if("string"===Fr(r))return xn({key:e,value:r});var n=[];for(var i in r)n.push.apply(n,xn({key:e+"."+i,value:r[i]}));return n}};function _n(t){var e=t.value,r=t.key,n=t.styleSpec,i=t.style;if(!e.type)return[new Ct(r,e,'"type" is required')];var a,o=Ot(e.type);switch(o){case"vector":case"raster":case"raster-dem":return a=Qr({key:r,value:e,valueSpec:n["source_"+o.replace("-","_")],style:t.style,styleSpec:n,objectElementValidators:bn});case"geojson":if(a=Qr({key:r,value:e,valueSpec:n.source_geojson,style:i,styleSpec:n,objectElementValidators:bn}),e.cluster)for(var s in e.clusterProperties){var l=e.clusterProperties[s],c=l[0],u=l[1],f="string"==typeof c?[c,["accumulated"],["get",s]]:c;a.push.apply(a,rn({key:r+"."+s+".map",value:u,expressionContext:"cluster-map"})),a.push.apply(a,rn({key:r+"."+s+".reduce",value:f,expressionContext:"cluster-reduce"}))}return a;case"video":return Qr({key:r,value:e,valueSpec:n.source_video,style:i,styleSpec:n});case"image":return Qr({key:r,value:e,valueSpec:n.source_image,style:i,styleSpec:n});case"canvas":return[new Ct(r,null,"Please use runtime APIs to add canvas sources, rather 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vi=function(t,e,r,n,i){this.property=t,this.value=e,this.begin=i+n.delay||0,this.end=this.begin+n.duration||0,t.specification.transition&&(n.delay||n.duration)&&(this.prior=r)};vi.prototype.possiblyEvaluate=function(t,e,r){var n=t.now||0,i=this.value.possiblyEvaluate(t,e,r),a=this.prior;if(a){if(n>this.end)return this.prior=null,i;if(this.value.isDataDriven())return this.prior=null,i;if(n<this.begin)return a.possiblyEvaluate(t,e,r);var o=(n-this.begin)/(this.end-this.begin);return this.property.interpolate(a.possiblyEvaluate(t,e,r),i,function(t){if(t<=0)return 0;if(t>=1)return 1;var e=t*t,r=e*t;return 4*(t<.5?r:3*(t-e)+r-.75)}(o))}return i};var yi=function(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)};yi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new _i(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n},yi.prototype.hasTransition=function(){for(var t=0,e=Object.keys(this._values);t<e.length;t+=1){var r=e[t];if(this._values[r].prior)return!0}return!1};var xi=function(t){this._properties=t,this._values=Object.create(t.defaultPropertyValues)};xi.prototype.getValue=function(t){return x(this._values[t].value)},xi.prototype.setValue=function(t,e){this._values[t]=new di(this._values[t].property,null===e?void 0:x(e))},xi.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i)}return t},xi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new _i(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n};var bi=function(t,e,r){this.property=t,this.value=e,this.parameters=r};bi.prototype.isConstant=function(){return"constant"===this.value.kind},bi.prototype.constantOr=function(t){return"constant"===this.value.kind?this.value.value:t},bi.prototype.evaluate=function(t,e,r,n){return this.property.evaluate(this.value,this.parameters,t,e,r,n)};var _i=function(t){this._properties=t,this._values=Object.create(t.defaultPossiblyEvaluatedValues)};_i.prototype.get=function(t){return this._values[t]};var wi=function(t){this.specification=t};wi.prototype.possiblyEvaluate=function(t,e){return t.expression.evaluate(e)},wi.prototype.interpolate=function(t,e,r){var n=Ge[this.specification.type];return n?n(t,e,r):t};var Ti=function(t,e){this.specification=t,this.overrides=e};Ti.prototype.possiblyEvaluate=function(t,e,r,n){return"constant"===t.expression.kind||"camera"===t.expression.kind?new bi(this,{kind:"constant",value:t.expression.evaluate(e,null,{},r,n)},e):new bi(this,t.expression,e)},Ti.prototype.interpolate=function(t,e,r){if("constant"!==t.value.kind||"constant"!==e.value.kind)return t;if(void 0===t.value.value||void 0===e.value.value)return new bi(this,{kind:"constant",value:void 0},t.parameters);var n=Ge[this.specification.type];return n?new bi(this,{kind:"constant",value:n(t.value.value,e.value.value,r)},t.parameters):t},Ti.prototype.evaluate=function(t,e,r,n,i,a){return"constant"===t.kind?t.value:t.evaluate(e,r,n,i,a)};var ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0===t.value)return new bi(this,{kind:"constant",value:void 0},e);if("constant"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n),a="resolvedImage"===t.property.specification.type&&"string"!=typeof i?i.name:i,o=this._calculate(a,a,a,e);return new bi(this,{kind:"constant",value:o},e)}if("camera"===t.expression.kind){var s=this._calculate(t.expression.evaluate({zoom:e.zoom-1}),t.expression.evaluate({zoom:e.zoom}),t.expression.evaluate({zoom:e.zoom+1}),e);return new bi(this,{kind:"constant",value:s},e)}return new bi(this,t.expression,e)},e.prototype.evaluate=function(t,e,r,n,i,a){if("source"===t.kind){var o=t.evaluate(e,r,n,i,a);return this._calculate(o,o,o,e)}return"composite"===t.kind?this._calculate(t.evaluate({zoom:Math.floor(e.zoom)-1},r,n),t.evaluate({zoom:Math.floor(e.zoom)},r,n),t.evaluate({zoom:Math.floor(e.zoom)+1},r,n),e):t.value},e.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},e.prototype.interpolate=function(t){return t},e}(Ti),Ai=function(t){this.specification=t};Ai.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0!==t.value){if("constant"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n);return this._calculate(i,i,i,e)}return this._calculate(t.expression.evaluate(new pi(Math.floor(e.zoom-1),e)),t.expression.evaluate(new pi(Math.floor(e.zoom),e)),t.expression.evaluate(new pi(Math.floor(e.zoom+1),e)),e)}},Ai.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},Ai.prototype.interpolate=function(t){return t};var Mi=function(t){this.specification=t};Mi.prototype.possiblyEvaluate=function(t,e,r,n){return!!t.expression.evaluate(e,null,{},r,n)},Mi.prototype.interpolate=function(){return!1};var Si=function(t){for(var e in this.properties=t,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[],t){var r=t[e];r.specification.overridable&&this.overridableProperties.push(e);var n=this.defaultPropertyValues[e]=new di(r,void 0),i=this.defaultTransitionablePropertyValues[e]=new mi(r);this.defaultTransitioningPropertyValues[e]=i.untransitioned(),this.defaultPossiblyEvaluatedValues[e]=n.possiblyEvaluate({})}};Nn("DataDrivenProperty",Ti),Nn("DataConstantProperty",wi),Nn("CrossFadedDataDrivenProperty",ki),Nn("CrossFadedProperty",Ai),Nn("ColorRampProperty",Mi);var Ei=function(t){function e(e,r){if(t.call(this),this.id=e.id,this.type=e.type,this._featureFilter={filter:function(){return!0},needGeometry:!1},"custom"!==e.type&&(e=e,this.metadata=e.metadata,this.minzoom=e.minzoom,this.maxzoom=e.maxzoom,"background"!==e.type&&(this.source=e.source,this.sourceLayer=e["source-layer"],this.filter=e.filter),r.layout&&(this._unevaluatedLayout=new xi(r.layout)),r.paint)){for(var n in this._transitionablePaint=new gi(r.paint),e.paint)this.setPaintProperty(n,e.paint[n],{validate:!1});for(var i in e.layout)this.setLayoutProperty(i,e.layout[i],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new _i(r.paint)}}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getCrossfadeParameters=function(){return this._crossfadeParameters},e.prototype.getLayoutProperty=function(t){return"visibility"===t?this.visibility:this._unevaluatedLayout.getValue(t)},e.prototype.setLayoutProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n="layers."+this.id+".layout."+t;if(this._validate(In,n,t,e,r))return}"visibility"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e},e.prototype.getPaintProperty=function(t){return g(t,"-transition")?this._transitionablePaint.getTransition(t.slice(0,-"-transition".length)):this._transitionablePaint.getValue(t)},e.prototype.setPaintProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n="layers."+this.id+".paint."+t;if(this._validate(Pn,n,t,e,r))return!1}if(g(t,"-transition"))return this._transitionablePaint.setTransition(t.slice(0,-"-transition".length),e||void 0),!1;var i=this._transitionablePaint._values[t],a="cross-faded-data-driven"===i.property.specification["property-type"],o=i.value.isDataDriven(),s=i.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var l=this._transitionablePaint._values[t].value;return l.isDataDriven()||o||a||this._handleOverridablePaintPropertyUpdate(t,s,l)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t<this.minzoom)||(!!(this.maxzoom&&t>=this.maxzoom)||"none"===this.visibility)},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var t={id:this.id,type:this.type,source:this.source,"source-layer":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),y(t,(function(t,e){return!(void 0===t||"layout"===e&&!Object.keys(t).length||"paint"===e&&!Object.keys(t).length)}))},e.prototype._validate=function(t,e,r,n,i){return void 0===i&&(i={}),(!i||!1!==i.validate)&&On(this,t.call(Ln,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:Lt,style:{glyphs:!0,sprite:!0}}))},e.prototype.is3D=function(){return!1},e.prototype.isTileClipped=function(){return!1},e.prototype.hasOffscreenPass=function(){return!1},e.prototype.resize=function(){},e.prototype.isStateDependent=function(){for(var t in this.paint._values){var e=this.paint.get(t);if(e instanceof bi&&zr(e.property.specification)&&(("source"===e.value.kind||"composite"===e.value.kind)&&e.value.isStateDependent))return!0}return!1},e}(Et),Li={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array},Ci=function(t,e){this._structArray=t,this._pos1=e*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8},Pi=function(){this.isTransferred=!1,this.capacity=-1,this.resize(0)};function Ii(t,e){void 0===e&&(e=1);var r=0,n=0;return{members:t.map((function(t){var i,a=(i=t.type,Li[i].BYTES_PER_ELEMENT),o=r=Oi(r,Math.max(e,a)),s=t.components||1;return n=Math.max(n,a),r+=a*s,{name:t.name,type:t.type,components:s,offset:o}})),size:Oi(r,Math.max(n,e)),alignment:e}}function Oi(t,e){return Math.ceil(t/e)*e}Pi.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},Pi.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},Pi.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},Pi.prototype.clear=function(){this.length=0},Pi.prototype.resize=function(t){this.reserve(t),this.length=t},Pi.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},Pi.prototype._refreshViews=function(){throw new Error("_refreshViews() must be implemented by each concrete StructArray layout")};var zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t},e}(Pi);zi.prototype.bytesPerElement=4,Nn("StructArrayLayout2i4",zi);var Di=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.int16[a+0]=e,this.int16[a+1]=r,this.int16[a+2]=n,this.int16[a+3]=i,t},e}(Pi);Di.prototype.bytesPerElement=8,Nn("StructArrayLayout4i8",Di);var Ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Ri.prototype.bytesPerElement=12,Nn("StructArrayLayout2i4i12",Ri);var Fi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t},e}(Pi);Fi.prototype.bytesPerElement=8,Nn("StructArrayLayout2i4ub8",Fi);var Bi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c){var u=this.length;return this.resize(u+1),this.emplace(u,t,e,r,n,i,a,o,s,l,c)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u){var f=9*t,h=18*t;return this.uint16[f+0]=e,this.uint16[f+1]=r,this.uint16[f+2]=n,this.uint16[f+3]=i,this.uint16[f+4]=a,this.uint16[f+5]=o,this.uint16[f+6]=s,this.uint16[f+7]=l,this.uint8[h+16]=c,this.uint8[h+17]=u,t},e}(Pi);Bi.prototype.bytesPerElement=18,Nn("StructArrayLayout8ui2ub18",Bi);var Ni=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f){var h=this.length;return this.resize(h+1),this.emplace(h,t,e,r,n,i,a,o,s,l,c,u,f)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=12*t;return this.int16[p+0]=e,this.int16[p+1]=r,this.int16[p+2]=n,this.int16[p+3]=i,this.uint16[p+4]=a,this.uint16[p+5]=o,this.uint16[p+6]=s,this.uint16[p+7]=l,this.int16[p+8]=c,this.int16[p+9]=u,this.int16[p+10]=f,this.int16[p+11]=h,t},e}(Pi);Ni.prototype.bytesPerElement=24,Nn("StructArrayLayout4i4ui4i24",Ni);var ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t},e}(Pi);ji.prototype.bytesPerElement=12,Nn("StructArrayLayout3f12",ji);var Ui=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint32[r+0]=e,t},e}(Pi);Ui.prototype.bytesPerElement=4,Nn("StructArrayLayout1ul4",Ui);var Vi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l){var c=this.length;return this.resize(c+1),this.emplace(c,t,e,r,n,i,a,o,s,l)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c){var u=10*t,f=5*t;return this.int16[u+0]=e,this.int16[u+1]=r,this.int16[u+2]=n,this.int16[u+3]=i,this.int16[u+4]=a,this.int16[u+5]=o,this.uint32[f+3]=s,this.uint16[u+8]=l,this.uint16[u+9]=c,t},e}(Pi);Vi.prototype.bytesPerElement=20,Nn("StructArrayLayout6i1ul2ui20",Vi);var Hi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Hi.prototype.bytesPerElement=12,Nn("StructArrayLayout2i2i2i12",Hi);var qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i){var a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)},e.prototype.emplace=function(t,e,r,n,i,a){var o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t},e}(Pi);qi.prototype.bytesPerElement=16,Nn("StructArrayLayout2f1f2i16",qi);var Gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=12*t,o=3*t;return this.uint8[a+0]=e,this.uint8[a+1]=r,this.float32[o+1]=n,this.float32[o+2]=i,t},e}(Pi);Gi.prototype.bytesPerElement=12,Nn("StructArrayLayout2ub2f12",Gi);var Yi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.uint16[i+0]=e,this.uint16[i+1]=r,this.uint16[i+2]=n,t},e}(Pi);Yi.prototype.bytesPerElement=6,Nn("StructArrayLayout3ui6",Yi);var Wi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g){var v=this.length;return this.resize(v+1),this.emplace(v,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v){var y=24*t,x=12*t,b=48*t;return this.int16[y+0]=e,this.int16[y+1]=r,this.uint16[y+2]=n,this.uint16[y+3]=i,this.uint32[x+2]=a,this.uint32[x+3]=o,this.uint32[x+4]=s,this.uint16[y+10]=l,this.uint16[y+11]=c,this.uint16[y+12]=u,this.float32[x+7]=f,this.float32[x+8]=h,this.uint8[b+36]=p,this.uint8[b+37]=d,this.uint8[b+38]=m,this.uint32[x+10]=g,this.int16[y+22]=v,t},e}(Pi);Wi.prototype.bytesPerElement=48,Nn("StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48",Wi);var Xi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S){var E=this.length;return this.resize(E+1),this.emplace(E,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E){var L=34*t,C=17*t;return this.int16[L+0]=e,this.int16[L+1]=r,this.int16[L+2]=n,this.int16[L+3]=i,this.int16[L+4]=a,this.int16[L+5]=o,this.int16[L+6]=s,this.int16[L+7]=l,this.uint16[L+8]=c,this.uint16[L+9]=u,this.uint16[L+10]=f,this.uint16[L+11]=h,this.uint16[L+12]=p,this.uint16[L+13]=d,this.uint16[L+14]=m,this.uint16[L+15]=g,this.uint16[L+16]=v,this.uint16[L+17]=y,this.uint16[L+18]=x,this.uint16[L+19]=b,this.uint16[L+20]=_,this.uint16[L+21]=w,this.uint16[L+22]=T,this.uint32[C+12]=k,this.float32[C+13]=A,this.float32[C+14]=M,this.float32[C+15]=S,this.float32[C+16]=E,t},e}(Pi);Xi.prototype.bytesPerElement=68,Nn("StructArrayLayout8i15ui1ul4f68",Xi);var Zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.float32[r+0]=e,t},e}(Pi);Zi.prototype.bytesPerElement=4,Nn("StructArrayLayout1f4",Zi);var Ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.int16[i+0]=e,this.int16[i+1]=r,this.int16[i+2]=n,t},e}(Pi);Ji.prototype.bytesPerElement=6,Nn("StructArrayLayout3i6",Ji);var Ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=2*t,a=4*t;return this.uint32[i+0]=e,this.uint16[a+2]=r,this.uint16[a+3]=n,t},e}(Pi);Ki.prototype.bytesPerElement=8,Nn("StructArrayLayout1ul2ui8",Ki);var Qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.uint16[n+0]=e,this.uint16[n+1]=r,t},e}(Pi);Qi.prototype.bytesPerElement=4,Nn("StructArrayLayout2ui4",Qi);var $i=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint16[r+0]=e,t},e}(Pi);$i.prototype.bytesPerElement=2,Nn("StructArrayLayout1ui2",$i);var ta=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.float32[n+0]=e,this.float32[n+1]=r,t},e}(Pi);ta.prototype.bytesPerElement=8,Nn("StructArrayLayout2f8",ta);var ea=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.float32[a+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,this.float32[a+3]=i,t},e}(Pi);ea.prototype.bytesPerElement=16,Nn("StructArrayLayout4f16",ea);var ra=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},anchorPoint:{configurable:!0}};return r.anchorPointX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorPointY.get=function(){return this._structArray.int16[this._pos2+1]},r.x1.get=function(){return this._structArray.int16[this._pos2+2]},r.y1.get=function(){return this._structArray.int16[this._pos2+3]},r.x2.get=function(){return this._structArray.int16[this._pos2+4]},r.y2.get=function(){return this._structArray.int16[this._pos2+5]},r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+8]},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.anchorPoint.get=function(){return new i(this.anchorPointX,this.anchorPointY)},Object.defineProperties(e.prototype,r),e}(Ci);ra.prototype.size=20;var na=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ra(this,t)},e}(Vi);Nn("CollisionBoxArray",na);var ia=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},glyphStartIndex:{configurable:!0},numGlyphs:{configurable:!0},vertexStartIndex:{configurable:!0},lineStartIndex:{configurable:!0},lineLength:{configurable:!0},segment:{configurable:!0},lowerSize:{configurable:!0},upperSize:{configurable:!0},lineOffsetX:{configurable:!0},lineOffsetY:{configurable:!0},writingMode:{configurable:!0},placedOrientation:{configurable:!0},hidden:{configurable:!0},crossTileID:{configurable:!0},associatedIconIndex:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.glyphStartIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.numGlyphs.get=function(){return this._structArray.uint16[this._pos2+3]},r.vertexStartIndex.get=function(){return this._structArray.uint32[this._pos4+2]},r.lineStartIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.lineLength.get=function(){return this._structArray.uint32[this._pos4+4]},r.segment.get=function(){return this._structArray.uint16[this._pos2+10]},r.lowerSize.get=function(){return this._structArray.uint16[this._pos2+11]},r.upperSize.get=function(){return this._structArray.uint16[this._pos2+12]},r.lineOffsetX.get=function(){return this._structArray.float32[this._pos4+7]},r.lineOffsetY.get=function(){return this._structArray.float32[this._pos4+8]},r.writingMode.get=function(){return this._structArray.uint8[this._pos1+36]},r.placedOrientation.get=function(){return this._structArray.uint8[this._pos1+37]},r.placedOrientation.set=function(t){this._structArray.uint8[this._pos1+37]=t},r.hidden.get=function(){return this._structArray.uint8[this._pos1+38]},r.hidden.set=function(t){this._structArray.uint8[this._pos1+38]=t},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+10]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+10]=t},r.associatedIconIndex.get=function(){return this._structArray.int16[this._pos2+22]},Object.defineProperties(e.prototype,r),e}(Ci);ia.prototype.size=48;var aa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ia(this,t)},e}(Wi);Nn("PlacedSymbolArray",aa);var oa=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},rightJustifiedTextSymbolIndex:{configurable:!0},centerJustifiedTextSymbolIndex:{configurable:!0},leftJustifiedTextSymbolIndex:{configurable:!0},verticalPlacedTextSymbolIndex:{configurable:!0},placedIconSymbolIndex:{configurable:!0},verticalPlacedIconSymbolIndex:{configurable:!0},key:{configurable:!0},textBoxStartIndex:{configurable:!0},textBoxEndIndex:{configurable:!0},verticalTextBoxStartIndex:{configurable:!0},verticalTextBoxEndIndex:{configurable:!0},iconBoxStartIndex:{configurable:!0},iconBoxEndIndex:{configurable:!0},verticalIconBoxStartIndex:{configurable:!0},verticalIconBoxEndIndex:{configurable:!0},featureIndex:{configurable:!0},numHorizontalGlyphVertices:{configurable:!0},numVerticalGlyphVertices:{configurable:!0},numIconVertices:{configurable:!0},numVerticalIconVertices:{configurable:!0},useRuntimeCollisionCircles:{configurable:!0},crossTileID:{configurable:!0},textBoxScale:{configurable:!0},textOffset0:{configurable:!0},textOffset1:{configurable:!0},collisionCircleDiameter:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.rightJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+2]},r.centerJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+3]},r.leftJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+4]},r.verticalPlacedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+5]},r.placedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+6]},r.verticalPlacedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+7]},r.key.get=function(){return this._structArray.uint16[this._pos2+8]},r.textBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.textBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+10]},r.verticalTextBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+11]},r.verticalTextBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+12]},r.iconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+13]},r.iconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+14]},r.verticalIconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+15]},r.verticalIconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+16]},r.featureIndex.get=function(){return this._structArray.uint16[this._pos2+17]},r.numHorizontalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+18]},r.numVerticalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+19]},r.numIconVertices.get=function(){return this._structArray.uint16[this._pos2+20]},r.numVerticalIconVertices.get=function(){return this._structArray.uint16[this._pos2+21]},r.useRuntimeCollisionCircles.get=function(){return this._structArray.uint16[this._pos2+22]},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+12]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+12]=t},r.textBoxScale.get=function(){return this._structArray.float32[this._pos4+13]},r.textOffset0.get=function(){return this._structArray.float32[this._pos4+14]},r.textOffset1.get=function(){return this._structArray.float32[this._pos4+15]},r.collisionCircleDiameter.get=function(){return this._structArray.float32[this._pos4+16]},Object.defineProperties(e.prototype,r),e}(Ci);oa.prototype.size=68;var sa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new oa(this,t)},e}(Xi);Nn("SymbolInstanceArray",sa);var la=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getoffsetX=function(t){return this.float32[1*t+0]},e}(Zi);Nn("GlyphOffsetArray",la);var ca=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getx=function(t){return this.int16[3*t+0]},e.prototype.gety=function(t){return this.int16[3*t+1]},e.prototype.gettileUnitDistanceFromAnchor=function(t){return this.int16[3*t+2]},e}(Ji);Nn("SymbolLineVertexArray",ca);var ua=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0}};return r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+0]},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+3]},Object.defineProperties(e.prototype,r),e}(Ci);ua.prototype.size=8;var fa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ua(this,t)},e}(Ki);Nn("FeatureIndexArray",fa);var ha=Ii([{name:"a_pos",components:2,type:"Int16"}],4).members,pa=function(t){void 0===t&&(t=[]),this.segments=t};function da(t,e){return 256*(t=l(Math.floor(t),0,255))+(e=l(Math.floor(e),0,255))}pa.prototype.prepareSegment=function(t,e,r,n){var i=this.segments[this.segments.length-1];return t>pa.MAX_VERTEX_ARRAY_LENGTH&&_("Max vertices per segment is "+pa.MAX_VERTEX_ARRAY_LENGTH+": bucket requested "+t),(!i||i.vertexLength+t>pa.MAX_VERTEX_ARRAY_LENGTH||i.sortKey!==n)&&(i={vertexOffset:e.length,primitiveOffset:r.length,vertexLength:0,primitiveLength:0},void 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t.paint._values)if(r(a)){var o=t.paint.get(a);if(o instanceof bi&&zr(o.property.specification)){var s=Va(a,t.type),l=o.value,c=o.property.specification.type,u=o.property.useIntegerZoom,f=o.property.specification["property-type"],h="cross-faded"===f||"cross-faded-data-driven"===f;if("constant"===l.kind)this.binders[a]=h?new Ra(l.value,s):new Da(l.value,s,c),i.push("/u_"+a);else if("source"===l.kind||h){var p=Ha(a,c,"source");this.binders[a]=h?new Na(l,c,u,e,p,t.id):new Fa(l,s,c,p),i.push("/a_"+a)}else{var d=Ha(a,c,"composite");this.binders[a]=new Ba(l,s,c,u,e,d),i.push("/z_"+a)}}}this.cacheKey=i.sort().join("")};ja.prototype.getMaxValue=function(t){var e=this.binders[t];return e instanceof Fa||e instanceof Ba?e.maxValue:0},ja.prototype.populatePaintArrays=function(t,e,r,n,i){for(var a in this.binders){var o=this.binders[a];(o instanceof Fa||o instanceof Ba||o instanceof Na)&&o.populatePaintArray(t,e,r,n,i)}},ja.prototype.setConstantPatternPositions=function(t,e){for(var r in 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c=t.readVarint();n=7&c,a=c>>3}if(a--,1===n||2===n)o+=t.readSVarint(),s+=t.readSVarint(),1===n&&(e&&l.push(e),e=[]),e.push(new i(o,s));else{if(7!==n)throw new Error("unknown command "+n);e&&e.push(e[0].clone())}}return e&&l.push(e),l},ws.prototype.bbox=function(){var t=this._pbf;t.pos=this._geometry;for(var e=t.readVarint()+t.pos,r=1,n=0,i=0,a=0,o=1/0,s=-1/0,l=1/0,c=-1/0;t.pos<e;){if(n<=0){var u=t.readVarint();r=7&u,n=u>>3}if(n--,1===r||2===r)(i+=t.readSVarint())<o&&(o=i),i>s&&(s=i),(a+=t.readSVarint())<l&&(l=a),a>c&&(c=a);else if(7!==r)throw new Error("unknown command "+r)}return[o,l,s,c]},ws.prototype.toGeoJSON=function(t,e,r){var n,i,a=this.extent*Math.pow(2,r),o=this.extent*t,s=this.extent*e,l=this.loadGeometry(),c=ws.types[this.type];function u(t){for(var e=0;e<t.length;e++){var r=t[e],n=180-360*(r.y+s)/a;t[e]=[360*(r.x+o)/a-180,360/Math.PI*Math.atan(Math.exp(n*Math.PI/180))-90]}}switch(this.type){case 1:var f=[];for(n=0;n<l.length;n++)f[n]=l[n][0];u(l=f);break;case 2:for(n=0;n<l.length;n++)u(l[n]);break;case 3:for(l=function(t){var e=t.length;if(e<=1)return[t];for(var r,n,i=[],a=0;a<e;a++){var o=ks(t[a]);0!==o&&(void 0===n&&(n=o<0),n===o<0?(r&&i.push(r),r=[t[a]]):r.push(t[a]))}r&&i.push(r);return i}(l),n=0;n<l.length;n++)for(i=0;i<l[n].length;i++)u(l[n][i])}1===l.length?l=l[0]:c="Multi"+c;var h={type:"Feature",geometry:{type:c,coordinates:l},properties:this.properties};return"id"in this&&(h.id=this.id),h};var As=Ms;function Ms(t,e){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=t,this._keys=[],this._values=[],this._features=[],t.readFields(Ss,this,e),this.length=this._features.length}function Ss(t,e,r){15===t?e.version=r.readVarint():1===t?e.name=r.readString():5===t?e.extent=r.readVarint():2===t?e._features.push(r.pos):3===t?e._keys.push(r.readString()):4===t&&e._values.push(function(t){var e=null,r=t.readVarint()+t.pos;for(;t.pos<r;){var n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function Es(t,e,r){if(3===t){var n=new As(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}Ms.prototype.feature=function(t){if(t<0||t>=this._features.length)throw new Error("feature index out of bounds");this._pbf.pos=this._features[t];var e=this._pbf.readVarint()+this._pbf.pos;return new _s(this._pbf,e,this.extent,this._keys,this._values)};var Ls={VectorTile:function(t,e){this.layers=t.readFields(Es,{},e)},VectorTileFeature:_s,VectorTileLayer:As},Cs=Ls.VectorTileFeature.types,Ps=Math.pow(2,13);function Is(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*Ps)+o,i*Ps*2,a*Ps*2,Math.round(s))}var Os=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new Ri,this.indexArray=new Yi,this.programConfigurations=new Ua(bs,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function zs(t,e){return t.x===e.x&&(t.x<0||t.x>8192)||t.y===e.y&&(t.y<0||t.y>8192)}function Ds(t){return t.every((function(t){return t.x<0}))||t.every((function(t){return t.x>8192}))||t.every((function(t){return t.y<0}))||t.every((function(t){return t.y>8192}))}Os.prototype.populate=function(t,e,r){this.features=[],this.hasPattern=ds("fill-extrusion",this.layers,e);for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.feature,s=a.id,l=a.index,c=a.sourceLayerIndex,u=this.layers[0]._featureFilter.needGeometry,f={type:o.type,id:s,properties:o.properties,geometry:u?Ya(o):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),f,r)){var h={id:s,sourceLayerIndex:c,index:l,geometry:u?f.geometry:Ya(o),properties:o.properties,type:o.type,patterns:{}};void 0!==o.id&&(h.id=o.id),this.hasPattern?this.features.push(ms("fill-extrusion",this.layers,h,this.zoom,e)):this.addFeature(h,h.geometry,l,r,{}),e.featureIndex.insert(o,h.geometry,l,c,this.index,!0)}}},Os.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.features;n<i.length;n+=1){var a=i[n],o=a.geometry;this.addFeature(a,o,a.index,e,r)}},Os.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},Os.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},Os.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},Os.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,bs),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},Os.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},Os.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=hs(e,500);a<o.length;a+=1){for(var s=o[a],l=0,c=0,u=s;c<u.length;c+=1){l+=u[c].length}for(var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray),h=0,p=s;h<p.length;h+=1){var d=p[h];if(0!==d.length&&!Ds(d))for(var m=0,g=0;g<d.length;g++){var v=d[g];if(g>=1){var y=d[g-1];if(!zs(v,y)){f.vertexLength+4>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var x=v.sub(y)._perp()._unit(),b=y.dist(v);m+b>32768&&(m=0),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,0,m),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,1,m),m+=b,Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,0,m),Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,1,m);var _=f.vertexLength;this.indexArray.emplaceBack(_,_+2,_+1),this.indexArray.emplaceBack(_+1,_+2,_+3),f.vertexLength+=4,f.primitiveLength+=2}}}}if(f.vertexLength+l>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray)),"Polygon"===Cs[t.type]){for(var w=[],T=[],k=f.vertexLength,A=0,M=s;A<M.length;A+=1){var S=M[A];if(0!==S.length){S!==s[0]&&T.push(w.length/2);for(var E=0;E<S.length;E++){var L=S[E];Is(this.layoutVertexArray,L.x,L.y,0,0,1,1,0),w.push(L.x),w.push(L.y)}}}for(var C=Do(w,T),P=0;P<C.length;P+=3)this.indexArray.emplaceBack(k+C[P],k+C[P+2],k+C[P+1]);f.primitiveLength+=C.length/3,f.vertexLength+=l}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},Nn("FillExtrusionBucket",Os,{omit:["layers","features"]});var Rs={paint:new Si({"fill-extrusion-opacity":new wi(Lt["paint_fill-extrusion"]["fill-extrusion-opacity"]),"fill-extrusion-color":new Ti(Lt["paint_fill-extrusion"]["fill-extrusion-color"]),"fill-extrusion-translate":new wi(Lt["paint_fill-extrusion"]["fill-extrusion-translate"]),"fill-extrusion-translate-anchor":new wi(Lt["paint_fill-extrusion"]["fill-extrusion-translate-anchor"]),"fill-extrusion-pattern":new ki(Lt["paint_fill-extrusion"]["fill-extrusion-pattern"]),"fill-extrusion-height":new Ti(Lt["paint_fill-extrusion"]["fill-extrusion-height"]),"fill-extrusion-base":new Ti(Lt["paint_fill-extrusion"]["fill-extrusion-base"]),"fill-extrusion-vertical-gradient":new wi(Lt["paint_fill-extrusion"]["fill-extrusion-vertical-gradient"])})},Fs=function(t){function e(e){t.call(this,e,Rs)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.createBucket=function(t){return new Os(t)},e.prototype.queryRadius=function(){return so(this.paint.get("fill-extrusion-translate"))},e.prototype.is3D=function(){return!0},e.prototype.queryIntersectsFeature=function(t,e,r,n,a,o,s,l){var c=lo(t,this.paint.get("fill-extrusion-translate"),this.paint.get("fill-extrusion-translate-anchor"),o.angle,s),u=this.paint.get("fill-extrusion-height").evaluate(e,r),f=this.paint.get("fill-extrusion-base").evaluate(e,r),h=function(t,e,r,n){for(var a=[],o=0,s=t;o<s.length;o+=1){var l=s[o],c=[l.x,l.y,n,1];xo(c,c,e),a.push(new i(c[0]/c[3],c[1]/c[3]))}return a}(c,l,0,0),p=function(t,e,r,n){for(var a=[],o=[],s=n[8]*e,l=n[9]*e,c=n[10]*e,u=n[11]*e,f=n[8]*r,h=n[9]*r,p=n[10]*r,d=n[11]*r,m=0,g=t;m<g.length;m+=1){for(var v=g[m],y=[],x=[],b=0,_=v;b<_.length;b+=1){var w=_[b],T=w.x,k=w.y,A=n[0]*T+n[4]*k+n[12],M=n[1]*T+n[5]*k+n[13],S=n[2]*T+n[6]*k+n[14],E=n[3]*T+n[7]*k+n[15],L=S+c,C=E+u,P=A+f,I=M+h,O=S+p,z=E+d,D=new i((A+s)/C,(M+l)/C);D.z=L/C,y.push(D);var R=new i(P/z,I/z);R.z=O/z,x.push(R)}a.push(y),o.push(x)}return[a,o]}(n,f,u,l);return function(t,e,r){var n=1/0;Ka(r,e)&&(n=Ns(r,e[0]));for(var i=0;i<e.length;i++)for(var a=e[i],o=t[i],s=0;s<a.length-1;s++){var l=a[s],c=a[s+1],u=o[s],f=o[s+1],h=[l,c,f,u,l];Za(r,h)&&(n=Math.min(n,Ns(r,h)))}return n!==1/0&&n}(p[0],p[1],h)},e}(Ei);function Bs(t,e){return t.x*e.x+t.y*e.y}function Ns(t,e){if(1===t.length){for(var r,n=0,i=e[n++];!r||i.equals(r);)if(!(r=e[n++]))return 1/0;for(;n<e.length;n++){var a=e[n],o=t[0],s=r.sub(i),l=a.sub(i),c=o.sub(i),u=Bs(s,s),f=Bs(s,l),h=Bs(l,l),p=Bs(c,s),d=Bs(c,l),m=u*h-f*f,g=(h*p-f*d)/m,v=(u*d-f*p)/m,y=1-g-v,x=i.z*y+r.z*g+a.z*v;if(isFinite(x))return x}return 1/0}for(var b=1/0,_=0,w=e;_<w.length;_+=1){var T=w[_];b=Math.min(b,T.z)}return b}var js=Ii([{name:"a_pos_normal",components:2,type:"Int16"},{name:"a_data",components:4,type:"Uint8"}],4).members,Us=Ls.VectorTileFeature.types,Vs=Math.cos(Math.PI/180*37.5),Hs=Math.pow(2,14)/.5,qs=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.patternFeatures=[],this.layoutVertexArray=new Fi,this.indexArray=new Yi,this.programConfigurations=new Ua(js,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};qs.prototype.populate=function(t,e,r){this.hasPattern=ds("line",this.layers,e);for(var n=this.layers[0].layout.get("line-sort-key"),i=[],a=0,o=t;a<o.length;a+=1){var s=o[a],l=s.feature,c=s.id,u=s.index,f=s.sourceLayerIndex,h=this.layers[0]._featureFilter.needGeometry,p={type:l.type,id:c,properties:l.properties,geometry:h?Ya(l):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),p,r)){h||(p.geometry=Ya(l));var d=n?n.evaluate(p,{},r):void 0,m={id:c,properties:l.properties,type:l.type,sourceLayerIndex:f,index:u,geometry:p.geometry,patterns:{},sortKey:d};i.push(m)}}n&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var g=0,v=i;g<v.length;g+=1){var y=v[g],x=y,b=x.geometry,_=x.index,w=x.sourceLayerIndex;if(this.hasPattern){var T=ms("line",this.layers,y,this.zoom,e);this.patternFeatures.push(T)}else this.addFeature(y,b,_,r,{});var k=t[_].feature;e.featureIndex.insert(k,b,_,w,this.index)}},qs.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},qs.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.patternFeatures;n<i.length;n+=1){var a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},qs.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},qs.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},qs.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,js),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},qs.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},qs.prototype.addFeature=function(t,e,r,n,i){for(var a=this.layers[0].layout,o=a.get("line-join").evaluate(t,{}),s=a.get("line-cap"),l=a.get("line-miter-limit"),c=a.get("line-round-limit"),u=0,f=e;u<f.length;u+=1){var h=f[u];this.addLine(h,t,o,s,l,c)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},qs.prototype.addLine=function(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,e.properties&&e.properties.hasOwnProperty("mapbox_clip_start")&&e.properties.hasOwnProperty("mapbox_clip_end")){this.clipStart=+e.properties.mapbox_clip_start,this.clipEnd=+e.properties.mapbox_clip_end;for(var o=0;o<t.length-1;o++)this.totalDistance+=t[o].dist(t[o+1]);this.updateScaledDistance()}for(var s="Polygon"===Us[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var c=0;c<l-1&&t[c].equals(t[c+1]);)c++;if(!(l<(s?3:2))){"bevel"===r&&(i=1.05);var u,f=this.overscaling<=16?122880/(512*this.overscaling):0,h=this.segments.prepareSegment(10*l,this.layoutVertexArray,this.indexArray),p=void 0,d=void 0,m=void 0,g=void 0;this.e1=this.e2=-1,s&&(u=t[l-2],g=t[c].sub(u)._unit()._perp());for(var v=c;v<l;v++)if(!(d=v===l-1?s?t[c+1]:void 0:t[v+1])||!t[v].equals(d)){g&&(m=g),u&&(p=u),u=t[v],g=d?d.sub(u)._unit()._perp():m;var y=(m=m||g).add(g);0===y.x&&0===y.y||y._unit();var x=m.x*g.x+m.y*g.y,b=y.x*g.x+y.y*g.y,_=0!==b?1/b:1/0,w=2*Math.sqrt(2-2*b),T=b<Vs&&p&&d,k=m.x*g.y-m.y*g.x>0;if(T&&v>c){var A=u.dist(p);if(A>2*f){var M=u.sub(u.sub(p)._mult(f/A)._round());this.updateDistance(p,M),this.addCurrentVertex(M,m,0,0,h),p=M}}var S=p&&d,E=S?r:s?"butt":n;if(S&&"round"===E&&(_<a?E="miter":_<=2&&(E="fakeround")),"miter"===E&&_>i&&(E="bevel"),"bevel"===E&&(_>2&&(E="flipbevel"),_<i&&(E="miter")),p&&this.updateDistance(p,u),"miter"===E)y._mult(_),this.addCurrentVertex(u,y,0,0,h);else if("flipbevel"===E){if(_>100)y=g.mult(-1);else{var L=_*m.add(g).mag()/m.sub(g).mag();y._perp()._mult(L*(k?-1:1))}this.addCurrentVertex(u,y,0,0,h),this.addCurrentVertex(u,y.mult(-1),0,0,h)}else if("bevel"===E||"fakeround"===E){var C=-Math.sqrt(_*_-1),P=k?C:0,I=k?0:C;if(p&&this.addCurrentVertex(u,m,P,I,h),"fakeround"===E)for(var O=Math.round(180*w/Math.PI/20),z=1;z<O;z++){var D=z/O;if(.5!==D){var R=D-.5;D+=D*R*(D-1)*((1.0904+x*(x*(3.55645-1.43519*x)-3.2452))*R*R+(.848013+x*(.215638*x-1.06021)))}var F=g.sub(m)._mult(D)._add(m)._unit()._mult(k?-1:1);this.addHalfVertex(u,F.x,F.y,!1,k,0,h)}d&&this.addCurrentVertex(u,g,-P,-I,h)}else if("butt"===E)this.addCurrentVertex(u,y,0,0,h);else if("square"===E){var B=p?1:-1;this.addCurrentVertex(u,y,B,B,h)}else"round"===E&&(p&&(this.addCurrentVertex(u,m,0,0,h),this.addCurrentVertex(u,m,1,1,h,!0)),d&&(this.addCurrentVertex(u,g,-1,-1,h,!0),this.addCurrentVertex(u,g,0,0,h)));if(T&&v<l-1){var N=u.dist(d);if(N>2*f){var j=u.add(d.sub(u)._mult(f/N)._round());this.updateDistance(u,j),this.addCurrentVertex(j,g,0,0,h),u=j}}}}},qs.prototype.addCurrentVertex=function(t,e,r,n,i,a){void 0===a&&(a=!1);var o=e.x+e.y*r,s=e.y-e.x*r,l=-e.x+e.y*n,c=-e.y-e.x*n;this.addHalfVertex(t,o,s,a,!1,r,i),this.addHalfVertex(t,l,c,a,!0,-n,i),this.distance>Hs/2&&0===this.totalDistance&&(this.distance=0,this.addCurrentVertex(t,e,r,n,i,a))},qs.prototype.addHalfVertex=function(t,e,r,n,i,a,o){var s=t.x,l=t.y,c=.5*this.scaledDistance;this.layoutVertexArray.emplaceBack((s<<1)+(n?1:0),(l<<1)+(i?1:0),Math.round(63*e)+128,Math.round(63*r)+128,1+(0===a?0:a<0?-1:1)|(63&c)<<2,c>>6);var u=o.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,u),o.primitiveLength++),i?this.e2=u:this.e1=u},qs.prototype.updateScaledDistance=function(){this.scaledDistance=this.totalDistance>0?(this.clipStart+(this.clipEnd-this.clipStart)*this.distance/this.totalDistance)*(Hs-1):this.distance},qs.prototype.updateDistance=function(t,e){this.distance+=t.dist(e),this.updateScaledDistance()},Nn("LineBucket",qs,{omit:["layers","patternFeatures"]});var Gs=new Si({"line-cap":new wi(Lt.layout_line["line-cap"]),"line-join":new Ti(Lt.layout_line["line-join"]),"line-miter-limit":new wi(Lt.layout_line["line-miter-limit"]),"line-round-limit":new wi(Lt.layout_line["line-round-limit"]),"line-sort-key":new Ti(Lt.layout_line["line-sort-key"])}),Ys={paint:new Si({"line-opacity":new Ti(Lt.paint_line["line-opacity"]),"line-color":new Ti(Lt.paint_line["line-color"]),"line-translate":new wi(Lt.paint_line["line-translate"]),"line-translate-anchor":new wi(Lt.paint_line["line-translate-anchor"]),"line-width":new Ti(Lt.paint_line["line-width"]),"line-gap-width":new Ti(Lt.paint_line["line-gap-width"]),"line-offset":new Ti(Lt.paint_line["line-offset"]),"line-blur":new Ti(Lt.paint_line["line-blur"]),"line-dasharray":new Ai(Lt.paint_line["line-dasharray"]),"line-pattern":new ki(Lt.paint_line["line-pattern"]),"line-gradient":new Mi(Lt.paint_line["line-gradient"])}),layout:Gs},Ws=new(function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(e,r){return r=new pi(Math.floor(r.zoom),{now:r.now,fadeDuration:r.fadeDuration,zoomHistory:r.zoomHistory,transition:r.transition}),t.prototype.possiblyEvaluate.call(this,e,r)},e.prototype.evaluate=function(e,r,n,i){return r=u({},r,{zoom:Math.floor(r.zoom)}),t.prototype.evaluate.call(this,e,r,n,i)},e}(Ti))(Ys.paint.properties["line-width"].specification);Ws.useIntegerZoom=!0;var Xs=function(t){function e(e){t.call(this,e,Ys)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._handleSpecialPaintPropertyUpdate=function(t){"line-gradient"===t&&this._updateGradient()},e.prototype._updateGradient=function(){var 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Eo({width:o||1,height:s||1});for(var c in t){var u=t[c],f=r[c].paddedRect;Eo.copy(u.data,l,{x:0,y:0},{x:f.x+1,y:f.y+1},u.data)}for(var h in e){var p=e[h],d=n[h].paddedRect,m=d.x+1,g=d.y+1,v=p.data.width,y=p.data.height;Eo.copy(p.data,l,{x:0,y:0},{x:m,y:g},p.data),Eo.copy(p.data,l,{x:0,y:y-1},{x:m,y:g-1},{width:v,height:1}),Eo.copy(p.data,l,{x:0,y:0},{x:m,y:g+y},{width:v,height:1}),Eo.copy(p.data,l,{x:v-1,y:0},{x:m-1,y:g},{width:1,height:y}),Eo.copy(p.data,l,{x:0,y:0},{x:m+v,y:g},{width:1,height:y})}this.image=l,this.iconPositions=r,this.patternPositions=n};Ll.prototype.addImages=function(t,e,r){for(var n in t){var i=t[n],a={x:0,y:0,w:i.data.width+2,h:i.data.height+2};r.push(a),e[n]=new Sl(a,i),i.hasRenderCallback&&this.haveRenderCallbacks.push(n)}},Ll.prototype.patchUpdatedImages=function(t,e){for(var r in 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e=0,r=t;e<r.length;e+=1){if(0!==r[e].positionedGlyphs.length)return!1}return!0}(L)&&C}Il.fromFeature=function(t,e){for(var r=new Il,n=0;n<t.sections.length;n++){var i=t.sections[n];i.image?r.addImageSection(i):r.addTextSection(i,e)}return r},Il.prototype.length=function(){return this.text.length},Il.prototype.getSection=function(t){return this.sections[this.sectionIndex[t]]},Il.prototype.getSectionIndex=function(t){return this.sectionIndex[t]},Il.prototype.getCharCode=function(t){return this.text.charCodeAt(t)},Il.prototype.verticalizePunctuation=function(){this.text=function(t){for(var e="",r=0;r<t.length;r++){var n=t.charCodeAt(r+1)||null,i=t.charCodeAt(r-1)||null;(!n||!Jn(n)||rl[t[r+1]])&&(!i||!Jn(i)||rl[t[r-1]])&&rl[t[r]]?e+=rl[t[r]]:e+=t[r]}return e}(this.text)},Il.prototype.trim=function(){for(var t=0,e=0;e<this.text.length&&zl[this.text.charCodeAt(e)];e++)t++;for(var r=this.text.length,n=this.text.length-1;n>=0&&n>=t&&zl[this.text.charCodeAt(n)];n--)r--;this.text=this.text.substring(t,r),this.sectionIndex=this.sectionIndex.slice(t,r)},Il.prototype.substring=function(t,e){var r=new Il;return r.text=this.text.substring(t,e),r.sectionIndex=this.sectionIndex.slice(t,e),r.sections=this.sections,r},Il.prototype.toString=function(){return this.text},Il.prototype.getMaxScale=function(){var t=this;return this.sectionIndex.reduce((function(e,r){return Math.max(e,t.sections[r].scale)}),0)},Il.prototype.addTextSection=function(t,e){this.text+=t.text,this.sections.push(Pl.forText(t.scale,t.fontStack||e));for(var r=this.sections.length-1,n=0;n<t.text.length;++n)this.sectionIndex.push(r)},Il.prototype.addImageSection=function(t){var e=t.image?t.image.name:"";if(0!==e.length){var r=this.getNextImageSectionCharCode();r?(this.text+=String.fromCharCode(r),this.sections.push(Pl.forImage(e)),this.sectionIndex.push(this.sections.length-1)):_("Reached maximum number of 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l=t.getSection(s);o+=Rl(t.getCharCode(s),l,n,i,e,a)}return o/Math.max(1,Math.ceil(o/r))}(t,e,r,n,i,o),u=t.text.indexOf("\u200b")>=0,f=0,h=0;h<t.length();h++){var p=t.getSection(h),d=t.getCharCode(h);if(zl[d]||(f+=Rl(d,p,n,i,e,o)),h<t.length()-1){var m=!!(!((s=d)<11904)&&(Yn["Bopomofo Extended"](s)||Yn.Bopomofo(s)||Yn["CJK Compatibility Forms"](s)||Yn["CJK Compatibility Ideographs"](s)||Yn["CJK Compatibility"](s)||Yn["CJK Radicals Supplement"](s)||Yn["CJK Strokes"](s)||Yn["CJK Symbols and Punctuation"](s)||Yn["CJK Unified Ideographs Extension A"](s)||Yn["CJK Unified Ideographs"](s)||Yn["Enclosed CJK Letters and Months"](s)||Yn["Halfwidth and Fullwidth Forms"](s)||Yn.Hiragana(s)||Yn["Ideographic Description Characters"](s)||Yn["Kangxi Radicals"](s)||Yn["Katakana Phonetic Extensions"](s)||Yn.Katakana(s)||Yn["Vertical Forms"](s)||Yn["Yi Radicals"](s)||Yn["Yi Syllables"](s)));(Dl[d]||m||p.imageName)&&l.push(Nl(h+1,f,c,l,Bl(d,t.getCharCode(h+1),m&&u),!1))}}return function t(e){return e?t(e.priorBreak).concat(e.index):[]}(Nl(t.length(),f,c,l,0,!0))}function Ul(t){var e=.5,r=.5;switch(t){case"right":case"top-right":case"bottom-right":e=1;break;case"left":case"top-left":case"bottom-left":e=0}switch(t){case"bottom":case"bottom-right":case"bottom-left":r=1;break;case"top":case"top-right":case"top-left":r=0}return{horizontalAlign:e,verticalAlign:r}}function Vl(t,e,r,n,i){if(n||i)for(var a=t[r],o=a.metrics.advance*a.scale,s=(t[r].x+o)*n,l=e;l<=r;l++)t[l].x-=s,t[l].y+=i}function Hl(t,e,r,n,i,a){var o,s=t.image;if(s.content){var l=s.content,c=s.pixelRatio||1;o=[l[0]/c,l[1]/c,s.displaySize[0]-l[2]/c,s.displaySize[1]-l[3]/c]}var u,f,h,p,d=e.left*a,m=e.right*a;"width"===r||"both"===r?(p=i[0]+d-n[3],f=i[0]+m+n[1]):f=(p=i[0]+(d+m-s.displaySize[0])/2)+s.displaySize[0];var 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F=Math.sin(R),B=Math.cos(R),N=[B,-F,F,B];C._matMult(N),P._matMult(N),O._matMult(N),I._matMult(N)}var j=n.stretch+n.fixed,U=l.stretch+l.fixed,V=a.stretch+a.fixed,H=c.stretch+c.fixed;return{tl:C,tr:P,bl:O,br:I,tex:{x:o.paddedRect.x+1+j,y:o.paddedRect.y+1+V,w:U-j,h:H-V},writingMode:void 0,glyphOffset:[0,0],sectionIndex:0,pixelOffsetTL:z,pixelOffsetBR:D,minFontScaleX:k/s/u,minFontScaleY:M/s/f,isSDF:r}};if(n&&(o.stretchX||o.stretchY))for(var L=ic(h,v,m),C=ic(p,y,g),P=0;P<L.length-1;P++)for(var I=L[P],O=L[P+1],z=0;z<C.length-1;z++){var D=C[z],R=C[z+1];a.push(E(I,D,O,R))}else a.push(E({fixed:0,stretch:-1},{fixed:0,stretch:-1},{fixed:0,stretch:l+1},{fixed:0,stretch:c+1}));return a}function nc(t,e,r){for(var n=0,i=0,a=t;i<a.length;i+=1){var o=a[i];n+=Math.max(e,Math.min(r,o[1]))-Math.max(e,Math.min(r,o[0]))}return n}function ic(t,e,r){for(var n=[{fixed:-1,stretch:0}],i=0,a=t;i<a.length;i+=1){var 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0===e&&(e=cc),this.data=t,this.length=this.data.length,this.compare=e,this.length>0)for(var r=(this.length>>1)-1;r>=0;r--)this._down(r)};function cc(t,e){return t<e?-1:t>e?1:0}function uc(t,e,r){void 0===e&&(e=1),void 0===r&&(r=!1);for(var n=1/0,a=1/0,o=-1/0,s=-1/0,l=t[0],c=0;c<l.length;c++){var u=l[c];(!c||u.x<n)&&(n=u.x),(!c||u.y<a)&&(a=u.y),(!c||u.x>o)&&(o=u.x),(!c||u.y>s)&&(s=u.y)}var f=o-n,h=s-a,p=Math.min(f,h),d=p/2,m=new lc([],fc);if(0===p)return new i(n,a);for(var g=n;g<o;g+=p)for(var v=a;v<s;v+=p)m.push(new hc(g+d,v+d,d,t));for(var y=function(t){for(var e=0,r=0,n=0,i=t[0],a=0,o=i.length,s=o-1;a<o;s=a++){var l=i[a],c=i[s],u=l.x*c.y-c.x*l.y;r+=(l.x+c.x)*u,n+=(l.y+c.y)*u,e+=3*u}return new hc(r/e,n/e,0,t)}(t),x=m.length;m.length;){var b=m.pop();(b.d>y.d||!y.d)&&(y=b,r&&console.log("found best %d after %d probes",Math.round(1e4*b.d)/1e4,x)),b.max-y.d<=e||(d=b.h/2,m.push(new hc(b.p.x-d,b.p.y-d,d,t)),m.push(new hc(b.p.x+d,b.p.y-d,d,t)),m.push(new hc(b.p.x-d,b.p.y+d,d,t)),m.push(new 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mc(t){switch(t){case"right":case"top-right":case"bottom-right":return"right";case"left":case"top-left":case"bottom-left":return"left"}return"center"}function gc(t,e,r,n,a,o,s,l,c,u,f,h,p,d,m){var g=function(t,e,r,n,a,o,s,l){for(var c=n.layout.get("text-rotate").evaluate(o,{})*Math.PI/180,u=[],f=0,h=e.positionedLines;f<h.length;f+=1)for(var p=h[f],d=0,m=p.positionedGlyphs;d<m.length;d+=1){var g=m[d];if(g.rect){var v=g.rect||{},y=4,x=!0,b=1,_=0,w=(a||l)&&g.vertical,T=g.metrics.advance*g.scale/2;if(l&&e.verticalizable){var k=24*(g.scale-1),A=(24-g.metrics.width*g.scale)/2;_=p.lineOffset/2-(g.imageName?-A:k)}if(g.imageName){var M=s[g.imageName];x=M.sdf,y=1/(b=M.pixelRatio)}var S=a?[g.x+T,g.y]:[0,0],E=a?[0,0]:[g.x+T+r[0],g.y+r[1]-_],L=[0,0];w&&(L=E,E=[0,0]);var C=(g.metrics.left-y)*g.scale-T+E[0],P=(-g.metrics.top-y)*g.scale+E[1],I=C+v.w*g.scale/b,O=P+v.h*g.scale/b,z=new i(C,P),D=new i(I,P),R=new i(C,O),F=new i(I,O);if(w){var B=new i(-T,T- -17),N=-Math.PI/2,j=12-T,U=g.imageName?j:0,V=new i(22-j,-U),H=new(Function.prototype.bind.apply(i,[null].concat(L)));z._rotateAround(N,B)._add(V)._add(H),D._rotateAround(N,B)._add(V)._add(H),R._rotateAround(N,B)._add(V)._add(H),F._rotateAround(N,B)._add(V)._add(H)}if(c){var q=Math.sin(c),G=Math.cos(c),Y=[G,-q,q,G];z._matMult(Y),D._matMult(Y),R._matMult(Y),F._matMult(Y)}var W=new i(0,0),X=new i(0,0);u.push({tl:z,tr:D,bl:R,br:F,tex:v,writingMode:e.writingMode,glyphOffset:S,sectionIndex:g.sectionIndex,isSDF:x,pixelOffsetTL:W,pixelOffsetBR:X,minFontScaleX:0,minFontScaleY:0})}}return u}(0,r,l,a,o,s,n,t.allowVerticalPlacement),v=t.textSizeData,y=null;"source"===v.kind?(y=[128*a.layout.get("text-size").evaluate(s,{})])[0]>32640&&_(t.layerIds[0]+': Value for "text-size" is >= 255. 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Reduce your "text-size".'),t.addSymbols(t.text,g,y,l,o,s,u,e,c.lineStartIndex,c.lineLength,p,m);for(var x=0,b=f;x<b.length;x+=1){h[b[x]]=t.text.placedSymbolArray.length-1}return 4*g.length}function vc(t){for(var e in t)return t[e];return null}function yc(t,e,r,n){var i=t.compareText;if(e in i){for(var a=i[e],o=a.length-1;o>=0;o--)if(n.dist(a[o])<r)return!0}else i[e]=[];return i[e].push(n),!1}var xc=Ls.VectorTileFeature.types,bc=[{name:"a_fade_opacity",components:1,type:"Uint8",offset:0}];function _c(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=s?Math.min(32640,Math.round(s[0])):0,d=s?Math.min(32640,Math.round(s[1])):0;t.emplaceBack(e,r,Math.round(32*n),Math.round(32*i),a,o,(p<<1)+(l?1:0),d,16*c,16*u,256*f,256*h)}function wc(t,e,r){t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r)}function Tc(t){for(var e=0,r=t.sections;e<r.length;e+=1){if($n(r[e].text))return!0}return!1}var kc=function(t){this.layoutVertexArray=new Ni,this.indexArray=new Yi,this.programConfigurations=t,this.segments=new pa,this.dynamicLayoutVertexArray=new ji,this.opacityVertexArray=new Ui,this.placedSymbolArray=new aa};kc.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length&&0===this.indexArray.length&&0===this.dynamicLayoutVertexArray.length&&0===this.opacityVertexArray.length},kc.prototype.upload=function(t,e,r,n){this.isEmpty()||(r&&(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Js.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,Ks.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,bc,!0),this.opacityVertexBuffer.itemSize=1),(r||n)&&this.programConfigurations.upload(t))},kc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},Nn("SymbolBuffers",kc);var Ac=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new pa,this.collisionVertexArray=new Gi};Ac.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,Qs.members,!0)},Ac.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},Nn("CollisionBuffers",Ac);var Mc=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=ho([]),this.placementViewportMatrix=ho([]);var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Gl(this.zoom,e["text-size"]),this.iconSizeData=Gl(this.zoom,e["icon-size"]);var r=this.layers[0].layout,n=r.get("symbol-sort-key"),i=r.get("symbol-z-order");this.sortFeaturesByKey="viewport-y"!==i&&void 0!==n.constantOr(1);var a="viewport-y"===i||"auto"===i&&!this.sortFeaturesByKey;this.sortFeaturesByY=a&&(r.get("text-allow-overlap")||r.get("icon-allow-overlap")||r.get("text-ignore-placement")||r.get("icon-ignore-placement")),"point"===r.get("symbol-placement")&&(this.writingModes=r.get("text-writing-mode").map((function(t){return Cl[t]}))),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id})),this.sourceID=t.sourceID};Mc.prototype.createArrays=function(){this.text=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^text/.test(t)}))),this.icon=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^icon/.test(t)}))),this.glyphOffsetArray=new la,this.lineVertexArray=new ca,this.symbolInstances=new sa},Mc.prototype.calculateGlyphDependencies=function(t,e,r,n,i){for(var a=0;a<t.length;a++)if(e[t.charCodeAt(a)]=!0,(r||n)&&i){var o=rl[t.charAt(a)];o&&(e[o.charCodeAt(0)]=!0)}},Mc.prototype.populate=function(t,e,r){var n=this.layers[0],i=n.layout,a=i.get("text-font"),o=i.get("text-field"),s=i.get("icon-image"),l=("constant"!==o.value.kind||o.value.value instanceof ne&&!o.value.value.isEmpty()||o.value.value.toString().length>0)&&("constant"!==a.value.kind||a.value.value.length>0),c="constant"!==s.value.kind||!!s.value.value||Object.keys(s.parameters).length>0,u=i.get("symbol-sort-key");if(this.features=[],l||c){for(var f=e.iconDependencies,h=e.glyphDependencies,p=e.availableImages,d=new pi(this.zoom),m=0,g=t;m<g.length;m+=1){var v=g[m],y=v.feature,x=v.id,b=v.index,_=v.sourceLayerIndex,w=n._featureFilter.needGeometry,T={type:y.type,id:x,properties:y.properties,geometry:w?Ya(y):[]};if(n._featureFilter.filter(d,T,r)){w||(T.geometry=Ya(y));var k=void 0;if(l){var A=n.getValueAndResolveTokens("text-field",T,r,p),M=ne.factory(A);Tc(M)&&(this.hasRTLText=!0),(!this.hasRTLText||"unavailable"===ui()||this.hasRTLText&&hi.isParsed())&&(k=el(M,n,T))}var S=void 0;if(c){var E=n.getValueAndResolveTokens("icon-image",T,r,p);S=E instanceof ie?E:ie.fromString(E)}if(k||S){var L=this.sortFeaturesByKey?u.evaluate(T,{},r):void 0,C={id:x,text:k,icon:S,index:b,sourceLayerIndex:_,geometry:Ya(y),properties:y.properties,type:xc[y.type],sortKey:L};if(this.features.push(C),S&&(f[S.name]=!0),k){var P=a.evaluate(T,{},r).join(","),I="map"===i.get("text-rotation-alignment")&&"point"!==i.get("symbol-placement");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(Cl.vertical)>=0;for(var O=0,z=k.sections;O<z.length;O+=1){var D=z[O];if(D.image)f[D.image.name]=!0;else{var R=Wn(k.toString()),F=D.fontStack||P,B=h[F]=h[F]||{};this.calculateGlyphDependencies(D.text,B,I,this.allowVerticalPlacement,R)}}}}}}"line"===i.get("symbol-placement")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+":"+n.x+":"+n.y}for(var c=0;c<t.length;c++){var u=t[c],f=u.geometry,h=u.text?u.text.toString():null;if(h){var p=l(h,f),d=l(h,f,!0);if(p in r&&d in e&&r[p]!==e[d]){var m=s(p,d,f),g=o(p,d,n[m].geometry);delete e[p],delete r[d],r[l(h,n[g].geometry,!0)]=g,n[m].geometry=null}else p in r?o(p,d,f):d in e?s(p,d,f):(a(c),e[p]=i-1,r[d]=i-1)}else a(c)}return n.filter((function(t){return t.geometry}))}(this.features)),this.sortFeaturesByKey&&this.features.sort((function(t,e){return t.sortKey-e.sortKey}))}},Mc.prototype.update=function(t,e,r){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(t,e,this.layers,r),this.icon.programConfigurations.updatePaintArrays(t,e,this.layers,r))},Mc.prototype.isEmpty=function(){return 0===this.symbolInstances.length&&!this.hasRTLText},Mc.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},Mc.prototype.upload=function(t){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(t),this.iconCollisionBox.upload(t)),this.text.upload(t,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(t,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},Mc.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},Mc.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},Mc.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),i=t.dist(e[t.segment]),a={},o=t.segment+1;o<e.length;o++)a[o]={x:e[o].x,y:e[o].y,tileUnitDistanceFromAnchor:n},o<e.length-1&&(n+=e[o+1].dist(e[o]));for(var s=t.segment||0;s>=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var c=a[l];this.lineVertexArray.emplaceBack(c.x,c.y,c.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},Mc.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=t.indexArray,p=t.layoutVertexArray,d=t.segments.prepareSegment(4*e.length,p,h,a.sortKey),m=this.glyphOffsetArray.length,g=d.vertexLength,v=this.allowVerticalPlacement&&o===Cl.vertical?Math.PI/2:0,y=a.text&&a.text.sections,x=0;x<e.length;x++){var b=e[x],_=b.tl,w=b.tr,T=b.bl,k=b.br,A=b.tex,M=b.pixelOffsetTL,S=b.pixelOffsetBR,E=b.minFontScaleX,L=b.minFontScaleY,C=b.glyphOffset,P=b.isSDF,I=b.sectionIndex,O=d.vertexLength,z=C[1];_c(p,s.x,s.y,_.x,z+_.y,A.x,A.y,r,P,M.x,M.y,E,L),_c(p,s.x,s.y,w.x,z+w.y,A.x+A.w,A.y,r,P,S.x,M.y,E,L),_c(p,s.x,s.y,T.x,z+T.y,A.x,A.y+A.h,r,P,M.x,S.y,E,L),_c(p,s.x,s.y,k.x,z+k.y,A.x+A.w,A.y+A.h,r,P,S.x,S.y,E,L),wc(t.dynamicLayoutVertexArray,s,v),h.emplaceBack(O,O+1,O+2),h.emplaceBack(O+1,O+2,O+3),d.vertexLength+=4,d.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(C[0]),x!==e.length-1&&I===e[x+1].sectionIndex||t.programConfigurations.populatePaintArrays(p.length,a,a.index,{},f,y&&y[I])}t.placedSymbolArray.emplaceBack(s.x,s.y,m,this.glyphOffsetArray.length-m,g,l,c,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,0,!1,0,u)},Mc.prototype._addCollisionDebugVertex=function(t,e,r,n,i,a){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n,i,Math.round(a.x),Math.round(a.y))},Mc.prototype.addCollisionDebugVertices=function(t,e,r,n,a,o,s){var l=a.segments.prepareSegment(4,a.layoutVertexArray,a.indexArray),c=l.vertexLength,u=a.layoutVertexArray,f=a.collisionVertexArray,h=s.anchorX,p=s.anchorY;this._addCollisionDebugVertex(u,f,o,h,p,new i(t,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,n)),this._addCollisionDebugVertex(u,f,o,h,p,new i(t,n)),l.vertexLength+=4;var d=a.indexArray;d.emplaceBack(c,c+1),d.emplaceBack(c+1,c+2),d.emplaceBack(c+2,c+3),d.emplaceBack(c+3,c),l.primitiveLength+=4},Mc.prototype.addDebugCollisionBoxes=function(t,e,r,n){for(var i=t;i<e;i++){var a=this.collisionBoxArray.get(i),o=a.x1,s=a.y1,l=a.x2,c=a.y2;this.addCollisionDebugVertices(o,s,l,c,n?this.textCollisionBox:this.iconCollisionBox,a.anchorPoint,r)}},Mc.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new Ac(Hi,$s.members,Qi),this.iconCollisionBox=new Ac(Hi,$s.members,Qi);for(var t=0;t<this.symbolInstances.length;t++){var e=this.symbolInstances.get(t);this.addDebugCollisionBoxes(e.textBoxStartIndex,e.textBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.verticalTextBoxStartIndex,e.verticalTextBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.iconBoxStartIndex,e.iconBoxEndIndex,e,!1),this.addDebugCollisionBoxes(e.verticalIconBoxStartIndex,e.verticalIconBoxEndIndex,e,!1)}},Mc.prototype._deserializeCollisionBoxesForSymbol=function(t,e,r,n,i,a,o,s,l){for(var c={},u=e;u<r;u++){var f=t.get(u);c.textBox={x1:f.x1,y1:f.y1,x2:f.x2,y2:f.y2,anchorPointX:f.anchorPointX,anchorPointY:f.anchorPointY},c.textFeatureIndex=f.featureIndex;break}for(var h=n;h<i;h++){var p=t.get(h);c.verticalTextBox={x1:p.x1,y1:p.y1,x2:p.x2,y2:p.y2,anchorPointX:p.anchorPointX,anchorPointY:p.anchorPointY},c.verticalTextFeatureIndex=p.featureIndex;break}for(var d=a;d<o;d++){var m=t.get(d);c.iconBox={x1:m.x1,y1:m.y1,x2:m.x2,y2:m.y2,anchorPointX:m.anchorPointX,anchorPointY:m.anchorPointY},c.iconFeatureIndex=m.featureIndex;break}for(var g=s;g<l;g++){var v=t.get(g);c.verticalIconBox={x1:v.x1,y1:v.y1,x2:v.x2,y2:v.y2,anchorPointX:v.anchorPointX,anchorPointY:v.anchorPointY},c.verticalIconFeatureIndex=v.featureIndex;break}return c},Mc.prototype.deserializeCollisionBoxes=function(t){this.collisionArrays=[];for(var e=0;e<this.symbolInstances.length;e++){var r=this.symbolInstances.get(e);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(t,r.textBoxStartIndex,r.textBoxEndIndex,r.verticalTextBoxStartIndex,r.verticalTextBoxEndIndex,r.iconBoxStartIndex,r.iconBoxEndIndex,r.verticalIconBoxStartIndex,r.verticalIconBoxEndIndex))}},Mc.prototype.hasTextData=function(){return this.text.segments.get().length>0},Mc.prototype.hasIconData=function(){return this.icon.segments.get().length>0},Mc.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},Mc.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},Mc.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},Mc.prototype.addIndicesForPlacedSymbol=function(t,e){for(var r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs,i=r.vertexStartIndex;i<n;i+=4)t.indexArray.emplaceBack(i,i+1,i+2),t.indexArray.emplaceBack(i+1,i+2,i+3)},Mc.prototype.getSortedSymbolIndexes=function(t){if(this.sortedAngle===t&&void 0!==this.symbolInstanceIndexes)return this.symbolInstanceIndexes;for(var e=Math.sin(t),r=Math.cos(t),n=[],i=[],a=[],o=0;o<this.symbolInstances.length;++o){a.push(o);var s=this.symbolInstances.get(o);n.push(0|Math.round(e*s.anchorX+r*s.anchorY)),i.push(s.featureIndex)}return a.sort((function(t,e){return n[t]-n[e]||i[e]-i[t]})),a},Mc.prototype.addToSortKeyRanges=function(t,e){var r=this.sortKeyRanges[this.sortKeyRanges.length-1];r&&r.sortKey===e?r.symbolInstanceEnd=t+1:this.sortKeyRanges.push({sortKey:e,symbolInstanceStart:t,symbolInstanceEnd:t+1})},Mc.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r<n.length;r+=1){var i=n[r],a=this.symbolInstances.get(i);this.featureSortOrder.push(a.featureIndex),[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t,r,n){t>=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),a.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,a.verticalPlacedTextSymbolIndex),a.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.placedIconSymbolIndex),a.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},Nn("SymbolBucket",Mc,{omit:["layers","collisionBoxArray","features","compareText"]}),Mc.MAX_GLYPHS=65535,Mc.addDynamicAttributes=wc;var Sc=new Si({"symbol-placement":new wi(Lt.layout_symbol["symbol-placement"]),"symbol-spacing":new wi(Lt.layout_symbol["symbol-spacing"]),"symbol-avoid-edges":new wi(Lt.layout_symbol["symbol-avoid-edges"]),"symbol-sort-key":new Ti(Lt.layout_symbol["symbol-sort-key"]),"symbol-z-order":new wi(Lt.layout_symbol["symbol-z-order"]),"icon-allow-overlap":new wi(Lt.layout_symbol["icon-allow-overlap"]),"icon-ignore-placement":new wi(Lt.layout_symbol["icon-ignore-placement"]),"icon-optional":new wi(Lt.layout_symbol["icon-optional"]),"icon-rotation-alignment":new wi(Lt.layout_symbol["icon-rotation-alignment"]),"icon-size":new Ti(Lt.layout_symbol["icon-size"]),"icon-text-fit":new wi(Lt.layout_symbol["icon-text-fit"]),"icon-text-fit-padding":new wi(Lt.layout_symbol["icon-text-fit-padding"]),"icon-image":new Ti(Lt.layout_symbol["icon-image"]),"icon-rotate":new Ti(Lt.layout_symbol["icon-rotate"]),"icon-padding":new wi(Lt.layout_symbol["icon-padding"]),"icon-keep-upright":new wi(Lt.layout_symbol["icon-keep-upright"]),"icon-offset":new Ti(Lt.layout_symbol["icon-offset"]),"icon-anchor":new Ti(Lt.layout_symbol["icon-anchor"]),"icon-pitch-alignment":new wi(Lt.layout_symbol["icon-pitch-alignment"]),"text-pitch-alignment":new wi(Lt.layout_symbol["text-pitch-alignment"]),"text-rotation-alignment":new wi(Lt.layout_symbol["text-rotation-alignment"]),"text-field":new Ti(Lt.layout_symbol["text-field"]),"text-font":new Ti(Lt.layout_symbol["text-font"]),"text-size":new Ti(Lt.layout_symbol["text-size"]),"text-max-width":new Ti(Lt.layout_symbol["text-max-width"]),"text-line-height":new wi(Lt.layout_symbol["text-line-height"]),"text-letter-spacing":new Ti(Lt.layout_symbol["text-letter-spacing"]),"text-justify":new Ti(Lt.layout_symbol["text-justify"]),"text-radial-offset":new Ti(Lt.layout_symbol["text-radial-offset"]),"text-variable-anchor":new wi(Lt.layout_symbol["text-variable-anchor"]),"text-anchor":new Ti(Lt.layout_symbol["text-anchor"]),"text-max-angle":new wi(Lt.layout_symbol["text-max-angle"]),"text-writing-mode":new wi(Lt.layout_symbol["text-writing-mode"]),"text-rotate":new Ti(Lt.layout_symbol["text-rotate"]),"text-padding":new wi(Lt.layout_symbol["text-padding"]),"text-keep-upright":new wi(Lt.layout_symbol["text-keep-upright"]),"text-transform":new Ti(Lt.layout_symbol["text-transform"]),"text-offset":new Ti(Lt.layout_symbol["text-offset"]),"text-allow-overlap":new wi(Lt.layout_symbol["text-allow-overlap"]),"text-ignore-placement":new wi(Lt.layout_symbol["text-ignore-placement"]),"text-optional":new wi(Lt.layout_symbol["text-optional"])}),Ec={paint:new Si({"icon-opacity":new Ti(Lt.paint_symbol["icon-opacity"]),"icon-color":new Ti(Lt.paint_symbol["icon-color"]),"icon-halo-color":new Ti(Lt.paint_symbol["icon-halo-color"]),"icon-halo-width":new Ti(Lt.paint_symbol["icon-halo-width"]),"icon-halo-blur":new Ti(Lt.paint_symbol["icon-halo-blur"]),"icon-translate":new wi(Lt.paint_symbol["icon-translate"]),"icon-translate-anchor":new wi(Lt.paint_symbol["icon-translate-anchor"]),"text-opacity":new Ti(Lt.paint_symbol["text-opacity"]),"text-color":new Ti(Lt.paint_symbol["text-color"],{runtimeType:Ut,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),"text-halo-color":new Ti(Lt.paint_symbol["text-halo-color"]),"text-halo-width":new Ti(Lt.paint_symbol["text-halo-width"]),"text-halo-blur":new Ti(Lt.paint_symbol["text-halo-blur"]),"text-translate":new wi(Lt.paint_symbol["text-translate"]),"text-translate-anchor":new wi(Lt.paint_symbol["text-translate-anchor"])}),layout:Sc},Lc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Ft,this.defaultValue=t};Lc.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},Lc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},Lc.prototype.outputDefined=function(){return!1},Lc.prototype.serialize=function(){return null},Nn("FormatSectionOverride",Lc,{omit:["defaultValue"]});var Cc=function(t){function e(e){t.call(this,e,Ec)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),"auto"===this.layout.get("icon-rotation-alignment")&&("point"!==this.layout.get("symbol-placement")?this.layout._values["icon-rotation-alignment"]="map":this.layout._values["icon-rotation-alignment"]="viewport"),"auto"===this.layout.get("text-rotation-alignment")&&("point"!==this.layout.get("symbol-placement")?this.layout._values["text-rotation-alignment"]="map":this.layout._values["text-rotation-alignment"]="viewport"),"auto"===this.layout.get("text-pitch-alignment")&&(this.layout._values["text-pitch-alignment"]=this.layout.get("text-rotation-alignment")),"auto"===this.layout.get("icon-pitch-alignment")&&(this.layout._values["icon-pitch-alignment"]=this.layout.get("icon-rotation-alignment")),"point"===this.layout.get("symbol-placement")){var n=this.layout.get("text-writing-mode");if(n){for(var i=[],a=0,o=n;a<o.length;a+=1){var s=o[a];i.indexOf(s)<0&&i.push(s)}this.layout._values["text-writing-mode"]=i}else this.layout._values["text-writing-mode"]=["horizontal"]}this._setPaintOverrides()},e.prototype.getValueAndResolveTokens=function(t,e,r,n){var i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||Yr(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,(function(e,r){return r in t?String(t[r]):""}))}(e.properties,i)},e.prototype.createBucket=function(t){return new Mc(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e.prototype._setPaintOverrides=function(){for(var t=0,r=Ec.paint.overridableProperties;t<r.length;t+=1){var n=r[t];if(e.hasPaintOverride(this.layout,n)){var i=this.paint.get(n),a=new Lc(i),o=new Gr(a,i.property.specification),s=null;s="constant"===i.value.kind||"source"===i.value.kind?new Xr("source",o):new Zr("composite",o,i.value.zoomStops,i.value._interpolationType),this.paint._values[n]=new bi(i.property,s,i.parameters)}}},e.prototype._handleOverridablePaintPropertyUpdate=function(t,r,n){return!(!this.layout||r.isDataDriven()||n.isDataDriven())&&e.hasPaintOverride(this.layout,t)},e.hasPaintOverride=function(t,e){var r=t.get("text-field"),n=Ec.paint.properties[e],i=!1,a=function(t){for(var e=0,r=t;e<r.length;e+=1){var a=r[e];if(n.overrides&&n.overrides.hasOverride(a))return void(i=!0)}};if("constant"===r.value.kind&&r.value.value instanceof ne)a(r.value.value.sections);else if("source"===r.value.kind){var o=function(t){if(!i)if(t instanceof ce&&se(t.value)===Gt){var e=t.value;a(e.sections)}else t instanceof pe?a(t.sections):t.eachChild(o)},s=r.value;s._styleExpression&&o(s._styleExpression.expression)}return i},e}(Ei),Pc={paint:new Si({"background-color":new wi(Lt.paint_background["background-color"]),"background-pattern":new Ai(Lt.paint_background["background-pattern"]),"background-opacity":new wi(Lt.paint_background["background-opacity"])})},Ic=function(t){function e(e){t.call(this,e,Pc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei),Oc={paint:new Si({"raster-opacity":new wi(Lt.paint_raster["raster-opacity"]),"raster-hue-rotate":new wi(Lt.paint_raster["raster-hue-rotate"]),"raster-brightness-min":new wi(Lt.paint_raster["raster-brightness-min"]),"raster-brightness-max":new wi(Lt.paint_raster["raster-brightness-max"]),"raster-saturation":new wi(Lt.paint_raster["raster-saturation"]),"raster-contrast":new wi(Lt.paint_raster["raster-contrast"]),"raster-resampling":new wi(Lt.paint_raster["raster-resampling"]),"raster-fade-duration":new wi(Lt.paint_raster["raster-fade-duration"])})},zc=function(t){function e(e){t.call(this,e,Oc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei);var Dc=function(t){function e(e){t.call(this,e,{}),this.implementation=e}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.is3D=function(){return"3d"===this.implementation.renderingMode},e.prototype.hasOffscreenPass=function(){return void 0!==this.implementation.prerender},e.prototype.recalculate=function(){},e.prototype.updateTransitions=function(){},e.prototype.hasTransition=function(){},e.prototype.serialize=function(){},e.prototype.onAdd=function(t){this.implementation.onAdd&&this.implementation.onAdd(t,t.painter.context.gl)},e.prototype.onRemove=function(t){this.implementation.onRemove&&this.implementation.onRemove(t,t.painter.context.gl)},e}(Ei),Rc={circle:_o,heatmap:Po,hillshade:Oo,fill:xs,"fill-extrusion":Fs,line:Xs,symbol:Cc,background:Ic,raster:zc};var Fc=self.HTMLImageElement,Bc=self.HTMLCanvasElement,Nc=self.HTMLVideoElement,jc=self.ImageData,Uc=self.ImageBitmap,Vc=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};Vc.prototype.update=function(t,e,r){var n=t.width,i=t.height,a=!(this.size&&this.size[0]===n&&this.size[1]===i||r),o=this.context,s=o.gl;if(this.useMipmap=Boolean(e&&e.useMipmap),s.bindTexture(s.TEXTURE_2D,this.texture),o.pixelStoreUnpackFlipY.set(!1),o.pixelStoreUnpack.set(1),o.pixelStoreUnpackPremultiplyAlpha.set(this.format===s.RGBA&&(!e||!1!==e.premultiply)),a)this.size=[n,i],t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,s.UNSIGNED_BYTE,t):s.texImage2D(s.TEXTURE_2D,0,this.format,n,i,0,this.format,s.UNSIGNED_BYTE,t.data);else{var l=r||{x:0,y:0},c=l.x,u=l.y;t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texSubImage2D(s.TEXTURE_2D,0,c,u,s.RGBA,s.UNSIGNED_BYTE,t):s.texSubImage2D(s.TEXTURE_2D,0,c,u,n,i,s.RGBA,s.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&s.generateMipmap(s.TEXTURE_2D)},Vc.prototype.bind=function(t,e,r){var n=this.context.gl;n.bindTexture(n.TEXTURE_2D,this.texture),r!==n.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(r=n.LINEAR),t!==this.filter&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,t),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,r||t),this.filter=t),e!==this.wrap&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,e),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,e),this.wrap=e)},Vc.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},Vc.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var Hc=function(t){var e=this;this._callback=t,this._triggered=!1,"undefined"!=typeof MessageChannel&&(this._channel=new MessageChannel,this._channel.port2.onmessage=function(){e._triggered=!1,e._callback()})};Hc.prototype.trigger=function(){var t=this;this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((function(){t._triggered=!1,t._callback()}),0))},Hc.prototype.remove=function(){delete this._channel,this._callback=function(){}};var qc=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.tasks={},this.taskQueue=[],this.cancelCallbacks={},m(["receive","process"],this),this.invoker=new Hc(this.process),this.target.addEventListener("message",this.receive,!1),this.globalScope=k()?t:self};function Gc(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}qc.prototype.send=function(t,e,r,n,i){var a=this;void 0===i&&(i=!1);var o=Math.round(1e18*Math.random()).toString(36).substring(0,10);r&&(this.callbacks[o]=r);var s=S(this.globalScope)?void 0:[];return this.target.postMessage({id:o,type:t,hasCallback:!!r,targetMapId:n,mustQueue:i,sourceMapId:this.mapId,data:Hn(e,s)},s),{cancel:function(){r&&delete a.callbacks[o],a.target.postMessage({id:o,type:"<cancel>",targetMapId:n,sourceMapId:a.mapId})}}},qc.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if("<cancel>"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else k()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},qc.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},qc.prototype.processTask=function(t,e){var r=this;if("<response>"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(qn(e.error)):n(null,qn(e.data)))}else{var i=!1,a=S(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:"<response>",sourceMapId:r.mapId,error:e?Hn(e):null,data:Hn(n,a)},a)}:function(t){i=!0},s=null,l=qn(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var c=e.type.split(".");s=this.parent.getWorkerSource(e.sourceMapId,c[0],l.source)[c[1]](l,o)}else o(new Error("Could not find function "+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},qc.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener("message",this.receive,!1)};var Yc=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Yc.prototype.setNorthEast=function(t){return this._ne=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.setSouthWest=function(t){return this._sw=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Wc)e=t,r=t;else{if(!(t instanceof Yc)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){var a=t;return this.extend(Yc.convert(a))}var o=t;return this.extend(Wc.convert(o))}return this}if(e=t._sw,r=t._ne,!e||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new Wc(e.lng,e.lat),this._ne=new Wc(r.lng,r.lat)),this},Yc.prototype.getCenter=function(){return new Wc((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Yc.prototype.getSouthWest=function(){return this._sw},Yc.prototype.getNorthEast=function(){return this._ne},Yc.prototype.getNorthWest=function(){return new Wc(this.getWest(),this.getNorth())},Yc.prototype.getSouthEast=function(){return new Wc(this.getEast(),this.getSouth())},Yc.prototype.getWest=function(){return this._sw.lng},Yc.prototype.getSouth=function(){return this._sw.lat},Yc.prototype.getEast=function(){return this._ne.lng},Yc.prototype.getNorth=function(){return this._ne.lat},Yc.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Yc.prototype.toString=function(){return"LngLatBounds("+this._sw.toString()+", "+this._ne.toString()+")"},Yc.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Yc.prototype.contains=function(t){var e=Wc.convert(t),r=e.lng,n=e.lat,i=this._sw.lat<=n&&n<=this._ne.lat,a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a},Yc.convert=function(t){return!t||t instanceof Yc?t:new Yc(t)};var Wc=function(t,e){if(isNaN(t)||isNaN(e))throw new Error("Invalid LngLat object: ("+t+", "+e+")");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error("Invalid LngLat latitude value: must be between -90 and 90")};Wc.prototype.wrap=function(){return new Wc(c(this.lng,-180,180),this.lat)},Wc.prototype.toArray=function(){return[this.lng,this.lat]},Wc.prototype.toString=function(){return"LngLat("+this.lng+", "+this.lat+")"},Wc.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return 6371008.8*Math.acos(Math.min(i,1))},Wc.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Yc(new Wc(this.lng-r,this.lat-e),new Wc(this.lng+r,this.lat+e))},Wc.convert=function(t){if(t instanceof Wc)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Wc(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&"object"==typeof t&&null!==t)return new Wc(Number("lng"in t?t.lng:t.lon),Number(t.lat));throw new Error("`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]")};var Xc=2*Math.PI*6371008.8;function Zc(t){return Xc*Math.cos(t*Math.PI/180)}function Jc(t){return(180+t)/360}function Kc(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Qc(t,e){return t/Zc(e)}function $c(t){var e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}var tu=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};tu.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Wc.convert(t);return new tu(Jc(r.lng),Kc(r.lat),Qc(e,r.lat))},tu.prototype.toLngLat=function(){return new Wc(360*this.x-180,$c(this.y))},tu.prototype.toAltitude=function(){return t=this.z,e=this.y,t*Zc($c(e));var t,e},tu.prototype.meterInMercatorCoordinateUnits=function(){return 1/Xc*(t=$c(this.y),1/Math.cos(t*Math.PI/180));var t};var eu=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=iu(0,t,t,e,r)};eu.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},eu.prototype.url=function(t,e){var r,n,i,a,o,s=(r=this.x,n=this.y,i=this.z,a=Gc(256*r,256*(n=Math.pow(2,i)-n-1),i),o=Gc(256*(r+1),256*(n+1),i),a[0]+","+a[1]+","+o[0]+","+o[1]),l=function(t,e,r){for(var n,i="",a=t;a>0;a--)i+=(e&(n=1<<a-1)?1:0)+(r&n?2:0);return i}(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace("{prefix}",(this.x%16).toString(16)+(this.y%16).toString(16)).replace("{z}",String(this.z)).replace("{x}",String(this.x)).replace("{y}",String("tms"===e?Math.pow(2,this.z)-this.y-1:this.y)).replace("{quadkey}",l).replace("{bbox-epsg-3857}",s)},eu.prototype.getTilePoint=function(t){var e=Math.pow(2,this.z);return new i(8192*(t.x*e-this.x),8192*(t.y*e-this.y))},eu.prototype.toString=function(){return this.z+"/"+this.x+"/"+this.y};var ru=function(t,e){this.wrap=t,this.canonical=e,this.key=iu(t,e.z,e.z,e.x,e.y)},nu=function(t,e,r,n,i){this.overscaledZ=t,this.wrap=e,this.canonical=new eu(r,+n,+i),this.key=iu(e,t,r,n,i)};function iu(t,e,r,n,i){(t*=2)<0&&(t=-1*t-1);var a=1<<r;return(a*a*t+a*i+n).toString(36)+r.toString(36)+e.toString(36)}nu.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},nu.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new nu(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new nu(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},nu.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?iu(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):iu(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},nu.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ<this.overscaledZ&&t.canonical.x===this.canonical.x>>e&&t.canonical.y===this.canonical.y>>e},nu.prototype.children=function(t){if(this.overscaledZ>=t)return[new nu(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new nu(e,this.wrap,e,r,n),new nu(e,this.wrap,e,r+1,n),new nu(e,this.wrap,e,r,n+1),new nu(e,this.wrap,e,r+1,n+1)]},nu.prototype.isLessThan=function(t){return this.wrap<t.wrap||!(this.wrap>t.wrap)&&(this.overscaledZ<t.overscaledZ||!(this.overscaledZ>t.overscaledZ)&&(this.canonical.x<t.canonical.x||!(this.canonical.x>t.canonical.x)&&this.canonical.y<t.canonical.y))},nu.prototype.wrapped=function(){return new nu(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.unwrapTo=function(t){return new nu(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},nu.prototype.toUnwrapped=function(){return new ru(this.wrap,this.canonical)},nu.prototype.toString=function(){return this.overscaledZ+"/"+this.canonical.x+"/"+this.canonical.y},nu.prototype.getTilePoint=function(t){return this.canonical.getTilePoint(new tu(t.x-this.wrap,t.y))},Nn("CanonicalTileID",eu),Nn("OverscaledTileID",nu,{omit:["posMatrix"]});var au=function(t,e,r){if(this.uid=t,e.height!==e.width)throw new RangeError("DEM tiles must be square");if(r&&"mapbox"!==r&&"terrarium"!==r)return _('"'+r+'" is not a valid encoding type. Valid types include "mapbox" and "terrarium".');this.stride=e.height;var n=this.dim=e.height-2;this.data=new Uint32Array(e.data.buffer),this.encoding=r||"mapbox";for(var i=0;i<n;i++)this.data[this._idx(-1,i)]=this.data[this._idx(0,i)],this.data[this._idx(n,i)]=this.data[this._idx(n-1,i)],this.data[this._idx(i,-1)]=this.data[this._idx(i,0)],this.data[this._idx(i,n)]=this.data[this._idx(i,n-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(n,-1)]=this.data[this._idx(n-1,0)],this.data[this._idx(-1,n)]=this.data[this._idx(0,n-1)],this.data[this._idx(n,n)]=this.data[this._idx(n-1,n-1)]};au.prototype.get=function(t,e){var r=new Uint8Array(this.data.buffer),n=4*this._idx(t,e);return("terrarium"===this.encoding?this._unpackTerrarium:this._unpackMapbox)(r[n],r[n+1],r[n+2])},au.prototype.getUnpackVector=function(){return"terrarium"===this.encoding?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},au.prototype._idx=function(t,e){if(t<-1||t>=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError("out of range source coordinates for DEM data");return(e+1)*this.stride+(t+1)},au.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},au.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},au.prototype.getPixels=function(){return new Eo({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},au.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error("dem dimension mismatch");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,c=a;c<o;c++)for(var u=n;u<i;u++)this.data[this._idx(u,c)]=t.data[this._idx(u+s,c+l)]},Nn("DEMData",au);var ou=function(t){this._stringToNumber={},this._numberToString=[];for(var e=0;e<t.length;e++){var r=t[e];this._stringToNumber[r]=e,this._numberToString[e]=r}};ou.prototype.encode=function(t){return this._stringToNumber[t]},ou.prototype.decode=function(t){return this._numberToString[t]};var su=function(t,e,r,n,i){this.type="Feature",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i},lu={geometry:{configurable:!0}};lu.geometry.get=function(){return void 0===this._geometry&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry},lu.geometry.set=function(t){this._geometry=t},su.prototype.toJSON=function(){var t={geometry:this.geometry};for(var e in this)"_geometry"!==e&&"_vectorTileFeature"!==e&&(t[e]=this[e]);return t},Object.defineProperties(su.prototype,lu);var cu=function(){this.state={},this.stateChanges={},this.deletedStates={}};cu.prototype.updateState=function(t,e,r){var n=String(e);if(this.stateChanges[t]=this.stateChanges[t]||{},this.stateChanges[t][n]=this.stateChanges[t][n]||{},u(this.stateChanges[t][n],r),null===this.deletedStates[t])for(var i in this.deletedStates[t]={},this.state[t])i!==n&&(this.deletedStates[t][i]=null);else if(this.deletedStates[t]&&null===this.deletedStates[t][n])for(var a in this.deletedStates[t][n]={},this.state[t][n])r[a]||(this.deletedStates[t][n][a]=null);else for(var o in r){this.deletedStates[t]&&this.deletedStates[t][n]&&null===this.deletedStates[t][n][o]&&delete this.deletedStates[t][n][o]}},cu.prototype.removeFeatureState=function(t,e,r){if(!(null===this.deletedStates[t])){var n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e){if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null}else this.deletedStates[t]=null}},cu.prototype.getState=function(t,e){var r=String(e),n=this.state[t]||{},i=this.stateChanges[t]||{},a=u({},n[r],i[r]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){var o=this.deletedStates[t][e];if(null===o)return{};for(var s in o)delete a[s]}return a},cu.prototype.initializeTileState=function(t,e){t.setFeatureState(this.state,e)},cu.prototype.coalesceChanges=function(t,e){var r={};for(var n in this.stateChanges){this.state[n]=this.state[n]||{};var i={};for(var a in this.stateChanges[n])this.state[n][a]||(this.state[n][a]={}),u(this.state[n][a],this.stateChanges[n][a]),i[a]=this.state[n][a];r[n]=i}for(var o in this.deletedStates){this.state[o]=this.state[o]||{};var s={};if(null===this.deletedStates[o])for(var l in this.state[o])s[l]={},this.state[o][l]={};else for(var c in this.deletedStates[o]){if(null===this.deletedStates[o][c])this.state[o][c]={};else for(var f=0,h=Object.keys(this.deletedStates[o][c]);f<h.length;f+=1){var p=h[f];delete this.state[o][c][p]}s[c]=this.state[o][c]}r[o]=r[o]||{},u(r[o],s)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(r).length)for(var d in t){t[d].setFeatureState(r,e)}};var uu=function(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new zn(8192,16,0),this.grid3D=new zn(8192,16,0),this.featureIndexArray=new fa,this.promoteId=e};function fu(t,e,r,n,i){return v(t,(function(t,a){var o=e instanceof _i?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function hu(t){for(var e=1/0,r=1/0,n=-1/0,i=-1/0,a=0,o=t;a<o.length;a+=1){var s=o[a];e=Math.min(e,s.x),r=Math.min(r,s.y),n=Math.max(n,s.x),i=Math.max(i,s.y)}return{minX:e,minY:r,maxX:n,maxY:i}}function pu(t,e){return e-t}uu.prototype.insert=function(t,e,r,n,i,a){var o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);for(var s=a?this.grid3D:this.grid,l=0;l<e.length;l++){for(var c=e[l],u=[1/0,1/0,-1/0,-1/0],f=0;f<c.length;f++){var h=c[f];u[0]=Math.min(u[0],h.x),u[1]=Math.min(u[1],h.y),u[2]=Math.max(u[2],h.x),u[3]=Math.max(u[3],h.y)}u[0]<8192&&u[1]<8192&&u[2]>=0&&u[3]>=0&&s.insert(o,u[0],u[1],u[2],u[3])}},uu.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new Ls.VectorTile(new al(this.rawTileData)).layers,this.sourceLayerCoder=new ou(this.vtLayers?Object.keys(this.vtLayers).sort():["_geojsonTileLayer"])),this.vtLayers},uu.prototype.query=function(t,e,r,n){var a=this;this.loadVTLayers();for(var o=t.params||{},s=8192/t.tileSize/t.scale,l=sn(o.filter),c=t.queryGeometry,u=t.queryPadding*s,f=hu(c),h=this.grid.query(f.minX-u,f.minY-u,f.maxX+u,f.maxY+u),p=hu(t.cameraQueryGeometry),d=this.grid3D.query(p.minX-u,p.minY-u,p.maxX+u,p.maxY+u,(function(e,r,n,a){return function(t,e,r,n,a){for(var o=0,s=t;o<s.length;o+=1){var l=s[o];if(e<=l.x&&r<=l.y&&n>=l.x&&a>=l.y)return!0}var c=[new i(e,r),new i(e,a),new i(n,a),new i(n,r)];if(t.length>2)for(var u=0,f=c;u<f.length;u+=1){if(io(t,f[u]))return!0}for(var h=0;h<t.length-1;h++){if(ao(t[h],t[h+1],c))return!0}return!1}(t.cameraQueryGeometry,e-u,r-u,n+u,a+u)})),m=0,g=d;m<g.length;m+=1){var v=g[m];h.push(v)}h.sort(pu);for(var y,x={},b=function(i){var u=h[i];if(u!==y){y=u;var f=a.featureIndexArray.get(u),p=null;a.loadMatchingFeature(x,f.bucketIndex,f.sourceLayerIndex,f.featureIndex,l,o.layers,o.availableImages,e,r,n,(function(e,r,n){return p||(p=Ya(e)),r.queryIntersectsFeature(c,e,n,p,a.z,t.transform,s,t.pixelPosMatrix)}))}},_=0;_<h.length;_++)b(_);return x},uu.prototype.loadMatchingFeature=function(t,e,r,n,i,a,o,s,l,c,u){var f=this.bucketLayerIDs[e];if(!a||function(t,e){for(var r=0;r<t.length;r++)if(e.indexOf(t[r])>=0)return!0;return!1}(a,f)){var h=this.sourceLayerCoder.decode(r),p=this.vtLayers[h].feature(n);if(i.filter(new pi(this.tileID.overscaledZ),p))for(var d=this.getId(p,h),m=0;m<f.length;m++){var g=f[m];if(!(a&&a.indexOf(g)<0)){var v=s[g];if(v){var y={};void 0!==d&&c&&(y=c.getState(v.sourceLayer||"_geojsonTileLayer",d));var x=l[g];x.paint=fu(x.paint,v.paint,p,y,o),x.layout=fu(x.layout,v.layout,p,y,o);var b=!u||u(p,v,y);if(b){var _=new su(p,this.z,this.x,this.y,d);_.layer=x;var w=t[g];void 0===w&&(w=t[g]=[]),w.push({featureIndex:n,feature:_,intersectionZ:b})}}}}}},uu.prototype.lookupSymbolFeatures=function(t,e,r,n,i,a,o,s){var l={};this.loadVTLayers();for(var c=sn(i),u=0,f=t;u<f.length;u+=1){var h=f[u];this.loadMatchingFeature(l,r,n,h,c,a,o,s,e)}return l},uu.prototype.hasLayer=function(t){for(var e=0,r=this.bucketLayerIDs;e<r.length;e+=1)for(var n=0,i=r[e];n<i.length;n+=1){if(t===i[n])return!0}return!1},uu.prototype.getId=function(t,e){var r=t.id;if(this.promoteId){var n="string"==typeof this.promoteId?this.promoteId:this.promoteId[e];"boolean"==typeof(r=t.properties[n])&&(r=Number(r))}return r},Nn("FeatureIndex",uu,{omit:["rawTileData","sourceLayerCoder"]});var du=function(t,e){this.tileID=t,this.uid=h(),this.uses=0,this.tileSize=e,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state="loading"};du.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<R.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},du.prototype.wasRequested=function(){return"errored"===this.state||"loaded"===this.state||"reloading"===this.state},du.prototype.loadVectorData=function(t,e,r){if(this.hasData()&&this.unloadVectorData(),this.state="loaded",t){for(var n in t.featureIndex&&(this.latestFeatureIndex=t.featureIndex,t.rawTileData?(this.latestRawTileData=t.rawTileData,this.latestFeatureIndex.rawTileData=t.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=t.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=function(){var t=a[i],n=t.layerIds.map((function(t){return e.getLayer(t)})).filter(Boolean);if(0!==n.length){t.layers=n,t.stateDependentLayerIds&&(t.stateDependentLayers=t.stateDependentLayerIds.map((function(t){return n.filter((function(e){return e.id===t}))[0]})));for(var o=0,s=n;o<s.length;o+=1){var l=s[o];r[l.id]=t}}},i=0,a=t;i<a.length;i+=1)n();return r}(t.buckets,e.style),this.hasSymbolBuckets=!1,this.buckets){var i=this.buckets[n];if(i instanceof Mc){if(this.hasSymbolBuckets=!0,!r)break;i.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var a in this.buckets){var o=this.buckets[a];if(o instanceof Mc&&o.hasRTLText){this.hasRTLText=!0,hi.isLoading()||hi.isLoaded()||"deferred"!==ui()||fi();break}}for(var s in this.queryPadding=0,this.buckets){var l=this.buckets[s];this.queryPadding=Math.max(this.queryPadding,e.style.getLayer(s).queryRadius(l))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new na},du.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state="unloaded"},du.prototype.getBucket=function(t){return this.buckets[t.id]},du.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploadPending()&&r.upload(t)}var n=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new Vc(t,this.imageAtlas.image,n.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new Vc(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},du.prototype.prepare=function(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)},du.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o,s,l,c){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:c,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}},du.prototype.querySourceFeatures=function(t,e){var r=this.latestFeatureIndex;if(r&&r.rawTileData){var n=r.loadVTLayers(),i=e?e.sourceLayer:"",a=n._geojsonTileLayer||n[i];if(a)for(var o=sn(e&&e.filter),s=this.tileID.canonical,l=s.z,c=s.x,u=s.y,f={z:l,x:c,y:u},h=0;h<a.length;h++){var p=a.feature(h);if(o.filter(new pi(this.tileID.overscaledZ),p)){var d=r.getId(p,i),m=new su(p,l,c,u,d);m.tile=f,t.push(m)}}}},du.prototype.hasData=function(){return"loaded"===this.state||"reloading"===this.state||"expired"===this.state},du.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},du.prototype.setExpiryData=function(t){var e=this.expirationTime;if(t.cacheControl){var r=A(t.cacheControl);r["max-age"]&&(this.expirationTime=Date.now()+1e3*r["max-age"])}else t.expires&&(this.expirationTime=new Date(t.expires).getTime());if(this.expirationTime){var n=Date.now(),i=!1;if(this.expirationTime>n)i=!1;else if(e)if(this.expirationTime<e)i=!0;else{var a=this.expirationTime-e;a?this.expirationTime=n+Math.max(a,3e4):i=!0}else i=!0;i?(this.expiredRequestCount++,this.state="expired"):this.expiredRequestCount=0}},du.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)},du.prototype.setFeatureState=function(t,e){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData&&0!==Object.keys(t).length){var r=this.latestFeatureIndex.loadVTLayers();for(var n in this.buckets)if(e.style.hasLayer(n)){var i=this.buckets[n],a=i.layers[0].sourceLayer||"_geojsonTileLayer",o=r[a],s=t[a];if(o&&s&&0!==Object.keys(s).length){i.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{});var l=e&&e.style&&e.style.getLayer(n);l&&(this.queryPadding=Math.max(this.queryPadding,l.queryRadius(i)))}}}},du.prototype.holdingForFade=function(){return void 0!==this.symbolFadeHoldUntil},du.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<R.now()},du.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},du.prototype.setHoldDuration=function(t){this.symbolFadeHoldUntil=R.now()+t},du.prototype.setDependencies=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){r[i[n]]=!0}this.dependencies[t]=r},du.prototype.hasDependency=function(t,e){for(var r=0,n=t;r<n.length;r+=1){var i=n[r],a=this.dependencies[i];if(a)for(var o=0,s=e;o<s.length;o+=1){if(a[s[o]])return!0}}return!1};var mu=self.performance,gu=function(t){this._marks={start:[t.url,"start"].join("#"),end:[t.url,"end"].join("#"),measure:t.url.toString()},mu.mark(this._marks.start)};gu.prototype.finish=function(){mu.mark(this._marks.end);var t=mu.getEntriesByName(this._marks.measure);return 0===t.length&&(mu.measure(this._marks.measure,this._marks.start,this._marks.end),t=mu.getEntriesByName(this._marks.measure),mu.clearMarks(this._marks.start),mu.clearMarks(this._marks.end),mu.clearMeasures(this._marks.measure)),t},t.Actor=qc,t.AlphaImage=So,t.CanonicalTileID=eu,t.CollisionBoxArray=na,t.Color=te,t.DEMData=au,t.DataConstantProperty=wi,t.DictionaryCoder=ou,t.EXTENT=8192,t.ErrorEvent=St,t.EvaluationParameters=pi,t.Event=Mt,t.Evented=Et,t.FeatureIndex=uu,t.FillBucket=gs,t.FillExtrusionBucket=Os,t.ImageAtlas=Ll,t.ImagePosition=Sl,t.LineBucket=qs,t.LngLat=Wc,t.LngLatBounds=Yc,t.MercatorCoordinate=tu,t.ONE_EM=24,t.OverscaledTileID=nu,t.Point=i,t.Point$1=i,t.Properties=Si,t.Protobuf=al,t.RGBAImage=Eo,t.RequestManager=H,t.RequestPerformance=gu,t.ResourceType=dt,t.SegmentVector=pa,t.SourceFeatureState=cu,t.StructArrayLayout1ui2=$i,t.StructArrayLayout2f1f2i16=qi,t.StructArrayLayout2i4=zi,t.StructArrayLayout3ui6=Yi,t.StructArrayLayout4i8=Di,t.SymbolBucket=Mc,t.Texture=Vc,t.Tile=du,t.Transitionable=gi,t.Uniform1f=Sa,t.Uniform1i=Ma,t.Uniform2f=Ea,t.Uniform3f=La,t.Uniform4f=Ca,t.UniformColor=Pa,t.UniformMatrix4f=Oa,t.UnwrappedTileID=ru,t.ValidationError=Ct,t.WritingMode=Cl,t.ZoomHistory=Gn,t.add=function(t,e,r){return t[0]=e[0]+r[0],t[1]=e[1]+r[1],t[2]=e[2]+r[2],t},t.addDynamicAttributes=wc,t.asyncAll=function(t,e,r){if(!t.length)return r(null,[]);var n=t.length,i=new Array(t.length),a=null;t.forEach((function(t,o){e(t,(function(t,e){t&&(a=t),i[o]=e,0==--n&&r(a,i)}))}))},t.bezier=o,t.bindAll=m,t.browser=R,t.cacheEntryPossiblyAdded=function(t){++ht>ot&&(t.getActor().send("enforceCacheSizeLimit",at),ht=0)},t.clamp=l,t.clearTileCache=function(t){var e=self.caches.delete("mapbox-tiles");t&&e.catch(t).then((function(){return t()}))},t.clipLine=ec,t.clone=function(t){var e=new fo(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e},t.clone$1=x,t.clone$2=function(t){var e=new fo(3);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e},t.collisionCircleLayout=tl,t.config=F,t.create=function(){var t=new fo(16);return 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vt(t,e,r,n,i,a){return[Math.round(r*(t*n-i)),Math.round(r*(e*n-a))]}function yt(t,e,r,n,i){for(var a=e===i.maxZoom?0:i.tolerance/((1<<e)*i.extent),o={features:[],numPoints:0,numSimplified:0,numFeatures:0,source:null,x:r,y:n,z:e,transformed:!1,minX:2,minY:1,maxX:-1,maxY:0},s=0;s<t.length;s++){o.numFeatures++,xt(o,t[s],a,i);var l=t[s].minX,c=t[s].minY,u=t[s].maxX,f=t[s].maxY;l<o.minX&&(o.minX=l),c<o.minY&&(o.minY=c),u>o.maxX&&(o.maxX=u),f>o.maxY&&(o.maxY=f)}return o}function xt(t,e,r,n){var i=e.geometry,a=e.type,o=[];if("Point"===a||"MultiPoint"===a)for(var s=0;s<i.length;s+=3)o.push(i[s]),o.push(i[s+1]),t.numPoints++,t.numSimplified++;else if("LineString"===a)bt(o,i,t,r,!1,!1);else if("MultiLineString"===a||"Polygon"===a)for(s=0;s<i.length;s++)bt(o,i[s],t,r,"Polygon"===a,0===s);else if("MultiPolygon"===a)for(var l=0;l<i.length;l++){var c=i[l];for(s=0;s<c.length;s++)bt(o,c[s],t,r,!0,0===s)}if(o.length){var u=e.tags||null;if("LineString"===a&&n.lineMetrics){for(var f in u={},e.tags)u[f]=e.tags[f];u.mapbox_clip_start=i.start/i.size,u.mapbox_clip_end=i.end/i.size}var h={geometry:o,type:"Polygon"===a||"MultiPolygon"===a?3:"LineString"===a||"MultiLineString"===a?2:1,tags:u};null!==e.id&&(h.id=e.id),t.features.push(h)}}function bt(t,e,r,n,i,a){var o=n*n;if(n>0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;l<e.length;l+=3)(0===n||e[l+2]>o)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n<i;a=n,n+=2)r+=(t[n]-t[a])*(t[n+1]+t[a+1]);if(r>0===e)for(n=0,i=t.length;n<i/2;n+=2){var o=t[n],s=t[n+1];t[n]=t[i-2-n],t[n+1]=t[i-1-n],t[i-2-n]=o,t[i-1-n]=s}}(s,a),t.push(s)}}function _t(t,e){var r=(e=this.options=function(t,e){for(var r in e)t[r]=e[r];return t}(Object.create(this.options),e)).debug;if(r&&console.time("preprocess data"),e.maxZoom<0||e.maxZoom>24)throw new Error("maxZoom should be in the 0-24 range");if(e.promoteId&&e.generateId)throw new Error("promoteId and generateId cannot be used together.");var n=function(t,e){var r=[];if("FeatureCollection"===t.type)for(var n=0;n<t.features.length;n++)tt(r,t.features[n],e,n);else"Feature"===t.type?tt(r,t,e):tt(r,{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd("preprocess data"),console.log("index: maxZoom: %d, maxPoints: %d",e.indexMaxZoom,e.indexMaxPoints),console.time("generate tiles"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=ot(t,1,-1-r,r,0,-1,2,e),a=ot(t,1,1-r,2+r,0,-1,2,e);return(i||a)&&(n=ot(t,1,-r,1+r,0,-1,2,e)||[],i&&(n=dt(i,1).concat(n)),a&&(n=n.concat(dt(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log("features: %d, points: %d",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd("generate tiles"),console.log("tiles generated:",this.total,JSON.stringify(this.stats)))}function wt(t,e,r){return 32*((1<<t)*r+e)+t}function Tt(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new g(n.features),a=_(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}U.prototype.load=function(t){var e=this.options,r=e.log,n=e.minZoom,i=e.maxZoom,a=e.nodeSize;r&&console.time("total time");var o="prepare "+t.length+" points";r&&console.time(o),this.points=t;for(var s=[],l=0;l<t.length;l++)t[l].geometry&&s.push(H(t[l],l));this.trees[i+1]=new N(s,Z,J,a,Float32Array),r&&console.timeEnd(o);for(var c=i;c>=n;c--){var u=+Date.now();s=this._cluster(s,c),this.trees[c]=new N(s,Z,J,a,Float32Array),r&&console.log("z%d: %d clusters in %dms",c,s.length,+Date.now()-u)}return r&&console.timeEnd("total time"),this},U.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],c=[],u=0,f=l.range(Y(r),W(a),Y(i),W(n));u<f.length;u+=1){var h=f[u],p=l.points[h];c.push(p.numPoints?q(p):this.points[p.index])}return c},U.prototype.getChildren=function(t){var e=this._getOriginId(t),r=this._getOriginZoom(t),n="No cluster with the specified id.",i=this.trees[r];if(!i)throw new Error(n);var a=i.points[e];if(!a)throw new Error(n);for(var o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=[],l=0,c=i.within(a.x,a.y,o);l<c.length;l+=1){var u=c[l],f=i.points[u];f.parentId===t&&s.push(f.numPoints?q(f):this.points[f.index])}if(0===s.length)throw new Error(n);return s},U.prototype.getLeaves=function(t,e,r){e=e||10,r=r||0;var n=[];return this._appendLeaves(n,t,e,r,0),n},U.prototype.getTile=function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options,o=a.extent,s=a.radius/o,l=(r-s)/i,c=(r+1+s)/i,u={features:[]};return this._addTileFeatures(n.range((e-s)/i,l,(e+1+s)/i,c),n.points,e,r,i,u),0===e&&this._addTileFeatures(n.range(1-s/i,l,1,c),n.points,i,r,i,u),e===i-1&&this._addTileFeatures(n.range(0,l,s/i,c),n.points,-1,r,i,u),u.features.length?u:null},U.prototype.getClusterExpansionZoom=function(t){for(var e=this._getOriginZoom(t)-1;e<=this.options.maxZoom;){var r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},U.prototype._appendLeaves=function(t,e,r,n,i){for(var a=0,o=this.getChildren(e);a<o.length;a+=1){var s=o[a],l=s.properties;if(l&&l.cluster?i+l.point_count<=n?i+=l.point_count:i=this._appendLeaves(t,l.cluster_id,r,n,i):i<n?i++:t.push(s),t.length===r)break}return i},U.prototype._addTileFeatures=function(t,e,r,n,i,a){for(var o=0,s=t;o<s.length;o+=1){var l=e[s[o]],c=l.numPoints,u={type:1,geometry:[[Math.round(this.options.extent*(l.x*i-r)),Math.round(this.options.extent*(l.y*i-n))]],tags:c?G(l):this.points[l.index].properties},f=void 0;c?f=l.id:this.options.generateId?f=l.index:this.points[l.index].id&&(f=this.points[l.index].id),void 0!==f&&(u.id=f),a.features.push(u)}},U.prototype._limitZoom=function(t){return Math.max(this.options.minZoom,Math.min(t,this.options.maxZoom+1))},U.prototype._cluster=function(t,e){for(var r=[],n=this.options,i=n.radius,a=n.extent,o=n.reduce,s=i/(a*Math.pow(2,e)),l=0;l<t.length;l++){var c=t[l];if(!(c.zoom<=e)){c.zoom=e;for(var u=this.trees[e+1],f=u.within(c.x,c.y,s),h=c.numPoints||1,p=c.x*h,d=c.y*h,m=o&&h>1?this._map(c,!0):null,g=(l<<5)+(e+1)+this.points.length,v=0,y=f;v<y.length;v+=1){var x=y[v],b=u.points[x];if(!(b.zoom<=e)){b.zoom=e;var _=b.numPoints||1;p+=b.x*_,d+=b.y*_,h+=_,b.parentId=g,o&&(m||(m=this._map(c,!0)),o(m,this._map(b)))}}1===h?r.push(c):(c.parentId=g,r.push(V(p/h,d/h,g,h,m)))}}return r},U.prototype._getOriginId=function(t){return t-this.points.length>>5},U.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},U.prototype._map=function(t,e){if(t.numPoints)return e?X({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?X({},n):n},_t.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},_t.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,c=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var u=1<<e,f=wt(e,r,n),h=this.tiles[f];if(!h&&(c>1&&console.time("creation"),h=this.tiles[f]=yt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),c)){c>1&&(console.log("tile z%d-%d-%d (features: %d, points: %d, simplified: %d)",e,r,n,h.numFeatures,h.numPoints,h.numSimplified),console.timeEnd("creation"));var p="z"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(h.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)||n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom||h.numPoints<=l.indexMaxPoints)continue;if(h.source=null,0!==t.length){c>1&&console.time("clipping");var m,g,v,y,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;m=g=v=y=null,x=ot(t,u,r-_,r+T,0,h.minX,h.maxX,l),b=ot(t,u,r+w,r+k,0,h.minX,h.maxX,l),t=null,x&&(m=ot(x,u,n-_,n+T,1,h.minY,h.maxY,l),g=ot(x,u,n+w,n+k,1,h.minY,h.maxY,l),x=null),b&&(v=ot(b,u,n-_,n+T,1,h.minY,h.maxY,l),y=ot(b,u,n+w,n+k,1,h.minY,h.maxY,l),b=null),c>1&&console.timeEnd("clipping"),s.push(m||[],e+1,2*r,2*n),s.push(g||[],e+1,2*r,2*n+1),s.push(v||[],e+1,2*r+1,2*n),s.push(y||[],e+1,2*r+1,2*n+1)}}},_t.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<<t,s=wt(t,e=(e%o+o)%o,r);if(this.tiles[s])return gt(this.tiles[s],i);a>1&&console.log("drilling down to z%d-%d-%d",t,e,r);for(var l,c=t,u=e,f=r;!l&&c>0;)c--,u=Math.floor(u/2),f=Math.floor(f/2),l=this.tiles[wt(c,u,f)];return l&&l.source?(a>1&&console.log("found parent tile z%d-%d-%d",c,u,f),a>1&&console.time("drilling down"),this.splitTile(l.source,c,u,f,t,e,r),a>1&&console.timeEnd("drilling down"),this.tiles[s]?gt(this.tiles[s],i):null):null};var kt=function(e){function r(t,r,n,i){e.call(this,t,r,n,Tt),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&"Idle"!==this._state?this._state="NeedsLoadData":(this._state="Coalescing",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if("object"!=typeof o)return r(new Error("Input data given to '"+n.source+"' is not a valid GeoJSON object."));f(o,!0);try{e._geoJSONIndex=n.cluster?new U(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var i={},a={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),c=0,u=l;c<u.length;c+=1){var f=u[c],h=n[f],p=h[0],d=h[1],m=t.createExpression(d),g=t.createExpression("string"==typeof p?[p,["accumulated"],["get",f]]:p);i[f]=m.value,a[f]=g.value}return r.map=function(t){s.properties=t;for(var e={},r=0,n=l;r<n.length;r+=1){var a=n[r];e[a]=i[a].evaluate(o,s)}return e},r.reduce=function(t,e){s.properties=e;for(var r=0,n=l;r<n.length;r+=1){var i=n[r];o.accumulated=t[i],t[i]=a[i].evaluate(o,s)}},r}(n)).load(o.features):function(t,e){return new _t(t,e)}(o,n.geojsonVtOptions)}catch(a){return r(a)}e.loaded={};var s={};if(i){var l=i.finish();l&&(s.resourceTiming={},s.resourceTiming[n.source]=JSON.parse(JSON.stringify(l)))}r(null,s)}))}},r.prototype.coalesce=function(){"Coalescing"===this._state?this._state="Idle":"NeedsLoadData"===this._state&&(this._state="Coalescing",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded,i=t.uid;return n&&n[i]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if("string"!=typeof e.data)return r(new Error("Input data given to '"+e.source+"' is not a valid GeoJSON object."));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error("Input data given to '"+e.source+"' is not a valid GeoJSON object."))}}},r.prototype.removeSource=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),e()},r.prototype.getClusterExpansionZoom=function(t,e){try{e(null,this._geoJSONIndex.getClusterExpansionZoom(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterChildren=function(t,e){try{e(null,this._geoJSONIndex.getChildren(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterLeaves=function(t,e){try{e(null,this._geoJSONIndex.getLeaves(t.clusterId,t.limit,t.offset))}catch(t){e(t)}},r}(l);var At=function(e){var r=this;this.self=e,this.actor=new t.Actor(e,this),this.layerIndexes={},this.availableImages={},this.workerSourceTypes={vector:l,geojson:kt},this.workerSources={},this.demWorkerSources={},this.self.registerWorkerSource=function(t,e){if(r.workerSourceTypes[t])throw new Error('Worker source with name "'+t+'" already registered.');r.workerSourceTypes[t]=e},this.self.registerRTLTextPlugin=function(e){if(t.plugin.isParsed())throw new Error("RTL text plugin already registered.");t.plugin.applyArabicShaping=e.applyArabicShaping,t.plugin.processBidirectionalText=e.processBidirectionalText,t.plugin.processStyledBidirectionalText=e.processStyledBidirectionalText}};return At.prototype.setReferrer=function(t,e){this.referrer=e},At.prototype.setImages=function(t,e,r){for(var n in this.availableImages[t]=e,this.workerSources[t]){var i=this.workerSources[t][n];for(var a in 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JSON support":"insufficient Object support":"insufficient Function support":"insufficent Array support"}t.exports?t.exports=e:window&&(window.mapboxgl=window.mapboxgl||{},window.mapboxgl.supported=e,window.mapboxgl.notSupportedReason=r);var n={};e.webGLContextAttributes={antialias:!1,alpha:!0,stencil:!0,depth:!0}})),r={create:function(e,r,n){var i=t.window.document.createElement(e);return void 0!==r&&(i.className=r),n&&n.appendChild(i),i},createNS:function(e,r){return t.window.document.createElementNS(e,r)}},n=t.window.document.documentElement.style;function i(t){if(!n)return t[0];for(var e=0;e<t.length;e++)if(t[e]in n)return t[e];return t[0]}var a,o=i(["userSelect","MozUserSelect","WebkitUserSelect","msUserSelect"]);r.disableDrag=function(){n&&o&&(a=n[o],n[o]="none")},r.enableDrag=function(){n&&o&&(n[o]=a)};var s=i(["transform","WebkitTransform"]);r.setTransform=function(t,e){t.style[s]=e};var l=!1;try{var c=Object.defineProperty({},"passive",{get:function(){l=!0}});t.window.addEventListener("test",c,c),t.window.removeEventListener("test",c,c)}catch(t){l=!1}r.addEventListener=function(t,e,r,n){void 0===n&&(n={}),"passive"in n&&l?t.addEventListener(e,r,n):t.addEventListener(e,r,n.capture)},r.removeEventListener=function(t,e,r,n){void 0===n&&(n={}),"passive"in n&&l?t.removeEventListener(e,r,n):t.removeEventListener(e,r,n.capture)};var u=function(e){e.preventDefault(),e.stopPropagation(),t.window.removeEventListener("click",u,!0)};function f(t){var e=t.userImage;if(e&&e.render&&e.render())return t.data.replace(new Uint8Array(e.data.buffer)),!0;return!1}r.suppressClick=function(){t.window.addEventListener("click",u,!0),t.window.setTimeout((function(){t.window.removeEventListener("click",u,!0)}),0)},r.mousePos=function(e,r){var n=e.getBoundingClientRect();return new t.Point(r.clientX-n.left-e.clientLeft,r.clientY-n.top-e.clientTop)},r.touchPos=function(e,r){for(var 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r=this.images[t];e.version=r.version+1,this.images[t]=e,this.updatedImages[t]=!0},r.prototype.removeImage=function(t){var e=this.images[t];delete this.images[t],delete this.patterns[t],e.userImage&&e.userImage.onRemove&&e.userImage.onRemove()},r.prototype.listImages=function(){return Object.keys(this.images)},r.prototype.getImages=function(t,e){var r=!0;if(!this.isLoaded())for(var n=0,i=t;n<i.length;n+=1){var a=i[n];this.images[a]||(r=!1)}this.isLoaded()||r?this._notify(t,e):this.requestors.push({ids:t,callback:e})},r.prototype._notify=function(e,r){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i];this.images[o]||this.fire(new t.Event("styleimagemissing",{id:o}));var s=this.images[o];s?n[o]={data:s.data.clone(),pixelRatio:s.pixelRatio,sdf:s.sdf,version:s.version,stretchX:s.stretchX,stretchY:s.stretchY,content:s.content,hasRenderCallback:Boolean(s.userImage&&s.userImage.render)}:t.warnOnce('Image "'+o+'" could not be loaded. Please make sure you have added the image with map.addImage() or a "sprite" property in your style. You can provide missing images by listening for the "styleimagemissing" map event.')}r(null,n)},r.prototype.getPixelSize=function(){var t=this.atlasImage;return{width:t.width,height:t.height}},r.prototype.getPattern=function(e){var r=this.patterns[e],n=this.getImage(e);if(!n)return null;if(r&&r.position.version===n.version)return r.position;if(r)r.position.version=n.version;else{var i={w:n.data.width+2,h:n.data.height+2,x:0,y:0},a=new t.ImagePosition(i,n);this.patterns[e]={bin:i,position:a}}return this._updatePatternAtlas(),this.patterns[e].position},r.prototype.bind=function(e){var r=e.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new t.Texture(e,this.atlasImage,r.RGBA),this.atlasTexture.bind(r.LINEAR,r.CLAMP_TO_EDGE)},r.prototype._updatePatternAtlas=function(){var e=[];for(var r in this.patterns)e.push(this.patterns[r].bin);var n=t.potpack(e),i=n.w,a=n.h,o=this.atlasImage;for(var s in o.resize({width:i||1,height:a||1}),this.patterns){var l=this.patterns[s].bin,c=l.x+1,u=l.y+1,f=this.images[s].data,h=f.width,p=f.height;t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u},{width:h,height:p}),t.RGBAImage.copy(f,o,{x:0,y:p-1},{x:c,y:u-1},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u+p},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:h-1,y:0},{x:c-1,y:u},{width:1,height:p}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c+h,y:u},{width:1,height:p})}this.dirty=!0},r.prototype.beginFrame=function(){this.callbackDispatchedThisFrame={}},r.prototype.dispatchRenderCallbacks=function(t){for(var e=0,r=t;e<r.length;e+=1){var n=r[e];if(!this.callbackDispatchedThisFrame[n]){this.callbackDispatchedThisFrame[n]=!0;var i=this.images[n];f(i)&&this.updateImage(n,i)}}},r}(t.Evented);var p=g,d=g,m=1e20;function g(t,e,r,n,i,a){this.fontSize=t||24,this.buffer=void 0===e?3:e,this.cutoff=n||.25,this.fontFamily=i||"sans-serif",this.fontWeight=a||"normal",this.radius=r||8;var o=this.size=this.fontSize+2*this.buffer;this.canvas=document.createElement("canvas"),this.canvas.width=this.canvas.height=o,this.ctx=this.canvas.getContext("2d"),this.ctx.font=this.fontWeight+" "+this.fontSize+"px "+this.fontFamily,this.ctx.textBaseline="middle",this.ctx.fillStyle="black",this.gridOuter=new Float64Array(o*o),this.gridInner=new Float64Array(o*o),this.f=new Float64Array(o),this.d=new Float64Array(o),this.z=new Float64Array(o+1),this.v=new Int16Array(o),this.middle=Math.round(o/2*(navigator.userAgent.indexOf("Gecko/")>=0?1.2:1))}function v(t,e,r,n,i,a,o){for(var s=0;s<e;s++){for(var l=0;l<r;l++)n[l]=t[l*e+s];for(y(n,i,a,o,r),l=0;l<r;l++)t[l*e+s]=i[l]}for(l=0;l<r;l++){for(s=0;s<e;s++)n[s]=t[l*e+s];for(y(n,i,a,o,e),s=0;s<e;s++)t[l*e+s]=Math.sqrt(i[s])}}function y(t,e,r,n,i){r[0]=0,n[0]=-m,n[1]=+m;for(var a=1,o=0;a<i;a++){for(var s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);s<=n[o];)o--,s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);r[++o]=a,n[o]=s,n[o+1]=+m}for(a=0,o=0;a<i;a++){for(;n[o+1]<a;)o++;e[a]=(a-r[o])*(a-r[o])+t[r[o]]}}g.prototype.draw=function(t){this.ctx.clearRect(0,0,this.size,this.size),this.ctx.fillText(t,this.buffer,this.middle);for(var e=this.ctx.getImageData(0,0,this.size,this.size),r=new Uint8ClampedArray(this.size*this.size),n=0;n<this.size*this.size;n++){var i=e.data[4*n+3]/255;this.gridOuter[n]=1===i?0:0===i?m:Math.pow(Math.max(0,.5-i),2),this.gridInner[n]=1===i?m:0===i?0:Math.pow(Math.max(0,i-.5),2)}for(v(this.gridOuter,this.size,this.size,this.f,this.d,this.v,this.z),v(this.gridInner,this.size,this.size,this.f,this.d,this.v,this.z),n=0;n<this.size*this.size;n++){var a=this.gridOuter[n]-this.gridInner[n];r[n]=Math.max(0,Math.min(255,Math.round(255-255*(a/this.radius+this.cutoff))))}return r},p.default=d;var x=function(t,e){this.requestManager=t,this.localIdeographFontFamily=e,this.entries={}};x.prototype.setURL=function(t){this.url=t},x.prototype.getGlyphs=function(e,r){var n=this,i=[];for(var a in e)for(var o=0,s=e[a];o<s.length;o+=1){var l=s[o];i.push({stack:a,id:l})}t.asyncAll(i,(function(t,e){var r=t.stack,i=t.id,a=n.entries[r];a||(a=n.entries[r]={glyphs:{},requests:{},ranges:{}});var o=a.glyphs[i];if(void 0===o){if(o=n._tinySDF(a,r,i))return a.glyphs[i]=o,void e(null,{stack:r,id:i,glyph:o});var s=Math.floor(i/256);if(256*s>65535)e(new Error("glyphs > 65535 not supported"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i<o.length;i+=1){(0,o[i])(t,e)}delete a.requests[s]}))),l.push((function(t,n){t?e(t):n&&e(null,{stack:r,id:i,glyph:n[i]||null})}))}}else e(null,{stack:r,id:i,glyph:o})}),(function(t,e){if(t)r(t);else if(e){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.stack,l=o.id,c=o.glyph;(n[s]||(n[s]={}))[l]=c&&{id:c.id,bitmap:c.bitmap.clone(),metrics:c.metrics}}r(null,n)}}))},x.prototype._doesCharSupportLocalGlyph=function(e){return!!this.localIdeographFontFamily&&(t.isChar["CJK Unified Ideographs"](e)||t.isChar["Hangul Syllables"](e)||t.isChar.Hiragana(e)||t.isChar.Katakana(e))},x.prototype._tinySDF=function(e,r,n){var i=this.localIdeographFontFamily;if(i&&this._doesCharSupportLocalGlyph(n)){var a=e.tinySDF;if(!a){var o="400";/bold/i.test(r)?o="900":/medium/i.test(r)?o="500":/light/i.test(r)&&(o="200"),a=e.tinySDF=new x.TinySDF(24,3,8,.25,i,o)}return{id:n,bitmap:new t.AlphaImage({width:30,height:30},a.draw(String.fromCharCode(n))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},x.loadGlyphRange=function(e,r,n,i,a){var o=256*r,s=o+255,l=i.transformRequest(i.normalizeGlyphsURL(n).replace("{fontstack}",e).replace("{range}",o+"-"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,(function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.parseGlyphPBF(r);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}}))},x.TinySDF=p;var b=function(){this.specification=t.styleSpec.light.position};b.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},b.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var _=new t.Properties({anchor:new t.DataConstantProperty(t.styleSpec.light.anchor),position:new b,color:new t.DataConstantProperty(t.styleSpec.light.color),intensity:new t.DataConstantProperty(t.styleSpec.light.intensity)}),w=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(_),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e,r){if(void 0===r&&(r={}),!this._validate(t.validateLight,e,r))for(var n in e){var i=e[n];t.endsWith(n,"-transition")?this._transitionable.setTransition(n.slice(0,-"-transition".length),i):this._transitionable.setValue(n,i)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r,n){return(!n||!1!==n.validate)&&t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.styleSpec})))},r}(t.Evented),T=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};T.prototype.getDash=function(t,e){var r=t.join(",")+String(e);return this.dashEntry[r]||(this.dashEntry[r]=this.addDash(t,e)),this.dashEntry[r]},T.prototype.getDashRanges=function(t,e,r){var n=[],i=t.length%2==1?-t[t.length-1]*r:0,a=t[0]*r,o=!0;n.push({left:i,right:a,isDash:o,zeroLength:0===t[0]});for(var s=t[0],l=1;l<t.length;l++){o=!o;var c=t[l];i=s*r,a=(s+=c)*r,n.push({left:i,right:a,isDash:o,zeroLength:0===c})}return n},T.prototype.addRoundDash=function(t,e,r){for(var n=e/2,i=-r;i<=r;i++)for(var a=this.nextRow+r+i,o=this.width*a,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=void 0,d=i/r*(n+1);if(l.isDash){var m=n-Math.abs(d);p=Math.sqrt(h*h+m*m)}else p=n-Math.sqrt(h*h+d*d);this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=l.isDash?h:-h;this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce("LineAtlas out of space"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];if(0!==a){var s=this.width/a,l=this.getDashRanges(e,this.width,s);r?this.addRoundDash(l,s,n):this.addRegularDash(l)}var c={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,c},T.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.ALPHA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.ALPHA,this.width,this.height,0,e.ALPHA,e.UNSIGNED_BYTE,this.data))};var k=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=i[a],s=new e.Actor(o,n,this.id);s.name="Worker "+a,this.actors.push(s)}};function A(e,r,n){var i=function(i,a){if(i)return n(i);if(a){var o=t.pick(t.extend(a,e),["tiles","minzoom","maxzoom","attribution","mapbox_logo","bounds","scheme","tileSize","encoding"]);a.vector_layers&&(o.vectorLayers=a.vector_layers,o.vectorLayerIds=o.vectorLayers.map((function(t){return t.id}))),o.tiles=r.canonicalizeTileset(o,e.url),n(null,o)}};return e.url?t.getJSON(r.transformRequest(r.normalizeSourceURL(e.url),t.ResourceType.Source),i):t.browser.frame((function(){return i(null,e)}))}k.prototype.broadcast=function(e,r,n){n=n||function(){},t.asyncAll(this.actors,(function(t,n){t.send(e,r,n)}),n)},k.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},k.prototype.remove=function(){this.actors.forEach((function(t){t.remove()})),this.actors=[],this.workerPool.release(this.id)},k.Actor=t.Actor;var M=function(e,r,n){this.bounds=t.LngLatBounds.convert(this.validateBounds(e)),this.minzoom=r||0,this.maxzoom=n||24};M.prototype.validateBounds=function(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]},M.prototype.contains=function(e){var r=Math.pow(2,e.z),n=Math.floor(t.mercatorXfromLng(this.bounds.getWest())*r),i=Math.floor(t.mercatorYfromLat(this.bounds.getNorth())*r),a=Math.ceil(t.mercatorXfromLng(this.bounds.getEast())*r),o=Math.ceil(t.mercatorYfromLat(this.bounds.getSouth())*r);return e.x>=n&&e.x<a&&e.y>=i&&e.y<o};var S=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type="vector",this.minzoom=0,this.maxzoom=22,this.scheme="xyz",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,t.extend(this,t.pick(n,["url","scheme","tileSize","promoteId"])),this._options=t.extend({type:"vector"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error("vector tile sources must have a tileSize of 512");this.setEventedParent(a)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles,e.map._requestManager._customAccessToken),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken,e.map._requestManager._customAccessToken),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme)),i={request:this.map._requestManager.transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSize*e.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function a(n,i){return delete e.request,e.aborted?r(null):n&&404!==n.status?r(n):(i&&i.resourceTiming&&(e.resourceTiming=i.resourceTiming),this.map._refreshExpiredTiles&&i&&e.setExpiryData(i),e.loadVectorData(i,this.map.painter),t.cacheEntryPossiblyAdded(this.dispatcher),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,e.actor&&"expired"!==e.state?"loading"===e.state?e.reloadCallback=r:e.request=e.actor.send("reloadTile",i,a.bind(this)):(e.actor=this.dispatcher.getActor(),e.request=e.actor.send("loadTile",i,a.bind(this)))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.actor&&t.actor.send("abortTile",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.unloadTile=function(t){t.unloadVectorData(),t.actor&&t.actor.send("removeTile",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),E=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type="raster",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme="xyz",this.tileSize=512,this._loaded=!1,this._options=t.extend({type:"raster"},n),t.extend(this,t.pick(n,["url","scheme","tileSize"]))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);e.request=t.getImage(this.map._requestManager.transformRequest(i,t.ResourceType.Tile),(function(i,a){if(delete e.request,e.aborted)e.state="unloaded",r(null);else if(i)e.state="errored",r(i);else if(a){n.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=n.map.painter.context,s=o.gl;e.texture=n.map.painter.getTileTexture(a.width),e.texture?e.texture.update(a,{useMipmap:!0}):(e.texture=new t.Texture(o,a,s.RGBA,{useMipmap:!0}),e.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.extTextureFilterAnisotropic&&s.texParameterf(s.TEXTURE_2D,o.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,o.extTextureFilterAnisotropicMax)),e.state="loaded",t.cacheEntryPossiblyAdded(n.dispatcher),r(null)}}))},r.prototype.abortTile=function(t,e){t.request&&(t.request.cancel(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),L=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type="raster-dem",this.maxzoom=22,this._options=t.extend({type:"raster-dem"},n),this.encoding=n.encoding||"mapbox"}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.serialize=function(){return{type:"raster-dem",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);function i(t,n){t&&(e.state="errored",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state="loaded",r(null))}e.request=t.getImage(this.map._requestManager.transformRequest(n,t.ResourceType.Tile),function(n,a){if(delete e.request,e.aborted)e.state="unloaded",r(null);else if(n)e.state="errored",r(n);else if(a){this.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=t.window.ImageBitmap&&a instanceof t.window.ImageBitmap&&t.offscreenCanvasSupported()?a:t.browser.getImageData(a,1),s={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:o,encoding:this.encoding};e.actor&&"expired"!==e.state||(e.actor=this.dispatcher.getActor(),e.actor.send("loadDEMTile",s,i.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state="unloaded",t.actor&&t.actor.send("removeDEMTile",{uid:t.uid,source:this.id})},r}(E),C=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type="geojson",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=i.getActor(),this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type),n.attribution&&(this.attribution=n.attribution),this.promoteId=n.promoteId;var o=t.EXTENT/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster||!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)*o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)*o,extent:t.EXTENT,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics||!1,generateId:n.generateId||!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,extent:t.EXTENT,radius:(n.clusterRadius||50)*o,log:!1,generateId:n.generateId||!1},clusterProperties:n.clusterProperties},n.workerOptions)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event("dataloading",{dataType:"source"})),this._updateWorkerData((function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:"source",sourceDataType:"metadata"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event("data",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event("dataloading",{dataType:"source"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:"source",sourceDataType:"content"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event("data",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send("geojson.getClusterExpansionZoom",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send("geojson.getClusterChildren",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send("geojson.getClusterLeaves",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;"string"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+".loadData",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+".coalesce",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?"reloadTile":"loadTile";e.actor=this.actor;var a={type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};e.request=this.actor.send(i,a,(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,"reloadTile"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send("removeTile",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send("removeSource",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),P=t.createLayout([{name:"a_pos",type:"Int16",components:2},{name:"a_texture_pos",type:"Int16",components:2}]),I=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type="image",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;o<s.length;o+=1){var l=s[o];r=Math.min(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.x),a=Math.max(a,l.y)}var c=i-r,u=a-n,f=Math.max(c,u),h=Math.max(0,Math.floor(-Math.log(f)/Math.LN2)),p=Math.pow(2,h);return new t.CanonicalTileID(h,Math.floor((r+i)/2*p),Math.floor((n+a)/2*p))}(n),this.minzoom=this.maxzoom=this.tileID.z;var i=n.map((function(t){return r.tileID.getTilePoint(t)._round()}));return this._boundsArray=new t.StructArrayLayout4i8,this._boundsArray.emplaceBack(i[0].x,i[0].y,0,0),this._boundsArray.emplaceBack(i[1].x,i[1].y,t.EXTENT,0),this._boundsArray.emplaceBack(i[3].x,i[3].y,0,t.EXTENT),this._boundsArray.emplaceBack(i[2].x,i[2].y,t.EXTENT,t.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new t.Texture(e,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];"loaded"!==i.state&&(i.state="loaded",i.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state="errored",e(null))},r.prototype.serialize=function(){return{type:"image",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented);var O=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type="video",this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1;var r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1){var a=i[n];this.urls.push(this.map._requestManager.transformRequest(a,t.ResourceType.Source).url)}t.getVideo(this.urls,(function(r,n){e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener("playing",(function(){e.map.triggerRepaint()})),e.map&&e.video.play(),e._finishLoading())}))},r.prototype.pause=function(){this.video&&this.video.pause()},r.prototype.play=function(){this.video&&this.video.play()},r.prototype.seek=function(e){if(this.video){var r=this.video.seekable;e<r.start(0)||e>r.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError("sources."+this.id,null,"Playback for this video can be set only between the "+r.start(0)+" and "+r.end(0)+"-second mark."))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];"loaded"!==i.state&&(i.state="loaded",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:"video",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(I),z=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return"number"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'"coordinates" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'missing required property "coordinates"'))),n.animate&&"boolean"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'optional "animate" property must be a boolean value'))),n.canvas?"string"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'"canvas" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'missing required property "canvas"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error("Canvas dimensions cannot be less than or equal to zero."))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];"loaded"!==a.state&&(a.state="loaded",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:"canvas",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var r=e[t];if(isNaN(r)||r<=0)return!0}return!1},r}(I),D={vector:S,raster:E,"raster-dem":L,geojson:C,video:O,image:I,canvas:z};function R(e,r){var n=t.identity([]);return t.translate(n,n,[1,1,0]),t.scale(n,n,[.5*e.width,.5*e.height,1]),t.multiply(n,n,e.calculatePosMatrix(r.toUnwrapped()))}function F(t,e,r,n,i,a){var o=function(t,e,r){if(t)for(var n=0,i=t;n<i.length;n+=1){var a=e[i[n]];if(a&&a.source===r&&"fill-extrusion"===a.type)return!0}else for(var o in e){var s=e[o];if(s.source===r&&"fill-extrusion"===s.type)return!0}return!1}(i&&i.layers,e,t.id),s=a.maxPitchScaleFactor(),l=t.tilesIn(n,s,o);l.sort(B);for(var c=[],u=0,f=l;u<f.length;u+=1){var h=f[u];c.push({wrappedTileID:h.tileID.wrapped().key,queryResults:h.tile.queryRenderedFeatures(e,r,t._state,h.queryGeometry,h.cameraQueryGeometry,h.scale,i,a,s,R(t.transform,h.tileID))})}var p=function(t){for(var e={},r={},n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.queryResults,s=a.wrappedTileID,l=r[s]=r[s]||{};for(var c in o)for(var u=o[c],f=l[c]=l[c]||{},h=e[c]=e[c]||[],p=0,d=u;p<d.length;p+=1){var m=d[p];f[m.featureIndex]||(f[m.featureIndex]=!0,h.push(m))}}return e}(c);for(var d in p)p[d].forEach((function(e){var r=e.feature,n=t.getFeatureState(r.layer["source-layer"],r.id);r.source=r.layer.source,r.layer["source-layer"]&&(r.sourceLayer=r.layer["source-layer"]),r.state=n}));return p}function B(t,e){var r=t.tileID,n=e.tileID;return r.overscaledZ-n.overscaledZ||r.canonical.y-n.canonical.y||r.wrap-n.wrap||r.canonical.x-n.canonical.x}var N=function(t,e){this.max=t,this.onRemove=e,this.reset()};N.prototype.reset=function(){for(var t in this.data)for(var e=0,r=this.data[t];e<r.length;e+=1){var n=r[e];n.timeout&&clearTimeout(n.timeout),this.onRemove(n.value)}return this.data={},this.order=[],this},N.prototype.add=function(t,e,r){var n=this,i=t.wrapped().key;void 0===this.data[i]&&(this.data[i]=[]);var a={value:e,timeout:void 0};if(void 0!==r&&(a.timeout=setTimeout((function(){n.remove(t,a)}),r)),this.data[i].push(a),this.order.push(i),this.order.length>this.max){var o=this._getAndRemoveByKey(this.order[0]);o&&this.onRemove(o)}return this},N.prototype.has=function(t){return t.wrapped().key in this.data},N.prototype.getAndRemove=function(t){return this.has(t)?this._getAndRemoveByKey(t.wrapped().key):null},N.prototype._getAndRemoveByKey=function(t){var e=this.data[t].shift();return e.timeout&&clearTimeout(e.timeout),0===this.data[t].length&&delete this.data[t],this.order.splice(this.order.indexOf(t),1),e.value},N.prototype.getByKey=function(t){var e=this.data[t];return e?e[0].value:null},N.prototype.get=function(t){return this.has(t)?this.data[t.wrapped().key][0].value:null},N.prototype.remove=function(t,e){if(!this.has(t))return this;var r=t.wrapped().key,n=void 0===e?0:this.data[r].indexOf(e),i=this.data[r][n];return this.data[r].splice(n,1),i.timeout&&clearTimeout(i.timeout),0===this.data[r].length&&delete this.data[r],this.onRemove(i.value),this.order.splice(this.order.indexOf(r),1),this},N.prototype.setMaxSize=function(t){for(this.max=t;this.order.length>this.max;){var e=this._getAndRemoveByKey(this.order[0]);e&&this.onRemove(e)}return this},N.prototype.filter=function(t){var e=[];for(var r in this.data)for(var n=0,i=this.data[r];n<i.length;n+=1){var a=i[n];t(a.value)||e.push(a)}for(var o=0,s=e;o<s.length;o+=1){var l=s[o];this.remove(l.value.tileID,l)}};var j=function(t,e,r){this.context=t;var n=t.gl;this.buffer=n.createBuffer(),this.dynamicDraw=Boolean(r),this.context.unbindVAO(),t.bindElementBuffer.set(this.buffer),n.bufferData(n.ELEMENT_ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?n.DYNAMIC_DRAW:n.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};j.prototype.bind=function(){this.context.bindElementBuffer.set(this.buffer)},j.prototype.updateData=function(t){var e=this.context.gl;this.context.unbindVAO(),this.bind(),e.bufferSubData(e.ELEMENT_ARRAY_BUFFER,0,t.arrayBuffer)},j.prototype.destroy=function(){var t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)};var U={Int8:"BYTE",Uint8:"UNSIGNED_BYTE",Int16:"SHORT",Uint16:"UNSIGNED_SHORT",Int32:"INT",Uint32:"UNSIGNED_INT",Float32:"FLOAT"},V=function(t,e,r,n){this.length=e.length,this.attributes=r,this.itemSize=e.bytesPerElement,this.dynamicDraw=n,this.context=t;var i=t.gl;this.buffer=i.createBuffer(),t.bindVertexBuffer.set(this.buffer),i.bufferData(i.ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?i.DYNAMIC_DRAW:i.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};V.prototype.bind=function(){this.context.bindVertexBuffer.set(this.buffer)},V.prototype.updateData=function(t){var e=this.context.gl;this.bind(),e.bufferSubData(e.ARRAY_BUFFER,0,t.arrayBuffer)},V.prototype.enableAttributes=function(t,e){for(var r=0;r<this.attributes.length;r++){var 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At=function(t,e,r){this.func=t,this.mask=e,this.range=r};At.ReadOnly=!1,At.ReadWrite=!0,At.disabled=new At(519,At.ReadOnly,[0,1]);var Mt=function(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a};Mt.disabled=new Mt({func:519,mask:0},0,0,7680,7680,7680);var St=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};St.disabled=new St(St.Replace=[1,0],t.Color.transparent,[!1,!1,!1,!1]),St.unblended=new St(St.Replace,t.Color.transparent,[!0,!0,!0,!0]),St.alphaBlended=new St([1,771],t.Color.transparent,[!0,!0,!0,!0]);var Et=function(t,e,r){this.enable=t,this.mode=e,this.frontFace=r};Et.disabled=new Et(!1,1029,2305),Et.backCCW=new Et(!0,1029,2305);var Lt=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension("OES_vertex_array_object"),this.clearColor=new q(this),this.clearDepth=new G(this),this.clearStencil=new Y(this),this.colorMask=new W(this),this.depthMask=new X(this),this.stencilMask=new Z(this),this.stencilFunc=new J(this),this.stencilOp=new K(this),this.stencilTest=new Q(this),this.depthRange=new $(this),this.depthTest=new tt(this),this.depthFunc=new et(this),this.blend=new rt(this),this.blendFunc=new nt(this),this.blendColor=new it(this),this.blendEquation=new at(this),this.cullFace=new ot(this),this.cullFaceSide=new st(this),this.frontFace=new lt(this),this.program=new ct(this),this.activeTexture=new ut(this),this.viewport=new ft(this),this.bindFramebuffer=new ht(this),this.bindRenderbuffer=new pt(this),this.bindTexture=new dt(this),this.bindVertexBuffer=new mt(this),this.bindElementBuffer=new gt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new vt(this),this.pixelStoreUnpack=new yt(this),this.pixelStoreUnpackPremultiplyAlpha=new xt(this),this.pixelStoreUnpackFlipY=new bt(this),this.extTextureFilterAnisotropic=t.getExtension("EXT_texture_filter_anisotropic")||t.getExtension("MOZ_EXT_texture_filter_anisotropic")||t.getExtension("WEBKIT_EXT_texture_filter_anisotropic"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension("OES_texture_half_float"),this.extTextureHalfFloat&&(t.getExtension("OES_texture_half_float_linear"),this.extRenderToTextureHalfFloat=t.getExtension("EXT_color_buffer_half_float")),this.extTimerQuery=t.getExtension("EXT_disjoint_timer_query")};Lt.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},Lt.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},Lt.prototype.createIndexBuffer=function(t,e){return new j(this,t,e)},Lt.prototype.createVertexBuffer=function(t,e,r){return new V(this,t,e,r)},Lt.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},Lt.prototype.createFramebuffer=function(t,e,r){return new kt(this,t,e,r)},Lt.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i|=n.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},Lt.prototype.setCullFace=function(t){!1===t.enable?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(t.mode),this.frontFace.set(t.frontFace))},Lt.prototype.setDepthMode=function(t){t.func!==this.gl.ALWAYS||t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)},Lt.prototype.setStencilMode=function(t){t.test.func!==this.gl.ALWAYS||t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)},Lt.prototype.setColorMode=function(e){t.deepEqual(e.blendFunction,St.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)},Lt.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var Ct=function(e){function r(r,n,i){var a=this;e.call(this),this.id=r,this.dispatcher=i,this.on("data",(function(t){"source"===t.dataType&&"metadata"===t.sourceDataType&&(a._sourceLoaded=!0),a._sourceLoaded&&!a._paused&&"source"===t.dataType&&"content"===t.sourceDataType&&(a.reload(),a.transform&&a.update(a.transform))})),this.on("error",(function(){a._sourceErrored=!0})),this._source=function(e,r,n,i){var a=new D[r.type](e,r,n,i);if(a.id!==e)throw new Error("Expected Source id to be "+e+" instead of "+a.id);return t.bindAll(["load","abort","unload","serialize","prepare"],a),a}(r,n,i,this),this._tiles={},this._cache=new N(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new t.SourceFeatureState}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;for(var t in this._tiles){var e=this._tiles[t];if("loaded"!==e.state&&"errored"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,(function(){}))},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,(function(){}))},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null),this._tiles){var r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}},r.prototype.getIds=function(){return t.values(this._tiles).map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.getRenderableIds=function(e){var r=this,n=[];for(var i in this._tiles)this._isIdRenderable(i,e)&&n.push(this._tiles[i]);return e?n.sort((function(e,n){var i=e.tileID,a=n.tileID,o=new t.Point(i.canonical.x,i.canonical.y)._rotate(r.transform.angle),s=new t.Point(a.canonical.x,a.canonical.y)._rotate(r.transform.angle);return i.overscaledZ-a.overscaledZ||s.y-o.y||s.x-o.x})).map((function(t){return t.tileID.key})):n.map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)"errored"!==this._tiles[t].state&&this._reloadTile(t,"reloading")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&("loading"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state="errored",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=t.browser.now(),"expired"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),"raster-dem"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._state.initializeTileState(e,this.map?this.map.painter:null),this._source.fire(new t.Event("data",{dataType:"source",tile:e,coord:e.tileID}))},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5*e),n="number"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(It(this._source.type)){for(var c={},u={},f=0,h=Object.keys(l);f<h.length;f+=1){var p=h[f],d=l[p],m=this._tiles[p];if(m&&!(m.fadeEndTime&&m.fadeEndTime<=t.browser.now())){var g=this.findLoadedParent(d,o);g&&(this._addTile(g.tileID),c[g.tileID.key]=g.tileID),u[p]=d}}for(var v in this._retainLoadedChildren(u,a,s,l),c)l[v]||(this._coveredTiles[v]=!0,l[v]=c[v])}for(var y in l)this._tiles[y].clearFadeHold();for(var x=0,b=t.keysDifference(this._tiles,l);x<b.length;x+=1){var _=b[x],w=this._tiles[_];w.hasSymbolBuckets&&!w.holdingForFade()?w.setHoldDuration(this.map._fadeDuration):w.hasSymbolBuckets&&!w.symbolFadeFinished()||this._removeTile(_)}this._updateLoadedParentTileCache()}},r.prototype.releaseSymbolFadeTiles=function(){for(var t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s={},l=0,c=t;l<c.length;l+=1){var u=c[l],f=this._addTile(u);n[u.key]=u,f.hasData()||e<this._source.maxzoom&&(s[u.key]=u)}this._retainLoadedChildren(s,e,o,n);for(var h=0,p=t;h<p.length;h+=1){var d=p[h],m=this._tiles[d.key];if(!m.hasData()){if(e+1>this._source.maxzoom){var g=d.children(this._source.maxzoom)[0],v=this.getTile(g);if(v&&v.hasData()){n[g.key]=g;continue}}else{var y=d.children(this._source.maxzoom);if(n[y[0].key]&&n[y[1].key]&&n[y[2].key]&&n[y[3].key])continue}for(var x=m.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(m=this.getTile(_))&&x&&(m=this._addTile(_)),m&&(n[_.key]=_,x=m.wasRequested(),m.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a<o.length;a+=1){var s=o[a];this._loadedParentTiles[s]=r}}},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[e.key]&&(clearTimeout(this._cacheTimers[e.key]),delete this._cacheTimers[e.key],this._setTileReloadTimer(e.key,r)));var n=Boolean(r);return n||(r=new t.Tile(e,this._source.tileSize*e.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n||this._source.fire(new t.Event("dataloading",{tile:r,coord:r.tileID,dataType:"source"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout((function(){r._reloadTile(t,"expired"),delete r._timers[t]}),n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&"reloading"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),c=s.map((function(t){return o.pointCoordinate(t)})),u=this.getIds(),f=1/0,h=1/0,p=-1/0,d=-1/0,m=0,g=c;m<g.length;m+=1){var v=g[m];f=Math.min(f,v.x),h=Math.min(h,v.y),p=Math.max(p,v.x),d=Math.max(d,v.y)}for(var y=function(e){var n=i._tiles[u[e]];if(!n.holdingForFade()){var s=n.tileID,m=Math.pow(2,o.zoom-n.tileID.overscaledZ),g=r*n.queryPadding*t.EXTENT/n.tileSize/m,v=[s.getTilePoint(new t.MercatorCoordinate(f,h)),s.getTilePoint(new t.MercatorCoordinate(p,d))];if(v[0].x-g<t.EXTENT&&v[0].y-g<t.EXTENT&&v[1].x+g>=0&&v[1].y+g>=0){var y=l.map((function(t){return s.getTilePoint(t)})),x=c.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:y,cameraQueryGeometry:x,scale:m})}}},x=0;x<u.length;x++)y(x);return a},r.prototype.getVisibleCoordinates=function(t){for(var e=this,r=this.getRenderableIds(t).map((function(t){return e._tiles[t].tileID})),n=0,i=r;n<i.length;n+=1){var a=i[n];a.posMatrix=this.transform.calculatePosMatrix(a.toUnwrapped())}return r},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(It(this._source.type))for(var e in this._tiles){var r=this._tiles[e];if(void 0!==r.fadeEndTime&&r.fadeEndTime>=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){t=t||"_geojsonTileLayer",this._state.updateState(t,e,r)},r.prototype.removeFeatureState=function(t,e,r){t=t||"_geojsonTileLayer",this._state.removeFeatureState(t,e,r)},r.prototype.getFeatureState=function(t,e){return t=t||"_geojsonTileLayer",this._state.getState(t,e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles){this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,"reloading")}this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function Pt(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function It(t){return"raster"===t||"image"===t||"video"===t}function Ot(){return new t.window.Worker(Zi.workerUrl)}Ct.maxOverzooming=10,Ct.maxUnderzooming=3;var zt="mapboxgl_preloaded_worker_pool",Dt=function(){this.active={}};Dt.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length<Dt.workerCount;)this.workers.push(new Ot);return this.active[t]=!0,this.workers.slice()},Dt.prototype.release=function(t){delete this.active[t],0===this.numActive()&&(this.workers.forEach((function(t){t.terminate()})),this.workers=null)},Dt.prototype.isPreloaded=function(){return!!this.active[zt]},Dt.prototype.numActive=function(){return Object.keys(this.active).length};var Rt,Ft=Math.floor(t.browser.hardwareConcurrency/2);function Bt(){return Rt||(Rt=new Dt),Rt}function Nt(e,r){var n={};for(var i in e)"ref"!==i&&(n[i]=e[i]);return t.refProperties.forEach((function(t){t in r&&(n[t]=r[t])})),n}function jt(t){t=t.slice();for(var e=Object.create(null),r=0;r<t.length;r++)e[t[r].id]=t[r];for(var n=0;n<t.length;n++)"ref"in t[n]&&(t[n]=Nt(t[n],e[t[n].ref]));return t}Dt.workerCount=Math.max(Math.min(Ft,6),1);var Ut={setStyle:"setStyle",addLayer:"addLayer",removeLayer:"removeLayer",setPaintProperty:"setPaintProperty",setLayoutProperty:"setLayoutProperty",setFilter:"setFilter",addSource:"addSource",removeSource:"removeSource",setGeoJSONSourceData:"setGeoJSONSourceData",setLayerZoomRange:"setLayerZoomRange",setLayerProperty:"setLayerProperty",setCenter:"setCenter",setZoom:"setZoom",setBearing:"setBearing",setPitch:"setPitch",setSprite:"setSprite",setGlyphs:"setGlyphs",setTransition:"setTransition",setLight:"setLight"};function Vt(t,e,r){r.push({command:Ut.addSource,args:[t,e[t]]})}function Ht(t,e,r){e.push({command:Ut.removeSource,args:[t]}),r[t]=!0}function qt(t,e,r,n){Ht(t,r,n),Vt(t,e,r)}function Gt(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&"data"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&"data"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;return!0}function Yt(e,r,n,i,a,o){var s;for(s in r=r||{},e=e||{})e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}))}function Wt(t){return t.id}function Xt(t,e){return t[e.id]=e,t}function Zt(e,r){if(!e)return[{command:Ut.setStyle,args:[r]}];var n=[];try{if(!t.deepEqual(e.version,r.version))return[{command:Ut.setStyle,args:[r]}];t.deepEqual(e.center,r.center)||n.push({command:Ut.setCenter,args:[r.center]}),t.deepEqual(e.zoom,r.zoom)||n.push({command:Ut.setZoom,args:[r.zoom]}),t.deepEqual(e.bearing,r.bearing)||n.push({command:Ut.setBearing,args:[r.bearing]}),t.deepEqual(e.pitch,r.pitch)||n.push({command:Ut.setPitch,args:[r.pitch]}),t.deepEqual(e.sprite,r.sprite)||n.push({command:Ut.setSprite,args:[r.sprite]}),t.deepEqual(e.glyphs,r.glyphs)||n.push({command:Ut.setGlyphs,args:[r.glyphs]}),t.deepEqual(e.transition,r.transition)||n.push({command:Ut.setTransition,args:[r.transition]}),t.deepEqual(e.light,r.light)||n.push({command:Ut.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r||{},e=e||{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)||Ht(a,n,i));for(a in r)r.hasOwnProperty(a)&&(e.hasOwnProperty(a)?t.deepEqual(e[a],r[a])||("geojson"===e[a].type&&"geojson"===r[a].type&&Gt(e,r,a)?n.push({command:Ut.setGeoJSONSourceData,args:[a,r[a].data]}):qt(a,r,n,i)):Vt(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach((function(t){i[t.source]?n.push({command:Ut.removeLayer,args:[t.id]}):o.push(t)})),n=n.concat(a),function(e,r,n){r=r||[];var i,a,o,s,l,c,u,f=(e=e||[]).map(Wt),h=r.map(Wt),p=e.reduce(Xt,{}),d=r.reduce(Xt,{}),m=f.slice(),g=Object.create(null);for(i=0,a=0;i<f.length;i++)o=f[i],d.hasOwnProperty(o)?a++:(n.push({command:Ut.removeLayer,args:[o]}),m.splice(m.indexOf(o,a),1));for(i=0,a=0;i<h.length;i++)o=h[h.length-1-i],m[m.length-1-i]!==o&&(p.hasOwnProperty(o)?(n.push({command:Ut.removeLayer,args:[o]}),m.splice(m.lastIndexOf(o,m.length-a),1)):a++,c=m[m.length-i],n.push({command:Ut.addLayer,args:[d[o],c]}),m.splice(m.length-i,0,o),g[o]=!0);for(i=0;i<h.length;i++)if(s=p[o=h[i]],l=d[o],!g[o]&&!t.deepEqual(s,l))if(t.deepEqual(s.source,l.source)&&t.deepEqual(s["source-layer"],l["source-layer"])&&t.deepEqual(s.type,l.type)){for(u in Yt(s.layout,l.layout,n,o,null,Ut.setLayoutProperty),Yt(s.paint,l.paint,n,o,null,Ut.setPaintProperty),t.deepEqual(s.filter,l.filter)||n.push({command:Ut.setFilter,args:[o,l.filter]}),t.deepEqual(s.minzoom,l.minzoom)&&t.deepEqual(s.maxzoom,l.maxzoom)||n.push({command:Ut.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(u)&&"layout"!==u&&"paint"!==u&&"filter"!==u&&"metadata"!==u&&"minzoom"!==u&&"maxzoom"!==u&&(0===u.indexOf("paint.")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}));for(u in l)l.hasOwnProperty(u)&&!s.hasOwnProperty(u)&&"layout"!==u&&"paint"!==u&&"filter"!==u&&"metadata"!==u&&"minzoom"!==u&&"maxzoom"!==u&&(0===u.indexOf("paint.")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}))}else n.push({command:Ut.removeLayer,args:[o]}),c=m[m.lastIndexOf(o)+1],n.push({command:Ut.addLayer,args:[l,c]})}(o,r.layers,n)}catch(t){console.warn("Unable to compute style diff:",t),n=[{command:Ut.setStyle,args:[r]}]}return n}var Jt=function(t,e){this.reset(t,e)};Jt.prototype.reset=function(t,e){this.points=t||[],this._distances=[0];for(var r=1;r<this.points.length;r++)this._distances[r]=this._distances[r-1]+this.points[r].dist(this.points[r-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(e||0,.5*this.length),this.paddedLength=this.length-2*this.padding},Jt.prototype.lerp=function(e){if(1===this.points.length)return this.points[0];e=t.clamp(e,0,1);for(var r=1,n=this._distances[r],i=e*this.paddedLength+this.padding;n<i&&r<this._distances.length;)n=this._distances[++r];var a=r-1,o=this._distances[a],s=n-o,l=s>0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Kt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCount*this.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};function Qt(e,r,n,i,a){var o=t.create();return r?(t.scale(o,o,[1/a,1/a,1]),n||t.rotateZ(o,o,i.angle)):t.multiply(o,i.labelPlaneMatrix,e),o}function $t(e,r,n,i,a){if(r){var o=t.clone(e);return t.scale(o,o,[a,a,1]),n||t.rotateZ(o,o,-i.angle),o}return i.glCoordMatrix}function te(e,r){var n=[e.x,e.y,0,1];fe(n,n,r);var i=n[3];return{point:new t.Point(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function ee(t,e){return.5+t/e*.5}function re(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ne(e,r,n,i,a,o,s,l){var c=i?e.textSizeData:e.iconSizeData,u=t.evaluateSizeForZoom(c,n.transform.zoom),f=[256/n.width*2+1,256/n.height*2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,m=n.transform.width/n.transform.height,g=!1,v=0;v<d.length;v++){var y=d.get(v);if(y.hidden||y.writingMode===t.WritingMode.vertical&&!g)ue(y.numGlyphs,h);else{g=!1;var x=[y.anchorX,y.anchorY,0,1];if(t.transformMat4(x,x,r),re(x,f)){var b=x[3],_=ee(n.transform.cameraToCenterDistance,b),w=t.evaluateSizeForFeature(c,u,y),T=s?w/_:w*_,k=new t.Point(y.anchorX,y.anchorY),A=te(k,a).point,M={},S=oe(y,T,!1,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,m);g=S.useVertical,(S.notEnoughRoom||g||S.needsFlipping&&oe(y,T,!0,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,m).notEnoughRoom)&&ue(y.numGlyphs,h)}else ue(y.numGlyphs,h)}}i?e.text.dynamicLayoutVertexBuffer.updateData(h):e.icon.dynamicLayoutVertexBuffer.updateData(h)}function ie(t,e,r,n,i,a,o,s,l,c,u){var f=s.glyphStartIndex+s.numGlyphs,h=s.lineStartIndex,p=s.lineStartIndex+s.lineLength,d=e.getoffsetX(s.glyphStartIndex),m=e.getoffsetX(f-1),g=le(t*d,r,n,i,a,o,s.segment,h,p,l,c,u);if(!g)return null;var v=le(t*m,r,n,i,a,o,s.segment,h,p,l,c,u);return v?{first:g,last:v}:null}function ae(e,r,n,i){if(e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i)return{useVertical:!0};return(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function oe(e,r,n,i,a,o,s,l,c,u,f,h,p,d){var m,g=r/24,v=e.lineOffsetX*g,y=e.lineOffsetY*g;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=ie(g,l,v,y,n,f,h,e,c,o,p);if(!w)return{notEnoughRoom:!0};var T=te(w.first.point,s).point,k=te(w.last.point,s).point;if(i&&!n){var A=ae(e.writingMode,T,k,d);if(A)return A}m=[w.first];for(var M=e.glyphStartIndex+1;M<x-1;M++)m.push(le(g*l.getoffsetX(M),v,y,n,f,h,e.segment,b,_,c,o,p));m.push(w.last)}else{if(i&&!n){var S=te(h,a).point,E=e.lineStartIndex+e.segment+1,L=new t.Point(c.getx(E),c.gety(E)),C=te(L,a),P=C.signedDistanceFromCamera>0?C.point:se(h,L,S,1,a),I=ae(e.writingMode,S,P,d);if(I)return I}var O=le(g*l.getoffsetX(e.glyphStartIndex),v,y,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,c,o,p);if(!O)return{notEnoughRoom:!0};m=[O]}for(var z=0,D=m;z<D.length;z+=1){var R=D[z];t.addDynamicAttributes(u,R.point,R.angle)}return{}}function se(t,e,r,n,i){var a=te(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function le(e,r,n,i,a,o,s,l,c,u,f,h){var p=i?e-r:e+r,d=p>0?1:-1,m=0;i&&(d*=-1,m=Math.PI),d<0&&(m+=Math.PI);for(var g=d>0?l+s:l+s+1,v=a,y=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((g+=d)<l||g>=c)return null;if(y=v,w.push(v),void 0===(v=h[g])){var T=new t.Point(u.getx(g),u.gety(g)),k=te(T,f);if(k.signedDistanceFromCamera>0)v=h[g]=k.point;else{var A=g-d;v=se(0===x?o:new t.Point(u.getx(A),u.gety(A)),T,y,_-x+1,f)}}x+=b,b=y.dist(v)}var M=(_-x)/b,S=v.sub(y),E=S.mult(M)._add(y);E._add(S._unit()._perp()._mult(n*d));var L=m+Math.atan2(v.y-y.y,v.x-y.x);return w.push(E),{point:E,angle:L,path:w}}Kt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Kt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Kt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Kt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Kt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Kt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s<this.boxKeys.length;s++)o.push({key:this.boxKeys[s],x1:this.bboxes[4*s],y1:this.bboxes[4*s+1],x2:this.bboxes[4*s+2],y2:this.bboxes[4*s+3]});for(var l=0;l<this.circleKeys.length;l++){var c=this.circles[3*l],u=this.circles[3*l+1],f=this.circles[3*l+2];o.push({key:this.circleKeys[l],x1:c-f,y1:u-f,x2:c+f,y2:u+f})}return a?o.filter(a):o}var h={hitTest:i,seenUids:{box:{},circle:{}}};return this._forEachCell(t,e,r,n,this._queryCell,o,h,a),i?o.length>0:o},Kt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var c=[],u={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,c,u,i),n?c.length>0:c},Kt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Kt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Kt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Kt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,c=this.boxCells[i];if(null!==c)for(var u=this.bboxes,f=0,h=c;f<h.length;f+=1){var p=h[f];if(!l.box[p]){l.box[p]=!0;var d=4*p;if(t<=u[d+2]&&e<=u[d+3]&&r>=u[d+0]&&n>=u[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:u[d],y1:u[d+1],x2:u[d+2],y2:u[d+3]})}}}var m=this.circleCells[i];if(null!==m)for(var g=this.circles,v=0,y=m;v<y.length;v+=1){var x=y[v];if(!l.circle[x]){l.circle[x]=!0;var b=3*x;if(this._circleAndRectCollide(g[b],g[b+1],g[b+2],t,e,r,n)&&(!s||s(this.circleKeys[x]))){if(o.hitTest)return a.push(!0),!0;var _=g[b],w=g[b+1],T=g[b+2];a.push({key:this.circleKeys[x],x1:_-T,y1:w-T,x2:_+T,y2:w+T})}}}},Kt.prototype._queryCellCircle=function(t,e,r,n,i,a,o,s){var l=o.circle,c=o.seenUids,u=this.boxCells[i];if(null!==u)for(var f=this.bboxes,h=0,p=u;h<p.length;h+=1){var d=p[h];if(!c.box[d]){c.box[d]=!0;var m=4*d;if(this._circleAndRectCollide(l.x,l.y,l.radius,f[m+0],f[m+1],f[m+2],f[m+3])&&(!s||s(this.boxKeys[d])))return a.push(!0),!0}}var g=this.circleCells[i];if(null!==g)for(var v=this.circles,y=0,x=g;y<x.length;y+=1){var b=x[y];if(!c.circle[b]){c.circle[b]=!0;var _=3*b;if(this._circlesCollide(v[_],v[_+1],v[_+2],l.x,l.y,l.radius)&&(!s||s(this.circleKeys[b])))return a.push(!0),!0}}},Kt.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToXCellCoord(t),c=this._convertToYCellCoord(e),u=this._convertToXCellCoord(r),f=this._convertToYCellCoord(n),h=l;h<=u;h++)for(var p=c;p<=f;p++){var d=this.xCellCount*p+h;if(i.call(this,t,e,r,n,d,a,o,s))return}},Kt.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},Kt.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},Kt.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s},Kt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s||u<=c)return!0;var f=l-s,h=u-c;return f*f+h*h<=r*r};var ce=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function ue(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(ce,3*n)}}function fe(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15],t}var he=function(t,e,r){void 0===e&&(e=new Kt(t.width+200,t.height+200,25)),void 0===r&&(r=new Kt(t.width+200,t.height+200,25)),this.transform=t,this.grid=e,this.ignoredGrid=r,this.pitchfactor=Math.cos(t._pitch)*t.cameraToCenterDistance,this.screenRightBoundary=t.width+100,this.screenBottomBoundary=t.height+100,this.gridRightBoundary=t.width+200,this.gridBottomBoundary=t.height+200};function pe(e,r,n){return r*(t.EXTENT/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}he.prototype.placeCollisionBox=function(t,e,r,n,i){var a=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),o=r*a.perspectiveRatio,s=t.x1*o+a.point.x,l=t.y1*o+a.point.y,c=t.x2*o+a.point.x,u=t.y2*o+a.point.y;return!this.isInsideGrid(s,l,c,u)||!e&&this.grid.hitTest(s,l,c,u,i)?{box:[],offscreen:!1}:{box:[s,l,c,u],offscreen:this.isOffscreen(s,l,c,u)}},he.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,c,u,f,h,p){var d=[],m=new t.Point(r.anchorX,r.anchorY),g=te(m,o),v=ee(this.transform.cameraToCenterDistance,g.signedDistanceFromCamera),y=(u?a/v:a*v)/t.ONE_EM,x=te(m,s).point,b=ie(y,i,r.lineOffsetX*y,r.lineOffsetY*y,!1,x,m,r,n,s,{}),_=!1,w=!1,T=!0;if(b){for(var k=.5*h*v+p,A=new t.Point(-100,-100),M=new t.Point(this.screenRightBoundary,this.screenBottomBoundary),S=new Jt,E=b.first,L=b.last,C=[],P=E.path.length-1;P>=1;P--)C.push(E.path[P]);for(var I=1;I<L.path.length;I++)C.push(L.path[I]);var O=2.5*k;if(l){var z=C.map((function(t){return te(t,l)}));C=z.some((function(t){return t.signedDistanceFromCamera<=0}))?[]:z.map((function(t){return t.point}))}var D=[];if(C.length>0){for(var R=C[0].clone(),F=C[0].clone(),B=1;B<C.length;B++)R.x=Math.min(R.x,C[B].x),R.y=Math.min(R.y,C[B].y),F.x=Math.max(F.x,C[B].x),F.y=Math.max(F.y,C[B].y);D=R.x>=A.x&&F.x<=M.x&&R.y>=A.y&&F.y<=M.y?[C]:F.x<A.x||R.x>M.x||F.y<A.y||R.y>M.y?[]:t.clipLine([C],A.x,A.y,M.x,M.y)}for(var N=0,j=D;N<j.length;N+=1){var U=j[N];S.reset(U,.25*k);var V=0;V=S.length<=.5*k?1:Math.ceil(S.paddedLength/O)+1;for(var H=0;H<V;H++){var q=H/Math.max(V-1,1),G=S.lerp(q),Y=G.x+100,W=G.y+100;d.push(Y,W,k,0);var X=Y-k,Z=W-k,J=Y+k,K=W+k;if(T=T&&this.isOffscreen(X,Z,J,K),w=w||this.isInsideGrid(X,Z,J,K),!e&&this.grid.hitTestCircle(Y,W,k,f)&&(_=!0,!c))return{circles:[],offscreen:!1,collisionDetected:_}}}}return{circles:!c&&_||!w?[]:d,offscreen:T,collisionDetected:_}},he.prototype.queryRenderedSymbols=function(e){if(0===e.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var c=l[s],u=new t.Point(c.x+100,c.y+100);n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y),r.push(u)}for(var f={},h={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var m=d[p],g=m.key;if(void 0===f[g.bucketInstanceId]&&(f[g.bucketInstanceId]={}),!f[g.bucketInstanceId][g.featureIndex]){var v=[new t.Point(m.x1,m.y1),new t.Point(m.x2,m.y1),new t.Point(m.x2,m.y2),new t.Point(m.x1,m.y2)];t.polygonIntersectsPolygon(r,v)&&(f[g.bucketInstanceId][g.featureIndex]=!0,void 0===h[g.bucketInstanceId]&&(h[g.bucketInstanceId]=[]),h[g.bucketInstanceId].push(g.featureIndex))}}return h},he.prototype.insertCollisionBox=function(t,e,r,n,i){var a={bucketInstanceId:r,featureIndex:n,collisionGroupID:i};(e?this.ignoredGrid:this.grid).insert(a,t[0],t[1],t[2],t[3])},he.prototype.insertCollisionCircles=function(t,e,r,n,i){for(var a=e?this.ignoredGrid:this.grid,o={bucketInstanceId:r,featureIndex:n,collisionGroupID:i},s=0;s<t.length;s+=4)a.insertCircle(o,t[s],t[s+1],t[s+2])},he.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return fe(i,i,e),{point:new t.Point((i[0]/i[3]+1)/2*this.transform.width+100,(-i[1]/i[3]+1)/2*this.transform.height+100),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3]*.5}},he.prototype.isOffscreen=function(t,e,r,n){return r<100||t>=this.screenRightBoundary||n<100||e>this.screenBottomBoundary},he.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary},he.prototype.getViewportMatrix=function(){var e=t.identity([]);return t.translate(e,e,[-100,-100,0]),e};var de=function(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r};de.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var me=function(t,e,r,n,i){this.text=new de(t?t.text:null,e,r,i),this.icon=new de(t?t.icon:null,e,n,i)};me.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var ge=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},ve=function(){this.invProjMatrix=t.create(),this.viewportMatrix=t.create(),this.circles=[]},ye=function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i},xe=function(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}};function be(e,r,n,i,a){var o=t.getAnchorAlignment(e),s=-(o.horizontalAlign-.5)*r,l=-(o.verticalAlign-.5)*n,c=t.evaluateVariableOffset(e,i);return new t.Point(s+c[0]*a,l+c[1]*a)}function _e(e,r,n,i,a,o){var s=e.x1,l=e.x2,c=e.y1,u=e.y2,f=e.anchorPointX,h=e.anchorPointY,p=new t.Point(r,n);return i&&p._rotate(a?o:-o),{x1:s+p.x,y1:c+p.y,x2:l+p.x,y2:u+p.y,anchorPointX:f,anchorPointY:h}}xe.prototype.get=function(t){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[t]){var e=++this.maxGroupID;this.collisionGroups[t]={ID:e,predicate:function(t){return t.collisionGroupID===e}}}return this.collisionGroups[t]};var we=function(t,e,r,n){this.transform=t.clone(),this.collisionIndex=new he(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=e,this.retainedQueryData={},this.collisionGroups=new xe(r),this.collisionCircleArrays={},this.prevPlacement=n,n&&(n.prevPlacement=void 0),this.placedOrientations={}};function Te(t,e,r,n,i){t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0)}we.prototype.getBucketParts=function(e,r,n,i){var a=n.getBucket(r),o=n.latestFeatureIndex;if(a&&o&&r.id===a.layerIds[0]){var s=n.collisionBoxArray,l=a.layers[0].layout,c=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),u=n.tileSize/t.EXTENT,f=this.transform.calculatePosMatrix(n.tileID.toUnwrapped()),h="map"===l.get("text-pitch-alignment"),p="map"===l.get("text-rotation-alignment"),d=pe(n,1,this.transform.zoom),m=Qt(f,h,p,this.transform,d),g=null;if(h){var v=$t(f,h,p,this.transform,d);g=t.multiply([],this.transform.labelPlaneMatrix,v)}this.retainedQueryData[a.bucketInstanceId]=new ye(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);var y={bucket:a,layout:l,posMatrix:f,textLabelPlaneMatrix:m,labelToScreenMatrix:g,scale:c,textPixelRatio:u,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:t.evaluateSizeForZoom(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(var x=0,b=a.sortKeyRanges;x<b.length;x+=1){var _=b[x],w=_.sortKey,T=_.symbolInstanceStart,k=_.symbolInstanceEnd;e.push({sortKey:w,symbolInstanceStart:T,symbolInstanceEnd:k,parameters:y})}else e.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:y})}},we.prototype.attemptAnchorPlacement=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d){var m,g=[f.textOffset0,f.textOffset1],v=be(t,r,n,g,i),y=this.collisionIndex.placeCollisionBox(_e(e,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate);if(d&&0===this.collisionIndex.placeCollisionBox(_e(d,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate).box.length)return;if(y.box.length>0)return this.prevPlacement&&this.prevPlacement.variableOffsets[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID].text&&(m=this.prevPlacement.variableOffsets[f.crossTileID].anchor),this.variableOffsets[f.crossTileID]={textOffset:g,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:m},this.markUsedJustification(h,t,f,p),h.allowVerticalPlacement&&(this.markUsedOrientation(h,p,f),this.placedOrientations[f.crossTileID]=p),{shift:v,placedGlyphBoxes:y}},we.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,c=a.textLabelPlaneMatrix,u=a.labelToScreenMatrix,f=a.textPixelRatio,h=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,m=a.collisionGroup,g=s.get("text-optional"),v=s.get("icon-optional"),y=s.get("text-allow-overlap"),x=s.get("icon-allow-overlap"),b="map"===s.get("text-rotation-alignment"),_="map"===s.get("text-pitch-alignment"),w="none"!==s.get("icon-text-fit"),T="viewport-y"===s.get("symbol-z-order"),k=y&&(x||!o.hasIconData()||v),A=x&&(y||!o.hasTextData()||g);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var M=function(e,a){if(!r[e.crossTileID])if(h)i.placements[e.crossTileID]=new ge(!1,!1,!1);else{var p,T=!1,M=!1,S=!0,E=null,L={box:null,offscreen:null},C={box:null,offscreen:null},P=null,I=null,O=0,z=0,D=0;a.textFeatureIndex?O=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(O=e.featureIndex),a.verticalTextFeatureIndex&&(z=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,n=a,i.markUsedOrientation(o,n,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i<s.length;i+=1){if(s[i]===t.WritingMode.vertical?(L=n(),C=L):L=r(),L&&L.box&&L.box.length)break}else L=r()};if(s.get("text-variable-anchor")){var N=s.get("text-variable-anchor");if(i.prevPlacement&&i.prevPlacement.variableOffsets[e.crossTileID]){var j=i.prevPlacement.variableOffsets[e.crossTileID];N.indexOf(j.anchor)>0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,c=e.textBoxScale,u=w&&!x?r:null,h={box:[],offscreen:!1},p=y?2*N.length:N.length,d=0;d<p;++d){var g=N[d%N.length],v=d>=N.length,k=i.attemptAnchorPlacement(g,t,a,s,c,b,_,f,l,m,v,e,o,n,u);if(k&&(h=k.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=k.shift;break}}return h};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox,n=L&&L.box&&L.box.length;return o.allowVerticalPlacement&&!n&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),L&&(T=L.box,S=L.offscreen);var V=F(L&&L.box);if(!T&&i.prevPlacement){var H=i.prevPlacement.variableOffsets[e.crossTileID];H&&(i.variableOffsets[e.crossTileID]=H,i.markUsedJustification(o,H.anchor,e,V))}}else{var q=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,y,f,l,m.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return q(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?q(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(L&&L.box&&L.box.length)}}if(T=(p=L)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),Y=t.evaluateSizeForFeature(o.textSizeData,d,G),W=s.get("text-padding"),X=e.collisionCircleDiameter;P=i.collisionIndex.placeCollisionCircles(y,G,o.lineVertexArray,o.glyphOffsetArray,Y,l,c,u,n,_,m.predicate,X,W),T=y||P.circles.length>0&&!P.collisionDetected,S=S&&P.offscreen}if(a.iconFeatureIndex&&(D=a.iconFeatureIndex),a.iconBox){var Z=function(t){var e=w&&E?_e(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,f,l,m.predicate)};M=C&&C.box&&C.box.length&&a.verticalIconBox?(I=Z(a.verticalIconBox)).box.length>0:(I=Z(a.iconBox)).box.length>0,S=S&&I.offscreen}var J=g||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,K=v||0===e.numIconVertices;if(J||K?K?J||(M=M&&T):T=M&&T:M=T=M&&T,T&&p&&p.box&&(C&&C.box&&z?i.collisionIndex.insertCollisionBox(p.box,s.get("text-ignore-placement"),o.bucketInstanceId,z,m.ID):i.collisionIndex.insertCollisionBox(p.box,s.get("text-ignore-placement"),o.bucketInstanceId,O,m.ID)),M&&I&&i.collisionIndex.insertCollisionBox(I.box,s.get("icon-ignore-placement"),o.bucketInstanceId,D,m.ID),P&&(T&&i.collisionIndex.insertCollisionCircles(P.circles,s.get("text-ignore-placement"),o.bucketInstanceId,O,m.ID),n)){var Q=o.bucketInstanceId,$=i.collisionCircleArrays[Q];void 0===$&&($=i.collisionCircleArrays[Q]=new ve);for(var tt=0;tt<P.circles.length;tt+=4)$.circles.push(P.circles[tt+0]),$.circles.push(P.circles[tt+1]),$.circles.push(P.circles[tt+2]),$.circles.push(P.collisionDetected?1:0)}i.placements[e.crossTileID]=new ge(T||k,M||A,S||o.justReloaded),r[e.crossTileID]=!0}};if(T)for(var S=o.getSortedSymbolIndexes(this.transform.angle),E=S.length-1;E>=0;--E){var L=S[E];M(o.symbolInstances.get(L),o.collisionArrays[L])}else for(var C=e.symbolInstanceStart;C<e.symbolInstanceEnd;C++)M(o.symbolInstances.get(C),o.collisionArrays[C]);if(n&&o.bucketInstanceId in this.collisionCircleArrays){var P=this.collisionCircleArrays[o.bucketInstanceId];t.invert(P.invProjMatrix,l),P.viewportMatrix=this.collisionIndex.getViewportMatrix()}o.justReloaded=!1},we.prototype.markUsedJustification=function(e,r,n,i){var a,o={left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex};a=i===t.WritingMode.vertical?n.verticalPlacedTextSymbolIndex:o[t.getAnchorJustification(r)];for(var s=0,l=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex,n.verticalPlacedTextSymbolIndex];s<l.length;s+=1){var c=l[s];c>=0&&(e.text.placedSymbolArray.get(c).crossTileID=a>=0&&c!==a?0:n.crossTileID)}},we.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o<s.length;o+=1){var l=s[o];e.text.placedSymbolArray.get(l).placedOrientation=i}n.verticalPlacedTextSymbolIndex&&(e.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)},we.prototype.commit=function(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;var e=this.prevPlacement,r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;var n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(var s in this.placements){var l=this.placements[s],c=i[s];c?(this.opacities[s]=new me(c,n,l.text,l.icon),r=r||l.text!==c.text.placed||l.icon!==c.icon.placed):(this.opacities[s]=new me(null,n,l.text,l.icon,l.skipFade),r=r||l.text||l.icon)}for(var u in i){var f=i[u];if(!this.opacities[u]){var h=new me(f,n,!1,!1);h.isHidden()||(this.opacities[u]=h,r=r||f.text.placed||f.icon.placed)}}for(var p in a)this.variableOffsets[p]||!this.opacities[p]||this.opacities[p].isHidden()||(this.variableOffsets[p]=a[p]);for(var d in o)this.placedOrientations[d]||!this.opacities[d]||this.opacities[d].isHidden()||(this.placedOrientations[d]=o[d]);r?this.lastPlacementChangeTime=t:"number"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)},we.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},we.prototype.updateBucketOpacities=function(e,r,n){var i=this;e.hasTextData()&&e.text.opacityVertexArray.clear(),e.hasIconData()&&e.icon.opacityVertexArray.clear(),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexArray.clear(),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexArray.clear();var a=e.layers[0].layout,o=new me(null,0,!1,!1,!0),s=a.get("text-allow-overlap"),l=a.get("icon-allow-overlap"),c=a.get("text-variable-anchor"),u="map"===a.get("text-rotation-alignment"),f="map"===a.get("text-pitch-alignment"),h="none"!==a.get("icon-text-fit"),p=new me(null,0,s&&(l||!e.hasIconData()||a.get("icon-optional")),l&&(s||!e.hasTextData()||a.get("text-optional")),!0);!e.collisionArrays&&n&&(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData())&&e.deserializeCollisionBoxes(n);for(var d=function(t,e,r){for(var n=0;n<e/4;n++)t.opacityVertexArray.emplaceBack(r)},m=function(n){var a=e.symbolInstances.get(n),s=a.numHorizontalGlyphVertices,l=a.numVerticalGlyphVertices,m=a.crossTileID,g=r[m],v=i.opacities[m];g?v=o:v||(v=p,i.opacities[m]=v),r[m]=!0;var y=s>0||l>0,x=a.numIconVertices>0,b=i.placedOrientations[a.crossTileID],_=b===t.WritingMode.vertical,w=b===t.WritingMode.horizontal||b===t.WritingMode.horizontalOnly;if(y){var T=Pe(v.text),k=_?Ie:T;d(e.text,s,k);var A=w?Ie:T;d(e.text,l,A);var M=v.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=M||_?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=M||w?1:0);var S=i.variableOffsets[a.crossTileID];S&&i.markUsedJustification(e,S.anchor,a,b);var E=i.placedOrientations[a.crossTileID];E&&(i.markUsedJustification(e,"left",a,E),i.markUsedOrientation(e,E,a))}if(x){var L=Pe(v.icon),C=!(h&&a.verticalPlacedIconSymbolIndex&&_);if(a.placedIconSymbolIndex>=0){var P=C?L:Ie;d(e.icon,a.numIconVertices,P),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=v.icon.isHidden()}if(a.verticalPlacedIconSymbolIndex>=0){var I=C?Ie:L;d(e.icon,a.numVerticalIconVertices,I),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=v.icon.isHidden()}}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var O=e.collisionArrays[n];if(O){var z=new t.Point(0,0);if(O.textBox||O.verticalTextBox){var D=!0;if(c){var R=i.variableOffsets[m];R?(z=be(R.anchor,R.width,R.height,R.textOffset,R.textBoxScale),u&&z._rotate(f?i.transform.angle:-i.transform.angle)):D=!1}O.textBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||_,z.x,z.y),O.verticalTextBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||w,z.x,z.y)}var F=Boolean(!w&&O.verticalIconBox);O.iconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,F,h?z.x:0,h?z.y:0),O.verticalIconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,!F,h?z.x:0,h?z.y:0)}}},g=0;g<e.symbolInstances.length;g++)m(g);if(e.sortFeatures(this.transform.angle),this.retainedQueryData[e.bucketInstanceId]&&(this.retainedQueryData[e.bucketInstanceId].featureSortOrder=e.featureSortOrder),e.hasTextData()&&e.text.opacityVertexBuffer&&e.text.opacityVertexBuffer.updateData(e.text.opacityVertexArray),e.hasIconData()&&e.icon.opacityVertexBuffer&&e.icon.opacityVertexBuffer.updateData(e.icon.opacityVertexArray),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexBuffer&&e.iconCollisionBox.collisionVertexBuffer.updateData(e.iconCollisionBox.collisionVertexArray),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexBuffer&&e.textCollisionBox.collisionVertexBuffer.updateData(e.textCollisionBox.collisionVertexArray),e.bucketInstanceId in this.collisionCircleArrays){var v=this.collisionCircleArrays[e.bucketInstanceId];e.placementInvProjMatrix=v.invProjMatrix,e.placementViewportMatrix=v.viewportMatrix,e.collisionCircleArray=v.circles,delete this.collisionCircleArrays[e.bucketInstanceId]}},we.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},we.prototype.zoomAdjustment=function(t){return Math.max(0,(this.transform.zoom-t)/1.5)},we.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},we.prototype.stillRecent=function(t,e){var r=this.zoomAtLastRecencyCheck===e?1-this.zoomAdjustment(e):1;return this.zoomAtLastRecencyCheck=e,this.commitTime+this.fadeDuration*r>t},we.prototype.setStale=function(){this.stale=!0};var ke=Math.pow(2,25),Ae=Math.pow(2,24),Me=Math.pow(2,17),Se=Math.pow(2,16),Ee=Math.pow(2,9),Le=Math.pow(2,8),Ce=Math.pow(2,1);function Pe(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*ke+e*Ae+r*Me+e*Se+r*Ee+e*Le+r*Ce+e}var Ie=0,Oe=function(t){this._sortAcrossTiles="viewport-y"!==t.layout.get("symbol-z-order")&&void 0!==t.layout.get("symbol-sort-key").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Oe.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex<t.length;){var o=t[this._currentTileIndex];if(e.getBucketParts(a,n,o,this._sortAcrossTiles),this._currentTileIndex++,i())return!0}for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,a.sort((function(t,e){return t.sortKey-e.sortKey})));this._currentPartIndex<a.length;){var s=a[this._currentPartIndex];if(e.placeLayerBucketPart(s,this._seenCrossTileIDs,r),this._currentPartIndex++,i())return!0}return!1};var ze=function(t,e,r,n,i,a,o){this.placement=new we(t,i,a,o),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};ze.prototype.isDone=function(){return this._done},ze.prototype.continuePlacement=function(e,r,n){for(var i=this,a=t.browser.now(),o=function(){var e=t.browser.now()-a;return!i._forceFullPlacement&&e>2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if("symbol"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Oe(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},ze.prototype.commit=function(t){return this.placement.commit(t),this.placement};var De=512/t.EXTENT/2,Re=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;n<e.length;n++){var i=e.get(n),a=i.key;this.indexedSymbolInstances[a]||(this.indexedSymbolInstances[a]=[]),this.indexedSymbolInstances[a].push({crossTileID:i.crossTileID,coord:this.getScaledCoordinates(i,t)})}};Re.prototype.getScaledCoordinates=function(e,r){var n=r.canonical.z-this.tileID.canonical.z,i=De/Math.pow(2,n);return{x:Math.floor((r.canonical.x*t.EXTENT+e.anchorX)*i),y:Math.floor((r.canonical.y*t.EXTENT+e.anchorY)*i)}},Re.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0;i<t.length;i++){var a=t.get(i);if(!a.crossTileID){var o=this.indexedSymbolInstances[a.key];if(o)for(var s=this.getScaledCoordinates(a,e),l=0,c=o;l<c.length;l+=1){var u=c[l];if(Math.abs(u.coord.x-s.x)<=n&&Math.abs(u.coord.y-s.y)<=n&&!r[u.crossTileID]){r[u.crossTileID]=!0,a.crossTileID=u.crossTileID;break}}}}};var Fe=function(){this.maxCrossTileID=0};Fe.prototype.generate=function(){return++this.maxCrossTileID};var Be=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Be.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},Be.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0;n<e.symbolInstances.length;n++){e.symbolInstances.get(n).crossTileID=0}this.usedCrossTileIDs[t.overscaledZ]||(this.usedCrossTileIDs[t.overscaledZ]={});var i=this.usedCrossTileIDs[t.overscaledZ];for(var a in this.indexes){var o=this.indexes[a];if(Number(a)>t.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var c=o[t.scaledTo(Number(a)).key];c&&c.findMatches(e.symbolInstances,t,i)}}for(var u=0;u<e.symbolInstances.length;u++){var f=e.symbolInstances.get(u);f.crossTileID||(f.crossTileID=r.generate(),i[f.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new Re(t,e.symbolInstances,e.bucketInstanceId),!0},Be.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1){var a=i[n];delete this.usedCrossTileIDs[t][a.crossTileID]}},Be.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Ne=function(){this.layerIndexes={},this.crossTileIDs=new Fe,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Ne.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new Be);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],c=l.getBucket(t);c&&t.id===c.layerIds[0]&&(c.bucketInstanceId||(c.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,c,this.crossTileIDs)&&(i=!0),a[c.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Ne.prototype.pruneUnusedLayers=function(t){var e={};for(var r in t.forEach((function(t){e[t]=!0})),this.layerIndexes)e[r]||delete this.layerIndexes[r]};var je=function(e,r){return t.emitValidationErrors(e,r&&r.filter((function(t){return"source.canvas"!==t.identifier})))},Ue=t.pick(Ut,["addLayer","removeLayer","setPaintProperty","setLayoutProperty","setFilter","addSource","removeSource","setLayerZoomRange","setLight","setTransition","setGeoJSONSourceData"]),Ve=t.pick(Ut,["setCenter","setZoom","setBearing","setPitch"]),He=function(){var e={},r=t.styleSpec.$version;for(var n in t.styleSpec.$root){var i=t.styleSpec.$root[n];if(i.required){var a=null;null!=(a="version"===n?r:"array"===i.type?[]:{})&&(e[n]=a)}}return e}(),qe=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new k(Bt(),this),this.imageManager=new h,this.imageManager.setEventedParent(this),this.glyphManager=new x(n._requestManager,i.localIdeographFontFamily),this.lineAtlas=new T(256,512),this.crossTileSymbolIndex=new Ne,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast("setReferrer",t.getReferrer());var o=this;this._rtlTextPluginCallback=r.registerForPluginStateChange((function(e){var r={pluginStatus:e.pluginStatus,pluginURL:e.pluginURL};o.dispatcher.broadcast("syncRTLPluginState",r,(function(e,r){if((t.triggerPluginCompletionEvent(e),r)&&r.every((function(t){return t})))for(var n in o.sourceCaches)o.sourceCaches[n].reload()}))})),this.on("data",(function(t){if("source"===t.dataType&&"metadata"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}}))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event("dataloading",{dataType:"style"}));var i="boolean"==typeof r.validate?r.validate:!t.isMapboxURL(e);e=this.map._requestManager.normalizeStyleURL(e,r.accessToken);var a=this.map._requestManager.transformRequest(e,t.ResourceType.Style);this._request=t.getJSON(a,(function(e,r){n._request=null,e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)}))},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event("dataloading",{dataType:"style"})),this._request=t.browser.frame((function(){n._request=null,n._load(e,!1!==r.validate)}))},r.prototype.loadEmpty=function(){this.fire(new t.Event("dataloading",{dataType:"style"})),this._load(He,!1)},r.prototype._load=function(e,r){if(!r||!je(this,t.validateStyle(e))){for(var n in this._loaded=!0,this.stylesheet=e,e.sources)this.addSource(n,e.sources[n],{validate:!1});e.sprite?this._loadSprite(e.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var i=jt(this.stylesheet.layers);this._order=i.map((function(t){return t.id})),this._layers={},this._serializedLayers={};for(var a=0,o=i;a<o.length;a+=1){var s=o[a];(s=t.createStyleLayer(s)).setEventedParent(this,{layer:{id:s.id}}),this._layers[s.id]=s,this._serializedLayers[s.id]=s.serialize()}this.dispatcher.broadcast("setLayers",this._serializeLayers(this._order)),this.light=new w(this.stylesheet.light),this.fire(new t.Event("data",{dataType:"style"})),this.fire(new t.Event("style.load"))}},r.prototype._loadSprite=function(e){var r=this;this._spriteRequest=function(e,r,n){var i,a,o,s=t.browser.devicePixelRatio>1?"@2x":"",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,".json"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,u())})),c=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,".png"),t.ResourceType.SpriteImage),(function(t,e){c=null,o||(o=t,a=e,u())}));function u(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],c=l.width,u=l.height,f=l.x,h=l.y,p=l.sdf,d=l.pixelRatio,m=l.stretchX,g=l.stretchY,v=l.content,y=new t.RGBAImage({width:c,height:u});t.RGBAImage.copy(e,y,{x:f,y:h},{x:0,y:0},{width:c,height:u}),r[s]={data:y,pixelRatio:d,sdf:p,stretchX:m,stretchY:g,content:v}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),c&&(c.cancel(),c=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast("setImages",r._availableImages),r.fire(new t.Event("data",{dataType:"style"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();("geojson"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer "'+n+'" does not exist on source "'+i.id+'" as specified by style layer "'+e.id+'"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=this._layers[i];"custom"!==a.type&&e.push(a.serialize())}return e},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error("Style is not done loading")},r.prototype.update=function(e){if(this._loaded){var r=this._changed;if(this._changed){var n=Object.keys(this._updatedLayers),i=Object.keys(this._removedLayers);for(var a in(n.length||i.length)&&this._updateWorkerLayers(n,i),this._updatedSources){var o=this._updatedSources[a];"reload"===o?this._reloadSource(a):"clear"===o&&this._clearSource(a)}for(var s in this._updateTilesForChangedImages(),this._updatedPaintProps)this._layers[s].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates()}for(var l in this.sourceCaches)this.sourceCaches[l].used=!1;for(var c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];h.recalculate(e,this._availableImages),!h.isHidden(e.zoom)&&h.source&&(this.sourceCaches[h.source].used=!0)}this.light.recalculate(e),this.z=e.zoom,r&&this.fire(new t.Event("data",{dataType:"style"}))}},r.prototype._updateTilesForChangedImages=function(){var t=Object.keys(this._changedImages);if(t.length){for(var e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies(["icons","patterns"],t);this._changedImages={}}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast("updateLayers",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),je(this,t.validateStyle(e)))return!1;(e=t.clone$1(e)).layers=jt(e.layers);var n=Zt(this.serialize(),e).filter((function(t){return!(t.command in Ve)}));if(0===n.length)return!1;var i=n.filter((function(t){return!(t.command in Ue)}));if(i.length>0)throw new Error("Unimplemented: "+i.map((function(t){return t.command})).join(", ")+".");return n.forEach((function(t){"setTransition"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error("An image with this name already exists.")));this.imageManager.addImage(e,r),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event("data",{dataType:"style"}))},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error("No image with this name exists.")));this.imageManager.removeImage(e),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event("data",{dataType:"style"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error("There is already a source with this ID");if(!r.type)throw new Error("The type property must be defined, but the only the following properties were given: "+Object.keys(r).join(", ")+".");if(!(["vector","raster","geojson","video","image"].indexOf(r.type)>=0)||!this._validate(t.validateStyle.source,"sources."+e,r,null,n)){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Ct(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error("There is no source with this ID");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source "'+e+'" cannot be removed while layer "'+r+'" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event("data",{sourceDataType:"metadata",dataType:"source",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id "'+i+'" already exists on this map')));else{var a;if("custom"===e.type){if(je(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if("object"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,"layers."+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id "'+r+'" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&"custom"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]="clear":(this._updatedSources[a.source]="reload",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id "'+r+'" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be moved.")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be removed.")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot have zoom extent.")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,"layers."+i.id+".filter",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be filtered.")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be styled.")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style.")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be styled.")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;"geojson"===o&&i?this.fire(new t.ErrorEvent(new Error("GeoJSON sources cannot have a sourceLayer parameter."))):"vector"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error("The feature id parameter must be provided."))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+n+"' does not exist in the map's style.")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o="vector"===a?e.sourceLayer:void 0;"vector"!==a||o?r&&"string"!=typeof e.id&&"number"!=typeof e.id?this.fire(new t.ErrorEvent(new Error("A feature id is requred to remove its specific state property."))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+n+"' does not exist in the map's style.")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if("vector"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error("The feature id parameter must be provided."))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+r+"' does not exist in the map's style.")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&"raster"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]="reload",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return"fill-extrusion"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s<l.length;s+=1){var c=l[s][o];if(c)for(var u=0,f=c;u<f.length;u+=1){var h=f[u];i.push(h)}}}}i.sort((function(t,e){return e.intersectionZ-t.intersectionZ}));for(var p=[],d=this._order.length-1;d>=0;d--){var m=this._order[d];if(r(m))for(var g=i.length-1;g>=0;g--){var v=i[g].feature;if(n[v.layer.id]<d)break;p.push(v),i.pop()}else for(var y=0,x=t;y<x.length;y+=1){var b=x[y][m];if(b)for(var _=0,w=b;_<w.length;_+=1){var T=w[_];p.push(T.feature)}}}return p},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,"queryRenderedFeatures.filter",r.filter,null,r);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error("parameters.layers must be an Array."))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error("The layer '"+s+"' does not exist in the map's style and cannot be queried for features."))),[];i[l.source]=!0}}var c=[];for(var u in r.availableImages=this._availableImages,this.sourceCaches)r.layers&&!i[u]||c.push(F(this.sourceCaches[u],this._layers,this._serializedLayers,e,r,n));return this.placement&&c.push(function(t,e,r,n,i,a,o){for(var s={},l=a.queryRenderedSymbols(n),c=[],u=0,f=Object.keys(l).map(Number);u<f.length;u+=1){var h=f[u];c.push(o[h])}c.sort(B);for(var p=function(){var r=m[d],n=r.featureIndex.lookupSymbolFeatures(l[r.bucketInstanceId],e,r.bucketIndex,r.sourceLayerIndex,i.filter,i.layers,i.availableImages,t);for(var a in n){var o=s[a]=s[a]||[],c=n[a];c.sort((function(t,e){var n=r.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-i}return e.featureIndex-t.featureIndex}));for(var u=0,f=c;u<f.length;u+=1){var h=f[u];o.push(h)}}},d=0,m=c;d<m.length;d+=1)p();var g=function(e){s[e].forEach((function(n){var i=n.feature,a=t[e],o=r[a.source].getFeatureState(i.layer["source-layer"],i.id);i.source=i.layer.source,i.layer["source-layer"]&&(i.sourceLayer=i.layer["source-layer"]),i.state=o}))};for(var v in s)g(v);return s}(this._layers,this._serializedLayers,this.sourceCaches,e,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(c)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,"querySourceFeatures.filter",r.filter,null,r);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map((function(e){return t.getTileByID(e)})),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]||(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called "'+t+'" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast("loadWorkerSource",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e,r){void 0===r&&(r={}),this._checkLoaded();var n=this.light.getLight(),i=!1;for(var a in e)if(!t.deepEqual(e[a],n[a])){i=!0;break}if(i){var o={now:t.browser.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e,r),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return void 0===a&&(a={}),(!a||!1!==a.validate)&&je(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.styleSpec},i)))},r.prototype._remove=function(){for(var e in this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),t.evented.off("pluginStateChange",this._rtlTextPluginCallback),this._layers){this._layers[e].setEventedParent(null)}for(var r in this.sourceCaches)this.sourceCaches[r].clearTiles(),this.sourceCaches[r].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(e,r,n,i,a){void 0===a&&(a=!1);for(var o=!1,s=!1,l={},c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];if("symbol"===h.type){if(!l[h.source]){var p=this.sourceCaches[h.source];l[h.source]=p.getRenderableIds(!0).map((function(t){return p.getTileByID(t)})).sort((function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)}))}var d=this.crossTileSymbolIndex.addLayer(h,l[h.source],e.center.lng);o=o||d}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((a=a||this._layerOrderChanged||0===n)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(t.browser.now(),e.zoom))&&(this.pauseablePlacement=new ze(e,this._order,a,r,n,i,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(t.browser.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(var m=0,g=this._order;m<g.length;m+=1){var v=g[m],y=this._layers[v];"symbol"===y.type&&this.placement.updateLayerOpacities(y,l[y.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(t.browser.now())},r.prototype._releaseSymbolFadeTiles=function(){for(var t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r),this._updateTilesForChangedImages();var n=this.sourceCaches[e.source];n&&n.setDependencies(e.tileID.key,e.type,e.icons)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r.prototype.getResource=function(e,r,n){return t.makeRequest(r,n)},r}(t.Evented);qe.getSourceType=function(t){return D[t]},qe.setSourceType=function(t,e){D[t]=e},qe.registerForPluginStateChange=t.registerForPluginStateChange;var Ge=t.createLayout([{name:"a_pos",type:"Int16",components:2}]),Ye=yr("#ifdef GL_ES\nprecision mediump float;\n#else\n#if !defined(lowp)\n#define lowp\n#endif\n#if !defined(mediump)\n#define mediump\n#endif\n#if !defined(highp)\n#define highp\n#endif\n#endif","#ifdef GL_ES\nprecision highp float;\n#else\n#if !defined(lowp)\n#define lowp\n#endif\n#if !defined(mediump)\n#define mediump\n#endif\n#if !defined(highp)\n#define highp\n#endif\n#endif\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}"),We=yr("uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}"),Xe=yr("uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}"),Ze=yr("varying vec3 v_data;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize mediump float radius\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize highp vec4 stroke_color\n#pragma mapbox: initialize mediump float stroke_width\n#pragma mapbox: initialize lowp float stroke_opacity\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\nvoid main(void) {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize mediump float radius\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize highp vec4 stroke_color\n#pragma mapbox: initialize mediump float stroke_width\n#pragma mapbox: initialize lowp float stroke_opacity\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}"),Je=yr("void main() {gl_FragColor=vec4(1.0);}","attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}"),Ke=yr("uniform highp float u_intensity;varying vec2 v_extrude;\n#pragma mapbox: define highp float weight\n#define GAUSS_COEF 0.3989422804014327\nvoid main() {\n#pragma mapbox: initialize highp float weight\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\n#pragma mapbox: define highp float weight\n#pragma mapbox: define mediump float radius\nconst highp float ZERO=1.0/255.0/16.0;\n#define GAUSS_COEF 0.3989422804014327\nvoid main(void) {\n#pragma mapbox: initialize highp float weight\n#pragma mapbox: initialize mediump float radius\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}"),Qe=yr("uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(0.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}"),$e=yr("varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}","attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}"),tr=yr("varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}","attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}"),er=yr("uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}","attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}"),rr=yr("#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float opacity\ngl_FragColor=color*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float opacity\ngl_Position=u_matrix*vec4(a_pos,0,1);}"),nr=yr("varying vec2 v_pos;\n#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 outline_color\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\n#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 outline_color\n#pragma mapbox: initialize lowp float opacity\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}"),ir=yr("uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}"),ar=yr("uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}"),or=yr("varying vec4 v_color;void main() {gl_FragColor=v_color;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\n#pragma mapbox: define highp float base\n#pragma mapbox: define highp float height\n#pragma mapbox: define highp vec4 color\nvoid main() {\n#pragma mapbox: initialize highp float base\n#pragma mapbox: initialize highp float height\n#pragma mapbox: initialize highp vec4 color\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}"),sr=yr("uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float base\n#pragma mapbox: initialize lowp float height\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float base\n#pragma mapbox: initialize lowp float height\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\n? a_pos\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}"),lr=yr("#ifdef GL_ES\nprecision highp float;\n#endif\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform float u_maxzoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggeration=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/ pow(2.0,(u_zoom-u_maxzoom)*exaggeration+19.2562-u_zoom);gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}"),cr=yr("uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\n#define PI 3.141592653589793\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}"),ur=yr("uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}"),fr=yr("uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp float v_lineprogress;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,vec2(v_lineprogress,0.5));gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define MAX_LINE_DISTANCE 32767.0\n#define scale 0.015873016\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_lineprogress;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_lineprogress=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0/MAX_LINE_DISTANCE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}"),hr=yr("uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\n#define LINE_DISTANCE_SCALE 2.0\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}"),pr=yr("uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\n#define LINE_DISTANCE_SCALE 2.0\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}"),dr=yr("uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}"),mr=yr("uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}"),gr=yr("#define SDF_PX 8.0\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}"),vr=yr("#define SDF_PX 8.0\n#define SDF 1.0\n#define ICON 0.0\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}");function yr(t,e){var r=/#pragma mapbox: ([\w]+) ([\w]+) ([\w]+) ([\w]+)/g,n={};return{fragmentSource:t=t.replace(r,(function(t,e,r,i,a){return n[a]=!0,"define"===e?"\n#ifndef HAS_UNIFORM_u_"+a+"\nvarying "+r+" "+i+" "+a+";\n#else\nuniform "+r+" "+i+" u_"+a+";\n#endif\n":"\n#ifdef HAS_UNIFORM_u_"+a+"\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n"})),vertexSource:e=e.replace(r,(function(t,e,r,i,a){var o="float"===i?"vec2":"vec4",s=a.match(/color/)?"color":o;return n[a]?"define"===e?"\n#ifndef HAS_UNIFORM_u_"+a+"\nuniform lowp float u_"+a+"_t;\nattribute "+r+" "+o+" a_"+a+";\nvarying "+r+" "+i+" "+a+";\n#else\nuniform "+r+" "+i+" u_"+a+";\n#endif\n":"vec4"===s?"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+a+" = a_"+a+";\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n":"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+a+" = unpack_mix_"+s+"(a_"+a+", u_"+a+"_t);\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n":"define"===e?"\n#ifndef HAS_UNIFORM_u_"+a+"\nuniform lowp float u_"+a+"_t;\nattribute "+r+" "+o+" a_"+a+";\n#else\nuniform "+r+" "+i+" u_"+a+";\n#endif\n":"vec4"===s?"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+r+" "+i+" "+a+" = a_"+a+";\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n":"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+r+" "+i+" "+a+" = unpack_mix_"+s+"(a_"+a+", u_"+a+"_t);\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n"}))}}var xr=Object.freeze({__proto__:null,prelude:Ye,background:We,backgroundPattern:Xe,circle:Ze,clippingMask:Je,heatmap:Ke,heatmapTexture:Qe,collisionBox:$e,collisionCircle:tr,debug:er,fill:rr,fillOutline:nr,fillOutlinePattern:ir,fillPattern:ar,fillExtrusion:or,fillExtrusionPattern:sr,hillshadePrepare:lr,hillshade:cr,line:ur,lineGradient:fr,linePattern:hr,lineSDF:pr,raster:dr,symbolIcon:mr,symbolSDF:gr,symbolTextAndIcon:vr}),br=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};br.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,c=0;!l&&c<n.length;c++)this.boundPaintVertexBuffers[c]!==n[c]&&(l=!0);var u=!this.vao||this.boundProgram!==e||this.boundLayoutVertexBuffer!==r||l||this.boundIndexBuffer!==i||this.boundVertexOffset!==a||this.boundDynamicVertexBuffer!==o||this.boundDynamicVertexBuffer2!==s;!t.extVertexArrayObject||u?this.freshBind(e,r,n,i,a,o,s):(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind())},br.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,c=this.context,u=c.gl;if(c.extVertexArrayObject)this.vao&&this.destroy(),this.vao=c.extVertexArrayObject.createVertexArrayOES(),c.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=c.currentNumAttributes||0;for(var f=l;f<s;f++)u.disableVertexAttribArray(f)}e.enableAttributes(u,t);for(var h=0,p=r;h<p.length;h+=1){p[h].enableAttributes(u,t)}a&&a.enableAttributes(u,t),o&&o.enableAttributes(u,t),e.bind(),e.setVertexAttribPointers(u,t,i);for(var d=0,m=r;d<m.length;d+=1){var g=m[d];g.bind(),g.setVertexAttribPointers(u,t,i)}a&&(a.bind(),a.setVertexAttribPointers(u,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(u,t,i)),c.currentNumAttributes=l},br.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var _r=function(t,e,r,n,i){var a=t.gl;this.program=a.createProgram();var o=r?r.defines():[];i&&o.push("#define OVERDRAW_INSPECTOR;");var s=o.concat(Ye.fragmentSource,e.fragmentSource).join("\n"),l=o.concat(Ye.vertexSource,e.vertexSource).join("\n"),c=a.createShader(a.FRAGMENT_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(c,s),a.compileShader(c),a.attachShader(this.program,c);var u=a.createShader(a.VERTEX_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(u,l),a.compileShader(u),a.attachShader(this.program,u);for(var f=r?r.layoutAttributes:[],h=0;h<f.length;h++)a.bindAttribLocation(this.program,h,f[h].name);a.linkProgram(this.program),a.deleteShader(u),a.deleteShader(c),this.numAttributes=a.getProgramParameter(this.program,a.ACTIVE_ATTRIBUTES),this.attributes={};for(var p={},d=0;d<this.numAttributes;d++){var m=a.getActiveAttrib(this.program,d);m&&(this.attributes[m.name]=a.getAttribLocation(this.program,m.name))}for(var g=a.getProgramParameter(this.program,a.ACTIVE_UNIFORMS),v=0;v<g;v++){var y=a.getActiveUniform(this.program,v);y&&(p[y.name]=a.getUniformLocation(this.program,y.name))}this.fixedUniforms=n(t,p),this.binderUniforms=r?r.getUniforms(t,p):[]}}};function wr(t,e,r){var n=1/pe(r,1,e.transform.tileZoom),i=Math.pow(2,r.tileID.overscaledZ),a=r.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(r.tileID.canonical.x+r.tileID.wrap*i),s=a*r.tileID.canonical.y;return{u_image:0,u_texsize:r.imageAtlasTexture.size,u_scale:[n,t.fromScale,t.toScale],u_fade:t.t,u_pixel_coord_upper:[o>>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}_r.prototype.draw=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m){var g,v=t.gl;if(!this.failedToCreate){for(var y in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[y].set(o[y]);p&&p.setUniforms(t,this.binderUniforms,f,{zoom:h});for(var x=(g={},g[v.LINES]=2,g[v.TRIANGLES]=3,g[v.LINE_STRIP]=1,g)[e],b=0,_=u.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new br)).bind(t,this,l,p?p.getPaintVertexBuffers():[],c,w.vertexOffset,d,m),v.drawElements(e,w.primitiveLength*x,v.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var Tr=function(e,r,n,i){var a=r.style.light,o=a.properties.get("position"),s=[o.x,o.y,o.z],l=t.create$1();"viewport"===a.properties.get("anchor")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var c=a.properties.get("color");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get("intensity"),u_lightcolor:[c.r,c.g,c.b],u_vertical_gradient:+n,u_opacity:i}},kr=function(e,r,n,i,a,o,s){return t.extend(Tr(e,r,n,i),wr(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},Ar=function(t){return{u_matrix:t}},Mr=function(e,r,n,i){return t.extend(Ar(e),wr(n,r,i))},Sr=function(t,e){return{u_matrix:t,u_world:e}},Er=function(e,r,n,i,a){return t.extend(Mr(e,r,n,i),{u_world:a})},Lr=function(e,r,n,i){var a,o,s=e.transform;if("map"===i.paint.get("circle-pitch-alignment")){var l=pe(n,1,s.zoom);a=!0,o=[l,l]}else 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o=e.transform,s=e.lineAtlas,l=jr(r,o),c="round"===n.layout.get("line-cap"),u=s.getDash(i.from,c),f=s.getDash(i.to,c),h=u.width*a.fromScale,p=f.width*a.toScale;return t.extend(Rr(e,r,n),{u_patternscale_a:[l/h,-u.height/2],u_patternscale_b:[l/p,-f.height/2],u_sdfgamma:s.width/(256*Math.min(h,p)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:u.y,u_tex_y_b:f.y,u_mix:a.t})};function jr(t,e){return 1/pe(t,1,e.tileZoom)}function Ur(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get("line-translate"),r.paint.get("line-translate-anchor"))}var Vr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get("raster-opacity"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get("raster-brightness-min"),u_brightness_high:i.paint.get("raster-brightness-max"),u_saturation_factor:(o=i.paint.get("raster-saturation"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get("raster-contrast"),a>0?1/(1-a):1+a),u_spin_weights:Hr(i.paint.get("raster-hue-rotate"))};var a,o};function Hr(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var qr,Gr=function(t,e,r,n,i,a,o,s,l,c){var u=i.transform;return{u_is_size_zoom_constant:+("constant"===t||"source"===t),u_is_size_feature_constant:+("constant"===t||"camera"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:u.cameraToCenterDistance,u_pitch:u.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:u.width/u.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:c,u_texture:0}},Yr=function(e,r,n,i,a,o,s,l,c,u,f){var h=a.transform;return t.extend(Gr(e,r,n,i,a,o,s,l,c,u),{u_gamma_scale:i?Math.cos(h._pitch)*h.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+f})},Wr=function(e,r,n,i,a,o,s,l,c,u){return t.extend(Yr(e,r,n,i,a,o,s,l,!0,c,!0),{u_texsize_icon:u,u_texture_icon:1})},Xr=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},Zr=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,c=Math.pow(2,n.tileID.overscaledZ),u=n.tileSize*Math.pow(2,r.transform.tileZoom)/c,f=u*(n.tileID.canonical.x+n.tileID.wrap*c),h=u*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/pe(n,1,r.transform.tileZoom),u_pixel_coord_upper:[f>>16,h>>16],u_pixel_coord_lower:[65535&f,65535&h]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},Jr={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_maxzoom:new t.Uniform1f(e,r.u_maxzoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function Kr(e,r,n,i,a,o,s){for(var l=e.context,c=l.gl,u=e.useProgram("collisionBox"),f=[],h=0,p=0,d=0;d<i.length;d++){var m=i[d],g=r.getTile(m),v=g.getBucket(n);if(v){var y=m.posMatrix;0===a[0]&&0===a[1]||(y=e.translatePosMatrix(m.posMatrix,g,a,o));var x=s?v.textCollisionBox:v.iconCollisionBox,b=v.collisionCircleArray;if(b.length>0){var _=t.create(),w=y;t.mul(_,v.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(_,_,v.placementViewportMatrix),f.push({circleArray:b,circleOffset:p,transform:w,invTransform:_}),p=h+=b.length/4}x&&u.draw(l,c.LINES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,Cr(y,e.transform,g),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&f.length){var T=e.useProgram("collisionCircle"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*h),k._trim();for(var A=0,M=0,S=f;M<S.length;M+=1)for(var E=S[M],L=0;L<E.circleArray.length/4;L++){var C=4*L,P=E.circleArray[C+0],I=E.circleArray[C+1],O=E.circleArray[C+2],z=E.circleArray[C+3];k.emplace(A++,P,I,O,z,0),k.emplace(A++,P,I,O,z,1),k.emplace(A++,P,I,O,z,2),k.emplace(A++,P,I,O,z,3)}(!qr||qr.length<2*h)&&(qr=function(e){var r=2*e,n=new t.StructArrayLayout3ui6;n.resize(r),n._trim();for(var i=0;i<r;i++){var a=6*i;n.uint16[a+0]=4*i+0,n.uint16[a+1]=4*i+1,n.uint16[a+2]=4*i+2,n.uint16[a+3]=4*i+2,n.uint16[a+4]=4*i+3,n.uint16[a+5]=4*i+0}return n}(h));for(var D=l.createIndexBuffer(qr,!0),R=l.createVertexBuffer(k,t.collisionCircleLayout.members,!0),F=0,B=f;F<B.length;F+=1){var N=B[F],j=Pr(N.transform,N.invTransform,e.transform);T.draw(l,c.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,j,n.id,R,D,t.SegmentVector.simpleSegment(0,2*N.circleOffset,N.circleArray.length,N.circleArray.length/2),null,e.transform.zoom,null,null,null)}R.destroy(),D.destroy()}}var Qr=t.identity(new Float32Array(16));function $r(e,r,n,i,a,o){var s=t.getAnchorAlignment(e),l=-(s.horizontalAlign-.5)*r,c=-(s.verticalAlign-.5)*n,u=t.evaluateVariableOffset(e,i);return new t.Point((l/a+u[0])*o,(c/a+u[1])*o)}function tn(e,r,n,i,a,o,s,l,c,u,f){var h=e.text.placedSymbolArray,p=e.text.dynamicLayoutVertexArray,d=e.icon.dynamicLayoutVertexArray,m={};p.clear();for(var g=0;g<h.length;g++){var v=h.get(g),y=e.allowVerticalPlacement&&!v.placedOrientation,x=v.hidden||!v.crossTileID||y?null:i[v.crossTileID];if(x){var b=new t.Point(v.anchorX,v.anchorY),_=te(b,n?l:s),w=ee(o.cameraToCenterDistance,_.signedDistanceFromCamera),T=a.evaluateSizeForFeature(e.textSizeData,u,v)*w/t.ONE_EM;n&&(T*=e.tilePixelRatio/c);for(var k=x.width,A=x.height,M=$r(x.anchor,k,A,x.textOffset,x.textBoxScale,T),S=n?te(b.add(M),s).point:_.point.add(r?M.rotate(-o.angle):M),E=e.allowVerticalPlacement&&v.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,L=0;L<v.numGlyphs;L++)t.addDynamicAttributes(p,S,E);f&&v.associatedIconIndex>=0&&(m[v.associatedIconIndex]={shiftedAnchor:S,angle:E})}else ue(v.numGlyphs,p)}if(f){d.clear();for(var C=e.icon.placedSymbolArray,P=0;P<C.length;P++){var I=C.get(P);if(I.hidden)ue(I.numGlyphs,d);else{var O=m[P];if(O)for(var z=0;z<I.numGlyphs;z++)t.addDynamicAttributes(d,O.shiftedAnchor,O.angle);else ue(I.numGlyphs,d)}}e.icon.dynamicLayoutVertexBuffer.updateData(d)}e.text.dynamicLayoutVertexBuffer.updateData(p)}function en(t,e,r){return r.iconsInText&&e?"symbolTextAndIcon":t?"symbolSDF":"symbolIcon"}function rn(e,r,n,i,a,o,s,l,c,u,f,h){for(var p=e.context,d=p.gl,m=e.transform,g="map"===l,v="map"===c,y=g&&"point"!==n.layout.get("symbol-placement"),x=g&&!v&&!y,b=void 0!==n.layout.get("symbol-sort-key").constantOr(1),_=e.depthModeForSublayer(0,At.ReadOnly),w=n.layout.get("text-variable-anchor"),T=[],k=0,A=i;k<A.length;k+=1){var M=A[k],S=r.getTile(M),E=S.getBucket(n);if(E){var L=a?E.text:E.icon;if(L&&L.segments.get().length){var C=L.programConfigurations.get(n.id),P=a||E.sdfIcons,I=a?E.textSizeData:E.iconSizeData,O=v||0!==m.pitch,z=e.useProgram(en(P,a,E),C),D=t.evaluateSizeForZoom(I,m.zoom),R=void 0,F=[0,0],B=void 0,N=void 0,j=null,U=void 0;if(a){if(B=S.glyphAtlasTexture,N=d.LINEAR,R=S.glyphAtlasTexture.size,E.iconsInText){F=S.imageAtlasTexture.size,j=S.imageAtlasTexture;var 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rt=et[tt];T.push({segments:new t.SegmentVector([rt]),sortKey:rt.sortKey,state:$})}else T.push({segments:L.segments,sortKey:0,state:$})}}}b&&T.sort((function(t,e){return t.sortKey-e.sortKey}));for(var nt=0,it=T;nt<it.length;nt+=1){var at=it[nt],ot=at.state;if(p.activeTexture.set(d.TEXTURE0),ot.atlasTexture.bind(ot.atlasInterpolation,d.CLAMP_TO_EDGE),ot.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),ot.atlasTextureIcon&&ot.atlasTextureIcon.bind(ot.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),ot.isSDF){var st=ot.uniformValues;ot.hasHalo&&(st.u_is_halo=1,nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,st)),st.u_is_halo=0}nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,ot.uniformValues)}}function nn(t,e,r,n,i,a,o,s,l){var c=n.context,u=c.gl;i.draw(c,u.TRIANGLES,a,o,s,Et.disabled,l,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function an(t,e,r,n,i,a,o){var s,l,c,u,f,h=t.context.gl,p=r.paint.get("fill-pattern"),d=p&&p.constantOr(1),m=r.getCrossfadeParameters();o?(l=d&&!r.getPaintProperty("fill-outline-color")?"fillOutlinePattern":"fillOutline",s=h.LINES):(l=d?"fillPattern":"fill",s=h.TRIANGLES);for(var g=0,v=n;g<v.length;g+=1){var y=v[g],x=e.getTile(y);if(!d||x.patternsLoaded()){var b=x.getBucket(r);if(b){var _=b.programConfigurations.get(r.id),w=t.useProgram(l,_);d&&(t.context.activeTexture.set(h.TEXTURE0),x.imageAtlasTexture.bind(h.LINEAR,h.CLAMP_TO_EDGE),_.updatePaintBuffers(m));var T=p.constantOr(null);if(T&&x.imageAtlas){var k=x.imageAtlas,A=k.patternPositions[T.to.toString()],M=k.patternPositions[T.from.toString()];A&&M&&_.setConstantPatternPositions(A,M)}var S=t.translatePosMatrix(y.posMatrix,x,r.paint.get("fill-translate"),r.paint.get("fill-translate-anchor"));if(o){u=b.indexBuffer2,f=b.segments2;var E=[h.drawingBufferWidth,h.drawingBufferHeight];c="fillOutlinePattern"===l&&d?Er(S,t,m,x,E):Sr(S,E)}else u=b.indexBuffer,f=b.segments,c=d?Mr(S,t,m,x):Ar(S);w.draw(t.context,s,i,t.stencilModeForClipping(y),a,Et.disabled,c,r.id,b.layoutVertexBuffer,u,f,r.paint,t.transform.zoom,_)}}}}function on(t,e,r,n,i,a,o){for(var s=t.context,l=s.gl,c=r.paint.get("fill-extrusion-pattern"),u=c.constantOr(1),f=r.getCrossfadeParameters(),h=r.paint.get("fill-extrusion-opacity"),p=0,d=n;p<d.length;p+=1){var m=d[p],g=e.getTile(m),v=g.getBucket(r);if(v){var y=v.programConfigurations.get(r.id),x=t.useProgram(u?"fillExtrusionPattern":"fillExtrusion",y);u&&(t.context.activeTexture.set(l.TEXTURE0),g.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),y.updatePaintBuffers(f));var b=c.constantOr(null);if(b&&g.imageAtlas){var _=g.imageAtlas,w=_.patternPositions[b.to.toString()],T=_.patternPositions[b.from.toString()];w&&T&&y.setConstantPatternPositions(w,T)}var k=t.translatePosMatrix(m.posMatrix,g,r.paint.get("fill-extrusion-translate"),r.paint.get("fill-extrusion-translate-anchor")),A=r.paint.get("fill-extrusion-vertical-gradient"),M=u?kr(k,t,A,h,m,f,g):Tr(k,t,A,h);x.draw(s,s.gl.TRIANGLES,i,a,o,Et.backCCW,M,r.id,v.layoutVertexBuffer,v.indexBuffer,v.segments,r.paint,t.transform.zoom,y)}}}function sn(t,e,r,n,i,a){var o=t.context,s=o.gl,l=e.fbo;if(l){var c=t.useProgram("hillshade");o.activeTexture.set(s.TEXTURE0),s.bindTexture(s.TEXTURE_2D,l.colorAttachment.get());var u=function(t,e,r){var n=r.paint.get("hillshade-shadow-color"),i=r.paint.get("hillshade-highlight-color"),a=r.paint.get("hillshade-accent-color"),o=r.paint.get("hillshade-illumination-direction")*(Math.PI/180);"viewport"===r.paint.get("hillshade-illumination-anchor")&&(o-=t.transform.angle);var s=!t.options.moving;return{u_matrix:t.transform.calculatePosMatrix(e.tileID.toUnwrapped(),s),u_image:0,u_latrange:Dr(t,e.tileID),u_light:[r.paint.get("hillshade-exaggeration"),o],u_shadow:n,u_highlight:i,u_accent:a}}(t,e,r);c.draw(o,s.TRIANGLES,n,i,a,Et.disabled,u,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}}function ln(e,r,n,i,a,o,s){var l=e.context,c=l.gl,u=r.dem;if(u&&u.data){var f=u.dim,h=u.stride,p=u.getPixels();if(l.activeTexture.set(c.TEXTURE1),l.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture||e.getTileTexture(h),r.demTexture){var d=r.demTexture;d.update(p,{premultiply:!1}),d.bind(c.NEAREST,c.CLAMP_TO_EDGE)}else r.demTexture=new t.Texture(l,p,c.RGBA,{premultiply:!1}),r.demTexture.bind(c.NEAREST,c.CLAMP_TO_EDGE);l.activeTexture.set(c.TEXTURE0);var m=r.fbo;if(!m){var g=new t.Texture(l,{width:f,height:f,data:null},c.RGBA);g.bind(c.LINEAR,c.CLAMP_TO_EDGE),(m=r.fbo=l.createFramebuffer(f,f,!0)).colorAttachment.set(g.texture)}l.bindFramebuffer.set(m.framebuffer),l.viewport.set([0,0,f,f]),e.useProgram("hillshadePrepare").draw(l,c.TRIANGLES,a,o,s,Et.disabled,function(e,r,n){var i=r.stride,a=t.create();return t.ortho(a,0,t.EXTENT,-t.EXTENT,0,0,1),t.translate(a,a,[0,-t.EXTENT,0]),{u_matrix:a,u_image:1,u_dimension:[i,i],u_zoom:e.overscaledZ,u_maxzoom:n,u_unpack:r.getUnpackVector()}}(r.tileID,u,i),n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments),r.needsHillshadePrepare=!1}}function cn(e,r,n,i,a){var o=i.paint.get("raster-fade-duration");if(o>0){var s=t.browser.now(),l=(s-e.timeAdded)/o,c=r?(s-r.timeAdded)/o:-1,u=n.getSource(),f=a.coveringZoomLevel({tileSize:u.tileSize,roundZoom:u.roundZoom}),h=!r||Math.abs(r.tileID.overscaledZ-f)>Math.abs(e.tileID.overscaledZ-f),p=h&&e.refreshedUponExpiration?1:t.clamp(h?l:1-c,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var un=new t.Color(1,0,0,1),fn=new t.Color(0,1,0,1),hn=new t.Color(0,0,1,1),pn=new t.Color(1,0,1,1),dn=new t.Color(0,1,1,1);function mn(t){var e=t.transform.padding;gn(t,t.transform.height-(e.top||0),3,un),gn(t,e.bottom||0,3,fn),vn(t,e.left||0,3,hn),vn(t,t.transform.width-(e.right||0),3,pn);var r=t.transform.centerPoint;!function(t,e,r,n){yn(t,e-1,r-10,2,20,n),yn(t,e-10,r-1,20,2,n)}(t,r.x,t.transform.height-r.y,dn)}function gn(t,e,r,n){yn(t,0,e+r/2,t.transform.width,r,n)}function vn(t,e,r,n){yn(t,e-r/2,0,r,t.transform.height,n)}function yn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function xn(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram("debug"),l=At.disabled,c=Mt.disabled,u=e.colorModeForRenderPass();i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,c,u,Et.disabled,Ir(o,t.Color.red),"$debug",e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var f=r.getTileByID(n.key).latestRawTileData,h=f&&f.byteLength||0,p=Math.floor(h/1024),d=r.getTile(n).tileSize,m=512/Math.min(d,512)*(n.overscaledZ/e.transform.zoom)*.5,g=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(g+=" => "+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext("2d");i.clearRect(0,0,r.width,r.height),i.shadowColor="white",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle="white",i.textBaseline="top",i.font="bold 36px Open Sans, sans-serif",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,g+" 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_=Math.pow(2,l.zoom-d.tileID.overscaledZ);tn(m,c,u,s,t.symbolSize,l,x,p.posMatrix,_,v,b)}}}}(i,e,n,r,n.layout.get("text-rotation-alignment"),n.layout.get("text-pitch-alignment"),a),0!==n.paint.get("icon-opacity").constantOr(1)&&rn(e,r,n,i,!1,n.paint.get("icon-translate"),n.paint.get("icon-translate-anchor"),n.layout.get("icon-rotation-alignment"),n.layout.get("icon-pitch-alignment"),n.layout.get("icon-keep-upright"),o,s),0!==n.paint.get("text-opacity").constantOr(1)&&rn(e,r,n,i,!0,n.paint.get("text-translate"),n.paint.get("text-translate-anchor"),n.layout.get("text-rotation-alignment"),n.layout.get("text-pitch-alignment"),n.layout.get("text-keep-upright"),o,s),r.map.showCollisionBoxes&&(Kr(e,r,n,i,n.paint.get("text-translate"),n.paint.get("text-translate-anchor"),!0),Kr(e,r,n,i,n.paint.get("icon-translate"),n.paint.get("icon-translate-anchor"),!1))}},circle:function(e,r,n,i){if("translucent"===e.renderPass){var a=n.paint.get("circle-opacity"),o=n.paint.get("circle-stroke-width"),s=n.paint.get("circle-stroke-opacity"),l=void 0!==n.layout.get("circle-sort-key").constantOr(1);if(0!==a.constantOr(1)||0!==o.constantOr(1)&&0!==s.constantOr(1)){for(var c=e.context,u=c.gl,f=e.depthModeForSublayer(0,At.ReadOnly),h=Mt.disabled,p=e.colorModeForRenderPass(),d=[],m=0;m<i.length;m++){var g=i[m],v=r.getTile(g),y=v.getBucket(n);if(y){var x=y.programConfigurations.get(n.id),b={programConfiguration:x,program:e.useProgram("circle",x),layoutVertexBuffer:y.layoutVertexBuffer,indexBuffer:y.indexBuffer,uniformValues:Lr(e,g,v,n)};if(l)for(var _=0,w=y.segments.get();_<w.length;_+=1){var T=w[_];d.push({segments:new t.SegmentVector([T]),sortKey:T.sortKey,state:b})}else d.push({segments:y.segments,sortKey:0,state:b})}}l&&d.sort((function(t,e){return t.sortKey-e.sortKey}));for(var k=0,A=d;k<A.length;k+=1){var M=A[k],S=M.state,E=S.programConfiguration,L=S.program,C=S.layoutVertexBuffer,P=S.indexBuffer,I=S.uniformValues,O=M.segments;L.draw(c,u.TRIANGLES,f,h,p,Et.disabled,I,n.id,C,P,O,n.paint,e.transform.zoom,E)}}}},heatmap:function(e,r,n,i){if(0!==n.paint.get("heatmap-opacity"))if("offscreen"===e.renderPass){var a=e.context,o=a.gl,s=Mt.disabled,l=new St([o.ONE,o.ONE],t.Color.transparent,[!0,!0,!0,!0]);!function(t,e,r){var n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);var i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{var a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1),function(t,e,r,n){var i=t.gl,a=t.extRenderToTextureHalfFloat?t.extTextureHalfFloat.HALF_FLOAT_OES:i.UNSIGNED_BYTE;i.texImage2D(i.TEXTURE_2D,0,i.RGBA,e.width/4,e.height/4,0,i.RGBA,a,null),n.colorAttachment.set(r)}(t,e,a,i)}}(a,e,n),a.clear({color:t.Color.transparent});for(var c=0;c<i.length;c++){var u=i[c];if(!r.hasRenderableParent(u)){var f=r.getTile(u),h=f.getBucket(n);if(h){var p=h.programConfigurations.get(n.id),d=e.useProgram("heatmap",p),m=e.transform.zoom;d.draw(a,o.TRIANGLES,At.disabled,s,l,Et.disabled,zr(u.posMatrix,f,m,n.paint.get("heatmap-intensity")),n.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,n.paint,e.transform.zoom,p)}}}a.viewport.set([0,0,e.width,e.height])}else"translucent"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(!a)return;n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o||(o=r.colorRampTexture=new t.Texture(n,r.colorRamp,i.RGBA));o.bind(i.LINEAR,i.CLAMP_TO_EDGE),e.useProgram("heatmapTexture").draw(n,i.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,function(e,r,n,i){var a=t.create();t.ortho(a,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:n,u_color_ramp:i,u_opacity:r.paint.get("heatmap-opacity")}}(e,r,0,1),r.id,e.viewportBuffer,e.quadTriangleIndexBuffer,e.viewportSegments,r.paint,e.transform.zoom)}(e,n))},line:function(e,r,n,i){if("translucent"===e.renderPass){var a=n.paint.get("line-opacity"),o=n.paint.get("line-width");if(0!==a.constantOr(1)&&0!==o.constantOr(1)){var s=e.depthModeForSublayer(0,At.ReadOnly),l=e.colorModeForRenderPass(),c=n.paint.get("line-dasharray"),u=n.paint.get("line-pattern"),f=u.constantOr(1),h=n.paint.get("line-gradient"),p=n.getCrossfadeParameters(),d=f?"linePattern":c?"lineSDF":h?"lineGradient":"line",m=e.context,g=m.gl,v=!0;if(h){m.activeTexture.set(g.TEXTURE0);var y=n.gradientTexture;if(!n.gradient)return;y||(y=n.gradientTexture=new t.Texture(m,n.gradient,g.RGBA)),y.bind(g.LINEAR,g.CLAMP_TO_EDGE)}for(var x=0,b=i;x<b.length;x+=1){var _=b[x],w=r.getTile(_);if(!f||w.patternsLoaded()){var T=w.getBucket(n);if(T){var k=T.programConfigurations.get(n.id),A=e.context.program.get(),M=e.useProgram(d,k),S=v||M.program!==A,E=u.constantOr(null);if(E&&w.imageAtlas){var L=w.imageAtlas,C=L.patternPositions[E.to.toString()],P=L.patternPositions[E.from.toString()];C&&P&&k.setConstantPatternPositions(C,P)}var I=f?Br(e,w,n,p):c?Nr(e,w,n,c,p):h?Fr(e,w,n):Rr(e,w,n);f?(m.activeTexture.set(g.TEXTURE0),w.imageAtlasTexture.bind(g.LINEAR,g.CLAMP_TO_EDGE),k.updatePaintBuffers(p)):c&&(S||e.lineAtlas.dirty)&&(m.activeTexture.set(g.TEXTURE0),e.lineAtlas.bind(m)),M.draw(m,g.TRIANGLES,s,e.stencilModeForClipping(_),l,Et.disabled,I,n.id,T.layoutVertexBuffer,T.indexBuffer,T.segments,n.paint,e.transform.zoom,k),v=!1}}}}}},fill:function(e,r,n,i){var a=n.paint.get("fill-color"),o=n.paint.get("fill-opacity");if(0!==o.constantOr(1)){var s=e.colorModeForRenderPass(),l=n.paint.get("fill-pattern"),c=e.opaquePassEnabledForLayer()&&!l.constantOr(1)&&1===a.constantOr(t.Color.transparent).a&&1===o.constantOr(0)?"opaque":"translucent";if(e.renderPass===c){var u=e.depthModeForSublayer(1,"opaque"===e.renderPass?At.ReadWrite:At.ReadOnly);an(e,r,n,i,u,s,!1)}if("translucent"===e.renderPass&&n.paint.get("fill-antialias")){var f=e.depthModeForSublayer(n.getPaintProperty("fill-outline-color")?2:0,At.ReadOnly);an(e,r,n,i,f,s,!0)}}},"fill-extrusion":function(t,e,r,n){var i=r.paint.get("fill-extrusion-opacity");if(0!==i&&"translucent"===t.renderPass){var a=new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D);if(1!==i||r.paint.get("fill-extrusion-pattern").constantOr(1))on(t,e,r,n,a,Mt.disabled,St.disabled),on(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();on(t,e,r,n,a,Mt.disabled,o)}}},hillshade:function(t,e,r,n){if("offscreen"===t.renderPass||"translucent"===t.renderPass){for(var i=t.context,a=e.getSource().maxzoom,o=t.depthModeForSublayer(0,At.ReadOnly),s=t.colorModeForRenderPass(),l="translucent"===t.renderPass?t.stencilConfigForOverlap(n):[{},n],c=l[0],u=0,f=l[1];u<f.length;u+=1){var h=f[u],p=e.getTile(h);p.needsHillshadePrepare&&"offscreen"===t.renderPass?ln(t,p,r,a,o,Mt.disabled,s):"translucent"===t.renderPass&&sn(t,p,r,o,c[h.overscaledZ],s)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if("translucent"===t.renderPass&&0!==r.paint.get("raster-opacity")&&n.length)for(var i=t.context,a=i.gl,o=e.getSource(),s=t.useProgram("raster"),l=t.colorModeForRenderPass(),c=o instanceof I?[{},n]:t.stencilConfigForOverlap(n),u=c[0],f=c[1],h=f[f.length-1].overscaledZ,p=!t.options.moving,d=0,m=f;d<m.length;d+=1){var g=m[d],v=t.depthModeForSublayer(g.overscaledZ-h,1===r.paint.get("raster-opacity")?At.ReadWrite:At.ReadOnly,a.LESS),y=e.getTile(g),x=t.transform.calculatePosMatrix(g.toUnwrapped(),p);y.registerFadeDuration(r.paint.get("raster-fade-duration"));var b=e.findLoadedParent(g,0),_=cn(y,b,e,r,t.transform),w=void 0,T=void 0,k="nearest"===r.paint.get("raster-resampling")?a.NEAREST:a.LINEAR;i.activeTexture.set(a.TEXTURE0),y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),i.activeTexture.set(a.TEXTURE1),b?(b.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),w=Math.pow(2,b.tileID.overscaledZ-y.tileID.overscaledZ),T=[y.tileID.canonical.x*w%1,y.tileID.canonical.y*w%1]):y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST);var A=Vr(x,T||[0,0],w||1,_,r);o instanceof I?s.draw(i,a.TRIANGLES,v,Mt.disabled,l,Et.disabled,A,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,v,u[g.overscaledZ],l,Et.disabled,A,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}},background:function(t,e,r){var n=r.paint.get("background-color"),i=r.paint.get("background-opacity");if(0!==i){var a=t.context,o=a.gl,s=t.transform,l=s.tileSize,c=r.paint.get("background-pattern");if(!t.isPatternMissing(c)){var u=!c&&1===n.a&&1===i&&t.opaquePassEnabledForLayer()?"opaque":"translucent";if(t.renderPass===u){var f=Mt.disabled,h=t.depthModeForSublayer(0,"opaque"===u?At.ReadWrite:At.ReadOnly),p=t.colorModeForRenderPass(),d=t.useProgram(c?"backgroundPattern":"background"),m=s.coveringTiles({tileSize:l});c&&(a.activeTexture.set(o.TEXTURE0),t.imageManager.bind(t.context));for(var g=r.getCrossfadeParameters(),v=0,y=m;v<y.length;v+=1){var x=y[v],b=t.transform.calculatePosMatrix(x.toUnwrapped()),_=c?Zr(b,i,t,c,{tileID:x,tileSize:l},g):Xr(b,i,n);d.draw(a,o.TRIANGLES,h,f,p,Et.disabled,_,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)xn(t,e,r[n])},custom:function(t,e,r){var n=t.context,i=r.implementation;if("offscreen"===t.renderPass){var a=i.prerender;a&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),a.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if("translucent"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(Mt.disabled);var o="3d"===i.renderingMode?new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,At.ReadOnly);n.setDepthMode(o),i.render(n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}},_n=function(t,e){this.context=new Lt(t),this.transform=e,this._tileTextures={},this.setup(),this.numSublayers=Ct.maxUnderzooming+Ct.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Ne,this.gpuTimers={}};_n.prototype.resize=function(e,r){if(this.width=e*t.browser.devicePixelRatio,this.height=r*t.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1){var a=i[n];this.style._layers[a].resize()}},_n.prototype.setup=function(){var e=this.context,r=new t.StructArrayLayout2i4;r.emplaceBack(0,0),r.emplaceBack(t.EXTENT,0),r.emplaceBack(0,t.EXTENT),r.emplaceBack(t.EXTENT,t.EXTENT),this.tileExtentBuffer=e.createVertexBuffer(r,Ge.members),this.tileExtentSegments=t.SegmentVector.simpleSegment(0,0,4,2);var n=new t.StructArrayLayout2i4;n.emplaceBack(0,0),n.emplaceBack(t.EXTENT,0),n.emplaceBack(0,t.EXTENT),n.emplaceBack(t.EXTENT,t.EXTENT),this.debugBuffer=e.createVertexBuffer(n,Ge.members),this.debugSegments=t.SegmentVector.simpleSegment(0,0,4,5);var i=new t.StructArrayLayout4i8;i.emplaceBack(0,0,0,0),i.emplaceBack(t.EXTENT,0,t.EXTENT,0),i.emplaceBack(0,t.EXTENT,0,t.EXTENT),i.emplaceBack(t.EXTENT,t.EXTENT,t.EXTENT,t.EXTENT),this.rasterBoundsBuffer=e.createVertexBuffer(i,P.members),this.rasterBoundsSegments=t.SegmentVector.simpleSegment(0,0,4,2);var a=new t.StructArrayLayout2i4;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,Ge.members),this.viewportSegments=t.SegmentVector.simpleSegment(0,0,4,2);var o=new t.StructArrayLayout1ui2;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=e.createIndexBuffer(o);var s=new t.StructArrayLayout3ui6;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=e.createIndexBuffer(s),this.emptyTexture=new t.Texture(e,{width:1,height:1,data:new Uint8Array([0,0,0,0])},e.gl.RGBA);var l=this.context.gl;this.stencilClearMode=new Mt({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)},_n.prototype.clearStencil=function(){var e=this.context,r=e.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var n=t.create();t.ortho(n,0,this.width,this.height,0,0,1),t.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram("clippingMask").draw(e,r.TRIANGLES,At.disabled,this.stencilClearMode,St.disabled,Et.disabled,Or(n),"$clipping",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},_n.prototype._renderTileClippingMasks=function(t,e){if(this.currentStencilSource!==t.source&&t.isTileClipped()&&e&&e.length){this.currentStencilSource=t.source;var r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(St.disabled),r.setDepthMode(At.disabled);var i=this.useProgram("clippingMask");this._tileClippingMaskIDs={};for(var a=0,o=e;a<o.length;a+=1){var s=o[a],l=this._tileClippingMaskIDs[s.key]=this.nextStencilID++;i.draw(r,n.TRIANGLES,At.disabled,new Mt({func:n.ALWAYS,mask:0},l,255,n.KEEP,n.KEEP,n.REPLACE),St.disabled,Et.disabled,Or(s.posMatrix),"$clipping",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},_n.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new Mt({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Mt({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s<a;s++)o[s+i]=new Mt({func:r.GEQUAL,mask:255},s+this.nextStencilID,255,r.KEEP,r.KEEP,r.REPLACE);return this.nextStencilID+=a,[o,n]}return[(e={},e[i]=Mt.disabled,e),n]},_n.prototype.colorModeForRenderPass=function(){var e=this.context.gl;if(this._showOverdrawInspector){return new St([e.CONSTANT_COLOR,e.ONE],new t.Color(1/8,1/8,1/8,0),[!0,!0,!0,!0])}return"opaque"===this.renderPass?St.unblended:St.alphaBlended},_n.prototype.depthModeForSublayer=function(t,e,r){if(!this.opaquePassEnabledForLayer())return At.disabled;var n=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon;return new At(r||this.context.gl.LEQUAL,e,[n,n])},_n.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},_n.prototype.render=function(e,r){var n=this;this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(t.browser.now()),this.imageManager.beginFrame();var i=this.style._order,a=this.style.sourceCaches;for(var o in a){var s=a[o];s.used&&s.prepare(this.context)}var l,c,u={},f={},h={};for(var p in a){var d=a[p];u[p]=d.getVisibleCoordinates(),f[p]=u[p].slice().reverse(),h[p]=d.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var m=0;m<i.length;m++){var g=i[m];if(this.style._layers[g].is3D()){this.opaquePassCutoff=m;break}}this.renderPass="offscreen";for(var v=0,y=i;v<y.length;v+=1){var x=y[v],b=this.style._layers[x];if(b.hasOffscreenPass()&&!b.isHidden(this.transform.zoom)){var _=f[b.source];("custom"===b.type||_.length)&&this.renderLayer(this,a[b.source],b,_)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.Color.black:t.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(e._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass="opaque",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){var w=this.style._layers[i[this.currentLayer]],T=a[w.source],k=u[w.source];this._renderTileClippingMasks(w,k),this.renderLayer(this,T,w,k)}for(this.renderPass="translucent",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){var A=this.style._layers[i[this.currentLayer]],M=a[A.source],S=("symbol"===A.type?h:f)[A.source];this._renderTileClippingMasks(A,u[A.source]),this.renderLayer(this,M,A,S)}this.options.showTileBoundaries&&(t.values(this.style._layers).forEach((function(t){t.source&&!t.isHidden(n.transform.zoom)&&(t.source!==(c&&c.id)&&(c=n.style.sourceCaches[t.source]),(!l||l.getSource().maxzoom<c.getSource().maxzoom)&&(l=c))})),l&&bn.debug(this,l,l.getVisibleCoordinates()));this.options.showPadding&&mn(this),this.context.setDefault()},_n.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||("background"===r.type||"custom"===r.type||n.length)&&(this.id=r.id,this.gpuTimingStart(r),bn[r.type](t,e,r,n,this.style.placement.variableOffsets),this.gpuTimingEnd())},_n.prototype.gpuTimingStart=function(t){if(this.options.gpuTiming){var e=this.context.extTimerQuery,r=this.gpuTimers[t.id];r||(r=this.gpuTimers[t.id]={calls:0,cpuTime:0,query:e.createQueryEXT()}),r.calls++,e.beginQueryEXT(e.TIME_ELAPSED_EXT,r.query)}},_n.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var t=this.context.extTimerQuery;t.endQueryEXT(t.TIME_ELAPSED_EXT)}},_n.prototype.collectGpuTimers=function(){var t=this.gpuTimers;return this.gpuTimers={},t},_n.prototype.queryGpuTimers=function(t){var e={};for(var r in t){var n=t[r],i=this.context.extTimerQuery,a=i.getQueryObjectEXT(n.query,i.QUERY_RESULT_EXT)/1e6;i.deleteQueryEXT(n.query),e[r]=a}return e},_n.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?"map"===i?this.transform.angle:0:"viewport"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]*l-n[1]*s,n[0]*s+n[1]*l]}var c=[a?n[0]:pe(r,n[0],this.transform.zoom),a?n[1]:pe(r,n[1],this.transform.zoom),0],u=new Float32Array(16);return t.translate(u,e,c),u},_n.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},_n.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},_n.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},_n.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=""+t+(e?e.cacheKey:"")+(this._showOverdrawInspector?"/overdraw":"");return this.cache[r]||(this.cache[r]=new _r(this.context,xr[t],e,Jr[t],this._showOverdrawInspector)),this.cache[r]},_n.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},_n.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},_n.prototype.initDebugOverlayCanvas=function(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=t.window.document.createElement("canvas"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var e=this.context.gl;this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,e.RGBA)}},_n.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var wn=function(t,e){this.points=t,this.planes=e};wn.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new wn(a,o)};var Tn=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};Tn.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a<r.length;a++)n[a]=r[a]?this.min[a]:this.center[a],i[a]=r[a]?this.center[a]:this.max[a];return i[2]=this.max[2],new Tn(n,i)},Tn.prototype.distanceX=function(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]},Tn.prototype.distanceY=function(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]},Tn.prototype.intersects=function(e){for(var r=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],n=!0,i=0;i<e.planes.length;i++){for(var a=e.planes[i],o=0,s=0;s<r.length;s++)o+=t.dot$1(a,r[s])>=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var c=Number.MAX_VALUE,u=-Number.MAX_VALUE,f=0;f<e.points.length;f++){var h=e.points[f][l]-this.min[l];c=Math.min(c,h),u=Math.max(u,h)}if(u<0||c>this.max[l]-this.min[l])return 0}return 1};var kn=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error("Invalid value for edge-insets, top, bottom, left and right must all be numbers");this.top=t,this.bottom=e,this.left=r,this.right=n};kn.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},kn.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},kn.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},kn.prototype.clone=function(){return new kn(this.top,this.bottom,this.left,this.right)},kn.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var An=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new kn,this._posMatrixCache={},this._alignedPosMatrixCache={}},Mn={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};An.prototype.clone=function(){var t=new An(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},Mn.minZoom.get=function(){return this._minZoom},Mn.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Mn.maxZoom.get=function(){return this._maxZoom},Mn.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Mn.minPitch.get=function(){return this._minPitch},Mn.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},Mn.maxPitch.get=function(){return this._maxPitch},Mn.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},Mn.renderWorldCopies.get=function(){return this._renderWorldCopies},Mn.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Mn.worldSize.get=function(){return this.tileSize*this.scale},Mn.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},Mn.size.get=function(){return new t.Point(this.width,this.height)},Mn.bearing.get=function(){return-this.angle/Math.PI*180},Mn.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},Mn.pitch.get=function(){return this._pitch/Math.PI*180},Mn.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},Mn.fov.get=function(){return this._fov/Math.PI*180},Mn.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},Mn.zoom.get=function(){return this._zoom},Mn.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},Mn.center.get=function(){return this._center},Mn.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Mn.padding.get=function(){return this._edgeInsets.toJSON()},Mn.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},Mn.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},An.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},An.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},An.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},An.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),c=s-1;c<=l+1;c++)0!==c&&r.push(new t.UnwrappedTileID(c,e));return r},An.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&r<e.minzoom)return[];void 0!==e.maxzoom&&r>e.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=wn.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var c=function(t){return{aabb:new Tn([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},u=[],f=[],h=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)u.push(c(-d)),u.push(c(d));for(u.push(c(0));u.length>0;){var m=u.pop(),g=m.x,v=m.y,y=m.fullyVisible;if(!y){var x=m.aabb.intersects(s);if(0===x)continue;y=2===x}var b=m.aabb.distanceX(o),_=m.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_)),T=3+(1<<h-m.zoom)-2;if(m.zoom===h||w>T&&m.zoom>=l)f.push({tileID:new t.OverscaledTileID(m.zoom===h?p:m.zoom,m.wrap,m.zoom,g,v),distanceSq:t.sqrLen([o[0]-.5-g,o[1]-.5-v])});else for(var k=0;k<4;k++){var A=(g<<1)+k%2,M=(v<<1)+(k>>1);u.push({aabb:m.aabb.quadrant(k),zoom:m.zoom+1,x:A,y:M,wrap:m.wrap,fullyVisible:y})}}return f.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},An.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Mn.unmodified.get=function(){return this._unmodified},An.prototype.zoomScale=function(t){return Math.pow(2,t)},An.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},An.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},An.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},Mn.point.get=function(){return this.project(this.center)},An.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},An.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},An.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},An.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},An.prototype.coordinateLocation=function(t){return t.toLngLat()},An.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[0]/i,s=n[0]/a,l=r[1]/i,c=n[1]/a,u=r[2]/i,f=n[2]/a,h=u===f?0:(0-u)/(f-u);return new t.MercatorCoordinate(t.number(o,s,h)/this.worldSize,t.number(l,c,h)/this.worldSize)},An.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},An.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},An.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},An.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},An.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},An.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},An.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,c=this.size,u=this._unmodified;if(this.latRange){var f=this.latRange;a=t.mercatorYfromLat(f[1])*this.worldSize,e=(o=t.mercatorYfromLat(f[0])*this.worldSize)-a<c.y?c.y/(o-a):0}if(this.lngRange){var h=this.lngRange;s=t.mercatorXfromLng(h[0])*this.worldSize,r=(l=t.mercatorXfromLng(h[1])*this.worldSize)-s<c.x?c.x/(l-s):0}var p=this.point,d=Math.max(r||0,e||0);if(d)return this.center=this.unproject(new t.Point(r?(l+s)/2:p.x,e?(o+a)/2:p.y)),this.zoom+=this.scaleZoom(d),this._unmodified=u,void(this._constraining=!1);if(this.latRange){var m=p.y,g=c.y/2;m-g<a&&(i=a+g),m+g>o&&(i=o-g)}if(this.lngRange){var v=p.x,y=c.x/2;v-y<s&&(n=s+y),v+y>l&&(n=l-y)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=u,this._constraining=!1}},An.prototype._calcMatrices=function(){if(this.height){var e=this._fov/2,r=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(e)*this.height;var n=Math.PI/2+this._pitch,i=this._fov*(.5+r.y/this.height),a=Math.sin(i)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-n-i,.01,Math.PI-.01)),o=this.point,s=o.x,l=o.y,c=1.01*(Math.cos(Math.PI/2-this._pitch)*a+this.cameraToCenterDistance),u=this.height/50,f=new Float64Array(16);t.perspective(f,this._fov,this.width/this.height,u,c),f[8]=2*-r.x/this.width,f[9]=2*r.y/this.height,t.scale(f,f,[1,-1,1]),t.translate(f,f,[0,0,-this.cameraToCenterDistance]),t.rotateX(f,f,this._pitch),t.rotateZ(f,f,this.angle),t.translate(f,f,[-s,-l,0]),this.mercatorMatrix=t.scale([],f,[this.worldSize,this.worldSize,this.worldSize]),t.scale(f,f,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=f,this.invProjMatrix=t.invert([],this.projMatrix);var h=this.width%2/2,p=this.height%2/2,d=Math.cos(this.angle),m=Math.sin(this.angle),g=s-Math.round(s)+d*h+m*p,v=l-Math.round(l)+d*p+m*h,y=new Float64Array(f);if(t.translate(y,y,[g>.5?g-1:g,v>.5?v-1:v,0]),this.alignedProjMatrix=y,f=t.create(),t.scale(f,f,[this.width/2,-this.height/2,1]),t.translate(f,f,[1,-1,0]),this.labelPlaneMatrix=f,f=t.create(),t.scale(f,f,[1,-1,1]),t.translate(f,f,[-1,-1,0]),t.scale(f,f,[2/this.width,2/this.height,1]),this.glCoordMatrix=f,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(f=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error("failed to invert matrix");this.pixelMatrixInverse=f,this._posMatrixCache={},this._alignedPosMatrixCache={}}},An.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},An.prototype.getCameraPoint=function(){var e=this._pitch,r=Math.tan(e)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,r))},An.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s<l.length;s+=1){var c=l[s];n=Math.min(n,c.x),i=Math.min(i,c.y),a=Math.max(a,c.x),o=Math.max(o,c.y)}return[new t.Point(n,i),new t.Point(a,i),new t.Point(a,o),new t.Point(n,o),new t.Point(n,i)]},Object.defineProperties(An.prototype,Mn);var Sn=function(e){var r,n,i,a,o;this._hashName=e&&encodeURIComponent(e),t.bindAll(["_getCurrentHash","_onHashChange","_updateHash"],this),this._updateHash=(r=this._updateHashUnthrottled.bind(this),n=300,i=!1,a=null,o=function(){a=null,i&&(r(),a=setTimeout(o,n),i=!1)},function(){return i=!0,a||o(),a})};Sn.prototype.addTo=function(e){return this._map=e,t.window.addEventListener("hashchange",this._onHashChange,!1),this._map.on("moveend",this._updateHash),this},Sn.prototype.remove=function(){return t.window.removeEventListener("hashchange",this._onHashChange,!1),this._map.off("moveend",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},Sn.prototype.getHashString=function(e){var r=this._map.getCenter(),n=Math.round(100*this._map.getZoom())/100,i=Math.ceil((n*Math.LN2+Math.log(512/360/.5))/Math.LN10),a=Math.pow(10,i),o=Math.round(r.lng*a)/a,s=Math.round(r.lat*a)/a,l=this._map.getBearing(),c=this._map.getPitch(),u="";if(u+=e?"/"+o+"/"+s+"/"+n:n+"/"+s+"/"+o,(l||c)&&(u+="/"+Math.round(10*l)/10),c&&(u+="/"+Math.round(c)),this._hashName){var f=this._hashName,h=!1,p=t.window.location.hash.slice(1).split("&").map((function(t){var e=t.split("=")[0];return e===f?(h=!0,e+"="+u):t})).filter((function(t){return t}));return h||p.push(f+"="+u),"#"+p.join("&")}return"#"+u},Sn.prototype._getCurrentHash=function(){var e,r=this,n=t.window.location.hash.replace("#","");return this._hashName?(n.split("&").map((function(t){return t.split("=")})).forEach((function(t){t[0]===r._hashName&&(e=t)})),(e&&e[1]||"").split("/")):n.split("/")},Sn.prototype._onHashChange=function(){var t=this._getCurrentHash();if(t.length>=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},Sn.prototype._updateHashUnthrottled=function(){var e=this.getHashString();try{t.window.history.replaceState(t.window.history.state,"",e)}catch(t){}};var En={linearity:.3,easing:t.bezier(0,0,.3,1)},Ln=t.extend({deceleration:2500,maxSpeed:1400},En),Cn=t.extend({deceleration:20,maxSpeed:1400},En),Pn=t.extend({deceleration:1e3,maxSpeed:360},En),In=t.extend({deceleration:1e3,maxSpeed:90},En),On=function(t){this._map=t,this.clear()};function zn(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function Dn(e,r,n){var i=n.maxSpeed,a=n.linearity,o=n.deceleration,s=t.clamp(e*a/(r/1e3),-i,i),l=Math.abs(s)/(o*a);return{easing:n.easing,duration:1e3*l,amount:s*(l/2)}}On.prototype.clear=function(){this._inertiaBuffer=[]},On.prototype.record=function(e){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:t.browser.now(),settings:e})},On.prototype._drainInertiaBuffer=function(){for(var e=this._inertiaBuffer,r=t.browser.now();e.length>0&&r-e[0].time>160;)e.shift()},On.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n<i.length;n+=1){var a=i[n].settings;r.zoom+=a.zoomDelta||0,r.bearing+=a.bearingDelta||0,r.pitch+=a.pitchDelta||0,a.panDelta&&r.pan._add(a.panDelta),a.around&&(r.around=a.around),a.pinchAround&&(r.pinchAround=a.pinchAround)}var o=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,s={};if(r.pan.mag()){var l=Dn(r.pan.mag(),o,t.extend({},Ln,e||{}));s.offset=r.pan.mult(l.amount/r.pan.mag()),s.center=this._map.transform.center,zn(s,l)}if(r.zoom){var c=Dn(r.zoom,o,Cn);s.zoom=this._map.transform.zoom+c.amount,zn(s,c)}if(r.bearing){var u=Dn(r.bearing,o,Pn);s.bearing=this._map.transform.bearing+t.clamp(u.amount,-179,179),zn(s,u)}if(r.pitch){var f=Dn(r.pitch,o,In);s.pitch=this._map.transform.pitch+f.amount,zn(s,f)}if(s.zoom||s.bearing){var h=void 0===r.pinchAround?r.around:r.pinchAround;s.around=h?this._map.unproject(h):this._map.getCenter()}return this.clear(),t.extend(s,{noMoveStart:!0})}};var Rn=function(e){function n(n,i,a,o){void 0===o&&(o={});var s=r.mousePos(i.getCanvasContainer(),a),l=i.unproject(s);e.call(this,n,t.extend({point:s,lngLat:l,originalEvent:a},o)),this._defaultPrevented=!1,this.target=i}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Fn=function(e){function n(n,i,a){var o="touchend"===n?a.changedTouches:a.touches,s=r.touchPos(i.getCanvasContainer(),o),l=s.map((function(t){return i.unproject(t)})),c=s.reduce((function(t,e,r,n){return t.add(e.div(n.length))}),new t.Point(0,0)),u=i.unproject(c);e.call(this,n,{points:s,point:c,lngLats:l,lngLat:u,originalEvent:a}),this._defaultPrevented=!1}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Bn=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={defaultPrevented:{configurable:!0}};return e.prototype.preventDefault=function(){this._defaultPrevented=!0},r.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(e.prototype,r),e}(t.Event),Nn=function(t,e){this._map=t,this._clickTolerance=e.clickTolerance};Nn.prototype.reset=function(){delete this._mousedownPos},Nn.prototype.wheel=function(t){return this._firePreventable(new Bn(t.type,this._map,t))},Nn.prototype.mousedown=function(t,e){return this._mousedownPos=e,this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseup=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.click=function(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.dblclick=function(t){return this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseover=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.mouseout=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.touchstart=function(t){return this._firePreventable(new Fn(t.type,this._map,t))},Nn.prototype.touchmove=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchend=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchcancel=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Nn.prototype.isEnabled=function(){return!0},Nn.prototype.isActive=function(){return!1},Nn.prototype.enable=function(){},Nn.prototype.disable=function(){};var jn=function(t){this._map=t};jn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},jn.prototype.mousemove=function(t){this._map.fire(new Rn(t.type,this._map,t))},jn.prototype.mousedown=function(){this._delayContextMenu=!0},jn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Rn("contextmenu",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},jn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new Rn(t.type,this._map,t)),this._map.listens("contextmenu")&&t.preventDefault()},jn.prototype.isEnabled=function(){return!0},jn.prototype.isActive=function(){return!1},jn.prototype.enable=function(){},jn.prototype.disable=function(){};var Un=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Vn(t,e){for(var r={},n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}Un.prototype.isEnabled=function(){return!!this._enabled},Un.prototype.isActive=function(){return!!this._active},Un.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Un.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Un.prototype.mousedown=function(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(r.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)},Un.prototype.mousemoveWindow=function(t,e){if(this._active){var n=e;if(!(this._lastPos.equals(n)||!this._box&&n.dist(this._startPos)<this._clickTolerance)){var i=this._startPos;this._lastPos=n,this._box||(this._box=r.create("div","mapboxgl-boxzoom",this._container),this._container.classList.add("mapboxgl-crosshair"),this._fireEvent("boxzoomstart",t));var a=Math.min(i.x,n.x),o=Math.max(i.x,n.x),s=Math.min(i.y,n.y),l=Math.max(i.y,n.y);r.setTransform(this._box,"translate("+a+"px,"+s+"px)"),this._box.style.width=o-a+"px",this._box.style.height=l-s+"px"}}},Un.prototype.mouseupWindow=function(e,n){var i=this;if(this._active&&0===e.button){var a=this._startPos,o=n;if(this.reset(),r.suppressClick(),a.x!==o.x||a.y!==o.y)return this._map.fire(new t.Event("boxzoomend",{originalEvent:e})),{cameraAnimation:function(t){return t.fitScreenCoordinates(a,o,i._map.getBearing(),{linear:!0})}};this._fireEvent("boxzoomcancel",e)}},Un.prototype.keydown=function(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent("boxzoomcancel",t))},Un.prototype.reset=function(){this._active=!1,this._container.classList.remove("mapboxgl-crosshair"),this._box&&(r.remove(this._box),this._box=null),r.enableDrag(),delete this._startPos,delete this._lastPos},Un.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var Hn=function(t){this.reset(),this.numTouches=t.numTouches};Hn.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},Hn.prototype.touchstart=function(e,r,n){(this.centroid||n.length>this.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n<i.length;n+=1){var a=i[n];r._add(a)}return r.div(e.length)}(r),this.touches=Vn(n,r)))},Hn.prototype.touchmove=function(t,e,r){if(!this.aborted&&this.centroid){var n=Vn(r,e);for(var i in this.touches){var a=this.touches[i],o=n[i];(!o||o.dist(a)>30)&&(this.aborted=!0)}}},Hn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var qn=function(t){this.singleTap=new Hn(t),this.numTaps=t.numTaps,this.reset()};qn.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},qn.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},qn.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},qn.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Gn=function(){this._zoomIn=new qn({numTouches:1,numTaps:2}),this._zoomOut=new qn({numTouches:2,numTaps:1}),this.reset()};Gn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Gn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Gn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Gn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Gn.prototype.touchcancel=function(){this.reset()},Gn.prototype.enable=function(){this._enabled=!0},Gn.prototype.disable=function(){this._enabled=!1,this.reset()},Gn.prototype.isEnabled=function(){return this._enabled},Gn.prototype.isActive=function(){return this._active};var Yn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};Yn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Yn.prototype._correctButton=function(t,e){return!1},Yn.prototype._move=function(t,e){return{}},Yn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},Yn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r&&(t.preventDefault(),this._moved||!(e.dist(r)<this._clickTolerance)))return this._moved=!0,this._lastPoint=e,this._move(r,e)},Yn.prototype.mouseupWindow=function(t){r.mouseButton(t)===this._eventButton&&(this._moved&&r.suppressClick(),this.reset())},Yn.prototype.enable=function(){this._enabled=!0},Yn.prototype.disable=function(){this._enabled=!1,this.reset()},Yn.prototype.isEnabled=function(){return this._enabled},Yn.prototype.isActive=function(){return this._active};var Wn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.mousedown=function(e,r){t.prototype.mousedown.call(this,e,r),this._lastPoint&&(this._active=!0)},e.prototype._correctButton=function(t,e){return 0===e&&!t.ctrlKey},e.prototype._move=function(t,e){return{around:e,panDelta:e.sub(t)}},e}(Yn),Xn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=.8*(e.x-t.x);if(r)return this._active=!0,{bearingDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Zn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=-.5*(e.y-t.y);if(r)return this._active=!0,{pitchDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Jn=function(t){this._minTouches=1,this._clickTolerance=t.clickTolerance||1,this.reset()};Jn.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new t.Point(0,0)},Jn.prototype.touchstart=function(t,e,r){return this._calculateTransform(t,e,r)},Jn.prototype.touchmove=function(t,e,r){if(this._active)return t.preventDefault(),this._calculateTransform(t,e,r)},Jn.prototype.touchend=function(t,e,r){this._calculateTransform(t,e,r),this._active&&r.length<this._minTouches&&this.reset()},Jn.prototype.touchcancel=function(){this.reset()},Jn.prototype._calculateTransform=function(e,r,n){n.length>0&&(this._active=!0);var i=Vn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var c=i[l],u=this._touches[l];u&&(a._add(c),o._add(c.sub(u)),s++,i[l]=c)}if(this._touches=i,!(s<this._minTouches)&&o.mag()){var f=o.div(s);if(this._sum._add(f),!(this._sum.mag()<this._clickTolerance))return{around:a.div(s),panDelta:f}}},Jn.prototype.enable=function(){this._enabled=!0},Jn.prototype.disable=function(){this._enabled=!1,this.reset()},Jn.prototype.isEnabled=function(){return this._enabled},Jn.prototype.isActive=function(){return this._active};var Kn=function(){this.reset()};function Qn(t,e,r){for(var n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}Kn.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},Kn.prototype._start=function(t){},Kn.prototype._move=function(t,e,r){return{}},Kn.prototype.touchstart=function(t,e,r){this._firstTwoTouches||r.length<2||(this._firstTwoTouches=[r[0].identifier,r[1].identifier],this._start([e[0],e[1]]))},Kn.prototype.touchmove=function(t,e,r){if(this._firstTwoTouches){t.preventDefault();var n=this._firstTwoTouches,i=n[0],a=n[1],o=Qn(r,e,i),s=Qn(r,e,a);if(o&&s){var l=this._aroundCenter?null:o.add(s).div(2);return this._move([o,s],l,t)}}},Kn.prototype.touchend=function(t,e,n){if(this._firstTwoTouches){var i=this._firstTwoTouches,a=i[0],o=i[1],s=Qn(n,e,a),l=Qn(n,e,o);s&&l||(this._active&&r.suppressClick(),this.reset())}},Kn.prototype.touchcancel=function(){this.reset()},Kn.prototype.enable=function(t){this._enabled=!0,this._aroundCenter=!!t&&"center"===t.around},Kn.prototype.disable=function(){this._enabled=!1,this.reset()},Kn.prototype.isEnabled=function(){return this._enabled},Kn.prototype.isActive=function(){return this._active};function $n(t,e){return Math.log(t/e)/Math.LN2}var ti=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._distance,delete this._startDistance},e.prototype._start=function(t){this._startDistance=this._distance=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._distance;if(this._distance=t[0].dist(t[1]),this._active||!(Math.abs($n(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:$n(this._distance,r),pinchAround:e}},e}(Kn);function ei(t,e){return 180*t.angleWith(e)/Math.PI}var ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},e.prototype._start=function(t){this._startVector=this._vector=t[0].sub(t[1]),this._minDiameter=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._vector;if(this._vector=t[0].sub(t[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:ei(this._vector,r),pinchAround:e}},e.prototype._isBelowThreshold=function(t){this._minDiameter=Math.min(this._minDiameter,t.mag());var e=25/(Math.PI*this._minDiameter)*360,r=ei(t,this._startVector);return Math.abs(r)<e},e}(Kn);function ni(t){return Math.abs(t.y)>Math.abs(t.x)}var ii=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ni(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid){this._lastPoints=t,this._active=!0;return{pitchDelta:-.5*((n.y+i.y)/2)}}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ni(t)&&ni(e)&&a}},e}(Kn),ai={panStep:100,bearingStep:15,pitchStep:10},oi=function(){var t=ai;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep};function si(t){return t*(2-t)}oi.prototype.reset=function(){this._active=!1},oi.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:"keyboardHandler",easing:si,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},oi.prototype.enable=function(){this._enabled=!0},oi.prototype.disable=function(){this._enabled=!1,this.reset()},oi.prototype.isEnabled=function(){return this._enabled},oi.prototype.isActive=function(){return this._active};var li=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=1/450,t.bindAll(["_onWheel","_onTimeout","_onScrollFrame","_onScrollFinished"],this)};li.prototype.setZoomRate=function(t){this._defaultZoomRate=t},li.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},li.prototype.isEnabled=function(){return!!this._enabled},li.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},li.prototype.isZooming=function(){return!!this._zooming},li.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&"center"===t.around)},li.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},li.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%4.000244140625==0?this._type="wheel":0!==r&&Math.abs(r)<4?this._type="trackpad":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?"trackpad":"wheel",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},li.prototype._onTimeout=function(t){this._type="wheel",this._delta-=this._lastValue,this._active||this._start(t)},li.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},li.prototype.renderFrame=function(){return this._onScrollFrame()},li.prototype._onScrollFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n="wheel"===this._type&&Math.abs(this._delta)>4.000244140625?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a="number"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),"wheel"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s="number"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,c=this._easing,u=!1;if("wheel"===this._type&&l&&c){var f=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),h=c(f);o=t.number(l,s,h),f<1?this._frameId||(this._frameId=!0):u=!0}else o=s,u=!0;return this._active=!0,u&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!u,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},li.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},li.prototype.reset=function(){this._active=!1};var ci=function(t,e){this._clickZoom=t,this._tapZoom=e};ci.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},ci.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},ci.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},ci.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var ui=function(){this.reset()};ui.prototype.reset=function(){this._active=!1},ui.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},ui.prototype.enable=function(){this._enabled=!0},ui.prototype.disable=function(){this._enabled=!1,this.reset()},ui.prototype.isEnabled=function(){return this._enabled},ui.prototype.isActive=function(){return this._active};var fi=function(){this._tap=new qn({numTouches:1,numTaps:1}),this.reset()};fi.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},fi.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},fi.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},fi.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},fi.prototype.touchcancel=function(){this.reset()},fi.prototype.enable=function(){this._enabled=!0},fi.prototype.disable=function(){this._enabled=!1,this.reset()},fi.prototype.isEnabled=function(){return this._enabled},fi.prototype.isActive=function(){return this._active};var hi=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};hi.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add("mapboxgl-touch-drag-pan")},hi.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove("mapboxgl-touch-drag-pan")},hi.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},hi.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var pi=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};pi.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},pi.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},pi.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},pi.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var di=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};di.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add("mapboxgl-touch-zoom-rotate")},di.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove("mapboxgl-touch-zoom-rotate")},di.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},di.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},di.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},di.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var mi=function(t){return t.zoom||t.drag||t.pitch||t.rotate},gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(t.Event);function vi(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var yi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new On(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll(["handleEvent","handleWindowEvent"],this);var i=this._el;this._listeners=[[i,"touchstart",{passive:!1}],[i,"touchmove",{passive:!1}],[i,"touchend",void 0],[i,"touchcancel",void 0],[i,"mousedown",void 0],[i,"mousemove",void 0],[i,"mouseup",void 0],[t.window.document,"mousemove",{capture:!0}],[t.window.document,"mouseup",void 0],[i,"mouseover",void 0],[i,"mouseout",void 0],[i,"dblclick",void 0],[i,"click",void 0],[i,"keydown",{capture:!1}],[i,"keyup",void 0],[i,"wheel",{passive:!1}],[i,"contextmenu",void 0],[t.window,"blur",void 0]];for(var a=0,o=this._listeners;a<o.length;a+=1){var s=o[a],l=s[0],c=s[1],u=s[2];r.addEventListener(l,c,l===t.window.document?this.handleWindowEvent:this.handleEvent,u)}};yi.prototype.destroy=function(){for(var e=0,n=this._listeners;e<n.length;e+=1){var i=n[e],a=i[0],o=i[1],s=i[2];r.removeEventListener(a,o,a===t.window.document?this.handleWindowEvent:this.handleEvent,s)}},yi.prototype._addDefaultHandlers=function(t){var e=this._map,r=e.getCanvasContainer();this._add("mapEvent",new Nn(e,t));var n=e.boxZoom=new Un(e,t);this._add("boxZoom",n);var i=new Gn,a=new ui;e.doubleClickZoom=new ci(a,i),this._add("tapZoom",i),this._add("clickZoom",a);var o=new fi;this._add("tapDragZoom",o);var s=e.touchPitch=new ii;this._add("touchPitch",s);var l=new Xn(t),c=new Zn(t);e.dragRotate=new pi(t,l,c),this._add("mouseRotate",l,["mousePitch"]),this._add("mousePitch",c,["mouseRotate"]);var u=new Wn(t),f=new Jn(t);e.dragPan=new hi(r,u,f),this._add("mousePan",u),this._add("touchPan",f,["touchZoom","touchRotate"]);var h=new ri,p=new ti;e.touchZoomRotate=new di(r,p,h,o),this._add("touchRotate",h,["touchPan","touchZoom"]),this._add("touchZoom",p,["touchPan","touchRotate"]);var d=e.scrollZoom=new li(e,this);this._add("scrollZoom",d,["mousePan"]);var m=e.keyboard=new oi;this._add("keyboard",m),this._add("blockableMapEvent",new jn(e));for(var g=0,v=["boxZoom","doubleClickZoom","tapDragZoom","touchPitch","dragRotate","dragPan","touchZoomRotate","scrollZoom","keyboard"];g<v.length;g+=1){var y=v[g];t.interactive&&t[y]&&e[y].enable(t[y])}},yi.prototype._add=function(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e},yi.prototype.stop=function(){if(!this._updatingCamera){for(var t=0,e=this._handlers;t<e.length;t+=1){e[t].handler.reset()}this._inertia.clear(),this._fireEvents({},{}),this._changes=[]}},yi.prototype.isActive=function(){for(var t=0,e=this._handlers;t<e.length;t+=1){if(e[t].handler.isActive())return!0}return!1},yi.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},yi.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},yi.prototype.isMoving=function(){return Boolean(mi(this._eventsInProgress))||this.isZooming()},yi.prototype._blockedByActive=function(t,e,r){for(var n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1},yi.prototype.handleWindowEvent=function(t){this.handleEvent(t,t.type+"Window")},yi.prototype._getMapTouches=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=i.target;this._el.contains(a)&&e.push(i)}return e},yi.prototype.handleEvent=function(t,e){if("blur"!==t.type){this._updatingCamera=!0;for(var n="renderFrame"===t.type?void 0:t,i={needsRenderFrame:!1},a={},o={},s=t.touches?this._getMapTouches(t.touches):void 0,l=s?r.touchPos(this._el,s):r.mousePos(this._el,t),c=0,u=this._handlers;c<u.length;c+=1){var f=u[c],h=f.handlerName,p=f.handler,d=f.allowed;if(p.isEnabled()){var m=void 0;this._blockedByActive(o,d,h)?p.reset():p[e||t.type]&&(m=p[e||t.type](t,l,s),this.mergeHandlerResult(i,a,m,h,n),m&&m.needsRenderFrame&&this._triggerRenderFrame()),(m||p.isActive())&&(o[h]=p)}}var g={};for(var v in this._previousActiveHandlers)o[v]||(g[v]=n);this._previousActiveHandlers=o,(Object.keys(g).length||vi(i))&&(this._changes.push([i,a,g]),this._triggerRenderFrame()),(Object.keys(o).length||vi(i))&&this._map._stop(!0),this._updatingCamera=!1;var y=i.cameraAnimation;y&&(this._inertia.clear(),this._fireEvents({},{}),this._changes=[],y(this._map))}else this.stop()},yi.prototype.mergeHandlerResult=function(e,r,n,i,a){if(n){t.extend(e,n);var o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}},yi.prototype._applyChanges=function(){for(var e={},r={},n={},i=0,a=this._changes;i<a.length;i+=1){var o=a[i],s=o[0],l=o[1],c=o[2];s.panDelta&&(e.panDelta=(e.panDelta||new t.Point(0,0))._add(s.panDelta)),s.zoomDelta&&(e.zoomDelta=(e.zoomDelta||0)+s.zoomDelta),s.bearingDelta&&(e.bearingDelta=(e.bearingDelta||0)+s.bearingDelta),s.pitchDelta&&(e.pitchDelta=(e.pitchDelta||0)+s.pitchDelta),void 0!==s.around&&(e.around=s.around),void 0!==s.pinchAround&&(e.pinchAround=s.pinchAround),s.noInertia&&(e.noInertia=s.noInertia),t.extend(r,l),t.extend(n,c)}this._updateMapTransform(e,r,n),this._changes=[]},yi.prototype._updateMapTransform=function(t,e,r){var n=this._map,i=n.transform;if(!vi(t))return this._fireEvents(e,r);var a=t.panDelta,o=t.zoomDelta,s=t.bearingDelta,l=t.pitchDelta,c=t.around,u=t.pinchAround;void 0!==u&&(c=u),n._stop(!0),c=c||n.transform.centerPoint;var f=i.pointLocation(a?c.sub(a):c);s&&(i.bearing+=s),l&&(i.pitch+=l),o&&(i.zoom+=o),i.setLocationAtPoint(f,c),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r)},yi.prototype._fireEvents=function(e,r){var n=this,i=mi(this._eventsInProgress),a=mi(e),o={};for(var s in e){var l=e[s].originalEvent;this._eventsInProgress[s]||(o[s+"start"]=l),this._eventsInProgress[s]=e[s]}for(var c in!i&&a&&this._fireEvent("movestart",a.originalEvent),o)this._fireEvent(c,o[c]);for(var u in e.rotate&&(this._bearingChanged=!0),a&&this._fireEvent("move",a.originalEvent),e){var f=e[u].originalEvent;this._fireEvent(u,f)}var h,p={};for(var d in this._eventsInProgress){var m=this._eventsInProgress[d],g=m.handlerName,v=m.originalEvent;this._handlersById[g].isActive()||(delete this._eventsInProgress[d],h=r[g]||v,p[d+"end"]=h)}for(var y in p)this._fireEvent(y,p[y]);var x=mi(this._eventsInProgress);if((i||a)&&!x){this._updatingCamera=!0;var b=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),_=function(t){return 0!==t&&-n._bearingSnap<t&&t<n._bearingSnap};b?(_(b.bearing||this._map.getBearing())&&(b.bearing=0),this._map.easeTo(b,{originalEvent:h})):(this._map.fire(new t.Event("moveend",{originalEvent:h})),_(this._map.getBearing())&&this._map.resetNorth()),this._bearingChanged=!1,this._updatingCamera=!1}},yi.prototype._fireEvent=function(e,r){this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},yi.prototype._triggerRenderFrame=function(){var t=this;void 0===this._frameId&&(this._frameId=this._map._requestRenderFrame((function(e){delete t._frameId,t.handleEvent(new gi("renderFrame",{timeStamp:e})),t._applyChanges()})))};var xi=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll(["_renderFrameCallback"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getCenter=function(){return new t.LngLat(this.transform.center.lng,this.transform.center.lat)},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.Point.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.getPadding=function(){return this.transform.padding},r.prototype.setPadding=function(t,e){return this.jumpTo({padding:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.resetNorthPitch=function(e,r){return this.easeTo(t.extend({bearing:0,pitch:0,duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.cameraForBounds=function(e,r){return e=t.LngLatBounds.convert(e),this._cameraForBoxAndBearing(e.getNorthWest(),e.getSouthEast(),0,r)},r.prototype._cameraForBoxAndBearing=function(e,r,n,i){var a={top:0,bottom:0,right:0,left:0};if("number"==typeof(i=t.extend({padding:a,offset:[0,0],maxZoom:this.transform.maxZoom},i)).padding){var o=i.padding;i.padding={top:o,bottom:o,right:o,left:o}}i.padding=t.extend(a,i.padding);var s=this.transform,l=s.padding,c=s.project(t.LngLat.convert(e)),u=s.project(t.LngLat.convert(r)),f=c.rotate(-n*Math.PI/180),h=u.rotate(-n*Math.PI/180),p=new t.Point(Math.max(f.x,h.x),Math.max(f.y,h.y)),d=new t.Point(Math.min(f.x,h.x),Math.min(f.y,h.y)),m=p.sub(d),g=(s.width-(l.left+l.right+i.padding.left+i.padding.right))/m.x,v=(s.height-(l.top+l.bottom+i.padding.top+i.padding.bottom))/m.y;if(!(v<0||g<0)){var y=Math.min(s.scaleZoom(s.scale*Math.min(g,v)),i.maxZoom),x=t.Point.convert(i.offset),b=(i.padding.left-i.padding.right)/2,_=(i.padding.top-i.padding.bottom)/2,w=new t.Point(x.x+b,x.y+_).mult(s.scale/s.zoomScale(y));return{center:s.unproject(c.add(u).div(2).sub(w)),zoom:y,bearing:n}}t.warnOnce("Map cannot fit within canvas with the given bounds, padding, and/or offset.")},r.prototype.fitBounds=function(t,e,r){return this._fitInternal(this.cameraForBounds(t,e),e,r)},r.prototype.fitScreenCoordinates=function(e,r,n,i,a){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(t.Point.convert(e)),this.transform.pointLocation(t.Point.convert(r)),n,i),i,a)},r.prototype._fitInternal=function(e,r,n){return e?(delete(r=t.extend(e,r)).padding,r.linear?this.easeTo(r,n):this.flyTo(r,n)):this},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return"zoom"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=t.LngLat.convert(e.center)),"bearing"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),"pitch"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),null==e.padding||n.isPaddingEqual(e.padding)||(n.padding=e.padding),this.fire(new t.Event("movestart",r)).fire(new t.Event("move",r)),i&&this.fire(new t.Event("zoomstart",r)).fire(new t.Event("zoom",r)).fire(new t.Event("zoomend",r)),a&&this.fire(new t.Event("rotatestart",r)).fire(new t.Event("rotate",r)).fire(new t.Event("rotateend",r)),o&&this.fire(new t.Event("pitchstart",r)).fire(new t.Event("pitch",r)).fire(new t.Event("pitchend",r)),this.fire(new t.Event("moveend",r))},r.prototype.easeTo=function(e,r){var n=this;this._stop(!1,e.easeId),(!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate||!e.essential&&t.browser.prefersReducedMotion)&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=this.getPadding(),c="zoom"in e?+e.zoom:a,u="bearing"in e?this._normalizeBearing(e.bearing,o):o,f="pitch"in e?+e.pitch:s,h="padding"in e?e.padding:i.padding,p=t.Point.convert(e.offset),d=i.centerPoint.add(p),m=i.pointLocation(d),g=t.LngLat.convert(e.center||m);this._normalizeCenter(g);var v,y,x=i.project(m),b=i.project(g).sub(x),_=i.zoomScale(c-a);e.around&&(v=t.LngLat.convert(e.around),y=i.locationPoint(v));var w={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||c!==a,this._rotating=this._rotating||o!==u,this._pitching=this._pitching||f!==s,this._padding=!i.isPaddingEqual(h),this._easeId=e.easeId,this._prepareEase(r,e.noMoveStart,w),clearTimeout(this._easeEndTimeoutID),this._ease((function(e){if(n._zooming&&(i.zoom=t.number(a,c,e)),n._rotating&&(i.bearing=t.number(o,u,e)),n._pitching&&(i.pitch=t.number(s,f,e)),n._padding&&(i.interpolatePadding(l,h,e),d=i.centerPoint.add(p)),v)i.setLocationAtPoint(v,y);else{var m=i.zoomScale(i.zoom-a),g=c>a?Math.min(2,_):Math.max(.5,_),w=Math.pow(g,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(m));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event("movestart",e)),this._zooming&&!n.zooming&&this.fire(new t.Event("zoomstart",e)),this._rotating&&!n.rotating&&this.fire(new t.Event("rotatestart",e)),this._pitching&&!n.pitching&&this.fire(new t.Event("pitchstart",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event("move",e)),this._zooming&&this.fire(new t.Event("zoom",e)),this._rotating&&this.fire(new t.Event("rotate",e)),this._pitching&&this.fire(new t.Event("pitch",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event("zoomend",e)),i&&this.fire(new t.Event("rotateend",e)),a&&this.fire(new t.Event("pitchend",e)),this.fire(new t.Event("moveend",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,["center","zoom","bearing","pitch","around"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),c=this.getPadding(),u="zoom"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,f="bearing"in e?this._normalizeBearing(e.bearing,s):s,h="pitch"in e?+e.pitch:l,p="padding"in e?e.padding:a.padding,d=a.zoomScale(u-o),m=t.Point.convert(e.offset),g=a.centerPoint.add(m),v=a.pointLocation(g),y=t.LngLat.convert(e.center||v);this._normalizeCenter(y);var x=a.project(v),b=a.project(y).sub(x),_=e.curve,w=Math.max(a.width,a.height),T=w/d,k=b.mag();if("minZoom"in e){var A=t.clamp(Math.min(e.minZoom,o,u),a.minZoom,a.maxZoom),M=w/a.zoomScale(A-o);_=Math.sqrt(M/k*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function L(t){return(Math.exp(t)-Math.exp(-t))/2}function C(t){return(Math.exp(t)+Math.exp(-t))/2}var P=E(0),I=function(t){return C(P)/C(P+_*t)},O=function(t){return w*((C(P)*(L(e=P+_*t)/C(e))-L(P))/S)/k;var e},z=(E(1)-P)/_;if(Math.abs(k)<1e-6||!isFinite(z)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var D=T<w?-1:1;z=Math.abs(Math.log(T/w))/_,O=function(){return 0},I=function(t){return Math.exp(D*_*t)}}if("duration"in e)e.duration=+e.duration;else{var R="screenSpeed"in e?+e.screenSpeed/_:+e.speed;e.duration=1e3*z/R}return e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==f,this._pitching=h!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*z,d=1/I(i);a.zoom=1===e?u:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,f,e)),n._pitching&&(a.pitch=t.number(l,h,e)),n._padding&&(a.interpolatePadding(c,p,e),g=a.centerPoint.add(m));var v=1===e?y:a.unproject(x.add(b.mult(O(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?v.wrap():v,g),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop()}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),bi=function(e){void 0===e&&(e={}),this.options=e,t.bindAll(["_updateEditLink","_updateData","_updateCompact"],this)};bi.prototype.getDefaultPosition=function(){return"bottom-right"},bi.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-attrib"),this._innerContainer=r.create("div","mapboxgl-ctrl-attrib-inner",this._container),e&&this._container.classList.add("mapboxgl-compact"),this._updateAttributions(),this._updateEditLink(),this._map.on("styledata",this._updateData),this._map.on("sourcedata",this._updateData),this._map.on("moveend",this._updateEditLink),void 0===e&&(this._map.on("resize",this._updateCompact),this._updateCompact()),this._container},bi.prototype.onRemove=function(){r.remove(this._container),this._map.off("styledata",this._updateData),this._map.off("sourcedata",this._updateData),this._map.off("moveend",this._updateEditLink),this._map.off("resize",this._updateCompact),this._map=void 0,this._attribHTML=void 0},bi.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(".mapbox-improve-map"));var r=[{key:"owner",value:this.styleOwner},{key:"id",value:this.styleId},{key:"access_token",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+"="+e.value+(n<r.length-1?"&":"")),t}),"?");e.href=t.config.FEEDBACK_URL+"/"+n+(this._map._hash?this._map._hash.getHashString(!0):""),e.rel="noopener nofollow"}},bi.prototype._updateData=function(t){!t||"metadata"!==t.sourceDataType&&"style"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},bi.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((function(t){return"string"!=typeof t?"":t}))):"string"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n];if(i.used){var a=i.getSource();a.attribution&&t.indexOf(a.attribution)<0&&t.push(a.attribution)}}t.sort((function(t,e){return t.length-e.length}));var o=(t=t.filter((function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0}))).join(" | ");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove("mapboxgl-attrib-empty")):this._container.classList.add("mapboxgl-attrib-empty"),this._editLink=null)}},bi.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add("mapboxgl-compact"):this._container.classList.remove("mapboxgl-compact")};var _i=function(){t.bindAll(["_updateLogo"],this),t.bindAll(["_updateCompact"],this)};_i.prototype.onAdd=function(t){this._map=t,this._container=r.create("div","mapboxgl-ctrl");var e=r.create("a","mapboxgl-ctrl-logo");return e.target="_blank",e.rel="noopener nofollow",e.href="https://www.mapbox.com/",e.setAttribute("aria-label",this._map._getUIString("LogoControl.Title")),e.setAttribute("rel","noopener nofollow"),this._container.appendChild(e),this._container.style.display="none",this._map.on("sourcedata",this._updateLogo),this._updateLogo(),this._map.on("resize",this._updateCompact),this._updateCompact(),this._container},_i.prototype.onRemove=function(){r.remove(this._container),this._map.off("sourcedata",this._updateLogo),this._map.off("resize",this._updateCompact)},_i.prototype.getDefaultPosition=function(){return"bottom-left"},_i.prototype._updateLogo=function(t){t&&"metadata"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?"block":"none")},_i.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t){if(t[e].getSource().mapbox_logo)return!0}return!1}},_i.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add("mapboxgl-compact"):e.classList.remove("mapboxgl-compact")}};var wi=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};wi.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},wi.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},wi.prototype.run=function(t){void 0===t&&(t=0);var e=this._currentlyRunning=this._queue;this._queue=[];for(var r=0,n=e;r<n.length;r+=1){var i=n[r];if(!i.cancelled&&(i.callback(t),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},wi.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Ti={"FullscreenControl.Enter":"Enter fullscreen","FullscreenControl.Exit":"Exit fullscreen","GeolocateControl.FindMyLocation":"Find my location","GeolocateControl.LocationNotAvailable":"Location not available","LogoControl.Title":"Mapbox logo","NavigationControl.ResetBearing":"Reset bearing to north","NavigationControl.ZoomIn":"Zoom in","NavigationControl.ZoomOut":"Zoom out","ScaleControl.Feet":"ft","ScaleControl.Meters":"m","ScaleControl.Kilometers":"km","ScaleControl.Miles":"mi","ScaleControl.NauticalMiles":"nm"},ki=t.window.HTMLImageElement,Ai=t.window.HTMLElement,Mi=t.window.ImageBitmap,Si={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:60,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:"sans-serif",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},Ei=function(n){function i(e){var r=this;if(null!=(e=t.extend({},Si,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error("maxZoom must be greater than or equal to minZoom");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error("maxPitch must be greater than or equal to minPitch");if(null!=e.minPitch&&e.minPitch<0)throw new Error("minPitch must be greater than or equal to 0");if(null!=e.maxPitch&&e.maxPitch>60)throw new Error("maxPitch must be less than or equal to 60");var i=new An(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new wi,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},Ti,e.locale),this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),"string"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error("Container '"+e.container+"' not found.")}else{if(!(e.container instanceof Ai))throw new Error("Invalid type: 'container' must be a String or HTMLElement.");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll(["_onWindowOnline","_onWindowResize","_contextLost","_contextRestored"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error("Failed to initialize WebGL.");this.on("move",(function(){return r._update(!1)})),this.on("moveend",(function(){return r._update(!1)})),this.on("zoom",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener("online",this._onWindowOnline,!1),t.window.addEventListener("resize",this._onWindowResize,!1)),this.handlers=new yi(this,e);var a="string"==typeof e.hash&&e.hash||void 0;this._hash=e.hash&&new Sn(a).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new bi({customAttribution:e.customAttribution})),this.addControl(new _i,e.logoPosition),this.on("style.load",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on("data",(function(e){r._update("style"===e.dataType),r.fire(new t.Event(e.dataType+"data",e))})),this.on("dataloading",(function(e){r.fire(new t.Event(e.dataType+"dataloading",e))}))}n&&(i.__proto__=n),i.prototype=Object.create(n&&n.prototype),i.prototype.constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&e.getDefaultPosition&&(r=e.getDefaultPosition()),void 0===r&&(r="top-right"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error("Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf("bottom")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error("Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event("movestart",e)).fire(new t.Event("move",e))),this.fire(new t.Event("resize",e)),a&&this.fire(new t.Event("moveend",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error("minZoom must be between -2 and the current maxZoom, inclusive")},i.prototype.getMinZoom=function(){return this.transform.minZoom},i.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error("maxZoom must be greater than the current minZoom")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error("minPitch must be greater than or equal to 0");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()<t&&this.setPitch(t),this;throw new Error("minPitch must be between 0 and the current maxPitch, inclusive")},i.prototype.getMinPitch=function(){return this.transform.minPitch},i.prototype.setMaxPitch=function(t){if((t=null==t?60:t)>60)throw new Error("maxPitch must be less than or equal to 60");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error("maxPitch must be greater than the current minPitch")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if("mouseenter"===t||"mouseover"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new Rn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if("mouseleave"===t||"mouseout"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new Rn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new Rn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;if(void 0===r)return n.prototype.off.call(this,t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o<a.length;o++){var s=a[o];if(s.layer===e&&s.listener===r){for(var l in s.delegates)i.off(l,s.delegates[l]);return a.splice(o,1),i}}}(this._delegatedListeners),this},i.prototype.queryRenderedFeatures=function(e,r){if(!this.style)return[];var n;if(void 0!==r||void 0===e||e instanceof t.Point||Array.isArray(e)||(r=e,e=void 0),r=r||{},(e=e||[[0,0],[this.transform.width,this.transform.height]])instanceof t.Point||"number"==typeof e[0])n=[t.Point.convert(e)];else{var i=t.Point.convert(e[0]),a=t.Point.convert(e[1]);n=[i,new t.Point(a.x,i.y),a,new t.Point(i.x,a.y),i]}return this.style.queryRenderedFeatures(n,r,this.transform)},i.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},i.prototype.setStyle=function(e,r){return!1!==(r=t.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},r)).diff&&r.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&e?(this._diffStyle(e,r),this):(this._localIdeographFontFamily=r.localIdeographFontFamily,this._updateStyle(e,r))},i.prototype._getUIString=function(t){var e=this._locale[t];if(null==e)throw new Error("Missing UI string '"+t+"'");return e},i.prototype._updateStyle=function(t,e){return this.style&&(this.style.setEventedParent(null),this.style._remove()),t?(this.style=new qe(this,e||{}),this.style.setEventedParent(this,{style:this.style}),"string"==typeof t?this.style.loadURL(t):this.style.loadJSON(t),this):(delete this.style,this)},i.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new qe(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},i.prototype._diffStyle=function(e,r){var n=this;if("string"==typeof e){var i=this._requestManager.normalizeStyleURL(e),a=this._requestManager.transformRequest(i,t.ResourceType.Style);t.getJSON(a,(function(e,i){e?n.fire(new t.ErrorEvent(e)):i&&n._updateDiff(i,r)}))}else"object"==typeof e&&this._updateDiff(e,r)},i.prototype._updateDiff=function(e,r){try{this.style.setState(e)&&this._update(!0)}catch(n){t.warnOnce("Unable to perform style diff: "+(n.message||n.error||n)+".  Rebuilding the style from scratch."),this._updateStyle(e,r)}},i.prototype.getStyle=function(){if(this.style)return this.style.serialize()},i.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce("There is no style added to the map.")},i.prototype.addSource=function(t,e){return this._lazyInitEmptyStyle(),this.style.addSource(t,e),this._update(!0)},i.prototype.isSourceLoaded=function(e){var r=this.style&&this.style.sourceCaches[e];if(void 0!==r)return r.loaded();this.fire(new t.ErrorEvent(new Error("There is no source with ID '"+e+"'")))},i.prototype.areTilesLoaded=function(){var t=this.style&&this.style.sourceCaches;for(var e in t){var r=t[e]._tiles;for(var n in r){var i=r[n];if("loaded"!==i.state&&"errored"!==i.state)return!1}}return!0},i.prototype.addSourceType=function(t,e,r){return this._lazyInitEmptyStyle(),this.style.addSourceType(t,e,r)},i.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0)},i.prototype.getSource=function(t){return this.style.getSource(t)},i.prototype.addImage=function(e,r,n){void 0===n&&(n={});var i=n.pixelRatio;void 0===i&&(i=1);var a=n.sdf;void 0===a&&(a=!1);var o=n.stretchX,s=n.stretchY,l=n.content;this._lazyInitEmptyStyle();if(r instanceof ki||Mi&&r instanceof Mi){var c=t.browser.getImageData(r),u=c.width,f=c.height,h=c.data;this.style.addImage(e,{data:new t.RGBAImage({width:u,height:f},h),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0})}else{if(void 0===r.width||void 0===r.height)return this.fire(new t.ErrorEvent(new Error("Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));var p=r.width,d=r.height,m=r.data,g=r;this.style.addImage(e,{data:new t.RGBAImage({width:p,height:d},new Uint8Array(m)),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0,userImage:g}),g.onAdd&&g.onAdd(this,e)}},i.prototype.updateImage=function(e,r){var n=this.style.getImage(e);if(!n)return this.fire(new t.ErrorEvent(new Error("The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.")));var i=r instanceof ki||Mi&&r instanceof Mi?t.browser.getImageData(r):r,a=i.width,o=i.height,s=i.data;if(void 0===a||void 0===o)return this.fire(new t.ErrorEvent(new Error("Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));if(a!==n.data.width||o!==n.data.height)return this.fire(new t.ErrorEvent(new Error("The width and height of the updated image must be that same as the previous version of the image")));var l=!(r instanceof ki||Mi&&r instanceof Mi);n.data.replace(s,l),this.style.updateImage(e,n)},i.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error("Missing required image id"))),!1)},i.prototype.removeImage=function(t){this.style.removeImage(t)},i.prototype.loadImage=function(e,r){t.getImage(this._requestManager.transformRequest(e,t.ResourceType.Image),r)},i.prototype.listImages=function(){return this.style.listImages()},i.prototype.addLayer=function(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)},i.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0)},i.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0)},i.prototype.getLayer=function(t){return this.style.getLayer(t)},i.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)},i.prototype.setFilter=function(t,e,r){return void 0===r&&(r={}),this.style.setFilter(t,e,r),this._update(!0)},i.prototype.getFilter=function(t){return this.style.getFilter(t)},i.prototype.setPaintProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setPaintProperty(t,e,r,n),this._update(!0)},i.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},i.prototype.setLayoutProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setLayoutProperty(t,e,r,n),this._update(!0)},i.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},i.prototype.setLight=function(t,e){return void 0===e&&(e={}),this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)},i.prototype.getLight=function(){return this.style.getLight()},i.prototype.setFeatureState=function(t,e){return this.style.setFeatureState(t,e),this._update()},i.prototype.removeFeatureState=function(t,e){return this.style.removeFeatureState(t,e),this._update()},i.prototype.getFeatureState=function(t){return this.style.getFeatureState(t)},i.prototype.getContainer=function(){return this._container},i.prototype.getCanvasContainer=function(){return this._canvasContainer},i.prototype.getCanvas=function(){return this._canvas},i.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]},i.prototype._detectMissingCSS=function(){"rgb(250, 128, 114)"!==t.window.getComputedStyle(this._missingCSSCanary).getPropertyValue("background-color")&&t.warnOnce("This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.")},i.prototype._setupContainer=function(){var t=this._container;t.classList.add("mapboxgl-map"),(this._missingCSSCanary=r.create("div","mapboxgl-canary",t)).style.visibility="hidden",this._detectMissingCSS();var e=this._canvasContainer=r.create("div","mapboxgl-canvas-container",t);this._interactive&&e.classList.add("mapboxgl-interactive"),this._canvas=r.create("canvas","mapboxgl-canvas",e),this._canvas.addEventListener("webglcontextlost",this._contextLost,!1),this._canvas.addEventListener("webglcontextrestored",this._contextRestored,!1),this._canvas.setAttribute("tabindex","0"),this._canvas.setAttribute("aria-label","Map");var n=this._containerDimensions();this._resizeCanvas(n[0],n[1]);var i=this._controlContainer=r.create("div","mapboxgl-control-container",t),a=this._controlPositions={};["top-left","top-right","bottom-left","bottom-right"].forEach((function(t){a[t]=r.create("div","mapboxgl-ctrl-"+t,i)}))},i.prototype._resizeCanvas=function(e,r){var n=t.browser.devicePixelRatio||1;this._canvas.width=n*e,this._canvas.height=n*r,this._canvas.style.width=e+"px",this._canvas.style.height=r+"px"},i.prototype._setupPainter=function(){var r=t.extend({},e.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),n=this._canvas.getContext("webgl",r)||this._canvas.getContext("experimental-webgl",r);n?(this.painter=new _n(n,this.transform),t.webpSupported.testSupport(n)):this.fire(new t.ErrorEvent(new Error("Failed to initialize WebGL")))},i.prototype._contextLost=function(e){e.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new t.Event("webglcontextlost",{originalEvent:e}))},i.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event("webglcontextrestored",{originalEvent:e}))},i.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},i.prototype._update=function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this},i.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},i.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},i.prototype._render=function(e){var r,n=this,i=0,a=this.painter.context.extTimerQuery;if(this.listens("gpu-timing-frame")&&(r=a.createQueryEXT(),a.beginQueryEXT(a.TIME_ELAPSED_EXT,r),i=t.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(e),!this._removed){var o=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var s=this.transform.zoom,l=t.browser.now();this.style.zoomHistory.update(s,l);var c=new t.EvaluationParameters(s,{now:l,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),u=c.crossFadingFactor();1===u&&u===this._crossFadingFactor||(o=!0,this._crossFadingFactor=u),this.style.update(c)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens("gpu-timing-layer")}),this.fire(new t.Event("render")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event("load"))),this.style&&(this.style.hasTransitions()||o)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens("gpu-timing-frame")){var f=t.browser.now()-i;a.endQueryEXT(a.TIME_ELAPSED_EXT,r),setTimeout((function(){var e=a.getQueryObjectEXT(r,a.QUERY_RESULT_EXT)/1e6;a.deleteQueryEXT(r),n.fire(new t.Event("gpu-timing-frame",{cpuTime:f,gpuTime:e}))}),50)}if(this.listens("gpu-timing-layer")){var h=this.painter.collectGpuTimers();setTimeout((function(){var e=n.painter.queryGpuTimers(h);n.fire(new t.Event("gpu-timing-layer",{layerTimes:e}))}),50)}return this._sourcesDirty||this._styleDirty||this._placementDirty||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&(this._fullyLoaded||(this._fullyLoaded=!0),this.fire(new t.Event("idle"))),this}},i.prototype.remove=function(){this._hash&&this._hash.remove();for(var e=0,r=this._controls;e<r.length;e+=1){r[e].onRemove(this)}this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),void 0!==t.window&&(t.window.removeEventListener("resize",this._onWindowResize,!1),t.window.removeEventListener("online",this._onWindowOnline,!1));var n=this.painter.context.gl.getExtension("WEBGL_lose_context");n&&n.loseContext(),Li(this._canvasContainer),Li(this._controlContainer),Li(this._missingCSSCanary),this._container.classList.remove("mapboxgl-map"),this._removed=!0,this.fire(new t.Event("remove"))},i.prototype.triggerRepaint=function(){var e=this;this.style&&!this._frame&&(this._frame=t.browser.frame((function(t){e._frame=null,e._render(t)})))},i.prototype._onWindowOnline=function(){this._update()},i.prototype._onWindowResize=function(t){this._trackResize&&this.resize({originalEvent:t})._update()},a.showTileBoundaries.get=function(){return!!this._showTileBoundaries},a.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},a.showPadding.get=function(){return!!this._showPadding},a.showPadding.set=function(t){this._showPadding!==t&&(this._showPadding=t,this._update())},a.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},a.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},a.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},a.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},a.repaint.get=function(){return!!this._repaint},a.repaint.set=function(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())},a.vertices.get=function(){return!!this._vertices},a.vertices.set=function(t){this._vertices=t,this._update()},i.prototype._setCacheLimits=function(e,r){t.setCacheLimits(e,r)},a.version.get=function(){return t.version},Object.defineProperties(i.prototype,a),i}(xi);function Li(t){t.parentNode&&t.parentNode.removeChild(t)}var Ci={showCompass:!0,showZoom:!0,visualizePitch:!1},Pi=function(e){var n=this;this.options=t.extend({},Ci,e),this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),this._container.addEventListener("contextmenu",(function(t){return t.preventDefault()})),this.options.showZoom&&(t.bindAll(["_setButtonTitle","_updateZoomButtons"],this),this._zoomInButton=this._createButton("mapboxgl-ctrl-zoom-in",(function(t){return n._map.zoomIn({},{originalEvent:t})})),r.create("span","mapboxgl-ctrl-icon",this._zoomInButton).setAttribute("aria-hidden",!0),this._zoomOutButton=this._createButton("mapboxgl-ctrl-zoom-out",(function(t){return n._map.zoomOut({},{originalEvent:t})})),r.create("span","mapboxgl-ctrl-icon",this._zoomOutButton).setAttribute("aria-hidden",!0)),this.options.showCompass&&(t.bindAll(["_rotateCompassArrow"],this),this._compass=this._createButton("mapboxgl-ctrl-compass",(function(t){n.options.visualizePitch?n._map.resetNorthPitch({},{originalEvent:t}):n._map.resetNorth({},{originalEvent:t})})),this._compassIcon=r.create("span","mapboxgl-ctrl-icon",this._compass),this._compassIcon.setAttribute("aria-hidden",!0))};Pi.prototype._updateZoomButtons=function(){var t=this._map.getZoom();this._zoomInButton.disabled=t===this._map.getMaxZoom(),this._zoomOutButton.disabled=t===this._map.getMinZoom()},Pi.prototype._rotateCompassArrow=function(){var t=this.options.visualizePitch?"scale("+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+") rotateX("+this._map.transform.pitch+"deg) rotateZ("+this._map.transform.angle*(180/Math.PI)+"deg)":"rotate("+this._map.transform.angle*(180/Math.PI)+"deg)";this._compassIcon.style.transform=t},Pi.prototype.onAdd=function(t){return this._map=t,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,"ZoomIn"),this._setButtonTitle(this._zoomOutButton,"ZoomOut"),this._map.on("zoom",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,"ResetBearing"),this.options.visualizePitch&&this._map.on("pitch",this._rotateCompassArrow),this._map.on("rotate",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Ii(this._map,this._compass,this.options.visualizePitch)),this._container},Pi.prototype.onRemove=function(){r.remove(this._container),this.options.showZoom&&this._map.off("zoom",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off("pitch",this._rotateCompassArrow),this._map.off("rotate",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},Pi.prototype._createButton=function(t,e){var n=r.create("button",t,this._container);return n.type="button",n.addEventListener("click",e),n},Pi.prototype._setButtonTitle=function(t,e){var r=this._map._getUIString("NavigationControl."+e);t.title=r,t.setAttribute("aria-label",r)};var Ii=function(e,n,i){void 0===i&&(i=!1),this._clickTolerance=10,this.element=n,this.mouseRotate=new Xn({clickTolerance:e.dragRotate._mouseRotate._clickTolerance}),this.map=e,i&&(this.mousePitch=new Zn({clickTolerance:e.dragRotate._mousePitch._clickTolerance})),t.bindAll(["mousedown","mousemove","mouseup","touchstart","touchmove","touchend","reset"],this),r.addEventListener(n,"mousedown",this.mousedown),r.addEventListener(n,"touchstart",this.touchstart,{passive:!1}),r.addEventListener(n,"touchmove",this.touchmove),r.addEventListener(n,"touchend",this.touchend),r.addEventListener(n,"touchcancel",this.reset)};function Oi(e,r,n){if(e=new t.LngLat(e.lng,e.lat),r){var i=new t.LngLat(e.lng-360,e.lat),a=new t.LngLat(e.lng+360,e.lat),o=n.locationPoint(e).distSqr(r);n.locationPoint(i).distSqr(r)<o?e=i:n.locationPoint(a).distSqr(r)<o&&(e=a)}for(;Math.abs(e.lng-n.center.lng)>180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ii.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ii.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ii.prototype.off=function(){var t=this.element;r.removeEventListener(t,"mousedown",this.mousedown),r.removeEventListener(t,"touchstart",this.touchstart,{passive:!1}),r.removeEventListener(t,"touchmove",this.touchmove),r.removeEventListener(t,"touchend",this.touchend),r.removeEventListener(t,"touchcancel",this.reset),this.offTemp()},Ii.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,"mousemove",this.mousemove),r.removeEventListener(t.window,"mouseup",this.mouseup)},Ii.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,"mousemove",this.mousemove),r.addEventListener(t.window,"mouseup",this.mouseup)},Ii.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ii.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ii.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:"mousedown",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ii.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ii.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Ii.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var zi={center:"translate(-50%,-50%)",top:"translate(-50%,0)","top-left":"translate(0,0)","top-right":"translate(-100%,0)",bottom:"translate(-50%,-100%)","bottom-left":"translate(0,-100%)","bottom-right":"translate(-100%,-100%)",left:"translate(0,-50%)",right:"translate(-100%,-50%)"};function Di(t,e,r){var n=t.classList;for(var i in zi)n.remove("mapboxgl-"+r+"-anchor-"+i);n.add("mapboxgl-"+r+"-anchor-"+e)}var Ri,Fi=function(e){function n(n,i){var a=this;if(e.call(this),(n instanceof t.window.HTMLElement||i)&&(n=t.extend({element:n},i)),t.bindAll(["_update","_onMove","_onUp","_addDragHandler","_onMapClick","_onKeyPress"],this),this._anchor=n&&n.anchor||"center",this._color=n&&n.color||"#3FB1CE",this._draggable=n&&n.draggable||!1,this._state="inactive",this._rotation=n&&n.rotation||0,this._rotationAlignment=n&&n.rotationAlignment||"auto",this._pitchAlignment=n&&n.pitchAlignment&&"auto"!==n.pitchAlignment?n.pitchAlignment:this._rotationAlignment,n&&n.element)this._element=n.element,this._offset=t.Point.convert(n&&n.offset||[0,0]);else{this._defaultMarker=!0,this._element=r.create("div"),this._element.setAttribute("aria-label","Map marker");var o=r.createNS("http://www.w3.org/2000/svg","svg");o.setAttributeNS(null,"display","block"),o.setAttributeNS(null,"height","41px"),o.setAttributeNS(null,"width","27px"),o.setAttributeNS(null,"viewBox","0 0 27 41");var s=r.createNS("http://www.w3.org/2000/svg","g");s.setAttributeNS(null,"stroke","none"),s.setAttributeNS(null,"stroke-width","1"),s.setAttributeNS(null,"fill","none"),s.setAttributeNS(null,"fill-rule","evenodd");var l=r.createNS("http://www.w3.org/2000/svg","g");l.setAttributeNS(null,"fill-rule","nonzero");var c=r.createNS("http://www.w3.org/2000/svg","g");c.setAttributeNS(null,"transform","translate(3.0, 29.0)"),c.setAttributeNS(null,"fill","#000000");for(var u=0,f=[{rx:"10.5",ry:"5.25002273"},{rx:"10.5",ry:"5.25002273"},{rx:"9.5",ry:"4.77275007"},{rx:"8.5",ry:"4.29549936"},{rx:"7.5",ry:"3.81822308"},{rx:"6.5",ry:"3.34094679"},{rx:"5.5",ry:"2.86367051"},{rx:"4.5",ry:"2.38636864"}];u<f.length;u+=1){var h=f[u],p=r.createNS("http://www.w3.org/2000/svg","ellipse");p.setAttributeNS(null,"opacity","0.04"),p.setAttributeNS(null,"cx","10.5"),p.setAttributeNS(null,"cy","5.80029008"),p.setAttributeNS(null,"rx",h.rx),p.setAttributeNS(null,"ry",h.ry),c.appendChild(p)}var d=r.createNS("http://www.w3.org/2000/svg","g");d.setAttributeNS(null,"fill",this._color);var m=r.createNS("http://www.w3.org/2000/svg","path");m.setAttributeNS(null,"d","M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z"),d.appendChild(m);var g=r.createNS("http://www.w3.org/2000/svg","g");g.setAttributeNS(null,"opacity","0.25"),g.setAttributeNS(null,"fill","#000000");var v=r.createNS("http://www.w3.org/2000/svg","path");v.setAttributeNS(null,"d","M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z"),g.appendChild(v);var y=r.createNS("http://www.w3.org/2000/svg","g");y.setAttributeNS(null,"transform","translate(6.0, 7.0)"),y.setAttributeNS(null,"fill","#FFFFFF");var x=r.createNS("http://www.w3.org/2000/svg","g");x.setAttributeNS(null,"transform","translate(8.0, 8.0)");var b=r.createNS("http://www.w3.org/2000/svg","circle");b.setAttributeNS(null,"fill","#000000"),b.setAttributeNS(null,"opacity","0.25"),b.setAttributeNS(null,"cx","5.5"),b.setAttributeNS(null,"cy","5.5"),b.setAttributeNS(null,"r","5.4999962");var _=r.createNS("http://www.w3.org/2000/svg","circle");_.setAttributeNS(null,"fill","#FFFFFF"),_.setAttributeNS(null,"cx","5.5"),_.setAttributeNS(null,"cy","5.5"),_.setAttributeNS(null,"r","5.4999962"),x.appendChild(b),x.appendChild(_),l.appendChild(c),l.appendChild(d),l.appendChild(g),l.appendChild(y),l.appendChild(x),o.appendChild(l),this._element.appendChild(o),this._offset=t.Point.convert(n&&n.offset||[0,-14])}this._element.classList.add("mapboxgl-marker"),this._element.addEventListener("dragstart",(function(t){t.preventDefault()})),this._element.addEventListener("mousedown",(function(t){t.preventDefault()})),this._element.addEventListener("focus",(function(){var t=a._map.getContainer();t.scrollTop=0,t.scrollLeft=0})),Di(this._element,this._anchor,"marker"),this._popup=null}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on("move",this._update),t.on("moveend",this._update),this.setDraggable(this._draggable),this._update(),this._map.on("click",this._onMapClick),this},n.prototype.remove=function(){return this._map&&(this._map.off("click",this._onMapClick),this._map.off("move",this._update),this._map.off("moveend",this._update),this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler),this._map.off("mouseup",this._onUp),this._map.off("touchend",this._onUp),this._map.off("mousemove",this._onMove),this._map.off("touchmove",this._onMove),delete this._map),r.remove(this._element),this._popup&&this._popup.remove(),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},n.prototype.getElement=function(){return this._element},n.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener("keypress",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute("tabindex")),t){if(!("offset"in t.options)){var e=Math.sqrt(Math.pow(13.5,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],"top-left":[0,0],"top-right":[0,0],bottom:[0,-38.1],"bottom-left":[e,-1*(24.6+e)],"bottom-right":[-e,-1*(24.6+e)],left:[13.5,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute("tabindex"),this._originalTabIndex||this._element.setAttribute("tabindex","0"),this._element.addEventListener("keypress",this._onKeyPress)}return this},n.prototype._onKeyPress=function(t){var e=t.code,r=t.charCode||t.keyCode;"Space"!==e&&"Enter"!==e&&32!==r&&13!==r||this.togglePopup()},n.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},n.prototype.getPopup=function(){return this._popup},n.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},n.prototype._update=function(t){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var e="";"viewport"===this._rotationAlignment||"auto"===this._rotationAlignment?e="rotateZ("+this._rotation+"deg)":"map"===this._rotationAlignment&&(e="rotateZ("+(this._rotation-this._map.getBearing())+"deg)");var n="";"viewport"===this._pitchAlignment||"auto"===this._pitchAlignment?n="rotateX(0deg)":"map"===this._pitchAlignment&&(n="rotateX("+this._map.getPitch()+"deg)"),t&&"moveend"!==t.type||(this._pos=this._pos.round()),r.setTransform(this._element,zi[this._anchor]+" translate("+this._pos.x+"px, "+this._pos.y+"px) "+n+" "+e)}},n.prototype.getOffset=function(){return this._offset},n.prototype.setOffset=function(e){return this._offset=t.Point.convert(e),this._update(),this},n.prototype._onMove=function(e){this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents="none","pending"===this._state&&(this._state="active",this.fire(new t.Event("dragstart"))),this.fire(new t.Event("drag"))},n.prototype._onUp=function(){this._element.style.pointerEvents="auto",this._positionDelta=null,this._map.off("mousemove",this._onMove),this._map.off("touchmove",this._onMove),"active"===this._state&&this.fire(new t.Event("dragend")),this._state="inactive"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._state="pending",this._map.on("mousemove",this._onMove),this._map.on("touchmove",this._onMove),this._map.once("mouseup",this._onUp),this._map.once("touchend",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on("mousedown",this._addDragHandler),this._map.on("touchstart",this._addDragHandler)):(this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||"auto",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&"auto"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Bi={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};var Ni=0,ji=!1,Ui=function(e){function n(r){e.call(this),this.options=t.extend({},Bi,r),t.bindAll(["_onSuccess","_onError","_onZoom","_finish","_setupUI","_updateCamera","_updateMarker"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.onAdd=function(e){var n;return this._map=e,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),n=this._setupUI,void 0!==Ri?n(Ri):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:"geolocation"}).then((function(t){Ri="denied"!==t.state,n(Ri)})):(Ri=!!t.window.navigator.geolocation,n(Ri)),this._container},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off("zoom",this._onZoom),this._map=void 0,Ni=0,ji=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitude<e.getWest()||r.longitude>e.getEast()||r.latitude<e.getSouth()||r.latitude>e.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case"WAITING_ACTIVE":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error");break;case"ACTIVE_LOCK":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting");break;case"BACKGROUND":this._watchState="BACKGROUND_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event("outofmaxbounds",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"BACKGROUND":case"BACKGROUND_ERROR":this._watchState="BACKGROUND",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background")}this.options.showUserLocation&&"OFF"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&"ACTIVE_LOCK"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove("mapboxgl-user-location-dot-stale"),this.fire(new t.Event("geolocate",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+"px",this._circleElement.style.height=i+"px"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState="OFF",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.disabled=!0;var r=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.title=r,this._geolocateButton.setAttribute("aria-label",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&ji)return;this._setErrorState()}"OFF"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add("mapboxgl-user-location-dot-stale"),this.fire(new t.Event("error",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener("contextmenu",(function(t){return t.preventDefault()})),this._geolocateButton=r.create("button","mapboxgl-ctrl-geolocate",this._container),r.create("span","mapboxgl-ctrl-icon",this._geolocateButton).setAttribute("aria-hidden",!0),this._geolocateButton.type="button",!1===e){t.warnOnce("Geolocation support is not available so the GeolocateControl will be disabled.");var i=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute("aria-label",i)}else{var a=this._map._getUIString("GeolocateControl.FindMyLocation");this._geolocateButton.title=a,this._geolocateButton.setAttribute("aria-label",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute("aria-pressed","false"),this._watchState="OFF"),this.options.showUserLocation&&(this._dotElement=r.create("div","mapboxgl-user-location-dot"),this._userLocationDotMarker=new Fi(this._dotElement),this._circleElement=r.create("div","mapboxgl-user-location-accuracy-circle"),this._accuracyCircleMarker=new Fi({element:this._circleElement,pitchAlignment:"map"}),this.options.trackUserLocation&&(this._watchState="OFF"),this._map.on("zoom",this._onZoom)),this._geolocateButton.addEventListener("click",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on("movestart",(function(e){var r=e.originalEvent&&"resize"===e.originalEvent.type;e.geolocateSource||"ACTIVE_LOCK"!==n._watchState||r||(n._watchState="BACKGROUND",n._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background"),n._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),n.fire(new t.Event("trackuserlocationend")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce("Geolocate control triggered before added to a map"),!1;if(this.options.trackUserLocation){switch(this._watchState){case"OFF":this._watchState="WAITING_ACTIVE",this.fire(new t.Event("trackuserlocationstart"));break;case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":case"BACKGROUND_ERROR":Ni--,ji=!1,this._watchState="OFF",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this.fire(new t.Event("trackuserlocationend"));break;case"BACKGROUND":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event("trackuserlocationstart"))}switch(this._watchState){case"WAITING_ACTIVE":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"ACTIVE_LOCK":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"ACTIVE_ERROR":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error");break;case"BACKGROUND":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background");break;case"BACKGROUND_ERROR":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background-error")}if("OFF"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","true"),++Ni>1?(e={maximumAge:6e5,timeout:0},ji=!0):(e=this.options.positionOptions,ji=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","false"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Vi={maxWidth:100,unit:"metric"},Hi=function(e){this.options=t.extend({},Vi,e),t.bindAll(["_onMove","setUnit"],this)};function qi(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&"imperial"===r.unit){var l=3.2808*s;if(l>5280)Gi(e,n,l/5280,t._getUIString("ScaleControl.Miles"));else Gi(e,n,l,t._getUIString("ScaleControl.Feet"))}else if(r&&"nautical"===r.unit){Gi(e,n,s/1852,t._getUIString("ScaleControl.NauticalMiles"))}else s>=1e3?Gi(e,n,s/1e3,t._getUIString("ScaleControl.Kilometers")):Gi(e,n,s,t._getUIString("ScaleControl.Meters"))}function Gi(t,e,r,n){var i,a,o,s=(i=r,a=Math.pow(10,(""+Math.floor(i)).length-1),o=(o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o),a*o),l=s/r;t.style.width=e*l+"px",t.innerHTML=s+"&nbsp;"+n}Hi.prototype.getDefaultPosition=function(){return"bottom-left"},Hi.prototype._onMove=function(){qi(this._map,this._container,this.options)},Hi.prototype.onAdd=function(t){return this._map=t,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-scale",t.getContainer()),this._map.on("move",this._onMove),this._onMove(),this._container},Hi.prototype.onRemove=function(){r.remove(this._container),this._map.off("move",this._onMove),this._map=void 0},Hi.prototype.setUnit=function(t){this.options.unit=t,qi(this._map,this._container,this.options)};var Yi=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce("Full screen control 'container' must be a DOM element.")),t.bindAll(["_onClickFullscreen","_changeIcon"],this),"onfullscreenchange"in t.window.document?this._fullscreenchange="fullscreenchange":"onmozfullscreenchange"in t.window.document?this._fullscreenchange="mozfullscreenchange":"onwebkitfullscreenchange"in t.window.document?this._fullscreenchange="webkitfullscreenchange":"onmsfullscreenchange"in t.window.document&&(this._fullscreenchange="MSFullscreenChange")};Yi.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display="none",t.warnOnce("This device does not support fullscreen mode.")),this._controlContainer},Yi.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},Yi.prototype._setupUI=function(){var e=this._fullscreenButton=r.create("button","mapboxgl-ctrl-fullscreen",this._controlContainer);r.create("span","mapboxgl-ctrl-icon",e).setAttribute("aria-hidden",!0),e.type="button",this._updateTitle(),this._fullscreenButton.addEventListener("click",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute("aria-label",t),this._fullscreenButton.title=t},Yi.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?"FullscreenControl.Exit":"FullscreenControl.Enter")},Yi.prototype._isFullscreen=function(){return this._fullscreen},Yi.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle("mapboxgl-ctrl-shrink"),this._fullscreenButton.classList.toggle("mapboxgl-ctrl-fullscreen"),this._updateTitle())},Yi.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var Wi={closeButton:!0,closeOnClick:!0,className:"",maxWidth:"240px"},Xi=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(Wi),r),t.bindAll(["_update","_onClose","remove","_onMouseMove","_onMouseUp","_onDrag"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(e){return this._map&&this.remove(),this._map=e,this.options.closeOnClick&&this._map.on("click",this._onClose),this.options.closeOnMove&&this._map.on("move",this._onClose),this._map.on("remove",this.remove),this._update(),this._trackPointer?(this._map.on("mousemove",this._onMouseMove),this._map.on("mouseup",this._onMouseUp),this._container&&this._container.classList.add("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.add("mapboxgl-track-pointer")):this._map.on("move",this._update),this.fire(new t.Event("open")),this},n.prototype.isOpen=function(){return!!this._map},n.prototype.remove=function(){return this._content&&r.remove(this._content),this._container&&(r.remove(this._container),delete this._container),this._map&&(this._map.off("move",this._update),this._map.off("move",this._onClose),this._map.off("click",this._onClose),this._map.off("remove",this.remove),this._map.off("mousemove",this._onMouseMove),this._map.off("mouseup",this._onMouseUp),this._map.off("drag",this._onDrag),delete this._map),this.fire(new t.Event("close")),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on("move",this._update),this._map.off("mousemove",this._onMouseMove),this._container&&this._container.classList.remove("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.remove("mapboxgl-track-pointer")),this},n.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off("move",this._update),this._map.on("mousemove",this._onMouseMove),this._map.on("drag",this._onDrag),this._container&&this._container.classList.add("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.add("mapboxgl-track-pointer")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement("body");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createContent=function(){this._content&&r.remove(this._content),this._content=r.create("div","mapboxgl-popup-content",this._container),this.options.closeButton&&(this._closeButton=r.create("button","mapboxgl-popup-close-button",this._content),this._closeButton.type="button",this._closeButton.setAttribute("aria-label","Close popup"),this._closeButton.innerHTML="&#215;",this._closeButton.addEventListener("click",this._onClose))},n.prototype._onMouseUp=function(t){this._update(t.point)},n.prototype._onMouseMove=function(t){this._update(t.point)},n.prototype._onDrag=function(t){this._update(t.point)},n.prototype._update=function(e){var n=this,i=this._lngLat||this._trackPointer;if(this._map&&i&&this._content&&(this._container||(this._container=r.create("div","mapboxgl-popup",this._map.getContainer()),this._tip=r.create("div","mapboxgl-popup-tip",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(" ").forEach((function(t){return n._container.classList.add(t)})),this._trackPointer&&this._container.classList.add("mapboxgl-popup-track-pointer")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||e)){var a=this._pos=this._trackPointer&&e?e:this._map.project(this._lngLat),o=this.options.anchor,s=function e(r){if(r){if("number"==typeof r){var n=Math.round(Math.sqrt(.5*Math.pow(r,2)));return{center:new t.Point(0,0),top:new t.Point(0,r),"top-left":new t.Point(n,n),"top-right":new t.Point(-n,n),bottom:new t.Point(0,-r),"bottom-left":new t.Point(n,-n),"bottom-right":new t.Point(-n,-n),left:new t.Point(r,0),right:new t.Point(-r,0)}}if(r instanceof t.Point||Array.isArray(r)){var i=t.Point.convert(r);return{center:i,top:i,"top-left":i,"top-right":i,bottom:i,"bottom-left":i,"bottom-right":i,left:i,right:i}}return{center:t.Point.convert(r.center||[0,0]),top:t.Point.convert(r.top||[0,0]),"top-left":t.Point.convert(r["top-left"]||[0,0]),"top-right":t.Point.convert(r["top-right"]||[0,0]),bottom:t.Point.convert(r.bottom||[0,0]),"bottom-left":t.Point.convert(r["bottom-left"]||[0,0]),"bottom-right":t.Point.convert(r["bottom-right"]||[0,0]),left:t.Point.convert(r.left||[0,0]),right:t.Point.convert(r.right||[0,0])}}return e(new t.Point(0,0))}(this.options.offset);if(!o){var l,c=this._container.offsetWidth,u=this._container.offsetHeight;l=a.y+s.bottom.y<u?["top"]:a.y>this._map.transform.height-u?["bottom"]:[],a.x<c/2?l.push("left"):a.x>this._map.transform.width-c/2&&l.push("right"),o=0===l.length?"bottom":l.join("-")}var f=a.add(s[o]).round();r.setTransform(this._container,zi[o]+" translate("+f.x+"px,"+f.y+"px)"),Di(this._container,o,"popup")}},n.prototype._onClose=function(){this.remove()},n}(t.Evented);var Zi={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:Ei,NavigationControl:Pi,GeolocateControl:Ui,AttributionControl:bi,ScaleControl:Hi,FullscreenControl:Yi,Popup:Xi,Marker:Fi,Style:qe,LngLat:t.LngLat,LngLatBounds:t.LngLatBounds,Point:t.Point,MercatorCoordinate:t.MercatorCoordinate,Evented:t.Evented,config:t.config,prewarm:function(){Bt().acquire(zt)},clearPrewarmedResources:function(){var t=Rt;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(zt),Rt=null):console.warn("Could not clear WebWorkers since there are active Map instances that still reference it. The pre-warmed WebWorker pool can only be cleared when all map instances have been removed with map.remove()"))},get accessToken(){return t.config.ACCESS_TOKEN},set accessToken(e){t.config.ACCESS_TOKEN=e},get baseApiUrl(){return t.config.API_URL},set baseApiUrl(e){t.config.API_URL=e},get workerCount(){return Dt.workerCount},set workerCount(t){Dt.workerCount=t},get maxParallelImageRequests(){return t.config.MAX_PARALLEL_IMAGE_REQUESTS},set maxParallelImageRequests(e){t.config.MAX_PARALLEL_IMAGE_REQUESTS=e},clearStorage:function(e){t.clearTileCache(e)},workerUrl:""};return Zi})),r}))},{}],235:[function(t,e,r){"use strict";e.exports=Math.log2||function(t){return Math.log(t)*Math.LOG2E}},{}],236:[function(t,e,r){"use strict";e.exports=function(t,e){e||(e=t,t=window);var r=0,i=0,a=0,o={shift:!1,alt:!1,control:!1,meta:!1},s=!1;function l(t){var e=!1;return"altKey"in t&&(e=e||t.altKey!==o.alt,o.alt=!!t.altKey),"shiftKey"in t&&(e=e||t.shiftKey!==o.shift,o.shift=!!t.shiftKey),"ctrlKey"in t&&(e=e||t.ctrlKey!==o.control,o.control=!!t.ctrlKey),"metaKey"in t&&(e=e||t.metaKey!==o.meta,o.meta=!!t.metaKey),e}function c(t,s){var c=n.x(s),u=n.y(s);"buttons"in s&&(t=0|s.buttons),(t!==r||c!==i||u!==a||l(s))&&(r=0|t,i=c||0,a=u||0,e&&e(r,i,a,o))}function u(t){c(0,t)}function f(){(r||i||a||o.shift||o.alt||o.meta||o.control)&&(i=a=0,r=0,o.shift=o.alt=o.control=o.meta=!1,e&&e(0,0,0,o))}function h(t){l(t)&&e&&e(r,i,a,o)}function p(t){0===n.buttons(t)?c(0,t):c(r,t)}function d(t){c(r|n.buttons(t),t)}function m(t){c(r&~n.buttons(t),t)}function g(){s||(s=!0,t.addEventListener("mousemove",p),t.addEventListener("mousedown",d),t.addEventListener("mouseup",m),t.addEventListener("mouseleave",u),t.addEventListener("mouseenter",u),t.addEventListener("mouseout",u),t.addEventListener("mouseover",u),t.addEventListener("blur",f),t.addEventListener("keyup",h),t.addEventListener("keydown",h),t.addEventListener("keypress",h),t!==window&&(window.addEventListener("blur",f),window.addEventListener("keyup",h),window.addEventListener("keydown",h),window.addEventListener("keypress",h)))}g();var v={element:t};return Object.defineProperties(v,{enabled:{get:function(){return s},set:function(e){e?g():function(){if(!s)return;s=!1,t.removeEventListener("mousemove",p),t.removeEventListener("mousedown",d),t.removeEventListener("mouseup",m),t.removeEventListener("mouseleave",u),t.removeEventListener("mouseenter",u),t.removeEventListener("mouseout",u),t.removeEventListener("mouseover",u),t.removeEventListener("blur",f),t.removeEventListener("keyup",h),t.removeEventListener("keydown",h),t.removeEventListener("keypress",h),t!==window&&(window.removeEventListener("blur",f),window.removeEventListener("keyup",h),window.removeEventListener("keydown",h),window.removeEventListener("keypress",h))}()},enumerable:!0},buttons:{get:function(){return r},enumerable:!0},x:{get:function(){return i},enumerable:!0},y:{get:function(){return a},enumerable:!0},mods:{get:function(){return o},enumerable:!0}}),v};var n=t("mouse-event")},{"mouse-event":238}],237:[function(t,e,r){var n={left:0,top:0};e.exports=function(t,e,r){e=e||t.currentTarget||t.srcElement,Array.isArray(r)||(r=[0,0]);var i=t.clientX||0,a=t.clientY||0,o=(s=e,s===window||s===document||s===document.body?n:s.getBoundingClientRect());var s;return r[0]=i-o.left,r[1]=a-o.top,r}},{}],238:[function(t,e,r){"use strict";function n(t){return t.target||t.srcElement||window}r.buttons=function(t){if("object"==typeof t){if("buttons"in t)return t.buttons;if("which"in t){if(2===(e=t.which))return 4;if(3===e)return 2;if(e>0)return 1<<e-1}else if("button"in t){var e;if(1===(e=t.button))return 4;if(2===e)return 2;if(e>=0)return 1<<e}}return 0},r.element=n,r.x=function(t){if("object"==typeof t){if("offsetX"in t)return t.offsetX;var e=n(t).getBoundingClientRect();return t.clientX-e.left}return 0},r.y=function(t){if("object"==typeof t){if("offsetY"in t)return t.offsetY;var e=n(t).getBoundingClientRect();return t.clientY-e.top}return 0}},{}],239:[function(t,e,r){"use strict";var n=t("to-px");e.exports=function(t,e,r){"function"==typeof t&&(r=!!e,e=t,t=window);var i=n("ex",t),a=function(t){r&&t.preventDefault();var n=t.deltaX||0,a=t.deltaY||0,o=t.deltaZ||0,s=1;switch(t.deltaMode){case 1:s=i;break;case 2:s=window.innerHeight}if(a*=s,o*=s,(n*=s)||a||o)return e(n,a,o,t)};return t.addEventListener("wheel",a),a}},{"to-px":309}],240:[function(t,e,r){(function(t,r){(function(){
+/*! Native Promise Only
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+    MIT License: http://getify.mit-license.org
+*/
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+/*
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+*/
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+/*
+ * @copyright 2016 Sean Connelly (@voidqk), http://syntheti.cc
+ * @license MIT
+ * @preserve Project Home: https://github.com/voidqk/polybooljs
+ */
+var n,i=t("./lib/build-log"),a=t("./lib/epsilon"),o=t("./lib/intersecter"),s=t("./lib/segment-chainer"),l=t("./lib/segment-selector"),c=t("./lib/geojson"),u=!1,f=a();function h(t,e,r){var i=n.segments(t),a=n.segments(e),o=r(n.combine(i,a));return n.polygon(o)}n={buildLog:function(t){return!0===t?u=i():!1===t&&(u=!1),!1!==u&&u.list},epsilon:function(t){return f.epsilon(t)},segments:function(t){var e=o(!0,f,u);return t.regions.forEach(e.addRegion),{segments:e.calculate(t.inverted),inverted:t.inverted}},combine:function(t,e){return{combined:o(!1,f,u).calculate(t.segments,t.inverted,e.segments,e.inverted),inverted1:t.inverted,inverted2:e.inverted}},selectUnion:function(t){return{segments:l.union(t.combined,u),inverted:t.inverted1||t.inverted2}},selectIntersect:function(t){return{segments:l.intersect(t.combined,u),inverted:t.inverted1&&t.inverted2}},selectDifference:function(t){return{segments:l.difference(t.combined,u),inverted:t.inverted1&&!t.inverted2}},selectDifferenceRev:function(t){return{segments:l.differenceRev(t.combined,u),inverted:!t.inverted1&&t.inverted2}},selectXor:function(t){return{segments:l.xor(t.combined,u),inverted:t.inverted1!==t.inverted2}},polygon:function(t){return{regions:s(t.segments,f,u),inverted:t.inverted}},polygonFromGeoJSON:function(t){return c.toPolygon(n,t)},polygonToGeoJSON:function(t){return c.fromPolygon(n,f,t)},union:function(t,e){return h(t,e,n.selectUnion)},intersect:function(t,e){return h(t,e,n.selectIntersect)},difference:function(t,e){return h(t,e,n.selectDifference)},differenceRev:function(t,e){return h(t,e,n.selectDifferenceRev)},xor:function(t,e){return h(t,e,n.selectXor)}},"object"==typeof window&&(window.PolyBool=n),e.exports=n},{"./lib/build-log":250,"./lib/epsilon":251,"./lib/geojson":252,"./lib/intersecter":253,"./lib/segment-chainer":255,"./lib/segment-selector":256}],250:[function(t,e,r){e.exports=function(){var t,e=0,r=!1;function n(e,r){return t.list.push({type:e,data:r?JSON.parse(JSON.stringify(r)):void 0}),t}return t={list:[],segmentId:function(){return e++},checkIntersection:function(t,e){return n("check",{seg1:t,seg2:e})},segmentChop:function(t,e){return n("div_seg",{seg:t,pt:e}),n("chop",{seg:t,pt:e})},statusRemove:function(t){return n("pop_seg",{seg:t})},segmentUpdate:function(t){return n("seg_update",{seg:t})},segmentNew:function(t,e){return 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t[e+3]|t[e+2]<<8|t[e+1]<<16|16777216*t[e]},o.prototype=Object.create(Error.prototype),o.prototype.constructor=o,r.ProbeError=o},{stream:280,"stream-parser":295}],258:[function(t,e,r){"use strict";function n(t,e){var r=new Error(t);return r.code=e,r}function i(t){try{return decodeURIComponent(escape(t))}catch(e){return t}}function a(t,e,r){this.input=t.subarray(e,r),this.start=e;var i=String.fromCharCode.apply(null,this.input.subarray(0,4));if("II*\0"!==i&&"MM\0*"!==i)throw n("invalid TIFF signature","EBADDATA");this.big_endian="M"===i[0]}a.prototype.each=function(t){this.aborted=!1;var e=this.read_uint32(4);for(this.ifds_to_read=[{id:0,offset:e}];this.ifds_to_read.length>0&&!this.aborted;){var r=this.ifds_to_read.shift();r.offset&&this.scan_ifd(r.id,r.offset,t)}},a.prototype.read_uint16=function(t){var e=this.input;if(t+2>e.length)throw n("unexpected EOF","EBADDATA");return this.big_endian?256*e[t]+e[t+1]:e[t]+256*e[t+1]},a.prototype.read_uint32=function(t){var e=this.input;if(t+4>e.length)throw n("unexpected EOF","EBADDATA");return this.big_endian?16777216*e[t]+65536*e[t+1]+256*e[t+2]+e[t+3]:e[t]+256*e[t+1]+65536*e[t+2]+16777216*e[t+3]},a.prototype.is_subifd_link=function(t,e){return 0===t&&34665===e||0===t&&34853===e||34665===t&&40965===e},a.prototype.exif_format_length=function(t){switch(t){case 1:case 2:case 6:case 7:return 1;case 3:case 8:return 2;case 4:case 9:case 11:return 4;case 5:case 10:case 12:return 8;default:return 0}},a.prototype.exif_format_read=function(t,e){var r;switch(t){case 1:case 2:return r=this.input[e];case 6:return(r=this.input[e])|33554430*(128&r);case 3:return r=this.read_uint16(e);case 8:return(r=this.read_uint16(e))|131070*(32768&r);case 4:return r=this.read_uint32(e);case 9:return 0|(r=this.read_uint32(e));case 5:case 10:case 11:case 12:case 7:default:return null}},a.prototype.scan_ifd=function(t,e,r){var a=this.read_uint16(e);e+=2;for(var o=0;o<a;o++){var s=this.read_uint16(e),l=this.read_uint16(e+2),c=this.read_uint32(e+4),u=this.exif_format_length(l),f=c*u,h=f<=4?e+8:this.read_uint32(e+8),p=!1;if(h+f>this.input.length)throw n("unexpected EOF","EBADDATA");for(var d=[],m=h,g=0;g<c;g++,m+=u){var v=this.exif_format_read(l,m);if(null===v){d=null;break}d.push(v)}if(Array.isArray(d)&&2===l&&(d=i(String.fromCharCode.apply(null,d)))&&"\0"===d[d.length-1]&&(d=d.slice(0,-1)),this.is_subifd_link(t,s)&&Array.isArray(d)&&Number.isInteger(d[0])&&d[0]>0&&(this.ifds_to_read.push({id:s,offset:d[0]}),p=!0),!1===r({is_big_endian:this.big_endian,ifd:t,tag:s,format:l,count:c,entry_offset:e+this.start,data_length:f,data_offset:h+this.start,value:d,is_subifd_link:p}))return void(this.aborted=!0);e+=12}0===t&&this.ifds_to_read.push({id:1,offset:this.read_uint32(e)})},e.exports.ExifParser=a,e.exports.get_orientation=function(t){var e=0;try{return new a(t,0,t.length).each((function(t){if(0===t.ifd&&274===t.tag&&Array.isArray(t.value))return e=t.value[0],!1})),e}catch(t){return-1}}},{}],259:[function(t,e,r){"use strict";var n=t("./common").readUInt16BE,i=t("./common").readUInt32BE;function a(t,e){if(t.length<4+e)return null;var r=i(t,e);return t.length<r+e||r<8?null:{boxtype:String.fromCharCode.apply(null,t.slice(e+4,e+8)),data:t.slice(e+8,e+r),end:e+r}}function o(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;switch(n.boxtype){case"ispe":e.sizes.push({width:i(n.data,4),height:i(n.data,8)});break;case"irot":e.transforms.push({type:"irot",value:3&n.data[0]});break;case"imir":e.transforms.push({type:"imir",value:1&n.data[0]})}r=n.end}}function s(t,e,r){for(var n=0,i=0;i<r;i++)n=256*n+(t[e+i]||0);return n}function l(t,e){for(var r=t[4]>>4&15,i=15&t[4],a=t[5]>>4&15,o=n(t,6),l=8,c=0;c<o;c++){var u=n(t,l),f=n(t,l+=2),h=s(t,l+=2,a),p=n(t,l+=a);if(l+=2,0===f&&1===p){var d=s(t,l,r),m=s(t,l+r,i);e.item_loc[u]={length:m,offset:d+h}}l+=p*(r+i)}}function c(t,e){for(var r=n(t,4),i=6,o=0;o<r;o++){var s=a(t,i);if(!s)break;if("infe"===s.boxtype){for(var l=n(s.data,4),c="",u=8;u<s.data.length&&s.data[u];u++)c+=String.fromCharCode(s.data[u]);e.item_inf[c]=l}i=s.end}}function u(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;"ipco"===n.boxtype&&o(n.data,e),r=n.end}}e.exports.unbox=a,e.exports.readSizeFromMeta=function(t){var e={sizes:[],transforms:[],item_inf:{},item_loc:{}};if(function(t,e){for(var r=4;;){var n=a(t,r);if(!n)break;"iprp"===n.boxtype&&u(n.data,e),"iloc"===n.boxtype&&l(n.data,e),"iinf"===n.boxtype&&c(n.data,e),r=n.end}}(t,e),e.sizes.length){var r,n,i,o=(r=e.sizes,n=r.reduce((function(t,e){return t.width>e.width||t.width===e.width&&t.height>e.height?t:e})),i=r.reduce((function(t,e){return t.height>e.height||t.height===e.height&&t.width>e.width?t:e})),n.width>i.height||n.width===i.height&&n.height>i.width?n:i),s=1;e.transforms.forEach((function(t){var e={1:6,2:5,3:8,4:7,5:4,6:3,7:2,8:1},r={1:4,2:3,3:2,4:1,5:6,6:5,7:8,8:7};if("imir"===t.type&&(s=0===t.value?r[s]:e[s=e[s=r[s]]]),"irot"===t.type)for(var n=0;n<t.value;n++)s=e[s]}));var f=null;return e.item_inf.Exif&&(f=e.item_loc[e.item_inf.Exif]),{width:o.width,height:o.height,orientation:e.transforms.length?s:null,variants:e.sizes,exif_location:f}}},e.exports.getMimeType=function(t){var e=String.fromCharCode.apply(null,t.slice(0,4)),r={};r[e]=!0;for(var n=8;n<t.length;n+=4)r[String.fromCharCode.apply(null,t.slice(n,n+4))]=!0;if(r.mif1||r.msf1||r.miaf)return"avif"===e||"avis"===e||"avio"===e?{type:"avif",mime:"image/avif"}:"heic"===e||"heix"===e?{type:"heic",mime:"image/heic"}:"hevc"===e||"hevx"===e?{type:"heic",mime:"image/heic-sequence"}:r.avif||r.avis?{type:"avif",mime:"image/avif"}:r.heic||r.heix||r.hevc||r.hevx||r.heis?r.msf1?{type:"heif",mime:"image/heif-sequence"}:{type:"heif",mime:"image/heif"}:{type:"avif",mime:"image/avif"}}},{"./common":257}],260:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt32BE,o=t("../miaf_utils"),s=t("../exif_utils"),l=n("ftyp");e.exports=function(t){if(i(t,4,l)){var e=o.unbox(t,0);if(e){var r=o.getMimeType(e.data);if(r){for(var n,c=e.end;;){var u=o.unbox(t,c);if(!u)break;if(c=u.end,"mdat"===u.boxtype)return;if("meta"===u.boxtype){n=u.data;break}}if(n){var f=o.readSizeFromMeta(n);if(f){var h={width:f.width,height:f.height,type:r.type,mime:r.mime,wUnits:"px",hUnits:"px"};if(f.variants.length>1&&(h.variants=f.variants),f.orientation&&(h.orientation=f.orientation),f.exif_location&&f.exif_location.offset+f.exif_location.length<=t.length){var p=a(t,f.exif_location.offset),d=t.slice(f.exif_location.offset+p+4,f.exif_location.offset+f.exif_location.length),m=s.get_orientation(d);m>0&&(h.orientation=m)}return h}}}}}}},{"../common":257,"../exif_utils":258,"../miaf_utils":259}],261:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt16LE,o=n("BM");e.exports=function(t){if(!(t.length<26)&&i(t,0,o))return{width:a(t,18),height:a(t,22),type:"bmp",mime:"image/bmp",wUnits:"px",hUnits:"px"}}},{"../common":257}],262:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt16LE,o=n("GIF87a"),s=n("GIF89a");e.exports=function(t){if(!(t.length<10)&&(i(t,0,o)||i(t,0,s)))return{width:a(t,6),height:a(t,8),type:"gif",mime:"image/gif",wUnits:"px",hUnits:"px"}}},{"../common":257}],263:[function(t,e,r){"use strict";var n=t("../common").readUInt16LE;e.exports=function(t){var e=n(t,0),r=n(t,2),i=n(t,4);if(0===e&&1===r&&i){for(var a=[],o={width:0,height:0},s=0;s<i;s++){var l=t[6+16*s]||256,c=t[6+16*s+1]||256,u={width:l,height:c};a.push(u),(l>o.width||c>o.height)&&(o=u)}return{width:o.width,height:o.height,variants:a,type:"ico",mime:"image/x-icon",wUnits:"px",hUnits:"px"}}}},{"../common":257}],264:[function(t,e,r){"use strict";var n=t("../common").readUInt16BE,i=t("../common").str2arr,a=t("../common").sliceEq,o=t("../exif_utils"),s=i("Exif\0\0");e.exports=function(t){if(!(t.length<2)&&255===t[0]&&216===t[1]&&255===t[2])for(var e=2;;){for(;;){if(t.length-e<2)return;if(255===t[e++])break}for(var r,i,l=t[e++];255===l;)l=t[e++];if(208<=l&&l<=217||1===l)r=0;else{if(!(192<=l&&l<=254))return;if(t.length-e<2)return;r=n(t,e)-2,e+=2}if(217===l||218===l)return;if(225===l&&r>=10&&a(t,e,s)&&(i=o.get_orientation(t.slice(e+6,e+r))),r>=5&&192<=l&&l<=207&&196!==l&&200!==l&&204!==l){if(t.length-e<r)return;var c={width:n(t,e+3),height:n(t,e+1),type:"jpg",mime:"image/jpeg",wUnits:"px",hUnits:"px"};return i>0&&(c.orientation=i),c}e+=r}}},{"../common":257,"../exif_utils":258}],265:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt32BE,o=n("\x89PNG\r\n\x1a\n"),s=n("IHDR");e.exports=function(t){if(!(t.length<24)&&i(t,0,o)&&i(t,12,s))return{width:a(t,16),height:a(t,20),type:"png",mime:"image/png",wUnits:"px",hUnits:"px"}}},{"../common":257}],266:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt32BE,o=n("8BPS\0\x01");e.exports=function(t){if(!(t.length<22)&&i(t,0,o))return{width:a(t,18),height:a(t,14),type:"psd",mime:"image/vnd.adobe.photoshop",wUnits:"px",hUnits:"px"}}},{"../common":257}],267:[function(t,e,r){"use strict";function n(t){return"number"==typeof t&&isFinite(t)&&t>0}var 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thickness * normal * .5 * lineOffset / viewport.zw;\n\n\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\n\n\tfragColor = color / 255.;\n}\n"]),frag:o(["precision highp float;\n#define GLSLIFY 1\n\nuniform float dashLength, pixelRatio, thickness, opacity, id;\nuniform sampler2D dashTexture;\n\nvarying vec4 fragColor;\nvarying vec2 tangent;\n\nvoid main() {\n\tfloat alpha = 1.;\n\n\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\n\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\n\n\tgl_FragColor = fragColor;\n\tgl_FragColor.a *= alpha * opacity * dash;\n}\n"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:16,divisor:1},aCoordFract:{buffer:t.prop("positionFractBuffer"),stride:8,offset:8,divisor:1},bCoordFract:{buffer:t.prop("positionFractBuffer"),stride:8,offset:16,divisor:1},color:{buffer:t.prop("colorBuffer"),stride:4,offset:0,divisor:1}}},n));try{e=t(a({cull:{enable:!0,face:"back"},vert:o(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec2 aCoord, bCoord, nextCoord, prevCoord;\nattribute vec4 aColor, bColor;\nattribute float lineEnd, lineTop;\n\nuniform vec2 scale, translate;\nuniform float thickness, pixelRatio, id, depth;\nuniform vec4 viewport;\nuniform float miterLimit, miterMode;\n\nvarying vec4 fragColor;\nvarying vec4 startCutoff, endCutoff;\nvarying vec2 tangent;\nvarying vec2 startCoord, endCoord;\nvarying float enableStartMiter, enableEndMiter;\n\nconst float REVERSE_THRESHOLD = -.875;\nconst float MIN_DIFF = 1e-6;\n\n// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead\n// TODO: precalculate dot products, normalize things beforehead etc.\n// TODO: refactor to rectangular algorithm\n\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\n\tvec2 diff = b - a;\n\tvec2 perp = normalize(vec2(-diff.y, diff.x));\n\treturn dot(p - a, perp);\n}\n\nbool isNaN( float val ){\n  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;\n}\n\nvoid main() {\n\tvec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;\n\n  vec2 adjustedScale;\n  adjustedScale.x = (abs(scale.x) < MIN_DIFF) ? MIN_DIFF : scale.x;\n  adjustedScale.y = (abs(scale.y) < MIN_DIFF) ? MIN_DIFF : scale.y;\n\n  vec2 scaleRatio = adjustedScale * viewport.zw;\n\tvec2 normalWidth = thickness / scaleRatio;\n\n\tfloat lineStart = 1. - lineEnd;\n\tfloat lineBot = 1. - lineTop;\n\n\tfragColor = (lineStart * aColor + lineEnd * bColor) / 255.;\n\n\tif (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;\n\n\tif (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);\n\tif (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);\n\n\tvec2 prevDiff = aCoord - prevCoord;\n\tvec2 currDiff = bCoord - aCoord;\n\tvec2 nextDiff = nextCoord - bCoord;\n\n\tvec2 prevTangent = normalize(prevDiff * scaleRatio);\n\tvec2 currTangent = normalize(currDiff * scaleRatio);\n\tvec2 nextTangent = normalize(nextDiff * scaleRatio);\n\n\tvec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);\n\tvec2 currNormal = vec2(-currTangent.y, currTangent.x);\n\tvec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);\n\n\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\n\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\n\n\t// collapsed/unidirectional segment cases\n\t// FIXME: there should be more elegant solution\n\tvec2 prevTanDiff = abs(prevTangent - currTangent);\n\tvec2 nextTanDiff = abs(nextTangent - currTangent);\n\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\n\t\tstartJoinDirection = currNormal;\n\t}\n\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\n\t\tendJoinDirection = currNormal;\n\t}\n\tif (aCoord == bCoord) {\n\t\tendJoinDirection = startJoinDirection;\n\t\tcurrNormal = prevNormal;\n\t\tcurrTangent = prevTangent;\n\t}\n\n\ttangent = currTangent;\n\n\t//calculate join shifts relative to normals\n\tfloat startJoinShift = dot(currNormal, startJoinDirection);\n\tfloat endJoinShift = dot(currNormal, endJoinDirection);\n\n\tfloat startMiterRatio = abs(1. / startJoinShift);\n\tfloat endMiterRatio = abs(1. / endJoinShift);\n\n\tvec2 startJoin = startJoinDirection * startMiterRatio;\n\tvec2 endJoin = endJoinDirection * endMiterRatio;\n\n\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\n\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\n\tstartBotJoin = -startTopJoin;\n\n\tendTopJoin = sign(endJoinShift) * endJoin * .5;\n\tendBotJoin = -endTopJoin;\n\n\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\n\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\n\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\n\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\n\n\t//miter anti-clipping\n\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\n\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\n\n\t//prevent close to reverse direction switch\n\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);\n\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);\n\n\tif (prevReverse) {\n\t\t//make join rectangular\n\t\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\n\t\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\n\t\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\n\t\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\n\t}\n\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\n\t\t//handle miter clipping\n\t\tbTopCoord -= normalWidth * endTopJoin;\n\t\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\n\t}\n\n\tif (nextReverse) {\n\t\t//make join rectangular\n\t\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\n\t\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\n\t\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\n\t\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\n\t}\n\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\n\t\t//handle miter clipping\n\t\taBotCoord -= normalWidth * startBotJoin;\n\t\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\n\t}\n\n\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\n\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\n\n\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\n\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\n\n\t//position is normalized 0..1 coord on the screen\n\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\n\n\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\n\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\n\n\tgl_Position = vec4(position  * 2.0 - 1.0, depth, 1);\n\n\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\n\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\n\n\t//bevel miter cutoffs\n\tif (miterMode == 1.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\n\t\t\tstartCutoff = vec4(aCoord, aCoord);\n\t\t\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\n\t\t\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tstartCutoff += viewport.xyxy;\n\t\t\tstartCutoff += startMiterWidth.xyxy;\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\n\t\t\tendCutoff = vec4(bCoord, bCoord);\n\t\t\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\n\t\t\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tendCutoff += viewport.xyxy;\n\t\t\tendCutoff += endMiterWidth.xyxy;\n\t\t}\n\t}\n\n\t//round miter cutoffs\n\telse if (miterMode == 2.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\n\t\t\tstartCutoff = vec4(aCoord, aCoord);\n\t\t\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\n\t\t\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tstartCutoff += viewport.xyxy;\n\t\t\tstartCutoff += startMiterWidth.xyxy;\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\n\t\t\tendCutoff = vec4(bCoord, bCoord);\n\t\t\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\n\t\t\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tendCutoff += viewport.xyxy;\n\t\t\tendCutoff += endMiterWidth.xyxy;\n\t\t}\n\t}\n}\n"]),frag:o(["precision highp float;\n#define GLSLIFY 1\n\nuniform float dashLength, pixelRatio, thickness, opacity, id, miterMode;\nuniform sampler2D dashTexture;\n\nvarying vec4 fragColor;\nvarying vec2 tangent;\nvarying vec4 startCutoff, endCutoff;\nvarying vec2 startCoord, endCoord;\nvarying float enableStartMiter, enableEndMiter;\n\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\n\tvec2 diff = b - a;\n\tvec2 perp = normalize(vec2(-diff.y, diff.x));\n\treturn dot(p - a, perp);\n}\n\nvoid main() {\n\tfloat alpha = 1., distToStart, distToEnd;\n\tfloat cutoff = thickness * .5;\n\n\t//bevel miter\n\tif (miterMode == 1.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\n\t\t\tif (distToStart < -1.) {\n\t\t\t\tdiscard;\n\t\t\t\treturn;\n\t\t\t}\n\t\t\talpha *= min(max(distToStart + 1., 0.), 1.);\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\n\t\t\tif (distToEnd < -1.) {\n\t\t\t\tdiscard;\n\t\t\t\treturn;\n\t\t\t}\n\t\t\talpha *= min(max(distToEnd + 1., 0.), 1.);\n\t\t}\n\t}\n\n\t// round miter\n\telse if (miterMode == 2.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\n\t\t\tif (distToStart < 0.) {\n\t\t\t\tfloat radius = length(gl_FragCoord.xy - startCoord);\n\n\t\t\t\tif(radius > cutoff + .5) {\n\t\t\t\t\tdiscard;\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\n\t\t\t}\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\n\t\t\tif (distToEnd < 0.) {\n\t\t\t\tfloat radius = length(gl_FragCoord.xy - endCoord);\n\n\t\t\t\tif(radius > cutoff + .5) {\n\t\t\t\t\tdiscard;\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\n\t\t\t}\n\t\t}\n\t}\n\n\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\n\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\n\n\tgl_FragColor = fragColor;\n\tgl_FragColor.a *= alpha * opacity * dash;\n}\n"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aColor:{buffer:t.prop("colorBuffer"),stride:4,offset:0,divisor:1},bColor:{buffer:t.prop("colorBuffer"),stride:4,offset:4,divisor:1},prevCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:0,divisor:1},aCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:16,divisor:1},nextCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:24,divisor:1}}},n))}catch(t){e=i}return{fill:t({primitive:"triangle",elements:function(t,e){return e.triangles},offset:0,vert:o(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec2 position, positionFract;\n\nuniform vec4 color;\nuniform vec2 scale, scaleFract, translate, translateFract;\nuniform float pixelRatio, id;\nuniform vec4 viewport;\nuniform float opacity;\n\nvarying vec4 fragColor;\n\nconst float MAX_LINES = 256.;\n\nvoid main() {\n\tfloat depth = (MAX_LINES - 4. - id) / (MAX_LINES);\n\n\tvec2 position = position * scale + translate\n       + positionFract * scale + translateFract\n       + position * scaleFract\n       + positionFract * scaleFract;\n\n\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\n\n\tfragColor = color / 255.;\n\tfragColor.a *= opacity;\n}\n"]),frag:o(["precision highp float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor;\n\nvoid main() {\n\tgl_FragColor = fragColor;\n}\n"]),uniforms:{scale:t.prop("scale"),color:t.prop("fill"),scaleFract:t.prop("scaleFract"),translateFract:t.prop("translateFract"),translate:t.prop("translate"),opacity:t.prop("opacity"),pixelRatio:t.context("pixelRatio"),id:t.prop("id"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]}},attributes:{position:{buffer:t.prop("positionBuffer"),stride:8,offset:8},positionFract:{buffer:t.prop("positionFractBuffer"),stride:8,offset:8}},blend:n.blend,depth:{enable:!1},scissor:n.scissor,stencil:n.stencil,viewport:n.viewport}),rect:i,miter:e}},v.defaults={dashes:null,join:"miter",miterLimit:1,thickness:10,cap:"square",color:"black",opacity:1,overlay:!1,viewport:null,range:null,close:!1,fill:null},v.prototype.render=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];e.length&&(t=this).update.apply(t,e),this.draw()},v.prototype.draw=function(){for(var t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];return(e.length?e:this.passes).forEach((function(e,r){var n;if(e&&Array.isArray(e))return(n=t).draw.apply(n,e);"number"==typeof e&&(e=t.passes[e]),e&&e.count>1&&e.opacity&&(t.regl._refresh(),e.fill&&e.triangles&&e.triangles.length>2&&t.shaders.fill(e),e.thickness&&(e.scale[0]*e.viewport.width>v.precisionThreshold||e.scale[1]*e.viewport.height>v.precisionThreshold||"rect"===e.join||!e.join&&(e.thickness<=2||e.count>=v.maxPoints)?t.shaders.rect(e):t.shaders.miter(e)))})),this},v.prototype.update=function(t){var e=this;if(t){null!=t.length?"number"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var r=this.regl,o=this.gl;if(t.forEach((function(t,f){var d=e.passes[f];if(void 0!==t)if(null!==t){if("number"==typeof t[0]&&(t={positions:t}),t=s(t,{positions:"positions points data coords",thickness:"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth",join:"lineJoin linejoin join type mode",miterLimit:"miterlimit miterLimit",dashes:"dash dashes dasharray dash-array dashArray",color:"color colour stroke colors colours stroke-color strokeColor",fill:"fill fill-color fillColor",opacity:"alpha opacity",overlay:"overlay crease overlap intersect",close:"closed close closed-path closePath",range:"range dataBox",viewport:"viewport viewBox",hole:"holes hole hollow",splitNull:"splitNull"}),d||(e.passes[f]=d={id:f,scale:null,scaleFract:null,translate:null,translateFract:null,count:0,hole:[],depth:0,dashLength:1,dashTexture:r.texture({channels:1,data:new Uint8Array([255]),width:1,height:1,mag:"linear",min:"linear"}),colorBuffer:r.buffer({usage:"dynamic",type:"uint8",data:new Uint8Array}),positionBuffer:r.buffer({usage:"dynamic",type:"float",data:new Uint8Array}),positionFractBuffer:r.buffer({usage:"dynamic",type:"float",data:new Uint8Array})},t=a({},v.defaults,t)),null!=t.thickness&&(d.thickness=parseFloat(t.thickness)),null!=t.opacity&&(d.opacity=parseFloat(t.opacity)),null!=t.miterLimit&&(d.miterLimit=parseFloat(t.miterLimit)),null!=t.overlay&&(d.overlay=!!t.overlay,f<v.maxLines&&(d.depth=2*(v.maxLines-1-f%v.maxLines)/v.maxLines-1)),null!=t.join&&(d.join=t.join),null!=t.hole&&(d.hole=t.hole),null!=t.fill&&(d.fill=t.fill?n(t.fill,"uint8"):null),null!=t.viewport&&(d.viewport=m(t.viewport)),d.viewport||(d.viewport=m([o.drawingBufferWidth,o.drawingBufferHeight])),null!=t.close&&(d.close=t.close),null===t.positions&&(t.positions=[]),t.positions){var y,x;if(t.positions.x&&t.positions.y){var b=t.positions.x,_=t.positions.y;x=d.count=Math.max(b.length,_.length),y=new Float64Array(2*x);for(var w=0;w<x;w++)y[2*w]=b[w],y[2*w+1]=_[w]}else y=l(t.positions,"float64"),x=d.count=Math.floor(y.length/2);var T=d.bounds=i(y,2);if(d.fill){for(var k=[],A={},M=0,S=0,E=0,L=d.count;S<L;S++){var C=y[2*S],P=y[2*S+1];isNaN(C)||isNaN(P)||null==C||null==P?(C=y[2*M],P=y[2*M+1],A[S]=M):M=S,k[E++]=C,k[E++]=P}if(t.splitNull){d.count-1 in A||(A[d.count]=d.count-1);var I=Object.keys(A).map(Number).sort((function(t,e){return t-e})),O=[],z=0,D=null!=d.hole?d.hole[0]:null;if(null!=D){var R=g(I,(function(t){return t>=D}));(I=I.slice(0,R)).push(D)}for(var F=function(t){var e=k.slice(2*z,2*I[t]).concat(D?k.slice(2*D):[]),r=(d.hole||[]).map((function(e){return e-D+(I[t]-z)})),n=c(e,r);n=n.map((function(e){return e+z+(e+z<I[t]?0:D-I[t])})),O.push.apply(O,n),z=I[t]+1},B=0;B<I.length;B++)F(B);for(var N=0,j=O.length;N<j;N++)null!=A[O[N]]&&(O[N]=A[O[N]]);d.triangles=O}else{for(var U=c(k,d.hole||[]),V=0,H=U.length;V<H;V++)null!=A[U[V]]&&(U[V]=A[U[V]]);d.triangles=U}}var q=new Float64Array(y);u(q,2,T);var G=new Float64Array(2*x+6);d.close?y[0]===y[2*x-2]&&y[1]===y[2*x-1]?(G[0]=q[2*x-4],G[1]=q[2*x-3]):(G[0]=q[2*x-2],G[1]=q[2*x-1]):(G[0]=q[0],G[1]=q[1]),G.set(q,2),d.close?y[0]===y[2*x-2]&&y[1]===y[2*x-1]?(G[2*x+2]=q[2],G[2*x+3]=q[3],d.count-=1):(G[2*x+2]=q[0],G[2*x+3]=q[1],G[2*x+4]=q[2],G[2*x+5]=q[3]):(G[2*x+2]=q[2*x-2],G[2*x+3]=q[2*x-1],G[2*x+4]=q[2*x-2],G[2*x+5]=q[2*x-1]);var Y=h(G);d.positionBuffer(Y);var W=p(G,Y);d.positionFractBuffer(W)}if(t.range?d.range=t.range:d.range||(d.range=d.bounds),(t.range||t.positions)&&d.count){var X=d.bounds,Z=X[2]-X[0],J=X[3]-X[1],K=d.range[2]-d.range[0],Q=d.range[3]-d.range[1];d.scale=[Z/K,J/Q],d.translate=[-d.range[0]/K+X[0]/K||0,-d.range[1]/Q+X[1]/Q||0],d.scaleFract=p(d.scale),d.translateFract=p(d.translate)}if(t.dashes){var $,tt=0;if(!t.dashes||t.dashes.length<2)tt=1,$=new Uint8Array([255,255,255,255,255,255,255,255]);else{tt=0;for(var et=0;et<t.dashes.length;++et)tt+=t.dashes[et];$=new Uint8Array(tt*v.dashMult);for(var rt=0,nt=255,it=0;it<2;it++)for(var at=0;at<t.dashes.length;++at){for(var ot=0,st=t.dashes[at]*v.dashMult*.5;ot<st;++ot)$[rt++]=nt;nt^=255}}d.dashLength=tt,d.dashTexture({channels:1,data:$,width:$.length,height:1,mag:"linear",min:"linear"},0,0)}if(t.color){var lt=d.count,ct=t.color;ct||(ct="transparent");var ut=new Uint8Array(4*lt+4);if(Array.isArray(ct)&&"number"!=typeof ct[0]){for(var ft=0;ft<lt;ft++){var ht=n(ct[ft],"uint8");ut.set(ht,4*ft)}ut.set(n(ct[0],"uint8"),4*lt)}else for(var pt=n(ct,"uint8"),dt=0;dt<lt+1;dt++)ut.set(pt,4*dt);d.colorBuffer({usage:"dynamic",type:"uint8",data:ut})}}else e.passes[f]=null})),t.length<this.passes.length){for(var f=t.length;f<this.passes.length;f++){var d=this.passes[f];d&&(d.colorBuffer.destroy(),d.positionBuffer.destroy(),d.dashTexture.destroy())}this.passes.length=t.length}for(var y=[],x=0;x<this.passes.length;x++)null!==this.passes[x]&&y.push(this.passes[x]);return this.passes=y,this}},v.prototype.destroy=function(){return 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e.borderColor.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:r.tooManyColors?4:2}:{constant:r.tooManyColors?a.slice(4*e.borderColor,4*e.borderColor+4):[e.borderColor]}},isActive:function(t,e){return!0===e.activation?{constant:[1]}:e.activation?e.activation:{constant:[0]}}},blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst alpha",dstAlpha:"one"}},scissor:{enable:!0,box:t.prop("viewport")},viewport:t.prop("viewport"),stencil:{enable:!1},depth:{enable:!1},elements:t.prop("elements"),count:t.prop("count"),offset:t.prop("offset"),primitive:"points"},s=f({},o);s.frag=h(["precision highp float;\n#define GLSLIFY 1\n\nuniform float opacity;\nuniform sampler2D markerTexture;\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragWidth, fragBorderColorLevel, fragColorLevel;\n\nfloat smoothStep(float x, float y) {\n  return 1.0 / (1.0 + exp(50.0*(x - y)));\n}\n\nvoid main() {\n  float dist = texture2D(markerTexture, gl_PointCoord).r, delta = fragWidth;\n\n  // max-distance alpha\n  if (dist < 0.003) discard;\n\n  // null-border case\n  if (fragBorderColorLevel == fragColorLevel || fragBorderColor.a == 0.) {\n    float colorAmt = smoothstep(.5 - delta, .5 + delta, dist);\n    gl_FragColor = vec4(fragColor.rgb, colorAmt * fragColor.a * opacity);\n  }\n  else {\n    float borderColorAmt = smoothstep(fragBorderColorLevel - delta, fragBorderColorLevel + delta, dist);\n    float colorAmt = smoothstep(fragColorLevel - delta, fragColorLevel + delta, dist);\n\n    vec4 color = fragBorderColor;\n    color.a *= borderColorAmt;\n    color = mix(color, fragColor, colorAmt);\n    color.a *= opacity;\n\n    gl_FragColor = color;\n  }\n\n}\n"]),s.vert=h(["precision highp float;\n#define GLSLIFY 1\n\nattribute float x, y, xFract, yFract;\nattribute float size, borderSize;\nattribute vec4 colorId, borderColorId;\nattribute float isActive;\n\nuniform bool constPointSize;\nuniform float pixelRatio;\nuniform vec2 scale, scaleFract, translate, translateFract, paletteSize;\nuniform sampler2D paletteTexture;\n\nconst float maxSize = 100.;\nconst float borderLevel = .5;\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragPointSize, fragBorderRadius, fragWidth, fragBorderColorLevel, fragColorLevel;\n\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\n\nbool isDirect = (paletteSize.x < 1.);\n\nvec4 getColor(vec4 id) {\n  return isDirect ? id / 255. : texture2D(paletteTexture,\n    vec2(\n      (id.x + .5) / paletteSize.x,\n      (id.y + .5) / paletteSize.y\n    )\n  );\n}\n\nvoid main() {\n  // ignore inactive points\n  if (isActive == 0.) return;\n\n  vec2 position = vec2(x, y);\n  vec2 positionFract = vec2(xFract, yFract);\n\n  vec4 color = getColor(colorId);\n  vec4 borderColor = getColor(borderColorId);\n\n  float size = size * maxSize / 255.;\n  float borderSize = borderSize * maxSize / 255.;\n\n  gl_PointSize = 2. * size * pointSizeScale;\n  fragPointSize = size * pixelRatio;\n\n  vec2 pos = (position + translate) * scale\n      + (positionFract + translateFract) * scale\n      + (position + translate) * scaleFract\n      + (positionFract + translateFract) * scaleFract;\n\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\n\n  fragColor = color;\n  fragBorderColor = borderColor;\n  fragWidth = 1. / gl_PointSize;\n\n  fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);\n  fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);\n}"]),this.drawMarker=t(s);var l=f({},o);l.frag=h(["precision highp float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragBorderRadius, fragWidth;\n\nuniform float opacity;\n\nfloat smoothStep(float edge0, float edge1, float x) {\n\tfloat t;\n\tt = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);\n\treturn t * t * (3.0 - 2.0 * t);\n}\n\nvoid main() {\n\tfloat radius, alpha = 1.0, delta = fragWidth;\n\n\tradius 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getColor(vec4 id) {\n  return isDirect ? id / 255. : texture2D(paletteTexture,\n    vec2(\n      (id.x + .5) / paletteSize.x,\n      (id.y + .5) / paletteSize.y\n    )\n  );\n}\n\nvoid main() {\n  // ignore inactive points\n  if (isActive == 0.) return;\n\n  vec2 position = vec2(x, y);\n  vec2 positionFract = vec2(xFract, yFract);\n\n  vec4 color = getColor(colorId);\n  vec4 borderColor = getColor(borderColorId);\n\n  float size = size * maxSize / 255.;\n  float borderSize = borderSize * maxSize / 255.;\n\n  gl_PointSize = (size + borderSize) * pointSizeScale;\n\n  vec2 pos = (position + translate) * scale\n      + (positionFract + translateFract) * scale\n      + (position + translate) * scaleFract\n      + (positionFract + translateFract) * scaleFract;\n\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\n\n  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\n  fragColor = color;\n  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\n  fragWidth = 1. / gl_PointSize;\n}\n"]),g&&(l.frag=l.frag.replace("smoothstep","smoothStep"),s.frag=s.frag.replace("smoothstep","smoothStep")),this.drawCircle=t(l)}b.defaults={color:"black",borderColor:"transparent",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},b.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this},b.prototype.draw=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];var i=this.groups;if(1===r.length&&Array.isArray(r[0])&&(null===r[0][0]||Array.isArray(r[0][0]))&&(r=r[0]),this.regl._refresh(),r.length)for(var a=0;a<r.length;a++)this.drawItem(a,r[a]);else i.forEach((function(e,r){t.drawItem(r)}));return this},b.prototype.drawItem=function(t,e){var r=this.groups,n=r[t];if("number"==typeof e&&(t=e,n=r[e],e=null),n&&n.count&&n.opacity){n.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,n,e));for(var a=[],o=1;o<n.activation.length;o++)n.activation[o]&&(!0===n.activation[o]||n.activation[o].data.length)&&a.push.apply(a,i(this.getMarkerDrawOptions(o,n,e)));a.length&&this.drawMarker(a)}},b.prototype.getMarkerDrawOptions=function(t,e,r){var i=e.range,a=e.tree,o=e.viewport,s=e.activation,l=e.selectionBuffer,c=e.count;this.regl;if(!a)return r?[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],count:r.length,elements:r,offset:0})]:[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],offset:0})];var u=[],h=a.range(i,{lod:!0,px:[(i[2]-i[0])/o.width,(i[3]-i[1])/o.height]});if(r){for(var p=s[t].data,d=new Uint8Array(c),m=0;m<r.length;m++){var g=r[m];d[g]=p?p[g]:1}l.subdata(d)}for(var v=h.length;v--;){var y=n(h[v],2),x=y[0],b=y[1];u.push(f({},e,{markerTexture:this.markerTextures[t],activation:r?l:s[t],offset:x,count:b-x}))}return u},b.prototype.update=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];if(r.length){1===r.length&&Array.isArray(r[0])&&(r=r[0]);var i=this.groups,a=this.gl,o=this.regl,s=this.maxSize,c=this.maxColors,h=this.palette;this.groups=i=r.map((function(e,r){var n=i[r];if(void 0===e)return n;null===e?e={positions:null}:"function"==typeof e?e={ondraw:e}:"number"==typeof e[0]&&(e={positions:e}),null===(e=p(e,{positions:"positions data points",snap:"snap cluster lod tree",size:"sizes size radius",borderSize:"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline",color:"colors color fill fill-color fillColor",borderColor:"borderColors borderColor stroke stroke-color strokeColor",marker:"markers marker shape",range:"range dataBox databox",viewport:"viewport viewPort viewBox viewbox",opacity:"opacity alpha transparency",bounds:"bound bounds boundaries limits",tooManyColors:"tooManyColors palette paletteMode optimizePalette enablePalette"})).positions&&(e.positions=[]),null!=e.tooManyColors&&(t.tooManyColors=e.tooManyColors),n||(i[r]=n={id:r,scale:null,translate:null,scaleFract:null,translateFract:null,activation:[],selectionBuffer:o.buffer({data:new Uint8Array(0),usage:"stream",type:"uint8"}),sizeBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"uint8"}),colorBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"uint8"}),positionBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"float"}),positionFractBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"float"})},e=f({},b.defaults,e)),e.positions&&!("marker"in e)&&(e.marker=n.marker,delete n.marker),e.marker&&!("positions"in e)&&(e.positions=n.positions,delete n.positions);var g=0,x=0;if(d(n,e,[{snap:!0,size:function(t,e){return null==t&&(t=b.defaults.size),g+=t&&t.length?1:0,t},borderSize:function(t,e){return null==t&&(t=b.defaults.borderSize),g+=t&&t.length?1:0,t},opacity:parseFloat,color:function(e,r){return null==e&&(e=b.defaults.color),e=t.updateColor(e),x++,e},borderColor:function(e,r){return null==e&&(e=b.defaults.borderColor),e=t.updateColor(e),x++,e},bounds:function(t,e,r){return"range"in r||(r.range=null),t},positions:function(t,e,r){var n=e.snap,i=e.positionBuffer,a=e.positionFractBuffer,s=e.selectionBuffer;if(t.x||t.y)return t.x.length?e.xAttr={buffer:o.buffer(t.x),offset:0,stride:4,count:t.x.length}:e.xAttr={buffer:t.x.buffer,offset:4*t.x.offset||0,stride:4*(t.x.stride||1),count:t.x.count},t.y.length?e.yAttr={buffer:o.buffer(t.y),offset:0,stride:4,count:t.y.length}:e.yAttr={buffer:t.y.buffer,offset:4*t.y.offset||0,stride:4*(t.y.stride||1),count:t.y.count},e.count=Math.max(e.xAttr.count,e.yAttr.count),t;t=m(t,"float64");var c=e.count=Math.floor(t.length/2),f=e.bounds=c?l(t,2):null;if(r.range||e.range||(delete e.range,r.range=f),r.marker||e.marker||(delete e.marker,r.marker=null),n&&(!0===n||c>n)?e.tree=u(t,{bounds:f}):n&&n.length&&(e.tree=n),e.tree){var h={primitive:"points",usage:"static",data:e.tree,type:"uint32"};e.elements?e.elements(h):e.elements=o.elements(h)}var p=v.float32(t);return i({data:p,usage:"dynamic"}),a({data:v.fract32(t,p),usage:"dynamic"}),s({data:new Uint8Array(c),type:"uint8",usage:"stream"}),t}},{marker:function(e,r,n){var i=r.activation;if(i.forEach((function(t){return t&&t.destroy&&t.destroy()})),i.length=0,e&&"number"!=typeof e[0]){for(var a=[],s=0,l=Math.min(e.length,r.count);s<l;s++){var c=t.addMarker(e[s]);a[c]||(a[c]=new Uint8Array(r.count)),a[c][s]=1}for(var u=0;u<a.length;u++)if(a[u]){var f={data:a[u],type:"uint8",usage:"static"};i[u]?i[u](f):i[u]=o.buffer(f),i[u].data=a[u]}}else{i[t.addMarker(e)]=!0}return e},range:function(t,e,r){var n=e.bounds;if(n)return 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n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30*(i.month()-1)+365*(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30*(n-1)+1;return this.newDate(r,n,i)}}),n.calendars.ethiopian=a},{"../main":341,"object-assign":242}],331:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}function o(t,e){return t-e*Math.floor(t/e)}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Hebrew",jdEpoch:347995.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29,29],hasYearZero:!1,minMonth:1,firstMonth:7,minDay:1,regionalOptions:{"":{name:"Hebrew",epochs:["BAM","AM"],monthNames:["Nisan","Iyar","Sivan","Tammuz","Av","Elul","Tishrei","Cheshvan","Kislev","Tevet","Shevat","Adar","Adar II"],monthNamesShort:["Nis","Iya","Siv","Tam","Av","Elu","Tis","Che","Kis","Tev","She","Ada","Ad2"],dayNames:["Yom Rishon","Yom Sheni","Yom Shlishi","Yom Revi'i","Yom Chamishi","Yom Shishi","Yom Shabbat"],dayNamesShort:["Ris","She","Shl","Rev","Cha","Shi","Sha"],dayNamesMin:["Ri","She","Shl","Re","Ch","Shi","Sha"],digits:null,dateFormat:"dd/mm/yyyy",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return this._leapYear(e.year())},_leapYear:function(t){return o(7*(t=t<0?t+1:t)+1,19)<7},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),this._leapYear(t.year?t.year():t)?13:12},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),this.toJD(-1===t?1:t+1,7,1)-this.toJD(t,7,1)},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),12===e&&this.leapYear(t)||8===e&&5===o(this.daysInYear(t),10)?30:9===e&&3===o(this.daysInYear(t),10)?29:this.daysPerMonth[e-1]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{yearType:(this.leapYear(i)?"embolismic":"common")+" "+["deficient","regular","complete"][this.daysInYear(i)%10-3]}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t<=0?t+1:t,o=this.jdEpoch+this._delay1(a)+this._delay2(a)+r+1;if(e<7){for(var s=7;s<=this.monthsInYear(t);s++)o+=this.daysInMonth(t,s);for(s=1;s<e;s++)o+=this.daysInMonth(t,s)}else for(s=7;s<e;s++)o+=this.daysInMonth(t,s);return o},_delay1:function(t){var e=Math.floor((235*t-234)/19),r=12084+13753*e,n=29*e+Math.floor(r/25920);return o(3*(n+1),7)<3&&n++,n},_delay2:function(t){var e=this._delay1(t-1),r=this._delay1(t);return this._delay1(t+1)-r==356?2:r-e==382?1:0},fromJD:function(t){t=Math.floor(t)+.5;for(var e=Math.floor(98496*(t-this.jdEpoch)/35975351)-1;t>=this.toJD(-1===e?1:e+1,7,1);)e++;for(var r=t<this.toJD(e,1,1)?7:1;t>this.toJD(e,r,this.daysInMonth(e,r));)r++;var n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.hebrew=a},{"../main":341,"object-assign":242}],332:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Islamic",jdEpoch:1948439.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Islamic",epochs:["BH","AH"],monthNames:["Muharram","Safar","Rabi' al-awwal","Rabi' al-thani","Jumada al-awwal","Jumada al-thani","Rajab","Sha'aban","Ramadan","Shawwal","Dhu al-Qi'dah","Dhu al-Hijjah"],monthNamesShort:["Muh","Saf","Rab1","Rab2","Jum1","Jum2","Raj","Sha'","Ram","Shaw","DhuQ","DhuH"],dayNames:["Yawm al-ahad","Yawm al-ithnayn","Yawm ath-thulaathaa'","Yawm al-arbi'aa'","Yawm al-kham\u012bs","Yawm al-jum'a","Yawm as-sabt"],dayNamesShort:["Aha","Ith","Thu","Arb","Kha","Jum","Sab"],dayNamesMin:["Ah","It","Th","Ar","Kh","Ju","Sa"],digits:null,dateFormat:"yyyy/mm/dd",firstDay:6,isRTL:!1}},leapYear:function(t){return(11*this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year()+14)%30<11},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return this.leapYear(t)?355:354},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),t=t<=0?t+1:t,(r=i.day())+Math.ceil(29.5*(e-1))+354*(t-1)+Math.floor((3+11*t)/30)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t)+.5;var e=Math.floor((30*(t-this.jdEpoch)+10646)/10631);e=e<=0?e-1:e;var r=Math.min(12,Math.ceil((t-29-this.toJD(e,1,1))/29.5)+1),n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.islamic=a},{"../main":341,"object-assign":242}],333:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Julian",jdEpoch:1721423.5,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Julian",epochs:["BC","AD"],monthNames:["January","February","March","April","May","June","July","August","September","October","November","December"],monthNamesShort:["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"],dayNames:["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"],dayNamesShort:["Sun","Mon","Tue","Wed","Thu","Fri","Sat"],dayNamesMin:["Su","Mo","Tu","We","Th","Fr","Sa"],digits:null,dateFormat:"mm/dd/yyyy",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()<0?e.year()+1:e.year())%4==0},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(4-(n.dayOfWeek()||7),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),r=i.day(),t<0&&t++,e<=2&&(t--,e+=12),Math.floor(365.25*(t+4716))+Math.floor(30.6001*(e+1))+r-1524.5},fromJD:function(t){var e=Math.floor(t+.5)+1524,r=Math.floor((e-122.1)/365.25),n=Math.floor(365.25*r),i=Math.floor((e-n)/30.6001),a=i-Math.floor(i<14?1:13),o=r-Math.floor(a>2?4716:4715),s=e-n-Math.floor(30.6001*i);return o<=0&&o--,this.newDate(o,a,s)}}),n.calendars.julian=a},{"../main":341,"object-assign":242}],334:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}function o(t,e){return t-e*Math.floor(t/e)}function s(t,e){return o(t-1,e)+1}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Mayan",jdEpoch:584282.5,hasYearZero:!0,minMonth:0,firstMonth:0,minDay:0,regionalOptions:{"":{name:"Mayan",epochs:["",""],monthNames:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17"],monthNamesShort:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17"],dayNames:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19"],dayNamesShort:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19"],dayNamesMin:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19"],digits:null,dateFormat:"YYYY.m.d",firstDay:0,isRTL:!1,haabMonths:["Pop","Uo","Zip","Zotz","Tzec","Xul","Yaxkin","Mol","Chen","Yax","Zac","Ceh","Mac","Kankin","Muan","Pax","Kayab","Cumku","Uayeb"],tzolkinMonths:["Imix","Ik","Akbal","Kan","Chicchan","Cimi","Manik","Lamat","Muluc","Oc","Chuen","Eb","Ben","Ix","Men","Cib","Caban","Etznab","Cauac","Ahau"]}},leapYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+"."+Math.floor(t/20)+"."+t%20},forYear:function(t){if((t=t.split(".")).length<3)throw"Invalid Mayan year";for(var e=0,r=0;r<t.length;r++){var n=parseInt(t[r],10);if(Math.abs(n)>19||r>0&&n<0)throw"Invalid Mayan year";e=20*e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,n.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate).day()},weekDay:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),!0},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate).toJD(),a=this._toHaab(i),o=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[o[0]-1],tzolkinDay:o[0],tzolkinTrecena:o[1]}},_toHaab:function(t){var e=o((t-=this.jdEpoch)+8+340,365);return[Math.floor(e/20)+1,o(e,20)]},_toTzolkin:function(t){return[s((t-=this.jdEpoch)+20,20),s(t+4,13)]},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return i.day()+20*i.month()+360*i.year()+this.jdEpoch},fromJD:function(t){t=Math.floor(t)+.5-this.jdEpoch;var e=Math.floor(t/360);t%=360,t+=t<0?360:0;var r=Math.floor(t/20),n=t%20;return this.newDate(e,r,n)}}),n.calendars.mayan=a},{"../main":341,"object-assign":242}],335:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new n.baseCalendar;var o=n.instance("gregorian");i(a.prototype,{name:"Nanakshahi",jdEpoch:2257673.5,daysPerMonth:[31,31,31,31,31,30,30,30,30,30,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Nanakshahi",epochs:["BN","AN"],monthNames:["Chet","Vaisakh","Jeth","Harh","Sawan","Bhadon","Assu","Katak","Maghar","Poh","Magh","Phagun"],monthNamesShort:["Che","Vai","Jet","Har","Saw","Bha","Ass","Kat","Mgr","Poh","Mgh","Pha"],dayNames:["Somvaar","Mangalvar","Budhvaar","Veervaar","Shukarvaar","Sanicharvaar","Etvaar"],dayNamesShort:["Som","Mangal","Budh","Veer","Shukar","Sanichar","Et"],dayNamesMin:["So","Ma","Bu","Ve","Sh","Sa","Et"],digits:null,dateFormat:"dd-mm-yyyy",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[""].invalidYear);return o.leapYear(e.year()+(e.year()<1?1:0)+1469)},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(1-(n.dayOfWeek()||7),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidMonth);(t=i.year())<0&&t++;for(var a=i.day(),s=1;s<i.month();s++)a+=this.daysPerMonth[s-1];return a+o.toJD(t+1468,3,13)},fromJD:function(t){t=Math.floor(t+.5);for(var e=Math.floor((t-(this.jdEpoch-1))/366);t>=this.toJD(e+1,1,1);)e++;for(var r=t-Math.floor(this.toJD(e,1,1)+.5)+1,n=1;r>this.daysInMonth(e,n);)r-=this.daysInMonth(e,n),n++;return this.newDate(e,n,r)}}),n.calendars.nanakshahi=a},{"../main":341,"object-assign":242}],336:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Nepali",jdEpoch:1700709.5,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{"":{name:"Nepali",epochs:["BBS","ABS"],monthNames:["Baisakh","Jestha","Ashadh","Shrawan","Bhadra","Ashwin","Kartik","Mangsir","Paush","Mangh","Falgun","Chaitra"],monthNamesShort:["Bai","Je","As","Shra","Bha","Ash","Kar","Mang","Pau","Ma","Fal","Chai"],dayNames:["Aaitabaar","Sombaar","Manglbaar","Budhabaar","Bihibaar","Shukrabaar","Shanibaar"],dayNamesShort:["Aaita","Som","Mangl","Budha","Bihi","Shukra","Shani"],dayNamesMin:["Aai","So","Man","Bu","Bi","Shu","Sha"],digits:null,dateFormat:"dd/mm/yyyy",firstDay:1,isRTL:!1}},leapYear:function(t){return this.daysInYear(t)!==this.daysPerYear},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){if(t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),void 0===this.NEPALI_CALENDAR_DATA[t])return this.daysPerYear;for(var e=0,r=this.minMonth;r<=12;r++)e+=this.NEPALI_CALENDAR_DATA[t][r];return e},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),void 0===this.NEPALI_CALENDAR_DATA[t]?this.daysPerMonth[e-1]:this.NEPALI_CALENDAR_DATA[t][e]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=n.instance(),o=0,s=e,l=t;this._createMissingCalendarData(t);var c=t-(s>9||9===s&&r>=this.NEPALI_CALENDAR_DATA[l][0]?56:57);for(9!==e&&(o=r,s--);9!==s;)s<=0&&(s=12,l--),o+=this.NEPALI_CALENDAR_DATA[l][s],s--;return 9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(c)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(c,1,1).add(o,"d").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),i=e.dayOfYear(),a=r+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)++o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var c=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,c)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)void 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n.baseCalendar,i(a.prototype,{name:"UmmAlQura",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Umm al-Qura",epochs:["BH","AH"],monthNames:["Al-Muharram","Safar","Rabi' al-awwal","Rabi' Al-Thani","Jumada Al-Awwal","Jumada Al-Thani","Rajab","Sha'aban","Ramadan","Shawwal","Dhu al-Qi'dah","Dhu al-Hijjah"],monthNamesShort:["Muh","Saf","Rab1","Rab2","Jum1","Jum2","Raj","Sha'","Ram","Shaw","DhuQ","DhuH"],dayNames:["Yawm al-Ahad","Yawm al-Ithnain","Yawm al-Thal\u0101th\u0101\u2019","Yawm al-Arba\u2018\u0101\u2019","Yawm al-Kham\u012bs","Yawm al-Jum\u2018a","Yawm al-Sabt"],dayNamesMin:["Ah","Ith","Th","Ar","Kh","Ju","Sa"],digits:null,dateFormat:"yyyy/mm/dd",firstDay:6,isRTL:!0}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 355===this.daysInYear(e.year())},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){for(var e=0,r=1;r<=12;r++)e+=this.daysInMonth(t,r);return e},daysInMonth:function(t,e){for(var r=this._validate(t,e,this.minDay,n.local.invalidMonth).toJD()-24e5+.5,i=0,a=0;a<o.length;a++){if(o[a]>r)return o[i]-o[i-1];i++}return 30},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate),a=12*(i.year()-1)+i.month()-15292;return i.day()+o[a-1]-1+24e5-.5},fromJD:function(t){for(var e=t-24e5+.5,r=0,n=0;n<o.length&&!(o[n]>e);n++)r++;var i=r+15292,a=Math.floor((i-1)/12),s=a+1,l=i-12*a,c=e-o[r-1]+1;return this.newDate(s,l,c)},isValid:function(t,e,r){var i=n.baseCalendar.prototype.isValid.apply(this,arguments);return i&&(i=(t=null!=t.year?t.year:t)>=1276&&t<=1500),i},_validate:function(t,e,r,i){var a=n.baseCalendar.prototype._validate.apply(this,arguments);if(a.year<1276||a.year>1500)throw i.replace(/\{0\}/,this.local.name);return a}}),n.calendars.ummalqura=a;var 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n=t("object-assign");function i(){this.regionalOptions=[],this.regionalOptions[""]={invalidCalendar:"Calendar {0} not found",invalidDate:"Invalid {0} date",invalidMonth:"Invalid {0} month",invalidYear:"Invalid {0} year",differentCalendars:"Cannot mix {0} and {1} dates"},this.local=this.regionalOptions[""],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(c.local.invalidDate||c.regionalOptions[""].invalidDate).replace(/\{0\}/,this._calendar.local.name)}function o(t,e){return"000000".substring(0,e-(t=""+t).length)+t}function s(){this.shortYearCutoff="+10"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[""]}n(i.prototype,{instance:function(t,e){t=(t||"gregorian").toLowerCase(),e=e||"";var r=this._localCals[t+"-"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+"-"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[""].invalidCalendar).replace(/\{0\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():"string"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+"").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n="",i=0;r>0;){var a=r%10;n=(0===a?"":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,"y")},month:function(t){return 0===arguments.length?this._month:this.set(t,"m")},day:function(t){return 0===arguments.length?this._day:this.set(t,"d")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(c.local.invalidDate||c.regionalOptions[""].invalidDate).replace(/\{0\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(c.local.differentCalendars||c.regionalOptions[""].differentCalendars).replace(/\{0\}/,this._calendar.local.name).replace(/\{1\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?"-":"")+o(Math.abs(this.year()),4)+"-"+o(this.month(),2)+"-"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear);return(e.year()<0?"-":"")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,c.local.invalidMonth||c.regionalOptions[""].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,c.local.invalidMonth||c.regionalOptions[""].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,c.local.invalidDate||c.regionalOptions[""].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,"d"===r||"w"===r){var n=t.toJD()+e*("w"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+("y"===r?e:0),o=t.monthOfYear()+("m"===r?e:0);i=t.day();"y"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):"m"===r&&(!function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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":r._dfltTitle.annotation),a("textangle"),n.coerceFont(a,"font",r.font),a("width"),a("align"),a("height")&&a("valign"),u){var f,h,p=a("arrowside");-1!==p.indexOf("end")&&(f=a("arrowhead"),h=a("arrowsize")),-1!==p.indexOf("start")&&(a("startarrowhead",f),a("startarrowsize",h)),a("arrowcolor",l?e.bordercolor:i.defaultLine),a("arrowwidth",2*(l&&c||1)),a("standoff"),a("startstandoff")}var d=a("hovertext"),m=r.hoverlabel||{};if(d){var g=a("hoverlabel.bgcolor",m.bgcolor||(i.opacity(o)?i.rgb(o):i.defaultLine)),v=a("hoverlabel.bordercolor",m.bordercolor||i.contrast(g));n.coerceFont(a,"hoverlabel.font",{family:m.font.family,size:m.font.size,color:m.font.color||v})}a("captureevents",!!d)}},{"../../lib":498,"../color":361}],348:[function(t,e,r){"use strict";var n=t("fast-isnumeric"),i=t("../../lib/to_log_range");e.exports=function(t,e,r,a){e=e||{};var o="log"===r&&"linear"===e.type,s="linear"===r&&"log"===e.type;if(o||s)for(var l,c,u=t._fullLayout.annotations,f=e._id.charAt(0),h=0;h<u.length;h++)l=u[h],c="annotations["+h+"].",l[f+"ref"]===e._id&&p(f),l["a"+f+"ref"]===e._id&&p("a"+f);function p(t){var r=l[t],s=null;s=o?i(r,e.range):Math.pow(10,r),n(s)||(s=null),a(c+t,s)}}},{"../../lib/to_log_range":526,"fast-isnumeric":185}],349:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../plots/cartesian/axes"),a=t("../../plots/array_container_defaults"),o=t("./common_defaults"),s=t("./attributes");function l(t,e,r){function a(r,i){return n.coerce(t,e,s,r,i)}var l=a("visible"),c=a("clicktoshow");if(l||c){o(t,e,r,a);for(var u=e.showarrow,f=["x","y"],h=[-10,-30],p={_fullLayout:r},d=0;d<2;d++){var m=f[d],g=i.coerceRef(t,e,p,m,"","paper");if("paper"!==g)i.getFromId(p,g)._annIndices.push(e._index);if(i.coercePosition(e,p,a,g,m,.5),u){var v="a"+m,y=i.coerceRef(t,e,p,v,"pixel",["pixel","paper"]);"pixel"!==y&&y!==g&&(y=e[v]="pixel");var x="pixel"===y?h[d]:.4;i.coercePosition(e,p,a,y,v,x)}a(m+"anchor"),a(m+"shift")}if(n.noneOrAll(t,e,["x","y"]),u&&n.noneOrAll(t,e,["ax","ay"]),c){var b=a("xclick"),_=a("yclick");e._xclick=void 0===b?e.x:i.cleanPosition(b,p,e.xref),e._yclick=void 0===_?e.y:i.cleanPosition(_,p,e.yref)}}}e.exports=function(t,e){a(t,e,{name:"annotations",handleItemDefaults:l})}},{"../../lib":498,"../../plots/array_container_defaults":544,"../../plots/cartesian/axes":549,"./attributes":344,"./common_defaults":347}],350:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../registry"),a=t("../../plots/plots"),o=t("../../lib"),s=o.strTranslate,l=t("../../plots/cartesian/axes"),c=t("../color"),u=t("../drawing"),f=t("../fx"),h=t("../../lib/svg_text_utils"),p=t("../../lib/setcursor"),d=t("../dragelement"),m=t("../../plot_api/plot_template").arrayEditor,g=t("./draw_arrow_head");function v(t,e){var r=t._fullLayout.annotations[e]||{},n=l.getFromId(t,r.xref),i=l.getFromId(t,r.yref);n&&n.setScale(),i&&i.setScale(),x(t,r,e,!1,n,i)}function y(t,e,r,n,i){var a=i[r],o=i[r+"ref"],s=-1!==r.indexOf("y"),c="domain"===l.getRefType(o),u=s?n.h:n.w;return t?c?a+(s?-e:e)/t._length:t.p2r(t.r2p(a)+e):a+(s?-e:e)/u}function x(t,e,r,a,v,x){var b,_,w=t._fullLayout,T=t._fullLayout._size,k=t._context.edits;a?(b="annotation-"+a,_=a+".annotations"):(b="annotation",_="annotations");var A=m(t.layout,_,e),M=A.modifyBase,S=A.modifyItem,E=A.getUpdateObj;w._infolayer.selectAll("."+b+'[data-index="'+r+'"]').remove();var L="clip"+w._uid+"_ann"+r;if(e._input&&!1!==e.visible){var C={x:{},y:{}},P=+e.textangle||0,I=w._infolayer.append("g").classed(b,!0).attr("data-index",String(r)).style("opacity",e.opacity),O=I.append("g").classed("annotation-text-g",!0),z=k[e.showarrow?"annotationTail":"annotationPosition"],D=e.captureevents||k.annotationText||z,R=O.append("g").style("pointer-events",D?"all":null).call(p,"pointer").on("click",(function(){t._dragging=!1,t.emit("plotly_clickannotation",Y(n.event))}));e.hovertext&&R.on("mouseover",(function(){var r=e.hoverlabel,n=r.font,i=this.getBoundingClientRect(),a=t.getBoundingClientRect();f.loneHover({x0:i.left-a.left,x1:i.right-a.left,y:(i.top+i.bottom)/2-a.top,text:e.hovertext,color:r.bgcolor,borderColor:r.bordercolor,fontFamily:n.family,fontSize:n.size,fontColor:n.color},{container:w._hoverlayer.node(),outerContainer:w._paper.node(),gd:t})})).on("mouseout",(function(){f.loneUnhover(w._hoverlayer.node())}));var F=e.borderwidth,B=e.borderpad,N=F+B,j=R.append("rect").attr("class","bg").style("stroke-width",F+"px").call(c.stroke,e.bordercolor).call(c.fill,e.bgcolor),U=e.width||e.height,V=w._topclips.selectAll("#"+L).data(U?[0]:[]);V.enter().append("clipPath").classed("annclip",!0).attr("id",L).append("rect"),V.exit().remove();var H=e.font,q=w._meta?o.templateString(e.text,w._meta):e.text,G=R.append("text").classed("annotation-text",!0).text(q);k.annotationText?G.call(h.makeEditable,{delegate:R,gd:t}).call(W).on("edit",(function(r){e.text=r,this.call(W),S("text",r),v&&v.autorange&&M(v._name+".autorange",!0),x&&x.autorange&&M(x._name+".autorange",!0),i.call("_guiRelayout",t,E())})):G.call(W)}else n.selectAll("#"+L).remove();function Y(t){var n={index:r,annotation:e._input,fullAnnotation:e,event:t};return a&&(n.subplotId=a),n}function W(r){return r.call(u.font,H).attr({"text-anchor":{left:"start",right:"end"}[e.align]||"middle"}),h.convertToTspans(r,t,X),r}function X(){var r=G.selectAll("a");1===r.size()&&r.text()===G.text()&&R.insert("a",":first-child").attr({"xlink:xlink:href":r.attr("xlink:href"),"xlink:xlink:show":r.attr("xlink:show")}).style({cursor:"pointer"}).node().appendChild(j.node());var n=R.select(".annotation-text-math-group"),f=!n.empty(),m=u.bBox((f?n:G).node()),b=m.width,_=m.height,A=e.width||b,D=e.height||_,B=Math.round(A+2*N),H=Math.round(D+2*N);function q(t,e){return"auto"===e&&(e=t<1/3?"left":t>2/3?"right":"center"),{center:0,middle:0,left:.5,bottom:-.5,right:-.5,top:.5}[e]}for(var W=!1,X=["x","y"],Z=0;Z<X.length;Z++){var J,K,Q,$,tt,et=X[Z],rt=e[et+"ref"]||et,nt=e["a"+et+"ref"],it={x:v,y:x}[et],at=(P+("x"===et?0:-90))*Math.PI/180,ot=B*Math.cos(at),st=H*Math.sin(at),lt=Math.abs(ot)+Math.abs(st),ct=e[et+"anchor"],ut=e[et+"shift"]*("x"===et?1:-1),ft=C[et],ht=l.getRefType(rt);if(it&&"domain"!==ht){var pt=it.r2fraction(e[et]);(pt<0||pt>1)&&(nt===rt?((pt=it.r2fraction(e["a"+et]))<0||pt>1)&&(W=!0):W=!0),J=it._offset+it.r2p(e[et]),$=.5}else{var dt="domain"===ht;"x"===et?(Q=e[et],J=dt?it._offset+it._length*Q:J=T.l+T.w*Q):(Q=1-e[et],J=dt?it._offset+it._length*Q:J=T.t+T.h*Q),$=e.showarrow?.5:Q}if(e.showarrow){ft.head=J;var mt=e["a"+et];if(tt=ot*q(.5,e.xanchor)-st*q(.5,e.yanchor),nt===rt){var gt=l.getRefType(nt);"domain"===gt?("y"===et&&(mt=1-mt),ft.tail=it._offset+it._length*mt):"paper"===gt?"y"===et?(mt=1-mt,ft.tail=T.t+T.h*mt):ft.tail=T.l+T.w*mt:ft.tail=it._offset+it.r2p(mt),K=tt}else ft.tail=J+mt,K=tt+mt;ft.text=ft.tail+tt;var vt=w["x"===et?"width":"height"];if("paper"===rt&&(ft.head=o.constrain(ft.head,1,vt-1)),"pixel"===nt){var yt=-Math.max(ft.tail-3,ft.text),xt=Math.min(ft.tail+3,ft.text)-vt;yt>0?(ft.tail+=yt,ft.text+=yt):xt>0&&(ft.tail-=xt,ft.text-=xt)}ft.tail+=ut,ft.head+=ut}else K=tt=lt*q($,ct),ft.text=J+tt;ft.text+=ut,tt+=ut,K+=ut,e["_"+et+"padplus"]=lt/2+K,e["_"+et+"padminus"]=lt/2-K,e["_"+et+"size"]=lt,e["_"+et+"shift"]=tt}if(W)R.remove();else{var bt=0,_t=0;if("left"!==e.align&&(bt=(A-b)*("center"===e.align?.5:1)),"top"!==e.valign&&(_t=(D-_)*("middle"===e.valign?.5:1)),f)n.select("svg").attr({x:N+bt-1,y:N+_t}).call(u.setClipUrl,U?L:null,t);else{var wt=N+_t-m.top,Tt=N+bt-m.left;G.call(h.positionText,Tt,wt).call(u.setClipUrl,U?L:null,t)}V.select("rect").call(u.setRect,N,N,A,D),j.call(u.setRect,F/2,F/2,B-F,H-F),R.call(u.setTranslate,Math.round(C.x.text-B/2),Math.round(C.y.text-H/2)),O.attr({transform:"rotate("+P+","+C.x.text+","+C.y.text+")"});var kt,At=function(r,n){I.selectAll(".annotation-arrow-g").remove();var l=C.x.head,f=C.y.head,h=C.x.tail+r,p=C.y.tail+n,m=C.x.text+r,b=C.y.text+n,_=o.rotationXYMatrix(P,m,b),w=o.apply2DTransform(_),A=o.apply2DTransform2(_),L=+j.attr("width"),z=+j.attr("height"),D=m-.5*L,F=D+L,B=b-.5*z,N=B+z,U=[[D,B,D,N],[D,N,F,N],[F,N,F,B],[F,B,D,B]].map(A);if(!U.reduce((function(t,e){return t^!!o.segmentsIntersect(l,f,l+1e6,f+1e6,e[0],e[1],e[2],e[3])}),!1)){U.forEach((function(t){var e=o.segmentsIntersect(h,p,l,f,t[0],t[1],t[2],t[3]);e&&(h=e.x,p=e.y)}));var V=e.arrowwidth,H=e.arrowcolor,q=e.arrowside,G=I.append("g").style({opacity:c.opacity(H)}).classed("annotation-arrow-g",!0),Y=G.append("path").attr("d","M"+h+","+p+"L"+l+","+f).style("stroke-width",V+"px").call(c.stroke,c.rgb(H));if(g(Y,q,e),k.annotationPosition&&Y.node().parentNode&&!a){var W=l,X=f;if(e.standoff){var Z=Math.sqrt(Math.pow(l-h,2)+Math.pow(f-p,2));W+=e.standoff*(h-l)/Z,X+=e.standoff*(p-f)/Z}var J,K,Q=G.append("path").classed("annotation-arrow",!0).classed("anndrag",!0).classed("cursor-move",!0).attr({d:"M3,3H-3V-3H3ZM0,0L"+(h-W)+","+(p-X),transform:s(W,X)}).style("stroke-width",V+6+"px").call(c.stroke,"rgba(0,0,0,0)").call(c.fill,"rgba(0,0,0,0)");d.init({element:Q.node(),gd:t,prepFn:function(){var t=u.getTranslate(R);J=t.x,K=t.y,v&&v.autorange&&M(v._name+".autorange",!0),x&&x.autorange&&M(x._name+".autorange",!0)},moveFn:function(t,r){var n=w(J,K),i=n[0]+t,a=n[1]+r;R.call(u.setTranslate,i,a),S("x",y(v,t,"x",T,e)),S("y",y(x,r,"y",T,e)),e.axref===e.xref&&S("ax",y(v,t,"ax",T,e)),e.ayref===e.yref&&S("ay",y(x,r,"ay",T,e)),G.attr("transform",s(t,r)),O.attr({transform:"rotate("+P+","+i+","+a+")"})},doneFn:function(){i.call("_guiRelayout",t,E());var e=document.querySelector(".js-notes-box-panel");e&&e.redraw(e.selectedObj)}})}}};if(e.showarrow&&At(0,0),z)d.init({element:R.node(),gd:t,prepFn:function(){kt=O.attr("transform")},moveFn:function(t,r){var n="pointer";if(e.showarrow)e.axref===e.xref?S("ax",y(v,t,"ax",T,e)):S("ax",e.ax+t),e.ayref===e.yref?S("ay",y(x,r,"ay",T.w,e)):S("ay",e.ay+r),At(t,r);else{if(a)return;var i,o;if(v)i=y(v,t,"x",T,e);else{var l=e._xsize/T.w,c=e.x+(e._xshift-e.xshift)/T.w-l/2;i=d.align(c+t/T.w,l,0,1,e.xanchor)}if(x)o=y(x,r,"y",T,e);else{var u=e._ysize/T.h,f=e.y-(e._yshift+e.yshift)/T.h-u/2;o=d.align(f-r/T.h,u,0,1,e.yanchor)}S("x",i),S("y",o),v&&x||(n=d.getCursor(v?.5:i,x?.5:o,e.xanchor,e.yanchor))}O.attr({transform:s(t,r)+kt}),p(R,n)},clickFn:function(r,n){e.captureevents&&t.emit("plotly_clickannotation",Y(n))},doneFn:function(){p(R),i.call("_guiRelayout",t,E());var e=document.querySelector(".js-notes-box-panel");e&&e.redraw(e.selectedObj)}})}}}e.exports={draw:function(t){var e=t._fullLayout;e._infolayer.selectAll(".annotation").remove();for(var r=0;r<e.annotations.length;r++)e.annotations[r].visible&&v(t,r);return a.previousPromises(t)},drawOne:v,drawRaw:x}},{"../../lib":498,"../../lib/setcursor":519,"../../lib/svg_text_utils":524,"../../plot_api/plot_template":538,"../../plots/cartesian/axes":549,"../../plots/plots":614,"../../registry":633,"../color":361,"../dragelement":380,"../drawing":383,"../fx":401,"./draw_arrow_head":351,"@plotly/d3":58}],351:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../color"),a=t("./arrow_paths"),o=t("../../lib"),s=o.strScale,l=o.strRotate,c=o.strTranslate;e.exports=function(t,e,r){var o,u,f,h,p=t.node(),d=a[r.arrowhead||0],m=a[r.startarrowhead||0],g=(r.arrowwidth||1)*(r.arrowsize||1),v=(r.arrowwidth||1)*(r.startarrowsize||1),y=e.indexOf("start")>=0,x=e.indexOf("end")>=0,b=d.backoff*g+r.standoff,_=m.backoff*v+r.startstandoff;if("line"===p.nodeName){o={x:+t.attr("x1"),y:+t.attr("y1")},u={x:+t.attr("x2"),y:+t.attr("y2")};var w=o.x-u.x,T=o.y-u.y;if(h=(f=Math.atan2(T,w))+Math.PI,b&&_&&b+_>Math.sqrt(w*w+T*T))return void z();if(b){if(b*b>w*w+T*T)return void z();var k=b*Math.cos(f),A=b*Math.sin(f);u.x+=k,u.y+=A,t.attr({x2:u.x,y2:u.y})}if(_){if(_*_>w*w+T*T)return void z();var M=_*Math.cos(f),S=_*Math.sin(f);o.x-=M,o.y-=S,t.attr({x1:o.x,y1:o.y})}}else if("path"===p.nodeName){var E=p.getTotalLength(),L="";if(E<b+_)return void z();var C=p.getPointAtLength(0),P=p.getPointAtLength(.1);f=Math.atan2(C.y-P.y,C.x-P.x),o=p.getPointAtLength(Math.min(_,E)),L="0px,"+_+"px,";var I=p.getPointAtLength(E),O=p.getPointAtLength(E-.1);h=Math.atan2(I.y-O.y,I.x-O.x),u=p.getPointAtLength(Math.max(0,E-b)),L+=E-(L?_+b:b)+"px,"+E+"px",t.style("stroke-dasharray",L)}function z(){t.style("stroke-dasharray","0px,100px")}function D(e,a,o,u){e.path&&(e.noRotate&&(o=0),n.select(p.parentNode).append("path").attr({class:t.attr("class"),d:e.path,transform:c(a.x,a.y)+l(180*o/Math.PI)+s(u)}).style({fill:i.rgb(r.arrowcolor),"stroke-width":0}))}y&&D(m,o,f,v),x&&D(d,u,h,g)}},{"../../lib":498,"../color":361,"./arrow_paths":343,"@plotly/d3":58}],352:[function(t,e,r){"use strict";var n=t("./draw"),i=t("./click");e.exports={moduleType:"component",name:"annotations",layoutAttributes:t("./attributes"),supplyLayoutDefaults:t("./defaults"),includeBasePlot:t("../../plots/cartesian/include_components")("annotations"),calcAutorange:t("./calc_autorange"),draw:n.draw,drawOne:n.drawOne,drawRaw:n.drawRaw,hasClickToShow:i.hasClickToShow,onClick:i.onClick,convertCoords:t("./convert_coords")}},{"../../plots/cartesian/include_components":562,"./attributes":344,"./calc_autorange":345,"./click":346,"./convert_coords":348,"./defaults":349,"./draw":350}],353:[function(t,e,r){"use strict";var n=t("../annotations/attributes"),i=t("../../plot_api/edit_types").overrideAll,a=t("../../plot_api/plot_template").templatedArray;e.exports=i(a("annotation",{visible:n.visible,x:{valType:"any"},y:{valType:"any"},z:{valType:"any"},ax:{valType:"number"},ay:{valType:"number"},xanchor:n.xanchor,xshift:n.xshift,yanchor:n.yanchor,yshift:n.yshift,text:n.text,textangle:n.textangle,font:n.font,width:n.width,height:n.height,opacity:n.opacity,align:n.align,valign:n.valign,bgcolor:n.bgcolor,bordercolor:n.bordercolor,borderpad:n.borderpad,borderwidth:n.borderwidth,showarrow:n.showarrow,arrowcolor:n.arrowcolor,arrowhead:n.arrowhead,startarrowhead:n.startarrowhead,arrowside:n.arrowside,arrowsize:n.arrowsize,startarrowsize:n.startarrowsize,arrowwidth:n.arrowwidth,standoff:n.standoff,startstandoff:n.startstandoff,hovertext:n.hovertext,hoverlabel:n.hoverlabel,captureevents:n.captureevents}),"calc","from-root")},{"../../plot_api/edit_types":531,"../../plot_api/plot_template":538,"../annotations/attributes":344}],354:[function(t,e,r){"use 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V,H,q,G,Y,W,X,Z,J,K,Q,$,tt,et=[],rt=[],nt={hLinePoint:null,vLinePoint:null},it=!1;if(Array.isArray(e))for(P="array",q=0;q<e.length;q++)(Y=t.calcdata[e[q].curveNumber||0])&&(W=Y[0].trace,"skip"!==Y[0].trace.hoverinfo&&(rt.push(Y),"h"===W.orientation&&(it=!0)));else{for(G=0;G<t.calcdata.length;G++)Y=t.calcdata[G],"skip"!==(W=Y[0].trace).hoverinfo&&v.isTraceInSubplots(W,l)&&(rt.push(Y),"h"===W.orientation&&(it=!0));var at,ot;if(!s)at="xpx"in e?e.xpx:_[0]._length/2,ot="ypx"in e?e.ypx:w[0]._length/2;else{if(!1===c.triggerHandler(t,"plotly_beforehover",e))return;var st=s.getBoundingClientRect();at=e.clientX-st.left,ot=e.clientY-st.top,u._calcInverseTransform(t);var lt=o.apply3DTransform(u._invTransform)(at,ot);if(at=lt[0],ot=lt[1],at<0||at>_[0]._length||ot<0||ot>w[0]._length)return d.unhoverRaw(t,e)}if(e.pointerX=at+_[0]._offset,e.pointerY=ot+w[0]._offset,V="xval"in e?v.flat(l,e.xval):v.p2c(_,at),H="yval"in e?v.flat(l,e.yval):v.p2c(w,ot),!i(V[0])||!i(H[0]))return o.warn("Fx.hover 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n=t("@plotly/d3"),i=t("../../lib"),a=t("../dragelement"),o=t("./helpers"),s=t("./layout_attributes"),l=t("./hover");e.exports={moduleType:"component",name:"fx",constants:t("./constants"),schema:{layout:s},attributes:t("./attributes"),layoutAttributes:s,supplyLayoutGlobalDefaults:t("./layout_global_defaults"),supplyDefaults:t("./defaults"),supplyLayoutDefaults:t("./layout_defaults"),calc:t("./calc"),getDistanceFunction:o.getDistanceFunction,getClosest:o.getClosest,inbox:o.inbox,quadrature:o.quadrature,appendArrayPointValue:o.appendArrayPointValue,castHoverOption:function(t,e,r){return i.castOption(t,e,"hoverlabel."+r)},castHoverinfo:function(t,e,r){return i.castOption(t,r,"hoverinfo",(function(r){return i.coerceHoverinfo({hoverinfo:r},{_module:t._module},e)}))},hover:l.hover,unhover:a.unhover,loneHover:l.loneHover,loneUnhover:function(t){var 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n=t("../../plots/cartesian/constants"),i=t("../../plot_api/plot_template").templatedArray;t("../../constants/axis_placeable_objects");e.exports=i("image",{visible:{valType:"boolean",dflt:!0,editType:"arraydraw"},source:{valType:"string",editType:"arraydraw"},layer:{valType:"enumerated",values:["below","above"],dflt:"above",editType:"arraydraw"},sizex:{valType:"number",dflt:0,editType:"arraydraw"},sizey:{valType:"number",dflt:0,editType:"arraydraw"},sizing:{valType:"enumerated",values:["fill","contain","stretch"],dflt:"contain",editType:"arraydraw"},opacity:{valType:"number",min:0,max:1,dflt:1,editType:"arraydraw"},x:{valType:"any",dflt:0,editType:"arraydraw"},y:{valType:"any",dflt:0,editType:"arraydraw"},xanchor:{valType:"enumerated",values:["left","center","right"],dflt:"left",editType:"arraydraw"},yanchor:{valType:"enumerated",values:["top","middle","bottom"],dflt:"top",editType:"arraydraw"},xref:{valType:"enumerated",values:["paper",n.idRegex.x.toString()],dflt:"paper",editType:"arraydraw"},yref:{valType:"enumerated",values:["paper",n.idRegex.y.toString()],dflt:"paper",editType:"arraydraw"},editType:"arraydraw"})},{"../../constants/axis_placeable_objects":467,"../../plot_api/plot_template":538,"../../plots/cartesian/constants":556}],407:[function(t,e,r){"use 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r=t[0].trace,o=n.select(this).select("g.legendpoints").selectAll("path.legendbox").data(r.visible&&i.traceIs(r,"box-violin")?[t]:[]);o.enter().append("path").classed("legendbox",!0).attr("d","M6,6H-6V-6H6Z").attr("transform",_),o.exit().remove(),o.each((function(){var t=n.select(this);if("all"!==r.boxpoints&&"all"!==r.points||0!==l.opacity(r.fillcolor)||0!==l.opacity((r.line||{}).color)){var i=w(void 0,r.line,5,2);t.style("stroke-width",i+"px").call(l.fill,r.fillcolor),i&&l.stroke(t,r.line.color)}else{var c=a.minExtend(r,{marker:{size:y?12:a.constrain(r.marker.size,2,16),sizeref:1,sizemin:1,sizemode:"diameter"}});o.call(s.pointStyle,c,e)}}))})).each((function(t){k(t,this,"funnelarea")})).each((function(t){k(t,this,"pie")})).each((function(t){var 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p,d="in"===l?.5:2,m=(1+d)/2,g=(1-d)/2;for(i=0;i<f.length;i++)if(!(r=f[i]).fixedrange)if(p=r._name,"auto"===l)u[p+".autorange"]=!0;else if("reset"===l){if(void 0===r._rangeInitial)u[p+".autorange"]=!0;else{var v=r._rangeInitial.slice();u[p+".range[0]"]=v[0],u[p+".range[1]"]=v[1]}void 0!==r._showSpikeInitial&&(u[p+".showspikes"]=r._showSpikeInitial,"on"!==h||r._showSpikeInitial||(h="off"))}else{var y=[r.r2l(r.range[0]),r.r2l(r.range[1])],x=[m*y[0]+g*y[1],m*y[1]+g*y[0]];u[p+".range[0]"]=r.l2r(x[0]),u[p+".range[1]"]=r.l2r(x[1])}}else"hovermode"!==s||"x"!==l&&"y"!==l||(l=c._isHoriz?"y":"x",o.setAttribute("data-val",l)),u[s]=l;c._cartesianSpikesEnabled=h,n.call("_guiRelayout",t,u)}function h(t,e){for(var r=e.currentTarget,i=r.getAttribute("data-attr"),a=r.getAttribute("data-val")||!0,o=t._fullLayout._subplots.gl3d||[],s={},l=i.split("."),c=0;c<o.length;c++)s[o[c]+"."+l[1]]=a;var u="pan"===a?a:"zoom";s.dragmode=u,n.call("_guiRelayout",t,s)}function p(t,e){for(var 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r=e.currentTarget,i=r.getAttribute("data-attr"),a=r.getAttribute("data-val")||!0,o=t._fullLayout,s=o._subplots.geo||[],l=0;l<s.length;l++){var c=s[l],u=o[c];if("zoom"===i){var f=u.projection.scale,h="in"===a?2*f:.5*f;n.call("_guiRelayout",t,c+".projection.scale",h)}}"reset"===i&&x(t,"geo")}function g(t){var e=t._fullLayout;return!e.hovermode&&(e._has("cartesian")?e._isHoriz?"y":"x":"closest")}function v(t){var e=g(t);n.call("_guiRelayout",t,"hovermode",e)}function y(t,e){for(var r=e.currentTarget.getAttribute("data-val"),i=t._fullLayout,a=i._subplots.mapbox||[],o={},s=0;s<a.length;s++){var l=a[s],c=i[l].zoom,u="in"===r?1.05*c:c/1.05;o[l+".zoom"]=u}n.call("_guiRelayout",t,o)}function x(t,e){for(var r=t._fullLayout,i=r._subplots[e]||[],a={},o=0;o<i.length;o++)for(var s=i[o],l=r[s]._subplot.viewInitial,c=Object.keys(l),u=0;u<c.length;u++){var f=c[u];a[s+"."+f]=l[f]}n.call("_guiRelayout",t,a)}u.toImage={name:"toImage",title:function(t){var e=(t._context.toImageButtonOptions||{}).format||"png";return c(t,"png"===e?"Download plot as a png":"Download plot")},icon:o.camera,click:function(t){var e=t._context.toImageButtonOptions,r={format:e.format||"png"};l.notifier(c(t,"Taking snapshot - this may take a few seconds"),"long"),"svg"!==r.format&&l.isIE()&&(l.notifier(c(t,"IE only supports svg.  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hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:v},u.hoverClosestGl2d={name:"hoverClosestGl2d",_cat:"hoverclosest",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:v},u.hoverClosestPie={name:"hoverClosestPie",_cat:"hoverclosest",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:"closest",icon:o.tooltip_basic,gravity:"ne",click:v},u.resetViewSankey={name:"resetSankeyGroup",title:function(t){return c(t,"Reset view")},icon:o.home,click:function(t){for(var e={"node.groups":[],"node.x":[],"node.y":[]},r=0;r<t._fullData.length;r++){var i=t._fullData[r]._viewInitial;e["node.groups"].push(i.node.groups.slice()),e["node.x"].push(i.node.x.slice()),e["node.y"].push(i.node.y.slice())}n.call("restyle",t,e)}},u.toggleHover={name:"toggleHover",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:function(t,e){var r=d(t,e);r.hovermode=g(t),n.call("_guiRelayout",t,r)}},u.resetViews={name:"resetViews",title:function(t){return c(t,"Reset views")},icon:o.home,click:function(t,e){var r=e.currentTarget;r.setAttribute("data-attr","zoom"),r.setAttribute("data-val","reset"),f(t,e),r.setAttribute("data-attr","resetLastSave"),p(t,e),x(t,"geo"),x(t,"mapbox")}},u.toggleSpikelines={name:"toggleSpikelines",title:function(t){return c(t,"Toggle Spike Lines")},icon:o.spikeline,attr:"_cartesianSpikesEnabled",val:"on",click:function(t){var e=t._fullLayout,r=e._cartesianSpikesEnabled;e._cartesianSpikesEnabled="on"===r?"off":"on",n.call("_guiRelayout",t,function(t){for(var e="on"===t._fullLayout._cartesianSpikesEnabled,r=a.list(t,null,!0),n={},i=0;i<r.length;i++){var o=r[i];n[o._name+".showspikes"]=!!e||o._showSpikeInitial}return n}(t))}},u.resetViewMapbox={name:"resetViewMapbox",_cat:"resetView",title:function(t){return c(t,"Reset view")},attr:"reset",icon:o.home,click:function(t){x(t,"mapbox")}},u.zoomInMapbox={name:"zoomInMapbox",_cat:"zoomin",title:function(t){return c(t,"Zoom in")},attr:"zoom",val:"in",icon:o.zoom_plus,click:y},u.zoomOutMapbox={name:"zoomOutMapbox",_cat:"zoomout",title:function(t){return c(t,"Zoom out")},attr:"zoom",val:"out",icon:o.zoom_minus,click:y}},{"../../fonts/ploticon":477,"../../lib":498,"../../plots/cartesian/axis_ids":553,"../../plots/plots":614,"../../registry":633,"../shapes/draw":445}],422:[function(t,e,r){"use strict";var n=t("./buttons"),i=Object.keys(n),a=["drawline","drawopenpath","drawclosedpath","drawcircle","drawrect","eraseshape"],o=["v1hovermode","hoverclosest","hovercompare","togglehover","togglespikelines"].concat(a),s=[];i.forEach((function(t){!function(t){if(-1===o.indexOf(t._cat||t.name)){var 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r,n,a,o,s=t._fullLayout,c=s._plots,u=s._subplots.cartesian;if(ut&&l.subplotsRegistry.splom.drag(t),ct)for(r=0;r<u.length;r++)if(a=(n=c[u[r]]).xaxis,o=n.yaxis,n._scene){var f=i.simpleMap(a.range,a.r2l),p=i.simpleMap(o.range,o.r2l);n._scene.update({range:[f[0],p[0],f[1],p[1]]})}if((ut||ct)&&(_(t),w(t)),ft){var d=e[2]/I._length,g=e[3]/O._length;for(r=0;r<u.length;r++){a=(n=c[u[r]]).xaxis,o=n.yaxis;var y,x,b,T,k=(st||nt.isSubplotConstrained)&&!a.fixedrange&&V[a._id],A=(lt||nt.isSubplotConstrained)&&!o.fixedrange&&Z[o._id];if(k?(y=d,b=v||nt.isSubplotConstrained?e[0]:Zt(a,y)):nt.xaHash[a._id]?(y=d,b=e[0]*a._length/I._length):nt.yaHash[a._id]?(y=g,b="ns"===at?-e[1]*a._length/O._length:Zt(a,y,{n:"top",s:"bottom"}[at])):b=Xt(a,y=Wt(a,d,g)),A?(x=g,T=m||nt.isSubplotConstrained?e[1]:Zt(o,x)):nt.yaHash[o._id]?(x=g,T=e[1]*o._length/O._length):nt.xaHash[o._id]?(x=d,T="ew"===it?-e[0]*o._length/I._length:Zt(o,x,{e:"right",w:"left"}[it])):T=Xt(o,x=Wt(o,d,g)),y||x){y||(y=1),x||(x=1);var M=a._offset-b/y,S=o._offset-T/x;n.clipRect.call(h.setTranslate,b,T).call(h.setScale,y,x),n.plot.call(h.setTranslate,M,S).call(h.setScale,1/y,1/x),y===n.xScaleFactor&&x===n.yScaleFactor||(h.setPointGroupScale(n.zoomScalePts,y,x),h.setTextPointsScale(n.zoomScaleTxt,y,x)),h.hideOutsideRangePoints(n.clipOnAxisFalseTraces,n),n.xScaleFactor=y,n.yScaleFactor=x}}}}function Wt(t,e,r){return t.fixedrange?0:st&&rt.xaHash[t._id]?e:lt&&(rt.isSubplotConstrained?rt.xaHash:rt.yaHash)[t._id]?r:0}function Xt(t,e){return e?(t.range=t._r.slice(),E(t,e),Zt(t,e)):0}function Zt(t,e,r){return t._length*(1-e)*b[r||t.constraintoward||"middle"]}return m.length*v.length!=1&&W(bt,(function(e){if(t._context._scrollZoom.cartesian||t._fullLayout._enablescrollzoom){if(It(),t._transitioningWithDuration)return e.preventDefault(),void e.stopPropagation();yt(),clearTimeout(Bt);var r=-e.deltaY;if(isFinite(r)||(r=e.wheelDelta/10),isFinite(r)){var n,a=Math.exp(-Math.min(Math.max(r,-20),20)/200),o=jt.draglayer.select(".nsewdrag").node().getBoundingClientRect(),s=(e.clientX-o.left)/o.width,l=(o.bottom-e.clientY)/o.height;if(st){for(v||(s=.5),n=0;n<J.length;n++)c(J[n],s,a);Vt("x"),Ft[2]*=a,Ft[0]+=Ft[2]*s*(1/a-1)}if(lt){for(m||(l=.5),n=0;n<K.length;n++)c(K[n],l,a);Vt("y"),Ft[3]*=a,Ft[1]+=Ft[3]*(1-l)*(1/a-1)}Yt(Ft),Ht(),t.emit("plotly_relayouting",ht),Bt=setTimeout((function(){t._fullLayout&&(Ft=[0,0,tt,et],Gt())}),Nt),e.preventDefault()}else i.log("Did not find wheel motion attributes: ",e)}function c(t,e,r){if(!t.fixedrange){var n=i.simpleMap(t.range,t.r2l),a=n[0]+(n[1]-n[0])*e;t.range=n.map((function(e){return t.l2r(a+(e-a)*r)}))}}})),bt},makeDragger:O,makeRectDragger:z,makeZoombox:N,makeCorners:j,updateZoombox:U,xyCorners:G,transitionZoombox:V,removeZoombox:H,showDoubleClickNotifier:q,attachWheelEventHandler:W}},{"../../components/color":361,"../../components/dragelement":380,"../../components/dragelement/helpers":379,"../../components/drawing":383,"../../components/fx":401,"../../constants/alignment":466,"../../lib":498,"../../lib/clear_gl_canvases":482,"../../lib/setcursor":519,"../../lib/svg_text_utils":524,"../../plot_api/subroutines":539,"../../registry":633,"../plots":614,"./axes":549,"./axis_ids":553,"./constants":556,"./scale_zoom":569,"./select":570,"@plotly/d3":58,"has-passive-events":224,tinycolor2:307}],559:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../components/fx"),a=t("../../components/dragelement"),o=t("../../lib/setcursor"),s=t("./dragbox").makeDragBox,l=t("./constants").DRAGGERSIZE;r.initInteractions=function(t){var e=t._fullLayout;if(t._context.staticPlot)n.select(t).selectAll(".drag").remove();else if(e._has("cartesian")||e._has("splom")){Object.keys(e._plots||{}).sort((function(t,r){if((e._plots[t].mainplot&&!0)===(e._plots[r].mainplot&&!0)){var n=t.split("y"),i=r.split("y");return n[0]===i[0]?Number(n[1]||1)-Number(i[1]||1):Number(n[0]||1)-Number(i[0]||1)}return e._plots[t].mainplot?1:-1})).forEach((function(r){var n=e._plots[r],o=n.xaxis,c=n.yaxis;if(!n.mainplot){var u=s(t,n,o._offset,c._offset,o._length,c._length,"ns","ew");u.onmousemove=function(e){t._fullLayout._rehover=function(){t._fullLayout._hoversubplot===r&&t._fullLayout._plots[r]&&i.hover(t,e,r)},i.hover(t,e,r),t._fullLayout._lasthover=u,t._fullLayout._hoversubplot=r},u.onmouseout=function(e){t._dragging||(t._fullLayout._hoversubplot=null,a.unhover(t,e))},t._context.showAxisDragHandles&&(s(t,n,o._offset-l,c._offset-l,l,l,"n","w"),s(t,n,o._offset+o._length,c._offset-l,l,l,"n","e"),s(t,n,o._offset-l,c._offset+c._length,l,l,"s","w"),s(t,n,o._offset+o._length,c._offset+c._length,l,l,"s","e"))}if(t._context.showAxisDragHandles){if(r===o._mainSubplot){var f=o._mainLinePosition;"top"===o.side&&(f-=l),s(t,n,o._offset+.1*o._length,f,.8*o._length,l,"","ew"),s(t,n,o._offset,f,.1*o._length,l,"","w"),s(t,n,o._offset+.9*o._length,f,.1*o._length,l,"","e")}if(r===c._mainSubplot){var 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e=t._fullLayout._zoomlayer;e&&e.selectAll(".select-outline").remove(),t._fullLayout._drawing=!1}}},{}],561:[function(t,e,r){"use strict";var n=t("../../lib").strTranslate;function i(t,e){switch(t.type){case"log":return t.p2d(e);case"date":return t.p2r(e,0,t.calendar);default:return t.p2r(e)}}e.exports={p2r:i,r2p:function(t,e){switch(t.type){case"log":return t.d2p(e);case"date":return t.r2p(e,0,t.calendar);default:return t.r2p(e)}},axValue:function(t){var e="y"===t._id.charAt(0)?1:0;return function(r){return i(t,r[e])}},getTransform:function(t){return n(t.xaxis._offset,t.yaxis._offset)}}},{"../../lib":498}],562:[function(t,e,r){"use strict";var n=t("../../registry"),i=t("../../lib"),a=t("./axis_ids");e.exports=function(t){return function(e,r){var o=e[t];if(Array.isArray(o))for(var s=n.subplotsRegistry.cartesian,l=s.idRegex,c=r._subplots,u=c.xaxis,f=c.yaxis,h=c.cartesian,p=r._has("cartesian")||r._has("gl2d"),d=0;d<o.length;d++){var m=o[d];if(i.isPlainObject(m)){var 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n=t("polybooljs"),i=t("../../registry"),a=t("../../components/drawing").dashStyle,o=t("../../components/color"),s=t("../../components/fx"),l=t("../../components/fx/helpers").makeEventData,c=t("../../components/dragelement/helpers"),u=c.freeMode,f=c.rectMode,h=c.drawMode,p=c.openMode,d=c.selectMode,m=t("../../components/shapes/draw_newshape/display_outlines"),g=t("../../components/shapes/draw_newshape/helpers").handleEllipse,v=t("../../components/shapes/draw_newshape/newshapes"),y=t("../../lib"),x=t("../../lib/polygon"),b=t("../../lib/throttle"),_=t("./axis_ids").getFromId,w=t("../../lib/clear_gl_canvases"),T=t("../../plot_api/subroutines").redrawReglTraces,k=t("./constants"),A=k.MINSELECT,M=x.filter,S=x.tester,E=t("./handle_outline").clearSelect,L=t("./helpers"),C=L.p2r,P=L.axValue,I=L.getTransform;function O(t,e,r,n,i,a,o){var s,l,c,u,f,h,d,g,v,y=e._hoverdata,x=e._fullLayout.clickmode.indexOf("event")>-1,b=[];if(function(t){return 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d=!1;for(n=0;n<e.length;n++){s=(o=(a=e[n]).cd)[0].trace,i.traceIs(s,"regl")&&(d=!0);var m=a._module,g=m.styleOnSelect||m.style;g&&(g(t,o,o[0].node3),o[0].nodeRangePlot3&&g(t,o,o[0].nodeRangePlot3))}d&&(w(t),T(t))}function V(t,e){if(Array.isArray(t))for(var r=e.cd,n=e.cd[0].trace,i=0;i<t.length;i++)t[i]=l(t[i],n,r);return t}function H(t,e){for(var r=[],n=0;n<t.length;n++){r[n]=[];for(var i=0;i<t[n].length;i++){r[n][i]=[],r[n][i][0]=i?"L":"M";for(var a=0;a<t[n][i].length;a++)r[n][i].push(t[n][i][a])}e||r[n].push(["Z",r[n][0][1],r[n][0][2]])}return r}e.exports={prepSelect:function(t,e,r,i,l){var c=u(l),v=f(l),x=p(l),_=h(l),w=d(l),T="drawcircle"===l,E="drawline"===l||T,L=i.gd,z=L._fullLayout,D=z._zoomlayer,j=i.element.getBoundingClientRect(),q=i.plotinfo,G=I(q),Y=e-j.left,W=r-j.top;z._calcInverseTransform(L);var X=y.apply3DTransform(z._invTransform)(Y,W);Y=X[0],W=X[1];var 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v(t,e){var r=t[0],n=t[1],i=t[2],a=t[3],o=e[0],s=e[1],l=e[2],c=e[3];return[r*o-n*s-i*l-a*c,r*s+n*o+i*c-a*l,r*l-n*c+i*o+a*s,r*c+n*l-i*s+a*o]}function y(t,e){if(t&&e){var r=function(t,e){return[t[1]*e[2]-t[2]*e[1],t[2]*e[0]-t[0]*e[2],t[0]*e[1]-t[1]*e[0]]}(t,e),n=Math.sqrt(k(r,r)),i=.5*Math.acos(Math.max(-1,Math.min(1,k(t,e)))),a=Math.sin(i)/n;return n&&[Math.cos(i),r[2]*a,-r[1]*a,r[0]*a]}}function x(t,e,r){var n=w(e,2,t[0]);n=w(n,1,t[1]),n=w(n,0,t[2]-r[2]);var i,a,o=e[0],l=e[1],c=e[2],u=n[0],f=n[1],h=n[2],p=Math.atan2(l,o)*s,d=Math.sqrt(o*o+l*l);Math.abs(f)>d?(a=(f>0?90:-90)-p,i=0):(a=Math.asin(f/d)*s-p,i=Math.sqrt(d*d-f*f));var m=180-a-2*p,g=(Math.atan2(h,u)-Math.atan2(c,i))*s,v=(Math.atan2(h,u)-Math.atan2(c,-i))*s;return b(r[0],r[1],a,g)<=b(r[0],r[1],m,v)?[a,g,r[2]]:[m,v,r[2]]}function b(t,e,r,n){var i=_(r-t),a=_(n-e);return Math.sqrt(i*i+a*a)}function _(t){return(t%360+540)%360-180}function w(t,e,r){var n=r*o,i=t.slice(),a=0===e?1:0,s=2===e?1:2,l=Math.cos(n),c=Math.sin(n);return i[a]=t[a]*l-t[s]*c,i[s]=t[s]*l+t[a]*c,i}function T(t){return[Math.atan2(2*(t[0]*t[1]+t[2]*t[3]),1-2*(t[1]*t[1]+t[2]*t[2]))*s,Math.asin(Math.max(-1,Math.min(1,2*(t[0]*t[2]-t[3]*t[1]))))*s,Math.atan2(2*(t[0]*t[3]+t[1]*t[2]),1-2*(t[2]*t[2]+t[3]*t[3]))*s]}function k(t,e){for(var r=0,n=0,i=t.length;n<i;++n)r+=t[n]*e[n];return r}e.exports=function(t,e){var r=t.projection;return(e._isScoped?h:e._isClipped?d:p)(t,r)}},{"../../lib":498,"../../registry":633,"@plotly/d3":58}],588:[function(t,e,r){"use strict";var n=t("../registry"),i=t("./cartesian/constants").SUBPLOT_PATTERN;r.getSubplotCalcData=function(t,e,r){var i=n.subplotsRegistry[e];if(!i)return[];for(var a=i.attr,o=[],s=0;s<t.length;s++){var l=t[s];l[0].trace[a]===r&&o.push(l)}return o},r.getModuleCalcData=function(t,e){var r,i=[],a=[];if(!(r="string"==typeof e?n.getModule(e).plot:"function"==typeof e?e:e.plot))return[i,t];for(var o=0;o<t.length;o++){var s=t[o],l=s[0].trace;!0===l.visible&&0!==l._length&&(l._module.plot===r?i.push(s):a.push(s))}return[i,a]},r.getSubplotData=function(t,e,r){if(!n.subplotsRegistry[e])return[];var a,o,s,l=n.subplotsRegistry[e].attr,c=[];if("gl2d"===e){var u=r.match(i);o="x"+u[1],s="y"+u[2]}for(var f=0;f<t.length;f++)a=t[f],"gl2d"===e&&n.traceIs(a,"gl2d")?a[l[0]]===o&&a[l[1]]===s&&c.push(a):a[l]===r&&c.push(a);return c}},{"../registry":633,"./cartesian/constants":556}],589:[function(t,e,r){"use strict";var n=t("mouse-change"),i=t("mouse-wheel"),a=t("mouse-event-offset"),o=t("../cartesian/constants"),s=t("has-passive-events");function l(t,e){this.element=t,this.plot=e,this.mouseListener=null,this.wheelListener=null,this.lastInputTime=Date.now(),this.lastPos=[0,0],this.boxEnabled=!1,this.boxInited=!1,this.boxStart=[0,0],this.boxEnd=[0,0],this.dragStart=[0,0]}e.exports=function(t){var e=t.mouseContainer,r=t.glplot,c=new l(e,r);function u(){t.xaxis.autorange=!1,t.yaxis.autorange=!1}function f(e,n,i){var a,s,l=t.calcDataBox(),f=r.viewBox,h=c.lastPos[0],p=c.lastPos[1],d=o.MINDRAG*r.pixelRatio,m=o.MINZOOM*r.pixelRatio;function g(e,r,n){var i=Math.min(r,n),a=Math.max(r,n);i!==a?(l[e]=i,l[e+2]=a,c.dataBox=l,t.setRanges(l)):(t.selectBox.selectBox=[0,0,1,1],t.glplot.setDirty())}switch(n*=r.pixelRatio,i*=r.pixelRatio,i=f[3]-f[1]-i,t.fullLayout.dragmode){case"zoom":if(e){var v=n/(f[2]-f[0])*(l[2]-l[0])+l[0],y=i/(f[3]-f[1])*(l[3]-l[1])+l[1];c.boxInited||(c.boxStart[0]=v,c.boxStart[1]=y,c.dragStart[0]=n,c.dragStart[1]=i),c.boxEnd[0]=v,c.boxEnd[1]=y,c.boxInited=!0,c.boxEnabled||c.boxStart[0]===c.boxEnd[0]&&c.boxStart[1]===c.boxEnd[1]||(c.boxEnabled=!0);var x=Math.abs(c.dragStart[0]-n)<m,b=Math.abs(c.dragStart[1]-i)<m;if(!function(){for(var e=t.graphDiv._fullLayout._axisConstraintGroups,r=t.xaxis._id,n=t.yaxis._id,i=0;i<e.length;i++)if(-1!==e[i][r]){if(-1!==e[i][n])return!0;break}return!1}()||x&&b)x&&(c.boxEnd[0]=c.boxStart[0]),b&&(c.boxEnd[1]=c.boxStart[1]);else{a=c.boxEnd[0]-c.boxStart[0],s=c.boxEnd[1]-c.boxStart[1];var _=(l[3]-l[1])/(l[2]-l[0]);Math.abs(a*_)>Math.abs(s)?(c.boxEnd[1]=c.boxStart[1]+Math.abs(a)*_*(s>=0?1:-1),c.boxEnd[1]<l[1]?(c.boxEnd[1]=l[1],c.boxEnd[0]=c.boxStart[0]+(l[1]-c.boxStart[1])/Math.abs(_)):c.boxEnd[1]>l[3]&&(c.boxEnd[1]=l[3],c.boxEnd[0]=c.boxStart[0]+(l[3]-c.boxStart[1])/Math.abs(_))):(c.boxEnd[0]=c.boxStart[0]+Math.abs(s)/_*(a>=0?1:-1),c.boxEnd[0]<l[0]?(c.boxEnd[0]=l[0],c.boxEnd[1]=c.boxStart[1]+(l[0]-c.boxStart[0])*Math.abs(_)):c.boxEnd[0]>l[2]&&(c.boxEnd[0]=l[2],c.boxEnd[1]=c.boxStart[1]+(l[2]-c.boxStart[0])*Math.abs(_)))}}else c.boxEnabled?(a=c.boxStart[0]!==c.boxEnd[0],s=c.boxStart[1]!==c.boxEnd[1],a||s?(a&&(g(0,c.boxStart[0],c.boxEnd[0]),t.xaxis.autorange=!1),s&&(g(1,c.boxStart[1],c.boxEnd[1]),t.yaxis.autorange=!1),t.relayoutCallback()):t.glplot.setDirty(),c.boxEnabled=!1,c.boxInited=!1):c.boxInited&&(c.boxInited=!1);break;case"pan":c.boxEnabled=!1,c.boxInited=!1,e?(c.panning||(c.dragStart[0]=n,c.dragStart[1]=i),Math.abs(c.dragStart[0]-n)<d&&(n=c.dragStart[0]),Math.abs(c.dragStart[1]-i)<d&&(i=c.dragStart[1]),a=(h-n)*(l[2]-l[0])/(r.viewBox[2]-r.viewBox[0]),s=(p-i)*(l[3]-l[1])/(r.viewBox[3]-r.viewBox[1]),l[0]+=a,l[2]+=a,l[1]+=s,l[3]+=s,t.setRanges(l),c.panning=!0,c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations()):c.panning&&(c.panning=!1,t.relayoutCallback())}c.lastPos[0]=n,c.lastPos[1]=i}return c.mouseListener=n(e,f),e.addEventListener("touchstart",(function(t){var r=a(t.changedTouches[0],e);f(0,r[0],r[1]),f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener("touchmove",(function(t){t.preventDefault();var r=a(t.changedTouches[0],e);f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener("touchend",(function(t){f(0,c.lastPos[0],c.lastPos[1]),t.preventDefault()}),!!s&&{passive:!1}),c.wheelListener=i(e,(function(e,n){if(!t.scrollZoom)return!1;var i=t.calcDataBox(),a=r.viewBox,o=c.lastPos[0],s=c.lastPos[1],l=Math.exp(5*n/(a[3]-a[1])),f=o/(a[2]-a[0])*(i[2]-i[0])+i[0],h=s/(a[3]-a[1])*(i[3]-i[1])+i[1];return i[0]=(i[0]-f)*l+f,i[2]=(i[2]-f)*l+f,i[1]=(i[1]-h)*l+h,i[3]=(i[3]-h)*l+h,t.setRanges(i),c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations(),t.relayoutCallback(),!0}),!0),c}},{"../cartesian/constants":556,"has-passive-events":224,"mouse-change":236,"mouse-event-offset":237,"mouse-wheel":239}],590:[function(t,e,r){"use strict";var n=t("../cartesian/axes"),i=t("../../lib/str2rgbarray");function a(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=["x","y"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=["sans-serif","sans-serif"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title="",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont="sans-serif",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}var o=a.prototype,s=["xaxis","yaxis"];o.merge=function(t){var e,r,n,a,o,l,c,u,f,h,p;for(this.titleEnable=!1,this.backgroundColor=i(t.plot_bgcolor),h=0;h<2;++h){var d=(e=s[h]).charAt(0);for(n=(r=t[this.scene[e]._name]).title.text===this.scene.fullLayout._dfltTitle[d]?"":r.title.text,p=0;p<=2;p+=2)this.labelEnable[h+p]=!1,this.labels[h+p]=n,this.labelColor[h+p]=i(r.title.font.color),this.labelFont[h+p]=r.title.font.family,this.labelSize[h+p]=r.title.font.size,this.labelPad[h+p]=this.getLabelPad(e,r),this.tickEnable[h+p]=!1,this.tickColor[h+p]=i((r.tickfont||{}).color),this.tickAngle[h+p]="auto"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[h+p]=this.getTickPad(r),this.tickMarkLength[h+p]=0,this.tickMarkWidth[h+p]=r.tickwidth||0,this.tickMarkColor[h+p]=i(r.tickcolor),this.borderLineEnable[h+p]=!1,this.borderLineColor[h+p]=i(r.linecolor),this.borderLineWidth[h+p]=r.linewidth||0;c=this.hasSharedAxis(r),o=this.hasAxisInDfltPos(e,r)&&!c,l=this.hasAxisInAltrPos(e,r)&&!c,a=r.mirror||!1,u=c?-1!==String(a).indexOf("all"):!!a,f=c?"allticks"===a:-1!==String(a).indexOf("ticks"),o?this.labelEnable[h]=!0:l&&(this.labelEnable[h+2]=!0),o?this.tickEnable[h]=r.showticklabels:l&&(this.tickEnable[h+2]=r.showticklabels),(o||u)&&(this.borderLineEnable[h]=r.showline),(l||u)&&(this.borderLineEnable[h+2]=r.showline),(o||f)&&(this.tickMarkLength[h]=this.getTickMarkLength(r)),(l||f)&&(this.tickMarkLength[h+2]=this.getTickMarkLength(r)),this.gridLineEnable[h]=r.showgrid,this.gridLineColor[h]=i(r.gridcolor),this.gridLineWidth[h]=r.gridwidth,this.zeroLineEnable[h]=r.zeroline,this.zeroLineColor[h]=i(r.zerolinecolor),this.zeroLineWidth[h]=r.zerolinewidth}},o.hasSharedAxis=function(t){var e=this.scene,r=e.fullLayout._subplots.gl2d;return 0!==n.findSubplotsWithAxis(r,t).indexOf(e.id)},o.hasAxisInDfltPos=function(t,e){var r=e.side;return"xaxis"===t?"bottom"===r:"yaxis"===t?"left"===r:void 0},o.hasAxisInAltrPos=function(t,e){var r=e.side;return"xaxis"===t?"top"===r:"yaxis"===t?"right"===r:void 0},o.getLabelPad=function(t,e){var r=e.title.font.size,n=e.showticklabels;return"xaxis"===t?"top"===e.side?r*(1.5+(n?1:0))-10:r*(1.5+(n?.5:0))-10:"yaxis"===t?"right"===e.side?10+r*(1.5+(n?1:.5)):10+r*(1.5+(n?.5:0)):void 0},o.getTickPad=function(t){return"outside"===t.ticks?10+t.ticklen:15},o.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return"inside"===t.ticks?-e:e},e.exports=function(t){return new a(t)}},{"../../lib/str2rgbarray":523,"../cartesian/axes":549}],591:[function(t,e,r){"use strict";var n=t("../../plot_api/edit_types").overrideAll,i=t("./scene2d"),a=t("../layout_attributes"),o=t("../../constants/xmlns_namespaces"),s=t("../cartesian/constants"),l=t("../cartesian"),c=t("../../components/fx/layout_attributes"),u=t("../get_data").getSubplotData;r.name="gl2d",r.attr=["xaxis","yaxis"],r.idRoot=["x","y"],r.idRegex=s.idRegex,r.attrRegex=s.attrRegex,r.attributes=t("../cartesian/attributes"),r.supplyLayoutDefaults=function(t,e,r){e._has("cartesian")||l.supplyLayoutDefaults(t,e,r)},r.layoutAttrOverrides=n(l.layoutAttributes,"plot","from-root"),r.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:c.hoverlabel},"plot","nested"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=u(r,"gl2d",o),c=s._scene2d;void 0===c&&(c=new i({id:o,graphDiv:t,container:t.querySelector(".gl-container"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=c),c.plot(l,t.calcdata,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl2d||[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];if(s._scene2d){var c=u(t,"gl2d",o);0===c.length&&(s._scene2d.destroy(),delete n._plots[o])}}l.clean.apply(this,arguments)},r.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage("png");e._glimages.append("svg:image").attr({xmlns:o.svg,"xlink:href":a,x:0,y:0,width:"100%",height:"100%",preserveAspectRatio:"none"}),i.destroy()}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){e._plots[r[n]]._scene2d.updateFx(e.dragmode)}}},{"../../components/fx/layout_attributes":402,"../../constants/xmlns_namespaces":475,"../../plot_api/edit_types":531,"../cartesian":563,"../cartesian/attributes":547,"../cartesian/constants":556,"../get_data":588,"../layout_attributes":605,"./scene2d":592}],592:[function(t,e,r){"use strict";var n,i,a=t("../../registry"),o=t("../../plots/cartesian/axes"),s=t("../../components/fx"),l=t("../../../stackgl_modules").gl_plot2d,c=t("../../../stackgl_modules").gl_spikes2d,u=t("../../../stackgl_modules").gl_select_box,f=t("webgl-context"),h=t("./convert"),p=t("./camera"),d=t("../../lib/show_no_webgl_msg"),m=t("../cartesian/constraints"),g=m.enforce,v=m.clean,y=t("../cartesian/autorange").doAutoRange,x=t("../../components/dragelement/helpers"),b=x.drawMode,_=x.selectMode,w=["xaxis","yaxis"],T=t("../cartesian/constants").SUBPLOT_PATTERN;function k(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context._scrollZoom.cartesian,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped||(this.glplotOptions=h(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=c(this.glplot),this.selectBox=u(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}e.exports=k;var A=k.prototype;A.makeFramework=function(){if(this.staticPlot){if(!(i||(n=document.createElement("canvas"),i=f({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error("Error creating static canvas/context for image server");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(".gl-canvas-focus"),e=f({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return d(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width="100%",r.style.height="100%",r.style.position="absolute",r.style.top="0px",r.style.left="0px",r.style["pointer-events"]="none",this.updateSize(r);var a=this.svgContainer=document.createElementNS("http://www.w3.org/2000/svg","svg");a.style.position="absolute",a.style.top=a.style.left="0px",a.style.width=a.style.height="100%",a.style["z-index"]=20,a.style["pointer-events"]="none";var o=this.mouseContainer=document.createElement("div");o.style.position="absolute",o.style["pointer-events"]="auto",this.pickCanvas=this.container.querySelector(".gl-canvas-pick");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener("mouseout",(function(){l.isMouseOver=!1,l.unhover()})),o.addEventListener("mouseover",(function(){l.isMouseOver=!0}))},A.toImage=function(t){t||(t="png"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var c=0;c<4;++c){var u=a[4*(r*o+l)+c];a[4*(r*o+l)+c]=a[4*(r*s+l)+c],a[4*(r*s+l)+c]=u}var f=document.createElement("canvas");f.width=r,f.height=i;var h,p=f.getContext("2d"),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case"jpeg":h=f.toDataURL("image/jpeg");break;case"webp":h=f.toDataURL("image/webp");break;default:h=f.toDataURL("image/png")}return this.staticPlot&&this.container.removeChild(n),h},A.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),t},A.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=t[e][r].text+"";return t},A.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(T),r="xaxis"+e[1],n="yaxis"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},A.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout,i={},o=i[e._name+".range"]=e.range.slice(),s=i[r._name+".range"]=r.range.slice();i[e._name+".autorange"]=e.autorange,i[r._name+".autorange"]=r.autorange,a.call("_storeDirectGUIEdit",t.layout,t._fullLayout._preGUI,i);var l=n[e._name];l.range=o,l.autorange=e.autorange;var c=n[r._name];c.range=s,c.autorange=r.autorange,i.lastInputTime=this.camera.lastInputTime,t.emit("plotly_relayout",i)},A.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},A.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod("annotations","drawOne")(t,r)}},A.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map((function(e){t[e].dispose(),delete t[e]})),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener("wheel",this.camera.wheelListener),this.camera=null}},A.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:r._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis,_size:r._size}};v(s,this.xaxis),v(s,this.yaxis);var l,c,u=r._size,f=this.xaxis.domain,h=this.yaxis.domain;for(o.viewBox=[u.l+f[0]*u.w,u.b+h[0]*u.h,i-u.r-(1-f[1])*u.w,a-u.t-(1-h[1])*u.h],this.mouseContainer.style.width=u.w*(f[1]-f[0])+"px",this.mouseContainer.style.height=u.h*(h[1]-h[0])+"px",this.mouseContainer.height=u.h*(h[1]-h[0]),this.mouseContainer.style.left=u.l+f[0]*u.w+"px",this.mouseContainer.style.top=u.t+(1-h[1])*u.h+"px",c=0;c<2;++c)(l=this[w[c]])._length=o.viewBox[c+2]-o.viewBox[c],y(this.graphDiv,l),l.setScale();g(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},A.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},A.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},A.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],c=this.traces[i.uid];c?c.update(i,l):(c=i._module.plot(this,i,l),this.traces[i.uid]=c)}this.glplot.objects.sort((function(t,e){return t._trace.index-e._trace.index}))},A.updateFx=function(t){_(t)||b(t)?(this.pickCanvas.style["pointer-events"]="none",this.mouseContainer.style["pointer-events"]="none"):(this.pickCanvas.style["pointer-events"]="auto",this.mouseContainer.style["pointer-events"]="auto"),this.mouseContainer.style.cursor="pan"===t?"move":"zoom"===t?"crosshair":null},A.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},A.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&"zoom"===i.dragmode){this.selectBox.enabled=!0;for(var c=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],u=0;u<2;u++)e.boxStart[u]===e.boxEnd[u]&&(c[u]=t.dataBox[u],c[u+2]=t.dataBox[u+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var f=i._size,h=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+f.l+h[0]*f.w,l/t.pixelRatio-(f.t+(1-p[1])*f.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,"plotly_click"),a&&"skip"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var m=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),m.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,"plotly_hover");var g=this.fullData[m.trace.index]||{},v=m.pointIndex,y=s.castHoverinfo(g,i,v);if(y&&"all"!==y){var x=y.split("+");-1===x.indexOf("x")&&(m.traceCoord[0]=void 0),-1===x.indexOf("y")&&(m.traceCoord[1]=void 0),-1===x.indexOf("z")&&(m.traceCoord[2]=void 0),-1===x.indexOf("text")&&(m.textLabel=void 0),-1===x.indexOf("name")&&(m.name=void 0)}s.loneHover({x:m.screenCoord[0],y:m.screenCoord[1],xLabel:this.hoverFormatter("xaxis",m.traceCoord[0]),yLabel:this.hoverFormatter("yaxis",m.traceCoord[1]),zLabel:m.traceCoord[2],text:m.textLabel,name:m.name,color:s.castHoverOption(g,v,"bgcolor")||m.color,borderColor:s.castHoverOption(g,v,"bordercolor"),fontFamily:s.castHoverOption(g,v,"font.family"),fontSize:s.castHoverOption(g,v,"font.size"),fontColor:s.castHoverOption(g,v,"font.color"),nameLength:s.castHoverOption(g,v,"namelength"),textAlign:s.castHoverOption(g,v,"align")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},A.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit("plotly_unhover"),s.loneUnhover(this.svgContainer))},A.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),"hover").text}}},{"../../../stackgl_modules":1119,"../../components/dragelement/helpers":379,"../../components/fx":401,"../../lib/show_no_webgl_msg":520,"../../plots/cartesian/axes":549,"../../registry":633,"../cartesian/autorange":548,"../cartesian/constants":556,"../cartesian/constraints":557,"./camera":589,"./convert":590,"webgl-context":326}],593:[function(t,e,r){"use strict";var n=t("../../plot_api/edit_types").overrideAll,i=t("../../components/fx/layout_attributes"),a=t("./scene"),o=t("../get_data").getSubplotData,s=t("../../lib"),l=t("../../constants/xmlns_namespaces");r.name="gl3d",r.attr="scene",r.idRoot="scene",r.idRegex=r.attrRegex=s.counterRegex("scene"),r.attributes=t("./layout/attributes"),r.layoutAttributes=t("./layout/layout_attributes"),r.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},"plot","nested"),r.supplyLayoutDefaults=t("./layout/defaults"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl3d,i=0;i<n.length;i++){var s=n[i],l=o(r,"gl3d",s),c=e[s],u=c.camera,f=c._scene;f||(f=new a({id:s,graphDiv:t,container:t.querySelector(".gl-container"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:u},e),c._scene=f),f.viewInitial||(f.viewInitial={up:{x:u.up.x,y:u.up.y,z:u.up.z},eye:{x:u.eye.x,y:u.eye.y,z:u.eye.z},center:{x:u.center.x,y:u.center.y,z:u.center.z}}),f.plot(l,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl3d||[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(".annotation-"+o).remove())}},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,c=s.toImage("png");e._glimages.append("svg:image").attr({xmlns:l.svg,"xlink:href":c,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:"none"}),s.destroy()}},r.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return"1"===e&&(e=""),"scene"+e}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=0;n<r.length;n++){e[r[n]]._scene.updateFx(e.dragmode,e.hovermode)}}},{"../../components/fx/layout_attributes":402,"../../constants/xmlns_namespaces":475,"../../lib":498,"../../plot_api/edit_types":531,"../get_data":588,"./layout/attributes":594,"./layout/defaults":598,"./layout/layout_attributes":599,"./scene":603}],594:[function(t,e,r){"use strict";e.exports={scene:{valType:"subplotid",dflt:"scene",editType:"calc+clearAxisTypes"}}},{}],595:[function(t,e,r){"use strict";var n=t("../../../components/color"),i=t("../../cartesian/layout_attributes"),a=t("../../../lib/extend").extendFlat,o=t("../../../plot_api/edit_types").overrideAll;e.exports=o({visible:i.visible,showspikes:{valType:"boolean",dflt:!0},spikesides:{valType:"boolean",dflt:!0},spikethickness:{valType:"number",min:0,dflt:2},spikecolor:{valType:"color",dflt:n.defaultLine},showbackground:{valType:"boolean",dflt:!1},backgroundcolor:{valType:"color",dflt:"rgba(204, 204, 204, 0.5)"},showaxeslabels:{valType:"boolean",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,title:{text:i.title.text,font:i.title.font},type:a({},i.type,{values:["-","linear","log","date","category"]}),autotypenumbers:i.autotypenumbers,autorange:i.autorange,rangemode:i.rangemode,range:a({},i.range,{items:[{valType:"any",editType:"plot",impliedEdits:{"^autorange":!1}},{valType:"any",editType:"plot",impliedEdits:{"^autorange":!1}}],anim:!1}),tickmode:i.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,minexponent:i.minexponent,separatethousands:i.separatethousands,tickformat:i.tickformat,tickformatstops:i.tickformatstops,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:"rgb(204, 204, 204)"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth,_deprecated:{title:i._deprecated.title,titlefont:i._deprecated.titlefont}},"plot","from-root")},{"../../../components/color":361,"../../../lib/extend":488,"../../../plot_api/edit_types":531,"../../cartesian/layout_attributes":564}],596:[function(t,e,r){"use strict";var n=t("tinycolor2").mix,i=t("../../../lib"),a=t("../../../plot_api/plot_template"),o=t("./axis_attributes"),s=t("../../cartesian/type_defaults"),l=t("../../cartesian/axis_defaults"),c=["xaxis","yaxis","zaxis"];e.exports=function(t,e,r){var u,f;function h(t,e){return i.coerce(u,f,o,t,e)}for(var p=0;p<c.length;p++){var d=c[p];u=t[d]||{},(f=a.newContainer(e,d))._id=d[0]+r.scene,f._name=d,s(u,f,h,r),l(u,f,h,{font:r.font,letter:d[0],data:r.data,showGrid:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelstep:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h("gridcolor",n(f.color,r.bgColor,13600/187).toRgbString()),h("title.text",d[0]),f.setScale=i.noop,h("showspikes")&&(h("spikesides"),h("spikethickness"),h("spikecolor",f.color)),h("showaxeslabels"),h("showbackground")&&h("backgroundcolor")}}},{"../../../lib":498,"../../../plot_api/plot_template":538,"../../cartesian/axis_defaults":551,"../../cartesian/type_defaults":577,"./axis_attributes":595,tinycolor2:307}],597:[function(t,e,r){"use strict";var n=t("../../../lib/str2rgbarray"),i=t("../../../lib"),a=["xaxis","yaxis","zaxis"];function o(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont=["Open Sans","Open Sans","Open Sans"],this.labelSize=[20,20,20],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}o.prototype.merge=function(t,e){for(var r=0;r<3;++r){var o=e[a[r]];o.visible?(this.labels[r]=t._meta?i.templateString(o.title.text,t._meta):o.title.text,"font"in o.title&&(o.title.font.color&&(this.labelColor[r]=n(o.title.font.color)),o.title.font.family&&(this.labelFont[r]=o.title.font.family),o.title.font.size&&(this.labelSize[r]=o.title.font.size)),"showline"in o&&(this.lineEnable[r]=o.showline),"linecolor"in o&&(this.lineColor[r]=n(o.linecolor)),"linewidth"in o&&(this.lineWidth[r]=o.linewidth),"showgrid"in o&&(this.gridEnable[r]=o.showgrid),"gridcolor"in o&&(this.gridColor[r]=n(o.gridcolor)),"gridwidth"in o&&(this.gridWidth[r]=o.gridwidth),"log"===o.type?this.zeroEnable[r]=!1:"zeroline"in o&&(this.zeroEnable[r]=o.zeroline),"zerolinecolor"in o&&(this.zeroLineColor[r]=n(o.zerolinecolor)),"zerolinewidth"in o&&(this.zeroLineWidth[r]=o.zerolinewidth),"ticks"in o&&o.ticks?this.lineTickEnable[r]=!0:this.lineTickEnable[r]=!1,"ticklen"in o&&(this.lineTickLength[r]=this._defaultLineTickLength[r]=o.ticklen),"tickcolor"in o&&(this.lineTickColor[r]=n(o.tickcolor)),"tickwidth"in o&&(this.lineTickWidth[r]=o.tickwidth),"tickangle"in o&&(this.tickAngle[r]="auto"===o.tickangle?-3600:Math.PI*-o.tickangle/180),"showticklabels"in o&&(this.tickEnable[r]=o.showticklabels),"tickfont"in o&&(o.tickfont.color&&(this.tickColor[r]=n(o.tickfont.color)),o.tickfont.family&&(this.tickFont[r]=o.tickfont.family),o.tickfont.size&&(this.tickSize[r]=o.tickfont.size)),"mirror"in o?-1!==["ticks","all","allticks"].indexOf(o.mirror)?(this.lineTickMirror[r]=!0,this.lineMirror[r]=!0):!0===o.mirror?(this.lineTickMirror[r]=!1,this.lineMirror[r]=!0):(this.lineTickMirror[r]=!1,this.lineMirror[r]=!1):this.lineMirror[r]=!1,"showbackground"in o&&!1!==o.showbackground?(this.backgroundEnable[r]=!0,this.backgroundColor[r]=n(o.backgroundcolor)):this.backgroundEnable[r]=!1):(this.tickEnable[r]=!1,this.labelEnable[r]=!1,this.lineEnable[r]=!1,this.lineTickEnable[r]=!1,this.gridEnable[r]=!1,this.zeroEnable[r]=!1,this.backgroundEnable[r]=!1)}},e.exports=function(t,e){var r=new o;return r.merge(t,e),r}},{"../../../lib":498,"../../../lib/str2rgbarray":523}],598:[function(t,e,r){"use strict";var n=t("../../../lib"),i=t("../../../components/color"),a=t("../../../registry"),o=t("../../subplot_defaults"),s=t("./axis_defaults"),l=t("./layout_attributes"),c=t("../../get_data").getSubplotData;function u(t,e,r,n){for(var o=r("bgcolor"),l=i.combine(o,n.paper_bgcolor),u=["up","center","eye"],f=0;f<u.length;f++)r("camera."+u[f]+".x"),r("camera."+u[f]+".y"),r("camera."+u[f]+".z");r("camera.projection.type");var h=!!r("aspectratio.x")&&!!r("aspectratio.y")&&!!r("aspectratio.z"),p=r("aspectmode",h?"manual":"auto");h||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},"manual"===p&&(e.aspectmode="auto"),t.aspectmode=e.aspectmode);var d=c(n.fullData,"gl3d",n.id);s(t,e,{font:n.font,scene:n.id,data:d,bgColor:l,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),a.getComponentMethod("annotations3d","handleDefaults")(t,e,n);var m=n.getDfltFromLayout("dragmode");if(!1!==m&&!m)if(m="orbit",t.camera&&t.camera.up){var g=t.camera.up.x,v=t.camera.up.y,y=t.camera.up.z;0!==y&&(g&&v&&y?y/Math.sqrt(g*g+v*v+y*y)>.999&&(m="turntable"):m="turntable")}else m="turntable";r("dragmode",m),r("hovermode",n.getDfltFromLayout("hovermode"))}e.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:"gl3d",attributes:l,handleDefaults:u,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{"../../../components/color":361,"../../../lib":498,"../../../registry":633,"../../get_data":588,"../../subplot_defaults":627,"./axis_defaults":596,"./layout_attributes":599}],599:[function(t,e,r){"use strict";var n=t("./axis_attributes"),i=t("../../domain").attributes,a=t("../../../lib/extend").extendFlat,o=t("../../../lib").counterRegex;function s(t,e,r){return{x:{valType:"number",dflt:t,editType:"camera"},y:{valType:"number",dflt:e,editType:"camera"},z:{valType:"number",dflt:r,editType:"camera"},editType:"camera"}}e.exports={_arrayAttrRegexps:[o("scene",".annotations",!0)],bgcolor:{valType:"color",dflt:"rgba(0,0,0,0)",editType:"plot"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:"enumerated",values:["perspective","orthographic"],dflt:"perspective",editType:"calc"},editType:"calc"},editType:"camera"},domain:i({name:"scene",editType:"plot"}),aspectmode:{valType:"enumerated",values:["auto","cube","data","manual"],dflt:"auto",editType:"plot",impliedEdits:{"aspectratio.x":void 0,"aspectratio.y":void 0,"aspectratio.z":void 0}},aspectratio:{x:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},y:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},z:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},editType:"plot",impliedEdits:{aspectmode:"manual"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:"enumerated",values:["orbit","turntable","zoom","pan",!1],editType:"plot"},hovermode:{valType:"enumerated",values:["closest",!1],dflt:"closest",editType:"modebar"},uirevision:{valType:"any",editType:"none"},editType:"plot",_deprecated:{cameraposition:{valType:"info_array",editType:"camera"}}}},{"../../../lib":498,"../../../lib/extend":488,"../../domain":579,"./axis_attributes":595}],600:[function(t,e,r){"use strict";var n=t("../../../lib/str2rgbarray"),i=["xaxis","yaxis","zaxis"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},e.exports=function(t){var e=new a;return e.merge(t),e}},{"../../../lib/str2rgbarray":523}],601:[function(t,e,r){"use strict";e.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],c=0;c<3;++c){var u=s[a[c]];if(u._length=(r[c].hi-r[c].lo)*r[c].pixelsPerDataUnit/t.dataScale[c],Math.abs(u._length)===1/0||isNaN(u._length))l[c]=[];else{u._input_range=u.range.slice(),u.range[0]=r[c].lo/t.dataScale[c],u.range[1]=r[c].hi/t.dataScale[c],u._m=1/(t.dataScale[c]*r[c].pixelsPerDataUnit),u.range[0]===u.range[1]&&(u.range[0]-=1,u.range[1]+=1);var f=u.tickmode;if("auto"===u.tickmode){u.tickmode="linear";var h=u.nticks||i.constrain(u._length/40,4,9);n.autoTicks(u,Math.abs(u.range[1]-u.range[0])/h)}for(var p=n.calcTicks(u,{msUTC:!0}),d=0;d<p.length;++d)p[d].x=p[d].x*t.dataScale[c],"date"===u.type&&(p[d].text=p[d].text.replace(/\<br\>/g," "));l[c]=p,u.tickmode=f}}e.ticks=l;for(c=0;c<3;++c){o[c]=.5*(t.glplot.bounds[0][c]+t.glplot.bounds[1][c]);for(d=0;d<2;++d)e.bounds[d][c]=t.glplot.bounds[d][c]}t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(l)};var n=t("../../cartesian/axes"),i=t("../../../lib"),a=["xaxis","yaxis","zaxis"],o=[0,0,0]},{"../../../lib":498,"../../cartesian/axes":549}],602:[function(t,e,r){"use strict";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}e.exports=function(t,e){return n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])))}},{}],603:[function(t,e,r){"use strict";var n,i,a=t("../../../stackgl_modules").gl_plot3d,o=a.createCamera,s=a.createScene,l=t("webgl-context"),c=t("has-passive-events"),u=t("../../registry"),f=t("../../lib"),h=f.preserveDrawingBuffer(),p=t("../../plots/cartesian/axes"),d=t("../../components/fx"),m=t("../../lib/str2rgbarray"),g=t("../../lib/show_no_webgl_msg"),v=t("./project"),y=t("./layout/convert"),x=t("./layout/spikes"),b=t("./layout/tick_marks");function _(t,e){var r=document.createElement("div"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS("http://www.w3.org/2000/svg","svg");i.style.position="absolute",i.style.top=i.style.left="0px",i.style.width=i.style.height="100%",i.style["z-index"]=20,i.style["pointer-events"]="none",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position="absolute",r.style.top=r.style.left="0px",r.style.width=r.style.height="100%",n.appendChild(r),this.fullLayout=e,this.id=t.id||"scene",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=y(e,e[this.id]),this.spikeOptions=x(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=u.getComponentMethod("annotations3d","convert"),this.drawAnnotations=u.getComponentMethod("annotations3d","draw"),this.initializeGLPlot()}var w=_.prototype;w.prepareOptions=function(){var t={canvas:this.canvas,gl:this.gl,glOptions:{preserveDrawingBuffer:h,premultipliedAlpha:!0,antialias:!0},container:this.container,axes:this.axesOptions,spikes:this.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:this.camera,pixelRatio:this.pixelRatio};if(this.staticMode){if(!(i||(n=document.createElement("canvas"),i=l({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error("error creating static canvas/context for image server");t.gl=i,t.canvas=n}return t};var T=!0;w.tryCreatePlot=function(){var t=this.prepareOptions(),e=!0;try{this.glplot=s(t)}catch(r){if(this.staticMode||!T||h)e=!1;else{f.warn(["webgl setup failed possibly due to","false preserveDrawingBuffer config.","The mobile/tablet device may not be detected by is-mobile module.","Enabling preserveDrawingBuffer in second attempt to create webgl scene..."].join(" "));try{h=t.glOptions.preserveDrawingBuffer=!0,this.glplot=s(t)}catch(r){h=t.glOptions.preserveDrawingBuffer=!1,e=!1}}}return T=!1,e},w.initializeGLCamera=function(){var t=this.fullSceneLayout.camera,e="orthographic"===t.projection.type;this.camera=o(this.container,{center:[t.center.x,t.center.y,t.center.z],eye:[t.eye.x,t.eye.y,t.eye.z],up:[t.up.x,t.up.y,t.up.z],_ortho:e,zoomMin:.01,zoomMax:100,mode:"orbit"})},w.initializeGLPlot=function(){var t=this;if(t.initializeGLCamera(),!t.tryCreatePlot())return g(t);t.traces={},t.make4thDimension();var e=t.graphDiv,r=e.layout,n=function(){var e={};return t.isCameraChanged(r)&&(e[t.id+".camera"]=t.getCamera()),t.isAspectChanged(r)&&(e[t.id+".aspectratio"]=t.glplot.getAspectratio(),"manual"!==r[t.id].aspectmode&&(t.fullSceneLayout.aspectmode=r[t.id].aspectmode=e[t.id+".aspectmode"]="manual")),e},i=function(t){if(!1!==t.fullSceneLayout.dragmode){var e=n();t.saveLayout(r),t.graphDiv.emit("plotly_relayout",e)}};return t.glplot.canvas&&(t.glplot.canvas.addEventListener("mouseup",(function(){i(t)})),t.glplot.canvas.addEventListener("wheel",(function(r){if(e._context._scrollZoom.gl3d){if(t.camera._ortho){var n=r.deltaX>r.deltaY?1.1:1/1.1,a=t.glplot.getAspectratio();t.glplot.setAspectratio({x:n*a.x,y:n*a.y,z:n*a.z})}i(t)}}),!!c&&{passive:!1}),t.glplot.canvas.addEventListener("mousemove",(function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=n();t.graphDiv.emit("plotly_relayouting",e)}})),t.staticMode||t.glplot.canvas.addEventListener("webglcontextlost",(function(r){e&&e.emit&&e.emit("plotly_webglcontextlost",{event:r,layer:t.id})}),!1)),t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(){t.render()},!0},w.render=function(){var t,e=this,r=e.graphDiv,n=e.svgContainer,i=e.container.getBoundingClientRect();r._fullLayout._calcInverseTransform(r);var a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,"viewBox","0 0 "+s+" "+l),n.setAttributeNS(null,"width",s),n.setAttributeNS(null,"height",l),b(e),e.glplot.axes.update(e.axesOptions);for(var c=Object.keys(e.traces),u=null,h=e.glplot.selection,m=0;m<c.length;++m)"skip"!==(t=e.traces[c[m]]).data.hoverinfo&&t.handlePick(h)&&(u=t),t.setContourLevels&&t.setContourLevels();function g(t,r,n){var i=e.fullSceneLayout[t+"axis"];return"log"!==i.type&&(r=i.d2l(r)),p.hoverLabelText(i,r,n)}if(null!==u){var y=v(e.glplot.cameraParams,h.dataCoordinate);t=u.data;var x,_=r._fullData[t.index],w=h.index,T={xLabel:g("x",h.traceCoordinate[0],t.xhoverformat),yLabel:g("y",h.traceCoordinate[1],t.yhoverformat),zLabel:g("z",h.traceCoordinate[2],t.zhoverformat)},k=d.castHoverinfo(_,e.fullLayout,w),A=(k||"").split("+"),M=k&&"all"===k;_.hovertemplate||M||(-1===A.indexOf("x")&&(T.xLabel=void 0),-1===A.indexOf("y")&&(T.yLabel=void 0),-1===A.indexOf("z")&&(T.zLabel=void 0),-1===A.indexOf("text")&&(h.textLabel=void 0),-1===A.indexOf("name")&&(u.name=void 0));var S=[];"cone"===t.type||"streamtube"===t.type?(T.uLabel=g("x",h.traceCoordinate[3],t.uhoverformat),(M||-1!==A.indexOf("u"))&&S.push("u: "+T.uLabel),T.vLabel=g("y",h.traceCoordinate[4],t.vhoverformat),(M||-1!==A.indexOf("v"))&&S.push("v: "+T.vLabel),T.wLabel=g("z",h.traceCoordinate[5],t.whoverformat),(M||-1!==A.indexOf("w"))&&S.push("w: "+T.wLabel),T.normLabel=h.traceCoordinate[6].toPrecision(3),(M||-1!==A.indexOf("norm"))&&S.push("norm: "+T.normLabel),"streamtube"===t.type&&(T.divergenceLabel=h.traceCoordinate[7].toPrecision(3),(M||-1!==A.indexOf("divergence"))&&S.push("divergence: "+T.divergenceLabel)),h.textLabel&&S.push(h.textLabel),x=S.join("<br>")):"isosurface"===t.type||"volume"===t.type?(T.valueLabel=p.hoverLabelText(e._mockAxis,e._mockAxis.d2l(h.traceCoordinate[3]),t.valuehoverformat),S.push("value: "+T.valueLabel),h.textLabel&&S.push(h.textLabel),x=S.join("<br>")):x=h.textLabel;var E={x:h.traceCoordinate[0],y:h.traceCoordinate[1],z:h.traceCoordinate[2],data:_._input,fullData:_,curveNumber:_.index,pointNumber:w};d.appendArrayPointValue(E,_,w),t._module.eventData&&(E=_._module.eventData(E,h,_,{},w));var L={points:[E]};if(e.fullSceneLayout.hovermode){var C=[];d.loneHover({trace:_,x:(.5+.5*y[0]/y[3])*s,y:(.5-.5*y[1]/y[3])*l,xLabel:T.xLabel,yLabel:T.yLabel,zLabel:T.zLabel,text:x,name:u.name,color:d.castHoverOption(_,w,"bgcolor")||u.color,borderColor:d.castHoverOption(_,w,"bordercolor"),fontFamily:d.castHoverOption(_,w,"font.family"),fontSize:d.castHoverOption(_,w,"font.size"),fontColor:d.castHoverOption(_,w,"font.color"),nameLength:d.castHoverOption(_,w,"namelength"),textAlign:d.castHoverOption(_,w,"align"),hovertemplate:f.castOption(_,w,"hovertemplate"),hovertemplateLabels:f.extendFlat({},E,T),eventData:[E]},{container:n,gd:r,inOut_bbox:C}),E.bbox=C[0]}h.buttons&&h.distance<5?r.emit("plotly_click",L):r.emit("plotly_hover",L),this.oldEventData=L}else d.loneUnhover(n),this.oldEventData&&r.emit("plotly_unhover",this.oldEventData),this.oldEventData=void 0;e.drawAnnotations(e)},w.recoverContext=function(){var t=this;t.glplot.dispose();var e=function(){t.glplot.gl.isContextLost()?requestAnimationFrame(e):t.initializeGLPlot()?t.plot.apply(t,t.plotArgs):f.error("Catastrophic and unrecoverable WebGL error. Context lost.")};requestAnimationFrame(e)};var k=["xaxis","yaxis","zaxis"];function A(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=k[i],o=a.charAt(0),s=n[a],l=e[o],c=e[o+"calendar"],u=e["_"+o+"length"];if(f.isArrayOrTypedArray(l))for(var h,p=0;p<(u||l.length);p++)if(f.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)h=s.d2l(l[p][d],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else h=s.d2l(l[p],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],u-1)}}w.plot=function(t,e,r){if(this.plotArgs=[t,e,r],!this.glplot.contextLost){var n,i,a,o,s,l,c=e[this.id],u=r[this.id];this.fullLayout=e,this.fullSceneLayout=c,this.axesOptions.merge(e,c),this.spikeOptions.merge(c),this.setViewport(c),this.updateFx(c.dragmode,c.hovermode),this.camera.enableWheel=this.graphDiv._context._scrollZoom.gl3d,this.glplot.setClearColor(m(c.bgcolor)),this.setConvert(s),t?Array.isArray(t)||(t=[t]):t=[];var f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&A(this,n,f);!function(t,e){for(var r=t.fullSceneLayout,n=r.annotations||[],i=0;i<3;i++)for(var a=k[i],o=a.charAt(0),s=r[a],l=0;l<n.length;l++){var c=n[l];if(c.visible){var u=s.r2l(c[o]);!isNaN(u)&&isFinite(u)&&(e[0][i]=Math.min(e[0][i],u),e[1][i]=Math.max(e[1][i],u))}}}(this,f);var h=[1,1,1];for(o=0;o<3;++o)f[1][o]===f[0][o]?h[o]=1:h[o]=1/(f[1][o]-f[0][o]);for(this.dataScale=h,this.convertAnnotations(this),a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&((i=this.traces[n.uid])?i.data.type===n.type?i.update(n):(i.dispose(),i=n._module.plot(this,n),this.traces[n.uid]=i):(i=n._module.plot(this,n),this.traces[n.uid]=i),i.name=n.name);var p=Object.keys(this.traces);t:for(a=0;a<p.length;++a){for(o=0;o<t.length;++o)if(t[o].uid===p[a]&&!0===t[o].visible&&0!==t[o]._length)continue t;(i=this.traces[p[a]]).dispose(),delete this.traces[p[a]]}this.glplot.objects.sort((function(t,e){return t._trace.data.index-e._trace.data.index}));var d,g=[[0,0,0],[0,0,0]],v=[],y={};for(a=0;a<3;++a){if((l=(s=c[k[a]]).type)in y?(y[l].acc*=h[a],y[l].count+=1):y[l]={acc:h[a],count:1},s.autorange){g[0][a]=1/0,g[1][a]=-1/0;var x=this.glplot.objects,b=this.fullSceneLayout.annotations||[],_=s._name.charAt(0);for(o=0;o<x.length;o++){var w=x[o],T=w.bounds,M=w._trace.data._pad||0;"ErrorBars"===w.constructor.name&&s._lowerLogErrorBound?g[0][a]=Math.min(g[0][a],s._lowerLogErrorBound):g[0][a]=Math.min(g[0][a],T[0][a]/h[a]-M),g[1][a]=Math.max(g[1][a],T[1][a]/h[a]+M)}for(o=0;o<b.length;o++){var S=b[o];if(S.visible){var E=s.r2l(S[_]);g[0][a]=Math.min(g[0][a],E),g[1][a]=Math.max(g[1][a],E)}}if("rangemode"in s&&"tozero"===s.rangemode&&(g[0][a]=Math.min(g[0][a],0),g[1][a]=Math.max(g[1][a],0)),g[0][a]>g[1][a])g[0][a]=-1,g[1][a]=1;else{var L=g[1][a]-g[0][a];g[0][a]-=L/32,g[1][a]+=L/32}if("reversed"===s.autorange){var C=g[0][a];g[0][a]=g[1][a],g[1][a]=C}}else{var P=s.range;g[0][a]=s.r2l(P[0]),g[1][a]=s.r2l(P[1])}g[0][a]===g[1][a]&&(g[0][a]-=1,g[1][a]+=1),v[a]=g[1][a]-g[0][a],this.glplot.setBounds(a,{min:g[0][a]*h[a],max:g[1][a]*h[a]})}var I=c.aspectmode;if("cube"===I)d=[1,1,1];else if("manual"===I){var O=c.aspectratio;d=[O.x,O.y,O.z]}else{if("auto"!==I&&"data"!==I)throw new Error("scene.js aspectRatio was not one of the enumerated types");var z=[1,1,1];for(a=0;a<3;++a){var D=y[l=(s=c[k[a]]).type];z[a]=Math.pow(D.acc,1/D.count)/h[a]}d="data"===I||Math.max.apply(null,z)/Math.min.apply(null,z)<=4?z:[1,1,1]}c.aspectratio.x=u.aspectratio.x=d[0],c.aspectratio.y=u.aspectratio.y=d[1],c.aspectratio.z=u.aspectratio.z=d[2],this.glplot.setAspectratio(c.aspectratio),this.viewInitial.aspectratio||(this.viewInitial.aspectratio={x:c.aspectratio.x,y:c.aspectratio.y,z:c.aspectratio.z}),this.viewInitial.aspectmode||(this.viewInitial.aspectmode=c.aspectmode);var R=c.domain||null,F=e._size||null;if(R&&F){var B=this.container.style;B.position="absolute",B.left=F.l+R.x[0]*F.w+"px",B.top=F.t+(1-R.y[1])*F.h+"px",B.width=F.w*(R.x[1]-R.x[0])+"px",B.height=F.h*(R.y[1]-R.y[0])+"px"}this.glplot.redraw()}},w.destroy=function(){this.glplot&&(this.camera.mouseListener.enabled=!1,this.container.removeEventListener("wheel",this.camera.wheelListener),this.camera=null,this.glplot.dispose(),this.container.parentNode.removeChild(this.container),this.glplot=null)},w.getCamera=function(){var t;return this.camera.view.recalcMatrix(this.camera.view.lastT()),{up:{x:(t=this.camera).up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]},projection:{type:!0===t._ortho?"orthographic":"perspective"}}},w.setViewport=function(t){var e,r=t.camera;this.camera.lookAt.apply(this,[[(e=r).eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]),this.glplot.setAspectratio(t.aspectratio),"orthographic"===r.projection.type!==this.camera._ortho&&(this.glplot.redraw(),this.glplot.clearRGBA(),this.glplot.dispose(),this.initializeGLPlot())},w.isCameraChanged=function(t){var e=this.getCamera(),r=f.nestedProperty(t,this.id+".camera").get();function n(t,e,r,n){var i=["up","center","eye"],a=["x","y","z"];return e[i[r]]&&t[i[r]][a[n]]===e[i[r]][a[n]]}var i=!1;if(void 0===r)i=!0;else{for(var a=0;a<3;a++)for(var o=0;o<3;o++)if(!n(e,r,a,o)){i=!0;break}(!r.projection||e.projection&&e.projection.type!==r.projection.type)&&(i=!0)}return i},w.isAspectChanged=function(t){var e=this.glplot.getAspectratio(),r=f.nestedProperty(t,this.id+".aspectratio").get();return void 0===r||r.x!==e.x||r.y!==e.y||r.z!==e.z},w.saveLayout=function(t){var e,r,n,i,a,o,s=this.fullLayout,l=this.isCameraChanged(t),c=this.isAspectChanged(t),h=l||c;if(h){var p={};if(l&&(e=this.getCamera(),n=(r=f.nestedProperty(t,this.id+".camera")).get(),p[this.id+".camera"]=n),c&&(i=this.glplot.getAspectratio(),o=(a=f.nestedProperty(t,this.id+".aspectratio")).get(),p[this.id+".aspectratio"]=o),u.call("_storeDirectGUIEdit",t,s._preGUI,p),l)r.set(e),f.nestedProperty(s,this.id+".camera").set(e);if(c)a.set(i),f.nestedProperty(s,this.id+".aspectratio").set(i),this.glplot.redraw()}return h},w.updateFx=function(t,e){var r=this.camera;if(r)if("orbit"===t)r.mode="orbit",r.keyBindingMode="rotate";else if("turntable"===t){r.up=[0,0,1],r.mode="turntable",r.keyBindingMode="rotate";var n=this.graphDiv,i=n._fullLayout,a=this.fullSceneLayout.camera,o=a.up.x,s=a.up.y,l=a.up.z;if(l/Math.sqrt(o*o+s*s+l*l)<.999){var c=this.id+".camera.up",h={x:0,y:0,z:1},p={};p[c]=h;var d=n.layout;u.call("_storeDirectGUIEdit",d,i._preGUI,p),a.up=h,f.nestedProperty(d,c).set(h)}}else r.keyBindingMode=t;this.fullSceneLayout.hovermode=e},w.toImage=function(t){t||(t="png"),this.staticMode&&this.container.appendChild(n),this.glplot.redraw();var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a),function(t,e,r){for(var n=0,i=r-1;n<i;++n,--i)for(var a=0;a<e;++a)for(var o=0;o<4;++o){var s=4*(e*n+a)+o,l=4*(e*i+a)+o,c=t[s];t[s]=t[l],t[l]=c}}(a,r,i),function(t,e,r){for(var n=0;n<r;++n)for(var i=0;i<e;++i){var a=4*(e*n+i),o=t[a+3];if(o>0)for(var s=255/o,l=0;l<3;++l)t[a+l]=Math.min(s*t[a+l],255)}}(a,r,i);var o=document.createElement("canvas");o.width=r,o.height=i;var s,l=o.getContext("2d"),c=l.createImageData(r,i);switch(c.data.set(a),l.putImageData(c,0,0),t){case"jpeg":s=o.toDataURL("image/jpeg");break;case"webp":s=o.toDataURL("image/webp");break;default:s=o.toDataURL("image/png")}return this.staticMode&&this.container.removeChild(n),s},w.setConvert=function(){for(var t=0;t<3;t++){var e=this.fullSceneLayout[k[t]];p.setConvert(e,this.fullLayout),e.setScale=f.noop}},w.make4thDimension=function(){var t=this.graphDiv._fullLayout;this._mockAxis={type:"linear",showexponent:"all",exponentformat:"B"},p.setConvert(this._mockAxis,t)},e.exports=_},{"../../../stackgl_modules":1119,"../../components/fx":401,"../../lib":498,"../../lib/show_no_webgl_msg":520,"../../lib/str2rgbarray":523,"../../plots/cartesian/axes":549,"../../registry":633,"./layout/convert":597,"./layout/spikes":600,"./layout/tick_marks":601,"./project":602,"has-passive-events":224,"webgl-context":326}],604:[function(t,e,r){"use strict";e.exports=function(t,e,r,n){n=n||t.length;for(var i=new Array(n),a=0;a<n;a++)i[a]=[t[a],e[a],r[a]];return i}},{}],605:[function(t,e,r){"use strict";var n=t("./font_attributes"),i=t("./animation_attributes"),a=t("../components/color/attributes"),o=t("../components/shapes/draw_newshape/attributes"),s=t("./pad_attributes"),l=t("../lib/extend").extendFlat,c=n({editType:"calc"});c.family.dflt='"Open Sans", verdana, arial, sans-serif',c.size.dflt=12,c.color.dflt=a.defaultLine,e.exports={font:c,title:{text:{valType:"string",editType:"layoutstyle"},font:n({editType:"layoutstyle"}),xref:{valType:"enumerated",dflt:"container",values:["container","paper"],editType:"layoutstyle"},yref:{valType:"enumerated",dflt:"container",values:["container","paper"],editType:"layoutstyle"},x:{valType:"number",min:0,max:1,dflt:.5,editType:"layoutstyle"},y:{valType:"number",min:0,max:1,dflt:"auto",editType:"layoutstyle"},xanchor:{valType:"enumerated",dflt:"auto",values:["auto","left","center","right"],editType:"layoutstyle"},yanchor:{valType:"enumerated",dflt:"auto",values:["auto","top","middle","bottom"],editType:"layoutstyle"},pad:l(s({editType:"layoutstyle"}),{}),editType:"layoutstyle"},uniformtext:{mode:{valType:"enumerated",values:[!1,"hide","show"],dflt:!1,editType:"plot"},minsize:{valType:"number",min:0,dflt:0,editType:"plot"},editType:"plot"},autosize:{valType:"boolean",dflt:!1,editType:"none"},width:{valType:"number",min:10,dflt:700,editType:"plot"},height:{valType:"number",min:10,dflt:450,editType:"plot"},margin:{l:{valType:"number",min:0,dflt:80,editType:"plot"},r:{valType:"number",min:0,dflt:80,editType:"plot"},t:{valType:"number",min:0,dflt:100,editType:"plot"},b:{valType:"number",min:0,dflt:80,editType:"plot"},pad:{valType:"number",min:0,dflt:0,editType:"plot"},autoexpand:{valType:"boolean",dflt:!0,editType:"plot"},editType:"plot"},computed:{valType:"any",editType:"none"},paper_bgcolor:{valType:"color",dflt:a.background,editType:"plot"},plot_bgcolor:{valType:"color",dflt:a.background,editType:"layoutstyle"},autotypenumbers:{valType:"enumerated",values:["convert types","strict"],dflt:"convert types",editType:"calc"},separators:{valType:"string",editType:"plot"},hidesources:{valType:"boolean",dflt:!1,editType:"plot"},showlegend:{valType:"boolean",editType:"legend"},colorway:{valType:"colorlist",dflt:a.defaults,editType:"calc"},datarevision:{valType:"any",editType:"calc"},uirevision:{valType:"any",editType:"none"},editrevision:{valType:"any",editType:"none"},selectionrevision:{valType:"any",editType:"none"},template:{valType:"any",editType:"calc"},newshape:o.newshape,activeshape:o.activeshape,meta:{valType:"any",arrayOk:!0,editType:"plot"},transition:l({},i.transition,{editType:"none"}),_deprecated:{title:{valType:"string",editType:"layoutstyle"},titlefont:n({editType:"layoutstyle"})}}},{"../components/color/attributes":360,"../components/shapes/draw_newshape/attributes":446,"../lib/extend":488,"./animation_attributes":543,"./font_attributes":580,"./pad_attributes":613}],606:[function(t,e,r){"use strict";var n=t("../../lib/sort_object_keys"),i='\xa9 <a target="_blank" href="https://www.openstreetmap.org/copyright">OpenStreetMap</a> contributors',a=['\xa9 <a target="_blank" href="https://carto.com/">Carto</a>',i].join(" "),o=['Map tiles by <a target="_blank" href="https://stamen.com">Stamen Design</a>','under <a target="_blank" href="https://creativecommons.org/licenses/by/3.0">CC BY 3.0</a>',"|",'Data by <a target="_blank" href="https://openstreetmap.org">OpenStreetMap</a> contributors','under <a target="_blank" href="https://www.openstreetmap.org/copyright">ODbL</a>'].join(" "),s={"open-street-map":{id:"osm",version:8,sources:{"plotly-osm-tiles":{type:"raster",attribution:i,tiles:["https://a.tile.openstreetmap.org/{z}/{x}/{y}.png","https://b.tile.openstreetmap.org/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-osm-tiles",type:"raster",source:"plotly-osm-tiles",minzoom:0,maxzoom:22}]},"white-bg":{id:"white-bg",version:8,sources:{},layers:[{id:"white-bg",type:"background",paint:{"background-color":"#FFFFFF"},minzoom:0,maxzoom:22}]},"carto-positron":{id:"carto-positron",version:8,sources:{"plotly-carto-positron":{type:"raster",attribution:a,tiles:["https://cartodb-basemaps-c.global.ssl.fastly.net/light_all/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-carto-positron",type:"raster",source:"plotly-carto-positron",minzoom:0,maxzoom:22}]},"carto-darkmatter":{id:"carto-darkmatter",version:8,sources:{"plotly-carto-darkmatter":{type:"raster",attribution:a,tiles:["https://cartodb-basemaps-c.global.ssl.fastly.net/dark_all/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-carto-darkmatter",type:"raster",source:"plotly-carto-darkmatter",minzoom:0,maxzoom:22}]},"stamen-terrain":{id:"stamen-terrain",version:8,sources:{"plotly-stamen-terrain":{type:"raster",attribution:o,tiles:["https://stamen-tiles.a.ssl.fastly.net/terrain/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-stamen-terrain",type:"raster",source:"plotly-stamen-terrain",minzoom:0,maxzoom:22}]},"stamen-toner":{id:"stamen-toner",version:8,sources:{"plotly-stamen-toner":{type:"raster",attribution:o,tiles:["https://stamen-tiles.a.ssl.fastly.net/toner/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-stamen-toner",type:"raster",source:"plotly-stamen-toner",minzoom:0,maxzoom:22}]},"stamen-watercolor":{id:"stamen-watercolor",version:8,sources:{"plotly-stamen-watercolor":{type:"raster",attribution:['Map tiles by <a target="_blank" href="https://stamen.com">Stamen Design</a>','under <a target="_blank" href="https://creativecommons.org/licenses/by/3.0">CC BY 3.0</a>',"|",'Data by <a target="_blank" href="https://openstreetmap.org">OpenStreetMap</a> contributors','under <a target="_blank" href="https://creativecommons.org/licenses/by-sa/3.0">CC BY SA</a>'].join(" "),tiles:["https://stamen-tiles.a.ssl.fastly.net/watercolor/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-stamen-watercolor",type:"raster",source:"plotly-stamen-watercolor",minzoom:0,maxzoom:22}]}},l=n(s);e.exports={requiredVersion:"1.10.1",styleUrlPrefix:"mapbox://styles/mapbox/",styleUrlSuffix:"v9",styleValuesMapbox:["basic","streets","outdoors","light","dark","satellite","satellite-streets"],styleValueDflt:"basic",stylesNonMapbox:s,styleValuesNonMapbox:l,traceLayerPrefix:"plotly-trace-layer-",layoutLayerPrefix:"plotly-layout-layer-",wrongVersionErrorMsg:["Your custom plotly.js bundle is not using the correct mapbox-gl version","Please install mapbox-gl@1.10.1."].join("\n"),noAccessTokenErrorMsg:["Missing Mapbox access token.","Mapbox trace type require a Mapbox access token to be registered.","For example:","  Plotly.newPlot(gd, data, layout, { mapboxAccessToken: 'my-access-token' });","More info here: https://www.mapbox.com/help/define-access-token/"].join("\n"),missingStyleErrorMsg:["No valid mapbox style found, please set `mapbox.style` to one of:",l.join(", "),"or register a Mapbox access token to use a Mapbox-served style."].join("\n"),multipleTokensErrorMsg:["Set multiple mapbox access token across different mapbox subplot,","using first token found as mapbox-gl does not allow multipleaccess tokens on the same page."].join("\n"),mapOnErrorMsg:"Mapbox error.",mapboxLogo:{path0:"m 10.5,1.24 c -5.11,0 -9.25,4.15 -9.25,9.25 0,5.1 4.15,9.25 9.25,9.25 5.1,0 9.25,-4.15 9.25,-9.25 0,-5.11 -4.14,-9.25 -9.25,-9.25 z m 4.39,11.53 c -1.93,1.93 -4.78,2.31 -6.7,2.31 -0.7,0 -1.41,-0.05 -2.1,-0.16 0,0 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t=i.numbersScaler(h);D+=t[2];var e,r=S(m,"numbersScale",1,t[0],D,Math.min);m._scaleNumbers||(r=1),e=m._isAngular?v-r*h.bottom:v-r*(h.top+h.bottom)/2,m._numbersTop=r*h.top+e;var n=h[T];"center"===T&&(n=(h.left+h.right)/2);var a=g-r*n;return a=S(m,"numbersTranslate",0,a,D,Math.max),l(a,e)+s(r)}))}(t,O,e,{numbersX:m,numbersY:E,numbersScaler:L,transitionOpts:r,onComplete:g}),z&&(C={range:I.gauge.axis.range,color:I.gauge.bgcolor,line:{color:I.gauge.bordercolor,width:0},thickness:1},P={range:I.gauge.axis.range,color:"rgba(0, 0, 0, 0)",line:{color:I.gauge.bordercolor,width:I.gauge.borderwidth},thickness:1});var Y=O.selectAll("g.angular").data(D?e:[]);Y.exit().remove();var W=O.selectAll("g.angularaxis").data(D?e:[]);W.exit().remove(),D&&function(t,e,r,a){var o,s,f,h,p=r[0].trace,m=a.size,g=a.radius,v=a.innerRadius,y=a.gaugeBg,x=a.gaugeOutline,b=[m.l+m.w/2,m.t+m.h/2+g/2],_=a.gauge,A=a.layer,M=a.transitionOpts,S=a.onComplete,E=Math.PI/2;function L(t){var e=p.gauge.axis.range[0],r=(t-e)/(p.gauge.axis.range[1]-e)*Math.PI-E;return r<-E?-E:r>E?E:r}function C(t){return n.svg.arc().innerRadius((v+g)/2-t/2*(g-v)).outerRadius((v+g)/2+t/2*(g-v)).startAngle(-E)}function P(t){t.attr("d",(function(t){return C(t.thickness).startAngle(L(t.range[0])).endAngle(L(t.range[1]))()}))}_.enter().append("g").classed("angular",!0),_.attr("transform",l(b[0],b[1])),A.enter().append("g").classed("angularaxis",!0).classed("crisp",!0),A.selectAll("g.xangularaxistick,path,text").remove(),(o=k(t,p.gauge.axis)).type="linear",o.range=p.gauge.axis.range,o._id="xangularaxis",o.ticklabeloverflow="allow",o.setScale();var I=function(t){return(o.range[0]-t.x)/(o.range[1]-o.range[0])*Math.PI+Math.PI},O={},z=d.makeLabelFns(o,0).labelStandoff;O.xFn=function(t){var e=I(t);return Math.cos(e)*z},O.yFn=function(t){var e=I(t),r=Math.sin(e)>0?.2:1;return-Math.sin(e)*(z+t.fontSize*r)+Math.abs(Math.cos(e))*(t.fontSize*u)},O.anchorFn=function(t){var e=I(t),r=Math.cos(e);return 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U=j.select("path");w(M)?(U.transition().duration(M.duration).ease(M.easing).each("end",(function(){S&&S()})).each("interrupt",(function(){S&&S()})).attrTween("d",(V=N,H=L(r[0].lastY),q=L(r[0].y),function(){var t=i(H,q);return function(e){return V.endAngle(t(e))()}})),p._lastValue=r[0].y):U.attr("d","number"==typeof r[0].y?N.endAngle(L(r[0].y)):"M0,0Z");var V,H,q;U.call(T),j.exit().remove(),F=[];var G=p.gauge.threshold.value;(G||0===G)&&F.push({range:[G,G],color:p.gauge.threshold.color,line:{color:p.gauge.threshold.line.color,width:p.gauge.threshold.line.width},thickness:p.gauge.threshold.thickness});var Y=_.selectAll("g.threshold-arc").data(F);Y.enter().append("g").classed("threshold-arc",!0).append("path"),Y.select("path").call(P).call(T),Y.exit().remove();var W=_.selectAll("g.gauge-outline").data([x]);W.enter().append("g").classed("gauge-outline",!0).append("path"),W.select("path").call(P).call(T),W.exit().remove()}(t,0,e,{radius:U,innerRadius:V,gauge:Y,layer:W,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:g});var X=O.selectAll("g.bullet").data(R?e:[]);X.exit().remove();var Z=O.selectAll("g.bulletaxis").data(R?e:[]);Z.exit().remove(),R&&function(t,e,r,n){var i,a,o,s,c,u=r[0].trace,f=n.gauge,p=n.layer,m=n.gaugeBg,g=n.gaugeOutline,v=n.size,x=u.domain,b=n.transitionOpts,_=n.onComplete;f.enter().append("g").classed("bullet",!0),f.attr("transform",l(v.l,v.t)),p.enter().append("g").classed("bulletaxis",!0).classed("crisp",!0),p.selectAll("g.xbulletaxistick,path,text").remove();var A=v.h,M=u.gauge.bar.thickness*A,S=x.x[0],E=x.x[0]+(x.x[1]-x.x[0])*(u._hasNumber||u._hasDelta?1-h.bulletNumberDomainSize:1);(i=k(t,u.gauge.axis))._id="xbulletaxis",i.domain=[S,E],i.setScale(),a=d.calcTicks(i),o=d.makeTransTickFn(i),s=d.getTickSigns(i)[2],c=v.t+v.h,i.visible&&(d.drawTicks(t,i,{vals:"inside"===i.ticks?d.clipEnds(i,a):a,layer:p,path:d.makeTickPath(i,c,s),transFn:o}),d.drawLabels(t,i,{vals:a,layer:p,transFn:o,labelFns:d.makeLabelFns(i,c)}));function L(t){t.attr("width",(function(t){return Math.max(0,i.c2p(t.range[1])-i.c2p(t.range[0]))})).attr("x",(function(t){return i.c2p(t.range[0])})).attr("y",(function(t){return.5*(1-t.thickness)*A})).attr("height",(function(t){return t.thickness*A}))}var C=[m].concat(u.gauge.steps),P=f.selectAll("g.bg-bullet").data(C);P.enter().append("g").classed("bg-bullet",!0).append("rect"),P.select("rect").call(L).call(T),P.exit().remove();var I=f.selectAll("g.value-bullet").data([u.gauge.bar]);I.enter().append("g").classed("value-bullet",!0).append("rect"),I.select("rect").attr("height",M).attr("y",(A-M)/2).call(T),w(b)?I.select("rect").transition().duration(b.duration).ease(b.easing).each("end",(function(){_&&_()})).each("interrupt",(function(){_&&_()})).attr("width",Math.max(0,i.c2p(Math.min(u.gauge.axis.range[1],r[0].y)))):I.select("rect").attr("width","number"==typeof r[0].y?Math.max(0,i.c2p(Math.min(u.gauge.axis.range[1],r[0].y))):0);I.exit().remove();var O=r.filter((function(){return u.gauge.threshold.value||0===u.gauge.threshold.value})),z=f.selectAll("g.threshold-bullet").data(O);z.enter().append("g").classed("threshold-bullet",!0).append("line"),z.select("line").attr("x1",i.c2p(u.gauge.threshold.value)).attr("x2",i.c2p(u.gauge.threshold.value)).attr("y1",(1-u.gauge.threshold.thickness)/2*A).attr("y2",(1-(1-u.gauge.threshold.thickness)/2)*A).call(y.stroke,u.gauge.threshold.line.color).style("stroke-width",u.gauge.threshold.line.width),z.exit().remove();var D=f.selectAll("g.gauge-outline").data([g]);D.enter().append("g").classed("gauge-outline",!0).append("rect"),D.select("rect").call(L).call(T),D.exit().remove()}(t,0,e,{gauge:X,layer:Z,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:g});var J=O.selectAll("text.title").data(e);J.exit().remove(),J.enter().append("text").classed("title",!0),J.attr("text-anchor",(function(){return R?x.right:x[I.title.align]})).text(I.title.text).call(f.font,I.title.font).call(p.convertToTspans,t),J.attr("transform",(function(){var t,e=B.l+B.w*b[I.title.align],r=h.titlePadding,n=f.bBox(J.node());if(z){if(D)if(I.gauge.axis.visible)t=f.bBox(W.node()).top-r-n.bottom;else t=B.t+B.h/2-U/2-n.bottom-r;R&&(t=E-(n.top+n.bottom)/2,e=B.l-h.bulletPadding*B.w)}else t=I._numbersTop-r-n.bottom;return l(e,t)}))}))}},{"../../components/color":361,"../../components/drawing":383,"../../constants/alignment":466,"../../lib":498,"../../lib/svg_text_utils":524,"../../plots/cartesian/axes":549,"../../plots/cartesian/axis_defaults":551,"../../plots/cartesian/layout_attributes":564,"../../plots/cartesian/position_defaults":567,"./constants":853,"@plotly/d3":58,"d3-interpolate":111}],857:[function(t,e,r){"use strict";var n=t("../../components/colorscale/attributes"),i=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,a=t("../../plots/template_attributes").hovertemplateAttrs,o=t("../mesh3d/attributes"),s=t("../../plots/attributes"),l=t("../../lib/extend").extendFlat,c=t("../../plot_api/edit_types").overrideAll;var u=e.exports=c(l({x:{valType:"data_array"},y:{valType:"data_array"},z:{valType:"data_array"},value:{valType:"data_array"},isomin:{valType:"number"},isomax:{valType:"number"},surface:{show:{valType:"boolean",dflt:!0},count:{valType:"integer",dflt:2,min:1},fill:{valType:"number",min:0,max:1,dflt:1},pattern:{valType:"flaglist",flags:["A","B","C","D","E"],extras:["all","odd","even"],dflt:"all"}},spaceframe:{show:{valType:"boolean",dflt:!1},fill:{valType:"number",min:0,max:1,dflt:.15}},slices:{x:{show:{valType:"boolean",dflt:!1},locations:{valType:"data_array",dflt:[]},fill:{valType:"number",min:0,max:1,dflt:1}},y:{show:{valType:"boolean",dflt:!1},locations:{valType:"data_array",dflt:[]},fill:{valType:"number",min:0,max:1,dflt:1}},z:{show:{valType:"boolean",dflt:!1},locations:{valType:"data_array",dflt:[]},fill:{valType:"number",min:0,max:1,dflt:1}}},caps:{x:{show:{valType:"boolean",dflt:!0},fill:{valType:"number",min:0,max:1,dflt:1}},y:{show:{valType:"boolean",dflt:!0},fill:{valType:"number",min:0,max:1,dflt:1}},z:{show:{valType:"boolean",dflt:!0},fill:{valType:"number",min:0,max:1,dflt:1}}},text:{valType:"string",dflt:"",arrayOk:!0},hovertext:{valType:"string",dflt:"",arrayOk:!0},hovertemplate:a(),xhoverformat:i("x"),yhoverformat:i("y"),zhoverformat:i("z"),valuehoverformat:i("value",1),showlegend:l({},s.showlegend,{dflt:!1})},n("",{colorAttr:"`value`",showScaleDflt:!0,editTypeOverride:"calc"}),{opacity:o.opacity,lightposition:o.lightposition,lighting:o.lighting,flatshading:o.flatshading,contour:o.contour,hoverinfo:l({},s.hoverinfo)}),"calc","nested");u.flatshading.dflt=!0,u.lighting.facenormalsepsilon.dflt=0,u.x.editType=u.y.editType=u.z.editType=u.value.editType="calc+clearAxisTypes",u.transforms=void 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0===e.isomax||null===e.isomin?s:e.isomax,n(t,e,{vals:[e._vMin,e._vMax],containerStr:"",cLetter:"c"})}},{"../../components/colorscale/calc":369,"../streamtube/calc":1040}],859:[function(t,e,r){"use strict";var n=t("../../../stackgl_modules").gl_mesh3d,i=t("../../lib/gl_format_color").parseColorScale,a=t("../../lib/str2rgbarray"),o=t("../../components/colorscale").extractOpts,s=t("../../plots/gl3d/zip3"),l=function(t,e){for(var r=e.length-1;r>0;r--){var n=Math.min(e[r],e[r-1]),i=Math.max(e[r],e[r-1]);if(i>n&&n<t&&t<=i)return{id:r,distRatio:(i-t)/(i-n)}}return{id:0,distRatio:0}};function c(t,e,r){this.scene=t,this.uid=r,this.mesh=e,this.name="",this.data=null,this.showContour=!1}var u=c.prototype;u.handlePick=function(t){if(t.object===this.mesh){var e=t.data.index,r=this.data._meshX[e],n=this.data._meshY[e],i=this.data._meshZ[e],a=this.data._Ys.length,o=this.data._Zs.length,s=l(r,this.data._Xs).id,c=l(n,this.data._Ys).id,u=l(i,this.data._Zs).id,f=t.index=u+o*c+o*a*s;t.traceCoordinate=[this.data._meshX[f],this.data._meshY[f],this.data._meshZ[f],this.data._value[f]];var h=this.data.hovertext||this.data.text;return Array.isArray(h)&&void 0!==h[f]?t.textLabel=h[f]:h&&(t.textLabel=h),!0}},u.update=function(t){var e=this.scene,r=e.fullSceneLayout;function n(t,e,r,n){return e.map((function(e){return t.d2l(e,0,n)*r}))}this.data=h(t);var l={positions:s(n(r.xaxis,t._meshX,e.dataScale[0],t.xcalendar),n(r.yaxis,t._meshY,e.dataScale[1],t.ycalendar),n(r.zaxis,t._meshZ,e.dataScale[2],t.zcalendar)),cells:s(t._meshI,t._meshJ,t._meshK),lightPosition:[t.lightposition.x,t.lightposition.y,t.lightposition.z],ambient:t.lighting.ambient,diffuse:t.lighting.diffuse,specular:t.lighting.specular,roughness:t.lighting.roughness,fresnel:t.lighting.fresnel,vertexNormalsEpsilon:t.lighting.vertexnormalsepsilon,faceNormalsEpsilon:t.lighting.facenormalsepsilon,opacity:t.opacity,contourEnable:t.contour.show,contourColor:a(t.contour.color).slice(0,3),contourWidth:t.contour.width,useFacetNormals:t.flatshading},c=o(t);l.vertexIntensity=t._meshIntensity,l.vertexIntensityBounds=[c.min,c.max],l.colormap=i(t),this.mesh.update(l)},u.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()};var f=["xyz","xzy","yxz","yzx","zxy","zyx"];function h(t){t._meshI=[],t._meshJ=[],t._meshK=[];var 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n=t("@plotly/d3"),i=t("../../components/drawing"),a=t("../../components/color");e.exports=function(t,e,r){var o=r||n.select(t).selectAll("g.ohlclayer").selectAll("g.trace");o.style("opacity",(function(t){return t[0].trace.opacity})),o.each((function(t){var e=t[0].trace;n.select(this).selectAll("path").each((function(t){if(!t.empty){var r=e[t.dir].line;n.select(this).style("fill","none").call(a.stroke,r.color).call(i.dashLine,r.dash,r.width).style("opacity",e.selectedpoints&&!t.selected?.3:1)}}))}))}},{"../../components/color":361,"../../components/drawing":383,"@plotly/d3":58}],876:[function(t,e,r){"use strict";var n=t("../../lib/extend").extendFlat,i=t("../../plots/attributes"),a=t("../../plots/font_attributes"),o=t("../../components/colorscale/attributes"),s=t("../../plots/template_attributes").hovertemplateAttrs,l=t("../../plots/domain").attributes,c=n({editType:"calc"},o("line",{editTypeOverride:"calc"}),{shape:{valType:"enumerated",values:["linear","hspline"],dflt:"linear",editType:"plot"},hovertemplate:s({editType:"plot",arrayOk:!1},{keys:["count","probability"]})});e.exports={domain:l({name:"parcats",trace:!0,editType:"calc"}),hoverinfo:n({},i.hoverinfo,{flags:["count","probability"],editType:"plot",arrayOk:!1}),hoveron:{valType:"enumerated",values:["category","color","dimension"],dflt:"category",editType:"plot"},hovertemplate:s({editType:"plot",arrayOk:!1},{keys:["count","probability","category","categorycount","colorcount","bandcolorcount"]}),arrangement:{valType:"enumerated",values:["perpendicular","freeform","fixed"],dflt:"perpendicular",editType:"plot"},bundlecolors:{valType:"boolean",dflt:!0,editType:"plot"},sortpaths:{valType:"enumerated",values:["forward","backward"],dflt:"forward",editType:"plot"},labelfont:a({editType:"calc"}),tickfont:a({editType:"calc"}),dimensions:{_isLinkedToArray:"dimension",label:{valType:"string",editType:"calc"},categoryorder:{valType:"enumerated",values:["trace","category 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i=n._has&&n._has("parcats"),a=e._has&&e._has("parcats");i&&!a&&n._paperdiv.selectAll(".parcats").remove()}},{"../../plots/get_data":588,"./plot":882}],878:[function(t,e,r){"use strict";var n=t("../../lib/gup").wrap,i=t("../../components/colorscale/helpers").hasColorscale,a=t("../../components/colorscale/calc"),o=t("../../lib/filter_unique.js"),s=t("../../components/drawing"),l=t("../../lib"),c=t("fast-isnumeric");function u(t,e,r){t.valueInds.push(e),t.count+=r}function f(t,e,r){return{categoryInds:t,color:e,rawColor:r,valueInds:[],count:0}}function h(t,e,r){t.valueInds.push(e),t.count+=r}e.exports=function(t,e){var r=l.filterVisible(e.dimensions);if(0===r.length)return[];var p,d,m,g=r.map((function(t){var e;if("trace"===t.categoryorder)e=null;else if("array"===t.categoryorder)e=t.categoryarray;else{e=o(t.values);for(var r=!0,n=0;n<e.length;n++)if(!c(e[n])){r=!1;break}e.sort(r?l.sorterAsc:void 0),"category descending"===t.categoryorder&&(e=e.reverse())}return function(t,e){e=null==e?[]:e.map((function(t){return t}));var r={},n={},i=[];e.forEach((function(t,e){r[t]=0,n[t]=e}));for(var a=0;a<t.length;a++){var o,s=t[a];void 0===r[s]?(r[s]=1,o=e.push(s)-1,n[s]=o):(r[s]++,o=n[s]),i.push(o)}var l=e.map((function(t){return r[t]}));return{uniqueValues:e,uniqueCounts:l,inds:i}}(t.values,e)}));p=l.isArrayOrTypedArray(e.counts)?e.counts:[e.counts],function(t){var e;if(function(t){for(var e=new Array(t.length),r=0;r<t.length;r++){if(t[r]<0||t[r]>=t.length)return!1;if(void 0!==e[t[r]])return!1;e[t[r]]=!0}return!0}(t.map((function(t){return t.displayindex}))))for(e=0;e<t.length;e++)t[e]._displayindex=t[e].displayindex;else for(e=0;e<t.length;e++)t[e]._displayindex=e}(r),r.forEach((function(t,e){!function(t,e){t._categoryarray=e.uniqueValues,null===t.ticktext||void 0===t.ticktext?t._ticktext=[]:t._ticktext=t.ticktext.slice();for(var r=t._ticktext.length;r<e.uniqueValues.length;r++)t._ticktext.push(e.uniqueValues[r])}(t,g[e])}));var v,y=e.line;y?(i(e,"line")&&a(t,e,{vals:e.line.color,containerStr:"line",cLetter:"c"}),v=s.tryColorscale(y)):v=l.identity;var x,b,_,w,T,k=r[0].values.length,A={},M=g.map((function(t){return t.inds}));for(m=0,x=0;x<k;x++){var S=[];for(b=0;b<M.length;b++)S.push(M[b][x]);d=p[x%p.length],m+=d;var E=(_=x,w=void 0,T=void 0,l.isArrayOrTypedArray(y.color)?T=w=y.color[_%y.color.length]:w=y.color,{color:v(w),rawColor:T}),L=S+"-"+E.rawColor;void 0===A[L]&&(A[L]=f(S,E.color,E.rawColor)),h(A[L],x,d)}var C,P=r.map((function(t,e){return function(t,e,r,n,i){return{dimensionInd:t,containerInd:e,displayInd:r,dimensionLabel:n,count:i,categories:[],dragX:null}}(e,t._index,t._displayindex,t.label,m)}));for(x=0;x<k;x++)for(d=p[x%p.length],b=0;b<P.length;b++){var I=P[b].containerInd,O=g[b].inds[x],z=P[b].categories;if(void 0===z[O]){var D=e.dimensions[I]._categoryarray[O],R=e.dimensions[I]._ticktext[O];z[O]={dimensionInd:b,categoryInd:C=O,categoryValue:D,displayInd:C,categoryLabel:R,valueInds:[],count:0,dragY:null}}u(z[O],x,d)}return n(function(t,e,r){var n=t.map((function(t){return t.categories.length})).reduce((function(t,e){return Math.max(t,e)}));return{dimensions:t,paths:e,trace:void 0,maxCats:n,count:r}}(P,A,m))}},{"../../components/colorscale/calc":369,"../../components/colorscale/helpers":372,"../../components/drawing":383,"../../lib":498,"../../lib/filter_unique.js":489,"../../lib/gup":495,"fast-isnumeric":185}],879:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../components/colorscale/helpers").hasColorscale,a=t("../../components/colorscale/defaults"),o=t("../../plots/domain").defaults,s=t("../../plots/array_container_defaults"),l=t("./attributes"),c=t("../parcoords/merge_length");function u(t,e){function r(r,i){return n.coerce(t,e,l.dimensions,r,i)}var i=r("values"),a=r("visible");if(i&&i.length||(a=e.visible=!1),a){r("label"),r("displayindex",e._index);var o,s=t.categoryarray,c=Array.isArray(s)&&s.length>0;c&&(o="array");var u=r("categoryorder",o);"array"===u?(r("categoryarray"),r("ticktext")):(delete t.categoryarray,delete t.ticktext),c||"array"!==u||(e.categoryorder="trace")}}e.exports=function(t,e,r,f){function h(r,i){return n.coerce(t,e,l,r,i)}var p=s(t,e,{name:"dimensions",handleItemDefaults:u}),d=function(t,e,r,o,s){s("line.shape"),s("line.hovertemplate");var l=s("line.color",o.colorway[0]);if(i(t,"line")&&n.isArrayOrTypedArray(l)){if(l.length)return s("line.colorscale"),a(t,e,o,s,{prefix:"line.",cLetter:"c"}),l.length;e.line.color=r}return 1/0}(t,e,r,f,h);o(e,f,h),Array.isArray(p)&&p.length||(e.visible=!1),c(e,p,"values",d),h("hoveron"),h("hovertemplate"),h("arrangement"),h("bundlecolors"),h("sortpaths"),h("counts");var m={family:f.font.family,size:Math.round(f.font.size),color:f.font.color};n.coerceFont(h,"labelfont",m);var g={family:f.font.family,size:Math.round(f.font.size/1.2),color:f.font.color};n.coerceFont(h,"tickfont",g)}},{"../../components/colorscale/defaults":371,"../../components/colorscale/helpers":372,"../../lib":498,"../../plots/array_container_defaults":544,"../../plots/domain":579,"../parcoords/merge_length":893,"./attributes":876}],880:[function(t,e,r){"use strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults"),calc:t("./calc"),plot:t("./plot"),colorbar:{container:"line",min:"cmin",max:"cmax"},moduleType:"trace",name:"parcats",basePlotModule:t("./base_plot"),categories:["noOpacity"],meta:{}}},{"./attributes":876,"./base_plot":877,"./calc":878,"./defaults":879,"./plot":882}],881:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("d3-interpolate").interpolateNumber,a=t("../../plot_api/plot_api"),o=t("../../components/fx"),s=t("../../lib"),l=s.strTranslate,c=t("../../components/drawing"),u=t("tinycolor2"),f=t("../../lib/svg_text_utils");function h(t,e,r,i){var a=t.map(F.bind(0,e,r)),o=i.selectAll("g.parcatslayer").data([null]);o.enter().append("g").attr("class","parcatslayer").style("pointer-events","all");var u=o.selectAll("g.trace.parcats").data(a,p),h=u.enter().append("g").attr("class","trace parcats");u.attr("transform",(function(t){return l(t.x,t.y)})),h.append("g").attr("class","paths");var y=u.select("g.paths").selectAll("path.path").data((function(t){return t.paths}),p);y.attr("fill",(function(t){return t.model.color}));var x=y.enter().append("path").attr("class","path").attr("stroke-opacity",0).attr("fill",(function(t){return t.model.color})).attr("fill-opacity",0);_(x),y.attr("d",(function(t){return t.svgD})),x.empty()||y.sort(m),y.exit().remove(),y.on("mouseover",g).on("mouseout",v).on("click",b),h.append("g").attr("class","dimensions");var w=u.select("g.dimensions").selectAll("g.dimension").data((function(t){return t.dimensions}),p);w.enter().append("g").attr("class","dimension"),w.attr("transform",(function(t){return l(t.x,0)})),w.exit().remove();var A=w.selectAll("g.category").data((function(t){return t.categories}),p),M=A.enter().append("g").attr("class","category");A.attr("transform",(function(t){return l(0,t.y)})),M.append("rect").attr("class","catrect").attr("pointer-events","none"),A.select("rect.catrect").attr("fill","none").attr("width",(function(t){return t.width})).attr("height",(function(t){return t.height})),T(M);var S=A.selectAll("rect.bandrect").data((function(t){return t.bands}),p);S.each((function(){s.raiseToTop(this)})),S.attr("fill",(function(t){return t.color}));var E=S.enter().append("rect").attr("class","bandrect").attr("stroke-opacity",0).attr("fill",(function(t){return t.color})).attr("fill-opacity",0);S.attr("fill",(function(t){return t.color})).attr("width",(function(t){return t.width})).attr("height",(function(t){return t.height})).attr("y",(function(t){return t.y})).attr("cursor",(function(t){return"fixed"===t.parcatsViewModel.arrangement?"default":"perpendicular"===t.parcatsViewModel.arrangement?"ns-resize":"move"})),k(E),S.exit().remove(),M.append("text").attr("class","catlabel").attr("pointer-events","none");var z=e._fullLayout.paper_bgcolor;A.select("text.catlabel").attr("text-anchor",(function(t){return d(t)?"start":"end"})).attr("alignment-baseline","middle").style("text-shadow",f.makeTextShadow(z)).style("fill","rgb(0, 0, 0)").attr("x",(function(t){return d(t)?t.width+5:-5})).attr("y",(function(t){return t.height/2})).text((function(t){return t.model.categoryLabel})).each((function(t){c.font(n.select(this),t.parcatsViewModel.categorylabelfont),f.convertToTspans(n.select(this),e)})),M.append("text").attr("class","dimlabel"),A.select("text.dimlabel").attr("text-anchor","middle").attr("alignment-baseline","baseline").attr("cursor",(function(t){return"fixed"===t.parcatsViewModel.arrangement?"default":"ew-resize"})).attr("x",(function(t){return t.width/2})).attr("y",-5).text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})).each((function(t){c.font(n.select(this),t.parcatsViewModel.labelfont)})),A.selectAll("rect.bandrect").on("mouseover",L).on("mouseout",C),A.exit().remove(),w.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on("dragstart",P).on("drag",I).on("dragend",O)),u.each((function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll("g.paths").selectAll("path.path"),t.dimensionSelection=n.select(this).selectAll("g.dimensions").selectAll("g.dimension")})),u.exit().remove()}function p(t){return t.key}function d(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function m(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor<e.model.rawColor?-1:0}function g(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){s.raiseToTop(this),w(n.select(this));var e=y(t),r=x(t);if(t.parcatsViewModel.graphDiv.emit("plotly_hover",{points:e,event:n.event,constraints:r}),-1===t.parcatsViewModel.hoverinfoItems.indexOf("none")){var i,a,l,c=n.mouse(this)[0],f=t.parcatsViewModel.graphDiv,h=t.parcatsViewModel.trace,p=f._fullLayout,d=p._paperdiv.node().getBoundingClientRect(),m=t.parcatsViewModel.graphDiv.getBoundingClientRect();for(l=0;l<t.leftXs.length-1;l++)if(t.leftXs[l]+t.dimWidths[l]-2<=c&&c<=t.leftXs[l+1]+2){var g=t.parcatsViewModel.dimensions[l],v=t.parcatsViewModel.dimensions[l+1];i=(g.x+g.width+v.x)/2,a=(t.topYs[l]+t.topYs[l+1]+t.height)/2;break}var b=t.parcatsViewModel.x+i,_=t.parcatsViewModel.y+a,T=u.mostReadable(t.model.color,["black","white"]),k=t.model.count,A=k/t.parcatsViewModel.model.count,M={countLabel:k,probabilityLabel:A.toFixed(3)},S=[];-1!==t.parcatsViewModel.hoverinfoItems.indexOf("count")&&S.push(["Count:",M.countLabel].join(" ")),-1!==t.parcatsViewModel.hoverinfoItems.indexOf("probability")&&S.push(["P:",M.probabilityLabel].join(" "));var E=S.join("<br>"),L=n.mouse(f)[0];o.loneHover({trace:h,x:b-d.left+m.left,y:_-d.top+m.top,text:E,color:t.model.color,borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontSize:10,fontColor:T,idealAlign:L<b?"right":"left",hovertemplate:(h.line||{}).hovertemplate,hovertemplateLabels:M,eventData:[{data:h._input,fullData:h,count:k,probability:A}]},{container:p._hoverlayer.node(),outerContainer:p._paper.node(),gd:f})}}}function v(t){if(!t.parcatsViewModel.dragDimension&&(_(n.select(this)),o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(m),-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip"))){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit("plotly_unhover",{points:e,event:n.event,constraints:r})}}function y(t){for(var e=[],r=z(t.parcatsViewModel),n=0;n<t.model.valueInds.length;n++){var i=t.model.valueInds[n];e.push({curveNumber:r,pointNumber:i})}return e}function x(t){for(var e={},r=t.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var i=r[n],a=i.categories[t.model.categoryInds[n]];e[i.containerInd]=a.categoryValue}return void 0!==t.model.rawColor&&(e.color=t.model.rawColor),e}function b(t){if(-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit("plotly_click",{points:e,event:n.event,constraints:r})}}function _(t){t.attr("fill",(function(t){return t.model.color})).attr("fill-opacity",.6).attr("stroke","lightgray").attr("stroke-width",.2).attr("stroke-opacity",1)}function w(t){t.attr("fill-opacity",.8).attr("stroke",(function(t){return u.mostReadable(t.model.color,["black","white"])})).attr("stroke-width",.3)}function T(t){t.select("rect.catrect").attr("stroke","black").attr("stroke-width",1).attr("stroke-opacity",1)}function k(t){t.attr("stroke","black").attr("stroke-width",.2).attr("stroke-opacity",1).attr("fill-opacity",1)}function A(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter((function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color}))}function M(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=n.select(t.parentNode).selectAll("rect.bandrect"),l=[];s.each((function(t){A(t).each((function(t){Array.prototype.push.apply(l,y(t))}))}));var c={};c[a.dimensionInd]=a.categoryValue,o.emit(e,{points:l,event:r,constraints:c})}function S(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=A(i),l=[];s.each((function(t){Array.prototype.push.apply(l,y(t))}));var c={};c[a.dimensionInd]=a.categoryValue,void 0!==i.rawColor&&(c.color=i.rawColor),o.emit(e,{points:l,event:r,constraints:c})}function E(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=n.select(r.parentNode).select("rect.catrect"),c=l.node().getBoundingClientRect(),u=l.datum(),f=u.parcatsViewModel,h=f.model.dimensions[u.model.dimensionInd],p=f.trace,d=c.top+c.height/2;f.dimensions.length>1&&h.displayInd===f.dimensions.length-1?(i=c.left,a="left"):(i=c.left+c.width,a="right");var m=u.model.count,g=u.model.categoryLabel,v=m/u.parcatsViewModel.model.count,y={countLabel:m,categoryLabel:g,probabilityLabel:v.toFixed(3)},x=[];-1!==u.parcatsViewModel.hoverinfoItems.indexOf("count")&&x.push(["Count:",y.countLabel].join(" ")),-1!==u.parcatsViewModel.hoverinfoItems.indexOf("probability")&&x.push(["P("+y.categoryLabel+"):",y.probabilityLabel].join(" "));var b=x.join("<br>");return{trace:p,x:o*(i-e.left),y:s*(d-e.top),text:b,color:"lightgray",borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontSize:12,fontColor:"black",idealAlign:a,hovertemplate:p.hovertemplate,hovertemplateLabels:y,eventData:[{data:p._input,fullData:p,count:m,category:g,probability:v}]}}function L(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){if(n.mouse(this)[1]<-1)return;var e,r=t.parcatsViewModel.graphDiv,i=r._fullLayout,a=i._paperdiv.node().getBoundingClientRect(),l=t.parcatsViewModel.hoveron;if("color"===l?(!function(t){var e=n.select(t).datum(),r=A(e);w(r),r.each((function(){s.raiseToTop(this)})),n.select(t.parentNode).selectAll("rect.bandrect").filter((function(t){return t.color===e.color})).each((function(){s.raiseToTop(this),n.select(this).attr("stroke","black").attr("stroke-width",1.5)}))}(this),S(this,"plotly_hover",n.event)):(!function(t){n.select(t.parentNode).selectAll("rect.bandrect").each((function(t){var e=A(t);w(e),e.each((function(){s.raiseToTop(this)}))})),n.select(t.parentNode).select("rect.catrect").attr("stroke","black").attr("stroke-width",2.5)}(this),M(this,"plotly_hover",n.event)),-1===t.parcatsViewModel.hoverinfoItems.indexOf("none"))"category"===l?e=E(r,a,this):"color"===l?e=function(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=r.getBoundingClientRect(),c=n.select(r).datum(),f=c.categoryViewModel,h=f.parcatsViewModel,p=h.model.dimensions[f.model.dimensionInd],d=h.trace,m=l.y+l.height/2;h.dimensions.length>1&&p.displayInd===h.dimensions.length-1?(i=l.left,a="left"):(i=l.left+l.width,a="right");var g=f.model.categoryLabel,v=c.parcatsViewModel.model.count,y=0;c.categoryViewModel.bands.forEach((function(t){t.color===c.color&&(y+=t.count)}));var x=f.model.count,b=0;h.pathSelection.each((function(t){t.model.color===c.color&&(b+=t.model.count)}));var _=y/v,w=y/b,T=y/x,k={countLabel:v,categoryLabel:g,probabilityLabel:_.toFixed(3)},A=[];-1!==f.parcatsViewModel.hoverinfoItems.indexOf("count")&&A.push(["Count:",k.countLabel].join(" ")),-1!==f.parcatsViewModel.hoverinfoItems.indexOf("probability")&&(A.push("P(color \u2229 "+g+"): "+k.probabilityLabel),A.push("P("+g+" | color): "+w.toFixed(3)),A.push("P(color | "+g+"): "+T.toFixed(3)));var M=A.join("<br>"),S=u.mostReadable(c.color,["black","white"]);return{trace:d,x:o*(i-e.left),y:s*(m-e.top),text:M,color:c.color,borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontColor:S,fontSize:10,idealAlign:a,hovertemplate:d.hovertemplate,hovertemplateLabels:k,eventData:[{data:d._input,fullData:d,category:g,count:v,probability:_,categorycount:x,colorcount:b,bandcolorcount:y}]}}(r,a,this):"dimension"===l&&(e=function(t,e,r){var i=[];return n.select(r.parentNode.parentNode).selectAll("g.category").select("rect.catrect").each((function(){i.push(E(t,e,this))})),i}(r,a,this)),e&&o.loneHover(e,{container:i._hoverlayer.node(),outerContainer:i._paper.node(),gd:r})}}function C(t){var e=t.parcatsViewModel;if(!e.dragDimension&&(_(e.pathSelection),T(e.dimensionSelection.selectAll("g.category")),k(e.dimensionSelection.selectAll("g.category").selectAll("rect.bandrect")),o.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(m),-1===e.hoverinfoItems.indexOf("skip"))){"color"===t.parcatsViewModel.hoveron?S(this,"plotly_unhover",n.event):M(this,"plotly_unhover",n.event)}}function P(t){"fixed"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll("g.category").select("rect.catrect").each((function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map((function(t){return t.displayInd})),e.model.dragY=e.y,s.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll("rect.bandrect").each((function(e){e.y<i&&i<=e.y+e.height&&(t.potentialClickBand=this)})))})),t.parcatsViewModel.dragDimension=t,o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function I(t){if("fixed"!==t.parcatsViewModel.arrangement&&(t.dragHasMoved=!0,null!==t.dragDimensionDisplayInd)){var e=t.dragDimensionDisplayInd,r=e-1,i=e+1,a=t.parcatsViewModel.dimensions[e];if(null!==t.dragCategoryDisplayInd){var o=a.categories[t.dragCategoryDisplayInd];o.model.dragY+=n.event.dy;var s=o.model.dragY,l=o.model.displayInd,c=a.categories,u=c[l-1],f=c[l+1];void 0!==u&&s<u.y+u.height/2&&(o.model.displayInd=u.model.displayInd,u.model.displayInd=l),void 0!==f&&s+o.height>f.y+f.height/2&&(o.model.displayInd=f.model.displayInd,f.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd||"freeform"===t.parcatsViewModel.arrangement){a.model.dragX=n.event.x;var h=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[i];void 0!==h&&a.model.dragX<h.x+h.width&&(a.model.displayInd=h.model.displayInd,h.model.displayInd=e),void 0!==p&&a.model.dragX+a.width>p.x&&(a.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=a.model.displayInd}j(t.parcatsViewModel),N(t.parcatsViewModel),R(t.parcatsViewModel),D(t.parcatsViewModel)}}function O(t){if("fixed"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll("text").attr("font-weight","normal");var e={},r=z(t.parcatsViewModel),i=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),o=t.initialDragDimensionDisplayInds.some((function(t,e){return t!==i[e]}));o&&i.forEach((function(r,n){var i=t.parcatsViewModel.model.dimensions[n].containerInd;e["dimensions["+i+"].displayindex"]=r}));var s=!1;if(null!==t.dragCategoryDisplayInd){var l=t.model.categories.map((function(t){return t.displayInd}));if(s=t.initialDragCategoryDisplayInds.some((function(t,e){return t!==l[e]}))){var c=t.model.categories.slice().sort((function(t,e){return t.displayInd-e.displayInd})),u=c.map((function(t){return t.categoryValue})),f=c.map((function(t){return 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strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults"),calc:t("./calc"),colorbar:{container:"line",min:"cmin",max:"cmax"},moduleType:"trace",name:"parcoords",basePlotModule:t("./base_plot"),categories:["gl","regl","noOpacity","noHover"],meta:{}}},{"./attributes":883,"./base_plot":886,"./calc":887,"./defaults":889}],886:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../plots/get_data").getModuleCalcData,a=t("./plot"),o=t("../../constants/xmlns_namespaces");r.name="parcoords",r.plot=function(t){var e=i(t.calcdata,"parcoords")[0];e.length&&a(t,e)},r.clean=function(t,e,r,n){var i=n._has&&n._has("parcoords"),a=e._has&&e._has("parcoords");i&&!a&&(n._paperdiv.selectAll(".parcoords").remove(),n._glimages.selectAll("*").remove())},r.toSVG=function(t){var e=t._fullLayout._glimages,r=n.select(t).selectAll(".svg-container");r.filter((function(t,e){return e===r.size()-1})).selectAll(".gl-canvas-context, .gl-canvas-focus").each((function(){var t=this.toDataURL("image/png");e.append("svg:image").attr({xmlns:o.svg,"xlink:href":t,preserveAspectRatio:"none",x:0,y:0,width:this.style.width,height:this.style.height})})),window.setTimeout((function(){n.selectAll("#filterBarPattern").attr("id","filterBarPattern")}),60)}},{"../../constants/xmlns_namespaces":475,"../../plots/get_data":588,"./plot":895,"@plotly/d3":58}],887:[function(t,e,r){"use strict";var n=t("../../lib").isArrayOrTypedArray,i=t("../../components/colorscale"),a=t("../../lib/gup").wrap;e.exports=function(t,e){var r,o;return i.hasColorscale(e,"line")&&n(e.line.color)?(r=e.line.color,o=i.extractOpts(e.line).colorscale,i.calc(t,e,{vals:r,containerStr:"line",cLetter:"c"})):(r=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=.5;return e}(e._length),o=[[0,e.line.color],[1,e.line.color]]),a({lineColor:r,cscale:o})}},{"../../components/colorscale":373,"../../lib":498,"../../lib/gup":495}],888:[function(t,e,r){"use strict";e.exports={maxDimensionCount:60,overdrag:45,verticalPadding:2,tickDistance:50,canvasPixelRatio:1,blockLineCount:5e3,layers:["contextLineLayer","focusLineLayer","pickLineLayer"],axisTitleOffset:28,axisExtentOffset:10,deselectedLineColor:"#777",bar:{width:4,captureWidth:10,fillColor:"magenta",fillOpacity:1,snapDuration:150,snapRatio:.25,snapClose:.01,strokeOpacity:1,strokeWidth:1,handleHeight:8,handleOpacity:1,handleOverlap:0},cn:{axisExtentText:"axis-extent-text",parcoordsLineLayers:"parcoords-line-layers",parcoordsLineLayer:"parcoords-lines",parcoords:"parcoords",parcoordsControlView:"parcoords-control-view",yAxis:"y-axis",axisOverlays:"axis-overlays",axis:"axis",axisHeading:"axis-heading",axisTitle:"axis-title",axisExtent:"axis-extent",axisExtentTop:"axis-extent-top",axisExtentTopText:"axis-extent-top-text",axisExtentBottom:"axis-extent-bottom",axisExtentBottomText:"axis-extent-bottom-text",axisBrush:"axis-brush"},id:{filterBarPattern:"filter-bar-pattern"}}},{}],889:[function(t,e,r){"use 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s.extendFlat({key:f,resolution:[x,b],viewBoxPos:[o+v,l],viewBoxSize:[c,u],i0:i,i1:a,dim0A:m[0].slice(0,16),dim0B:m[0].slice(16,32),dim0C:m[0].slice(32,48),dim0D:m[0].slice(48,64),dim1A:m[1].slice(0,16),dim1B:m[1].slice(16,32),dim1C:m[1].slice(32,48),dim1D:m[1].slice(48,64),drwLayer:h,contextColor:[A[0]/255,A[1]/255,A[2]/255,A[3]<1?A[3]:Math.max(1/255,Math.pow(1/t.lines.color.length,1/3))],scissorX:(n===e?0:o+v)+(_-v)+k*y.x[0],scissorWidth:(n===r?x-o+v:c+.5)+(n===e?o+v:0),scissorY:l+w+T*y.y[0],scissorHeight:u,viewportX:_-v+k*y.x[0],viewportY:w+T*y.y[0],viewportWidth:x,viewportHeight:b},p)}function x(t){var e=Math.max(0,Math.floor(2047*t[0]),0),r=Math.min(2047,Math.ceil(2047*t[1]),2047);return[Math.min(e,r),Math.max(e,r)]}e.exports=function(t,e){var r,n,l,g,b,_=e.context,w=e.pick,T=e.regl,k=T._gl,A=k.getParameter(k.ALIASED_LINE_WIDTH_RANGE),M=Math.max(A[0],Math.min(A[1],e.viewModel.plotGlPixelRatio)),S={currentRafs:{},drawCompleted:!0,clearOnly:!1},E=function(t){for(var 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I(t){r=t.model,n=t.viewModel,l=n.dimensions.slice(),g=l[0]?l[0].values.length:0;var e=r.lines,i=w?e.color.map((function(t,r){return r/e.color.length})):e.color,a=function(t,e,r){for(var n,i=new Array(t*(o+4)),a=0,s=0;s<t;s++){for(var l=0;l<o;l++)i[a++]=l<e.length?e[l].paddedUnitValues[s]:.5;i[a++]=d(s,2),i[a++]=d(s,1),i[a++]=d(s,0),i[a++]=(n=r[s],Math.max(1e-6,Math.min(.999999,n)))}return i}(g,l,i);!function(t,e,r){for(var n=0;n<=o;n+=4)t[v(n)](m(n/4,e,r))}(E,g,a),_||w||(L=T.texture(s.extendFlat({data:p(r.unitToColor,255)},u)))}return{render:function(t,e,n){var i,a,o,s=t.length,c=1/0,u=-1/0;for(i=0;i<s;i++)t[i].dim0.canvasX<c&&(c=t[i].dim0.canvasX,a=i),t[i].dim1.canvasX>u&&(u=t[i].dim1.canvasX,o=i);0===s&&f(T,0,0,r.canvasWidth,r.canvasHeight);var p=function(t){var e,r,n,i=[[],[]];for(n=0;n<64;n++){var a=!t&&n<l.length?l[n].brush.filter.getBounds():[-1/0,1/0];i[0][n]=a[0],i[1][n]=a[1]}var o=new Array(16384);for(e=0;e<16384;e++)o[e]=255;if(!t)for(e=0;e<l.length;e++){var s=e%8,c=(e-s)/8,u=Math.pow(2,s),f=l[e].brush.filter.get();if(!(f.length<2)){var h=x(f[0])[1];for(r=1;r<f.length;r++){var p=x(f[r]);for(n=h+1;n<p[0];n++)o[8*n+c]&=~u;h=Math.max(h,p[1])}}}var d={shape:[8,2048],format:"alpha",type:"uint8",mag:"nearest",min:"nearest",data:o};return b?b(d):b=T.texture(d),{maskTexture:b,maskHeight:2048,loA:i[0].slice(0,16),loB:i[0].slice(16,32),loC:i[0].slice(32,48),loD:i[0].slice(48,64),hiA:i[1].slice(0,16),hiB:i[1].slice(16,32),hiC:i[1].slice(32,48),hiD:i[1].slice(48,64)}}(_);for(i=0;i<s;i++){var d=t[i],m=d.dim0.crossfilterDimensionIndex,v=d.dim1.crossfilterDimensionIndex,k=d.canvasX,A=d.canvasY,M=k+d.panelSizeX,E=d.plotGlPixelRatio;if(e||!C[m]||C[m][0]!==k||C[m][1]!==M){C[m]=[k,M];var L=y(r,a,o,i,m,v,k,A,d.panelSizeX,d.panelSizeY,d.dim0.crossfilterDimensionIndex,_?0:w?2:1,p,E);S.clearOnly=n;var I=e?r.lines.blockLineCount:g;h(T,P,S,I,g,L)}}},readPixel:function(t,e){return T.read({x:t,y:e,width:1,height:1,data:c}),c},readPixels:function(t,e,r,n){var i=new Uint8Array(4*r*n);return T.read({x:t,y:e,width:r,height:n,data:i}),i},destroy:function(){for(var e in t.style["pointer-events"]="none",L.destroy(),b&&b.destroy(),E)E[e].destroy()},update:I}}},{"../../lib":498,"./constants":888,glslify:222}],893:[function(t,e,r){"use strict";e.exports=function(t,e,r,n){var i,a;for(n||(n=1/0),i=0;i<e.length;i++)(a=e[i]).visible&&(n=Math.min(n,a[r].length));for(n===1/0&&(n=0),t._length=n,i=0;i<e.length;i++)(a=e[i]).visible&&(a._length=n);return n}},{}],894:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../lib"),a=i.numberFormat,o=t("color-rgba"),s=t("../../plots/cartesian/axes"),l=i.strRotate,c=i.strTranslate,u=t("../../lib/svg_text_utils"),f=t("../../components/drawing"),h=t("../../components/colorscale"),p=t("../../lib/gup"),d=p.keyFun,m=p.repeat,g=p.unwrap,v=t("./helpers"),y=t("./constants"),x=t("./axisbrush"),b=t("./lines");function _(t,e,r){return i.aggNums(t,null,e,r)}function w(t,e){return k(_(Math.min,t,e),_(Math.max,t,e))}function 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0!==(x=c[r])&&""!==x||(x=r);var _=d[x=String(x)];void 0===_?(d[x]=a.length,(b=-1!==l.indexOf(x))||(g+=y),a.push({v:y,label:x,color:m(u[r],x),i:r,pts:[r],hidden:b})):(v=!0,(i=a[_]).v+=y,i.pts.push(r),i.hidden||(g+=y),!1===i.color&&u[r]&&(i.color=m(u[r],x)))}return a=a.filter((function(t){return t.v>=0})),("funnelarea"===e.type?v:e.sort)&&a.sort((function(t,e){return e.v-t.v})),a[0]&&(a[0].vTotal=g),a},crossTraceCalc:function(t,e){var r=(e||{}).type;r||(r="pie");var n=t._fullLayout,i=t.calcdata,a=n[r+"colorway"],s=n["_"+r+"colormap"];n["extend"+r+"colors"]&&(a=l(a,o));for(var c=0,u=0;u<i.length;u++){var f=i[u];if(f[0].trace.type===r)for(var h=0;h<f.length;h++){var p=f[h];!1===p.color&&(s[p.label]?p.color=s[p.label]:(s[p.label]=p.color=a[c%a.length],c++))}}},makePullColorFn:s,generateExtendedColors:l}},{"../../components/color":361,"fast-isnumeric":185,tinycolor2:307}],899:[function(t,e,r){"use strict";var n=t("fast-isnumeric"),i=t("../../lib"),a=t("./attributes"),o=t("../../plots/domain").defaults,s=t("../bar/defaults").handleText;function l(t,e){var r=Array.isArray(t),a=i.isArrayOrTypedArray(e),o=Math.min(r?t.length:1/0,a?e.length:1/0);if(isFinite(o)||(o=0),o&&a){for(var s,l=0;l<o;l++){var c=e[l];if(n(c)&&c>0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}e.exports={handleLabelsAndValues:l,supplyDefaults:function(t,e,r,n){function c(r,n){return i.coerce(t,e,a,r,n)}var u=l(c("labels"),c("values")),f=u.len;if(e._hasLabels=u.hasLabels,e._hasValues=u.hasValues,!e._hasLabels&&e._hasValues&&(c("label0"),c("dlabel")),f){e._length=f,c("marker.line.width")&&c("marker.line.color"),c("marker.colors"),c("scalegroup");var h,p=c("text"),d=c("texttemplate");if(d||(h=c("textinfo",Array.isArray(p)?"text+percent":"percent")),c("hovertext"),c("hovertemplate"),d||h&&"none"!==h){var m=c("textposition");s(t,e,n,c,m,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(m)||"auto"===m||"outside"===m)&&c("automargin"),("inside"===m||"auto"===m||Array.isArray(m))&&c("insidetextorientation")}o(e,n,c);var g=c("hole");if(c("title.text")){var v=c("title.position",g?"middle center":"top center");g||"middle center"!==v||(e.title.position="top center"),i.coerceFont(c,"title.font",n.font)}c("sort"),c("direction"),c("rotation"),c("pull")}else e.visible=!1}}},{"../../lib":498,"../../plots/domain":579,"../bar/defaults":647,"./attributes":896,"fast-isnumeric":185}],900:[function(t,e,r){"use strict";var n=t("../../components/fx/helpers").appendArrayMultiPointValues;e.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,bbox:t.bbox,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),"funnelarea"===e.type&&(delete r.v,delete r.i),r}},{"../../components/fx/helpers":397}],901:[function(t,e,r){"use strict";var n=t("../../lib");function i(t){return-1!==t.indexOf("e")?t.replace(/[.]?0+e/,"e"):-1!==t.indexOf(".")?t.replace(/[.]?0+$/,""):t}r.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+"%"},r.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},r.getFirstFilled=function(t,e){if(Array.isArray(t))for(var r=0;r<e.length;r++){var n=t[e[r]];if(n||0===n||""===n)return n}},r.castOption=function(t,e){return Array.isArray(t)?r.getFirstFilled(t,e):t||void 0},r.getRotationAngle=function(t){return("auto"===t?0:t)*Math.PI/180}},{"../../lib":498}],902:[function(t,e,r){"use strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults").supplyDefaults,supplyLayoutDefaults:t("./layout_defaults"),layoutAttributes:t("./layout_attributes"),calc:t("./calc").calc,crossTraceCalc:t("./calc").crossTraceCalc,plot:t("./plot").plot,style:t("./style"),styleOne:t("./style_one"),moduleType:"trace",name:"pie",basePlotModule:t("./base_plot"),categories:["pie-like","pie","showLegend"],meta:{}}},{"./attributes":896,"./base_plot":897,"./calc":898,"./defaults":899,"./layout_attributes":903,"./layout_defaults":904,"./plot":905,"./style":906,"./style_one":907}],903:[function(t,e,r){"use strict";e.exports={hiddenlabels:{valType:"data_array",editType:"calc"},piecolorway:{valType:"colorlist",editType:"calc"},extendpiecolors:{valType:"boolean",dflt:!0,editType:"calc"}}},{}],904:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("./layout_attributes");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r("hiddenlabels"),r("piecolorway",e.colorway),r("extendpiecolors")}},{"../../lib":498,"./layout_attributes":903}],905:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../plots/plots"),a=t("../../components/fx"),o=t("../../components/color"),s=t("../../components/drawing"),l=t("../../lib"),c=l.strScale,u=l.strTranslate,f=t("../../lib/svg_text_utils"),h=t("../bar/uniform_text"),p=h.recordMinTextSize,d=h.clearMinTextSize,m=t("../bar/constants").TEXTPAD,g=t("./helpers"),v=t("./event_data"),y=t("../../lib").isValidTextValue;function x(t,e,r){var i=r[0],o=i.cx,s=i.cy,c=i.trace,u="funnelarea"===c.type;"_hasHoverLabel"in c||(c._hasHoverLabel=!1),"_hasHoverEvent"in c||(c._hasHoverEvent=!1),t.on("mouseover",(function(t){var r=e._fullLayout,f=e._fullData[c.index];if(!e._dragging&&!1!==r.hovermode){var h=f.hoverinfo;if(Array.isArray(h)&&(h=a.castHoverinfo({hoverinfo:[g.castOption(h,t.pts)],_module:c._module},r,0)),"all"===h&&(h="label+text+value+percent+name"),f.hovertemplate||"none"!==h&&"skip"!==h&&h){var p=t.rInscribed||0,d=o+t.pxmid[0]*(1-p),m=s+t.pxmid[1]*(1-p),y=r.separators,x=[];if(h&&-1!==h.indexOf("label")&&x.push(t.label),t.text=g.castOption(f.hovertext||f.text,t.pts),h&&-1!==h.indexOf("text")){var b=t.text;l.isValidTextValue(b)&&x.push(b)}t.value=t.v,t.valueLabel=g.formatPieValue(t.v,y),h&&-1!==h.indexOf("value")&&x.push(t.valueLabel),t.percent=t.v/i.vTotal,t.percentLabel=g.formatPiePercent(t.percent,y),h&&-1!==h.indexOf("percent")&&x.push(t.percentLabel);var _=f.hoverlabel,w=_.font,T=[];a.loneHover({trace:c,x0:d-p*i.r,x1:d+p*i.r,y:m,_x0:u?o+t.TL[0]:d-p*i.r,_x1:u?o+t.TR[0]:d+p*i.r,_y0:u?s+t.TL[1]:m-p*i.r,_y1:u?s+t.BL[1]:m+p*i.r,text:x.join("<br>"),name:f.hovertemplate||-1!==h.indexOf("name")?f.name:void 0,idealAlign:t.pxmid[0]<0?"left":"right",color:g.castOption(_.bgcolor,t.pts)||t.color,borderColor:g.castOption(_.bordercolor,t.pts),fontFamily:g.castOption(w.family,t.pts),fontSize:g.castOption(w.size,t.pts),fontColor:g.castOption(w.color,t.pts),nameLength:g.castOption(_.namelength,t.pts),textAlign:g.castOption(_.align,t.pts),hovertemplate:g.castOption(f.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[v(t,f)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e,inOut_bbox:T}),t.bbox=T[0],c._hasHoverLabel=!0}c._hasHoverEvent=!0,e.emit("plotly_hover",{points:[v(t,f)],event:n.event})}})),t.on("mouseout",(function(t){var r=e._fullLayout,i=e._fullData[c.index],o=n.select(this).datum();c._hasHoverEvent&&(t.originalEvent=n.event,e.emit("plotly_unhover",{points:[v(o,i)],event:n.event}),c._hasHoverEvent=!1),c._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),c._hasHoverLabel=!1)})),t.on("click",(function(t){var r=e._fullLayout,i=e._fullData[c.index];e._dragging||!1===r.hovermode||(e._hoverdata=[v(t,i)],a.click(e,n.event))}))}function b(t,e,r){var n=g.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=g.castOption(t._input.textfont.color,e.pts));var i=g.castOption(t.insidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.insidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n||o.contrast(e.color),family:i,size:a}}function _(t,e){for(var r,n,i=0;i<t.length;i++)if((n=(r=t[i][0]).trace).title.text){var a=n.title.text;n._meta&&(a=l.templateString(a,n._meta));var o=s.tester.append("text").attr("data-notex",1).text(a).call(s.font,n.title.font).call(f.convertToTspans,e),c=s.bBox(o.node(),!0);r.titleBox={width:c.width,height:c.height},o.remove()}}function w(t,e,r){var n=r.r||e.rpx1,i=e.rInscribed;if(e.startangle===e.stopangle)return{rCenter:1-i,scale:0,rotate:0,textPosAngle:0};var a,o=e.ring,s=1===o&&Math.abs(e.startangle-e.stopangle)===2*Math.PI,l=e.halfangle,c=e.midangle,u=r.trace.insidetextorientation,f="horizontal"===u,h="tangential"===u,p="radial"===u,d="auto"===u,m=[];if(!d){var g,v=function(r,i){if(function(t,e){var r=t.startangle,n=t.stopangle;return r>e&&e>n||r<e&&e<n}(e,r)){var s=Math.abs(r-e.startangle),l=Math.abs(r-e.stopangle),c=s<l?s:l;(a="tan"===i?k(t,n,o,c,0):T(t,n,o,c,Math.PI/2)).textPosAngle=r,m.push(a)}};if(f||h){for(g=4;g>=-4;g-=2)v(Math.PI*g,"tan");for(g=4;g>=-4;g-=2)v(Math.PI*(g+1),"tan")}if(f||p){for(g=4;g>=-4;g-=2)v(Math.PI*(g+1.5),"rad");for(g=4;g>=-4;g-=2)v(Math.PI*(g+.5),"rad")}}if(s||d||f){var y=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/y,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return a;m.push(a)}(d||p)&&((a=T(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a)),(d||h)&&((a=k(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a));for(var x=0,b=0,_=0;_<m.length;_++){var w=m[_].scale;if(b<w&&(b=w,x=_),!d&&b>=1)break}return m[x]}function T(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.width/t.height,o=S(a,n,e,r);return{scale:2*o/t.height,rCenter:A(a,o/e),rotate:M(i)}}function k(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.height/t.width,o=S(a,n,e,r);return{scale:2*o/t.width,rCenter:A(a,o/e),rotate:M(i+Math.PI/2)}}function A(t,e){return Math.cos(e)-t*e}function M(t){return(180/Math.PI*t+720)%180-90}function S(t,e,r,n){var i=t+1/(2*Math.tan(e));return r*Math.min(1/(Math.sqrt(i*i+.5)+i),n/(Math.sqrt(t*t+n/2)+t))}function E(t,e){return t.v!==e.vTotal||e.trace.hole?Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2):1}function L(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i*=-1),n<0&&(a*=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)*(i>0?1:-1)/2,y:a/(1+r*r/(n*n)),outside:!0}}function C(t,e){var r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=I(a),-1!==a.title.position.indexOf("top")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf("bottom")&&(o.y+=(1+i)*t.r);var l,c,u=(l=t.r,c=t.trace.aspectratio,l/(void 0===c?1:c)),f=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf("left")?(f+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf("center")?f*=2:-1!==a.title.position.indexOf("right")&&(f+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=f/t.titleBox.width,n=P(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function P(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function I(t){var e,r=t.pull;if(!r)return 0;if(Array.isArray(r))for(r=0,e=0;e<t.pull.length;e++)t.pull[e]>r&&(r=t.pull[e]);return r}function O(t,e){for(var r=[],n=0;n<t.length;n++){var i=t[n][0],a=i.trace,o=a.domain,s=e.w*(o.x[1]-o.x[0]),l=e.h*(o.y[1]-o.y[0]);a.title.text&&"middle center"!==a.title.position&&(l-=P(i,e));var c=s/2,u=l/2;"funnelarea"!==a.type||a.scalegroup||(u/=a.aspectratio),i.r=Math.min(c,u)/(1+I(a)),i.cx=e.l+e.w*(a.domain.x[1]+a.domain.x[0])/2,i.cy=e.t+e.h*(1-a.domain.y[0])-l/2,a.title.text&&-1!==a.title.position.indexOf("bottom")&&(i.cy-=P(i,e)),a.scalegroup&&-1===r.indexOf(a.scalegroup)&&r.push(a.scalegroup)}!function(t,e){for(var r,n,i,a=0;a<e.length;a++){var o=1/0,s=e[a];for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var l;if("pie"===i.type)l=r.r*r.r;else if("funnelarea"===i.type){var c,u;i.aspectratio>1?(c=r.r,u=c/i.aspectratio):(u=r.r,c=u*i.aspectratio),c*=(1+i.baseratio)/2,l=c*u}o=Math.min(o,l/r.vTotal)}for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var f=o*r.vTotal;"funnelarea"===i.type&&(f/=(1+i.baseratio)/2,f/=i.aspectratio),r.r=Math.sqrt(f)}}}(t,r)}function z(t,e){return[t*Math.sin(e),-t*Math.cos(e)]}function D(t,e,r){var n=t._fullLayout,i=r.trace,a=i.texttemplate,o=i.textinfo;if(!a&&o&&"none"!==o){var s,c=o.split("+"),u=function(t){return-1!==c.indexOf(t)},f=u("label"),h=u("text"),p=u("value"),d=u("percent"),m=n.separators;if(s=f?[e.label]:[],h){var v=g.getFirstFilled(i.text,e.pts);y(v)&&s.push(v)}p&&s.push(g.formatPieValue(e.v,m)),d&&s.push(g.formatPiePercent(e.v/r.vTotal,m)),e.text=s.join("<br>")}if(a){var x=l.castOption(i,e.i,"texttemplate");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:g.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:g.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,"customdata")}}(e),_=g.getFirstFilled(i.text,e.pts);(y(_)||""===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,i._meta||{})}else e.text=""}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}e.exports={plot:function(t,e){var r=t._fullLayout,a=r._size;d("pie",r),_(e,t),O(e,a);var h=l.makeTraceGroups(r._pielayer,e,"trace").each((function(e){var h=n.select(this),d=e[0],m=d.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=g.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,c="px0",u="px1";if("counterclockwise"===o.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;s+=l*t[e].v,l*=-1,c="px1",u="px0"}for(n=z(a,s),e=0;e<t.length;e++)(r=t[e]).hidden||(r[c]=n,r.startangle=s,s+=l*r.v/2,r.pxmid=z(a,s),r.midangle=s,s+=l*r.v/2,n=z(a,s),r.stopangle=s,r[u]=n,r.largeArc=r.v>i.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),h.attr("stroke-linejoin","round"),h.each((function(){var v=n.select(this).selectAll("g.slice").data(e);v.enter().append("g").classed("slice",!0),v.exit().remove();var y=[[[],[]],[[],[]]],_=!1;v.each((function(i,a){if(i.hidden)n.select(this).selectAll("path,g").remove();else{i.pointNumber=i.i,i.curveNumber=m.index,y[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var o=d.cx,c=d.cy,u=n.select(this),h=u.selectAll("path.surface").data([i]);if(h.enter().append("path").classed("surface",!0).style({"pointer-events":"all"}),u.call(x,t,e),m.pull){var v=+g.castOption(m.pull,i.pts)||0;v>0&&(o+=v*i.pxmid[0],c+=v*i.pxmid[1])}i.cxFinal=o,i.cyFinal=c;var T=m.hole;if(i.v===d.vTotal){var k="M"+(o+i.px0[0])+","+(c+i.px0[1])+C(i.px0,i.pxmid,!0,1)+C(i.pxmid,i.px0,!0,1)+"Z";T?h.attr("d","M"+(o+T*i.px0[0])+","+(c+T*i.px0[1])+C(i.px0,i.pxmid,!1,T)+C(i.pxmid,i.px0,!1,T)+"Z"+k):h.attr("d",k)}else{var A=C(i.px0,i.px1,!0,1);if(T){var M=1-T;h.attr("d","M"+(o+T*i.px1[0])+","+(c+T*i.px1[1])+C(i.px1,i.px0,!1,T)+"l"+M*i.px0[0]+","+M*i.px0[1]+A+"Z")}else h.attr("d","M"+o+","+c+"l"+i.px0[0]+","+i.px0[1]+A+"Z")}D(t,i,d);var S=g.castOption(m.textposition,i.pts),E=u.selectAll("g.slicetext").data(i.text&&"none"!==S?[0]:[]);E.enter().append("g").classed("slicetext",!0),E.exit().remove(),E.each((function(){var u=l.ensureSingle(n.select(this),"text","",(function(t){t.attr("data-notex",1)})),h=l.ensureUniformFontSize(t,"outside"===S?function(t,e,r){var n=g.castOption(t.outsidetextfont.color,e.pts)||g.castOption(t.textfont.color,e.pts)||r.color,i=g.castOption(t.outsidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.outsidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n,family:i,size:a}}(m,i,r.font):b(m,i,r.font));u.text(i.text).attr({class:"slicetext",transform:"","text-anchor":"middle"}).call(s.font,h).call(f.convertToTspans,t);var v,y=s.bBox(u.node());if("outside"===S)v=L(y,i);else if(v=w(y,i,d),"auto"===S&&v.scale<1){var 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n=t("../../plots/font_attributes"),i=t("../../plots/attributes"),a=t("../../components/color/attributes"),o=t("../../components/fx/attributes"),s=t("../../plots/domain").attributes,l=t("../../plots/template_attributes").hovertemplateAttrs,c=t("../../components/colorscale/attributes"),u=t("../../plot_api/plot_template").templatedArray,f=t("../../plots/cartesian/axis_format_attributes").descriptionOnlyNumbers,h=t("../../lib/extend").extendFlat,p=t("../../plot_api/edit_types").overrideAll;(e.exports=p({hoverinfo:h({},i.hoverinfo,{flags:[],arrayOk:!1}),hoverlabel:o.hoverlabel,domain:s({name:"sankey",trace:!0}),orientation:{valType:"enumerated",values:["v","h"],dflt:"h"},valueformat:{valType:"string",dflt:".3s",description:f("value")},valuesuffix:{valType:"string",dflt:""},arrangement:{valType:"enumerated",values:["snap","perpendicular","freeform","fixed"],dflt:"snap"},textfont:n({}),customdata:void 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h={_id:"x",c2p:u.identity,_offset:r._sankey.translateX,_length:r._sankey.width},p={_id:"y",c2p:u.identity,_offset:r._sankey.translateY,_length:r._sankey.height},d={gd:t,element:o.node(),plotinfo:{id:e,xaxis:h,yaxis:p,fillRangeItems:u.noop},subplot:e,xaxes:[h],yaxes:[p],doneFnCompleted:function(r){var n,i=t._fullData[e],a=i.node.groups.slice(),o=[];function s(t){for(var e=i._sankey.graph.nodes,r=0;r<e.length;r++)if(e[r].pointNumber===t)return e[r]}for(var l=0;l<r.length;l++){var c=s(r[l].pointNumber);if(c)if(c.group){for(var u=0;u<c.childrenNodes.length;u++)o.push(c.childrenNodes[u].pointNumber);a[c.pointNumber-i.node._count]=!1}else o.push(c.pointNumber)}n=a.filter(Boolean).concat([o]),f.call("_guiRestyle",t,{"node.groups":[n]},e)},prepFn:function(t,e,r){c(t,e,r,d,i)}};l.init(d)}}r.name="sankey",r.baseLayoutAttrOverrides=n({hoverlabel:o.hoverlabel},"plot","nested"),r.plot=function(t){var e=i(t.calcdata,"sankey")[0];a(t,e),r.updateFx(t)},r.clean=function(t,e,r,n){var i=n._has&&n._has("sankey"),a=e._has&&e._has("sankey");i&&!a&&(n._paperdiv.selectAll(".sankey").remove(),n._paperdiv.selectAll(".bgsankey").remove())},r.updateFx=function(t){for(var e=0;e<t._fullData.length;e++)h(t,e)}},{"../../components/dragelement":380,"../../components/fx/layout_attributes":402,"../../lib":498,"../../lib/setcursor":519,"../../plot_api/edit_types":531,"../../plots/cartesian/select":570,"../../plots/get_data":588,"../../registry":633,"./plot":918}],914:[function(t,e,r){"use strict";var n=t("strongly-connected-components"),i=t("../../lib"),a=t("../../lib/gup").wrap,o=i.isArrayOrTypedArray,s=i.isIndex,l=t("../../components/colorscale");function c(t){var e,r=t.node,a=t.link,c=[],u=o(a.color),f=o(a.customdata),h={},p={},d=a.colorscales.length;for(e=0;e<d;e++){var m=a.colorscales[e],g=l.extractScale(m,{cLetter:"c"}),v=l.makeColorScaleFunc(g);p[m.label]=v}var y=0;for(e=0;e<a.value.length;e++)a.source[e]>y&&(y=a.source[e]),a.target[e]>y&&(y=a.target[e]);var x,b=y+1;t.node._count=b;var _=t.node.groups,w={};for(e=0;e<_.length;e++){var T=_[e];for(x=0;x<T.length;x++){var k=T[x],A=b+e;w.hasOwnProperty(k)?i.warn("Node "+k+" is already part of a group."):w[k]=A}}var M={source:[],target:[]};for(e=0;e<a.value.length;e++){var S=a.value[e],E=a.source[e],L=a.target[e];if(S>0&&s(E,b)&&s(L,b)&&(!w.hasOwnProperty(E)||!w.hasOwnProperty(L)||w[E]!==w[L])){w.hasOwnProperty(L)&&(L=w[L]),w.hasOwnProperty(E)&&(E=w[E]),L=+L,h[E=+E]=h[L]=!0;var C="";a.label&&a.label[e]&&(C=a.label[e]);var P=null;C&&p.hasOwnProperty(C)&&(P=p[C]),c.push({pointNumber:e,label:C,color:u?a.color[e]:a.color,customdata:f?a.customdata[e]:a.customdata,concentrationscale:P,source:E,target:L,value:+S}),M.source.push(E),M.target.push(L)}}var I=b+_.length,O=o(r.color),z=o(r.customdata),D=[];for(e=0;e<I;e++)if(h[e]){var R=r.label[e];D.push({group:e>b-1,childrenNodes:[],pointNumber:e,label:R,color:O?r.color[e]:r.color,customdata:z?r.customdata[e]:r.customdata})}var F=!1;return function(t,e,r){for(var a=i.init2dArray(t,0),o=0;o<Math.min(e.length,r.length);o++)if(i.isIndex(e[o],t)&&i.isIndex(r[o],t)){if(e[o]===r[o])return!0;a[e[o]].push(r[o])}return n(a).components.some((function(t){return t.length>1}))}(I,M.source,M.target)&&(F=!0),{circular:F,links:c,nodes:D,groups:_,groupLookup:w}}e.exports=function(t,e){var r=c(e);return a({circular:r.circular,_nodes:r.nodes,_links:r.links,_groups:r.groups,_groupLookup:r.groupLookup})}},{"../../components/colorscale":373,"../../lib":498,"../../lib/gup":495,"strongly-connected-components":301}],915:[function(t,e,r){"use strict";e.exports={nodeTextOffsetHorizontal:4,nodeTextOffsetVertical:3,nodePadAcross:10,sankeyIterations:50,forceIterations:5,forceTicksPerFrame:10,duration:500,ease:"linear",cn:{sankey:"sankey",sankeyLinks:"sankey-links",sankeyLink:"sankey-link",sankeyNodeSet:"sankey-node-set",sankeyNode:"sankey-node",nodeRect:"node-rect",nodeLabel:"node-label"}}},{}],916:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("./attributes"),a=t("../../components/color"),o=t("tinycolor2"),s=t("../../plots/domain").defaults,l=t("../../components/fx/hoverlabel_defaults"),c=t("../../plot_api/plot_template"),u=t("../../plots/array_container_defaults");function f(t,e){function r(r,a){return n.coerce(t,e,i.link.colorscales,r,a)}r("label"),r("cmin"),r("cmax"),r("colorscale")}e.exports=function(t,e,r,h){function p(r,a){return n.coerce(t,e,i,r,a)}var d=n.extendDeep(h.hoverlabel,t.hoverlabel),m=t.node,g=c.newContainer(e,"node");function v(t,e){return n.coerce(m,g,i.node,t,e)}v("label"),v("groups"),v("x"),v("y"),v("pad"),v("thickness"),v("line.color"),v("line.width"),v("hoverinfo",t.hoverinfo),l(m,g,v,d),v("hovertemplate");var y=h.colorway;v("color",g.label.map((function(t,e){return a.addOpacity(function(t){return y[t%y.length]}(e),.8)}))),v("customdata");var x=t.link||{},b=c.newContainer(e,"link");function _(t,e){return 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function(e){return-1!==e.node.sourceLinks.indexOf(t.link)||-1!==e.node.targetLinks.indexOf(t.link)}}function v(t,e,r){e&&r&&h(r,e).selectAll("."+c.sankeyLink).filter(m(e)).call(x.bind(0,e,r,!1))}function y(t,e,r){e&&r&&h(r,e).selectAll("."+c.sankeyLink).filter(m(e)).call(b.bind(0,e,r,!1))}function x(t,e,r,n){var i=n.datum().link.label;n.style("fill-opacity",(function(t){if(!t.link.concentrationscale)return.4})),i&&h(e,t).selectAll("."+c.sankeyLink).filter((function(t){return t.link.label===i})).style("fill-opacity",(function(t){if(!t.link.concentrationscale)return.4})),r&&h(e,t).selectAll("."+c.sankeyNode).filter(g(t)).call(v)}function b(t,e,r,n){var i=n.datum().link.label;n.style("fill-opacity",(function(t){return t.tinyColorAlpha})),i&&h(e,t).selectAll("."+c.sankeyLink).filter((function(t){return t.link.label===i})).style("fill-opacity",(function(t){return t.tinyColorAlpha})),r&&h(e,t).selectAll(c.sankeyNode).filter(g(t)).call(y)}function _(t,e){var r=t.hoverlabel||{},n=i.nestedProperty(r,e).get();return!Array.isArray(n)&&n}e.exports=function(t,e){for(var r=t._fullLayout,i=r._paper,h=r._size,m=0;m<t._fullData.length;m++)if(t._fullData[m].visible&&t._fullData[m].type===c.sankey&&!t._fullData[m]._viewInitial){var g=t._fullData[m].node;t._fullData[m]._viewInitial={node:{groups:g.groups.slice(),x:g.x.slice(),y:g.y.slice()}}}var w=u(t,"source:")+" ",T=u(t,"target:")+" ",k=u(t,"concentration:")+" ",A=u(t,"incoming flow count:")+" ",M=u(t,"outgoing flow count:")+" ";o(t,i,e,{width:h.w,height:h.h,margin:{t:h.t,r:h.r,b:h.b,l:h.l}},{linkEvents:{hover:function(e,r,i){!1!==t._fullLayout.hovermode&&(n.select(e).call(x.bind(0,r,i,!0)),"skip"!==r.link.trace.link.hoverinfo&&(r.link.fullData=r.link.trace,t.emit("plotly_hover",{event:n.event,points:[r.link]})))},follow:function(e,i){if(!1!==t._fullLayout.hovermode){var o=i.link.trace.link;if("none"!==o.hoverinfo&&"skip"!==o.hoverinfo){for(var c=[],u=0,h=0;h<i.flow.links.length;h++){var m=i.flow.links[h];if("closest"!==t._fullLayout.hovermode||i.link.pointNumber===m.pointNumber){i.link.pointNumber===m.pointNumber&&(u=h),m.fullData=m.trace,o=i.link.trace.link;var g=y(m),v={valueLabel:a(i.valueFormat)(m.value)+i.valueSuffix};c.push({x:g[0],y:g[1],name:v.valueLabel,text:[m.label||"",w+m.source.label,T+m.target.label,m.concentrationscale?k+a("%0.2f")(m.flow.labelConcentration):""].filter(f).join("<br>"),color:_(o,"bgcolor")||l.addOpacity(m.color,1),borderColor:_(o,"bordercolor"),fontFamily:_(o,"font.family"),fontSize:_(o,"font.size"),fontColor:_(o,"font.color"),nameLength:_(o,"namelength"),textAlign:_(o,"align"),idealAlign:n.event.x<g[0]?"right":"left",hovertemplate:o.hovertemplate,hovertemplateLabels:v,eventData:[m]})}}s.loneHover(c,{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t,anchorIndex:u}).each((function(){i.link.concentrationscale||p(this,.65),d(this)}))}}function y(t){var e,r;t.circular?(e=(t.circularPathData.leftInnerExtent+t.circularPathData.rightInnerExtent)/2,r=t.circularPathData.verticalFullExtent):(e=(t.source.x1+t.target.x0)/2,r=(t.y0+t.y1)/2);var n=[e,r];return"v"===t.trace.orientation&&n.reverse(),n[0]+=i.parent.translateX,n[1]+=i.parent.translateY,n}},unhover:function(e,i,a){!1!==t._fullLayout.hovermode&&(n.select(e).call(b.bind(0,i,a,!0)),"skip"!==i.link.trace.link.hoverinfo&&(i.link.fullData=i.link.trace,t.emit("plotly_unhover",{event:n.event,points:[i.link]})),s.loneUnhover(r._hoverlayer.node()))},select:function(e,r){var i=r.link;i.originalEvent=n.event,t._hoverdata=[i],s.click(t,{target:!0})}},nodeEvents:{hover:function(e,r,i){!1!==t._fullLayout.hovermode&&(n.select(e).call(v,r,i),"skip"!==r.node.trace.node.hoverinfo&&(r.node.fullData=r.node.trace,t.emit("plotly_hover",{event:n.event,points:[r.node]})))},follow:function(e,i){if(!1!==t._fullLayout.hovermode){var o=i.node.trace.node;if("none"!==o.hoverinfo&&"skip"!==o.hoverinfo){var l=n.select(e).select("."+c.nodeRect),u=t._fullLayout._paperdiv.node().getBoundingClientRect(),h=l.node().getBoundingClientRect(),m=h.left-2-u.left,g=h.right+2-u.left,v=h.top+h.height/4-u.top,y={valueLabel:a(i.valueFormat)(i.node.value)+i.valueSuffix};i.node.fullData=i.node.trace,t._fullLayout._calcInverseTransform(t);var x=t._fullLayout._invScaleX,b=t._fullLayout._invScaleY,w=s.loneHover({x0:x*m,x1:x*g,y:b*v,name:a(i.valueFormat)(i.node.value)+i.valueSuffix,text:[i.node.label,A+i.node.targetLinks.length,M+i.node.sourceLinks.length].filter(f).join("<br>"),color:_(o,"bgcolor")||i.tinyColorHue,borderColor:_(o,"bordercolor"),fontFamily:_(o,"font.family"),fontSize:_(o,"font.size"),fontColor:_(o,"font.color"),nameLength:_(o,"namelength"),textAlign:_(o,"align"),idealAlign:"left",hovertemplate:o.hovertemplate,hovertemplateLabels:y,eventData:[i.node]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t});p(w,.85),d(w)}}},unhover:function(e,i,a){!1!==t._fullLayout.hovermode&&(n.select(e).call(y,i,a),"skip"!==i.node.trace.node.hoverinfo&&(i.node.fullData=i.node.trace,t.emit("plotly_unhover",{event:n.event,points:[i.node]})),s.loneUnhover(r._hoverlayer.node()))},select:function(e,r,i){var a=r.node;a.originalEvent=n.event,t._hoverdata=[a],n.select(e).call(y,r,i),s.click(t,{target:!0})}}})}},{"../../components/color":361,"../../components/fx":401,"../../lib":498,"./constants":915,"./render":919,"@plotly/d3":58}],919:[function(t,e,r){"use strict";var n=t("d3-force"),i=t("d3-interpolate").interpolateNumber,a=t("@plotly/d3"),o=t("@plotly/d3-sankey"),s=t("@plotly/d3-sankey-circular"),l=t("./constants"),c=t("tinycolor2"),u=t("../../components/color"),f=t("../../components/drawing"),h=t("../../lib"),p=h.strTranslate,d=h.strRotate,m=t("../../lib/gup"),g=m.keyFun,v=m.repeat,y=m.unwrap,x=t("../../lib/svg_text_utils"),b=t("../../registry"),_=t("../../constants/alignment"),w=_.CAP_SHIFT,T=_.LINE_SPACING;function k(t,e,r){var n,i=y(e),a=i.trace,u=a.domain,f="h"===a.orientation,p=a.node.pad,d=a.node.thickness,m=t.width*(u.x[1]-u.x[0]),g=t.height*(u.y[1]-u.y[0]),v=i._nodes,x=i._links,b=i.circular;(n=b?s.sankeyCircular().circularLinkGap(0):o.sankey()).iterations(l.sankeyIterations).size(f?[m,g]:[g,m]).nodeWidth(d).nodePadding(p).nodeId((function(t){return t.pointNumber})).nodes(v).links(x);var _,w,T,k=n();for(var A in n.nodePadding()<p&&h.warn("node.pad was reduced to ",n.nodePadding()," to fit within the figure."),i._groupLookup){var M,S=parseInt(i._groupLookup[A]);for(_=0;_<k.nodes.length;_++)if(k.nodes[_].pointNumber===S){M=k.nodes[_];break}if(M){var E={pointNumber:parseInt(A),x0:M.x0,x1:M.x1,y0:M.y0,y1:M.y1,partOfGroup:!0,sourceLinks:[],targetLinks:[]};k.nodes.unshift(E),M.childrenNodes.unshift(E)}}if(function(){for(_=0;_<k.nodes.length;_++){var t,e,r=k.nodes[_],n={};for(w=0;w<r.targetLinks.length;w++)t=(e=r.targetLinks[w]).source.pointNumber+":"+e.target.pointNumber,n.hasOwnProperty(t)||(n[t]=[]),n[t].push(e);var i=Object.keys(n);for(w=0;w<i.length;w++){var a=n[t=i[w]],o=0,s={};for(T=0;T<a.length;T++)s[(e=a[T]).label]||(s[e.label]=0),s[e.label]+=e.value,o+=e.value;for(T=0;T<a.length;T++)(e=a[T]).flow={value:o,labelConcentration:s[e.label]/o,concentration:e.value/o,links:a},e.concentrationscale&&(e.color=c(e.concentrationscale(e.flow.labelConcentration)))}var l=0;for(w=0;w<r.sourceLinks.length;w++)l+=r.sourceLinks[w].value;for(w=0;w<r.sourceLinks.length;w++)(e=r.sourceLinks[w]).concentrationOut=e.value/l;var u=0;for(w=0;w<r.targetLinks.length;w++)u+=r.targetLinks[w].value;for(w=0;w<r.targetLinks.length;w++)(e=r.targetLinks[w]).concenrationIn=e.value/u}}(),a.node.x.length&&a.node.y.length){for(_=0;_<Math.min(a.node.x.length,a.node.y.length,k.nodes.length);_++)if(a.node.x[_]&&a.node.y[_]){var L=[a.node.x[_]*m,a.node.y[_]*g];k.nodes[_].x0=L[0]-d/2,k.nodes[_].x1=L[0]+d/2;var C=k.nodes[_].y1-k.nodes[_].y0;k.nodes[_].y0=L[1]-C/2,k.nodes[_].y1=L[1]+C/2}if("snap"===a.arrangement)!function(t){t.forEach((function(t){var e,r,n,i=0,a=t.length;for(t.sort((function(t,e){return t.y0-e.y0})),n=0;n<a;++n)(e=t[n]).y0>=i||(r=i-e.y0)>1e-6&&(e.y0+=r,e.y1+=r),i=e.y1+p}))}(function(t){var e,r,n=t.map((function(t,e){return{x0:t.x0,index:e}})).sort((function(t,e){return t.x0-e.x0})),i=[],a=-1,o=-1/0;for(_=0;_<n.length;_++){var s=t[n[_].index];s.x0>o+d&&(a+=1,e=s.x0),o=s.x0,i[a]||(i[a]=[]),i[a].push(s),r=e-s.x0,s.x0+=r,s.x1+=r}return i}(v=k.nodes));n.update(k)}return{circular:b,key:r,trace:a,guid:h.randstr(),horizontal:f,width:m,height:g,nodePad:a.node.pad,nodeLineColor:a.node.line.color,nodeLineWidth:a.node.line.width,linkLineColor:a.link.line.color,linkLineWidth:a.link.line.width,valueFormat:a.valueformat,valueSuffix:a.valuesuffix,textFont:a.textfont,translateX:u.x[0]*t.width+t.margin.l,translateY:t.height-u.y[1]*t.height+t.margin.t,dragParallel:f?g:m,dragPerpendicular:f?m:g,arrangement:a.arrangement,sankey:n,graph:k,forceLayouts:{},interactionState:{dragInProgress:!1,hovered:!1}}}function A(t,e,r){var n=c(e.color),i=e.source.label+"|"+e.target.label+"__"+r;return e.trace=t.trace,e.curveNumber=t.trace.index,{circular:t.circular,key:i,traceId:t.key,pointNumber:e.pointNumber,link:e,tinyColorHue:u.tinyRGB(n),tinyColorAlpha:n.getAlpha(),linkPath:M,linkLineColor:t.linkLineColor,linkLineWidth:t.linkLineWidth,valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,parent:t,interactionState:t.interactionState,flow:e.flow}}function M(){return function(t){if(t.link.circular)return e=t.link,r=e.width/2,n=e.circularPathData,"top"===e.circularLinkType?"M "+n.targetX+" "+(n.targetY+r)+" L"+n.rightInnerExtent+" "+(n.targetY+r)+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightSmallArcRadius+r)+" 0 0 1 "+(n.rightFullExtent-r)+" "+(n.targetY-n.rightSmallArcRadius)+"L"+(n.rightFullExtent-r)+" "+n.verticalRightInnerExtent+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightLargeArcRadius+r)+" 0 0 1 "+n.rightInnerExtent+" "+(n.verticalFullExtent-r)+"L"+n.leftInnerExtent+" "+(n.verticalFullExtent-r)+"A"+(n.leftLargeArcRadius+r)+" "+(n.leftLargeArcRadius+r)+" 0 0 1 "+(n.leftFullExtent+r)+" "+n.verticalLeftInnerExtent+"L"+(n.leftFullExtent+r)+" "+(n.sourceY-n.leftSmallArcRadius)+"A"+(n.leftLargeArcRadius+r)+" "+(n.leftSmallArcRadius+r)+" 0 0 1 "+n.leftInnerExtent+" "+(n.sourceY+r)+"L"+n.sourceX+" "+(n.sourceY+r)+"L"+n.sourceX+" "+(n.sourceY-r)+"L"+n.leftInnerExtent+" "+(n.sourceY-r)+"A"+(n.leftLargeArcRadius-r)+" "+(n.leftSmallArcRadius-r)+" 0 0 0 "+(n.leftFullExtent-r)+" "+(n.sourceY-n.leftSmallArcRadius)+"L"+(n.leftFullExtent-r)+" "+n.verticalLeftInnerExtent+"A"+(n.leftLargeArcRadius-r)+" "+(n.leftLargeArcRadius-r)+" 0 0 0 "+n.leftInnerExtent+" "+(n.verticalFullExtent+r)+"L"+n.rightInnerExtent+" "+(n.verticalFullExtent+r)+"A"+(n.rightLargeArcRadius-r)+" "+(n.rightLargeArcRadius-r)+" 0 0 0 "+(n.rightFullExtent+r)+" "+n.verticalRightInnerExtent+"L"+(n.rightFullExtent+r)+" "+(n.targetY-n.rightSmallArcRadius)+"A"+(n.rightLargeArcRadius-r)+" "+(n.rightSmallArcRadius-r)+" 0 0 0 "+n.rightInnerExtent+" "+(n.targetY-r)+"L"+n.targetX+" 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e.group&&(s=h.randstr()),e.trace=t.trace,e.curveNumber=t.trace.index,{index:e.pointNumber,key:s,partOfGroup:e.partOfGroup||!1,group:e.group,traceId:t.key,trace:t.trace,node:e,nodePad:t.nodePad,nodeLineColor:t.nodeLineColor,nodeLineWidth:t.nodeLineWidth,textFont:t.textFont,size:t.horizontal?t.height:t.width,visibleWidth:Math.ceil(a),visibleHeight:o,zoneX:-n,zoneY:-i,zoneWidth:a+2*n,zoneHeight:o+2*i,labelY:t.horizontal?e.dy/2+1:e.dx/2+1,left:1===e.originalLayer,sizeAcross:t.width,forceLayouts:t.forceLayouts,horizontal:t.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:u.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,graph:t.graph,arrangement:t.arrangement,uniqueNodeLabelPathId:[t.guid,t.key,s].join("_"),interactionState:t.interactionState,figure:t}}function E(t){t.attr("transform",(function(t){return p(t.node.x0.toFixed(3),t.node.y0.toFixed(3))}))}function L(t){t.call(E)}function 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y={padded:!0},x={padded:!0};s&&(y.ppad=x.ppad=s);var b=c<2||i[0]!==i[c-1]||o[0]!==o[c-1];b&&("tozerox"===v||"tonextx"===v&&(p||"h"===d))?y.tozero=!0:(e.error_y||{}).visible||"tonexty"!==v&&"tozeroy"!==v&&(l.hasMarkers(e)||l.hasText(e))||(y.padded=!1,y.ppad=0),b&&("tozeroy"===v||"tonexty"===v&&(p||"v"===d))?x.tozero=!0:"tonextx"!==v&&"tozerox"!==v||(x.padded=!1),f&&(e._extremes[f]=a.findExtremes(r,i,y)),h&&(e._extremes[h]=a.findExtremes(n,o,x))}function p(t,e){if(l.hasMarkers(t)){var r,n=t.marker,o=1.6*(t.marker.sizeref||1);if(r="area"===t.marker.sizemode?function(t){return Math.max(Math.sqrt((t||0)/o),3)}:function(t){return Math.max((t||0)/o,3)},i.isArrayOrTypedArray(n.size)){var s={type:"linear"};a.setConvert(s);for(var c=s.makeCalcdata(t.marker,"size"),u=new Array(e),f=0;f<e;f++)u[f]=r(c[f]);return u}return r(n.size)}}function d(t,e){var r=m(e),n=t._firstScatter;n[r]||(n[r]=e.uid)}function m(t){var e=t.stackgroup;return t.xaxis+t.yaxis+t.type+(e?"-"+e:"")}function g(t,e,r,n){var 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n=t("../../registry"),i=t("../../lib"),a=t("../scatter/subtypes"),o=t("../scatter/marker_defaults"),s=t("../scatter/line_defaults"),l=t("../scatter/text_defaults"),c=t("./attributes");e.exports=function(t,e,r,u){function f(r,n){return i.coerce(t,e,c,r,n)}if(function(t,e,r,i){var a=0,o=r("x"),s=r("y"),l=r("z");n.getComponentMethod("calendars","handleTraceDefaults")(t,e,["x","y","z"],i),o&&s&&l&&(a=Math.min(o.length,s.length,l.length),e._length=e._xlength=e._ylength=e._zlength=a);return a}(t,e,f,u)){f("text"),f("hovertext"),f("hovertemplate"),f("xhoverformat"),f("yhoverformat"),f("zhoverformat"),f("mode"),a.hasLines(e)&&(f("connectgaps"),s(t,e,r,u,f)),a.hasMarkers(e)&&o(t,e,r,u,f,{noSelect:!0}),a.hasText(e)&&(f("texttemplate"),l(t,e,u,f,{noSelect:!0}));var h=(e.line||{}).color,p=(e.marker||{}).color;f("surfaceaxis")>=0&&f("surfacecolor",h||p);for(var d=["x","y","z"],m=0;m<3;++m){var g="projection."+d[m];f(g+".show")&&(f(g+".opacity"),f(g+".scale"))}var 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n=t("../../lib"),i=t("../scatter/constants"),a=t("../scatter/subtypes"),o=t("../scatter/marker_defaults"),s=t("../scatter/line_defaults"),l=t("../scatter/line_shape_defaults"),c=t("../scatter/text_defaults"),u=t("../scatter/fillcolor_defaults"),f=t("./attributes");e.exports=function(t,e,r,h){function p(r,i){return n.coerce(t,e,f,r,i)}p("carpet"),e.xaxis="x",e.yaxis="y";var d=p("a"),m=p("b"),g=Math.min(d.length,m.length);if(g){e._length=g,p("text"),p("texttemplate"),p("hovertext"),p("mode",g<i.PTS_LINESONLY?"lines+markers":"lines"),a.hasLines(e)&&(s(t,e,r,h,p),l(t,e,p),p("connectgaps")),a.hasMarkers(e)&&o(t,e,r,h,p,{gradient:!0}),a.hasText(e)&&c(t,e,h,p);var v=[];(a.hasMarkers(e)||a.hasText(e))&&(p("marker.maxdisplayed"),v.push("points")),p("fill"),"none"!==e.fill&&(u(t,e,r,p),a.hasLines(e)||l(t,e,p)),"tonext"!==e.fill&&"toself"!==e.fill||v.push("fills"),"fills"!==p("hoveron",v.join("+")||"points")&&p("hovertemplate"),n.coerceSelectionMarkerOpacity(e,p)}else 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0;var p=s.trace,d=p._carpet,m=p._module.formatLabels(h,p);s.yLabel=m.yLabel,delete s.text;var g=[];if(!p.hovertemplate){var v=(h.hi||p.hoverinfo).split("+");-1!==v.indexOf("all")&&(v=["a","b","text"]),-1!==v.indexOf("a")&&y(d.aaxis,h.a),-1!==v.indexOf("b")&&y(d.baxis,h.b),g.push("y: "+s.yLabel),-1!==v.indexOf("text")&&i(h,p,g),s.extraText=g.join("<br>")}return o}function y(t,e){var r;r=t.labelprefix&&t.labelprefix.length>0?t.labelprefix.replace(/ = $/,""):t._hovertitle,g.push(r+": "+e.toFixed(3)+t.labelsuffix)}}},{"../../lib":498,"../scatter/hover":933}],962:[function(t,e,r){"use 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s,l,c,u=r[0][0].carpet,f={xaxis:i.getFromId(t,u.xaxis||"x"),yaxis:i.getFromId(t,u.yaxis||"y"),plot:e.plot};for(n(t,f,r,o),s=0;s<r.length;s++)l=r[s][0].trace,c=o.selectAll("g.trace"+l.uid+" .js-line"),a.setClipUrl(c,r[s][0].carpet._clipPathId,t)}},{"../../components/drawing":383,"../../plots/cartesian/axes":549,"../scatter/plot":943}],964:[function(t,e,r){"use strict";var n=t("../../plots/template_attributes").hovertemplateAttrs,i=t("../../plots/template_attributes").texttemplateAttrs,a=t("../scatter/attributes"),o=t("../../plots/attributes"),s=t("../../components/colorscale/attributes"),l=t("../../components/drawing/attributes").dash,c=t("../../lib/extend").extendFlat,u=t("../../plot_api/edit_types").overrideAll,f=a.marker,h=a.line,p=f.line;e.exports=u({lon:{valType:"data_array"},lat:{valType:"data_array"},locations:{valType:"data_array"},locationmode:{valType:"enumerated",values:["ISO-3","USA-states","country 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n;if(f.hasText(e)&&(n.text=y(t,e),n.textSel=_(t,e,e.selected),n.textUnsel=_(t,e,e.unselected)),f.hasMarkers(e)&&(n.marker=x(e),n.markerSel=b(e,e.selected),n.markerUnsel=b(e,e.unselected),!e.unselected&&s.isArrayOrTypedArray(e.marker.opacity))){var a=e.marker.opacity;for(n.markerUnsel.opacity=new Array(a.length),r=0;r<a.length;r++)n.markerUnsel.opacity[r]=m*a[r]}if(f.hasLines(e)){n.line={overlay:!0,thickness:e.line.width*i,color:e.line.color,opacity:e.opacity};var o=(d.DASHES[e.line.dash]||[1]).slice();for(r=0;r<o.length;++r)o[r]*=e.line.width*i;n.line.dashes=o}return e.error_x&&e.error_x.visible&&(n.errorX=w(e,e.error_x,i)),e.error_y&&e.error_y.visible&&(n.errorY=w(e,e.error_y,i)),e.fill&&"none"!==e.fill&&(n.fill={closed:!0,fill:e.fillcolor,thickness:0}),n},markerStyle:x,markerSelection:b,linePositions:function(t,e,r){var n,i,a=r.length,o=a/2;if(f.hasLines(e)&&o)if("hv"===e.line.shape){for(n=[],i=0;i<o-1;i++)isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN,NaN,NaN):(n.push(r[2*i],r[2*i+1]),isNaN(r[2*i+2])||isNaN(r[2*i+3])?n.push(NaN,NaN):n.push(r[2*i+2],r[2*i+1]));n.push(r[a-2],r[a-1])}else if("hvh"===e.line.shape){for(n=[],i=0;i<o-1;i++)if(isNaN(r[2*i])||isNaN(r[2*i+1])||isNaN(r[2*i+2])||isNaN(r[2*i+3]))isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+1]),n.push(NaN,NaN);else{var s=(r[2*i]+r[2*i+2])/2;n.push(r[2*i],r[2*i+1],s,r[2*i+1],s,r[2*i+3])}n.push(r[a-2],r[a-1])}else if("vhv"===e.line.shape){for(n=[],i=0;i<o-1;i++)if(isNaN(r[2*i])||isNaN(r[2*i+1])||isNaN(r[2*i+2])||isNaN(r[2*i+3]))isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+1]),n.push(NaN,NaN);else{var l=(r[2*i+1]+r[2*i+3])/2;n.push(r[2*i],r[2*i+1],r[2*i],l,r[2*i+2],l)}n.push(r[a-2],r[a-1])}else if("vh"===e.line.shape){for(n=[],i=0;i<o-1;i++)isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN,NaN,NaN):(n.push(r[2*i],r[2*i+1]),isNaN(r[2*i+2])||isNaN(r[2*i+3])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+3]));n.push(r[a-2],r[a-1])}else n=r;var c=!1;for(i=0;i<n.length;i++)if(isNaN(n[i])){c=!0;break}var u=c||n.length>d.TOO_MANY_POINTS||f.hasMarkers(e)?"rect":"round";if(c&&e.connectgaps){var h=n[0],p=n[1];for(i=0;i<n.length;i+=2)isNaN(n[i])||isNaN(n[i+1])?(n[i]=h,n[i+1]=p):(h=n[i],p=n[i+1])}return{join:u,positions:n}},errorBarPositions:function(t,e,r,i,a){var s=o.getComponentMethod("errorbars","makeComputeError"),l=c.getFromId(t,e.xaxis),u=c.getFromId(t,e.yaxis),f=r.length/2,h={};function p(t,i){var a=i._id.charAt(0),o=e["error_"+a];if(o&&o.visible&&("linear"===i.type||"log"===i.type)){for(var l=s(o),c={x:0,y:1}[a],u={x:[0,1,2,3],y:[2,3,0,1]}[a],p=new Float64Array(4*f),d=1/0,m=-1/0,g=0,v=0;g<f;g++,v+=4){var y=t[g];if(n(y)){var x=r[2*g+c],b=l(y,g),_=b[0],w=b[1];if(n(_)&&n(w)){var T=y-_,k=y+w;p[v+u[0]]=x-i.c2l(T),p[v+u[1]]=i.c2l(k)-x,p[v+u[2]]=0,p[v+u[3]]=0,d=Math.min(d,y-_),m=Math.max(m,y+w)}}}h[a]={positions:r,errors:p,_bnds:[d,m]}}}return p(i,l),p(a,u),h},textPosition:function(t,e,r,n){var i,a=e._length,o={};if(f.hasMarkers(e)){var s=r.font,l=r.align,c=r.baseline;for(o.offset=new Array(a),i=0;i<a;i++){var u=n.sizes?n.sizes[i]:n.size,h=Array.isArray(s)?s[i].size:s.size,p=Array.isArray(l)?l.length>1?l[i]:l[0]:l,d=Array.isArray(c)?c.length>1?c[i]:c[0]:c,m=g[p],v=g[d],y=u?u/.8+1:0,x=-v*y-.5*v;o.offset[i]=[m*y/h,x/h]}}return o}}},{"../../components/drawing":383,"../../components/fx/helpers":397,"../../constants/interactions":473,"../../lib":498,"../../lib/gl_format_color":494,"../../plots/cartesian/axis_ids":553,"../../registry":633,"../scatter/make_bubble_size_func":939,"../scatter/subtypes":947,"./constants":977,"./helpers":982,"color-normalize":84,"fast-isnumeric":185,"svg-path-sdf":305}],979:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../registry"),a=t("./helpers"),o=t("./attributes"),s=t("../scatter/constants"),l=t("../scatter/subtypes"),c=t("../scatter/xy_defaults"),u=t("../scatter/period_defaults"),f=t("../scatter/marker_defaults"),h=t("../scatter/line_defaults"),p=t("../scatter/fillcolor_defaults"),d=t("../scatter/text_defaults");e.exports=function(t,e,r,m){function g(r,i){return n.coerce(t,e,o,r,i)}var v=!!t.marker&&a.isOpenSymbol(t.marker.symbol),y=l.isBubble(t),x=c(t,e,m,g);if(x){u(t,e,m,g),g("xhoverformat"),g("yhoverformat");var b=x<s.PTS_LINESONLY?"lines+markers":"lines";g("text"),g("hovertext"),g("hovertemplate"),g("mode",b),l.hasLines(e)&&(g("connectgaps"),h(t,e,r,m,g),g("line.shape")),l.hasMarkers(e)&&(f(t,e,r,m,g),g("marker.line.width",v||y?1:0)),l.hasText(e)&&(g("texttemplate"),d(t,e,m,g));var _=(e.line||{}).color,w=(e.marker||{}).color;g("fill"),"none"!==e.fill&&p(t,e,r,g);var T=i.getComponentMethod("errorbars","supplyDefaults");T(t,e,_||w||r,{axis:"y"}),T(t,e,_||w||r,{axis:"x",inherit:"y"}),n.coerceSelectionMarkerOpacity(e,g)}else e.visible=!1}},{"../../lib":498,"../../registry":633,"../scatter/constants":926,"../scatter/fillcolor_defaults":930,"../scatter/line_defaults":935,"../scatter/marker_defaults":941,"../scatter/period_defaults":942,"../scatter/subtypes":947,"../scatter/text_defaults":948,"../scatter/xy_defaults":949,"./attributes":974,"./helpers":982}],980:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../components/color"),a=t("../../constants/interactions").DESELECTDIM;e.exports={styleTextSelection:function(t){var e,r,o=t[0],s=o.trace,l=o.t,c=l._scene,u=l.index,f=c.selectBatch[u],h=c.unselectBatch[u],p=c.textOptions[u],d=c.textSelectedOptions[u]||{},m=c.textUnselectedOptions[u]||{},g=n.extendFlat({},p);if(f.length||h.length){var v=d.color,y=m.color,x=p.color,b=Array.isArray(x);for(g.color=new Array(s._length),e=0;e<f.length;e++)r=f[e],g.color[r]=v||(b?x[r]:x);for(e=0;e<h.length;e++){r=h[e];var _=b?x[r]:x;g.color[r]=y||(v?_:i.addOpacity(_,a))}}c.glText[u].update(g)}}},{"../../components/color":361,"../../constants/interactions":473,"../../lib":498}],981:[function(t,e,r){"use strict";var n=t("../scatter/format_labels");e.exports=function(t,e,r){var i=t.i;return"x"in t||(t.x=e._x[i]),"y"in t||(t.y=e._y[i]),n(t,e,r)}},{"../scatter/format_labels":931}],982:[function(t,e,r){"use strict";var n=t("./constants");r.isOpenSymbol=function(t){return"string"==typeof t?n.OPEN_RE.test(t):t%200>100},r.isDotSymbol=function(t){return"string"==typeof t?n.DOT_RE.test(t):t>200}},{"./constants":977}],983:[function(t,e,r){"use strict";var n=t("../../registry"),i=t("../../lib"),a=t("../scatter/get_trace_color");function o(t,e,r,o){var s=t.xa,l=t.ya,c=t.distance,u=t.dxy,f=t.index,h={pointNumber:f,x:e[f],y:r[f]};h.tx=Array.isArray(o.text)?o.text[f]:o.text,h.htx=Array.isArray(o.hovertext)?o.hovertext[f]:o.hovertext,h.data=Array.isArray(o.customdata)?o.customdata[f]:o.customdata,h.tp=Array.isArray(o.textposition)?o.textposition[f]:o.textposition;var p=o.textfont;p&&(h.ts=i.isArrayOrTypedArray(p.size)?p.size[f]:p.size,h.tc=Array.isArray(p.color)?p.color[f]:p.color,h.tf=Array.isArray(p.family)?p.family[f]:p.family);var d=o.marker;d&&(h.ms=i.isArrayOrTypedArray(d.size)?d.size[f]:d.size,h.mo=i.isArrayOrTypedArray(d.opacity)?d.opacity[f]:d.opacity,h.mx=i.isArrayOrTypedArray(d.symbol)?d.symbol[f]:d.symbol,h.mc=i.isArrayOrTypedArray(d.color)?d.color[f]:d.color);var m=d&&d.line;m&&(h.mlc=Array.isArray(m.color)?m.color[f]:m.color,h.mlw=i.isArrayOrTypedArray(m.width)?m.width[f]:m.width);var g=d&&d.gradient;g&&"none"!==g.type&&(h.mgt=Array.isArray(g.type)?g.type[f]:g.type,h.mgc=Array.isArray(g.color)?g.color[f]:g.color);var v=s.c2p(h.x,!0),y=l.c2p(h.y,!0),x=h.mrc||1,b=o.hoverlabel;b&&(h.hbg=Array.isArray(b.bgcolor)?b.bgcolor[f]:b.bgcolor,h.hbc=Array.isArray(b.bordercolor)?b.bordercolor[f]:b.bordercolor,h.hts=i.isArrayOrTypedArray(b.font.size)?b.font.size[f]:b.font.size,h.htc=Array.isArray(b.font.color)?b.font.color[f]:b.font.color,h.htf=Array.isArray(b.font.family)?b.font.family[f]:b.font.family,h.hnl=i.isArrayOrTypedArray(b.namelength)?b.namelength[f]:b.namelength);var _=o.hoverinfo;_&&(h.hi=Array.isArray(_)?_[f]:_);var w=o.hovertemplate;w&&(h.ht=Array.isArray(w)?w[f]:w);var T={};T[t.index]=h;var k=o._origX,A=o._origY,M=i.extendFlat({},t,{color:a(o,h),x0:v-x,x1:v+x,xLabelVal:k?k[f]:h.x,y0:y-x,y1:y+x,yLabelVal:A?A[f]:h.y,cd:T,distance:c,spikeDistance:u,hovertemplate:h.ht});return h.htx?M.text=h.htx:h.tx?M.text=h.tx:o.text&&(M.text=o.text),i.fillText(h,o,M),n.getComponentMethod("errorbars","hoverInfo")(h,o,M),M}e.exports={hoverPoints:function(t,e,r,n){var i,a,s,l,c,u,f,h,p,d,m=t.cd,g=m[0].t,v=m[0].trace,y=t.xa,x=t.ya,b=g.x,_=g.y,w=y.c2p(e),T=x.c2p(r),k=t.distance;if(g.tree){var A=y.p2c(w-k),M=y.p2c(w+k),S=x.p2c(T-k),E=x.p2c(T+k);i="x"===n?g.tree.range(Math.min(A,M),Math.min(x._rl[0],x._rl[1]),Math.max(A,M),Math.max(x._rl[0],x._rl[1])):g.tree.range(Math.min(A,M),Math.min(S,E),Math.max(A,M),Math.max(S,E))}else i=g.ids;var L=k;if("x"===n){var C=!!v.xperiodalignment,P=!!v.yperiodalignment;for(u=0;u<i.length;u++){if(l=b[a=i[u]],f=Math.abs(y.c2p(l)-w),C){var I=y.c2p(v._xStarts[a]),O=y.c2p(v._xEnds[a]);f=w>=Math.min(I,O)&&w<=Math.max(I,O)?0:1/0}if(f<L){if(L=f,c=_[a],h=x.c2p(c)-T,P){var z=x.c2p(v._yStarts[a]),D=x.c2p(v._yEnds[a]);h=T>=Math.min(z,D)&&T<=Math.max(z,D)?0:1/0}d=Math.sqrt(f*f+h*h),s=i[u]}}}else for(u=i.length-1;u>-1;u--)l=b[a=i[u]],c=_[a],f=y.c2p(l)-w,h=x.c2p(c)-T,(p=Math.sqrt(f*f+h*h))<L&&(L=d=p,s=a);return t.index=s,t.distance=L,t.dxy=d,void 0===s?[t]:[o(t,b,_,v)]},calcHover:o}},{"../../lib":498,"../../registry":633,"../scatter/get_trace_color":932}],984:[function(t,e,r){arguments[4][891][0].apply(r,arguments)},{"./base_index":975,"./plot":985,dup:891}],985:[function(t,e,r){"use strict";var n=t("regl-scatter2d"),i=t("regl-line2d"),a=t("regl-error2d"),o=t("gl-text"),s=t("../../lib"),l=t("../../components/dragelement/helpers").selectMode,c=t("../../lib/prepare_regl"),u=t("../scatter/subtypes"),f=t("../scatter/link_traces"),h=t("./edit_style").styleTextSelection,p={};function d(t,e,r,n){var i=t._size,a=t.width*n,o=t.height*n,s=i.l*n,l=i.b*n,c=i.r*n,u=i.t*n,f=i.w*n,h=i.h*n;return[s+e.domain[0]*f,l+r.domain[0]*h,a-c-(1-e.domain[1])*f,o-u-(1-r.domain[1])*h]}(e.exports=function(t,e,r){if(r.length){var m,g,v=t._fullLayout,y=e._scene,x=e.xaxis,b=e.yaxis;if(y)if(c(t,["ANGLE_instanced_arrays","OES_element_index_uint"],p)){var _=y.count,w=v._glcanvas.data()[0].regl;if(f(t,e,r),y.dirty){if(!0===y.error2d&&(y.error2d=a(w)),!0===y.line2d&&(y.line2d=i(w)),!0===y.scatter2d&&(y.scatter2d=n(w)),!0===y.fill2d&&(y.fill2d=i(w)),!0===y.glText)for(y.glText=new Array(_),m=0;m<_;m++)y.glText[m]=new o(w);if(y.glText){if(_>y.glText.length){var T=_-y.glText.length;for(m=0;m<T;m++)y.glText.push(new o(w))}else if(_<y.glText.length){var k=y.glText.length-_;y.glText.splice(_,k).forEach((function(t){t.destroy()}))}for(m=0;m<_;m++)y.glText[m].update(y.textOptions[m])}if(y.line2d&&(y.line2d.update(y.lineOptions),y.lineOptions=y.lineOptions.map((function(t){if(t&&t.positions){for(var e=t.positions,r=0;r<e.length&&(isNaN(e[r])||isNaN(e[r+1]));)r+=2;for(var n=e.length-2;n>r&&(isNaN(e[n])||isNaN(e[n+1]));)n-=2;t.positions=e.slice(r,n+2)}return t})),y.line2d.update(y.lineOptions)),y.error2d){var A=(y.errorXOptions||[]).concat(y.errorYOptions||[]);y.error2d.update(A)}y.scatter2d&&y.scatter2d.update(y.markerOptions),y.fillOrder=s.repeat(null,_),y.fill2d&&(y.fillOptions=y.fillOptions.map((function(t,e){var n=r[e];if(t&&n&&n[0]&&n[0].trace){var i,a,o=n[0],s=o.trace,l=o.t,c=y.lineOptions[e],u=[];s._ownfill&&u.push(e),s._nexttrace&&u.push(e+1),u.length&&(y.fillOrder[e]=u);var f,h,p=[],d=c&&c.positions||l.positions;if("tozeroy"===s.fill){for(f=0;f<d.length&&isNaN(d[f+1]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h+1]);)h-=2;0!==d[f+1]&&(p=[d[f],0]),p=p.concat(d.slice(f,h+2)),0!==d[h+1]&&(p=p.concat([d[h],0]))}else if("tozerox"===s.fill){for(f=0;f<d.length&&isNaN(d[f]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h]);)h-=2;0!==d[f]&&(p=[0,d[f+1]]),p=p.concat(d.slice(f,h+2)),0!==d[h]&&(p=p.concat([0,d[h+1]]))}else 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B=i.extendFlat({},g,m.unselectedOptions,w),N=i.extendFlat({},g,m.selectedOptions,w);m.matrix.update(B,N),P=!1}}else d.xpx=d.ypx=null;if(P){var j=i.extendFlat({},g,w);m.matrix.update(j,null)}}}e.exports=function(t,e,r){if(r.length)for(var n=0;n<r.length;n++)s(t,r[n][0])}},{"../../components/dragelement/helpers":379,"../../lib":498,"../../plots/cartesian/axis_ids":553,"regl-splom":277}],1037:[function(t,e,r){"use strict";var n=t("../../lib");e.exports=function(t,e){var r=t._fullLayout,i=e.uid,a=r._splomScenes;a||(a=r._splomScenes={});var o={dirty:!0,selectBatch:[],unselectBatch:[]},s=a[e.uid];return 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T=l.matrixOptions;return b.length||_.length?l.selectBatch.length||l.unselectBatch.length||l.matrix.update(l.unselectedOptions,n.extendFlat({},T,l.selectedOptions,l.viewOpts)):l.matrix.update(T,null),l.selectBatch=b,l.unselectBatch=_,h}},{"../../lib":498,"../scatter/subtypes":947,"./helpers":1033}],1039:[function(t,e,r){"use strict";var n=t("../../components/colorscale/attributes"),i=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,a=t("../../plots/template_attributes").hovertemplateAttrs,o=t("../mesh3d/attributes"),s=t("../../plots/attributes"),l=t("../../lib/extend").extendFlat,c={x:{valType:"data_array",editType:"calc+clearAxisTypes"},y:{valType:"data_array",editType:"calc+clearAxisTypes"},z:{valType:"data_array",editType:"calc+clearAxisTypes"},u:{valType:"data_array",editType:"calc"},v:{valType:"data_array",editType:"calc"},w:{valType:"data_array",editType:"calc"},starts:{x:{valType:"data_array",editType:"calc"},y:{valType:"data_array",editType:"calc"},z:{valType:"data_array",editType:"calc"},editType:"calc"},maxdisplayed:{valType:"integer",min:0,dflt:1e3,editType:"calc"},sizeref:{valType:"number",editType:"calc",min:0,dflt:1},text:{valType:"string",dflt:"",editType:"calc"},hovertext:{valType:"string",dflt:"",editType:"calc"},hovertemplate:a({editType:"calc"},{keys:["tubex","tubey","tubez","tubeu","tubev","tubew","norm","divergence"]}),uhoverformat:i("u",1),vhoverformat:i("v",1),whoverformat:i("w",1),xhoverformat:i("x"),yhoverformat:i("y"),zhoverformat:i("z"),showlegend:l({},s.showlegend,{dflt:!1})};l(c,n("",{colorAttr:"u/v/w 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e,r,i,a,s,l,c,u,f,h,p,d,m=t._x,g=t._y,v=t._z,y=t._len,x=-1/0,b=1/0,_=-1/0,w=1/0,T=-1/0,k=1/0,A="";for(y&&(c=m[0],f=g[0],p=v[0]),y>1&&(u=m[y-1],h=g[y-1],d=v[y-1]),e=0;e<y;e++)x=Math.max(x,m[e]),b=Math.min(b,m[e]),_=Math.max(_,g[e]),w=Math.min(w,g[e]),T=Math.max(T,v[e]),k=Math.min(k,v[e]),a||m[e]===c||(a=!0,A+="x"),s||g[e]===f||(s=!0,A+="y"),l||v[e]===p||(l=!0,A+="z");a||(A+="x"),s||(A+="y"),l||(A+="z");var M=o(t._x),S=o(t._y),E=o(t._z);A=(A=(A=A.replace("x",(c>u?"-":"+")+"x")).replace("y",(f>h?"-":"+")+"y")).replace("z",(p>d?"-":"+")+"z");var L=function(){y=0,M=[],S=[],E=[]};(!y||y<M.length*S.length*E.length)&&L();var C=function(t){return"x"===t?m:"y"===t?g:v},P=function(t){return"x"===t?M:"y"===t?S:E},I=function(t){return t[y-1]<t[0]?-1:1},O=C(A[1]),z=C(A[3]),D=C(A[5]),R=P(A[1]).length,F=P(A[3]).length,B=P(A[5]).length,N=!1,j=function(t,e,r){return R*(F*t+e)+r},U=I(C(A[1])),V=I(C(A[3])),H=I(C(A[5]));for(e=0;e<B-1;e++){for(r=0;r<F-1;r++){for(i=0;i<R-1;i++){var q=j(e,r,i),G=j(e,r,i+1),Y=j(e,r+1,i),W=j(e+1,r,i);if(O[q]*U<O[G]*U&&z[q]*V<z[Y]*V&&D[q]*H<D[W]*H||(N=!0),N)break}if(N)break}if(N)break}return N&&(n.warn("Encountered arbitrary coordinates! 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v=n[u];r.xMax=Math.max(r.xMax,v),r.xMin=Math.min(r.xMin,v);var y=o[u];r.yMax=Math.max(r.yMax,y),r.yMin=Math.min(r.yMin,y);var x=l[u];r.zMax=Math.max(r.zMax,x),r.zMin=Math.min(r.zMin,x)}e._slen=c,e._normMax=f,e._xbnds=[r.xMin,r.xMax],e._ybnds=[r.yMin,r.yMax],e._zbnds=[r.zMin,r.zMax]},filter:s,processGrid:a}},{"../../components/colorscale/calc":369,"../../lib":498}],1041:[function(t,e,r){"use strict";var n=t("../../../stackgl_modules").gl_streamtube3d,i=n.createTubeMesh,a=t("../../lib"),o=t("../../lib/gl_format_color").parseColorScale,s=t("../../components/colorscale").extractOpts,l=t("../../plots/gl3d/zip3"),c={xaxis:0,yaxis:1,zaxis:2};function u(t,e){this.scene=t,this.uid=e,this.mesh=null,this.data=null}var f=u.prototype;function h(t){var e=t.length;return e>2?t.slice(1,e-1):2===e?[(t[0]+t[1])/2]:t}function p(t){var e=t.length;return 1===e?[.5,.5]:[t[1]-t[0],t[e-1]-t[e-2]]}function d(t,e){var r=t.fullSceneLayout,i=t.dataScale,u=e._len,f={};function d(t,e){var n=r[e],o=i[c[e]];return a.simpleMap(t,(function(t){return n.d2l(t)*o}))}if(f.vectors=l(d(e._u,"xaxis"),d(e._v,"yaxis"),d(e._w,"zaxis"),u),!u)return{positions:[],cells:[]};var m=d(e._Xs,"xaxis"),g=d(e._Ys,"yaxis"),v=d(e._Zs,"zaxis");if(f.meshgrid=[m,g,v],f.gridFill=e._gridFill,e._slen)f.startingPositions=l(d(e._startsX,"xaxis"),d(e._startsY,"yaxis"),d(e._startsZ,"zaxis"));else{for(var y=g[0],x=h(m),b=h(v),_=new Array(x.length*b.length),w=0,T=0;T<x.length;T++)for(var k=0;k<b.length;k++)_[w++]=[x[T],y,b[k]];f.startingPositions=_}f.colormap=o(e),f.tubeSize=e.sizeref,f.maxLength=e.maxdisplayed;var A=d(e._xbnds,"xaxis"),M=d(e._ybnds,"yaxis"),S=d(e._zbnds,"zaxis"),E=p(m),L=p(g),C=p(v),P=[[A[0]-E[0],M[0]-L[0],S[0]-C[0]],[A[1]+E[1],M[1]+L[1],S[1]+C[1]]],I=n(f,P),O=s(e);I.vertexIntensityBounds=[O.min/e._normMax,O.max/e._normMax];var z=e.lightposition;return I.lightPosition=[z.x,z.y,z.z],I.ambient=e.lighting.ambient,I.diffuse=e.lighting.diffuse,I.specular=e.lighting.specular,I.roughness=e.lighting.roughness,I.fresnel=e.lighting.fresnel,I.opacity=e.opacity,e._pad=I.tubeScale*e.sizeref*2,I}f.handlePick=function(t){var e=this.scene.fullSceneLayout,r=this.scene.dataScale;function n(t,n){var i=e[n],a=r[c[n]];return i.l2c(t)/a}if(t.object===this.mesh){var i=t.data.position,a=t.data.velocity;return t.traceCoordinate=[n(i[0],"xaxis"),n(i[1],"yaxis"),n(i[2],"zaxis"),n(a[0],"xaxis"),n(a[1],"yaxis"),n(a[2],"zaxis"),t.data.intensity*this.data._normMax,t.data.divergence],t.textLabel=this.data.hovertext||this.data.text,!0}},f.update=function(t){this.data=t;var e=d(this.scene,t);this.mesh.update(e)},f.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()},e.exports=function(t,e){var r=t.glplot.gl,n=d(t,e),a=i(r,n),o=new u(t,e.uid);return o.mesh=a,o.data=e,a._trace=o,t.glplot.add(a),o}},{"../../../stackgl_modules":1119,"../../components/colorscale":373,"../../lib":498,"../../lib/gl_format_color":494,"../../plots/gl3d/zip3":604}],1042:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../components/colorscale/defaults"),a=t("./attributes");e.exports=function(t,e,r,o){function s(r,i){return n.coerce(t,e,a,r,i)}var l=s("u"),c=s("v"),u=s("w"),f=s("x"),h=s("y"),p=s("z");l&&l.length&&c&&c.length&&u&&u.length&&f&&f.length&&h&&h.length&&p&&p.length?(s("starts.x"),s("starts.y"),s("starts.z"),s("maxdisplayed"),s("sizeref"),s("lighting.ambient"),s("lighting.diffuse"),s("lighting.specular"),s("lighting.roughness"),s("lighting.fresnel"),s("lightposition.x"),s("lightposition.y"),s("lightposition.z"),i(t,e,o,s,{prefix:"",cLetter:"c"}),s("text"),s("hovertext"),s("hovertemplate"),s("uhoverformat"),s("vhoverformat"),s("whoverformat"),s("xhoverformat"),s("yhoverformat"),s("zhoverformat"),e._length=null):e.visible=!1}},{"../../components/colorscale/defaults":371,"../../lib":498,"./attributes":1039}],1043:[function(t,e,r){"use strict";e.exports={moduleType:"trace",name:"streamtube",basePlotModule:t("../../plots/gl3d"),categories:["gl3d","showLegend"],attributes:t("./attributes"),supplyDefaults:t("./defaults"),colorbar:{min:"cmin",max:"cmax"},calc:t("./calc").calc,plot:t("./convert"),eventData:function(t,e){return t.tubex=t.x,t.tubey=t.y,t.tubez=t.z,t.tubeu=e.traceCoordinate[3],t.tubev=e.traceCoordinate[4],t.tubew=e.traceCoordinate[5],t.norm=e.traceCoordinate[6],t.divergence=e.traceCoordinate[7],delete t.x,delete t.y,delete t.z,t},meta:{}}},{"../../plots/gl3d":593,"./attributes":1039,"./calc":1040,"./convert":1041,"./defaults":1042}],1044:[function(t,e,r){"use strict";var n=t("../../plots/attributes"),i=t("../../plots/template_attributes").hovertemplateAttrs,a=t("../../plots/template_attributes").texttemplateAttrs,o=t("../../components/colorscale/attributes"),s=t("../../plots/domain").attributes,l=t("../pie/attributes"),c=t("./constants"),u=t("../../lib/extend").extendFlat;e.exports={labels:{valType:"data_array",editType:"calc"},parents:{valType:"data_array",editType:"calc"},values:{valType:"data_array",editType:"calc"},branchvalues:{valType:"enumerated",values:["remainder","total"],dflt:"remainder",editType:"calc"},count:{valType:"flaglist",flags:["branches","leaves"],dflt:"leaves",editType:"calc"},level:{valType:"any",editType:"plot",anim:!0},maxdepth:{valType:"integer",editType:"plot",dflt:-1},marker:u({colors:{valType:"data_array",editType:"calc"},line:{color:u({},l.marker.line.color,{dflt:null}),width:u({},l.marker.line.width,{dflt:1}),editType:"calc"},editType:"calc"},o("marker",{colorAttr:"colors",anim:!1})),leaf:{opacity:{valType:"number",editType:"style",min:0,max:1},editType:"plot"},text:l.text,textinfo:{valType:"flaglist",flags:["label","text","value","current 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c=n.select(this),f=s.ensureSingle(c,"path","surface",(function(t){t.style("pointer-events","all")}));i.rpx0=H(i.y0),i.rpx1=H(i.y1),i.xmid=(i.x0+i.x1)/2,i.pxmid=q(i.rpx1,i.xmid),i.midangle=-(i.xmid-Math.PI/2),i.startangle=-(i.x0-Math.PI/2),i.stopangle=-(i.x1-Math.PI/2),i.halfangle=.5*Math.min(s.angleDelta(i.x0,i.x1)||Math.PI,Math.PI),i.ring=1-i.rpx0/i.rpx1,i.rInscribed=function(t){return 0===t.rpx0&&s.isFullCircle([t.x0,t.x1])?1:Math.max(0,Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2))}(i),v?f.transition().attrTween("d",(function(t){var e=function(t){var e,r=R[b.getPtId(t)],n={x0:t.x0,x1:t.x1,rpx0:t.rpx0,rpx1:t.rpx1};if(r)e=r;else if(D)if(t.parent)if(X){var i=(t.x1>X?2*Math.PI:0)+U;e={x0:i,x1:i}}else e={rpx0:I,rpx1:I},s.extendFlat(e,K(t));else e={rpx0:0,rpx1:0};else e={x0:U,x1:U};return a(e,n)}(t);return function(t){return G(e(t))}})):f.attr("d",G),c.call(y,M,t,e,{eventDataKeys:x.eventDataKeys,transitionTime:x.CLICK_TRANSITION_TIME,transitionEasing:x.CLICK_TRANSITION_EASING}).call(b.setSliceCursor,t,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:t._transitioning}),f.call(g,i,k);var p=s.ensureSingle(c,"g","slicetext"),_=s.ensureSingle(p,"text","",(function(t){t.attr("data-notex",1)})),w=s.ensureUniformFontSize(t,b.determineTextFont(k,i,h.font));_.text(r.formatSliceLabel(i,M,k,e,h)).classed("slicetext",!0).attr("text-anchor","middle").call(o.font,w).call(l.convertToTspans,t);var A=o.bBox(_.node());i.transform=m(A,i,T),i.transform.targetX=Y(i),i.transform.targetY=W(i);var S=function(t,e){var r=t.transform;return d(r,e),r.fontSize=w.size,u(k.type,r,h),s.getTextTransform(r)};v?_.transition().attrTween("transform",(function(t){var e=function(t){var e,r=R[b.getPtId(t)],n=t.transform;if(r)e=r;else if(e={rpx1:t.rpx1,transform:{textPosAngle:n.textPosAngle,scale:0,rotate:n.rotate,rCenter:n.rCenter,x:n.x,y:n.y}},D)if(t.parent)if(X){var i=t.x1>X?2*Math.PI:0;e.x0=e.x1=i}else s.extendFlat(e,K(t));else e.x0=e.x1=U;else e.x0=e.x1=U;var o=a(e.transform.textPosAngle,t.transform.textPosAngle),l=a(e.rpx1,t.rpx1),c=a(e.x0,t.x0),f=a(e.x1,t.x1),p=a(e.transform.scale,n.scale),d=a(e.transform.rotate,n.rotate),m=0===n.rCenter?3:0===e.transform.rCenter?1/3:1,g=a(e.transform.rCenter,n.rCenter);return function(t){var e=l(t),r=c(t),i=f(t),a=function(t){return g(Math.pow(t,m))}(t),s={pxmid:q(e,(r+i)/2),rpx1:e,transform:{textPosAngle:o(t),rCenter:a,x:n.x,y:n.y}};return u(k.type,n,h),{transform:{targetX:Y(s),targetY:W(s),scale:p(t),rotate:d(t),rCenter:a}}}}(t);return function(t){return S(e(t),A)}})):_.attr("transform",S(i,A))}))}function w(t){return e=t.rpx1,r=t.transform.textPosAngle,[e*Math.sin(r),-e*Math.cos(r)];var e,r}r.plot=function(t,e,r,i){var a,o,s=t._fullLayout,l=s._sunburstlayer,c=!r,u=!s.uniformtext.mode&&b.hasTransition(r);(f("sunburst",s),(a=l.selectAll("g.trace.sunburst").data(e,(function(t){return t[0].trace.uid}))).enter().append("g").classed("trace",!0).classed("sunburst",!0).attr("stroke-linejoin","round"),a.order(),u)?(i&&(o=i()),n.transition().duration(r.duration).ease(r.easing).each("end",(function(){o&&o()})).each("interrupt",(function(){o&&o()})).each((function(){l.selectAll("g.trace").each((function(e){_(t,e,this,r)}))}))):(a.each((function(e){_(t,e,this,r)})),s.uniformtext.mode&&v(t,s._sunburstlayer.selectAll(".trace"),"sunburst"));c&&a.exit().remove()},r.formatSliceLabel=function(t,e,r,n,i){var a=r.texttemplate,o=r.textinfo;if(!(a||o&&"none"!==o))return"";var l=i.separators,c=n[0],u=t.data.data,f=c.hierarchy,h=b.isHierarchyRoot(t),p=b.getParent(f,t),d=b.getValue(t);if(!a){var m,g=o.split("+"),v=function(t){return-1!==g.indexOf(t)},y=[];if(v("label")&&u.label&&y.push(u.label),u.hasOwnProperty("v")&&v("value")&&y.push(b.formatValue(u.v,l)),!h){v("current path")&&y.push(b.getPath(t.data));var x=0;v("percent parent")&&x++,v("percent entry")&&x++,v("percent root")&&x++;var _=x>1;if(x){var w,T=function(t){m=b.formatPercent(w,l),_&&(m+=" of "+t),y.push(m)};v("percent parent")&&!h&&(w=d/b.getValue(p),T("parent")),v("percent entry")&&(w=d/b.getValue(e),T("entry")),v("percent root")&&(w=d/b.getValue(f),T("root"))}}return v("text")&&(m=s.castOption(r,u.i,"text"),s.isValidTextValue(m)&&y.push(m)),y.join("<br>")}var k=s.castOption(r,u.i,"texttemplate");if(!k)return"";var A={};u.label&&(A.label=u.label),u.hasOwnProperty("v")&&(A.value=u.v,A.valueLabel=b.formatValue(u.v,l)),A.currentPath=b.getPath(t.data),h||(A.percentParent=d/b.getValue(p),A.percentParentLabel=b.formatPercent(A.percentParent,l),A.parent=b.getPtLabel(p)),A.percentEntry=d/b.getValue(e),A.percentEntryLabel=b.formatPercent(A.percentEntry,l),A.entry=b.getPtLabel(e),A.percentRoot=d/b.getValue(f),A.percentRootLabel=b.formatPercent(A.percentRoot,l),A.root=b.getPtLabel(f),u.hasOwnProperty("color")&&(A.color=u.color);var M=s.castOption(r,u.i,"text");return(s.isValidTextValue(M)||""===M)&&(A.text=M),A.customdata=s.castOption(r,u.i,"customdata"),s.texttemplateString(k,A,i._d3locale,A,r._meta||{})}},{"../../components/drawing":383,"../../lib":498,"../../lib/svg_text_utils":524,"../bar/style":657,"../bar/uniform_text":659,"../pie/helpers":901,"../pie/plot":905,"./constants":1047,"./fx":1049,"./helpers":1050,"./style":1055,"@plotly/d3":58,"d3-hierarchy":110,"d3-interpolate":111}],1055:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../components/color"),a=t("../../lib"),o=t("../bar/uniform_text").resizeText;function s(t,e,r){var n=e.data.data,o=!e.children,s=n.i,l=a.castOption(r,s,"marker.line.color")||i.defaultLine,c=a.castOption(r,s,"marker.line.width")||0;t.style("stroke-width",c).call(i.fill,n.color).call(i.stroke,l).style("opacity",o?r.leaf.opacity:null)}e.exports={style:function(t){var e=t._fullLayout._sunburstlayer.selectAll(".trace");o(t,e,"sunburst"),e.each((function(t){var e=n.select(this),r=t[0].trace;e.style("opacity",r.opacity),e.selectAll("path.surface").each((function(t){n.select(this).call(s,t,r)}))}))},styleOne:s}},{"../../components/color":361,"../../lib":498,"../bar/uniform_text":659,"@plotly/d3":58}],1056:[function(t,e,r){"use strict";var n=t("../../components/color"),i=t("../../components/colorscale/attributes"),a=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,o=t("../../plots/template_attributes").hovertemplateAttrs,s=t("../../plots/attributes"),l=t("../../lib/extend").extendFlat,c=t("../../plot_api/edit_types").overrideAll;function u(t){return{show:{valType:"boolean",dflt:!1},start:{valType:"number",dflt:null,editType:"plot"},end:{valType:"number",dflt:null,editType:"plot"},size:{valType:"number",dflt:null,min:0,editType:"plot"},project:{x:{valType:"boolean",dflt:!1},y:{valType:"boolean",dflt:!1},z:{valType:"boolean",dflt:!1}},color:{valType:"color",dflt:n.defaultLine},usecolormap:{valType:"boolean",dflt:!1},width:{valType:"number",min:1,max:16,dflt:2},highlight:{valType:"boolean",dflt:!0},highlightcolor:{valType:"color",dflt:n.defaultLine},highlightwidth:{valType:"number",min:1,max:16,dflt:2}}}var f=e.exports=c(l({z:{valType:"data_array"},x:{valType:"data_array"},y:{valType:"data_array"},text:{valType:"string",dflt:"",arrayOk:!0},hovertext:{valType:"string",dflt:"",arrayOk:!0},hovertemplate:o(),xhoverformat:a("x"),yhoverformat:a("y"),zhoverformat:a("z"),connectgaps:{valType:"boolean",dflt:!1,editType:"calc"},surfacecolor:{valType:"data_array"}},i("",{colorAttr:"z or surfacecolor",showScaleDflt:!0,autoColorDflt:!1,editTypeOverride:"calc"}),{contours:{x:u(),y:u(),z:u()},hidesurface:{valType:"boolean",dflt:!1},lightposition:{x:{valType:"number",min:-1e5,max:1e5,dflt:10},y:{valType:"number",min:-1e5,max:1e5,dflt:1e4},z:{valType:"number",min:-1e5,max:1e5,dflt:0}},lighting:{ambient:{valType:"number",min:0,max:1,dflt:.8},diffuse:{valType:"number",min:0,max:1,dflt:.8},specular:{valType:"number",min:0,max:2,dflt:.05},roughness:{valType:"number",min:0,max:1,dflt:.5},fresnel:{valType:"number",min:0,max:5,dflt:.2}},opacity:{valType:"number",min:0,max:1,dflt:1},opacityscale:{valType:"any",editType:"calc"},_deprecated:{zauto:l({},i.zauto,{}),zmin:l({},i.zmin,{}),zmax:l({},i.zmax,{})},hoverinfo:l({},s.hoverinfo),showlegend:l({},s.showlegend,{dflt:!1})}),"calc","nested");f.x.editType=f.y.editType=f.z.editType="calc+clearAxisTypes",f.transforms=void 0},{"../../components/color":361,"../../components/colorscale/attributes":368,"../../lib/extend":488,"../../plot_api/edit_types":531,"../../plots/attributes":545,"../../plots/cartesian/axis_format_attributes":552,"../../plots/template_attributes":628}],1057:[function(t,e,r){"use strict";var n=t("../../components/colorscale/calc");e.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:"",cLetter:"c"}):n(t,e,{vals:e.z,containerStr:"",cLetter:"c"})}},{"../../components/colorscale/calc":369}],1058:[function(t,e,r){"use strict";var n=t("../../../stackgl_modules").gl_surface3d,i=t("../../../stackgl_modules").ndarray,a=t("../../../stackgl_modules").ndarray_linear_interpolate.d2,o=t("../heatmap/interp2d"),s=t("../heatmap/find_empties"),l=t("../../lib").isArrayOrTypedArray,c=t("../../lib/gl_format_color").parseColorScale,u=t("../../lib/str2rgbarray"),f=t("../../components/colorscale").extractOpts;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=h.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 0===r?i:n.d2l(i,0,r)},p.getZat=function(t,e,r,n){var i=this.data.z[e][t];return null===i&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[e][t]),void 0===r?i:n.d2l(i,0,r)},p.handlePick=function(t){if(t.object===this.surface){var e=(t.data.index[0]-1)/this.dataScaleX-1,r=(t.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(e),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,i],t.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],t.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){var o=t.dataCoordinate[a];null!=o&&(t.dataCoordinate[a]*=this.scene.dataScale[a])}var s=this.data.hovertext||this.data.text;return Array.isArray(s)&&s[i]&&void 0!==s[i][n]?t.textLabel=s[i][n]:t.textLabel=s||"",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}};var d=[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,101,103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,181,191,193,197,199,211,223,227,229,233,239,241,251,257,263,269,271,277,281,283,293,307,311,313,317,331,337,347,349,353,359,367,373,379,383,389,397,401,409,419,421,431,433,439,443,449,457,461,463,467,479,487,491,499,503,509,521,523,541,547,557,563,569,571,577,587,593,599,601,607,613,617,619,631,641,643,647,653,659,661,673,677,683,691,701,709,719,727,733,739,743,751,757,761,769,773,787,797,809,811,821,823,827,829,839,853,857,859,863,877,881,883,887,907,911,919,929,937,941,947,953,967,971,977,983,991,997,1009,1013,1019,1021,1031,1033,1039,1049,1051,1061,1063,1069,1087,1091,1093,1097,1103,1109,1117,1123,1129,1151,1153,1163,1171,1181,1187,1193,1201,1213,1217,1223,1229,1231,1237,1249,1259,1277,1279,1283,1289,1291,1297,1301,1303,1307,1319,1321,1327,1361,1367,1373,1381,1399,1409,1423,1427,1429,1433,1439,1447,1451,1453,1459,1471,1481,1483,1487,1489,1493,1499,1511,1523,1531,1543,1549,1553,1559,1567,1571,1579,1583,1597,1601,1607,1609,1613,1619,1621,1627,1637,1657,1663,1667,1669,1693,1697,1699,1709,1721,1723,1733,1741,1747,1753,1759,1777,1783,1787,1789,1801,1811,1823,1831,1847,1861,1867,1871,1873,1877,1879,1889,1901,1907,1913,1931,1933,1949,1951,1973,1979,1987,1993,1997,1999,2003,2011,2017,2027,2029,2039,2053,2063,2069,2081,2083,2087,2089,2099,2111,2113,2129,2131,2137,2141,2143,2153,2161,2179,2203,2207,2213,2221,2237,2239,2243,2251,2267,2269,2273,2281,2287,2293,2297,2309,2311,2333,2339,2341,2347,2351,2357,2371,2377,2381,2383,2389,2393,2399,2411,2417,2423,2437,2441,2447,2459,2467,2473,2477,2503,2521,2531,2539,2543,2549,2551,2557,2579,2591,2593,2609,2617,2621,2633,2647,2657,2659,2663,2671,2677,2683,2687,2689,2693,2699,2707,2711,2713,2719,2729,2731,2741,2749,2753,2767,2777,2789,2791,2797,2801,2803,2819,2833,2837,2843,2851,2857,2861,2879,2887,2897,2903,2909,2917,2927,2939,2953,2957,2963,2969,2971,2999];function m(t,e){if(t<e)return 0;for(var r=0;0===Math.floor(t%e);)t/=e,r++;return r}function g(t){for(var e=[],r=0;r<d.length;r++){var n=d[r];e.push(m(t,n))}return e}function v(t){for(var e=g(t),r=t,n=0;n<d.length;n++)if(e[n]>0){r=d[n];break}return r}function y(t,e){if(!(t<1||e<1)){for(var r=g(t),n=g(e),i=1,a=0;a<d.length;a++)i*=Math.pow(d[a],Math.max(r[a],n[a]));return i}}p.calcXnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getXat(e-1,0),i=this.getXat(e,0);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r},p.calcYnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getYat(0,e-1),i=this.getYat(0,e);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r};var x=[1,2,4,6,12,24,36,48,60,120,180,240,360,720,840,1260],b=x[9],_=x[13];function w(t,e,r){var n=r[8]+r[2]*e[0]+r[5]*e[1];return t[0]=(r[6]+r[0]*e[0]+r[3]*e[1])/n,t[1]=(r[7]+r[1]*e[0]+r[4]*e[1])/n,t}function T(t,e,r){return function(t,e,r,n){for(var i=[0,0],o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var c=0;c<s;c++)r(i,[l,c],n),t.set(l,c,a(e,i[0],i[1]))}(t,e,w,r),t}function k(t,e){for(var r=!1,n=0;n<t.length;n++)if(e===t[n]){r=!0;break}!1===r&&t.push(e)}p.estimateScale=function(t,e){for(var r=1+function(t){if(0!==t.length){for(var e=1,r=0;r<t.length;r++)e=y(e,t[r]);return e}}(0===e?this.calcXnums(t):this.calcYnums(t));r<b;)r*=2;for(;r>_;)r--,r/=v(r),++r<b&&(r=_);var n=Math.round(r/t);return n>1?n:1},p.refineCoords=function(t){for(var e=this.dataScaleX,r=this.dataScaleY,n=t[0].shape[0],a=t[0].shape[1],o=0|Math.floor(t[0].shape[0]*e+1),s=0|Math.floor(t[0].shape[1]*r+1),l=1+n+1,c=1+a+1,u=i(new Float32Array(l*c),[l,c]),f=[1/e,0,0,0,1/r,0,0,0,1],h=0;h<t.length;++h){this.surface.padField(u,t[h]);var p=i(new Float32Array(o*s),[o,s]);T(p,u,f),t[h]=p}},p.setContourLevels=function(){var t,e,r,n=[[],[],[]],i=[!1,!1,!1],a=!1;for(t=0;t<3;++t)if(this.showContour[t]&&(a=!0,this.contourSize[t]>0&&null!==this.contourStart[t]&&null!==this.contourEnd[t]&&this.contourEnd[t]>this.contourStart[t]))for(i[t]=!0,e=this.contourStart[t];e<this.contourEnd[t];e+=this.contourSize[t])r=e*this.scene.dataScale[t],k(n[t],r);if(a){var o=[[],[],[]];for(t=0;t<3;++t)this.showContour[t]&&(o[t]=i[t]?n[t]:this.scene.contourLevels[t]);this.surface.update({levels:o})}},p.update=function(t){var e,r,n,a,l=this.scene,h=l.fullSceneLayout,p=this.surface,d=c(t),m=l.dataScale,g=t.z[0].length,v=t._ylength,y=l.contourLevels;this.data=t;var 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A=[!0,!0,!0],M=["x","y","z"];for(e=0;e<3;++e){var S=t.contours[M[e]];A[e]=S.highlight,w.showContour[e]=S.show||S.highlight,w.showContour[e]&&(w.contourProject[e]=[S.project.x,S.project.y,S.project.z],S.show?(this.showContour[e]=!0,w.levels[e]=y[e],p.highlightColor[e]=w.contourColor[e]=u(S.color),S.usecolormap?p.highlightTint[e]=w.contourTint[e]=0:p.highlightTint[e]=w.contourTint[e]=1,w.contourWidth[e]=S.width,this.contourStart[e]=S.start,this.contourEnd[e]=S.end,this.contourSize[e]=S.size):(this.showContour[e]=!1,this.contourStart[e]=null,this.contourEnd[e]=null,this.contourSize[e]=0),S.highlight&&(w.dynamicColor[e]=u(S.highlightcolor),w.dynamicWidth[e]=S.highlightwidth))}(function(t){var e=t[0].rgb,r=t[t.length-1].rgb;return e[0]===r[0]&&e[1]===r[1]&&e[2]===r[2]&&e[3]===r[3]})(d)&&(w.vertexColor=!0),w.objectOffset=this.objectOffset,w.coords=b,p.update(w),p.visible=t.visible,p.enableDynamic=A,p.enableHighlight=A,p.snapToData=!0,"lighting"in 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r=[],n=0;n<32;n++){var i=n/31,a=e+(1-e)*(1-Math.pow(Math.sin(t*i*Math.PI),2));r.push([i,Math.max(0,Math.min(1,a))])}return r}(1,.1):function(t){var e=0;if(!Array.isArray(t)||t.length<2)return!1;if(!t[0]||!t[t.length-1])return!1;if(0!=+t[0][0]||1!=+t[t.length-1][0])return!1;for(var r=0;r<t.length;r++){var n=t[r];if(2!==n.length||+n[0]<e)return!1;e=+n[0]}return!0}(i)||(e.opacityscale=void 0)}function l(t,e,r){e in t&&!(r in t)&&(t[r]=t[e])}e.exports={supplyDefaults:function(t,e,r,c){var u,f;function h(r,n){return i.coerce(t,e,o,r,n)}var p=h("x"),d=h("y"),m=h("z");if(!m||!m.length||p&&p.length<1||d&&d.length<1)e.visible=!1;else{e._xlength=Array.isArray(p)&&i.isArrayOrTypedArray(p[0])?m.length:m[0].length,e._ylength=m.length,n.getComponentMethod("calendars","handleTraceDefaults")(t,e,["x","y","z"],c),h("text"),h("hovertext"),h("hovertemplate"),h("xhoverformat"),h("yhoverformat"),h("zhoverformat"),["lighting.ambient","lighting.diffuse","lighting.specular","lighting.roughness","lighting.fresnel","lightposition.x","lightposition.y","lightposition.z","hidesurface","connectgaps","opacity"].forEach((function(t){h(t)}));var g=h("surfacecolor"),v=["x","y","z"];for(u=0;u<3;++u){var y="contours."+v[u],x=h(y+".show"),b=h(y+".highlight");if(x||b)for(f=0;f<3;++f)h(y+".project."+v[f]);x&&(h(y+".color"),h(y+".width"),h(y+".usecolormap")),b&&(h(y+".highlightcolor"),h(y+".highlightwidth")),h(y+".start"),h(y+".end"),h(y+".size")}g||(l(t,"zmin","cmin"),l(t,"zmax","cmax"),l(t,"zauto","cauto")),a(t,e,c,h,{prefix:"",cLetter:"c"}),s(t,e,c,h),e._length=null}},opacityscaleDefaults:s}},{"../../components/colorscale/defaults":371,"../../lib":498,"../../registry":633,"./attributes":1056}],1060:[function(t,e,r){"use strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults").supplyDefaults,colorbar:{min:"cmin",max:"cmax"},calc:t("./calc"),plot:t("./convert"),moduleType:"trace",name:"surface",basePlotModule:t("../../plots/gl3d"),categories:["gl3d","2dMap","showLegend"],meta:{}}},{"../../plots/gl3d":593,"./attributes":1056,"./calc":1057,"./convert":1058,"./defaults":1059}],1061:[function(t,e,r){"use strict";var n=t("../../components/annotations/attributes"),i=t("../../lib/extend").extendFlat,a=t("../../plot_api/edit_types").overrideAll,o=t("../../plots/font_attributes"),s=t("../../plots/domain").attributes,l=t("../../plots/cartesian/axis_format_attributes").descriptionOnlyNumbers;(e.exports=a({domain:s({name:"table",trace:!0}),columnwidth:{valType:"number",arrayOk:!0,dflt:null},columnorder:{valType:"data_array"},header:{values:{valType:"data_array",dflt:[]},format:{valType:"data_array",dflt:[],description:l("cell value")},prefix:{valType:"string",arrayOk:!0,dflt:null},suffix:{valType:"string",arrayOk:!0,dflt:null},height:{valType:"number",dflt:28},align:i({},n.align,{arrayOk:!0}),line:{width:{valType:"number",arrayOk:!0,dflt:1},color:{valType:"color",arrayOk:!0,dflt:"grey"}},fill:{color:{valType:"color",arrayOk:!0,dflt:"white"}},font:i({},o({arrayOk:!0}))},cells:{values:{valType:"data_array",dflt:[]},format:{valType:"data_array",dflt:[],description:l("cell 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e,r=t.slice(),n=1/0,i=0;for(e=0;e<r.length;e++)Array.isArray(r[e])||(r[e]=[r[e]]),n=Math.min(n,r[e].length),i=Math.max(i,r[e].length);if(n!==i)for(e=0;e<r.length;e++){var a=i-r[e].length;a&&(r[e]=r[e].concat(c(a)))}return r}function c(t){for(var e=new Array(t),r=0;r<t;r++)e[r]="";return e}function u(t){return t.calcdata.columns.reduce((function(e,r){return r.xIndex<t.xIndex?e+r.columnWidth:e}),0)}function f(t,e){return Object.keys(t).map((function(r){return i({},t[r],{auxiliaryBlocks:e})}))}function h(t,e){for(var r,n={},i=0,a=0,o={firstRowIndex:null,lastRowIndex:null,rows:[]},s=0,l=0,c=0;c<t.length;c++)r=t[c],o.rows.push({rowIndex:c,rowHeight:r}),((a+=r)>=e||c===t.length-1)&&(n[i]=o,o.key=l++,o.firstRowIndex=s,o.lastRowIndex=c,o={firstRowIndex:null,lastRowIndex:null,rows:[]},i+=a,s=c+1,a=0);return n}e.exports=function(t,e){var r=l(e.cells.values),p=function(t){return t.slice(e.header.values.length,t.length)},d=l(e.header.values);d.length&&!d[0].length&&(d[0]=[""],d=l(d));var m=d.concat(p(r).map((function(){return c((d[0]||[""]).length)}))),g=e.domain,v=Math.floor(t._fullLayout._size.w*(g.x[1]-g.x[0])),y=Math.floor(t._fullLayout._size.h*(g.y[1]-g.y[0])),x=e.header.values.length?m[0].map((function(){return e.header.height})):[n.emptyHeaderHeight],b=r.length?r[0].map((function(){return e.cells.height})):[],_=x.reduce(s,0),w=h(b,y-_+n.uplift),T=f(h(x,_),[]),k=f(w,T),A={},M=e._fullInput.columnorder.concat(p(r.map((function(t,e){return e})))),S=m.map((function(t,r){var n=Array.isArray(e.columnwidth)?e.columnwidth[Math.min(r,e.columnwidth.length-1)]:e.columnwidth;return a(n)?Number(n):1})),E=S.reduce(s,0);S=S.map((function(t){return t/E*v}));var L=Math.max(o(e.header.line.width),o(e.cells.line.width)),C={key:e.uid+t._context.staticPlot,translateX:g.x[0]*t._fullLayout._size.w,translateY:t._fullLayout._size.h*(1-g.y[1]),size:t._fullLayout._size,width:v,maxLineWidth:L,height:y,columnOrder:M,groupHeight:y,rowBlocks:k,headerRowBlocks:T,scrollY:0,cells:i({},e.cells,{values:r}),headerCells:i({},e.header,{values:m}),gdColumns:m.map((function(t){return t[0]})),gdColumnsOriginalOrder:m.map((function(t){return t[0]})),prevPages:[0,0],scrollbarState:{scrollbarScrollInProgress:!1},columns:m.map((function(t,e){var r=A[t];return A[t]=(r||0)+1,{key:t+"__"+A[t],label:t,specIndex:e,xIndex:M[e],xScale:u,x:void 0,calcdata:void 0,columnWidth:S[e]}}))};return C.columns.forEach((function(t){t.calcdata=C,t.x=u(t)})),C}},{"../../lib/extend":488,"./constants":1064,"fast-isnumeric":185}],1066:[function(t,e,r){"use strict";var n=t("../../lib/extend").extendFlat;r.splitToPanels=function(t){var 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0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0|r.words[o])-(0|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var d=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,c=0,u=0|o[0],f=8191&u,h=u>>>13,p=0|o[1],d=8191&p,m=p>>>13,g=0|o[2],v=8191&g,y=g>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,A=0|o[5],M=8191&A,S=A>>>13,E=0|o[6],L=8191&E,C=E>>>13,P=0|o[7],I=8191&P,O=P>>>13,z=0|o[8],D=8191&z,R=z>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,H=0|s[1],q=8191&H,G=H>>>13,Y=0|s[2],W=8191&Y,X=Y>>>13,Z=0|s[3],J=8191&Z,K=Z>>>13,Q=0|s[4],$=8191&Q,tt=Q>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ct=st>>>13,ut=0|s[8],ft=8191&ut,ht=ut>>>13,pt=0|s[9],dt=8191&pt,mt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var gt=(c+(n=Math.imul(f,U))|0)+((8191&(i=(i=Math.imul(f,V))+Math.imul(h,U)|0))<<13)|0;c=((a=Math.imul(h,V))+(i>>>13)|0)+(gt>>>26)|0,gt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(m,U)|0,a=Math.imul(m,V);var vt=(c+(n=n+Math.imul(f,q)|0)|0)+((8191&(i=(i=i+Math.imul(f,G)|0)+Math.imul(h,q)|0))<<13)|0;c=((a=a+Math.imul(h,G)|0)+(i>>>13)|0)+(vt>>>26)|0,vt&=67108863,n=Math.imul(v,U),i=(i=Math.imul(v,V))+Math.imul(y,U)|0,a=Math.imul(y,V),n=n+Math.imul(d,q)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(m,q)|0,a=a+Math.imul(m,G)|0;var yt=(c+(n=n+Math.imul(f,W)|0)|0)+((8191&(i=(i=i+Math.imul(f,X)|0)+Math.imul(h,W)|0))<<13)|0;c=((a=a+Math.imul(h,X)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(b,U),i=(i=Math.imul(b,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(v,q)|0,i=(i=i+Math.imul(v,G)|0)+Math.imul(y,q)|0,a=a+Math.imul(y,G)|0,n=n+Math.imul(d,W)|0,i=(i=i+Math.imul(d,X)|0)+Math.imul(m,W)|0,a=a+Math.imul(m,X)|0;var xt=(c+(n=n+Math.imul(f,J)|0)|0)+((8191&(i=(i=i+Math.imul(f,K)|0)+Math.imul(h,J)|0))<<13)|0;c=((a=a+Math.imul(h,K)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(b,q)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,q)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(v,W)|0,i=(i=i+Math.imul(v,X)|0)+Math.imul(y,W)|0,a=a+Math.imul(y,X)|0,n=n+Math.imul(d,J)|0,i=(i=i+Math.imul(d,K)|0)+Math.imul(m,J)|0,a=a+Math.imul(m,K)|0;var bt=(c+(n=n+Math.imul(f,$)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,$)|0))<<13)|0;c=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(M,U),i=(i=Math.imul(M,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,q)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,q)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(b,W)|0,i=(i=i+Math.imul(b,X)|0)+Math.imul(_,W)|0,a=a+Math.imul(_,X)|0,n=n+Math.imul(v,J)|0,i=(i=i+Math.imul(v,K)|0)+Math.imul(y,J)|0,a=a+Math.imul(y,K)|0,n=n+Math.imul(d,$)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(m,$)|0,a=a+Math.imul(m,tt)|0;var _t=(c+(n=n+Math.imul(f,rt)|0)|0)+((8191&(i=(i=i+Math.imul(f,nt)|0)+Math.imul(h,rt)|0))<<13)|0;c=((a=a+Math.imul(h,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,U),i=(i=Math.imul(L,V))+Math.imul(C,U)|0,a=Math.imul(C,V),n=n+Math.imul(M,q)|0,i=(i=i+Math.imul(M,G)|0)+Math.imul(S,q)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,W)|0,i=(i=i+Math.imul(T,X)|0)+Math.imul(k,W)|0,a=a+Math.imul(k,X)|0,n=n+Math.imul(b,J)|0,i=(i=i+Math.imul(b,K)|0)+Math.imul(_,J)|0,a=a+Math.imul(_,K)|0,n=n+Math.imul(v,$)|0,i=(i=i+Math.imul(v,tt)|0)+Math.imul(y,$)|0,a=a+Math.imul(y,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(m,rt)|0,a=a+Math.imul(m,nt)|0;var wt=(c+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;c=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(I,U),i=(i=Math.imul(I,V))+Math.imul(O,U)|0,a=Math.imul(O,V),n=n+Math.imul(L,q)|0,i=(i=i+Math.imul(L,G)|0)+Math.imul(C,q)|0,a=a+Math.imul(C,G)|0,n=n+Math.imul(M,W)|0,i=(i=i+Math.imul(M,X)|0)+Math.imul(S,W)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(T,J)|0,i=(i=i+Math.imul(T,K)|0)+Math.imul(k,J)|0,a=a+Math.imul(k,K)|0,n=n+Math.imul(b,$)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,$)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(v,rt)|0,i=(i=i+Math.imul(v,nt)|0)+Math.imul(y,rt)|0,a=a+Math.imul(y,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(m,at)|0,a=a+Math.imul(m,ot)|0;var Tt=(c+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ct)|0)+Math.imul(h,lt)|0))<<13)|0;c=((a=a+Math.imul(h,ct)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(D,U),i=(i=Math.imul(D,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(I,q)|0,i=(i=i+Math.imul(I,G)|0)+Math.imul(O,q)|0,a=a+Math.imul(O,G)|0,n=n+Math.imul(L,W)|0,i=(i=i+Math.imul(L,X)|0)+Math.imul(C,W)|0,a=a+Math.imul(C,X)|0,n=n+Math.imul(M,J)|0,i=(i=i+Math.imul(M,K)|0)+Math.imul(S,J)|0,a=a+Math.imul(S,K)|0,n=n+Math.imul(T,$)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,$)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(v,at)|0,i=(i=i+Math.imul(v,ot)|0)+Math.imul(y,at)|0,a=a+Math.imul(y,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ct)|0)+Math.imul(m,lt)|0,a=a+Math.imul(m,ct)|0;var kt=(c+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;c=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(D,q)|0,i=(i=i+Math.imul(D,G)|0)+Math.imul(R,q)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(I,W)|0,i=(i=i+Math.imul(I,X)|0)+Math.imul(O,W)|0,a=a+Math.imul(O,X)|0,n=n+Math.imul(L,J)|0,i=(i=i+Math.imul(L,K)|0)+Math.imul(C,J)|0,a=a+Math.imul(C,K)|0,n=n+Math.imul(M,$)|0,i=(i=i+Math.imul(M,tt)|0)+Math.imul(S,$)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(v,lt)|0,i=(i=i+Math.imul(v,ct)|0)+Math.imul(y,lt)|0,a=a+Math.imul(y,ct)|0,n=n+Math.imul(d,ft)|0,i=(i=i+Math.imul(d,ht)|0)+Math.imul(m,ft)|0,a=a+Math.imul(m,ht)|0;var At=(c+(n=n+Math.imul(f,dt)|0)|0)+((8191&(i=(i=i+Math.imul(f,mt)|0)+Math.imul(h,dt)|0))<<13)|0;c=((a=a+Math.imul(h,mt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,q),i=(i=Math.imul(B,G))+Math.imul(N,q)|0,a=Math.imul(N,G),n=n+Math.imul(D,W)|0,i=(i=i+Math.imul(D,X)|0)+Math.imul(R,W)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(I,J)|0,i=(i=i+Math.imul(I,K)|0)+Math.imul(O,J)|0,a=a+Math.imul(O,K)|0,n=n+Math.imul(L,$)|0,i=(i=i+Math.imul(L,tt)|0)+Math.imul(C,$)|0,a=a+Math.imul(C,tt)|0,n=n+Math.imul(M,rt)|0,i=(i=i+Math.imul(M,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ct)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ct)|0,n=n+Math.imul(v,ft)|0,i=(i=i+Math.imul(v,ht)|0)+Math.imul(y,ft)|0,a=a+Math.imul(y,ht)|0;var Mt=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,mt)|0)+Math.imul(m,dt)|0))<<13)|0;c=((a=a+Math.imul(m,mt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,W),i=(i=Math.imul(B,X))+Math.imul(N,W)|0,a=Math.imul(N,X),n=n+Math.imul(D,J)|0,i=(i=i+Math.imul(D,K)|0)+Math.imul(R,J)|0,a=a+Math.imul(R,K)|0,n=n+Math.imul(I,$)|0,i=(i=i+Math.imul(I,tt)|0)+Math.imul(O,$)|0,a=a+Math.imul(O,tt)|0,n=n+Math.imul(L,rt)|0,i=(i=i+Math.imul(L,nt)|0)+Math.imul(C,rt)|0,a=a+Math.imul(C,nt)|0,n=n+Math.imul(M,at)|0,i=(i=i+Math.imul(M,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ct)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ct)|0,n=n+Math.imul(b,ft)|0,i=(i=i+Math.imul(b,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var St=(c+(n=n+Math.imul(v,dt)|0)|0)+((8191&(i=(i=i+Math.imul(v,mt)|0)+Math.imul(y,dt)|0))<<13)|0;c=((a=a+Math.imul(y,mt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,J),i=(i=Math.imul(B,K))+Math.imul(N,J)|0,a=Math.imul(N,K),n=n+Math.imul(D,$)|0,i=(i=i+Math.imul(D,tt)|0)+Math.imul(R,$)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(I,rt)|0,i=(i=i+Math.imul(I,nt)|0)+Math.imul(O,rt)|0,a=a+Math.imul(O,nt)|0,n=n+Math.imul(L,at)|0,i=(i=i+Math.imul(L,ot)|0)+Math.imul(C,at)|0,a=a+Math.imul(C,ot)|0,n=n+Math.imul(M,lt)|0,i=(i=i+Math.imul(M,ct)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ct)|0,n=n+Math.imul(T,ft)|0,i=(i=i+Math.imul(T,ht)|0)+Math.imul(k,ft)|0,a=a+Math.imul(k,ht)|0;var Et=(c+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,mt)|0)+Math.imul(_,dt)|0))<<13)|0;c=((a=a+Math.imul(_,mt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,$),i=(i=Math.imul(B,tt))+Math.imul(N,$)|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,i=(i=i+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(I,at)|0,i=(i=i+Math.imul(I,ot)|0)+Math.imul(O,at)|0,a=a+Math.imul(O,ot)|0,n=n+Math.imul(L,lt)|0,i=(i=i+Math.imul(L,ct)|0)+Math.imul(C,lt)|0,a=a+Math.imul(C,ct)|0,n=n+Math.imul(M,ft)|0,i=(i=i+Math.imul(M,ht)|0)+Math.imul(S,ft)|0,a=a+Math.imul(S,ht)|0;var Lt=(c+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,mt)|0)+Math.imul(k,dt)|0))<<13)|0;c=((a=a+Math.imul(k,mt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)|0,i=(i=i+Math.imul(D,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(I,lt)|0,i=(i=i+Math.imul(I,ct)|0)+Math.imul(O,lt)|0,a=a+Math.imul(O,ct)|0,n=n+Math.imul(L,ft)|0,i=(i=i+Math.imul(L,ht)|0)+Math.imul(C,ft)|0,a=a+Math.imul(C,ht)|0;var Ct=(c+(n=n+Math.imul(M,dt)|0)|0)+((8191&(i=(i=i+Math.imul(M,mt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((a=a+Math.imul(S,mt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,i=(i=i+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ct)|0,n=n+Math.imul(I,ft)|0,i=(i=i+Math.imul(I,ht)|0)+Math.imul(O,ft)|0,a=a+Math.imul(O,ht)|0;var Pt=(c+(n=n+Math.imul(L,dt)|0)|0)+((8191&(i=(i=i+Math.imul(L,mt)|0)+Math.imul(C,dt)|0))<<13)|0;c=((a=a+Math.imul(C,mt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ct))+Math.imul(N,lt)|0,a=Math.imul(N,ct),n=n+Math.imul(D,ft)|0,i=(i=i+Math.imul(D,ht)|0)+Math.imul(R,ft)|0,a=a+Math.imul(R,ht)|0;var It=(c+(n=n+Math.imul(I,dt)|0)|0)+((8191&(i=(i=i+Math.imul(I,mt)|0)+Math.imul(O,dt)|0))<<13)|0;c=((a=a+Math.imul(O,mt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863,n=Math.imul(B,ft),i=(i=Math.imul(B,ht))+Math.imul(N,ft)|0,a=Math.imul(N,ht);var Ot=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,mt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((a=a+Math.imul(R,mt)|0)+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863;var zt=(c+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,mt))+Math.imul(N,dt)|0))<<13)|0;return c=((a=Math.imul(N,mt))+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863,l[0]=gt,l[1]=vt,l[2]=yt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=At,l[10]=Mt,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=Pt,l[16]=It,l[17]=Ot,l[18]=zt,0!==c&&(l[19]=c,r.length++),r};function m(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(d=p),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?d(this,t,e):r<63?p(this,t,e):r<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),c=Math.max(0,a-t.length+1);c<=l;c++){var u=a-c,f=(0|t.words[u])*(0|e.words[c]),h=67108863&f;s=67108863&(h=h+s|0),i+=(o=(o=o+(f/67108864|0)|0)+(h>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):m(this,t,e)},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),c=Math.sin(2*Math.PI/s),u=0;u<i;u+=s)for(var f=l,h=c,p=0;p<o;p++){var d=r[u+p],m=n[u+p],g=r[u+p+o],v=n[u+p+o],y=f*g-h*v;v=f*v+h*g,g=y,r[u+p]=d+g,n[u+p]=m+v,r[u+p+o]=d-g,n[u+p+o]=m-v,p!==s&&(y=l*f-c*h,h=l*h+c*f,f=y)}},g.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},g.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),c=new Array(n),u=new Array(n),f=new Array(n),h=r.words;h.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,c,n),this.transform(o,a,s,l,n,i),this.transform(c,a,u,f,n,i);for(var p=0;p<n;p++){var d=s[p]*u[p]-l[p]*f[p];l[p]=s[p]*f[p]+l[p]*u[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,h,a,n,i),this.conjugate(h,a,n),this.normalize13b(h,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),m(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n("number"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n("number"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n("number"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var c=0;c<o;c++)l.words[c]=this.words[c];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,c=0;c<this.length;c++)this.words[c]=this.words[c+o];else this.words[0]=0,this.length=1;var u=0;for(c=this.length-1;c>=0&&(0!==u||c>=i);c--){var f=0|this.words[c];this.words[c]=u<<26-a|f>>>a,u=f&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n("number"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n("number"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,"imaskn works only with positive numbers"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n("number"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n("number"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if("mod"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c<s.length;c++)s.words[c]=0}var u=n.clone()._ishlnsubmul(i,1,l);0===u.negative&&(n=u,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),"div"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),"mod"!==e&&(i=s.div.neg()),"div"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),"mod"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),"div"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?"div"===e?{div:this.divn(t.words[0]),mod:null}:"mod"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,"div",!1).div},a.prototype.mod=function(t){return this.divmod(t,"mod",!1).mod},a.prototype.umod=function(t){return this.divmod(t,"mod",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(u),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,m=1;0==(r.words[0]&m)&&d<26;++d,m<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(u),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- 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this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,"Number is too big");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new T(t)},a.prototype.toRed=function(t){return n(!this.red,"Already a number in reduction context"),n(0===this.negative,"red works only with positives"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,"fromRed works only with numbers in reduction context"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,"Already a number in reduction 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0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(b,y),i(_,y),i(w,y),w.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*(0|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(v[t])return v[t];var e;if("k256"===t)e=new x;else if("p224"===t)e=new b;else if("p192"===t)e=new _;else{if("p25519"!==t)throw new Error("Unknown prime "+t);e=new w}return v[t]=e,e},T.prototype._verify1=function(t){n(0===t.negative,"red works only with positives"),n(t.red,"red works only with red numbers")},T.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),"red works only with positives"),n(t.red&&t.red===e.red,"red works only with red numbers")},T.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},T.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},T.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},T.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},T.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},T.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},T.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},T.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},T.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},T.prototype.isqr=function(t){return this.imul(t,t.clone())},T.prototype.sqr=function(t){return this.mul(t,t)},T.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),c=this.m.subn(1).iushrn(1),u=this.m.bitLength();for(u=new a(2*u*u).toRed(this);0!==this.pow(u,c).cmp(l);)u.redIAdd(l);for(var f=this.pow(u,i),h=this.pow(t,i.addn(1).iushrn(1)),p=this.pow(t,i),d=o;0!==p.cmp(s);){for(var m=p,g=0;0!==m.cmp(s);g++)m=m.redSqr();n(g<d);var v=this.pow(f,new a(1).iushln(d-g-1));h=h.redMul(v),f=v.redSqr(),p=p.redMul(f),d=g}return h},T.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},T.prototype.pow=function(t,e){if(e.isZero())return new a(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new a(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var i=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var c=e.words[n],u=l-1;u>=0;u--){var f=c>>u&1;i!==r[0]&&(i=this.sqr(i)),0!==f||0!==o?(o<<=1,o|=f,(4===++s||0===n&&0===u)&&(i=this.mul(i,r[o]),s=0,o=0)):s=0}l=26}return i},T.prototype.convertTo=function(t){var e=t.umod(this.m);return e===t?e.clone():e},T.prototype.convertFrom=function(t){var e=t.clone();return e.red=null,e},a.mont=function(t){return new k(t)},i(k,T),k.prototype.convertTo=function(t){return this.imod(t.ushln(this.shift))},k.prototype.convertFrom=function(t){var e=this.imod(t.mul(this.rinv));return e.red=null,e},k.prototype.imul=function(t,e){if(t.isZero()||e.isZero())return t.words[0]=0,t.length=1,t;var r=t.imul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),a=i;return i.cmp(this.m)>=0?a=i.isub(this.m):i.cmpn(0)<0&&(a=i.iadd(this.m)),a._forceRed(this)},k.prototype.mul=function(t,e){if(t.isZero()||e.isZero())return new a(0)._forceRed(this);var 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r=0;r<t;++r)if(!(e[r]<=e[r+t]))return!0;return!1}function l(t,e,r,n){for(var i=0,a=0,o=0,l=t.length;o<l;++o){var c=t[o];if(!s(e,c)){for(var u=0;u<2*e;++u)r[i++]=c[u];n[a++]=o}}return a}function c(t,e,r,n){var s=t.length,c=e.length;if(!(s<=0||c<=0)){var u=t[0].length>>>1;if(!(u<=0)){var f,h=i.mallocDouble(2*u*s),p=i.mallocInt32(s);if((s=l(t,u,h,p))>0){if(1===u&&n)a.init(s),f=a.sweepComplete(u,r,0,s,h,p,0,s,h,p);else{var d=i.mallocDouble(2*u*c),m=i.mallocInt32(c);(c=l(e,u,d,m))>0&&(a.init(s+c),f=1===u?a.sweepBipartite(u,r,0,s,h,p,0,c,d,m):o(u,r,n,s,h,p,c,d,m),i.free(d),i.free(m))}i.free(h),i.free(p)}return f}}}function u(t,e){n.push([t,e])}function f(t){return n=[],c(t,t,u,!0),n}function h(t,e){return n=[],c(t,e,u,!1),n}},{"./lib/intersect":37,"./lib/sweep":41,"typedarray-pool":308}],36:[function(t,e,r){"use strict";function n(t){return t?function(t,e,r,n,i,a,o,s,l,c,u){return i-n>l-s?function(t,e,r,n,i,a,o,s,l,c,u){for(var f=2*t,h=n,p=f*n;h<i;++h,p+=f){var 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f=e[e.length-2];f.left===r?f.left=null:f.right===r&&(f.right=null),e.pop();for(l=0;l<e.length;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(r.left||r.right){r.left?d(r,r.left):r.right&&d(r,r.right),r._color=1;for(l=0;l<e.length-1;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(1===e.length)return new s(this.tree._compare,null);for(l=0;l<e.length;++l)e[l]._count--;var h=e[e.length-2];return function(t){for(var e,r,n,s,l=t.length-1;l>=0;--l){if(e=t[l],0===l)return void(e._color=1);if((r=t[l-1]).left===e){if((n=r.right).right&&0===n.right._color){if(s=(n=r.right=i(n)).right=i(n.right),r.right=n.left,n.left=r,n.right=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).left===r?c.left=n:c.right=n;return void(t[l-1]=n)}if(n.left&&0===n.left._color){if(s=(n=r.right=i(n)).left=i(n.left),r.right=s.left,n.left=s.right,s.left=r,s.right=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).left===r?c.left=s:c.right=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.right=a(0,n));r.right=a(0,n);continue}n=i(n),r.right=n.left,n.left=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).left===r?c.left=n:c.right=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}else{if((n=r.left).left&&0===n.left._color){if(s=(n=r.left=i(n)).left=i(n.left),r.left=n.right,n.right=r,n.left=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).right===r?c.right=n:c.left=n;return void(t[l-1]=n)}if(n.right&&0===n.right._color){if(s=(n=r.left=i(n)).right=i(n.right),r.left=s.right,n.right=s.left,s.right=r,s.left=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).right===r?c.right=s:c.left=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.left=a(0,n));r.left=a(0,n);continue}var c;n=i(n),r.left=n.right,n.right=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).right===r?c.right=n:c.left=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}}}(e),h.left===r?h.left=null:h.right=null,new s(this.tree._compare,e[0])},Object.defineProperty(p,"key",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(p,"value",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(p,"index",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),p.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,"hasNext",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),p.update=function(t){var e=this._stack;if(0===e.length)throw new Error("Can't update empty node!");var r=new Array(e.length),i=e[e.length-1];r[r.length-1]=new n(i._color,i.key,t,i.left,i.right,i._count);for(var a=e.length-2;a>=0;--a)(i=e[a]).left===e[a+1]?r[a]=new n(i._color,i.key,i.value,r[a+1],i.right,i._count):r[a]=new n(i._color,i.key,i.value,i.left,r[a+1],i._count);return new s(this.tree._compare,r[0])},p.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,"hasPrev",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},{}],70:[function(t,e,r){"use strict";e.exports=function(t,e){var r=new u(t);return r.update(e),r};var n=t("./lib/text.js"),i=t("./lib/lines.js"),a=t("./lib/background.js"),o=t("./lib/cube.js"),s=t("./lib/ticks.js"),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function c(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function u(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=["auto","auto","auto"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont="sans-serif",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=["auto","auto","auto"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var f=u.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),c=e.bind(this,!0,(function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]})),u=!1,f=!1;if("bounds"in t)for(var h=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)h[p][d]!==this.bounds[p][d]&&(f=!0),this.bounds[p][d]=h[p][d];if("ticks"in t){r=t.ticks,u=!0,this.autoTicks=!1;for(p=0;p<3;++p)this.tickSpacing[p]=0}else a("tickSpacing")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&("ticks"in t||"tickSpacing"in t||(this.autoTicks=!0),f=!0,u=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),u=!0),u){for(p=0;p<3;++p)r[p].sort((function(t,e){return t.x-e.x}));s.equal(r,this.ticks)?u=!1:this.ticks=r}o("tickEnable"),l("tickFont")&&(u=!0),a("tickSize"),a("tickAngle"),a("tickPad"),c("tickColor");var m=l("labels");l("labelFont")&&(m=!0),o("labelEnable"),a("labelSize"),a("labelPad"),c("labelColor"),o("lineEnable"),o("lineMirror"),a("lineWidth"),c("lineColor"),o("lineTickEnable"),o("lineTickMirror"),a("lineTickLength"),a("lineTickWidth"),c("lineTickColor"),o("gridEnable"),a("gridWidth"),c("gridColor"),o("zeroEnable"),c("zeroLineColor"),a("zeroLineWidth"),o("backgroundEnable"),c("backgroundColor"),this._text?this._text&&(m||u)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&u&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new h,new h,new h];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var f=a,h=s,p=o,d=l;c&1<<u&&(f=s,h=a,p=l,d=o),f[u]=r[0][u],h[u]=r[1][u],i[u]>0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var m=[0,0,0],g={model:l,view:l,projection:l,_ortho:!1};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var v=[0,0,0],y=[0,0,0],x=[0,0,0];f.draw=function(t){t=t||g;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,u=o(r,n,i,a,s),f=u.cubeEdges,h=u.axis,b=n[12],_=n[13],w=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*b+i[7]*_+i[11]*w+i[15]*T)/e.drawingBufferHeight,A=0;A<3;++A)this.lastCubeProps.cubeEdges[A]=f[A],this.lastCubeProps.axis[A]=h[A];var M=p;for(A=0;A<3;++A)d(p[A],A,this.bounds,f,h);e=this.gl;var S,E=m;for(A=0;A<3;++A)this.backgroundEnable[A]?E[A]=h[A]:E[A]=0;this._background.draw(r,n,i,a,E,this.backgroundColor),this._lines.bind(r,n,i,this);for(A=0;A<3;++A){var L=[0,0,0];h[A]>0?L[A]=a[1][A]:L[A]=a[0][A];for(var C=0;C<2;++C){var P=(A+1+C)%3,I=(A+1+(1^C))%3;this.gridEnable[P]&&this._lines.drawGrid(P,I,this.bounds,L,this.gridColor[P],this.gridWidth[P]*this.pixelRatio)}for(C=0;C<2;++C){P=(A+1+C)%3,I=(A+1+(1^C))%3;this.zeroEnable[I]&&Math.min(a[0][I],a[1][I])<=0&&Math.max(a[0][I],a[1][I])>=0&&this._lines.drawZero(P,I,this.bounds,L,this.zeroLineColor[I],this.zeroLineWidth[I]*this.pixelRatio)}}for(A=0;A<3;++A){this.lineEnable[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].primalOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio),this.lineMirror[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].mirrorOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio);var O=c(v,M[A].primalMinor),z=c(y,M[A].mirrorMinor),D=this.lineTickLength;for(C=0;C<3;++C){var R=k/r[5*C];O[C]*=D[C]*R,z[C]*=D[C]*R}this.lineTickEnable[A]&&this._lines.drawAxisTicks(A,M[A].primalOffset,O,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio),this.lineTickMirror[A]&&this._lines.drawAxisTicks(A,M[A].mirrorOffset,z,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio)}this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio);var F,B;function N(t){(B=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(A=0;A<3;++A){var U=M[A].primalMinor,V=M[A].mirrorMinor,H=c(x,M[A].primalOffset);for(C=0;C<3;++C)this.lineTickEnable[A]&&(H[C]+=k*U[C]*Math.max(this.lineTickLength[C],0)/r[5*C]);var q=[0,0,0];if(q[A]=1,this.tickEnable[A]){-3600===this.tickAngle[A]?(this.tickAngle[A]=0,this.tickAlign[A]="auto"):this.tickAlign[A]=-1,F=1,"auto"===(S=[this.tickAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(""+S[0]),B=[0,0,0],j(A,U,V);for(C=0;C<3;++C)H[C]+=k*U[C]*this.tickPad[C]/r[5*C];this._text.drawTicks(A,this.tickSize[A],this.tickAngle[A],H,this.tickColor[A],q,B,S)}if(this.labelEnable[A]){F=0,B=[0,0,0],this.labels[A].length>4&&(N(A),F=1),"auto"===(S=[this.labelAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(""+S[0]);for(C=0;C<3;++C)H[C]+=k*U[C]*this.labelPad[C]/r[5*C];H[A]+=.5*(a[0][A]+a[1][A]),this._text.drawLabel(A,this.labelSize[A],this.labelAngle[A],H,this.labelColor[A],[0,0,0],B,S)}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{"./lib/background.js":71,"./lib/cube.js":72,"./lib/lines.js":73,"./lib/text.js":75,"./lib/ticks.js":76}],71:[function(t,e,r){"use strict";e.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var c=(l+1)%3,u=(l+2)%3,f=[0,0,0],h=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=p,h[l]=p;for(var d=-1;d<=1;d+=2){f[c]=d;for(var m=-1;m<=1;m+=2)f[u]=m,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var g=c;c=u,u=g}var v=n(t,new Float32Array(e)),y=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:v,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:v,type:t.FLOAT,size:3,offset:12,stride:24}],y),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,v,x,b)};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders").bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{"./shaders":74,"gl-buffer":78,"gl-vao":150}],72:[function(t,e,r){"use strict";e.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var y=0,x=0;x<2;++x){u[2]=a[x][2];for(var b=0;b<2;++b){u[1]=a[b][1];for(var _=0;_<2;++_)u[0]=a[_][0],h(l[y],u,s),y+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)c[x][k]=l[x][k]/T;p&&(c[x][2]*=-1),T<0&&(w<0||c[x][2]<c[w][2])&&(w=x)}if(w<0){w=0;for(var A=0;A<3;++A){for(var M=(A+2)%3,S=(A+1)%3,E=-1,L=-1,C=0;C<2;++C){var P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);o(c[O],c[P],c[I],f)<0||(C?E=1:L=1)}if(E<0||L<0)L>E&&(w|=1<<A);else{for(C=0;C<2;++C){P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);var 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e=0;e<p.length;++e)if((t=a.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],c=s[0]/s[3]-n,u=s[1]/s[3]-i,f=l[0]/l[3]-n,h=l[1]/l[3]-i;o+=Math.abs(c*h-u*f)}return o}var m=[1,1,1],g=[0,0,0],v={cubeEdges:m,axis:g}},{"bit-twiddle":32,"gl-mat4/multiply":100,"robust-orientation":284,"split-polygon":300}],73:[function(t,e,r){"use strict";e.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var h=o.length/3|0,d=0;d<r[f].length;++d){var m=+r[f][d].x;o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}var g=o.length/3|0;s[f]=h,l[f]=g-h;h=o.length/3|0;for(var v=0;v<r[f].length;++v){m=+r[f][v].x;o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}g=o.length/3|0;c[f]=h,u[f]=g-h}var y=n(t,new Float32Array(o)),x=i(t,[{buffer:y,type:t.FLOAT,size:3,stride:0,offset:0}]),b=a(t);return b.attributes.position.location=0,new p(t,y,x,b,l,s,u,c)};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders").line,o=[0,0,0],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[1,1];function f(t){return t[0]=t[1]=t[2]=0,t}function h(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,u[0]=this.gl.drawingBufferWidth,u[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=u,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,i){var a=f(s);this.shader.uniforms.majorAxis=s,a[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=a;var o,u=h(c,r);u[t]+=e[0][t],this.shader.uniforms.offset=u,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=f(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=f(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=f(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=f(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var u=f(s);u[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=u;var p=h(c,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=f(o);d[t]=1,this.shader.uniforms.majorAxis=d;var m=f(l);m[t]=1,this.shader.uniforms.screenAxis=m,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,i,a){var o=f(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var u=h(c,n);u[t]+=r[0][t],this.shader.uniforms.offset=u;var p=f(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},{"./shaders":74,"gl-buffer":78,"gl-vao":150}],74:[function(t,e,r){"use strict";var n=t("glslify"),i=t("gl-shader"),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\nuniform float lineWidth;\nuniform vec2 screenShape;\n\nvec3 project(vec3 p) {\n  vec4 pp = projection * view * model * vec4(p, 1.0);\n  return pp.xyz / max(pp.w, 0.0001);\n}\n\nvoid main() {\n  vec3 major = position.x * majorAxis;\n  vec3 minor = position.y * minorAxis;\n\n  vec3 vPosition = major + minor + offset;\n  vec3 pPosition = project(vPosition);\n  vec3 offset = project(vPosition + screenAxis * position.z);\n\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\n\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = color;\n}"]);r.line=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"}])};var s=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\nuniform vec3 offset, axis, alignDir, alignOpt;\nuniform float scale, angle, pixelScale;\nuniform vec2 resolution;\n\nvec3 project(vec3 p) {\n  vec4 pp = projection * view * model * vec4(p, 1.0);\n  return pp.xyz / max(pp.w, 0.0001);\n}\n\nfloat computeViewAngle(vec3 a, vec3 b) {\n  vec3 A = project(a);\n  vec3 B = project(b);\n\n  return atan(\n    (B.y - A.y) * resolution.y,\n    (B.x - A.x) * resolution.x\n  );\n}\n\nconst float PI = 3.141592;\nconst float TWO_PI = 2.0 * PI;\nconst float HALF_PI = 0.5 * PI;\nconst float ONE_AND_HALF_PI = 1.5 * PI;\n\nint option = int(floor(alignOpt.x + 0.001));\nfloat hv_ratio =       alignOpt.y;\nbool enableAlign =    (alignOpt.z != 0.0);\n\nfloat mod_angle(float a) {\n  return mod(a, PI);\n}\n\nfloat positive_angle(float a) {\n  return mod_angle((a < 0.0) ?\n    a + TWO_PI :\n    a\n  );\n}\n\nfloat look_upwards(float a) {\n  float b = positive_angle(a);\n  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\n    b - PI :\n    b;\n}\n\nfloat look_horizontal_or_vertical(float a, float ratio) {\n  // ratio controls the ratio between being horizontal to (vertical + horizontal)\n  // if ratio is set to 0.5 then it is 50%, 50%.\n  // when using a higher ratio e.g. 0.75 the result would\n  // likely be more horizontal than vertical.\n\n  float b = positive_angle(a);\n\n  return\n    (b < (      ratio) * HALF_PI) ? 0.0 :\n    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\n    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\n    (b < (4.0 - ratio) * HALF_PI) ? 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HALF_PI :\n                      computeViewAngle(dataPosition, dataPosition + axis);\n    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\n\n    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\n    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\n\n    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\n                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\n\n    beta += applyAlignOption(clipAngle, flip * PI);\n  }\n\n  //Compute plane offset\n  vec2 planeCoord = position.xy * pixelScale;\n\n  mat2 planeXform = scale * mat2(\n     cos(beta), sin(beta),\n    -sin(beta), cos(beta)\n  );\n\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\n\n  //Compute clip position\n  vec3 clipPosition = project(dataPosition);\n\n  //Apply text offset in clip coordinates\n  clipPosition += vec3(viewOffset, 0.0);\n\n  //Done\n  gl_Position = vec4(clipPosition, 1.0);\n}"]),l=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = color;\n}"]);r.text=function(t){return i(t,s,l,null,[{name:"position",type:"vec3"}])};var c=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec3 normal;\n\nuniform mat4 model, view, projection;\nuniform vec3 enable;\nuniform vec3 bounds[2];\n\nvarying vec3 colorChannel;\n\nvoid main() {\n\n  vec3 signAxis = sign(bounds[1] - bounds[0]);\n\n  vec3 realNormal = signAxis * normal;\n\n  if(dot(realNormal, enable) > 0.0) {\n    vec3 minRange = min(bounds[0], bounds[1]);\n    vec3 maxRange = max(bounds[0], bounds[1]);\n    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\n    gl_Position = projection * view * model * vec4(nPosition, 1.0);\n  } else {\n    gl_Position = vec4(0,0,0,0);\n  }\n\n  colorChannel = abs(realNormal);\n}"]),u=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 colors[3];\n\nvarying vec3 colorChannel;\n\nvoid main() {\n  gl_FragColor = colorChannel.x * colors[0] +\n                 colorChannel.y * colors[1] +\n                 colorChannel.z * colors[2];\n}"]);r.bg=function(t){return i(t,c,u,null,[{name:"position",type:"vec3"},{name:"normal",type:"vec3"}])}},{"gl-shader":132,glslify:231}],75:[function(t,e,r){(function(r){(function(){"use strict";e.exports=function(t,e,r,a,s,l){var u=n(t),f=i(t,[{buffer:u,size:3}]),h=o(t);h.attributes.position.location=0;var p=new c(t,h,u,f);return p.update(e,r,a,s,l),p};var n=t("gl-buffer"),i=t("gl-vao"),a=t("vectorize-text"),o=t("./shaders").text,s=window||r.global||{},l=s.__TEXT_CACHE||{};s.__TEXT_CACHE={};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}var u=c.prototype,f=[0,0];u.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,f[0]=this.gl.drawingBufferWidth,f[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=f},u.unbind=function(){this.vao.unbind()},u.update=function(t,e,r,n,i){var o=[];function s(t,e,r,n,i,s){var c=l[r];c||(c=l[r]={});var u=c[e];u||(u=c[e]=function(t,e){try{return a(t,e)}catch(e){return console.warn('error vectorizing text:"'+t+'" error:',e),{cells:[],positions:[]}}}(e,{triangles:!0,font:r,textAlign:"center",textBaseline:"middle",lineSpacing:i,styletags:s}));for(var f=(n||12)/12,h=u.positions,p=u.cells,d=0,m=p.length;d<m;++d)for(var g=p[d],v=2;v>=0;--v){var y=h[g[v]];o.push(f*y[0],-f*y[1],t)}}for(var c=[0,0,0],u=[0,0,0],f=[0,0,0],h=[0,0,0],p={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},d=0;d<3;++d){f[d]=o.length/3|0,s(.5*(t[0][d]+t[1][d]),e[d],r[d],12,1.25,p),h[d]=(o.length/3|0)-f[d],c[d]=o.length/3|0;for(var m=0;m<n[d].length;++m)n[d][m].text&&s(n[d][m].x,n[d][m].text,n[d][m].font||i,n[d][m].fontSize||12,1.25,p);u[d]=(o.length/3|0)-c[d]}this.buffer.update(o),this.tickOffset=c,this.tickCount=u,this.labelOffset=f,this.labelCount=h},u.drawTicks=function(t,e,r,n,i,a,o,s){this.tickCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t]))},u.drawLabel=function(t,e,r,n,i,a,o,s){this.labelCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.labelCount[t],this.labelOffset[t]))},u.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}}).call(this)}).call(this,t("_process"))},{"./shaders":74,_process:5,"gl-buffer":78,"gl-vao":150,"vectorize-text":311}],76:[function(t,e,r){"use strict";function n(t,e){var r=t+"",n=r.indexOf("."),i=0;n>=0&&(i=r.length-n-1);var a=Math.pow(10,i),o=Math.round(t*e*a),s=o+"";if(s.indexOf("e")>=0)return s;var l=o/a,c=o%a;o<0?(l=0|-Math.ceil(l),c=0|-c):(l=0|Math.floor(l),c|=0);var u=""+l;if(o<0&&(u="-"+u),i){for(var f=""+c;f.length<i;)f="0"+f;return u+"."+f}return u}r.create=function(t,e){for(var r=[],i=0;i<3;++i){for(var a=[],o=(t[0][i],t[1][i],0);o*e[i]<=t[1][i];++o)a.push({x:o*e[i],text:n(e[i],o)});for(o=-1;o*e[i]>=t[0][i];--o)a.push({x:o*e[i],text:n(e[i],o)});r.push(a)}return r},r.equal=function(t,e){for(var r=0;r<3;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;++n){var i=t[r][n],a=e[r][n];if(i.x!==a.x||i.text!==a.text||i.font!==a.font||i.fontColor!==a.fontColor||i.fontSize!==a.fontSize||i.dx!==a.dx||i.dy!==a.dy)return!1}}return!0}},{}],77:[function(t,e,r){"use strict";e.exports=function(t,e,r,l,f){var h=e.model||c,p=e.view||c,v=e.projection||c,y=e._ortho||!1,x=t.bounds,b=(f=f||a(h,p,v,x,y)).axis;o(u,p,h),o(u,v,u);for(var _=m,w=0;w<3;++w)_[w].lo=1/0,_[w].hi=-1/0,_[w].pixelsPerDataUnit=1/0;var T=n(s(u,u));s(u,u);for(var k=0;k<3;++k){var A=(k+1)%3,M=(k+2)%3,S=g;t:for(w=0;w<2;++w){var E=[];if(b[k]<0!=!!w){S[k]=x[w][k];for(var L=0;L<2;++L){S[A]=x[L^w][A];for(var C=0;C<2;++C)S[M]=x[C^L^w][M],E.push(S.slice())}var P=y?5:4;for(L=P;L===P;++L){if(0===E.length)continue t;E=i.positive(E,T[L])}for(L=0;L<E.length;++L){M=E[L];var I=d(g,u,M,r,l);for(C=0;C<3;++C)_[C].lo=Math.min(_[C].lo,M[C]),_[C].hi=Math.max(_[C].hi,M[C]),C!==k&&(_[C].pixelsPerDataUnit=Math.min(_[C].pixelsPerDataUnit,Math.abs(I[C])))}}}}return _};var n=t("extract-frustum-planes"),i=t("split-polygon"),a=t("./lib/cube.js"),o=t("gl-mat4/multiply"),s=t("gl-mat4/transpose"),l=t("gl-vec4/transformMat4"),c=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),u=new Float32Array(16);function f(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var h=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=h,s=p,c=0;c<3;++c)s[c]=o[c]=r[c];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var u=(o[0]/o[3]-s[0]/s[3])*n,f=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.25*Math.sqrt(u*u+f*f)}return t}var m=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],g=[0,0,0]},{"./lib/cube.js":72,"extract-frustum-planes":67,"gl-mat4/multiply":100,"gl-mat4/transpose":109,"gl-vec4/transformMat4":221,"split-polygon":300}],78:[function(t,e,r){"use strict";var n=t("typedarray-pool"),i=t("ndarray-ops"),a=t("ndarray"),o=["uint8","uint8_clamped","uint16","uint32","int8","int16","int32","float32"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function c(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error("gl-buffer: If resizing buffer, must not specify offset");return t.bufferSubData(e,a,i),r}function u(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if("number"!=typeof e&&(e=-1),this.bind(),"object"==typeof t&&void 0!==t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r="float32"),this.type===this.gl.ELEMENT_ARRAY_BUFFER)r=gl.getExtension("OES_element_index_uint")&&"uint16"!==r?"uint32":"uint16";if(r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=c(this.gl,this.type,this.length,this.usage,t.data,e):this.length=c(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=c(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?u(t,"uint16"):u(t,"float32"),this.length=c(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if("object"==typeof t&&"number"==typeof t.length)this.length=c(this.gl,this.type,this.length,this.usage,t,e);else{if("number"!=typeof t&&void 0!==t)throw new Error("gl-buffer: Invalid data type");if(e>=0)throw new Error("gl-buffer: Cannot specify offset when resizing buffer");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},e.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error("gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error("gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},{ndarray:259,"ndarray-ops":254,"typedarray-pool":308}],79:[function(t,e,r){"use strict";var n=t("gl-vec3");e.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,c=1/0,u=-1/0,f=1/0,h=-1/0,p=null,d=null,m=[],g=1/0,v=!1,y=0;y<r.length;y++){var x=r[y];s=Math.min(x[0],s),l=Math.max(x[0],l),c=Math.min(x[1],c),u=Math.max(x[1],u),f=Math.min(x[2],f),h=Math.max(x[2],h);var b=i[y];if(n.length(b)>o&&(o=n.length(b)),y){var _=2*n.distance(p,x)/(n.length(d)+n.length(b));_?(g=Math.min(g,_),v=!1):v=!0}v||(p=x,d=b),m.push(b)}var w=[s,c,f],T=[l,u,h];e&&(e[0]=w,e[1]=T),0===o&&(o=1);var k=1/o;isFinite(g)||(g=1),a.vectorScale=g;var A=t.coneSize||.5;t.absoluteConeSize&&(A=t.absoluteConeSize*k),a.coneScale=A;y=0;for(var M=0;y<r.length;y++)for(var S=(x=r[y])[0],E=x[1],L=x[2],C=m[y],P=n.length(C)*k,I=0;I<8;I++){a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vertexIntensity.push(P,P,P),a.vertexIntensity.push(P,P,P);var O=a.positions.length;a.cells.push([O-6,O-5,O-4],[O-3,O-2,O-1])}return a};var i=t("./lib/shaders");e.exports.createMesh=t("./create_mesh"),e.exports.createConeMesh=function(t,r){return e.exports.createMesh(t,r,{shaders:i,traceType:"cone"})}},{"./create_mesh":80,"./lib/shaders":81,"gl-vec3":169}],80:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("gl-mat4/multiply"),l=t("gl-mat4/invert"),c=t("ndarray"),u=t("colormap"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e,r,n,i,a,o,s,l,c,u){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=i,this.triangleVectors=a,this.triangleColors=s,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=c,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=u,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=f,this._view=f,this._projection=f,this._resolution=[1,1]}var p=h.prototype;function d(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}function m(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"lightPosition"in t&&(this.lightPosition=t.lightPosition),"opacity"in t&&(this.opacity=t.opacity),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=u({colormap:t,nshades:256,format:"rgba"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],f=[];this.cells=r,this.positions=n,this.vectors=i;var h=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,m=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],m=+t.vertexIntensityBounds[1];else for(var g=0;g<p.length;++g){var v=p[g];d=Math.min(d,v),m=Math.max(m,v)}else for(g=0;g<n.length;++g){v=n[g][2];d=Math.min(d,v),m=Math.max(m,v)}this.intensity=p||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(g=0;g<n.length;++g)for(var y=n[g],x=0;x<3;++x)!isNaN(y[x])&&isFinite(y[x])&&(this.bounds[0][x]=Math.min(this.bounds[0][x],y[x]),this.bounds[1][x]=Math.max(this.bounds[1][x],y[x]));var b=0;t:for(g=0;g<r.length;++g){var _=r[g];switch(_.length){case 3:for(x=0;x<3;++x){y=n[T=_[x]];for(var w=0;w<3;++w)if(isNaN(y[w])||!isFinite(y[w]))continue t}for(x=0;x<3;++x){var T;y=n[T=_[2-x]];a.push(y[0],y[1],y[2],y[3]);var k=i[T];o.push(k[0],k[1],k[2],k[3]||0);var A,M=h;3===M.length?s.push(M[0],M[1],M[2],1):s.push(M[0],M[1],M[2],M[3]),A=p?[(p[T]-d)/(m-d),0]:[(y[2]-d)/(m-d),0],l.push(A[0],A[1]),f.push(g)}b+=1}}this.triangleCount=b,this.trianglePositions.update(a),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(f))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var c={model:r,view:n,projection:i,inverseModel:f.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};c.inverseModel=l(c.inverseModel,c.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);s(u,c.view,c.model),s(u,c.projection,u),l(u,u);for(o=0;o<3;++o)c.eyePosition[o]=u[12+o]/u[15];var h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];c.lightPosition[o]=p/h}if(this.triangleCount>0){var m=this.triShader;m.bind(),m.uniforms=c,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return"cone"===this.traceType?i.index=Math.floor(r[1]/48):"streamtube"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},e.exports=function(t,e,r){var n=r.shaders;1===arguments.length&&(t=(e=t).gl);var s=d(t,n),l=m(t,n),u=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));u.generateMipmap(),u.minFilter=t.LINEAR_MIPMAP_LINEAR,u.magFilter=t.LINEAR;var f=i(t),p=i(t),g=i(t),v=i(t),y=i(t),x=a(t,[{buffer:f,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:v,type:t.FLOAT,size:2},{buffer:p,type:t.FLOAT,size:4}]),b=new h(t,u,s,l,f,p,y,g,v,x,r.traceType||"cone");return b.update(e),b}},{colormap:53,"gl-buffer":78,"gl-mat4/invert":98,"gl-mat4/multiply":100,"gl-shader":132,"gl-texture2d":146,"gl-vao":150,ndarray:259}],81:[function(t,e,r){var n=t("glslify"),i=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  float index = rawIndex - floor(rawIndex /\n    (segmentCount * 6.0)) *\n    (segmentCount * 6.0);\n\n  float segment = floor(0.001 + index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  float nextAngle = (\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\n  ) ? 1.0 : 0.0;\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex < 3.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec3 vector;\nattribute vec4 color, position;\nattribute vec2 uv;\n\nuniform float vectorScale, coneScale, coneOffset;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 eyePosition, lightPosition;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * conePosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\n  vec4 t_position  = view * conePosition;\n  gl_Position      = projection * t_position;\n\n  f_color          = color;\n  f_data           = conePosition.xyz;\n  f_position       = position.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}\n"]),o=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  float index = rawIndex - floor(rawIndex /\n    (segmentCount * 6.0)) *\n    (segmentCount * 6.0);\n\n  float segment = floor(0.001 + index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  float nextAngle = (\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\n  ) ? 1.0 : 0.0;\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex < 3.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform float vectorScale, coneScale, coneOffset;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n  gl_Position = projection * view * conePosition;\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec3"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec3"}]}},{glslify:231}],82:[function(t,e,r){e.exports={0:"NONE",1:"ONE",2:"LINE_LOOP",3:"LINE_STRIP",4:"TRIANGLES",5:"TRIANGLE_STRIP",6:"TRIANGLE_FAN",256:"DEPTH_BUFFER_BIT",512:"NEVER",513:"LESS",514:"EQUAL",515:"LEQUAL",516:"GREATER",517:"NOTEQUAL",518:"GEQUAL",519:"ALWAYS",768:"SRC_COLOR",769:"ONE_MINUS_SRC_COLOR",770:"SRC_ALPHA",771:"ONE_MINUS_SRC_ALPHA",772:"DST_ALPHA",773:"ONE_MINUS_DST_ALPHA",774:"DST_COLOR",775:"ONE_MINUS_DST_COLOR",776:"SRC_ALPHA_SATURATE",1024:"STENCIL_BUFFER_BIT",1028:"FRONT",1029:"BACK",1032:"FRONT_AND_BACK",1280:"INVALID_ENUM",1281:"INVALID_VALUE",1282:"INVALID_OPERATION",1285:"OUT_OF_MEMORY",1286:"INVALID_FRAMEBUFFER_OPERATION",2304:"CW",2305:"CCW",2849:"LINE_WIDTH",2884:"CULL_FACE",2885:"CULL_FACE_MODE",2886:"FRONT_FACE",2928:"DEPTH_RANGE",2929:"DEPTH_TEST",2930:"DEPTH_WRITEMASK",2931:"DEPTH_CLEAR_VALUE",2932:"DEPTH_FUNC",2960:"STENCIL_TEST",2961:"STENCIL_CLEAR_VALUE",2962:"STENCIL_FUNC",2963:"STENCIL_VALUE_MASK",2964:"STENCIL_FAIL",2965:"STENCIL_PASS_DEPTH_FAIL",2966:"STENCIL_PASS_DEPTH_PASS",2967:"STENCIL_REF",2968:"STENCIL_WRITEMASK",2978:"VIEWPORT",3024:"DITHER",3042:"BLEND",3088:"SCISSOR_BOX",3089:"SCISSOR_TEST",3106:"COLOR_CLEAR_VALUE",3107:"COLOR_WRITEMASK",3317:"UNPACK_ALIGNMENT",3333:"PACK_ALIGNMENT",3379:"MAX_TEXTURE_SIZE",3386:"MAX_VIEWPORT_DIMS",3408:"SUBPIXEL_BITS",3410:"RED_BITS",3411:"GREEN_BITS",3412:"BLUE_BITS",3413:"ALPHA_BITS",3414:"DEPTH_BITS",3415:"STENCIL_BITS",3553:"TEXTURE_2D",4352:"DONT_CARE",4353:"FASTEST",4354:"NICEST",5120:"BYTE",5121:"UNSIGNED_BYTE",5122:"SHORT",5123:"UNSIGNED_SHORT",5124:"INT",5125:"UNSIGNED_INT",5126:"FLOAT",5386:"INVERT",5890:"TEXTURE",6401:"STENCIL_INDEX",6402:"DEPTH_COMPONENT",6406:"ALPHA",6407:"RGB",6408:"RGBA",6409:"LUMINANCE",6410:"LUMINANCE_ALPHA",7680:"KEEP",7681:"REPLACE",7682:"INCR",7683:"DECR",7936:"VENDOR",7937:"RENDERER",7938:"VERSION",9728:"NEAREST",9729:"LINEAR",9984:"NEAREST_MIPMAP_NEAREST",9985:"LINEAR_MIPMAP_NEAREST",9986:"NEAREST_MIPMAP_LINEAR",9987:"LINEAR_MIPMAP_LINEAR",10240:"TEXTURE_MAG_FILTER",10241:"TEXTURE_MIN_FILTER",10242:"TEXTURE_WRAP_S",10243:"TEXTURE_WRAP_T",10497:"REPEAT",10752:"POLYGON_OFFSET_UNITS",16384:"COLOR_BUFFER_BIT",32769:"CONSTANT_COLOR",32770:"ONE_MINUS_CONSTANT_COLOR",32771:"CONSTANT_ALPHA",32772:"ONE_MINUS_CONSTANT_ALPHA",32773:"BLEND_COLOR",32774:"FUNC_ADD",32777:"BLEND_EQUATION_RGB",32778:"FUNC_SUBTRACT",32779:"FUNC_REVERSE_SUBTRACT",32819:"UNSIGNED_SHORT_4_4_4_4",32820:"UNSIGNED_SHORT_5_5_5_1",32823:"POLYGON_OFFSET_FILL",32824:"POLYGON_OFFSET_FACTOR",32854:"RGBA4",32855:"RGB5_A1",32873:"TEXTURE_BINDING_2D",32926:"SAMPLE_ALPHA_TO_COVERAGE",32928:"SAMPLE_COVERAGE",32936:"SAMPLE_BUFFERS",32937:"SAMPLES",32938:"SAMPLE_COVERAGE_VALUE",32939:"SAMPLE_COVERAGE_INVERT",32968:"BLEND_DST_RGB",32969:"BLEND_SRC_RGB",32970:"BLEND_DST_ALPHA",32971:"BLEND_SRC_ALPHA",33071:"CLAMP_TO_EDGE",33170:"GENERATE_MIPMAP_HINT",33189:"DEPTH_COMPONENT16",33306:"DEPTH_STENCIL_ATTACHMENT",33635:"UNSIGNED_SHORT_5_6_5",33648:"MIRRORED_REPEAT",33901:"ALIASED_POINT_SIZE_RANGE",33902:"ALIASED_LINE_WIDTH_RANGE",33984:"TEXTURE0",33985:"TEXTURE1",33986:"TEXTURE2",33987:"TEXTURE3",33988:"TEXTURE4",33989:"TEXTURE5",33990:"TEXTURE6",33991:"TEXTURE7",33992:"TEXTURE8",33993:"TEXTURE9",33994:"TEXTURE10",33995:"TEXTURE11",33996:"TEXTURE12",33997:"TEXTURE13",33998:"TEXTURE14",33999:"TEXTURE15",34e3:"TEXTURE16",34001:"TEXTURE17",34002:"TEXTURE18",34003:"TEXTURE19",34004:"TEXTURE20",34005:"TEXTURE21",34006:"TEXTURE22",34007:"TEXTURE23",34008:"TEXTURE24",34009:"TEXTURE25",34010:"TEXTURE26",34011:"TEXTURE27",34012:"TEXTURE28",34013:"TEXTURE29",34014:"TEXTURE30",34015:"TEXTURE31",34016:"ACTIVE_TEXTURE",34024:"MAX_RENDERBUFFER_SIZE",34041:"DEPTH_STENCIL",34055:"INCR_WRAP",34056:"DECR_WRAP",34067:"TEXTURE_CUBE_MAP",34068:"TEXTURE_BINDING_CUBE_MAP",34069:"TEXTURE_CUBE_MAP_POSITIVE_X",34070:"TEXTURE_CUBE_MAP_NEGATIVE_X",34071:"TEXTURE_CUBE_MAP_POSITIVE_Y",34072:"TEXTURE_CUBE_MAP_NEGATIVE_Y",34073:"TEXTURE_CUBE_MAP_POSITIVE_Z",34074:"TEXTURE_CUBE_MAP_NEGATIVE_Z",34076:"MAX_CUBE_MAP_TEXTURE_SIZE",34338:"VERTEX_ATTRIB_ARRAY_ENABLED",34339:"VERTEX_ATTRIB_ARRAY_SIZE",34340:"VERTEX_ATTRIB_ARRAY_STRIDE",34341:"VERTEX_ATTRIB_ARRAY_TYPE",34342:"CURRENT_VERTEX_ATTRIB",34373:"VERTEX_ATTRIB_ARRAY_POINTER",34466:"NUM_COMPRESSED_TEXTURE_FORMATS",34467:"COMPRESSED_TEXTURE_FORMATS",34660:"BUFFER_SIZE",34661:"BUFFER_USAGE",34816:"STENCIL_BACK_FUNC",34817:"STENCIL_BACK_FAIL",34818:"STENCIL_BACK_PASS_DEPTH_FAIL",34819:"STENCIL_BACK_PASS_DEPTH_PASS",34877:"BLEND_EQUATION_ALPHA",34921:"MAX_VERTEX_ATTRIBS",34922:"VERTEX_ATTRIB_ARRAY_NORMALIZED",34930:"MAX_TEXTURE_IMAGE_UNITS",34962:"ARRAY_BUFFER",34963:"ELEMENT_ARRAY_BUFFER",34964:"ARRAY_BUFFER_BINDING",34965:"ELEMENT_ARRAY_BUFFER_BINDING",34975:"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING",35040:"STREAM_DRAW",35044:"STATIC_DRAW",35048:"DYNAMIC_DRAW",35632:"FRAGMENT_SHADER",35633:"VERTEX_SHADER",35660:"MAX_VERTEX_TEXTURE_IMAGE_UNITS",35661:"MAX_COMBINED_TEXTURE_IMAGE_UNITS",35663:"SHADER_TYPE",35664:"FLOAT_VEC2",35665:"FLOAT_VEC3",35666:"FLOAT_VEC4",35667:"INT_VEC2",35668:"INT_VEC3",35669:"INT_VEC4",35670:"BOOL",35671:"BOOL_VEC2",35672:"BOOL_VEC3",35673:"BOOL_VEC4",35674:"FLOAT_MAT2",35675:"FLOAT_MAT3",35676:"FLOAT_MAT4",35678:"SAMPLER_2D",35680:"SAMPLER_CUBE",35712:"DELETE_STATUS",35713:"COMPILE_STATUS",35714:"LINK_STATUS",35715:"VALIDATE_STATUS",35716:"INFO_LOG_LENGTH",35717:"ATTACHED_SHADERS",35718:"ACTIVE_UNIFORMS",35719:"ACTIVE_UNIFORM_MAX_LENGTH",35720:"SHADER_SOURCE_LENGTH",35721:"ACTIVE_ATTRIBUTES",35722:"ACTIVE_ATTRIBUTE_MAX_LENGTH",35724:"SHADING_LANGUAGE_VERSION",35725:"CURRENT_PROGRAM",36003:"STENCIL_BACK_REF",36004:"STENCIL_BACK_VALUE_MASK",36005:"STENCIL_BACK_WRITEMASK",36006:"FRAMEBUFFER_BINDING",36007:"RENDERBUFFER_BINDING",36048:"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE",36049:"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME",36050:"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL",36051:"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE",36053:"FRAMEBUFFER_COMPLETE",36054:"FRAMEBUFFER_INCOMPLETE_ATTACHMENT",36055:"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT",36057:"FRAMEBUFFER_INCOMPLETE_DIMENSIONS",36061:"FRAMEBUFFER_UNSUPPORTED",36064:"COLOR_ATTACHMENT0",36096:"DEPTH_ATTACHMENT",36128:"STENCIL_ATTACHMENT",36160:"FRAMEBUFFER",36161:"RENDERBUFFER",36162:"RENDERBUFFER_WIDTH",36163:"RENDERBUFFER_HEIGHT",36164:"RENDERBUFFER_INTERNAL_FORMAT",36168:"STENCIL_INDEX8",36176:"RENDERBUFFER_RED_SIZE",36177:"RENDERBUFFER_GREEN_SIZE",36178:"RENDERBUFFER_BLUE_SIZE",36179:"RENDERBUFFER_ALPHA_SIZE",36180:"RENDERBUFFER_DEPTH_SIZE",36181:"RENDERBUFFER_STENCIL_SIZE",36194:"RGB565",36336:"LOW_FLOAT",36337:"MEDIUM_FLOAT",36338:"HIGH_FLOAT",36339:"LOW_INT",36340:"MEDIUM_INT",36341:"HIGH_INT",36346:"SHADER_COMPILER",36347:"MAX_VERTEX_UNIFORM_VECTORS",36348:"MAX_VARYING_VECTORS",36349:"MAX_FRAGMENT_UNIFORM_VECTORS",37440:"UNPACK_FLIP_Y_WEBGL",37441:"UNPACK_PREMULTIPLY_ALPHA_WEBGL",37442:"CONTEXT_LOST_WEBGL",37443:"UNPACK_COLORSPACE_CONVERSION_WEBGL",37444:"BROWSER_DEFAULT_WEBGL"}},{}],83:[function(t,e,r){var n=t("./1.0/numbers");e.exports=function(t){return n[t]}},{"./1.0/numbers":82}],84:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),o=i(e,[{buffer:r,type:e.FLOAT,size:3,offset:0,stride:40},{buffer:r,type:e.FLOAT,size:4,offset:12,stride:40},{buffer:r,type:e.FLOAT,size:3,offset:28,stride:40}]),l=a(e);l.attributes.position.location=0,l.attributes.color.location=1,l.attributes.offset.location=2;var c=new s(e,r,o,l);return c.update(t),c};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders/index"),o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.shader=n,this.buffer=e,this.vao=r,this.pixelRatio=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1,this.hasAlpha=!1}var l=s.prototype;function c(t,e){for(var r=0;r<3;++r)t[0][r]=Math.min(t[0][r],e[r]),t[1][r]=Math.max(t[1][r],e[r])}l.isOpaque=function(){return!this.hasAlpha},l.isTransparent=function(){return this.hasAlpha},l.drawTransparent=l.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view||o,i=r.projection=t.projection||o;r.model=t.model||o,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var a=n[12],s=n[13],l=n[14],c=n[15],u=(t._ortho||!1?2:1)*this.pixelRatio*(i[3]*a+i[7]*s+i[11]*l+i[15]*c)/e.drawingBufferHeight;this.vao.bind();for(var f=0;f<3;++f)e.lineWidth(this.lineWidth[f]*this.pixelRatio),r.capSize=this.capSize[f]*u,this.lineCount[f]&&e.drawArrays(e.LINES,this.lineOffset[f],this.lineCount[f]);this.vao.unbind()};var u=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var i=-1;i<=1;i+=2){var a=[0,0,0];a[(n+e)%3]=i,r.push(a)}t[e]=r}return t}();function f(t,e,r,n){for(var i=u[n],a=0;a<i.length;++a){var o=i[a];t.push(e[0],e[1],e[2],r[0],r[1],r[2],r[3],o[0],o[1],o[2])}return i.length}l.update=function(t){"lineWidth"in(t=t||{})&&(this.lineWidth=t.lineWidth,Array.isArray(this.lineWidth)||(this.lineWidth=[this.lineWidth,this.lineWidth,this.lineWidth])),"capSize"in t&&(this.capSize=t.capSize,Array.isArray(this.capSize)||(this.capSize=[this.capSize,this.capSize,this.capSize])),this.hasAlpha=!1,"opacity"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var e=t.color||[[0,0,0],[0,0,0],[0,0,0]],r=t.position,n=t.error;if(Array.isArray(e[0])||(e=[e,e,e]),r&&n){var i=[],a=r.length,o=0;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.lineCount=[0,0,0];for(var s=0;s<3;++s){this.lineOffset[s]=o;t:for(var l=0;l<a;++l){for(var u=r[l],h=0;h<3;++h)if(isNaN(u[h])||!isFinite(u[h]))continue t;var p=n[l],d=e[s];if(Array.isArray(d[0])&&(d=e[l]),3===d.length?d=[d[0],d[1],d[2],1]:4===d.length&&(d=[d[0],d[1],d[2],d[3]],!this.hasAlpha&&d[3]<1&&(this.hasAlpha=!0)),!isNaN(p[0][s])&&!isNaN(p[1][s])){var m;if(p[0][s]<0)(m=u.slice())[s]+=p[0][s],i.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,m[0],m[1],m[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,m),o+=2+f(i,m,d,s);if(p[1][s]>0)(m=u.slice())[s]+=p[1][s],i.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,m[0],m[1],m[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,m),o+=2+f(i,m,d,s)}}this.lineCount[s]=o-this.lineOffset[s]}this.buffer.update(i)}},l.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},{"./shaders/index":85,"gl-buffer":78,"gl-vao":150}],85:[function(t,e,r){"use strict";var n=t("glslify"),i=t("gl-shader"),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, offset;\nattribute vec4 color;\nuniform mat4 model, view, projection;\nuniform float capSize;\nvarying vec4 fragColor;\nvarying vec3 fragPosition;\n\nvoid main() {\n  vec4 worldPosition  = model * vec4(position, 1.0);\n  worldPosition       = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\n  gl_Position         = projection * view * worldPosition;\n  fragColor           = color;\n  fragPosition        = position;\n}"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float opacity;\nvarying vec3 fragPosition;\nvarying vec4 fragColor;\n\nvoid main() {\n  if (\n    outOfRange(clipBounds[0], clipBounds[1], fragPosition) ||\n    fragColor.a * opacity == 0.\n  ) discard;\n\n  gl_FragColor = opacity * fragColor;\n}"]);e.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"offset",type:"vec3"}])}},{"gl-shader":132,glslify:231}],86:[function(t,e,r){"use strict";var n=t("gl-texture2d");e.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var c=t.getExtension("WEBGL_draw_buffers");!l&&c&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;a<n;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(a=n;a<r;++a)i[a]=t.NONE;l[n]=i}}(t,c);Array.isArray(e)&&(n=r,r=0|e[1],e=0|e[0]);if("number"!=typeof e)throw new Error("gl-fbo: Missing shape parameter");var u=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0||e>u||r<0||r>u)throw new Error("gl-fbo: Parameters are too large for FBO");var f=1;if("color"in(n=n||{})){if((f=Math.max(0|n.color,0))<0)throw new Error("gl-fbo: Must specify a nonnegative number of colors");if(f>1){if(!c)throw new Error("gl-fbo: Multiple draw buffer extension not supported");if(f>t.getParameter(c.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error("gl-fbo: Context does not support "+f+" draw buffers")}}var h=t.UNSIGNED_BYTE,p=t.getExtension("OES_texture_float");if(n.float&&f>0){if(!p)throw new Error("gl-fbo: Context does not support floating point textures");h=t.FLOAT}else n.preferFloat&&f>0&&p&&(h=t.FLOAT);var m=!0;"depth"in n&&(m=!!n.depth);var g=!1;"stencil"in n&&(g=!!n.stencil);return new d(t,e,r,h,f,m,g,c)};var i,a,o,s,l=null;function c(t){return[t.getParameter(t.FRAMEBUFFER_BINDING),t.getParameter(t.RENDERBUFFER_BINDING),t.getParameter(t.TEXTURE_BINDING_2D)]}function u(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function f(t){switch(t){case i:throw new Error("gl-fbo: Framebuffer unsupported");case a:throw new Error("gl-fbo: Framebuffer incomplete attachment");case o:throw new Error("gl-fbo: Framebuffer incomplete dimensions");case s:throw new Error("gl-fbo: Framebuffer incomplete missing attachment");default:throw new Error("gl-fbo: Framebuffer failed for unspecified reason")}}function h(t,e,r,i,a,o){if(!i)return null;var s=n(t,e,r,a,i);return s.magFilter=t.NEAREST,s.minFilter=t.NEAREST,s.mipSamples=1,s.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,o,t.TEXTURE_2D,s.handle,0),s}function p(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var m=this,g=[0|e,0|r];Object.defineProperties(g,{0:{get:function(){return m._shape[0]},set:function(t){return m.width=t}},1:{get:function(){return m._shape[1]},set:function(t){return m.height=t}}}),this._shapeVector=g,function(t){var e=c(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,m=t._useDepth,g=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var v=0;v<o;++v)t.color[v]=h(r,i,a,g,r.RGBA,r.COLOR_ATTACHMENT0+v);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var y=r.getExtension("WEBGL_depth_texture");y?d?t.depth=h(r,i,a,y.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):m&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null);for(v=0;v<t.color.length;++v)t.color[v].dispose(),t.color[v]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),u(r,e),f(x)}u(r,e)}(this)}var m=d.prototype;function g(t,e,r){if(t._destroyed)throw new Error("gl-fbo: Can't resize destroyed FBO");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error("gl-fbo: Can't resize FBO, invalid dimensions");t._shape[0]=e,t._shape[1]=r;for(var a=c(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),u(n,a),f(s)),u(n,a)}}Object.defineProperties(m,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error("gl-fbo: Shape vector must be length 2");var e=0|t[0],r=0|t[1];return g(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return g(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,g(this,this._shape[0],t),t},enumerable:!1}}),m.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},m.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{"gl-texture2d":146}],87:[function(t,e,r){var n=t("sprintf-js").sprintf,i=t("gl-constants/lookup"),a=t("glsl-shader-name"),o=t("add-line-numbers");e.exports=function(t,e,r){"use strict";var s=a(e)||"of unknown name (see npm glsl-shader-name)",l="unknown type";void 0!==r&&(l=r===i.FRAGMENT_SHADER?"fragment":"vertex");for(var c=n("Error compiling %s shader %s:\n",l,s),u=n("%s%s",c,t),f=t.split("\n"),h={},p=0;p<f.length;p++){var d=f[p];if(""!==d&&"\0"!==d){var m=parseInt(d.split(":")[2]);if(isNaN(m))throw new Error(n("Could not parse error: %s",d));h[m]=d}}var g=o(e).split("\n");for(p=0;p<g.length;p++)if(h[p+3]||h[p+2]||h[p+1]){var v=g[p];if(c+=v+"\n",h[p+1]){var y=h[p+1];y=y.substr(y.split(":",3).join(":").length+1).trim(),c+=n("^^^ %s\n\n",y)}}return{long:c.trim(),short:u.trim()}}},{"add-line-numbers":9,"gl-constants/lookup":83,"glsl-shader-name":223,"sprintf-js":301}],88:[function(t,e,r){"use strict";e.exports=function(t,e){var r=t.gl,n=o(r,l.vertex,l.fragment),i=o(r,l.pickVertex,l.pickFragment),a=s(r),u=s(r),f=s(r),h=s(r),p=new c(t,n,i,a,u,f,h);return p.update(e),t.addObject(p),p};var n=t("binary-search-bounds"),i=t("iota-array"),a=t("typedarray-pool"),o=t("gl-shader"),s=t("gl-buffer"),l=t("./lib/shaders");function c(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var u,f=c.prototype,h=[0,0,1,0,0,1,1,0,1,1,0,1];f.draw=(u=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],c=a[3]-a[1];u[0]=2*o/l,u[4]=2*s/c,u[6]=2*(r[0]-a[0])/l-1,u[7]=2*(r[1]-a[1])/c-1,e.bind();var f=e.uniforms;f.viewTransform=u,f.shape=this.shape;var h=e.attributes;this.positionBuffer.bind(),h.position.pointer(),this.weightBuffer.bind(),h.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),h.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,c=a[2]-a[0],u=a[3]-a[1],f=l[2]-l[0],h=l[3]-l[1];t[0]=2*c/f,t[4]=2*u/h,t[6]=2*(a[0]-l[0])/f-1,t[7]=2*(a[1]-l[1])/h-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var m=i.attributes;return this.positionBuffer.bind(),m.position.pointer(),this.weightBuffer.bind(),m.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),m.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},f.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var c,u,f,p,d=t.colorLevels||[0],m=t.colorValues||[0,0,0,1],g=d.length,v=this.bounds;l?(c=v[0]=r[0],u=v[1]=o[0],f=v[2]=r[r.length-1],p=v[3]=o[o.length-1]):(c=v[0]=r[0]+(r[1]-r[0])/2,u=v[1]=o[0]+(o[1]-o[0])/2,f=v[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=v[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var y=1/(f-c),x=1/(p-u),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(h.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),A=a.mallocUint8(2*w),M=a.mallocUint32(w),S=0,E=l?b-1:b,L=l?_-1:_,C=0;C<L;++C){var P,I;l?(P=x*(o[C]-u),I=x*(o[C+1]-u)):(P=C<_-1?x*(o[C]-(o[C+1]-o[C])/2-u):x*(o[C]-(o[C]-o[C-1])/2-u),I=C<_-1?x*(o[C]+(o[C+1]-o[C])/2-u):x*(o[C]+(o[C]-o[C-1])/2-u));for(var O=0;O<E;++O){var z,D;l?(z=y*(r[O]-c),D=y*(r[O+1]-c)):(z=O<b-1?y*(r[O]-(r[O+1]-r[O])/2-c):y*(r[O]-(r[O]-r[O-1])/2-c),D=O<b-1?y*(r[O]+(r[O+1]-r[O])/2-c):y*(r[O]+(r[O]-r[O-1])/2-c));for(var R=0;R<h.length;R+=2){var F,B,N,j,U=h[R],V=h[R+1],H=s[l?(C+V)*b+(O+U):C*b+O],q=n.le(d,H);if(q<0)F=m[0],B=m[1],N=m[2],j=m[3];else if(q===g-1)F=m[4*g-4],B=m[4*g-3],N=m[4*g-2],j=m[4*g-1];else{var G=(H-d[q])/(d[q+1]-d[q]),Y=1-G,W=4*q,X=4*(q+1);F=Y*m[W]+G*m[X],B=Y*m[W+1]+G*m[X+1],N=Y*m[W+2]+G*m[X+2],j=Y*m[W+3]+G*m[X+3]}T[4*S]=255*F,T[4*S+1]=255*B,T[4*S+2]=255*N,T[4*S+3]=255*j,k[2*S]=.5*z+.5*D,k[2*S+1]=.5*P+.5*I,A[2*S]=U,A[2*S+1]=V,M[S]=C*b+O,S+=1}}}this.positionBuffer.update(k),this.weightBuffer.update(A),this.colorBuffer.update(T),this.idBuffer.update(M),a.free(k),a.free(T),a.free(A),a.free(M)},f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{"./lib/shaders":89,"binary-search-bounds":31,"gl-buffer":78,"gl-shader":132,"iota-array":235,"typedarray-pool":308}],89:[function(t,e,r){"use strict";var n=t("glslify");e.exports={fragment:n(["precision lowp float;\n#define GLSLIFY 1\nvarying vec4 fragColor;\nvoid main() {\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\n}\n"]),vertex:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 color;\nattribute vec2 weight;\n\nuniform vec2 shape;\nuniform mat3 viewTransform;\n\nvarying vec4 fragColor;\n\nvoid main() {\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\n  fragColor = color;\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\n}\n"]),pickFragment:n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragId;\nvarying vec2 vWeight;\n\nuniform vec2 shape;\nuniform vec4 pickOffset;\n\nvoid main() {\n  vec2 d = step(.5, vWeight);\n  vec4 id = fragId + pickOffset;\n  id.x += d.x + d.y*shape.x;\n\n  id.y += floor(id.x / 256.0);\n  id.x -= floor(id.x / 256.0) * 256.0;\n\n  id.z += floor(id.y / 256.0);\n  id.y -= floor(id.y / 256.0) * 256.0;\n\n  id.w += floor(id.z / 256.0);\n  id.z -= floor(id.z / 256.0) * 256.0;\n\n  gl_FragColor = id/255.;\n}\n"]),pickVertex:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 pickId;\nattribute vec2 weight;\n\nuniform vec2 shape;\nuniform mat3 viewTransform;\n\nvarying vec4 fragId;\nvarying vec2 vWeight;\n\nvoid main() {\n  vWeight = weight;\n\n  fragId = pickId;\n\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\n}\n"])}},{glslify:231}],90:[function(t,e,r){var n=t("glslify"),i=t("gl-shader"),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, nextPosition;\nattribute float arcLength, lineWidth;\nattribute vec4 color;\n\nuniform vec2 screenShape;\nuniform float pixelRatio;\nuniform mat4 model, view, projection;\n\nvarying vec4 fragColor;\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\n\nvec4 project(vec3 p) {\n  return projection * view * model * vec4(p, 1.0);\n}\n\nvoid main() {\n  vec4 startPoint = project(position);\n  vec4 endPoint   = project(nextPosition);\n\n  vec2 A = startPoint.xy / startPoint.w;\n  vec2 B =   endPoint.xy /   endPoint.w;\n\n  float clipAngle = atan(\n    (B.y - A.y) * screenShape.y,\n    (B.x - A.x) * screenShape.x\n  );\n\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\n    sin(clipAngle),\n    -cos(clipAngle)\n  ) / screenShape;\n\n  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\n\n  worldPosition = position;\n  pixelArcLength = arcLength;\n  fragColor = color;\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3      clipBounds[2];\nuniform sampler2D dashTexture;\nuniform float     dashScale;\nuniform float     opacity;\n\nvarying vec3    worldPosition;\nvarying float   pixelArcLength;\nvarying vec4    fragColor;\n\nvoid main() {\n  if (\n    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\n    fragColor.a * opacity == 0.\n  ) discard;\n\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\n  if(dashWeight < 0.5) {\n    discard;\n  }\n  gl_FragColor = fragColor * opacity;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\n#define FLOAT_MAX  1.70141184e38\n#define FLOAT_MIN  1.17549435e-38\n\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\nvec4 packFloat(float v) {\n  float av = abs(v);\n\n  //Handle special cases\n  if(av < FLOAT_MIN) {\n    return vec4(0.0, 0.0, 0.0, 0.0);\n  } else if(v > FLOAT_MAX) {\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\n  } else if(v < -FLOAT_MAX) {\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\n  }\n\n  vec4 c = vec4(0,0,0,0);\n\n  //Compute exponent and mantissa\n  float e = floor(log2(av));\n  float m = av * pow(2.0, -e) - 1.0;\n\n  //Unpack mantissa\n  c[1] = floor(128.0 * m);\n  m -= c[1] / 128.0;\n  c[2] = floor(32768.0 * m);\n  m -= c[2] / 32768.0;\n  c[3] = floor(8388608.0 * m);\n\n  //Unpack exponent\n  float ebias = e + 127.0;\n  c[0] = floor(ebias / 2.0);\n  ebias -= c[0] * 2.0;\n  c[1] += floor(ebias) * 128.0;\n\n  //Unpack sign bit\n  c[0] += 128.0 * step(0.0, -v);\n\n  //Scale back to range\n  return c / 255.0;\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform float pickId;\nuniform vec3 clipBounds[2];\n\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\nvarying vec4 fragColor;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\n\n  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\n}"]),l=[{name:"position",type:"vec3"},{name:"nextPosition",type:"vec3"},{name:"arcLength",type:"float"},{name:"lineWidth",type:"float"},{name:"color",type:"vec4"}];r.createShader=function(t){return i(t,a,o,null,l)},r.createPickShader=function(t){return i(t,a,s,null,l)}},{"gl-shader":132,glslify:231}],91:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=f(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),u=c(new Array(1024),[256,1,4]),p=0;p<1024;++p)u.data[p]=255;var d=a(e,u);d.wrap=e.REPEAT;var m=new v(e,r,o,s,l,d);return m.update(t),m};var n=t("gl-buffer"),i=t("gl-vao"),a=t("gl-texture2d"),o=new Uint8Array(4),s=new Float32Array(o.buffer);var l=t("binary-search-bounds"),c=t("ndarray"),u=t("./lib/shaders"),f=u.createShader,h=u.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function m(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function v(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var y=v.prototype;y.isTransparent=function(){return this.hasAlpha},y.isOpaque=function(){return!this.hasAlpha},y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.drawTransparent=y.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:m(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:m(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;"dashScale"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,"opacity"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,u=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position||t.positions;if(h){var p=t.color||t.colors||[0,0,0,1],m=t.lineWidth||1,g=!1;t:for(e=1;e<h.length;++e){var v,y,x,b=h[e-1],_=h[e];for(a.push(s),o.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])||isNaN(_[r])||!isFinite(b[r])||!isFinite(_[r])){if(!n&&i.length>0){for(var w=0;w<24;++w)i.push(i[i.length-12]);u+=2,g=!0}continue t}f[0][r]=Math.min(f[0][r],b[r],_[r]),f[1][r]=Math.max(f[1][r],b[r],_[r])}Array.isArray(p[0])?(v=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],y=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):v=y=p,3===v.length&&(v=[v[0],v[1],v[2],1]),3===y.length&&(y=[y[0],y[1],y[2],1]),!this.hasAlpha&&v[3]<1&&(this.hasAlpha=!0),x=Array.isArray(m)?m.length>e-1?m[e-1]:m.length>0?m[m.length-1]:[0,0,0,1]:m;var T=s;if(s+=d(b,_),g){for(r=0;r<2;++r)i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3]);u+=2,g=!1}i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3],b[0],b[1],b[2],_[0],_[1],_[2],T,-x,v[0],v[1],v[2],v[3],_[0],_[1],_[2],b[0],b[1],b[2],s,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],s,x,y[0],y[1],y[2],y[3]),u+=4}}if(this.buffer.update(i),a.push(s),o.push(h[h.length-1].slice()),this.bounds=f,this.vertexCount=u,this.points=o,this.arcLength=a,"dashes"in t){var k=t.dashes.slice();for(k.unshift(0),e=1;e<k.length;++e)k[e]=k[e-1]+k[e];var A=c(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)A.set(e,0,r,0);1&l.le(k,k[k.length-1]*e/255)?A.set(e,0,0,0):A.set(e,0,0,255)}this.texture.setPixels(A)}},y.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},y.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=function(t,e,r,n){return o[0]=n,o[1]=r,o[2]=e,o[3]=t,s[0]}(t.value[0],t.value[1],t.value[2],0),r=l.le(this.arcLength,e);if(r<0)return null;if(r===this.arcLength.length-1)return new g(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],a=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),c=1-a,u=[0,0,0],f=0;f<3;++f)u[f]=c*n[f]+a*i[f];var h=Math.min(a<.5?r:r+1,this.points.length-1);return new g(e,u,h,this.points[h])}},{"./lib/shaders":90,"binary-search-bounds":31,"gl-buffer":78,"gl-texture2d":146,"gl-vao":150,ndarray:259}],92:[function(t,e,r){e.exports=function(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}},{}],93:[function(t,e,r){e.exports=function(){var t=new Float32Array(16);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],94:[function(t,e,r){e.exports=function(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],c=t[8],u=t[9],f=t[10],h=t[11],p=t[12],d=t[13],m=t[14],g=t[15];return(e*o-r*a)*(f*g-h*m)-(e*s-n*a)*(u*g-h*d)+(e*l-i*a)*(u*m-f*d)+(r*s-n*o)*(c*g-h*p)-(r*l-i*o)*(c*m-f*p)+(n*l-i*s)*(c*d-u*p)}},{}],95:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,c=r*o,u=n*o,f=n*s,h=i*o,p=i*s,d=i*l,m=a*o,g=a*s,v=a*l;return t[0]=1-f-d,t[1]=u+v,t[2]=h-g,t[3]=0,t[4]=u-v,t[5]=1-c-d,t[6]=p+m,t[7]=0,t[8]=h+g,t[9]=p-m,t[10]=1-c-f,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],96:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,c=a+a,u=n*s,f=n*l,h=n*c,p=i*l,d=i*c,m=a*c,g=o*s,v=o*l,y=o*c;return t[0]=1-(p+m),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+m),t[6]=d+g,t[7]=0,t[8]=h+v,t[9]=d-g,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],97:[function(t,e,r){e.exports=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],98:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=e[9],h=e[10],p=e[11],d=e[12],m=e[13],g=e[14],v=e[15],y=r*s-n*o,x=r*l-i*o,b=r*c-a*o,_=n*l-i*s,w=n*c-a*s,T=i*c-a*l,k=u*m-f*d,A=u*g-h*d,M=u*v-p*d,S=f*g-h*m,E=f*v-p*m,L=h*v-p*g,C=y*L-x*E+b*S+_*M-w*A+T*k;if(!C)return null;return C=1/C,t[0]=(s*L-l*E+c*S)*C,t[1]=(i*E-n*L-a*S)*C,t[2]=(m*T-g*w+v*_)*C,t[3]=(h*w-f*T-p*_)*C,t[4]=(l*M-o*L-c*A)*C,t[5]=(r*L-i*M+a*A)*C,t[6]=(g*b-d*T-v*x)*C,t[7]=(u*T-h*b+p*x)*C,t[8]=(o*E-s*M+c*k)*C,t[9]=(n*M-r*E-a*k)*C,t[10]=(d*w-m*b+v*y)*C,t[11]=(f*b-u*w-p*y)*C,t[12]=(s*A-o*S-l*k)*C,t[13]=(r*S-n*A+i*k)*C,t[14]=(m*x-d*_-g*y)*C,t[15]=(u*_-f*x+h*y)*C,t}},{}],99:[function(t,e,r){var n=t("./identity");e.exports=function(t,e,r,i){var a,o,s,l,c,u,f,h,p,d,m=e[0],g=e[1],v=e[2],y=i[0],x=i[1],b=i[2],_=r[0],w=r[1],T=r[2];if(Math.abs(m-_)<1e-6&&Math.abs(g-w)<1e-6&&Math.abs(v-T)<1e-6)return n(t);f=m-_,h=g-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*m+o*g+s*v),t[13]=-(l*m+c*g+u*v),t[14]=-(f*m+h*g+p*v),t[15]=1,t}},{"./identity":97}],100:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],m=e[12],g=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*m,t[1]=x*i+b*l+_*h+w*g,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*m,t[5]=x*i+b*l+_*h+w*g,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*m,t[9]=x*i+b*l+_*h+w*g,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*m,t[13]=x*i+b*l+_*h+w*g,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],101:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},{}],102:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],103:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);if(Math.abs(P)<1e-6)return null;E*=P=1/P,L*=P,C*=P,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],m=e[8],g=e[9],v=e[10],y=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,M=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+m*_,t[1]=l*x+h*b+g*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+m*k,t[5]=l*w+h*T+g*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*A+f*M+m*S,t[9]=l*A+h*M+g*S,t[10]=c*A+p*M+v*S,t[11]=u*A+d*M+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],104:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],105:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],106:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],107:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],108:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,m=r[0],g=r[1],v=r[2];e===t?(t[12]=e[0]*m+e[4]*g+e[8]*v+e[12],t[13]=e[1]*m+e[5]*g+e[9]*v+e[13],t[14]=e[2]*m+e[6]*g+e[10]*v+e[14],t[15]=e[3]*m+e[7]*g+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*m+s*g+f*v+e[12],t[13]=i*m+l*g+h*v+e[13],t[14]=a*m+c*g+p*v+e[14],t[15]=o*m+u*g+d*v+e[15]);return t}},{}],109:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],110:[function(t,e,r){"use strict";var n=t("barycentric"),i=t("polytope-closest-point/lib/closest_point_2d.js");function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}function s(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}e.exports=function(t,e,r,a,l,c){if(1===t.length)return[0,t[0].slice()];for(var u=new Array(t.length),f=0;f<t.length;++f)u[f]=o(t[f],r,a,l,c);var h=0,p=1/0;for(f=0;f<u.length;++f){for(var d=0,m=0;m<2;++m)d+=Math.pow(u[f][m]-e[m],2);d<p&&(p=d,h=f)}var g=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),a=Math.sqrt(a),r+a<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(u,e),v=0;for(f=0;f<3;++f){if(g[f]<-.001||g[f]>1.0001)return null;v+=g[f]}if(Math.abs(v-1)>.001)return null;return[h,s(t,g),g]}},{barycentric:14,"polytope-closest-point/lib/closest_point_2d.js":270}],111:[function(t,e,r){var n=t("glslify"),i=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, normal;\nattribute vec4 color;\nattribute vec2 uv;\n\nuniform mat4 model\n           , view\n           , projection\n           , inverseModel;\nuniform vec3 eyePosition\n           , lightPosition;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvec4 project(vec3 p) {\n  return projection * view * model * vec4(p, 1.0);\n}\n\nvoid main() {\n  gl_Position      = project(position);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * vec4(position , 1.0);\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  f_color          = color;\n  f_data           = position;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness\n            , fresnel\n            , kambient\n            , kdiffuse\n            , kspecular;\nuniform sampler2D texture;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (f_color.a == 0.0 ||\n    outOfRange(clipBounds[0], clipBounds[1], f_data)\n  ) discard;\n\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\n\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * f_color.a;\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\n\nuniform mat4 model, view, projection;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n  f_color = color;\n  f_data  = position;\n  f_uv    = uv;\n}"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\n\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}"]),l=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\nattribute float pointSize;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\n  } else {\n    gl_Position = projection * view * model * vec4(position, 1.0);\n  }\n  gl_PointSize = pointSize;\n  f_color = color;\n  f_uv = uv;\n}"]),c=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\n  if(dot(pointR, pointR) > 0.25) {\n    discard;\n  }\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}"]),u=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n  f_id        = id;\n  f_position  = position;\n}"]),f=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]),h=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3  position;\nattribute float pointSize;\nattribute vec4  id;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\n  } else {\n    gl_Position  = projection * view * model * vec4(position, 1.0);\n    gl_PointSize = pointSize;\n  }\n  f_id         = id;\n  f_position   = position;\n}"]),p=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n}"]),d=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec3 contourColor;\n\nvoid main() {\n  gl_FragColor = vec4(contourColor, 1.0);\n}\n"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec3"},{name:"normal",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},r.wireShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},r.pointShader={vertex:l,fragment:c,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"pointSize",type:"float"}]},r.pickShader={vertex:u,fragment:f,attributes:[{name:"position",type:"vec3"},{name:"id",type:"vec4"}]},r.pointPickShader={vertex:h,fragment:f,attributes:[{name:"position",type:"vec3"},{name:"pointSize",type:"float"},{name:"id",type:"vec4"}]},r.contourShader={vertex:p,fragment:d,attributes:[{name:"position",type:"vec3"}]}},{glslify:231}],112:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("normals"),l=t("gl-mat4/multiply"),c=t("gl-mat4/invert"),u=t("ndarray"),f=t("colormap"),h=t("simplicial-complex-contour"),p=t("typedarray-pool"),d=t("./lib/shaders"),m=t("./lib/closest-point"),g=d.meshShader,v=d.wireShader,y=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,T,k,A,M,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=g,this.edgeUVs=v,this.edgeIds=m,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=T,this.pointSizes=k,this.pointIds=b,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=M,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function A(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function M(t){var e=n(t,g.vertex,g.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}function S(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}function E(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function L(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function C(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function P(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var f=c[0];2===c.length&&(f=c[u]);for(var d=n[f][0],m=n[f][1],g=i[f],v=1-g,y=this.positions[d],x=this.positions[m],b=0;b<3;++b)o[s++]=g*y[b]+v*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},k.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"contourEnable"in t&&(this.contourEnable=t.contourEnable),"contourColor"in t&&(this.contourColor=t.contourColor),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"lightPosition"in t&&(this.lightPosition=t.lightPosition),this.hasAlpha=!1,"opacity"in t&&(this.opacity=t.opacity,this.opacity<1&&(this.hasAlpha=!0)),"opacityscale"in t&&(this.opacityscale=t.opacityscale,this.hasAlpha=!0),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t,e){for(var r=f({colormap:t,nshades:256,format:"rgba"}),n=new Uint8Array(1024),i=0;i<256;++i){for(var a=r[i],o=0;o<3;++o)n[4*i+o]=a[o];n[4*i+3]=e?255*A(i/255,e):255*a[3]}return u(n,[256,256,4],[4,0,1])}(t.colormap,this.opacityscale)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],c=[],h=[],p=[],d=[],m=[],g=[],v=[],y=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,M=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,M)),T||w||(w=s.vertexNormals(r,n,k));var S=t.vertexColors,E=t.cellColors,L=t.meshColor||[1,1,1,1],C=t.vertexUVs,P=t.vertexIntensity,I=t.cellUVs,O=t.cellIntensity,z=1/0,D=-1/0;if(!C&&!I)if(P)if(t.vertexIntensityBounds)z=+t.vertexIntensityBounds[0],D=+t.vertexIntensityBounds[1];else for(var R=0;R<P.length;++R){var F=P[R];z=Math.min(z,F),D=Math.max(D,F)}else if(O)if(t.cellIntensityBounds)z=+t.cellIntensityBounds[0],D=+t.cellIntensityBounds[1];else for(R=0;R<O.length;++R){F=O[R];z=Math.min(z,F),D=Math.max(D,F)}else for(R=0;R<n.length;++R){F=n[R][2];z=Math.min(z,F),D=Math.max(D,F)}this.intensity=P||(O||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n)),this.pickVertex=!(O||E);var B=t.pointSizes,N=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(R=0;R<n.length;++R)for(var j=n[R],U=0;U<3;++U)!isNaN(j[U])&&isFinite(j[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],j[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],j[U]));var V=0,H=0,q=0;t:for(R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(j=n[W=G[0]],U=0;U<3;++U)if(isNaN(j[U])||!isFinite(j[U]))continue t;v.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?y.push(X[0],X[1],X[2],this.opacity):(y.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],x.push(Z[0],Z[1]),B?b.push(B[W]):b.push(N),_.push(R),q+=1;break;case 2:for(U=0;U<2;++U){j=n[W=G[U]];for(var Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t}for(U=0;U<2;++U){j=n[W=G[U]];p.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?d.push(X[0],X[1],X[2],this.opacity):(d.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],m.push(Z[0],Z[1]),g.push(R)}H+=1;break;case 3:for(U=0;U<3;++U)for(j=n[W=G[U]],Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t;for(U=0;U<3;++U){var W,X,Z,J;j=n[W=G[2-U]];i.push(j[0],j[1],j[2]),(X=S?S[W]:E?E[R]:L)?this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?a.push(X[0],X[1],X[2],this.opacity):(a.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)):a.push(.5,.5,.5,1),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],c.push(Z[0],Z[1]),J=w?w[W]:T[R],l.push(J[0],J[1],J[2]),h.push(R)}V+=1}}this.pointCount=q,this.edgeCount=H,this.triangleCount=V,this.pointPositions.update(v),this.pointColors.update(y),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(m),this.edgeIds.update(new Uint32Array(g)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(c),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},k.drawTransparent=k.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:w.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};s.inverseModel=c(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var f,h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=t.coord[0],s=t.coord[1];if(!this.pickVertex){var l=this.positions[r[0]],c=this.positions[r[1]],u=this.positions[r[2]],f=[(l[0]+c[0]+u[0])/3,(l[1]+c[1]+u[1])/3,(l[2]+c[2]+u[2])/3];return{_cellCenter:!0,position:[o,s],index:e,cell:r,cellId:e,intensity:this.intensity[e],dataCoordinate:f}}var h=m(i,[o*this.pixelRatio,this._resolution[1]-s*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!h)return null;var p=h[2],d=0;for(a=0;a<r.length;++a)d+=p[a]*this.intensity[r[a]];return{position:h[1],index:r[h[0]],cell:r,cellId:e,intensity:d,dataCoordinate:this.positions[r[h[0]]]}},k.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=t.getExtension("OES_standard_derivatives")||t.getExtension("MOZ_OES_standard_derivatives")||t.getExtension("WEBKIT_OES_standard_derivatives");if(!r)throw new Error("derivatives not supported");var n=M(t),s=S(t),l=E(t),c=L(t),f=C(t),h=P(t),p=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),m=i(t),g=i(t),v=i(t),y=i(t),x=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:m,type:t.FLOAT,size:4},{buffer:g,type:t.FLOAT,size:2},{buffer:v,type:t.FLOAT,size:3}]),b=i(t),_=i(t),w=i(t),k=i(t),A=a(t,[{buffer:b,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),I=i(t),O=i(t),z=i(t),D=i(t),R=i(t),F=a(t,[{buffer:I,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:O,type:t.FLOAT,size:4},{buffer:z,type:t.FLOAT,size:2},{buffer:D,type:t.FLOAT,size:1}]),B=i(t),N=a(t,[{buffer:B,type:t.FLOAT,size:3}]),j=new T(t,p,n,s,l,c,f,h,d,y,m,g,v,x,b,k,_,w,A,I,R,O,z,D,F,B,N);return j.update(e),j}},{"./lib/closest-point":110,"./lib/shaders":111,colormap:53,"gl-buffer":78,"gl-mat4/invert":98,"gl-mat4/multiply":100,"gl-shader":132,"gl-texture2d":146,"gl-vao":150,ndarray:259,normals:261,"simplicial-complex-contour":291,"typedarray-pool":308}],113:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e,[0,0,0,1,1,0,1,1]),s=i(e,a.boxVert,a.lineFrag);return new o(t,r,s)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("./shaders");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,i){var a=this.plot,o=this.shader,c=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":116,"gl-buffer":78,"gl-shader":132}],114:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,o.gridVert,o.gridFrag),l=i(e,o.tickVert,o.gridFrag);return new s(t,r,a,l)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("binary-search-bounds"),o=t("./shaders");function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var c,u,f,h,p,d=s.prototype;d.draw=(c=[0,0],u=[0,0],f=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,h=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,m=0;m<2;++m){var g=a[m],v=a[m+2]-g,y=.5*(o[m+2]+o[m]),x=o[m+2]-o[m];u[m]=2*v/x,c[m]=2*(g-y)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=c,r.uniforms.dataScale=u;var b=0;for(m=0;m<2;++m){f[0]=f[1]=0,f[m]=1,r.uniforms.dataAxis=f,r.uniforms.lineWidth=l[m]/(s[m+2]-s[m])*d,r.uniforms.color=h[m];var _=6*n[m].length;p[m]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,c=this.vbo,u=this.tickShader,f=this.ticks,h=s.gl,p=s._tickBounds,d=s.dataBox,m=s.viewBox,g=s.pixelRatio,v=s.screenBox,y=v[2]-v[0],x=v[3]-v[1],b=m[2]-m[0],_=m[3]-m[1],w=0;w<2;++w){var T=p[w],k=p[w+2]-T,A=.5*(d[w+2]+d[w]),M=d[w+2]-d[w];e[w]=2*k/M,t[w]=2*(T-A)/M}e[0]*=b/y,t[0]*=b/y,e[1]*=_/x,t[1]*=_/x,u.bind(),c.bind(),u.attributes.dataCoord.pointer();var S=u.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,L=s.tickMarkWidth,C=s.tickMarkColor,P=6*f[0].length,I=Math.min(a.ge(f[0],(d[0]-p[0])/(p[2]-p[0]),l),f[0].length),O=Math.min(a.gt(f[0],(d[2]-p[0])/(p[2]-p[0]),l),f[0].length),z=0+6*I,D=6*Math.max(0,O-I),R=Math.min(a.ge(f[1],(d[1]-p[1])/(p[3]-p[1]),l),f[1].length),F=Math.min(a.gt(f[1],(d[3]-p[1])/(p[3]-p[1]),l),f[1].length),B=P+6*R,N=6*Math.max(0,F-R);i[0]=2*(m[0]-E[1])/y-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[1]*g/y,o[1]=L[1]*g/x,N&&(S.color=C[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/y-1,i[1]=2*(m[1]-E[0])/x-1,o[0]=L[0]*g/y,o[1]=E[0]*g/x,D&&(S.color=C[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D)),i[0]=2*(m[2]+E[3])/y-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[3]*g/y,o[1]=L[3]*g/x,N&&(S.color=C[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/y-1,i[1]=2*(m[3]+E[2])/x-1,o[0]=L[2]*g/y,o[1]=E[2]*g/x,D&&(S.color=C[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D))}}(),d.update=(h=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18*(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],c=r[o],u=r[o+2],f=0;f<l.length;++f){var d=(l[f].x-c)/(u-c);s.push(d);for(var m=0;m<6;++m)n[i++]=d,n[i++]=h[m],n[i++]=p[m]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{"./shaders":116,"binary-search-bounds":31,"gl-buffer":78,"gl-shader":132}],115:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e,[-1,-1,-1,1,1,-1,1,1]),s=i(e,a.lineVert,a.lineFrag);return new o(t,r,s)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("./shaders");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,i,a){var o=this.plot,c=this.shader,u=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,c.uniforms.start=s,c.uniforms.end=l,c.uniforms.width=i*o.pixelRatio,c.uniforms.color=a,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":116,"gl-buffer":78,"gl-shader":132}],116:[function(t,e,r){"use strict";var n=t("glslify"),i=n(["precision lowp float;\n#define GLSLIFY 1\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\n}\n"]);e.exports={lineVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 coord;\n\nuniform vec4 screenBox;\nuniform vec2 start, end;\nuniform float width;\n\nvec2 perp(vec2 v) {\n  return vec2(v.y, -v.x);\n}\n\nvec2 screen(vec2 v) {\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\n}\n\nvoid main() {\n  vec2 delta = normalize(perp(start - end));\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\n}\n"]),lineFrag:i,textVert:n(["#define GLSLIFY 1\nattribute vec3 textCoordinate;\n\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\nuniform float angle;\n\nvoid main() {\n  float dataOffset  = textCoordinate.z;\n  vec2 glyphOffset  = textCoordinate.xy;\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\n    glyphMatrix * glyphOffset * textScale + screenOffset;\n  gl_Position = vec4(screenCoordinate, 0, 1);\n}\n"]),textFrag:i,gridVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 dataCoord;\n\nuniform vec2 dataAxis, dataShift, dataScale;\nuniform float lineWidth;\n\nvoid main() {\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\n  gl_Position = vec4(pos, 0, 1);\n}\n"]),gridFrag:i,boxVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 coord;\n\nuniform vec4 screenBox;\nuniform vec2 lo, hi;\n\nvec2 screen(vec2 v) {\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\n}\n\nvoid main() {\n  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\n}\n"]),tickVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 dataCoord;\n\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\n\nvoid main() {\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\n  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);\n}\n"])}},{glslify:231}],117:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,s.textVert,s.textFrag);return new l(t,r,a)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("text-cache"),o=t("binary-search-bounds"),s=t("./shaders");function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var c,u,f,h,p,d,m=l.prototype;m.drawTicks=(c=[0,0],u=[0,0],f=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,h=e.screenBox,p=e.pixelRatio,d=e.tickEnable,m=e.tickPad,g=e.tickColor,v=e.tickAngle,y=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],T=this.labelCount[t],k=o.lt(n,l[t]),A=o.le(n,l[t+2]);c[0]=c[1]=0,c[t]=1,u[t]=(s[2+t]+s[t])/(h[2+t]-h[t])-1;var M=2/h[2+(1^t)]-h[1^t];u[1^t]=M*s[1^t]-1,d[t]&&(u[1^t]-=M*p*m[t],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t],r.uniforms.angle=v[t],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t]&&T&&(u[1^t]-=M*p*x[t],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,T)),u[1^t]=M*s[2+(1^t)]-1,d[t+2]&&(u[1^t]+=M*p*m[t+2],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t+2],r.uniforms.angle=v[t+2],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t+2]&&T&&(u[1^t]+=M*p*x[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,T))}),m.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,c=r.pixelRatio;if(this.titleCount){for(var u=0;u<2;++u)e[u]=2*(o[u]*c-a[u])/(a[2+u]-a[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),m.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,c=.5*(n[o+2]+n[o]),u=n[o+2]-n[o],f=a[o],m=a[o+2]-f,g=i[o],v=i[o+2]-g;p[o]=2*l/u*m/v,h[o]=2*(s-c)/u*m/v}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),m.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,c=t.bounds;for(o=0;o<2;++o){var u=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e<h.length;++e){var p=h[e],d=p.x,m=p.text,g=p.font||"sans-serif";i=p.fontSize||12;for(var v=1/(c[o+2]-c[o]),y=c[o],x=m.split("\n"),b=0;b<x.length;b++)for(n=a(g,x[b]).data,r=0;r<n.length;r+=2)s.push(n[r]*i,-n[r+1]*i-b*i*1.2,(d-y)*v);u.push(Math.floor(s.length/3)),f.push(d)}this.tickOffset[o]=u,this.tickX[o]=f}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{textAlign:"center"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},m.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":116,"binary-search-bounds":31,"gl-buffer":78,"gl-shader":132,"text-cache":303}],118:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e,[e.drawingBufferWidth,e.drawingBufferHeight]),c=new l(e,r);return c.grid=i(c),c.text=a(c),c.line=o(c),c.box=s(c),c.update(t),c};var n=t("gl-select-static"),i=t("./lib/grid"),a=t("./lib/text"),o=t("./lib/line"),s=t("./lib/box");function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var c=l.prototype;function u(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function f(t,e){return t.x-e.x}c.setDirty=function(){this.dirty=this.pickDirty=!0},c.setOverlayDirty=function(){this.dirty=!0},c.nextDepthValue=function(){return this._depthCounter++/65536},c.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var c=this.borderColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var u=this.backgroundColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var f=this.zeroLineEnable,h=this.zeroLineColor,p=this.zeroLineWidth;if(f[0]||f[1]){o.bind();for(var d=0;d<2;++d)if(f[d]&&n[d]<=0&&n[d+2]>=0){var m=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(m,e[1],m,e[3],p[d],h[d]):o.drawLine(e[0],m,e[2],m,p[d],h[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var g=this.borderLineEnable,v=this.borderLineWidth,y=this.borderLineColor;for(g[1]&&o.drawLine(r[0],r[1]-.5*v[1]*i,r[0],r[3]+.5*v[3]*i,v[1],y[1]),g[0]&&o.drawLine(r[0]-.5*v[0]*i,r[1],r[2]+.5*v[2]*i,r[1],v[0],y[0]),g[3]&&o.drawLine(r[2],r[1]-.5*v[1]*i,r[2],r[3]+.5*v[3]*i,v[3],y[3]),g[2]&&o.drawLine(r[0]-.5*v[0]*i,r[3],r[2]+.5*v[2]*i,r[3],v[2],y[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},c.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl,this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},c.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),c=this.objects,u=0;u<c.length;++u){var f=c[u].pick(a,o,l);if(f)return f}return null}},c.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},c.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},c.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},c.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=u(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=u(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=u(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!("titleEnable"in t)||!!t.titleEnable,this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=u(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=u(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=u(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks||[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(f),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels||["x","y"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||["sans-serif","sans-serif"],title:t.title||"",titleSize:t.titleSize||18,titleFont:t.titleFont||"sans-serif"}),this.static=!!t.static,this.setDirty()},c.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();this.objects.length=0;for(t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},c.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},c.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},c.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},c.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},{"./lib/box":113,"./lib/grid":114,"./lib/line":115,"./lib/text":117,"gl-select-static":131}],119:[function(t,e,r){"use strict";e.exports=function(t,e){t=t||document.body,e=e||{};var r=[.01,1/0];"distanceLimits"in e&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]);"zoomMin"in e&&(r[0]=e.zoomMin);"zoomMax"in e&&(r[1]=e.zoomMax);var c=i({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||"orbit",distanceLimits:r}),u=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],f=0,h=t.clientWidth,p=t.clientHeight,d={keyBindingMode:"rotate",enableWheel:!0,view:c,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:c.modes,_ortho:e._ortho||e.projection&&"orthographic"===e.projection.type||!1,tick:function(){var e=n(),r=this.delay,i=e-2*r;c.idle(e-r),c.recalcMatrix(i),c.flush(e-(100+2*r));for(var a=!0,o=c.computedMatrix,s=0;s<16;++s)a=a&&u[s]===o[s],u[s]=o[s];var l=t.clientWidth===h&&t.clientHeight===p;return h=t.clientWidth,p=t.clientHeight,a?!l:(f=Math.exp(c.computedRadius[0]),!0)},lookAt:function(t,e,r){c.lookAt(c.lastT(),t,e,r)},rotate:function(t,e,r){c.rotate(c.lastT(),t,e,r)},pan:function(t,e,r){c.pan(c.lastT(),t,e,r)},translate:function(t,e,r){c.translate(c.lastT(),t,e,r)}};return Object.defineProperties(d,{matrix:{get:function(){return c.computedMatrix},set:function(t){return c.setMatrix(c.lastT(),t),c.computedMatrix},enumerable:!0},mode:{get:function(){return c.getMode()},set:function(t){var e=c.computedUp.slice(),r=c.computedEye.slice(),i=c.computedCenter.slice();if(c.setMode(t),"turntable"===t){var a=n();c._active.lookAt(a,r,i,e),c._active.lookAt(a+500,r,i,[0,0,1]),c._active.flush(a)}return c.getMode()},enumerable:!0},center:{get:function(){return c.computedCenter},set:function(t){return c.lookAt(c.lastT(),null,t),c.computedCenter},enumerable:!0},eye:{get:function(){return c.computedEye},set:function(t){return c.lookAt(c.lastT(),t),c.computedEye},enumerable:!0},up:{get:function(){return c.computedUp},set:function(t){return c.lookAt(c.lastT(),null,null,t),c.computedUp},enumerable:!0},distance:{get:function(){return f},set:function(t){return c.setDistance(c.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return c.getDistanceLimits(r)},set:function(t){return c.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener("contextmenu",(function(t){return t.preventDefault(),!1})),d._lastX=-1,d._lastY=-1,d._lastMods={shift:!1,control:!1,alt:!1,meta:!1},d.enableMouseListeners=function(){function e(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s="rotate"===o,l="pan"===o,u="zoom"===o,h=!!a.control,p=!!a.alt,m=!!a.shift,g=!!(1&e),v=!!(2&e),y=!!(4&e),x=1/t.clientHeight,b=x*(r-d._lastX),_=x*(i-d._lastY),w=d.flipX?1:-1,T=d.flipY?1:-1,k=Math.PI*d.rotateSpeed,A=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&g&&!h&&!p&&!m||g&&!h&&!p&&m)&&c.rotate(A,w*k*b,-T*k*_,0),(l&&g&&!h&&!p&&!m||v||g&&h&&!p&&!m)&&c.pan(A,-d.translateSpeed*b*f,d.translateSpeed*_*f,0),u&&g&&!h&&!p&&!m||y||g&&!h&&p&&!m)){var M=-d.zoomSpeed*_/window.innerHeight*(A-c.lastT())*100;c.pan(A,0,0,f*(Math.exp(M)-1))}return d._lastX=r,d._lastY=i,d._lastMods=a,!0}}d.mouseListener=a(t,e),t.addEventListener("touchstart",(function(r){var n=s(r.changedTouches[0],t);e(0,n[0],n[1],d._lastMods),e(1,n[0],n[1],d._lastMods)}),!!l&&{passive:!0}),t.addEventListener("touchmove",(function(r){var n=s(r.changedTouches[0],t);e(1,n[0],n[1],d._lastMods),r.preventDefault()}),!!l&&{passive:!1}),t.addEventListener("touchend",(function(t){e(0,d._lastX,d._lastY,d._lastMods)}),!!l&&{passive:!0}),d.wheelListener=o(t,(function(t,e){if(!1!==d.keyBindingMode&&d.enableWheel){var r=d.flipX?1:-1,i=d.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))c.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-c.lastT())/20;c.pan(a,0,0,f*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=t("right-now"),i=t("3d-view"),a=t("mouse-change"),o=t("mouse-wheel"),s=t("mouse-event-offset"),l=t("has-passive-events")},{"3d-view":7,"has-passive-events":232,"mouse-change":247,"mouse-event-offset":248,"mouse-wheel":250,"right-now":278}],120:[function(t,e,r){var n=t("glslify"),i=t("gl-shader"),a=n(["precision mediump float;\n#define GLSLIFY 1\nattribute vec2 position;\nvarying vec2 uv;\nvoid main() {\n  uv = position;\n  gl_Position = vec4(position, 0, 1);\n}"]),o=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform sampler2D accumBuffer;\nvarying vec2 uv;\n\nvoid main() {\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\n  gl_FragColor = min(vec4(1,1,1,1), accum);\n}"]);e.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec2"}])}},{"gl-shader":132,glslify:231}],121:[function(t,e,r){"use strict";var n=t("./camera.js"),i=t("gl-axes3d"),a=t("gl-axes3d/properties"),o=t("gl-spikes3d"),s=t("gl-select-static"),l=t("gl-fbo"),c=t("a-big-triangle"),u=t("mouse-change"),f=t("gl-mat4/perspective"),h=t("gl-mat4/ortho"),p=t("./lib/shader"),d=t("is-mobile")({tablet:!0,featureDetect:!0});function m(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function g(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}if(e>0){r=Math.round(Math.pow(10,e));return Math.ceil(t/r)*r}return Math.ceil(t)}function v(t){return"boolean"!=typeof t||t}e.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;if(!e){if(e=document.createElement("canvas"),t.container)t.container.appendChild(e);else document.body.appendChild(e)}var r=t.gl;r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext("webgl",e))||(r=t.getContext("experimental-webgl",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d}));if(!r)throw new Error("webgl not supported");var y=t.bounds||[[-10,-10,-10],[10,10,10]],x=new m,b=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),_=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&"orthographic"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||"turntable",_ortho:w},k=t.axes||{},A=i(r,k);A.enable=!k.disable;var M=t.spikes||{},S=o(r,M),E=[],L=[],C=[],P=[],I=!0,O=!0,z=new Array(16),D=new Array(16),R={view:null,projection:z,model:D,_ortho:!1},F=(O=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),B=t.cameraObject||n(e,T),N={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:B,axes:A,axesPixels:null,spikes:S,bounds:y,objects:E,shape:F,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:v(t.autoResize),autoBounds:v(t.autoBounds),autoScale:!!t.autoScale,autoCenter:v(t.autoCenter),clipToBounds:v(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:R,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,O=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},j=[r.drawingBufferWidth/N.pixelRatio|0,r.drawingBufferHeight/N.pixelRatio|0];function U(){if(!N._stopped&&N.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*N.pixelRatio),a=0|Math.ceil(n*N.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||"absolute",o.left="0px",o.top="0px",o.width=r+"px",o.height=n+"px",I=!0}}}N.autoResize&&U();function V(){for(var t=E.length,e=P.length,n=0;n<e;++n)C[n]=0;t:for(n=0;n<t;++n){var i=E[n],a=i.pickSlots;if(a){for(var o=0;o<e;++o)if(C[o]+a<255){L[n]=o,i.setPickBase(C[o]+1),C[o]+=a;continue t}var l=s(r,F);L[n]=e,P.push(l),C.push(a),i.setPickBase(1),e+=1}else L[n]=-1}for(;e>0&&0===C[e-1];)C.pop(),P.pop().dispose()}function H(){if(N.contextLost)return!0;r.isContextLost()&&(N.contextLost=!0,N.mouseListener.enabled=!1,N.selection.object=null,N.oncontextloss&&N.oncontextloss())}window.addEventListener("resize",U),N.update=function(t){N._stopped||(t=t||{},I=!0,O=!0)},N.add=function(t){N._stopped||(t.axes=A,E.push(t),L.push(-1),I=!0,O=!0,V())},N.remove=function(t){if(!N._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),L.pop(),I=!0,O=!0,V())}},N.dispose=function(){if(!N._stopped&&(N._stopped=!0,window.removeEventListener("resize",U),e.removeEventListener("webglcontextlost",H),N.mouseListener.enabled=!1,!N.contextLost)){A.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();b.dispose();for(t=0;t<P.length;++t)P[t].dispose();_.dispose(),r=null,A=null,S=null,E=[]}},N._mouseRotating=!1,N._prevButtons=0,N.enableMouseListeners=function(){N.mouseListener=u(e,(function(t,e,r){if(!N._stopped){var n=P.length,i=E.length,a=x.object;x.distance=1/0,x.mouse[0]=e,x.mouse[1]=r,x.object=null,x.screen=null,x.dataCoordinate=x.dataPosition=null;var o=!1;if(t&&N._prevButtons)N._mouseRotating=!0;else{N._mouseRotating&&(O=!0),N._mouseRotating=!1;for(var s=0;s<n;++s){var l=P[s].query(e,j[1]-r-1,N.pickRadius);if(l){if(l.distance>x.distance)continue;for(var c=0;c<i;++c){var u=E[c];if(L[c]===s){var f=u.pick(l);f&&(x.buttons=t,x.screen=l.coord,x.distance=l.distance,x.object=u,x.index=f.distance,x.dataPosition=f.position,x.dataCoordinate=f.dataCoordinate,x.data=f,o=!0)}}}}}a&&a!==x.object&&(a.highlight&&a.highlight(null),I=!0),x.object&&(x.object.highlight&&x.object.highlight(x.data),I=!0),(o=o||x.object!==a)&&N.onselect&&N.onselect(x),1&t&&!(1&N._prevButtons)&&N.onclick&&N.onclick(x),N._prevButtons=t}}))},e.addEventListener("webglcontextlost",H);var q=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],G=[q[0].slice(),q[1].slice()];function Y(){if(!H()){U();var t=N.camera.tick();R.view=N.camera.matrix,I=I||t,O=O||t,A.pixelRatio=N.pixelRatio,S.pixelRatio=N.pixelRatio;var e=E.length,n=q[0],i=q[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<e;++o){(C=E[o]).pixelRatio=N.pixelRatio,C.axes=N.axes,I=I||!!C.dirty,O=O||!!C.dirty;var s=C.bounds;if(s)for(var l=s[0],u=s[1],p=0;p<3;++p)n[p]=Math.min(n[p],l[p]),i[p]=Math.max(i[p],u[p])}var d=N.bounds;if(N.autoBounds)for(p=0;p<3;++p){if(i[p]<n[p])n[p]=-1,i[p]=1;else{n[p]===i[p]&&(n[p]-=1,i[p]+=1);var m=.05*(i[p]-n[p]);n[p]=n[p]-m,i[p]=i[p]+m}d[0][p]=n[p],d[1][p]=i[p]}var v=!1;for(p=0;p<3;++p)v=v||G[0][p]!==d[0][p]||G[1][p]!==d[1][p],G[0][p]=d[0][p],G[1][p]=d[1][p];if(O=O||v,I=I||v){if(v){var y=[0,0,0];for(o=0;o<3;++o)y[o]=g((d[1][o]-d[0][o])/10);A.autoTicks?A.update({bounds:d,tickSpacing:y}):A.update({bounds:d})}var T=r.drawingBufferWidth,k=r.drawingBufferHeight;F[0]=T,F[1]=k,j[0]=0|Math.max(T/N.pixelRatio,1),j[1]=0|Math.max(k/N.pixelRatio,1),function(t,e){var r=t.bounds,n=t.cameraParams,i=n.projection,a=n.model,o=t.gl.drawingBufferWidth,s=t.gl.drawingBufferHeight,l=t.zNear,c=t.zFar,u=t.fovy,p=o/s;e?(h(i,-p,p,-1,1,l,c),n._ortho=!0):(f(i,u,p,l,c),n._ortho=!1);for(var d=0;d<16;++d)a[d]=0;a[15]=1;var m=0;for(d=0;d<3;++d)m=Math.max(m,r[1][d]-r[0][d]);for(d=0;d<3;++d)t.autoScale?a[5*d]=t.aspect[d]/(r[1][d]-r[0][d]):a[5*d]=1/m,t.autoCenter&&(a[12+d]=.5*-a[5*d]*(r[0][d]+r[1][d]))}(N,w);for(o=0;o<e;++o){(C=E[o]).axesBounds=d,N.clipToBounds&&(C.clipBounds=d)}x.object&&(N.snapToData?S.position=x.dataCoordinate:S.position=x.dataPosition,S.bounds=d),O&&(O=!1,function(){if(!H()){r.colorMask(!0,!0,!0,!0),r.depthMask(!0),r.disable(r.BLEND),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL);for(var t=E.length,e=P.length,n=0;n<e;++n){var i=P[n];i.shape=j,i.begin();for(var a=0;a<t;++a)if(L[a]===n){var o=E[a];o.drawPick&&(o.pixelRatio=1,o.drawPick(R))}i.end()}}}()),N.axesPixels=a(N.axes,R,T,k),N.onrender&&N.onrender(),r.bindFramebuffer(r.FRAMEBUFFER,null),r.viewport(0,0,T,k),N.clearRGBA(),r.depthMask(!0),r.colorMask(!0,!0,!0,!0),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL),r.disable(r.BLEND),r.disable(r.CULL_FACE);var M=!1;A.enable&&(M=M||A.isTransparent(),A.draw(R)),S.axes=A,x.object&&S.draw(R),r.disable(r.CULL_FACE);for(o=0;o<e;++o){(C=E[o]).axes=A,C.pixelRatio=N.pixelRatio,C.isOpaque&&C.isOpaque()&&C.draw(R),C.isTransparent&&C.isTransparent()&&(M=!0)}if(M){b.shape=F,b.bind(),r.clear(r.DEPTH_BUFFER_BIT),r.colorMask(!1,!1,!1,!1),r.depthMask(!0),r.depthFunc(r.LESS),A.enable&&A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){(C=E[o]).isOpaque&&C.isOpaque()&&C.draw(R)}r.enable(r.BLEND),r.blendEquation(r.FUNC_ADD),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.colorMask(!0,!0,!0,!0),r.depthMask(!1),r.clearColor(0,0,0,0),r.clear(r.COLOR_BUFFER_BIT),A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){var C;(C=E[o]).isTransparent&&C.isTransparent()&&C.drawTransparent(R)}r.bindFramebuffer(r.FRAMEBUFFER,null),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.disable(r.DEPTH_TEST),_.bind(),b.color[0].bind(0),_.uniforms.accumBuffer=0,c(r),r.disable(r.BLEND)}I=!1;for(o=0;o<e;++o)E[o].dirty=!1}}}return N.enableMouseListeners(),function t(){if(N._stopped||N.contextLost)return;Y(),requestAnimationFrame(t)}(),N.redraw=function(){N._stopped||(I=!0,Y())},N},createCamera:n}},{"./camera.js":119,"./lib/shader":120,"a-big-triangle":8,"gl-axes3d":70,"gl-axes3d/properties":77,"gl-fbo":86,"gl-mat4/ortho":101,"gl-mat4/perspective":102,"gl-select-static":131,"gl-spikes3d":141,"is-mobile":238,"mouse-change":247}],122:[function(t,e,r){var n=t("glslify");r.pointVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\n\nuniform mat3 matrix;\nuniform float pointSize;\nuniform float pointCloud;\n\nhighp float rand(vec2 co) {\n  highp float a = 12.9898;\n  highp float b = 78.233;\n  highp float c = 43758.5453;\n  highp float d = dot(co.xy, vec2(a, b));\n  highp float e = mod(d, 3.14);\n  return fract(sin(e) * c);\n}\n\nvoid main() {\n  vec3 hgPosition = matrix * vec3(position, 1);\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\n    // if we don't jitter the point size a bit, overall point cloud\n    // saturation 'jumps' on zooming, which is disturbing and confusing\n  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\n  if(pointCloud != 0.0) { // pointCloud is truthy\n    // get the same square surface as circle would be\n    gl_PointSize *= 0.886;\n  }\n}"]),r.pointFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec4 color, borderColor;\nuniform float centerFraction;\nuniform float pointCloud;\n\nvoid main() {\n  float radius;\n  vec4 baseColor;\n  if(pointCloud != 0.0) { // pointCloud is truthy\n    if(centerFraction == 1.0) {\n      gl_FragColor = color;\n    } else {\n      gl_FragColor = mix(borderColor, color, centerFraction);\n    }\n  } else {\n    radius = length(2.0 * gl_PointCoord.xy - 1.0);\n    if(radius > 1.0) {\n      discard;\n    }\n    baseColor = mix(borderColor, color, step(radius, centerFraction));\n    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\n  }\n}\n"]),r.pickVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 pickId;\n\nuniform mat3 matrix;\nuniform float pointSize;\nuniform vec4 pickOffset;\n\nvarying vec4 fragId;\n\nvoid main() {\n  vec3 hgPosition = matrix * vec3(position, 1);\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\n  gl_PointSize = pointSize;\n\n  vec4 id = pickId + pickOffset;\n  id.y += floor(id.x / 256.0);\n  id.x -= floor(id.x / 256.0) * 256.0;\n\n  id.z += floor(id.y / 256.0);\n  id.y -= floor(id.y / 256.0) * 256.0;\n\n  id.w += floor(id.z / 256.0);\n  id.z -= floor(id.z / 256.0) * 256.0;\n\n  fragId = id;\n}\n"]),r.pickFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragId;\n\nvoid main() {\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\n  if(radius > 1.0) {\n    discard;\n  }\n  gl_FragColor = fragId / 255.0;\n}\n"])},{glslify:231}],123:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("typedarray-pool"),o=t("./lib/shader");function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}e.exports=function(t,e){var r=t.gl,a=i(r),l=i(r),c=n(r,o.pointVertex,o.pointFragment),u=n(r,o.pickVertex,o.pickFragment),f=new s(t,a,l,c,u);return f.update(e),t.addObject(f),f};var l,c,u=s.prototype;u.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},u.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r("sizeMin",.5),this.sizeMax=r("sizeMax",20),this.color=r("color",[1,0,0,1]).slice(),this.areaRatio=r("areaRatio",1),this.borderColor=r("borderColor",[0,0,0,1]).slice(),this.blend=r("blend",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),c=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e<n;e++)c[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(c),i||a.free(l),o||a.free(c),this.pointCount=n,this.pickOffset=0},u.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],c=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),u=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=u<5,r.uniforms.pointSize=u,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(c[0]=255&t,c[1]=t>>8&255,c[2]=t>>16&255,c[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=c,this.pickOffset=t);var f=n.getParameter(n.BLEND),h=n.getParameter(n.DITHER);return f&&!this.blend&&n.disable(n.BLEND),h&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),f&&!this.blend&&n.enable(n.BLEND),h&&n.enable(n.DITHER),t+this.pointCount}),u.draw=u.unifiedDraw,u.drawPick=u.unifiedDraw,u.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},{"./lib/shader":122,"gl-buffer":78,"gl-shader":132,"typedarray-pool":308}],124:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c=e[0],u=e[1],f=e[2],h=e[3],p=r[0],d=r[1],m=r[2],g=r[3];(a=c*p+u*d+f*m+h*g)<0&&(a=-a,p=-p,d=-d,m=-m,g=-g);1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n);return t[0]=s*c+l*p,t[1]=s*u+l*d,t[2]=s*f+l*m,t[3]=s*h+l*g,t}},{}],125:[function(t,e,r){"use strict";e.exports=function(t){return t||0===t?t.toString():""}},{}],126:[function(t,e,r){"use strict";var n=t("vectorize-text");e.exports=function(t,e,r){var a=i[e];a||(a=i[e]={});if(t in a)return a[t];var o={textAlign:"center",textBaseline:"middle",lineHeight:1,font:e,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},s=n(t,o);o.triangles=!1;var l,c,u=n(t,o);if(r&&1!==r){for(l=0;l<s.positions.length;++l)for(c=0;c<s.positions[l].length;++c)s.positions[l][c]/=r;for(l=0;l<u.positions.length;++l)for(c=0;c<u.positions[l].length;++c)u.positions[l][c]/=r}var f=[[1/0,1/0],[-1/0,-1/0]],h=u.positions.length;for(l=0;l<h;++l){var p=u.positions[l];for(c=0;c<2;++c)f[0][c]=Math.min(f[0][c],p[c]),f[1][c]=Math.max(f[1][c],p[c])}return a[t]=[s,u,f]};var i={}},{"vectorize-text":311}],127:[function(t,e,r){var n=t("gl-shader"),i=t("glslify"),a=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform vec4 highlightId;\nuniform float highlightScale;\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = 1.0;\n    if(distance(highlightId, id) < 0.0001) {\n      scale = highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1);\n    vec4 viewPosition = view * worldPosition;\n    viewPosition = viewPosition / viewPosition.w;\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\n\n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}"]),o=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float highlightScale, pixelRatio;\nuniform vec4 highlightId;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = pixelRatio;\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\n      scale *= highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1.0);\n    vec4 viewPosition = view * worldPosition;\n    vec4 clipPosition = projection * viewPosition;\n    clipPosition /= clipPosition.w;\n\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}"]),s=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform float highlightScale;\nuniform vec4 highlightId;\nuniform vec3 axes[2];\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float scale, pixelRatio;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float lscale = pixelRatio * scale;\n    if(distance(highlightId, id) < 0.0001) {\n      lscale *= highlightScale;\n    }\n\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\n\n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = dataPosition;\n  }\n}\n"]),l=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 fragClipBounds[2];\nuniform float opacity;\n\nvarying vec4 interpColor;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (\n    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\n    interpColor.a * opacity == 0.\n  ) discard;\n  gl_FragColor = interpColor * opacity;\n}\n"]),c=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 fragClipBounds[2];\nuniform float pickGroup;\n\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\n\n  gl_FragColor = vec4(pickGroup, pickId.bgr);\n}"]),u=[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"glyph",type:"vec2"},{name:"id",type:"vec4"}],f={vertex:a,fragment:l,attributes:u},h={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:a,fragment:c,attributes:u},m={vertex:o,fragment:c,attributes:u},g={vertex:s,fragment:c,attributes:u};function v(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}r.createPerspective=function(t){return v(t,f)},r.createOrtho=function(t){return v(t,h)},r.createProject=function(t){return v(t,p)},r.createPickPerspective=function(t){return v(t,d)},r.createPickOrtho=function(t){return v(t,m)},r.createPickProject=function(t){return v(t,g)}},{"gl-shader":132,glslify:231}],128:[function(t,e,r){"use strict";var n=t("is-string-blank"),i=t("gl-buffer"),a=t("gl-vao"),o=t("typedarray-pool"),s=t("gl-mat4/multiply"),l=t("./lib/shaders"),c=t("./lib/glyphs"),u=t("./lib/get-simple-string"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function p(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function m(t){return!0===t||t>1?1:t}function g(t,e,r,n,i,a,o,s,l,c,u,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=u,this.pickProjectShader=f,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}e.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),c=l.createPickOrtho(e),u=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),m=a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),v=new g(e,r,n,o,f,h,p,d,m,s,c,u);return v.update(t),v};var v=g.prototype;v.pickSlots=1,v.setPickBase=function(t){this.pickId=t},v.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},v.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var y=[0,0],x=[0,0,0],b=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],T=f.slice(),k=[0,0,0],A=[[0,0,0],[0,0,0]];function M(t){return t[0]=t[1]=t[2]=0,t}function S(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function E(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function L(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,c=r.model||f,u=r.view||f,h=r.projection||f,d=e.axesBounds,m=function(t){for(var e=A,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],y[0]=2/o.drawingBufferWidth,y[1]=2/o.drawingBufferHeight,t.bind(),l.view=u,l.projection=h,l.screenSize=y,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=m,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var g=0;g<3;++g)if(a[g]){l.scale=e.projectScale[g],l.opacity=e.projectOpacity[g];for(var v=T,L=0;L<16;++L)v[L]=0;for(L=0;L<4;++L)v[5*L]=1;v[5*g]=0,i[g]<0?v[12+g]=d[0][g]:v[12+g]=d[1][g],s(v,c,v),l.model=v;var C=(g+1)%3,P=(g+2)%3,I=M(x),O=M(b);I[C]=1,O[P]=1;var z=p(0,0,0,S(_,I)),D=p(0,0,0,S(w,O));if(Math.abs(z[1])>Math.abs(D[1])){var R=z;z=D,D=R,R=I,I=O,O=R;var F=C;C=P,P=F}z[0]<0&&(I[C]=-1),D[1]>0&&(O[P]=-1);var B=0,N=0;for(L=0;L<4;++L)B+=Math.pow(c[4*C+L],2),N+=Math.pow(c[4*P+L],2);I[C]/=Math.sqrt(B),O[P]/=Math.sqrt(N),l.axes[0]=I,l.axes[1]=O,l.fragClipBounds[0]=E(k,m[0],g,-1e8),l.fragClipBounds[1]=E(k,m[1],g,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}var C=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function P(t,e,r,n,i,a,o){var s=r.gl;if((a===r.projectHasAlpha||o)&&L(e,r,n,i),a===r.hasAlpha||o){t.bind();var l=t.uniforms;l.model=n.model||f,l.view=n.view||f,l.projection=n.projection||f,y[0]=2/s.drawingBufferWidth,y[1]=2/s.drawingBufferHeight,l.screenSize=y,l.highlightId=r.highlightId,l.highlightScale=r.highlightScale,l.fragClipBounds=C,l.clipBounds=r.axes.bounds,l.opacity=r.opacity,l.pickGroup=r.pickId/255,l.pixelRatio=i,r.vao.bind(),r.vao.draw(s.TRIANGLES,r.vertexCount),r.lineWidth>0&&(s.lineWidth(r.lineWidth*i),r.vao.draw(s.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function I(t,e,r,i){var a;a=Array.isArray(t)?e<t.length?t[e]:void 0:t,a=u(a);var o=!0;n(a)&&(a="\u25bc",o=!1);var s=c(a,r,i);return{mesh:s[0],lines:s[1],bounds:s[2],visible:o}}v.draw=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},v.drawTransparent=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},v.drawPick=function(t){P(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,1,!0,!0)},v.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},v.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},v.update=function(t){if("perspective"in(t=t||{})&&(this.useOrtho=!t.perspective),"orthographic"in t&&(this.useOrtho=!!t.orthographic),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"project"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if("projectScale"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,"projectOpacity"in t){if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{r=+t.projectOpacity;this.projectOpacity=[r,r,r]}for(var n=0;n<3;++n)this.projectOpacity[n]=m(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,"opacity"in t&&(this.opacity=m(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l=t.font||"normal",c=t.alignment||[0,0];if(2===c.length)i=c[0],a=c[1];else{i=[],a=[];for(n=0;n<c.length;++n)i[n]=c[n][0],a[n]=c[n][1]}var u=[1/0,1/0,1/0],f=[-1/0,-1/0,-1/0],h=t.glyph,p=t.color,d=t.size,g=t.angle,v=t.lineColor,y=-1,x=0,b=0,_=0;if(s.length){_=s.length;t:for(n=0;n<_;++n){for(var w=s[n],T=0;T<3;++T)if(isNaN(w[T])||!isFinite(w[T]))continue t;var k=(N=I(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;x+=3*k.cells.length,b+=2*A.edges.length}}var S=x+b,E=o.mallocFloat(3*S),L=o.mallocFloat(4*S),C=o.mallocFloat(2*S),P=o.mallocUint32(S);if(S>0){var O=0,z=x,D=[0,0,0,1],R=[0,0,0,1],F=Array.isArray(p)&&Array.isArray(p[0]),B=Array.isArray(v)&&Array.isArray(v[0]);t:for(n=0;n<_;++n){y+=1;for(w=s[n],T=0;T<3;++T){if(isNaN(w[T])||!isFinite(w[T]))continue t;f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T])}k=(N=I(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;var N,j=N.visible;if(j)if(Array.isArray(p)){if(3===(U=F?n<p.length?p[n]:[0,0,0,0]:p).length){for(T=0;T<3;++T)D[T]=U[T];D[3]=1}else if(4===U.length){for(T=0;T<4;++T)D[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else D[0]=D[1]=D[2]=0,D[3]=1;else D=[1,1,1,0];if(j)if(Array.isArray(v)){var U;if(3===(U=B?n<v.length?v[n]:[0,0,0,0]:v).length){for(T=0;T<3;++T)R[T]=U[T];R[T]=1}else if(4===U.length){for(T=0;T<4;++T)R[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else R[0]=R[1]=R[2]=0,R[3]=1;else R=[1,1,1,0];var V=.5;j?Array.isArray(d)?V=n<d.length?+d[n]:12:d?V=+d:this.useOrtho&&(V=12):V=0;var H=0;Array.isArray(g)?H=n<g.length?+g[n]:0:g&&(H=+g);var q=Math.cos(H),G=Math.sin(H);for(w=s[n],T=0;T<3;++T)f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T]);var Y=i,W=a;Y=0;Array.isArray(i)?Y=n<i.length?i[n]:0:i&&(Y=i);W=0;Array.isArray(a)?W=n<a.length?a[n]:0:a&&(W=a);var X=[Y*=Y>0?1-M[0][0]:Y<0?1+M[1][0]:1,W*=W>0?1-M[0][1]:W<0?1+M[1][1]:1],Z=k.cells||[],J=k.positions||[];for(T=0;T<Z.length;++T)for(var K=Z[T],Q=0;Q<3;++Q){for(var $=0;$<3;++$)E[3*O+$]=w[$];for($=0;$<4;++$)L[4*O+$]=D[$];P[O]=y;var tt=J[K[Q]];C[2*O]=V*(q*tt[0]-G*tt[1]+X[0]),C[2*O+1]=V*(G*tt[0]+q*tt[1]+X[1]),O+=1}for(Z=A.edges,J=A.positions,T=0;T<Z.length;++T)for(K=Z[T],Q=0;Q<2;++Q){for($=0;$<3;++$)E[3*z+$]=w[$];for($=0;$<4;++$)L[4*z+$]=R[$];P[z]=y;tt=J[K[Q]];C[2*z]=V*(q*tt[0]-G*tt[1]+X[0]),C[2*z+1]=V*(G*tt[0]+q*tt[1]+X[1]),z+=1}}}this.bounds=[u,f],this.points=s,this.pointCount=s.length,this.vertexCount=x,this.lineVertexCount=b,this.pointBuffer.update(E),this.colorBuffer.update(L),this.glyphBuffer.update(C),this.idBuffer.update(P),o.free(E),o.free(L),o.free(C),o.free(P)},v.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},{"./lib/get-simple-string":125,"./lib/glyphs":126,"./lib/shaders":127,"gl-buffer":78,"gl-mat4/multiply":100,"gl-vao":150,"is-string-blank":239,"typedarray-pool":308}],129:[function(t,e,r){"use strict";var n=t("glslify");r.boxVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 vertex;\n\nuniform vec2 cornerA, cornerB;\n\nvoid main() {\n  gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\n}\n"]),r.boxFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec4 color;\n\nvoid main() {\n  gl_FragColor = color;\n}\n"])},{glslify:231}],130:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("./lib/shaders");function o(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}e.exports=function(t,e){var r=t.gl,s=i(r,[0,0,0,1,1,0,1,1]),l=n(r,a.boxVertex,a.boxFragment),c=new o(t,s,l);return c.update(e),t.addOverlay(c),c};var s=o.prototype;s.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),a=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,c=t.viewBox,u=t.pixelRatio,f=(e[0]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],h=(e[1]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1],p=(e[2]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],d=(e[3]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1];if(f=Math.max(f,c[0]),h=Math.max(h,c[1]),p=Math.min(p,c[2]),d=Math.min(d,c[3]),!(p<f||d<h)){o.bind();var m=s[2]-s[0],g=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,m,h,i),o.drawBox(0,h,f,d,i),o.drawBox(0,d,m,g,i),o.drawBox(p,h,m,d,i)),this.innerFill&&o.drawBox(f,h,p,d,n),r>0){var v=r*u;o.drawBox(f-v,h-v,p+v,h+v,a),o.drawBox(f-v,d-v,p+v,d+v,a),o.drawBox(f-v,h-v,f+v,d+v,a),o.drawBox(p-v,h-v,p+v,d+v,a)}}}},s.update=function(t){t=t||{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor||[0,0,0,.5]).slice(),this.outerColor=(t.outerColor||[0,0,0,.5]).slice(),this.borderColor=(t.borderColor||[0,0,0,1]).slice(),this.borderWidth=t.borderWidth||0,this.selectBox=(t.selectBox||this.selectBox).slice()},s.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},{"./lib/shaders":129,"gl-buffer":78,"gl-shader":132}],131:[function(t,e,r){"use strict";e.exports=function(t,e){var r=e[0],a=e[1],o=n(t,r,a,{}),s=i.mallocUint8(r*a*4);return new l(t,o,s)};var n=t("gl-fbo"),i=t("typedarray-pool"),a=t("ndarray"),o=t("bit-twiddle").nextPow2;function s(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}function l(t,e,r){this.gl=t,this.fbo=e,this.buffer=r,this._readTimeout=null;var n=this;this._readCallback=function(){n.gl&&(e.bind(),t.readPixels(0,0,e.shape[0],e.shape[1],t.RGBA,t.UNSIGNED_BYTE,n.buffer),n._readTimeout=null)}}var c=l.prototype;Object.defineProperty(c,"shape",{get:function(){return this.gl?this.fbo.shape.slice():[0,0]},set:function(t){if(this.gl){this.fbo.shape=t;var e=this.fbo.shape[0],r=this.fbo.shape[1];if(r*e*4>this.buffer.length){i.free(this.buffer);for(var n=this.buffer=i.mallocUint8(o(r*e*4)),a=0;a<r*e*4;++a)n[a]=255}return t}}}),c.begin=function(){var t=this.gl;this.shape;t&&(this.fbo.bind(),t.clearColor(1,1,1,1),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT))},c.end=function(){var t=this.gl;t&&(t.bindFramebuffer(t.FRAMEBUFFER,null),this._readTimeout||clearTimeout(this._readTimeout),this._readTimeout=setTimeout(this._readCallback,1))},c.query=function(t,e,r){if(!this.gl)return null;var n=this.fbo.shape.slice();t|=0,e|=0,"number"!=typeof r&&(r=1);var 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t=this.enable,e=this.width,r=this.color,n=this.center,i=this.plot,a=i.line,o=i.dataBox,s=i.viewBox;if(a.bind(),o[0]<=n[0]&&n[0]<=o[2]&&o[1]<=n[1]&&n[1]<=o[3]){var l=s[0]+(n[0]-o[0])/(o[2]-o[0])*(s[2]-s[0]),c=s[1]+(n[1]-o[1])/(o[3]-o[1])*(s[3]-s[1]);t[0]&&a.drawLine(l,c,s[0],c,e[0],r[0]),t[1]&&a.drawLine(l,c,l,s[1],e[1],r[1]),t[2]&&a.drawLine(l,c,s[2],c,e[2],r[2]),t[3]&&a.drawLine(l,c,l,s[3],e[3],r[3])}},i.dispose=function(){this.plot.removeOverlay(this)}},{}],140:[function(t,e,r){"use strict";var n=t("glslify"),i=t("gl-shader"),a=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position, color;\nattribute float weight;\n\nuniform mat4 model, view, projection;\nuniform vec3 coordinates[3];\nuniform vec4 colors[3];\nuniform vec2 screenShape;\nuniform float lineWidth;\n\nvarying vec4 fragColor;\n\nvoid main() {\n  vec3 vertexPosition = mix(coordinates[0],\n    mix(coordinates[2], coordinates[1], 0.5 * (position + 1.0)), abs(position));\n\n  vec4 clipPos = projection * view * model * vec4(vertexPosition, 1.0);\n  vec2 clipOffset = (projection * view * model * vec4(color, 0.0)).xy;\n  vec2 delta = weight * clipOffset * screenShape;\n  vec2 lineOffset = normalize(vec2(delta.y, -delta.x)) / screenShape;\n\n  gl_Position   = vec4(clipPos.xy + clipPos.w * 0.5 * lineWidth * lineOffset, clipPos.z, clipPos.w);\n  fragColor     = color.x * colors[0] + color.y * colors[1] + color.z * colors[2];\n}\n"]),o=n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor;\n\nvoid main() {\n  gl_FragColor = fragColor;\n}"]);e.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"},{name:"color",type:"vec3"},{name:"weight",type:"float"}])}},{"gl-shader":132,glslify:231}],141:[function(t,e,r){"use strict";var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders/index");e.exports=function(t,e){var r=[];function o(t,e,n,i,a,o){var s=[t,e,n,0,0,0,1];s[i+3]=1,s[i]=a,r.push.apply(r,s),s[6]=-1,r.push.apply(r,s),s[i]=o,r.push.apply(r,s),r.push.apply(r,s),s[6]=1,r.push.apply(r,s),s[i]=a,r.push.apply(r,s)}o(0,0,0,0,0,1),o(0,0,0,1,0,1),o(0,0,0,2,0,1),o(1,0,0,1,-1,1),o(1,0,0,2,-1,1),o(0,1,0,0,-1,1),o(0,1,0,2,-1,1),o(0,0,1,0,-1,1),o(0,0,1,1,-1,1);var l=n(t,r),c=i(t,[{type:t.FLOAT,buffer:l,size:3,offset:0,stride:28},{type:t.FLOAT,buffer:l,size:3,offset:12,stride:28},{type:t.FLOAT,buffer:l,size:1,offset:24,stride:28}]),u=a(t);u.attributes.position.location=0,u.attributes.color.location=1,u.attributes.weight.location=2;var f=new s(t,l,c,u);return f.update(e),f};var o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n,this.pixelRatio=1,this.bounds=[[-1e3,-1e3,-1e3],[1e3,1e3,1e3]],this.position=[0,0,0],this.lineWidth=[2,2,2],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.enabled=[!0,!0,!0],this.drawSides=[!0,!0,!0],this.axes=null}var l=s.prototype,c=[0,0,0],u=[0,0,0],f=[0,0];l.isTransparent=function(){return!1},l.drawTransparent=function(t){},l.draw=function(t){var e=this.gl,r=this.vao,n=this.shader;r.bind(),n.bind();var i,a=t.model||o,s=t.view||o,l=t.projection||o;this.axes&&(i=this.axes.lastCubeProps.axis);for(var h=c,p=u,d=0;d<3;++d)i&&i[d]<0?(h[d]=this.bounds[0][d],p[d]=this.bounds[1][d]):(h[d]=this.bounds[1][d],p[d]=this.bounds[0][d]);f[0]=e.drawingBufferWidth,f[1]=e.drawingBufferHeight,n.uniforms.model=a,n.uniforms.view=s,n.uniforms.projection=l,n.uniforms.coordinates=[this.position,h,p],n.uniforms.colors=this.colors,n.uniforms.screenShape=f;for(d=0;d<3;++d)n.uniforms.lineWidth=this.lineWidth[d]*this.pixelRatio,this.enabled[d]&&(r.draw(e.TRIANGLES,6,6*d),this.drawSides[d]&&r.draw(e.TRIANGLES,12,18+12*d));r.unbind()},l.update=function(t){t&&("bounds"in t&&(this.bounds=t.bounds),"position"in t&&(this.position=t.position),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"colors"in t&&(this.colors=t.colors),"enabled"in t&&(this.enabled=t.enabled),"drawSides"in t&&(this.drawSides=t.drawSides))},l.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{"./shaders/index":140,"gl-buffer":78,"gl-vao":150}],142:[function(t,e,r){var n=t("glslify"),i=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the tube vertex and normal at the given index.\n//\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\n//\n// Each tube segment is made up of a ring of vertices.\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n// The indexes of tube segments run from 0 to 8.\n//\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n  float segmentCount = 8.0;\n\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d);\n  vec3 y = v * sin(angle) * length(d);\n  vec3 v3 = x + y;\n\n  normal = normalize(v3);\n\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 color, position;\nattribute vec2 uv;\n\nuniform float vectorScale, tubeScale;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 eyePosition, lightPosition;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * tubePosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  // vec4 m_position  = model * vec4(tubePosition, 1.0);\n  vec4 t_position  = view * tubePosition;\n  gl_Position      = projection * t_position;\n\n  f_color          = color;\n  f_data           = tubePosition.xyz;\n  f_position       = position.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}\n"]),o=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the tube vertex and normal at the given index.\n//\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\n//\n// Each tube segment is made up of a ring of vertices.\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n// The indexes of tube segments run from 0 to 8.\n//\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n  float segmentCount = 8.0;\n\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d);\n  vec3 y = v * sin(angle) * length(d);\n  vec3 v3 = x + y;\n\n  normal = normalize(v3);\n\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform float tubeScale;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  gl_Position = projection * view * tubePosition;\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec4"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec4"}]}},{glslify:231}],143:[function(t,e,r){"use strict";var n=t("gl-vec3"),i=t("gl-vec4"),a=["xyz","xzy","yxz","yzx","zxy","zyx"],o=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,c=0;c<l.length;c++)o=Math.max(o,n.length(l[c]));var u=t.map((function(t){return function(t,e,r,a){for(var o=t.points,s=t.velocities,l=t.divergences,c=[],u=[],f=[],h=[],p=[],d=[],m=0,g=0,v=i.create(),y=i.create(),x=0;x<o.length;x++){var b=o[x],_=s[x],w=l[x];0===e&&(w=.05*r),g=n.length(_)/a,v=i.create(),n.copy(v,_),v[3]=w;for(var T=0;T<8;T++)p[T]=[b[0],b[1],b[2],T];if(h.length>0)for(T=0;T<8;T++){var k=(T+1)%8;c.push(h[T],p[T],p[k],p[k],h[k],h[T]),f.push(y,v,v,v,y,y),d.push(m,g,g,g,m,m);var A=c.length;u.push([A-6,A-5,A-4],[A-3,A-2,A-1])}var M=h;h=p,p=M;var S=y;y=v,v=S;var E=m;m=g,g=E}return{positions:c,cells:u,vectors:f,vertexIntensity:d}}(t,r,a,o)})),f=[],h=[],p=[],d=[];for(s=0;s<u.length;s++){var m=u[s],g=f.length;f=f.concat(m.positions),p=p.concat(m.vectors),d=d.concat(m.vertexIntensity);for(c=0;c<m.cells.length;c++){var v=m.cells[c],y=[];h.push(y);for(var x=0;x<v.length;x++)y.push(v[x]+g)}}return{positions:f,cells:h,vectors:p,vertexIntensity:d,colormap:e}},s=function(t,e){var r,n=t.length;for(r=0;r<n;r++){var i=t[r];if(i===e)return r;if(i>e)return r-1}return r},l=function(t,e,r){return t<e?e:t>r?r:t},c=function(t){var e=1/0;t.sort((function(t,e){return t-e}));for(var r=t.length,n=1;n<r;n++){var i=Math.abs(t[n]-t[n-1]);i<e&&(e=i)}return e};e.exports=function(t,e){var r=t.startingPositions,i=t.maxLength||1e3,u=t.tubeSize||1,f=t.absoluteTubeSize,h=t.gridFill||"+x+y+z",p={};-1!==h.indexOf("-x")&&(p.reversedX=!0),-1!==h.indexOf("-y")&&(p.reversedY=!0),-1!==h.indexOf("-z")&&(p.reversedZ=!0),p.filled=a.indexOf(h.replace(/-/g,"").replace(/\+/g,""));var d=t.getVelocity||function(e){return function(t,e,r){var i=e.vectors,a=e.meshgrid,o=t[0],c=t[1],u=t[2],f=a[0].length,h=a[1].length,p=a[2].length,d=s(a[0],o),m=s(a[1],c),g=s(a[2],u),v=d+1,y=m+1,x=g+1;if(d=l(d,0,f-1),v=l(v,0,f-1),m=l(m,0,h-1),y=l(y,0,h-1),g=l(g,0,p-1),x=l(x,0,p-1),d<0||m<0||g<0||v>f-1||y>h-1||x>p-1)return n.create();var b,_,w,T,k,A,M=a[0][d],S=a[0][v],E=a[1][m],L=a[1][y],C=a[2][g],P=(o-M)/(S-M),I=(c-E)/(L-E),O=(u-C)/(a[2][x]-C);switch(isFinite(P)||(P=.5),isFinite(I)||(I=.5),isFinite(O)||(O=.5),r.reversedX&&(d=f-1-d,v=f-1-v),r.reversedY&&(m=h-1-m,y=h-1-y),r.reversedZ&&(g=p-1-g,x=p-1-x),r.filled){case 5:k=g,A=x,w=m*p,T=y*p,b=d*p*h,_=v*p*h;break;case 4:k=g,A=x,b=d*p,_=v*p,w=m*p*f,T=y*p*f;break;case 3:w=m,T=y,k=g*h,A=x*h,b=d*h*p,_=v*h*p;break;case 2:w=m,T=y,b=d*h,_=v*h,k=g*h*f,A=x*h*f;break;case 1:b=d,_=v,k=g*f,A=x*f,w=m*f*p,T=y*f*p;break;default:b=d,_=v,w=m*f,T=y*f,k=g*f*h,A=x*f*h}var z=i[b+w+k],D=i[b+w+A],R=i[b+T+k],F=i[b+T+A],B=i[_+w+k],N=i[_+w+A],j=i[_+T+k],U=i[_+T+A],V=n.create(),H=n.create(),q=n.create(),G=n.create();n.lerp(V,z,B,P),n.lerp(H,D,N,P),n.lerp(q,R,j,P),n.lerp(G,F,U,P);var Y=n.create(),W=n.create();n.lerp(Y,V,q,I),n.lerp(W,H,G,I);var X=n.create();return n.lerp(X,Y,W,O),X}(e,t,p)},m=t.getDivergence||function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=d(r);n.subtract(a,a,e),n.scale(a,a,1/i),n.add(r,t,[0,i,0]);var o=d(r);n.subtract(o,o,e),n.scale(o,o,1/i),n.add(r,t,[0,0,i]);var s=d(r);return n.subtract(s,s,e),n.scale(s,s,1/i),n.add(r,a,o),n.add(r,r,s),r},g=[],v=e[0][0],y=e[0][1],x=e[0][2],b=e[1][0],_=e[1][1],w=e[1][2],T=function(t){var e=t[0],r=t[1],n=t[2];return!(e<v||e>b||r<y||r>_||n<x||n>w)},k=10*n.distance(e[0],e[1])/i,A=k*k,M=1,S=0,E=r.length;E>1&&(M=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,l=0;l<s;l++){var u=t[l],f=u[0],h=u[1],p=u[2];i[f]||(e.push(f),i[f]=!0),a[h]||(r.push(h),a[h]=!0),o[p]||(n.push(p),o[p]=!0)}var d=c(e),m=c(r),g=c(n),v=Math.min(d,m,g);return isFinite(v)?v:1}(r));for(var L=0;L<E;L++){var C=n.create();n.copy(C,r[L]);var P=[C],I=[],O=d(C),z=C;I.push(O);var D=[],R=m(C,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F),g.push({points:P,velocities:I,divergences:D});for(var B=0;B<100*i&&P.length<i&&T(C);){B++;var N=n.clone(O),j=n.squaredLength(N);if(0===j)break;if(j>A&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,C),O=d(N),n.squaredDistance(z,N)-A>-1e-4*A){P.push(N),z=N,I.push(O);R=m(N,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F)}C=N}}var U=o(g,t.colormap,S,M);return f?U.tubeScale=f:(0===S&&(S=1),U.tubeScale=.5*u*M/S),U};var u=t("./lib/shaders"),f=t("gl-cone3d").createMesh;e.exports.createTubeMesh=function(t,e){return f(t,e,{shaders:u,traceType:"streamtube"})}},{"./lib/shaders":142,"gl-cone3d":79,"gl-vec3":169,"gl-vec4":205}],144:[function(t,e,r){var n=t("gl-shader"),i=t("glslify"),a=i(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec4 uv;\nattribute vec3 f;\nattribute vec3 normal;\n\nuniform vec3 objectOffset;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 lightPosition, eyePosition;\nuniform sampler2D colormap;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  vec3 localCoordinate = vec3(uv.zw, f.x);\n  worldCoordinate = objectOffset + localCoordinate;\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\n  vec4 clipPosition = projection * view * worldPosition;\n  gl_Position = clipPosition;\n  kill = f.y;\n  value = f.z;\n  planeCoordinate = uv.xy;\n\n  vColor = texture2D(colormap, vec2(value, value));\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * worldPosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  lightDirection = lightPosition - cameraCoordinate.xyz;\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\n}\n"]),o=i(["precision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat beckmannSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness) {\n  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 lowerBound, upperBound;\nuniform float contourTint;\nuniform vec4 contourColor;\nuniform sampler2D colormap;\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform float vertexColor;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  if (\n    kill > 0.0 ||\n    vColor.a == 0.0 ||\n    outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\n  ) discard;\n\n  vec3 N = normalize(surfaceNormal);\n  vec3 V = normalize(eyeDirection);\n  vec3 L = normalize(lightDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  //decide how to interpolate color \u2014 in vertex or in fragment\n  vec4 surfaceColor =\n    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\n    step(.5, vertexColor) * vColor;\n\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\n}\n"]),s=i(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec4 uv;\nattribute float f;\n\nuniform vec3 objectOffset;\nuniform mat3 permutation;\nuniform mat4 model, view, projection;\nuniform float height, zOffset;\nuniform sampler2D colormap;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\n  worldCoordinate = objectOffset + dataCoordinate;\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\n\n  vec4 clipPosition = projection * view * worldPosition;\n  clipPosition.z += zOffset;\n\n  gl_Position = clipPosition;\n  value = f + objectOffset.z;\n  kill = -1.0;\n  planeCoordinate = uv.zw;\n\n  vColor = texture2D(colormap, vec2(value, value));\n\n  //Don't do lighting for contours\n  surfaceNormal   = vec3(1,0,0);\n  eyeDirection    = vec3(0,1,0);\n  lightDirection  = vec3(0,0,1);\n}\n"]),l=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec2 shape;\nuniform vec3 clipBounds[2];\nuniform float pickId;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 surfaceNormal;\n\nvec2 splitFloat(float v) {\n  float vh = 255.0 * v;\n  float upper = floor(vh);\n  float lower = fract(vh);\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\n}\n\nvoid main() {\n  if ((kill > 0.0) ||\n      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\n\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\n}\n"]);r.createShader=function(t){var e=n(t,a,o,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,a,l,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,s,o,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{"gl-shader":132,glslify:231}],145:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=y(e),n=b(e),s=x(e),l=_(e),c=i(e),u=a(e,[{buffer:c,size:4,stride:40,offset:0},{buffer:c,size:3,stride:40,offset:16},{buffer:c,size:3,stride:40,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),m=o(e,1,256,e.RGBA,e.UNSIGNED_BYTE);m.minFilter=e.LINEAR,m.magFilter=e.LINEAR;var g=new M(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,m,s,l,f,h,p,d,[0,0,0]),v={levels:[[],[],[]]};for(var w in t)v[w]=t[w];return v.colormap=v.colormap||"jet",g.update(v),g};var n=t("bit-twiddle"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("typedarray-pool"),l=t("colormap"),c=t("ndarray-ops"),u=t("ndarray-pack"),f=t("ndarray"),h=t("surface-nets"),p=t("gl-mat4/multiply"),d=t("gl-mat4/invert"),m=t("binary-search-bounds"),g=t("ndarray-gradient"),v=t("./lib/shaders"),y=v.createShader,x=v.createContourShader,b=v.createPickShader,_=v.createPickContourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],T=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],k=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function A(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=k[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();function M(t,e,r,n,i,a,o,l,c,u,h,p,d,m,g){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=g,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new A([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=m,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var S=M.prototype;S.genColormap=function(t,e){var r=!1,n=u([l({colormap:t,nshades:256,format:"rgba"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return c.divseq(n,255),this.hasAlphaScale=r,n},S.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},S.isOpaque=function(){return!this.isTransparent()},S.pickSlots=1,S.setPickBase=function(t){this.pickId=t};var E=[0,0,0],L={showSurface:!1,showContour:!1,projections:[w.slice(),w.slice(),w.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function C(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||E,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=L.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var c=L.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return L.showSurface=o,L.showContour=s,L}var P={model:w,view:w,projection:w,inverseModel:w.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},I=w.slice(),O=[1,0,0,0,1,0,0,0,1];function z(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=P;n.model=t.model||w,n.view=t.view||w,n.projection=t.projection||w,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=O,n.vertexColor=this.vertexColor;var s=I;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var c=s[12+i];for(o=0;o<3;++o)c+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=c/l}var u=C(n,this);if(u.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=u.projections[i],this._shader.uniforms.clipBounds=u.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(u.showContour){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=k[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=u.projections[i],f.uniforms.clipBounds=u.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=k[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var m=0;m<this.contourLevels[o].length;++m)m===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==m&&m-1!==this.highlightLevel[o]||(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][m]&&(f.uniforms.height=this.contourLevels[o][m],h.draw(r.LINES,this._contourCounts[o][m],this._contourOffsets[o][m]))}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=k[i],r.lineWidth(this.dynamicWidth[i]*this.pixelRatio),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=u.projections[o],f.uniforms.clipBounds=u.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}S.draw=function(t){return z.call(this,t,!1)},S.drawTransparent=function(t){return z.call(this,t,!0)};var D={model:w,view:w,projection:w,inverseModel:w,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function R(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function F(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function B(t){if(Array.isArray(t)){if(Array.isArray(t))return[F(t[0]),F(t[1]),F(t[2])];var e=F(t);return[e.slice(),e.slice(),e.slice()]}}S.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=D;r.model=t.model||w,r.view=t.view||w,r.projection=t.projection||w,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.objectOffset=this.objectOffset,r.permutation=O;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=C(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]*this.pixelRatio),s.uniforms.permutation=k[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=k[a],e.lineWidth(this.contourWidth[a]*this.pixelRatio);for(var c=0;c<this.contourLevels[a].length;++c)this._contourCounts[a][c]&&(s.uniforms.height=this.contourLevels[a][c],l.draw(e.LINES,this._contourCounts[a][c],this._contourOffsets[a][c]))}l.unbind()}},S.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;u<2;++u)for(var f=u?a:1-a,h=0;h<2;++h)for(var p=i+u,d=s+h,g=f*(h?l:1-l),v=0;v<3;++v)c[v]+=this._field[v].get(p,d)*g;for(var y=this._pickResult.level,x=0;x<3;++x)if(y[x]=m.le(this.contourLevels[x],c[x]),y[x]<0)this.contourLevels[x].length>0&&(y[x]=0);else if(y[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][y[x]],_=this.contourLevels[x][y[x]+1];Math.abs(b-c[x])>Math.abs(_-c[x])&&(y[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],v=0;v<3;++v)r.dataCoordinate[v]=this._field[v].get(r.index[0],r.index[1]);return r},S.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();c.assign(t.lo(1,1).hi(r[0],r[1]),e),c.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),c.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),c.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),c.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},S.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,"contourWidth"in t&&(this.contourWidth=R(t.contourWidth,Number)),"showContour"in t&&(this.showContour=R(t.showContour,Boolean)),"showSurface"in t&&(this.showSurface=!!t.showSurface),"contourTint"in t&&(this.contourTint=R(t.contourTint,Boolean)),"contourColor"in t&&(this.contourColor=B(t.contourColor)),"contourProject"in t&&(this.contourProject=R(t.contourProject,(function(t){return R(t,Boolean)}))),"surfaceProject"in t&&(this.surfaceProject=t.surfaceProject),"dynamicColor"in t&&(this.dynamicColor=B(t.dynamicColor)),"dynamicTint"in t&&(this.dynamicTint=R(t.dynamicTint,Number)),"dynamicWidth"in t&&(this.dynamicWidth=R(t.dynamicWidth,Number)),"opacity"in t&&(this.opacity=t.opacity),"opacityscale"in t&&(this.opacityscale=t.opacityscale),"colorBounds"in t&&(this.colorBounds=t.colorBounds),"vertexColor"in t&&(this.vertexColor=t.vertexColor?1:0),"colormap"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),"field"in t||"coords"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error("gl-surface: invalid coordinates for x/y");for(o=0;o<2;++o){var c=l[o];for(v=0;v<2;++v)if(c.shape[v]!==a[v])throw new Error("gl-surface: coords have incorrect shape");this.padField(this._field[o],c)}}else if(t.ticks){var u=t.ticks;if(!Array.isArray(u)||2!==u.length)throw new Error("gl-surface: invalid ticks");for(o=0;o<2;++o){var p=u[o];if((Array.isArray(p)||p.length)&&(p=f(p)),p.shape[0]!==a[o])throw new Error("gl-surface: invalid tick length");var d=f(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var m=[0,0];m[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],m,0)}this._field[0].set(0,0,0);for(var v=0;v<a[0];++v)this._field[0].set(v+1,0,v);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),v=0;v<a[1];++v)this._field[1].set(0,v+1,v);this._field[1].set(0,a[1]+1,a[1]-1)}var y=this._field,x=f(s.mallocFloat(3*y[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)g(x.pick(o),y[o],"mirror");var b=f(s.mallocFloat(3*y[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(v=0;v<a[1]+2;++v){var _=x.get(0,o,v,0),w=x.get(0,o,v,1),k=x.get(1,o,v,0),A=x.get(1,o,v,1),M=x.get(2,o,v,0),S=x.get(2,o,v,1),E=k*S-A*M,L=M*w-S*_,C=_*A-w*k,P=Math.sqrt(E*E+L*L+C*C);P<1e-8?(P=Math.max(Math.abs(E),Math.abs(L),Math.abs(C)))<1e-8?(C=1,L=E=0,P=1):P=1/P:P=1/Math.sqrt(P),b.set(o,v,0,E*P),b.set(o,v,1,L*P),b.set(o,v,2,C*P)}s.free(x.data);var I=[1/0,1/0,1/0],O=[-1/0,-1/0,-1/0],z=1/0,D=-1/0,F=(a[0]-1)*(a[1]-1)*6,N=s.mallocFloat(n.nextPow2(10*F)),j=0,U=0;for(o=0;o<a[0]-1;++o)t:for(v=0;v<a[1]-1;++v){for(var V=0;V<2;++V)for(var H=0;H<2;++H)for(var q=0;q<3;++q){var G=this._field[q].get(1+o+V,1+v+H);if(isNaN(G)||!isFinite(G))continue t}for(q=0;q<6;++q){var Y=o+T[q][0],W=v+T[q][1],X=this._field[0].get(Y+1,W+1),Z=this._field[1].get(Y+1,W+1);G=this._field[2].get(Y+1,W+1),E=b.get(Y+1,W+1,0),L=b.get(Y+1,W+1,1),C=b.get(Y+1,W+1,2),t.intensity&&(J=t.intensity.get(Y,W));var J=t.intensity?t.intensity.get(Y,W):G+this.objectOffset[2];N[j++]=Y,N[j++]=W,N[j++]=X,N[j++]=Z,N[j++]=G,N[j++]=0,N[j++]=J,N[j++]=E,N[j++]=L,N[j++]=C,I[0]=Math.min(I[0],X+this.objectOffset[0]),I[1]=Math.min(I[1],Z+this.objectOffset[1]),I[2]=Math.min(I[2],G+this.objectOffset[2]),z=Math.min(z,J),O[0]=Math.max(O[0],X+this.objectOffset[0]),O[1]=Math.max(O[1],Z+this.objectOffset[1]),O[2]=Math.max(O[2],G+this.objectOffset[2]),D=Math.max(D,J),U+=1}}for(t.intensityBounds&&(z=+t.intensityBounds[0],D=+t.intensityBounds[1]),o=6;o<j;o+=10)N[o]=(N[o]-z)/(D-z);this._vertexCount=U,this._coordinateBuffer.update(N.subarray(0,j)),s.freeFloat(N),s.free(b.data),this.bounds=[I,O],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===z&&this.intensityBounds[1]===D||(r=!0),this.intensityBounds=[z,D]}if("levels"in t){var K=t.levels;for(K=Array.isArray(K[0])?K.slice():[[],[],K],o=0;o<3;++o)K[o]=K[o].slice(),K[o].sort((function(t,e){return t-e}));for(o=0;o<3;++o)for(v=0;v<K[o].length;++v)K[o][v]-=this.objectOffset[o];t:for(o=0;o<3;++o){if(K[o].length!==this.contourLevels[o].length){r=!0;break}for(v=0;v<K[o].length;++v)if(K[o][v]!==this.contourLevels[o][v]){r=!0;break t}}this.contourLevels=K}if(r){y=this._field,a=this.shape;for(var Q=[],$=0;$<3;++$){var tt=this.contourLevels[$],et=[],rt=[],nt=[0,0,0];for(o=0;o<tt.length;++o){var it=h(this._field[$],tt[o]);et.push(Q.length/5|0),U=0;t:for(v=0;v<it.cells.length;++v){var at=it.cells[v];for(q=0;q<2;++q){var ot=it.positions[at[q]],st=ot[0],lt=0|Math.floor(st),ct=st-lt,ut=ot[1],ft=0|Math.floor(ut),ht=ut-ft,pt=!1;e:for(var dt=0;dt<3;++dt){nt[dt]=0;var mt=($+dt+1)%3;for(V=0;V<2;++V){var gt=V?ct:1-ct;for(Y=0|Math.min(Math.max(lt+V,0),a[0]),H=0;H<2;++H){var vt=H?ht:1-ht;if(W=0|Math.min(Math.max(ft+H,0),a[1]),G=dt<2?this._field[mt].get(Y,W):(this.intensity.get(Y,W)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(G)||isNaN(G)){pt=!0;break e}var yt=gt*vt;nt[dt]+=yt*G}}}if(pt){if(q>0){for(var xt=0;xt<5;++xt)Q.pop();U-=1}continue t}Q.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[$]=et,this._contourCounts[$]=rt}var bt=s.mallocFloat(Q.length);for(o=0;o<Q.length;++o)bt[o]=Q[o];this._contourBuffer.update(bt),s.freeFloat(bt)}},S.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},S.highlight=function(t){var e,r;if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;for(r=this.snapToData?t.dataCoordinate:t.position,e=0;e<3;++e)r[e]-=this.objectOffset[e];if(this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,c=(o+2)%3,u=this._field[o],f=this._field[l],p=this._field[c],d=h(u,r[o]),m=d.cells,g=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<m.length;++e)for(var v=m[e],y=0;y<2;++y){var x=g[v[y]],b=+x[0],_=0|b,w=0|Math.min(_+1,i[0]),T=b-_,k=1-T,A=+x[1],M=0|A,S=0|Math.min(M+1,i[1]),E=A-M,L=1-E,C=k*L,P=k*E,I=T*L,O=T*E,z=C*f.get(_,M)+P*f.get(_,S)+I*f.get(w,M)+O*f.get(w,S),D=C*p.get(_,M)+P*p.get(_,S)+I*p.get(w,M)+O*p.get(w,S);if(isNaN(z)||isNaN(D)){y&&(n-=1);break}a[2*n+0]=z,a[2*n+1]=D,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},{"./lib/shaders":144,"binary-search-bounds":31,"bit-twiddle":32,colormap:53,"gl-buffer":78,"gl-mat4/invert":98,"gl-mat4/multiply":100,"gl-texture2d":146,"gl-vao":150,ndarray:259,"ndarray-gradient":252,"ndarray-ops":254,"ndarray-pack":255,"surface-nets":302,"typedarray-pool":308}],146:[function(t,e,r){"use strict";var n=t("ndarray"),i=t("ndarray-ops"),a=t("typedarray-pool");e.exports=function(t){if(arguments.length<=1)throw new Error("gl-texture2d: Missing arguments for texture2d constructor");o||c(t);if("number"==typeof arguments[1])return v(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return v(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if("object"==typeof arguments[1]){var e=arguments[1],r=u(e)?e:e.raw;if(r)return y(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return x(t,e)}throw new Error("gl-texture2d: Invalid arguments for texture2d constructor")};var o=null,s=null,l=null;function c(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}function u(t){return"undefined"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||"undefined"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||"undefined"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||"undefined"!=typeof ImageData&&t instanceof ImageData}var f=function(t,e){i.muls(t,e,255)};function h(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error("gl-texture2d: Invalid texture size");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function p(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var d=p.prototype;function m(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function g(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function v(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0||e>a||r<0||r>a)throw new Error("gl-texture2d: Invalid texture shape");if(i===t.FLOAT&&!t.getExtension("OES_texture_float"))throw new Error("gl-texture2d: Floating point textures not supported on this platform");var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new p(t,o,e,r,n,i)}function y(t,e,r,n,i,a){var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new 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+/*!
+ * Determine if an object is a Buffer
+ *
+ * @author   Feross Aboukhadijeh <https://feross.org>
+ * @license  MIT
+ */
+e.exports=function(t){return null!=t&&(n(t)||function(t){return"function"==typeof t.readFloatLE&&"function"==typeof t.slice&&n(t.slice(0,0))}(t)||!!t._isBuffer)}},{}],238:[function(t,e,r){"use strict";e.exports=a,e.exports.isMobile=a,e.exports.default=a;var n=/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series[46]0|symbian|treo|up\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino/i,i=/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series[46]0|symbian|treo|up\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino|android|ipad|playbook|silk/i;function a(t){t||(t={});var e=t.ua;if(e||"undefined"==typeof 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+ * Licensed under the MIT License.
+ */
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c=0;c<16;++c)this.computedMatrix[c]=.5;this.recalcMatrix(0)}var p=h.prototype;p.setDistanceLimits=function(t,e){t=t>0?Math.log(t):-1/0,e=e>0?Math.log(e):1/0,e=Math.max(e,t),this.radius.bounds[0][0]=t,this.radius.bounds[1][0]=e},p.getDistanceLimits=function(t){var e=this.radius.bounds[0];return t?(t[0]=Math.exp(e[0][0]),t[1]=Math.exp(e[1][0]),t):[Math.exp(e[0][0]),Math.exp(e[1][0])]},p.recalcMatrix=function(t){this.center.curve(t),this.up.curve(t),this.right.curve(t),this.radius.curve(t),this.angle.curve(t);for(var e=this.computedUp,r=this.computedRight,n=0,i=0,a=0;a<3;++a)i+=e[a]*r[a],n+=e[a]*e[a];var l=Math.sqrt(n),u=0;for(a=0;a<3;++a)r[a]-=e[a]*i/n,u+=r[a]*r[a],e[a]/=l;var f=Math.sqrt(u);for(a=0;a<3;++a)r[a]/=f;var h=this.computedToward;o(h,e,r),s(h,h);var p=Math.exp(this.computedRadius[0]),d=this.computedAngle[0],m=this.computedAngle[1],g=Math.cos(d),v=Math.sin(d),y=Math.cos(m),x=Math.sin(m),b=this.computedCenter,_=g*y,w=v*y,T=x,k=-g*x,A=-v*x,M=y,S=this.computedEye,E=this.computedMatrix;for(a=0;a<3;++a){var L=_*r[a]+w*h[a]+T*e[a];E[4*a+1]=k*r[a]+A*h[a]+M*e[a],E[4*a+2]=L,E[4*a+3]=0}var C=E[1],P=E[5],I=E[9],O=E[2],z=E[6],D=E[10],R=P*D-I*z,F=I*O-C*D,B=C*z-P*O,N=c(R,F,B);R/=N,F/=N,B/=N,E[0]=R,E[4]=F,E[8]=B;for(a=0;a<3;++a)S[a]=b[a]+E[2+4*a]*p;for(a=0;a<3;++a){u=0;for(var j=0;j<3;++j)u+=E[a+4*j]*S[j];E[12+a]=-u}E[15]=1},p.getMatrix=function(t,e){this.recalcMatrix(t);var r=this.computedMatrix;if(e){for(var n=0;n<16;++n)e[n]=r[n];return e}return r};var d=[0,0,0];p.rotate=function(t,e,r,n){if(this.angle.move(t,e,r),n){this.recalcMatrix(t);var i=this.computedMatrix;d[0]=i[2],d[1]=i[6],d[2]=i[10];for(var o=this.computedUp,s=this.computedRight,l=this.computedToward,c=0;c<3;++c)i[4*c]=o[c],i[4*c+1]=s[c],i[4*c+2]=l[c];a(i,i,n,d);for(c=0;c<3;++c)o[c]=i[4*c],s[c]=i[4*c+1];this.up.set(t,o[0],o[1],o[2]),this.right.set(t,s[0],s[1],s[2])}},p.pan=function(t,e,r,n){e=e||0,r=r||0,n=n||0,this.recalcMatrix(t);var i=this.computedMatrix,a=(Math.exp(this.computedRadius[0]),i[1]),o=i[5],s=i[9],l=c(a,o,s);a/=l,o/=l,s/=l;var u=i[0],f=i[4],h=i[8],p=u*a+f*o+h*s,d=c(u-=a*p,f-=o*p,h-=s*p),m=(u/=d)*e+a*r,g=(f/=d)*e+o*r,v=(h/=d)*e+s*r;this.center.move(t,m,g,v);var y=Math.exp(this.computedRadius[0]);y=Math.max(1e-4,y+n),this.radius.set(t,Math.log(y))},p.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},p.setMatrix=function(t,e,r,n){var a=1;"number"==typeof r&&(a=0|r),(a<0||a>3)&&(a=1);var o=(a+2)%3;e||(this.recalcMatrix(t),e=this.computedMatrix);var s=e[a],l=e[a+4],f=e[a+8];if(n){var h=Math.abs(s),p=Math.abs(l),d=Math.abs(f),m=Math.max(h,p,d);h===m?(s=s<0?-1:1,l=f=0):d===m?(f=f<0?-1:1,s=l=0):(l=l<0?-1:1,s=f=0)}else{var g=c(s,l,f);s/=g,l/=g,f/=g}var v,y,x=e[o],b=e[o+4],_=e[o+8],w=x*s+b*l+_*f,T=c(x-=s*w,b-=l*w,_-=f*w),k=l*(_/=T)-f*(b/=T),A=f*(x/=T)-s*_,M=s*b-l*x,S=c(k,A,M);if(k/=S,A/=S,M/=S,this.center.jump(t,q,G,Y),this.radius.idle(t),this.up.jump(t,s,l,f),this.right.jump(t,x,b,_),2===a){var E=e[1],L=e[5],C=e[9],P=E*x+L*b+C*_,I=E*k+L*A+C*M;v=R<0?-Math.PI/2:Math.PI/2,y=Math.atan2(I,P)}else{var O=e[2],z=e[6],D=e[10],R=O*s+z*l+D*f,F=O*x+z*b+D*_,B=O*k+z*A+D*M;v=Math.asin(u(R)),y=Math.atan2(B,F)}this.angle.jump(t,y,v),this.recalcMatrix(t);var N=e[2],j=e[6],U=e[10],V=this.computedMatrix;i(V,e);var H=V[15],q=V[12]/H,G=V[13]/H,Y=V[14]/H,W=Math.exp(this.computedRadius[0]);this.center.jump(t,q-N*W,G-j*W,Y-U*W)},p.lastT=function(){return Math.max(this.center.lastT(),this.up.lastT(),this.right.lastT(),this.radius.lastT(),this.angle.lastT())},p.idle=function(t){this.center.idle(t),this.up.idle(t),this.right.idle(t),this.radius.idle(t),this.angle.idle(t)},p.flush=function(t){this.center.flush(t),this.up.flush(t),this.right.flush(t),this.radius.flush(t),this.angle.flush(t)},p.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},p.lookAt=function(t,e,r,n){this.recalcMatrix(t),e=e||this.computedEye,r=r||this.computedCenter;var i=(n=n||this.computedUp)[0],a=n[1],o=n[2],s=c(i,a,o);if(!(s<1e-6)){i/=s,a/=s,o/=s;var l=e[0]-r[0],f=e[1]-r[1],h=e[2]-r[2],p=c(l,f,h);if(!(p<1e-6)){l/=p,f/=p,h/=p;var d=this.computedRight,m=d[0],g=d[1],v=d[2],y=i*m+a*g+o*v,x=c(m-=y*i,g-=y*a,v-=y*o);if(!(x<.01&&(x=c(m=a*h-o*f,g=o*l-i*h,v=i*f-a*l))<1e-6)){m/=x,g/=x,v/=x,this.up.set(t,i,a,o),this.right.set(t,m,g,v),this.center.set(t,r[0],r[1],r[2]),this.radius.set(t,Math.log(p));var b=a*v-o*g,_=o*m-i*v,w=i*g-a*m,T=c(b,_,w),k=i*l+a*f+o*h,A=m*l+g*f+v*h,M=(b/=T)*l+(_/=T)*f+(w/=T)*h,S=Math.asin(u(k)),E=Math.atan2(M,A),L=this.angle._state,C=L[L.length-1],P=L[L.length-2];C%=2*Math.PI;var I=Math.abs(C+2*Math.PI-E),O=Math.abs(C-E),z=Math.abs(C-2*Math.PI-E);I<O&&(C+=2*Math.PI),z<O&&(C-=2*Math.PI),this.angle.jump(this.angle.lastT(),C,P),this.angle.set(t,E,S)}}}}},{"filtered-vector":68,"gl-mat4/invert":98,"gl-mat4/rotate":103,"gl-vec3/cross":157,"gl-vec3/dot":162,"gl-vec3/normalize":179}],306:[function(t,e,r){"use strict";e.exports=function(t,e,r){var i=t*e,a=n*t,o=a-(a-t),s=t-o,l=n*e,c=l-(l-e),u=e-c,f=s*u-(i-o*c-s*c-o*u);if(r)return r[0]=f,r[1]=i,r;return[f,i]};var n=+(Math.pow(2,27)+1)},{}],307:[function(t,e,r){"use strict";e.exports=function(t,e,r){var n=t+e,i=n-t,a=e-i,o=t-(n-i);if(r)return r[0]=o+a,r[1]=n,r;return[o+a,n]}},{}],308:[function(t,e,r){(function(e){(function(){"use strict";var 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@@ -331,66 +2263,51 @@
 
 
 
-<h1 class="title toc-ignore">Standard Operating Procedure</h1>
+<h1 class="title toc-ignore">Open Specy Tutorial</h1>
 <h4 class="author"><p>Jessica Meyers, Jeremy Conkle, Win Cowger,
 Zacharias Steinmetz, Andrew Gray, Chelsea Rochman, Sebastian Primpke,
 Jennifer Lynch, Hannah Hapich, Hannah De Frond, Keenan Munno, Bridget
 O’Donnell</p></h4>
-<h4 class="date">2022-07-06</h4>
-
-
-
-<p>Open Specy Raman and (FT)IR spectral analysis tool for plastic
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-reading spectral data files (.asp, .csv, .jdx, .spc, .spa, .0),
-smoothing spectral intensities with <code>smooth_intens()</code>,
-correcting background with <code>subtr_bg()</code>, and identifying
-spectra using an onboard reference library. Analyzed spectra can be
-shared with the Open Specy community. A Shiny app is available via
-<code>run_app()</code> or online at <a href="https://openanalysis.org/openspecy/">https://openanalysis.org/openspecy/</a>.</p>
-<p>This document outlines a common workflow for using Open Specy and
-highlights some topics that users are often requesting a tutorial on. If
-the document is followed sequentially from beginning to end, the user
-will have a better understanding of every procedure involved in using
-Open Specy as a tool for interpreting spectra. It takes approximately 45
-minutes to read through and follow along with this standard operating
-procedure the first time. Afterward, knowledgeable users should be able
-to thoroughly analyze individual spectra at an average speed of 1
-min<sup>-1</sup>.</p>
-<div id="getting-started" class="section level1">
-<h1>Getting started</h1>
-<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(OpenSpecy)</span></code></pre></div>
-</div>
-<div id="viewing-and-sharing-spectra" class="section level1">
-<h1>Viewing and Sharing Spectra</h1>
+<h4 class="date">2023-08-30</h4>
+
+
+
+<div id="document-overview" class="section level1">
+<h1>Document Overview</h1>
+<p>This document outlines a common workflow for using the Open Specy
+package and app and highlights some topics that users are often
+requesting a tutorial on. If the document is followed sequentially from
+beginning to end, the user will have a better understanding of every
+procedure involved in using the Open Specy R package and app as a tool
+for interpreting spectra. It takes approximately 45 minutes to read
+through and follow along with this standard operating procedure the
+first time. Afterward, knowledgeable users should be able to thoroughly
+analyze spectra at an average speed of 1 min<sup>-1</sup> or faster with
+the new batch and automated procedures.</p>
+</div>
+<div id="r-package-and-app" class="section level1">
+<h1>R Package and App</h1>
+<p>The Open Specy R package is the backbone of the Shiny app. The choice
+is yours as to which you start with, we use both on a regular basis. The
+tutorial will talk through the R functions and app features step by step
+together.</p>
+<div id="installation" class="section level2">
+<h2>Installation</h2>
+<p>After installing the R package, you just need to read in the
+library.</p>
+<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">library</span>(OpenSpecy)</span></code></pre></div>
+</div>
+<div id="running-the-app" class="section level2">
+<h2>Running the App</h2>
 <p>To get started with the Open Specy user interface, access <a href="https://openanalysis.org/openspecy/">https://openanalysis.org/openspecy/</a>
-or start the Shiny GUI directly from R typing</p>
-<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="fu">run_app</span>()</span></code></pre></div>
-<p>Then click the <strong>Upload File</strong> tab at the top of the
-page.</p>
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n8e1LCLnIw7E5dxbiqI3CXFsoUlKiO3lJ327dq8uNw48QMzEcuz6Lojli7JgEtqLnV19QPhjHn3XC223NUUj0VSlgpAc5SSNqAsulXQ1olrC/pA7xAsaXX9WCMXwWV7MRa3PViLiUBwQzI25OvKCFdXvzcp322q16AUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoCQlSvepJ+oU6tz5NXlX1a/ot3CTwx8U2Ya+OcSHD7iWoHuWwW3TseiZdblyWIshT8kKUA2CpIUEICikE7J7K+rv+zq6LNEN2C10U2msme20pwSk4k2GXNnFj9kht91MhAS3sXW3Snndb7B7QAH5SOrc+TV5U6tz5NXlX6vpvR49FHLxeZcrN0T+lcKYzBW/HReLVsh8gJPI2G2ypxbYJ65Ps9Xt3qB3q5Mv6LnogsIlZVFm9G7pFNkWa4Q2bVAZsSW3JLT0dlZdUpRUOQOOKTz7BI2AJBpwC1PyO9W58mryp1bnyavKv1gP9H/0RUe0IvknomtMo0eRAXNiuTbelpJaQp1JbWepIQ8tTWzbfaFc3apO3avXAT0O9oWhDXRN6fy1p6oSmI1iQXULWhvmaSCgBbiFuBKkb7jYnYbHYS6Pyf9W58mryp1bnyavKv1r6ddGp0RGfZ3HwhrortL0IdlPx1XaHbG34qy1127jSihBcbBZ5VHYFKnEjY9prLn+ww6IP/wDp46V/+GUfrQp+Mzq3Pk1eVQQUnYjb66/YRqH0QXRFaf3K32tPRV4belXUlqE7ZMNaeb9I3GzLqucdQCnmX1rmzQDagVBXKlX5y/6QroRo3w1dLVqbo1oFptacRxW1RbGq3WGxxupjR1O2mM64UI3O3M4pSz/FRoCp/R0czxDBOmg0KvObPMtRZOQTbdGdfUAlEyVbZUeN2n4VOuIQP4rFa6a46Kas2jjDyvh6uuKzznA1Fm2b1KptRlOz1z1toQlJ9pRWtSSkj3wUCNwd6x1arrcrFdI17stwfiTIb6H4kuK8pt1h1CgpDiFpIKFJUAQoEEEAit44f9Ii6QhlmNktxtWkly1DhW5MKFrJdNJLa/lzLaW+rQoTynYuBHs86myTuSdyd6A+mGVarS8R6czjGyXS3KzGynT/AIEJzLl5tjgW5FvcK3W5zrEq7QXGllsdu+ykbHurRvo6tfdd+KnovOOHTTio1XyLOcOx3S2BlNnmZleXrku05KmWoxlsPSFLU048psAgKHNyd3tK3sToRuOaz8OPEVxD8SOtmv8AGs2X5Bw75UMayPKXvSXrvlD78aRHQetStL77jqFL5XAUr2UD2HY4o4k+l64o+JTh8c4XTh2menmDXK5s3PJ7BpNp5Fx9vIZrWym3p3UE9cUrAWEjlRzBJ5fYSAB9JuPrLOFrhetvDFp1buk81g0BsuHaIY5f8JxrTTTJ6fa7lIkBT794cksz2ES33ndw4lxCgNiO0Oq3oY/xqaeZFqT0lfGx0ft/vuKm46NY1coN3XZl2qdGuy3AzNlIZXuphxbpdcCx7QUvnSd9jXzv0G6Zriw0S0Ux/h/v+E6Vam45hq1nBGtXdNYeQP4wFnmKILz2y2kBQBCFFSU8oAAAAFkX/pNeMrLrrrRkGY6oM3e4a/2yPb9Tp1ws8dbk+MwsLZbZ2SBESjZKUhoJAQlKQNhQG10HVXU/X7+jY6pXbXLUe/ZjMxTihsqccm5Pd3p79vS9Ab61DTr6lLSlXWuEp323Wo7dprWHovpPHnh/Eo7rp0eFjduGa6bYzOyS7Rm3IykKsrSUtzQ7HfcR6WypDgSplvmcPMCgBQBFg2Di/wBcsa4Tsg4KbRf4SNP8ny+Nk14ty7U0p924sNobbcTII6xCQlCRyA8p2PxmvK4beJfXPhD1ks+v/DjqNOxXLbE4pVuu0DlUQlaSlbbjawUPNLSSlTawUqB7R3ULZn0T4Ybf0b/TxarzeG17gojaA695HZbldMdznSm8OOYvcZ8eOuStE60vgpiNKCF+00d9yBzg7A9LiK1/4guHz+jzcJGLaP60ZNh7F31GzpvIfctf3oPpqotyd6lLjrCkqWhC1uKSnm5SdlbEpBGFcv6efjRu+HX/ABvTTT/RnTG6ZXbnYGT5rpbpJAst9uUd3+uQua3zKR1h7VKbCVb9oKTWY4HSs45w19DRw16CaTO6T6gX+2ZTlr+oemWo2FRr/GhoXOW7b5LrEhILTgC1qbW2sEhwhW4JTQWZt5oZjeJ8XXGR0X2ufGFGj3zUDK9JcknZHJvLKS/fF2lt1+xyZPNt6QtSt3UlXatQ33rD2FceXA7geda+J4hOlB141kY1UwvILLl+mGYaLS2ITU5YUWX0JXPeTDXEcTyo5W0htI29kJG3zh4iekm4weJrigsvF/n2qjkDNcVRDawuTjEVFujY0xEUVRmIDDXssNoUSeXt5ipXMVbmsr6s9Oxxrar4dlFlGKaS4vkWdWl22Z1qPhOlUC2ZLkER4cshqTPbBVs8OxwtpQVDs3AoQzH0mWums3CrwG8D2lXCxqtkWEYZd9Dm8wnv4feH7cLnlL8nmnSHnWFIL7zSyAOYnq+fuAIrJvBPxba/8W3SN5DwzdIjo3GwF/i04f2cJubj9gk283qaiE4ux5E8h5RLkh1TSkB1OyFFSOVI2rSbhm6Xbih4atFIHDm7h+meo+E2K4u3DE8f1d08i5C1jcp1XM67ALxCo/Or2igEo5iSEjmO978HWX5T0gfSEjjj45+kBwzT93C8ls2T5RkuX3n0C4TIkSQFph2OIw0UuOtojpbQw2EhsOIICiSCBevS/Wq88FnBTwz9FLco4hZFj+PS9R9WoSXeYi/3Z5xEdl3bs6xiKlaf8Lia+d9Zx6Sfi9uHHjx0amcV0v0lEXLcmedscaWf2ka1tAMQmVD4FJjttbgf2iqsHUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQGyXADxx8dPAAvItS+CzVGBjL+TxEQr45Ms0SZ17MVfWA/8AS2lNt8heKtwpKiOY7EJrZlP9Ir/pAJUoHiLiJ5UknmwGxAdh27+o2337B8ZNfN2FeLtbno78C5PMrirUqMW17dWpQAUR9YAB+Ou57uMxICVZPOUEoKEpXIKgASCRsf4gH/IU0B9FHf6Rj0+ceOuVM4oLUyw226ovu4TYgk8iVqKQQx7SiEL5QN+blO3caoS/6SL08cWKzcXuKu0GK8GtpbeD2ItoLgbKQsiPuggOI3BG43r55e7jLg0pgZHM5HGnG3E9d75CySpJ+MEqV5nbbc1wmZfklwtKrJPuzr0dSWUcjqt9kNb8iB/Ab/gPiFVWvqD6b3zpxenjzC8xLHa+JCyXxtMklhy66a2FgIfaWtC1pQ6wpQSnkUoLISSntCfgqb107X9IOgWi4nIOJqxpMPrES4bmFWFYLaEKU4VH0fl22Cezt5g4P4gfMBF/vrbyZDd8mpcR1fI4mY4FJ5DujY8245T2j4vg2rtXPN8vvEKHbrlks11i3tuNw2lSCA0lZJWBttvzEncncn4atVxnWqyh4sWlkVrtO6vd31TV7K11pq7HJB0oxoqSbafju6WZa2yq2j4Xvc+geN/0jzpsYuHsTcQ4ssdZioUsero+nNlYEcBSQSeaIlHap1HvSSSv+B29Bf8ASPOn2Q6WFcS9sCw4tHKcFx/fdIUT/wBh3ewrY9x27N6+cUG/Xq2NIZt11kMJbcDjaWnSkJXzJVzDb4d0JP1pHxV2Rm2XpmesE5NND4O4d688wPtdu/x+0rzNZOM+ibf9JB6eeSlT0Lips7zaUA9YMEsSdyWg6E7Kjg8xSdh2dpBA32rSjjJ4i+Iriu4ir9rtxXZEm653eURU3mci3x4qXEsx0Ms7Nx0pbTs0hA9kdu257d6sNObZghlDCconBDe/Vo9JVsndJSdv/ZJH1E10bjcrjd5rlyus52TIdILjzy+ZSuzbtP1CgKNKVnPgG4dtJOILVm4McQWeM4zg1isbkm9Xh69M28offcRDhIS88lSeyQ+h5aOUlTUZ5I5SQoAYNClJ96SN/iqK+g+SdDxp1MTZCnUu941cU45ZGbzZEwIdwVdLk9Ghonz7a4qajroMV95xyYVhHo6AAgLG5RjPWvo2sJ0N4ds11ZuWqtzv9zti4UjDHLRGiot12tizag9cty4t1yOXLktpC0AAKj7qO6lJQWsblSzcO2lzVvT7Ens+z2yYLHmpjOXq7xoCJC0FSWi86lsKIHeBzb7fwrfI9CFiyVFK+I+57gkHlxlrb/41aU8Of/6QmCf+udr/APxTdfd3TSxWHKNTrLjeUSVM26feGo811L4aKG1r2JCz2J7x2nsr0vcXYWx9qYLEVsbSz5LW1ktLN8jXMeN91DebeDYePweH2bW733xSv4sXd3SWsou1r8h89z0ImKf7x90/8NNf86g6EPFP9466f+Gmv+dX1jwrhRxmbasdv+X3K4wnHEoTkVtdS2lph9JQtTDjvWJUw46282G0BKjzDc7BXZ07Zwf226BQa1LDYTAceD70RkNulLSXEyEbPFSoaipSA7sDzNqBA7Ae0eB9wczj3nhy3qc9v2vX6LHRVvJ3UHTjJYjjyZaV+CeqcdOm/B6M+VA6ETFD38R90/8ADTX/ADqf7EXFiOX/AFj7rsO4e5pr/nV9UrloZpfiOUs2TIbjJVHjYZc7lKlT5KoyZMhictllwCP1ywytsJUgIBUtJSrYbmu5K4ZsAvOBx8qteasW15uzKnSGWiuUpznYadb3QVcyW0EqQtewO7iSUp22rT2HuPHK3hnlfB3qW4tftX5ObmMQ3n7pNTMo4uOaOrVqafJzwty8/Iz44cQfRIWbRjRXJdWLTrpMuL2PWtc4wZVhQ0l9KCOZPOl1RSdj2HY9taUKGxI/jX2a44lFXBvqUsjYnD5J2+L3tfGflCneU79qtuz666Jv7sfZ2xtpUqWDhli4Xeret3zt8x6n3Ldv7X3h2PWrbQqZ5RqWTslplTt4qS4tnGgJHaPOvoBnfRz8C+c6sXXSrSfiHex262LUMYs/Hj3D11EeQ8ZbkR56TJbiFuUpuBJaWzHTIa656IA4OdW3n570Qmj+nmLSMzyDihv0a1xrHKuiZ8zDGIzc4IXcGhEjB+Yha5DC4KTJCkJDbbxUjnISlfR1FtXPTTQ+lbzZF0QmB2Wb6v8A9ZO4tqduzVudemY3DQ3Y0GW/HF1uhE//AKPbJAYDkV9BWtxLo3QNk9Z6k3oadN2LkYsXiWvjqGslat01C8MbadjJVFDvVcjklK3pBcPLysoXsjZ3kUglQymmHpxNBaVu7w49G7pTqqrPtLb7MvK7/YdSX8ajZb65jM2+FHbWw0h4JYMhl2SXXVK6h51pqSkBuLILyVJr2f8AY26beomr9N4pJ9skSZaGxi10xRn1xb3OraX6umttSlMxrhI63/oranQ27+zClJUshB2Su+3KOU0JpW71z6LrRJ/EbPKsmr+dQb9dob8SJaL5j1sDsi+By9KRb1ttzSYy0t2thlaAp5ZenM7ABQSOeadFNofgSk4vf+Ke8DJXIkUiI3jUAxGZMlN2DbLzhnhxHVrtLgdIQSPSG+UE7gpNQeognUlaPE0epUJO6EqI98kHb4txvU7E9gOx+AmgFK+lWedD9wmyMiy60aZa4XN5MrMIQwWOxfIU6ZFsrDE5u6pks7t+kSvWMRcdhKSFKbYLiW3OtQKtpvoj9C8bzzGY07V7Jr9bWcgiWzKlQIEExVLRep0KVJceTKbXBgqbhoaSsda6h57t39hKkfGdlzpe0anz6pW5miXAxw8ZnrjesPYm5LldimYXbL1i6nLgxaXIRlyurcTcVRzMWyGkJcSJCG3WG1KbXJ6tski8770LenVkuF/i3fiukWEWOC4sx8ixlkSXkgJUm6pbiyXSuzkq6v0sbK5kqPIEgE3LIchoBSt5b/0XvDtiuE3695TrZqBa5ON3SS3cpM/HLShpcVKG24zzTQnFRakOqW82+pxIUw2shtRSFK6d96KvSnS+0zZGtnENkNrnxmLzLatlrxiA84uLb3ZJCz109sgyWGWXWdgQfSBueUAq0qcmrlknGOZmk1KuXWbT5rSXWDLNKmL83dUYzk0+0oujKAlEwRpDjIeSApQAWEcwAJ25u899W2kAqAPx1xxamk1ykIpW7GEdG5p5rXwyYlmWnTV9tuVXGyxpS1z7oy8u+THFqL0eFFCQ0+yhkF3rGXy9H9HdblMIU4hYvad0H2Fwp1yjjiAyx5q2IhftmcKhETWZS4CRdI5M8BVtj+nbSH1cpSphQSFAkp1JZVdjm6e3zPnjSt8Mb6J/h8tM7HrhqdxO3x22XbLLDYUIsFntyFXb1i/HbF0gvPTCFWrZ5aUSVt9YXGHApobHltLQngA0g1c0qtOW3S/X21uOKv8AbMkujkplQs92Zu1vi21hyNukl5xiU6v0fnBeUj2FAIVtL3lZdu1zWV6GnlKyJxX6HQ+G/X7INGbdf3rpHs645YnSG2UOOIejNPgLS04tKFAOcpSSFgjZaG1hSE47qJ3RGrClKVSClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAc2WHpCihhsqIG5A+Kqgt08EKERW47j2dldcgHvG9ORHhHlQFb1TJ229Wj7tNT6qlg7iBsfjCRvVDkR4B5U5EeEeVAXtw/MuW/XjCbhPCWI8fLra6++84lCG0JktlSlKJ2SAO0k9gr7Lva0aMl1f/AOd7EyCo/wD8Sw9j2/8A2lfDLkRttyD/ADFQGWPkUfYFdv3a3trbt0akIUlPO09W1a3qOg757h4bfKtRqVa7p97TWiTvdp63a5j7pq150oXaW7EvWnFjCZkrkMxTk8Tq0OrSlK1hPWbBRShIJ+EJHxV1/wDTJot/3s4j37//AEjh9/x/1lfDTqWPkUfYFOqZ+RR9gV2Vd1HFx4YWPvPqOmy7iOCla+Om/wAK6z7lp1l0XT2J1bxIdu/ZkkP/AJlDrLouTudWsRPbv25JD/5lfDTqWPkUfYFOqZ+RR9gVf1pYx/8AxY+8+on6kMD99l7q6z6/8aGqel9+4TNRLNZNTcbmTJWKSG40WJf4zjrqyU7JSlLhKj/ACvkUu2z+cn0VXf8AwrrBpkHdLSPr5BTkR4R5V03eTeGrvHioV6lNQcY5bJ35W/meh7nbpUNz8BUw1Kq6inLNdq1tEraegrm2ziADCJA7gQNhXfvV1zDIotsg36fLmM2W3iDaGpDnMmHGDzj3Utg+8R1rzq9h/acUfhryeRHhHlTkR4R5V1zkO3XKxtMkjY24fVyJp6okfRo+7TVHkR4R5U5EeEeVAenj03KcSyCBlmMSZNvudqmNS7bcIawh6K+0sLbdbUO1CkqAUCO4gGurIgXCU8uRJhlbjiipa1pBKiTuSf8AM11uRHhHlTkR4R5UuwVvVMob7W8dvf7Ce2nqmVtsLf2fFyCqPIjwjypyI8I8qA7Hq6f80X+FPV0/5ov8K6/IjwjypyI8I8qArG0yidzbwfrQmhtMk99uB/8AYTVHkR4R5U5EeEeVAV/VUs++gb/WkGvRy285nnuRS8uzW4zLtdJzvWTLhcHetefVsBzKUe0nYAb/ABAV4/IjwjypyI8I8qAreqJI7rcB9SE0FqlpO6bft9SAKo8iPCPKnIjwjyoCuLbOA2ERQHwAbU9Wz/mi/wAKociPCPKnIjwjyoCt6pkk7qtwPb27oT21cU/UXVq6RLlAuOYXZ9m8WuDbbq07KKkzIcMNCJHcH9ttkMMhtJ7E9UjbuFWvyI8I8qciPCPKj1BW9VS99/V/ae88g7ak2uYo7qg7nbbcpHd8VUORHhHlTkR4R5UBXFsnJGyYRA+IAAU9Wz/mi/wqhyI8I8qciPCPKgK/q2f80X+FPVs/5ov8KociPCPKnIjwjyoCv6tn/NF/hT1bP+aL/CqHIjwjypyI8I8qAr+rZ/zRf4U9Wz/mi/wqhyI8I8qciPCPKgK/q2f80X+FPVs/5ov8KociPCPKnIjwjyoCv6tn/NF/hT1bP+aL/CqHIjwjypyI8I8qAr+rZ/zRf4U9Wz/mi/wqhyI8I8qciPCPKgK/q2f80X+FPVs/5ov8KociPCPKnIjwjyoCv6tn/NF/hT1bP+aL/CqHIjwjypyI8I8qAr+rZ/zRf4U9Wz/mi/wqhyI8I8qciPCPKgK/q2f80X+FPVs/5ov8KociPCPKnIjwjyoCv6tn/NF/hT1bP+aL/CqHIjwjypyI8I8qAr+rZ/zRf4U9Wz/mi/wqhyI8I8qciPCPKgK/q2f80X+FPVs/5ov8KociPCPKnIjwjyoCv6tn/NF/hT1bP+aL/CqHIjwjypyI8I8qAr+rZ/zRf4U9Wz/mi/wqhyI8I8qciPCPKgK/q2f80X+FPVs/5ov8KociPCPKnIjwjyoCv6tn/NF/hT1bP+aL/CqHIjwjypyI8I8qAr+rZ/zRf4U9Wz/mi/wqhyI8I8qciPCPKgK/q2f80X+FPVs/5ov8KociPCPKnIjwjyoCv6tn/NF/hT1bP+aL/CqHIjwjypyI8I8qAr+rZ/zRf4U9Wz/mi/wqhyI8I8qciPCPKgK/q2f80X+FPVs/5ov8KociPCPKnIjwjyoCv6tn/NF/hT1bP+aL/CqHIjwjypyI8I8qAr+rZ/zRf4U9Wz/mi/wqhyI8I8qciPCPKgK/q2f80X+FPVs/5ov8KociPCPKnIjwjyoCv6tn/NF/hT1bP+aL/CqHIjwjypyI8I8qAr+rZ/zRf4VTdadYX1byClQ7wa4ciPCPKpGwGwAH1CgMz8FGgeC6/wCRai2zPHLilvFdE8tyu1+rpQaJn223LkRg5ulXM0XAOdA2Kh2BQ76yzhnRoWTWThE0612wjVGy4nMmYpmOS6j3vO7rITbIlutN9ttrYMduJFee6wm4I3QErLh97sQEnAHDNxG5hwu6n/6ScTsVnvLcqyXCy33HshjrdgXi1z4y4syE+ltaHAhxpxQ521oWhQStKgpINbNaLdKLpxb8XznBNTOHrF7dgsbQm5YdpzpbZRdXbY7MnZBbLnLclzFzTN53fRXHDI6/2VMMISkJGxlS9k483Dp1+hIJ5nfhf8tPqVHOiRylOkXuEgz8dn6iydWoMO25jEypYxx7En8ReyH1r1q207RhGR6Up0th1KEKbKOcclY7s/Rc6oZHldkt+N64afXTGsmxCRkeNZzaF3abCu0ePN9CkMR4ce3ruTkpl/scYETmbQOtUQ0Qs+490xOv7eStXq0aV4DCgRcjizIeOtW2Su3M2pjG3MaTYurW+VKhKtjq2lqK+vUtanOtCjvXWsfSu5pjVqf00sPDXgMTTReBLxSNprFn3piNHjLuqbs5IE9qcm4LfdmIQtxSpBQ42hDRRyISBVfN0fTr+Zp2yrn+r+RkjhK6GTJFcZmMaS8aOeYVjth/01OYLLsL2Tymp+WyYjcd+a1alxoy9kpalxSHn1MJK5CGwefmSnWTgn0z0V1g4vMM0X19u1wtuLZZfzYX7rbZaWXLdJlhcaFKUpaVAtNSlx1upI9ptKxukkKGf7d03+rytb/9PuZ8L2lmRXu26lu53hibki7Nt4vd34kSLK9F6iahTrDqYMZZZkF1KHGwtO25B1d1qzXSnJL9j83RPT6VjDNtxG1xLw8/OLj1zvTTIM24++IZ6x8qKEJOyUIQdgoqFVWvG/r9mv53Mu936Pn1WNlsR6OfHsY0Fhvav4rPd1Quo1HuvqJzKvVsa2WLFbY+29JWExX1OvrujMpKG/ZS4m3ONFbfWdajpRuhP4sJEi2WMZVhaL4/cbXbb/YXps5D+Oz7lb351tiy3FQww45IEdUcejOvpakONtPFsq3Fsau9KnxB638RU7iVz3HMbcvE/SW44Au3xIzzURuFPtsmFLloSlzmEp1yZKmKWTyqkPrUU8vs16urPS+8RuryMWvmQ49bWspxzILJeX8pZyG9uG4S7WECOswHZy7fFKy22p4x47fWLQFDk3VvFx9nxfxXIaeXL7flb87lrai9GdxFaU6Qt64ZzJsEKwu6TWvPm3TPdU4YlwuTdvjQCkNezPK3UOqZJASyecr/ALNcNLOje1q1l4Yb1xSYFl2MS7dYcfud8uGPocnLnNwLesCU4t5ERUJh1KN3UxXpKJDjSedDZCkc3ocS3SkcRPFJpZmWj+eWrH4lozTVxzPpYtUV1tcJ5Uf0du1xypZCLe0lLakMnchbSTzHtrv6a9KdqXpxwyscNjeimGXQQtOchwa15VOlXRE2FZry4+9KQiO1MTCVIDshxSZK2FO8vK2pSkpGzjF9u2unoVw+Kt27LX8i4c16FPiWxnURGlGM6uaXZXkETUCJh+T2/H8jlD3NTpMCVcGnZzkmI02mP6LClurdaU71Xo60rAVsDa/GHwl6L8P2g+gWW4ZqlZMmk59CyF/J80xeZNk22UiLeTFZWyzLYjutKaZ3C2y2kqUnft3BPrI6YTiMia15brtacFw+Pd8y1QtebXSIuJIeidfCt1wtyoHVre3VEkRblJbdSo85BHItG1Y74g+NW4a02PTbEML0Mw/TuxaUonDE7Ri7k6W2lUqd6c4t5dykSFvK67xHlKdklOwqQcrLMubt7S6Zem3536rG7mO9FfwT3nie0201tWmuo83Acn1VgWDF9VIGoEG9Y5qHaXY8lTzy5MFhs2CcOrZfRDWpxfIp5taUqZ51aqxOi11KvOT4zBxnX3Ta8WHLsUuN+sWXWOTd5sWWiBN9ClxGorVuVPflNvEbttRlgtkvBXVpUoXJg/TFZ5pRmQz/AEZ4TtKMPul0z225fm6rBGuaI+R3GB6SqOlUdyYtmAwHZb7pZhpZHMoJBS2kIq1NFuk71L0f0mtmhkvSHFMjxaHhd5xifAuUy5xHrjDuN4j3dZVJgSmH2VolRmgOqWhK2+ZtxKwqiTu+1wsuV3Lhu3QwcVNjuk7D7lmWBN5UjVgad4/iAvckz8jvBj2+UFQ/+jdUmOItyYkKdkLY6tAWFALAQcZcS/A/m3Cx7jcgzfUHHrziebPzWbTmOMNTlReshSUMTmy1NjRpHOz1rTn9XyuNutqbUoK7MjaldMRxKZ5qnYdZLDiWJ4vfMb1b/wBIVpcssWS4y1cfVVsthjluQ85zxSza2TyElW7ro59inlx3xQ8cmc8TGZ4xmjuFQccdxNxb9tYayK83sGQt5DxWpV6mS1ciS2hKWk8rYQnYhW5Nbdk1zafBfO4ko8j7Xfyt+ZtJrh0T2juWZTYtKOFHF8rgqynPI9i0u1jvOdQcgwzPIKo8h12VJkwIyTYpYQy2+mGrrV8jjqFpSWSs4RtfRPa05ELdleH6xaeXzAZ+Gz8ld1Os825PWmHFhTmoEll1kQfTjJTLkRmksIjKU56Q2tO7ZK05N4Uel3wvT7iMsGU5Rw0YHp5jF31IhZdqxM09tdxddv8AKhsThGCIb8xbENkPTXlliIlpP7TYFKEJQLEwfpetXtKnLXimk+i+I4tgNtw2fjq9P7Dd73HZkImz2bjJmG4InC4NyzKjRlJdRISEIYS1ylsrSrKTyrtydY0tp2+J17z0QXEJhd8mY5qpqxpfhst3I12PDE5VljsRvMZabfGuINvdMcttsqjTYSg/MVGb55bTalJXzhHv6i9EparXZrZleAcXWDixxdALFqVml7y1q5QmLci4y0REMMBuCtx/nfdaS0nk6w+1zhHs72rcelFyfKo86DqhwuaZ5pEZyiVkGGQ8xTdrk3jE6TDjxH+rMictdwZWiJFcUzPVIQp5gL22UtCvJyrpJtTcu0CkaET9L8UaE7SC1acXLI45mCZKtNtuzFyhLKC8WUvtqYDJWhCQttaipJXsoZnmssvbTr+RFbOub69R1dfOje1q4f8Ah1t3E9ecvxe+41Lu1utk1zHnZy/QpU6G7LjID0iIzHmoU2y8lTsJ2Q0hxsoUoEpKtfK2k4qulT1M4tdHb3pRmmh+E2iRlGQ2O+ZPk1kkXUy5861wpEKOpLMiY7GislmQvdiO022lXakAbAat1p2zPtyL53JplXPy9vRYUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgMn8LHDNM4mMkySLJ1Gs+IWDC8QlZNl2TXuNJfat9vZeYYKksxW1vPuKeksNpQhP8Ab3JASaz5n3Qg8VdqtWPZbpBmGCZ7jN4sEe43HL7bl0O3W20mQ/IajpfcnutLaaf6hPUPuIbS8p0NpTzgita+H/XPWzh/1DbzHQPK5lqvsyI7bCIcNuUJzEjlSuK5HdbcbkIWQj9mtCgVJSQOYAjYdHGf00WNXZd/nZnq8JM67xLXIcu2GOPCbPSZBjRXUvw1JfeCpjykNLClcy0qSOZKCm2VtO3P8icE79u2p47fQwdIr1MpczRKBDdtsF2Re4M/MbYzJtCkNNPCPMZU/wA8WQtl9l1tlwBS21849lKiMf6+dH1xdcMudYZplrJpDItt/wBQG2vcpamJ8eS7MeW8hn0XZpaurkJdcQhTStlJK0/Ad6vBvpLuk9xnJImBvcRedMX60LetbMCdBbXcm5LhQ0pLgdYL65aQ220hxzmfbSkNoUkezXDO9VOlHz7M7VqHqFa9Trjf7fE9yVqvU/BXTLQtSQfRku+iBSpmzaVB3cyh1YUFgpBrKvoFms78TvN9Dpx5zIsudZMCxW7Mxbq5bEu2TUmzTUyZqGi45FZLElXWvJILZaHtl32AkkHboYl0XHEhkq8rsE2VjVqyHFMqkWGZabnksRmOH4dsfulzcdnLdTGjtQ4jAU6ta+1biW0gkK29KB0hnSq5pnkzDsc1bzuVkrV7jXadZ7HjDKJrMy2SG5SHFMRogcb6mRHbfWgpCC4jncSVFRPocL3Ftx6aKaiOaf4vpK/kOa6u3aXdMW912PvGbIut5gy7W7cYiHglqT6Q3MXv1qHI61MtqI2QoG6vh6vy+v5Gkknr24/QqMdBv0khuEm2ztGrPFdt8ZuRdm3s4tinLe0tyShCn0IfUtHMiHLeSCN1tRnFp5htv7urXQP8b2FZjfmtO7XYsnwqzz57DGoMzJLfaIMpqG44y9KKZMklhlT8eS02pxQLhYPYlSgmu65knTa5DnOpePXfUC/KulrxyzHUaXfclsTUUW95T79qe9KkuCO7z+myCw9HcUtSZKkoUUq2rw+IPV/pgtKcXlYzrLqFlkizTbfdLhcn4SIlyjsRJl5mMy+vkx2liM05co8wpbWtCOfmU2kBY31JcseHb5duULK2dGZ0HnSLWiLMvd+0rx+JZbTcpkW/5CrUC0rt9pRD60TJUiQmR1bUdhceQy46VcqXmVNH29gcecenA5f+BrU1vB5uodvyu2yJ13gxL/AguRULlWy5yLdMaU04pRSUvMcyVBRStt5tQIO4HYtHSi8fNjQtmDxL3wtuX+6Xl1qREhvocl3JTy5/Ml1hQUzIXIfW5HP7BS3VK6vftqjrH0ifEHrlY7RbM5Rj706z5E3f28gXZG3ri/dfS5syRKcdd5kkyZU5x55oIDSyzHTycrQTWeREtZvt20PZwfomeOXUbTW06v4jplaZGOXrGpF9g3NeY25pJispjqWFBbwKHeWVHUGyN9nNztyq2sPih4Mdf+Dq82yx68Y7a4Lt3blehvWnJIdyZ62M91EqOtyK4tLT7LmyXGl7LRzJ3HaKvaJ0tHSKQbZa7RD4pr60xZLe9CtAbgQQqGy4200oNrEfmQQ2wy2lQPM2hpKUFIAFTxP9KBxL8V+SuZJqIjHGVsZZGv2PNQ7CyfUK2FPrTGiqcSo+jrdkKddS4FqfW20XVL6tIo/K04FjbL43Ht29ZfWD9C3xJah6et6i2DJceYj3fT+zZDhcK8XaJCkZJLuD9oZ9AjNuSOYJaVeIyVS1gNFxTbR5VupA1f1j0kzfQXVS/wCi2pkKLEyPF7m5br9BiXFmUmJLaPK6wXGVKQVtr3QsAnlWhST2pNbB5Z0yHHRcZTEfTjU0YPZ4dptkGDYcfhsusxvQmIjaHm3JbbrrbjjkKPIX1akILzTaglPVt8uFuITiV1Q4oMjt+a6vyLdOvsO3rjTr7GtTUeXeHFyXpC5c5xsD0qUpTxSXlDmKEIB3IJOpZc3i8NfoRdJYFUprq2WOds7Hf4qq117l+6n/ABCsg63rGV4x9mnrGV4x9mqFKAr+sZXjH2aesZXjH2aoV2mLPPktB5iOpST3EbdtctGhXxEstKLk+hXBw9YyvGPs09YyvGPs1V9QXf5krzFPUF3+ZK8xX6fBm0vMz919RbMpesZXjH2aesZXjH2aq+oLuO+ErtG47RT1Bd/mSvMU8GbS8zP3X1CzKXrGV4x9mnrGV4x9mqvqC7/MleYp6gu/zJXmKeDNpeZn7r6hZlL1jK8Y+zT1jK8Y+zVX1Bd/mSvMU9QXf5krzFPBm0vMz919Qsyl6xleMfZp6xleMfZqr6gu/wAyV5inqC7nuhK8xTwZtLzM/dfULMpesZXjH2aesZXjH2aq+oLv8yV5inqC7/MleYp4M2l5mfuvqFmUvWMrxj7NPWMrxj7NVfUF3+ZK8xT1Bd/mSvMU8GbS8zP3X1CzKXrGV4x9mnrGV4x9mqvqC7/MleYp6gu/zJXmKeDNpeZn7r6hZlL1jK8Y+zT1jK8Y+zVX1Bd/mSvMU9z93239CV293dTwZtLzM/dfULMpesZXjH2aesZXjH2aq+oLv8yV5inqC7/MleYp4M2l5mfuvqFmUvWMrxj7NPWMrxj7NVfUF3+ZK8xT1Bd/mSvMU8GbS8zP3X1CzKXrGV4x9mnrGV4x9mqvqC7/ADJXmKeoLv8AMleYp4M2l5mfuvqFmUvWMrxj7NPWMrxj7NVRYLuTsISvMU9QXf5krzFPBm0vMz919Qsyl6xleMfZp6xleMfZqr6gu/zJXmKeoLv8yV5ingzaXmZ+6+oWZS9YyvGPs09YyvGPs1V9QXf5krzFPUF3+ZK8xTwZtLzM/dfULMpesZXjH2aesZXjH2aq+oLv8yV5inqC7/MleYp4M2l5mfuvqFmUvWMrxj7NPWMrxj7NVfUF3+ZK8xT1Bd/mSvMU8GbS8zP3X1CzKXrGV4x9mnrGV4x9mqvqC7/MleYp6gu/zJXmKeDNpeZn7r6hZlL1jK8Y+zT1jK8Y+zVX1Bd/mSvMU9QXf5krzFPBm0vMz919Qsyl6xleMfZp6xleMfZqr6gu/wAyV5inqC7/ADJXmKeDNpeZn7r6hZlL1jK8Y+zT1jK8Y+zVX1Bdx3wleYp6gu/zJXmKeDNpeZn7r6hZlL1jK8Y+zT1jK8Y+zVX1DdQCTCX3fGK6ihynY1wVsLicNbvsHG/OmviQresZXjH2aesZXjH2aoUrgBX9YyvGPs09YyvGPs1QpQFf1jK8Y+zT1jK8Y+zVClAV/WMrxj7NPWMrxj7NUKUBX9YyvGPs09YyvGPs1QpQHqRXFOsJcWe099VCQlKlHuSkk7fwG9UYP7oj6j+dVv4EbgjYj4xQG6d26B7jZg32QzDu+n67DHsa7qrKJ+aR4bIjIt7kxS1R3CZTaOZp2MHFNcinke+CDz1b926EDpJ7BjxyW+6HW2CyqCJDDE3N7Uy/JX6NIlORmm1yApyS2xFfccZTupCWyT2dtWa30qvSComomDiov63Wre3AKVsw1BUVBWUsKQWCFI/aOApUCFBZB3HZXC5dKRx+3n0RVy4ob+65CQhth4tRA6VJgSoAUtYZ5nXPRJslkrWVLUl07kkJIqWupIp28bjb89Py4mUY3QWcZysburt3uWEWjIrPdVQ3sevGZwmUS1Fy1MMojyw6plby37qw31S+rCdgec9YgGy9JuiZ4tdT9WNQ9EJ1kt2P5Pptp17rL7arnKLq1FyM1Ii2sejhfJPkJdSEMr5eVSVJcKSkiug90qfHPkF4jvaj643bJbOEiPcMbmoZjxJkIriKcibRmm1MIX6DG9pkocbU0HG1Ic9uvMuXSY8ax1sznXrDtdb1il91EzxvLsnOMSTGRIuDLj646T3rXHa9IcSlhalNlJHOlewNTX4/Q34tkZUl9CNxQw+GlGuRvdkdvyGZUq5YKxPjLkWqKxERNWZcgSOrjv8Aoqy8Yqk9akAJUApaQfOh9B7x9ITMOV45hePKj2uXMjNXrUG3pVOLDrTRYaDS1/tVreSE8/KnxKRzJ5ujB6aPj4jaYysBm6sKnXF25Q5ELK5sRlU2Cyw0405GShLYYeQ+haUPLfbdWttlDe/ICD4EnpdekemXP1tK4rr+t5U2RLe3hQeV955BQtbqPR+V0hJITzhQb7OTl2FFy3MO/IXZH6C/pJ7hdW7TZNIMfuiipQkSLRqFaJbMMbOcipDjUhQYS4tl5pBXsFuNLQO0GvF1O6IHjR0R0hy7WjWSz4hjdtwuzMz7tBmZxCenFbjtvR6GGI63FIlJTc4TpZd5DyPp2JUQk27j3SmdIHiy3lWTiiyBpMlARLZUxEW1JQBKAQ62tgodSPTJJCVgjmc5tt0pKfGy3pCuM7O8Pv2A5lxAXm52bJ4RiX+3TGIy2pzRZtzOyx1XeG7TbkhQ2Un0VBBBKiqLNbXszSty9ly/QwyRsdvipQkk7mlUgpSlAKUpQF6aA6xuaBaoWzVu3YZAvF1sUpidYVT58yOIE5iQ0+zLQqI80srQWyACrl2WTtuEkb4ZR/SMtSnsHshwvQzG4eTvP3SPmSpNynm3P2yQ5EKY8RtEwFqQ4mLu5N5UvtrVvHU2CoVrt0Sul+j2rHFzFsGsdth3FmNZZUuyWi4JCmZ85BRyoUg9jnKguOBB3Cigbg7bV+ijov8AhB4OtR38ouGoug2D3i7QCwiBbLlisJ1DcdSVczyWlNEKJV7BUQeUADs5u35Nfa0IbThgMl3JXvey+vZHwcRtqFLbdPZyhecle7dlpd6aO/R0n5v73xuY0zxPWbi0wfQZiNlNvZiT3PWmZXeShq/MTg+1cI7xlelAIYbYjJQ886TyrdUStSeXZrL/AOkK5zcsQx04/wANmMG+9dMGXsXSbcF2t6OqWh+O3EYRL5UO7ttuOSVo6zrUlKNmlKbN/wDTUcJ3R76FdNwzpXpPheOx8UueA+tMwwqFfGLZAteQOMTOqZbDkmM0gqCIckwTIjB0r6tLrPWisY5FwZdGnqPqXaMKtGtOP4yu1W9n1/IxfUqELZdi/MvW4iuXFDriJDCI0PnDjriS24lIV2Jfe+uknFPt2+h9+LdmzGg6XHJMd4wsj4z9PtB7NDyDP7IhvUK1TL9cREn3NN2ZuaJcVyO+1IitpdhwE9T1qg4ll1LnMl5QFpZN0n+teb8aEHjuzLCrBdM1s0Wypx6PJemi32l63JioS6xHS+AlLrbDqVsb9UPTHVISlQQpN26t8HHANiOjufZJgvFFc7tkuI2+G3bYrV9tT7VzmOQ2JBfQ0lKFrjrdecjBDS3HGVxlKWV84Qm+LBpN0aeW5NfcMVmGFY9bbbilrjeu5HXFaJ0uwy3JMxDztxe9NXGlobXsw2wkuKDSmz7AqNLLf9nT23ZXmSkn6zEF06VLXxhjP4Om2PWjC4mdYrjOMNs41LmNrslmsbaWosOO648pxxLjSeR5TylqWFHu3rJOqnT28U+q+j+e6LXPTXDLbbM+h3FifItQmIfjenSLi7I5VLeV1iVIuTrQQ4CkBtCvfFZVY3FhpXwS6HYFmWn2lLhym8xrxaW8byt3IW5MttTqXHZYUqJI9EeiJZjhKNo4dLk4Fa0BgJd1Tq5rxcVwZm2twTud6UpUDdxSlKAUpSgFde5fup/xCuxXXuX7qf8AEKA8+lKUBd2g+mjOsus2KaTyLu7b0ZLkMO1qnsW9Utcfr30NdYGUqSXSnn35QRvt3ivUyPEZOA5LdsHkvLW5ZrvKgrW6yWlKLL629yg9qSeXfb4N6sKDOmWyWidb5TjDzSuZt1lwpUk/GCCCDV02uZJnwRMmyFuvOqUp111ZUpaiokkk9pO/wmu5bj38MSd9Mj/mj9Tkp+UV/a8X409rxfjUUr1Y/Qc3AfZIUPeD4a4+14vxqXe9P+AVxrMSIn2vF+NPa8X41FK0Un2vF+NPa8X41FKAn2vF+NSkKJ23+A/DXGpT3/5H8qWAPNv2K/GnteL8ailRKwJ9rxfjT2vF+NRSqCfa8X409rxfjUUoCfa8X41yUk8iSFDuPw/xrhXJfvEfUfzrLRGR7Xi/GnteL8ailasUn2vF+NPa8X41FKAn2vF+NPa8X41FKA5N7843V+NR7Xi/Gjfvx9dRQE+14vxp7Xi/GopQE+14vxp7Xi/GopQE+14vxp7Xi/GopSwORCgkHm+E/DUe14vxofeD6zUVEiE+14vxp7Xi/GopVKT7Xi/Gg5t/ffjUVI76WB6mU4bk+FyIsTKLS7Cdm22NcIiHSN3YshpLrLo2J9laFBQ+HY9oFeWOYkbn4fjrLHF62pGU4WVIICtJMUKf4j1WyN/MGsTJ7x9dfL2Ljam0tl0sTUSTmru3Arioto5K5gojm+H46j2vF+NQrvP10r6aMok83Vq7e8H4f4VZatyrtNXmr+qP1H8qstXefrrzvfzyqH4v6Thq8TJ2N6O6F3bHoV0vnFpj9pmSIyXJVskYteHVxFnvbUtqMpCiPjSSD8BrtStE+H2PDefjcZGNyHUMrU0wjEL2C4oAkIBVFABJ2G5IA37eysT0ry2WDxMpuSxM10Wp29H93f8AO5jMrcPj1m0/DrwH6ccQeCY9dbdqXJhXa+WKRzNtdRKDN5auy2hATGSpLri1W1tUkI5gSSFcwQCKuK79GZprMsdo9yvE/i6LgmMpq9+mT4xQmV6fJbQHN5CBDKmUssobK3QuU26kuIQUuVp/a7tc7JMTcbPcHoshAUEPxnVNrSFJKSApJBG4JB7e0Ej4a65WSSdh2/8A1RX79VGxjW5uez0YWmVtt96tN74ncfeuIkRxaLjGKExmR1BkORXWi6XXJ60bIbipAAUUlTm6uUWtmPBHozpvbMNbvmrjt9XkOpEO1TbnjyY7SBanGyXlR2ZLyVKktKHKsPdUht09USrYrrVnnPxD7IoVE/APKrft6+yLyG5Vy6LzErnEt+S2/X2xYza7rcFRYa75KMpltaGrctUdyQURlonrFwHLBLHMksrC3UgpWrrYv0aOmVzvT+O3PihidZJiRXLRdI1ibRDSt28x4Drcn0iY2607HYdclPtBvdttAUFLSSa1Geu9ykQGbU9NdVFjuLcYjKcJbbWsJC1JT3JKuRG5A7eUb77Cuvzn4h9kVOSwfJY2mxHo9MDyjH7fnUrijsNtsF3XCbtsqXbWvSXHH5KI6kqY9M9gsrcSXfaPKncjfbasGa36ZWPSvLmLDjmbRshhS7NCuMa4sMBpXLIYS71TjYccDbiCooUnnV2p/jVmcx222HlUb70B6UH90R9R/Oq6e/8A/wC1Qg/uiPqP51WAJ7hv/CqnZ3B9JukE41+AbV3gnm6b6QXuzXCRLNi/0Z4NBwJUCZgHUFJm+kSyyhLnM0HGD1br3pKnQ8vlKaxxw+8WfR7xuH3EdIeIrSmRJuWPYIbbIu9rxlClXB6TlJnSYsxaVocebajNxnmnkKS5ymXF5gl1BTfvSBf0bziO6P3gKtfHDm2uWMX5CVW0ZbilugutO2f01SEN9XIUopl8rjiEL2SjtVunmAJrWDS7gmtGseg0DVbC9a4rF5kSr23NsV+sxhQ4aLXEiy5Tip/Xr5gWpscNpDPM44VI2SAFH6u09q4ja9dVakIxajGPixUVaKUbtLla1b4t3b1Pm7P2ZQ2ZRdGnOTzSk/GbbvJ3aT5lyLkM22bXLoiU5PAseT6DQnre1b1SLnfLZjE5nrsg5mUJdbbXIWUWk/t3TELal8uw23KWxqFrzddNL5rRlN40ax9FqxSTfJDmP21pbqkR4pV7CEl4Bzl+ILHMBsD3Vm/Ieia4pMXZKrzdcGZddUn1fGfzFtlyckFsPrbDqEBKGC6OtU4UBISojmABPN3om+JKO7KhS8x08jy2Jfo7EKVmHVPSSqQzEaU2hbIJS5KkMsJ5uU869yEoBWPltN204duo+m9Fr2/5NYKVtfmHRNaoY/Bal2vWLC3ZUiRb2W8fmTZCLgyqVJmRwXuqZcZAQYL7iuRxfsDYbqBFeZkvRN8TuFmScxv+BW1FsZXKv65WYo2s8AOTGm50nlbPJHdcgvpbKedajyboTzisXWmvH5EWvD0/M1jpWWuILgp1y4ZcGxrUDVOHZm4OU7+rRbL41LcALSH2lqCOzq3WHW3m3EFaFJWN1BQKRiWqLClKUApSlAKUpQFWDcZtoms3O2XB6LJjuByPJjvFtxpYO4UlSSCkg9xB3rIEDjJ4srTITLtXFVqFEeQCEPRc4nNLSD3gKS6CN/rrvcI1ntN41Klpu1tYlJYs7jjSZDKVpSrrG08wCgRvsSP862qsWhV9ynF7lnGNaNvXGzWbf1vdoWOB2PC2RznrXEtlKPZ9o7nsT2ns7axKFOTzSS0MSpUptOUU2uF1c0Iu17nX+5yb1fbw7NmTXlPTJcySXXX3FHdS1rWSpaie0kkk11usjEcpW2R8XMK3i9zOI93uctP/APgM/wDDU+5nEt9vc3au07D/AKAz3/ZraNmjnWR9wesb3HceYdlT1rPf1rfZ3e0Oyt4xi2Kkbpxi1kHuItzX/DWw/RmaM6P6h8QNysme6UY1eobeJSXm4l1sMd9tLgkRgFhK0EBQClDfv2J+Og4HyUDkdIAS42AO4BQ7KnrmflkfbFfpYHBtwflXIOFvTnmA3KfcbB32+P8AqqHg14QQtKDwtacgq35QcMg7n/7qhnMfmn65n5ZH2xTrmflkfbFfpVRwe8HbjQfb4XtOChR2SsYbB2J322/qvjG1cneDjg9YR1r3C5pyhIIBUrDII7zt8lQZj81HXM/LI+2Kdcz8sj7Yr9J44TeCtSUrTw26ZELCikjEYHaB3n+r+Cu1/qZ8IXwcLGnf/guD/wAqgzH5puuZ+WR9sVKXG1HlS4kn4goV+lj/AFM+EL/dY07/APBcH/lVhHpI+EjhZsHAfqrk2O8OeE2y5WvEH5luuNsxmNGfjPtrQUOIcaQlSSPr2O5B3oMx8Fq69y/dT/iFdlQ2UQPjrrXL91P+IUNHn0pSgFXRYP8Aqhr6z+Zq2OU/FV0WD/qlr/P8zXctx/8AFpfwP4xOSn5R26UpXqx+g5O96f8AAK41yd70/wCAVxqR4BcBSlKoFKUoBUp7/wDI/lUVKe//ACP5UBFKUoBSlKAUpSgFcl+8R9R/OuNcl+8R9R/OoyM40pSqUUpSgFKUoCW/fj66ipb9+PrqKAUpSgFKUoBSlKAk+8H1moqT7wfWaigFKUoBQDfsHxUoDsd/4UD4GaONtkMZdgaA3ypVoxiJT2bf/utrt896wunvH11nXj1iriZjp00v/uPw9QO3YQbY2ezzrBSe8fXXV9y5983Wwsv3UbqWVSXpCu8/XSiu8/XSu0LgYXAK/qj9R/KrLV3n66vRX9UfqP5VZau8/XXnW/nlUPxf0nBV4ilKV54cQpSlAKUpQClKUApSlAelB/dEfUfzqttuNiNwRsR8dUYP7oj6j+dVuzY7jfs7t+/+FAZ21w6S3j14juHzHeGDXTiayfI8Axws+pbDcVtho9Qnka6x1KEuSuqT2J61a+T6wKxfi+smq2FWEYriGot4tsDnmLRCgzFNoC5TbDckgDuLjcaOlRHaQyj4hX1o6W/pmuiy4uuiOwzhO4bdEplvziAqzG22p/E0RGcLEUJ9J6uUPZe6xKVsjqiesS6Vr5SNq1X4c+kI4Rsa4csN4b9ftKb1fLdYrAluf10MToLlwFyvMltwQnXgz+zM6A8XwjrHkx3Yqwpsp30uHEjbTWhrvlnGhxqZhMh5xlmvebSXGlvtwLm7LWhIWvl64IWlITzKKUle25JG57Sd/OvnGTxV5HeUX2+6/wCVSJiSx1Di7mR1YYfakMpbSAAhLbzDLiEpASlTSSANq3ItvSY8B+RYtC0o1G0MlSMPsboexPGRizTtotEkP3pfXGKp8l9Janst7LKlgrUrm3ZSTall4yujiiv3HH2dD49oxa8R7U4i0RdJbfKlWz0SXEdchuTJEtbk5Lim5Cy+sBfIvqldYFBLeFKXN20NTSSs9e3wRrfbeOXjFtb4kweIzL0LDhXzG5cxUS4XCDuk7gqKyUncHrHBtstYVwe45OMF7LlZ7L4issdu7iUIcmvXLmLiEGSUNqSU8qkAzJXsEFP/AEhfZ29myGRcXvRizcun23GeGa22+zmEp2wXh3S+G+7BvLrs/rJb0dUkCXDS29E5IS1dUC2CEjqwFYj4wtbODLUnSXDsa4bNEY+N3y3SnnMiuJx8QnpBUlQX7SJDoW044UvIZUVmOP2SV8o9rUko2ItHZduHb1MxFqRrzrJrBarZZtUtSbtkDFmDgtwusnrls853V7ZHOr4hzE8oAA2HZVo0pUApSlAKUpQClKUBlvg2/wDOXcP/AEI5/wDFar6C8MnGDaOHvT644bcdLBfn5N+duMWUuYy0Gw7A9CcaJUwt1sFG5KmVtlQ3bXzIUpJ0u6MHR+BrNxAXCy3XJJ1uiwcZflvi3obK5H7ZpsNkuJUEjdYVuBv7IHZvW/X+o/pr/fHJPvo3/IrtGyNz9tbawaxOGUcrdtXbhboOk7e7oG7u7e0PseMlLviSlpG614cp3rb0mt0iSU3a76TpvUxyXIelNXq8Ieic/Wz32H22uo3RI66VFU8srUl5NuZQEoB9nvwulAtDDhU/w8sBlu6KkW6DGvaG2LchcMsKWykRwTIStS3ULcKghSvZCe3fwv8AUf01/vjkn30b/kU/1H9Nf745J99G/wCRX1F3Od5F/lh730Pifrd3N/aqe59TDOqOfYNqVlt/zw4rdYV0vlwem8rdwiiKy844Fq/ZNxkex2q9lJT2kH4DvmvomxvxK3X/ANS5X/4mLXrYZ0cmG50+8xaM4vTZYdiNrMqXHQN5EluM3ttHP9t1O/xDc9vdWRdLej31c0DvErPNMstyS13VQNreHWWuQVNuKCiP2rKmwetbbRy7hznWlIT7W1fjqbjbYoT73UnTi+Zz+nSftpd07dzE089ONWS51Tb+D6H7DaK+4uLzJMlEpLZU0hCt0b8wSVHYkEdntH/MA11zhI61Tont7laykqZJOyttwSVbnfYA9vaCRWH52DcZlps02/3vWvI4MWDbxLWuVbLGC6grQkJbSIhLi91jdIHsj33L2b82tPONp99cVnV7JFONqWl0CJj3scm3WEn0bYBvcBZPY2VAL5SQKfoTta1++Urf6i6OtEfdK3fUsve61/8ASfbkMtM4O426h03BohspKGxG2SCHErHZzdw5SB8XNXYhY3Lt0JLCZqX1Ccl/tRyjbl5T2EkfCVfETWKrdpJxuzLsm1TNachh7qWFvu2+wrSjkSSrsbiqUrYgJPKDyqUArbeqb+kvHXFiekv6p5SFBt1bsYW2xF1pDaW1KWpIidg/aJSPhKt0gbisfoZtPNbvtL/2I3+sbYeXN3qtb/SfblMqqxKYh705M9Drm6lKbKClKiQdhuVHs7f7W+39naqz+MuyG0odltK5YiWAVtEkAAjcEKG2+/tfHsK1rGda/KSFJ4kMh2IBH/kWz/8AyVPdxxAf7yGQ/wAls/8A8lX7/wBXO8vND3vofO/W7ud+1U9z6mzNnsBtLxeVM609QEBIRypSr+0oD+PKj7P8aw50nH/6vTWT/wBQ5f8A7yKsn3ccQH+8hkP8ls//AMlWOuLljWTVDhjzvCck4jckXBlYxLclMJtlsbS+llsvdUstRUL6tRbAUEqBIPfWKnc83jpU3NqFkr+V9Dlod1jdCtWjTjKd5NLyOd25z4nr9+frrr3L92P+IVWSvrEhe3vgD51RuP7t/wC0K6MenGQNMeDXiF1fwtnULB8Rt7lokSXmI8u45TbIHXLaKQ5yIlyW1qCSoAqAKd+zfcGrKz3T/LtKc0uWn2fWZUC72mUqPOiKeQ51bgG/YttSkLGxBCkkggggkGsq6D4Jhk3T673i86o6WLk3yxzLO1as6nS2ZdidL7DiJ0bkYcQXClCkpUD2B1wEb7GsW6mWG34vmk/HbVnMHJI8J4Mt3q2db6NL5UjdTRdSlZQDukFSQSEg7AdlalZPQ1ZZbmz1x4keB2fqrhWfR9PQzBx1mcH7GrDI6GFNyInVQ4rpadBf9DfSXjJILroe3Tspsb4g1ry3TzOtXMmzLSiwqtmN3O9yJFmt6orccx2FL3Sjq2/ZQB27AfBt8O9Yhq6LB/1Q19Z/M13Dcf8AxaX8D/NxNweaR3d0+H8abp8P41FK9WOc5ulPs+z/AGB8Ncd0+H8al3vT/gFcazEiJ3T4fxpunw/jUUrRSd0+H8abp8P41FKAndPh/GpQU778vwH4a41Ke/8AyP5UsASnfsT+NN0+H8ailRKyBO6fD+NN0+H8ailUE7p8P403T4fxqKUBO6fD+Nclkcidx8B+H+NcK5L94j6j+dRojI3T4fxpunw/jUUqlJ3T4fxpunw/jUUoCd0+H8abp8P41FKA5IKSsDl/Go3T4fxo378fXUUBO6fD+NN0+H8ailATunw/jTdPh/GopQE7p8P403T4fxqKUsDkVJ5B7Pwn4ajdPh/Gh94PrNRUSsQndPh/Gm6fD+NRSqUndPh/GgKT/Z+D46inwH6jQj4GwXSGoUjMtMudsjfQTDSN/wD0akfnWvySncez8Px1sf0lza0Z5petSSAvQLDyk/GPQSPzH4VrenvH1107ufyz7nYOX7nzZyVVarL0nJRTzE8vw/HUbp8P41Cu8/XSu4I40SSOrJ5fj+H+FWWr3xq81f1R+o/lVlq7z9ded7+caH4v6Thq8RSlK88OIUpSgFKUoBSlKAUpSgPSg/uiPqP51WqjB/dEfUfzqtQClKUApSlAKUpQClKUApSlAKUpQClKUBmTgh4qYnCPqvM1BuOGu3uNcLI5bn4seWllxAU424FpUoEHYtgEH4DW1v8Att9Pf932+/z6P/y60DxDCsnzy7Gx4nalS5IaU4ptK0pCUDbckqIAHaPMVdI4Ytbdv/ocn+YMf8ddi2ZvVtzZGG+z4Wrlhdu2WL49LTZ1DbW427W8GN+146jmqWSvmktFw0TSN0/9tvp7/u+33+fR/wDl0/22+nv+77ff59H/AOXWln+rFrb/AHPT/MGP+On+rFrb/c9P8wY/46+h+n29Pn17kOo+T+qrcn7u/fn/ALjePHeniseIynZuLaNZNAefZ6p1yNkMYFSOYKA7Wz3KSlQPeCkEEEV790/pGF8us9y4O6cZUwXNv2MS/wAZtpGw2HKgNbA/Dze+5va332I+fv8Aqxa2/wBz0/zBj/jp/qxa2/3PT/MGP+OuCpvpt+rPPOcW+d06bf8AKfppdzbdSjTyU6c1HmVWol/Ob73j+kL3DILa/Z75p1l8uLJjpYfZfySMoLbSrmCSeq3992k77kgbk7Cpe/pDNyfbfZd07y8pkpUmQBkcUdYFJ5VA7Nf2h77xdhVuQDWg/wDqxa2/3PT/ADBj/jroZNoNqtiFlfyG/YqWokYBT7rcppzkTvtuQlRO25HbUW+W3krKcbf6dPo/d6EV9zjdZyu4Tvw/van+/wBJ9DWf6RPdo9pVZ4+m2VttrluSFvN5DGDylLHtDn6rflJ9rlHYVEk7/BRe/pDd0kIkNv6e5gtMtKkykqyOKQ6FJSlXMOq7SQhO5790g943r5k0rK3w24ndSh/66f8AtNPudbsOKThOy/8Atq/7z6IjpttPANhw+33Yf/36P/y6f7bfT3/d9vv8+j/8uvndSv1fp9vT59e5DqPx/qq3J+7v35/7j6I/7bfT3/d9vv8APo//AC68PU3pk8SzLTm/YfZNCLpHl3ezyYLMiZfGVNNF5pTZWoIRzEAKJ2HftWhVKzPfveepBwlXVmreRDqOSl3Lty6NWNSOHd4tNePPivxEISEJCAd9gBvVG5fux/xCq9ULj+7f+0K6eegm3XRhcNuG6m4PmupOrunOI5XizFzttjTaL/nVsx15mW+l9fp3rGRJRJgRo6UJW6tliR1oWlHVq5SU6q6pKwRed3NWmtrnQbL6URAiXG7InOtJAAIMhDLIeHMFcq+rQSkp3G+9br9DZqNAwXFtQcnzxm6+5jH7lY3VyGs4ftMOO9LdfZdZdRHcS68t5louIUncJcgtpUUJcJrRfI3I8q/zXoaXAyqY6psOvdYoI5yRurYcx27zsN++tT1krcxuyyHn1dFg/wCqGvrP5mr/AJvAfr/C1CxnS9FkgSLxlBloisRro2tMR6Iyl+azJV3NLjsqS44PaASewqO4Fq3LEb1g1zmYdf4TrE62THostl2O40pLiFlJBS4lK093cpKT29oFdw3Ha8LS/gfxiWnpOzOtSp6tz5NXlTq3Pk1eVerXR+gl3vT/AIBXGqjrayU7IPvB8FcOrc+TV5VmLVguBFKnq3Pk1eVOrc+TV5Vq6BFKnq3Pk1eVOrc+TV5UugRUp7/8j+VOrc+TV5VyS25v/Vq7j8FLoHClT1bnyavKnVufJq8qXQIpU9W58mryp1bnyavKl0CKVPVufJq8qdW58mrypdAiuS/eI+o/nUdW58mryrmttzkT+zV3H4P41G0RlOlT1bnyavKnVufJq8qt0UilT1bnyavKnVufJq8qXQIpU9W58mryp1bnyavKl0A378fXUVzbbc5x+zV3/FXHq3Pk1eVLoEUqerc+TV5U6tz5NXlS6BFKnq3Pk1eVOrc+TV5UugRSp6tz5NXlTq3Pk1eVLoA+8H1mormW3OQfs1d5+CuPVufJq8ql0CKVPVufJq8qdW58mryq3QIp8B/wn8qnq3Pk1eVSptwIUooO3Kfg/hUuSXA2Y6UJptjUDStpkkoToDiaU79/sxlg/iK1lT3j662f6UZpw5/pcrkPs6GY22obdykNupUPMGtYktubj9mrv+Kukdzd/wDg+C/hf80jnxH99I4q7z9dK5Kbc3P7NXf8VR1bnyavKu7pqxwrgQr+qP1H8qstXefrq9lIX1ZHIe4/B/CrKV74/XXnW/nlUPxf0nBV4kUpSvPTiFKUoBSlKAUpSgFKUoD0oP7oj6j+dVqowf3RH1H86rUApSlAKUpQClKUApSlAKUpQClKUApSlAZb4Nf/ADl3D/0Iv/4rVb8cPfBdn/EhhLea4RkduaSNQrdjE6JLaXzQ2pTXWLua1Ds9GY9kOf2hzpPca0D4PZcSLqZMRKlNtl6zOJaC1hPOoONnYb952BP+Vbd4Zrfqdp1aJNhwPU+42eFMU6ZcW33ANoeLraGnOYDv5kIQkg9hCR8VCO/IZcybo49e8WhXG63S544xGjyG2rWmVc1NP3LrZzERgpa5CWutEll4c52CFbEhfs1S/wBnrrRb2blGye+Y3b7k1HivWO2m8Bxd1ZdlRWDKQEp5kRUCUkl0p5itBb6vfcixU8XvEaia1c0a93oSmEKS3KE1rrQC+iRsXOXmVs622tO5PIUDl5QNq7J41OJ/nUscQl6TzvNuqCJLCRu2hCEJACNg2EtNp6oANkIG6TSOnlGna2hdN+6PjX6xOOwBIxuXcIlkm3S42yLeiXYzUZ6Q2pO6mwha1JjOuIAVsUjYkL2SaWK8Bes2fYximU4LeMbmt5daI023Q5l6TDkbuqbC0BDgPOlkPMqW5uAA52A8qqtd3jK4mHkTUPcQN6WLih9E4KlsnrUvBYdHvPZ5g46PZ22DrgGwWoGlppxY606V33F7zj2pL7zOILb9S2qZJC4jbKS2fRygEEtK6lsFIIJCB21l5uQzK91bhyl147wH6zZBfbZblZDiEa33ixPXm25AciD0KTAQppAfbU0hS1pUt5tCfZB35twAg1rPrmtD+iuROp7UrtClD6ipNZzHGbxMp5uTiBvSQqW3J5UymQlLjYbCOUcmyEANN/sk7NnkG6TWCNfLjb2tHMiS5OYSXbeUNp6xI5lladkgD4f4CtM07WNQVd5+ulD2k0oQUpSgFKUoBVC4/u3b4hVeqFx/dj/iFAfRHoGoCsOsWpGreTY7OdsU2bZ7Ai6QNQ4lm9FfUp99ZfbkXCJtGCQguS/2io6dwhtwur5fnhlL0dWSXFyMGw2ua8WwzILyAkuK22WrtWNu5R7SO099bWdGrxCcP+g+C5vA1a1ZcxyZlEmAhiRakXaJcorcNS3AWptvbWQ08X1Bxk7AmO2o9oTtqXe3mpF0kvsuqWlchakrWSSQVEgkntJ2+PtrUvKXoN3fe7dJke4cZ3EddZ0C43PUX0p+2sLZiuyrLCcVyONlp4LKmT1peaPVvlfMZDYSh0uJSAPNvee5dqXd5ed5zfHrjd7pIU9OmvbBTiz2dyQEpAACQkAABIAAAArH9XRYP+qGvrP5mu4bjr/u0v4H/NEtPyju86/EfOnOvxHzqKV6rlR+g5uqUCn2j70fDXHnX4j51Lven/AK41lJEXAnnX4j5051+I+dRStZUUnnX4j5051+I+dRSmVAnnX4j51KVr398e4/DXGpT3/5H8qmVAFat+xR86c6/EfOopTKgTzr8R86c6/EfOopVyoE86/EfOnOvxHzqKUyoE86/EfOuS1r5E+0e4/nXCuS/eI+o/nUcURkc6/EfOnOvxHzqKVcqKTzr8R86c6/EfOopTKgTzr8R86c6/EfOopTKgc21q5wCo+dcedfiPnRv34+uoqZUCedfiPnTnX4j51FKuVAnnX4j5051+I+dRSmVAnnX4j5051+I+dRSmVA5Fa+Qe0e8/DUc6/EfOh94PrNRUyohPOvxHzpzr8R86ilXKik86/EfOsp8PGl9i1D071gv95tfpD+J6Zqu9sdK1D0eQLpAZ6wbEAnq3XU7HcbLPZvsRiutmeAWJFk6OcSiZbQIc0MlNtK+ELEtl8D/wC4J/yrqm+uNrbM3eniKLakp0dVo7OtTT9qbT6Llik5W9PwZX6UUKVnWmkhKtk/6IbQ3t8O6VPAn8a1gSte49o9/wAdbRdKGArMNPHknsGnUZpKfiCH3QPzrVxPePrr8fc4iluTg1+7L+eRyV9a0jkpauY+0e/46jnX4j51Cu8/XSu7KKOFEqUvkKuY9x+H+FWWokqJPx1eav6o/Ufyqy1d5+uvO9+1Z0Pxf0nDV4ilKV56cQpSlAKUpQClKUApSlAelB/dEfUfzqtVGD+6I+o/nVagFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBtv8G9Nj8ArJHC9huM5tqBKgZVZ2p0di1LeQw/uUFfOhO5APbsFGtl8C4RMc1RvTmNabaBIv1wZgvzXYNpti3nURmUc7zxSk7hCEjmUfgFAaQbH4jTY/Ea3Ga0H0ifaXIY0wti0NoC3FoiKUEJPcpRB2SP4nsrj/oN0b+HTe0fcH9acRymnex+I02PxGtyGtAtJn2nn4+lttcRHQFvrbhqUGklQSCog+yCogbnbtIHeRVSDw86Y3RfVWrSGHKVuByxba46dydh7zfvPd8dAaZ7H4jQj4xW5U3h80stkgxLnpPAjPBIUWZMFbawCNweVWx2IIIP8awhxX4FiGE3KyOYlYWLemXHf9IbjAhKyhSOU7b9h2UaAxJSlKAUpSgFKUoBXXuX7qf8QrsVQuP7sf8AEKA3q6DbK8LxS+6hHJ8KwW/KuIsUN1jLLvBhOW6CuU/6TcyuYhSXIEbZlyW01yurT1HKtKUrNaK5Utp3Ip7jC4ikKmuqSqA2UsEFaiC2D2hG3vQe0Dat7+g8xdnPmdVMAc1Fx3G3LgzYnI8qTk821Xd5bT8pSkQ3YZSssobLr8kKVyBuOhRBKU1ojkLSGMhnR1SEvcst5KXW3esS4Qs7KCv7QPfv8O+9WXlK/Mb17327f8Hncqj3JPlV0WAH1Q12fCfzNZ/1C0I4IYubYbIsGscSNiiFOnKpMLIkTZkuGlLSojyWkpUuPJkr9IacbDaxECUOLb2IC8aa2Y9pzims2YYxpFfWblikDK7kxjM+NLVIbkW9MpwR1pdUlBdBaCPbKU83vtgDXctyLLasn+4/jEsFadi1qVOyPEfKmyPEfKvVT9BLven/AACuNVHeT2e0+8HwVw2R4j5VmPAIilTsjxHypsjxHyrQIpU7I8R8qbI8R8qAipT3/wCR/KmyPEfKpSEb957j8FAcaVJ5D27nypsjxHyqLgCKVOyPEfKmyPEfKqCKVOyPEfKmyPEfKgIrkv3iPqP51GyPEfKuSuTkTvv3H4P41GRnClTsjxHypsjxHyqlIpU7I8R8qbI8R8qAilTsjxHypsjxHyoA378fXUVyQE842J8qjZHiPlQEUqdkeI+VNkeI+VARSp2R4j5U2R4j5UBFKnZHiPlTZHiPlQA+8H1morkeTlGxPefgqNkeI+VRAilTsjxHypsjxHyqgitnOj/SXNNdbYKPfXLT563tjxKciz1hP1ktDyNaybI8R8q2d6PFSPRMggK7Rc8pxy1qG3wShc2f/wDYD/Oukd0W63PxDXG9P8qsH8jkof38e3Izl0mqjIyPBnwRtHxuRC2HwdTLWNv47cwG/wANavp7x9dbPdIs0pbljdWSTDynJ7co7dxZfhEp/wAusrWIBO42J7/iqdziSluZhejOv/0mK399L1fBEK7z9dK5K5OY9p7/AIqjZHiPlXeEcaIV/VH6j+VWWrvP11ep5erPae4/B/CrLV74151v55VD8X9JwVeJFKUrzw4hSlKAUpSgFKUoBSlKA9KD+6I+o/nVaqMH90R9R/Oq1AKUpQClKUApSlAKUpQClKUApSlAKUpQGW+DX/zl3D/0G5/8Vqvrp0Q2unAvwwe6LVziJ1SELLbhHl2SJYbhgEi6R0215lsKfbkR9ltrcKnm1tE8q0oSDtuFJ+LGkmqFw0mydzI4NrZmh6IqO6w84UbpJSrcKAOxBSPgrJH+uvdx/wDy6ifzJf8AwVU7JkaTPpFatd+FnQfONRcH0xzPMMiwO/S2pNqdtdhZtLksKt77S2eUOt+iobdkuNhtxuTHcZSQWOflcru5nxkcJccrGlvDzBaW7bn0vSbtgFmUVSgECI4EK6xKA2AoKCdgs7KKTvsPmh/rsXf/ALuof8yX/wAFP9de7/8Ad1D/AJkv/gqK8Vb1CSzJ35T6pZLxfcDWLZIuDjuiabtYrnEti5MC143BhNRIwNukuW91SFhU9SJEdx1Re7A6jqklTa1LrGkHi+05xi6ZTFxPTt6LZ8k1Csl2chW8vW5lNviRXGZKQxGlhBceU4pXUOFyP7at0p7APnyeNi7nv06ifzJf/BUf6693/wC7qH/Ml/8ABUSSfbtyGm7xt24W+Z9EOIfip4cdWtIZuM4pou7Dyh9cBpq/XG2sLeSxGaiNo5H0vc7IQ2w6yGglaFIc3PKokp+eXGx++43/APYy/wD3mq4f6693/wC7qJ/Ml/8ABVhax6y3LWCbBkzbGxAbgNLS02y8pwqKyCokkDwjYbVXq2+cNtlmUpShBSlKAUpSgFULj+7dniFV6oXH927fEKA386GzULHLDpxn9twfS6FdM4t12st6mruGYz7ei62aO7IW9FX6LAkJERK+rElh0huUh5AOxaSFaAZE+iTepclphhpLkpxSGop/ZoBUSEo/+qO4fw2rfvoI0NP3/PBNm3xTFrumMXlFvtOISLjtMiy5Pos5DjKT1cmK86l5DLnMiQ36QnqnVoQkaD5W6H8hnPi4Klc815XpLjHVKe3WTzlH9gq7+X4N9vgqy4r0G15DPP5ztt2+dXRYVE2hrc79p/M1a1XRYP8Aqhr6z+ZruO4/+LS/gf8ANEtPyjuUpSvVj9Byd70/4BXGuTven/AK41I8AuApSlUClKUAqU9/+R/KoqU9/wDkfyoCKUpQClKUApSlAK5L94j6j+dca5L94j6j+dRkZxpSlUopSlAKUpQEt+/H11FS378fXUUApSlAKUpQClKUBJ94PrNRUn3g+s1FAKUpQCs3cOWeT9LNFcn1OtcRqRKsGpOF3CNHeJCHVx13N4IWR28p6vY7dvbWEayhh+7PCBnbp9nrs9xpsHxbRLwoj8jXXd6KNLE7NhRqK8Z1aKa506sLr2G4Npu3M/gXZqjq3f8AXnh7z/PL5HRG6/XCPfGLUw6pTFuVc4dwLyGubtCSYzKd+89UnfurAae8fXWVtJkxr1w36tYuVLD8Nuw39gBIIUI85UNxJ7dwdrklW/b7wj4axWoAKAFY3dw2HwE8Xg6EcsKdRKK5EnRpP4t68rvfW5Ja2fbizirvP10orvP10rsq4GVwCv6o/Ufyqy1d5+ur0V/VH6j+VWWrvP1151v55VD8X9JwVeIpSleeHEKUpQClKUApSlAKUpQHpQf3RH1H86rVRg/uiPqP51WoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFde5fup/wAQrsV17l+6n/EKA3q6Gzie4ftNouR6f8ROr+O4VHavVqvmLSZeCWyU5KucZ5xz0p6fLgS1MmG0HFR0BI5nZPIkjnUa0XyiUi5ZJPlsTjJS9OeWmStBSXQpaiFkHuJ332+DevOStSB7KiD8YNShwoJO2+42NVu7TNZnlsXG5o3qyzdrXYX9Nb81OvkVMmyQ3bQ8l24MqBIdZSU7uoISTzJ3GwNd2PZrtj6V2O+21+FNiPLalRJTKm3WXEqIUhaVAFKgewggEGsqZxx12jVXJceyHVTh7sl7bslycucm3OXqY3HuEx8NCUXAlW4Zc9Hj7MpISjkWASHCBYmdakXvVzNrzqhkkl9+4X+7SJ812W+HXFOuuKWoqUEoB7T8CUgDYAAACu47kf4tL+B/GJqn5eh5FKnmPxDyFOY/EPIV6sfoJd70/wCAVxqo6s+z2D3g+CuHMfiHkKzHgERSp5j8Q8hTmPxDyFaBFKnmPxDyFOY/EPIUBFSnv/yP5U5j8Q8hUoWd+4dx+CgOPdSuaSVDf2Rt/AVyQy66VBloq5UlR2RvsB8PZ8FZvZagpVKEKWdkjf8AgKFRHZsPsiuTSlc422+zWuIPRyTDMnxH0D3SWd2H60tbNxt/Xbft4ru/Vup2J9lXKdvh7K8vbt2rOnGxZxZoujbqEACdoNjsg7J23JXKTv8AX2Vg0EqXy9nf8Qr4+wNpy2xsqni5JJyvw4aScenmK1Z2II2G+9Sv3iPqP51mPKtCMVtHApiHEZEL3ru86j3ayz+Z79l6KzEjuMbI7grrBI3PedwP7IrDy1kJT2DuPeP41ybM2rhtr0qk6F/EnOm7q3jU5OMvVdadBGmrX7XKdKnnPxDyFOY/EPIV9QEUqeY/EPIU5j8Q8hQEUqeY/EPIU5j8Q8hQBv34+uorkhRKwOzyoklR2AH2RQHGlVORY2JT2E7b8tce09w/AVLpEurnGlc1JcT3p22+NPw1x5j8Q8hVTuUilTzH4h5CnOfiHkKAH3g+s1Fcio8o7B3n4KjmPxDyFRXBFKnmPxDyFOY/EPIVQRWUbIC1wY5M4j/t9T7Gg7/CEW26q7P81fjWL+Y/EPIVlV11Ns4KYzbg2XedVHltAj3yYdrbSoj6jNA/zr4O3/Gp4en+1Wp/k8z/ACiyxdmXH0c950gHE7bcE1/eZawrMYT1lyR5+SWUJaWtqQ3uoAnZT8ZlGw2JC9gQdjWJdUceRiWpuRYq3GDKbZf5kNLQ7kBp9aAn/IJ2/wAq8WJPl219NxgvFt6OQ6y4jsKVp9pJH1EA1lHjcbZTxW5tOjstITcruLjytNJQkmUy1JJCUjYbl0ns+OvxQwjwm+kq8Zu2Io6xv4qlRmlmS/alGqot81OJXK8Lcz+K+hilXefrpXJSzzHsHf8AFUcx+IeQrtaMohX9UfqP5VZau8/XV6lR6s9g7j8H8KstXvjXnW/nlUPxf0nBV4kUpSvPDiFKUoBSlKAUpSgFKUoD0oP7oj6j+dVqowf3RH1H86rUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQCuvcv3U/wCIV2K69y/dT/iFAefSlKAVdFg/6oa+s/materqsDazaGjt2dvb/ma7luOn4Wl/A/jE5KflHapsa78fGcikMtSmbDNW07FclNOoiLUlbCFFLjoIHahJSQpQ7EkEEjas2ZR0d3EDZdAmeIOwxYN/t8W2szsstFmeUu44w0+117CprCkpUlK45Q9zJ3CErHN3Ejvu0d4dh7IqUqeMxEKbqSyxu0ry4WvwV3or2vJqK1aR+mMZzvlV7GBne9P+AVx2NdlVvnLlohphPF7YDqQ2effbfbbbfu7fq7e6s3aRcAmtWbybxedV4EjTbFcYaU7k+T5fa3mUwUBZbPIwQlyQsO8iC23uodYk7EVdp7e2PsXD9+xteMFyXervZJRiryk22klFNttJK7QjGU9Iq5gjY9+1KyXlfCprXiurTmkKsJmzJir+zaYNwgsKehzHHy2Y6232+ZtSHUOsuJIUfYcSap6icJXEdpjLyT3WaOZE1BxS8rtl4vLdmfMFD6XS2OR4oCVpWQChQ98FJPw1iG8mwKkqcY4qneolKKzrxk7JNXet20lz3GWVuBjigG/dWbrH0c3HBk9hZv1g4aMnktyELW1HRDAkqCXA2dmFEOd5BA5dyndQ3SCa62EcBXFPnGl1+1ctmk90YtFisTt2KpkRbbk2OzJMeR1CSPaW0UrWtB5VcjaikK2AP5Zb57pQg5PH0bKSg/7SDtKWii9dG3f2N8jNOlV5Iv2GGtj8VSnv/wAj+VbWSehh4/RZ7ZebBpbAu6bnaW54j2/II4ejhxAWhl5t4tqQ8oE7IG/akjcEVQxfoe+ODJc2OIuaeRbdFFqROXklxnhFsSlTbKi31yQrdxKng2Ugdq23Qkq5Ca+R+s/ud97nNbVw7UU2/wC1hdWdndXv6Fa75Ea+z4j9h+ws3gb06s+oT2rarzaI8sWTQ3I7nDEllKw1IQ2yltxPMDstPOSkjtB7RVfgXxk5EjWaWWucW3QXI5W+3cT6Ogf+8azrbLDwa8Drxg49xCvZFPv17g4fq3azCSX7Jb1sPG6+j8g/6SgPJQnmAUB1aUgqKq6OL2bgs4NrhItuP8WbeW2TWCyIs9zct9mK5WMY9JadU89LDalJXKDgjgMt7HdpzmCfZ5vP9o72Vdqfb3hKNdrEqj3i9GrqqWtb/L4rteUc1s68jNdHKqKWVSty31Xq7cho8olSyr4zvQdiSr4hW/Vj4W+j21V0swfSux8bViRcLLBusuTMvERFofnybpHC4CDzFad48iMEvNLWORCiSpHOnfxI/R4cB2FtDINTekMtF1tVng+m39OHtsuvXJl5bxjogcyzu51TCy4khZDgCQNloKu0U+69uzFyp16WIhUTaUPs9dyks0oxaXe1bOlFpO1s6T5bcbw1VrS3tXWY56R2AiFbtDYrbZC4OhdngSCR/wBqyt0qB+rrQK1nQOZfft8O/wAVbxYBrN0fvFOjO9O+Ju+XzDEC7z5mnOXONpkt2m29VBHVFHYpcpxNvQEoCCD6Q8kbKINXxdpfQt6JIxCxZLkFyz2Vgky8TG02yAiTGvbTyPS40GYsFAe2MhCEujbZ2OttwpQO3rmxd+cXunsylsWvsrFVcRDM2oUm4uMlOopKfkeVJU2syalrayV+SVFVZOcZK3p9C4fmedpBatN9O9EmuBziEsZazu54U/Ox2xT4qVmLdrsLkGlHbmDbjUVUZ4FRSrd9HL2g1oLZMdv+ThScfscuetiG5JkohRVvFplA5luKCAeVCR2lR7AO0kVdVp16zCBxDxuJGatE+9sZYi/OJlEhuQ8mR1xbVt2hB95sO5J2HdW0WN9KHgGLYPdNWsA0Aw7AtVGr7bYsNnB7Y7EhXqxCYZkuNLSCW0p3YZY3H7RaXVduwIH06WB3r3Lq1sTgsI8XUxjhKdpqMKdeUmpWi80lSeZNyWZxs3LThjPTqxSk7KN7aXbWn5+w0jI9rYEH6jXctGPX2/JkqstllzBDjGRLMWMtwMNBSUlxfKDyp5lJG57N1AfCK3wf45ujx1K1Ny7SPUfhSxe36Z3L0+baMusNsXHvLcssFZcSVoWW3Hy22wkAJSlai6rbc1YWlXSxXTQ3Kshn6L8OWG4tY7/HmyZWPwYqpDL11cQkRZK+vJ6tphaElMdrlRsVjtKuavp09899sbhZrDbBqQrKMZJVatOMGpapZ45nnspJxy3i0s1lJMjp0YvWfsT7es1fh6X6jXDCp2pMLBLq5j9tUgT72iA56JHK3Q0lKnSOQEuKCNt9+Y7bVknhg4GtZ+KyBkzunVrU3JsWOt3a3sT462kXZKpKGS2y6rZG4SXHObcghpSe81k+Z0w3EVcZmMG9Y/jt6tlps78bIcbvtsadtl9mPSlPLnORkIShDoaDDKOxXIGQR2nst7Oelm40svxzGscteozOOIxeVLfgOYxARC3U+FoSjq0jq0NMsrLTTaUgIA5vfnmrOLx/dbxuHdHDYLD0KjlpUlWlOMYptpOCpJzcsqTaccsaitrFsiWHUtZNr0fM8KF0afGJLxCNk8jS9qDLuCA5aMWul7ixb3cmdiS7Gt7riX3Ujb4E7ncFIUN9qkbov+PKTN9Bb4a72pwwnZTOzrBQ+ltbaFoQtLhQp0KdALe/MOVzs/Zr5cP5pqZn+o2VLzrPczud4vLjnObncZq3nwQsrHKtRJSAokgJ2CSewCvVsnEFrbjmKqwew6q36HaFXb1mu3R7m6lpcvlWnrVAHtJDjgO/YrnVuCVE19iphe6QsOnTxWFc3xTo1bR6E1W8a370Vm18nQzejfg/auo2xa6DXWtjMn7Pcta8KjWdFpMlF7XcE8zUjbdLD7BXztpUW5ezqSpJERe3b2C49LuiW0g0r1Xm4/xUcQuAz7fHei3G2otuYpjKlWMqkMSZTrSuVTag6uJyIS4SSFj2hvWkmUa26r5tPvk7J8+uUpWS3JM++N+kFLUuQlby0rUhOyQEqkvlKQAlPWr2A3NWn2D+yn6uUV1z9Ee6ftDDzo47b0YKUVH+yw8YyT0bkp57qV7rRWa4JM5e+YdO6p+1m6i9SuiK061AtOEzOHzMb1bMYvLwvGRemMSJF4diOpba5kFwMuQ5SS+pxCeVSerYKFDmWK9+Dxk9F1rBmNrmamcHEnHJ8m3HHfW7MlD1ttkBS/RGJL7APM+/Gg8qw8E8/Wt+9OyV1oZuabnv3r9tXuUbIxCU62Nxcqqi45/tNVPW7k0lLLq2rppx8WKtprPtE0vJXsPohgNt6I7VPRzOsgGnF1s1oxOXb7vfpBkFq4OIfTIt7UCC8pRCuV0NzOXl/aqWtKgerAFhW3gg4JNd89hYfw/8YmNsR7JjJhXh+/qftr9/vKgv0WXFTKSUBhx5+Kw6jdKkFtZSk8wNaYekLS0ppDqglZBWkKOytu7cdx2rilQB3V/lvXDhu5vtbZ1SvUwO28TBy8hTlGrGPiRjeSqRbk1aTTzR8qKd8kWadeMrKUF6tO3bnNvOIXoi9atPJdub0Qlw8+i+qAm4XC2XSOj026oVNVJiwWVqS5IUy1GTztJ53ULKtxt3YRa4M+KJ/Rl3iCb0Pv8A7kmZbcc3P0BXtlS1N86W/wCsU2lxPVqcCeVK1oSSCoCvDx/iD1qxWz2GwY3qdd4MPGL+7e7AxGmFAg3F0IS5JQR2haghIJJIIGxHad/avXGZxW5FqHD1avPEPlz2SW+MI8O8evHUvstbH2QUkDY79vZ7Xw77CvobNwPdPwFCNCtisNXyttzlCpCUkm9HGLypyVvGWlNppQqaM4pOg3dJr1p/Hs+dFa/8FXFTiuRY9imV6D5Ja5uUMpfsrU22LHXNEblZIB5AlIKlhWxSlJUQADXj6h8M2vWl2S5Di+Z6S3+M/is9UO+vItLy2IroICeZ0J5eVQKVJVvspKkkdhq8rX0jnHXZnJjtu4rc1T6fJkPyULvBUguPb9YtKVAhsnc7coHLv7O1ZV0Q6ZLiowLAb1gWoGoMi+R/cu5Dx5+TbGHpAl9ZGS16S857TjKY7b7ex5ietJUFEkj8+LxvdbwFKNWGFwle1lKMalWDd5ayWanJK0dLa3fjZlbKaSw0na7XqXWax4No9qZqY1eHdP8AB593VYojUq6R4EcuPMtOPtx0qDQ9tYLrqE7JBI33OwBIz9wq8CuGZrprcNW+JG8XuxQZeXKwnErXAbS0/IyRbKyhuUXk/sGEL6sKI9oqCkEJNX//ALcrXeHnttyvHsFtFri22beFKYgpbQ/cob6XhbYcxxLY6xqEpxBTycvWch32J3rBXFRxz6scT18jPyJruPWVn0OccZtEgogJvKG1qk3Jtodjbj0h6Q8T2qHW7FSikGvnVK/dY3hq/Y6uFp4GlLK++wrKpOKTalDyEs0mlJSSSUHZ2ki/9NCOa7k+a1jG1w0d1WsuYs6eXvTW/Qb8680ymzTbO+1JLjm/InqijmJPKvbYdoSdt9jWXONXRfLOFzDNO+GzUKTazkdsj3a9X2LariiSmI9MlIabaWtHYHOphNqI+ALFZA1l6Zzim1Ej2U4VfHsdlwsObtN1nLdblPyZyV+1dWXC2lUaUpvmb50k8qXXANubcak5BkN9yu9y8lya8ybhcZz6n5s6a+p16Q6o7qWtaiSpR+MnevvbFhvvtnGYXFbao0sNCjmk6cJOrKdRxlBPNaMYQUZN5fHburuNnfE1RgmoNvp4advQdQe9UOz3p7/qrYbjJ0A1Xdk4zxIwsaNxxXNsKtUuDd7U6JKG3YlohtzW3g3uWVsrSefnAA+M7HbXlBAPbWZuGXjr184TcfyDF9Krpa3IWRtNNyo16tiZrbBQtSiWkOHlSHErcbcTsUuIcPMNwkj6+8mF3gVWhjtjxpzq080XCo3GM4TccyU0pZJJxjJNxkmk42V7qRyWaly/L/kw7LjPxZTkWSwtp1tZS424kpUlQOxBB7QQe8Huqlsfir6K6pdJD0amrmjV1y/OuB223TUS9zo7V8tvo4gKmcrfMqai4MBSmx1iQC2OVxQX2lXao4cj3bomNcsWfN3xvO9GMnbjyjH9XyV320PKCAI4UV7vJ7EbqCUDmccVsoJ226xsvuhbcq4Z1Np7BxVDLLLJxVOqrpK7UYT77KF7pSjTaenSlySowTtGon7V8rfmanK/qyP4H8qspXefrrcnJODrhOaixZGLdI1haHLkjr4sC949OaXFadaS8w1JcYS6hp4Nkpd70NvAN83aSNOXkhLhSD3V+LeTb+ztuqlLC5/FzXz0qlPjZK3fIRv5L1V/Y03+OvFxaT+KfwOFKUrqxwClKUApSlAKUpQClKUB6UH90R9R/Oq1UYP7oj6j+dVqAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBXXuX7qf8QrsV17l+6n/EKA8+lKUBfHDrpRB1o1Xg4BcrpKisPwbhLc9AiJfkyBEgvy/Ro7alJC33uo6lpJPa46gdvcdocjvuinCfOY0kxXQ3S/MMpxRT8aVmGXvyy4p/0uQ4A9AS+I7jraDGTurmSkoW2oLB7NKo8qRFcS9HdUhaFcyFpOxSfgIPwH+Ne/ar5BYgIakyyHBuVbgk7kk19fYuz9nbQxuXG1HCEU35bgpPRWbjKN1Zt5XdNpXWhzUpuD04m7tv6VPiDxqztQ8FxXSOxzYcJhm0XODaWA5bCpXPcPR0ABttqa4VreYCerBcXyBO4Iw3b+JXiHtOtF315t2sdvbvl/uwn3lly79bDm8r3WojusOFSXY6dkoDSt0hCQj3vZWDfdFaPno+warwbjFuLwYgl19w77Nsx1rVsO87AE12vZ+624Gy1V+zU6Ee+rLO+V5o3Ts3JvS6zW4Zry4tt80q9Sds0+BuBP6Uni0npYddvWmvpkZiF1N1FnjCV1zAaS46p1IBcL7bamXUq3QWn3EJCQRtibWbiQ4jdf8Wt+C6qa4xLnY7TPemWyzuXVAYjvOLWoqHZzLKesUlJWpRSjZIIArDLlxjBAcK3NgwHCeoX2I325+73u5A37t/hrr+6K0fPR9g1vZu7W4GyK0a2DpYeE4u8Wo07xdrXi+MXZvVW4vnYlXqyVnNm1uA9IlxVacaGx+HrHc8xEY9ExuRaIiXnQp9kuPOONy0uc26ZDKXVttLGwSjl7CUJI8qT0gvH5KnOz3ONC7JU6wlnqmsjbS02gKKgltsI5W9iTtygHbs32rWf3RWj56PsGnuitHz0fYNZhut3PIVqlbvGGcqknKTcKUm5PVvW+rfHn05h3+q0lnenSZuzXiW4pdQ7u5f8ALOJ2e/NeVEU9JZyksLcXFjrjMOKLXLu4ll11HP74hxe5PMa9uTxu8bkzJbdl0vi9uTtwtEiQ9bH15E2fR1PoQh5KU8nLyLShIKNuXs7ANzWu3uitHz0fYNPdFaPno+wa/bLY+5MoRg6eGainFLJSsotWaStomtLcLGe/VP237TP+JcWPEph9gyWywtcW3ncmxw2V64vZEr0iLFVcFTnQ0sbbKcccfClHc8r6wCOzaxmcu1VYxJ7AmtaliyyX2X5Fq91C+occZQUNKUnfY8iCUpHcB3Csc+6K0fPR9g1WRc4i0laHVFIISVJaUQCoHlG+3w7Hb49uyv0UMHurhZznSdCLm1J2VPWUUlF+lWVubjxbI6kpcZHvqxN5aiteT2cknck3RO5PlUe5Jzu90ll23329Zp/SvBfuUZhCnXlLQlDhQorZUAFjvSdx74fF31R90Vo+ej7Br662lgUrLFQ9seszdX4lye5FexHuls2xG23rNO35U9yKt9/dFZAfjFyR+lW37orR89H2DT3RWj56PsGnhPA/eoe2PWPFfKXKnE3EncZJZt9tt/Waf0qPci5tscms38B60T+lW37orR89H2DT3RWj56PsGnhPA/eoe2PWLp8pcXuOX/eWy/zNP6VyVh6ylI90tl7B9Jp+P6qt1u/WtzcIlg7Dc+wa7MiQIyeWU2+0UhPMHYy0kc25T3j4djt8ex2p4TwP3qHtj1jNflPY9x7g7sls38zT+lR7jl/3lsv8zT+leA/erfGcUzIkFC0qKVIW0oEEdhBG3ZXD3RWj56PsGnhPA/eoe2PWXN0lxe45f95bL/M0/pT3HL/vLZf5mn9Kt33RWj56PsGnuitHz0fYNPCeB+9Q9sesZukuL3HL/vLZf5mn9Ke45f8AeWy/zNP6VbvuitHz0fYNSnILSs8qZg3/AMBp4TwP3qHtj1jN0lxow9YUD7pbL/M0/pXH3HL/ALy2X+Zp/SvFVOZYWoPFxHIhK187C08qTtso7jsB3GxPYdxXF64x4yVqfLiA2QHOZhY5CRuAdx2EgEjfv2p4TwP3qHvR6xm6T3Pccv8AvLZf5mn9Ke45f95bL/M0/pVu+6K0fPR9g090Vo+ej7Bp4TwP3qHtj1jN0lxe45f95bL/ADNP6U9xy/7y2X+Zp/Srd90Vo+ej7Bp7orR89H2DTwngfvUPbHrGbpLi9xy/7y2X+Zp/SnuOX/eWy/zNP6VbvuitHz0fYNVY91gywsxXFudWgrcKGVHkSO9R2HYP4nsp4TwP3qHtj1jN0nvHD18oHulsvf8ASaf0qPccv+8tl/maf0rxnpSI7aXJCXkJU31gU5HWkFBIAX2j3u+w37tztVGReYER0sSn1NrG26FtKBHw9xG4otp4H71D2x6xm6T3/ccv+8tl/maf0p7jl/3lsv8AM0/pVu+6K0fPR9g090Vo+ej7Bp4TwP3qHtj1jN0lxe45f95bL/M0/pT3HL/vLZf5mn9Kt33RWj56PsGnuitHz0fYNPCeB+9Q9sesZukuL3HL/vLZf5mn9KkYesEf/lLZf5mn9KtwZFaD/wDtg+wa7HprQCVKDo5mutTvHWN2/H3e97Pfd38aeE8D96h7Y9YzdJ7Zw9ZJ2yWy9/0mn9KJxBxJ3GS2bu+k0/pXhruMZAK1KWAEBwksq7EEgBXd3EkbHuO4qh7orR89H2DTwngfvUPbHrGZc5cbuKuIbURk1m7jsPWiezs+qsaOdiz271dByC1KSQmaO7wmrWV7410ffLE0MR3nvdWM7ZuFtPJ5rnFNrQilKV0gwKUpQClKUApSlAKUpQHpQf3RH1H86rVRg/uiPqP51WoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUpSgFde5fup/xCuxXXuX7qf8QoDz6UpQCm5+OlKAVlPhR17gcPuZ3HI7vEuE233Szqt11tEB5poXOMZDDyorrq0lbLaywnd1koeQQkpV3g4spS40ZujeOlKw244zLsTWis1CpMGQRz3xtTQU7AkQha+XqtzaW/SPSEMb8wdbB37eYaYOqCj2fw/KuNKt3aweruKUpUApSlAc4zgafQ4oqASoElPePq3rduJ0rOC2rNLtlMbR27zkXC6ol+jT73GSh5aoVmjLfdS2xyqkMG0F6KsDZpya4SPZPPpDSqnYltbm0HEVx74zrXo1L0rt+n1zjuut2+M3NuV4bfS8Iiuf1k8hLSSq5P8Aa269vsWzt21rArvNRSjZXq7ilKVAKUpQGVOHzXay6Q4bnmMXWNdXFZfZrdDiuWx6OgRXot2iXFLy+uQrmI9E5ABsP2pKuYJCTsPZOlYw+y3x2/yNGrjc5j01Uq5vyr2y2i9zHvV5euMllDJbblNqg9ZGDIDTLkhawkbqC9KYaIzkpCZi1pa5h1qm0gqCd+0gEgE7b7DevoZjHQq6L3TUiyYc7xs264xLpOlsS1WC2RpEuIlmKZxUlj0kmQr0KZZXeVv+3MlN7j0JxR25ScUuTgTRGlPEdqvC1r1Ym6gW63y47T8G3xEesJQflSPRIMeIZL7iQAt94sF5wgdq3Vdp7zYgBPcK390s6G7SPNdQo2H37jOtbLc2TeI9satlsYfmz37PJXEubbbIkkgpeVHdY336+Ot1xI3aIrwL90VGm02PKxPR7iLl5Xm1utLUlyxtWGJHYush+LJlMMQHFTS44Q1DfLgcbbUglIAUd6xHXgbad9TSIIUe4VGx7virfLKOi34VcRmXDDLnxgX13JGLo9b4iYWJ21y3uOuTL7FiOLf9ZBYb/wDIhceKW1FImNBHP75V9ZT0MPDvluomRO4jxDTcKtzNjk3O04vfbWxMnuspUylE+P1Mxxcm1buqHpCwh4lpX7MJ9qrfUwmpWsfNMAnuFc2SEOArHZ8R7q2ac4ZuH/CeIzJ9KrVfrjm8TFdK73d58i6R0wY7t3jWd2YgNeiSVqcjoc6sBRWhS+U8yUg7VlSL0VXDlZk25OpPEpl9sZujVqZtl7g4ba5EC7PzIEuaqVDWq7JW7BbTEUyXVpQsuuIBQk8wRtwlGKb7a2F7SylvYx0nuDYPChWWyaS3e9Wyz2uIxAiZXe2ZLlxUhi4tvs3N4Nc02Ot64rkoRskNKYZbQnlTzV42tXSN4nqbpJf9MYOmV36y5Wl6A3dbzfG5Ds4vSIT/AKXO5Wk9fIj+h9THUkjkbeIPcQqxtQcS4YNC8ly/A2JF1yZq56fQp2JXu521jrESZ7FuuUULZYf5YjrTLj0d5QdkAqJ5Up+Cz+LrA8P051sk45glj9W2xeP2Ke1BEl11LTkuzwpboSp1SllPWvuFPMokJIG523qWunLty9RbOP5fQxm4oKUSPjrjSlZuBSlKgFZX4Udesf4fMmvuXXaBepMyXjUqBZkW24IbjNyXk9WHZbCxtMaQhTiksKIQXOrUvnSgtrxRSnFWBudjPSjYljUOO9cNIJmRXNiKwLjPvdzZSjIC1GnRWYs9hpoNGK0zNQUpbSlS3Ija17qIKcC8XPEBaeIvUaFl9nstxjNQrK3BVJvVxTKnTVB994uvupQkKUnrw0ns7G2Gx8GwxXSrJ5nd8QvFTS5RSlKgFKUoADsd6220e6RfFtKNKLRpo/g+QXtdriqULnOv7SHHCQwTbVJDRC7ctTJC0n9opvqke9aAVqTSj1VmOW5uZl/Sh4jkmEXXFGtG5iHbjarg2HHb02tvmlxLjGEBaQ0Oa2xvWHWx2d+ZC4yN9twU6Zk7nelKctxdilKUApSlAKUpQClKUApSlAKUpQHpQf3RH1H86rVRg/uiPqP51WoBSlKAUpSgFKUoBSlKAUpSgFKUoBSlKAUrk0ttCiXWesG23KVlP+fZXPron0cn75X60BSpVXron0cn75X6066J9HJ++V+tAUqVV66J9HJ++V+tOuifRyfvlfrQFKlVeuifRyfvlfrTron0cn75X60BSpVXron0cn75X6066J9HJ++V+tAUqVV66J9HJ++V+tOuifRyfvlfrQFKlVeuifRyfvlfrTron0cn75X60BSqFIQscq0gj4iKrddE+jk/fK/WnXRPo5P3yv1oDreiRvkE+VPRI3yCfKuz10T6OT98r9addE+jk/fK/WgOt6JG+QT5U9EjfIJ8q7PXRPo5P3yv1p10T6OT98r9aA63okb5BPlT0SN8gnyrs9dE+jk/fK/WnXRPo5P3yv1oDreiRvkE+VPRI3yCfKuz10T6OT98r9addE+jk/fK/WgOt6JG+QT5U9EjfIJ8q7PXRPo5P3yv1p10T6OT98r9aA63okb5BPlT0SN8gnyrs9dE+jk/fK/WnXRPo5P3yv1oDreiRvkE+VPRI3yCfKuz10T6OT98r9addE+jk/fK/WgOt6JG+QT5U9EjfIJ8q7PXRPo5P3yv1p10T6OT98r9aA63okb5BPlT0SN8gnyrs9dE+jk/fK/WnXRPo5P3yv1oDreiRvkE+VPRI3yCfKuz10T6OT98r9addE+jk/fK/WgOsIscHcMp8q5dQx2fsEdnxoFV+uifRyfvlfrTron0cn75X60vYFAMMD/sEf5oFAwwP+wR9gVX66J9HJ++V+tOuifRyfvlfrS4Ov6Owf8AsEfYFOoY+QR9gV2OuifRyfvlfrTron0cn75X60B2cXybJMIuwv2GZBOtM4MOsemW2WthzqnEFDiOZBB5VIUpKk9ykqIO4NeeWGD3sI+wKr9dE+jk/fK/WnXRPo5P3yv1q3YKCGm21pcbQEqSQUlI22IrvZPkWQZte38lzK+zbtcZPL6TPuUpb7zvKkITzLWSpWyUpSNz2BIA7ABVDron0cn75X6066J9HJ++V+tLsHW9EjfIJ8qeiRvkE+Vdnron0cn75X6066J9HJ++V+tQHW9EjfIJ8qeiRvkE+Vdnron0cn75X6066J9HJ++V+tAdb0SN8gnyp6JG+QT5V2euifRyfvlfrTron0cn75X60B1vRI3yCfKnokb5BPlXZ66J9HJ++V+tOuifRyfvlfrQHW9EjfIJ8qeiRvkE+Vdnron0cn75X6066J9HJ++V+tAdb0SN8gnyp6JG+QT5V2euifRyfvlfrTron0cn75X60B1vRI3yCfKnokb5BPlXZ66J9HJ++V+tOuifRyfvlfrQHW9EjfIJ8qeiRvkE+Vdnron0cn75X6066J9HJ++V+tAdb0SN8gnyp6JG+QT5V2euifRyfvlfrTron0cn75X60B1vRI3yCfKnokb5BPlXZ66J9HJ++V+tOuifRyfvlfrQHW9EjfIJ8qeiRvkE+Vdnron0cn75X6066J9HJ++V+tAdb0SN8gnyp6JG+QT5V2euifRyfvlfrTron0cn75X60B1vRI3yCfKnokb5BPlXZ66J9HJ++V+tOuifRyfvlfrQHW9EjfIJ8qeiRvkU+Vdnron0cn75X6066J9HJ++V+tAUUpShIShIAHcBU1V66J9HJ++V+tOuifRyfvlfrQFKlVeuifRyfvlfrTron0cn75X60BSpVXron0cn75X6066J9HJ++V+tAUqVV66J9HJ++V+tOuifRyfvlfrQFKlVeuifRyfvlfrTron0cn75X60BSpVXron0cn75X6066J9HJ++V+tAUqVV66J9HJ++V+tOuifRyfvlfrQFKlVeuifRyfvlfrXBxSFK3ba5B4eYn8TQHGlKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUpQClKUApSlAKUoCQdx8FAXpZuHPXjIMEY1RtGkOQvYzJddaj5ELYtMF1xtLhUhL6gEFX7JxIG/tKQUJ3WOWvCi4DnEzIJWKRsPuirnBU8J0D1e4HopZ/rutSU7tdX/bK9gj+1tWftMOkBs2nGP4tLVovLmZZimCSMQt96ZzN6LBNvUq4ONOqhNs+1JQ5P5udTqkfsErShDp60ZcuXTWtybVeLdB4f7zHdv99vVwus46kuOPKFx9ZKdQhaom6d13BsKHahaITQKAolYrsuGpFra+nP0GjF2sl0sd1k2S5w1Nyokhxl9pJC9ltqKVgFJIVsQQSkkfxr1sO0o1Q1DYck4Fp1e7021IDDq7VbHXwh0x35IQeQHZXo8aQ9t8mw4ruQSNwGOmkya0xJZxHRZ60TJsqK71kbMVpj2xtm4R5S2be0mMkxES22VImALWJDjnWEJCS2rGWgfSM5Rw6Zvn2Z4ZhLlxczSNbEQ/X18U+7Z3Yr6OdbTiWkhaFwXJ9vSjlT1UecUgqLY5sq7djbUUtGYZxbQXW3N85GmOHaS5Fc8iMFE31HCtDrkoRltNvIeLYG4bU060sLOwKXEbH2hvbd2s12sFxfs99tkmFLivrZlRZcdbTjLqFFC0LSoApUlQKVJI3BGx2NZ4vPGfhGW626naiZfotPcx3Vi3mJkGNWzK0x3oDbd0jz4rcGUqIsNNMiFEjhDjTnM2hR5gop5MsY50yt9wqNBXjOgMN1+DEIiRb/k71yg26UwyRAlQ2X2StpaZKWJsnrHHTMksJWso5lVrSyf5E0NKEsvL5eRlauZXKnlQTur4h8Z/h316WHYRmGoWRRMSwbGpl1uc4qEODCZ53HuVJUrlHw7JSSfiANfQe0dMnpPZNN5WWxtLZLN+evCoVl01ts1aLNYbSlc/qJTLzjKki4MsSo8duUApSkRm0rbSEcysFaucfWmOu+vWB8Q2sOjNzyA4o6pibp89eI7NmdistpcjKa5Y3IhTk1x5+QyGA04lCEbHrFkZTeazXbQy8xgo8POvCdVn9Cl6O5InNIqlCViqrQ6J7HK0Hlc7JHMgBshZJ2ASQSdq8y+aW6kYtlTOEZZgl2s93kPhlu3XmA5DdKy+pjtDwTyjrkqbKjskKSoE7g7ZSw7iwxnDOIy46+R8YzG5OX2NdEZELvmMR+ZcHJ3MHVuLVbjFfQoKIcjvxnWXQVApT7PLsBH6bRyHaFWiBwwxI7KOoZhx15UZbMaEzPnyW4KRLiuqEcNzuXkBBS6wh1so9lCK+F+Ur4mi022zrfLdgzIq0OsLWl1O3MElCilXaNwQCD2gkdnfVW0Y7kGQTjbLFYps2SEuKMeHDcdcAbbU44eVIJ9lCVLV8SUknYAmt1rZ02upNmuzNytmmT7KIl5tEiEw1l7qUx7fDYtzbtsRsxsI8hcAurQByFUhXMhZBKlk6arK7dpzbcHuGk8gTm7Z6DkGR27LQxPvgXaUwHpjzy4q3BNJSXA+Fn2dm3A4ASo/JVjTUVO19Of19WppJHttxlzmrXFgPuSX3EtsR0MqLji1EBKUp23JJIAA7969lGlGp7mpS9Gm9PL2rL27qu1uYum1um4InIWULjGPy84dStKklBG4KTvtsa2E1J47tMM313wTidY0syq5XnBvRYlpxPL8tTcrYzBgRAIB9KSw1IeV6cpcl5K+1whX7QF39nbt7427ZfOJ6BxVydFw1e5VrfgZxAiZE8iNeA/aVWuQ/HWttb0SQ6y464pxbkj9usLO/tJU5u3oIrN66cPqYWzjTzPdM8ol4TqJhd1sd4glPplsusBxh9kKQHElSFAHYoUlYV3FJCgSDvXWxnE8ozS8t47iGPTbpcHW3nGoUCMp11aGm1OuqCUgkhDaFrUfgShRPYK3N026XzHNLb9ZYeL8LDkbFLBbn2Y2KQs7daaekOerAuU8PRlNKW43b1tLQGggIlrDYb5Rv4urvSC6cJ4gsD1709xh+8XjC8bk2mexLt7cK1XV+VaC1MufUuJcX1z9xmT33EOJKFNtsp2AWoJk7xXi6sqSbd2auRNKdTJ9+tuLQ8BvDtyvFtTcLTAbt7hemRFNKeS+0gDdbZbQtYUOwpQT3CvPvWJ5PjjUN/IMfmwkXGE1MgKlRlIEiO4VBt5BI9pC+RfKodh5VbdxrPTHH401xAafcR6tKLab5hGLMWR+BGkxWLZPbYtrkNtxERqKltgbuuOLbAW2obI5R7SlZ14V+mVwfAZmO49q5oQy3FjxbXCud8xeczHQ01CU0tElm2NRUsoeSWTyFot8pecUor2AqryG3x5jjvK/A0Yg4Bl90VaW7dYnX3L5MVEtTLSkqckPh1DRRyA8yT1jiE+0E7lQ23qpE001DuGoQ0mtmE3SVk5uiranH4kJbstUxKygsBpAKlLCgRsPiPwdtbgxumKLDVshN6I3Zty0PwnvWkbUPkul69GaQ0GrtJ9CJntPBJckJ5G+tdDa908mxsHMuP63ZxrJiXErkuF3y65dbYt6smURJ195mrjZpiJqEFEstl5EtLVzksJWpK0NoixiEqG6Bm7utNCwu28ysvq/kYQZ4fdcpGqatD29Isi92KEqUrFzaXRO5Usl8r6ojm5QyC7ze96sc+/L214eW4VmGA5PLwrN8WuFovECSY0213GGtl9h4bbtqQoAhWxB2+EEEdhBrK2JcUeD6Y60RNWtL9I5aPQ8OuWPMWbLcmRem3WZFmdtbTjiX4wS4EB1bq2CjqF9jSG2mxtWerp0zM29NShK0DnwnLhCVCeNmzgxk2gKRLHptoT6GRbrgn0ssokjnCIkaPHCNkc9bsrC7vw0NMsqwLM8Idt7OW41Lt6rrZ491tokNbekwn0c7UhG2+6FJ7Qfi79q8otOjcFpQ2UEqBSewntAP8SO4fDW7tt6avUmzzY8qDp7dOWA7Yk2wPZu6tyNHgyor8tkL9HT2ThF5X+VKUr61RUlfvT2ML6aO9Yzh6oN30Jk3XJFRoyF5PIzVwuOyGCx1MxaFxlH0htMcIQ4laVBCiCVcqdiXC7+nb4mklmtf1mkVnsl4yG9RccsNqkTLhOlNxYcGMyVuvvrWEIaQgdqlqUQkJ7ySBXa9xOYiBdLocWuHo1klNxry/6IvkgvOLWhtt07bIUpbbiQk9pKFD4DWdHOLPR+XxC45xN5npTkmT5BFtFolX1mRlaYW9/tzkVLUxt8MOl9t9qEhb6XkFanJb5SpKkIcPX0U4ssOwbJcpXd8KvUJvLsmsV9ZvFuv7aXrFc4Et2V6UEqhuCQx17619QEpcCEJR1ilbrJ20t0hONm36u3sMApZdUEqS2ohSglJCT7RPcB8ZPxV6eR4RluIRrVMyjH5MBq92tFytLklHKJURa1oS8g/CkqacH1pO4rdzO+mXHu+udm0/0WdYwCHLuUbE7PBvi7ebbCdkteiSozfVOpiTo8dtSWXj1imlvFW6gClWLeIXj9gcUWlEzSzUzH8sZiWq3W9zDOvyZNyWi5xTPQtySt1lsbSW7ityQ42lK3HIbP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width="98%" style="display: block; margin: auto;" /></p>
-<p>Accessibility is extremely important to us and we are making strives
-to improve the accessibility of Open Specy for all spectroscopists.
-Please reach out if you have ideas for improvement.</p>
-<p>We added a Google translate plugin to all pages in the app so that
-you can easily translate the app. We know that not all languages will be
-fully supported but we will continue to try and improve the
-translations.</p>
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width="133" style="display: block; margin: auto;" /></p>
-<div id="download-a-test-dataset" class="section level2">
-<h2>Download a test dataset</h2>
-<p><img 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" width="138" style="display: block; margin: auto;" /></p>
-<p>If you don’t have your own data to use right away, that is ok. You
-can download test data to try out the tool by clicking on the test data
-button. A .csv file of HDPE Raman spectrum will download on your
-computer. This file can also be used as a template for formatting .csv
-data into an Open Specy accepted format. The following line of code does
-the same:</p>
-<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="fu">data</span>(<span class="st">&quot;raman_hdpe&quot;</span>)</span></code></pre></div>
-</div>
-<div id="choose-whether-to-share-your-uploaded-data-or-not" class="section level2">
-<h2>Choose whether to share your uploaded data or not</h2>
-<p><img 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" width="287" style="display: block; margin: auto;" /></p>
+or start the Shiny GUI directly from your own computer in R.</p>
+<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="fu">run_app</span>()</span></code></pre></div>
+</div>
+<div id="reading-data" class="section level2">
+<h2>Reading Data</h2>
+<p>If you have your own data, click <strong>Browse</strong> at the top
+left hand corner of the Analyze Spectra tab.</p>
+<p><img src="data:image/jpeg;base64,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" width="98%" style="display: block; margin: auto;" /></p>
 <p>Before uploading, indicate if you would like to share the uploaded
 data or not using the slider. If selected, any data uploaded to the tool
 will automatically be shared under <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0
@@ -399,26 +2316,20 @@ <h2>Choose whether to share your uploaded data or not</h2>
 Some users may choose not to share if they need to keep their data
 private. If switched off, none of the uploaded data will be stored or
 shared in Open Specy.</p>
-</div>
-<div id="uploadread-data" class="section level2">
-<h2>Upload/Read Data</h2>
-<p>Open Specy allows for upload of .csv, .asp, .jdx, .0, .spc, and .spa
-files. .csv files should always load correctly but the other file types
-are still in beta development, though most of the time these files work
-perfectly. It is best practice to cross check files in the proprietary
-software they came from and Open Specy before use in Open Specy. Due to
-the complexity of these file types, we haven’t been able to make them
-fully compatible yet. If your file is not working, please contact the
-administrator and share the file so that we can get it to work.</p>
-<p>For the most consistent results, files should be converted to .csv
-format before uploading to Open Specy. The specific steps to converting
-your instrument’s native files to .csv can be found in its software
-manual or you can check out <a href="https://www.effemm2.de/spectragryph/">Spectragryph</a>, which
-supports many spectral file conversions (see Mini Tutorial section: File
-conversion in Spectragryph to Open Specy accepted format).</p>
-<p>If uploading a .csv file, label the column with the wavenumbers
-<code>wavenumber</code> and name the column with the intensities
-<code>intensity</code>.</p>
+<p><img src="data:image/jpeg;base64,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width="274" style="display: block; margin: auto;" /></p>
+<p>Open Specy allows for upload of native Open Specy .y(a)ml, .json, or
+.rds files. In addition, .csv, .asp, .jdx, .0, .spa, .spc, and .zip
+files can be imported. .zip files can either contain multiple files with
+individual spectra in them of the non-zip formats or it can contain a
+.hdr and .dat file that form an ENVI file for a spectral map. Open Specy
+and .csv files should always load correctly but the other file types are
+still in development, though most of the time these files work
+perfectly. If uploading a .csv file, label the column with the
+wavenumbers <code>wavenumber</code> and name the column with the
+intensities <code>intensity</code>. Wavenumber units must be
+cm<sup>-1</sup>. Any other columns are not used by the software. Always
+keep a copy of the original file before alteration to preserve metadata
+and raw data for your records.</p>
 <table>
 <caption>Sample data <code>raman_hdpe</code></caption>
 <thead>
@@ -454,182 +2365,385 @@ <h2>Upload/Read Data</h2>
 </tr>
 </tbody>
 </table>
-<p>Wavenumber units must be cm<sup>-1</sup>. Any other columns are not
-used by the software. Always keep a copy of the original file before
-alteration to preserve metadata and raw data for your records.</p>
-<p>To upload data, click <strong>Browse</strong> and choose one of your
-files to upload, or drag and drop your file into the gray box. At this
-time you can only upload one file at a time.</p>
-<p>Upon upload and throughout the analysis, intensity values are min-max
-normalized (Equation 1).</p>
-<span class="math display">\[\frac{x - \mathrm{min}(x)}{\mathrm{min}(x)
-- \mathrm{max}(x)}\]</span>
-<p style="text-align: center;">
-Equation 1: Max-Min Normalization
-</p>
-<p>The following R functions from the Open Specy package will also read
-in spectral data accordingly:</p>
-<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="fu">read_text</span>(<span class="st">&quot;.csv&quot;</span>)</span>
-<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a><span class="fu">read_asp</span>(<span class="st">&quot;.asp&quot;</span>)</span>
-<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a><span class="fu">read_0</span>(<span class="st">&quot;.0&quot;</span>)</span></code></pre></div>
-</div>
-<div id="viewing-spectra-plot" class="section level2">
-<h2>Viewing Spectra Plot</h2>
-<p>After spectral data are uploaded, it will appear in the main window.
-This plot is selectable, zoomable, and provides information on hover.
-You can also save a .png file of the plot view using the camera icon at
-the top right when you hover over the plot. This plot will change the
-view based on updates from the <strong>Intensity Adjustment</strong>
-selection.</p>
-</div>
-<div id="intensity-adjustment" class="section level2">
-<h2>Intensity Adjustment</h2>
-<p><img 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" width="153" style="display: block; margin: auto;" /></p>
+<p>It is best practice to cross check files in the proprietary software
+they came from and Open Specy before use in Open Specy. Due to the
+complexity of some proprietary file types, we haven’t been able to make
+them fully compatible yet. If your file is not working, please contact
+the administrator and share the file so that we can work on integrating
+it.</p>
+<p>The specific steps to converting your instrument’s native files to
+.csv can be found in its software manual or you can check out <a href="https://www.effemm2.de/spectragryph/">Spectragryph</a>, which
+supports many spectral file conversions (see Mini Tutorial section: File
+conversion in Spectragryph to Open Specy accepted format).</p>
+<p>The following line of code will read in your data when using the
+package and interprets which reading function to use based on the file
+extension.</p>
+<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a><span class="fu">read_any</span>(<span class="st">&quot;path/to/your/data&quot;</span>)</span></code></pre></div>
+<p>These file type specific functions will also read in spectral data
+accordingly if you have a particular format in mind.</p>
+<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="fu">read_text</span>(<span class="st">&quot;.csv&quot;</span>)</span>
+<span id="cb4-2"><a href="#cb4-2" tabindex="-1"></a><span class="fu">read_asp</span>(<span class="st">&quot;.asp&quot;</span>)</span>
+<span id="cb4-3"><a href="#cb4-3" tabindex="-1"></a><span class="fu">read_opus</span>(<span class="st">&quot;.0&quot;</span>)</span></code></pre></div>
+<p>If you don’t have your own data, you can use a test dataset.</p>
+<p><img src="data:image/jpeg;base64,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" width="267" style="display: block; margin: auto;" /></p>
+<p>A .csv file of an HDPE Raman spectrum will download on your computer.
+This file can also be used as a template for formatting .csv data into
+an Open Specy accepted format.</p>
+<p>The following line of code does the same to load the example file
+into R:</p>
+<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="fu">data</span>(<span class="st">&quot;raman_hdpe&quot;</span>)</span></code></pre></div>
+<p>We also have many onboard files that you can call to test different
+formats:</p>
+<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a>spectral_map <span class="ot">&lt;-</span> <span class="fu">read_extdata</span>(<span class="st">&quot;CA_tiny_map.zip&quot;</span>) <span class="sc">|&gt;</span> </span>
+<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a>  <span class="fu">read_any</span>() <span class="co"># preserves some metadata</span></span>
+<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a>asp_example <span class="ot">&lt;-</span> <span class="fu">read_extdata</span>(<span class="st">&quot;ftir_ldpe_soil.asp&quot;</span>) <span class="sc">|&gt;</span> </span>
+<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a>  <span class="fu">read_any</span>() </span>
+<span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a>ps_example <span class="ot">&lt;-</span> <span class="fu">read_extdata</span>(<span class="st">&quot;ftir_ps.0&quot;</span>) <span class="sc">|&gt;</span> </span>
+<span id="cb6-6"><a href="#cb6-6" tabindex="-1"></a>  <span class="fu">read_any</span>() <span class="co"># preserves some metadata</span></span>
+<span id="cb6-7"><a href="#cb6-7" tabindex="-1"></a>spc_example <span class="ot">&lt;-</span> <span class="fu">read_extdata</span>(<span class="st">&quot;raman_atacamit.spc&quot;</span>) <span class="sc">|&gt;</span> </span>
+<span id="cb6-8"><a href="#cb6-8" tabindex="-1"></a>  <span class="fu">read_any</span>()</span>
+<span id="cb6-9"><a href="#cb6-9" tabindex="-1"></a>csv_example <span class="ot">&lt;-</span> <span class="fu">read_extdata</span>(<span class="st">&quot;raman_hdpe.csv&quot;</span>) <span class="sc">|&gt;</span> </span>
+<span id="cb6-10"><a href="#cb6-10" tabindex="-1"></a>  <span class="fu">read_any</span>()</span>
+<span id="cb6-11"><a href="#cb6-11" tabindex="-1"></a>json_example <span class="ot">&lt;-</span> <span class="fu">read_extdata</span>(<span class="st">&quot;raman_hdpe.json&quot;</span>) <span class="sc">|&gt;</span> </span>
+<span id="cb6-12"><a href="#cb6-12" tabindex="-1"></a>  <span class="fu">read_any</span>() <span class="co"># read in exactly as an OpenSpecy object</span></span>
+<span id="cb6-13"><a href="#cb6-13" tabindex="-1"></a>rds_example <span class="ot">&lt;-</span> <span class="fu">read_extdata</span>(<span class="st">&quot;raman_hdpe.rds&quot;</span>) <span class="sc">|&gt;</span> </span>
+<span id="cb6-14"><a href="#cb6-14" tabindex="-1"></a>  <span class="fu">read_any</span>() <span class="co"># read in exactly as an OpenSpecy object</span></span>
+<span id="cb6-15"><a href="#cb6-15" tabindex="-1"></a>yml_example <span class="ot">&lt;-</span> <span class="fu">read_extdata</span>(<span class="st">&quot;raman_hdpe.yml&quot;</span>) <span class="sc">|&gt;</span> </span>
+<span id="cb6-16"><a href="#cb6-16" tabindex="-1"></a>  <span class="fu">read_any</span>() <span class="co"># read in exactly as an OpenSpecy object</span></span>
+<span id="cb6-17"><a href="#cb6-17" tabindex="-1"></a>batch_example <span class="ot">&lt;-</span> <span class="fu">read_extdata</span>(<span class="st">&quot;testdata_zipped.zip&quot;</span>) <span class="sc">|&gt;</span> </span>
+<span id="cb6-18"><a href="#cb6-18" tabindex="-1"></a>  <span class="fu">read_any</span>()</span></code></pre></div>
+<p>You will notice now that the R package reads in files into an object
+with class <code>OpenSpecy</code>. This is a class we created for high
+throughput spectral analysis which now also preserves spectral metadata.
+You can even create these from scratch if you’d like:</p>
+<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a>scratch_OpenSpecy <span class="ot">&lt;-</span> <span class="fu">as_OpenSpecy</span>(<span class="at">x =</span> <span class="fu">seq</span>(<span class="dv">1000</span>,<span class="dv">2000</span>, <span class="at">by =</span> <span class="dv">5</span>), </span>
+<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a>                                  <span class="at">spectra =</span>  <span class="fu">data.frame</span>(<span class="fu">runif</span>(<span class="at">n =</span> <span class="dv">201</span>)), </span>
+<span id="cb7-3"><a href="#cb7-3" tabindex="-1"></a>                                  <span class="at">metadata =</span> <span class="fu">list</span>(<span class="at">file_name =</span> <span class="st">&quot;fake_noise&quot;</span>)) </span></code></pre></div>
+<p>We have some generic functions built for inspecting the spectra:</p>
+<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a><span class="fu">print</span>(scratch_OpenSpecy) <span class="co"># shows the raw object</span></span>
+<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a><span class="co">#&gt;      wavenumber runif.n...201.</span></span>
+<span id="cb8-3"><a href="#cb8-3" tabindex="-1"></a><span class="co">#&gt;   1:       1000     0.54651092</span></span>
+<span id="cb8-4"><a href="#cb8-4" tabindex="-1"></a><span class="co">#&gt;   2:       1005     0.83551928</span></span>
+<span id="cb8-5"><a href="#cb8-5" tabindex="-1"></a><span class="co">#&gt;   3:       1010     0.02988866</span></span>
+<span id="cb8-6"><a href="#cb8-6" tabindex="-1"></a><span class="co">#&gt;   4:       1015     0.76262890</span></span>
+<span id="cb8-7"><a href="#cb8-7" tabindex="-1"></a><span class="co">#&gt;   5:       1020     0.80840769</span></span>
+<span id="cb8-8"><a href="#cb8-8" tabindex="-1"></a><span class="co">#&gt;  ---                          </span></span>
+<span id="cb8-9"><a href="#cb8-9" tabindex="-1"></a><span class="co">#&gt; 197:       1980     0.51529198</span></span>
+<span id="cb8-10"><a href="#cb8-10" tabindex="-1"></a><span class="co">#&gt; 198:       1985     0.06580319</span></span>
+<span id="cb8-11"><a href="#cb8-11" tabindex="-1"></a><span class="co">#&gt; 199:       1990     0.53815839</span></span>
+<span id="cb8-12"><a href="#cb8-12" tabindex="-1"></a><span class="co">#&gt; 200:       1995     0.80906019</span></span>
+<span id="cb8-13"><a href="#cb8-13" tabindex="-1"></a><span class="co">#&gt; 201:       2000     0.73927894</span></span>
+<span id="cb8-14"><a href="#cb8-14" tabindex="-1"></a><span class="co">#&gt; </span></span>
+<span id="cb8-15"><a href="#cb8-15" tabindex="-1"></a><span class="co">#&gt; $metadata</span></span>
+<span id="cb8-16"><a href="#cb8-16" tabindex="-1"></a><span class="co">#&gt;    x y  file_name                          file_id         col_id</span></span>
+<span id="cb8-17"><a href="#cb8-17" tabindex="-1"></a><span class="co">#&gt; 1: 1 1 fake_noise be5d6463062fcf4ac45e229d1f4820a2 runif.n...201.</span></span></code></pre></div>
+<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a><span class="fu">summary</span>(scratch_OpenSpecy) <span class="co"># summarizes the contents of the spectra</span></span>
+<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a><span class="co">#&gt; $wavenumber</span></span>
+<span id="cb9-3"><a href="#cb9-3" tabindex="-1"></a><span class="co">#&gt;  Length Min. Max. Res.</span></span>
+<span id="cb9-4"><a href="#cb9-4" tabindex="-1"></a><span class="co">#&gt;     201 1000 2000    5</span></span>
+<span id="cb9-5"><a href="#cb9-5" tabindex="-1"></a><span class="co">#&gt; </span></span>
+<span id="cb9-6"><a href="#cb9-6" tabindex="-1"></a><span class="co">#&gt; $spectra</span></span>
+<span id="cb9-7"><a href="#cb9-7" tabindex="-1"></a><span class="co">#&gt;  Number Min. Intensity Max. Intensity</span></span>
+<span id="cb9-8"><a href="#cb9-8" tabindex="-1"></a><span class="co">#&gt;       1    0.002008419      0.9965232</span></span>
+<span id="cb9-9"><a href="#cb9-9" tabindex="-1"></a><span class="co">#&gt; </span></span>
+<span id="cb9-10"><a href="#cb9-10" tabindex="-1"></a><span class="co">#&gt; $metadata</span></span>
+<span id="cb9-11"><a href="#cb9-11" tabindex="-1"></a><span class="co">#&gt;   Min. Max.</span></span>
+<span id="cb9-12"><a href="#cb9-12" tabindex="-1"></a><span class="co">#&gt; x    1    1</span></span>
+<span id="cb9-13"><a href="#cb9-13" tabindex="-1"></a><span class="co">#&gt; y    1    1</span></span>
+<span id="cb9-14"><a href="#cb9-14" tabindex="-1"></a><span class="co">#&gt; [1] &quot;x&quot;         &quot;y&quot;         &quot;file_name&quot; &quot;file_id&quot;   &quot;col_id&quot;</span></span></code></pre></div>
+<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" tabindex="-1"></a><span class="fu">head</span>(scratch_OpenSpecy) <span class="co"># shows the top wavenumbers and intensities</span></span>
+<span id="cb10-2"><a href="#cb10-2" tabindex="-1"></a><span class="co">#&gt;    wavenumber runif.n...201.</span></span>
+<span id="cb10-3"><a href="#cb10-3" tabindex="-1"></a><span class="co">#&gt; 1:       1000     0.54651092</span></span>
+<span id="cb10-4"><a href="#cb10-4" tabindex="-1"></a><span class="co">#&gt; 2:       1005     0.83551928</span></span>
+<span id="cb10-5"><a href="#cb10-5" tabindex="-1"></a><span class="co">#&gt; 3:       1010     0.02988866</span></span>
+<span id="cb10-6"><a href="#cb10-6" tabindex="-1"></a><span class="co">#&gt; 4:       1015     0.76262890</span></span>
+<span id="cb10-7"><a href="#cb10-7" tabindex="-1"></a><span class="co">#&gt; 5:       1020     0.80840769</span></span>
+<span id="cb10-8"><a href="#cb10-8" tabindex="-1"></a><span class="co">#&gt; 6:       1025     0.50201981</span></span></code></pre></div>
+</div>
+<div id="visualization" class="section level2">
+<h2>Visualization</h2>
+<div id="spectra" class="section level3">
+<h3>Spectra</h3>
+<p>In the app after spectral data are uploaded, it will appear in the
+main window. This plot is selectable, zoomable, and provides information
+on hover. You can also save a .png file of the plot view using the
+camera icon at the top right when you hover over the plot.</p>
+<p><img 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" width="420" style="display: block; margin: auto;" /></p>
+<p>In R, we have two ways to visualize your spectra, one is quick and
+efficient and the other is interactive. Here is an example of quick and
+efficient plotting.</p>
+<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a><span class="fu">plot</span>(scratch_OpenSpecy) <span class="co"># quick and efficient</span></span></code></pre></div>
+<p><img 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" style="display: block; margin: auto;" /></p>
+<p>This is an example of an interactive plot.</p>
+<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" tabindex="-1"></a><span class="fu">plotly_spec</span>(scratch_OpenSpecy) <span class="co"># this will min-max normalize your data even</span></span></code></pre></div>
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+<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" tabindex="-1"></a>                               <span class="co"># if it isn&#39;t already but are not changing your</span></span>
+<span id="cb13-2"><a href="#cb13-2" tabindex="-1"></a>                               <span class="co"># underlying data</span></span></code></pre></div>
+<p>With interactive plots you can also plot two different datasets
+simultaneously to compare.</p>
+<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" tabindex="-1"></a><span class="fu">plotly_spec</span>(scratch_OpenSpecy, rds_example) <span class="co"># interactive comparison</span></span></code></pre></div>
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+</div>
+<div id="maps" class="section level3">
+<h3>Maps</h3>
+<p>Spectral maps can also be visualized as overlaid spectra but in
+addition the spatial information can be plotted as a heatmap. A similar
+plot should appear in the app if you upload multiple spectra or a
+spectral map. It is important to note that when multiple spectra are
+uploaded in batch they are prescribed <code>x</code> and <code>y</code>
+coordinates, this can be helpful for visualizing summary statistics and
+navigating vast amounts of data but shouldn’t be confused with data
+which actually has spatial coordinates. Hovering over the map will
+reveal information about the signal and noise and correlation values.
+Clicking the map will provide the selected spectrum underneath. In the
+app, the colors of the heatmap are either the signal and noise or the
+spectral identifications depending on whether the identification is
+turned on or not. Pixels are black if the spectra does not pass the
+signal-noise threshold and/or the correlation threshold.</p>
+<p><img 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" width="420" style="display: block; margin: auto;" /></p>
+<p>The same plot can be created in R but the user can control what
+values form the colors of the heatmap by specifying <code>z</code>.</p>
+<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" tabindex="-1"></a><span class="fu">heatmap_spec</span>(spectral_map,</span>
+<span id="cb15-2"><a href="#cb15-2" tabindex="-1"></a>             <span class="at">z =</span> spectral_map<span class="sc">$</span>metadata<span class="sc">$</span>x) <span class="co"># will show more advanced example </span></span></code></pre></div>
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+<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" tabindex="-1"></a>                                          <span class="co"># later in tutorial</span></span></code></pre></div>
+<p>An interactive plot of the heatmap and spectra overlayed can be
+generated with the <code>interactive_plot()</code> function. A user can
+hover over the heatmap to identify the next row id they are interested
+in and update the value of select to see that spectrum.</p>
+<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" tabindex="-1"></a><span class="fu">interactive_plot</span>(spectral_map, <span class="at">select =</span> <span class="dv">100</span>, <span class="at">z =</span> spectral_map<span class="sc">$</span>metadata<span class="sc">$</span>x)</span></code></pre></div>
+<div class="plotly html-widget html-fill-item-overflow-hidden html-fill-item" id="htmlwidget-f412c9891a1a4791365b" style="width:98%;height:288px;"></div>
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+</div>
+</div>
+<div id="processing" class="section level2">
+<h2>Processing</h2>
+<p>After uploading data, you can preprocess the data using intensity
+adjustment, baseline subtraction, smoothing, flattening, and range
+selection and save your preprocessed data. Once the process button is
+selected the default processing will initiate. This is an absolute
+derivative transformation, it is kind of magic, it does something
+similar to smoothing, baseline subtraction, and intensity correction
+simultaneously and really quickly. By clicking preprocessing you should
+see the spectrum update with the processed spectra.</p>
+<p><img 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oBqTKutrapaW9K1oSGU2tY3WYa0AASSSY01JJPeealph+k8v2jhMC2NnvZltNuoPSHqgxbtgLBjVX1gqsBtzm95UktExWqg26y797eckOPEKKatY9NE9I3SZce5Y+Y2y1kzWjnWgtvWtULGpUK0K7QpBTKdJN+RUZcOc+62TdMjx9G9x5ttFFp5r2VCI3pbI7vY8Qf04Olsju9jxB/Ti806BblA0+e0LQHXYjX0UqVX9JVmVcFBiUm4Mmdn+4qrTrYgW5Tb0umy3LwvCmlChSoiyY0qe6+jIuOSG5owmBbjMy2FdBteWfBz00z0ld6U2RW6j/d42aHa/X4awahdTdjvRbn0dKqNypLczlSUZb0WotQilab/I05ldCNx83IpOlsju9jxB/Tg6WyO72PEH9OAUrcCANPmgG6HI+ikEzd20K/nHYV2WPWck9n62pF31WnVqo3xZ9gN0u/AkQKjOnmY1EyN38JCTFivbqhvN01pV3jdkK5plhkdLZHd7HiD+nB0tkd3seIP6cOwU2CGKt1Ou8guHp/e9PfBhkdLZHd7HiD+nB0tkd3seIP6cNnal4NXZPfDltS4I1tVN6oyrcoF0NO0mZTRp1xsPSILJyozscJII24Bco0riGKqqp7P2LoY4j6WyO72PEH9ODpbI7vY8Qf04M7d0cGrslW6P9tH9MDq6uo8dHWwXDmOehpz8ltxJTkRml5wi9KbjOHFZXm6HwNxE3R6061TrT3pjmiqNddqcdI5xWmBF5Ht8HSMl0Qk00VE9+Ou30bCkn3PJtmihEgqecGooSoK/4bQ+tMZLog5SPeb6rbagsYWu5wVyP3N+NUv9OY/XwaxI3sE7JlubXmbVx2TdV4VO0qHatlnd8tq3Y0eTdFfX1xiEEeGj6E2Atk+jrzqg4oigogpv7wxyXN+Ncr9OY/k1jKFFrtetmqMVq261WrdrMRSSLV6BVZNEqsZDRRNG5LBg4KEiqioJJqi6LrjSATmAWWpEMnb6BZn2gcpqDk3m3nTlTTpz9cLKSqTozdzR54S4dZbiAw6jb7HJJyD5C+LKiLhIMlsg0JF1HaaL6L7OaqFISi5obPU/fGI5SGZGYT1Pn1JuQ9EbdcfZ9UNIgsDJ5VVdMuUQEANXXWmijzqNwXDWOUSr1us1VHZCy3UqNSfm8s6SqSunvku8aqRKplqWpKuuqrhFRoRXUWgFevUW0FetF93vRF/Vhi15jK6PkqgWgkkeakjtr0YWdFZkxkq2Y2QtCpk0afHZrcbMBbmgRZlU/wBJFkhHjoacRcAyYR8uUQBBt1D3xbVp+jkzou6r3VbkS78m4VxW7R7Mr9Opk2+hZbuuJequyoTkJw2gc34lKbCsyY6sk+EWXGDkkkOIwkf6sNkm6TDZDx9kmRUeKaKumn2pw192FCrT6jXpz1Trs2dW6lIFsH6jWZTlVqDyNNAw0hvuqRqgNtNNiiroINAKaIKIkZasRmHT8/VTNOdG6fH8fRSa1b0UOekW8eYaTfuTcq2FqI09u+rguhy1oRojSyX5HN/JSJCNtx9JKIq7xtONrugpiitJ70YmfwxVmxL12dpscYUioPAecDdJqUNpkxbbSTDlQ2nWnXlMdGlRVaVV5ZWUFSxHvU59RrUkZlZmzqvMCExTRl1SU7UZQxorLcaLGRxxSJGmWWWWm2kXcbbaABQREUTwo0KKpI0G8uupcmm8uvBdV014/b78LkrZYzifh+R4KQ6lpmb3a69/QrYvaB2aLs2czs2Pdt1Zd3RJvBurkymXlbk3JApa0iYzEMH5pxmWSJ8JMeQ2LSkYtujyotkooWumie5PhioggpoIIKa66CG6nwT8yfDFdF9y/DFoBjUpHZS6WiB8VbonuT4YNE9yfDF2i+5fhg0X3L8MTCWFbonuT4Yctq2hXL0qT1Jt6LGlTmKXLrDrcmoRqY2jEKO5JeVHHjAVLdbXQUXVVVOpNSRuaL7l+GKohIuqIqLoo6pqi6EiiSfmVFVFT7UVU6lwQphNq50RKaPBEX1wE1RE1T2T+3HWT6Njj9zybZn/AGzf/ptDxyb3QipTA1RU/DA6/wDqeOsj0bHD7nk2zP8Atm//AE2h4xXfs/8ATfVdC0/1n4Fcj11ip3LUG001ckNtpr1IpA2ia/HHsjWbV5klqHCdObLeJRZiw2pMuS8qIqqgNgikWiIq8EXgirhvXQxVqlfkmlUdqZNqVRqkan0uBCFXZUuQ+LLbDTQJ1mZmIinvVMZgs6jbYuU9ZeujL+lZ8WPXhFy30uGz6XUWKhJB7kjcYiyo4Hy7TicgSuRiNsxNpd9RMd53Pa0nMq4cWty7BMiXYVxwGgfnMTYLDhIDb8yFMisOEqKqCJkKCqqiLwRdeC4T0tiaqapPYVPeLjqp/PC1VbY2j57VyXRWbezbkjUKs+xd9Rl0Woo67LRhirSCqLSNoTf3qQxKV10BDRxCQuvHjmWHtCTZIOTLCzddkmIxWtbFqrJOI0KgACIRkQlRAVOCKq7hKuqoS4gVacoDKs6Hy+C8CW1LUlBKhHUxTUgR51TFPeqa6/anxxXoxN109fY1VNUTlHtV/VrhXmWhtGJQY8GbZ+bI2/S3X57TLtn1BuPTSAXTfedP1dHGxQTeJSeJB0Q1TgKqNlRy32iKK/0fqNgZsQ5Ex0JgQHLQqDpyiBgHBNtwWS3txuUCkLZqg8qm+iLpoCozZQG1dx0SZ0XnduZ/bexRLZmKqik9hVH/ADIjjyqOvVrx4Y+5Za5+iy5IPL7NsGW9OUJyzqs0QITZOiqgTKFooCRIWm7oK8deGMfP3dcs2PBYer9RfjQWjCngkhEbYB5xXj3FFEVUIyU9SVevgqJwxPEp7Iy1QAZHRPvovO7cz+29g6Lzu3M/tvYx3z9W+96h4osU5+rfe9R8UWDiU9ioy1Nx0WRei87tzP7b2DovO7cz+29jHfP1b73qHiixTn6t971HxRYOJT2KA2ruOiyL0XnduZ/bewdF53bmf23sY75+rfe9Q8UWKc/Vvveo+KLBxKexRlqbjontUqLJp8cZD0ht4FdRrdEnFJFVF0X2uH2LjrW9HDXKXF+5/drmgPyXAq1aj51TqbFSDJdbkNQqZbqyiKQLasNqHrDOguuAR7/sIehacdz1XqskOTkVGY+2hIaA6+RiipwRdP1r8cbBZZbUW0jZVmPZE2fnxmxbGSt41SQF2ZT0K9plOy+uVKpybVRSdSwJGXvWQbbF3fRd9ARF6sVVYqZQ3cHoVdScWA5ucEJgXPRblC75Nao4Px32JrFQptQiTgiSorrItE262W+hgYGCEJJoqKKKmHm/mvtMyEjoeamZYpFgpTY6R78ehI0wiEm4iNvCnHeXUl9pdB1X2A3TBjSaLSZKobUdlASG/eefcpioxZV+39JiViKxCq8SRezz0WqsxmwZYZktq/uutg22DaAaKm4KCqKnDC2/mvtMyJc+c7mrmasmqTXKjUDC/XmgkvOiQGfJi8gD7JmCCAoIgZCKCJEimDC/t6abiOX3Yze2mmSqZHmdf88Kzbc20KpGrF2pXYM2m1BtWpkU48hxxrRwV/zoKGJIhCQkiLjxwM0NpOl1Cn1WDmdmM1UKUctymyHL2cmJBWc0DM3km3XSAUkA22Lo7uhoA7yLprgwYP29OZ7/AJI4jlamZ20mkhqZ/anmeU1jd5GceYUlyc3uAbY/f1kK4uguOImpL/8AQ16yVVxQ5atyvOOPOwScddcJ11wpbCkZEqkRL7fWqqq/rwYMTwGI4ryIKs6I3D3f/FsfXg6I3D3f/FsfXgwYOAzcqM5R0RuHu/8Ai2PrwdEbh7v/AItj68GDBwGblGco6I3D3f8AxbH14OiNw93/AMWx9eDBg4DNyjOUdEbh7v8A4tj68LFvWtXY9do8h6CgNMVFp501lMluiJbyrohKvUn2YMGDgtGoRncv/9n/2wBDAAEBAQEBAQEBAQEBAQEBAQIBAQEBAQIBAQECAgICAgICAgIDAwQDAwMDAwICAwQDAwQEBAQEAgMFBQQEBQQEBAT/2wBDAQEBAQEBAQIBAQIEAwIDBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAT/wAARCAFSAaQDASIAAhEBAxEB/8QAHwABAAEDBQEBAAAAAAAAAAAAAAcBAwYEBQgJCgIL/8QAXhAAAAQEAgQHCggKCgEBBQkAAQIDBAAFBgcIERITIZQJFBYxUVXSFUFTVHGRkqGx0RciMlJhgaTjMzQ1OXJzdbKztQojN0J3eILBxPAYNiQlRWLhGRooQ2NkhKLC/8QAHAEBAAIDAQEBAAAAAAAAAAAAAAECAwQFBgcI/8QAPREAAQMBBgQDBwIFAwQDAAAAAQACEQMEEhMhMVFBYXHwBTKBBhQiM5GhwQexQlJictEjkvEIgqLhg8LS/9oADAMBAAIRAxEAPwDpSfv0ZazWfL5imgTWZFLpGMOwAAA8ohEeDcRXMdGUp5ZiICLz4w59PxRy8kZRWGyn3oD4Mv7xYg+PTkwuBSpMeJKkj4RVuqU98+7h8Iq3VKe+fdxG4CHfHIADSEctgBEuWtsVdO8zty2t7SjucIMVCozCbOFSSyRy45hyAiztTIgG/wDkLpGDnEADLPStfiFlsNA2m21GspjUuIAWK11/DbBZzarbUDKY1LiAPv0W1/CKt1Snvn3cPhFW6pT3z7uOSocHpiYEQAJLQw57ci3BamHZz5Bq9sS3bjge8f13k3a9tLOS6s2jJQUXMwlFcMSSpM4AAmT44uCSAnABKOgB9LbtAI49n9sPZ211cGy2tj37Nk/sCuNZPaj2Vt1ZtnsVqbUqHQNlxMRwAK4H/CKt1Snvn3cPhFW6pT3z7uJGxHYRcSWEepWlJ4i7QVfayazMpzyVefMyrSCoCp56w0vmiInaudDL4xUlBMXPMSgGQjxw0gz74B9IfG2jkGzvB9I7I71O0MqtDqZkL0DW2Zwlqkn4RVuqU98+7h8Iq3VKe+fdxG/QHf5xDIQEod7PoEe8A96EZC8tMEK2FQzyOWSkj4RVuqU98+7h8Iq3VKe+fdxG8IgVCVIo0ToOE+mv4UkfCKt1Snvn3cPhFW6pT3z7uI3hEl5GZHeqChRMQO9fwpI+EVbqlPfPu4fCKt1Snvn3cRvCBeQYI7mP3UYNCAY7Ckj4RVuqU98+7h8Iq3VKe+fdxHGX0gH0iORQ8o9H0xXRMAFMJTZHEAL8UfjZ7cg6R8mfPE3nRMcvWJ/ZTgUQbsZ6+ikb4RVuqU98+7h8Iq3VKe+fdxG+wQAQ2gIZgMIi+dkFGjEgd9hSR8Iq3VKe+fdw+EVbqlPfPu4jeEL52UYNDZSR8Iq3VKe+fdw+EVbqlPfPu4jeEMQpg0dlJHwirdUp7593D4RVuqU98+7iN4QxCmDR2UkfCKt1Snvn3cPhFW6pT3z7uI3hDEKYNHZSR8Iq3VKe+fdw+EVbqlPfPu4jeEMQpg0dlJHwirdUp7593D4RVuqU98+7iN4QxCmDR2UkfCKt1Snvn3cPhFW6pT3z7uI3hDEKYNHZSR8Iq3VKe+fdw+EVbqlPfPu4jeEMQpg0dlJHwirdUp7593D4RVuqU98+7iN4QxCmDR2UkfCKt1Snvn3cPhFW6pT3z7uI3hDEKYNHZSR8Iq3VKe+fdw+EVbqlPfPu4jeEMQpg0dlJHwirdUp7593D4RVuqU98+7iN4QxCmDR2UkfCKt1Snvn3cPhFW6pT3z7uI3hDEKYNHZSR8Iq3VKe+fdw+EVbqlPfPu4jeEMQpg0dlJHwirdUp7593D4RVuqU98+7iN4QxCmDR2UkfCKt1Unvn3cXELhFMqmVzLtUiY2R1COdaJPpy0A2dMRnFB/2H90YlrgdVDqLIhi5MJqaZCnIcAKcoHDMuY7QhFhp+Kt/1JfYEIutUsZOZWwVl+QXv6BfaWIOicay/IL39AvtLEHRiq6ZLYokim6O9FcSIKq6KIG0BWVKgUwhmBTHMBQNlzDlnzDHqJtpb2RWtoWnKEp5mi0YSCWJs1DIp6s71zoFB26UHnMdZQDmMYRzHMI8uhREBzAwkNmGRw5yCGZgMH6OQj9Ud/WF/FfRF16PkkhqSfy2n7lSqXISuaymcPU2HKA6RSpFeMVFDFKqCxS6ZiAYVCmzESgA6UfDP1v8ADvF7Z4bZ6tiYXWdjnGoAJMGLpjYQQeoXw/8AXTwzxi2+F2apYWF1Cm5xqACcjF0xsIIPMhc/7cUoWu7h0FRKjgzVKsa0ldLquSbFECP3qDY5yjkOQgVQ2Q5bByHvR7AqKpCm6BpmSUfSksbSen5DL05ZLZazT1aKBEyhtH5xjbTGOPxjGMYRERERjxt0vWnJGpqequTzaVpTamJ60qGWqDMUDgRdkum4SEQ0tvxkw2befvx6prB4t7L34peXTiQV3TDCoDMSGn9GTKo2SE+kTkCgKxDJGUAyiQGz0F08yHKADmA6RQ8v+jtTw6i62UCAy0mC08S2I+xnLVc7/p+reF2d9vs9VrW2t5ZdkAOdTGoHGZmR0WKcINhht1i5wjXptDcKVtXCbyiJjPqOnirQisyomfS5k4eSqbsVMtJJVFVIAOKYgKqSqyRsyKGAfzHsJ1D03d/EhhvtxWDZ4vSN0Lv0rSdTs5Y+Ulb9aXzh+zbPEkHSfx0jikscpVC7S6Q5Zc0e+jhfOFmsThMw/wBy7Y2+uJS1dYn7h0m+oqj6JpKbNqjdUGaZN1Wa8+nwonMRom0SVOok3WEFnCwIlKnqxUUJ4BsOFyJTZC/ljLtzaVzKdSO0t0qer2YSWTLIoTeatJLMWrxRs1UXEqRVVE0DEIZUSlATlEwgXII/SHhDblZrniGE8V+iPEZNCoaPnAB9ZXpbxecExgpldvcerez1lsV+HWrMFFMNaypy9V0aqcVVYG/eubA6VlUrVfGMoobnQFRuJDprKJiJlQzRNwAr/gIMS1D1Vbe27S9mGitLuXcnUqbW/tHTdXvUbgTWTPZerMpjVjtguiVVnJpWk3dlcu1SDpKNhIkBxOkB+P3CF8JtejG7ea4kwaXIvbJMNFRVZL6kozD7V9XNzSKlism7QokXZsTi1VMLhFw4IB1FikMsQ3yylEOU9VcMjTiXCf2Xx/27s/V6FK2+sZKbHVhbWr51K29T1MybtJyzmSrN82Mq3TMJZk3Wb6wcxVYZKashhyxeHstJpNdWyLjmDreFMyTtfdAMaASCCSstsrBlbCptBa1pIPAy5sRuWtvESMyYOSjiYcCXfZS72Hu2tEX6w4XVpXEbU9S0HS16bfTubTe3dM1DScvezKaySck4txpNcUWKwJHSKcpzAYNmiaNmu1wMN+bXyO0lUSq9mHC79L3JxCMMMFTVHaGrXlWSizVXv1ytSsKiUKjtKksJklTNszJnKmQxAMsln2s4WuEOtpid4TLBLI6Bu1jWm9FSa4Vb1/VTPGNcylpxRcicvaLnqErYU+iwbonQ7n8ZetirPFVFFSOdWKixwA0casSHCe4ZbQqSPDbhts7d5nSFIcIe4xY4kZ9XNXyifzSr5vKKi446ldKLJnBIGrhdokqksvqipJt0QHTMdUwa9CvbjaadN4ADnSP5jTa6kHzkBkC+7EaNmcyq2xrW2SvUsgmo1sifKahFRzY4glwYCNJk5TC48zvgHsRknvLLbFI3+wrTuumbSf1TddvKa8dKt8P1KyQW4o1TWICjrGbaZJu2qrNqYpXByHOJgKCSxko+mvAoYslL7WSs7bqpbNXmpHEFRT241t8RNuauUfWPeyOUg2NO5g6fHS1yJGXHWRRKBTiuZ4iVITiYdGW6A4Xa3dM8IRjPxJTuzlczfDljgotxba5luEZzLZfdOSSxeTMZaR4zdpqGZGcpnRemAmt0DJOzAB9YmUBnKjuG+slYS4WFi2+H7D1cRbBThys3VFj5rTFw6rYub2XAZ1epL3ExmZXaY8USWRWljVQiBzFBcVXQaSBTJaG1RdaHMo1nj4yHEjheAqQHn+EAtpgbyVd+DUqVKOjZgEaxDTLR/FmXAzpC4Yyjge66qm5tbUlQ2LrB3XVrLUWyJc68uJal67fTCzNp01nr1i1lE3VKgZfui4OwcKJoIaYAiXWHMmAkBTdpFwJOI19dy91u6nu3YGgqEsZbOn7wT7EBPJ9M5jauo6eqkzwJFMZWVs2M8OmuEufmVMoQhGwNvjmHTKMcjcN3Cg4GcGtV3mtzhmtnjNoXDliCoZmyratGlyaeQxC0JVLJ48UZz+lljFUaERTaPuJnbONLMWSR9WqBlwV1VpOFwsNRuK6sr6VTcPhPZ2xa05S9JW0rSZXhoqoq2eyqRrvX87k1WyY7IsoesJw7cAqQpEROyBZwKAtDG1kA60F7Q3IFovE5fEQeGeYcchlLReMnJLtBwdVcACSbt3PIFrczsWg3jndcYBAXFW2/A63Fr2j7tXRf4sMHND2atjeJex8nvRUdxHr21V0p02bprKGlE4btxRQZ6SpEeNvRTJrtYQAOCYnNNqXBuzS/mG3gvKQtnQeGG2tfYnKoranS4ipRVtTTyobqGkJZo6Fepmnc4iKKAllxxbHZKODHzSNkgUTkHkFYDhpcJFqbsYjLzrWaxU0aa8FzJzW7zD9QNZUhM8OFyJc+lbWVspfUNOPm5kWL0OLHeOn8tKQ6qzs5TrOEtFKIjtrwytnKHacHUk5sBcOXlwW3KuFXlUyOkJnIUacm7esyT3iUrpkFXBTposBm6CWi+BHNJpkXMRLGtWNtcxjBpNFx6CL/AHPDQK1QU6RqYOZBqgdY+H8Z/dce7r8CRiZt5IbOzmi7oYeL+K3bv2TDQsys7Xak2bW8rE/GCqS2dP1Uytw4uozdpuhRETtlETFOnzZ7Lif4IatMMGH64OIabYsMLlyqetxco9mJ5TFvH1QK1M4q1s4TbzGnWxnDMjczxoBlFlEzHLmmicSiOwI5kYTuEItld57YXCMjS1TW/ntY8LetjHTuZWNcMKQoKRSea1bM6jSkzp+2W46nMBTcJMhMmQWyq6pSazVKxzp4b22VPVfhJuhcutrz3qtPMKEvyvUVqMPtyZRaenaLvtNpu+K3fTSTNKUL3QfnRZrKrJTOdLKOSJpG1xBOoKoUtL/EqVBz3CCXgD+25SLjlsXVB1AV7O2haLQ5pyugx/V8VQNjqLh6TkvGsPRs6PLCA5Z5Z57cwHp+mEdhxzPr9zP5WmRDiBp2EhCEQiQhCCJCEIIkIQgiQhCCJCEIIkIQgiQhCCJCEIIkIQgiQhCCJCEIIkIQgNUVBEhQzOoQhc9HM5wJtHmyERAB6Ms8xzCPkqqJwzIsicRD4pU1QUObnzAobM8tnN9MStaO7c3s3UbypZNS9vKtcvpSeTKy25NFNa4kyaaiiaplEm6/xUlM0ihrS7QKJg78S/X2L+sK/pGfUa/tRhykbKo2YMnk3pSysrp+o2pdMimsaPS5nRUESB/WE2gCmWUWIjii4m7O8ICHeEo6RR8g9+KD/sP7oxXPPbmI57cx5x8sUH/Yf3RiG6hSNVyRafirf9SX2BCDT8Vb/qS+wIRnXPdqVsFZfkF7+gX2liDonGsvyC9/QL7SxB0Y6nBbVn8h72SKCUpgyMUpg6DABg80VhGN0uEFZTmIOf3VrUIbP6hHZtD+pLs9UfJ2rVQMlGrVQA7yjYigesIvwgQL15uROqiACXMyPIn/AIXwRNNIuikmmkT5iSYJkD6gDKPuEIaNDdjKBoAhuQT3Q/7thCJJJJLuKteOnfRfJyEUASKEIqQecihAOQfKA7IqAAUAAoaIAGQAXYAeQIrCHG9Ge/KI+4yJ1KqARk05af4+nBP+80IQhJzI1UnPXuNEhCECQRdjJMymz37IQhEcSTx/9ZdMvuUEN0XyYpTAJTlKco7BKcAOUwdAgMWk2zZERMg1aoGHnMg2IgYfKJQCL8IQOOYGnJS03DebkU8vuhCEFUCBBSEIQUpCEIIkIQgiQhCCJCEIIkIQgiQhCCJCEIIkIQgiQhCCJCEIIkIQgiQhCCJCEIiESKD/ALD+6MVig/7D+6MWbqFI1XJFp+Kt/wBSX2BCDT8Vb/qS+wIRnXPdqVsFZfkF7+gX2liDonCtAzp98GYhmQu0phIb5ReYQ5ogrVB85fejxjqcFtWfyHvZXYRa1QfOX3o8NUHzl96PGNZVdhFrVB85fejw1QfOX3o8EV2EWtUHzl96PDVB85fejwRXYRa1QfOX3o8NUHzl96PBFdhFrVB85fejw1QfOX3o8EV2EWtUHzl96PDVB85fejwRXYRa1QfOX3o8NUHzl96PBFdhFrVB85fejw1QfOX3o8EV2EWtUHzl96PDVB85fejwRXYRa1QfOX3o8NUHzl96PBFdhFrVB85fejw1QfOX3o8EV2EWtUHzl96PDVB85fejwRXYRa1QfOX3o8NUHzl96PBFdhFrVB85fejw1QfOX3o8EV2EWtUHzl96PDVB85fejwRXYRa1QfOX3o8NUHzl96PBFdhFrVB85fejw1QfOX3o8EV2EWtUHzl96PDVB85fejwRXYRa1QfOX3o8NUHzl96PBFdhFrVB85fejw1QfOX3o8EV2EWtUHzl96PDVB85fejwRXYRa1QfOX3o8NUHzl96PBFdhFrVB85fejw1QfOX3o8EV2EWtUHzl96PDVB89fejxIEpqYV2KD/sP7ox65+Bk4K/Avi2wOU/ebEBZmaVzcV7dqsaXcz9G6dXUsmsxlL9ugwR4pLpki2AUyLHDT1ZTn/vCYdHLtXHgGuCoOQ4lw2TUwaJygYl/LgfKLpFEM+7OWwxTAPfASmDnAQjTNsa10c1nbZ3EXpXhSafirf9SX2Qi4kJUiasCiAEUOUoEKBwAAOYC8+YhsAMgHIcss9u0UdZtNz2hw4rlOaZIWOVl+QXv6BfaWIOicay/IL39AvtLEHRgqcFs2fyHvZIpmGYh3wgYRKUxss9EuYBzAI5hsz+uMxJSgmIQVn6iauj8YqbcogQejMR2xQAnRXL2t1Kw+EZjyRS6xV3RP3xUaRS6xV3RP3xa45VxWTErDYRmPJFLrBbdExivJFLrFbLPxRP3xFxxMAKTUYMz+x/dYbCMx5IpdYLbomMV5JI5bJgtn382ieUSGPMkDRVFakSRezWGwjMeSKfWCu6Jw5Ip9YK7onEXHKcWnusOhGY8kU+sFd0ThyRT6wV3ROFxyYtPdYdCMx5Ip9YK7onDkin1gruicLjkxae6w6EZjyRT6wV3ROHJFPrBXdE4XHJi091h0IzHkin1gruicOSKfWCu6JwuOTFp7rDoRmPJFPrBXdE4ckU+sFd0ThccmLT3WHQjMeSKfWCu6Jw5Ip9YK7onC45MWnusOhGY8kU+sFd0ThyRT6wV3ROFxyYtPdYdCMx5Ip9YK7onDkin1gruicLjkxae6w6EZjyRT6wV3ROHJFPrBXdE4XHJi091h0IzHkin1gruicOSKfWCu6JwuOTFp7rDoRmPJFPrBXdE4ckU+sFd0ThccmLT3WHQjMeSKfWCu6Jw5Ip9YK7onC45MWnusOhGY8kU+sFd0ThyRT6wV3ROFxyYtPdYdCMx5Ip9YK7onDkin1gruicLjkxae6w6EZjyRT6wV3ROHJFPrBXdE4XHJi091h0IzHkin1gruicOSKfWCu6JwuOTFp7rDoRmPJFPrBXdE4ckU+sFd0ThccmLT3WHQjMeSKfWCu6JwGkicxJgoJx2FzbEIGflDbC45MWnusOy5vpDMOgYRUxDpnOmplppnFI4AOYAJREBCKRU5GFlGhXvX/o+iTd3wXMili09cU2pM743HZoTiXTJvLJwz0poxAyzVVcpylVKGwDgQ2hpAIABgAwdxlqLd0pamTvKZpes6hnsrfPDv2kqqirGdRKMF1AUOuLMSJkUKK5jGUUJ8YpjiJ/lCYTec3ghSJn4MezQmIU/wD+IG62QKEA5Q/95SUB2R2sWZSSLdOhzAkkUQnQ5CCRcwHizjIY4deMbLddWmf9LPZeAxIoiUw7MtcplmYA/wDzDQj4REwEHRy2qHEQERyAdM3MHR/9YR6qnkwDkvJvyeRzWO1l+QXv6BfaWIOicay/IL39AvtLEHRq1OC6Nn8h72VlcM0jZ9IfVty/3iZQHPMekwj64htb8Gbyh7QiZh2COXTEM1WOuCCEANIcg5x2B39sZxQFtq1uhOgkFDSF1PZiCevcakxEWkvSEfwzlcwgmkUMhDIw5m7wZxg5Q0jFDpMGfPllnt5tsd4+D+i5VSli6NmLNuj3RrZgFWTl7qgK7cnXMIIpKDz6KKQJkKUNgfGHYJhjxP6he17vY7wP3yi0OrVHBjAdATJk8oC8B7fe1x9j/Bfe6QmtUcGM2BzMnlAXBJLAJe9RJNQ81twiZQgHFI9QvROnn3hErIQz8giH0jGVUpwZ2Juu5oEkoljStWzgyeuCW087mk3dJkDYJzlTl/xS8wZiPkCO2BQ4pprKDtBNIyp8tph0SiYP3Y9X+EKytKWXshRMqk0vaFnU+kLSpKqnRUShMJy9et03BxVV+UYhNboJpiOiUoAABHzH2J9u/bf2ut1SmX06VCmAX3acmToASeO/JeE/Tn2p9t/b3xSrRfWp0bPTaHPIp3jJMBok6nWeq/OZxD4EMVOFlihOr02iqml6bcqlbpVQaXrHkJVDCAFIosZMpkxHP4utKUBzANLPZHEMQy2DlpBsHIcw74bOnIQEBGP1krk24oa7FC1Nbu4dOSqqaKq6ULSSoZDNmpHLF82cEEigaJgHROGekU4fGKYpRAQEAj8ujEXa+U2AxP3ss2YVqhkNmr4zmhDaxfi7ifS+TTZVECGUAcyKLNkgIJ9uRhEQAY+6eGV7TW/0rW4PI0dofoMivuz7La7K0Mr1cRucGA3QcQMioDyHoHzRSPS67Z8E0Tg+G+OdPg054RF9iCcYekrd/wDk/UJ5m3cpS5V2E47p6zUmJmTLi4IaWkIZGy2Rm9ecFBa3FjZng+G1lbj4e8OV4a6wZFrhC3LullVLgX/mibRjMXUweKtTJZlbEUKkrM3HGFAM4H+qMACEdCs+pZ3PFWmQGOuuP8pu38wJ/hzy4LaZYiWNqPeAHNvN/q+IMjhBvGM+IXlrhHbfSnBULTXCrbDFPXOKyzdrJbeBGfSG3dtaokTxWtKyq2VTZ7JpdScqMDxJN04mThgsAOS6CTfXIaRTgYRLnl7uBLvLZ21NTVk2vRbSuLl2wSplW9tnpbTs5p8bdJ1S6bsmijWp3JQl81Bss5IVyLcqWqIVU+Y6JCjR1potDnOOQ1PeWupmBIkwQVhbZbS4SGagkbED/wB5DcggZgrpUhHoNkPBT0ZhOxp4Lbe3lvzaG99RVviFp2kLi4eHlrJ/JQeMn7B2+XVbunxVJfOZelq0011SikUDqJk1emJkg0mPfgmTN6rvhe7DJc6yNaydLFuysY+w1WulS8md2VmFTvmTGRyZV6Jwba1M0wlp3DcrdErZN6YxTrAmYxqG20TWZSpguDpEgaEXQJEyQS4ZiQOJVxY65pueRBABjeb37Bp58AF5/YR3J4heB3rGxNrbt3ApvEvZC+1ZYZV5MGKKz1BtX0uq2z6U4ImokoZwuodN6BQOBzAKLU4pAcxCiJNUPMTGxwX1LXlxzXlkdqprZrB/hxsJhYo26N4LhuqZKwoWijzJi/MVYspaHRFd06Fm4VUUE6YARqYxxOcSEPsOqNFJtYeUuI6AMD739pBgHjwWWh4dXtDajm/wNvdfjbTI6hzhIOi81EMhyzyHLp70d33DLYf7LYf5DgEldnZHbJNtUmFxGa1Tca2NPpyWV3edpjL0iVOsIAVVwZ2Q53JBWEVMnOQmjmjiXwBYRbUYGZbcSx2Cqv8AFVT04w7DWJsbVp8Qp3dTUpVJmZ1zLTmjyGMmSWNRAyzkzdspqU0zIKETUAVg1qdupPszrXBuh72cyWEgnOIm6YnjAWFlmdUtQsrXCbjXH/uAgc9RpwzXlwhHolvFwa1RX7nWEqRy02FXDlR0q4PxPEndm6Nu7eTaUoN5AwSl4uZzUjAiwrTibGFfMyjc6QiAriY5g0CxGC3AlKzC9NjLKUPjOtRWU5v1Y2b4h6Um5bVTynm7OmGLZm4lczcoLPTCCUyM6OmUQMBkOKqiombYQbstVN4kbv8Aoxzmk/8Ai49B6qRZKrycEXgLsnTzNDv/ALAdSBEldFMI7bbx8FUlav8A8TJ/KsYuH2vLR4qqzmVvZbemWsJtKaBoubSlFdV6mofNdZ6gYzZ23QWSKkZZyRJMxESnBUvMqxXBOyiw2Mzg+airWtrbYrsN2JK4s/pcSTC18yo+XuHkqpqezDiczp6bGUOogIswWSVUHI5mv4IoaAny41ORnkXXQYIz25cNdxCw4VQzdEwL2WeW+XqfQrzkRXIebIc+jKOZWJKyU/leKG/HJe0dXsLOyDEfPKaYzGm7dzVC3MkljapDMhapTJFuLJFFIukh8VQoJZaPxRDIPTzXnApYQ2eNKxjmjbVg+w1zmi6goG89sDVpO1Apmsm9NBUdOzTj4u+OlI+bKqAKYKgmCrEvxMjmLGkPFLN7hS8QcYa8ExqRDQ7MDeQBuSstay1KVrq2PVzCBPAy67keMQSdgF4u4R3OWH4Hip730DQNwqnxI2Yw8vsRVc1HSOFS2Vw2EwmlTXjVkzl8QCprIqlBmQwNQ1ZtB0oZIwKCTM6ZTSax4Pi51U4L7L2ecW8w00dd+pOESf4YprcN9Tb1teuWTVqWepum0yqUqwoOpMiDFU6bZFPWKgk00C6YnMbdZUxCGMBvkAx1IB/2yL38s/FCwQA1lVxhjiROf8rnZDUyGmN+EroWhHc3VPA5VMGKeicKFp8Tlr7q1zNmU8nN0V3Fvqjt05sdLKdOinMJtNWDsFBds1jrlSZKs1hF0qORQBPRWHjXjj4P+pMGMptFXDO6FOXqtNe5tNCUPcCQ0hNrdOgfyRYiM1YPpHMtJ0gYmmQ6agnEqhNIQAmwBxi12ZxYGvEOMDXOSQPqQYJgc1mNktALgWEXROeWUA6dCDGvJdfEIbO8OYQjYWukIQgiQhCCJCEIIkIQgiQhCCJCEIIkI+TGIQMznBMMwDSMIAAZ/SOwOnP6Mu/Ek1lbh1RdEWird5Opa9a3epyaVBKmTUBTcywsqnC8nOmqYw6KonO2MrmnogGlo8+yJj4S86SB9f8AhBBddGsE/SP8qOIqAZ+Yfq2RT/fbz5xUOf6h9kQDIkKQPigqI3Q5vpkI84zJwI/WqYYtRdc/jsx/aK/8Q0Wow1PmFdAeUdPyvZnwQX5sazP+YG638zksdrNmv7UqH/bX/HcR1TcEF+bGsz/mBut/M5LHazZr+1Kh/wBtf8dxHArfO9V1KfyR0XgGR/B/6zfvDCCP4P8A1m/eGEerZ5B0XlanzHdSsdrL8gvf0C+0sQdE41l+QXv6BfaWIOjVqcF0LP5D3srS34M3lD2hEzG5x8sQyt+DN5Q9oRMxucfLEM1VbTqO9kAch9/MP0D9HTl3o7U8GuIil+STC09ZTdpIpzT5lEqYmM1dFZy2dsznMoRsCpviEXQExigUwhpkMTLMQGOquGWwQER25D3tghtAfKEed9rfZaw+1vhB8Mt5LRIc1wElrhx9QYK8j7WezFh9q/CT4bbXXcwWuGocNDxyzzXppTT4wkJiEFZFZPMDAQVUliiADkAhsEBDZmA9+PRJgpxuW4qu21L29uRUjGkLhUhLEKeE1QqhLJXVDZsmVFu7bOjiCYKCmUhVEjmKIHKIhmAhH5vgPnhQACvHxQDYAA+VAoeQNKKGevThomfPjF25lO9VMU31Cb/v0R8/9lf0r8U9lrc+2WK3h7HC65hYc9j5tRv9l4v2J/T3xT2H8Tf4h4f4gyoxzQ17CDBgyDkRmN1+lXjK4UDCfg7ttPaoqa5tJVjXabFYtF2moqo2dQ1lV8wKTNu2MkgocGqAnEutdOBKRNMDj8YwAQfzhrn3EqS7tyK+utWLhJ1VdyazmleVIqiTQQM+mrxZ4vqw2ZFKZYCl2BmUhc9ucYLsARMABpmKBTHANE5wAcwARDnAB5gHMA5+cRGKR9cstk93ZLz8ZEL65UtFa0EOqZRt+VzoWxjsVeDrZ4Evg+f90WmJVXEAFzhqVEJcdJRkdl3K7k8WFTT2gfjGvAuzLVjHOC03DFya2N0sDNxz4fp9OEcG+F6ZYc3snQuYzYK3DVmDFg1CcIrDLhBmRMzVQ3FlCqmHTD+tzARN0bwjfq1XVjVL9aji49TTNIxsLhOWk56gK7q9RzGU3H4WiANgHioPo8A/Y5LsWupj5VrnDTgysXTFvHlJ1PhCufUFz2FeP6mRnUuqx1N5+pP2iRZcVsmogVspqCHEyxwVBMRApMwAvMPFnwvVA4qaMnRJ3bDFPT1ZXAayCWXMoSU40J+ywxPmsqesV5wjLqPIQUSFnLdu5anKYpdRryLf1qpTHP0TQjA+nTqeds/EXergAfQgDLTLRW96rFjabjLWtuAcIvFw9QSSDzzlehaseGwtv3Fw4Urb7D5d1/S9kMR0qxBnQvbfwt1KqkKEqQcpIUhSk7cszumzEuuImRd6dYxECGSAggcBJAc34XR8nIMUiVEWgmNOVlfvGvT+Majqkmlbt5xLLer0+5p9yjJXzIjJM78FhkqqaiySqAat5sJmUdLplhGBljs9O0OtTR/qGc5PHDnlnhM4cOZV6dtr0wADkI+17/8AR+q70cSHC4WjuTazFHKrI4Upjaa82OEskLiVuDUNy+WEhOSTJJomTp+XAiQUjOSkPmooJALr1BEhziU5JSqThqbIV7ce6k4r/CfVk+thibwzSKxeI+hUrmNmszdTCnTzEsqmtPPQb/1KKaEzdJmTU0VROKCgHAyIgp53IRkNBjmGk6S0iI/pDbob/aBAA4QIUs8QtlMEU3kTtl/E150ykua0k6mM12QcIVjrozGkOHOWUHZOcWUpLDpaw1ppBT8zuEWvnMylyKjQGI8dK2bqEOmi0TTMoppnMcROAljmHbzhZMMmH+1tyGuF7BLPrN3munZ81pKgP8O8wqaxDVUzPipqkCmVyCRw9MImMOmVNRTMdYsbTUA/Q5Aeb6tvfjEbHZ22V1lDfgJcTmZl/mMzOck6xKw+8ObX95IF6ANMgGwBlplA+i9V+AjhAqGxG33t+9mUnY2YnOGTg4X2H+lm0+vPJaeqO7U1BWTtuMU5MJmVpJmjoAZHV1E5B2kQqxT5CKZjhaxQYq7F4K8VFhsXzWuLnYlL4VvYyqrZ3XsdUuIGj7gPrVSx+Vq3kqyM+kLRaRsw00XohLmRDEU1p1fiGA+flRUSTXTMkuki4TOIGORdIqxDiAZBpFMAgOW3LMO+MUIkkiQU0EUUCc+SCRUSAIiAmHRDYGY5+eIfYqDA33Q3Gw4Hch5cX5/1XyNwMgQtizW6tSvtIzJByOUgMDdf5bo5HMkHh3YWB4WynLNUfgAo2bYeXFYo4LbgVZWE2mEwrJoolWZKpZTlkU0qbHZCLF3L+6iaqSqxlgOLc5Q1emByTZXXDf0pOq8wiz+mrDXYeSfCtf6eXePM7tX+Pc2vLisqglc4lbxmtMFmhTt10u7Cx2xROq2bpNm7chNWQBDz0eQAAM9gB3oRs1abK1Q1XZkuveuRmOcZrVo1alnbcpGPhLfSCPsHGOvIR2C324QS79dVxdyTWouLeegcLlzLlPq9DD1Oa+CZyFNObTYk8mzRyVJIEjFcvTOlxKBRAoLgUdMQMc3ZTSnD9TSlcXF4cQKOH+dPrXXhthTtIzGzTu5rTunIp7TbZVk0qJpN+5xkCmVbOXLdZIG+ZkxSyNmQQN50YRqHw6yGytsd3/Sa0tjkW3DJ1OWmeXBZHWqu+0PtTjL3EEnmHXhG2f14rvWw/wDC72oo212Hql8QOFmc3jr/AAa17Oq+wv1fT9z+R0slJ5qq5UasqgaGQU4xxHXkAi5NMDA1biCRDkMc8ezDhWqeuNamk7b34sBOq8WaY45njPruY0bdpW2rOo1JgE5MSRy0W7Uz5gZqrMmqqTsjnP8A9hEBKAnAwdNcI2BQp4prcftqCYjS8Wgu3hYhUqCk2leyH1OTgAZ4AOIGy9F854efuLdnDRVdurM1/P6PsfSlTUVWswvbdstWXuuhK6qFmdwyGqW7Uh0BYHl7NVs4VKqoc6BRU25njrYx4Yw6QxTOqBZ0AXFMhTtGLTSZOUcTOJ2bYhHTt3NFkVCmlyTvMkvI2TTM2ySMYyyOqAxs08zde8IqbLSc8VHZkGfWZ+gMx1WYWuuGlgOREekR9+KeccgyzEcxGEIRsLWSEIQRIQhBEhCEESEIQRIQhBEhCEEWaW+r6o7Y1ZK61pNSWJT2Ua4rM04kraoJdkuidBQFGrghkjZlOYAEwfFEQENoBHOO6WOeup5am0cjkE5oJ3UkwoafS260v+CyUiSVuXM2dlaJNCnbimiJmB0TDqAy1mZxyUzjrnHPMBDLMNn0iA84eQe/FcxyyzH6xEdveH6RDvZxJMswzpr+/wDlG/C6+NYj6x/hUAuiAFzz0QAoCG3mDKPoOf6h9kUioc/1D7IqBAgKQZdJURufx2Y/tFf+IaLUXXP47Mf2iv8AxDRajFU+aV0G+QdPyvZnwQX5sazP+YG638zksdrNmv7UqH/bX/HcR1TcEF+bGsz/AJgbrfzOSx2s2a/tSof9tf8AHcRwK3zvVdSn8kdF4Bkfwf8ArN+8MII/g/8AWb94YR6tnkHReVqfMd1Kx2svyC9/QL7SxB0TjWX5Be/oF9pYg6NWpwXQs/kPeytLfgzeUPaETLnnmI9I+2IaXARSNtyANvQIjmGUTGmYqhAUTEDkENIDlHSKIdOcQzVY60kr6hFch6B80Mh6B80ZZCw/EFSEVyHoHzQyHoHzREiQRqhkxKpCK5D0D5oZD0D5omQogqkIrkPQPmhkPQPmhISCqQiuQ9A+aGQ9A+aEhIKpCK5D0D5oZD0D5oSEgqkIrkPQPmhkPQPmhISCqQiuQ9A+aGQ9A+aEiZSJVIRXIegfNDIegfNC8l2YVIRXIegfNDIegfNCQgGypCK5D0D5oZD0D5oSEgqkIrkPQPmhkPQPmhISCqQiuQ9A+aGQ9A+aEhIKpCK5D0D5oZD0D5oSEgqkIrkPQPmhkPQPmhISCqQiuQ9A+aGQ9A+aEhIKpCK5D0D5oZD0D5oSEgqkIrkPQPmhkPQPmhISCqQiuQ9A+aGQ9A+aEhIKpCK5D0D5oZD0D5oSEgqkIrkPQPmhkPQPmhISCqRUOf6h9kMh6B80MhDbzAG0RHYAB39sJCkSDKiNz+OzH9or/wAQ0WouuDFO7fHIICUz9cSiA6QD/WG25xajA8zUkLoDy+i9mfBBfmxrM/5gbrfzOSx2s2a/tSof9tf8dxHVNwQX5sazP+YG638zksdrNmv7UqH/AG1/x3EcGt871XUp/JHReAZH8H/rN+8MII/g/wDWb94YR6tnkHReVqfMd1Kx2svyC9/QL7SxB0TjWX5Be/oF9pYg6NWpwXQs/kPeyDzDsAeko8xo+Q1xQAqbx+kQAy0CPVSFL9AABsgD6Aj6Hvc23pHKPvUriUDFauzlMGZTFaqGAQ6QEAjGsqt5L9YzHfl+3DJfrGY78v241HFXniT3c1ezDirzxJ7uavZgqkNOq0+S/WMx35ftwyX6xmO/L9uNRxV54k93NXsw4q88Se7mr2YKLrFp8l+sZjvy/bhkv1jMd+X7cajirzxJ7uavZhxV54k93NXswS6xafJfrGY78v24ZL9YzHfl+3Go4q88Se7mr2YcVeeJPdzV7MEusWnyX6xmO/L9uGS/WMx35ftxqOKvPEnu5q9mHFXniT3c1ezBLrFp8l+sZjvy/bhkv1jMd+X7cajirzxJ7uavZhxV54k93NXswS6xafJfrGY78v24ZL9YzHfl+3Go4q88Se7mr2YcVeeJPdzV7MEusWnyX6xmO/L9uGS/WMx35ftxqOKvPEnu5q9mHFXniT3c1ezBLrFp8l+sZjvy/bhkv1jMd+X7cajirzxJ7uavZhxV54k93NXswS6xafJfrGY78v24ZL9YzHfl+3Go4q88Se7mr2YcVeeJPdzV7MEusWnyX6xmO/L9uGS/WMx35ftxqOKvPEnu5q9mHFXniT3c1ezBLrFp8l+sZjvy/bhkv1jMd+X7cajirzxJ7uavZhxV54k93NXswS6xafJfrGY78v24ZL9YzHfl+3Go4q88Se7mr2YcVeeJPdzV7MEusWnyX6xmO/L9uGS/WMx35ftxqOKvPEnu5q9mHFXniT3c1ezBLrFp8l+sZjvy/bhkv1jMd+X7cajirzxJ7uavZhxV54k93NXswS6xafJfrGY78v24ZL9YzHfl+3Go4q88Se7mr2YcVeeJPdzV7MEusWnyX6xmO/L9uGS/WMx35ftxqOKvPEnu5q9mHFXniT3c1ezBLrFp8l+sZjvy/bhkv1jMd+X7cajirzxJ7uavZhxV54k93NXswS6xafJfrGY78v24ZL9YzHfl+3Go4q88Se7mr2YcVeeJPdzV7MEusWnyX6xmO/L9uGS/WMx35ftxqOKvPEnu5q9mHFXniT3c1ezBLrFp8l+sZjvy/bhkv1jMd+X7cajirzxJ7uavZhxV54k93NXswS6xafJfrGY78v24oJVRAQM+fKFEMhIo8WMQ3lATCEanirzxJ7uavZihmzooCYzR2UChmJjtVCFDyiIZQSGDNWAAC5AUClKBQyKUNgDtz/2isVEAyAQzEBHIBEMgHLLmikFkGkr2Z8EF+bGsz/mBut/M5LHazZr+1Kh/21/x3EdU3BBfmxrM/wCYG638zksdrNmv7UqH/bX/AB3EcKt871XUp/JHReAZH8H/AKzfvDCCP4P/AFm/eGEerZ5B0XlanzHdSsdrL8gvf0C+0sQdE41l+QXv6BfaWIOjVqcF0LP5D3srawiCRshyzyDziGcTGBQIAEJ8QpA0CgUADIA2RDa34M3lD2hEzDzj5YU4vZqK5iAV8aBej1w0C7dnNz/RH0ACOwu0390MwDMe9t98dlGEXC7TFT041ujcWWpzttMljhSlNutMZYCCKihBfOk8wFUTnKYEyCOQFSzHMTBHC9qPabwz2T8LPiniRN2YaB/EYmJ4aLy3tL7TeG+yvhjvE/EnG6MgBq4xMDnkutHTb7Q1qOYbBDXF2euAHQHYCqIj0AsX3x6Oi24twmQpS2/oMqaaeRQNRUsOUgFAOYTIZ820RHYG36Y7xMGnBXYfZzQFP3PvhamiKnmtXsCzeS0W5pGXt5HJ2KwFM3M8IRApll1SAVUSmMBE9MCgURzGPB+zf6qn2otpsfh3hr8hJcajQAP9q8f7HfqRW9tfEj4d4V4a4XWhznGo2GtMQfLJ6LwC6BRDSAAEo8xgHMPPDRL0euP0FMY3AXYNsQtup4naa3lP4fbxNJWqpRNZUA0NJ6cUekKJm7adykoig4aqnACHUTKVZMFNMhh0NA3gOrOlKit9VlU0NWEsUktU0VUD6lqnlDs4ApLX8vcqNXaBx2gIEOip8cRyMAAbYBsg+pWO1i1CC0h22v4X1R9KrQIFoF31n/CxzQL0euGgXo9cdm5eCEx2FsOfES6trScsowtvT3WGnZxc2VSW6BaeSRFwabGkC4kMmmCAa/QVXKpqxDNMFBBMOtErR2dPXEZvhSBsD0VeJLAmREwaQLGNo5FIIAI6Zsi7B27BjaD6Re9gObddgdp35fVVDXlrXwYdpwJyBy5cJ30labQL0euGgXo9cX1kF24JmcoLtgVS4wiLlA7YFiCOQKEEwBpEEdgGLmGffiq7Zw1OCbxu4ZqCTWgR63OzUMTPR0wKcAES57NLmz2ZxYFmpOUkeo4DdVJABMHLl9PQmM/8LT6Bej1w0C9HrjIZFStR1NUVO0lJZPMHNQVZOWlPU7LjM1G601dvnCTVqmjpgUBAyjlHM/yQBQBEctsc6bZcGtiDru9WIewNSK0nau5OGm0M0vJcGV1i7XmzBdnKkGDpVgwcy8iqay6icwanIcDaoMzAY4DsiJaPMQMzx/lAc6N4Ek8lkZTe94pt1JaOl8w2TwBjVdeugXo9cNAvR645B4a7Cf8AkRdqR2qmV1rZ2DJOJA9n4V7fSZL0rRTIrFsLkEVltADgq52po7QAxtgZiGQwo9kz1pMHsvQIpNgZu3DZN9KWq7thMCNVTIqOWxtDM6BhIJwUy+ScojoiOiEXmSwE+Zt7oJIMnkQcteSl1Gq2fhORu+vAf+9NNVs+gXo9cNAvR641LZq6eaYM2rt7qwAynEmqj3VgbYUTaBRyAe8I88cgcP8AhtrDEE4vH3FnMjpGW2LsZUF/K5m9WtnYNm8rkCSY8TTSSIJ+NPVlkW7cByLrDlEwgEWJpgFxcAA0uMmMmiT9B9s1jGZbAJvENGWpOQ6ZgjPjwXHXQL0euGgXo9cdqzfgXeEHmNppHeanLU0nWNOVHQbC5UkkdIXRlU4uDMZPMGiL1BdCSGBJVQ5UXBBMkRQVM9hQObIo9Wwy98VR0gLF9rmBjpvkSslTKsDJmMQ5XBALmkJRIbPWAXIQEByEDAGNlajUcadMy4ajjz+hy23WR1NzGNqGbp0y+n1GYGvVaLQL0euGgXo9cakrR4qkVZBi8ckUTUUQFuzUckciiUDm0BAogYCgbMwlzEvRHIjETh2Lh+mNAMGV47SXub1zbSXXGcTSzM3c1FLKJGYCrlJp0qYgFRfpAQpjJ5hmG0QKPxRvUdSpNa97ovGBO8TntIGpy3RtJ7w4tBN0SekhuW5nKFxu0C9HrhoF6PXGqKzeHMgQjJ8oo6IKjRNNkqoo7KGWZkSgXNQoZhmJMwDPbEzWjw5XlvoxutMbX0U8qZnZG3zi6FzlBdoyoaakzXSBZwci5iGVU+IbJukBlR0REC5ZxLixmdQwOJPDgZ6HVVY0veymzNziAOcxH14KD9AvR64aBej1xyTqPDmNPYcraYgm14LUVJMLh1a/pZextOzRy8vXQ5WKjshpjOpZqxBBsrxYhiHDL4rtHnEwgXjnqF9UZbUONQmcUzrigcEEzBlmUymWiBtobBHPbFGPZUvBp+JpAI5wDlvMiI1kcckcyoxoc4ZHP6Ej9wVY0C9HrhoF6PXF9ZBw3MCblu5bKGICpU3TY7VQxBEQKcCnABEoiAgBg2CIDti1FgWEm6ZjvvmqkFphwI77j0XzoF6PXDQL0euPqETkokL50C9HrhoF6PXH1CGSSF86Bej1w0C9Hrj6hDJJC+dAvR64aBej1x9QhkkhfOgXo9cNAvR64+oQySQvnQL0euGgXo9cfUIZJIXzoF6PXDQL0euPqEMkkL50C9HrhoF6PXH1FRKYClOJTFIcBEhjFECnAB0REo9/bs2d+JgRPop0AK+NAvR64qUpQHm7w5gO0B2cwxXm54qHP9Q+yIcBBCNIJCiNwUCPHxChkUj5UhQ+aAHEACLUXXP47Mf2iv8AxDRajXcLrrq3x5R0/K9mfBBfmxrM/wCYG638zksdrNmv7UqH/bX/AB3EdU3BBfmxrM/5gbrfzOSx2s2a/tSof9tf8dxHBrfO9V1KfyR0XgGR/B/6zfvDCCP4P/Wb94YR6tnkHReVqfMd1Kx2svyC9/QL7SxB0TjWX5Be/oF9pYg6NWpwXQs/kPeytLfgzeUPaETMbnHyxDK34M3lD2hEzG5x8sQzVVtOo72QNHMBNzAOY/VtjvPwjVbKKmsPQrJgukZ7SErLSM5aFMHGmyzUTaJ1S97XEOVUB74H5+eOjAP+gIZgPSAh0CGYD5YkG3V066tTOjT2h564lLpZMqD5ucpXssmiRREQTctj5kPlpGEDZAYomEQHbs8R+onsjV9sfARYqD7tWm68yR8JdEQeOi+e/qD7JVPa7wIWShUu1qbr1OR8N6IIPHReiFYgqoLJlyAVUDkKIgIl2lEoCI98AEwc3NkMevvDtWMgrqydsKkp12m6lr+imCJTJmKY7dVs2SbOETgHMdJVJRMxecpgEBABj84QuP8AvMUhSnp626wgAAZQ8nmRTG2ZZ5A+AAEe/kGX0RySsRw12M3Dqd03t8S1Z5A+XF29pKoqbmc2ptwsIFKKxUgmJDpKCBcjHROQTbNIRyj57+n/ALC+1vsrb6zrbSY6z1AAXCoJBGwyK8l+lfsv7TexHitod4jQa6zVmhpc1zZaW9SCW6r9Ed9MmMuZOX0xdIS9i0bHdvXr1YrVozSTKKiqyqhhApCJkAxzGMIFKBRER2DH5a+M+4VM3kxb4pbo0eqL6jrkX3qurKbXMJUOPy59NHKjZTMRApQUTLplMYQKBVAMIhsEObGLThr8c2L+hJja+rKjoi2Fu5+2FlVlLWYp11SwVggOYGazGYOnjp8ZucBEFG6K6SaobDlMGyOpMBEBzAQzzzAwlAxu8Ic+weYNghlsDIAj7h4fZatnN9xz+/XvRfc7ZaG1wKbB8I3/ACvaG7sY6xL4U1pvwl9qbASxha/CaCttuEFsffxilO1UCS9JZrT0zliIpmXWE5ia9BMzmXPVUTgVEmuTKM34Pr2upbPuCzw8Hd26c2gu3wbT2Z3YlM4pmTu3lRuZTLpMnL2kweLFMsm3Art5psDjoHO4U0yGEBy8KIrKi0LL9YYJem5M9TYZiLJNc5RIZcqI/wBWColESioUoGEBHb340pm7U+nptWxgUHSUKdApyqDt2mAQEDZ585sx2BkIRnNJrzUMBrXvLoA1LmPAcehe0nTy+qobUHUGUHA3xF1x4APpuug8AQwjjqva9QN1LRYh7a8EPffGk4tZN54OJKv6JJN5lT8npqm5UdsjWEvpCXrMG5CtkGZHUmkIJlUIDfWt0Tm2m0jb5eNzTJpHhcX4WpeyDu8xeEReJWdGXEkayBLclWfFlRJmm0ASjIszMxWK+0g+OyFf44jl4iNUjmcwoIaagaKimqDTUDLIAMPfAMsgAeYObpi+uss7UBV2sq7W1JGwLO1BdKgknsTS0jZjqyBsKTmKAjkAAOUadLw57ntfiZBwOWUAFhPqXMM/0mOayPtwdSex4Ol2eJ84g8oeDG4C9wmI+7l3bK0i+uZi0qOxdPV9S3CN01L8BE9ljOm3s/k9r388lsvnBkioFMYjHk+6nALncZGT0AOcQORsIZfP63xH0twleLyrbnL0qW0MhwFVxPMI79yjTz6TzqWoqUo6mCbgqJxWeAMyIAGSmICYxB0S5pjt8JqyyzjUg5VWXBq2Bi14wsZczVAoiJUExMI6KZRERAgfFAQAQAADKPl0oo+I3I8NxorNuVqzK6zckZpF2FSRA4jq0y5jokJkUMx2bYzmzE4cgCBUAyzAfSbTvHPN2RdO7jkso8QphpaKYiWOOWZu1L4b/aBk0azxK9wFkbqK3rv/AMCrf250zo6a3XuJhJu4tcmqm7CUyE8wXSk0iO0QXboaKSCSajh0KSIlKUorKaAAOnljWEe/E/tPZDgXKLo6b0TLZRe651wbaXhPMJFKZtO31PJqVPNkmAPliGcMklXjVmqcUjJgqKSZTZhkEeJwUG460RbtxMqbTUEyBT60wD8Ux8w+MYMgDMfpyy2ZBbthFQeLtxMp+FMZAhxV6APmHxtmz42ecZLRZTXwWUzda06bjEe8g9b5bOwWMW9wa4Ppy45gz/Q1oz6i99OIXuN4PqXTOjrZXlnNkKOqhijRfCh3GGupJh4o+lqzqS8NLsXc1FjTM9F++ZBK5amLtom2VQWUyTaNxIkVNY+j034gKqu3JcO/C44hr+0VP7d3jxKX/pfCXIqSrNNpKaspOSoHVrJ/LlBbADdxxeUoU4mLhuY6KpSnOVRQD5jwmw78JtdvDtZqj7FNLP4d7sURbu4T251vVbtUlPX0+oybP1de4VaOJZOGBFAFTM5QdprCURyzEoAQIdxKY6sS+LRk4lF7K2l09kS1z5jd5GSyml2NPt2M6mTJOWKKEXSJxhRFBkii0borqKAikQch0hE0aNSxE1nNBvsLWskzm2aTXHqWscI0gndbL7cyoxz2NuHEc8R/8rmD0c5pnWYletNhZ6qHd4+CfxkHuTbm2uH3DHgoYEvfXFS3GYyByZN1TiJgkoMdZrViKmVSVU0wKmQW5flH0ShrMF91rYVDh5f3YwZWvqy5T+u8YNeVLiGt3aqo7Y0bUU9ZTOeTY8q5cBVgFV5PKy9yyXTTl6yLhNJwUxdLJYI8LpWrMqgrAzZgqJ9YZUGiZVBHMR5wL0jn05iO3mANWmssiC4IrLIcabCydCgsdIzpERAwpKCA/HIJgEwkNmXSHPRjOLE5tnfSc/VziN/iqOqOBP8AKS4ZbABaVOuWPbcBuwL2eXwsDGkf1AA57kr1LXYxUTvDhwS3LzC/KKWtJNKvxr3aoGUyWfSeRV5V9uKanM+qksxlLFYoqt0dLi5GwumgiQUUktScC6Axy1qaraPrPEZb/BFUc+k0soTGhwPMjtkxmATBqRpJarlDWduZQ6FXPQK5Kko80dLbm2S2ZiWPFWVFEp9YVFEimjoCdNMCmEMsgDPn2BsAAENmz6IpxdDROTUIaChtJRMW6YpKD8bIxg0dohpDtHoDv7YtZ7BSo2f3WsbwvHMyTdNBtJrf9wNTqc1nd4kHte4MhxGX92K1970Aule1q3TybTvGldW1FhLlmkTDg8cEdLWPpmgLOyihV7m3TnaiRJjUAUvN6mTVlcrEjkrVk6enIO1PI5iFyMPLSd3SraWYu8XltMPs6oCVXUujwe9O3wtvJCqUwu4rS5Td5UcmCYLPwKVk9WTSTlKKxRHi5i6o5igQSmj8/pusqzMB2aijQ4InbFO0VO1MVM4CU6eZBAdExRyMGfxg584ozVUl66LmXnMwcNlBWbrsTCxXQOIaInTOnoiQRATZiUQz0zcwbIe5PqU2sL5ddde5ucHS6eTnAjjkNko28Wd76gpCAWlk6XWFkAjm1pBjW9zXrtpDEZWWGPCvwdF3bzOJUyuTK+EXr2Q4jZej3P15CVBPKuk9Tgu2aCZHRSM8OqUEc0CmbIiQRKBRGWKskeFrCJf7A9geraa29f2quvjAuRi5r004dtX9IlOsrOWlr2szXMYURTK6dSzSRcAKOtZJGHICiI+LUSJiqdUUyaxUR1ioEKCxwEc9p8to/SbPyDHwRBBMh0yN26RFchUTRRKmkce/mTLRHZ8UAENgCIbc84x2ewCgxku+EHUCMrtMBoHC7Ubf9VjfbSXVCGgzvuS4k+rXgem69QPDt1XU80s5bWSXYsVdaT1zL7/zn4OL7XSra1tROJvTQMHCrqSShtSgpuSSc5l5euyNMWxFCi1XKqYVNER8wcXXCyro6SrhVZdRFuVmiddYy6iKJBEU0SGMIiVImY6KYDolzHIAzi1GzZqBoNcC6ZM/j8LFarR7zUFQiMo/c/lIQhGwtZIQhBEhCEESEIQRIQhBEhCEESEIQRZpb6T0hP6tlUprusnFAUu6FUZjVjWnFaqXlgkSOZHJmkchzgc4FKIgYAABERAeaOwa9VssNwWPw5gOIx42SkttKnGiHrezj1Ve4gcony46whFynaatz/7JkqJ8yBpBkAZR1iCGeQbMs8x0i6QeaMmnVY1NUMlpKnZzOHUwktCy9zK6Rl7gCGQkaDt6eYOE0gyzEDuFFFMziOWllE1CX2bCHmk/SFNMXat86QVjIZ5AIgBTCUBMUOYoiG0A+gI+g5/qH2RQYqHP9Q+yBickBkgqI3P47Mf2iv8AxDRai65/HZj+0V/4hotRgqfNK6DfIOn5Xsz4IL82NZn/ADA3W/mcljtZs1/alQ/7a/47iOqbggvzY1mf8wN1v5nJY7WbNf2pUP8Atr/juI4Fb53qupT+SOi8AyP4P/Wb94YQR/B/6zfvDCPVs8g6LytT5jupWO1l+QXv6BfaWIOicay/IL39AvtLEHRq1OC6Fn8h72Vpb8Gbyh7QiZjc4+WIZW/Bm8oe3P8A2HzRMoiAiI9I7PpiGaqto4d7JCK5D0D5oZD0D5ozAkGQtYZG8NVSEVyHoHzQyHoHzRAyiOGiaG9xVIRXIegfNDIegfNBOEKmXr2QiuQ9A+aGQ9A+aJBIMhD8WTlSEVyHoHzQyHoHzREZQpkgyCqf7hlCK5D0D5oZD0D5oHMydVAy0VIRXIegfNDIegfNDXVOEKkIrkPQPmhkPQPmiZMQkZzxVIRXIegfNDIegfNEAkaIqQiuQ9A+aGQ9A+aGmiKkP94rkPQPmhkPQPmgcxB0QZaKkIrkPQPmhkPQPmiSSTeOqHMQVSEVyHoHzQyHoHzRCKkIrkPQPmhkPQPmgipCK5D0D5oZD0D5oIqQiuQ9A+aGQ9A+aCKkIrkPQPmhkPQPmgipCK5D0D5oZD0D5oIqQiuQ9A+aGQ9A+aCKkIrkPQPmhkPQPmgipCK5D0D5oZD0D5okEgyEVIqHP9Q+yGQ9A+aHycxHYAAO0dgRCluoURufx2Y/tFf+IaLUXXIZPZj+0Vw8ypgH1gPmi1GGpnUJC6A8vovbDwLNDVXWvBkWoLTEkWmxpZf+6Jn2rcoNdQCszlIJ561QgG0tA3yc8tCO3S2VorjyKv6UnM3pZdlLJdNBcvnZpizVK3T1CxdISkVMYQzMXmDv96ODf9HU28GZSme3K/dxMs+9/wC9Gkd54gAlPmGf9Wbn/RGODXEVvVdWjnTjl+F+Wilnoc5Sf1h9h9o7TmEMsh5g2Bt27IRVMAEDiIAI65TaIf8A6hoR6qnFxvReTfm8nmsdrL8gvf0C+0sQdE41l+QXv6BfaWIOjVqcF0bP5D3svg5dIolDvh/32jGVp1gsRMgKyo6ixSgCiiTkiZDj3xDMueXljF4RQEgyFkcxr8nLKeWYdUOd9S7MOWYdUOd9S7MYrmPQPqhmPQPqi19ypg0+5WVcsw6oc76l2Ycsw6oc76l2YxXMegfVDMegfVC+5MGn3KyrlmHVDnfUuzDlmHVDnfUuzGK5j0D6oZj0D6oX3Jg0+5WVcsw6oc76l2Ycsw6oc76l2YxXMegfVDMegfVC+5MGn3KyrlmHVDnfUuzDlmHVDnfUuzGK5j0D6oZj0D6oX3Jg0+5WVcsw6oc76l2Ycsw6oc76l2YxXMegfVDMegfVC+5MGn3KyrlmHVDnfUuzDlmHVDnfUuzGK5j0D6oZj0D6oX3Jg0+5WVcsw6oc76l2Ycsw6oc76l2YxXMegfVDMegfVC+5MGn3KyrlmHVDnfUuzDlmHVDnfUuzGK5j0D6oZj0D6oX3Jg0+5WVcsw6oc76l2Ycsw6oc76l2YxXMegfVDMegfVC+5MGn3KyrlmHVDnfUuzDlmHVDnfUuzGK5j0D6oZj0D6oX3Jg0+5WVcsw6oc76l2Ycsw6oc76l2YxXMegfVDMegfVC+5MGn3KyrlmHVDnfUuzDlmHVDnfUuzGK5j0D6oZj0D6oX3Jg0+5WVcsw6oc76l2Ycsw6oc76l2YxXMegfVDMegfVC+5MGn3KyrlmHVDnfUuzDlmHVDnfUuzGK5j0D6oZj0D6oX3Jg0+5WVcsw6oc76l2Ycsw6oc76l2YxXMegfVDMegfVC+5MGn3KyrlmHVDnfUuzDlmHVDnfUuzGK5j0D6oZj0D6oX3Jg0+5WVcsw6oc76l2Ycsw6oc76l2YxXMegfVDMegfVC+5MGn3KyrlmHVDnfUuzDlmHVDnfUuzGK5j0D6oZj0D6oX3Jg0+5WVcsw6oc76l2Ycsw6oc76l2YxXMegfVDMegfVC+5MGn3KyrlmHVDnfUuzDlmHVDnfUuzGK5j0D6oZj0D6oX3Jg0+5WVcsw6oc76l2YoNZCOwspXA390TvCGKA97MALmPkjFsx6B9UMx7wCA9MQXkiFIosBkJpHUMoqqICqusddTRLolAxzCYQAPKI7YrCEVWQaFe/H+jp/mzKV/wAe7ifzRnHeePyT/qzfujHRh/R0/wA2ZSv+PdxP5ozjvPH5J/1Zv3RjiWj5w6hdWh8v0/AX5aaXyT/rlP4hoQS+Sf8AXKfxDQj1FP5begXk3+c9VjlZfkF7+gX2liDonGsvyC9/QL7SxB0a1TgujZ/Ie9l8HPoFMYQzyDPy7Bzy9Q/VEipU5KiFKmogLhTRAFFTrKJ5m5hHIDAHPnsiOFvwZvKHtCJmNzj5YhgBOaiu4tgBbJyek/if2hXtQ5PSfxP7Qr2o3r/YM+nmjk5howj3lxVz2Yyu2UnaJSeRCmSpq1qBwaXUrT5liidJE6wFMZVwcoCYiCRTHyABNolEBG5DQJhYA55MAriTyek/if2hXtQ5PSfxP7Qr2o7j7ocEjcC1FJOaxe3DmNcMJWxM/njK2NnzVPNZQimQTqqcVXnrVdcpAARMZsQ4gH9wOaOusaZsN3rzXBHvgJsPCeY5jkGYcp8/oyygLpEhCXtMSoE5PSfxP7Qr2ocnpP4n9oV7UT08tfKZrLJlNrYV6yuIWSslZrOKZc045ou4DRmiGm4mCUpUWcJu0ESaJ1jMnKx0SCBjpFKBjBD4BnlltAeYQHMB5uYe/wA4c3TE3RoBn+VAe6RJyWy8npP4n9oV7UOT0n8T+0K9qN6Ecg59nOIgOffy98BAcwLkOkI5AGW0w9GXTzbOfvxYMbMGI49/RC8tguMA/wCYH1Wy8npP4n9oV7UOT0n8T+0K9qN55wzAe9pZ/R/0Biu3yh84AzKOzp5u9FYAALh+c9lJNQZA59VsvJ6T+J/aFe1Dk9J/E/tCvajec9meYd/6sgAdv1CHnj5FVIFARFVLXCQTlRBUoqmKGeZgLnmIBkOYhsCBEAuIyEfcgAfUgJedv03PQbZfZbRyek/if2hXtQ5PSfxP7Qr2o3oRAuekOWQCI5jzZc/mgI5ZAbZls2jkG0cvPmMSGToP+dkvPABJ+/ekBbLyek/if2hXtQ5PSfxP7Qr2o3rn27e/9PMGYj5A9UfB1UkSGUWWSRTLsMqqqVJIo82QmEcg2iHfibjb0d8/oM1F5+QBzOi2jk9J/E/tCvahyek/if2hXtRuhV0DnMkRwgoqQumdJNYqipCjzGMUBEQAe8I7Bz2Re7+XMOYBkOwdvNFYbx7yn9j+UvOEZ6/5A/f9lsvJ6T+J/aFe1Dk9J/E/tCvajeshDvDlzc2zmz/2GK5D0ZeXZ0+6JLQMgM+nL/MD1Ci+6dclsnJ6T+J/aFe1Dk9J/E/tCvajeRENohnllpfJHPLyR9ZCGezmHIRDaAZ83tgWZkAb/j9/wl93ArZOT0n8T+0K9qHJ6T+J/aFe1G9AA83OI9HPzCP+w+aHPt2iG3bkOWwQAdvTmIbOfbANDswMuam+9vmP0z10Wy8npP4n9oV7UOT0n8T+0K9qN6hEXRoRmhc9pgnNbLyek/if2hXtQ5PSfxP7Qr2o3qEIGyX3brZeT0n8T+0K9qHJ6T+J/aFe1G9QhA2S+7dbLyek/if2hXtQ5PSfxP7Qr2o3qEIGyX3brZeT0n8T+0K9qHJ6T+J/aFe1G9QhA2S+7dbLyek/if2hXtQ5PSfxP7Qr2o3qEIGyX3brZeT0n8T+0K9qHJ6T+J/aFe1G9QhA2S+7dbLyek/if2hXtQ5PSfxP7Qr2o3qEIGyX3brZeT0n8T+0K9qHJ6T+J/aFe1G9f98wCPsAfND183Nt58hD2hCBsl9262Xk9J/E/tCvaig07KBAQK2MQcthyrKGEuznyE0b3FQ5/qH2QLRClr3BwzURLJ6lZdATCYyC6iAmEMhNoHEmeXlKMW4uufx6Y/tFf+IaLUYXC666t4eWeS9+P9HT/NmUr/j3cT+aM47zx+Sf9Wb90Y6MP6On+bMpX/Hu4n80Zx3nj8k/6s37oxw7R84dQurQ+X6fgL8tNL5J/wBcp/ENCCXyT/rlP4hoR6in8tvQLyb/ADnqscrL8gvf0C+0sQdE41l+QXv6BfaWIOjWqcF0bP5D3srS34M3lD2hEzG5x8sQyt+DN5Q9oRMxucfLEM1VbTqO9kDLSLpZZaQbRz2bQ6Nv1htCPYlwdtHSOj8GNggkjJNoaqqITrueqFANa+mc1VVWdrqiABmYdEpC6WYlTRTKHxQKAeO0ByEB5shAcw2c22O8rg5uEboO1VBS2wV/X7mn5LTrhUlv7g8VUmMslrNdU65pXNSJgZVIiKiimocgUxATOBD6IEKaJqNJGSx03Bpgr0PFESiBinMQ4CChBIAicmiICAgAAI7RyKHQI57ctnjh4QahKdtzjGvhTFKtUWElNO2dTtpe0ICTRgpOpWzmrpFAobCkIu9XyKHNkPN3vSNV3CD4S5JTk2mlM30tJXU+aNNOWUm0uA1p084XMGSSR37wqaCBNMSlMoY2YAY2gU5tEB8yF2JHWl5bk1rdKrrq4bzVDXNQrz+Yptr/AFPg0YiqIFSatwFUBBJBIiaJA2bE8xyEYxUWOa4k6LJWewiBquPFOVHN6Rn8jqmQKqoTqmpohO5QZIdYcV2xwOmlo8xiqZCkYo/LIsYhswGMsvtI5bS11rtyCREI2lMkqyat5Si3EDJs0wFRcqCY94qJlBTKAbAKXLIcs4zGTSeiLWPmtWVDV9FXGqeSuSTGl6CoeYjVtNOnyJgUbuZ7OiEBmDNuqCShmjQzhV0JSpiKKYmOaEZu+ez17NpjNXi7yZTpy5eTN8uIGcul3ZlFHCp+gxjKCYS5ZFE2XNsDaabrg4cCP3Wq6bpAEr1MX84MzD5iDxK4YrO0wrJMMFPLcGP/AOQtW1Nbe3zFylP5xLXsrQWdzZmGhrjKJP1lFVSiK5itylKO0Yw/D1wUWC5liQwCVYa+9Y3+w7YsKeqqeUNI6ytcNKK19O6VRbORlswQRBNRpLHDVZ+5KdxksB5cRITnKsARwUDhlbwhdqi7vfAragZxROEJ7g6Yygagnoyt7JnqzJY84WPpawr0vESEBIg6kSqnz26IhGlD8KdeK30pwESunaAoBuOAJ/OnFEP1n8xcubjt6gQBrM2k6IPxG5ToioQFGWRgFTPLYARzbJZrVTdk7LEqnPPJ9Ws5pz/lY6mdusLbqPsosQYCXVYYCIyyZTvDqXNP/K5+UvwPlhMQF4MVtb2uu3exbDTZ65hLay6T2ssYpVF1xq51MXBZzIZZKVyiASWQJqsyd0VCqKLFOcdPJITnxFbgX7U2zXxoTXElijqu21A4Ra+pdFWs6YtSWqXVZUvVMrZTRo+NLAFR0hMQM8I01RNYmRRsc5yHJohECt+Fsk6M7xAU85wcWkfYc8Sc3Z1rciwqlwalaIJVWgsu5eVTK5+isR40dvFlGwqoNxTSKDJIUxSOJ9KB33CKVIWzmMKxNKWLtfQtv8X9TSeopxLJHUM+efBb3GaS5qi1kQOVj61NQZdrVOOCIAo7WEhQLoFLiFPxClSpU6ZzFNjT/cGtva5zN8yeSzipYXuLq/Go52n8JLi37XZ5yuxKS8B5Qi2KzEdZOZ4gbjT2hrKWTpa9tLtreW9lk9v/AHJa1Qd8UGrWSqqA1UOwFgtrzNyDp8dZFKQhjGKbV0rhpoSR4Ir6W6nV6qoleGaQcJFStqqonFSYXmtMXxI3eO6ebGmhuNCM2ZLprLptxZmRyEiZjlBNNcyYcYV+F/XqnENM8RV0cHlkq1q9am6SkNNvZfX1W0nVdAGo47xZgtJp62ckckTdqO0xeNBASOCNSEUFRPNMYuvhwrl7L722vxbupaAtzJ1L74i5JiRmVVSF1Mu6VIzCngkhJVLZe0WMZBZsmnIWhTLOM1VBUVMOQiXR6AFUWhkCWC4JyOj2OnmSGuJkcuKw2z3d1JrrGf8AUAJEzqaLwQNhiuAAnQ8pHadi04LrArcHHTWGGOwF0qus3eccPjm4VLWJkFv0vg1LNpVJGj6VMkpu7MKzg85SO8cuBbqGFvxExQABMBY6XsXGD+RYT7P4Q6gn1Z1I/vbiItS4vJXtsnsnaMZRbWVrLFRk4Irl/wDallXf9bpA4+SLU4CA7BDsCoHHtQuMbHPh7xKXYnFh8BlwbGcXrG5d/F5nPKmLfVCUdzpenTJJYooVJFRRgMxTRITSyB0uJtcJEwjgjwhuL+SYtcb9aX6lEpUnlqZNPJXTVsaRn2ukjaYUpTxk02zVwimOtaEmIldqqETyUTK+2gVQogGsynXZUstNzvhJLqh1yY98Ef33tONw8CFa8x9lqljQXhsNGnxuDbw6NAmc/OBwXd9UHBvWmbcFS6tunYKXt8WdP4R2WM19fMtEnLOnzk04cTR5S556KIqmUSlaYpGlgKaWiZIdEADOL/BWcHnZHD5fDBhca8943s8xH4ibJ1Fd2hsPZrZo1DbXk46lSI617OTpn1T5u3dorlE5iEMpriFTHRA0daf/ANuXi6UxNza/zxBk/t/OKEPb9fCa5uHUn/jqg1PLEpaZylLtaIldDqzLiuBAOY6xyiIEyAMttZw5Nf22klkFHmFGwNe3Ow805Mbb2nutVU4njSo6apKYlImaSJ8XMUAMki3ZtONiI6xFkQxkwVOoYcFpo2+tj1aWTqoPwzF2cWBMQCCWTuMlanUsop0qB1YCQegpjPQnR5HVcnpnhFf4mLGcHTZ6pLmy+lrdXwxs3ZomWt6Os/I5fV9C9z1q/mAOAnQmFV+U/c7VAi5IQhSrE0hUFBMY4ZM+Cso+aWMqS6be+k1kU1lXCHhgebhU0kYNaIZy5So05EFTTN0Q5VyHKmsC6iJDlS+Jo6QAbSDklf8Ax3Wvw12U4OOVYe7yWsxE3vw83+qvEtXRKLav3Fs5TypTnijqnnDkxtaJ8qkdtCmTOKoEamW0SjoljjdePhjHl1aLVtWywVYZabtQ+xAy/EdP6FM5nc1lteztB8aYzElQp6ZU3qczcnEVj6BRAuiXQOBdEdmmXiqadJsta4g/9uEZk5EFoe2eKmyVKHu1IWofEACeQcKsjkbxaRtouyayvA8YLLRY8LU2Xu1XWIe5zuZsakXlturuYejUlae7q8ql6ZxdMKrYmK0UZpEcqOiIJn1wKs0wP8XSIbhNSPBqYHbmMMXt6mWLu6tG4dMOl7JNb9aogsqR3OHJp25TZu5chKTE40U7SaukpY2X0DpHQQBdUFAETRhSvDaXJp2qMMj60OHy2tsaAw1VXO6wk1uTVrUlcNapdVDLnstmTPunMFlHLJkkR+5Fq1a5FQEUwAoJEKiECVhwlJ3ltcU1nLXYabW2jtniluFTNy55KJNVc8nDyj5lTr+WTRRZgZYwprd0ncuMq41mxMXq2rDICABotMPc8k5ZHT+NhOQy8t/LosNR9FtRhpgFupEbteBn/cGel5c9r88CHZS10pxZ01ReLesa4v3hwswniNa25mVp0ZRIXdKuAdcSQmU2DNI8xcjLpkUosjETTNxcx0AKYRDT1twCM2o7DVVtanuhcl3iDozDmviIfyD4JdVhyckbtlXy9KNKuNkqabEbpjmImAgnLpCiCZtnFi4fDI3kuLcrE7cyYWatZLZriiwxtML1TS+X1HPeJ0rLWhZuCU2lqpjgod6Izcw6C46ootURDPM+eF3q4TtXERZwlIXhww2krG+jSzXwEsMSZKsqaRVW0lANjIt5ieRNnRWCk0QNpHIusUxCmOplkRQ6ZtfDt4sRYwxUJI12LrvpduuO8wt4v8NkOdy6aNn73o9OS55OOBEw+rNG9GSvF/Xhr81bg9NizoC3T+07YtMnl7Vm3O+LNZuQRTFEXbtm3SKgcjohFDKHBUIizEnhUwC0XwTGFC/dMVzVUvvbcJpPJjTNUJ24cd0r2VAi51M2p2fKiApy9nJ1CO0GzspUwcFZpm0j6wM4ZLwyd4SXkpW8oWWtUac0lhEeYOmknNUU87mPZK8XYrmnCqoG1hXxeIkICRMkRKqbPaBRDj0vjyCoMCNKYILhWDt7XaFrHszfWSvG8n80lVZ2sPOXqr16qmyQMDdyqIrLpE12SYpqFKomOgUw5bRTtLrAcRxxDcJAPAVKkgD+Z1M0n8vinRY6L/D6NRjmjNt8dZY2Dns+QOWmq6/ByzHLp8kIbNuQZBnzdEI6J8xGscd+fquXF0R1+kmPtCQhCIRIQhBEhCEESEIQRIQhBEhCEESEIQRapmxfTFyk0lrN6/eqiIoNJc1Ueu1tH4wgVJMpjmyAMx0QEcgHvZiE319YmoKMt9Z2tUmVWv1rl0vN6hnsvUpJ21SpQ0tnbiWFTOoCfxSqpIAsIqaOQHzD4uQxGFF1tVtu6jY1dQ9RzWlKklgHKxnklccWftCrEMgqBTZCGiYipimzAdgjHL242N+8NS2ztRTUju3cZtUTOj51JLvOTuSM0arcvZo7O0EFil/rgKwVTRExdH4uzIcos4A0SWeeR95/wjJNQh2kE/t/lcFwHSABzAcwzzDmj6Dn+ofZFMgL8UuWRdgZc2QRUOf6h9kUGglSPNkojc/jsx/aK/8AENFqLrn8dmP7RX/iGi1GKp80roN8g6fle/H+jp/mzKV/x7uJ/NGcd54/JP8AqzfujHRh/R0/zZlK/wCPdxP5ozjvPH5J/wBWb90Y4Vo+cOoXVofL9PwF+Wml8k/65T+IaEEvkn/XKfxDQj1FP5begXk3+c9VjlZfkF7+gX2liDonGsvyC9/QL7SxB0a1TgujZ/Ie9laW/Bm8oe0ImY3OPliGVvwZvKHtCJmHnHy+SIpgk5KtoBJEd6KkVARAAABEAD5OQ5aPk6IpCM0HZa8HZfesPzaZsshDn5s9g5dEBUUHPM5xz58zDtj4hC6dkhyqIiPOImHviYdIR58tv1j54+cgAAAADIAyAO8HkisIXTshaSIISHn88IRMOU/H3y0+iZj084ZD9IdA/R9HNDIA5gy+gNgD9Pl+mEIiDt32FEOiFQQzAQHmHnDvDtz2+aK5jnnmOeeefMMIQunZIdMr6Kc5NpTnKIho5lOJRyDmDMO9z7PpHpj5+oAzERyAMg2iIj6xGEIXTERz9VFyciEh055jnz7R8/l+mEIkXhmFJDiC0jIr6Oc5xzOYTDmI5iOY5iICO36g80fPeEOkdIfpHpH6fp54QiACBAGSEOIg6IAZbA2Bszy2Z5CAhn08wc8UyABEcto7RHvj5YrCEGZjNT8Y0lM9ufP9AhmA+UIbMgAQAQAMgAQzDLo8n0QhAtJ1Crc1y1T17MtvegO3LPvbQ+jyQhC6SSSMypIcTJSEIQg7JB2SEIQg7JB2SEIQg7JB2SEIQg7JB2SEIQg7JB2SEIQg7JB2SEIQg7JB2SEIQg7JB2SHTzbRzMGWw3Pzh3+cfJCEIdskOGif96YqHP8AUPsikVDn+ofZCCOCkAyMlEbn8dmP7RX/AIhotRddBk+mID1iv/ENFqMFTOoSFvt8o6L34/0dP82ZSv8Aj3cT+aM47zx+Sf8AVm/dGOjD+jp/mzKV/wAe7ifzRnHeePyT/qzfujHCtHzh1C6tD5fp+Avy00vkn/XKfxDQgl8k/wCuU/iGhHqKfy29AvJv856rHKy/IL39AvtLEHRONZfkF7+gX2liDo1qnBdGz+Q97K0sH9WboDI2eWfMIRMKS6LhMiyCpFE1SgchgHLYIZ//AEiJM+cOkMo0pmjcwiIpFzEcxyDLOKAkGQpqMviFNMIhTiLXwJPRCHEWvgSeiEXxCseBzU1wiFOItfAk9EIcRa+BJ6IQxCmBzU1wiFOItfAk9EIcRa+BJ6IQxCmBzU1wiFOItfAk9EIcRa+BJ6IQxCmBzU1wiFOItfAk9EIcRa+BJ6IQxCmBzU1wiFOItfAk9EIcRa+BJ6IQxCmBzU1wiFOItfAk9EIcRa+BJ6IQxCmBzU1wiFOItfAk9EIcRa+BJ6IQxCmBzU1wiFOItfAk9EIcRa+BJ6IQxCmBzU1wiFOItfAk9EIcRa+BJ6IQxCmBzU1wiFOItfAk9EIcRa+BJ6IQxCmBzU1wiFOItfAk9EIcRa+BJ6IQxCmBzU1wiFOItfAk9EIcRa+BJ6IQxCmBzU1wiFOItfAk9EIcRa+BJ6IQxCmBzU1wiFOItfAk9EIcRa+BJ6IQxCmBzU1wiFOItfAk9EIcRa+BJ6IQxCmBzU1wiFOItfAk9EIcRa+BJ6IQxCmBzU1wiFOItfAk9EIcRa+BJ6IQxCmBzU1wiFOItfAk9EIcRa+BJ6IQxCmBzU1wiFOItfAk9EIcRa+BJ6IQxCmBzU1wiFOItfAk9EIcRa+BJ6IQxCmBzU1xQTkTATqHImQAHSOcwEIUMucRGIV4i18CT0QipWbYogYqRQENoCAZRBeSIQUIzla5ZQqzl4umOaa7xVZMekpjiIRbhzAAAAAAcwAGQB9UIothghsFe/H+jp/mzKV/x7uJ/NGcd54/JP8AqzfujHRh/R0/zZlK/wCPdxP5ozjvPH5J/wBWb90Y4lo+cOoXWofL9PwF+Wml8k/65T+IaEEvkn/XKfxDQj1FP5begXk3+c9VjlZfkF7+gX2liDonGsvyC9/QL7SxB0a1TgujZ/Ie9lQdnxhzAme0fJ3g8ucbuSQzVQhVSNkylULpl1ropDZDtDMvejZF/wAEbyZecS5RMvNFWi8YU1XmnF1RvyfnHi7ffCQ5PzjxdvvhIkgRyi4gis5XRbNkVXLlwqCDds3TMu4XOIiAEIQoCYxhyHYAZ7B6BixYBxWPHqxIUZ8npwHO3b74SHJ+ceLt98JHKNrYC+79BN0xspdp63VICqa7O3E4dJHKb5JgMVuIZD3umNV/454g+cbE3kDLnztjOwEPs0Lg3VhVqkSSuKXJ+ceLt98JDk/OPF2++EiZ6ipOqqQfhKqtpioqWmhiiYktqORupI/UAuQGEqSxCmEAzDmDvxsGYAADmGRg0ijmGRgHvh9H080CzLJVFoqkwFG/J+ceLt98JDk/OPF2++EiSP8AuXfDoz8ve6e9CJwxuq+81JhRvyfnHi7ffCQ5PTgeZu3H/wDmEiSIf9y7/R/vE4WUypFoqnIKN+T045uLt8+jjhIcn5x4u33wkSR3+Y2YbRASj9A/7hCK4ZJy7ylPeKgMFRvyfnHi7ffCQ5PzjxdvvhIkiHQHfHmANoj5A78ThhPeKqjfk/OPF2++EhyfnHi7ffCRJAbc8toAbREQ2gA9A+eHnD6BDIYYYT3iqo35PzjxdvvhIcn5x4u33wkSRCGGE94qqN+T848Xb74SHJ+ceLt98JEkQ820cg29+GHOQT3moNSo35PzjxdvvhIcn5x4u33wkSRzfWOX19Ht80OaGHxnJPeKoUb8n5x4u33wkOT848Xb74SJIhDDCe8VVG/J+ceLt98JDk/OPF2++EiSIQwwnvFVRvyfnHi7ffCQ5PzjxdvvhIkiEMMJ7xVUb8n5x4u33wkOT848Xb74SJIhDDCe8VVG/J+ceLt98JDk/OPF2++EiSIQwwnvFVRvyfnHi7ffCQ5PzjxdvvhIkiEMMJ7xVUb8n5x4u33wkOT848Xb74SJIhDDCe8VVG/J+ceLt98JDk/OPF2++EiSIQwwnvFVRvyfnHi7ffCQ5PzjxdvvhIkiEMMJ7xVUb8n5x4u33wkOT848Xb74SJIhDDCe8VVG/J+ceLt98JFBp+bgAiLZIwAAjkm6Kc45bdgd+JJioc/1D7IjDG6kWioTBUPDmAiUwZGKIgcMtExB+aIdIQi65/HZj+0V/wCIaLUUcLrrq2R5Z5L34/0dP82ZSn+PdxP5ozjvPHmP+rN+6MdBnACO6ul/BRN39BSSSVPWjO8lyXVLU5U1SKUjT07ekmTIyLN1NE2rk7RNUQMTjAIKFTMcDHIJdIQ7SMJ14cQV7aJn1X36w1p4YnKM8cySlqKmdwz1rWc2TYqrNHswft+57ZNm3Mslq2o61c7khTrABEjIqK8Kuf8AW9V1aOVOeS/N+S+Sf9cp/ENCCXyT/rlP4hoR6mn8tvQLyb/Oeqxit1ASpyYKiAiBEyiIBzj8Yof7xx97qpeBV85ffE+19/6Wmn6kn75I4zxpV3EOgLo2fyHvZbwpM0zlEoJKAI5DmIhkGQ5xng3Al4iI9zn4Zjn8tL3xFcIxCo4ZhXcxr/MpT5fy4QyGXzAAHYbI6WeQ8/fjsXwVY2MJWGuWTWeXIsLdW5V1Zs+USJUUqmUhbySQS4BLqm0v17lNwmsqIGOuuGgcck0ymAhBE/UrCBqPOqhtNjTIC7+bwcJNwdN7aefSOrcFVzJa+dIGKzq6jyUXSdXSZcwZFdIO2zggnOXn0F9YQ4BonzAREOl93XkjK7chLmE5MwK5ODA74rZJ8ZEDjqTLETUMmCgl0ROBBEulpZCIZRE0IkVXgQpLWlcsKHxJpSlqaja6l1RVvaia/wBRO6NdTBJdzKvimKnMKfcODH7nv24iByKJCVJYC6twRQg7IkPX8tKdXUsZicgGOKRlTJJnOGYgTTADCBRyyzAoiBdoF6YiqESKzwqGjTIhcrZtWOHMlNqLSKa4g3NY8QIZOXTugaRYUcdyJS60hniFQKuyogbSAihW2sEoFzKA5gES8v5fkGcvfiPf+MkH/wDqIshEYr5lThMiIUp/CBL+rn/ppe+A3Al+QgEuf55ZlzUTABEOYB28w8w/QI7BiLIRIrVAZUGjTc26QuweX3H4P4aklAzilsbp6OC0aic/Slc+twnVRq/MoUEnLE6iYtgp0iYqiKKod0DHBMAOQuccTBr+XAI5S9+IZ7B0kgEf/wCw+0fKMRXCKF7i8PnP/BnvlkpbSY1gYBkpT5fy/q9/6aXvi+3r6TGXRK8YzYjQVig5O1Kgs5KnpBpimQyhSmNo5iBTGKAiAAJgDMYiWEXxnqMFmy5J13WtmWy8r+C15eCdNjth7tGufSlO0s5bK5k0SMiyybviqJiAq5isKQgIlyIO2I/Cv5fltl7/ADy+ekP+8RZCGM9MFmylPl/L+r3/AKaXvhy/l/V7/wBNL3xFkIYz0wWbKVPhAl/V7/00/fG8tK+o7KWHmDarhOaaETniLBtLxTTl4HIKgslFFtrgS6eiRZMqYG0RE4hmEQlCJFeo0gjghoUyIIXKe81w8P6tezJXDvI73sLXqM2wylnfSY05M7jN3AJADwrpzJwKyVSFQBOkJCEOAHEDFEQ0jRWFfy/q9/8AWdL3xFkIq2q9rboPGfUqzqbHEEjgB6BSny/l/V7/ANNL3w5fy/q9/wCml74iyETjPVcFmylTl/L+r33pp++Kcv5f1e/9NL3xFkIYz0wWbKU+X8v6vf8Appe+HL+X9Xv/AE0vfEWQhjPTBZspT5fy/q9/6aXvhy/l/V7/ANNL3xFkIYz0wWbKU+X8v6vf+ml74cv5f1e/9NL3xFkIYz0wWbKU+X8v6vf+ml74ry/l/V7700/fEVwhjPTBZspT5fy/q9/6aXvhy/l/V7/00vfEWQhjPTBZspT5fy/q9/6aXvhy/l/V7/00vfEWQhjPTBZspT5fy/q9/wCml74cv5f1e/8ATS98RZCGM9MFmylPl/L+r3/ppe+HL+X9Xv8A00vfEWQhjPTBZspT5fy/q9/6aXvioXAlwf8Aw9/zDzHS98RXCGM9SKTBwW/LTdFRw6WBFUCrulHBSiJdIoHOJgAdvOGcfHdVLwKvnL742SEVL3E3ic1k0EL0zcF3w5ticBmE6UYebgWKvdX1QS241T1saoaDm1MspCdGevEXKCIFfO019YkVLRPmQC5j8UxgHZ2I/wD3pfCiIGKGFXFAAaAlKA1FRGQZgb/9/sz0uYMg2AOWeYx4iIqHP9Q+yNc0Kb3S7VZ2V6g+EHJcwUDaaRVAAQBURVAB2iAHMJgAfPCPlp+Kt/1JfYEI7bMmAclx3MBcSsXr7/0tNP1JP3yRxnjkxX3/AKWmn6kn75I4zxoWjzrdsxhhKoOwREcgACCYRPsKXLbn5A2CIjmAAA5htjkLWeE3Exbu1UhvjXNj7iUpaWowZnlddTmRmaycSTAgqSxd0TauzRfEATNVXiSKboADUGVzDOAG6wN3DdcyBHBGzhN0ducRKR2CShDiiYQH5JwKID5Y747k33w8PLm46MSkgv8A0bcdpjuoWQW5tthwbyioPhLpJ5MqgpJ0qyrFs4ZklrNnSQSRdJksg6cEdHUbi0TKmKgFqymagY2mJc4wB14eug5q9MONZrQJGWuQzcATP9IN6OIBGUrolp+WTCrZj3IpVotUk1IxdzRWXSHRm75NswZOJk/XFFPSNq2zNo7dKmHIE0WqyhhKUhjBvdXUVUVBSuhZ1VraXyeU3LoxtcCinpqilr9GdSl25dNG7odQ4UFuZRVm5KDZ1qnBQTKYUtE5TD6qLr4m7SWGxRXqttWd/bD09XLjEfdajbPTugrVpyFhhNpKaW8nlOy6T1e9Qp5ESEXqNWTnBkgm/Fj3PWdmWOk6MSOC9C3+w8tqIpSQUPfXDvazFPLcOVs6UnmISurWqzu3K0wklw65m9zpUDwaeXA8xm7B3Tzwky4iIPmTYzErlEygEDUp1RXDHUtDTa4nhLnRHo2J21beBBWd7GU2Ak5mo5sjOGtEh5AzIccmwP7o0XQeZ00KqRAztoVwrlqm5nSZV1cwzDQIJtI2YCAhkG0BjUS4Czd6hLJUq3mUydPUZaiyZuk1VjOF1SIIIiAG+Kc6ihCABsszGCPQTcDGRgsed1rc2oe22pKw9fUligJVdKvLGoNnozGpHai9ogWc9y1nzYplTGdS5Jm6EktTX0FTojmAQDjCvxaSoatwaXMoe8NtjVrQtwGE1qehbEy882szbGTSpWmSsJ7KVndNSuZMVXncuYHXpl+E2OyOiuqk8NxgERzU3F1emx/lc5oJ2BLgSdoLJnZw3CpTaHsc85OAcIO7cNw/3B8ZZS1wBMLq8vLairrCXJnlpLmKUrLrg00uDOfU/T1dSatRlDsMyuJa4cS90uiV60UKZFyz09c3WTOmoUohlEXg6aGFUCu2ZxQLpOCldJm4uG0M1NvxQ2DtNlzDHpVrDGXhRuLV91q4ml5bQvLYTi6l+ZniMtvWNoVZndbFZKqiQdtLPOaUdmk2nqmbYsuaokVdS1WWOGirxcqmsHWcaaFvHRt/+Ey4Oy31JL0PV1iqOnVo6TZUxL7XS2UU44m/JWRNK17ptBlqK0zWUmiDwjhR6Rcq4o5pmFPITY7Ea9pFCm5l11Qtn+kuaTntBA9TnEgKpLLz3tya1oIniZaI6/ESRwAynMjo9B8wFEXATCXi3KcUxc8eSFuUwAIiUVNLRzAAERDPPYMXCOWqqh0UnTVVZMoHVQTcpqLpFEMwMYgDmACG3MQyyj0B1Xi1tJbuQT9zO7tWAufi0pfCZdqlqcvFQlmG02o5pUU5r+m5hbenUCOKfbtHc3k8sZztYs0XZIpsyOCMxcKCkGnnksxR4DRWpOcXUq+y9eYc3E+si8tXh2lVk1HN2rFTanlWyt1JxVhyytuLts5VPMFXZSP3wTzum3BJJLiwgWtCqyuyi+DdcGGeEFzm+pAbeI/rbndzGZ9JrXGk93xRrwgtBn0mMs8jC83QTGWmTKqEylopKK6hNUJgiZI6ngym0shN/wDKG2Psz1iQ65DvmJFG5dNyQ75IqjYOlQomzIH0my5wj0d2hxNWeoxO5y94cRGEa+l/1Ll05VTCtyFUtVaCsbbsCzxd/QC00JbWZaZlHD1o6dyhrLmizhq7bNyTbTZ6gNFRWJPDyzoXDJM07m4QZFWVviKUpIrNDMJwwtC5l7mlKilziezqaq0OtNqUq1k8mjRshMzqzyWvnZEHx2iAI5BNOo4wy7AgH1Ia4j0vFp4S0wSACYNJl0gOkn8A/SbojjmOJXQNMKIqSV29pe6r5owQt/WVQzSlqaqQKglq6cxfyZNmpMkOLEcGcpakr5qYqzhJJJXXDqjqaJ9DCTv5emRNZSYS4iS/4BU79FJFxlz6BxMAGyzDPIdn0R6Cp/iWwX0PU7mZ0Xdukrk1DT77EpNpRcWr7JM1Z9PJhU9vaMa20VeJkkCLF1oTxhNkGDjiiKKZ2Yu1GjAHHx+beGasreXWkDC81pGlsKhupW9TWrt9OlHVHy23lOXwuMlZUWFUUpULxemHxGzNCfTFjNVlJW3Zgq9TFcjpUgCdI6qRTa8gmWB7hByl925PBwADjsHDdMICqGBwzcWzyAc6ehJAHPlAXlbo229eXCdSBtRlKTWoeU9fye1cjes0ipSh9UlQKHTkclF6oJWyTp6KaxkUlVCaSbdU+loJqKhjFRSt5SU+qamaiBtLJ3Rs+e0vVDJWYtlwkr+XOVWb1sssmodEDJLIqpiJTiURJmAiAgI9xGD/ABDSm1ln6wtFdG/FLW1n9BcIlbe+lxacqNE74K5pin5i8aVslJnTCWuyO3aD9Ni7M2SVQ1iKAnbqmDSSNyUkGNXChcurWNX3WrG3Tq9IzHEbTdtrkuaIb26llvST+p6ddWqezGaI0rMUG6aksJUrdhMFZVNF5Ys/1jhFERBQg1L1amxgkGniEzkDcY6AePxOcwDjHJY7pp2dr3j4y97COIGM9gceRY1r73AEbrzmHdtEkQcqvGiTUQAQdKuk02pgN8kQUEdEc8wy27c4oDxmKpW5XrIzg4aRGwPExcHDLPMqelpCGQgOwO/Hd5ZjEHZxTH3fW4c1muFy1tFVxRMtoZ5VbetZmvKGrkitPln1V2+qJSh1GhJ2uWWO3ayT+nWrF9r37VEiBXSagcl7u19Yi22HigiVLdG0NWWIujgVrkabtE8s00py/wDf2rpxdGrGtGV+kZCSlIwBuhLEnCjkZmiDRNis0BtorkAReXMBIzcAek3svS6P9yvhh1Z1IEQ0wDvF3P8A8j9DqvNaV4yyXNx1kYjT8cNx1LQaZbR1xtL+r/1CAhnnG5nlswRkEsqpdkulS86mjySSmozkEkkmr2Xps1pgzbu89WZZqnMZeosQg5pFftxOUgKkAfSpcXFlgsmV0mQ0TUGGOTUo5lFdvsJVz5s0c1i+w1zKa0WjI6Nl9T0gWg2DaSy5k8bnAyazmo1WkyArwDKonMsbALZYtbcsG+Flhf3FrYa5knsRixrep79yhG3iz+kbkMZ9SMtJS06lEoLS6Sc1ZMZkyeNXWaTcdeIKiiqkomcLiq68510gi67OI1DS3LMnOYy3zGas2lTdUY2o+A4PGXAhrnNJPAEsDergvOmk/l65ypt5jLnChyaxMjd+iuooUNmmUpTCIl2c4bI1cd7jDENYS4mBKuqXvLiCtzU17L12n0nkpqekkKcntr7hmuDLHZUpXIZLSCaaTJpIyKLjP1p0oVZuJ5egxSKjoj0l1xT8rpKtKrpeR1hT9wJNT1Qu5LKa7pRF43pis2zZY6SUyl6TtNN0Rs4KQqqQOEyKgU4AYoRDXuFQUnbaiSPqQOMdRzlHU2Gi2sx2TjEcQMoJGoyn1mVi8IQi6wJCEIIkIQgiQhCCJCEIIkIQgiQhCCJCEIIkIQgiQhCCJCEIIkIQgiQhCCJFQ5/qH2RSKhz/AFD7IluoVmeYLl+0/FW/6kvsCEGn4q3/AFJfYEI6rPIOi5ztSsXr7/0tNP1JP3yRxnjlFWLNw/p+YtGxDKLKIgCZCFE5jCUxRyAA294Y43jJZyXPOVP+cQACtTmEMunZGjXBLpAWzQIuEd8FtsfYKHKZMxDGIZI4KJnKcQMQwbQMUc/iiUchAS5DntzCNd3Hm/VUx3M/uh3Hm/VUx3M/ujCwva4PbII05c/RZlafzB/NnzqaTZ+/m0zfODO38ymj5eZTKYKnHM6rhwocyqpz/wB5RQxjD3xHII0giOYiAjnzgYQ+MI5gOYj05gA9HxQ2c8bh3HnHVUx3M/uh3Hm/VUx3M/uijWXGhjWwAIHIbeiLbunLmzHIADRyz59nNmO0RyyDMR2RXMR2iYREMxLs6SgGXPkHMA5gGe0Y3DuPN+qpjuZ/dDuPN+qpjuZ/dFodsi24M9uYgG0RDRDLLPn7+Xxtmez+6EauXzGYyl+zmkqmD+VzKXuCu2Eylb1aXTFgqTMSKoOEjlUTOXvHIIGLmIgIDz3u4836qmO5n90O4836qmO5n90BiN+JkgoROS0BjmOYxjmMYxjCYxjGE5jCIiYRER2iIiIiI98R747R+QEQzyHbmAl2ZaIhnkId8BABNkIZfLHPONx7jzjqqY7mf3Q7jzfqqY7mf3RF2AGMbDRkByyj8oSSZK28pjBkGkIAAZZAGQAG0cg+gMxDLvgI8wwzNs+MJekC7CmyKJQ2B0AOQfohzxuHceb9VTHcz+6Hceb9VTHcz+6Jh2yLbsx74/RsEcuYAjO6Rujc2gZfPpTQtyLh0PKaqa8RqiVUXXU2pOW1Gj8X+rft2jhJNcMi6ICoAiBRMADkYwDivceb9VTHcz+6Hceb9VTHcz+6Dg5zbokd/wDH0TRbcAmDICiIAX5OQAQC7P7oBsLt25gHMAd8RGKZCACGf93ZtEdEcgzDb3hHS25Z5HHaEbl3Hm/VUx3M/uh3Hm/VUx3M/uirWXR8LYEQBsEzLi5xklbeIiI5gIgOWjpAIgOWwdod/vhtEOcB2jGsczSZPUZcg/mMwfISdmEulDd8/XfN5Q2A6ioN2pFDmKikB1ljgkkUhNNY5tHSERG53Hm/VUx3M/uh3Hm4/wDwqY7mf3RJDo0UiAZ48FtwCYNENMw6OQaX98cgyHPMRDbzfWPPFQEQ6MubIQEMgDLINg7cuYPoyzjcO4836qmO5n90O4836qmO5n90WdJeXAaqom6AVt3MAlDLRE2Yl0cgMGeYc31Dl9GwQ775wjziOel/eHpz8sbj3Hm/VUx3M/uh3Hm/VUx3M/uiIdspW3Qjce4836qmO5n90O4836qmO5n90Idsi26Ebj3Hm/VUx3M/uh3Hm/VUx3M/uhDtkW3Qjce4836qmO5n90O4836qmO5n90Idsi26Ebj3Hm/VUx3M/uh3Hm/VUx3M/uhDtkW3Qjce4836qmO5n90O4836qmO5n90Idsi26Ebj3Hm/VUx3M/uh3Hm/VUx3M/uhDtkW3Qjce4836qmO5n90O4836qmO5n90Idsi26Ebj3Hm/VUx3M/uh3Hm/VUx3M/uhDtkW3Qjce4836qmO5n90O4836qmO5n90Idsi26Ebj3Hm/VUx3M/uh3Hm/VUx3M/uhDtkW3Qjce4836qmO5n90O4836qmO5n90Idsi26Ebj3Hm/VUx3M/uh3Hm/VUx3M/uhDtkW3Qjce4836qmO5n90O4836qmO5n90Idsi26Khz/UPsjcO4836qmO5n90X0JBOVlk0iyx8AqHAmZmxilDMchERHIIlrTMoHBpBK5UNPxVv+pL7AhFWpTEboEOGRyJFIfygAZwjfbWYGgFaRYSVeEAEBAQAQHnAdoDGk1SWYjq08x5x0AzGEIlnmCszRV1afgyeiENWn4MnohCEWHf2V01afgyeiENWn4MnohCEB39kTVp+DJ6IQ1afgyeiEIQHf2RNWn4MnohDVp+DJ6IQhAd/ZE1afgyeiENWn4MnohCEB39kTVp+DJ6IQ1afgyeiEIQHf2RNWn4MnohDVp+DJ6IQhAd/ZE1afgyeiENWn4MnohCEB39kTVp+DJ6IQ1afgyeiEIQHf2RNWn4MnohDVp+DJ6IQhAd/ZE1afgyeiENWn4MnohCEB39kTVp+DJ6IQ1afgyeiEIQHf2RNWn4MnohDVp+DJ6IQhAd/ZE1afgyeiENWn4MnohCEB39kTVp+DJ6IQ1afgyeiEIQHf2RNWn4MnohDVp+DJ6IQhAd/ZE1afgyeiENWn4MnohCEB39kTVp+DJ6IQ1afgyeiEIQHf2RNWn4MnohDVp+DJ6IQhAd/ZE1afgyeiENWn4MnohCEB39kTVp+DJ6IQ1afgyeiEIQHf2RNWn4MnohDVp+DJ6IQhAd/ZE1afgyeiENWn4MnohCEB39kTVp+DJ6IQ1afgyeiEIQHf2RNWn4MnoBAE0yiAlIQohzCBQAQhCMT9R3squ1Hey1cIQig7+yqO/sv/2Q==" width="420" style="display: block; margin: auto;" /></p>
+<p>To view the raw data again, just deselect preprocessing. Toggling
+preprocessing on and off can help you to make sure that the spectra are
+processed correctly.</p>
+<p>The <code>process_spec()</code> function is a monolithic function for
+all processing procedures which is optimized by default to result in
+high signal to noise in most cases, same as the app.</p>
+<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" tabindex="-1"></a>processed <span class="ot">&lt;-</span> <span class="fu">process_spec</span>(raman_hdpe)</span>
+<span id="cb18-2"><a href="#cb18-2" tabindex="-1"></a><span class="fu">summary</span>(processed)</span>
+<span id="cb18-3"><a href="#cb18-3" tabindex="-1"></a><span class="co">#&gt; $wavenumber</span></span>
+<span id="cb18-4"><a href="#cb18-4" tabindex="-1"></a><span class="co">#&gt;  Length Min. Max. Res.</span></span>
+<span id="cb18-5"><a href="#cb18-5" tabindex="-1"></a><span class="co">#&gt;     579  305 3195    5</span></span>
+<span id="cb18-6"><a href="#cb18-6" tabindex="-1"></a><span class="co">#&gt; </span></span>
+<span id="cb18-7"><a href="#cb18-7" tabindex="-1"></a><span class="co">#&gt; $spectra</span></span>
+<span id="cb18-8"><a href="#cb18-8" tabindex="-1"></a><span class="co">#&gt;  Number Min. Intensity Max. Intensity</span></span>
+<span id="cb18-9"><a href="#cb18-9" tabindex="-1"></a><span class="co">#&gt;       1              0              1</span></span>
+<span id="cb18-10"><a href="#cb18-10" tabindex="-1"></a><span class="co">#&gt; </span></span>
+<span id="cb18-11"><a href="#cb18-11" tabindex="-1"></a><span class="co">#&gt; $metadata</span></span>
+<span id="cb18-12"><a href="#cb18-12" tabindex="-1"></a><span class="co">#&gt;   Min. Max.</span></span>
+<span id="cb18-13"><a href="#cb18-13" tabindex="-1"></a><span class="co">#&gt; x    1    1</span></span>
+<span id="cb18-14"><a href="#cb18-14" tabindex="-1"></a><span class="co">#&gt; y    1    1</span></span>
+<span id="cb18-15"><a href="#cb18-15" tabindex="-1"></a><span class="co">#&gt; [1] &quot;x&quot;                 &quot;y&quot;                 &quot;user_name&quot;        </span></span>
+<span id="cb18-16"><a href="#cb18-16" tabindex="-1"></a><span class="co">#&gt; [4] &quot;spectrum_type&quot;     &quot;spectrum_identity&quot; &quot;organization&quot;     </span></span>
+<span id="cb18-17"><a href="#cb18-17" tabindex="-1"></a><span class="co">#&gt; [7] &quot;license&quot;           &quot;session_id&quot;        &quot;file_id&quot;</span></span>
+<span id="cb18-18"><a href="#cb18-18" tabindex="-1"></a><span class="fu">summary</span>(raman_hdpe)</span>
+<span id="cb18-19"><a href="#cb18-19" tabindex="-1"></a><span class="co">#&gt; $wavenumber</span></span>
+<span id="cb18-20"><a href="#cb18-20" tabindex="-1"></a><span class="co">#&gt;  Length   Min.    Max. Res.</span></span>
+<span id="cb18-21"><a href="#cb18-21" tabindex="-1"></a><span class="co">#&gt;     964 301.04 3198.12 2.54</span></span>
+<span id="cb18-22"><a href="#cb18-22" tabindex="-1"></a><span class="co">#&gt; </span></span>
+<span id="cb18-23"><a href="#cb18-23" tabindex="-1"></a><span class="co">#&gt; $spectra</span></span>
+<span id="cb18-24"><a href="#cb18-24" tabindex="-1"></a><span class="co">#&gt;  Number Min. Intensity Max. Intensity</span></span>
+<span id="cb18-25"><a href="#cb18-25" tabindex="-1"></a><span class="co">#&gt;       1             26            816</span></span>
+<span id="cb18-26"><a href="#cb18-26" tabindex="-1"></a><span class="co">#&gt; </span></span>
+<span id="cb18-27"><a href="#cb18-27" tabindex="-1"></a><span class="co">#&gt; $metadata</span></span>
+<span id="cb18-28"><a href="#cb18-28" tabindex="-1"></a><span class="co">#&gt;   Min. Max.</span></span>
+<span id="cb18-29"><a href="#cb18-29" tabindex="-1"></a><span class="co">#&gt; x    1    1</span></span>
+<span id="cb18-30"><a href="#cb18-30" tabindex="-1"></a><span class="co">#&gt; y    1    1</span></span>
+<span id="cb18-31"><a href="#cb18-31" tabindex="-1"></a><span class="co">#&gt; [1] &quot;x&quot;                 &quot;y&quot;                 &quot;user_name&quot;        </span></span>
+<span id="cb18-32"><a href="#cb18-32" tabindex="-1"></a><span class="co">#&gt; [4] &quot;spectrum_type&quot;     &quot;spectrum_identity&quot; &quot;organization&quot;     </span></span>
+<span id="cb18-33"><a href="#cb18-33" tabindex="-1"></a><span class="co">#&gt; [7] &quot;license&quot;           &quot;session_id&quot;        &quot;file_id&quot;</span></span></code></pre></div>
+<p>You can compare the processed and unprocessed data in an overlay
+plot:</p>
+<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" tabindex="-1"></a><span class="fu">plotly_spec</span>(raman_hdpe, processed)</span></code></pre></div>
+<p>We want people to use the <code>process_spec()</code> function for
+most processing operations, all processing functions can be tuned using
+its parameters. However, we recognize that nesting of functions and
+order of operations can be useful for users to control so you can also
+use individual functions for each operation if you’d like.</p>
+<div id="threshold-signal-noise" class="section level4">
+<h4>Threshold Signal-Noise</h4>
+<p>Considering whether you have enough signal to analyze spectra is
+important. Classical spectroscopy would recommend your highest peak to
+be at least 10 times the baseline of your processed spectra before you
+begin analysis. If your spectra is below that threshold, you may want to
+consider recollecting it. In practice, we are rarely able to collect
+spectra of that good quality and more often use 5. The Signal Over Noise
+technique searches your spectra for high and low regions and conducts
+division on them to derive the signal to noise ratio. Signal Times Noise
+multiplies the mean signal by the standard deviation of the signal and
+Total Signal sums the intensities. The latter can be really useful for
+thresholding spectral maps to identify particles which we will discuss
+later.</p>
+<p><img 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" width="400" style="display: block; margin: auto;" /></p>
+<p>If analyzing spectra in batch, we recommend looking at the heatmap
+and optimizing the percent of spectra that are above your signal to
+noise threshold to determine the correct settings instead of looking
+through spectra individually. Good Signal tells you what percent of your
+data are above your signal threshold.</p>
+<p><img 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" width="636" style="display: block; margin: auto;" /></p>
+</div>
+<div id="intensity-adjustment" class="section level4">
+<h4>Intensity Adjustment</h4>
+<p><img 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width="390" style="display: block; margin: auto;" /></p>
 <p>Open Specy assumes that intensity units are in absorbance units but
 Open Specy can adjust reflectance or transmittance spectra to absorbance
-units using this selection in the upload file tab. The transmittance
-adjustment uses the <span class="math inline">\(\log_{10} 1/T\)</span>
-calculation which does not correct for system or particle
-characteristics. The reflectance adjustment use the Kubelka-Munk
-equation <span class="math inline">\(\frac{(1-R)^2}{2R}\)</span>. If
-none is selected, Open Specy assumes that the uploaded data is an
-absorbance spectrum and does not apply an adjustment.</p>
-<p>This is the respective R code:</p>
-<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(dplyr)</span>
-<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; </span></span>
-<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; Attaching package: &#39;dplyr&#39;</span></span>
-<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; The following object is masked from &#39;package:OpenSpecy&#39;:</span></span>
-<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; </span></span>
-<span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;     filter</span></span>
-<span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; The following objects are masked from &#39;package:stats&#39;:</span></span>
-<span id="cb5-8"><a href="#cb5-8" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; </span></span>
-<span id="cb5-9"><a href="#cb5-9" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;     filter, lag</span></span>
-<span id="cb5-10"><a href="#cb5-10" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; The following objects are masked from &#39;package:base&#39;:</span></span>
-<span id="cb5-11"><a href="#cb5-11" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; </span></span>
-<span id="cb5-12"><a href="#cb5-12" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;     intersect, setdiff, setequal, union</span></span>
-<span id="cb5-13"><a href="#cb5-13" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb5-14"><a href="#cb5-14" aria-hidden="true" tabindex="-1"></a>raman_adj <span class="ot">&lt;-</span> raman_hdpe <span class="sc">%&gt;%</span></span>
-<span id="cb5-15"><a href="#cb5-15" aria-hidden="true" tabindex="-1"></a>  <span class="fu">adj_intens</span>()</span>
-<span id="cb5-16"><a href="#cb5-16" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb5-17"><a href="#cb5-17" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(raman_adj)</span>
-<span id="cb5-18"><a href="#cb5-18" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   wavenumber  intensity</span></span>
-<span id="cb5-19"><a href="#cb5-19" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 1    301.040 0.00000000</span></span>
-<span id="cb5-20"><a href="#cb5-20" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 2    304.632 0.03037975</span></span>
-<span id="cb5-21"><a href="#cb5-21" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 3    308.221 0.02784810</span></span>
-<span id="cb5-22"><a href="#cb5-22" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 4    311.810 0.02405063</span></span>
-<span id="cb5-23"><a href="#cb5-23" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 5    315.398 0.02531646</span></span>
-<span id="cb5-24"><a href="#cb5-24" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 6    318.983 0.02025316</span></span></code></pre></div>
-</div>
-<div id="share-metadata-on-known-spectra" class="section level2">
-<h2>Share metadata on known spectra</h2>
-<p><img 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" width="238" style="display: block; margin: auto;" /></p>
-<p>To share metadata about your spectrum, click the metadata input
-button. When sharing data, please provide as much metadata as you can.
-Metadata helps make shared data as useful as possible. Metadata inputs
-each have examples provided in the input. The examples disappear when
-the box is clicked and will not be saved if nothing is input by the
-user. Mandatory inputs are marked with a red asterisk. If these inputs
-are not filled, the data will be considered uninterpretable and will be
-discarded. Inputs left blank will be left blank in the metadata sheet
-and interpreted as “unknown” or “not applicable”. To share metadata,
-click the share data button at the bottom of the metadata inputs.</p>
-<p>When the user clicks the <strong>Share Data</strong> button their
-current uploaded data and metadata is sent to an <a href="https://osf.io/rjg3c/">open-access online repository</a>.</p>
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" width="150" style="display: block; margin: auto;" /></p>
-<p>All inputs from the metadata (described below) are input to a
-metadata sheet. The metadata sheet is given the same unique name as the
-data, but it ends with “_form”. The exact same data is saved as would be
-downloaded using the download data button (described below). All high
-quality uploaded data with metadata will eventually be reviewed by
-spectroscopy experts and added to the internal library if it passes
-review. If multiple files are going to be uploaded with metadata, the
-cells will also stay filled with the last input after the share data
-button is clicked. Just upload the next dataset and change the metadata
-inputs that are different. If more than 50 files will be shared at once,
-you can contact the website administrator to get a bulk upload sheet for
-more rapid upload.</p>
-<p>Type</p>
-<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="fu">share_spec</span>(raman_hdpe,</span>
-<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>           <span class="at">metadata =</span> <span class="fu">c</span>(<span class="at">user_name =</span> <span class="st">&quot;Win Cowger&quot;</span>,</span>
-<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>                        <span class="at">contact_info =</span> <span class="st">&quot;wincowger@gmail.com&quot;</span>,</span>
-<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a>                        <span class="at">spectrum_type =</span> <span class="st">&quot;Raman&quot;</span>,</span>
-<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a>                        <span class="at">spectrum_identity =</span> <span class="st">&quot;HDPE&quot;</span>)</span>
-<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a>           )</span></code></pre></div>
-<p>to share your spectral data from the R console.</p>
-</div>
-</div>
-<div id="preprocessing" class="section level1">
-<h1>Preprocessing</h1>
-<p>After uploading data, you can preprocess the data using baseline
-correction, smoothing, and range selection and save your preprocessed
-data. Go to the <strong>Preprocess Spectrum</strong> tab to select your
-parameters for processing the spectrum.</p>
-<div id="preprocess-spectra-plot" class="section level2">
-<h2>Preprocess Spectra Plot</h2>
-<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAADACAIAAABXroqMAAAKW3pUWHRSYXcgcHJvZmlsZSB0eXBlIGV4aWYAAHjanZhrciu7DYT/zyqyBJIA+FgO+KrKDrL8fJBkX/vmVCoVuSSNZoYkCDS6e/ycf/3zPv/gVUzTo9Z6HbUmXjp0FOegp/crjifvwvu8T/Wavl45jdfn6/VYf90WJ35dWF93l9/nv76z/20i/aye5+8LX+dL/9tE72WTxAIcZ/1MND4HWTj9h5Xr6C3pX7/9s8D9bLE209qbPXXXUpJIORJzHVZiNYl3Fudb+CwS93CdY5HKp0qLWWIqVuf4SZ+IXkMZnPLIceHr/GsD8sn8j/PynviVrJjr+ftWUnlf+Pr9XQz5fX6l36/nFZ5wWX5MVv9cmf9WmOe/Vea1rR+JVv0cld/nvaT+/Azv3t3vPa871LUC0PpB2Nfm8+c+YKrymjK//57Xqu8f5ftPk3wfG7/it7Jh411/fDf20d/fDx9xqtMKndP+/fc+t3g7n58r37H8XDYWk4e1jFvaa17///+e//XGe6PgOUdDl/Hqa+IqUZaSXzmS+OI2qpD3d03kk7c/vewV/RuoGgmJ0VRNZv4qtX1fjAS3H4O/8K0xTc6EAv6zCAeaLVt5Us+aq5RGawzxXKKZcs+5aZl5qpj0Jk2b9TLLJ/L5ivUr8g+y//MVF5WbYgP2HsrcbxTJJ+Z36X+1yBtkkbC4wSJoyRFuy2zCTIm07BwnpUSsM6+coQwxLVZa6cXzLA/I8FcPyl8TxupyQfBKCxC5ibfadM2tWW/ausdRCLqkY20wSx/ygLnqe1U9mvZse8w7WXzcIuNayX2tLWfcarqZMq89R5nCFLX0XZr0rYdbnjVNdnFbdfka9Y46FVh7ZCnXYQS40rmpahuqTevp7eRZc4M6ZV87Q2wpE3UizLtkH4TsiQWK7bMW21/9tA1H2CREuZ+flfqeBAumvq72PcX7TE876+xz0hjD5srl3FrLkKNNjmv+NeNnirT6JaHNck13jz032X/IFcOvVAZoXvD4HyYo2tfYLp+JKN15T3FomdU3cTztV27Tr3jkszb56am/dvC1gc/wz+hV26PvCYhoF/KrTl16ljtGWg0x2EUohysBjcUw3TW5lUmqrZBa9dPRqANnt9NojFRWXqPn+lrQbeqU2cWcHpm5pTlpFVA0rZzq3aYAgbJ3K1qQtPWUvQgNRCP7Iv2cToHoM6+zmQwbetotNtu0Jnk1gyFozOvjjFxbIwGS+rDniNu+5WynedSWhELeS+i+6rlygcelxrP4PU5f9NWIZQVItaVNTZztTnBEURGgW/YVGwZIq2Vft1KaRZSz+8x1U6d1kx2/dUk29wuLBGbamvXk9hwDmZupfHmuWldkAl4xWifAIr00h4COzdu6ycqTgTsN97XT7L1U+nD4M4xUbfVp1kciE9K99OxNZ527Kj3byeFWyZPOX2bDh22xfCO1ym75bOdpea0LWayBx2Kvo+15I3tUyDtZdrax0UBCoMEJYbCG7+sTBQRsZQQlwNmK6QhCuFGGvM9u/W5KsKlzmdVBlCNGfv3IhKsgKM9Grg/lFhodfgEHz6ixByMz0tPugDG80wBU7L0fc8oPHR7LmxDTvt042GAieMNHg41YfDwTikjbxolcJmwlSl30rvAeRnV7CA6o631vwsnbrzS6IwYzNh93Rxj92WrazVgZYFPKfjBSg/7cU+vYdVymALJQt9IpNzmYHXN6gTUHzDd07bXtKT78wCs7oAS/wlm0JzAGRPAW9LejxUE0XZHB6WRGdGiG5YF8NhlDmtJDfyUXnCBke9ll6zTpqXdCmWD1tLGPSaFlq8vOOKDgOIzCpXg2bmMcnSzPLQMqUjJINxwtUDKUxT5BNU6UMm0P4pqt5457DCWoQo0TCQB5MEmda9/nrwggBL0o46o2VvAtCVl1VnrGpSvt3/TYmp6hbnCMBQDgiZrqtf7sCs+ke8Bndru7HhhJivOF5tDj5RaeDEAK6SPpbIDqhKQTQS5AhACzQSNr3kIp1jh5jCkbSYG6+mx15nNbux3gzc58ZEHaMigp0K705mbWzbJ1jAedWzEYcgWnPd8NMwcnSb27QQSwiAIhr9tpvGqVOH2Tv+YcthMV2V0eKHbNIpSX/cRwsDDrOvTkqIOcEM1gi7NamjRNQP7kl1Y3R5RaX+cAl0foWBqghcmV7AcS24cWGLBcqCXqJy3nK6HYaP+Et/KNdiDfCiNwDkS3h6ZHrdnRQi2IacKRKzA+7mGJa9gHphjk5NDGDMH7zLaaH6h/1lrh39XqY/QVar6QC7j1DuUqOt0aPHSa997zplMPvX0ChQZpvzl3M0G106fgFxw5Mqf9bOH/ziok4OAYoblrwnJ4sgVhwSmV9u2oH6ks1KdfCzLgRnRGZ0dFmsJ6cFYhrjXBXWB+aKhPhxVsJMgTUK0Tuhd6D91CIMgi7XfwBToOVItG0GiiAGuUjl4WGhUua20TDkqgvkoX6NxPHRksBqszZZv4pc4w/MSgRbLBrJe7b4ZXZ/ZgD4FRcSd6aPLrKKBQT6BEB90cDmxliyaGVLGZpEafzjwNRCUKgjmlLeCYhJeiXECE9g4YSjyTYGA4UzM0OJBzJ+8WieydFXBsEDlSAaF35MH4pKwhdWkXA/sNLqDHADFYjomw630PVrho31RUF8hMnrKXUIPkYA2pAi00HTDAJtSQ5Q52YRU2iT+t1ZDycmBGrjomT9AbxyX4cxOPsRi90K6bx8W9DixdWeXOuehuxmS6I7hQYJpGjIhIvYJRJDacTA/r+PhmYugGX7tXqFlPVBAr1d1YAggG7vBWl14xOq0QTlJ657BBugCsVQr+wGeX3DaB5zquAX9PFyIBVBF2O+izTuwajhc8IfZNsQnAA9GcPBIg8QCt8ARJTywoOVckIpSoC4LNzDyjHOiS/ZKlPoFTLxAQrmVDqCvEhqH4I1JUvT3EiaVRnE7GE9aXXz00neq6N0Scxl0tfiAMOKQMxrCfEEqc0cqzK/67CHLUdmE7ZGS9eH2QyQ63qwuVR0WxkXQ7fIDbgp82hAerFeswEOS26avRMaPpDK7DTXg8zR1SSTO6wTFJVI494HAgA54T9AROwQc2lFrwUFarGj/A8YO/O04x6ZF84GhCyFkxJWzIm9y9ZcYmSFLDJ++GZmMLClJTV0uxq4lpBpCGILEe9s9gT6B04gkBM4KBx7HeBdH3KzhWxrYovoQoNucxOVh+kC4eOp4CZRA5Ld/icanZKpMGpmX2igcNpeIhiwBhCBLtUTAxuLy3OfBjp5Mj3whk007hrYASTDVPeFg33Aht3nctuIpRaOzgH+ji1BNInAK7LnwBngQxR9UfFA5LhFxHZWwB33nwuLXG8wHMyJMAxrsEQ541VfHAWErH1W6qRm0v0FFRjBYT8vRKL6AbyakbdhuP2QKIg48VBMVtCMNQulbK6tAzBt0n+lGC3BWGpFF28I+MAy704OgUN4wMeA+EYQzEGi4Yf10hc5JLgtk23Q7NDS8Nu30f9JuN8uzK81CHY18NB9NhgrCXGX+i8Y+M8fwbH54at7duS70AAAADc0JJVAgICNvhT+AAACAASURBVHic7N15fBXV9QDwc+6dmbe/l+RlI4EECPsWNhWCCAoWxRWruFVr1VqtWqvYWrW14K5VsbWbWpfa9ldUXECpgtaFxQVBZBGEsIXse96+zMw9vz8mpCEGZYskvPv9AJ/wlsnMe2/mvHPvPfcCSJIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkHTOwizaLwLtmy5IkSdJBIzCO9i50X8qR3iACEAePF0oIjC4LtMA4CLOLtt19MQ5CANDR3o/vFmNABJRiR40IAKl41IggxNHej+9cF1/QGEE8CCsIUu+VPTBHPBBaEEEFwK4LhF246W4sZY86BaXsUafsh7wrj5oBpF7ecDC6KBACAKVc5iJJktQddeHVmDGGiEIIOkLNF5zztq0hImOMiMS3tRIwxr71Md/gCAdCxphIwSZLSZKklNQWfqyISEREZIUlIuKcA4Bpmm0/tAVORLR+gL1hzHq8abZGEGtrbf+1ttBhs9bWTNMUQnDOiahtH6wttP0LAN8QUI/wkBbr1zOw26EQQHRh0yhLue4TSNmjTsGWshQ+asSU/JB37amNAEYCdn9rXsiBMcAD+MNaH8/58OHDR44cmUgkgsFgWwRqDQR7w5t1S/sA2XaLtZ32P9xyyy1EVFFRAQCZmZmTJ08uKCioqanRdb3DZttvrbCwsLm5mfZq21oHuJ/z6kgGQkVRhg0bHg6HDB3t2FcGwiMuRY86VUNCCurRgRAP413rJoGQWselffsf69KenpH+yiuv1NfXX3nlleXl5cOGDfN6vUKIKVOm1NbWxuPxU045paioaM+ePSUlJQUFBWVlZSNGjDjuuONaWlp69+49YcKEhoYGn883efLkuro6wzBOP/30W2+99fPPP9+yZUteXt6zzz772WefFRcXJ5PJ/Pz80aNHR6PRYDA4ZcoUa2sDBw6cOHFicXHxX/7yly1btqSlpY0aNSorK8vKFAsLC/1+//jx4zMzM4uKijIyMqqrqzuNhUemadTKRi+66KJ58+6eMWPa9u1ViKyHfpolSZIOgQ5ERBqwHnflQ0ACymIZtziu4sDoG+MlgXCA45nESxuMrQrylpaWN954o6Sk5MEHH2xpaXnkkUeeeOKJHTt2zJw5c+fOnWPGjFm1atWMGTNycnIURTn//PNHjBjx+uuvFxUVzZo1a+HChQ6H44ILLigvLz/33HNDoRAiVldXG4YBAETk9Xpzc3OXLl2aTCaXLFnyr3/966KLLtq0adOwYcOI6NRTTx06dOjixYsHDBgQCARsNtuCBQuefvppIurTp8+iRYuuueYan8+3a9euMWPGfPrpp2PGjLn++uu/+uqrr3coHsk+wldfffWCC2armgZA1je7rtPV2++eUvaoU1DKHnVP/JAjgikox27jwKvjEZXxgx058l0c9bftkQmiRQQYMPrGpjwBIobJJBgAYJqm2+2+7bbbFi5cOGnSpE8++WT79u1ZWVkXXnjh4sWLTzrppCuvvHLNmjX//ve/Q6HQ1q1bQ6HQ9u3be/XqtWbNmjfffDM3N9fj8RQUFPznP//p37//9OnTp06d+uc//1lVVQCorq6eOXPm0KFD58+f//HHH69cufLee+997bXXvv/9769evXrr1q0nnXRSKBT6xz/+0bdv3yFDhixbtqyuru4vf/nLlClTsrKympubE4lEKBR65ZVXHA7HsmXL8vLycnJytmzZ8vWk8EgGwmg0KoQIh8NHcJv71eO+dB0RqXnUktS9IUIE2F0XGiMzXLPmE7AomgeZFx7VU9tKAZtEy0Oxpw70OVYoYajr+rx588rLy0855ZTs7OyWlpbq6uq//e1vGzdufPXVV++8887169d/+eWXhYWFoVAoMzMzEAjk5ORMnz597dq148aN27lz58aNG91u95YtWzRN+93vfldcXPzmm28CQFFR0S233LJ06dLdu3fv2rXrpJNOuu+++7Zs2bJgwYKTTz5ZCLFs2bJTTz117ty5tbW1Ho/nJz/5CSLm5uZWVVWNGjWqT58+iURCUZS0tDSbzeZ2uzVNaxt60+nRHC6rafS000574IEH/vCH+c89t8DPThMiKQvqjyxZUJ86ZEF9D8IYBITy2I3hCwv6j/+F0cIrVVP75gbGjlvo+oL6ALz/rQX1/IBHjQgQBMQ5z83NbWho0HXd7/cbhtHc3Ox0OocMGbJ+/XrTNEeOHImIGzZsGDx4cHp6+q5du/r06RONRktLS4uLixOJxMaNGz0ez7Bhw2pqasrKysaOHRsKhSoqKmKxGAAMGzbMepaqqi+99NLPf/7ztWvXJpPJoUOHer3e9evXu1yufv36bdmyxefzZWVlNTQ01NfXJ5PJoqIit9tdXl6uaVooFHK5XJFIxOPxtLS0xOPxrx/OkQxUffv2ZYwLYZbvbvLBSXJmmSNOBsLUIQNhD2IFwoevC1/Yp+iEO5IBXtVDA+ER/q17u+KsTOmbH/PND7PZbE6ns7m5GQDal1h06hu2sz9Hsml09+7d1g8cfEdws5IkSd0cAtiYksGcdqCWVPquaoUlqyiwrUTBqvMzDIMxBgBCCOuHtgKGttvbCgqtcoj2tfntt5lIJBKJhFVcb5UPwr5FGrA3/rXVaUC7qow2+wuQRzIQIiIiIxKp9DGQJEkiBuhBuwKMA6bU9c9KztpCoBWrrGGf7bWFQCsoWg9oP3EMY6xDntcWtKwA2fbc9vX4bXGxfQV9h4J6yzfPO8MO92XYd7+P4EQ7kiRJPcgxMKskZ8A5cPZtf3hr0z3nPDs7GxEdDkdaWlpbCJg4ceJll11m5WpW9Gq7y7rx/PPPLykpabvRmlZt9uzZEyZMsPZEUZQxY8aMGDFCVVWxl/WwtklkrOempaVZP1h3ta/WF+3sr5TeciQDoSRJUsqymsJ6dIOYKcA0wRTf9scEIgYA+fm9X3vtNVVVzzrrrLlz52ZlZZ1wwgl5eXkjRoyYPXu2EGL06NH9+/cXQgwaNGjs2LFWVBs5cuTPfvazgQMHOp3O448/3hrMWVxc/LOf/Wz06NEA4PF4nnzyyTFjxsyePfvEE08sKCgYP358RkaGaZqDBg0aNWqUaZo+n++444474YQT3nzzzXHjxuXl5Y0aNWrw4MEZGRk2my0nJyczM7O4uLioqGjQoEH9+/f/5gyt6ybdliRJSgkEgIA+dCCCDxyVRNijskNrNh+PB8+YqTH8lj0nATYbfPChWbZHcA6cc6tHkIiefPLJ5uZmVVXr6uq2bdt24YUXnnHGGYqiLFq0yG63FxcX67q+e/fuCRMmNDU1ud3uhx56KJFIzJo1q6qqaty4caFQKBqNAoCmaaNHj3733Xefe+65eDz+7rvvvvPOO2lpae+999706dMTicSmTZtGjx69cePGjIwMl8s1YMCAP/zhD6tXry4tLR05cuRTTz11yy23WClgTk7Ozp07+/bte+utt27cuHF/42hkRihJknS4EMAubMwb8QLvcaNeLYoCeb1Yft63/MnLY/n53G4H2NuNF4vFrLbHRCIxd+7cPXv2TJo0qaWl5bTTTrv//vuXLl16+umnJxIJwzCGDBly9tlnP/744ytWrJgyZUpxcfHWrVs55+eee+6cOXM+//xzu90OAI2NjdOnTw+Hw0888cQVV1yxZcuW2267zeFw3HjjjaZpbtmyZerUqW63+7HHHrv//vvLyspefPHFcDj8+OOPr1q1ynq6aZpEtGDBgk2bNr3zzjt79uwZNGgQ7B2k08mxf2evsiRJ0jGMmAAuRA9c/NbKkZqb6bHHYwf+LMZYZWX19u3b58yZM2HChEWLFp199tmXX355fn7+mjVrevXq9cknn9x5553JZHLPnj033njj+vXrAWDNmjU//vGPR44c+dZbbw0ePFhV1Q0bNhDRr371q4kTJ77wwgsA0KtXrxtuuOGNN96oq6uLx+Mul+vmm2+ORqPPPffcpEmTmpqali1bNmPGjJtuuikcDrvd7h/84Aculys7OzsQCPTv33/OnDkulwsA/H6/d69vbho94nV+1gr1Ph9MlnWER5ysI0wdso6wB0FGMaG9/huYnp99yrX2T2GzC+wH1VnYTeoI95MvdcIalUlE6enpJSUl9fX1q1evXrFixeLFixcuXBgIBLKzs7/66qsZM2Y0NzevXr16zJgxmZmZ5eXlu3fvnjx5MhF99NFHHo9n3LhxVVVVGzZsmDFjRjQa/fLLLxsaGhBx8uTJ6enp1uOXL19+3333vffee7W1tSeccILf71+zZo2macXFxVZNfUFBQV1d3a5du4LB4Pjx47OysrZt26aqalNTk9frDQaD6enpDQ0NjY2N+2salYGwJ5GBMHXIQNiDIKOEsL0zTxmb4z3rWu9y2OjumYHwcCDigAEDSktL29/SVsZwyNUEdru9T58+1ma/dfXdQ/5FsmlUkiTpCGCAAigL3AIIe9ZomcPQvhawLVxZVYPt6xw6LNsLewv72p7bft1daLfMbzweLy0tbV9u2GFd3/Z1921V+W1FFNbP7Vfr7ZQMhJIkSYeLWseOAnZZM1j3RO0WkW8LV7B3HE3bXe0zufaF820/d6imb//49olg2w/tV9/d3xPb7v3WNFEGQkmSpMNCAAowBCRKrSjYwTe3W3a3zbYnA6EkSdKhQ0CDzDRw2Al0JYHsO57aWjoCZB2hJEnS4WKAJBh4wn5NM4FSrYG0p5MZoSRJ0uEiICsPtKOaGqNkjikyI5QkSTosBGQHVUUmUq7g5RghM0JJkqRDhwAmCC/YbGQalHp1vscEmRFKkiQdLgISnhAQOlA72vsiHTQZCCVJkg4TEpBwxgDAz1wkk8KeRgZCSZKkwyJIuMCmkmIK2UfYI8lAKEmSdFgEkAs0lRTTFUXFONq7Ix20Qx8sg4jWPHLt59dhjAthynpSSZJSCgGBQLInUDFlgtHjHHogJCLD2Oe7jxDCmguHy2JSSZJSDAcWFUkfc9pA7YmrEqayQ/nmgogAkJWVdfPNN8+aNQv2Lvt7/PHH33DDjRMnTiBIWo+RJEk6tiGgCcIHDhsqSTA4yGtfz3MogdAKe/fee6+u65dffvm5554LAJmZmQ899FBlZcX999+bk52XJBkLJUlKCQSgAmfA5EiZHupQmkat9s+srKwXXnghLy9v8ODBQoimpqa6urrzzjvvi/XrG5pqfHyoIKPrQiEiYOq1w1vLlqZaw3PrIafYNaZ1QbtUe6974KndekYSgDXjKBDuXU36IDbyHRy1bKzdv0NvGuWc5+fn+/3+aDTaq1evsWPHcs7vnnf3pEmTBvYfEjNjTGaEkiSlCAIkjAndDTYNuIDUXpCpp+mYEVoDQdsvY8gY67BkouW555674447wuHwa6+99vDDDz/11FO7d+++8qqr3n///aqacgWzTbMLv8MT7p3jNpUQAlHK5UbEgCj15nBEAEjVo+5Rpza1rcrLhc6STqZyUEyhs4Opq+/qC1qqfY4OVsdA+PWY9/VbrKbR119/fenSpbFYDAAuu+wyAFixYoXX6w0GgwAuP3ABesq17EiSlJKQkUgL2r3JiN1IvSbtHq81ECIiEdnt9jvvvFPXdV3XFUUxDIMx5vP57rrrrng8bj2m7ZmMMSsKtt2OiMFgEBE5qJRyX2UlSUpRCJiFHp1MTMHe7GPCPhmhy+WaOHHi1VdfXVlZqeu6pmmqqi5YsMDr9cbj8Q7PtPJCAGiLeUQEgEQkp9qTJCmlqNjaFGq14suMsGdpHSxjBbNAIPDTn/60oqKCMTZmzBjTNCORyM0339zc3AztAp4kSZLUHgEgoA6mG+3WYJmjvUfSQdhn1KhhGNu2bTMMo3fv3vPnz/d4PACwfft2XdeP0u5JkiT1DAhggHChTQNOKVj70pN1LJ+wiuWFEOFw2EoBrVskSZKkb8YQZM9QT9RJkFMUpbq6+rrrrovFYooil7CXJEnqBAPEvWkfARFhIgFgS8pcsMdpDYRWjXxaWtoTTzzBGIvH4+Xl5clk0jCMhx9+2O/3tz1GkiRJQsAwJJJgWLHQzhTThEAIWXqIZCDsafZJ+EzTLCgomDNnTigU4pwbhsE5HzZsmOwjlCRJaoMAMUheZ5/6uVG2xijjwDLRnSQTEWXDaE/UGgit7sBQKHTppZf6/X5EbKsOfOGFF4LBIMhRo5IkSXuXm/Ci41fOGb+PvbfK2KECA0rNOXGPER27AIPBoBX2JEmSpK9DAB2MPpiewdwx0jkgAWOeaNQwQzHyMbsGihwx07N0DIRtiaA1TEYI0WHqUUmSpBRnVQ2qwDOYE601M2zJpBAJHRxc5XI9pp5mn0BoRcGCggJVVXfs2GHdaJVPyFgoSZLUhgAAyIVa6/8IW1df6lEzhkuWTgJhYWHhD3/4w7q6uk8//XT9+vW7d+8+SvsmSZLUTXFAAnCjHQEEAKFVPoFgTwCA7CvsWfYJhNb0oatWreKc33XXXTNnzvzkk08AYO7cuTU1NR0m3ZYkSUpNAiiLeRCZF+0IgIA+dOgmBULI0kIATA6b6Vn2CYScc9M0r7/++pkzZ/7mN79Zv359bm6u0+mU5ROSJEntqcgTpDtQQ0AE8KLDIIEIckXenqjjYBlN03JyclauXLlq1aof/ehHF1100YwZM6y7ZDooSZIEe5O9JhFORwe0Lszbbok6WVDf0/wvEFotn8cdd9zEiRMZY4qieL3eRx99tG320aO3k5IkSd2LAixJhgHChkqETLAm3SYRJyMLPOUkbLJxtOf4XyC0Er5Vq1ZNmzbt6O2PJElSt2YV1OfxtDLRqJOZhs4gBawckAgISOlsDmepO+uYEQ4fPry4uNjtdquqqijK9u3blyxZchT3T5IkqRviwHQwCYgDay2fQOAcSKBMBHucjhlhVVVVKBQSQiiKEo1GZYuoJElSBwTEAMKU4MAymHO7aOQ2I5aEQIQr7oRcfaLH2SeFZ4w1Nzdby9N7PJ4rrrhCrjshSZL0dTZUE2QQAEME4DlprCmRbGpUtLQIAYJMC3uUjgX1iHjttdemp6drmqbr+j333DN79uz9Pfnr42gYYwAEMo2UJOkYhQACRC/07TYbB/CsDHQBmBoogMQZAKGcaLTH2ScjtKYVffHFF91u9+bNm5ctW7Z582bYf0YohBBCtL9XCFMIIVsGJEk6tjEAE4gAODDYWz5hBUAV+FHdNemgdQyEALBt27aqqioAGDJkyLp162D/FYQTJ07s168fEbXFwhNPnDxq1EgCs2v3WpIk6ehCjEDCAOFjDgCyyupNECaIbPCYIFDmAz3HPk2jjDHTNK+99lpFUf785z9zzpPJJHwtEFoT0Nx+++29e/fu1avX/fffv3btWsbY3XffbRjGokWLEAgRZf29JEnHKHKBbZdoNLmwgwogQC5G2JN1nFkGAPbs2VNUVBSJRBhjnY4ateLixIkTr7zyyttvv/3kk09eu3ZtQUHBjBkzXn554Yknlmz5chfognVlLQ0idOn2uydEYJhyDc/WgqcpN/gAASAVjxqxu1fhWcvxZHF3UEQRwEBTARsDTKKpMCRCBGLMKqo4UF19QROtf6XO7RMIrQhXXV3t8/luv/12xlhpaelLL730zZuwChBzcnJ279798MMPrVn76ZpPtn3+WcTDXF1XfYEAKZhxtn7lTLEDRwSglHu72wq0U0qPOGpr7wQBJXkdC2egyw42hhg3zFiUae6ECRqRVVx/oFLzgtZ9dLL6xMqVK1euXPkNz7F6BD/++ON58+bl5uYuWLDgmWee+etf/xoIBO69995Nm77cubvUhkOEEF331lLqXRmh7ahT7MBbrykpdtQWedTdkLV3Bk+m5YWNZsNGqhXyhMBQBG05STt4TKKDCoRd/Qnv3q/o0dexj1AIkZ+f/8gjj9TX12/cuNHtds+fP79DG6lpmgDwwAMPTJw4saamZteuXfF4vLS09Kabbho9esxXX21ubIz5caQgPeVa8SRJOtYRIy4cLKfp9Ds+qP3F0OpAu3YvJCD0g0vI0NOj7NMsbaV6l156qWEY4XA4FotNnjwZ9j9q9OOPP961axcibty4MR6PRyKRVatWNjY2KWiTS1VIknRMEiRsoLltaPdHIo6WTPB0+L4vo2CPs08gFEJwzt9++22bzTZ+/PgLL7zw2Wefhb2F8508mTHGGBFZD0BExrhcv1eSpGMZAgEa7rDNJjSnjsQ5cJsvFkqYsQjn/KDaRKVuoeMUa6Zpjh079rPPPrvggguef/75mTNnwt620K+zCuphb+ciEQkhZBSUJOnYRgCmlrA7TJdL6KbwgcumYox0EggAaeCU6/P2LB1Xnzj++OOvvvpqIopGo6NGjaqoqIC9fYdHbyclSZK6BWsIsx1Uh1tXXXrEEcygvhz2Bj4UQMwNdgKSIyR6kI6rT3z11Vd33XVXKBTasWPH3//+93A4DHJVXkmSpL0ISAPF5RRC0e0e3dzbrsa4CAdVXYsRJoEQ5JyjPUfHgvpgMPj+++/fcsstJSUljLGysrJXX331qOyZJElSd4RAAMITAQR0R5xMA4EEgAiJmGJoCYYmyBmXe5ROpli7+eabBwwYcOedd3LOY7HY0dozSZKk7okAgDAc4OAJ+7lTGK2pn8KgxYx5mF0VaheWUUtHWidTrFVWVhYWFjY1NX33eyNJktS9oQCygS3dZ24o53aPbqLpBzcH1MFkoCbBVIAxmQ/2KJ1MsVZRUZGbm/urX/0KEUtLSxcuXHiU9k2SJKnbISAVuN2jV1Wr/V1RQpEDXgZogAAABkzImVx6mo5TrHHOV69efeGFF9rtdkTcX+GEJElSyhJAABRsVh2+pMlAASaArMnhE6ArwHjqzZfeo3Usnxg1alRJSYnX67Vu3LZt2yuvvHKU9k2SJKk7ap37XvCoEtOA0sChCwIAlbFmM+oGux1VWVHdg3Qsn1i3bp21GK8kSZL0dYLAAcxt53V7WCzN8HkTGFFVuyEIGmo17kiqdpPCspOwJ+k4dxoi7m9CNUmSJIkEaGCCpgebtCjEGCcfuB2epC5EMMC5KoALIeNgj9Jx1CgREZHL5bI6COPxuEzwJUmS2rOqBvUET4ik3Z3MALcNkgaYGseoSLrBpgE3wEAZDnuITpZhGjx48IMPPtjQ0PD555/n5OTMnTtXTrEmSZLUDiKSMFhEJB0ac4NqQhwAETAGugc1FZgOxOSQmR6ik2WYzjvvvGg0Wl9fr+v6iBEjYP/LMEmSJKUUBBBAWehGRpEoCgGKM6mACgBozTYKmKrLSPdgHZdhQsS///3vDQ0NI0aMmDp16kMPPQR7A6QkSVKqQyBAu91I2MP11XZECNmCfnCTqsfjSASEIK+XPU4nBfVVVVWPPfZYQ0OD3++vrq4GmRFKkiS1I0AgQjKJjAHTDAU4eUOREA+1aEmtKcuhqc1qBOLcWqxC6vb2yQg55wBwzTXX3HbbbZFIZNKkSX/84x9BZoSSJEl7EQAHzhjFYiweZ3Ff42Do1UJRhmjqSMzUmJxirYfppFJCVVW73Q4ADofD4/GAzAglSZIAoLWPUGQzDwEAivpaVc0Ie8AWF6aqUnNcsdvAZicTSIbCHmSfQGiaJiI+9dRTpaWld911V0lJydy5c0FmhJIkSXsRMO7Q42o0FNACAa77mjPBrbhjwQAXgMCFCaYGisweepCOo0aJKDMz0+/3E9HmzZsHDx4M+1mYFxEVRbFaU/+3OcY63CJJknSsYaZAUxjY3KQ60xMcCOyJWJQTYF3QiKc1ZIHPBFMmED1FJ+sRXn755YZh3HPPPd/8TCIyDAP2hk9oLUM0AYDLRgFJko5dDBgAIhONDYojIwoASIwxIkDDQEWTy/L2MJ30EW7durV3795+vz8zM9PqI+zAain1eDxXXnnltGnTrCjIORdCDB8+4uSTpxIkZWuqJEnHHquPMAu9QIiMwi0qaUlUdV0IRCBgdWHD8AaGQF5SzizTc3RePlFbW3vZZZch4s6dOxctWtThOVbieO+999bW1p5zzjmKoixbtsw0zf79+y9c+PKWrza9//6HiAgkPwSSJB1rCFDVzCRPxJNKIqLWmKHeaY2mJ9TYoCBgOGFWaLXjYfA/j/Z+Sgeu43qEALB69erVq1fbbDbOeTwe//pzrIcVFBTcddddHo9n9OjRS5cuHT58+COPPPLuO++mZXg11QVEXTp1NyKk4NzgrUedYr3wiK3TdaQWa5qS1DtqxM7aqboNRGAmMpueVBK6YUvGIUpJ7o0xBEFoAoiYrd5XkYnH+biNhDjAY+nqC5po/St1rpO5RidPnvzoo49WVlaGQqE9e/b8+te/7jDXqNUpyDnPyMjwer21tbUej8ftdq9cubJfv34jR4xIS0tPNhocsAvrLlJyFqPWuZtS7MARU/HttppTUvCoqXsfNbXuISIhB4qG1KCZAHdE6JxxEgBmXC1Xa8aQx4FahIwDXaE39T7h3co+gdDq2DvllFOam5tLS0s//PDDSy+9FPZTR/jSSy/NmzfPNM3Fixc/+eSTjzzyyH333XfmmWfFE7G6+vJMNtYUya7bb+zeZ0sXab1GpNiBE7X+SUEpeNTd/9QmABAIAIgUNpSwmVR8UcWpBwMcABIRNeptSgL1Qt9mqrODSgdwxnb1UXfvV/To66Rp9I033njnnXeKi4tnz5796aefQrtxoe0f9s9//nPp0qXBYDCRSFx99dXJZFJRlKVLly5btlRBnxCmtVbJd3s4kiRJXQkBiDKdajyBAoAA65sQ/C12uwiFGABFg6ovPdoIoYE89wuocoB2IIFQOro6GSzz+eefu91uIcRHH320fv162E8dIWOsvr4eABAxGo22v4uDowt3WZIk6WhCe1o8EEKTAIHC9a6FuYtdsRqMOQBEoEG7/BfbcnZsXbMw2wCDyb65nmCf/lmrpr232gAAIABJREFUHP74449/5ZVXhgwZ8stf/vK8886zbv/6M62lKqxksV2xhEwBJUk6xhG1LjFBQM21jto+m8sSQSOmOsBct8G96OWstElfZah21q3H/Uj/03FmGdM0p06dumrVqueff/73v/+91Ue4v1V5reXsQU5GKklSamn9xs8Admy3nzo1kZcjggFuB1FRp/77b3kt/spsp6qAIlJu4G+P1HGwzOTJkxFxypQpZ5111oknnhgOh2HvaNKjtIeSJEndCzGTBBCgimLPbtvmLx27dtqiMc6QHCASUaVJRHMzkAcOaKSMdNR10kcYDodfeuklAFixYkVZWRnsPyOUJElKKQTEQPX44/UBMoGpQE1N6o0/LhKADKzmMTCTvDGqZ6ehAiqBLjuMuj+lw/9XrFjRv39/v9//xhtvHJUdkiRJ6rYIiIPiVtQ6Iaz4xoEIQEGyinwYUNLgDTF9SDoqoBAmUc6x1e11DISMsdra2gsvvHD79u2IGAqF3nvvvaOyZ5IkSd0TtVttkPbeYkEEk3htWNfSo37w1UJYBS4bSLu5jusREtEnn3zy1ltvzZw5c9q0aZWVlSDXI5QkSTpADAxgLTHD5o1ngdeQizH1BB3LJwBg7Nix48ePnz9/fjQaPffcc0EGQkmSJAsCAUJ6sCXIBLCvXxoRyQTWHCZPWjIbvDqYcg2K7q+ThXlLSkoaGhq2bt36wQcfnHjiiSCrIyRJkgAAgIBUUJwaxnRBnUY4AgWgqZmBM54FDoPkSMMeYJ9AaNXIP//8816v96abbrr00ksfffRRkBmhJElS65zgpAH3oF0Hs9NqeSJAoGCLGna05DAn69pleKQjo5PyiYqKit/+9rdut3v58uXr1q0DWT4hSZLUDhGy/WYHyIDCzbYmR0MuO04VcqRMD7BPIOScm6b5s5/9LCcnp6Gh4eSTT/7000/vu+++DpNuS5IkpSwCRFc00owCOu/+4yBaqpzm0G35o8fwNQ5iJgjZqNatdZK2J5PJcePGjRs37v33358wYcJ3v0+SJEndFgJT3LFQtPNETwiwMXPbbu/ba4yikioEG8lZ1rq9TpZheuqpp8rKynbt2pVIJJYsWXKUdkySJKm7QRPIAUo62nQ0sHUh4Y7ZHiKaAGtWe845IewGFhfE5YqA3ds+GaG1joTNZhs+fPjMmTOvuuqqurq6o7VnkiRJ3RACY8AA9ztyggg0oD3lKvZqyAGnsc/6PFJ3tE8g5JwT0XXXXTdixIj58+dXVlY+9thjR2vPJEmSuh0CAFMww0go+wtuRMCBqio1Zk/k+XgSSMh8sHvr2EfocDh0XUdEr9fb0NDg8/mOym5JkiR1P0SEKpjMrgeaVdZZu6hFAQoEeCDIc/LjAtDlkqGwW+tYUH/xxRe73e7q6urZs2cXFRW98sorR2vPJEmSuhsC0kBxoS2OSQadD6cnAoVBi87KI1GPzzhuVOz2uXviwFCWFHZX+wyWMQzj2WefZYydeeaZbrc7HA7v2LED5MwykiRJBwMZCMFqa5WzflChfRnN75NwMlMIZAjyatoNdewjZIxdc801jz322Jlnntm3b98f//jHsHcOUknqABHkIAAp1QgQAN9SJU8mqGC8uyRzpC9j2qkBUEyPWwg56Wh31THCCSFUVV28eHFFRYVhGHa7HfaTETLGFEXhnLfdwjlXFIVzGTVTAgIYhLpca01KHQiCIE3hCVukuVHj++8jNAk8jC3akvy0IjQzuzAnkutIj8tA2G3t0zRqmiYAPPnkk4yx448/vqSk5MEHH9zfM4UQHaZeM00TwAQALt/vYx0iGIQZabpmo9pajckqKSkFIKAA8qEDUP/WeScZgo7mqmBF1ttj+6Y7PP6tZpkPUcim0W6o48K8AJBMJgFg+fLly5cvt27pkBFaM64VFRVdddVVZWVlzz77rK7rNpvtpptucrlcK1Yuf/edlYwxU05QeuxijCImnzUtMHBQ7O4HCj3MlPPRSqkBiZtCSRr6/mcbBQAAU6CLzMXPFb4RES/NDRWma+tAniTdVOfNmIhotXx22jto3Th37twNGzZMnz79+9//PmPMbrdv2LBh6dJl9913j8vhMYSxv0YD6dggAD0eMytH54c9mkr2NUo9B5KWTGjxUED5lk8+AQOqq3ZsDcZrWkRfr9MA2ZHQTXWSEQIAERHR/hadsG53uVxLliwZOXJkQUGBECIQCLz99tvXXffTVStXReOBTEURItl1sRARUnAEDyIw7BZfMBgDZoKqkaoRA2Ds0MfCIYAuUADYWOfbQARASLX5GhGBIOWOGhAQ9/P1vBvgCGRSpl0zbNFwSFWQvvU7nMJJM7CuTjnr+w3//K8/EVJZZ8tRdPUFTbT+lTrXeSD8Zm2LUYwYMSI/P7+srKy4uLiiouLCCy+cfOLkq66+koGNRBfXXFBKjkK2zqBucOBEQACmiT6fwYEOuV0UEZKEmX7dZhNV1TaOnQ3Gw1R9uyHljtqau7PbHjUBmAA+BxpkRiN2dgC7KkwUpK9/v9+pZzSl+2PlQZuNzE6e1cWf8O76inYXhxIILY8//vjVV19dXV398ssvP/DAA//4xz8mTpy4ddvWa6655v/+8Wa8UVcQu+6txW58tnSd1qPuBgcuBHIgr8/gCuFhNI0iQozYlT+pyctP/vT6Ac7ONoUEJANhyujOpzYhCGBKWlSP2Egc0J4SIWBy59Z0V22+6tlmAkJnT+vOR50KDiUQWk2j7UfTXHvttQCwatUqRMYYgun2wVAi2U14LGNAnIHXZyqMDIEMDyVCmwK8zBw0JOZym+l2Ixpnh7YdSfpOEAE6vEkzphKwA/ykIrAAhjDizMwwt3X1DkqH5NCbpRlj1pia9v8SkVWDIaUCIYDzw+jbQCAARSFVo2iE9emb0IHJUTNSt2V13Gbn6HVNLAHI2LcHQgJSAAMUjzTbsjNkP103deiBUAhhDahp/+8R3DPpiDuCgzOJQAFKSzc4J1+aKQ5pw2gNPfUagRbe3KRmZukGyOGjRx5j8lU9grgzr6WxmRnADqTBiwA4siAk4kEt208CSL4X3VC3HZ8lHYpvPsesiWCO1GmIAFwhxkDVDv0bkADweM1gi9LSrGRl6wYwTLmBkl0FEZABAsQE0wnZvi8s551/FBhLxfHYB4gIEMiXZiQbPHSgtRDEgQUgFglomT6SuUL3JD/yxwgEEADJ/ZybiBAHdsq0lmnTWiLAj9SVTghUNeH1GQIOceVRqxgxGmUNDUpmpkEApiEj4QFpH8na5/rWm4sAOmFCMANw1NBI716JCCmcUdsDAqbS9q2o7QVnCHHBIoLh1/LI/QXOlCIEamBke3my1gdgHkgoJAAOGIB4vMWemUFCdoB3SzIQHgsYowiwMWMjP7m2utMeWkTQAU+YFDyhJJQEPPysSxDYGLndRmO9atMOcbwbIggAh1MYgmqrVU+a4QTzjrv3DB4SiwPDA+iASUFt0ShoKgYhZ8Q5JQkNQsaAACKCIYJg0LtXYsiAaAzYZVfW3nZXeU5OMio4YxAVjDE4/6z6XjnJJCAicEZWK18L8XGjQ1NKAjFgUcFMQs5bC3YCpmJS53tiYawnRUrrq8PB7jMBKNzIc2u11TaCJB5YTsiAJUBvbMFMl6KAnGKtO5KB8FiACAbg6HHhSScFOx2pay2Z7fGYpnnEvpEiI11n4TBP9+vmIVW8IxIAMxzR89TxlzafJZzRvrn6WbOaBgyKGYeaYna1Iz4JTqcdeIyTVWHd4UpNAElCAkCEH8yuLewTDwilwVQvvrh+6sktGX796X+UXn5JbZKwRaiTTw48/c9tP7y4xuagyScHb7yu8oYbKxWFBhdF+/aP3/KrynkPlikKTTwx+MD83USQ4Tfum1d2w5yqKacEOINTTmpxOswWU0EGNpu4bHZtRrphIMLeVyBh7Yn1mgBEBTepxwwTNwl0wrDgBz5rPCKYgA6XmZUByXofgHngo+IZiKqg7mM2OxokesqLlEIOvY5Q6j6EQBtQYd+EIFA5GSbyr60YigAZfiOZYBoQHamZngjJZKp6qLGVgAB6ZYk+4M9sHLPD8dLEU5oaG9TcvCSHA+6C+W5Z1021s6Ivawacg+0FigqmAqkoAEEItJq4wyZ3gkgAMwHsIFRGBEhACHj9T6s+eDdt1w77tTdVZ/iNtM+MvNzkKd9rsdnNXdsd1VXaqac3Z/iNj1Z7p0wP6Dq7+traLzc6n/xDr5nnNjqdIiPdGDIsunO749230nLy9DGjwtNOazlhUtDnM2bNbhg3NuTP0tetcednJe57ZPfuXbbXFmb+/eWcoRnRW+6o1Gz0zD9yM1Q9rnOONLAwVl2tJZLMIORAJ5UEvtrkbA4qKjv0CRY6hUd0DT+rZVizCU2jCy5sQEZ/+2uvb5013kofdWJnn1eXERgnAu6DmKmFEMCoDZjDVLvTbSRC2tdPT+nokhnhMYGAAWXnJh1OoSqdnJ9EoCG5PabNLpTDX2gZgQCdDiJF37YHsrJN/eDTJEQQgG5uXHFJaHVz/fKK5uPzPSOPCyxe6C/slzj8+Uu7AgFk+/WsDF3s2zBotS7qhAnR2tLYljhaY0+s/1q3tE2mZQXOE08I9MpJhIhHBGcIBqLLZc46s9HjMU8qCZw6pcXrMZuFmhQYERyITj+7qf+AuKpRMMCHDItedVXNI0/stNnFlo2uc85vfOavOXfc2u+0M5v/+mRp8ZiIEPDF5+4F/8pesig9w28sXZJeUJjI6aWfOrN59SeeT1Z5bphTZSTxzVf9v31gz7AR0Ucf6C0E+nzG6HHhzz9zOx3isivqzp3eeNNtlTu22k+YGEyzGXW6ZtNEeob+uz/uGjEqmiBWkJdwO8Wtd1QMHhZNAJrWi8Cp065o664OHxds9yp93aEt9dXpb2cIUcEQ4a579sz/047rbqqaOj3gcprf3MlNAAahYTJDsMIBkeolo5M6Azio/j5RHTJ8ig2YQAYHUnchfZdkRngsEAB2Rdgdwu0yFZUosc+91pJJPqfhcAjG4PCXTEIAE9CdFe1n5Pvjec8oGw9oIHmHLRCGSBncv7FPsO87C4d/Gd7+qN+1GcuffrjvbXfvyfQZzQFF6eyLszWv6SGHyU6f3uEiuL97Y8Ruu6vcZhc3/GQAEDEEAmAIhoAgKb3TEr40s3S3I0noBIFAHCEmGAGoQNbUoSoTScGShC4mkgI50N0Pl732sv+DD9NGDY8sWJClAx43LPbLX1fcHSu45VeV3jTj4xWet5dmVO7UFBtlZhhujxg2IlJbre4stWsaFPRLPHZf700bnbEY9/jMmiotkFDuvLVvbl6yf1Gcc/r7UzlNCSXdYdx/V8HGL1w1derw4dFLfli3fp27b7/4r+/Z8+yTuf9dkfbOuxs//K/v0zWeXTvtI4sj005rWfCPrBeez7n5lxV3ztuT7jcefzA/K1t//M87Xnw569yzG99flpaWbgwfFUkmcN7DZa/8O8vjNT1egyFlpOs1TVrCRBuQDURrPodWIyolBEuY3AUmAwAEIuCMYoKZgDYQRGhTWhNKKy4SQVa63tKstL0v1juISGL/bYwCMCbQiaLtHRQCECEBbHxxCAj7D4xHQvyWn/W/6ppaj8cIxbmKrR+Mju8+A47kcpvJJE4rCQzoa1a96A5jDdJBRTNR2yL8Nvv4kpplb/lUAAcKosP9MEtHigyEPZ4V57LS9UiI2zRyu82WcMe31QRIyzBsduFwmjYmTIFsb3OTdZU/qFMREUxiadnRkYnRfeqLX/NtUFAcVEumAZjl108eHQyrwZy6YXWV7mX4eeyNyZ9Gm3ZU2/Qk9imI12z0qPsGQgQAhIhgKpDGBNH/5vA7kNYz66ITFUzZ+/S2u6zxk6J1jB/wdt8VGANdIANiDOxM+NIMh1NMKglOmdby6P29EyZLEvbrFS8+LnL6zKYRo6LvLUvbtNG14j1vKMyTOhs+KNIrP9nSrHy1xWnTzPKQfXJxyxnnNN1zd4HbLs45r6G2RutXlMjOqh8/Mdy7IBEO8T6FicZG5YxzmurrFFNA8bjI8RNCLS2KZiObRg116gmTQqfObPngXR9XyOFU//C3PBuQBmLdhr4qkAvFuo1ufSNyIASwg3CjiMXYa0sy7WAiwOovPJs3OmMmVxV6dUHm5o1OIHjk3t5bNjsjSfbIvb3vf2y3x2vqSfzgs4zCf8dLTgo2Nyuffeop3eO4/OLa66+trqtXf3Bl7dYtjplnNc3+QQNndMa5jVwhm42cQA/+ftfL/5eVmaXv2m776CMfEjEAINAB48SHFETPm93w/NO5DQFFJWCMGoU6aVQwJze54kOfx23UNGoaCo6kCxYhPrxv+I555Tf9pCgYVxxMCAExwRSgBDEnCMZa2/mxtb8SiMAAzEzXR48Nv/NuugDgADqhDQQDEgCXXF5vmFBTpT3z55yVG9PPPqepeGx061suAMME1EAojASgNfmnolCToQ7tE51zR3koyKedGlxXSvXVDk0B0A/0A08EDKghZnLAm35R5TQcDpfxxut+a4wuB7Im72YMZCX20SIDYY+HCCahL82Mx1hjo5KeYZTV2LFdCLEipT9Lj8dYOMRVlczE/85hgxDh4NJERDIA8nNFMqK9H9yV5/Mk6SCGSXBOMcFmzW64+NLGz0rNwPLetRTajdVfPnPVHqMpAVuCTVq/AfHVX3oZFwDIGAoBQGACCoLRQ8N1NVpls6YBaUiAIASYBLxdLYF1QbGukm3xPiyYAjBhfLC6wrazxq6CsCr6OVBuVjIWYzY7/eLO8tcX+j/8OM3FTGEiIkQEy/Louo6BuOJ369EoM02cNCUw85ym557MnXBicNDQWCjAf/rz6qVL0ld+6HU4zTPObpx9SX1Tg/LRCu855zdmZBgbvnC2NCu5efr8+fmXXVVXPCaMCH37x8ceH372rzm5uXrxuEjpFvv001oQ6LNPPAv/nfnzX1Y9cl/+RZfVp6WL5ibF4RTvv5O2dp27bJvttw/sSSTYxi9ciFBdaeMM3NzUDbQBEYEgcDDhxNYIIQQIAgbgZoZ1iwtNw0QbiNpa9fY7+tlAOEG88366CmQHqqnWNJv413NZS9/K8LLkG6/53383rS6o2oAEwLN/y3lxYdbwwdGf/rzqkQf6XH1d9YlTgls2OQv6xrd+6Rg/ITxqTLSwX+Kc8xrKdtsuvrz+izXND93TJxphOuI111QrCpVMDo4sjub1STbUq48/lC9MHDMkfOW1NX37x4+bEErPMB58oE9zrdJA2uh+4ay8ZCLC8non7n9s9/NP53y0zpvGjaKCmB7HfkWJlSu9CcHtIBhQlBQORAAuJuKCpWcY18+pcjjF2jXuWIQNGhJb/7k7YSAnUm1i2KDY1i3OtRs9Tqa/+qL/kivqG5oVI45NLUpDtdaYUBQgDqQiBQxlygktXrc5ZFisuUnZud1eUYZ5QdVUDNAP/IwBjjxM8fqtmbWG89Zfl2s2UVFuW7HWN7E4WF2pVTfYbKaIEXOw/zW3tn23k/nid0AGwmOBCehLM8JRTCQVr6/jisiIZAJm5+jVFTbDQJdbRBKc7V3Hwuc2TIHRKMeDiYUGQO9sCETFW2uSl5zBctzJRFi1CtI6BEQBoOz9ZVa4TJrMBOyVl6ypU/Qm+9ZXRtWqH5h68gt1+ynagMXBtdu22fr2SyQFCwvFANQEKUDIyOs27py3Z/S4yMYvnH/6Q148wvdU24DAhsJhE8E4R0AGZBA6QBBARHDNSv4A4oJffH7dV1ucN99W+X9/zxqciBaPCr++MPPMcxuf+HP+DbdUDRgc13V0Os3xx4ffXuXnggggTuy8mQ0/vKouHGLr1rjzeifiUb5+reOs7zfW1Wg3zqnKzU/26x9XVPH2m+kP39+7rk4TgDYU4yeECgoTN8ypuvvOgk07XLfPKR8yLBaLs9cWf/n6y5l/fDT/tt+WP/FIXp/CRNkuR22VVjQo9ps7+g0fGvndn3auW+teudxXPCay/L20dWvc/YrigRZumlhRbtte53CDOe/2wpoqLRjlKhIAuMjUBUK7ckAhvv4+QFtDIhFa87kigJMJIYAAHEwQgUmYTODy933PP5lbH1ZdKARBIKi4mCACDgAAoRa+7jP3dT8cGEmyO27vd83V1eVltq92Op2q+eu79wwYGF+6JP1Xv+oHRM8N3Hb8xNDDv9+Z1DEW4716JXNyk42N6oP39xk/PnTy9IDXa5oCTj4lsHmz45MV3lmzG2prtWdf2LbpC+e6De7hQ6MDB8W++Nzl85ljxoc9HvO0rc1fbnRedV3te0t9Z57X9H/PZZ04Nfinx/IaG5UfXl278kNfMolrPvE4FXP0+LDPZ954a9UnKz1FA+O+DOPHlwzaXWv7zR17Ro6KIGtd0cyB4oNVaVOnB37/xx07djh8PuOVFzO/+sqR5jPT0ox/Lsg+63tNP/9lRWOj4vaYqz/yPP5Yvt2mP6CoDWZYgc6WU+oMATBiER6seGrWX9XqGx/aREn1imtqd/3a/vNfVtbVqb+Y03/02MjU6S33PFhgB2G9OyZZE7qBgwkCIKtn3hqNdfCtONI340d6gwgADOx2KAQQXz8bj9ivOYwF8Hqu9kfNeGvHC2MUAzZqWKRfPtXUM8PAL7e6HVy0XfgQQQdWckJQ08huFx+t8EZjnCNwTgFSr/9p1cDBsY8/99oZUWdz03Qc14DAGSQJzpse9zT3evm/GT+ZQR+Fy7fuTEt36iRwb1MVcE5h4t+b1nLuBY3vrEznACqnJLE++Ylemcmp0wPP/y1ryb/7/EBM+buxql7ETEE/cU5eG2+Iq+GSaU1+jzHxhFBRYdxpMw0da6JaUX785tsr31+WFo2wCy5q8KUZW7c7ivrEW5qVB+fvHlgUVzmBgIH9Y9V1GgFMm9oSj7LasBYmZfbZDTfcUj2qOJKWbg4aGpt6cmDI8NjosZGSk0LDh0YysoyRxdHt2+yPP5h/xrnNx48Jnff9xsknBYYMiF70gwbGIL93ckRx1OkSby/L+GCZ74JLGl76V9aosZFXX8x89qnc995O//c/s0NhRUWy+jVLK5ybNzm3rHeu/shb0Wir2Gp7fVFm38KEYbAn/pC3fq37s4+9by7NmDWrcd0a95vv+D/70FPToDXVKn37Jd9blrZ9j2P5srRIlNc2a1t3OCoq7RXVtliEe5jJAKqbNFPHTjtQD1bbFlrzZgDDwHeXp1EStb3bZ/s2OysIRKCbqCGACavWeLeXOpsa1F119nSHMf6E0Juv+Vdv8GTYjbffz3AoYtSY6PZS+6ChsaZG5ebrByx62b9iTdpnyz1LFmWc+/3Gwr6JxkZ1Z6n9uWdy6mrVWIQnEszjNfsVxsccF3Y4hM1GD93Xp2yHfdr3Av0HxYcNj2oaDRsRTcT5wCFxVRV5vZO+NPPyq+tGjwt/b2Zz/4HxiRNCF1zSEAmxcJgX9E3YHcLhFFUVWn5u8sxZTc89n+tPN3Zsd6z4yGvnlBSsqU4dc1zYl2ZoGo0cE5l0YmjAwNjAoTGv0/zNfWVvvpYRi/J/PZ+9+JXMqno1EtQuc47/b2JruWjSQDmQd8A6gQTQbCxZEihdvBKWv5U585ymM85qzszShwyPFfZOjBgV6T8wnpOh985P7K60D+gXGzww1q9vvG9hYn2ZWxASgQBMEuNABqAJyA9iFUMEMBKwW85nvz8plBG2XdCPjQgaMhWrUUgI5ISjT6qf9eWPdzrKXin8b5LykobJ945RtMYkFA1IrF/nmnJK0Jdm1LWo1ouhoCjsl2hsUKyxDILQAFCBOCMAtMYXJAlVoLaRCzph3OA6GPlerX6TugMblc2Dhw3/4q1l7JY7Kld/5Hn1P5k+biRMxkzQgeX3TkwoCY4ZGm6sVfc02bLc+v2P7XI4qbpcW/Wxc7CeY6THGiIRJ9jWmRVBM7HMc/2tFU87/Nt+8YtoUlBtterPNGqq1UWvZdZWqJ997Hn4d72vuaa6aHB83Vr3T66uOWlayx9+l9+3f2LyyYH+/02LhHlOr+QlPxoyODf28BO7nnsy99U3/MVDIxf/sG7O9f0vvaKOCPoUJpqblbJdjs1fun79y77z/7JD4fTrOX0/+sgTDTOP15z2vRbDxGQcJ08JLnzRv3Ob4+obavQE/uPZnH+/mZ2Byd/d03vDF66FL2bqCRYjRlaRQ7vI5GEGEa5a7bOD8DN923ZHEvDfL2RFI7yyUXOA+GStx4HihWeyt2xyxiNsV8TuQGEKnHdHARE4UDAEAHAA7U3egAhMgQDgZKJLR1i4mdm+/7XDL7L+yxEEASK40SqnI43Eolf8H77na6hXvWAmEyweZ88/nfPy/2VVNmhpDsPlMqsaNAUonenRKA9ElTtu6etwCOvrXXml/W9/75WhGURgmGgIHD4sEo3wujq1LqaWbnbs3m2Lhvm8h8qWLUm3empHFEd+/pMBP/hR3RXX1D50dx9/pn7+xQ0lk4O7dtpvuqaosUk9/Yyms2Y1bd7oGDEq+sMf12o2enBunzeXZ5aX2rdvtdsBhMnsIEq3Oq770cD5f9mRTGJ2tv78UzmvLc2cNrH5gfm7n/pj7gvP5ySiLAFMAbKjcKBqAzVIMXbAlbMEoABvpKiqkidu/7xGcYK2e6dtyvRAS7Ny710FZ89qHD0uvKfMdv7F9Q112qmnN7vcIhzmLc1KdraOHHJzkhvWuQr7JQr6xZ99Ojc/K8k5VVZqOqFJyIE0aP0WS2RNrUdArefv199B6eu6aSBkDEwBuHehzsOFgARJQg4kAJVv3OSRLVpq2yZjZJrY/pY2nf669v1b7Z/CGACBIfCUkpbSrY5wmCWSbGBh+LiBCp8/peT0z5c5/tvHH09z6jvK7QgEBA5FOIlyesc+e9pfMDg0clR03U4PB10HbgPhcpvxOAOCOMORwyIzzmx+4tH8sKkoQBoIg7AgL1HSSZWbAAAgAElEQVRbo+qCWaPYBxTER4yM1gbExAnJBQtsjVTV9MUJM2+N29nOQYNjdTUqRwqayumnNNls4qW3snJydU+a+bvHd73xWsZLi7LOPrUxL18HgEfm9QnpNNiRXmtEWiDqANUg89LgM5c7JpwSmFhdu/jdHZ4NGxzle2w3zqmKRdnsi+qX/Sd921fOmqCjuUnRbGLk6Eh1lfbZx547793z+wfzVQddcWUtU2Bnqe2GK6uOnxh67SX/oCHR35/SUlFue/uN9HUb3Btvd007qfkXv6545s+5yz/0mUlsMZR4jOs6vPq23wkCgJ7+Y25NvXbJD+p2bbd/tsazfr1r5LDImk/cT/6pVzLOMrgOAv/zboYNCAAYkJOZANghMom9EUsIMAUqSCrQrj12BuBCUxBad726OFMFcqAAAGF1CLV+Jlp/oE4am7t8SMU3jMZs03qwBKJ1zBECQCCkNIZUFYS1Yq0DRTTGwjHuZCIa46EYd6AgAFMgR1KAauq1ttfMgcLJKJlERBAEHOiLzW4EUIHSmdHUpLzzQYYG4ufX9q/YY29KKD+7trJPYaKq3PbwPX2mTAvU1arPv5yTnmHMPKdpR6njs00eHVhJczAaZU//uRdocOmldSWTg2tWuzNYcvVatwogQI+ScIBGgupa1HiM7Si1e73m7l32aJSv+tD3+EP5r/5/e9cdXmWR9c+Zedvt6T2EkNB7lSaoFJGmoqJYwFVX17K21ZXV/da2uixiF7ssoq4CilgQkSLSe+8QUkhvN8ltb535/niTEKq4i6tgfs998tzcO3feOdPOnDpz4iIGkZA7kQEHnXMRBDfKfh6mPyX2DAFMsPxWJIF4RMJkYP98Nn3ux/H9BtbN+TJ+zQ+eO+8vbds+LIpccbC2HXQqwJE8yTSxQ5fQM11DksQqK0VKOSXQsVPYF23GxJiT78+srBKzsiJFhXJBkWwfZCVkJkfVoiIwBbnOCQIXCdja1WacCr9S1ajKiYj1ocknjS76SapRew6kp+lahCTEG+EwPVXMkC0A4Zm129b+cV4fNXXij+qjxwAMwBCnMh7dMwxADsgALUABAQlAfcwZB0BEiHAKALSB/yGCwZEgaIyonPicxhszD+o6HTm2umuXiOUKDacddyxsb8aVyn12Z3bwP/pkoRbB2lohNtoUEF54Z783FHv5vL+psWU9J+4UI1JUlHnZyOqkJGP0uOqYWFMEvmZb9MALau97uHjhV9FZ2WqsTy+sVvpfUPvqOznbNrk5hyuuqFIkNvWV3MFDarM71ybv67V0ftIGPfdKo2/cNWs69C9zKgAcxl5e3atnYPTl1aZJfA7rtrtKQwEqSrx9x8j4qyq69wkV5suSyP89M6kiZFwpd1dA/FTf6AYFAcp4IMz1G+QLHlue881C79Zdnspi8buF0d98EdOyldbrguD2za6tu5yleVJVudh/UF11pfiPJ1vkH1aWfhe1Zqs3I1lb+b03u4064MJAYqI++YHMxd9Gb93onvlh4o5tLoXwkEmi3KYvynr9zRTLQOTAEV0y27jOk5vnUAgDjvvznKXl0vb17q2b3XnFigS8qkJcu8obCAumUZ+cTiYMAUj9cfuUF1A3/ZwDCGirGbHxK5mwU91y/utMrHN6UASRHHXGtWNLKALnR99Ak28pHn1xDpxj05TfEuEicnt/J1jfV2VVMmE8AqR3t2BCojHvyziR8Ng4Y+d2V2mZtGO9+8vP4lZ+7xM45xycMgOAb76LKa6WrSAaOlm10occJAImWCkkuhWJL+J+EQkgGDrZuc2pKHz9aq8aIshh6w43snpVsH0isYBFo3OC0vs9dbUJ1plHDSGgCuZwqUMNj6w38lxc0k0orZTXb/H6qGVo+OXyuPR4zTRxxxbXBf2DNdWCy80ohZ1bXRvWet94PSVUS1f/4H3xhdRhl9a07xhGxF59g6Mvr75kWG1aur5oafTf/54vID942BkXZfTpGZQFVuSXU5MiIuV+VUKwdMhtZoanwllfbQjAKfh8cCGH013Me5zghU28+S0OXbuF7nmw+L03klas8ymccQB6rG8xocBOce+hLTMxDoRyzhABBJmpOpn20uH4eEOQ+O03tfGHBftI3rQZ9lE0OUWvq6WhMCX1tXHOkTe5Gh4REDnnaHEIAfWiZXDUgCjAbLd7QjhjSBBMjvaBPj1V69w1tGBBDAFOESzAhDhd1wggFyVeXCFzADs4ygSUgTHAPj3rykulgmKZcxCBM4C4OKOuRhw8pKasVDQNfOzpAlHiaphqYaEKAp2+/N3S+Ulc1u96fvOubt/mbEhq3yVomVhXR4vyHO60Gvrk/Zk5vZ0d8/nbj4ZDpLJcdHutYIAWHZF7XhAoKZJmvpd41bVVWdmR559NG36ZX3GyikpRElhMrFleJu7c6pr85JGyEqmiVOQMiTci3jxtbt2uD62V87x351z4dWTgGini7H5BQFaY12u5vda8T+IQefdeoZyDSlKy/tX8mH6DAp07h+fPjd2X41i3xFcW0d9y33DQKv9n5Ns4cFvAGICC4hzf7/9S8+VGOOwjssk4IgY4+dvk/DFXVU+4ot2RIhkBKPD5i3bv2+O6+4FsEZgCTBK4ahIA+HDOPlHmzz2btmWDm3M0ABVkAPXRbAS5IIBp1jMwBIgAIcDlholgBzsbjBDglNQHq/ETTGU/N36bSi377HgakZcQAOAhRi+8oC6thfbvufFuwmz3MIJgcTABhQYvaA7AAAXkdtAha0ggQoHUQvgt903XKr1G17y61sx1gxwAEIBLTVjFcXsUAQyD3kto+YzrinG1b5hg4U9IsYZ+CL/oGh/g6l/DX8SBmyGzdxLLQttvO8pnMgaeaOuykf4LBtSlpunr13j+MjlTAK4CoQ2eaIkxet/+AYfT6j+4LjrKTE7V62qF0mKRAUoiO3zIccGAQEaGumq5L/eIMHEkP1hirdgkiJL63PM5usHPkpLtfMMvJhFa9VIQQMN8pYQTghrHlunatOmHY2PMIZfW9e9X5/VZW3d57OhjAkAoJwQQkbN6W7GtFrdlMkIgyIgFKCIPM0EF0q51+Il/FFSViwMGBRYvjk1P1SWFFRfK4QgVCBgcKdZntVA5UUQ2fUZO/mFl7xGXzZCCnDKOFgcCQEl9rEKEEwYY4zWvGFO1c48rPUUb0Leu1i+oGrEAVU4clIUZjXJbUVFmSUgedpH/T48WLVkQpRsEACOM/PXxgpt/Xzbycn9mlrZ3t7Nzh1DnTuHuXYOdO4TUEAkG6PT3DgXq6JodvnYZ4WCdwCg8/veCzGz12hsrZZmltdB3bXfV1Ag/LI3au9t50x0lyjs3zSjZHWBap4K+Lx3Y/enMlMuvqfL6zOpKMSbenDYladjO8f8UP+0SbntQq/77DPx0bgrhvN+FdY/9KbOoSG7ZSr3y6mrOYMsmd/dewZQ0vVWWmp6mtWipP/mXjLh48+Y7yr6YE5fWQv/2q5hXpqesXRI1MjjoO77zoFV8tdR73S7huaUVm1YkDLm0+tB+x85drnUrvH36BZJS9Pv+kFXjF9xeNu3l9IVfx+hBXL/Gs3h1jGxxA6wHHEPnapsLmF8CygEEIOUQGCt2jRBtvXXYwWUOXBA542io+MNS3/bdLhk4JcAI1lQKO7e5i47ILsHiDBlDisAQU1P1DWs98xbFuQhDAIEc41TCOZoWNo16lAinTTYHzpFzpA3S3tFp/b/F2ZIIEYDUKybOATSaok8FW3CUgOcXybv3OB3A7dOMTR7BerG7vjZbauf149igj0ENzJYkbrzcc6G++3K522faFgJEIZyelkkQICHQ+wgtW9OE2fpG4adsngRIGPTeYmYKifpK3+4CmQHn/OgEowQCYRpRaXWNsHGj98Aux97dziXfRlfXiDJhCjIRQUQuAg9E6J4Dzj27XcsX+5YvjiovFctKpY3rvc+9nBb208lPHPlhSdSC+fGdBpR3quwSNXOSp1X5NX0dsTHGu1+UMvNXmsL3F8fPZSM8cTbbJlwE4BwZA4/LDIcooRw4InJRZNWqIAGPAMnMVndt8bzyVtJ140tvvNHv8Zq9+wW2bXLPmpmIhAcsAQBcyAC4bSt2INM5ahY1AYf18w8cXPvpnPiDOcp9fyxc/G10fILRtXto8t8KP/0k7o3348prjHvvqOzUOfSXRzJ1holxenmlZHAUgMfH6cOH1qSma30H1u076IgECCCOHFJdWSF27hqa/2lsYbWEAFGydeWY6u+XRk24sfyeB4vzcuW0dH3a9MOfz4nLOaSkp2tz58St2eJLjtYe+nNhapp+913ZXbuHNA1feSfnm69iXno7/Y7ri3r3C8oS0w3SoXP4n9MOR0VbRYVS+44RQWRFBXJNtYAA7TuGn/1r7rCR/kcfaDlijL9Hn2DPPsG1qzx9+gUEgT9yf+buAy4RuNdrvP1Uuzsr4vOgHIhp5Q5dsjMbaHDKE+nX3lj+1isp6/corQVvXBTbVVt6gFYVvj1wA3zpBHnBFzHhEC0pkt6fm1iUL6Vn6CtXe9OStFvvLFuyMNrjNXMOOcZdW1lZIX70r4TBQ2rnfhy/b6+zvEQ6UiYFwZEQ7SwO1wJAOQtkeRwYZJzj6y+kVJSLucUKAMgK27jOc7DQUVYs/rDM5yEWZ/DvOQkC8CRqVlqRgULrWOLebRbLTbzvEGCrWdCVpjfyKmahAmzdJi8DcCIDjsiAAP/im1gEcACzzMbAAKDAX30pBQBiiVkvWJywtR0n251U/ji3RLHGDdzmeVaTNJgMuAo6AiogIiCvFyzsBALHE0kAz4l7gjiAhBxO4chzfNFjgQAamK1onITis+GF3/ruzSRxOaxCYeKPhkMwYGOlrqW8LgSaAyTrp1w4z4GXW3XdhRYnZbYcQEAOACIAIN+137V1v1sBpgA7zmQrIpeQcwDLwOoacd78eA5gAjqA7d3lnPpU+vxP40tVsmaDOKvu94+Xb5j7g3aN1GNSVprEHjJAPxeG9xfAzyIRAigUWgrEQsQGFw/OOIa4oHKicZIQZbz774P+anHzQW+E0wv71T765BEtQN0u66JBtfc/XDL/G8eyFYnWES/3BUUQWmSqMbFmUpK+dGPMxGtK+/QMHjrg0CySlqJdd135xs2euGjj1ltLK0qktm3Do6/wd+8eDNYI9zxYkpRkpLfUDux2LfjO8+lnvmvlbhX74p79N1xzVW1igh4OkVfezolPMK4YV7l/l3PAgLo/PVa0ZGH0ZWP8nTqGR471Xz2hcty1FfEJ5pARNfv3OC8b4U9L0gYNqb31zlJZ5F17hl5+PnXSreVde4Y+nJHodFvXXF+ZkaG1bRcpKZT+/mxe524hQyeXj62qrBC3bnFfMrymolTyKcbE35ft2OoqKpQSk8y3XklOy9BlhcfFGYzh7p1Op4Ot+N73/juJd/yx5PAhx5ZNnptvL0tK0YO1Qm6OMv3F1DbtIzPfTtq41esmXACo1Ay6vUtvaDVLW13L1AnOHrm8+IBRWVDgWfx1VHWFrILWj2b2E7NeVRe3pon9XC3m61s8SGuDdMtOtwDcTazcQmXXHlcgQKtKpW++jFm72bt+jXf3PtfSr6Nq6wRNJ19+FltXS3ftdRUXKQJqWULscLHjx9rGGoh0FdIT0bdQ3+MGmlsqBwJUQS4SvmJ1VF6B4kbGODJeH6YoESYAhpkZg66Pfb9/MbxklXnIDRJr0PzoYHlQGSt3+0TbSKE+EoMDiIRLyJATDQwDmARUIkw8mfRA4Hhz1LmIn3pyp0AAeB2oYdAVezsFJIAEsAtNiyauah5mwG05xgRLA9P2/kdA+7cAEAFDaMhMQAAJkDMMlTtb+FGJ8L8HARIE7U7H4ACLzNFXj5F6mMDWmIddIJ2GWBGoH8KDhNaPOEe8FFl62KqQzyx2wobNfTNobB+h5ZfadgrHp2dDUp+u3bbCSIQrtv34ZLXZZhpEoAgCcoFwGRnhEFHJxh1eMLlAtev8o1Iw+jFztotLW6yi7yoLAtZBBqewJ/3mcZYlQvtk6nKyjBh1f6FEGxijwTFOMcaMqM7Mjqxe5dPDKIh82Eh/aoaemyenJ2stWmpPT83buN7tcsCbb0dXL+u0wjchOey6aPLbQ67b2y5TefnlpOnvHHpUybt4SK3FYOgIf95hRRD5RUNqF30Tc9tdpcnJevuOYVHizz+bygCnvpz7/WJfaroeCdF7/9jSocd+6r3ahXK84nmk5qu/Pq5OfbYyq426eaNn1Fh/XIK+fbO7Z5/grHcT9uxyDR5a4/JYosi1CM58J+nyq6oqy4XrJ5WLEieEKw42f07cdRPL//FEi9lfJUd5zUuG17z3dlJMtLF7h2vjDvfEG8qfe/7w3I/jU9M0i2Gr1uqjT2S2S49c0C/QpUdw0CU1q37w3fdIVvvU8LRXD2/d5J79TcJ9txVdfnXl9q2u/MPyX2cnqDVEFPi9t2dv2+vyilbP3oH5c2M3bfFUFEsc4KH7WnEAGVgd050gWWj1diRU80AlhAQgh8zKHqTlQtgTTRSd2Rf2Wm2EpCOWn4O13Sy8Ru4VA54w10UkInJgyBjKhANw5MA4IIAdQC0hD6uEABAEw0AEkJATgDBnyRhVB2qAqwRoCavtJWaIQK2mnv0clPobFeonhg3OiA5mDHF95v3DCv3ADG11NDgb74biADKIB6xyiqQlic1n1Q4QOTAAYKxeN9WNpglIN5p5biafSnz5z3bSRoHp3IIt2/khRIHcLPdvTROeDi+QQWTAqqHm787xNyv9OPAfjAO3BN4PAyggRqEjgXh2WcUeUHQwQ6B5QBGAdqQpOVaFBZwCCYOug+UFBRp4KgAw4OdEF51K5KVAdLCyMf5SqcPvAx8gCF9o26+Re76jrmxKV9OZQAApkABoCohPuy6fFvnuc31zPHjNnyYOggLiYavShw4ZhBqIRIHzNALlSVMinFjp0WUDyAAEIArVUiHmI89tGULMmJrphAkUSBQqIQga3DzzBv/WcDYZISG2zokkJBivvXVo8ULv90ujBMrHXVvFGLhcVqcuoapKsVUrVZL5W68mt8jQHv7LkYpyEQm88lyKoPDbbi/dvcM16+PUle4bvmXbJUOc67tx+KfPE8p0Tdq9wznoorp77s5+/Il8ABAlHp9gvPVq0nOv5paVipNuaTP+isr/e7rgpSlpmw55Wr8WKSqUjuQrtRVKjR562TekioWG1039gzLsfu/AwSXbSkuFgRfX3nZ962CY6mG8+8HiI/nyk5MzYmLYR+8lzfk4DpFTCoW18t5dTjVC8o4oWhDDIdq5W2jffmdaC+3QfsVJ9E8/ifvkowSCvNovfvBJggHk3enkh8VRi5dHOQlLTtUUB1eQFRdJd9za+rLLqmWZz/s81kfMqkrxrw+3DAQoRVZaLCLAq9NSIioJBAQZmWnijr0uF2GmgffdnhXSKANQBM4Z2LeoMg79hFYFVnUND7UiCTvMIgpoAdtmHukrZsogHg2oB+xKU7eZBQjSPrNURiGDxu4wC0VOOecBUEWgDiayJuzD5l6cN7rz1asTbd85E6xUEhVkWgBUGYQjrDoOPQIQgGPu3znqXtTkYEsBq0F9UB5WwQL3hD6OBmfT3YoDV0DIZZUqNzoIyfv0UgbcDaLdHAuYG+W3PDeqYIyufc3g1pnHcjV2xWn2cQsY/bEKbd3jifygkVVw+B/dQm5LchoYScT3mHPkZ9qWux0XyShEowsB/xz+qCWmjpN7Xiv3urr2zUoe/MBzy2POUWuMnNXGwT87L73FMeiewIf/0n7IxOTr5D4z1FXDxA7zfHdPCS14LvKdBubv5AEUcZa6VgSqgRkEjQC6QRZOkGZ+bUBADQwLuPNYIc+2Dgag+nHHqC3mkbXmYR94F+q7/uAY3J2m77SKFBAZcFu3jA1jqoIRgnBP2uodz8Qcq/wDdV0UuH4SFwQADlwGIZdVAfB3PTfP0tZ8pe/wgeMnKVdPQy8DRoHoYNZZ4Wnua/OsqkdD87ebhS6QLODAOQVyjp7z/jc4m4yQNdhkPF6rqkJyR1v/mJYfDpHt25wA4HTinZPaVtXQSy+rvvfh4v97KHPzWtRU0qtfoCBXXrooOr9WPrjdXQXBP8i9QDSe8S8kgNmeuEWuPz0R+mo5HJj1Rur7b1uH/fLGtZ6SEmnhdzGp8dqBIkdKum4aaAEuWhCthmlpkRJDzH/NTBYAALAWansILfqR1mMCryngmq9tu8s5qC/JnPlR0F8pFuQpT09p0T4tnJyqv/NqsmrC3jJt77sxMSAw4AAYS6zFy2IQGIDtrcPXbfQi8L893BIBFcYBiACAHCmCRDjnZmGxnFusOJExBnlHFADiAMY4GhGcOy8eACTgIoChkQM5LgWZzPmRfHnrZndlhWgCKoTZWZbq/VoBIhqx/cjrEwFzUgtqXyHzu6gHP9M2TQi82ZLErjdyEdAJ4hoj5wblAhmEKghGgxMBZRAzaNxsbbMANABaoVXdniZvNQsIkAjog4U2FSxwgJW5QOYNvnZHFwxH+76Zo761ABxYComq5WENdC84y1jAg7IHZY2bJy42BDCBUSAEkAM3gcWjZ5Tc5dHgPHtxwgkcRQPzoFnWhiYgQC8hY511SAHJAVIFBP4oXVLOAhbyPkLmd8YeW15p6sXO6uPxjof9dB1MEWhj+aaxfwjoQjHINQmEU21PCFAHqgmWC2S5fobUs0YNzCCoAEgBPaAQIOyM97hGYavpJ6S+J48y3SZiGbPHTgfLgPCd8uDRcpdeQsYsbe2LkSXZJGF51J80MC4W28UTz99CX+yyioOg/jO86Hn3NVfLPY5YfgnplbWvvuG+IYPG9hIyWtOEa+QemSTu5fDiYVKHRfqejVbeaLlTbzGzq5D2++CMQUKHJ1xjdptF/wx/F+IabVCZ2mcCbIj5b9Rv2zrVkx4ISH3u9GMMmY0/OXGnbho8dSYnDDtirw1NlEDYZRU5QOL1/sIQBqOvkHmb46beQovr6t52gCQBLeI1m438y+Wu68L7XBBHgEfA8IDMABjwagiNEDve4hjYXUh7K7JiauQ7Hzj+M40xBVLDw/O0rfe5hqdS31rjcJjrIlB7QM+khsZDWFNREQFNsCJgfOS59UN1vQj0QjH74poXynkguonQ2cwAT4+zYyMkhHDO77jjjttuu9XnSywu3Oz2pD/5bMbmrTj347hvvk1c+UPUupXunCrOdangkGPpIt+hEqiMWFu3+jZ+H/3d0hhDI15i5ZTKoTLfq96rnw4uzOeVPnR8re9sQaMec41cZRzcGSmLqFIMgXWbPbkHnMSC2oCoIF+7wndgj5OrCAy35kh1psq4RYmlcc1Evb+UPcM9aYa6eoGxMxbdZbwuCX1j5Y6vFu7YuDZB4NxJWF2tsHhJbHkdTySuG5ULRMJzrHIKoIEe4JqDoAMIRU4RCaJIuAgYtlitpVlg6WBGQFfB1MDSuEm5ICFKhHOOBFBAHsFICAwNDAroIkRETgEDoKmgW6hHuOEE6q+UVy/3ASBFAI726ZtyAoAASNH2ckRCkHCigqGC8Zp7wgJ9eyaNi0NfP6nVp+rmYl6joFjDI1fIXZ1EdKOcy6pqIBiDrglK7znapjqIhFG7UMyWQFhqbg2DOVHpN90zYaTceb9VtoMVEUAdLAQQgAAgBaLWG+QEqN+SEAENYDcofXJZ5SrzkIISBbxM6vytvruI+yUQKJDGEEoCaALzoKKhEQTdBXI1hC8R210gtfxHZKEbZYYNPn8NL4KooZFAPBMd/foIma94rhOQ5FlVVTzkAOmfnnFPhxe0onE6N9eYOV5wRMAIgqqCEQY9Arp8bAMamkHqQAWAFBIV4GoYdA1MExgHToEIQAKgDRSz5/ju+FbflccrXaA0RG8eJQQAw2BMUvo94Bi2zTxSwQMOEDlACLQARFJJ1Hi51y1K/+5Ci+XGARV0EQRqXz30Y68gqAZYMgjQ8Kww6CHQDLDCoBNAEajNOWogrIEhguCH0AAha4ZnUnchc5Tc+bbArKmRRd8b+z2oVPNwBQ8+6Bg2S1t7c+Bf+60yBUUnSoXM/6666mNtwx8dQ14ML/7C2LjOzH/KNXabeWRM3WsJxFvDw1Mi3/YXWwW5pnHjDseFo2tfvdc5pJjVPeUa+72xv42Q2FPMmKuvQRDsPgyBRoDoYJpgGsAEoLa7TQ1EDLBkEE8kNgy6vWQUEO1eJYAMwA8hE5iCIiI2JDKqF8gioGtgNe2i07xEEKoh8Iz7ylc8183RNufxcgu4BUwAagF7zTPBz8PPhBfsNIsVFDlyhryWR551X5lJk77Wd9RCuCWNWxb90AGrdJO1twNt8Z530noj92Ntwyx1nRtlROQnTNoze3ER6R6rZGpk4QVC5lC53Wx9IwMeAk0EQSCE89PRRQDrQA03dLgBlg6WBSwImgnWE64x18g9WwsJl8mdngsvWmcejkW3iVbDo7E5xdrpcRYkQkRkjGVmZl555ZX33nvP9FdmPRf+pmNl9mbp/s37c543F3EsMTkaYdeNcruv9O0Vmll1WBkndYwhriXG7gPBSidSjYPBiIrB3ylDC426hcZuLzgs4ABkcmjuIb3qTefEF7RFB62yDWa+AwWDcmYCIBgcOEDQjyIYEdD7ipk30QEZNMaN8sva0pFC575Cq1mhda/qyz3gVLnpBscr4eWLvPddJrVfaO7kzMEYQ7CdeqxnHDd2omlUJDNw1QYrt5eQEbS09dbhnbxYAcE+BhJOIqB3EdL+Tx7tAMmJ0h6zREYhiXjzedWj4c/ruE54g6DD4WqxdzfSIp1Gz9BXLjb3uEBRQR8stL1fHlrBQsWs+i39h1JeJ1gUgCOgAdaVYvctVkEuq5TthzbMXgFIkOsXCq2nOa4JM/3F0PdZGL8w6r552uadZqkEIgKphvAevewhaUS+UOWXIkv1vReKrU0Tcq1qEUQG+o98T5EAABqESURBVHat+HbHhXHgTkRfJyHl8uo3W5CYp11XBLjmQRk45LLKg6xcATEMek/awoXyCvOgEyT73GrvyBkk9tPwVsIFAKzmEcqEV5zXb2K532l7VloHnCDbUksYItdJfZ90XJ5nVb2pff+lsd2F0n3y0IWhPWFuKnASIx8HroC8SN3bFVrIKNzq/+A6pXdfZ9aQwAuDhNaUCUuNA2k89veOgTO1tWW8ZqjQcaLUv8yqC3C1lNd+ZmwOgEqbnNkRUAdrpNjpdmlwRyHtFXXxPqtUBqGn2OJaqf/D4Y+/NLYBwNVSz5Bpfu6++x1txYvqkkb/kcZKImC87rq+M6bvNAtnu++4IfjOHlYiAB0rdr1K7NmOJhWxmq1GwSCx7QRv3+XmvkfCn0XAPFHkPW71GGDeJV8MAK9py2zHzjDoo4TOY6Vui4xdlwjt5xgbV5gHZRCi0HmXeHEUcb6hL7tHuvgu+eIXIkuGie03q0eWGQe84PCA0+JcAGGGuvYrdWc1hGSQKKDFbUGfA0CIqxf7nw+BHgMxW8zCAdXPmcAkkJ6LLCaADNhXkV1/dlz6R2nIjNDaXVbh68EVH7jv+Le69m+Rz1tByiLvfdMdtyw0d1wp9nCDcsiqmG2uzyRxTznGfWFsfiLylQAkCp1/lUflsarZ+objwjYIkCFiOwT0gPK5saVxOnlQmSyNzGdVc42NElCrXtQmYVCvlfrcKl4IiB/qaz/S10unTXVNAGvBbEdSukKLOaEtn7r+sNo4nEy8KhrXBt8eLLSJ454xgdctYF5QTM4BQAF5k3HknprZ/3RfZTjYBivvAXloraneLw2TgU5WRs0MrpuifyGCww2y1aju/w9BImBSECYHv1jqfWCc2MMDjr5C5rPqN2W8lp6ONGTAhgsdLhd7fGpujCfuIqvGi44qHrpDGpxN4jnw4dWvlPA6BPBD2AUO9ViLYDMDPD3OQlAJIYQxNmjQoHvuuWf8+PFff7v0i/efeSZv0hOH93d2xwwJ9MzVqnU0Y9DVTkjcYRQHQfUSuQNNcaK8zywt4TUCp7ZqXgezJ2k5TZn/Ca6JApetqReAlvO6u8jQJ/Vrc62qPVYxBQJYHz9kKz0AATiaYLWlSTvch5bBrnjuezRyzVxl5RT+ZZkVjCZOe5JRIH4I/Z5ffLt66WaW72iwCphgxXMvOs1x+EI7mjTNujFRi3NRsVKsDajmEdMvYP3eSgA1MNuTlC2ufd+QrW5wZGGCyvVCrL5Jv1gKK8XgF0EA4BZwF5dTHZ6FyqZY5u0f6rjbKhEAgWMbKeFz52pKyACjnRh0HoFqe4VzAATSS2xxxKopZNXisWuDIGrM7Ca0+NC19HVrmQwiBcwkcQWsWgNTBAIAOljZmBAEtYIHJwkDemMrP4S+tratYgcdIBpgRYPrI+ue/WJhMa1+31xdyPwMrefYjaP13kGMSCAWsupC5hdB0MBsT5NEpLuMIgGPtoRy0l5MGi+9WIY1EggW8BvJgM6QNk4dsJcV5ViVIhDWwNS70xbTHQviBdcNkYs3qUdSiK/MWXk/zrL4KS8wRAALeB1TAThFwgibyx4gISmBeJcp254mn8kgvs5uSY4kHIby7jRjtWtHGamNQ09nI4MFpWLwH9tvnHLaXk78xLliDTs4VZ3oAElCWs3D3yqbrwz2386OuLmSKnsniq+lUN9U/abiSDCIkaaufQy4myupime88NIeXvRPct2V4f6bWZ6PO1sqUZ84Vq5hBzebBSo30kl0WzHxLuPSqFB0IVSJDeenk5LJACQutFZiCZC9apmFDAEZ5+3ExP3Ogl5qm1ypOCoSddAsZ8gzMb7SXREBo0Mwu1aueVKes1I/FEtcEghB0IQmvJ8AMbgpID3ObcgWc01uUSSNxRDRVlwDAAeo4+FJwkCK+I6xIhqdYa53o+n7WRkHbnCrG02/FS6+LNznE8fy7ULeUN6pdaClSc33XcsmhC+s0iJ+DKVDrOAyZUsqiYTCqDW14wpA+9BMg5g61fdFKlTUKRADrBSIpm5TMeXCSEAnOtSH/SHjrLOQ9qFraRUJ3hces1+r0NE4TUykvYd0JKnLnFtf4AsvFToNwDZ+CPbV2tWEzRTqm+VY9h4sjwKn0cR/EgFreLgHbfE4uzrbSn5cnv2tueMmOuD/tAlTpE9ftb6LQTcHflbseXYn+HloBOnyYuR3O6RcgRJv0HsYKqRTTBVb5+zgcqrDc1gu7hzIdqCgookABrcWuTctgV3bzIIIN2xFa9Ol2lADMZmaW/oD580XHp4cZ4ERIiLnPDExcebMmS88/8KfH3vo7vEPxlb23enIMTnraLVoA0kmMAOsIl7dAuMoEBOsEl7DgceA24MKa3J4j3B9u5CrEwOb7pMIFuOdrBbAMQG9JjDS4G3cCA5AAWt4eDXdxwgTOO1qttxNjyABB4pWk9IcucCE7mYrJ0qNj+YAlJMdmF8t1qnciLeiWrNkFfXDUN4OUqLBZcLRcB4EVLmxmu6LEI1wYoFFAE1k6WZ8D57J8WidAifbMf+AUOxiygCrrRcdFjARhBxevkPItZDFWb7+rA1p4ixPAI/w6hhwO/F4f24kQBip4eG1wn4HEQlHDlwFQwbRNinxBj5tHyDqeMTOf+FAyQmSbV2wgCuGUkuDEdS96FBA0NH0mM7WLEUFAwBcILlRsYALQKp50AArEX2N5Nv9XMED+8RChrZqE4Ncc4PS3WplcisKj/q/EMAg1zbQgzUk1MnMaAcpOjfXkYOaoEn8lBzCppXW5wtHA41YM6qT1cIAazct8NMAALpMpRfLIoh1PLKa7tPRNNGKs7wDWLumnWk3WOT0AJbsEvJFoB3MFgRQ5YYL5T3kyECrvYIi5aQAKw8IRSoYrcykjjzNwGPyhnAAkZPDWH5IKFFQZBb0trIVFAiQg7xkp1DgQsmJks1aakGNs7wXsNY/GgLBgYtc2I2FCohZkGjvzgKQcqjbRnO7sZbbIP8Cnu0FBwNucvYD2U2RXGR13otHcoXSaHSZ3OIA9ASr1Wk8I5p+hfVc6hjZtw5U4NyHTgbMllCVeiUnBEGVmNTLytpCcmppmHJ6kdkphOpauq+HldWSx5vIkMNqciCWuzrxFgaax3VjDQ9HQK/FcCdIa0zOghxXkL1JPKojpOrY6EQMBNDPQ+uEAxoYXa3MLJ54XIUnggAGuLqeHqCEhLluATPBamOldIWMWohsJjlA+YkOUQLSGh5KsKKjuXuXkOcDl8qN3ix7G+YKlPw0j6wzAAKq3OxhZB2mpSoYA1hbOw/yqWDP4b1YdJiUDrI6GMDCoAGAE6RVwl5EdIPcOAdOMu5IGFfLw8v5WeLl5x/OTpoBWyi89NJLBwwYuHz5smXLVrhxuMgRAVXQdTABAAEFoCZYHDgCCkAAwAJ+nKGYACogn9gsuyr7XIYNWbmPo8TeEZwg20s9AroC0km9qAEgAho7Zv3bLs6SABQBTTA1MAgQCQQNDKvhkrCm7XGBTIDwJp1ogBkBvWkZDlwBUQaJAQuBZu8sHLgIggMk+0Fh0I5rnwjUAnYS528CFqunsdGlxT5xH+cd19iZ9jvWxHMBAS2wbHOOBZw3uMlpYJAmhRv70/Z2aSTffiMAlUFsOmp2hwNA48HZLkmAOEGiQCOg6WAiYOMAnTgoJwUFNMCKNESF0wb/AnsvIEBcINt2FBOsMGgnZQkSCApIAKCCzusjx5kMUhhUu2dEEOzzhAaGBsZxLWysRAaR1Yfc1c8fu+ZG35AGy6gVBu30WtHGamUQbUeexqOGANQBkgq6DGIEdKtBKe0CBYAHQZVBlEG0gGGDq+oZduaZgNavzfpxbDrBKBAGLAy6AyTbfTQIEXsIwqAbYNokO0G2Z9SJ3Wh7fNj+rk2/dYFigqWDQbBel3vcirZ3ktPPnIYph84mXlQIqIGugkEAHSCfhmoDLAsspeHIaG8gcLa71wYBDIMmgUAAQ6DREza0Y+mqnycSCCFQsSFVEAfuAoXXu32dppGEgxqA75sZ4alwdhjhcRAx2scHWmBAg5/bT8JJb5jBJvs7nDbXaFNx5FSX1RxXW5NHQ9PUG9Coej2zdjb1cDuuzhOf2Ohid/oTblMQCvZd7f+lg75tuDpOaXbmzbBxXBtOU0OjqNr03zMHIQAN8fgnDlDTCk9PRaOTZ9OGNS1/YoFTVXLcg04cjv+gP48HcltNeqxEZT/95A6W/xtgw75sP77R5ZUc386flhLAHouT5ho9k3E5sbbjjq14ss+PAx4bfXiaDeSsoEFpzBHwNBtaUxw3Y+HMPGltRljbzAhPjbMZPkEpRSSMWYwdlWbOVkwVP+Fc9qNDeppHn1jbCQWOLoAzJ+A0y+ZUT/zRlhyHszKRT3ziT23Gyer8kRr+m5lwYseetKvPhIrjmnFi+TNp5xnMn/+2P7GBxpMN1i+5nR3XpFOv9J/MQhrOiycn7+zOnzMp9nPHgzaxy3A446XdHAv4c+BsMkLLsgAYAD/redua0YxmNKMZzfiZ8PNdzMubXXab0YxmNONXgObd+EfwMzFCxHpPip/FBgkNIaq/Nfxmqf4N4jdL9W92kv+cVFOA5kSjp8PPwgg5mAZUAlhnc3ARKSHQmMiNEotzzhghBBEtxn5Oq/YvBESCaNNLKeUAFrcAkRDCzmvabSfkxhG3gDPOCSWcc8YYEmJ3Cz+PqCaEIkL9sFKKABazAIBQygGYZWGTcf+lG3vWYA+lPaw21RyAcQvtHrAsew7YBX7pxp492EQdO6wWZ9hkOdOGcT9LjyQctLNUVTN+fTh6t+9vA0398H5rtNfjfBcWTjqsx/hfnpc9cFKiflNU/2gPNOPnxM/n14Jn64WIABgbGzd48KDOnbsEgwFJkkaMGBEOhWtqavte0Ld37945OYcZY3bJ8+BlIz4+PqtVVnl5BaV05MhRqampeXl5KSkpw4YNO49pdzpd3bp2qyivcDgcF110UdcuXQEgFAqNHXu5QIXy8vLMzFaXXHxxtd8fCgWxnm/88s3+b16IeNFFF/fo0VNVVb+/ZsCAAT179jpw4ABwGD16dEpKal5eXnx8wogRIwzDqK6uRiQNcT3n6suerl27drvwwoGKopSUlPTt26979+4ZGRm5uXmDBw/q0qXLgf0HvF7vqFGjCCEV5RUNd6v/8o3/L6gmAJCSkjpixGXx8fH5eXlJScnDhw/XNN3v93fq1HnAgAFFRcW6ro0ePSY5KTk/P/+sNqAZp8Q54OBJKeWcXX/9hAcffPCbb76pqKiYNm2aaZoTJkyoq6u78847vV7voEGDli1bapf8pdv738Je8E6nc8qUKWPGjJk9+5PJkyf36tVr8ODBlNKbb76ZUnrdddedf7QjIgC//fbbpz0/7a233urRo8dHH300Z+6cgoKCZ599VlGUSZMmFRQUPPzww5Zl3XzzzV9//fW5riC1SXY4HFdffVUwGJw8ebKmaTfccENiYmK7du06de508cUXDx48CAAmTZoky/Idd9yxatWq2tpa+4e/dPP/c9jtv/LKKxhjf/7zn0tLS6dPn15cXLxjx47+/ftfe+21LVu2TEtLGzt2bHR09C233LJt27aKigo7uf8v3fb/HIQg5/ySSy5RFGXixJucTucNN1wvy/LNN08qLCx8+OGHKKXDhw9v06btgAH9L7r4Isuy9u7dex4s7V8/zgH9mj31t27dunr16jFjxvTt29fpdE6bNs3hcNx3332bNm169dVX+/XrJwiCZVn406J4f43gnCNiKBSaMWOG3+8nhPTp02fmzJmzZ8+eOHGiKIrnMe0A8PHHH+/bt8/tdufl5c2bN2/cuHEtW7ZMTEycMmVKUVHRTTfdFAqFpk2blpGRkZmZyRgj5ByYw6eCPbdVVZ0yZcqePXsikcjAgQO3bNny2muvDRo0aNiwYe++++5nn302dOhQh8Px7LPPMsbatGnDOT+nqYaGSf7mm29+/PHHlNKamppZs2bFx8d37dp10KBBy5cvf++996644ors7Oynn37a7/d36tTJ/skv3fD/Crbm5osvvti+fbvP5zMMw+fzPfPMM6Zp3nTTTTk5OS+//HKPHj2uumrcW2+99eWXX/bq1QsaTsbN+Fnx84VPnDXYdvItW7Zs2bLl4YcfHj9+fDAYdLvdgiDU1tY6HA6n02lLBufZjNE0zTRNm3yXy+Xz+UKhkCAIDofjPKZdVVXOuWVZRUVFf/zjH6+//vq77rorHA57PB6Hw1FWVhYXF+dwOAghmnY+2P9tz6DOnTs/9dRTt91224QJE6KiopxOp2mamqa53W6v12sYhsPhcDgckiQZhvFLN/kswJ6u8fHxzz333N/+9rcNGzZs2LDB5/PNnTu3uLiYUupyuezxdbvdsiyb5nni9Mg5dzqdVVVVu3fvTk9PBwCHwyHLsr2cHQ4HY8w0TXvczysXoV83zoHd094pbrvttvj4+M6dO7/00kujR4+OiYlRVfWNN954/PHHLctatWrVe++9Rym1zpqf1S+GRtXon/70p6FDh957772ZmZnXXHONLMtTp04dNWrU+Uq7nb39uuuue+SRRx599NFAIHD55ZcnJCR88MEHHTp06NSpk8PhePzxx++9915KaTgcnjx5MjZ41Z6jsMdaEITFixfv379/9erVOTk5t912myiKn332WTgc/t3vfieK4tSpU4cNG5aSkqIoykMPPeT3+6FBmjxHYS/q6dOnZ2RkzJkzJycnZ+zYsbquq6o6f/78Rx99FBH/9a9/tW3btkuXLg6H45FHHikpKYFznGp7kU6aNCk5OblDhw7//ve/+/Tpk5ycjIhTp0599NFHLcvatWvXoUOHJk6cKAjCtGnT1q9fX+9E3YxmAIDL5WrXrl1iYiIAUEqzsrIkSQKAmJiYVq1a/dKtO/sQBCEzMzMtLS02NhYAMjIyfiO0p6enp6SkJCUlEUJat25tn5oBICsry+12A4Asy9nZ2ee6brApCCHx8fEtW7Zs3749ISQmJiYzM9P+qkWLFva4I2JWVpYsnzJn9LmImJiY9PT0jh07er3e1NTUNm3a2MOalJSUkZFhl8nKynK5XL9oM88a7HOPJElt27ZNTk6GhuVsD6vb7c7KyrJLZmRkJCQk/IJNbcavHU13wMb3541i8KRoJPM3SDscS/V5T2wjbM9h+/1vqgdOOtvPS6pPReBJe6AZPyvOmenVOFFs3Yj9b6Nt7JxWmJwKTUluSub5TfsZUg3nF+E/SvVx788P/DaphlMQeN5T3YxmNKMZR0EpbT7sN6MZzWhGM84BUErPRClnJ8r60WKEEEqPidxFxOM+aUYzmvG/R/MibMa5CjvVKjSYUux/CSF22DWl1FY32d/aQpgdgWej8YfHlWlaia2bsqs6aSWNyqvGN/bntoWv8b1dv12hoij3339/dHT0wYMHoVn91Yxm/ArQrJ9pxrkKxpgdQ9n4lzFmWZYdRmJnGGj8tvHzxjKNXx1XprGAy+V67rnnvF5vY7KCEyuxW9KzZ8+ZM2deddVVtqe7XUljGfsN57xDhw6jR4/2eDydO3fOzs6Oj4+fOnXq888/b1d+XvqDNKMZ5wTOgYD6ZjTjONgcLjs7m1JaWFjYqlWr/Pz8Nm3aCIKQlJRUXl6+adOm7t27r1+/PiUlJTo6+sCBA6NGjaqurl6xYkXPnj3j4+NFUVyyZEmbNm3y8/MZY+np6ZWVlV26dPF4PDk5ORkZGdXV1Vu2bLnkkkvy8vIOHz68aNEiALjssssQccGCBdnZ2a1atRIEYeXKlYFAoHXr1pIkzZs3j3PevXv3zMzMgwcPcs6zsrLC4XBlZWWrVq3WrVtn58EJBAKbNm2Kjo6urKxcsGDBlClTCCHnegxoM5pxTqOZETbj3IPNCLt27Tpu3LhVq1bdeeedU6dOHT58+JIlSwRBuP3222fMmHHvvfdOnDjRDkx2Op2lpaUDBw7s2bPn+PHjP//88zZt2nTr1q1Hjx4vvfSSrut33nnn6tWr77jjjjfeeOPll19+6qmn7rzzztdffz0cDhuGceONN7Zs2dLpdCYnJ7vd7kGDBvl8vtatWz/55JO2YlPX9XA4zDkfNmzYAw888P777/fq1evuu+/+8ssvW7duHR8fP3/+/OnTp19xxRX79u2Ljo7OyspijEmSFAwGA4HAL92dzWjGbx3NqtFmnKtYt25dfHz80KFD9+/fP378+MWLF9fV1SUlJfn9/uTk5NmzZz/22GOtW7deuXLlsGHDamtrV69eXVBQUFJS8u677y5btiwlJSUUCtXV1fn9fr/fzzmfP3/+O++8c/jw4XfeeWfv3r1t2rQpKyubNWvWu+++O3LkyCFDhpSXl+/evTs/P58Q8t57761YsUJVVWhiZbzqqqvmzZs3e/bsf/3rX1VVVa+//vrSpUs3btz45ptvxsTEJCYm2nk177///gcffFDTNEEQmt1Hm9GMXxzNi7AZ5x5sHWNxcbEsyy6Xa/78+aNGjdqzZ8+TTz757bffImKLFi0++OCDSZMmcc7Xr18fDocLCws/++yzQ4cOpaWlxcTE2JlrTNPs3r37JZdckpCQ4HA4kpOTY2Ji4uPjZVlOSEgQRTEhIaFDhw4jR47cvHlzYWFhOByeO3fu6tWrvV6vXUOjz6f9fseOHSNGjGjbtu1FF10UExOTkJAQHx8fGxsbExMTFRVlGwsBgFIqCEKzy2gzmvErQfM6bMY5CVs7GgqFVqxY8f333wcCgTlz5pSUlPTr1y8QCCxdunTfvn11dXULFiw4ePDgli1bRowY0alTp7KyskOHDm3fvl1V1SNHjnz++ecXXnih2+1evHjxwYMHi4qK9u/fzzlft24dIu7Zs6eioqJNmzaBQOD555/fuHHjoEGDOnfurGnanj17Dhw4UFBQYHvHdOzYMS0t7euvv966dWtsbGy3bt3C4fDOnTu3b99u82C72o0bNxqG0ZgflXPu8XhGjBjx4Ycfnk9p05vRjGY0oxm/CjTylZ+Vwdgi3ahRo9atWzd27Ngz/JUdXJGamvryyy//8MMPjVEcP187m9GMZpwG/w8nAIdulYIyIQAAAABJRU5ErkJggg==" width="600" style="display: block; margin: auto;" /></p>
-<p>The preprocess spectra plot shows the uploaded spectra in comparison
-to the processed spectra that has been processed using the processing
-inputs on the page. It will automatically update with any new slider
-inputs. This allows the user to tune the inputs to optimize the signal
-to noise ratio. The goal with preprocessing is to make peak regions have
-high intensities and non-peak regions should have low intensities.</p>
-</div>
-<div id="preprocessing-tools" class="section level2">
-<h2>Preprocessing Tools</h2>
-<p><img 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" width="252" style="display: block; margin: auto;" /></p>
-<p>When the slider is green for the tool type, that means that that tool
-is being used to preprocess the spectrum. If the slider is clicked
-blank, the cog button to the right will disappear to indicate that the
-tool is no longer being used.</p>
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width="148" style="display: block; margin: auto;" /></p>
-<p>If the cog button is clicked, any functions associated with that tool
-will be displayed and can be manipulated to process the spectrum.</p>
-</div>
-<div id="smoothing-polynomial" class="section level2">
-<h2>Smoothing Polynomial</h2>
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" width="531" style="display: block; margin: auto;" /></p>
+units using this selection. The transmittance adjustment uses the <span class="math inline">\(\log_{10} 1/T\)</span> calculation which does not
+correct for system or particle characteristics. The reflectance
+adjustment use the Kubelka-Munk equation <span class="math inline">\(\frac{(1-R)^2}{2R}\)</span>. If none is selected,
+Open Specy assumes that the uploaded data is an absorbance spectrum and
+does not apply an adjustment.</p>
+<p>This is the respective R code for a scenario where the spectra
+doesn’t need intensity adjustment:</p>
+<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" tabindex="-1"></a>raman_adj <span class="ot">&lt;-</span> raman_hdpe <span class="sc">|&gt;</span> </span>
+<span id="cb20-2"><a href="#cb20-2" tabindex="-1"></a>  <span class="fu">adj_intens</span>(<span class="at">type =</span> <span class="st">&quot;none&quot;</span>)</span>
+<span id="cb20-3"><a href="#cb20-3" tabindex="-1"></a></span>
+<span id="cb20-4"><a href="#cb20-4" tabindex="-1"></a><span class="fu">plot</span>(raman_adj)</span></code></pre></div>
+<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
+</div>
+<div id="conforming" class="section level4">
+<h4>Conforming</h4>
+<p>Conforming spectra is essential before comparing to a reference
+library and can be useful for summarizing data when you don’t need it to
+be highly resolved spectrally. In the app, conforming happens behind the
+scenes without any user input to make sure that the spectra the user
+uploads and the library spectra will be compatible. In code, we set the
+default spectral resolution to 5 because this tends to be pretty good
+for a lot of applications and is in between 4 and 8 which are commonly
+used wavenumber resolutions. There are several ways that this is
+function can be specified.</p>
+<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" tabindex="-1"></a><span class="fu">conform_spec</span>(raman_hdpe) <span class="sc">|&gt;</span> <span class="co"># default convert res to 5 wavenumbers.</span></span>
+<span id="cb21-2"><a href="#cb21-2" tabindex="-1"></a>  <span class="fu">summary</span>()</span>
+<span id="cb21-3"><a href="#cb21-3" tabindex="-1"></a><span class="co">#&gt; $wavenumber</span></span>
+<span id="cb21-4"><a href="#cb21-4" tabindex="-1"></a><span class="co">#&gt;  Length Min. Max. Res.</span></span>
+<span id="cb21-5"><a href="#cb21-5" tabindex="-1"></a><span class="co">#&gt;     579  305 3195    5</span></span>
+<span id="cb21-6"><a href="#cb21-6" tabindex="-1"></a><span class="co">#&gt; </span></span>
+<span id="cb21-7"><a href="#cb21-7" tabindex="-1"></a><span class="co">#&gt; $spectra</span></span>
+<span id="cb21-8"><a href="#cb21-8" tabindex="-1"></a><span class="co">#&gt;  Number Min. Intensity Max. Intensity</span></span>
+<span id="cb21-9"><a href="#cb21-9" tabindex="-1"></a><span class="co">#&gt;       1       41.65339       662.0608</span></span>
+<span id="cb21-10"><a href="#cb21-10" tabindex="-1"></a><span class="co">#&gt; </span></span>
+<span id="cb21-11"><a href="#cb21-11" tabindex="-1"></a><span class="co">#&gt; $metadata</span></span>
+<span id="cb21-12"><a href="#cb21-12" tabindex="-1"></a><span class="co">#&gt;   Min. Max.</span></span>
+<span id="cb21-13"><a href="#cb21-13" tabindex="-1"></a><span class="co">#&gt; x    1    1</span></span>
+<span id="cb21-14"><a href="#cb21-14" tabindex="-1"></a><span class="co">#&gt; y    1    1</span></span>
+<span id="cb21-15"><a href="#cb21-15" tabindex="-1"></a><span class="co">#&gt; [1] &quot;x&quot;                 &quot;y&quot;                 &quot;user_name&quot;        </span></span>
+<span id="cb21-16"><a href="#cb21-16" tabindex="-1"></a><span class="co">#&gt; [4] &quot;spectrum_type&quot;     &quot;spectrum_identity&quot; &quot;organization&quot;     </span></span>
+<span id="cb21-17"><a href="#cb21-17" tabindex="-1"></a><span class="co">#&gt; [7] &quot;license&quot;           &quot;session_id&quot;        &quot;file_id&quot;</span></span>
+<span id="cb21-18"><a href="#cb21-18" tabindex="-1"></a></span>
+<span id="cb21-19"><a href="#cb21-19" tabindex="-1"></a><span class="co"># Force one spectrum to have the exact same wavenumbers as another</span></span>
+<span id="cb21-20"><a href="#cb21-20" tabindex="-1"></a><span class="fu">conform_spec</span>(asp_example, <span class="at">range =</span> ps_example<span class="sc">$</span>wavenumber, <span class="at">res =</span> <span class="cn">NULL</span>) <span class="sc">|&gt;</span> </span>
+<span id="cb21-21"><a href="#cb21-21" tabindex="-1"></a>  <span class="fu">summary</span>()</span>
+<span id="cb21-22"><a href="#cb21-22" tabindex="-1"></a><span class="co">#&gt; $wavenumber</span></span>
+<span id="cb21-23"><a href="#cb21-23" tabindex="-1"></a><span class="co">#&gt;  Length     Min.     Max.     Res.</span></span>
+<span id="cb21-24"><a href="#cb21-24" tabindex="-1"></a><span class="co">#&gt;    2126 399.2239 4497.537 1.928618</span></span>
+<span id="cb21-25"><a href="#cb21-25" tabindex="-1"></a><span class="co">#&gt; </span></span>
+<span id="cb21-26"><a href="#cb21-26" tabindex="-1"></a><span class="co">#&gt; $spectra</span></span>
+<span id="cb21-27"><a href="#cb21-27" tabindex="-1"></a><span class="co">#&gt;  Number Min. Intensity Max. Intensity</span></span>
+<span id="cb21-28"><a href="#cb21-28" tabindex="-1"></a><span class="co">#&gt;       1             NA             NA</span></span>
+<span id="cb21-29"><a href="#cb21-29" tabindex="-1"></a><span class="co">#&gt; </span></span>
+<span id="cb21-30"><a href="#cb21-30" tabindex="-1"></a><span class="co">#&gt; $metadata</span></span>
+<span id="cb21-31"><a href="#cb21-31" tabindex="-1"></a><span class="co">#&gt;   Min. Max.</span></span>
+<span id="cb21-32"><a href="#cb21-32" tabindex="-1"></a><span class="co">#&gt; x    1    1</span></span>
+<span id="cb21-33"><a href="#cb21-33" tabindex="-1"></a><span class="co">#&gt; y    1    1</span></span>
+<span id="cb21-34"><a href="#cb21-34" tabindex="-1"></a><span class="co">#&gt; [1] &quot;x&quot;          &quot;y&quot;          &quot;file_name&quot;  &quot;license&quot;    &quot;session_id&quot;</span></span>
+<span id="cb21-35"><a href="#cb21-35" tabindex="-1"></a><span class="co">#&gt; [6] &quot;file_id&quot;    &quot;col_id&quot;</span></span>
+<span id="cb21-36"><a href="#cb21-36" tabindex="-1"></a></span>
+<span id="cb21-37"><a href="#cb21-37" tabindex="-1"></a><span class="co"># Specify the wavenumber resolution and use a rolling join instead of linear</span></span>
+<span id="cb21-38"><a href="#cb21-38" tabindex="-1"></a><span class="co"># approximation (faster for large datasets). </span></span>
+<span id="cb21-39"><a href="#cb21-39" tabindex="-1"></a><span class="fu">conform_spec</span>(spectral_map, <span class="at">range =</span> ps_example<span class="sc">$</span>wavenumber, <span class="at">res =</span> <span class="dv">10</span>,</span>
+<span id="cb21-40"><a href="#cb21-40" tabindex="-1"></a>             <span class="at">type =</span> <span class="st">&quot;roll&quot;</span>) <span class="sc">|&gt;</span> </span>
+<span id="cb21-41"><a href="#cb21-41" tabindex="-1"></a>  <span class="fu">summary</span>()</span>
+<span id="cb21-42"><a href="#cb21-42" tabindex="-1"></a><span class="co">#&gt; $wavenumber</span></span>
+<span id="cb21-43"><a href="#cb21-43" tabindex="-1"></a><span class="co">#&gt;  Length Min. Max. Res.</span></span>
+<span id="cb21-44"><a href="#cb21-44" tabindex="-1"></a><span class="co">#&gt;     329  720 4000   10</span></span>
+<span id="cb21-45"><a href="#cb21-45" tabindex="-1"></a><span class="co">#&gt; </span></span>
+<span id="cb21-46"><a href="#cb21-46" tabindex="-1"></a><span class="co">#&gt; $spectra</span></span>
+<span id="cb21-47"><a href="#cb21-47" tabindex="-1"></a><span class="co">#&gt;  Number Min. Intensity Max. Intensity</span></span>
+<span id="cb21-48"><a href="#cb21-48" tabindex="-1"></a><span class="co">#&gt;     208      -1.317072       1.168225</span></span>
+<span id="cb21-49"><a href="#cb21-49" tabindex="-1"></a><span class="co">#&gt; </span></span>
+<span id="cb21-50"><a href="#cb21-50" tabindex="-1"></a><span class="co">#&gt; $metadata</span></span>
+<span id="cb21-51"><a href="#cb21-51" tabindex="-1"></a><span class="co">#&gt;   Min. Max.</span></span>
+<span id="cb21-52"><a href="#cb21-52" tabindex="-1"></a><span class="co">#&gt; x    0   12</span></span>
+<span id="cb21-53"><a href="#cb21-53" tabindex="-1"></a><span class="co">#&gt; y    0   15</span></span>
+<span id="cb21-54"><a href="#cb21-54" tabindex="-1"></a><span class="co">#&gt;  [1] &quot;x&quot;             &quot;y&quot;             &quot;file_name&quot;     &quot;license&quot;      </span></span>
+<span id="cb21-55"><a href="#cb21-55" tabindex="-1"></a><span class="co">#&gt;  [5] &quot;description&quot;   &quot;samples&quot;       &quot;lines&quot;         &quot;bands&quot;        </span></span>
+<span id="cb21-56"><a href="#cb21-56" tabindex="-1"></a><span class="co">#&gt;  [9] &quot;header offset&quot; &quot;data type&quot;     &quot;interleave&quot;    &quot;z plot titles&quot;</span></span>
+<span id="cb21-57"><a href="#cb21-57" tabindex="-1"></a><span class="co">#&gt; [13] &quot;pixel size&quot;    &quot;session_id&quot;    &quot;file_id&quot;       &quot;col_id&quot;</span></span></code></pre></div>
+</div>
+<div id="smoothing" class="section level4">
+<h4>Smoothing</h4>
+<p><img 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" width="460" style="display: block; margin: auto;" /></p>
 <p>The first step of the Open Specy preprocessing routing is spectral
 smoothing. The goal of this function is to increase the signal to noise
 ratio (S/N) without distorting the shape or relative size of the peaks.
 The value on the slider is the polynomial order of the <a href="https://en.wikipedia.org/wiki/Savitzky%E2%80%93Golay_filter">Savitzky-Golay
-(SG) filter</a>. The SG filter is fit to a moving window of 11 data
-points where the center point in the window is replaced with the
-polynomial estimate. The number of data points in the window is not user
-adjustable. Higher numbers lead to more wiggly fits and thus less
-smooth, lower numbers lead to more smooth fits, a 7th order polynomial
-will make the spectrum have almost no smoothing. If smoothing is set to
-0 then no smoothing is conducted on the spectrum. When smoothing is done
-well, peak shapes and relative heights should not change. Typically a
-3rd order polynomial (3 on the slider) works to increase the signal to
-noise without distortion, but if the spectrum is noisy, decrease
-polynomial order and if it is already smooth, increase the polynomial
-order to the maximum (7). Examples of smoothing below:</p>
-<div class="figure">
-<img 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" alt />
-<p class="caption">Sample <code>raman_hdpe</code> spectrum with
-different smoothing polynomials (p) from Cowger et al. (2020).</p>
-</div>
-<p>The different degrees of smoothing were achieved with the following R
-commands:</p>
-<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="fu">smooth_intens</span>(raman_hdpe, <span class="at">p =</span> <span class="dv">1</span>)</span>
-<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a><span class="fu">smooth_intens</span>(raman_hdpe, <span class="at">p =</span> <span class="dv">4</span>)</span></code></pre></div>
-<p>The intensity-adjusted sample spectrum <code>raman_adj</code> is
-smoothed accordingly:</p>
-<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>raman_smooth <span class="ot">&lt;-</span> raman_adj <span class="sc">%&gt;%</span> </span>
-<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">smooth_intens</span>()</span>
-<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(raman_smooth)</span>
-<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   wavenumber  intensity</span></span>
-<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 1    301.040 0.00000000</span></span>
-<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 2    304.632 0.01568318</span></span>
-<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 3    308.221 0.02461353</span></span>
-<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 4    311.810 0.02828915</span></span>
-<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 5    315.398 0.02820811</span></span>
-<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 6    318.983 0.02586852</span></span></code></pre></div>
-</div>
-<div id="baseline-correction-polynomial" class="section level2">
-<h2>Baseline Correction Polynomial</h2>
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" width="529" style="display: block; margin: auto;" /></p>
+(SG) filter</a>. Higher numbers lead to more wiggly fits and thus less
+smooth, lower numbers lead to more smooth fits. The SG filter is fit to
+a moving window of 11 data points by default where the center point in
+the window is replaced with the polynomial estimate. Larger windows will
+produce smoother fits. The derivative order is set to 1 by default which
+transforms the spectra to their first derivative. A zero order
+derivative will have no derivative transformation. When smoothing is
+done well, peak shapes and relative heights should not change. The
+absolute value is primarily useful for first derivative spectra where
+the absolute value results in an absorbance-like spectrum which is why
+we set it as the default.</p>
+<p>Examples of smoothing with the R package:</p>
+<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
+<span id="cb22-2"><a href="#cb22-2" tabindex="-1"></a><span class="fu">data</span>(<span class="st">&quot;raman_hdpe&quot;</span>)</span>
+<span id="cb22-3"><a href="#cb22-3" tabindex="-1"></a></span>
+<span id="cb22-4"><a href="#cb22-4" tabindex="-1"></a>none <span class="ot">&lt;-</span> <span class="fu">adj_intens</span>(raman_hdpe, <span class="at">make_rel =</span> T)</span>
+<span id="cb22-5"><a href="#cb22-5" tabindex="-1"></a>p1 <span class="ot">&lt;-</span> <span class="fu">smooth_intens</span>(raman_hdpe, <span class="at">polynomial =</span> <span class="dv">1</span>)</span>
+<span id="cb22-6"><a href="#cb22-6" tabindex="-1"></a>p4 <span class="ot">&lt;-</span> <span class="fu">smooth_intens</span>(raman_hdpe, <span class="at">polynomial =</span> <span class="dv">4</span>)</span>
+<span id="cb22-7"><a href="#cb22-7" tabindex="-1"></a></span>
+<span id="cb22-8"><a href="#cb22-8" tabindex="-1"></a>compare_derivative <span class="ot">&lt;-</span> <span class="fu">c_spec</span>(<span class="fu">list</span>(none, p1, p4))</span>
+<span id="cb22-9"><a href="#cb22-9" tabindex="-1"></a>    </span>
+<span id="cb22-10"><a href="#cb22-10" tabindex="-1"></a><span class="fu">plot</span>(compare_derivative)</span></code></pre></div>
+<div class="figure" style="text-align: center">
+<img 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" alt="Sample `raman_hdpe` spectrum with different smoothing polynomials from Cowger et al. (2020)." />
+<p class="caption">
+Sample <code>raman_hdpe</code> spectrum with different smoothing
+polynomials from Cowger et al. (2020).
+</p>
+</div>
+<p>Derivative transformation can happen with the same function. You’ll
+notice a new function we are using <code>c_spec()</code> which is used
+to combine spectral objects into one OpenSpecy object.</p>
+<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" tabindex="-1"></a></span>
+<span id="cb23-2"><a href="#cb23-2" tabindex="-1"></a>none <span class="ot">&lt;-</span> <span class="fu">adj_intens</span>(raman_hdpe, <span class="at">make_rel =</span> T)</span>
+<span id="cb23-3"><a href="#cb23-3" tabindex="-1"></a>d1 <span class="ot">&lt;-</span> <span class="fu">smooth_intens</span>(raman_hdpe, <span class="at">derivative =</span> <span class="dv">1</span>, <span class="at">abs =</span> T)</span>
+<span id="cb23-4"><a href="#cb23-4" tabindex="-1"></a>d2 <span class="ot">&lt;-</span> <span class="fu">smooth_intens</span>(raman_hdpe, <span class="at">derivative =</span> <span class="dv">2</span>)</span>
+<span id="cb23-5"><a href="#cb23-5" tabindex="-1"></a></span>
+<span id="cb23-6"><a href="#cb23-6" tabindex="-1"></a>compare_derivative <span class="ot">&lt;-</span> <span class="fu">c_spec</span>(<span class="fu">list</span>(none, d1, d2))</span>
+<span id="cb23-7"><a href="#cb23-7" tabindex="-1"></a>    </span>
+<span id="cb23-8"><a href="#cb23-8" tabindex="-1"></a><span class="fu">plot</span>(compare_derivative)</span></code></pre></div>
+<div class="figure" style="text-align: center">
+<img 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" alt="Sample `raman_hdpe` spectrum with different derivatives from Cowger et al. (2020)." />
+<p class="caption">
+Sample <code>raman_hdpe</code> spectrum with different derivatives from
+Cowger et al. (2020).
+</p>
+</div>
+</div>
+<div id="baseline-correction" class="section level4">
+<h4>Baseline Correction</h4>
+<p><img 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" width="400" style="display: block; margin: auto;" /></p>
 <p>The second step of Open Specy’s preprocessing routine is baseline
 correction. The goal of baseline correction is to get all non-peak
 regions of the spectra to zero absorbance. The higher the polynomial
@@ -642,127 +2756,165 @@ <h2>Baseline Correction Polynomial</h2>
 baseline fit. The data in the peak region is removed from the fit to
 make sure that the baseline is less likely to fit to the peaks. The
 iterative fitting terminates once the difference between the new and
-previous fit is small. An example of a good baseline fit below.</p>
-<div class="figure">
-<img 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" alt />
-<p class="caption">Sample <code>raman_hdpe</code> spectrum with
-different degrees of background subtraction (Cowger et al., 2020).</p>
-</div>
-<p>The smoothed sample spectrum <code>raman_smooth</code> is
-background-corrected as follows:</p>
-<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>raman_bgc <span class="ot">&lt;-</span> raman_smooth <span class="sc">%&gt;%</span> </span>
-<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">subtr_bg</span>()</span>
-<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(raman_bgc)</span>
-<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   wavenumber   intensity</span></span>
-<span id="cb9-6"><a href="#cb9-6" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 1    301.040 0.000000000</span></span>
-<span id="cb9-7"><a href="#cb9-7" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 2    304.632 0.000000000</span></span>
-<span id="cb9-8"><a href="#cb9-8" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 3    308.221 0.006298355</span></span>
-<span id="cb9-9"><a href="#cb9-9" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 4    311.810 0.008146146</span></span>
-<span id="cb9-10"><a href="#cb9-10" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 5    315.398 0.007025667</span></span>
-<span id="cb9-11"><a href="#cb9-11" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; 6    318.983 0.004412447</span></span></code></pre></div>
-</div>
-<div id="spectral-range" class="section level2">
-<h2>Spectral Range</h2>
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width="531" style="display: block; margin: auto;" /></p>
-<p>The final step of preprocessing is restricting the spectral range.
-Sometimes the instrument operates with high noise at the ends of the
-spectrum and sometimes the baseline fit can produce distortions at the
-ends of the spectrum, both can be removed using this routine. You should
-look into the signal to noise ratio of your specific instrument by
-wavelength to determine what wavelength ranges to use. Distortions due
-to baseline fit can be assessed from looking at the preprocess spectra
-plot. Additionally, you can restrict the range to examine a single peak
-or a subset of peaks of interests. This function allows users to isolate
-peaks of interest for matching, while removing noise and influence from
-less relevant spectral data.</p>
-</div>
-<div id="download-data" class="section level2">
-<h2>Download Data</h2>
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" width="239" style="display: block; margin: auto;" /></p>
-<p>After you have the preprocessing parameters set, we recommend that
-you download the preprocessed data for your records. The download data
-button will append the uploaded data to three columns created by the
-preprocessing parameters. “Wavelength” and “Absorbance” are columns from
-the data uploaded by the user. “NormalizedIntensity” is the max-min
-normalized value (Equation 1) of the “Absorbance”. “Smoothed” is the
-Savitzky-Golay filter specified by the slider explained above.
-“BaselineRemoved” is the smoothed and baseline corrected value that is
-visible on the center plot.</p>
+previous fit is small. An example of a good baseline fit below. For
+those who have been with OpenSpecy a while, you will notice the app no
+longer supports manual baseline correction, it was a hard choice but it
+just didn’t make sense to keep it now that we are moving toward high
+throughput automated methods. It does still exist in the R function
+though.</p>
+<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" tabindex="-1"></a><span class="fu">data</span>(<span class="st">&quot;raman_hdpe&quot;</span>)</span>
+<span id="cb24-2"><a href="#cb24-2" tabindex="-1"></a></span>
+<span id="cb24-3"><a href="#cb24-3" tabindex="-1"></a>none <span class="ot">&lt;-</span> <span class="fu">adj_intens</span>(raman_hdpe)</span>
+<span id="cb24-4"><a href="#cb24-4" tabindex="-1"></a>d2 <span class="ot">&lt;-</span> <span class="fu">subtr_baseline</span>(raman_hdpe, <span class="at">degree =</span> <span class="dv">2</span>)</span>
+<span id="cb24-5"><a href="#cb24-5" tabindex="-1"></a>d8 <span class="ot">&lt;-</span> <span class="fu">subtr_baseline</span>(raman_hdpe, <span class="at">degree =</span> <span class="dv">8</span>)</span>
+<span id="cb24-6"><a href="#cb24-6" tabindex="-1"></a></span>
+<span id="cb24-7"><a href="#cb24-7" tabindex="-1"></a>compare_subtraction <span class="ot">&lt;-</span> <span class="fu">c_spec</span>(<span class="fu">list</span>(none, d2, d8))</span>
+<span id="cb24-8"><a href="#cb24-8" tabindex="-1"></a></span>
+<span id="cb24-9"><a href="#cb24-9" tabindex="-1"></a><span class="fu">plot</span>(compare_subtraction)</span></code></pre></div>
+<div class="figure" style="text-align: center">
+<img 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" alt="Sample `raman_hdpe` spectrum with different degrees of background subtraction (Cowger et al., 2020)." />
+<p class="caption">
+Sample <code>raman_hdpe</code> spectrum with different degrees of
+background subtraction (Cowger et al., 2020).
+</p>
 </div>
 </div>
-<div id="matching" class="section level1">
-<h1>Matching</h1>
-<p>After uploading data and preprocessing it (if desired) you can now
-identify the spectrum. To identify the spectrum go to the <strong>Match
-Spectrum</strong> tab.</p>
-<p>You will see your spectrum and the top matches, but before looking at
-matches, you need to check the three selectable parameters below.</p>
-<div id="spectrum-type" class="section level2">
-<h2>Spectrum Type</h2>
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" width="110" style="display: block; margin: auto;" /></p>
-<p>The spectra type input on the “Match spectra” tab specifies the type
-of spectra (Raman or FTIR) that the user has uploaded and wants to match
-to. This input will tell the website whether to use the FTIR library or
-the Raman library to make the match.</p>
-</div>
-<div id="spectrum-to-analyze" class="section level2">
-<h2>Spectrum To Analyze</h2>
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" width="147" style="display: block; margin: auto;" /></p>
-<p>The spectra to analyze input specifies if the tool will match the
-<strong>Uploaded</strong> spectra (unaltered by the inputs on the
-<strong>Preprocess Spectra</strong> tab) or the <strong>Processed
-Spectra</strong> (manipulated by the inputs in the Preprocess Spectra
-Tab).</p>
-</div>
-<div id="region-to-match" class="section level2">
-<h2>Region To Match</h2>
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" width="126" style="display: block; margin: auto;" /></p>
-<p>The region to match input specifies if the “Full Spectrum” will match
-the entire range of the spectra (including non peak regions) in the
-reference database. This is the most intuitive match. Or should the
-<strong>Peaks Only</strong> match just the peak regions in the reference
-database. This is an advanced feature proposed in Renner et al. (2017).
-This can be a less intuitive approach but in cases where there are few
-peaks and high baseline interference, it could be the best option. In
-cases where non-peak regions are important for the interpretation of the
-match, this is not the best approach.</p>
-</div>
-<div id="match-table" class="section level2">
-<h2>Match Table</h2>
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" width="370" style="display: block; margin: auto;" /></p>
-<p>The selectable table shows the top material matches returned by the
-tool, their Pearson’s r value, and the organization they were provided
-by. When rows are selected their spectra are added to the match plot.
-The spectrum being matched and reference library are determined by the
-previously mentioned parameters. During the matching process, one final
-cleaning step happens using a simple minimum subtraction algorithm
-(Equation 2) which in many cases will allow unprocessed spectra to
-remove subtle baseline, but will not harm the spectra which has no
-baseline. Then, these aligned data are tested for correlation using the
-Pearson’s r. The Pearson’s r is used as a match quality indicator and
-the spectra from the top 1000 best matches are returned from the
-library. You can restrict the libraries which are displayed in the table
-by clicking the box that says <strong>All</strong> under the
-Organization column.</p>
-<p>Similarly you can restrict the range of Pearson&#39;s r values or search
-for specific material types.</p>
-<span class="math display">\[\mathrm{for~each}~peak~group^{1,n}: x -
-\mathrm{min}(x)\]</span>
-<p style="text-align: center;">
-Equation 2: Minimum Subtraction
+<div id="range-selection" class="section level4">
+<h4>Range Selection</h4>
+<p><img 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width="380" style="display: block; margin: auto;" /></p>
+<p>Sometimes an instrument operates with high noise at the ends of the
+spectrum and, a baseline fit produces distortions, or there are regions
+of interest for analysis. Range selection accomplishes those goals. You
+should look into the signal to noise ratio of your specific instrument
+by wavelength to determine what wavelength ranges to use. Distortions
+due to baseline fit can be assessed from looking at the preprocess
+spectra. Additionally, you can restrict the range to examine a single
+peak or a subset of peaks of interests. The maximum and minimum
+wavenumbers will specify the range to keep in the app. In the R
+function, multiple ranges can be specified simultaneously.</p>
+<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" tabindex="-1"></a><span class="fu">data</span>(<span class="st">&quot;raman_hdpe&quot;</span>)</span>
+<span id="cb25-2"><a href="#cb25-2" tabindex="-1"></a></span>
+<span id="cb25-3"><a href="#cb25-3" tabindex="-1"></a>none <span class="ot">&lt;-</span> <span class="fu">adj_intens</span>(raman_hdpe)</span>
+<span id="cb25-4"><a href="#cb25-4" tabindex="-1"></a>r1 <span class="ot">&lt;-</span> <span class="fu">restrict_range</span>(raman_hdpe, <span class="at">min =</span> <span class="dv">1000</span>, <span class="at">max =</span> <span class="dv">2000</span>)</span>
+<span id="cb25-5"><a href="#cb25-5" tabindex="-1"></a>r2 <span class="ot">&lt;-</span> <span class="fu">restrict_range</span>(raman_hdpe, <span class="at">min =</span> <span class="fu">c</span>(<span class="dv">1000</span>, <span class="dv">1800</span>), <span class="at">max =</span> <span class="fu">c</span>(<span class="dv">1200</span>, <span class="dv">2000</span>))</span>
+<span id="cb25-6"><a href="#cb25-6" tabindex="-1"></a></span>
+<span id="cb25-7"><a href="#cb25-7" tabindex="-1"></a>compare_ranges <span class="ot">&lt;-</span> <span class="fu">c_spec</span>(<span class="fu">list</span>(none, r1, r2), <span class="at">range =</span> <span class="st">&quot;common&quot;</span>)</span>
+<span id="cb25-8"><a href="#cb25-8" tabindex="-1"></a><span class="co"># Common argument crops the ranges to the most common range between the spectra</span></span>
+<span id="cb25-9"><a href="#cb25-9" tabindex="-1"></a><span class="co"># when joining. </span></span>
+<span id="cb25-10"><a href="#cb25-10" tabindex="-1"></a></span>
+<span id="cb25-11"><a href="#cb25-11" tabindex="-1"></a><span class="fu">plot</span>(compare_ranges)</span></code></pre></div>
+<div class="figure" style="text-align: center">
+<img src="data:image/png;base64,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" alt="Sample `raman_hdpe` spectrum with different degrees of background subtraction (Cowger et al., 2020)." />
+<p class="caption">
+Sample <code>raman_hdpe</code> spectrum with different degrees of
+background subtraction (Cowger et al., 2020).
+</p>
+</div>
+</div>
+<div id="flattening-ranges" class="section level4">
+<h4>Flattening Ranges</h4>
+<p><img 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" width="390" style="display: block; margin: auto;" /></p>
+<p>Sometimes there are peaks that really shouldn’t be in your spectra
+and can distort your interpretation of the spectra but you don’t
+necessarily want to remove the regions from the analysis because you
+believe those regions should be flat instead of having a peak. One way
+to deal with this is to replace the peak values with the mean of the
+values around the peak. This is the purpose of the
+<code>flatten_range</code> function. By default it is set to flatten the
+CO2 region for FTIR spectra because that region often needs to be
+flattened when atmouspheric artefacts occur in spectra. Like
+<code>restrict_range</code>, the R function can accept multiple
+ranges.</p>
+<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" tabindex="-1"></a>single <span class="ot">&lt;-</span> <span class="fu">filter_spec</span>(spectral_map, <span class="dv">120</span>) <span class="co"># Function to filter spectra by index</span></span>
+<span id="cb26-2"><a href="#cb26-2" tabindex="-1"></a>                                         <span class="co"># number or name or a logical vector. </span></span>
+<span id="cb26-3"><a href="#cb26-3" tabindex="-1"></a>none <span class="ot">&lt;-</span> <span class="fu">adj_intens</span>(single)</span>
+<span id="cb26-4"><a href="#cb26-4" tabindex="-1"></a>f1 <span class="ot">&lt;-</span> <span class="fu">flatten_range</span>(single)</span>
+<span id="cb26-5"><a href="#cb26-5" tabindex="-1"></a>f2 <span class="ot">&lt;-</span> <span class="fu">flatten_range</span>(single, <span class="at">min =</span> <span class="fu">c</span>(<span class="dv">1000</span>, <span class="dv">2500</span>), <span class="at">max =</span> <span class="fu">c</span>(<span class="dv">1200</span>, <span class="dv">3000</span>))</span>
+<span id="cb26-6"><a href="#cb26-6" tabindex="-1"></a></span>
+<span id="cb26-7"><a href="#cb26-7" tabindex="-1"></a>compare_flats <span class="ot">&lt;-</span> <span class="fu">c_spec</span>(<span class="fu">list</span>(none, f1, f2))</span>
+<span id="cb26-8"><a href="#cb26-8" tabindex="-1"></a></span>
+<span id="cb26-9"><a href="#cb26-9" tabindex="-1"></a><span class="fu">plot</span>(compare_flats)</span></code></pre></div>
+<div class="figure" style="text-align: center">
+<img 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" alt="Sample `raman_hdpe` spectrum with different degrees of background subtraction (Cowger et al., 2020)." />
+<p class="caption">
+Sample <code>raman_hdpe</code> spectrum with different degrees of
+background subtraction (Cowger et al., 2020).
 </p>
-<p>The same table can be returned using the Open Specy library commands
+</div>
+</div>
+<div id="min-max-normalization" class="section level4">
+<h4>Min-Max Normalization</h4>
+<p>Min-Max normalization is used throughout the app but is not one of
+the options the user can specify. Often we regard spectral intensities
+as arbitrary and min-max normalization allows us to view spectra on the
+same scale without drastically distorting their shapes or relative peak
+intensities. In the app, it is primarily used for plotting and as a
+processing step before correlation analysis. In the package, most of the
+processing functions will min-max transform your spectra relative by
+default if you do not specify otherwise. This plot shows the two spectra
+on such different scales that one looks like a straight line.</p>
+<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" tabindex="-1"></a>none <span class="ot">&lt;-</span> <span class="fu">adj_intens</span>(raman_hdpe, <span class="at">make_rel =</span> F)</span>
+<span id="cb27-2"><a href="#cb27-2" tabindex="-1"></a>relative <span class="ot">&lt;-</span> <span class="fu">adj_intens</span>(raman_hdpe, <span class="at">make_rel =</span> T)</span>
+<span id="cb27-3"><a href="#cb27-3" tabindex="-1"></a></span>
+<span id="cb27-4"><a href="#cb27-4" tabindex="-1"></a>compare_rel <span class="ot">&lt;-</span> <span class="fu">c_spec</span>(<span class="fu">list</span>(none, relative))</span>
+<span id="cb27-5"><a href="#cb27-5" tabindex="-1"></a></span>
+<span id="cb27-6"><a href="#cb27-6" tabindex="-1"></a><span class="fu">plot</span>(compare_rel)</span></code></pre></div>
+<div class="figure" style="text-align: center">
+<img 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" alt="Sample `raman_hdpe` spectrum with different degrees of background subtraction (Cowger et al., 2020)." />
+<p class="caption">
+Sample <code>raman_hdpe</code> spectrum with different degrees of
+background subtraction (Cowger et al., 2020).
+</p>
+</div>
+</div>
+</div>
+<div id="identifying-spectra" class="section level2">
+<h2>Identifying Spectra</h2>
+<p><img 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" width="420" style="display: block; margin: auto;" /></p>
+<p>After uploading data and preprocessing it (if desired) you can now
+identify the spectrum. To identify the spectrum go to the
+<strong>Identification</strong> box. Pro tip: if you select
+*Identification** without uploading data to the app, you’ll be able to
+explore the library by itself.</p>
+<div id="reading-libraries" class="section level3">
+<h3>Reading Libraries</h3>
+<p>These options define the strategy for identification. The ID Library
+will inform which library is used. Both (default) will search both FTIR
+and Raman libraries. Deriv will search against a derivative transformed
+library. No Baseline will search against a baseline corrected library.
+This should be in line with how you choose to process your spectra. Cor
+options use a simple Pearson correlation search algorithm. AI is
+currently experimental and uses either a multinomial model or
+correlation on mediod spectra from the library. Correlation thresholding
+will set the minimum value from matching to use as a ‘positive
+identification’</p>
+<p>In the R package, you’ll download and load the libraries into your
+working environment.</p>
+<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" tabindex="-1"></a><span class="fu">get_lib</span>() <span class="co">#will load all libraries unless specified otherwise. </span></span>
+<span id="cb28-2"><a href="#cb28-2" tabindex="-1"></a>lib <span class="ot">&lt;-</span> <span class="fu">load_lib</span>(<span class="at">type =</span> <span class="st">&quot;derivative&quot;</span>)</span></code></pre></div>
+</div>
+<div id="matches" class="section level3">
+<h3>Matches</h3>
+<p><img src="data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/4Rb6RXhpZgAASUkqAAgAAAAKAAsAAgAOAAAAhgAAAAABCQABAAAA0AIAAAEBCQABAAAAlQEAABIBCQABAAAAAQAAABoBCQABAAAASAAAABsBCQABAAAASAAAACgBCQABAAAAAgAAADIBAgAUAAAAlAAAABMCCQABAAAAAQAAAGmHBAABAAAAqAAAAPYAAABnVGh1bWIgMy4xMi4yADIwMjM6MDg6MzAgMDk6NDM6MTQABgAAkAcABAAAADAyMjEBkQcABAAAAAECAwAAoAcABAAAADAxMDABoAkAAQAAAAEAAAACoAkAAQAAANACAAADoAkAAQAAAJUBAAAAAAAABgADAQMAAQAAAAYAAAAaAQkAAQAAAEgAAAAbAQkAAQAAAEgAAAAoAQkAAQAAAAIAAAABAgQAAQAAAEQBAAACAgQAAQAAAK4VAAAAAAAA/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAEBAQEBAQEBAQEBAQEBAQIBAQEBAQIBAQECAgICAgICAgIDAwQDAwMDAwICAwQDAwQEBAQEAgMFBQQEBQQEBAT/2wBDAQEBAQEBAQIBAQIEAwIDBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAT/wAARCABIAIADASIAAhEBAxEB/8QAHgAAAQQDAQEBAAAAAAAAAAAABgAEBQcCCAkDAQr/xABHEAABAwMDAgMDBQsKBwEAAAABAgMEBQYRAAcSEyEIIjEUFVEJMkFSkRYZI1VWYZWYotLVFxgkU1lxgZOh0Sc0QkVHhcHT/8QAHAEAAQUBAQEAAAAAAAAAAAAAAAECAwQFBggH/8QAPREAAQMCAgYGBQoHAAAAAAAAAQIDEQAhBAUGBxIxQVETIiNhcfAUgZGh0RUXMjNCUlODk7FjZKKys8HS/9oADAMBAAIRAxEAPwDgHHuagsUH3TI25pM+sJZksLut6465HqC1OrWph4RGpCYvNjkkDKClYbTyBychGZHbzSfT1yvvrkVKr1fm3bcUN67rliobqcwxWmKrKUHVJkqHTz1MIARyVnBHkCcdwQ961d/LC7f06/8Ava+j6O6HaRaUYJWYZS0ktJWUEqWEnaABNj3KFVmsA44mUm1daMyPjJ+1elmR8ZP2r1yW6td/LC7v06/+9o4cse+GbRReci8biZpT8ZiZCbNySFy5jT1SnUorCAfLwkQHwQoglPEjOdbLuq/TFkAutNiSAO1TcmwFSpyvEKVspN4J9QEk+oCumOZHxk/avSzI+Mn7V65LdWu/lhd36df/AHtfetXfyvu39Ov/AL2pfmq02/Ab/VTSfJj/AN6utGZHxk/avSzI+Mn7V65LdWu/lhd36df/AHtZF2ugAi8LuGc/99f7ftaT5qtNfwG/1U0fJj/OushdcBwXXQR6guqBH+ul1l/1zn+af99eGySKAm2rGl3jDqNy0VFrRmqhBhXYLWuCW4qGgNPpnriS0hSVkLIWysOcSk4zy1Zt1CxZEZCbIoNYth3jwnyLk3DZu5uS2U5KUtt0qJw/CBCgcnypwck8hwDzLjDy2HB1kkg+IMH31lqUUkjlVc9Zf9c5/mn/AH1guWlrHVmBrl83qyuny+OMnvq37mVtRNoy49oWTc9vXAoxyKpXd3GbqpTXA/0pIgookZeHRgI5PEt9yepot2U3Pb2vlXD7xk3dIj1xERl9Nh3xSrWkpEZDyU9ZM+iVFp08XMI4paKfN5lZ1FBjdSBZm9a6JkFaQtEkuIV3StuQVoV/cQcay6y/65z/ADT/AL6sK+KrIu28q5diJriFVaSJINbq8ao1dIDDbKg8/HhRmFnyKAU3HaHEpynkCoztOc2gFIordRsi65NcixG01qowN3GIlIrjoLnNxmGqirciJWFNYAfe4lk4B5+Vdk0bZmBQXZFcoFDumkVS7baj3vb0V5Zqdrz67OoESqIU0tCQuXEPtLfBakOAtdyWwD2J1O2ZFpsy5KTLamOGUivU2pQqFBZmSpHNdVaCKcw73Sp1KCghx7DRKeBcClBeggUl8ABUymFQACiJKgCfpIHHt/do82rpTyd0tr1GXTjx3Lt1WEyVFRxWoJwPJqq/hi5tLbUUrIAmVRYz9GQJvciCbAyBFWMqfwuAzUZliGelEbJQVHZI60QlQWgGVXUEbRteUpKeHqX4kS6LpiTKG2/PqNYkSKXV5EqTFfpzaJEgudFlKg06l4ApJcSrjglJB1J6ObpTXGpEcUiYRQH7VgyLsjIqbJDslVQqpiBxhag4SgjISykhAIKsZJIR5vrfta9L6kRGib4/mF/2NV0GE+q88qx1t9U7YLXhMtu9zKQoVOpv2giAWwHGTTrjkz+uF57hz3upGMDHQPc57ah+b637Wt+avRmXPk9rRuUyZHtcfeKbbohjiIhZed9tLxPzuYWylA+jio/TrvdI3+hXl6ZsrEtp9oUf3ArfythTycWpH2WFn1SkH960E0tZeb637Wl5vrfta6WsqsdZH5qf8dLzfW/a19OcJ7/HPm0UV2U8P+0t3XvtTbVYoUigNQXaFHo7yapIW1KBTFjKc4gMOBOQpGFpIV2IyBnN0MeFLdSRTavVoTFDk06jRS/WKhGmy34lMbJHmfWI3FAJKQCogeYarnw2b3wNutoLUoEq3qhVXXKexUvao09qM2A7EjJCOKkk5HSznPfOtkIviIcq1DuCq0+wqw7SqHE61alG5YEdMNDmEJUWVlLjvdSezSFnv6djj4Yzlmpt3DFzOsatGOJV0ie1gK2jIGy2R4QSPGvNGfZlrfaz/EtZNg0KwQWoNk9FJRwJ2nAfaAfCgiveEveOlt0l+4afRKQzOhcqO5Nek09E9lJDhW2fZQXB+GSeasnC09yMa8qF4Ut4q27U4tvQKNWlswkJnNU0yqo7GZdy2lRAiKUnJaI5pAI+gjJ1Zdxb9zLWYpEmv7fVKG1XIpmUst3hSqqt9oJbVyUhhxxTXldbwl0IUQew7HErt54mKpOqVWNpbWNXFLFOaTKi1mo0h9cdvm752EyVt+Yk4UWiVBKfNhJOXuZTqKSzLeYKLn54H+Mnu376ym8113qd7TAoCPySeH8T/W6qZqvhJ3atuWiJXIdGos5TYfajVWVNhPLQcgLCFxQSnIIyO2QR6jUvSvBxvxctHhSqPb0Sr0kPOJiTaZHmSGn3AroOAPNxCV4UwU8eSgkpVgDJ1cN9eLedScW3dGz9vUKXJZTNZcoqaIKkG+ryJblxVu4HJC0KRyyByBA0xtXde7LkpMKpW7tpV50CTIdjxHk39RKUpxYdcSsJZfkNuDCgQVFIHYHOCCWIyrUcpsbWPUlXE9uRwi3Rg8+J3cacvNNdiVkpwSVJtH1IPt6Qj3CZqs3fA34gWM9ez1tcQFK5sVEBIUSEkn2TsDg9z27aMbA8FG/VE3A25rFRtFUeDEv6iVFTy256A61HqkOS6W+UUBRS2hSiM9gO+Bk6Mlbj3+h0R1bUV/mvsQNwaEtvuQPMr2riAc+pOMA/A6K7Mvu/FXpZSJO1taYiuXVRn3Zqb/oU1mMy5VY7aneLclSlFIC1FpALhSB5cKTmJ/KNSKWFKazIlcGOq/cxb7POpGM210l9Ady8BEiesxYSJ3L5V+TiNCgy7sul1yoPoq0euyUUylIpqpTdQQt+QHlGRzAZ6Y4kApVz5YyNSmPzHQ2m4G7bvW5aj7C1MkGpTWYbyn1IXT1qlqPWQkHgtXEKRhwKSA4VAckpIlndx4L6y47bNPK1oUhakqW2VBaio+jnzsk+f5359Ymq3TbJNHMiewGavJQVPKUB1pgpQJskiDFrzYyN1eosGcEML2rpS5JsUkgiBEESZ3zI5QafYJB4/O4nj2z3wcf/ADXRHfBNHsnw/wAPaeizmHYRlQNyY1JbmOTpdJbmJgJWmWtYBDilyy4Ae3B4cSQnOtKbQ3WsGVVoMGrbd7Y0CCFOuvV26FXbXYTR6PFKX2Yc9by0kp8oQ2QlxwqOAAEwELewUuu3NOl29RLxhXGy/TKtT677W9R6gwqQh9tUfDzUqOW1MtllaFpcaT2+kjXZZprA0OzPEYZ707ZDCw4ElKusoERuBiE7W+LkX31o4LM2cAHkpUFdKgoJ61gQeYH2tkmxsLb6h3WXmHFsvtOMutq4ONutlpxs/BSSAQfzHWHFWM8Tj447as6u+KH7uasqsX9tlt1dddmSIyJtzViPWINScYjI9njsOmDUWApppgNteVHUUlhvKlKSCXtd33sWgvLolD2l2Ju2liOyo1yFb10MuPqJS8pvqzqg3LSpChwUpISFAlIKkHWonWxokEJ23xt2mJI74JAJ7pA8BWep1kHqOAjvChbw2T7JPjVSYPwPx9NfVAhKcgj19dHLfiEtVD7D58P2z7gZhGCpl1m4FMPpU6XVOLSKwPwpypHVGFJQQlJTxSQfLvugXfQKdNtDYHbG66qOa6/QrBtvcFNXtcOKUGDOfTUTEUHSlXDoqX80g4xpw1saHbQBft4Gk6VqR2if6v8Amtt7BdaRY1nBaXifuYgnLakhP/LN/EasWDRV1KmVessLpiI9AZS/KbqFdgQKq6FFKQIUVxQekq83dMdKlAA5HY6BrP4P2pbT0iiuUB1yhxVLoTZeYTRSWUZi8Hyt8dI5Rh1al+XzEnOjeJRoUqm1SpqqlKiOUsILVKnVFTdZq5WQMQmUsKSvj/1Fa0AY9fTXmXHuofxzz7RlKlqI8Coke41xzkdIrxNOK3by7bbpj0t+hyPfUMzmW6BclOrkiMkcfLNRHUox3DzBDb2FEZ7djom26fuEzKsm1XrQjyXICEzEXtJtxlh9oLUcRveyekVJI5K6JCwkAny9tQFdtmm0NikvMXNbNxqqcUyXo9sVZ+c/RjhtQamh2I0lDh6hHFsuDLa8kYGS3a5i61Tq2myrvZs6Uac37yfnVN2AzUWA6SGy43DfThCsKw5wBJTgqVgCnwvTRY04vu8bvjxl2lcD9gSocxCJ5VY9KtGW0MPcxioUtjkhQW2QUFYOMjBQruOULbqo3NBj1OJIsplmSl5SBX7/AKBQZqegsoWlxiS+h1BJTlAWkdRKklHIHR3uLWt1W6c5Qbnviq3jbEtKJT79Mffm2yHA/wAghx52AxwdDrYOMDJwQTnQLR7EplYpbNTcvjb6jOuqdBpNwXO9ArLAbc4AuNJhLSOY86MLPJPfse2gbrU43POpFW0dWS008KjtosPP+ztto3UtnrlWCrzJMkcU5SU8lYTywM986OtrNpqu1ubtvKM3blCYm49AlL/4n20p9Yaq8BwhpsSebiyM8UIBUpSSAM4BDBtZSg682rdPZxKW1hCHk31IebkJKsc0cacTgDzEKCVY7YJ7aOts9rqVG3O29fb3R2elGFufQSw1EveQuRVEtVmCtKo6DT093CAlCHC2oqIBCe5CEkiJpBE1W8HZeLU6FSrkFw7BQ2K248RTKve1Bh3RS+kpwKVUacpsvsZ4AgrB5dVv6V41jR9mIdaotFrjFwbAwWqylCl0qt3lQaLcdCCpKoxNSgOtB1gI4h5R8+GVpX3zgeLNYstNDWxJi7h/dMmG82ibEuinotwSQpXQX7IqIXw0BgLR1uXqUqHYaf0SvbcNU2G1cdJ3Sm1VAJnSqDe9MplOcUFrKfZ471PdWgcS2DzcUQUqx2UOIRyqxNhVUqpFGSpSfc1vniopymhQVJOCRkHpdwcZB+B18900b8S0D9Awf/y1cUiv7TkLMag7vNnyltD24NIdT2+cFKFMB7/QQMj8+s3rh2lUqN0Le3caQEq9tLu4tKecUeICCyBTAAOWSoL5dsAEdzpRu3Ul+dVFDoNGly4kT3basT2uU3F9rn0iDGgROotKOq+70TwaRy5LXg8UpUcHGNWBcmzkK2afLqMiv7A1tERKl+xWhetv3TVZYSUj8BGYaK1k80kJ7EjOB5VcfehVywWWJQual7j1CSqatcJygXnApLDMcklpDqHoLqlugFIUtJSkkHCRkYcVav7cu06otUWkbnRKouKhNJl1e+qfUKfHe4gOKkx24Da3GyruEocQoA4JV66S82oqu3rUpTS6qj2KyXhSY7cl5yNHpj7U0OLYbCYag3/SFgvpKkN5KQ28T2bVi207FOU1UkM7g+GuEkRTJdVA3ct9BkJS11kI4so5LcV81LWCoLVxISdAKqrSjGq4CbnEx1mOKC4qtMKjRHEhv2pUxHR5OpWQ700tFsoCkcivieRcbg2xUUH3PuwwhMMIcQ1ftLfW/IzlTvJVOAQ1jI6QSVdgeoO6dBFqAYqrlSAnjllKypIX51KbUnP0FIOAdSkRqmv0uqzZFVgwJ0II9gorkWdJl1sqOFBp5CSy3w7kl5Sc47ZzrJ1qjSpD7qZVSjtKfPRbkxkz5fT7cVOuIUhJWTnICQO/qfXTVyNTEtlTMyQ48BlLbkHooUc9/N1D9Hf006q53mpOuxKJTWqUulXHTbmdmwzIqLEGnVKme4nfJiO4uShAeUeSvOzlALZGfTJvtPSLruGoVxi0bkrdpSotMbkzpNChXHUDLZLik8HzSmnXUJBOU9ZPBSlYB5aqcoZSOSuKU+gKl4Bz/jr2ZcUySqLIfYKuxVFlLZK8Z9SlQz6n7TpDuigb5q99zaRvHQ6a6i4K5uVdlmOJQqbVq1Du2lW0h72gISy81VG2xzDqUFKgkgko7hXlFcUWi2xUoDMqo7gW7bktRcL1LqVDr019gJWUow7GjuNKKwAoAKHEKHIg9tCjkuW+hTT1QnyGl45tPVF59peCCMpUog4IBGfpA026Tf1f9ToFKo3q2fuN2+LjSf5cLJ4LeKHXTZ93gMIGfwhT7DlWcDyp7+bRrthaViNbk7dPNbzWdIkx9xrddiQWrTupLlRX74gHoocXDCELKvKC4Qg+pUBrXHpN/V/1Oj3alpv+VTa7y/8Ak23D6n8dwNEd9ANxatxEfJ0eJp1mHKRaG0BZnwY1VjlXiksVC3WZbDclorT70yhRbeQVNqwpCspUAoEDwk/JweKJzh7NbO0EUJB558Tthyup6YPmqgxj83x0tLVI4tw8BWknDtm96a/e2/FX+JNn/wBZKwP4rpfe2/FX+JNn/wBZKwP4rpaWk9Kc5Dz66UYdvv8AMUvvbfir/Emz/wCslYH8V0/pnyZPi2q85inQaFswqVI5dISfE9t3AZPFJWeTrtYS2nsk/OUMnsO5A0tLR6U5yHn10no6Inzwos+9J+Nj8ndhP1wdqf4/r6PkkvGuSB9zuwgyQMnxg7U4Gf8A3+lpaPSnOQ8+ug4dEcfMU3pmyWxG28ZFm7h+M3YK3L/oMp6JuDZFa8Ktb3hcsittulip0VF0w0Ow6m0wuOEJmU19cR1KypheFFR9ItgeFyGpCmvHnsCrgEDjI8DVzSkr6Ywkq5xjk57k+qj87PcFaWrYClAK2j7vhVIqExHHv+NOPuM8NCOoqJ4/dhqa86tSnJdN8C1wxpSgtfNxHIwlDgo4yjGMJSBgAaGattF4XK3MM6o/KI7YKkFpDKhD8IV3UqMoIyUksR4rbfLJyVceSj6k4GlpalacfYUVsOqSSCCQYMHeLDceI41GtDToCXUAixvJva++oaRsJ4TJSA2/8onYKkpVzHHwt30yQe/0paB+k9s400/m7eEP+0RsX9WTcD93S0tOxD2KxTnTYp5S181GT7TJpGmmGU7DLaUp5AQOHI0v5u3hD/tEbF/Vk3A/d1Zu2nht8GzN1WPWo3yk1gTLjgX5SJVLstPhV3IZl159irQlxoqKgW/ZmTJWEthx08WyvK+w0tLUOyr7x93wp20mQNke/wCNf//Z/9sAQwABAQEBAQEBAQEBAQEBAQECAQEBAQECAQEBAgICAgICAgICAwMEAwMDAwMCAgMEAwMEBAQEBAIDBQUEBAUEBAQE/9sAQwEBAQEBAQECAQECBAMCAwQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQE/8AAEQgBlQLQAwEiAAIRAQMRAf/EAB8AAQAABgMBAQAAAAAAAAAAAAACAwUHCAkBBAYKC//EAG0QAAAFAwECBA8ICQ8JBAoCAwECAwQFAAYHEQgSCRMWIRQVMUFRU1VhkZOVl9HS1BcZIjJSVHGSI1ZXWIGxssHVChgzNjdyc3V2oaKztNPhJCU0NTlCdLbCJkNilCdERWOCg4WjpPBG8WR4tf/EABwBAQADAAMBAQAAAAAAAAAAAAABAgMEBQYIB//EAE8RAAEDAQQECwUFBQYEBAcAAAEAAhEDBBIhMQUGQVETFFJhcYGRobHR8AciMlTBFRYzkuEXI0KT8TQ2U3JzsiQ1Q2IIRKLCGCUmY4Kj0v/aAAwDAQACEQMRAD8A+fKYuGaTlZBNCRct0knRkU0kTARMpS8wc2lU7lJcHdh94wPRXXmf9byf/HKflDWV2zBg7GeZrYzD09f3nceV7WiGz7GOFsc3jbFnX1fLUWcu5mJeNQm0jEnDxZ2UaVS34w6Mi4RkFlkTm4gUx9HVe1oN7ICV0rWNutwzjv71izykuDuw+8YHopykuDuw+8YHorYdBcGNle6JRCOtvKeK7qJCIm9115a8VdU2vhxcbZhLrZtHseSL6Lk1njGfYlRLDpuS9EEcJHOQUTGHpRPB7mhbwfWLmnaJxVjC8WeLr9y4ez4iHuHI02SCsuPuZw2mjOmLA7AWckva7wSJEXF4VmdJcEBOskiOV5hOeMSOeBj3DNSGtc0OaBGGOzGI7cFr+5SXB3YfeMD0U5SXB3YfeMD0VnOHB73y7hcjvILKuObmuDFWHGWZLxtiEty7zt4pB9a5b0ShV7gViiQ6Un0mMV0CB3Wh1RFuQTHKcxdfoCBgAwc4CGoD1hDqgNSC0uLD8QzG3m7cVY0/dkgbvA/UKt8pLg7sPvGB6KcpLg7sPvGB6KotKlVus5IVa5SXB3YfeMD0U5SXB3YfeMD0VRaUS6zkhVrlJcHdh94wPRTlJcHdh94wPRVFpRLrOSFWuUlwd2H3jA9FOUlwd2H3jA9FUWlEus5IVa5SXB3YfeMD0U5SXB3YfeMD0VRaUS6zkhVrlJcHdh94wPRTlJcHdh94wPRVFpRLrOSFWuUlwd2H3jA9FOUlwd2H3jA9FUWlEus5IVa5SXB3YfeMD0U5SXB3YfeMD0VRaUS6zkhVrlJcHdh94wPRTlJcHdh94wPRVFpRLrOSFWuUlwd2H3jA9FOUlwd2H3jA9FUWlEus5IVa5SXB3YfeMD0U5SXB3YfeMD0VRaUS6zkhVrlJcHdh94wPRTlJcHdh94wPRVFpRLrOSFWuUlwd2H3jA9FOUlwd2H3jA9FUWlEus5IVa5SXB3YfeMD0U5SXB3YfeMD0VRaUS6zkhVrlJcHdh94wPRTlJcHdh94wPRVFpRLrOSFWuUlwd2H3jA9FOUlwd2H3jA9FUWlEus5IVa5SXB3YfeMD0U5SXB3YfeMD0VRaUS6zkhVrlJcHdh94wPRTlJcHdh94wPRVFpRLrOSFWuUlwd2H3jA9FOUlwd2H3jA9FUWlEus5IVa5SXB3YfeMD0U5SXB3YfeMD0VRaUS6zkhVrlJcHdh94wPRTlJcHdh94wPRVFpRLrOSFWuUlwd2H3jA9FOUlwd2H3jA9FUWlEus5IVa5SXB3YfeMD0U5SXB3YfeMD0VRaUS6zkhVrlJcHdh94wPRTlJcHdh94wPRVFpRLrOSFWuUlwd2H3jA9FOUlwd2H3jA9FUWlEus5IVa5SXB3YfeMD0U5SXB3YfeMD0VRaUS6zkhVrlJcHdh94wPRTlJcHdh94wPRVFpRLrOSFWuUlwd2H3jA9FOUlwd2H3jA9FUWlEus5IVa5SXB3YfeMD0U5SXB3YfeMD0VRaUS6zkhVrlJcHdh94wPRTlJcHdh94wPRVFpRLrOSFWuUlwd2H3jA9FOUlwd2H3jA9FUWlEus5IVa5SXB3YfeMD0U5SXB3YfeMD0VRaUS6zkhVrlJcHdh94wPRTlJcHdh94wPRVFpRLrOSFWuUlwd2H3jA9FOUlwd2H3jA9FUWlEus5IVa5SXB3YfeMD0U5SXB3YfeMD0VRaUS6zkhVrlJcHdh94wPRTlJcHdh94wPRVFpRLrOSFWuUlwd2H3jA9FOUlwd2H3jA9FUWlEus5IVa5SXB3YfeMD0U5SXB3YfeMD0VRaUS6zkhVrlJcHdh94wPRTlJcHdh94wPRXTjGXTKTjY7jOJ6YSLdgKoE3+K49ZNHe06+m/rp19K3JwXBhoz9u2rO2/bl9XNyzeO2VqQkHeLN/fVzkYGcpPZFrAJJi9FimqydJGdgnxBVEjE39QLr5rT+tWj9XX06dtY9xcCRcbegAgYxvJAXgdcvaJoHUivQs2lKVV9SqHOa2mwvIDS0EmDgJcI5yFp45SXB3YfeMD0U5SXB3YfeMD0VnXtRbHzfAlisbjd2/kG0pV+aPkoxheoGIWdjX6p26btBNRFM4E3yiJVA5jbpgEOuGvquXoLTth1hsbrbYLwY1xaQ4QQREgjHeF2eqGt2itddGO0nopjmsa4sLajSxwIAJkHHaFWuUlwd2H3jA9FOUlwd2H3jA9FUWqzE29OTx1Sw0W8kQQ0Bc7dPVNITdQBMIgXUexrrXZ2m1Wax0uMWuo1jBmSYHaV+gaF0DpfWG3M0XoCx1LRaHTdp0mOe4wJMNbJwXPKS4O7D7xgeis/+Con5tfhKdhxBeUeLIrbRkKRVI6gCRQBSecw81YEPrRuiOKdR9b8s3STKJ1FTMjnSIAc4iJi6gAAGo669as4OCi/2luwz/8A7HQn9U8rGhbrFbqDqljqte3/ALSD4ErlaY1Z1g1YtLbLrBYatmqHJtWm6mTsMBwGRzWB8z/reT/45T8oaurizaDyzhOPuJhjKfhoA1xnK9NJP7DgLsnLffpM3bBvLQj9+yWcxj9FB+7TI7YKIqACwjvbxSHJaqZ/1vJ/8cp+UNU2uc+C5zSMCF59mLWndHgs4sj7f+db0u9vNQLm27NtBhAjbiONORdvXLaM82cW1B2xKhc6K0cCdwKPG0AxKK0uRwqimggRJQgolUG3cTtjZ+hWltsmdx2kq3tNGZjIMr/EtpSCjWJuBOaSlraMopHCc8CsW4pvSDMYWCYyKnFoEAqQJ4w0qjWMa2APXqe1Wb7rQG5AAeAHgFk0w2xNoqOh70hG+QEFWN/o8RcCr6ybfkn6RTWkjYa3S1yqxMpHGXt9u3iV1GAoqKtkCAY4m1OOMgAAAAAGgBzAH0VzSp23o96AJ5hl2YoJAifW1KUpUolKUoiUpSiJSlKIlKUoiUpSiJSlKIlKUoiUpSiJSlKIlKUoiUpSiJSlKIlKUoiUpSiJSlKIlKUoiUpSiJSlKIlKUoiUpSiJSlKIlKUoiUpSiJSlKIlKUoiUpSiJSlKIlKUoiUpSiJSlKIlKUoiUpSiJSlKIlKUoiUpSiJSlKIlKUoi7sa9NHSMfIkICpo+QQfkSMbcKqKCxFgKI9bUSaa9bWt1GPOFzfY4irWj4O0G657RayEXDv7gxzA3LNNY2VXWev4I79RcqysaZ2ud2VubTRcCnAQAAJWkqriWDall3SpKlvHLEFi4rFJE8epN2lNXWWbMoKgKFTCPSUFIUt0giK26BuNDd10Np0GmdWdH6dfTq2suDmZFri05g4xnBAheJ1p1D0HrbaqFt0kaja1EFrXMeWmHFpIMESJaInKAswtrvbecbUzWSdzzOce3fOOYosvPSrFlEIHQh0SJN9EGxzAZU5UiAc2hQEROYecdK181W7jjYqInZWMg7jZ3dEMnQoR9zR8Y7hGU2mBSiCybV0UrhIoiIhuqlA3wRHTQQr2+LML5HzQ/n4/HsI1kuS0Epc11S0pMNYC37VjiAcDyEk8XOUiLYpibplefdExQEOcK5OhtCWHQNmdZbCDDnF7iTJJMSSSSuz1Y1V0VqlYH2HRd6695e4vcXOLnQJJJJyAhWu0Hm5tNeprXvpp05jbQsuOaLrNiv2zmefAgqZMXCijgUkxOICGu6QnMA83P1K97m+/sP3izxjEYkxgNiIWNZSdu3Rc70rVvcOVHwptDnl5Ju3+wpLFUK6TACGPvJmTMIgYxgrtWETGR8gYFHMpnZMVHaJkvYzIXQLgzBZ0Bt0W+q+gKijv8AF8+5v9QNRrh6YpMtOlNHWeoAWcI8wciRTdBg9ZE7l9D+za12jRupOt2l7A4stLbLRYHNJD2tfaqF6CIIkANMbCQc1aOCu+YhHaah3bmQYCQzd7FPXSirR4ioUSKkEBMO6Ih1DB1Po1rYTwY9vM2vCTbCk/bywOrYf7ScImkYx951DrCk8EWjkvVAxecCmHmMAdnq9XOuybY1yWdknaOwBddmN8XW43MunaUGjLuIGSdRa0WyudxDSr0QI2ZEeyR+lzB8Yjxy2QIZBFUDbwWg2Acnv8SbXOBbzaCQ5YHKUJMmQXTFdATISCBBOJPlAis4KAgOob2vWrg6bsjdCF+nNHxTLAOFaAA19PCTA2tBJBzOXR3WoGsdp9o1Fnss1se608MXcSqucTUs9pLRdY1xONGq5rWVGHAGHjEY4jzP+t5P/jlPyhqm1UZhFZSXlDE44S9HqB8BMDF6v70ap3QzjsOvEh6texqODXEFfP7Gksb0DwSlOhnHYdeJD1adDOOw68SHq1WRvVoKUp0M47DrxIerToZx2HXiQ9WkjekFKU6Gcdh14kPVp0M47DrxIerSRvRKU6Gcdh14kPVp0M47DrxIerSRvRKU6Gcdh14kPVp0M47DrxIerSRvRKU6Gcdh14kPVp0M47DrxIerSRvRKU6Gcdh14kPVp0M47DrxIerSRvRKU6Gcdh14kPVp0M47DrxIerSRvRKU6Gcdh14kPVp0M47DrxIerSRvRKU6Gcdh14kPVp0M47DrxIerSRvRKU6Gcdh14kPVp0M47DrxIerSRvRKU6Gcdh14kPVp0M47DrxIerSRvRKU6Gcdh14kPVp0M47DrxIerSRvRKU6Gcdh14kPVp0M47DrxIerSRvRKU6Gcdh14kPVp0M47DrxIerSRvRKU6Gcdh14kPVp0M47DrxIerSRvRKU6Gcdh14kPVp0M47DrxIerSRvRKU6Gcdh14kPVp0M47DrxIerSRvRKU6Gcdh14kPVp0M47DrxIerSRvRKU6Gcdh14kPVp0M47DrxIerSRvRKU6Gcdh14kPVp0M47DrxIerSRvRKU6Gcdh14kPVrgW6xQ1MZwQvUExkgKUPw7tSkFc0qXuG7cp4CerTcN25TwE9WioXgGFMpUvcN25TwE9Wm4btyngJ6tEvhTKVL3DduU8BPVpuG7cp4CerRL4UylS9w3blPAT1abhu3KeAnq0S+FMpXdaQz94zkX7cVDtotMijs/wQEoHNuhoG7z6BqYe8FU7cN25TwE9WiXwplKl7hu3KeAnq03DduU8BPVol8KZSpe4btyngJ6tNw3blPAT1aJfCmUqXuG7cp4CerTcN25TwE9WiXwplK7kXEPph4RixFRVwoQ6gAbcKUAIUTGER3e9p9IhXQFNQoiUyipTAbQSiUoCUQ6oD8HqhRL4UdK9NZNhXxku6Yixsc2rdV/XpcKqiEFaNnwS1x3LNHSRUcKEaskEzKqmKkkqoIEKIgRI4jzAI1UnGKslMsns8JyVk3dD5hf3U0sZpi+fhFLevxSYfuEWjOONGOiJrEWWWcIJkKoUoCKxB10EBqQJIaMzlz7MO0KQ4ESvD0r2uR8YZBxBIQ0TlC2piw5K4bdC7oRlchW7FxJxgv30WD9IN4dUejIyQbb46fZGigadQRTGL8hW9Zzi/wC4LXnYCz2l4I4+dzk8gjCps5lxEpTqDBZssJHJDqx66LwhzpAkdFZMxVB3ygNcAJOGYx3gxHaY6VF4Xroz+kA/VeKpXUKqgZQUCPkjrAG9xKa6J1tBAB3twOfTQQHXTTQQ7NT9w3blPAT1alC8BTKVL3DduU8BPVpuG7cp4CerRL4Uyld15Dv2LSNfOeMI3lETLtDhuAIgU27zhu8wjzGDvGAevVO3DduU8BPVol8KZSpe4btyngJ6tNw3blPAT1aJfCmUqXuG7cp4CerTcN25TwE9WiXwplKl7hu3KeAnq03DduU8BPVol8KZSpe4btyngJ6tNw3blPAT1aJfCmUqXuG7cp4CerTcN25TwE9WiXwplepti+rzsok6Sz7ruG1yXRDKW5cpYGVWjCXBHq/sjF6UggCyBuukpqUexXktw3blPAT1abhu3KeAnq0S+FHoAABQDmAebvAHU/NVwGCYXNaC0YkAHmbROpJR6Yc6z5gsbV0mUOuZI+h9A1HQe/VvNw3blPAT1a7sc+fxL1vIR75w0eNVAUQXSAm8UeoOoCXQQEBEBAeYQrp9NWCrbrOx9kcG16bg9hO8YQYnBwlp3Svf+zrW7R+rOl61n07TNTRdspOs9pY34uDeQWvbOBfSeG1GTgXMAOBKrLa8rua2y/stpdNxN7NlXoSUnajabcI2zJuSmRMVw4YgcEFFSi2biChyiYBQJoIaBWU+wTiaSy9tW4HsxosjHp3PleFtdF+4UBJEy7p4modMg6aicEEnB90A11KUObWsdz3fbr0xncvYsU7kj6GVcM5BaOauj/7x1EC/BAR6uhevW1TgRreYZq4UnZchbkO6iYKxHk9lK24a2lSRjNtJ27BPHzAFtUziqkZTUFQHQxyiIAcnVro7fU01pqynRtexmixwio5zmkXcJDA0kmRIBMRgV+maBo+zf2Z6VbrpozT7NIWuiS+x0KNGqx4qkfu32g1WNY0UnQ4tpuqXi2JAMr5ZNsWVk2OZnqTORfNUukbY/FNnijdMBFVyAjulMAa8wc9YrDcE7oA9Opbn1/8AaS/rVkztn/u1Pf4ha/1rqsTx+KX8Nd3aCeGdjtX4TQA4FnQPBVblDPd2pbykv69OUM93alvKS/r1R65ANRDq6a6DWMu3rWAqvyhnu7Ut5SX9anKGe7tS3lJf16JwsmchDlZqCUxQOU26G6YBDUB8FR9IpT5mr9UK7xurusD2h1OyVSDiCGOjHqVgwnIKDlDPd2pbykv69OUM93alvKS/r1z0ilfmSvgD0U6RSvzJXwB6Kn7t6xfJVv5bvJODPJ7lxyhnu7Ut5SX9enKGe7tS3lJf1656RSvzJXwB6KdIpX5kr4A9FPu3rD8lW/lu8k4M8nuXHKGe7tS3lJf16coZ7u1LeUl/XrnpFK/MlfAHop0ilfmSvgD0U+7esPyVb+W7yTgzye5ccoZ7u1LeUl/Xpyhnu7Ut5SX9euekUr8yV8AeinSKV+ZK+APRT7t6w/JVv5bvJODPJ7lxyhnu7Ut5SX9enKGe7tS3lJf1656RSvzJXwB6KdIpX5kr4A9FPu3rD8lW/lu8k4M8nuXHKGe7tS3lJf16coZ7u1LeUl/XqMYKWAdOgleoA9brhr+f+eoekUr8yV8Aein3a1h+Srfy3eScGeT3LjlDPd2pbykv69OUM93alvKS/r1z0ilfmSvgD0U6RSvzJXwB6KfdvWH5Kt/Ld5JwZ5PcuOUM93alvKS/r05Qz3dqW8pL+vXPSKV+ZK+APRTpFK/MlfAHop929Yfkq38t3knBnk9y45Qz3dqW8pL+vTlDPd2pbykv69c9IpX5kr4A9FOkUr8yV8Aein3b1h+Srfy3eScGeT3LjlDPd2pbykv69OUM93alvKS/r1z0ilfmSvgD0U6RSvzJXwB6KfdvWH5Kt/Ld5JwZ5PcuOUM93alvKS/r05Qz3dqW8pL+vXPSKV+ZK+APRTpFK/MlfAHop929Yfkq38t3knBnk9y45Qz3dqW8pL+vTlDPd2pbykv69RBBSwjoDJT+b0Vx0ilfmSvgD0U+7esPyVb+W7yTgzye5ccoZ7u1LeUl/Xpyhnu7Ut5SX9euekUr8yV8AeinSKV+ZK+APRT7t6w/JVv5bvJODPJ7lxyhnu7Ut5SX9enKGe7tS3lJf1656RSvzJXwB6KdIpX5kr4A9FPu3rD8lW/lu8k4M8nuXHKGe7tS3lJf16coZ7u1LeUl/XrnpFK/MlfAHop0ilfmSvgD0U+7esPyVb+W7yTgzye5ccoZ7u1LeUl/Xpyhnu7Ut5SX9euekUr8yV8AeinSKV+ZK+APRT7t6w/JVv5bvJODPJ7lxyhnu7Ut5SX9enKGe7tS3lJf1656RSvzJXwB6KdIpX5kr4A9FPu3rD8lW/lu8k4M8nuXHKGe7tS3lJf16coZ7u1LeUl/XrnpFK/MlfAHoqLpDK7u8DJTQTCQB5ucQ62n4f56fdvWH5Kt/Ld5JwZ5PcoOUM93alvKS/r05Qz3dqW8pL+vXPSKV+ZK+APRXPSKV+ZKeAPRT7t6w/JVv5bvJODdye5Q8oZ3m/zzLeUl/Wq+ezlLSrvMNrIOpORcomTfidFd6qqibSPdCGpRMIDoOg8/XAKsd0ilOf8AyNXvcwVfDZxi37XMNqrLtjpJESf75h6gax7oOt+Cn2Dp2ieFrWSq1jcSSxwAG0kkQFnWY4UzhsWz6lKVZdElK5AoGEC83OOga9StoZOCS2nbgwviTL+K3lm5fd5Utm1bwWxtaxJS37rsWOvGKnZmGdycnKNm0EoiVtbcud4o2fm6BBBIy5SFWSMYfcbfdlIHWcR3BS1rnkhgmBPVgPqAtXdKzqiODP27J1SFQi9nK6XLu4MjTGJGTE1w283kmtyQLeYdSkVINzyIKMFU0bfmVSGfFRTXIwUMidQBIJ58fwY23ZLXBb9rRez3MSM9dNqr3vBx7O+LTXO6iUnzSNRfqKdNeLRSeuXzJGPFcxOmRnaPQQOd4BqpqMGMiO7L1/RTwVSJIPZzx4rA6lZ6xHBu7UKEDat65Rs9HCmN75s6dum0L/vyQYu4yYcxFpTF4NIVVgzdKP2b6VbQj5uyI+QRA7lI6egnIcgeOS2Adr1TMMrs/jhp0jmO38cK5aueyHV9Ws2d2rAN1Eknb6WeGkwZshbHWTKu3dLJuENRFRIpQ1qWuYXlgcJAnqiSepG0nuAe0GMMdmMR25dKx+te52cVbk9HrpNzOTkE7MgpgYXpligkJD9nc5jfvRq3mmnMHUDmCuTFEhjEEQExDCQ2ggYNQHQecOYep1Q5hripVSIwKUrLbZm2L8ubWdq5xuLEYRMnIYLiLZmpm0nBHR7guhG5p8kCgEWCaZkxFqc5nS/HmIAIIqCUTGDdrIkeCF2y5LM91YitK2bSu2MtDaCDZtmsuM7wYReO2E+JCnIu7brK9N2zI4iKRHKrHdVVAE0+MUMQhque1hDXHGJ6sB9QEbLpuYxgeyVrApWeLfgw9u96Fsnj9nedfN7zPMFtSRQvO1+kk0WDQeOnrkr8ZPodJqdvHvnLd4udNB43ZrrNlF00zGC4ifBX7Rj/ACVEWrARTyTx8jN40t7IuVHDOJZJ40cZHRts6Tpzb6css/eR7FS5mZQkWgGbuUxTUA6IKbiccJSBALhJMdfPu5kqTTvFwiBJ6BHhOK1lUr12QbSWx/kC/rBcPU5New75mbHXkkW4tkZI8PJuo07giQiIkKqLUVAIIiJQOACIiGteRqadRtSmHsMggEHmIkKXNcw3HiCDB6Qvb2HONISXWM+4tNu8aGRM5MXU6Ak+yF0HrAYS7o98S15SRdhIP3r4EioA7dHcAiQu4VPfMIgGn4+/rXTpVlCy72ENoCz9lvaoxvnS/YSduS1LMirpZycHbYAEvJmmrQn4BqiU3RLYxExcSjfjlEl01U0QVMkbjCkAdqmNuF9wrJY0j32e8LCrmKKyRbUoW3cZY4hndjP4+1pzGq1nzSV0yr1a5G8haUJZEtFM2yi7gJA0+ZVw5ATr1pa2eMFXdtKZgtPDdkv7ehpm5iSEk9uS7nx4u07QioaNdzM1Mya5E1FCtWLFg7cqAkmoocEgIQhjnKFZM2Fwbmfs2XPxWzfIWBn/ABIvlKExOy2h7QuALZxt0dPCwTaOXsbKAhPs2qS0k3bLLrR4kBwBkkjLnEgGiq5kt4QwQMOYAiDzY7VpSe4GG7DPSYy6hmtnj/hPdhh7brxm/tPNc3OW1azptYT17hy1npZGQCPzhBM0X53UqcWrIU8mQMuYUwVORxDqJAkcxSLV2H3C27LN03ry0vKGy3IXhcgsJWNyO/wNY123Hs5SCeImFj8dBNHbviJ7oGZaSD5AHhW5DI3AqYoJnbJEHVYfgw9uowsVmmz/ADMlDSdzS9sRt1sLwtpWzXJ4Msso/kTyppIrZvGbkFLGSlXR0WS/QCxU1znKJK7TvgtNv1g+jo9xs1XYL2VyIpiiPRbXJbj4684md+j0P9ikjbiB14uSaJyB91ko6YrtiODLkFKsXup1B77viM584Jw6YWl2q114gyN2zCMeqYncssNrzhGdmvPGzvlHB+LMGPcYx1yTra8cfQhMdWrHsLLlwyzd11vXPTlqbo8pFramoyPBBMRQTXTcIkSIgUix9KNZOZd2NtpXBNvSd3ZVxi4ta1YpzBMnNxp3XA3JBLLXIadLDotHbB84SdHWNbM+U5WplRbjFrAvxQ7m9jHV6QZLnMMyccdvUq1aj6jGh4jMjCM0pSlaLFXCuq6GkxAQDJBJEFyJAs+IQugMTpFFEEydgDaCb97u1b2lclKJjFKGmomAOfqdWmakCTC4pW1rBPBYXdnW08F3HEZTaRKueLTgblhUVMfrSMLbR56TylFoMZKRF+kUiwnxk4MkYqYpq9NvhGRBsYytmmnBf7eDlZ02W2fX0a4ZXw3xs46c5JsyEaOJ1zER88gxZOnEwmg9BSPlGDxNwzOqgog4KcqogBtJcLjix2DtqsGOcA8CQVgVSs4F+DZ24G1sy13Odnu5G8TCz0xbb5u4uO3k7kF7b08W2ZxFvC9MOmLgGMkdJsuq3bqJp8cmoJ+KOVQbY4+2ZL1vLJ2a8PzKUrb9+4Qx1fd4XJAQcIjkB6pJWGkcZCGEzV2VECnXSUbneoKOCJiTUiTjUAoy7UeWDOCf/wARiSN4hQWPaJeCMQOs+ulY20rMjas2JMr7HNuYDdZlBvBXxmu27kuGTxxvtH8tjYbeuA0F0HIPWrpw2VcOAAjgySZiqNRMZBchViHKGG9VDmOksMxh2HftUupvYYeIwHeJSlKVKolKUoiUpSiJSlKICRklbs/1PD/tXMFF63Ia/ObrftVejWkyt2f6nh/2ruCv5DX5/wAqPazrAcC/oWlE/vmDnXybbZ/7tT3+IWv9a6rE7rB9I/mrLHbP/dqe/wAQtf611WJ3WD6R/NXU2j8Z3Su3ofgM6B4LirmYuxNe2YZq4IGw2EbIylsY/uDJ8s1kLij7eMEPa8Q7nZpZv0Usn0SsiyYuVitG3GOFQRNxaRtB0tnV4cJ5ln8F3dJXjbULaE7Iy1iXDjty0vWA5RRRY66Id3ATAEQ41PdVWj5B82KsA7yYOjmJocCHLVnx+HTsWqmM0z9BtNCGHVskYBAgmEdSBXY4pTe03DfSBB0rrNA3mrTQQ06GTDq6iHwAqaIaCIdivvjQzWHRVmvDG4z/AGhdrTEUwAcFFxanaz/VGnFqdrP9UagpXZXGbgrqPi1O1n+qNOLU7Wf6o1BSlxm4Io+LU7Wf6o04tTtZ/qjUFKXGbgij4tTtZ/qjTi1O1n+qNQUpcZuCKPi1O1n+qNOLU7Wf6o1BSlxm4IppyHEwaEOPwC/7o/JCoeLU7Wf6o0U+MH7wv5IVBS4zcEU0UzdQCKD3xINcCmfXmTPoPP8AFGsvNhPZlabX+07YmA5C4JW1ou57eu65pa4YVgjJyUY1tSzLguxcyaKogmbfCEBIRMPwSrGENRAAHENId4yA6BochD7vVAN4pTafz1xG2qzOtVSyN/Ea0OIjIGQO2CswWl9316xXcXj3yLdo6VYOkW78FBZOVGxyNnQJH3FeLOIaG3DfBNuiO6I6DoPNXT4s/N9jP39CjW1Pb9gYqL2aeDLdQ8c3jkHGyuRo/TjbNJBRij9VyhNPTKyQAB5B8sebI7cHMIlRTesilHUylaqgEdNNdOwGnVrq9XNLjTejnW007v7yoyJnCm9zJ67srutM6KOiLYyzF969So1MojhaTKkQYyvRzrni1O1n+qNOLU7Wf6o1BSu/uM3BdSo+LU7Wf6o04tTtZ/qjUFKXGbgij4tTtZ/qjTi1O1n+qNQUpcZuCKaRM4GDUhw5hDXdEOsNQgmoPOBDjrz6gURon8cPoH8Q1BS4zcEUfFqdrP8AVGnFqdrP9UagpS4zcEUfFqdrP9UacWp2s/1RqClLjNwRR8Wp2s/1Rpxanaz/AFRqClLjNwRR8Wp2s/1Rpxanaz/VGoKUuM3BFHxanaz/AFRpxanaz/VGoKUuM3BFHxanaz/VGpm4YUShuG1FUefdHsBUiuxoHFBzB+zmDqf+EKq5rcBCgkDNSuLU7Wf6o04tTtZ/qjUFKtcZuClR8Wp2s/1Rq7eDSHLk63hMQwaJvB+EXQP9CcVaIBDQwdnTSrt4L/dOt7+Def2JxXQa0saNXLaR/hO8FhafwH9C2CUrkC6iAagGogGo9QNauj7k0515+zg7wyT8B/sVfGIBcYC8crXAOghoIAOvNrWwTH/CcbV2OL0wTdkHdbBzFbPeEE9nuy8av1JcuL5C3el7qMdnfRKMgmBZN2g60XlmJ2roTtm5iHJxYAOJnuSznd+zfKT/ANip7ks53fs3yk/9iqwZLbr8ebqgdkwpaY+E+pB8QFsHf8MXtZv7KZ2MMNhRGPbzdxT7qU5ESj2fl17jj7ziXJnrleWPx50Wl9TSKbpUpnSnEsTOHDkWxdaNjLhZtorE9h4hxfbNgbPD7H+G7KZ2hBWvdWOH10M5xaJmIK4YKZkll5MV+i4uVtyLkWqDNRswK56KOLIeiVC1gj7ks53fs3yk/wDYqe5LOd37N8pP/Yqz4u26WwIn+nYrGrUcWknIQOYEgnwV982bdeddoKw1Me5OLYctEOZqyrikpBnaZo+cmnliRdwxMOs7XBwYhuORueUO7AiZQVUOkYnEgTdH0OWeEJzbmC8b1vKbt3FME4vzZWX2O5iJty1Hxo9G0HLxu+WMmu8fuXqsjxrZMqb164ccSgUiBCAkmmUmM4Ylmw6k/ZofRJP/AGKnuSznd+zfKT/2Krig1ktaMDJ7RB7pHWjajxGOQAHQII74VrRNvGEw6aiOo6Bza9elXS9yWc7v2b5Sf+xV1VsYTCC7JuactM5nyiiSRk5F6KaYppGVEVBFmAgAgUQDQB5xDXQOep4MhUV4dmfbPz7shtclJYEuSLs+Symxi424riXg05mcZoxRpIUU2JlTcSmCpZV2RXjEld4BTEvFnIU9Zis+Gl2wI9m8QZwmBWz6SzY0zvKzaGMnKUpKyzOZi5xFu50keKWbivFJJGOsRR10MsoiVyQgJ7muP3JZzu/ZvlJ/7FT3JZzu/ZvlJ/7FVHWdj8XAH9II8B2IHFpJbhPlHms4Lt4VnaPurG0NiRK2cN2xYdu40uvDUDb9tW1Ot4iIta6YaVt7pQixWmFWf+a4+YdtmDw6JnZSlR49ZyCehvRW5ww21hatlOLLhYHBTZJwlaTQbhDGzotyEbWZHWfGw7cyhZEEVgAljwZzncpLGKYz0EBbkdKErX77ks53fs3yk/8AYqe5LOd37N8pP/YqhtlpNdfAxkGecf1R7nVcamMgg9BjDuXn8jXq9yVkO/sjSUXCwkpkO95W+pOGt1BZrb8W5l368i5QYpKqqqkQIq5UBMqiihgLoAnMPPXjaul7ks53fs3yk/8AYqe5LOd37N8pP/YquygKbRTp4AAADcBgApc8vcXOMk4npKtbSrpe5LOd37N8pP8A2Kuq9xhMMGbl6rN2msm1RFc6beRenXUAvWIBmZQEe8Ih9NTcKhVfAucb72b8s2pmXGysKF12mL1ukxuaGTuK159jKMHUTLRUqwOIFcM3zJ67arJbxREi4iUxTAUwZ3WXwuG0Fi2yXGO8O4n2W8PWgOSo3J0VD48xE7iW8A5iJa2Ztk0RKeTORdEHlqx5zrPiuXRiuHhAXIVYQDAQcSzYCIcoLN5h0/1m/wDYqe5LOd37N8pP/Yqh9BtUXXwcIx3TMHeFLSWkFvT3eS217P3DPX1Z13Y1jMuY4tVDCmMUrtC0LQwlDyMU5tcLgY3AjFsE4t5PpsHkdFuLjklU01ToPxIokQJIpUSlN4a9OGGy/H3rKuMC47x7YFhR99ry1jK5Bi3eSswDbQS0/cTG1LiuRV8BpBkhNXRPTRA0Bcrl8mmZ2ukgQD6zfclnO79m+Un/ALFT3JZzu/ZvlJ/7FWb7LTJBjAAjdtGe9ai0VgCATmDnjKvvlfaoJeux5so7IVusbrbW1gCTuW+LnlbnkGLhGdnbhdHFuziEGyZRRiYtBV/0MV0Y7gy029MO4TiyjhtV0vclnO79m+Un/sVPclnO79m+Un/sVaNota4lubsT66sFkXSBOwR661a2lXS9yWc7v2b5Sf8AsVPclnO79m+Un/sVTcKhWtrkBEogYOYQHUKuj7ks53fs3yk/9irqpYwmFXLxqWctMqjIUgUUPIPQRV41PjC8WIMxEdA5jagXQepr1aGmizMxTwne0PhzH+J8cWtbmGn0LiKPYQ8O8uayHczLXCxi3t8PmLGWOV+mmqikbIl0JiCREjqJu0t4+qQGNXjcK3tImyDj6/lLUwSoTFczOyGPbNDHjttaFotJyxrfx2ESwKSQK7btY6GtiJIxVQcEeILEXUF0oKxihg/7ks53fs3yk/8AYqe5LOd37N8pP/YqFl6pwjsXY49MSrNc5rbg+HDuyWxPGPCs5Mjtp6zNqTL0BHT12YntrKTjHtv4yh2lpwc9PZMkHsy8QuYXqjkVIZKRkFHQotQBwUGDQhDCJRVDFvH+0pAYPF/f2EYzJzHP94WDNY/yJe+Tbogrzsd6jdDQU7ikYWLbRrV61fGdFScs13bt0VDQ4KpuBMBgsh7ks53fs3yk/wDYqe5LOd37N8pP/YqsymKbwWYEC7IzgzI6DJU8JUMhxmTPXAA7IV69rjbSyftlzFmTGSLVxTaI2Ma43UZH4os9xaMdIPrumz3Lcsm/Iq8ciq6fyizl6c5RTIU7tQpSAXdAuIVXS9yWc7v2b5Sf+xU9yWc7v2b5Sf8AsVUbQawXWYBQ97nmXmT5QB4K1tKul7ks53fs3yk/9ip7ks53fs3yk/8AYqm4VVWtpV0vclnO79m+Un/sVPclnO79m+Un/sVLhRWtpV0vclnO79m+Un/sVPclnO79m+Un/sVLhRWtpV0vclnO79m+Un/sVPclnO79m+Un/sVLhRWtrdn+p4f9q7gr+Q1+f8qPa1Pe5LOd37N8pP8A2Ktx/ADWdIWvwq+z6s8kYR+V7ZV/pJliHThydISWo8MIqcYgkAAO8ABoIjqHUCsq7SKDzzLSj+MzpXyEbZ/7tT3+IWv9a6rE7rB9I/mrLHbP/dqe/wAQtf611WJ3WD6R/NXT2j8Z3Su3ofgM6B4LiuS9UPpriuS9UPprNnxjpWqvI1DRi15tNWyY/wBEKjqBr/oLX/hk/wAktR19+aH/AOWUP8jP9oXbM/Db0JSlK7JWSlKURKUpREpSlESlKURRqfGD94X8kKgqNT4wfvC/khUFEW9n9TxxMC84QGal54CGTtbZEy9JsAWSFVAXb+0VrfTTMAAI6KlnFUfpWAOoI1opQ5itRABKIJJcxQ5x+AXqf/vXrcVwLEiZhnzP4E3+OkNkG44RuCNvq3Suos+uqxmyCaUcmG84VMY4FTR6hjCAditPipRTWUIcgFFJQSCUw6iUS8whr19BDTv14bR9e9rnpOlyaNDHpNTCM855t2S7A6HdR0TQ02X4VqtSmGwcODbRdM5Gb8Rzc625cIZ0xHZV4NpSUCSRUa4GRj45K5bkTNPKMRtizHTQ7aATHdZxOrpwLKRH7JIGUdAoOrMpS6hhDUQHQBDTTd16lbWNuUGQbM+xu3YhagFiLTi2bhvAMXdxP2ijzF2MHxlXt0qDxZ1HCijg6luJF/zM5I8HeMV4StUwc5g0AOr1OtWXs5H/ANORmeFrHtqudsjfuXotfL3240OwijZ2xBEXaNNu0ndsMboUNKUr3y8alKUoiUpSiKNP44fQP4hqCo0/jh9A/iGoKIlKUoiUpSiJSlKIlKUoiUpSiKIvxgqeH7EH8Ob8kKkE+MFTw/Yg/hzfkhUOyHSs3bfW5dalKVK0SruYL/dOt7+Def2JxVo6u5gv9063v4N5/YnFdBrV/dy2/wCm7wWFp/s7+hbBK7zUsm+WTaslHzhwqIgmii4OBh0AREdRMAAAAAiIiIAABqIgFdGqzChqaZAecOTEgGn/AMkK+LwSMl44CTC7vJ66OsmqYOyE+zEDf/kU5PXR2pby+z9pqmwtuytyyIRMBErS0mds4elZMkinXFJqgq6cqaDoGiaSKqhh16hB01HQB7MhacvFQ8VPPWKSUTNlRPGuQWSUFcF2wO0h3AHeLvJCBucA06g8/NVvfDZnBaBgIzXZ5PXR2pby+z9ppyeujtS3l9n7TXluLT7WT6oU4tPtZPqhUX3KbgXqeT10dqW8vs/aacnro7Ut5fZ+015bi0+1k+qFOLT7WT6oUvuS4F6NxDXI2RWcLJOwRbpiqsZGWRdmRIGmpxImsY26GoajpoGvPoFUUXDkBDV05ESjqAi4OIl6w6c/4KqluEIWRMJSlKPSqRDUpQAdBjXmvPVEL1A+il9yo4AGAqwwZzUoZYGPRjjiCgdY/RwN0kgMIgXeUUUKQBMICABrqOg6AOg13xt66O1LD/8AX2ntFdJMANbbwDABg5RteYwagH+RvqmxFnz081F9EwyztgWfj7YVflKQjNu/lDqEYN1DiIborCkpoI8wbg6iHNrLb5iDigbIkrscnro7Ut5fZ+005PXR2pby+z9pqlzEA+gXSLOVaJN3C7BGTRIU5Ft5FwUTpG1KIhzgA83VDr1SuLT7WT6oVF9yvcC9TyeujtS3l9n7TTk9dHalvL7P2mvLcWn2sn1QpxafayfVCl9yXAvUcnrpESlKg5OYR0IRKbaqqnEeoBSFcCYwj1AAoCIiOgAI1QDLufhEO4ddcpyHXU+gQEBH+YahakIDpoIEIAg7SHUChqH2QtVCc/13N/x08/tStL7lRzQ3JSmZJSQcJtWZnrhwoAiRJNyYoiBQExhERMAABQARExhAAAB1EKrHJ66O1qj9E+09oroxH7HPB1htl0Ah2fsrWpMRb0pPrPUIWKXk14+JdTz5JoiCijdmySFZ24MHyUiBvG05+wAjzUvO3o1t5VPk9dHalvL7P2mnJ66O1LeX2ftNdaXtGagmEfJSjBFuzlTEKyVKuk4FYVGLKSJ8EoiIat5Bofn051BDqlMAef4tPtZPqhUlzgYJV7jSJC9TyeujtS3l9n7TTk9dHalvL7P2mvLcWn2sn1QpxafayfVCovuS4F6nk9dHalvL7P2mnJ66O1LeX2ftNeW4tPtZPqhTi0+1k+qFL7kuBVt8xm4wqSj3oxBNYwkSVLIFdImMUAExN9NQxQMACA7oiA6DrppVMBw4ARHolyAm0Ewg5PqbTmDUdefSqogUpbae7oAXS42g6AGgf6HIVRy9UPppfcsyIMKvt4a43SCTlFN2KK5OMROvKoszKl1EAOUiixTCURAdDaaDpzCNTuT10dqW8vs/aa6E+mQz1oIkKI9I4wgCJQEf9XNeaq8xxleMgqxRQt9RNSTi4+djgeKJMwfM5RyZowcpiY3ORVUpigPVDdERAA56gXjiJ9f0Vg1u0rocnro7Ut5fZ+005PXR2pby+z9pryoESMUpykIIGKBtdwOoIahXPFp9rJ9UKm+5WuBep5PXR2pby+z9ppyeujtS3l9n7TXluLT7WT6oU4tPtZPqhS+5LgXqeT10dqW8vs/aacnro7Ut5fZ+015bi0+1k+qFOLT7WT6oUvuS4F6nk9dHalvL7P2mnJ66O1LeX2ftNeW4tPtZPqhTi0+1k+qFL7kuBep5PXR2pby+z9ppyeujtS3l9n7TXluLT7WT6oU4tPtZPqhS+5LgXqeT10dqW8vs/aacnro7Ut5fZ+015bi0+1k+qFOLT7WT6oUvuS4F6nk9dHalvL7P2mt0f6nzjZVhwrWBDSRTlKrY9/FRA8ii+HULVeCPMRQ+nNpzjprWjPi0+1k+qFbtv1PAUpeFcwVulKGtiX5zgUAH9qz2sa5JoPk7FekwCqw86+TbbP8A3anv8Qtf611WJ3WD6R/NWWO2f+7U9/iFr/WuqxO6wfSP5q6m0fjO6V2dD8BnQPBd+PYPZWRZxsYzcyEjIOk2TFgybndvXqypwTSRRSIAnOc5jFKUhQExhMAAAiIBXt2+J8muronLJbY6vxxeVsN3Du5bSQs2SVue3UmgFM6VfR4Ii4blRAxRUMqQoEAwCYQ1CvQ7O+Xl8A5wxXmdtBIXMtjS92F4BAuHxowssVmsVQ6BXRSnFBQxd7cXAhxSPuH3Dbu6OxjFPCaWjiTMV05ehMJXjJyk7jGzMQIFmcwMpF0tDY7Qx4nbKjw6kCZM0k5NYqx38q3Ik5SUl0Vos8YszBVxRpAcIEn1C0EkxGAjrxWuJrr0G11Af9HT5+p/uB1vz1OHUo8w9Tr0RV3m7cQTSTAyBDFIUm8VMN0PggJtRH6RERHriI89R8YYw8wE5/8A3ZR/NX33oWfsmzzncZ/tC7Wn8A3KXSo+MN2CeLL6KcYbsE8WX0V2S0UFKj4w3YJ4svopxhuwTxZfRRFBSo+MN2CeLL6KcYbsE8WX0URQUqPjDdgniy+inGG7BPFl9FEUFKj4w3YJ4svopxhuwTxZfRREU+MH7wv5IVBU05xAQ5ifEL/3ZR/3Q71Q8YbsE8WX0URbgOBlO+Ln/MbeObXO6XlMAIwIEte4kbIfgZ9kvG6CQKXEqYqcOmdTi0+mhxAEDnS6561IzCItZWUbGMRQW8i5RFQiYpJKbiqhd4peqADpqAD1OpW0fgnOhAy5lY8m2tpeJPj6020qveb1ZGz49BxmHGaJnErHIBx8szJv6LxaH2RVIx1SiAtwrW3esPKx0vKSbmMfoQ8rPS6cHLuI1VvGzPQjxVJwLRwcgEW4owkKpxYjuGOAG3RHSvzjR1Rg150tJg8DQAE5xwhyznHZ3L2OkQ1mo2jIETWtBmBuojfjg3cCOcLart2klVNlXAh3CtxLRcNdFsxCBE4ZvZ9kMHY4DxIZdAkTr0QpJESRaAvMiHFSaBWixRE2uunQNAHmH8OlbgdvVs0b4Ytlo+QhRueKu3GyPRlxXSrOZRNGOtnnHThmigiTVoWDDdA7YuvHtuNQbHL9jNpqB3x1ANCGEQ6m4UNP5q29nDi/QT5/xXd8HbjtVdeLn2yCyCLjIOAmAB/CSOsR0KVSo+MN2CeLL6KcYbsE8WX0V+gryCgpUfGG7BPFl9FOMN2CeLL6KIoKVHxhuwTxZfRTjDdgniy+iiITmNr3h/ENQVNIcRNoIE6g/wDdl7A96oeMN2CeLL6KIoKVHxhuwTxZfRTjDdgniy+iiKClR8YbsE8WX0U4w3YJ4svooigpUfGG7BPFl9FOMN2CeLL6KIoKVHxhuwTxZfRTjDdgniy+iiKClR8YbsE8WX0U4w3YJ4svooi4J8YKmCYeJDn5+OEf6IVwU5t4OYniy+ipuv2IOYv7MYPiB8kKh2Q6VV0YyurSo+MN2CeLL6KcYbsE8WX0VKsoKu5gv9063v4N5/YnFWm4w3YJ4svoq7eDTCbJtvAIF0FN4A6EKH/qTjvV0GtX93Lb/pu8FhafwH9C2BVWYT48z/JiQ/qQqjVUI14kxcKHXRM4bOma0e6QSW6HXMkuQSHFM4lMBTBzCAiBg5tBDnr4uXjwYMr3GNcghjaXkZ5vBx85KLtEGMeWV4w8cyTCQaO3gnTIchzGVTaAiQSmAC8acTFOURKPfvm/YO5beiYGHhZOKSh5BJdqV87QdJkbotVmiaOpAARMUgoDvaaDofm+LXhRVtfUf8jufq902Ps1ONtb5nc/lNj7NVi9xZcOSveaCSNv6foqFSq7xtrfM7n8psfZqcba3zO5/KbH2aqqb4VCpVd421vmdz+U2Ps1ONtb5nc/lNj7NRL4XFu/6yN/Fcj/AP8ANeVQi9QPor0aUhCMuOWjmMyLw7RdqieQkmyrVLj0ToHOYiaBTGECKH3Q3gDUQEddNB871O9RUcQTIVZT15NvdA1Ebja/T/ob6rq2JlqNs+3WVsOLTQkmZpc9xTEgRxxUys+RfxTyNM1UE3FkSQLEpEOVQhzGFdUSnIA6Dalg+YpNHMfItni7Vw5SfEPHuk2rpFVIiqYc6iZyCQxVjgIaAICUogPVqbxtrfM7n8psfZqsxxZi3P8AVS0tiHKp3vcjO6ZdnIsmrxmk2gWcMZJ6omosczQFEwUASc2hyiQdB5wHeDnDQR8fVd421vmdz+U2Ps1ONtb5nc/lNj7NVQAMlbhAc1QqVXeNtb5nc/lNj7NTjbW+Z3P5TY+zUS+FSG3+ktf+KS/rC13pz/Xc3/HTz+1K13EnNrJKJqgwuRQyShVSkUlWZEzCUwGAphK21AB00HTn0HmqjOnB3jpy7VApVXblR2qUgCBCmVUMoYA72ph071FRzg7JVKH/AGOeHsWy6H/7rWvc4wyO2xs7lJDk4yuF3KqMGC5ZMTC0SjknQuJFFMpTlMC7gCNyEUMJkygQ+8mfUAC30a8SZrL9EoquGrxkpHu0kFgbuOLV3BEyZxKYAOUSEMG8UQHQQEOeu5xtrfM7n8psfZqlpgyjSALpXsL3viJueEgYeMi5SPJCOETkNIOkXBVEkYOHhyl1IADvaxJVNdNBBcQ6pdRtlVdBS1g5gZ3P5UY+zU421vmdz+U2Ps1QSSSSr32gAAYBUKlV3jbW+Z3P5TY+zU421vmdz+U2Ps1EvhUKlV3jbW+Z3P5TY+zU421vmdz+U2Ps1EvhQpftae/yhaf2SQqil6ofTVYdvo8WIR0Y1fIIqPSv3K0m8SdLnOmmokmUgJpkKUoAqoI6gIiJg6mnPRw6oc+nP1exRZuMmVWZ4CmeNin3wIeDjSn4s26oBRj2oCJR6mumumvNrpWQ4bQUI5etnTqyDx5YuRbt4gkC7IibpS0kI541bOuNMYDuEytXJRVT3ExM4AQTKAG1x+O/hHhGykkxmOjEWaLJU8dIt0mixW6REUzgRRA5imEhCbwbwhqAiGmugQcba3zO5/KbH2arseWgtG39fNSS0wSMR+ioBSgBSlDUQIUCgI9UQANA1qKq7xtrfM7n8psfZqcba3zO5/KbH2aqK98KhUqu8ba3zO5/KbH2anG2t8zufymx9mol8KhUqu8ba3zO5/KbH2anG2t8zufymx9mol8KhUqu8ba3zO5/KbH2anG2t8zufymx9mol8KhUqu8ba3zO5/KbH2anG2t8zufymx9mol8KhUqu8ba3zO5/KbH2anG2t8zufymx9mol8KhVu1/U8P8AtXMFfyEvz/lZ7WmfjbW+Z3P5TY+zVum/U9x4keFawN0rQlUjlsa/uOGRdoOQMHJV5ugQE0iaDrrqIiP4Kzrfgv6Fek8Gqwc6+SbbP/dqe/xC1/rXVYndYPpH81ZY7Z/7tT3+IWv9a6rE7rB9I/mrqbR+M7pXZ0PwGdA8EKYSjqHMIdQexUYKH5g15tewFS65L1Q+ms2fGOlaq8bYABm10D/uCfkhUyoG/wDobX+AJ+SFR19+aH/5VZv8jP8AaF2tP4AlKUrsldKUpREpSlESlKURKUpRFGp8YP3hfyQqCo1PjB+8L+SFQURbT+CvdLR2SsmP26kmm8GMxtHxJrbtpO6L/B6tnTFxkeSySv2BKXAqC52x3P2JXi1Gxv8ASigN5NuCxlU+Cr4NzIRySRFR2mNp+zH6co36FkGu/dFrvmiLhMBEhFinZvzHIXQN45w592rGcHA3dKNdoVfpc/lIQWGJo+4o4t3lsS15Ju4zrjoxG1wSwf5Q0YKKIEJ0Y0DjWrkWi3xEz1nnt6JAhwKWxtGFbFTNG7fWbHTorWT6dR8eo7mrzbA3RdaiY5TFiOZUf2UqIH1EddPw23vfT9oNWowwS6nOeINJ4PNmRuyXp9KvB1O0YBBipV3Ti4bR72zCZE5HYsW9t8zVTA92s4GQZK2pDZ6xa2jWFlWqZ9j0HSuAIRB6ue5Fg6JCTA7IU3MZrxXRHR6xA0TJWnMNR5gLzBzCGvV/DW6bb7TkFscZ8dSTa8jOWG1Ri6GO9umRa2auzMlhEUDslLPSMJVBArRIEJcoCHQzVIDiB3ogOlj42o9fq6dXWvW+zQH7Eqf6h2bLjOrsJWmuMHSYIGJBnImQ9wzAB2ZuAd3KGlKV+iryiUpSiJSlKIo0/jh9A/iGoKjT+OH0D+IagoiUpSiJSlKIlKUoiUpSiJSlKIoifGCp4fsQfw5vyQqQT4wVPD9iD+HN+SFQ7IdKzdt9bl1qUpUrRKu5gv8AdOt7+Def2JxVo6u5gv8AdOt7+Def2JxXQa1f3ctv+m7wWFq/s7+hbBAAdQAA1Ew6AHUERHqV7/3Kcmdaw7o/DGGD89eES/ZUu8qUdB6g/CCtvmgaE5g+IAiH4NK/B/Zl7P7BrwLUbbWczgi2LoBmZzkcy+fPaNr5btTHWZtkotqCpemSRERER07Vq/8AcpyX9ol0eSzemnuU5M69h3R5MMP562gaB1NObsVxoAdQAD8FfrH7AdA/O1Oxnkvy79uumvk6f5nLV/7lOTNf2iXPp/Fhtfx0HFOTOtYd0fhjDB+etoGgdgPBTQB6oAP4KfsA0Cf/ADtTsYn7ddNfJ0/zOWsD3Kcl/aJdHks3pp7lOTOvYl0eTDD+etoGgdTTm7FccwdgP5qn/wCH/QAGNtqdjfJP266a+TZ2uWr/ANynJnXsO6PJhh/PXXdYzyExbqu3tlXK1aoABl3C0aYiSQCYCAJh15tTGKH4a2kCABz6AHPoPNpoPMOn84V5G/gAbOn9es2REP8AzbauHbfYRoKzWSraGW2pLWuIkNGIGW9cqye3DTNotdKg6yUxecATLsAYWu4cUZMARAbDugBARKIDFmAQEPw09ynJf2iXR5LN6a2hrAUFltdADjjAIjz/AO8IVLASmHcAS68/wdQAebq83h8FclvsA0BdDnW2p/6fJYH266aBjilOel3MtYHuU5M69h3R5MMP5649ynJmv7RLn0/iw2v462fb6ZS73GJAXXTeE4AUR5+bXs8/U79RaB1dA7OtSPYFq+TAttTsaqn26abGdjZ2uWr8cU5M61h3R+GMMH5659ynJf2iXR5LN6a2f6APVAB/BXOgdTTm7FT+wHQHztTsYo/brpr5On+Zy1f+5Tkzr2JdHkww/nrj3KcmAGo2Hc/kww1tB0AOoGlcaB2A8FQfYDoECeO1Oxilvt101Iix0+1y1co4yyG5FcG9lXKsLV0dk44uNMYUFk93fSN2DF3i6h3wqf7lOTOtYl0eTDB+eti1qlLrd/MAbt+SWnN/4WtepASm1ADFNujumADAYSj2BrjWL2EaBtNBtR9sqAmcg3YYXJtftv0zZ7Q6mLJTIEbTtA+q1ge5Tkv7RLo8lm9NPcpyZ17DujyYYfz1s/ASj8USiAG3RABAdBDqgPhDm79NSgOmpAMIb25qG8IB1R06v/8Adcn9gGgAP7bU7GLjft001McTZ2uWr/3Kcma/tEufT+LDa/joOKcmdaw7o/DGGD89bPgMmJzEA6YqlLqJAOUVQ17Jer36i0AeqAD+CpH/AIf9BHDjlTsYn7dNNDOyU+1y1ge5Tkv7RLo8lm9NPcpyZ17EujyYYfz1tA0Dqac3YpoAdQNKfsB0B87U7GKP266a+Tp/mctX3uU5M69h3R5MMP56kusZZEZNnDx3ZVxtmjREzhy4WjjFSQTIUTHOcdeYAABER7AVtG0AOoAB+CvK31oFk3eOgajbL4NdP/8AGUrj2r2CaDo2epWbbKhIE5N2dXMuRZvbjperaadF1kpi8QM3bSFrpDFOTB0HkHdG6Ic2sWYBH+eufcpyX9ol0eSzemtnxQACl5gABIAj2OoFciIF+MIFAR0+EOgDr1PDrWlP2BaBcwONtqdjfqFnV9ummm1C02Onhzu8FrA9ynJnXsO6PJhh/PXHuU5M1/aJc+n8WG1/HWz8BTE4pgYgKAXeEm8G+Aa6aiHV015tepXJt0uphEhQKAiYwiAAXTnHUet2a0/YBoAT/wAbU/8AT67VX9ummx/5Nna5avxxTkzrWHdH4Ywwfnrn3Kcl/aJdHks3prZ+IFHQeYQHTQR6nP1Px07w9bQBAebTqaa+EPDVR7AdAYg2yp2N5lLfbnpsmOJ056XLWB7lOTOvYl0eTDD+euPcpyZ17DujyYYfz1tAESgIF1KAibdAomABEex/N/NTQA05gLv8xREALvCPUAOzroPgqT7ANAfO1exvkqj266bJ/sdPtK1f+5TkzX9olz6fxYbX8dc+5TkzrWJdHkwwfnrZ/oAjzaa8w8/MNB06+n4afsB0ADHHanYxP266a+TZ2uWsD3Kcl/aJdHks3pp7lOTOvYd0eTDD+etoGnN1Ob6OaoAOiKgpFOlxwFAxkgOHHAHWES666d+pHsA0BMG21OxvkpHtz018mztctYPuU5M1/aJc+n8WG1/HQcU5M61h3R+GMMH562gaAGohpzDoI83MPU0/n/nrkQLroIF1105xDXXsf4VA9gOr7TBttTsaoHt100RhYmdrub11rV/7lOS/tEujyWb01uV/U/8AaF02twq+Al7jgJWDSe2Rf6TVSSai3I4OS1HhjlII9USgICP01akA15gDX6OcA+n+atgnBIlKPChbKgc3PbmSSCIDpp/2OW5hryWvfsd0Pq1qpa9N2a1PfUptBDTdgy4N2Y4TK9VqT7W9Kaxaz2XQ1ezMY2oSCRMiGkiJ3kepXw1bZRDKZucEIAmOpBtSFKHxjCKzoAAK7LrYqy22m7ktYk9hF/eVptZhzO2RFbQVnyd3tTW+zePplumxTfidVw2RYPDCglvHMZuYpAMYQKMjbIABzguAqAmAwjMBUMIiVMOOc/CHvB1auvO4PIaz2uLnGRdkWNiscXjft9rbQkPnu2Je6MnR60NGDERQxKD40uoiZS3DhHNTICsRxdS4KpJfZBD5mtTQHuMbTtHMvpaytBpMncPDzw5lr2MQBMBSFH4QalAR6gdWm5zAYCjoA669b/8AepV8dm64sYWjn/EFzZnhk7jxTB5Ai5S/YZaJCfQexqTkhnBVmAiAOkyhoc7YR0WImZPn39B2XWFlXg+WOdchXvfzi3JqJu7F1sWAV/buGHcBDQs1EMcdoZEvC3YQkeLJq+ubi7+GFIsxIgzcCUztqxTdJmQxZmMPXrJWbLnEHAD1uWr1tr0G15//AFdPm06nwAqbUwotATILUjjoYUii36IMUF+L3Q3N/dDd3tNNdObXXSojcUXT4Bh1/wDH/hX3zoZ17RVnd/2M/wBoXa0xDQ3cpNKmbyXazeM/wpvJdrN4z/CuzV1LpUzeS7Wbxn+FN5LtZvGf4URS6VM3ku1m8Z/hTeS7Wbxn+FEUulTN5LtZvGf4U3ku1m8Z/hRFLpUzeS7Wbxn+FN5LtZvGf4URcKfGD94X8kKgqcYSa85DD8EvPv6f7od6od5LtZvGf4URbNeD0UZsITOky7QtFQ7G4MOpxy1zCvJumy45etpYyrGEJqWVNxDV0RdioAgZqq5OUonIQQzq29bkYv8AgoMMWmi4glXtpbcd6OXi0U3UhF3hJO4c5ukXKsOoimdiU5S6pNzCcSpgUhuLMQUwwb2A3LljYW0E+aqzJUi3xhBFwyt2GI5nZAAytHOCEZy6heLinBTtUjJOjGKB1NxEREqpwrLnhDHU612GrNtyfNfrZw32lJSZShb6thvJu407m99pPjyDdbfVNwuVZRVN0mYxuPfdMFEjcS1II/PWnbU+l7Qmsb8LqrAcBlwRjGRtg7+rFfpdi0fZtJ6hVeGEvoUy9mLsHcYAMAMmC0n+K7tJvYKyO3idqvb+1DvktuMfJbWuPSowrqVdX9eZSe5bIpulmdwfsHQIHK3Fdof4fGOGBE9SM1NNO4bwiAB1up1tK3G7biyr+09tZ6wk5aRtxPbRxcYw2Na5bSwwq7WxzehSOHbJcoPGsjoi5TaJlACKJ9ODqaiCNadN5Ln+Abn/APH/AIV+iezlt3QzwBgXtOUZ02c+PcvP64uJ0iyT7wD8JdIitV2OAI65O87BLpUzeS7Wbxn+FN5LtZvGf4V+gryil0qZvJdrN4z/AApvJdrN4z/CiKXSpm8l2s3jP8KbyXazeM/woi4T+OH0D+IagqcUSCYN0hgHQeqfXrD3qhAyenOQwj1x4zTX+aiKXSpm8l2s3jP8KbyXazeM/wAKIpdKmbyXazeM/wAKbyXazeM/woil0qZvJdrN4z/Cm8l2s3jP8KIpdKmbyXazeM/wpvJdrN4z/CiKXSpm8l2s3jP8KbyXazeM/wAKIoSfGCp4fsQfw5vyQqWUU9Q0IYO/xmv5qmbxeJAd0dONEfjdfdDvVDsh0qjm3iutSpm8l2s3jP8ACm8l2s3jP8KlXUuruYL/AHTre/g3n9icVafeS7Wbxn+FXawcJBybb+6UQHi3nVNva/5E471dBrV/dy2/6bvBce042d/QVsGS/ZE/4Qv5Ra2+j1CfvArUEl+yJ/whfyi1t9HqE/eBXhf/AA85aS6Wf+5fIXt3+Ow9D/8A2rilKV9Mr50SlKURKzOxpbtkXHs3XRFgTGRLvl1pqNTkLjloiHv1nc6ktZqVlJtHTtVNVCPOxVuoyxyGK0EAeGdGAyaAlwxqEQKbnEpRHQS6iUBHQeqFdZpPR7tI0m02VSwtcHSMZjYRhhtzzErnaPtbLDWdVdTDwQWwefCdvgtxuNo/ZjfJYvfT1uY3jJtzaSbVW1ZE1iuXMZCqHsVo7fLujvlGj9MFSXa8cOpMUJwjJ/Ig0RIq1bqE0oZMTQRt270mp2yrVNYyLVVmZQ7JZIsiiVM6JlPsgpiUCiQVPhiUSib4Wteh4tPUQFNIQE2+JBIG6Jub4Qh2eYOfvBXlL9/adcH/AAqP9rbV0FLQL9EWO3V3V3VA9hwOTYvHCSd/iV3v2wzSdqsVAUWsLHtlwzdi3d0dWSyk2fTY8LkiYHKZCGswuMr36MKmqwRlDuuTEr0uLGHegLYJAXXEdCCoA/5RxWgCOlZ1Oldno9ocRCkxB7mBmIrXwS6lrTPl1NEmILRCB6CVQAr7ppymM7BQsUHEmedHdFFFMFdNUa37Mt/DG6oa/wC8I1K0Dm5g1KAgA6c4APV5620vqydL2htq4y9nuht0ZYYyMQAccDsIB2LjWDTv2fRFmNBr4eXScCZ2TBwwxG2VuAlciYDn7n2ho6yIWyVbktZurFWhdzW0cUWW0uBFe5g4hjBtJQFId0xjWbdUppQ2ko6bPdS7m4WtQAgYBED6b4Dofd5y69fTva61CJCmAAMUpg5tAMUDAGneqKuXoHV+noIVBSql4fd+LEggQcZyJxwGCy0zpt2meDc6k1hbewbgMThhGYyzOSUpSvQrokpSlQ7IqzPjHSvKWzru3lprry6k9NA1EfgNq23ru9mnIB7Zxwe3bXui47Xxfal0wpG8pbmCLck3w29brKetwbtZnBw+V4x/Lzi6soZNYjqEM2T3gOqB9SdqFET3du9Ub9ki/wBFr/hWSTHZ3y9L2u5u6NtJGUgmsA1uNyVtcEYEt0M8jHE2iVKMUcFduFulzVWROg0RWUTagVU5SlHWvE2yx2K02Gz1rVauBLC66ZAxLhmcDzYEYHNess9stFnt1enZrOKpcGgiJwA2YRjgdqzWe4p2X7pjmbp7exhsjD9oso1xeMTMJInuaGlr1uu1otZyVJPQj5k7Xh5RUypSncMVDjxYpFLpVm2LNm0se6tF0vbMMLi5m92yWOWmYmF3qrSTKHv9rBxKF0g5RP0PMJNYN4sRNymCK8gkjxyBlUSkwnuXDG0FYdqScXcVvz0NZYt1ciXFEoXOxeQLFaJBiycLyrZs6UIhJMSTjBM7R2Uj1BKRIIolIYRChO9nrMUeW5Qd49lUS2hE3DN3CmB2ivS9ha5I89wPN0qo8Yg2CVj9VUt4iouQ4oVRKbd6hmimVaTidNfu7xulrhGF074kOknPZEbedx6rTc0nRQLsjLT2DAmIOWHXgVnncLvZqkrGt+wTmYpWRju1Hcy0td1kmMYXRc0gm/y2/K3fv2xVxSeqKRNqIrKsFjgulJMy/DKZqIY/ZzxlguAxxL3Ri9zHEkkcnt2rMXeTUbudSMVJxiTwkfENUFzHOSKWFdB2vIoIuB0bKEXWKodMlgY/B2VpS5JWz4yx5F7csGvCNpOIarNVHLI1xi3CGEw8bubrgHKB98BEqRDCdUyZCHMWqr7OmXUItOaStRi+QWsB3lToeFu6CnZ5C2mLQsi4mV4xs8UepNCtTg4IoqiUFUx3kwOHPXMs+jrDou0sr/apALg9wLsHT7xmXbRJ6OZY17dbbfQdSOjgTBaCG4jANAGEiDI6edWVpV81Nm3NKCLRRezUUnEjdKNmRMMpdsGnc87JrhGCRtHRIvAeO9AmYwx1WyKiSYOgFQ5AKcS+PvbFV648atXtzsotJo9l14NB7B3RF3fHqOUGjJ+JAdsHC7YxTtpBqsmcipiqFMfQdUzgX2NDS2i7TVFGz2hrnGYAIJMZwJnavL1NGaQs7C+tReGiMSDtyxiNit5XlL7/AGk3d/Jp9/ZlK9XXlL7/AGk3d/Jp9/ZlK2t/9hrf5XeBVLAJt1EDlt8Qsl9nBC118zWPyxQiHsA36PfOI2aUj0m0u4Rh36ke0RF+IRwulXZWhWpZIQZGc8QVxqkY5TZ3ylkYFgclrTtmweMbyi7kZ38xmnima4ux4eNmHFtXAziLKRhSrg3adEmBguaWaiZBN6+UTZuE0U0ShqiAAMQpTFAxTJABiiGpTAJecBCgkIPVImPwdwPgAOhfk9Tqd7qV5+3av19I1m2unaXMHB3C0TBmccCDhIjoXdWPTdLRzXUKlnbUN+8HOgkZYAEEQYxnetwcUrsZwclYWMpWYtJG17euDIThinIy7jI1suJeSbx7QrmceIgBTFiEumCEU6U1TeqNW6hREAMKnm8T2diUJbAktkVtg2eu2zIe37pdFtTIVs4xs9do7uqB6ITl1GzjeeO7Yh03D90k+3VHa8w6QMd0RioWtUIFKAaAAAGgBp9HUrji0+1phzgI/AD4Qh1BHvh2a612pNcCKdvfJHvEycZOLTeBE3jexJOGOAXJOtNJ5IqWRkbAIHJEEERIuyMMMcCtwGLcX7L8AezLvmn2Op66LiTfDOQ/LW3rmZS7KUfW+96btY2ZdgybHjm7mcaPiKJtRK2RfAwKs6bpuR6ONIvCLaJxoxvmCwq7kenkzGOrhiTWDKRTa0U5G2XQSiTdw+RO6YrR6cyyVdTBhuBs6knirZJQEBTS1G7iYgOqZB3h1NqQPhCGmgj2R5g5x7AUEhB3vgE+EYDmHcAREwdQ30h1h6oVm7Um1VC51XSD3EnAmZAxiDeyF7KN61p610KV0U7G1sZ4iCcJOWeE9fbtftqFwvLRDBuNvY0BnOYxc2TAY2ksmWdZyl7s0JCxnJboPdjLi5Fo4XL01MdpLqmdkUYOyp6FK4Try9qWpiaNbsmUNH4iviFXtfNVj22g+yNA2Bc2UGppSHPYxpyRByVy1du2yMgDZ4qRuoCae4TixMTXWQJCG3t4hDAc+8YTEAd8dADU3ZHmDn6tcCmmbUDEIYB+MBiAYDfTXJbqfaabblO2OjE4g5kOE/FnjnnnOyM3a1UnvD3WRstiMhkWmB7uRjEHq2ztXVxls0TPQ9qlmbSjbGtK8Z6PsNyjeVpPpXaGXIpdz6PbOZtuZvNxwNjIQ0adR45QauU1WhkhRVcJKD44cebNlrv8XSVlyfQp7iyyjDXHct/T9q3jF2bGqTUu1moOZiV3blqCjSMZsF2smi2VIoo8BQzs5VEyk1t7hNTGEhRE4AU4iUBA4B1AHsh3hrgCED4RSEKbd3AECAAgHY+jvdSlPVG2sIaNIvu5luw9MnEmZJPRllWprLZagg2FgdsdtAwywwiMOnfitmuOn+zZecfimfax+IGt7zm1azuzKdqXva6Fr2/E25LN5sXsE1F2snHlhIhilHHIcpgFR+Y2oCp0OlXt7FDCNz4MtbCt7NMetm0Cki+vuRbZnYQEtbahr1vtKWuZsqQ5gduWMc3hXAMWxlyu0XDEnFqJFR11KCAG1AdB16u8G8FcbhNADiyfBEBL8EB0EOoIVS0alurC4221A0ODmyTLS2YAIIluOAjCBuCUdaruLrKyYIMRBBG2QceeehbiGMRsuzUZHH6HsSyr9lcf8XGoOo3H8izsK3l30M3VfO0VHjiOknDVuWWcce4Es6szcOBKkisRI5fEX4+wlZcjZUvbDLB7yNgcIXU8loZ/AWrcTXIDqJh4VO2egt0FlwaSEk/droHe8TPKdCySTkRSRREuq7dIBdNwmm9v/sYAIG113vp159aAUpRESlKUTG3jCUN0TD2RHs9+q0NSalKsHvtj3Ng4dIIzJmBMiduO1aVNa2PpXG2Rgdh707iDlGZiDv6lnrEuLUu9jG204jMEP5BrsaXJMTdyuIu1LJeku6bbvXcE1bLAZqkEpH9CRLRMAA7gh3LzjOZbeDOXZSYYsZcJzwepsaI2S2MnjrK8ZLFtI0QqWVQbWRFdASZDMFDmOxdHUkjtlJcCy4n6MI6ASpoDWiUSlNzCUogHU1KFbA+CQKUOFD2U9CgG/buSjH3ebeEbOWARHv8AMHP3q8X7WNWzQ1Ntttp2ghrGg3NhJeJJxjbAgbAvZ+yzTora42Oyvoi89xF4xIhmAGE7OslfDTtnfu1u+/BNQ/8AvOqvnGbOOz5LbJMLkq6LxyVYObG1kyeRDNLTs+Uy/YM5b4XDI29Dyl2D0ExTtZN5LJoQrZyyfTRXCxkjLMmQmMY9jds392x31P8AUTXq9T9ldVnTk2JuK9uDihciSu0nJSsfEWrEsLWwbcG07N3S5iIqOvRzDrRaNpKgRqAx67yPfpJHIOiUuuomJAZarfFVqgF5O8eu5faNmINOmDtA7YWmc5gL8QddQ1HXqhzVAUw694R6nWCoa5L1Q+muOz4x0q6vI3/0Nr/AE/JCo6gb/wChtf4An5IVHX35of8A5VZv8jP9oXa0/gCUpSuyV0pSlESlKURKUpREpSlEUanxg/eF/JCoKjU+MH7wv5IVBRFsr2KSGSwZtNyLgE+lKGRcHx8wrK3L0ktFsitkFy4E8w3KYFHDX/IxAwp/CQEeN5t0BDLDb/jQb7IUKaOVj07XaZXgXrVLH+UzT+HyrSty7RZkkWMKucz1NPoZoARqxx1LGocYrqtKbw4W7KCjFHZ92jFju7dbSq2XsKx8SDmEUm7yATXNPOFloQmgoiZMECcciqGixDkIXqmCvoUY44tTKrTb2tBzGWRcE3bmwFkW+U1bhxOrZeZWsowb5oklHZ40U+gUFRWVQcvAbAVRu8cRjRuJU2jgofM+uFqpWHXhtrqAG7WbtaMqbdpE5OJX7LqzRqnUa2tkkcXLoioR+O+CYIZmMyQNl0nFaZttKOcGJwiKsw3uJGZgtsfGySQZOulJtk9kC9t38kukvENtWjoTlQa9EvCc7UW7EhR0dqa6fg5yiABz9X6a24bTqccEBwk7Vk9tZFk32xMcqRUZYyC+RLYfAm0yKgKrC7Vd5VFumQ4iCahw6OMsHMIsy7uo8dd7UADUA6n4K/WPZ869o2sRlNPcc6NM5jPrx5l4fXEEaSY0yIFTeAP39XKZEf5SRzyoKUpX6AvJJSlKIlKUoijT+OH0D+IagqNP44fQP4hqCiJSlKIlKUoiUpSiJSlKIlKUoiiJ8YKnaAKIB1uPN+SFSSfGCp4fsQfw5vyQqHZDpVHOunBdalKVKulXcwX+6db38G8/sTirR1dzBf7p1vfwbz+xOK6DWr+7lt/03eCwtX9nf0LYQl+yJ/whfyi1t9HqE/eBWoFMdFEx100UKJteppvBW1Mb8snQv/ay3+YoAIdNEtfx1+eewS32KxDSAtlVrCSyLzgJi9lJHMvkz24WG3Wx1hNjoufF+brS6JuxMA7l6qleT5eWTp+223vo6aJa/jpy8sjr3bbwd8ZRL019G/b2g/m6f52+a+ffsPTPylT8jvJespXk+Xlk/bbb/lRL005eWT9ttvj3+miXpp9vaD+bp/nb5p9h6Z+Uqfkd5L1lK8ny8snT9ttvfR00S1/HTl5ZP2228H0yiXpp9vaD+bp/nb5p9iaZ+Uqfkd5L1leRv39p1wf8Kj/a21RcvLJ+223w7/TRL015e9r0s9zak43bXRBOFlWyQJpJSSZ1FBB03MIAGuo8wCP4Brr9Kab0M/RldjLXTJLHR77dy5ujNC6YbpGg99lqABzf4HbxjkrsLfsy38Mf8oal15dW/LIFVUS3dbwgZU4gISqQ6/CEezUvl5ZOn7bbe+jpolr+OuazTuhS1v8AxlPIfxt81w3aE0yXE8UqZ8h3kvWUryfLyyOvdtvB3xlEvTTl5ZP222/5US9NX+3tB/N0/wA7fNR9h6Z+Uqfkd5L1lK8ny8sn7bbfHv8ATRL005eWTp+223vo6aJa/jp9vaD+bp/nb5p9iaZ+Uqfkd5L1lK8ny8sn7bbeD6ZRL005eWT9tlv+VEvTVTp7QkH/AIun+dvmpZoPTIeDxWp+R3korXLvcsCiOgGvuSLvAPOGpWvOFZ5y21LIW/bTBhjYjFlcUryVuSVuWRt1m/f2k6iLGQsqSiYpRwgZZr0QRoKh3rFQhlWsgogJgHf01z23elnomugVrogkgc3tIOkAUkky8emcG4EULz85TbptB6g6DXoxvyyfttt4f/qiXprz1OtqzpOx0m2+0U3BhOBc2Djtk44wV3lSy6wWG21H2Oz1ASAJDXbhkY6VsXk9qPFL2Jyfa7bG+RI+2M3StyXrkMhLvh3M1FzlxKwagpQqh2fFdLmoRLoCg8KLhbpiUDiXiA3+zA7aydvnhAWx6F4FY43tfC8me9pVN+u6teGPOml02vEFSKi/kyybABcq8cmTpWXfSXAScXrf5eWTp+223vo6aJa/joF+WT9ttv8A0jKJemuP9lakmmab67C0wSDVBxGG+coGeQC3Ft1y4RtTgHhzZgimdsHKIOOPatkUPthWtZt4ZCvKzcf3Osvli1rYsK/4K9JqIl4R5BwpItjMRzNRJoVdPprHsnTUXIiCyBnm8QwgBgN1/wBeKzgkrac2Pb93xkrZeDSYUtVKdXtpzExyaKMU3JIOHLaOSkHoLpRgJuWjxdRISOlipAmUd0dcvLyyfttt/wAqJemnLyyfttt8f/qiXpqPsrUgkGpXYTAGNUGQBdAMnYMN/OpGktc2uJZQe3EkRSOBLg4nLa7HpWyXIe2Fal/qFK1s/ItgMGeZF8oxEbY9yW4k8iU1CW4m1bs5tzFqSbRwwLAj0Oq0UIioKqXHpKbgiNqc45gsbI1rM2tqQrmFlZK+07lucHMRHQruYWj7caw5p163jm6EYm/mHC71ddGPTKkQrJET7yipzGww5eWTp+223vo6aJa/jpy8sjr3bbwd8ZRL01rYrBqfo+rRqWK0MaWTH70HOMDJk75MlZWu062W6m9tqs73XwAZpu2bsIE9G1esryl9/tJu7+TT7+zKVxy8sn7bbfDv9NEvTXmr1vWz3Nn3U2b3RBLuF7eeIoIpSSZ1VjmbqAUpS66iIiIAAd+u60hpzQzrDWaLXTJuu/jbu6V1Vh0JpgW2k42WoBeb/A7eOZXQL8Un7wv4grmvJBfllAUmt2W9qBQAQ6aJc3MHfqLl3ZH23W95UT9Ncihp3QopMAtdPLlt81lX0Lpg1XEWSp+R3kvV0ryfLyyOvdtvB3xlEvTTl5ZP222/5US9Na/b2g/m6f52+ax+w9M/KVPyO8l6yleT5eWT9ttvj3+miXppy8snT9ttvfR00S1/HT7e0H83T/O3zT7E0z8pU/I7yXrKV5Pl5ZP2228H0yiXppy8sn7bbfDv9NEvTT7e0H83T/O3zT7E0z8pU/I7yXrKV5Pl5ZP222/5US9NBvyyfttt4f8A6ol6afb2g/m6f52+afYmmflKn5HeS9ZSvJ8vLJ0/bbb30dNEtfx05eWR17tt4O+Mol6afb2g/m6f52+afYemflKn5HeS9ZSvJ8vLJ+223/KiXppy8sn7bbfHv9NEvTT7e0H83T/O3zT7D0zlxSp+R3kvWVsD4JH/AGoWyj/JzJX/ACctWtDl5ZOn7bbe+jpolr+Oti/A+3BBzfCibLJIaYjJVRrbOSTrkYvCODIgazlgKJgKPMAiAh+Cvzj2saY0VaPZ9pGjQtNNzy0QGuaSfebkAZK/QPZbonSln16sFWvZ3tYHGS5pAHukYkjDNfEBtmfu2uv4jaf1zqszMiYlyHPcGlh/J8OW4bOsOzsfOkbvtWOlLZJamRj+6hIt0LtftEnhZYXAmuOKjSHctlVDhBgIGSbkTAMNNsz9211p1ekbTTrf986rLjK+WoG2dgDFOH2qL+2mWSsWsbwgca3DtF3Te7OTkGeQ59CSyEwtNpbTaDj374Yd7FA2fS6gpMGYqg0O5VScm+FLXjfLc7wA+vd62r7fs08HT3QJ6MFqSrkvVD6avns14ytnM2fcQYrvC5VLRtfIGQYy1Zy4UFW6DqObvHJElBQUX+wEVPqCSZ1tUyqKkEwCUBAdjmNeDpw9dO1q2xXkXKM7hPEk3YDOVt0l8zDFHIDu8nVg27c0pYBpcWBYwj625C5WqEqs7btVAbs1CptAdqkQJi0AOBJwnwWzRffcbmtarYdGjQer9gJ+QFRiIaFDsa60bkIRq2JxxTbjdMBMUBMQ47oaiA9co8+g9cNKj3U+2f0Br750KQ7RNmcMjTZ/tC7SnJptlS6VM3U+2f0Bpup9s/oDXaK6l0qZup9s/oDTdT7Z/QGiKXSpm6n2z+gNN1Ptn9AaIpdKmbqfbP6A03U+2f0Boil0qZup9s/oDTdT7Z/QGiLhT4wfvC/khUFTjAQRDU+nwC/7oj/uhUO6n2z+gNEWwrZgWkWuyptNrsnVypoONoPBMY6ZRTZJCEciMnfDpA7+REBUaKEO1DocyegHOosJv2MAr6osJSdx2LkXb2hHcLer5UdhGMtVG0b+v2Ef46A10uMn2+1cusgmMUwEQb3DInaGPqMlvSy59VQbFr5QsCFSa7JG0BLFKzWXa7TuEmwEdTpmY8UdHJC6gdKgMHRxDGbJAdTQehdCaaC55/p8xwNjNNoDbQYC/wBnroB/jDBdupz0MymZy1OPk7olXp0IbG4mK6eP1SmbEBloUkWi0AmgdEjvfJHtfr1bNpK02ii4g3nRGchlHeDh72P8O9fRnsq0ZY9K2Kjo620r9F7abXi5IIdWtBI917cwNxqQIaIgnQVtIKTb6J4Uo5lLqk2bDa2x/wBOJGPiWuKrdFdF1frFmtL2mO6qmsoAuQbIJF/yM4ueMAONAa1EfFMAiAgHWDq1tv2mm7MZnhX0ZgIhjNxe1hbYN2WRboXvLLBHKNz3gyfpspdEQbPTplMIP3JwEFSlQOloOuupMNzTnU1Dqc5Br909nZLtGVJwP7rDHPgKW8Dx69i/JNdg1ulgGgBs1YwGXGK0ZGD0gBu4bVJpUzdT7Z/QGm6n2z+gNfoa8cpdKmbqfbP6A03U+2f0Boil0qZup9s/oDTdT7Z/QGiLhP44fQP4hqCpxCk3g0U1HQebcHsDUAFJ11NO9uCNEUFKmbqfbP6A03U+2f0Boil0qZup9s/oDTdT7Z/QGiKXSpm6n2z+gNN1Ptn9AaIpdKmbqfbP6A03U+2f0Boil0qZup9s/oDTdT7Z/QGiKEnxgqeH7EH8Ob8kKllKTUNFNe9uCFTNC8WA73Nxwjrujp1AqHZDpVHAk4LrUqZup9s/oDTdT7Z/QGpV1Lq7mC/3Tre/g3n9icVafdT7Z/QGrtYOAoZNt4Sn3h4t5zboh/6k4roNav7uW3/Td4LC0/2d/QtgVVbp/P8AWnp0A7ATTrT+sqk0r4wa97PhMLxsA4qrdP7g+2Ce8tOvXp0/uD7YJ7y069eqTSrcLV5RU4bh2KrdP7g+2Ce8tOvXp0/uD7YJ7y069eqTSnC1eUUw3DsVW6f3B9sE95adevTp/cH2wT3lp169Ums17I2dLNujZPu7K5m18vslpQV33tbUhFvE+Q0ajZs/j6AG3nbAGqizh/LjfCjhNYq6RkOgmZSIrg4VEsGtUaCbxwhQ1t5waIk5dQnwCw+6f3B9sE95adevUCk3OKkMmrNzSyZg0MmtLOVUzc4DzlE+g84APP2K3QYu4P3ZeyDFYzvBDIM47su52Dayn8sllBswJN3NIKY0aNlnABCqLQb4z+95yMaW64TcJunkLFAvIs0H6rgNLEiyVjZKSjV03KS0ZJOI1ZJ6gVs8SO2XUQOVdIpjARUopiByFMYCmAwAYwBqNeHc5xpl2OffCvwRawVCM+boXaG4Lg5zDcE9zj1pp11//mU6f3B9sE95adevV8tl7EtsZsybL2Pd1xs7QiEMN37fCd0yr9zGQNvPbbtCXnI95KKtmrpx0Cm4YomclQbqqGR3wKXUda2HyWwlgCPh4y1myt7XNct4Rj95E5wtC+HDvGVolgsFWXlJxJrxbmISO7iJKQnX7UF3J2Lhs3cstwp1CCRSKlpfRYXOJgRlzqtwQ0gQCSB0gA/Vafhn7gDq3BPB9M06D/rp0/uD7YJ7y069et0eQNkHYls+U2kAZvGzmUwOirDyViye09LSluwyyt4GgINzJ3LE2q6cJTUk1ReipbQtjotHDUgLP0AV3A0iAOoAbdMXfADbh/jk1DXQ3fDqD9FVp2upUJALlpVoPpfGBnHZ661WOn9wfbBPeWnXr06f3B9sE95adevVJpW3C1eUVnhuHYqt0/uD7YJ7y069enT+f68/PCHXAZp1oP8ATqk0pwtXlFMNw7FUyzk2lvinNzKQqHFRQU5dyQTmNpqY2h+cR0DUR5x0Coxn7g5/+0E9rrp/rp1/eVRlTbiShwABEiRjhvdTUAEQ/FW3i6tiPZxf25bjeFyU5xtd1tYvsjKWSkYufX2jrslYS67etEFZNxZzVsxUhHJLku6LZIMBfqgrGKLuxABRIC9HVn02yHECYw8grMpg/CB6y7Vqg6f3B9sE95adevTp/cH2wT3lp169bU5jgz5ScfWha1rXlbFvXTaVsNoDNrl90bKwKM09ua9LZg51u4McBKymJeBZQSaCZCigssioJDmVFKuYzg6rSk7EcSLHKYuIlzcSt5J5anbKcWRKwdo27D5DRuxmtaT+TSQF302swwtFV5BJNVqJFjOGxBXTTrxw3LzXmMuwx4qrWyRIzjZt51qr6f3B9sE95adevTp/cH2wT3lp169bcbj2GcGK4ysqItnIMSefgLFc3/lLPcRDT90xUw1QkczuFV4KJJI9BvmasdiwEWxUyNFSKqb51lwXNxGKu0DsakwdY1z3mwyihkdS1cro2DORUJZRoUbYjZSOTk7Zlpwyz8zhitMIKGM3akbOGwmau0umRl0DIjY2ktqmm55kGPDHoxVuCEAgDGemRBI6lhx0/uD7YJ7y069enT+4PtgnvLTr16pNKvwtXllVgblVun9wfbBPeWnXr1ApNziyZ0lpybVSVKJFElZdwomoUeYQMUT6CA9cB5qplKcLU5SYbu5Vbp/Ph/7fnQAOt06daB/9ynT+4PtgnvLTr16ups92HbmR8osbfu5C4H9tRloXTf8AMwtqOyR90XQjatrTNyjDx7k6SoILvhiitgccSqKJFlFCpKGIUo7EZnYCxJNzyVmY7u15HSFrZDjX2cJ2/r9CBlcVWlcHRMjCtWca8iWcVLv1GjdRgCzeVKspJkSTFgmVRXoerqzmQLxEoxoN4xl6wjsC1M8oLg5v8/z3P1P89Ouf+nXPT+4PtgnvLTr163mP+DTwe8n4zHcXI3TGzEE/zXK3CWRuiJaZNulS3ZyVgcc2+wVWkulLlBEkX0fLumLMqqRSOAMoXjm6KFnIzg/MXW1PbNVnX7K3LcUpnjCs66l7xtDJcOSzojIDB6+6HhbZFgykiSpzkKxjmbV8uwbv3zeQMZ+3TIQpai1mMHnMjbOB8ZCl1MBpIxgxhju81qW6f3B9sE95adevTp/cH2wT3lp169bu4ng19n9+6gEHFx3uiR7ZrexpQyt/N23QeSHCkGmNur6Q5uh7lKMm+4i1R349wLZn/n8nRPNYu29kbAF1WXiqai2t5Nb+zDjp/B2Vj6eybItYR1kKImmzOQgpSeStEwxkoRgsZ2tDumxI9FRdmUsyoQ7gyAWl4ycfXjl1q7aN7mwnHoBWrgLguDn/AM/zw6dmadf3lOn9wfbBPeWnXr1tImNiHEljw1h2fcs1f95ZEdjmeNvy4bOhHFmxENcFj4sti+oaMZpvCuyykU1XkHaSz9qmzVfdHFBPiyJJHVodzcH1bdrYExZmW4MmyEPGnxJM5DzhcFqxkbmePYSik3aMZblu2xExj9NdcyQXcyQlHztdNJu4buylKIkQScxxl8wXntzwnwUOolpaCBJ8wMVrT6f3B9sE95adevTp/cH2wT3lp169bHY3Ypx2wufMGDlL3lL/ANpC1GqVvWvbb+LmcJY95QsIafnLrQYzqkdJN5ZKPax8eq2UenhknhVFx30yikoaro7DOLbXyBk/Ddy5IVyVfETie7Zm25awZYtuObcvGzWlwKyFrdIiN5FGWdujwoPEEXcjEqdJivHJ0UnhEmQ2NpcHFl8zE9I5vXMjad4wPDLLPtWsvp/cH2wT3lp169On9wfbBPeWnXr1t7U2S9k2OhZ67Wze+bsx3kHLGK8c4QmrfuqUkrol4a5L6u62rnmRVexkYWKlwNayzYjKSipJmiVJY6QrmcEVaegwJsFbOk20gZzJT693jmGzRdIXVaycuurIyVnW/cl423HrOIRsi1XJFvXFsJt1ZtrKEeA/Ou1BkgiZB4NXWlzPicR6E+ITgJ94GQAD1EYdq0ydP7g+2Ce8tOvXp0/uD7YJ7y069etoeeNg7HdjQdx35CXw5g8a46txqwui7rcSkszTd6XfLBb54+HbWr0KwVhWyZ7hbAdfo+WQIyUZHB07eHVQNS5rYPtbFkVMu8mur7uOJe7PN05Nkr7h1GdgobOk5a6j6JVhrmhQ6ZhJvH0+e3olnHkeM3Bwk3Qqt266Bk0bNtL3NBvEH6wo4K6JOW/s81rQ6f3B9sE95adevW6n9T2SMjIcK3gcj+QkHxUrGv0EyvXyrsExG1HmolA5h0EdOqHYqGV4OLBqWRbzkm9x5DlsbSOXmOAbfs+Dj1rOufFtxyEhCMXkku8fA9GchohOaaLg5IDQHi6xm6hmpSEUVvnwMuFYvAvDA7PFgK3q9vG+o/Fl3uciptrJc2vaFtyD2wSSxGEPJrLqDMIghJth6PTTbEExDbqR0zJLH49W0ipRcC7MYdyvRZ+9a4RE+v6L4uts4d3Nb0dNf8wNQ/8AuuqhuraXNcOBonBiMHdjWGt1MrW2wksjnuCBt1uaUVmHLdlHKx4HRTWduXjgSJuik452qfdETCA87Z/7tT3+IWv9a6rE7TmAezXXWgu4RzdhPgu2s+FJjhnA8FyQ24YDaFNoOuhg1Aang4MHwdCgBh+EAF017FdauS9UPprKmSHiN61V423+hNB7LdP8gKmVC2DVo0AO0E/ICozAACIBX35oiDouz/5Gf7Qu1piKbQuKUpXYq6UpSiJSlKIlKUoiUpSiKNT4wfvC/khUFRqdUP3hfyQrgC6hqIgAB1tecfooTCgkDErOLEa0ew2LM4PlnKCT5baxxAUUDQhnjxVq0hslrKqIv9N1uVM6yQKNzDvORUQMUNGpxD6m4S6rna7a+2LCpP8ANBbsl0dliHLETmJ463tomWXXu6OWFo7ukpelcIJF3qJnEmYSlkXJ44hDAmVQQ+VqyCPUNg7LrlJC8Olrna9xs1knjKQQJYqSyVq5AVZpyDMfsqj8QO9MzVKPFpopyZVOdVKvomumSbsdrLbTZsICLJYcdL7IKc10DtJr3nssQaAS9tps1ZyaMKkrcEUum5XK0bI8YoyQcyCioCdknp8m+1ymaltrOxkmpyx8qMxIjp3ZHFfR/soqUxToU33T7rM+BJwNrdHvw7Zk2XGRD2ksWnrNCpVkOGISgHrUtshtIWtJpIY4tMVsbOk0ch3M2ZdDuXIA6j2CZXSgtCjodXVJJUBEObULzAHZEf5q26ZcJJvFeGRO9CZcOkc8Q8pLu7rkkbFugiw5XmEjmkIBDRus9Oq6EF2SIcWzOKyiehEK1GiAaCIBoIDX7d7OwRo2oDmBS3/4FPp9da/HNcve0pfBmTVxwknjFY7IJOObgDuEQoKUpX6IvKJSlKIlKUoijT+OH0D+IagqNP44fQP4hqCiJSlKIlKUoiUpSiJSlKIlKUoiiJ8YKnaBxIB1uPN+SFSSfGCp4fsQfw5vyQqHZDpVHGMl1qUpUq6VdzBf7p1vfwbz+xOKtHV3MF/unW9/BvP7E4roNav7uW3/AE3eCwtP9nf0LYJSlK+Ll45KUpREpSlESve2xlXKNkxfSOzMj35aUJylZ3r0oti7pCCjOnEeJRYSvEIKlJ0W3EhBScacYQUyCBtSE3fBUocRBy9fXFFdxptAZ4j38HKMM15ZZSVt2kawbeftcizCDyCgzmIc0QzUBxqizEySRuIT0JvIpGAAMmmJbRiYRMIiIiIiJhER3hERHUREev8ATSlRdE+ulTeJF05evIdi78bKykOuu6iZORiHLmPcxDlxGPlY9dw0eoHbPGqh0zFMZFwiooiqkIiRRNQxDgYphAfZky5lhK3JSzyZRySW0Z1NsjOWqW/ZYltTSbJu0aM03bAHHELEQQj2CCRFSGKmkxbkKAFSTKW3tKsMo2HP1zbNyEkYj16296uFAZcyxaczcVx2tlDI9s3Dd4mC7p+3b6lYWburfWM5P0zdouCqu95UxlR6IMfU5hN8YRGreiIiIiIiIiOoiI6iIjSlVgAyhc5x94yUpSlSoSlKURcGABAQEAEBDQQENQEOvXuYHKuSLNuxhels5Jva1r5Siiw0ZdURej+GusrFNqmwI0bvCLFXFuVugm2KkQwplTQIQCgCZQL4Y2/um4v9k3R4sOtvac389boHG0Ph/DGGBtq54dzfcXfVv47Y2ziyLTgk4m7bKdYYSi3r2cWdsVnwNWFyPLgeIFiV2rrp2yAVFk/hKAcDwTnROQjfiMB3yrMhz7hMCJndER3/AKLV/H7QF/Nca3Ri9vPIvYq63UUxl7gdybyTvFtHQsqvcTO3GjwzoU20f05XPMqN00QWO+IVUVtAMQ0Cm0XndxcJruXzrlVzdjh+wlTXMtkqUXnl3LBq8YRq/RRnAqGOi2fyDdIdR0SeuiBqVVUDbbssXxhzIFmZzsqXzhsuTd4XhH39H7LFzw8pCWZbuOsdOpLHrm0LYkH6cegSKel6XSyrSOfCL1oLKU4w5AdgCscTmzY3uDpqnkqbxpbQ5XwA1xfeCdh2zHXg3icg5PCRDJN4LKGFsEbHRQ2paIIumPHmYFlVehW6oOFhJxjUBBNzHZO0kT47d62exoDSXgzM8wECZ24EnoWmZ3lLIr4Xqz3I97OzSjdZtIqOr1kFwkUVRlDuElhFf7ImYZubMcptS/54fiIf5SvvxzeX8jXZb6Nl3HlO87nthpLlmkbVm75ezkO2fcVxBHIM1VzkBbiw3Cm3dQDXd01HXcJYt7bKFg5sz1fGR74wbdlq5NtrFMfhl9Y0zE3knju5o4tvsHd1y1ui1NvM7fedETDuKVKn0enGmA3GE3yKVR7nzAsJaliRF/XbjrKth2/sJOcTXjZ8TlK3btJPXdJxFtw9xL27byEK2koua0CdeNpd4+dFdO0EVU+hgMJTaPddiGySJ7BMStG0A6oWF4EEiekgT1z2BaKRdNQOVIXbQFTlE5EhckBU5Q11MBddRANDaiHN8EewNTE1CLJEWRUIsiqAmTWQOCqKoAOgiQ4cxtB1DmEdBCt+uXs3YNYpR0FhTKGz/eluQG2EpdMgxvDI0Nj3FGRYKODHBYV3cdrGgFph6xeKREip0UxdoJxu48FZq4AD64ibc0pbc9YcLcUfkH3SXU9nBaYi5VzOwN4yNvqrWBbfLC3wuOGYMY2cSjJEkAgaYaNUiulzH4wplyLnNN/AQM47wPqe5ZPoNpuLLwPRzTInetZFKUrRYKsW9cM/aU7EXRas5L21csA/JKQVwwEktDzcM5SHVNw1dJGKokoUeocggPOIdQRCvU3NlrKl528W0rwyXf8AdlqkuRxeJbaue8ZCfgAl3Sq67mT6EXVOn0Uoo6dKGX03990uYBAyqgmt9Shg4O9bfoEmARvXpXV6Xk+fsJR9d10PZSKWkHEXJPLheO5CMUljrKyqjZY6gnSM9O4cHcmTEouDLqGV3xOYRq9pZTydYLF3F2LkW+7KjJCCcWu9jbSu6QtyOcRrxZRw7YGQbrEJxCyqyyp0gDdMdZQwhvHMI+DpUQIhSDBkK76W0JnxEW5kc35dSFpZw47ailkiYTFtADuf5mJo4+Cz+xp/YA+D9jJ8kuk5TaM2g1QW43O2ZFQcWd7nrkFcnTSnREF8P/M59XPwmn2RX7CPMPGn+UbWzVKmBM7Ua5ww9YRCvGjtFbQTVaAcNM7ZmZr2tb42lbC7TKU61WtyKMm3SNGsTEdAKDUxGjQhkE90hitECmAwJEAvkrSyXkewXbZ9Y1/XrZjxk3kGjR1al0PbfWapS5kDSqZBQUJuleGbNTOChzLGaomPvGSTMXxNKgNaMh69dyXjEK77zaFz7IwT613+cMvvrakbWSseRt97kmZdwz+GQKoVGKWbHcCmdqUqqpARMAl3FTkEBIYSjMd7RW0C/ayrJ7nTMTpnO2+2tSZauMmTSiMrGMwUBqwclFz9kRSBZYCkNroCygc4HOBrOUpdEyAok5etnrqV1pHPGcJe5ErxlMz5YkbsRVYro3O7yLLq3AieLK7JGqEedEcaU7QJCQBE4G3khfuhKICsqJ+Xeec5v2Ksa+zPll6wXuNW8V2rvJEy4RXl10VG68ooBnIiZ0omsqQ65hE5gVPqI7xhG1FKFrCIcPXqFJJOM+vRV1Wed84R/SzoDMmVGXSSyj42huhMhSzYYi3VBIJ4NturhxbERSSHoUmiYCikIF1TTEtVc7Se0W7RctnWfs2rtn0Qnb7xqrleeFm7YokVTSZqo9FcWdAhV1wBIxRL9nVHQROcRsrSnuqJMRsV3XG0Bnl29TknWbMuOpFGzF8cpyDnJEy4fEt90bfcwgLGcCfoFUwAY7bXcMJSiICJSiG4bgBch3/eXCj7MkFd983hdUJZeM76hbPh7juV5NxlqsyWk7IRrHILKGI3SKQhCFIkBQAiZChoUhShocrdn+p4f9q7gr+Q1+f8qPayrMAovgbFpRJNdnSvk22z/wB2p7/ELX+tdVid1g+kfzVljtn/ALtT3+IWv9a6rE7rB9I/mrq7R+M7pXb0PwGdA8E163e0/n1rkOfd5uoP4erV0cJYnuHOuWse4ftV1GMZ/Il1NLVjZCaWUQiI07pQCGcujJkOpxSRAOocEyHOJUxApTGEAHJe3tiVzO7QDjAqma8bQrqTx5B5OsG9ZaKuELZvSKuW24u74p2sVJgovFN0IaUCSknkoVBtGt456dZU3FlA9GzfA6FpMmBn5rHlt/oTT/h0/wAgKnfGN2Nfw1C2SP0CzNpqBmqRi6BoOgpgPoqZxZwHmAebqDpX33oYg6Ks5GVxn+0LtKQ/dt3wpdKj4s/yRpxZ/kjXZLVQUqPiz/JGnFn+SNEUFKj4s/yRpxZ/kjRFBSo+LP8AJGnFn+SNEUFKj4s/yRpxZ/kjRFGcR13A5/gFAPqhW5bY3wLie9eDM4QK7L1tKHl8r3Isg8wHeblgi7nrBNiKJRv29SM1DBxiacpHXAwYr8WJd4gFE28BAANNRimHX4IjqQv5IVvG2XSyTbYYw7j+LRmn0lmbI21ZIKQMBcDe03Uq3i8I2c0RVdSS4CQrNM7SQOuxLoMkk0VblMB93TxOvVvq6N0TTrUZk1GZYYAycp2CSIxiDmu10Jokac0i3R5q8HLXkEgHENJaDeLRi6GzOE4TktfEU1ik9hXIDkCWuafT2t7WaIGXlXCd7AzGzrpMoVqxAehzMBVIiLhwcONTWKyKQd1RQK3V3DeNv/rztriZh32EnlxFyVs1IWbcEFhOSsfIyLhrcNrGfqWhYK6Qs2slvIFGYI8AEVVEFE24GJI1qNf49v20uDfhb5uGAumJsjMe1U0lscTLix2ylo3qjbltXFDTLhvchVBWTcMXigtjxhyEBQqpnAb3EgNbTcqXFfbXas213txr5+K8WvzZpRu1veTaMkbiVat5O21mBbru1ApDsEycS1CPcM9FHAqR5lRHiDmr8Q9oDKdsq1LS2HCauw5ipZBmHAYQRlO7DP8Ab/ZdaBQbTZfxF3NwAjgbcSACwkg4ZODNjpJBbr+uo7ZYOFuaunMA3c8rY6SYxzlVW+3rpVHLjdJQI2dLqBjpJu1RPJKjuum4rF1E7glatxARES82gDrr1a2es1pKSDhcCR6ciLN1Z5LhlTWm0QhbWI1QznZhi9HMlPsqTDjXLYUEEdFE3RWBTAKYK6axtw3OOggIG6gf71fsmoLQyxVGjk0T/wDpZtnEdAHQvyrXAg22Tgb9YEbR++ecokZ4SSedSKVHxZ/kjTiz/JGv0FeQUFKj4s/yRpxZ/kjRFBSo+LP8kacWf5I0RE/jh9A/iGoKmkIcDaiUeoP4hqHiz/JGiKClR8Wf5I04s/yRoigpUfFn+SNOLP8AJGiKClR8Wf5I04s/yRoigpUfFn+SNOLP8kaIoKVHxZ/kjTiz/JGiLgnxgqeH7EH8Ob8kKlFIcBARKNTdDcUX4I/sxh/DuhzVDsh0qjgTkutSo+LP8kacWf5I1KuoKu5gv9063v4N5/YnFWm4s/yRq7eDCHDJtvCJRAATef2JxXQa1f3ctv8Apu8Fhaf7O/oWwKudB7A+CuAHqCH01UeUR+4VqeQQ/vK+Ll45U/QewPgpoPYHwVUOURu4VqeQQ/vKcojdwrU8gh/eURU/QewPgpoPYHwVUOURu4VqeQQ/vKcojdwrU8gh/eURU/QewPgpoPYHwVUOURu4VqeQQ/vKcojdwrU8gh/eURU/QewPgpoPYHwVUOURu4VqeQQ/vK4NOmWKKQwlsJgfQoqIwgJrE5wH4Jt/m6mn0CNEXQ0HsD4KaD2B8FVAbiMIiPSG0w1HXQIAAD+spyiN3CtTyCH95RFT9B7A+Cmg9gfBVQ5RG7hWp5BD+8pyiN3CtTyCH95RFT9B7A+Cmg9gfBVQ5RG7hWp5BD+8pyiN3CtTyCH95RFT9B7A+Cmg9gfBVQ5RG7hWp5BD+8pyiN3CtTyCH95RFT9B7A+CuRMoYAATKGKUNClMImAgaiOgdgNREdOyI9mu6SfMTe/zLbBxVMK32WEA/F73+4T4YaFDTmDn01HnqLlEbuFankEP7yiDAyF0dVNddT69nUda41P2T9XXr13+URu4VqeQQ/vKcojdwrU8gh/eVaTn69YIPdyXREyggACJxAOoAiI6UEVBDQROIaaaCIjXe5RG7hWp5BD+8pyiN3CtTyCH95VUyyXR3lecAMoACOumo6VwIqCBSmE5gJ8UBERAvZ0DqB+Cu/yiN3CtTyCH95TlEbuFankEP7yigADJU/QewPgpoPYHwVUOURu4VqeQQ/vK4NPGVKZLpLbCYKFFMVEYME1ia82pTcZzCHWHrUUroaD2B8FNB7A+CqhyiN3CtTyCH95TlEbuFankEP7yiKn6D2B8FNB7A+CqhyiN3CtTyCH95TlEbuFankEP7yiKn6D2B8FNB7A+CqhyiN3CtTyCH95TlEbuFankEP7yiKn6D2B8FNB7A+CqhyiN3CtTyCH95TlEbuFankEP7yiKn6D2B8FNB7A+CqhyiN3CtTyCH95TlEbuFankEP7yiKn6D2B8FNB7A+CqhyiN3CtTyCH95TlEbuFankEP7yiKn6D2B8FNB7A+CqhyiN3CtTyCH95TlEbuFankEP7yiKn6D2B8Fbsv1PCA++u4K1AQ/wCw1+dX+Sj2tLvKI3cK1PIIf3lbq/1PTJmkOFbwOmMfDsgSsa/R3ouOBidXetR5zHHeHeANOYObTUazrfgv6FpR/GZ0r5Lds/8Adqe/xC1/rXVYndYPpH81ZY7Z/wC7U9/iFr/WuqxO6wfSP5q6m0fjO6V29D8BnQPBeitK6rjsa54C87QmpK27ptaYbz9u3BCvDx8tDPWipVmzlusX4RFE1CFMUwdcvXrIuP22NqFlI3XIo5gudVxfEc8hrsSet4+SYzbB+SAScx6zVZqdDoMU7Wt1IjQpARSTh2pE0yFIAVijU5ARBQNOvzfzhVWOuuDt3orZolwAV4GuqjZqI6fCblOOo84jugI/jqccNzQAEdBDUSj1QGvRTaJumTUhzvDgrFxqhVHkUWIWMB2LYQ0QLzbnOO4oH7IUCn/3xqtZHi0Yu85ligk7bJJGTEqEhCltx4jvIpnEijMvMmICOnweY3xg5jV9waC0xSq2TR1mLYdVoCoImAGhgP8AuAXr62grRQsdqthdLKFVtIkwCS4PIMAnD92cpGOeU2/pXIhp1hAB6mvVrivWLpgQRISlKUUpSlKIlKUoiUpSiKabQwgHYIXTr/7oVvN2cDR4xXBCWHKI287jL8uHMzqTa3ZDuFbbEl1zUlaCZ3xE/sjpP/NG8UyOooiQxQATGEB0bkDeVSKGvPugIdceYoVuet+4CYr2iOA4Os7Vj2dhWXjHIMmD10UyTJO5Mz3HcbhUmgiCaKyEgVQSG0MJhWOIAVQtfn+vlLjdms1jHxOc/owo1OcRjAx8V3mr1pZYbRWt73QabA4ZD/q0piQScJJjGJ2SF7vbmuuEZcETwYmFI1qgSYxKa4bvul7D5IO4brPr7GYu1NGUtQUCJpvUW7loBZEiywilvJGBLjCkGXtpSNhYY219smwbNtx+jG8r8IzdjW6Fxyr2Gi1GkVbU7IN3BTCZBxvKPnKKZJA5SIg4VBIAMBShavhbLwyPiTJtzcH7MLxjizMEyFmXSmgpbqUNP2/ODim24Q8KQ6JwT6EZMxatQ3yGXVUacaqscxhALHbfWWLAzTwgmVMmYxuePuywLrum1RhbgQTcIQ8umxte2Y1yYxDkIruEcs3KY7xQH7EYQ6w1+b2bVi2WrVttTSI9+oyrWvQDAc+nVDZkiSGRM45hev0XrJY9Ha70KGhLx0fhSgPfdqOdTfS4U3g051HODS0ABxbEGF1sfzaV4e+Vz79aGXd3Nhd5dCKk1PrR7tZ0rliynRlY/cKUHroCqrD0MqUEzIHdLCUDIk014nH4Q6jr8IebXUK2BYGWcrK8IEggq4Od5sr3AdZU1rFnzKpo37ZLg5VNAHoATiQn+XlEOKEOL10WGtf5h0MbdEd4TjqAl0641+l6lhrTVaP8Oge2mB07F0et93jRunAVKw2xhUJyyGewnPFSubm5vpHs0/B/hVQjI1/MyMfExTJ1JScq9RjY2OYoGdPZBw4UKiggikX4R1FFDkIUoc4mOAB1aqN1Wrcti3PcllXjBS1rXdZ867te6bZuBgpFTtuyUe4UaPmD1qoAKIuG6ySqSiRwAxDpmAQAQr3JqsD7hPvRMbY3wvHBwJjavPUqIS6a6D8XSoa0Ugg4hKUpRSo0/jh9A/iGoKjT+OH0D+IagoiUpSiJSlKIlKUoiUpSiJSlKIoifGCp2mqIBr/3xg1/+EKkl+MFTw/Yg/hzfkhUOyHSqOJGIXWpSlSrpV3MF/unW9/BvP7E4q0dXcwX+6db38G8/sTiug1q/u5bf9N3gsLT/Z39C2CVT6qFU+vi5eOSlKURKUpREq+0Ns9XtOYNuXPTaUtFvbVtkk5Dk08l1k75nIqCeQMdcE2wZFQMkdnGOrngkFhVWTVOZ6oKKSxW65iWJrKPHO1FK2Dip1iJ9jWwr6t927kWS8jcbyajJw1vz0pbUxctrgqxeIADSUdWlDHM5AAdty9FlQVIDg27DvhN3PD6T6+qlsX2z8OM9n9PqruW9wb2fLlkYGNYzWM015a3emkqZWallws2SO3tB2hb8oRGNOp0YdC/bUcHctCuI5sjIrKuXqCbN2dLAYyKjdyo3WKUqzdc7dYpFSrEKdMwkMBTlESmDUo6GKIlENBARAQGtiVh8JVlzHkRja24W0YR1bWMIRFnBRkpka93sh0e3e2++TdpSppUXjNgcbZjWy9uszpxTlms9bqtzEXDitebh0o+kHb5VNsio/fLPlUGTYjJkiZdU6piIIkACpplE4gRMoAUhQKUOYAqo4ThDPwx3zu2Dcr3aYpC6fe7sh+oXtMbY4m8ozk1AW+5jGjyCx9cuSXisssog3OxtaEez8gkmJCHEVlG7BYiRRACmUMQDGIAiYMpsd7BGSsow2HZK08h4kcSuXGFv3EraDuTmmt045grnuWRtKIuGaT6WigqxNJxqrdbpYs7cN+iWgqIACxRDG7DOVnmGb1XvFpbFv3kk/sq4sfzNs3Q4kGcPLRlzwr2BkkzLsXCDpJQG75YyaqKpDEUKUecAEBymtDhBr8x17nS2NsS4nsqVxs2gLfi55m7uiYm5G37YumUvKFtxdV1KqFI1SlZIjhVw3Im8cBGMiqOBKQ4KUqcOCBSjMTO6DO7bCrILPd+LHPq8Me5US9thW9bDxHO5ulMn46kMftmkdI2TJW9bN8y58lNZWITmGLlvpAFLDkUTOZEBuUY0BWSMUonKZM58Hx5hEOwOg1lCrtZ5LmcQX9iK/VJLJRcg3urkOVv67cl3hy2NKKQrGCId6KEmm0lkEW8e1Mi1l0HSSShNSlANADF7riPXHq1dnCSb2WEdgz655lEtLQf4pM9E4d3elKUq6hKUpRFwcxSEA49QiG+P0BvCNZyyXB957VhbSksdkt3MspcTeEGdtXHfTJvO49XuG1Gt5xTeWVlGjJicFIx2iYzhg6coJuDEbGUKsqgRXBpQoHIBBEQA6G4Ih1Q13g/PWabXblyvIT5VshRcVlPHgYhgMLlwtd1y3ChjdhE20FrLRq0Ym3fEXj3JntnQr9wozOQrhQrghycWrukrUcbkM+LD119KuA0yXZf0/qvC3Dsk5vh47GT+Lse5bqUyXaLm4UY6Gt5cklbb9gedPKW+9QUEFOj2baAdO1UwKXVM26QDKEOQPRuNh3aOYR7pCUx5OxV+pX7F2Syxs/bt0ZmSQk4GfuDp4SS6J6WkYNUbckEnC6rgpEVElCqnSMiqQuQUTwk2Qmlo5Fud0LEmdrpd2xHW+8Y2kmezY8sPfU/fslci6qrsxknrrp++twY5ogVFaNdqHVcAcoFP5iJ4R6/bdaqW9bGIsZW1j8GryIbWLCTFxoJR7CXSvEbgbIS53x35VH7u9pJ8V0KgqNFWjQiQCkVVNXK/aQ0gtEzv2Th3bdx3qPdgY7uveeboXmJ3g9c9xFhY0uFpBTU1kPI7M8mjhtjaDpG5oBAJO7I4SvZI6vQabhItlzbtw3cdDikgmUSnWEq4IY539s9ZuxXDvLgyPjO5LKhmF6vMdOHs+LRrvTUf/prNJEFzKrFSH4JnSJDtRNoUq5jCAVfKR26sqybK42Ly3rOfkueyndhyT+YXmbhn3DN2GUCnXcSDl6dd06AuWLgAXDkxxUFiwMcDCC/HUTMG2Fd+acdSmPrlsWyGYS17xd8K3CydS8k8hloqIShk0IRo8dLN4ortug2B+ZgVLo7oJrxxBMiCg3fwwqkQInDokfTvVncFgBliO4EHtz3LEWlKVpslZpURPjl/fB+OoaiJ8cv74Px0RXq2csHXDtJZrsLClsSTKFlL3fO0zzsi3F4zg2kdHPJaQeGbgomZYUmrBwYiBVCGVPuEAxd4TF9FIbJG0czcPAQw/ebuOb2I7ykjLijHtY9e22jo7M0oqv0YZuicViAgLA6wvCrmBEETqaANsMX5FmMT3zD3/AMYmSloRnKsmrScRUcRapZaFkoNwKhEzkOIkQk1zk0MGihExHUAEo5i5A4Qa48n2AhiS8sD4YkMVM4JBnH2I0dXJDQ8VJMnyUlGybEW0gmLVJmuVyVKKbAmz4l8sQxDKbrgKvLwQaYB3yY6/Ba0xTDf3hMz3Yfr0K1ORdj7ImM5zK9tTt0Y/VuHDGJPdjvOH6JnIF+4jSyTGMcIQxZGLalk1kDyLVVRVmY7PiTHMR0oYhyFuNefB150sFO7XdzTWPWkRZlooXDKzTVzcEkyVkHDuUYIW+imlEGcC4UXhZLi5JRIkMqigm4JJmQWRVPQ5HbVkX0W8t73EcbDbg4anMMQcRK3hetzKWy0uW4k7qnHqLx3LKOHCi8k2YnSavTrNWyDIqJEjAs5UWu894VDObp68fNrQs2OMdis5iSEue6noQM2stdCxJQhlZIwuGDULwm0WdsO+NhmiJ2yaTYAQDfzJrXdkyezZ3JFO9enCBhz4Tu51Z64NhbJFuS8nbjzJGEF7ka2jcdyQ0CyvKQcP7te2X055b29Hm6XAiD+AGAkyvFHSiDMxitgbu3HRKImgPsR3iV+xjyZbwqqpO4Yd54tFwL+6Gba8IGONKIyKrQVoNMyApOIpVuirIFat3xnjE7Jd0k5TUr085t9T0+yvVg/wACYZBG+rNlbQkHDKRuuJk4vlNPObnvZ2xdISpFE+Ukiq3M/baiiLSPQZJgm2M4Iv4K99sa8rqj1IaGsu0rLiE8Khg2IIymZ+8Jq34ob1YX4czWWlXzl4YpH8cg0as11FWrKO1aopAGqgy3hjhlhnsmD3TCk8EJLe/M4jxEqr3FsNZHtmUmYF7kbCTm4o6zbjumIt+PvF+vI3g5sk04S+YKOMaPKiD63z27KpvFHajdmcybcG7px0SiJrmynBm5XtSJyDO37kXGtpw1iJW2zVmU207cMa8lLjmZmFTjVG6TIkoUEFrfkwI6ZsHyUgJ2PS8HiTsjgnnpzhAbjn0r4SeYHwqie+bXkrddO2Lq6GT6FPcVyu7wvJyxVJJhoS4pZwgrIMlQO3UaRzZiUpWhnCS8GQ+EEvfKK6aV5YssOShDSsdNvI8t3Xi3uArqOkbwl25425CSoSkam2eXrJKxzdosCUSmyYotSFSTUItB4W8GjficMP6qGCkS69uMc5wieuVbua2J822peru1b1ZwdnwKGVXOFGWTpNd7KWBcFyIKFQTjo0rFq4lXJl11E2pTpsBIi6MZu5M3XTVSJDLbE+dGbA0lAsLYvxk6zxP7PttLWVc6L0LzlLYZvXk3LxouQQKEG2COfpHmHZm7cFWLkBECoqnJ7e9tvS77+yPjTMVwYgxMvlPD+QeWVhXgktcKakfHll3M8nAPGgyApPUwkXSzs8m43pBZRQ5lV1DnMcepiThBdoDFFjwuMTPoW9seQqbqIb2/ckenHySMI+g52DdwTSZZERftW5guKQekWTVMsm83T750zKoqXa5933x7w7Dl449p6zhTv4H3fDAiADzxidithGbKOYE1I17kWIb4Pst9dzuxHORculdQ9qQ8uzWdtDMHKDNB3Jcas6YPWjbi2hk3K7NyRFQ4oLbns7r2FNoG1HFlQfSFrcl83/dVxWrb9hWohJScmoa1ZSeiJl6rMKM0oIjZBa25NUxjSIKptiJuFUkkjCcty2vCPZJPIY7lrnx1Z+QH2MJtWYttO9r5vOXh3pkX0/KW+tLMglCpyMhBPLieOWMs84xwCjZoZQFBTOZXy1qbc0zZdpXRYcJhiwRs3Jij9pmWBmr7vi6ksnxr9jcTAzE6z6XWUjVgLc8ispJRwpunK6TdRYxwBYi+ZNct90Ce45frs81AuSJy283nOG76Kh2nsQZHutti9yW+cXwhsn3+/wATtmUs4uR3KWzdLFIi3J903Zw7g712cDlKc8J0wbtDgYrtdsIc9CU2NcvvmMCtYzmx8rzMnfMxjO4bXxndJZuSx7OwjLpk9ZTT1wm3jCkBoAr9GNXa7Ug7qZ1iKnTTP7W1ttpWzoWwbZt/A9hxVtY8v0+T4GLico5GinUPOFagyavYKRTneioQOJASvUYxRNKUMoY7ohjAkKXpWHCIXlHHiAYYYxPAt0oq74m62dlS902WzvYL6lUpy43yiDeSFFjIrP2UOok/YkIoghBMWZCg0TFA1iKrnTkN23LDvkdnOjA0H3+710d6kT3ByZtt/HNm3vIStsxk7eGM18is8bXCi7hLxduEULllBtqMIUqwO5dOJtKbk3DdcrMiHEotQUXcrETHaFwJ+y9lPZz4T3Y1l8qw6ttTGVMVZTmiWg/ImlPWl0qjZaJBvIgRZQoKuAbHcAmG6dANUliprFMQNWERwh+Y4cxX6Vs2O7uqHimpLFvWUPLytzWRLt2d2RZ7jKus8P0wfrsLyl2yvTLjkjHTaONwVkjGV2n8DJtFPtobhbtmF0tjmxsZsLTsfML1rDWO5l3zZ8/u9KZu24HzhaRdOFd5eSkHR00UzFSQSFNIoG3N8eNWNpLDIF2ceYQYjrhWohvCMjMAdZwnqXxa7Z/7tT3+IWv9a6rE7rB9I/mrLHbP/dqe/wAQtf611WJ3WD6R/NXDtH4zuldrQ/AZ0DwXFTkOZQvNr3tdNalAGo6dSrwYfwVljO85I29iOypS95qIiyy8gxizt250EVXjWObE311UyGWdO3rNo2bEMZdy4dooopqKHKQcm4uAW7TDgVch9HOHjy2nJWShWzm34cXB15wJZBPeRSQA6zoOZuUwkHRE+goFAC8+lXYexLSWue6nDyNK4UkLGdKQ7R5cRLpcrrNhFqVRk+6gFQ6EVOQFh3hbtzAURMZPXxjC7T2jZ1hSUEvBll3TcDSCDQ+pRIwcHFNtMMDaprmOYxFgUOA7xN0oAG4OvShL/SRaLM3bSMLx8om5UalYkQipRJV2k5WbSBiCCxkExRIKaaQgYupwA2mgV9J2Wy6dt2hbNa7IyKdFnBNIc69dDm3jG6WDGcRlC+itHW3VjRVXiGkq16raLld4LWFgcaRLGh8TJFUyIAaRk84C1xy/DEoabxuYpQHUA5v5tecdKqMjBykOVkeQaHZlkWJJRgKol1cIKGOUioAAiIAIkPzDoPN1KvJOWdAWGorMOUUrjbshbNRadM0zR5nyhxOcXBQ3FQbrtyKLICQd8AMmJzFHUld9/CsbzQj7jUPGs4DpCvBRclPTC6Lgi7NYqDRJ4ummKXRKaKiBipEAqQoJk1EDAYR9g/X+zg2a1UaZ4m6WueRiXxg1omZ2mRjGC8032aWxrbVYrRVb9osh7aTTgKd66ajnXSIB3HDbGRx2MXTXm059O8Ndpu1XeKpt2yKy65zbqaSKQrHMOmvMUuojoACOgB1Ar16lkOReCVN80Si1o4ZGOmXqLiOj5MAIcSpNzKJgJjnUSWQIAgBTHRPobdARr3kGERjJdkrPtLkbXqMgovGy9u3EzbM49FMxmnGIqFBQFDmOV0Uw6lKAIlABEDGEO80lrfYqNlB0WOGtBEtYMNkyQYgD+LaNy6LROpVutVs/+bu4vZGmH1XSQBIwaWzLn/8ATmGu5QElWMEglHmDUAHn63OFcCHPzD8INN0NNNKuJcliyrB84Bk3cSCZ3QpuU2yJnZ4lZU6527ZwqQBSMqoijx5eJMYhiH3gNzGAPRk2fsuPYmPn7esW5rttuVmQttjdFpwTy4LZWkjBvdLwkEkhb9E6AI8Tv726QTaboCNcyhrVoJ1GnVrWljb4nExEZ4mO/PCF19p1U0/Z69amLM8hjoJAnbhl0c8HA4qy4CY2mnVDrjXBQ11DqCPMH56z+S4LzbmGMfTzrBrqItmNk5eIe3Zcd9WxbVpoqwjSEfvTFk3Ukm3UTUbXDErtDoHU6PI4N0IDjilQJM2Qdhk+0Hed3I5myk32WcP44t2VkcoZtvqwZectrHL1IU4yBaSCSREyCMhNO2LEyaSxnCSJXzgqChWpwHO0a4atUaTq3HGENiQ03j2NnPJcJugdNEOebI8ATJc0gYQCASBJG4SVr9HdDTdER7+lRc4gPPqH0VslzbwaGcbZyzcFubNkVK7WeHHspDe5VmPErBK4GV7M7qFoa12kgwQUMtFTz0jxMTW89KnIJ8Q5OKApIKqku9hfgdcyXQ1hHe0fkjHmx/y7apDjeOy9LskrkmFk1bhdyqkvFC7ScxbZlDWjcMoU65RVeipDItUVemJFUqVddNWaVlba3Wtl07AZcDkRdGM7wVRuhNMl1zirwYmC0jCAQZMZjEb9i1AiIAfUC6gUgG5tQN8EgCP4qzP21ZoI/K2K4WGfQILYy2ZMS2w2kLRWcA2RfM7FgHrk6p1NDdGJvHCwOuL0IDlFYEx3SlMOYeTeCld5FXtW6+DyvaY2oMMyuOOkz287qj0seXXc+TIZ+rEXFZcLbhw6MK+lCJ9P4OHcF6Ncw5nBt9VVm4AMG9uq4o65NrPNq8VckvdUXB3QjYUbOz8Qyt+Wdt7ZjWFtoEVZtdG6XEkiioFBPXUqBTGExjGMPV0dMaJ1l05ZTYageymyo4iMWl11oBGyRMDtXJNktWj9E2jjtMsdUcxoDpaS33nEgRiJDcSdoidnG3TtUye23taZu2q5m2E7Lk803Ujc7u10ZYZxGFMjGMI0ESuhST4wNGAHAdwugKbunNqOPNlaBd9siYR3xnGgAZH4S2vRKWgFDqCPU0D6K8gIgIhoJep1N4KnpOTt1CKoqcUsmcp01UzgRVISiBtSCHOAgIAICHOFemrWCh9ku0VZHBjLhY3aAIgdgXWaMtDNH2+jbLt4U3tdG+CCR1rZHsos03tz7fzAXEi3RPsUZFcKpspToU7joO4bTekI4HQRcJAduU50Q5z8Xrr8GtbagABzG5w1MIgYR6mo9f01sl4MDJ2B7G2g7+abSlxMoLGuXdna9cMvZqWTl3TQkjciDFGPM4VjQF4kALI7/RBDEKQxAMc4E3gHJzJWwrgPPrF9grg+op1kLaW2dZKNtiam2V1PX8Xt/RazNi2u68rJj3wlI1Wta4FXKKkY1KTj7dcpSJiAZg+EPD2HSVm1Y06/RukXENdSoAPODZYHNJJMbbo255LvNJ1K+l7NU0lZKBLDWrON0E3Q4Uy2TiAJJAwHXswd4PFpGRW09auXp+3pS6La2aLdnNp+XiI2KUlE3y9ixbiZt9B4UAEqbNxPJ262crK6JpoOVTGEdAKO83JuztiTLWVOECTvSxreufaL4RW+0i7M0xKtU0ZXHN1rYnh8/OXcQIlHiFbkuGegLVFYmgCWXVRAd06oVqTZbLmX9lLDW3/KZ2x3IWPc9mK2xsrQ7uWl1mUepcFxXWnKvVYVw3MLOaaqQlnTpiOEFFWijd8g5TMqApGr321xtnnk7f4M65MY3Nc0ZcuB8AQc/crQVW0OZleEQ7j4QrxnJtlDOVSKRtkWsiB3RgUSGM3QIAicynndPaQ0npbT4tWgnh1I3qbXNxE0w2qfeBGDj7sCQSMcJC51l1f0M/RnGNJVHU67Q1zsxDajixmAa7Iw8k3Za7AHAnUEsUCn03TpjvamTVKJFC94wDz6gOoCAhzCA1K6gjujz9TTTnrfVtK8HhjnO+fsrTGz7tF2G8z5tCNpHakwJsLtMY3AwyHJRtxmPd5LWLcAJhbzeYUiH3R8bENXDk7ohCNtGyx0E1pVw8GPiu98c4yxrs7ycpe2acI7SjLEG33n5C4iyWGLbZXHa6NxvZ2LNukbMLYss8JdEUtNuDiEo7RFYpwTcx7cfdUNftBOoUTVqXXOHvAgiMMyDGE+6ImSvK1NG6SoValJ1B3uEAkAkY5GQNsSN4WhodR6oiGnV1DQa5MGhQ10N3+ppW3Pad4JTaBs7L2RHWzRjq7sp7KjqYeXVgnMEzclvs3F1WEpGs5yLueV41w1M2Y9CSLZurLOG7ZiZ8RZqVQHBBQC4s3wHOdx2fWeTsVZo2edovLS8w6lG+B9n3KMXk647ptMrKP6DnLXVbK9ETDvpnykjnUY3alM2LbS7oFVW5yKDzRrtqyKdN7rWwX4AE4gkSA4RgenapOj9ICmKxs77pnEtdsMYGNi0kkAoGANdB0HUdNdeaoQDdDnHQTdTm1rcVh/giMiobRcbjnasvSDw3gmPwPKZzyZtN2DKs8rYjxigg2fxDSNkrhacbFg9aXYi3t2UbJrHM2WQfAmZXiyHN7XCnA63Swyph62tqjLWF8YTshe435kzZzkclt4XLk7hyCeLHuK/LWmygpCP0XraInTRjRm7VkXrJuSTaNnTQQPS067au0CQ+0NMNBF33pBmIjbhjlEgmJXG4K0NcWCm6RzHm5t2K0gbo9XUNf90QHSoipnEA010EdOYOqPYAezX0QXHwffBpvm9hZdsbOuXbewDtv2irjHYaTyxLQUZflkZLRfz0dOTWSkmwmIztOBko62YkxteNc8uW7kFzkj3Wt2dlvZh4Py0J/I2zxtLYPtHJd+bDex+623tp6+IDIsma5cpXVFALq8cPRkkxlDwqjKIi7lthgmszS3wnLek1jPFUVQTDr6ntB0SyiKtGm9/NEGNu0ZGGnn5sVUstGYpOjfBjHI5bdi+YQNN0vNprzU03zFAmu8I6aAHPW4iQ2FNmPPFg35ZvB0ZFz7tXbTmKl4zI0tAyGNVLOjMpWVOy60Oszte1zNhlQl7UO8tNaZdKrKNnKczKLNkyNYzopfJOyODS2P9nDbaZyG0ltQ7O+ethzHkmxgbujbezxbsHnS/bnfg2tRe1lbIZSYTscSLul3JKuZJQiTPpVazh0LgCrJEU5btdtDU6Zv3m1IJDCPeMRHRenCTj1KbtoIl1Nw6j6wXzxgXUB0HTXqhpQCjoOvMGug82vfrbRgfgx77YbcVq4I2zrGzbg3Z8h567bgy/ml9YMnaMUwsWyWk89mrqjJd0xUZmaHThDCg7AqqSouEtwD8YmBsx3PBhbKU6F47ENq30nbe3BhDHFq7T+f9pq7MoNb02byWeRgtJ33G2tGRbfeUbwtv3Db1xqrEXeu3poV4i1ApfsZ5tmuuhrJUYy+XS1riWiQ1pmSTOzIgY4jBLlfLg3di+dANC8+mvYHqa0AAEN4ecQ5xDqVssa8Fln+TmsltYvIGzeWyrEt62LitDMl2ZvjcXYYzqS82LeYtthZ1xTZWbZaRcxiy8grHyIsl2hGDlFyVFzxKC2ZeDuBWmBl7SynkLaJ2YcjYXtLIDG67/bYhytHXsS8LBtBoaXzHJxy5lkVdbTWJGw7xIzYeiRnUnbQ6zQCKq3r67atUGw21Nc6AQGmSSYgdcjA852LkOsVvFPhhRddIkEtIkTEzEZ4StBP+6I728A9QQ5wrgADTXUSmDq81b582cHYXbEu/IG1rspTGDsf482kLcYZY2aNl+35tupfNz3UulEqZPxxBwjQoBGBY7t3IrrGfkbEWYLxYs03HRIijcnLHBS7EOJYKamrp2qrmtmwszt4/HWxttG3sKbLC8je9qw02rlF/dzdjGO5FWACet8IOPCMIZdse5Y5VYzkCHA3FOvugQxhqON8gS0CS34ZBjMicYnBpjEQsm0bWW3uCdGUwYnbGGzNfOdz6Bzjprz83Uqf1EQH/wB6Ywd/4IV9DVm8Cvje0UbotLaW2q8TWxceXYpvD7HWfbLyQzcbI8rLxFsSt45Af3FcB2p1XsdBpQx7e34wScZKSKG4Km8kkpPgeBLse38c3+4u3aXsrNWWrih7rwXgOytm6XSuCBcZ/t+Of3qWx3ciqURlWx7WiWf2ZkmmmrL3lHMkVVDtlhPWp7QNXWuuCsSZbHun3gcyOYYzJEQeZVdQtP8AhnZsO1fOuAa/73OPW0rkADQR15+uOnU1reH7wptdtZTG9t3NeuBrAu+bi3r3OkHfWV4mPabKLh2q+Sx62vaSbrrtyFvg7Vu0hXDYyiZ3zoWzjiDpHNVjLr4InaltXM+HseNG8LknFeZ8mQ+K7c2qMOtpC/8AZ4RlHj58wnGqk4CCJeiIFSGuHo5A26GkE4OkooiZJU/aUtb9XKpustTZic8MOfKTBIGZGSkU64APBu7CtV4iGug66B3uers4MDTJ1vB1+Lea/wDknFZmveCw2p0dodTC7K030tjcZtJ+12t2Fty6myu5shw0LNI5HC7hbAzLAjCG6bioJ+OAhTocWLkOJr1TTg689YPu3JGSLwUtUuEcYEYmx/nFKYKhjvafSuhFqpbBsbLqbqs0L6KkgnFiN0zDGtGTkj/oVwQEDdTrLrJoStoC1UadpaXPokgTibwgdZJyznYuPaadXi7/AN27I7DhAkg7ozXdqn1UKp9fJS8clKUoiUpSiJSlKIlRp/HL9NQVGn8cv00RQUpSiJSlKIlKUoiUpSiLhQwEIBx0NuIb+6ACO9pvCAfh00/DW36LwLsgwOH5aWy60t+0gQGyceNcjnuq62t8PJO4cNtr5LL27FMyKw75yedm2YPCTfENEohgpxapFCCYdQmohxYgOggkUQH8I16OfvG57pQgW1wTTyUb2zbbG0YNBwcpEmUfGJqosG4lIUAUFBJY6JFVd5UEikT3+LIQpYdiwsGZjHdB+qlhDXTsg98Qtv8AlXYqxwWxNol7auzzkbHl/wCHXOQbAxZa6k/dF6yObmtpSWPU47IJGiyYqOBUJcL9JyEUUIpUksyMgkmduY5q1CbD+zZfhJYtuNZO0T35s8Nr3xkbIN/r2e5gchZQLIDjqxmibwQGWcwIWZc3GtwAXEkMwjvag20rTQF83wDmLehfF6lfQcOW34R+necom/hY8g7xGDNcFwUQalHnBsiYqQfJqlqTc2sYh1pubWOk4I8SUWmXSyiSyYmFNYpjKCJVCCc4lOGhiic2ghvDrlwVXFt7A4dog9EkzO8Ld9WmboDAIJPaQQOgQRzrcbjjYWxDLZy2gbTyRbV32HaFn21iyWwpGXK9nLXbZQuG6E4JMbMazyiBkiDPykmnFjJqb6UaV2ocpiHKiFVANkzZqNB2PEX5Y7vFhpPYRWzHeV/INMlMZjH17SsPbabB9MuJURtx1HRkpNqKrRsOYzhFozcEeDxhAGtNz+7LslEWzaUuy7JRozKQjNrKXTISTVmCZiqJgimqsYqe6YhDFAgBumIUQ0EAEOHt2XZIsyxsjdd1yMaQqiacbJXVISUaQFedUAbqLGTADj8cALobr61NWk98YxhBjbhn2qwtFIVC8sEE5Z7RhG6MFuxzJsY4Bw4tDWvJ4RvS5ZQm1q4xRfbGw22Rr9yujCwhceDLMrXeMhNa/HHPMzZCklxB67B4j0FzkSMXDjbFwXZ+MbSjrjt6x7Usg8jlFqlbo2ZL3c8iLoti5LHi7liXCjO5wLOsHDdZu9MeOkyg6ahLcWdRdMG6xsGkrxvFBuq0QvG8W7Rw9Tkl2iF3SSDNdykKZknKiRVwIZZMUURIqYBOUUUxAwbhdO/cOQ74u6JZwt03VN3ExYXBIXYl07fnln68nKosGz9+6eqiZy5WVRi49HfcKKCRNoQhNwuoDZzCbsuyjuAB6zHeqvq03vJa0D0fqceheNqInxy/vg/HUNRE+OX98H46uuOoaUpREpSlESlKURKUpREpSlESlKURKUpRErd1+p2v9q9gz+Qt+f8AKr2tItbuv1O1/tXsGfyFvz/lV7Wdb8F/QtKP4zOlfJ/tn/u1Pf4ha/1rqsTusH0j+assds/92p7/ABC1/rXVYndYPpH81dTaPxndK7eh+AzoHggDoID2Kyh2Y9qu+dlq5Je47OgLRugZY0JI9Kr1aPXcawlbYuKNuq2pZLoVy3V45hJxLNYETnM3XTFZJZJQimgYu06lZtdBC2BIyWQja67l6EbGLOSZQFAmoEeHKUdSgOugcwc4jzBzB1gqbysuUQHWele9/lpx0/nqyqdxyaSZCEWTAqZAIX7CQR0KAAHPp3gqILnluuun4gnP/NX0tYPa1qjZbDRs1SzPlrWgw1pEgAHGQuWLUC0BwMq9nK25TCInuGX5w1ATPVDDrp2RH/8Aqpxb4u4jczMtzTwMzqAc7UsqsVucQHXeFPe3d7v6VY7lPLiOgLEH/wCST0UG55brLkH/AOQQPzVofaxqQ8XX2NxaCDjTYcRkfiz5965FPSlopPdUpvcHOBBIJkg4EHHLm2q+xr8vJVmWOPdM8dmVQqwNjyyx0QMQpik0KI8wFA5wAA5g3zdmunysuQR0Celih8XQrwwl06wdWrK8ppfTUXCf0cQTn/mpynlh6ixNddP2Amn4qil7WNSKR/dWN7cZwpsGJiT8W1TX0paLRd4eo90ADFxOAyAxwAxhXzb3zeLMwGZ3XcDUSgOgtpZZvp8ESf7pg/3TCX6BEOuNXgsja72qsZtLUjsdbS2fLDjLFlXM1ZETZ+XZ63Ia0Xb3jejnUczQdEQbrOeOVBVVIhTKAqcDCIGHXCwLmlh/74vf+wE9FOU0v24mnW+wED81ZO9qeobx79icZif3bOr+LZ5o7Stpe0MdUdAy94wJzgTt2rJBfL2UnMYhCucj3y8hWz1tJt4p7dDxzHtnTIzg7NykgZQSEWQM6cikqUAOmK6m6Jd4aulkvbQ2t8yRzmEyrtN56yDb7qbNczi2rryrMy9ruJNQpyKySkaZfoUzs4KKAZyZMVTAofU47w64OBc8v110++PEE5v5qcp5fT9nT1/gCeisx7TtQG48QM5/hs5p/i9Qru0zbHlpfWeS3KXHCN2OzYsqbcz9m2zHir+zcvZMtR2tx/GuLavaRgTqC7jnEQ6UMCCxCmUVZO3TQ6ohvmRcqEE2hhCvKP8AIt9SrVFjKXjdEmxanIs1aSE65eM2hyppoFMkkc5iFEqaKKYCUA0IkQoaAUACwXKaXEdAWJ+FAnorN/Z/2WJzaAxrO5Fgsw2bbyVgzpxyjDzluSKzuzoFCFnrgczKJkEji+MmxtySV6EQIGoi3T48FVSpVdntP1ApuD6dgcIEAilTwG4Y4KammrdXvmpWeb+LpcTMbTiZhWgbZIyDGHbdLr6vCOFm6QkGgsLkdsTM10RFRBdLi1A3FUzGMYihdDkMYwlEBERGmK3hdKyh1VbhmFlFTCoqqrIKqKqmMIiYxjCIiIiIiIiI6iIiI1nvL8GNlS3y3KtO5uxi1aWNDOsm3su0gZl6aEx+k0vB2zutMnQxRcKOi2RMFLEJf5UmK7PjBKB1RQ145zsq9cC5gyVhq6JOIk57Gd5P7MlJKFIKkRJKMVzpA4bCoQqnFqlAqhQUKU4AcAMUBAQraj7WtSKBPAWR7ZxMMaM9/vLi1Le6uQ6q4ugCCTOECMycFUeVtz93pX/zp/TTlbc/d6V/86f01ZjlPLfOE/El9WnKeW+cJ+JL6tb/ALY9UPl6n5Gf/wBKvGWbir0crLk106fSu7/xp/TXobayxkuy5ljcNnZAvO07gilePi562rkdwM3GH0EoqN3SChVUjCBjBvEMAiBhAeqNY68p5bt5PEk9FcjdEtrzLEEP4EnP/NWFX2s6lWht20WR7huLGEd7ulbWfSVWzG/Z3uYeYkYbRgVl1P7TO0LdViR+L7nzhle5ccREiymYew7hvuRm7ShHUa0esI9ViwWVMk26HbSL5BMqBSFKR2oABz1a8LpuEDCJZ2TLvm3lNxyYN4epqI9cdAANR6wVZULmltNePJp1/sJBEP5q45Ty3bieJJ6KUPavqPZqYpULG5rRkBTYAMv+5Vq6Rq16rq1dznPdmSSSeucetZDwuU8jW07jn9vX3d8C/iXIvYl/DXE6i30WsJiKCs1WSOUyKm+mmffSEpt4hTa6gA1HHZXyXFNJaPi8gXrHMLgblaXBHx90vmcfPJFUIsRN8iRQCOClUTTUAqpTAB0ymAAEoCGO3KaX0144nf8AsJOb+auOU8trzLE8QTX8VYO9p2oFTF9gcZzmlTxxnHHHEnvXKGndINc0tr1BdiPeOEREY4RGELIR3k7ILw7gXV63UfomIbwTgoTa6ZHLJoKZmzRQpTAU6KRkkzlTMAlA5APpv/CqqQOacuWw9i5O2sn5At6ThI13DQ0lA3Y+hn8OzfpuEnzVquioU6STgjt0VUhBApyuVAMAgYdcZ+U8t28niSeiqvByr+YmYmLdTkXAtZOSQYOZuWQUGLh01VCJnduuJSUV4pEpjKH4pM591M26Qw6FF+07UAtDOIG6NnBs6OUn27bg81OHqSZk3jlz4486yOaZ3zOxtl/YzHKmQmdkyjsZGUs5pd75rako4ADiVZzGlUBsqcnGKCQyhDCQyhzFEomMI0VfK+R3R2Kju+LodHjUGraOI4mFVkGKbFMUmREkzGEpQQIYxU9ADcAwgGmo1mNH8HTeFzXXPWFYG0BjG9L/AG76yy29YbeGewF8nj7zSjTozc5CvOKfRbKONKMyyH2F0u0K4TUVQTTMJieQyVsF5BsPE89mGMy/YN3WwGN0M2WC1YwMnES9+WcC1lNH8uCbhACMlmri/IRLoB0fjlQQemIOiaXH5D2kezthj7NIwM/umbYJ27SO5cga0aaMEWuqJMj33DEYA5jEDAHcsXDZHvkUCtj3XPHbFQBqRA0ooKJUiq8eVMC66AUFNT82nwufq1PbZNv9om5SbXnciKTtBw1cIoyyySa6TvcF0kcoG0EqwpkE5R5jCQoiAiAVj0e5pgphKKyepR014gghzfgoNzy3PouQQDr8SQPzVc+032ekBv2eYz/Dp5kQTnu2qG6z6ZaQRaquGXvuwgzAxyCySjswZOiXKj2Lv67YtyskKC7mOnXDNZZMUehxTMYhiiYhkvgGTNqUxdAMA1S1si3q5KoC9zzKwqIdCnMq7MsoqmCorgU5jamH7IInERHUREddasBynlu3k8QT0Vzynl+rx5Of/wBwT0VUe0r2dXr50bjhjwVOcMtuxS7WjTTgQ62VoMz77sZz27du9ZmONq/aadW1O2W52g80OrOugrZO5LSdZLl3Fr3AVmh0MzK+jzLi3cFbJfY0AVIIIFAATAmgV4BllnI0cuDmPvS5GTk6IN1Vm0qoidVMEBa8UbQQ1SFERSMmOpDEHdMUwc1Y4hc8t11yfTxBPRTlPLdvJ4gnoqo9pHs5a0sGjTdOY4KnEEycJ3+ClutOmmG8y11QZB+N2wAb92A5lk28zTl6RtuEs5/lDIDq0rZduJK3bUWu98e2oBy8Okd25ZseN4hFZYW7fjFiEBQ3Q6WphAhAChp5IvwhBIneFzET4tZIShOuQASud3jy67/UV3S7/wAsA0NqFWB5Ty3byeJJ6K45Ty/bieJJ6K1/aZ7PjgdHmM/wmZ7888TisRrBpJri5tepJwJvmTjMdCyNZ5ZyPGyKUvG3tc8ZLIP+mreTippeMkEHIplSFwVdISqFU3ClKBimAQAodiqnIZ0zFKRNuQUlk++5KDtEVeS0NIXM7eRNtce6M9cGYtjnFJEyzgwrKmTKBlFNDHEwgGmMAXPLDr9nJ4knP/NQbmlw6qxNexxJOb+aqH2leztwBOjsR/8AaZ57JK1+9GmbxfxurOJ+N23DOdow6FkO5ylkR7HMoZ1etyuIdgiu2YRLiXWVi2SblYjhwVFuJuLTBRRNNQ26UNTJkEfil07kdlzJ0Q8jZqKyBeUZLxM4WfiZSNuJ0wkIyQSAhknzdchwOk4IJCCVZMQOUSFEDAIBpK2dMSyG0DM3jAlyfaWP5G2rEk7zYHumEkXkZIli2LqRdndvWzc7eNZIIM1DLyD45E0zKtyFIqZTdLmurwYGY+gCrNcw40dvHElIY3i47pPLtV5LI0PGz8rM2UBztgKl0O2tqR3ZhbcZKqKNygJQModIfaX7OwI+zjGJP7pmM4nb1+Kgaz6Yc67xurOEe+7Zlt2bNwWEb7KmRpFy7fSN8XRIvZJ0m/kXj+aXeOn66Sh1iLOFDmEypyqKKKAc4iO8cxtdREa9JAbRGdbVjxirXzHk62o0SLJmjrevmRhWQA4BUF9EkVSkAVCrrlMIBqJV1C66HMAy9qrZ/vTZbuW14KVve2b4bXRFzC7eZtuNdsGzZ9bd1T1lXAxURdpEV/yeVtySImsBdxdAUVQAgnFMmK3KaX6yxObq6oEAfxVA9pXs6c0A6Oyj/pU90b9yg6y6ZAui1VQMovuyJk7d+PSsxn21TtKSMA3tR9nvMTm12lqN7GaWurkeXNbDeFaqCs2hyR4Lg36CSVEyybUE+KIqYyhSgcRNVfxVlbJV03VYtl3Ffl4ztoW6nJFt21pm4XMnb1uA6RVcugj2ahxSbcesmmqrxBScadMhj7xilEMGhueXAdOOT5uwiT0Ve/Z1m5F7l61m7hUpkjpPxEoJFAR0j3Ih1A7IBXXaR191Btejq1ksWj7tV7SGng2CDGBkEnA48ywtesWl61nqUn2qqQ4GZecZEGcdowPMtm4dbX8OlVgGlp8+r26R/wDpbENP/wAqqPSvyATGK8IBAgKs9CWl88unyWx9qp0JaXzy6fJbH2qqNSpUqs9CWl88unyWx9qp0JaXzy6fJbH2qqNSiKs9CWl88unyWx9qp0JaXzy6fJbH2qqNXsGePL9kbJmslsLLul7ju2phvb1wXy1gnC9pQj90BTNmbp+BeJTVUA6e6Qxg51UwHQVEwNE+vXP3oMTAVI6EtL55dPktj7VUB21sFIJm7q5BWDnTBeNZkSEdQ11EHAiHNr1AHnAPpq7EPs0bRNwObZZweC8sy7u87ZJeloNmFiSC690RKiyTdORYEBPVdAyjhuQDJ6iIuEebRRMTWVWRVbrLtnCSiDhsudu5QVKKaqCiZhIchyjzgYpimKIDzgICFJBwBQY5Kq9CWl1nt0iHWEYpiAj/APlU6EtL55dPktj7VSCt2eud27YW5DSU49YQj+5HrSLameOGsfFNFX8k9UKHURatkFl1VB5iJpGMPMFXPHZyz8NlTmSC4YyUewLaRZObhvBK1HK0DCJSMcwl2KrlYoCJElmMpGuyqiHFgg/bqGMUqhRGS7M7BHfl2oASQB69Zq2HQlpfPLp8lsfaqdCWl88unyWx9qrIRXYq2u28/J2s52a8xtbihrdC7paId2eq0cx8YLkWYPlTnMVMEwcFMgbQ4mKqG4YpTc1Yxc4CICAgIDoID1QqrXNcYBUlrxmI9T5dqrPQlpfPLp8lsfaqdCWl88unyWx9qqjUqyhVnoS0vnl0+S2PtVOhLS+eXT5LY+1VRqURVYja2B3+Od3GAFUEqPFRjId5MPiibVyGhh1HUA1AOsI9aPoS0vnl0+S2PtVUbeAB+EIAABqIiO6AAFXNvvC2XcX25aF4ZHxpeli2nkBglJ2Nc10QikVA3Wgu2SeoHZujfYzcY2XRcEIYSnMkqVQCiQd6oJAxKLxfQlpfPLp8lsfaqdCWl88unyWx9qqdPWtcNrNrXeXDGKRTW9bVQve1lnK6Qlmoly7eMUHyW6YRBM68e8SAFAKbVub4OglEeEbYnXFmv8hIsDKWTF3Q1sp/cZXKPS5pKvWbuQasTjv73GKN2LtUBAolAERATAIlAYvDFxOAMdcxCRh69ZKV0JaXzy6fJbH2qnQlpfPLp8lsfaq9fL4iyLb+OLcy5M26Vhjy7EyrwFxKTsYcXyR3z+NRcGYlci8SQWdRUmgiusiRJY8c5BM5+LMNW0BZExgKRVE5zF4whU1inMco9QwAA85e/wBSrEgGCcZjrCQQASM8VXOhLS+eXT5LY+1U6EtL55dPktj7VVGpRFWehLS+eXT5LY+1VAo2tcpDmbu7lMsQoikVaNZFRMYOoBhByIgHZEAEe9VJpRFWAaWnp8J7dAj3otiAf2quehLS+eXT5LY+1VHbNsXJetxQto2dAzF03TccinEwFuwEepKzcy6VEQTQbN0wE5zjoI6AHMBREdAARC5jTZx2gn52KbPCGV3J5K+3WL2JUrDkf8ouNiRZR7BlEUgAHjcrdwZVE2gkBssJhAEz7ouAEn1kPqkHHmVsOhLS+eXT5LY+1U6EtL55dPktj7VXvE8G5kO+LHKYvvmPcnm5+2SDN264t9qaTtWNXmLkjQcOipoi7jGTZZw6bAfjUiFDeIAmIBvE8lbiGCs65U4pwtC5Ckn8NZDxqYjw90uoxVkg+bs0EzGVUUSUkmJN0CamM5IBN4ddIDmnFp9HLtHapLXAnDFSehLS+eXT5LY+1U6EtL55dPktj7VVy2+ztnx106FHC+TwLbeQWmJ7kUXsl+zTty5nzpuxawMgKiZehnyjh21Q4hbdMRR0iU+4KiYGtpdkBJWTctx2hcZotrP2lNL27Pt2E/H3AxYvGyoorpFfM1lmqwFOUxeMQVUTMJTbph0qLwm7OOfVAKAOIkDDzToS0vnl0+S2PtVOhLS+eXT5LY+1VNtW1rnvqVXhLJt2cvCZbRruZcxNrRK9wSTVkwbndPniqCBDnIg2RTUVWWMAESImYxzFABGu7YNh3vlO4C2njO0bjyDdB2C8oW3bMh17jmhbNkuOcOBbIFMcE0yaGMYQAPhFDnMYpRFzAYJ9epQCclTehLS+eXT5LY+1U6EtL55dPktj7VVfZY0yA/td/e7e0J4LOjIWRuJ3dLpiaNgBZxEhExUqsi6W3CLizeTsO3XTQE50lJFEDlLqOnrbY2dc+3o3s55aOFsn3O2yGg5dWG4gbLeyiF5JMgVF2tGHIQQXTS4lUDKE1KApiXUTaANskLSTEZYK2fQlpfPLp8lsfaqdCWl88unyWx9qq+kNskbRc/YxskxeMX5rNJb7y6Rk39xQkI+FgxPMEcrhGuXqb4dOT0+cqYIcYqnBv1EyHTbqnLjYVy2OBDFdNhKoiC5BK4Jocghrvhz/ABdP97qVF4OJaDiM+bp7UuuuhxGBVf6EtL55dPktj7VToS0vnl0+S2PtVcWpbk/flyQ1nWRDSV33Zcb4kbA21bTQ81OzThTXcRbNkgE5zCACPMGgAUTCIFARC5rPZ02gJA8amywjlZyeZvt5i+JKjYcj/nC448iyj+DTEUgDoxsVs5MqiOgpg2X3hDilN2ZEwjWuOJy9HwVs+hLS+eXT5LY+1Vun/U9yMOlwreBRinEsqYbHv0FgkmjdsQoclHglEgpqnER111AdOtzjWmuLsK+Jq/E8XxFn3LJZKWmlbbJj9rDOFLzCQROcizI0buguRZIySoKEOUOL4o4m3QKIhuX/AFP3EStucLviG27gjn0JcVv2rkKKnoCWanj5qFdN7YfJLNnbU4AokoQ5TFMQ5QEBCsqzgKDjvHl5rSiDwzCRtC+SbbP/AHanv8Qtf611WJ4/FL+Gssds8Nc1PB5ueAaiPwg1/ZXVYnCHMUNQ5tdecK6q0A8M7pXbUPwGdA8FDSudB73hCmg97whWUFarilc6D3vCFNB73hCkFFxSud0e94QpoPe8IVEFFxSudB73hCmg97whUwUXFK50HveEKaD3vCFIKLilc6D3vCFNB73hCkFFEAlDTs6DrzVfaydpjOeObehLXsbKF42nBW5cid2REfASYRiTd8kZY5FDmIUDqp77hyYWyxjoCLhURSETmEbDgHfAPwhXIBzDrpr1uepBcMlMwsnybZu1Kmq2W93vKSqjW93mRkjOLpWdf54kE3yL10cD6gcFk5SUIdA+8gISbwOK+zq79gLpum4b3uSdvC7ZqUuS6LnlnE9cVwTj08jMTb12qZdy6dLnETKKqqHOcxzDqImEa89oPe8IU0HveEKiCoXFK50HveEKaD3vCFIKLilc6D3vCFNB73hCkFFxSudB73hCmg97whSCi4pXOg97whTQe94QpBRcVWIKck7bmIm4YR85jJmDkkJeJkWhwI6j3TVUi7ddIw66HTUTIco6cwlCqRoPe8IU0HveEKlpc0yFBAOBWT01tl7TlwvrvlZTNd/Ky9+zyF0XjMtpROMmrkkm7lq9I/dvEEyLqLmcsWThRQT7yyzNBRUVDJlEKVee1dtEZChLwtm9MyZAuO3L+fNJK8IOTnzrRM4sxTYpNOMbhomVNMsVFACKQESHpUyExDC3SEmO2g97whTQe94QpLlaSg84iPZGuK50HveEKaD3vCFRBULilc6D3vCFNB73hCkFFxSudB73hCmg97whSCi4pXOg97whTQe94QpBRcUrnQe94QpoPe8IUgort42zjlPEDG9ovHF7zlqReSLfTtW/YyNVRNG3dHIuk3qLOQbqJnTWSTcIouCFOX4KqKZw0MQohcB5ti7UEizfx7zPOUnTKTx+ni6QbLXWuZN7BJlcpgwV59TFFN69TMqI8ccj1yQ6hirKAbGUQ16gAH/xa0ANBAebq9kKmXEQg90yFc3J+YMnZnmmFxZUvq57+nIuGTt9hKXRJnlHjVomqs5BFMxuoBl3TpwcfjKLOllTidRQ5htqJBEdRHTX8Pera9sN444Ne9MTXBJbYOSJ2xslxuQHkPARkVcD6PQnInpMzuto+WTSbqEInraFyWrvEMB+j8kwjgxeLYn3r5tcMcFQdvhZpdWVICId3RYM3cl7XHZGRZd/FR69sxmPrvasXEcud27ZvblQUyNaLYHQNTJSyEc4MxaIAU64tDCQMYz6xO7Hn58EBLhPrctFnFm5tNB1HQKvvs1bxczWoUQ5+KfgA6af+znVbSELK4LWLtW2brTZNLhXc4di7nQj7h2gXjEJm5Zd7YKM9AycQxUGWaLQBpG9+h1lCsW7ttFIG/y8Q6KUprjD+xTaEbF3ngrJkFdd6q3tB2nakGGRVJu8nDZv7sLa85J7DC3S4tsZvHY3VbuTAQhwkRM3IcFVyoa02kVGO3mOgjf9N6zqY0nRu2+u0KsUpSu5XRpSlKIlKUoiVnLi3PuIYzZxk8L5B90uPlZONuvHSjm0oKOuC3ekN8XDj6dlJ0UnD5sY0pGFshVug0EATc9HNDC5bg2MB8GqUdDmlhyKkEsIe3MT34eC2bz+23YjyPwm5CBlp6+8aW/eV4P79RsGPsWSUvuWxtC2DaL46BZV4m6GHGAjnS0okDIVehWpko8iyRzKaxiFAhCJlEwgQgEATiJjiABoAiPVEebnEerUVKiGyXDMx3Zdyk1CWhmwZd3l3q/2zVmOOwTkmTyBI28xukFMTXvY0dBy9vsbrt52/uW1ZSCYGlYx4IN3TAi75I7lBQDb6JDgBDjoUc9JLb8w9MoQt4vbMvaKyJZUbJFtqz7Qs63rZxLNup3CdnYnkGzoqL4po+KbLwUk/Sj2bRYq6IskR6G+yCnqNpValNlRpY8YGO70VW8YbzEkdJAnuAW1+b4QCwMgSG1BbV12Bb9p4oypNuJ3FNuRmALdys1hnr+7TXFMzlxRMtMESWnZFNGOA0q2cmBouzAzZBMnMOp4CgUAKUTGKQAIUx+c5gANAE3fHTUe/UVKMpMpmWbo7FrUrVKxmods9ZjySlKVdZJSlKIoFCidNQgbup0jEDUObnAQ5/DW2WzuEMtmNuk0aWCf4msk2KrLhUMk41x1E3HmlveFqW7aVvL3I/LISAtXgOIyFuGAQBBRpxEfcqhzJHUFYptTgiUoCcRACFATKCAa6AHOI+CtmlkcHknfuPZ65GGRLojbot6Ft5CSI5x4hI4vt+bncdmyOk2uK5OmCIxDJZBxFwzR0LZ2ZxJvQLxZSCXSr2tNMmpiAR27Fdt68G0/iPgIJV+orbS2fl7MuHJD7FdsdAY/cRENifCsu9hyTDKQnbxu8bvjo9gXjFFoQbHnytxeqpItW0myapFTNvF14Q4RDBSE2R4+ZZUuy4GjkzuPzVcNgQ0Reh5JWIyHHxdxLRMdLIAV1baV1QUUzBB6RwsyilTkctlEmyVYtX/sRW3b1i5ovyxc0P75Y7Patz2bk9lIYyNZzkLwtR7a7Z5HQ29Ir9FRTpO4nRmskuDdfjIVUqrJIF0lK9cXg3JqTC7xs/KLe8Txmz5M7StlDA2Q6kE7wt5QqA4+I5FBdTpc6ufoa5R4tcBKwC3TcYZUVyAXjltG66chic+nZ1laOo1GlsjMx1iAeyQDslXBluEftuQBF2aAuXlBa9hP7fxy9jLLgYaAseWcKZsWSfw8Wq6c9ANwWyDZ64NwVXFI1vr/AAlDItjq2n2oNrjGGdMQ3DakLGXrG3PK5vQyvGMVLXjbUhEFXkSm2uh9OuWj9VKXdvnaRXDRXoNq4Zi4eEO4cIKJIp9PG/B83Bk3KOeMewOQChH7P1lWjlC77m5IqPVVbbnWMfJzL5vGpuOOWcxzV8IN2DcVFZByCCJOK40Tp+ti+DmJdEFj1S1skXQjcuTNld3tLWlyzsOGgbBuVwMREyUNaUfKN55y8F+9cTTGNM4esGrdNyunxZnBDbwXqljX8I74iAduOMzHrJXFKu6pdAykHpMA9+WxawKVtBu3g7ISxYWHkrszJNW+jM5/bYNd3hNY9iojFlgb5LOVcyN0SC88nItDJlulYCotmDlIDRYg4dNQUEyeOe0Zs0s8HMHTtjI5JOvD5TWxrOwuVcfssd3Oy4+24u5Yd8Ro0kZBsqg6RcSZActHrlquVmgs3WOmsIF1vtEEnPyB+o61jUo1aZLXYHP10LEqlKVZZq82Ab+t3G+TWlwXdygStaUs66sfXBIWmgi7umBaXXa0zbKsnHIKqpJrLs+mwOQQOqkCxUTp8amJwOGwvKW25gDK7B+zex+UoE0nG23jLoaWx/BZEs5CBsu4Zm5Yi4ZSDXlEU5J1MLPmrKWhTHTT6FWkt16vxxUzajqVBAc4HaMu711qzXuph13r7p8O5bcR238GhlGTyhHyGfYWUk9qq8c5NW6VowUulabK5sbvrCRcx4qzW50W3cqtZYsfxRW6SJCsyulOh011Isp7dWIMi4iybjAX2aBRvSOfW2um/sO2iR13ysgljRubK0kKMgVRtcTY1lT0iRg1ECKOblKUX5Q6LUU1GUqnBMukO2x3AAd3aVd1VznFxzPrxW4C0tubAWPM45zznbKmaJSQyrle3r3jI6Vxzb0Jd8G3irss253BkLlTm3CqCKhbfkkXsQCaiMqqMSoosh0Obi7gw/CQ4CaW/YaTyByi9yPBMJ16zyzdlns7wn8bSc+SAcywMyozzNR+0fvYmUTO3aGiiIsp44GB0sZ4dzo+7He6nep/+hR1MPAnZAHUobUcxtxoAEz1xC3DqbcOzQ7Ns2kPEZhgCYdWu+Sl3NuWHEHfRyN22US33NsxRVLgTRGITlln8og0TBq3atuhkOJdOBcuzdS2duvBUfZWJsbrw182xZloY3tW2XwWdjpgS77Ok7NmLHnV1IqSWnuIdMLne2Yo8eNQbsjNXkuDlU786BhV1BaB2A6mlc0NNl4kCD6/VVc69iRmI6sPKVughuE2sGabWy1dWTfGzbJW9C3xbcDdWCV0sjJWIzuq48f3GAxUNMPW6IrPF7YnVJBXfKbomXIqlvkUWTTt+/2tNke87rse6sh2RkRdvamUX2QbXt+0sTWpa7vG0I0Xm5eBx0vLM5VqvcUAeblGEguLkWqrdCHMg2KQzs6iOp+lVbQptAa2QBEYnZjn2+CsXukzjPlEfot2Vu8KoLe3Ldsq4b4z25YxcU1j3N3MLdt4HjWQCIzCxdXC0hDPuJKt0Zftrv02YuN0vSFdMFQ3ETKwn4RvAAuWIkx7cKjEmS+UV6LyuL4SZuPJj1NlCto/Jo6yqcawuZm6jF5AWSzJ6kqKwtyvUk3LtStJ9KrxWmC4ifeMnHbhu6FFN7qbW0xk3KcY7VuHt7bkwJE4Wn8VXVNZ/wAn3TfMtcCd15huizIhvc0W4uSGvK2pi7YtuWbFJJdzHXLHb8WQhVnJ4tx0TKHBRqRCrXZwiWHLuczblCHvuBC8bdZ4OuiHuPHsbkS0HNkRbq9VzyT2PTmmSzuRn0LrSayDRBw1M3MD1wi/VOdNMdMVK0NJhIe7GCD2YdmYThHTLcDjjtxjvwlbt7j4S3C11SDaRQsB/ZrR7dd0Ppu23WKY7IMYd1KMrojIq7XBVJxBo8MqwmYyNmreUa6SDGPUahJ8RxIBnfwVe0njHPPCj7NDTGaGRohulL5nyJPW7fKCRo1m5mbefERcxSiMgqiggq1TZECO4gVWvECmeQdkIgCfys1uz/U8P+1dwV/Ia/P+VHtVqNDKTi3MNI6jmtKVRxqtGwkd2S0uS1vWpKzcu4nk40rkr0ySSjq1Up5dQgCI6cYIgJQKPMBefq9aulyMxx2YTzcI+tXfmf8AW8n/AMcp+UNU2tnBoeRARpIY2DsCj5GY47MJ5uEfWpyMxx2YTzcI+tUFKQzkhTLt5UfIzHHZhPNwj61OReOOzCebhH1qgpSGckJLt5XCVm48MkmZZODRUMXVREMeoLgmPXADgYAH6QAPoqZyMxx2YTzcI+tUFKghg2BJdvKj5GY47MJ5uEfWpyMxx2YTzcI+tUFKmGckJLt5UfIzHHZhPNwj61ORmOOzCebhH1qgpSGckJLt5UfIzHHZhPNwj61ORmOOzCebhH1qgpSGckJLt5UfIzHHZhPNwj61ORmOOzCebhH1qgpSGckJLt5UfIzHHZhPNwj61ORmOOzCebhH1qgpSGckJLt5UfIzHHZhPNwj61ORmOOzCebhH1qgpSGckJLt5UfIzHHZhPNwj61ORmOOzCebhH1qgpSGckJLt5UfIzHHZhPNwj61Sxs3HhVSFAkGZIxTCor7nqBRTEN3dDc3ufe1NzgIabvUHWuaUhnJCS7eVHyMxx2YTzcI+tTkZjjswnm4R9aoKUhnJCS7eVHyMxx2YTzcI+tTkZjjswnm4R9aoKUhnJCS7eVHyMxx2YTzcI+tTkZjjswnm4R9aoKUhnJCS7eVHyMxx2YTzcI+tTkZjjswnm4R9aoKUhnJCS7eVHyMxx2YTzcI+tUstm48E6oGTgypl3eKUDHqBuN1DU3wNdS6Dzc4jrrrzdSuaUhnJCkl7dpUfIzHHZhPNwj61Sxs3HfGlIBIMURIYxlfc9RKYpgEAKXc3ufUBMO9rzbumnPXNKQzkhAXNOJKj5GY47MJ5uEfWpyMxx2YTzcI+tUFKQzkhRLt5XBrNx4B0gKnBmTNvcaoOPUC8VoGpfga6m1Hm5hDTTXn6lTORmOOzCebhH1qgpSGckJLt5UfIzHHZhPNwj61ORmOOzCebhH1qgpSGckJLt5U0LOxyXXQ0KACOogXHSQdkPl98fDXBrQx4IAUVIcd0NC647S5gEOcA+HUulRcp7kBcDIcVOC0MeAAACsRoHMAe54loHU/8feCoTW5Z0eXoqJUjRfpDqiVCzE4lUd7UD7rgDCJOYxtebn5w69S6UhoMgY9CHEQUpQOfvfTzVWek5O71qeXy+pUrjqjUqs9Jyd3rU8vl9SnScnd61PL5fUoio1KrPScnd61PL5fUp0nJ3etTy+X1KIqNSqz0nJ3etTy+X1KdJyd3rU8vl9SiKjUqs9Jyd3rU8vl9SoFIoiaZjhN2woJQDRJGcKqsfUQDQpdwNerr9ADRFSaVWRhyd37THvhcBRAf6FOk5O71qeXy+pRFRqVWek5O71qeXy+pTpOTu9anl8vqURUalVnpOTu9anl8vqU6Tk7vWp5fL6lEVGpVZ6Tk7vWp5fL6lOk5O71qeXy+pRFRtANzCACAjoYDc4CA9UBq9eRM73nkW2rbtOQBvGw0Ihbr6RRYCB15+dtq3ErSY3Aq4EoLJODRLSPbmRTOKQKMgXKAKH+DaskSQ+//ny2C7igphxk4UgKAGnwy/A5yjrzD19B5qi6Tk7vWp5fL6lR/CWnI+u5Q0uDpGeXbE+CyllduraImnbxeVkcaSDecjZdneMK8wpazi2ciup9WFXm5q5Y0WXESUs9PbsKKsk4KKw9AE3RJvq8Z5aI2vtoe3jQY21kFW1Qt1a2zRaNqQjG22woWh055PRzlNukQHMe2C4JYDR7njGy4OgBZNTi09ywfScnd61PL5fUp0nJ3etTy+X1KrwbM7o37+jPdOG5XL3wATlO3eB4wFke+2z9oB5N8pms9Z9uXQpI2xLv7pszF1v2hck25sx7Hv7ZXkXjVqQ7k7FaMZ7nG6gciZkzgciihTUKf2qcw3E1couFcfxL95jomJXly2nii3bQvJ5baPQXQkQpLtGqboUGnS5iDXQ+82BokCZi6VY3pOTu9anl8vqU6Tk7vWp5fL6lSQ07OjmwVjWqySHY/rPislLw21M9ZEmVblyG8xnf9zq5Ce5RLct64QtG551tMyZoo0qog4XYG3Gz/pLHg6ZAXodcEzgZMeMPr5PK20bemYbNtazLli7XimNqzvTSPQtCDbWpb7Fm0hWEBBQ0dDtkyNmLCLZtHYIpJbxlVZZ0qoYTiA1ZfpOTu9anl8vqU6Tk7vWp5fL6lWLWuERu7svW5HVKjviMn1h0KjUqs9Jyd3rU8vl9SoDxREkzqdPLZV3CCpxaE4VVZTQNdCF3OcR6wUWapNKrPScnd61PL5fUp0nJ3etTy+X1KIqNSqz0nJ3etTy+X1KdJyd3rU8vl9SiKjUqs9Jyd3rU8vl9SnScnd61PL5fUoio1KrPScnd61PL5fUp0nJ3etTy+X1KIqNSqz0nJ3etTy+X1KdJyd3rU8vl9SiKjUqs9Jyd3rU8vl9SnScnd61PL5fUoio1KrPScnd61PL5fUp0nJ3etTy+X1KIqNW7P9Tw/wC1dwV/Ia/P+VHtaZek5O71qeXy+pW6j9T2sStOFbwMYJGHe8ZY9+huxkiD86elqPOc4AUNAHXmHr6DWdb8F/QtKP4zOlaUJn/W8n/xyn5Q1TaqUz/reT/45T8oay+2dtgXaY2kZLEbi2cc3PbWNc0Xs7x3ZWbbmtl8OLnsw0Yyj0WQPESGUMdU0S7apmITcO4KKYHExDlDR0Bxc7L0fQU32taCdw+iwvpWcqnBzbXBYuBaIYUyg7yxL5HncdSGDSY5lUL4twYKBgrhWk3T45CxZ0Fm1wMRIRJyZQONSMIaLob1Jsjg8NtK/wCx7iyTB7PF/s7HtvHE3lVW4boZpWk1mYi3XaLGXNFpOzkWdLorLCAIFIUVAbriQxuLHXNtWkQYcMMT2T4YytLrjiBImOuYKwwpWQWHdmnJeebJv258Vw8zelw2fdlo2XD47ta0pS4rkvt/eK0skwRYOUETM250wiHCokfKoisTeFLe4pXdh2n9nO9dlDMs5g3IbyLe3jbVt27PzXSlJwi2jz3Db8bcBGKhVigcF2hJMjZfQBJxzdTcMYm6YbGo29c/i/oVZlKo6majRIAk9oHiVj/Su7GMVJOTjItA5E1pSSbRaKimoppHcrpoEMbTn0AygCOnPoA1tWvHgddqKFYZV5BHaZrunEki8YTFi44sq4TXBcScfekzZj17CKOmyTd83bqwrh6sdA5hTSUIkIC4AydRUqMpRwhicuqJ+krNoL6nBt+KJhanKVlipsH7aCNljkZTZdzSSxS2D7qYXSa0TdKz22DYHgzZPh8YdoCAisKpCDoRNQwgAJqbkF57Cm2Zjpok/vvZjzFaDJaFmLjI7nrYKyQCPt+LGbmnqhuNHi0WceAvVDqboChocm+Ahq4RgkFwwzV2tLgNoOXPl5hYo0rJXFGyllzOOPBvLEdt3DkS43OZGmGIXGlo2hKTFyzzxzbUndKrxF8VII5MjZpFODKoLrkXAmqwF4ohzB57aZwFdGy3nrJez5ekvDzl1Ytm0IGclbfTcIwztdaOZSP2Aq5SrABCviJmA5QHfSPpqGgiNVrXCmT752dQPgc1ADjiBhE9UxPaCrF0pSrqEpSlESlKURKUpREpSlESlKURKUpREpSr17OuBrx2msxWjhKwpG04e6LyTkl2UtfEypb9pxSEREvpt+5fPCIqmSTSaRzo4mKmcdSgGnPqEbCd2PUM1Uvaz3nnDzy71ZSlZtZE4OzbBx5MOWaeGriyJbQyluRMDk7FKJr0xfeZ7uYxcjbKkPKgVIV036E1FCUxkicUd+imvxJzlKanseDu27JF3NsWWyTnV06tq6y2NcCSdmiBYeXMyayQMF1BUBMqgs3rR3v7wpdDrlV4zi944VbVpuwaR6jzCsWubF7M+U+AJ6isNaVlay2FdsyRRsxyw2XM2vG+Rbmk7NsNdCyFzo3fKwyUivKMGBtdFFW6cPLHHqFULGuRSMpxRtPUvODz2uGsOg5Qw9dEleLe5but27sSx8csfJ+MgsxvbLmTkLjZHIRu1Zrku2G6EVK4UFwZfQhNRTA832TnsJ6hmepWuOwgZrCmlXWmMF5kt3E1p55ncZXjEYWv2XUt+ycoP4sG9m3W+RO9TXaMHQm1WVTNHPSnKUvwBbH3tNS71qaBzTN05evAqIM+vWaUq8DzZ9zhH4pY52fYpvVpheUK3GMym4iwJYz8zqSew7dNF/v7hlTvY5+24gPspTtFBMQpQAw2fqZF47xgelRBgHelKVdiGwTma4rFXyfA4yvCXx01iJ+edXuwjAcW01Z2qeLTuRwq5A+hCxxpuHKuB9DAMm23Sn4wKk4CSpaC4w3NWnpXIgICICGggOghWUezrsjZG2l7bzbetpXLjKyLK2frRZ3dka78qXYtasAwLJruW0WzTURauT8c7VZuCFVWIk1SEgcc4S3yb0SGtLjkBJO4byo2jnwWLdKy7S2Att1aRsOLS2Us5HkMoQTm5sfNgsw4hdkezbMnjp21V3+KBNNGRj1BFU5BMD1ACAYVCANSsTg8ttHItqSd8W9s9382tSOxpO5ZbzVztErRb3BDW26QZy6kUm7ORVysks4IQqIEKKm4oJDCBBGql7Mccvpmpa0kAtxBjviO3DtWGVKy5uXYG217OSTXuvZazPbyK7F/JpnlrVBrvN4uMUmpBbQVd4CN49I704mAPsBd8NSiAjcSE4Lrb2fXhYtpXPsyZRxchf8AfcNjuLvLKNtr2xYcZJT7xWPiyyEgQqwopquETo75UziUwkAQDjE99wtNzokTh44eSgmAb2wSejDwWAVKzYzTsD5xwbhO1M93JJ4uuKybji7YlpmNse+yz1742RvVtIO7TUuSIOgkq0TlU4mSBuukKyQqMzkMcphABwnqWuaXOAOIMHmKlzXsaC8QDiOhK6Go9kfDXfqn1ZcZc6j2R8NNR7I+GuKURc6j2R8NNR7I+GuKURc6j2R8NQgoG+CfGFBUxRORMVABUwAIAIgXXUQDUNRDqahXNbGcQKYruHY4vDHjy7cH21eN0OrjiHLW/H0Vb1+jeLy4Mee55NoyblEzlvDsIlG9yuHKKpGjcFH4OimO5b7w4NLhsjvMI0S4Nymcd0CZ7sdy1wmdIJ677pAgEU4owncEACn013B5+Y2mg6Dz89dhMRE5ecRAR1Dn5h79brVs9YItRvs2XXDO8T21c9r41u+4rosVshYuRbWcxcJjyAUtCyXTlvGHOJJq74t+7BtMCMyVSUk0lVk0VkhV0qccqusZw4ORRwuoZdyqmiRumoocROoYqZQApAEwmEClAClAQAAAAAKqHOL7sYfrs+qsQLgcTnGHjmpQn3dN5QChqBQETboCI8wAGvX62nfqULpuAqlM6bgZAN5cguCgdEvyjhr8EO+bQKya2T1LQ91K4WF6TVkW9HT+CMj2rDy+RZNrCWm1m5SyZplAgu+cgKDZQ79VqRFwpu8WqYhgMUQAwbUMG5NwBYcHsrwmYsq7NU1ZmNIewGDaBhHlt3ldtm33H5NuN/d1wunaTI6vSo9smTScOH6yrJ8LuJKmioZIDJZ1axpiS0nEDtxnq8VDvdaHjHPAZ4RHbPdK0Jg8aiKIA9ZiLnnbgDtMRcdb7H8L4XX+Lr1K7Go9kfDW4m/dpzGWUtkXMLezHjOz8hzd3M4W4U7rvjHVgZYy3FR1nRrRxcdyQ6MBuSq0i76JQCPthWPOks0TVUXcHMoZTTqI6iI8wgI82lWZUvuc0tiI65x7EAhgeDnPcY71zqPZHw01Hsj4a4pWihc6j2R8NNR7I+GuKURRCJh3R3jCIpAI8/V5xqSm5RXMoRFyksZE24sVFYqhkh7BwAdSj3h0qNbXijboCJhaiBQL8YREDAGnfrd2/wAwbI+XFLLw9cETjy+HePMN2LcdkTOX7zisN4WQnELTsyFue0GlwQiLKVK3Km5umacdOH5jrTkEgmlqBjJuYcSG3ony29gkq7GhxMmB6AWkcQOG6I73wg1LzCXX6PTT4WmupuroYefQB+mt4DqwNhK/7ZaXPOXFJSmHdnOyoO0WV7R0jIhJzsHceQr6tOLiJIyJClQl4gj2HusqaiZHTpiDgxkVUi8UFQYwOwknDO7ClpbBsWRxeTbIMviqz82vLrxNLXJH23k+NtRoldDuVbOBYv2oWqtJ6SzdBvIyiaB3bIq5k0OOLQCy9dMyREbAYnpiCqhkkHonPDDzwlaLx3ygQTb4AoJgTEQEAOJNN8Cj1xLvF1AOpvBrpqFcam7Ij+HnGt4N3Zb2OZ3GtsYxXGzi4yxJj13JWniJ/mGZeIXHMt5LPku1j5d8wdiiu7M6Tx4RR9FHRO6TnWhTLKEURFHFrafs3ZCZYin7hwQ4xtG3Mjm1upEt4zJj+/LluKBlohJ0tHRTI8kZWOYwDoHDdUZZksq7Kq0VRllVAXaho6qBUNMDI57IyHmtCxouhpxM90T1YwJzK1x6j2R8NNR7I+GuKVqslzqPZHw1EQR3y84/GDr9+oKiJ8cv74Px0RcGOBCnMc4EIUBMY5zbpSgAaiIiPMAAHPrUsF0jHFMq6Z1ADeFIixTKaDoIDugOuggIDr1NBrIjZXkbdic4Wy+uKQs6HOlAXKnZs1kRNqpYkHdalrzKVnPpfooh2hWyE2eJUFR2QzZI5E1Fg4oh62CXhmzZgaTsjB3hOWdeOJH21eFxylr45xxAzq7OOtazY2TMMeKKTB0Fv3PfUxKi6Oi8KCsbCHSbJkSMUg1Lg1wb194GG9SLpGO+Okx9YWnUFANqIHAREANzHARMA9Qfo79c7/WEw6iAiAb2hubq83VrcbdMZs05BvCVQTyFsru8crbeuQL8t5o8GJxxNz1pPrVcOGDFyxYP2MghDOZxgVlGJupRqzblct1ONZNnClXEfx3BdEir3xBZNyWk8Kx2mj3xDrX2ueNgb5A1oXoa3rYQyMg/O7SsttIFtpg9fEKgody9VOqsAHReN6uqloktM+7gMTjE9knsWjqTG1ODa/CSJ2c3blzLRSdwkQyhTuEiGTLxihTrFIZMvXMYBHmDn6o81R8aTe3AVJxm5xop8YAqgXrG3ddd3q8/U5hrdTY1x4jYY5tiPe3Fsv4rybZDW74fBcJbmVLUyviuUvKWY3+8YzdwmfRy76NLAPXMCwYSE7Jv45yVzHmAFQSByldXIF/cHGSVxzcjCetxHE9mZvNdpsb2fblsXfGZHudSevUz51N290Ixn2dtthLarjog0oZg+hnbVkgxIdFUxINSKgY0SDIndlHbmoFOcs46pOzf15LQGJ9CGUE+iRA3jKibRIodXUTdTTq8/eGm/vEFQT/Yik4wyu9qkUvWMJuoAd/qV9AMfcGxVZ0dlEcZXps6OD5OyMrcto3tddyFb3E/vNnP5gPBJSVmuVlomItaCXd4/eMXhmwNl03ya66yyXGN2lMnpjZabW5lpxhLI2ybZWScuuLXiLhyrfl5Njx8LesdkK1V5szKz1iHhwtFs0j5C5EpBKOOR0oooRNXQEmgUdXIbIaevfhgZ6c1NWlwbWkOmYyxwwnLaPWS0H8cXcBXjCikJOM40FAFIS6agO9rpppz69Soim3igchwOTd3wOQ2+QQHqDqHNp3+pW6LJyOyhLLXdf8AYUrhdxnR5mciMJfTrLNnwNvQKsWe1WsJOLxJ1lbfVaXMyRuOUfkbw8g3YTEgALuGaTA6bm/pbk2HGN3ZYvJpdOzS2HKBI+38oTF1XuE1LwLlC4cOvuTTK3YoW8POR7xvH3g7m7hjGJWzp8zkDICxQ3Gyx1cBt4A5E+WHrGFD2XBI3js2k7O9fPARUqhCqpH4xI/xFUzb6Z+p1DBzD1etUsHCJgTMVwiYqphKiYq5TFXEOqBBAfhCGg8wa9QexW/HNFx7JORLSvxm7ntnNnJNl3l/NXcxlSNfX3jm8V8e2owgBSSt9Jvb11FdT7AjSadto1VgEei4MZu3VTXWUqN5XTsc3TY91Rl4ZMxvaN9XFYdsR+ebssSfx3dlynaMIaURMzZLM7fBjcDh/IgiY/JFOLdRybyDM5OsePWE9hWPwubGAPb5beZWNENddBkb+oeJK0AGVIU5EzKkKooAmTTMoBVDgUNTCBRHUdA6unUrgFSHSUcEWIduiXeWdEUA7ZEBHdATqAO6XUeYN4Q1Hm6tby2i+yhY81nyBsxfZ1hcSyFmtU4OCuPM1qXrA3BdMZczd1BSYFUCXuMGktEN2LdNVlKETiXq0s+kI1NBUGBLuW1njZ5t57cUC9u7Eri5sh5ARydd9tM8o2qoFtSbbHdwoRFqQF1JxqFkroRUi+aqxstMxLts0erigoKrghXJQqkxAnxxP0E9o3pTolxIkYAz+ncPpgvnWM4SATgZdIopp8acDLFKKZOf4Y8/MXmH4Q83frd5+p2zkNwruCxA5TgFh34fQDgoXQbVe8/4f56vIpeewu0dRzy0LyxICVuyt7O8Y21CS1jWZa1039IvMhnQmVRlYF25t9Fi3C1yRknNLvoNcslEFSYpmZAonkXwRamNQ4RjZbjbCv7FmQTxWWNphSKnrTY2/D5KmbVkICGWtyRuZpGtW5yEXcN7gM0BchSJpGAEEkG6iKQZVapdTLQ2ARM7M1anTDajTOMx3c3RC+XuYEQmJIQHQQfqCAh1Q+FWz3BHCy5nwHjDEuNrdxVh+4VMVNWVrJ3jcZp8s9d1qRcvck/F2s/QbP0mqSLeRuqVc9HtE0nqhQQSOsJCDva4Att9Pyc4o0fW60K1kjJqFnLmYW8ooJtTAKZXCpBOAac5iagA6AOgiFdn3PJnuzYPnHhPaa5j23yWkSMD1j9MFmA0NB2xHcPrit3DThurhsWzMUL40xjDJXcwPcMBkPH7wZO38Z2hagW5Ytp2XbtqzyUirOruWDawouQWl3Z01jOFlUhIoUQULiVO8JrceQXjuTylgXGF33NeOzpKbKWXb/5TXW1u/ItmvHbN5FlUTCSBo3kYdRk3WRdtyJmfKI/5YY4HONa/Pc8me7Ng+ceE9pp7nkz3ZsHzjwntNZNszGuLmjF2ee6I7MFsyo5jLrXR5zMnf2LOfBO3BAbF60+x2ZIK5cj29kaYhZjJrfaTgY6AkXqcIzuSKGKjTW9JGKm2kmF2S6TpwcSuETlbGbGKJTCbFjaYz9N7TeYJjL89aNrWI5kbZtyzmNpWWvIOrcgo61rejLZikEFXy67tQQZxTXjFFlTmOpvm1DXSrf8AueTPdmwfOPCe00DHk0IgHTmwOcdOfI8IAf2mrikLweRLhhOMwrCu+6abTDTsHTPiJ7F5Bg9XjX7CRaCQrqNfISTUVCcYkCrdUi6QmLzahvJl1DUNQ1DUK3RG4b3Mqt3vrtX2dcAO1FLoDIcLFu5W7xa29cyF5yeQI+bSUTkyHWFrNyi7grNfebHSTTROmYoCYdQzaxJdy2QdJS1kppuEwWIm6v8Ah2bkgDrzKJHcAcg83xTAA96p3ueTPdmwfOPCe01WpRbWDQ4YgyPNZNhr+Ebg6Inmz9StkdkcLjmay/dSfFxBhWduXKOCrZwA5uqSTnGk5bkPbdoS1nCZkqi8KIhIN5dZ84ZnHoTo5BBYqOiZSBMPwjqd24r25onISF1sL726snWgpdkPYMM0c2Zi214ds2aXE9t97JPTuzScmxbJw5WCoFa9DkA6jnTdSLrY9zyZ7s2D5x4T2mnueTPdmwfOPCe01UWSkHOdd+ICeo/X6qzHljW02GIMjpiB3LOrEu3Rb+yzASOM8D2TI5TxLelxyl05Nido5gla9y3KWcsidx7LQKC9tyYlbsF4qaOuDpI5HqbtIokMCYCBsTNpXPFx7T2dMkZ7u2Ct62LjyZMN5mUt60uixtuIM2jmUYkiz6KVVcCQEmCI6rKHMJjG+F1K8R7nkz3ZsHzjwntNPc8me7Ng+ceE9pqTQaXtrEEuAIB5sPpCl1UkBoMNGzv7ySvC0r3XueTPdmwfOPCe009zyZ7s2D5x4T2mtrjlSRvXhaV7r3PJnuzYPnHhPaae55M92bB848J7TS65JG9eFpXuvc8me7Ng+ceE9pp7nkz3ZsHzjwntNLrkkb14Wle69zyZ7s2D5x4T2mnueTPdmwfOPCe00uuSRvXhaV7r3PJnuzYPnHhPaae55M92bB848J7TS45JG9eFpXuvc8me7Ng+ceE9pqQaw5cjhBsMvY4ncJKLEOXIEOZuQEtwDAdUHG6Uw8YXdKYQE2htNd02i45SvGUr3XueTPdmwfOPCe009zyZ7s2D5x4T2ml0qJG9eFq/mzDtEXlso5xsrPuPo2DlbysJOVCBa3F0SEURWVhZGEMuYW6iaoHQJIqLpbpgDjUEt4DF3ijb33PJnuzYPnHhPaae55M92bB848J7TUQ4y2MCCOojFVe1jxdfBGB7DI78Vsmxdwwe0vZGLorGOQIu29oROPzEnlyUuvM9wzs7O3yB5KGln0FcqJXINpRis4gmB0eiU95oJA4oBBNEE63dPDHZuvHLmLsrzuKccFVxRkZ/kiHt6EvS8oROXcPbEgse9DSEmWSF2ukWOgW6huME4OFXbsi5FUFRQDV97nkz3ZsHzjwntNPc8me7Ng+ceE9pqgoNmSPWEeAVi8OIc4yQZHMSD5kLaDK8MnnKRhGkWywps/Wy8MzewU7I2rFTcEzmoYsJkGBtqFQi033Qce3t9vkmfFqdgmmo4Ogy6JMoVMxT2gsLhJ8j2VtDZT2jXmK8Z3jP5Ww/H4dmLGuGUuH3NCM42AhLeRXeRJHvFyhFkYFkuuykuObmW+GQETESMTB33PJnuzYPnHhPaae55M92bB848J7TQUGmZbnIPWPrmVo2s4ANBwER1ZL12Q84XNkjGGz1ieYjodjb2zhZ89ZtoOYtR0DyZSuG5XtzvHMgmoqZEFiLvjNyGbkTDiEUgMBjF3qsvXuvc8me7Ng+ceE9pqSSxJdRZwgWXsgDtQTFQ6l/w6SCnGlExeKUFxun0ANDbgjuiIAbQRCrMp3RdYI9Zqrnl+JOSyDmNry5ZnY8tbYsWxxj5rjyz8jHy/E3g0XmS3+pc7lWQJJSq5zOxZqA7YPEIwzUW4IpIxjdRIpFzLKqYkV7r3PJnuzYPnHhPaalGsKXI5TajLWRxijc7kigX/Di1ApDkIJTLdEbhTiKhRAgjvGADCACBREHB3XFzRiTJ58B+iXiWtZOAwHNt8V4kQ15w5+bqdYazpsXawY494P/ADdsnRC91yN3Z7zdC3ZOGdRLFCy7HtuGbNFXyUc/BUzxZ5PvI+DI8b8Ukgkhbjc3GKnV3SYo+55M92bB848J7TT3PJnuzYPnHhPaaPYXsLCMDE9AIMdBIChr7rg8GCJ8I8F4WsyNjfbPunYruW974sbG1j3telzWoe3IKavG4bmh0rVEyTtI5VWUXIN2ksxc9FALqKmEXLVcWrcd0gkETY1nsSXTWboGl7IE7oFBTOnf8OqgnxRQMbjVAcbpNQHQu+IbwgIF1EBqd7nkz3ZsHzjwntNSA4yN+CrgYJW7Od4ci9LKvmKUwBhq0IzGgWJHRF8srxkpmKvnJ84Sy7EtlxMuH7J+YIkzBSx48rAkQCKZkSCddMVVTbmJM/wm923tIubjyBgTEl5Xrc+zbL7KeVb0fXPdzCbyrabzoQ8Mm8IlI8QzeRCjBoqR4xImo8MkYXIn3zVgF7nkz3ZsHzjwntNPc8me7Ng+ceE9prEWdgMwZx7/AF1LQVnimKQOAjuiPDtWytvwvGZhLtGuJbD+GJ+X2h7+nchKSsktcZD4wd3NZLSwp1OCSTfgVRNeNYNOLK/48EFCH3A0P8H0jXhqdoppfCV9p4vwweQTvNa9yRqh7jPFldLZQbZWFLcGQ3uLLItiMi8+8DMxg1FXRStWHueTPdmwfOPCe009zyZ7s2D5x4T2mqtslEVBUDMQInoy9c6zcGmSdszzzHksvM5cINknOOB7ewG8xxiyxYdnC2Vb173paCUyve+VGWO2so2s9pLnePVmqKLA01JuOLYIIccs5AyhjbhQrAmvde55M92bB848J7TXQk7Ok4lks/cSlouEURKB0Yu9YuXfG3jAUNxuiuZQ2giAjulHQAER0AK2FMNeXxicSd5j1hkruqueGscZAEDH15rylU+qhVPqVxkpSlESlKURK5ARDrj+AdK4pRJOS5Exh+Nr1BDq9nqhXKYfDLzBzDzc3UqGo0/jl+mgECAoAgQoQEQHUBEB7IDoNc75+pvmENdQAR6lQ0qQSMlacIUQGMACACOg9UOtz1DSlQoSlKURKUpRFyJd7iw7KZeqOnXGsp4jY3zlPWgF5QjKy5BFS3GE+ztc+RYqMyHNhIW06vNOOiIFwom6kn6UC0NLrM2BVlE2qiY6GOIplxWOUqhSkNruHQAh90PhAA7wDp3+caz7uTblvSGshta2HHz6zpS6gsm6b/uYY1n03ZyVtY8Y4+fQccqq3UXSYuk4pF4d2xcNlF0ZFZmqUyYH3jhDDd+LZuzxlTTDXVIdlB7RGXPKtzfeyjtJ42sy4TXIjEDYtrs5G+b6jray7FXbb1lydvnhY6WYzrJk8URaXFGkuiIKswXJ0Yk3kDboiQiwE6UpsX7SMOF7C9sJL/0f2fd2QLkIjc0c6ULBWR0pCel2ZSqiLtoHTxgDVZvvlfCZYG/G8Sru5Ay+3ZjeTiMsWels9XFFWJtAyl133maGjc1l6cO7pux1bTlw6t58rCKpsYtlyeVK3YOW7pc4TK/GuzcSiFRWtwkFzWce3AY4st65UojGFjYHlByNMp3y4lLGs4LpK6hWp1WJQYupYtyJcbJty8agMUluJnBQwF44dWLXS33jh3Z54gGBG7JcgihDQwkwTPRIgCdpE9BhYyQmyfnm5L3uzHUJaDeRu6yFLaJdTBK5Y8jSITuwjdeIdKuzqggDYEXRHLpyJwSZoEWVXMmRM4hWQ2Oc4PItOQhSY4u5+bCD/aPc2bZuWoO6MjRlkxsSlOKzzyARWF4giqwXSdNkzkBVymYeLIYQEKyKguERa2BeuTr8xlh6QhZTONq2bj7McHd2SkL1s+8bbtgYdtKW8m16St1kWc9Gxi0e731lVUyvROmp8DcP1nvCKTkdFWSlYNr37bMpjTZ/SwDjtCey22u2yrJbNQgiN5ZjFhBt1zOliW+1I9TcO1EXYLrl3Uin3as/hIaKQzac9jujPPuxVmizB8FxIw7JHfEqxTjYkz60Wt+NcMccN7ovHIKWLrIx+rl630sj33LqBAmMSEhDOAcPG6JbliDru0w4lEi6hjmAElNLV5HwjduMYhjcEtN45umBf3U8stC4MX5FjMm20Z+zjoyWFMskwOo2OVZpKt1UhIcRHiXJDlTOiclZu5X4RZjl5s7jZTFuQLGttbaAc51bWRijPxrJtRudxyWK3ZgfpEq/TcxhLaKEbINnKIthckE6C3E/ZLObT+1BbGe7TjkIq3JqGuiSyOjeF6yFwrspW4bmND2jH2wynJuUZtGTV/NzCh5R5IOW7NuUOhmYG45UyqxpLqhAMbp7BPYZ6VV7aAJuOkc+/Hmx9YLCWoifHL++D8dQ1ET45f3wfjrVcZQ01HQA+SO8H09mlKIuQOfQQ3jaCGmgjzDTfMPVEQ59er8bq8/89cUpMiPXrBFzqPZHw0Axg6gjzjrzjqJa4pSE5lzvG+UPP1efq1yBzh1DGD6DCFQ0qZgQi5Exh1ERMIj1dTaiP4fB4KFMYNPhG8OvU6n4g8FcUoDGSiMIXOo6aajp2Neam8PZHw//AL2R8NcUqFJxzXOpuyPV3ucezzjTUepr+Aer2K4pUgkZIud4erqPU06v/wC9kfDW7v8AU7hzG4V3BQGMIgFiX5zCOoB/2Ve1pDrd1+p2v9q9gz+Qt+f8qvayrfgv6FrQN2swbJC0yTg6S8l1P9OV6oa/71UnUe94Aqqzv+t5L/jlfyqygtrY1yzf+N7eyVYKMFIxkjYLi8ZBndN3wllP3zlFze4pxVut1ngryrhRjYc67BIqaKoCyXLxe4CSq2tVzQSXmAP6/RWptkAnbCxK1HveAKaj3vAFbBJ3gzdp5jcklAWq2xvkhuyuKMthpPWZkqNXi5dzIpwIrmR445BIjGr3LCtZBZYSFaqSKAm3iGMZPzcPsD5pCRjHF1GtBlZMlckbbkfddm3/AAN78sDy9s8q2C1rFB4i3lUhYKN1lFCuEikKdXQTmRUKXK/SBxd38/oLQUqgcARnl4rB/Ue94ApqPe8AVl1lLYozDixm9mV5HG96WvE4/SyFOXZYl+s5yGt9EbetK4l46QLqVVtIEQvWCIi0UKCjsXIHQA5dd3EUQEBEB6oVf3Q6AcQoLXNEkehmojCOoh1g6moa1DqPe8AVybnMPOUvU1McRKQvfMIc4AHVEQ6wDWw7MvBzZMw3dCUZJXlaDi1WrSGUmrsuh0hixdZ3LzErFtY2AjpBwZSYcPkoOTlIsjQwnex6JFTpNDm4irXG3mztMDnO5Q1rnEhgk+a14aj3vAFNR73gCtnTjgy7+ZDmpuN6WlOvLUveJsTCnI++bduGPycpIXhcttSL+SWTcmFmjBI2nMOJZu3Kou0Oife+woqKG8DNbBFwmm8t2Nj3J9vZDyRhzMzfFVxQalqv7Asx20fDMnYTyN0yKpGZEhZwUhMOW6xCC0i0VHB1jGSWRJiH0oBnZI7uzMKSwge94+plYB6j3vAFNR73gCtirrg4snSGSXFpWNeNh3NaEE+xtE3jfyV0MZFK3T5Di7WcoyibBoc6ruHRe3QzZpP0RAHPGImKUAFUyPp47gz7hmLwyNYjLK4R9zY1yBaNn3AzujEUvbyMeneKMW4jmTxTopQErk3JJTS20uP400W5IWQ3ztyrBVpXQQcCMOiB2DEYqGDhPeaMIE9eHngtYeo97wBTUe94AraA24M65ELis+Hmr+nEkrwy1bthESZ4nXj5u0Ye5WdoPo2cu1q8kE1rfUOW8WbboN4gsmR6xcoHdE+xnPZq1NiS5ns42hLyv60Ih0vkLEtvMkbKcFyPG3ZbmWZhaIjLsgJ9qfpW5bNVW65DNzqFVOqQ5dCAmoYJv0hEnPAZjFS5hbi7DyWEmo97wBTUe94ArPh7wfuTI228kTikyyul9bDlkawIPDqLHNQZOYP1J5uWWRfMZAhGzJq6t93HvVyJuhaPBMRRMqZDK1Qpvg9dp+1krpkrns+IZW5ZUIa5J64Yq6mFwNnzRoSVXmk4Miahemb2IbwM6u9ZIGIZIIlYOMHeSFSL1OMDG0ycO1WNNweWuGIWEmo97wBTUe94ArO24dhuUY23l2+bZyYznLIw7s/w2bpyUuCwJCy5uRfTrZtLs7OQjeiHX+dEoN23nXJgWOk2auESqaKqkTG6dq8HTCyw5tZSufrTK7x3OxFuWRKpKsLLgrrNK4rnMqllZNCYXSesY5NlFJNTqgmY6RlHSygFTbGKNzdAJ3CT0DbHTlvVW0nVIDdu3ZkDHPmJWr/Ue94ApqPe8AVtHtvgos6TcRbrh5ORkfOPZwnKlrF248vCzLfg1FpgicpHXSzUNHy7w6MMZ+WHaiRYzGYiXALAVybifFXRwf0pZmO2uULmvy64q23ebBwsSMd4FlW15pPl3zqLj2akYpIEAs0u4bpKnhuO3CMXqLpN85LvJBQvYCBO7viPHtVWtLhe2evALXbqPe8AU1HveAKzxe7HFgJzchBMNpiLdHjdqiI2Ulp15heaa2xIy8k5coun0c4Rerru0Y1NtxzpMiBRAHTcE1FONKaqrH8H/PLY/sfIk1laDtuAvPacDZoWk3Vgy8vAQT011yloFci8bGOdw7K4ijvlIpJADkj3bdcrhQ3HIpS2pS92TgSN+2PMI0gyDsBJ6MfIrXzqPe8AVEA/BN1Ot1grL5fY+u2FwhmbNl1z9vw8Xjxyi2sm30Z1g5uzISZMgcgJCWGGFXo5rEpu0X5UZAyYkWcMlEQ5gE9Yf66FHviAVFKpTqOLGnERPr6KwA7/AATUe94ApqPe8AVk/s37MshtEGusre9omyRh5GGs62TSkC8ni3fdFyFmDwMKPQ5iizQXLBSQrSKoHI33UfsKvGDuXjwLwfd7Z8xXj3KMBeZIxpfV1EiVmClgSctFwbE08rbQaTSahWa82d8RHi7d1SWO3kGbgXJE1DCneGNmTlHfl651AbLL5GExPOPRWv3Ue94ApqPe8AVsOmtgdS3W+Q3MxlaSR9yzPUFg28WrbB826fA9uRLjYuOjkeiwF1cQakK5t8BTI1MY4GkjcWavcy3BdX5bkwhFXHkVKPZv8zzGK2d0oYzkHVmRjWDVdNHkjOSajxJGMeKPGbti3i1xMCrhmqmL1MTtuPz4SkBnI/ofqFLabiLzMd/bH0WrjUe94ApqPe8AVnvbGw+lc43+3NleQtGWxFeYRWWYDImFZe0JmyIUYm4J3o8Ci7VSeSnS+2ZN8EE2UMJmxkFCvjAKnFe3uPg2562basW8JbMtutrSv/OFu4khrs9zmbc26RpdjhNKDkjnSOdcX5m7hnJOIMqPGIMnZFAdLGIqmSWvpkxtwG3bEePcdyFrhiRlM9Xl/Va0dR73gCmo97wBWeGM9iqNyQ2nm6OYXUddFsZdnMSylqNsNydxSNwO7di7ln5Qlqi3fb8w+LF25x6ccRFudRxMxzYFd9XfC5t8cGHfmNmLOQu+/HxmpchxlmS57Uw1O3KYqM/Ky0NbxIswrIpvJ105i0iuLbOds4jyTUeo4cbplhSB9K60k5xGeMxHbIQtMnmn9fXMtYWo97wBXOo6D1OqAdQNOvWX2atlBvh+2styaWWIi87hwfnVHBeRbfY2LLW7FtXr4Z40ceOl3RuKeuQSt9yu9YJpgLIjpD7O40UEmIHWH6Q/PV2ua8Sw4f0PhCgtcAC71s8U1HveAKiAw6Dz9QObm1rJrH+zBdV9bO2U9o1N3MpWvjmcVtltHwNivLxUk3bSPaSsitKOkViEiWDdtIMC9HKkXAyzsoGTTSIqunjHppvB2ObsdcKgFpcWbRB6jl4JHuh2wpqPe8AU1HveAKuvi/Fxcmx+XXSV1x1vv8WYblswNod9EPZBe9koZdim7jWrhEOKarAm948FnYlTEETEDU5ihWT1q7BtzXfEXHdMDfDm57Tt7ANo5tPJ2LiibuyadOLybzqzGE6TidFyUrIbbmOjZEN8hE2xVEG7jjCEGxuNkzl69dCXSYI2kgdUT496wLAR105uoI9QOxXGo97wBUCRwUImoXdEFEgU5jAcobxdeYwcwhz9UOYavvgjZ9vnaIe5OgcbR7+fvGwMRyOVYmz4piV9L3t0uk4aPUjGonVTTSUMSXM441QRKBWZw3REwCCAGychipa0l4btJA6yQB3qxeo97wBTUe94ArZrdvBwL2tKTNkHzUxVyvDXHdbbkS+xs8jod/DWTNW1DXNIFmwfKESfNRuMXZY1VApVUIpyBXgKikmb1OReDCWxxbOZLzfZMyVKw2KGJnB4Rrs1uInIyIpyd5xJ3twwbyeSNERCqtmLOWsoVV4LplKtV022gGINXup0wCciJHdj0YqXU3teWEYgwRz5jtkLVDqPe8AU1HveAKya2dtnZDO8HmKZWvCUg3OJ7JQu9K07Ox8vli/bqIsZ0Vdy2hEHjZyaMjgbFUknzQHSzRN42U6CVTFU6XoDbD2fEbXib3eJYujbLmseNslx15SuYoGMtJ0ydzDa30macgosVFSQ6YvGjQWSQnMJ3KYgYSb5yHFrSA7CY78lDTIkb48O5Yi6j3vAFRFEd4O9z8waVnfffB85lx7im676n3Voo3RYkzIPrkswt7xIqzFqtbTty7EbltgTKlXl0+hZ/jnKTdIBbINROfQwinWPdo4Kum7cJ5Yzqwfw6FtYhuSBtydjHT1NKakDzhnZQWapCYDGK3FFuB9Cjr0VrqUEjiNqQbVdgcgObDCM+ztWTiAyTtwHSNnd4KzNdHcP8k31RrvUqVlzro7h/km+qNNw/wAk31RrvUoi6O4f5JvqjTcP8k31RrvUoi6O4f5JvqjTcP8AJN9Ua71KIujuH+Sb6o1EQpgOURKYAAef4I12wEB6ggP0DrXNEXR3D/JN9Uabh/km+qNd6lEXR3D/ACTfVGm4f5JvqjXepRF0dw/yTfVGm4f5JvqjXepRF0dw/wAk31RpuH+Sb6o13qURdISKfY/gm0BMAHm6g6jXG4f5JvqjXepRF0dw/wAk31RpuH+Sb6o13qURdHcP8k31RpuH+Sb6o13qURdHcP8AJN9Uabh/km+qNd6lEXR3D/JN9Ua5KUwGKIlNoBgHqDXdpRF0dw/yTfVGm4f5JvqjXepRF0dw/wAk31RpuH+Sb6o13qURdHcP8k31RpuH+Sb6o13qURdHcP8AJN9Uabh/km+qNd6lEXR3D/JN9Uabh/km+qNd6lEXR3D/ACTfVGm4f5JvqjXepRF0dw/yTfVGm4f5JvqjXepRF0dw/wAk31Rrd1+p2ymDhXsF6gIANi34Aa83/wDFXtaU63Z/qeH/AGruCv5DX5/yo9rOt+C/oWlH8ZnStLU7/reS/wCOV/Kq+dr7VOa7Otq0LRgLhh20FYiRkLbaObUYvVWpTtb0ZmBVYxBOrqlkG6S/DEed6iPVbo7tjJ3/AFvJf8cr+VVJq9Rge4tdjIxByOzHqVmH3QVmWvt5bQTkttKqucfGk7OuVheVszo49bBLRMszaxDRxJB9l4lRaQJBRRnvHpKEVO2MchEDLLipTbQ238/2LCdJbakrHYmGzYHHJ5xTH7FzcTiAtpsg1h4lwuYeLXRRK2ROCiyR1yqCsZNZPj1wVxGpVeDpuJcRJK0DnNIgmRksv7l2vLqunAl8Ykk40gXFk2YsRvfdzxyLOHg5aFxxDDE26QzFNIVlJdwUseSQkTqlTcN4GMIDcFCKLGxApSrBoaS4ZnPsA+nioBdAZsC7bV46jpBpIMVjtn0c8RkWLtLQFWq7dQiyCpNQEN4iiZDBqAhqUNQHqVl1dG3PnG8ol7b9wx+In1uuJY13R9vDiSMJC2zcqsnLzLu7YlLURazTp5Py667khhSMLwABuUqKBUsPD/GGoakANIdtBkHdlBHn2ICQcDBWWd57bu0beRZAiV4R+PUpJ0jIqI4et9HFHQLwkrcU68fM1o8xFmy8g/uuecvjpKADro0qZigikkkT0r7hBNpCXfJy1wPcaXJNFyNL5SdztwYrjZOdmpWfjSwc0WQdCIGdNXcSXpOZstvFTjjGbIiiQRrCelU4OmAGlogc3rcokudeOfoLPFlwkm1HFtXDKIksZRLZVw3K2bxuJ4pBrFMGzS2GBIZo3HVEjA7az7fbqJmIdY6TRQnRAFWVA3gHO2ZlRw0kmR7TwYVtKwELYzsieGY0i57Tt9WFWirJMvvispBIHt6HMDVY51/8j5nRQOoBsTaVPB04u3cMurcglsXMIM4LMFhtyZzibkkr1iEMTRN8SctEuVr0ZYghwulKLgnkK/ibVI6MU2sC2WtyEEkaoU+pGBUzLGTOqVTqE21s0tnD1eOjMPxQKr2SpCox2H4pNGx0MdOk39nMLfA290E0j3RVnIIhv8es9dnWMqKx6xJpQ06ZjDIz1qQ4iOZZfONt/NXTZS4IeLw7atyMbZVs+yLotDDkRAXHiaOdmfqSiFpOygY8d0xVlpVd2OqoqLSThQnEmPzUi9dsTL9/wmTbbuNpjxSCyy86d3TFx9mmjWzWYO2fM3U9G7rrVq/coSLpNUxRM2EoobjZMWzfi8WKVApUwMvU+E9UqwqOab0mc/XhKyODa32hk5XKUk3yVNM2+aLeXtjJVsx7lWMse5GbmOiolcp4RFQjMih2MNHtBXIQFQRTOmBwKoYB9W722s2vblkLmVZ4kRGZXZLz9uMcQRLGzLiKws2Yx8gi9jC/AOmNvzshHHKUxBMToc+oKoJqBiNSrQD2R1bjvGaNeW5evUBZfLbcmeVH8rJpHxyxeXJdJLjvBSPxnGtEb5bIRzuGaW5MoB9jcQTaOerx6MYBSEBsCCZjn6GbmS7c1t5bQ84eJWdvsfoP4G4LdmYiUYY0jGr9iytGcC5LVtso86ZoSJkgBy2YHIYQ3QIdZRMNysNqUuMaQYyjuiOyFUOcNvTzzn651eW2s9ZEtlSzDNl7blkLCy9IZ1gGFz2o0uGOVueTRjEXMg+SUD/Kih0oj1EkFhFNJRETlDU59btm2485G6DMZhiEx2l5M7zKYcQxYpOTxtzSV6xkaujrxZ41lcEvIzCDQS6ldOR1UOkUiRcP6VMNkYZZc2WW7IdiOMkzjv5+nt71k7eG1zl69seXNjKZbY0b27dztQ0s6gsZx0JciTFS6V72CFZyCf2RtGJzjt5IptEw0Ko9WLvikJUi4yB8U34KhqIPim/BVG02UzLRGXcoJ3q8+Hs/5JwUF2e569gmx7vYtkXSk7bje4lIR6wB4WLnIkVgHoOVjwkpDoV6nqZLo1X4BtQ3fe2jteZRsCOfRVjW/h20IualCPbojrexNHR7O8WRWCsaeBlkgHdXilEnDgVGhQIY6iu8ZYRTR4rFmlWIBz5u6I8FcOcMj6Oay1k9tHKk0S5kZmz8CSyV8BHRl7pP8HQ7ot5QUOCRYe1ZQw/CXiGHQ7QWyJh6JSMxaiDsRRT3e/O7cucLtaTcdecdhy84u4TjHy0XdWHIiXYuYEV2bstnFKIAZOATdR7B4mwTMU6Tlkksm4KcBE2HdKgsYQBAMb92GHcovuiPXoLM19t558kixpXrXDqwRV7P77a6Yah25juJOIRtt8wXMmBQWj1YJuhAAzVAwJxaQNUzkKJjG5lNvbaDnFY9xMDi6VdRdxQ84zdSGKIxwYja3Jd1PWzb5k9QTNDQ8g6UdsmO59hORIoqnTTKnWGNKcGwEEDaCOoQD5diguccCcII7cT27d6v272j7/k5VrITEPjOXjW2Wp/Nh7Oe47Zksl5P3MxZR0wuqwSMQwoqJMGxkkSqlBsqUyqIkOYRr1cjtg5YnkLhZ3NC4hutnfF5LXtkplcuJYySaZQXUTfotmk+lqUqzNinJOSskW4NzNeLaCRTVm1FHFmlAxrQABgI7owG5RJJJ34nrWS+X9rPLedLVTtDIrbGr5gjekhkBvKwuMY22LoQk5ToYjxYH7YCmMU7djHMClUKYxGUY0blMCaQAONPWH6Q/PXFc9YfpD89SGgEkDPH6fRSXEnH1t8Veiys+5Fx9jy8cYW8tbfJy8yyQKu5a1msvc1sGm4pOBnzwMkoHGsDSsYklHvDJa8c3SIAbhigcLL/ACvo/OFcVyHUN9H5wqIF6/twE8wyHVJVIwjZie2FdHGWXrnxKS+S2xE2JJe6JYr3G9xmvWyWl5GJESO50cgyFcf8lOtxaW8sl8P7AnoIbvPeaJ22c1wJos0NH4ijAgbDhbBt8jDEMU3Sgk7b6aBbku1Lz7sxFhOTINZI28oQsq6AxVAOUC4j0qzmtdOGf0VwSMj6wnthcJEIkRNJMN1NJIEky66iUpS7oBr9ABXqbevKdtiJyBBRK7QkXlGwnOM70au45GQCTh3b6Nklm6YqAIonFxEsTgsnocoJGKA6HNr5gOr+AfxVxUEAiFIcQ69tmev+qy+k9ufaJlWUm3czNikkJeYeSzu6WmMIZreZEpGVhZqTikJQEuOQjXru3odRyxR3SLlZ8WYRSOoma3LPaLyARnlmNno/H99R2bb4RyRf7fIFhMrtVPONxmeIlIpZb4ce4RLPypUTtx0SBwQCkEEiAFh6Uc1pABaCOcdHhCgvc4y4kmZ696yktbaQicSzLa4MC4ftOw55uAO29xZGkEs93NbckgmuiwmLbkHzBstEPG6bx4UTIiqkuKyZlUjiikIexuHhBNpm5ZC1pN9P2Wg6s+ah7ki+l+O45BN1IwkzETzN67KcD8acz2CjVFCBuIiRAUk0kkh3AwqpUFrSQTs85UDCWjI5rMC6Nt7Nt+v0i3u6tl/a6kFI2ZJWnbtttrUQXgZiCYWvKxjZ8BF3KBlouNbNyPTiu4SUAzjeUWMYxvDzO0BIMrFvDEeJoiZxlh/IqzWUvywZO/XOSk7hkmSiZm77oxdq2FISFbNSlTKmbdFETAfU4gGO9RE+MFAwNgsEHm+u9QWtuXCMJnvld1sUp3DYiiR1iHcpkOgkfilFwE5QEhTaaFMbUQAR6giA1d6XsqJOEtLHA0I1btk1mEO2inbeQYlHowSHk2p+POTUzQETKAoRI++CpTgA7g2a/AA94ecKajz847xg3TDqOpg7Aj1w73eq4IAg5LEmTJV6JDH0SLpVYHEmCrh/IuUYmKZipx5Gyr4CxzY5khTK5AGqWhTHOYSqjol8EN/qBYEY9jyu2yM5HkStI0i4cPFE1kDPyFkVTpnIVIRAoAx4o/wiAQ5gDXeEqZrRaj1hMG6beDn05+sP09/vVzqOm7qYSh1jHHQefXqfTz0BxkKFfV5YMKvJSDdArqJLIygJN0X6SfHsUk3pyAszAgD9hcgIIpbwGETF5hNqAV5yKtq2FF7pZPXCqacc+jTMH0jxkc6bEAF3T5sdM5UxE6iSKqJRMQoiciYgUu+AVazsd4AAPoDqB+ChvhfCNoYerqfnER7I/wCNBE4or3nx/BCu/kV28qVmeWk28dEsXRTdEJFQlzsSN1BTH7IRSOIUSCZQxt8upCal36anZsGiaQh3Sc0RY7hk3i5hUjdNJ4oo2kVjOW+oc7M4pJgIAJjjul+GA6gFoA5hAQ1AQH4PXEvfDsdQPBUWoiAAIiIAGgAI6gH0VCiIMzuV1ndgRiCbpNtISj14hb4yyYlaHbxbgxSFWFYHZ0ASBExBU3C7wgYUhLx+8YpBtTXOo6aaju7u5przaa66fR3q4qSSTJUpSlKhEpSlESlKURKUpREpSlESlKURKUpREpSlESlKURKUpREpSlESlKURKUpREpSlESlKURKUpRErdn+p4f8Aau4K/kNfn/Kj2tJlbs/1PD/tXcFfyGvz/lR7Wdb8F/QtKP4zOlaWp3/W8l/xyv5VUmqxNlA0vJ668z5Qeb99VM3A7I1s/wCIqzfhb0KVSpu4HZGm4HZGqqylUqbuB2RpuB2RoigP8YahqcJAEdeeuNwOyNEUqlTdwOyNNwOyNEUqlTdwOyNNwOyNEUqlTdwOyNNwOyNEUqlTdwOyNNwOyNEUqlTdwOyNNwOyNEUqlTdwOyNNwOyNEUqlTdwOyNNwOyNEUqog+Kb8FR7gdkabhe/RFKpU3cDsjTcDsjRFKpU3cDsjTcDsjRFKpU3cDsjTcDsjRFKpU3cDsjTcDsjRFKrnrD9IfnqZuB2RpuF79EUquQ6hvo/OFTNwOyNc7gaCHPoPVoik0qbuB2RpuB2Roilh1fwD+KuKm7he/TcDsjRFKpU3cDsjTcDsjRFKpU3cDsjTcDsjRFKqInxgqPcDsjXIEAB156IoqV3ouLkZuVjISIZLyUvNSbeGiI1oTjXci7drJtmrdEvXUVVVTTKHXMcArIm89knNlkRmRJV5D27drbFEmxiMimxTd7LLydmqvUp06xpReHM5RZlj1LclW0h0WoiLNdNIigaKEOMOcGtl2WHeQPEhYhpcTdxI+uSxopXtHeNMksDMCv8AHWQGJpaNaTUUV7Y8qzPKM36Tpdi8bAduHGoOUmL1VFZPeIqRm4MQxipKCX0AYSyq4hoeci7Fui4G8vb766VWds23I3DL27GR6oprP5lqg3MZi3OQOiEV19ElW4gqB9wdakkCeZRdMwrV0q6kDhDL9xzWMoNhji7WbrM8u1gsVPblhlbKtu/nLw6abYsXMSIN49YhzLJfZir8UAKFET/CLrAwwll6YiL0n4TG15z8Hjq9mWOL2lret9xPMrbnJJw8aR8Y4M3KcePcrsHKKRSgYDnKQoDvKpAeJbMFWax5zG7v/RWupV1oHCmR502SiKQrO1FMPtEHWSEsk3AwxY4tU7p0dk1ZLpSyrZQXy7ghkUmJSi4OcN0E9aq2G9nfL+eLtx7aWP7Jn3Xum3iexbXuyRt+TbWGaRQaKyD1FaWSaqp7zRogu6XRRBVciSQjxJhEpRsPePu+goIIHvD0MVZOlXdi8E5XuC1XV9W3ZUxcdlI5fj8Cs7kiWhzM5q7JZJ24jYZi3VBN0su4RaHVAoIlFIFUQWBE6pCD6uM2Us+S95Zfx8ysJQ15YIkHUHkyBc3DFM3UXJtFHyZ4ZiY7kCSMkqMXJi3YR5l13JY1yZIhypiNVBGBn1h4giFBGJG0fT+hWO9KyyV2H9ppJSSTDHzBcYi3FLgdqs7+t58gZZAzxNzAIKJvRKtcDc8dIlWt9ITSSQsF95uG5qOP8JjfI10MYGUtrHl/3HGXTKrQFryNv2RKzbC5n7ZEHDhjHLotzkcuUkhBRRugJ1UyfCMQA56lpa6bpkQhaR3/AK9ncvG0q9wbNW0EVnZ8mthXKTeMvt7KMLZkVrDlRavFIORCKmxVAiBlG5Y5zvJuhcEIKPFnMYN0omqhrYSy0E0ELGY6ve6VXF0zFmwslZdnyt2QV3P4BRUkujCPG7YxH/QoInUUBtvGTT0OcpAoHNJgHf3IGuGQ9ESrW0rZTdPB7dB44xheMFlFG25i78XxmWbjTzgxYY+sc0K5sZS9pmWtqQZunsg9ZwgJkinguY9BQ7560TQBUVBIXx10cG9tJWd0ME8nj1ASrCjciaV4LFXsUW7GYfyZpkirNMUko4sI5ScO0eOa8c4akRXX4wRIddpVOCqYOHnGHXgrGm+MB6/VYD0rP25OD0y6hPXO0taSspdtFScm/ZWpc17tmmUGNssnc5HtbnlWDdAzUjF29t6SYJKt1lDi5BDeQTRXSXNaq89kTJOOsfWpk28pezGVq3fdUTacYlCyrq5Z4qsxb0PczZZZFJsDVApWc5H7yLl0k5MYypkkFkUjL1rY6TrdaGWazwXOIAyxLiAMekxisa1VlCm6rUyY2T0DPDfGKxXpXrWFvsHjdiseQekO4mgg3CBI9IgGVMRQ5QbKKLkKfd3EwUMoKZSdEJ8484VVhsBZsUi0jJlatlZh3HIqHadDAqk3ScHRcAdY6aZeiDtjppkOcvV3hMACGvt7L7NtbLXT4az2cOZhJvNgTdicREyvO1tbdB2apcrVSDsF0yYOIEK3lKnvUBYvHTJYTJLNXJ2qiTkSJuEzEEQEpyAYwAbQNRABENOcBEOevfRVgjJM4J4EioUky2O4EiLVNTTQhjFTSOZYpd7mABFYUwNvl4sVNefrdB6m6waxW2vYNE0L9al8bZaI94N2nGDnC5mkNPaL0ZZ6dqtlSGP+EwTOE7N4yVuqV7qMs1OQeMWhZJQ5nKkigsCLACGTOwXTRAqfGqkKPG8aU4CoZPTQS6CbTXtnx29UbIrMHpHyrhqkYiYIiiRJc7w7c6amvwilBIoOCiIbwlExd3Uo13Nn9lmu1rom0WWxl7QDkWkmGtcYAJJN1wykyY2Lr6uuurdGo2nXtAaScMDGZbiYgYgzKt1SvdIWDJnOuLt/EsWzYrgDPVnBzInURaGfAluATjAE6IFV+EUBKQ+pgAQEleFrzWmdWdN6v06VXS9mNIVCQ0OgE3YnDOBO3qldtYNL6N0pUdT0fVDy0AuuzhOWyMYSlKV0K7NKUpREpSlESlKURKUpREpSlESlKURKUpRErdn+p4f9q7gr+Q1+f8qPa0mVuz/U8P8AtXcFfyGvz/lR7Wdb8F/QtKP4zOladZSFdq9OJsyrJBmEyqwbpuHQJvJFYoJnUTbJaDviQqyZhDUBEDfBA2g6dYbWnhIUOlb3osz4zDpYDVQZIDFbkdCcUd3Xc4s5R3q9E0u1a2X82Roy6IcOJXjxM5kFDxhjJLJKpHVY6bh1EjJDxaoGKYvGm+NzBXbTyW7SXeGCM3m7kwgUhnyQuWhBKkYqaJ+huLKBFUSHKIpCOgnAdRHfDdzQDM4qQXQIXkWdr3C+anet4eQFmm0M/M7O1ORuKBFk251QHTUxSKKkA26AiGo83MNSjW7OAokmlEyTkrl2syYrNmCyqMgo3MYqoIG3A3t3cMI8wCAFHUA0HSvqXoK6qzpzGce9dw6MRIOBlVCEddCrNl2yxU9wQIYBap8YAagpvH03BGp7zIDt03elLGIpOZJoozfOU3ypynTFvIN0AST0ACGTLIrCYwCPGbpdQKAm3gaL0TtUkmTAVAZWtOvyPjNmJ9+NlmcK9bLm4h23cPVVEECGSNobTjEzFMP+4Il16utRktC5VXLtshDPXJ2hnpFFW6IqtVhjt/owUldN04Jgmcfg9XTmCqw3v+UZyE5INWjRNacepvHBTidZNAU0FUQAgDoOoCqCpTjzlOkUQ1rtucjO3ifEuoshkFmjtg8boPuh27lFyDwE+cEhUBRLoxQAOJzAIB+xlExhFda71zJLpAAXnI60bjkpNCKSiX6DldArwxnLJVJBsgfXdcKju/BTESiG91NebrDUhG2LicM1nyUJJmaoMEpNRYWhwAUF1SoJqEAQ1MUxzlDUuvV16nPXtT5NEVGZU7daJNY5RNwxaN5JRuRqsko9UJruJgBki9HrFBIQ1+CQRUE28Y3VDICQx7GKNbMeaNZJpgLMr06SC501mDjUQKQB3DGY/CIbeMbjz6qDoFRADoJSXbl4B4zdx7lVm/auGbtuIAs2comQXS3gAwbxRDUNQEBDsgNdaq7cc4e4pIsgdoRkCUe3jUW6aoLBuNkwSIYTAQhd4QANQKQpQ6wAGgVQqqrJSlKIlKUoiUpSiJSlKIlKUoiUpSiJSlKIlKUoiUpSiJSlKIlKUoiUpSiJSlKIlKUoiUpSiJSlKIlKUoiUpXAmKXUxjFKUoCY5jCBSlAOqIj1gDs0Rd+LdNWUpGPHzNZ+xZyTd29YN5FSHcPkUliKKopvCFMduc5SmKVchTHSEwHKAiUArZrLcJI5CabSFu47utwBkGaN2XBf2VEp7IWWgZ25km3m4XnJMolmhMi3RyC3QSWdtzLKNrXakcHXVWMsjqx6YR3dGO/8APo+tXHTGN7pxvlBH1qq8NqMNN2RjuIPiBgsqb3sJczM/RbgY/hXJm3pySmYDEbx4E0WauddG9crObrNb10XDMxL5+7hDGYlK1iGke2nYePijkOZBC6XyhnBzgBD+PW4Tm5Up8k1F4wPFMWuSBvJtbsZk98wjzwze45ubjbTUOVqAnZt2kkxi95QpiHJFEVFAN/iiarumMb3TjfKCPrU6YxvdON8oI+tUPp0nQXDLLoGQ54/RS19VpkHn64AW2XaT2mcGXBLbIN12xd925WbYUmoi4bixmw46zI9wtFW7Yqakm4kn0UYzWSfyNspNF2SJH7ZJnEJmRWIZwImtHh7bWh8Q3TmaXjMd3i4t7J2ZGuYYiCZ5Y6TyduLAyvCJftHMgnHARyfoO9pJZm7SQQUav4yMX3VCpHTNr26YxvdON8oI+tTpjG9043ygj61VuU3TO3ZzGMPLcritUEBuER3YDuJlbEbc24GFvZbvLJoWXkJ5HyWNbYxjBWa4y8mvD3pH2naxrWj0cmCrFqcpE3ROJfOwSBmbj0hIkYpDgZOp4Z2/3eGcdYvtOKsGck5/Hlvx1kryCeUXMLZcnExM9c9yNXraETaiLWdWd3Mqg6lAWUBVoz3ASKdwqcNbfTGN7pxvlBH1qdMY3unG+UEfWqWtpiHA44Ds/r1qgc5rwQMv0EdyzrxPt5ZRxFhO3sOQlmYfmVLJuJrcFiXpc+P0ZebgVOIupKXXclFUoO3z0bpVEkmcxHLYrQhCHOIJGQu4pt1Y4jbxvjLMNh58W/8ALN+uc3Tltxl1FgLDx5d8Src0ZZwtCixMs+YFirleKSaJTJKOHabc6LhvqunWrjpjG9043ygj61OmMb3TjfKCPrVJawye/s8ggc6QN2MeuxbXh4TBVWMs2HlcYz12pYzBvfFhTl1X3Fp3WrfjdKbQCeuBxHQjRvJMRTnDFUbCim+cqR6SziRVUWW1tRa+3XP2fgiwcPQMBcMO/sjEs7iAZe37pYW/CzDeRir6j4yeOCUZ02RlmR77fKGFGRK2XK250iHWOdPXx0xje6cb5QR9anTGN7pxvlBH1qi5TuFgMA8/VhuzRrql4OOY29MSto1/cJJN3q0mV21kXVb1wznTGLXcMcuOxg04uRv60b/XSFAGpVVHKzi2nLFdQVCpLNpIBMkJkzgr7Jtwoqq0pb7+XxxdjWHbLybS87Dti+LeaWTkGMcy9zSTdlI9F26vIGMsjdD5i+Mm6TBUA6Lbg1ciUU9Q/TGN7pxvlBH1qdMY3unG+UEfWqBToxHT35qwrVoIGEmTAz6VdyZzDfcvc0/dIS5mL64McKYaWK3bornQtDpYnBt7f486fGKJIRqDZkVyceijFQBQywqmMoPuJ3auzzcL6+pR7e7ZpL5RxoviXKEzB2fCW9MZLhXS7Ry7LPOm7Qij14uoxa8ZJKj0YYCnLxwAqsCuNfTGN7pxvlBH1qdMY3unG+UEfWq5uudeOJiJOfrzWYLrxc3M+Of9NyzDR26dqVFZo6Nk1J09b3Kpcy8g+si33slLiq9lJE8XIuDMuMdxAu5uYchDLiZiCskuYEAES7vmLx2tM637C3BAXPdEC4YXXOw1wXIaNxxbVuv51S22xGlutnS7NgkdRnFppJgzZmHiW4lASFL1Kxj6YxvdON8oI+tTpjG9043ygj61aWeo6zVGV7M4te0ghzTBBBBkRkRge9VqUxXYadVt5pBBBEgg5g7wfqvWGueWUSaoLDGrIszLCgirCMlEiA4MZRYu6KWglOY4iJR5tQL8kukPKidMdcy8gZ6Ry9PIOG8ginIM1ljkOmYxkVCmJ8VQwAAAAF11AAEAEPK9MY3unG+UEfWp0xje6cb5QR9au/Ot2s0A/aFbCP8AqO2AAbdgAwyXVfd/QzsW2SmJ/wCwZnGelXXb5bvJqwSjg5JLpJRh4oHL/HsBIyiyR7hQugx1narMyyi3RzZMvHnMKoNBUZb3Qih25qRJZAuaXkpOUeOIvoiZkFpSRbs7djoyLcnWVUW4voNFEiBUUjKmBBuQgJNyAUiRSFKUoW/6YxvdON8oI+tTpjG9043ygj61cGxad0vo60PtdgtdSnVf8TmuIJkyZIxMlcmvoyw2mi2hXoNcxsQC0ECMBA5h3L1wXZOBulM5argU66qhXUa2c9EmcqEWXFfeT1V3lE0lPsmuhkiCGglCiV33IkooqnLOAOsKJ1xMRM4LCgoqqkJwEug6GXW11+MCpgNqA6V5HpjG9043ygj61OmMb3TjfKCPrV2LNcdaWEOZpGtIy/euwwu4Y7sFxTq9oQiDZKZ6WA7Z3byvXIXZONyCi3ct0GgqHODBOPbFjtVSnIpohubnwinOQebUSG3ddOavO10+mMb3TjfKCPrU6YxvdON8oI+tXW6Q01pXSrGM0jaX1GsmA5xcBMZSTE4SFy7No6x2Jzn2Og1hdElrQCYymIynBdyldPpjG9043ygj61OmMb3TjfKCPrV1shcuCu5Sun0xje6cb5QR9anTGN7pxvlBH1qSEgruUrp9MY3unG+UEfWp0xje6cb5QR9akhIK7lK6fTGN7pxvlBH1qdMY3unG+UEfWpISCu5Sun0xje6cb5QR9anTGN7pxvlBH1qSEgruUrp9MY3unG+UEfWp0xje6cb5QR9akhIK7lK6fTGN7pxvlBH1qdMY3unG+UEfWpIRdyldTphHd0Y7/wA+j61OmEd3Rjv/AD6PrUkJBXbrdn+p4f8Aau4K/kNfn/Kj2tIPTCO7ox3/AJ9H1q3cfqd1y2X4V7BYIOmy4lsW/DGBBwRcShyVehqO6I6BWdYjgXjmWlEHhmHnWmaYIc0vJiUphDo5TnAoiHxqpu4f5JvqjX2IcDBsl7J+WthS5sp5k2S8U5/v5DaguGz05i7LKa3BcjeNBS2kEgO4UHfBoxB85XMQgGEpd/dLzmENgeadhnYYsm/XkBb+xZsoN4xCMaOU03GFIt6qJ1SHFQ3GG59BEAEA6gdasalrY2s4QcFy6dmc5jXTsC/Ps3D/ACTfVGm4f5JvqjX3efrRtjL7zTZJ8xURT9aNsZfeabJPmKiKjjo3FW4q9fCHuH+Sb6o03D/JN9Ua+7z9aNsZfeabJPmKiKfrRtjL7zTZJ8xURTjo3FOKvXwh7h/km+qNNw/yTfVGvu8/WjbGX3mmyT5ioin60bYy+802SfMVEU46NxTir18Ie4f5JvqjTcP8k31Rr7vP1o2xl95psk+YqIp+tG2MvvNNknzFRFOOjcU4q9fCHuH+Sb6o03D/ACTfVGvu8/WjbGX3mmyT5ioin60bYy+802SfMVEU46NxTir18Ie4f5JvqjTcP8k31Rr7vP1o2xl95psk+YqIp+tG2MvvNNknzFRFOOjcU4q9fCHuH+Sb6o03D/JN9Ua+7z9aNsZfeabJPmKiKfrRtjL7zTZJ8xURTjo3FOKvXwh7h/km+qNNw/yTfVGvu8/WjbGX3mmyT5ioin60bYy+802SfMVEU46NxTir18Ie4f5JvqjTcP8AJN9Ua+7z9aNsZfeabJPmKiKfrRtjL7zTZJ8xURTjo3FOKvXwh7h/km+qNNw/yTfVGvu8/WjbGX3mmyT5ioin60bYy+802SfMVEU46NxTir18Ie4f5JvqjTcP8k31Rr7vP1o2xl95psk+YqIp+tG2MvvNNknzFRFOOjcU4q9fCHuH+Sb6o03D/JN9Ua+7z9aNsZfeabJPmKiKfrRtjL7zTZJ8xURTjo3FOKvXwh7h/km+qNNw/wAk31Rr7vP1o2xl95psk+YqIp+tG2MvvNNknzFRFOOjcU4q9fCHuH+Sb6o03D/JN9Ua+7z9aNsZfeabJPmKiKfrRtjL7zTZJ8xURTjo3FOKvXwh7h/km+qNNw/yTfVGvu8/WjbGX3mmyT5ioin60bYy+802SfMVEU46NxTir18Ie4f5JvqjTcP8k31Rr9GzB3B28H5fFmuZm4tiPZYdP059wwKq1xFHsEwSTSbmKG4QdNQFQ3P1R171Xi9674N77xrZh817P01HHae5W4o7evzMdw/yTfVGm4f5JvqjX6Z3vXfBvfeNbMPmvZ+mnvXfBvfeNbMPmvZ+mnHaW4pxQ8pfmY7h/km+qNNw/wAk31Rr9M73rvg3vvGtmHzXs/TT3rvg3vvGtmHzXs/TTjtLcU4oeUvzMdw/yTfVGm4f5JvqjX6Z3vXfBvfeNbMPmvZ+mnvXfBvfeNbMPmvZ+mnHaW4pxQ8pfmY7h/km+qNNw/yTfVGv0zveu+De+8a2YfNez9NPeu+De+8a2YfNez9NOO0txTih5S/Mx3D/ACTfVGm4f5JvqjX6Z3vXfBvfeNbMPmvZ+mnvXfBvfeNbMPmvZ+mnHaW4pxQ8pfmY7h/km+qNXw2Y2LWQ2l9nFhIsmz6OfbQNkMpBjINSPGD1Ba6YlNZFdFQBTUTUIYxDkOAlMU4gYBARCv0Xveu+De+8a2YfNez9Na0+F62KtkHZw2Asw5t2f9mjDGGsw48ueyJuxMm44slvbd62g8C9YEoOo98Qd9FQCiYN4vWEaNtbHuugZqrrM5vvuOS233ZgrCbBTLLcuzJgCGiLatcZC0rlTw/aTpaWcmRkTuvgDHiCQtDoNigQ4CJuM3xESmKAYHBb1gaB/wCjfCXU+4lZP6Lr4yj8KFwjp98o7cm05unEQOHuoOjFOA9XUNNBAdR5q6vvmvCJfftbSH4chqiH5FU4lUBgvQWykB8C+0Dk9j/7m+EvMlZH6Lpyex/9zfCXmSsj9F18X/vmvCJffs7SH0e6Irp+RT3zXhEvv2dpAPoyGqH/AEU4m7lpxunyPBfaByex/wDc3wl5krI/RdOT2P8A7m+EvMlZH6Lr4v8A3zXhEvv2dpDXs+6Grr+RT3zXhEvv2dpAfpyGqP8A0U4m7lpxunyPBfaByex/9zfCXmSsj9F05PY/+5vhLzJWR+i6+L/3zXhEvv2dpD6PdEV0/Ip75rwiXW22dpAO8GRFQD8ip4m7lqeN0+T4L7QOT2P/ALm+EvMlZH6Lpyex/wDc3wl5krI/RdfF/wC+a8Il9+ztIB9GQ1Q/6Ke+a8Il9+ztIa9n3Q1dfyKjibuWnG6fJ8F9oHJ7H/3N8JeZKyP0XTk9j/7m+EvMlZH6Lr4v/fNeES6+2ztID3hyIqIfkU9814RL79naQ+j3RFdPyKcTdy043T5PgvtA5PY/+5vhLzJWR+i6cnsf/c3wl5krI/RdfF/75rwiX37O0gH0ZDVD/op75rwiX37O0hr2fdDV1/IpxN3LTjdPk+C+0Dk9j/7m+EvMlZH6Lpyex/8Ac3wl5krI/RdfF/75rwiX37O0gP05DVH/AKKe+a8Il9+ztIfR7oiun5FTxN3LTjdPk+C+0Dk9j/7m+EvMlZH6Lpyex/8Ac3wl5krI/RdfF/75rwiXW22dpAO8GRFQD8invmvCJffs7SAfRkNUP+io4m7lpxunyfBfaByex/8Ac3wl5krI/RdOT2P/ALm+EvMlZH6Lr4v/AHzXhEvv2dpDXs+6Grr+RT3zXhEuvts7SA94ciKiH5FTxN3LTjdPk+C+0Dk9j/7m+EvMlZH6Lpyex/8Ac3wl5krI/RdfF/75rwiX37O0h9HuiK6fkU9814RL79naQD6Mhqh/0U4m7lpxunyfBfaByex/9zfCXmSsj9F05PY/+5vhLzJWR+i6+L/3zXhEvv2dpDXs+6Grr+RT3zXhEvv2dpAfpyGqP/RUcTdy1HG6fI8F9oHJ7H/3N8JeZKyP0XTk9j/7m+EvMlZH6Lr4v/fNeES+/Z2kPo90RXT8invmvCJdbbZ2kA7wZEVAPyKcTdy1PG6fJ8F9oHJ7H/3N8JeZKyP0XTk9j/7m+EvMlZH6Lr4v/fNeES+/Z2kA+jIaof8ART3zXhEvv2dpDXs+6Grr+RU8Tdy1HG6fI8F9oHJ7H/3N8JeZKyP0XTk9j/7m+EvMlZH6Lr4v/fNeES6+2ztID3hyIqIfkU9814RL79naQ+j3RFdPyKcTdy043T5HgvtA5PY/+5vhLzJWR+i6cnsf/c3wl5krI/RdfF/75rwiX37O0gH0ZDVD/op75rwiX37O0hr2fdDV1/IpxN3LTjdPkeC+0Dk9j/7m+EvMlZH6Lpyex/8Ac3wl5krI/RdfF/75rwiX37O0gP05DVH/AKKe+a8Il9+ztIfR7oiun5FOJu5acbp8jwX2gcnsf/c3wl5krI/RdOT2P/ub4S8yVkfouvi/9814RLrbbO0gHeDIioB+RT3zXhEvv2dpAPoyGqH/AEU4m7lpxunyPBfaByex/wDc3wl5krI/RdOT2P8A7m+EvMlZH6Lr4v8A3zXhEvv2dpDXs+6Grr+RT3zXhEuvts7SA94ciKiH5FOJu5acbp8jwX2gcnsf/c3wl5krI/RdcDb1gaD/AOjfCXU+4lZP6Lr4wPfNeES+/Z2kPo90RXT8invmvCJffs7SH/w5DVDX+hTibuWo43S5Hgv0SrJ2fdniUs205J9gLALt6/txk9eOlMI2lvuVVW6ZzqDpHgGphER5gAOevUfrb9m773nZ+8yNqewV+csXhQuEeIUCE25dpxMpQ3SlLk90UpQDqAAAXQA7wVF76JwkH38+0950XfoqvE38pRxxnJX6LEps6bO7WMlXLDZz2f3L5rFOnLBt7h1rKg5cJN1FEEtwrADG31CkJulEBHe0AQEQGsD2dmwsRtf8EbeLjAeONnvJt6Q+WXmSrGsG34WPNb782MGbheKXfx7ZErojZZZXd397cERKIiYpjD8S/vonCQffz7T3nRd+itovAl7V20ztI8Kfs1Ru0BnvKmZmNpWlkOTthnka6lrkbQLh1aLpFws0KcPsZ1E0yEMIdUpACqusz6bS4mYBWlO003PDbsGR4haysEcJXtubJdqzGJ9nnOLrHVgKXrI3gpb6NjW1cRDSUgVsk8ddEP49dfVQrRuG5xm4HF/BKGptbmSHDQ8JVMOjPZXaJYybwUwTM7kMJWA9cnKmGhCioeEE2hQHQA15utSlc6pSpkkkbVxqVWoKYx2BdP343hGPu9QfmHx5+g6e/G8Ix93qD8w+PP0HSlRwFLkhbCrU3+sE9+N4Rj7vUH5h8efoOnvxvCMfd6g/MPjz9B0pTgKXJCCrU3+sE9+N4Rj7vUH5h8efoOnvxvCMfd6g/MPjz9B0pTgKXJCCrU3+sE9+N4Rj7vUH5h8efoOnvxvCMfd6g/MPjz9B0pTgKXJCCrU3+sE9+N4Rj7vUH5h8efoOnvxvCMfd6g/MPjz9B0pTgKXJCCrU3+sE9+N4Rj7vUH5h8efoOnvxvCMfd6g/MPjz9B0pTgKXJCCrU3+sE9+N4Rj7vUH5h8efoOnvxvCMfd6g/MPjz9B0pTgKXJCCrU3+sE9+N4Rj7vUH5h8efoOnvxvCMfd6g/MPjz9B0pTgKXJCCrU3+sE9+N4Rj7vUH5h8efoOnvxvCMfd6g/MPjz9B0pTgKXJCCrU3+sE9+N4Rj7vUH5h8efoOnvxvCMfd6g/MPjz9B0pTgKXJCCrU3+sE9+N4Rj7vUH5h8efoOnvxvCMfd6g/MPjz9B0pTgKXJCCrU3+sE9+N4Rj7vUH5h8efoOnvxvCMfd6g/MPjz9B0pTgKXJCCrU3+sE9+N4Rj7vUH5h8efoOnvxvCMfd6g/MPjz9B0pTgKXJCCrU3+sE9+N4Rj7vUH5h8efoOnvxvCMfd6g/MPjz9B0pTgKXJCCrU3+sE9+N4Rj7vUH5h8efoOnvxvCMfd6g/MPjz9B0pTgKXJCCrU3+sFV4/hteE8hkTM4jaa6VNRUFczaNw7YbFuY5gADHEhIUA3hApQEeqO6XsV3/AH8vhUfvrJDzVWP+h6UqzaFLK6Flwj96e/l8Kj99ZIeaqx/0PT38vhUfvrJDzVWP+h6UqwoUuSPUJwj96e/l8Kj99ZIeaqx/0PT38vhUfvrJDzVWP+h6UoKFLkj1CcI/env5fCo/fWSHmqsf9D09/L4VH76yQ81Vj/oelKChS5I9QnCP3p7+XwqP31kh5qrH/Q9Pfy+FR++skPNVY/6HpSgoUuSPUJwj96e/l8Kj99ZIeaqx/wBD09/L4VH76yQ81Vj/AKHpSgoUuSPUJwj96e/l8Kj99ZIeaqx/0PVpM68Kpt9bTGLbkwtm/aAeXzjK7Tsl7gthWwLWgySB456jIsjC6ZxqLgnFuGiCn2NQu9uaG1KIgKlQylTvD3VZ1R5YATgte2uoj3h8NKUqy4LcglKUopSlKURKUpREpSlESlKURKUpREpSlESlKURKUpREpSlESlKURKUpREpSlESlKURKUpREpSlESlKURKUpREpSlESlKURK3Z/qeH/au4K/kNfn/Kj2lKzrfgv6FpR/GZ0r/9k=" width="720" style="display: block; margin: auto;" /></p>
+<p>Top matches in the app can be assessed by clicking the cog in the
+right hand corner of the Spectra box. This will open a side window with
+the matches sorted from most to least similar. Clicking on rows in the
+table will add the selected match to the spectra viewer. Using the
+table’s filter options, you can restrict the range of Pearson&#39;s r values
+or search for specific material types.</p>
+<p>The same table can be returned using the OpenSpecy package commands
 in the R console.</p>
-<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Fetch current spectral library from https://osf.io/x7dpz/</span></span>
-<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a><span class="fu">get_lib</span>()</span>
-<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a><span class="co"># Load library into global environment</span></span>
-<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a>spec_lib <span class="ot">&lt;-</span> <span class="fu">load_lib</span>()</span>
-<span id="cb10-5"><a href="#cb10-5" aria-hidden="true" tabindex="-1"></a><span class="co"># Match spectrum with library and retrieve meta data</span></span>
-<span id="cb10-6"><a href="#cb10-6" aria-hidden="true" tabindex="-1"></a><span class="fu">match_spec</span>(raman_bgc, <span class="at">library =</span> spec_lib, <span class="at">which =</span> <span class="st">&quot;raman&quot;</span>)</span></code></pre></div>
-</div>
-<div id="selection-metadata" class="section level2">
-<h2>Selection Metadata</h2>
+<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" tabindex="-1"></a><span class="fu">data</span>(<span class="st">&quot;test_lib&quot;</span>)</span>
+<span id="cb29-2"><a href="#cb29-2" tabindex="-1"></a></span>
+<span id="cb29-3"><a href="#cb29-3" tabindex="-1"></a>processed <span class="ot">&lt;-</span> <span class="fu">process_spec</span>(ps_example, </span>
+<span id="cb29-4"><a href="#cb29-4" tabindex="-1"></a>                        <span class="at">conform_spec_args =</span> <span class="fu">list</span>(<span class="at">range =</span> test_lib<span class="sc">$</span>wavenumber, </span>
+<span id="cb29-5"><a href="#cb29-5" tabindex="-1"></a>                                                 <span class="at">res =</span> <span class="cn">NULL</span>))</span>
+<span id="cb29-6"><a href="#cb29-6" tabindex="-1"></a></span>
+<span id="cb29-7"><a href="#cb29-7" tabindex="-1"></a>test <span class="ot">&lt;-</span> <span class="fu">match_spec</span>(processed, test_lib, <span class="at">add_object_metadata =</span> <span class="st">&quot;col_id&quot;</span>,</span>
+<span id="cb29-8"><a href="#cb29-8" tabindex="-1"></a>                   <span class="at">add_library_metadata =</span> <span class="st">&quot;sample_name&quot;</span>)</span></code></pre></div>
+</div>
+<div id="selection-metadata" class="section level3">
+<h3>Selection Metadata</h3>
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" width="98%" style="display: block; margin: auto;" /></p>
 <p>Whatever match is selected from the match table may have additional
 metadata about it. That metadata will be displayed below the plot. Some
@@ -772,10 +2924,10 @@ <h2>Selection Metadata</h2>
 match.</p>
 <p>The R command for manual metadata selection using
 <code>sample_name == 5381</code> as example is:</p>
-<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="fu">find_spec</span>(sample_name <span class="sc">==</span> <span class="dv">5381</span>, <span class="at">library =</span> spec_lib, <span class="at">which =</span> <span class="st">&quot;raman&quot;</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" tabindex="-1"></a><span class="fu">find_spec</span>(sample_name <span class="sc">==</span> <span class="dv">5381</span>, <span class="at">library =</span> spec_lib, <span class="at">which =</span> <span class="st">&quot;raman&quot;</span>)</span></code></pre></div>
 </div>
-<div id="match-plot" class="section level2">
-<h2>Match Plot</h2>
+<div id="match-plot" class="section level3">
+<h3>Match Plot</h3>
 <p><img src="data:image/png;base64,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" width="600" style="display: block; margin: auto;" /></p>
 <p>This plot is dynamically updated by selecting matches from the match
 table. The red spectrum is the spectrum that you selected from the
@@ -784,152 +2936,48 @@ <h2>Match Plot</h2>
 data table in this tab will be updated. These plots can be saved as a
 .png by clicking the camera button at the top of the plot.</p>
 </div>
-<div id="how-to-interpret-the-reported-matches" class="section level2">
-<h2>How to interpret the reported matches</h2>
-<p>There are several important things to consider when interpreting a
-spectral match including the library source, the Pearson’s r, and other
-metrics.</p>
-<div id="the-library-source" class="section level3">
-<h3>The library source</h3>
-<p>When you click on a spectrum, all of the metadata that we have in
-Open Specy about that source will be displayed in a metadata window
-below to the matches table. Each library has different methodologies
-used to develop it. It is useful to read up on the library sources from
-the literature that they came from. E.g. Chabuka et al. 2020 focuses on
-weathered plastics, so matching to it may suggest that your spectrum is
-of a weathered polymer. Primpke et al. 2018 only has a spectral range up
-to 2000, so some polymers may be difficult to differentiate with it.
-Make sure to cite the libraries that you use during your search when you
-publish your results. The authors were kind enough to make their data
-open access so that it could be used in Open Specy and we should return
-the favor by citing them.</p>
-</div>
-<div id="pearsons-r" class="section level3">
-<h3>Pearson’s r</h3>
-<p>Correlation values are used to identify the closest matches available
-in the current Open Specy spectral libraries to improve material
-identification and reduce sample processing times. Pearson’s r values
-range from 0 - 1 with 0 being a completely different spectrum and 1
-being an exact match. Some general guidelines that we have observed from
-using Open Specy. If no matches are &gt; ~0.3 the material may require
-additional processing or may not exist in the Open Specy library.
-Correlation values are not the only metric you should use to assess your
-spectra’s match to a material in the library, matches need to make
-sense.</p>
-</div>
-<div id="things-to-consider-beyond-correlation" class="section level3">
-<h3>Things to consider beyond correlation</h3>
-<p>Peak position and height similarities are more important than
-correlation and need to be assessed manually. Peak position correlates
-with specific bond types. Peak height correlates to the concentration of
-a compound. Therefore, peak height and peak position should match as
-closely as possible to the matched spectrum. When there are peaks that
-exist in the spectra you are trying to interpret that do not exist in
-the match, there may be additional materials to identify. In this case,
-restrict the preprocessing range to just the unidentified peak and try
-to identify it as an additional component (see also <a href="https://www.compoundchem.com/2015/02/05/irspectroscopy/">https://www.compoundchem.com/2015/02/05/irspectroscopy/</a>).</p>
-<p>Also, check the match metadata to see if the match makes sense.
-Example: A single fiber cannot be a “cotton blend” since there would be
-no other fibers to make up the rest of the blend. Example: Cellophane
-does not degrade into fibers, so a match for a fiber to cellophane
-wouldn’t make sense. Example: You are analyzing a particle at room
-temperature, but the matched material is liquid at room temperature. The
-material may be a component of the particle but it cannot be the whole
-particle.</p>
-</div>
-<div id="how-specific-do-you-need-to-be-in-the-material-type-of-the-match" class="section level3">
-<h3>How specific do you need to be in the material type of the
-match?</h3>
-<p>You can choose to be specific about how you classify a substance
-(e.g. polyester, cellophane) or more general (e.g. synthetic,
-semi-synthetic, natural, etc.). The choice depends on your research
-question. Using more general groups can speed up analysis time but will
-decrease the information you have for interpretation. To identify
-materials more generally, you can often clump the identities provided by
-Open Specy to suit your needs. For example, matches to “polyester” and
-“polypropylene” could be clumped to the category “plastic”.</p>
-</div>
-<div id="how-to-differentiate-between-similar-spectra" class="section level3">
-<h3>How to differentiate between similar spectra?</h3>
-<p>One common challenge is differentiating between LDPE and HDPE. But,
-even with a low resolution instrument (MacroRAM, 2 cm<sup>-1</sup>
-pixel<sup>-1</sup>), you can still see some differences. From a wide
-view, these low, medium, and high density PE samples all look relatively
-similar (figures courtesy of Bridget O&#39;Donnell, Horiba Scientific):</p>
-<p><img src="data:image/png;base64,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" width="600" style="display: block; margin: auto;" /></p>
-<p>But, a closer look at the 1450 cm<sup>-1</sup> band reveals clear
-differences:</p>
-<p><img 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" width="600" style="display: block; margin: auto;" /></p>
-<p>When you overlay them, you start to see differences in other spectral
-regions too:</p>
-<p><img 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" width="600" style="display: block; margin: auto;" /></p>
-<p>So, the question is, how do we deal with samples that are very
-similar with only subtle differences? Usually, researchers will use MVA
-techniques after they’ve collected multiple reference spectra of known
-samples (LDPE and HDPE in this case). They can then develop models and
-apply them to distinguish between different types of PE. With a
-reference database like Open Specy, this is complicated by the fact that
-researchers are measuring samples on different instruments with
-correspondingly different spectral responses and spectral resolutions.
-That makes it even more difficult to accurately match definitively to
-LDPE and HDPE as opposed to generic ‘PE’.</p>
-<p>One possibility is to place more emphasis (from a computational
-perspective) on the bands that show the most difference (the triplet at
-1450 cm<sup>-1</sup>) by restricting the range used to match in Open
-Specy.</p>
-<p>The other, much simpler option is to just match any PE hit to generic
-‘PE’ and not specifically HDPE or LDPE.</p>
-<p>Another challenge is in differentiating between types of nylons. But,
-Raman has a pretty easy time distinguishing nylons. These spectra were
-recorded of a series of nylons and the differences are much more
-distinguishable compared to the PE results above (nylon 6, 6-6, 6-9,
-6-10, and 6-12 top to bottom):</p>
-<p><img 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" width="600" style="display: block; margin: auto;" /></p>
-<p>The differences are even more pronounced when you overlay the
-spectra:</p>
-<p><img 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" width="600" style="display: block; margin: auto;" /></p>
-</div>
-<div id="what-to-do-when-matches-arent-making-sense" class="section level3">
-<h3>What to do when matches aren’t making sense</h3>
-<ol style="list-style-type: decimal">
-<li>Double check that the baseline correction and smoothing parameters
-result in the best preprocessing of the data.</li>
-<li>Try reprocessing your spectrum, but limit it to specific peak
-regions with a higher signal to noise ratio.</li>
-<li>Restrict the spectral range to include or exclude questionable peaks
-or peaks that were not present in the previous matches.</li>
-<li>Restrict the spectral range to exclude things like CO<sub>2</sub>
-(2200 cm<sup>-1</sup>) or H<sub>2</sub>O (~1600 cm<sup>-1</sup>) in
-spikes in the IR spectrum.</li>
-<li>If nothing above works to determine a quality match, you may need to
-measure the spectrum of your material again or use another spectral
-analysis tool.</li>
-</ol>
-</div>
-</div>
-</div>
-<div id="mini-tutorials" class="section level1">
-<h1>Mini Tutorials</h1>
-<div id="file-conversion-in-spectragryph-to-open-specy-accepted-format" class="section level2">
-<h2>File Conversion in SpectraGryph to Open Specy Accepted Format</h2>
-<ol style="list-style-type: decimal">
-<li><p>Download Spectragryph from <a href="https://www.effemm2.de/spectragryph/down.html">https://www.effemm2.de/spectragryph/down.html</a></p></li>
-<li><p>Open Spectragryph and upload your file by dragging and dropping
-it into the console.</p></li>
-</ol>
-<p><img 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" width="98%" style="display: block; margin: auto;" /></p>
-<ol start="3" style="list-style-type: decimal">
-<li>Click File, Save/export data, save data as, and save it as an spc
-file. ¸</li>
-</ol>
-<p><img 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" width="98%" style="display: block; margin: auto;" /></p>
-<ol start="4" style="list-style-type: decimal">
-<li>Then upload that .spc file to Open Specy.</li>
-</ol>
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" width="287" style="display: block; margin: auto;" /></p>
-</div>
-<div id="conceptual-diagram-of-data-flow-through-open-specy" class="section level2">
-<h2>Conceptual diagram of data flow through Open Specy</h2>
+<div id="sharing-reference-data" class="section level3">
+<h3>Sharing Reference Data</h3>
+<p>If you have reference data or AI models that you think would be
+useful for sharing with the spectroscopy community through OpenSpecy
+please contact the website administrator to discuss options for
+collaborating.</p>
+</div>
+</div>
+<div id="characterizing-particles" class="section level2">
+<h2>Characterizing Particles</h2>
+<div id="thresholding" class="section level3">
+<h3>Thresholding</h3>
+</div>
+<div id="collapse-spectra" class="section level3">
+<h3>Collapse Spectra</h3>
+</div>
+</div>
+<div id="additional-app-features" class="section level2">
+<h2>Additional App Features</h2>
+<p>Accessibility is extremely important to us and we are making strives
+to improve the accessibility of Open Specy for all spectroscopists.
+Please reach out if you have ideas for improvement.</p>
+<p>We added a Google translate plugin to all pages in the app so that
+you can easily translate the app. We know that not all languages will be
+fully supported but we will continue to try and improve the
+translations.</p>
+<div id="download-data" class="section level3">
+<h3>Download Data</h3>
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width="239" style="display: block; margin: auto;" /></p>
+<p>After you have the preprocessing parameters set, we recommend that
+you download the preprocessed data for your records. The download data
+button will append the uploaded data to three columns created by the
+preprocessing parameters. “Wavelength” and “Absorbance” are columns from
+the data uploaded by the user. “NormalizedIntensity” is the max-min
+normalized value (Equation 1) of the “Absorbance”. “Smoothed” is the
+Savitzky-Golay filter specified by the slider explained above.
+“BaselineRemoved” is the smoothed and baseline corrected value that is
+visible on the center plot.</p>
+</div>
+</div>
+<div id="conceptual-diagram-of-open-specy" class="section level2">
+<h2>Conceptual diagram of Open Specy</h2>
 <p><img src="data:image/png;base64,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" width="600" style="display: block; margin: auto;" /></p>
 </div>
 </div>
diff --git a/vignettes/sop/baselinecorrectionpoly.jpg b/vignettes/sop/baselinecorrectionpoly.jpg
new file mode 100644
index 00000000..1f01c895
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diff --git a/vignettes/images/deselection.jpg b/vignettes/sop/deselection.jpg
similarity index 100%
rename from vignettes/images/deselection.jpg
rename to vignettes/sop/deselection.jpg
diff --git a/vignettes/images/download.jpg b/vignettes/sop/download.jpg
similarity index 100%
rename from vignettes/images/download.jpg
rename to vignettes/sop/download.jpg
diff --git a/vignettes/sop/flattten_example.jpg b/vignettes/sop/flattten_example.jpg
new file mode 100644
index 00000000..8864d63c
Binary files /dev/null and b/vignettes/sop/flattten_example.jpg differ
diff --git a/vignettes/images/flowchart.png b/vignettes/sop/flowchart.png
similarity index 100%
rename from vignettes/images/flowchart.png
rename to vignettes/sop/flowchart.png
diff --git a/vignettes/images/hoverexample.jpg b/vignettes/sop/hoverexample.jpg
similarity index 100%
rename from vignettes/images/hoverexample.jpg
rename to vignettes/sop/hoverexample.jpg
diff --git a/vignettes/sop/identification.jpg b/vignettes/sop/identification.jpg
new file mode 100644
index 00000000..7faff78a
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diff --git a/vignettes/sop/intensityadjustment.jpg b/vignettes/sop/intensityadjustment.jpg
new file mode 100644
index 00000000..3c0dc754
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diff --git a/vignettes/sop/mainpage.jpg b/vignettes/sop/mainpage.jpg
new file mode 100644
index 00000000..efbae6ab
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diff --git a/vignettes/sop/map_vis.jpg b/vignettes/sop/map_vis.jpg
new file mode 100644
index 00000000..342b9991
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diff --git a/vignettes/sop/matches.jpg b/vignettes/sop/matches.jpg
new file mode 100644
index 00000000..f41c11f3
Binary files /dev/null and b/vignettes/sop/matches.jpg differ
diff --git a/vignettes/images/matchplot.png b/vignettes/sop/matchplot.png
similarity index 100%
rename from vignettes/images/matchplot.png
rename to vignettes/sop/matchplot.png
diff --git a/vignettes/sop/processing.jpg b/vignettes/sop/processing.jpg
new file mode 100644
index 00000000..665537c5
Binary files /dev/null and b/vignettes/sop/processing.jpg differ
diff --git a/vignettes/sop/rangeselection.jpg b/vignettes/sop/rangeselection.jpg
new file mode 100644
index 00000000..e4ecd419
Binary files /dev/null and b/vignettes/sop/rangeselection.jpg differ
diff --git a/vignettes/sop/samplefile.jpg b/vignettes/sop/samplefile.jpg
new file mode 100644
index 00000000..6f0dcbe8
Binary files /dev/null and b/vignettes/sop/samplefile.jpg differ
diff --git a/vignettes/images/selectionmetadata.jpg b/vignettes/sop/selectionmetadata.jpg
similarity index 100%
rename from vignettes/images/selectionmetadata.jpg
rename to vignettes/sop/selectionmetadata.jpg
diff --git a/vignettes/sop/sig_over_noise.jpg b/vignettes/sop/sig_over_noise.jpg
new file mode 100644
index 00000000..927d8b0a
Binary files /dev/null and b/vignettes/sop/sig_over_noise.jpg differ
diff --git a/vignettes/sop/signal_settings.jpg b/vignettes/sop/signal_settings.jpg
new file mode 100644
index 00000000..1b6e94da
Binary files /dev/null and b/vignettes/sop/signal_settings.jpg differ
diff --git a/vignettes/sop/smoothingpoly.jpg b/vignettes/sop/smoothingpoly.jpg
new file mode 100644
index 00000000..71bd2be8
Binary files /dev/null and b/vignettes/sop/smoothingpoly.jpg differ
diff --git a/vignettes/sop/snr_threshold.jpg b/vignettes/sop/snr_threshold.jpg
new file mode 100644
index 00000000..312b1f30
Binary files /dev/null and b/vignettes/sop/snr_threshold.jpg differ
diff --git a/vignettes/sop/spectra_vis.jpg b/vignettes/sop/spectra_vis.jpg
new file mode 100644
index 00000000..1ed0199e
Binary files /dev/null and b/vignettes/sop/spectra_vis.jpg differ
diff --git a/vignettes/sop/uploadfile.jpg b/vignettes/sop/uploadfile.jpg
new file mode 100644
index 00000000..cf0a4d96
Binary files /dev/null and b/vignettes/sop/uploadfile.jpg differ
diff --git a/vignettes/spectragryph.Rmd b/vignettes/spectragryph.Rmd
new file mode 100644
index 00000000..83c50d6d
--- /dev/null
+++ b/vignettes/spectragryph.Rmd
@@ -0,0 +1,44 @@
+---
+title: "File conversion in SpectraGryph"
+author: >
+  Jessica Meyers, Jeremy Conkle, Win Cowger, Zacharias Steinmetz,
+  Andrew Gray, Chelsea Rochman, Sebastian Primpke, Jennifer Lynch,
+  Hannah Hapich, Hannah De Frond, Keenan Munno, Bridget O’Donnell
+date: "`r Sys.Date()`"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{File conversion in SpectraGryph}
+  %\VignetteEncoding{UTF-8}
+  %\VignetteEngine{knitr::rmarkdown}
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  warning = FALSE
+)
+```
+
+1. Download Spectragryph from
+[https://www.effemm2.de/spectragryph/down.html](https://www.effemm2.de/spectragryph/down.html)
+
+2. Open Spectragryph and upload your file by dragging and dropping it into the
+console.
+
+```{r, fig.align="center", out.width="98%", echo=FALSE}
+knitr::include_graphics("spectragryph/spectragryph-1.png")
+```
+
+3. Click File, Save/export data, save data as, and save it as an spc
+file. ¸
+
+```{r, fig.align="center", out.width="98%", echo=FALSE}
+knitr::include_graphics("spectragryph/spectragryph-2.png")
+```
+
+4. Then upload that .spc file to Open Specy.
+
+```{r, fig.align="center", echo=FALSE}
+knitr::include_graphics("sop/uploadfile.jpg")
+```
diff --git a/vignettes/images/spectragryph-1.png b/vignettes/spectragryph/spectragryph-1.png
similarity index 100%
rename from vignettes/images/spectragryph-1.png
rename to vignettes/spectragryph/spectragryph-1.png
diff --git a/vignettes/images/spectragryph-2.png b/vignettes/spectragryph/spectragryph-2.png
similarity index 100%
rename from vignettes/images/spectragryph-2.png
rename to vignettes/spectragryph/spectragryph-2.png