diff --git a/.Rbuildignore b/.Rbuildignore
index 5af5293d..f1d67fcb 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -8,9 +8,11 @@
 ^Meta$
 ^cran-comments\.md$
 ^CRAN-RELEASE$
+^CRAN-SUBMISSION$
 ^_pkgdown\.yml$
 ^docs$
 ^pkgdown$
 ^codecov\.yml$
-^vignettes/spectragryph.*
 ^vignettes/advanced.*
+^vignettes/app.*
+^vignettes/spectragryph.*
diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION
new file mode 100644
index 00000000..e7ae3d08
--- /dev/null
+++ b/CRAN-SUBMISSION
@@ -0,0 +1,3 @@
+Version: 1.0.0
+Date: 2023-09-01 14:53:15 UTC
+SHA: 1924ab057e7bd36723eccdd4649204fef0b2366c
diff --git a/DESCRIPTION b/DESCRIPTION
index d13ebc54..43b56724 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -2,7 +2,7 @@ Package: OpenSpecy
 Type: Package
 Title: Analyze, Process, Identify, and Share Raman and (FT)IR Spectra
 Version: 1.0.0
-Date: 2023-08-29
+Date: 2023-09-01
 Authors@R: c(person("Win", "Cowger", role = c("cre", "aut", "dtc"),
       email = "wincowger@gmail.com",
       comment = c(ORCID = "0000-0001-9226-3104")),
@@ -31,13 +31,16 @@ Authors@R: c(person("Win", "Cowger", role = c("cre", "aut", "dtc"),
     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"),
+    person("Aleksandra", "Karapetrova", role = c("ctb", "dtc", "rev"),
       comment = c(ORCID = "0000-0002-9856-1644")),
-    person("Vesna","Teofilovic", role = c("ctb"),
+    person("Vesna", "Teofilovic", role = c("ctb"),
       comment = c(ORCID = "0000-0002-3557-1482")),
-    person("Laura A. T.","Markley", role = c("ctb"),
+    person("Laura A. T.", "Markley", role = c("ctb"),
       comment = c(ORCID = "0000-0003-0620-8366")),
-    person("Shreyas","Patankar", role = c("ctb", "dtc")))
+    person("Shreyas", "Patankar", role = c("ctb", "dtc")),
+    person("Katherine", "Lasdin", role = c("ctb")),
+    person("National Renewable Energy Laboratory", role = c("fnd")), 
+    person("Possibility Lab", role = c("fnd")))
 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>). With read_any(), Open Specy provides a
diff --git a/NAMESPACE b/NAMESPACE
index e62d5632..90eaf4d6 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -4,6 +4,8 @@ S3method(adj_intens,OpenSpecy)
 S3method(adj_intens,default)
 S3method(ai_classify,OpenSpecy)
 S3method(ai_classify,default)
+S3method(as.data.frame,OpenSpecy)
+S3method(as.data.table,OpenSpecy)
 S3method(as_OpenSpecy,OpenSpecy)
 S3method(as_OpenSpecy,data.frame)
 S3method(as_OpenSpecy,default)
@@ -32,6 +34,8 @@ S3method(heatmap_spec,default)
 S3method(interactive_plot,OpenSpecy)
 S3method(interactive_plot,default)
 S3method(lines,OpenSpecy)
+S3method(make_rel,OpenSpecy)
+S3method(make_rel,default)
 S3method(match_spec,OpenSpecy)
 S3method(match_spec,default)
 S3method(plot,OpenSpecy)
diff --git a/NEWS.md b/NEWS.md
index 12f37b71..7b89b11e 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -6,7 +6,8 @@
 - 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
+  with a set of new functions including `c_spec()` for concatenating spectra or
+  converting them back to tables
 - 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
diff --git a/R/adj_intens.R b/R/adj_intens.R
index a1ded912..1a887602 100644
--- a/R/adj_intens.R
+++ b/R/adj_intens.R
@@ -9,7 +9,7 @@
 #' 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)}
+#' The transmittance adjustment uses the \eqn{log(1 / T)}
 #' calculation which does not correct for system and particle characteristics.
 #' The reflectance adjustment uses the Kubelka-Munk equation
 #' \eqn{(1 - R)^2 / 2R}. We assume that the reflectance intensity
@@ -62,7 +62,7 @@ adj_intens.OpenSpecy <- function(x, type = "none", make_rel = TRUE, ...) {
 
   adj <- switch(type,
                 "reflectance" = (1 - spec/100)^2 / (2 * spec/100),
-                "transmittance" = log10(1/adj_neg(spec, ...)),
+                "transmittance" = log(1/adj_neg(spec, ...)),
                 "none" = adj_neg(spec, ...)
   )
 
diff --git a/R/as_OpenSpecy.R b/R/as_OpenSpecy.R
index 542648c3..c337699f 100644
--- a/R/as_OpenSpecy.R
+++ b/R/as_OpenSpecy.R
@@ -318,7 +318,6 @@ check_OpenSpecy <- function(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)))
diff --git a/R/conform_spec.R b/R/conform_spec.R
index 04762c48..f1e3ea7c 100644
--- a/R/conform_spec.R
+++ b/R/conform_spec.R
@@ -60,7 +60,7 @@ conform_spec.OpenSpecy <- function(x, range = NULL, res = 5, type = "interp",
 
     wn <- conform_res(range, res = res)
   } else {
-    wn <- range
+    wn <- range[range >= min(x$wavenumber) & range <= max(x$wavenumber)]
   }
 
   if(type == "interp")
@@ -70,7 +70,8 @@ conform_spec.OpenSpecy <- function(x, range = NULL, res = 5, type = "interp",
   if(type == "roll") {
     join <- data.table("wavenumber" = wn)
     # Rolling join option
-    spec <- x$spectra[,"wavenumber" := x$wavenumber]
+    spec <- x$spectra
+    spec$wavenumber <- x$wavenumber
     spec <- spec[join, roll = "nearest", on = "wavenumber"]
     spec <- spec[,-"wavenumber"]
   }
diff --git a/R/data_norm.R b/R/data_norm.R
index a6ea0cf1..eacfc482 100644
--- a/R/data_norm.R
+++ b/R/data_norm.R
@@ -75,15 +75,6 @@ adj_neg <- function(y, na.rm = FALSE) {
   }
 }
 
-#' @rdname data_norm
-#'
-#' @export
-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
 #'
diff --git a/R/def_features.R b/R/def_features.R
index a587f5e3..68f63568 100644
--- a/R/def_features.R
+++ b/R/def_features.R
@@ -154,6 +154,13 @@ def_features.OpenSpecy <- function(x, features, ...) {
   features_dt <- rbindlist(lapply(seq_along(convex_hulls), function(i) {
     hull <- convex_hulls[[i]]
     id <- names(convex_hulls)[i]
+    if(nrow(hull) == 1)
+      return(data.table(feature_id = id,
+                        area = 1,
+                        perimeter = 4,
+                        feret_min = 1,
+                        feret_max = 1)
+      )
 
     # Calculate Feret dimensions
     dist_matrix <- as.matrix(dist(hull))
diff --git a/R/gen_OpenSpecy.R b/R/gen_OpenSpecy.R
index 25b9f267..8369ec07 100644
--- a/R/gen_OpenSpecy.R
+++ b/R/gen_OpenSpecy.R
@@ -2,7 +2,7 @@
 #' @rdname gen_OpenSpecy
 #'
 #' @description
-#' Methods to visualize \code{OpenSpecy} objects.
+#' Methods to visualize and convert \code{OpenSpecy} objects.
 #'
 #' @details
 #' \code{head()} shows the first few lines of an \code{OpenSpecy} object.
@@ -19,6 +19,8 @@
 #' \code{head()}, \code{print()}, and \code{summary()} return a textual
 #' representation of an \code{OpenSpecy} object.
 #' \code{plot()} and \code{lines()} return a plot.
+#' \code{as.data.frame()} and \code{as.data.table()} convert \code{OpenSpecy}
+#' objects into tabular data.
 #'
 #' @examples
 #' data("raman_hdpe")
@@ -38,7 +40,9 @@
 #' @seealso
 #' \code{\link[utils]{head}()}, \code{\link[base]{print}()},
 #' \code{\link[base]{summary}()}, \code{\link[graphics]{matplot}()}, and
-#' \code{\link[graphics]{matlines}()}
+#' \code{\link[graphics]{matlines}()},
+#' \code{\link[base]{as.data.frame}()},
+#' \code{\link[data.table]{as.data.table}()}
 #'
 #' @importFrom utils head
 #' @importFrom graphics matplot matlines
@@ -93,7 +97,7 @@ summary.OpenSpecy <- function(object, ...) {
 
   cat("\n$spectra\n")
   sl <- length(object$spectra)
-  sr <- range(object$spectra)
+  sr <- range(object$spectra, na.rm = T)
   array(c(sl, sr), c(1,3), list("", c("Number", "Min. Intensity",
                                       "Max. Intensity"))) |>
     print()
@@ -104,3 +108,21 @@ summary.OpenSpecy <- function(object, ...) {
   t(array(c(xr, yr), c(2,2), list(c("Min.", "Max."), c("x", "y")))) |> print()
   names(object$metadata) |> print()
 }
+
+#' @rdname gen_OpenSpecy
+#'
+#' @method as.data.frame OpenSpecy
+#' @export
+as.data.frame.OpenSpecy <- function(x, ...) {
+  data.frame(wavenumber = x$wavenumber, x$spectra)
+}
+
+#' @rdname gen_OpenSpecy
+#'
+#' @method as.data.table OpenSpecy
+#'
+#' @importFrom data.table data.table
+#' @export
+as.data.table.OpenSpecy <- function(x, ...) {
+  data.table(wavenumber = x$wavenumber, x$spectra)
+}
diff --git a/R/interactive_plots.R b/R/interactive_plots.R
index 5f437bf6..5d1519de 100644
--- a/R/interactive_plots.R
+++ b/R/interactive_plots.R
@@ -89,7 +89,7 @@ plotly_spec.OpenSpecy <- function(x, x2 = NULL,
   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) |>
+              name = "x1", showlegend = F) |>
     layout(xaxis = list(title = "wavenumber [cm<sup>-1</sup>]",
                         autorange = "reversed"),
            yaxis = list(title = "intensity [-]"),
@@ -100,7 +100,7 @@ plotly_spec.OpenSpecy <- function(x, x2 = NULL,
   # Add dummy trace for Your Spectra
   p <- p |>
     add_trace(x = NULL, y = NULL,
-              line = line, name = "Your Spectra", showlegend = T)
+              line = line, name = "x1", showlegend = T)
 
   if (!is.null(x2)) {
     dt2 <- cbind(wavenumber = x2$wavenumber, x2$spectra) |>
@@ -109,14 +109,14 @@ plotly_spec.OpenSpecy <- function(x, x2 = NULL,
     p <- p |>
       add_trace(data = dt2, x = ~wavenumber, y = ~make_rel(intensity, na.rm = T),
                 color = ~id, type = "scatter", mode = "lines",
-                name = "Library Spectra",
+                name = "x2",
                 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)
+                name = "x2", showlegend = T)
   }
 
   return(p)
diff --git a/R/make_rel.R b/R/make_rel.R
new file mode 100644
index 00000000..74cd4b40
--- /dev/null
+++ b/R/make_rel.R
@@ -0,0 +1,57 @@
+#' @rdname make_rel
+#' @title Make spectral intensities relative
+#'
+#' @description
+#' \code{make_rel()} converts intensities \code{x} 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{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 OpenSpecy object
+#' @param na.rm logical. Should missing values be removed?
+#' @param \ldots further arguments passed to \code{make_rel()}.
+#'
+#' @return
+#' \code{make_rel()} return numeric vectors (if vector provided) or an
+#' \code{OpenSpecy} object with the normalized intensity data.
+#'
+#' @examples
+#' make_rel(c(-1000, -1, 0, 1, 10))
+#'
+#' @author
+#' Win Cowger, Zacharias Steinmetz
+#'
+#' @seealso
+#' \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
+make_rel <- function(x, ...) {
+  UseMethod("make_rel")
+}
+
+#' @rdname make_rel
+#'
+#' @export
+make_rel.default <- function(x, na.rm = FALSE, ...) {
+  r <- range(x, na.rm = na.rm)
+
+  return((x - r[1]) / (r[2] - r[1]))
+}
+
+#' @rdname make_rel
+#'
+#' @export
+make_rel.OpenSpecy <- function(x, na.rm = FALSE, ...) {
+  x$spectra <- x$spectra[, lapply(.SD, make_rel, na.rm = na.rm, ...)]
+
+  return(x)
+}
diff --git a/R/manage_spec.R b/R/manage_spec.R
index b5b645e1..355c776c 100644
--- a/R/manage_spec.R
+++ b/R/manage_spec.R
@@ -12,6 +12,8 @@
 #' spectra having all the same wavenumber range.
 #' @param res defaults to \code{NULL}, the resolution you want the output
 #' wavenumbers to be.
+#' @param size the number of spectra to sample. 
+#' @param prob probabilities to use for the sampling.
 #' @param \ldots further arguments passed to submethods.
 #'
 #' @return
@@ -115,14 +117,10 @@ sample_spec.default <- function(x, ...) {
 #' @rdname manage_spec
 #'
 #' @export
-sample_spec.OpenSpecy <- function(x, ...) {
+sample_spec.OpenSpecy <- function(x, size = 1, prob = NULL, ...) {
   # replace = false is mandatory currently because we don't have a way to
   # rename and recoordinate duplicates.
-  cols <- sample(1:ncol(x$spectra), ...)
+  cols <- sample(1:ncol(x$spectra), size = size, replace = FALSE, prob = prob, ...)
 
-  as_OpenSpecy(
-    x = x$wavenumber,
-    spectra = x$spectra[, cols, with = F],
-    metadata = x$metadata[cols, ]
-  )
+  filter_spec(x, cols)
 }
diff --git a/R/match_spec.R b/R/match_spec.R
index 86eefcd0..aacdf1ed 100644
--- a/R/match_spec.R
+++ b/R/match_spec.R
@@ -22,6 +22,8 @@
 #' If \code{NULL} (default), all matches will be returned.
 #' @param cor_matrix a correlation matrix for object and library,
 #' can be returned by \code{cor_spec()}
+#' @param order an \code{OpenSpecy} used for sorting, ideally the unprocessed
+#' one; \code{NULL} skips sorting.
 #' @param add_library_metadata name of a column in the library metadata to be
 #' joined; \code{NULL} if you don't want to join.
 #' @param add_object_metadata name of a column in the object metadata to be
@@ -53,19 +55,23 @@
 #'
 #' @examples
 #' data("test_lib")
+#'
 #' unknown <- read_extdata("ftir_ldpe_soil.asp") |>
 #'   read_any() |>
 #'   conform_spec(range = test_lib$wavenumber,
 #'                res = spec_res(test_lib)) |>
 #'   process_spec()
-#' matches <- cor_spec(unknown, test_lib)
+#' cor_spec(unknown, test_lib)
 #'
 #' test_lib_extract <- filter_spec(test_lib,
 #'   logic = grepl("polycarbonate", test_lib$metadata$polymer_class,
 #'                 ignore.case = TRUE)
 #' )
 #'
-#' matches2 <- cor_spec(unknown, library = test_lib_extract)
+#' cor_spec(unknown, library = test_lib_extract)
+#'
+#' match_spec(unknown, test_lib, add_library_metadata = "sample_name",
+#'            top_n = 1)
 #'
 #' @author
 #' Win Cowger, Zacharias Steinmetz
@@ -136,16 +142,24 @@ match_spec.default <- function(x, ...) {
 #'
 #' @export
 match_spec.OpenSpecy <- function(x, library, na.rm = T, top_n = NULL,
-                                 add_library_metadata = NULL,
+                                 order = NULL, add_library_metadata = NULL,
                                  add_object_metadata = NULL, fill = NULL, ...) {
   if(is_OpenSpecy(library)) {
-    cor_spec(x, library =  library) |>
+    res <- 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)
+    res <- ai_classify(x, library, fill)
+  }
+
+  if(!is.null(order)) {
+    .r <- NULL
+    match <- match(colnames(order$spectra), res$object_id)
+    setorder(res[, .r := order(match)], .r)[, .r := NULL]
   }
+
+  return(res)
 }
 
 #' @rdname match_spec
diff --git a/R/process_spec.R b/R/process_spec.R
index 2d31a6ff..0c51bc9e 100644
--- a/R/process_spec.R
+++ b/R/process_spec.R
@@ -34,6 +34,9 @@
 #' \code{\link{subtr_baseline}()}.
 #' @param make_rel logical; if \code{TRUE} spectra are automatically normalized
 #' with \code{\link{make_rel}()}.
+#' @param make_rel_args named list of arguments passed to
+#' \code{\link{make_rel}()}.
+#' @param na.rm Whether to allow NA or set all NA values to 
 #' @param \ldots further arguments passed to subfunctions.
 #'
 #' @return
@@ -105,6 +108,8 @@ process_spec.OpenSpecy <- function(x, active = TRUE,
                                      polynomial = 3, window = 11,
                                      derivative = 1, abs = TRUE),
                                    make_rel = TRUE,
+                                   make_rel_args = list(
+                                       na.rm = TRUE),
                                    ...) {
   if(active) {
     if(adj_intens)
@@ -124,7 +129,8 @@ process_spec.OpenSpecy <- function(x, active = TRUE,
       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)]
+      x <- do.call("make_rel", c(list(x),
+                                      make_rel_args))
   }
 
   return(x)
diff --git a/R/read_ext.R b/R/read_ext.R
index b4e41353..4602cf39 100644
--- a/R/read_ext.R
+++ b/R/read_ext.R
@@ -303,7 +303,7 @@ read_jdx <- function(file, share = NULL,
                        other_info = NULL,
                        license = "CC BY-NC"),
                      ...) {
-  jdx <- read.jdx(file, ...)
+  jdx <- read.jdx(file, encoding = 'latin1')
 
   x <- jdx@wavelength
   y <- as.numeric(unname(jdx@data$spc[1,]))
diff --git a/R/sig_noise.R b/R/sig_noise.R
index aae90245..ecef02c8 100644
--- a/R/sig_noise.R
+++ b/R/sig_noise.R
@@ -10,8 +10,11 @@
 #' Options include \code{"sig"} (mean intensity), \code{"noise"} (standard
 #' deviation of intensity), \code{"sig_times_noise"} (absolute value of
 #' signal times noise), \code{"sig_over_noise"} (absolute value of signal /
-#' noise), or \code{"tot_sig"} (total signal = signal * number of data
-#' points).
+#' noise), \code{"run_sig_over_noise"} (absolute value of signal /
+#' noise where signal is estimated as the max intensity and noise is 
+#' estimated as the height of a low intensity region.), 
+#' \code{"log_tot_sig"} (sum of the inverse log intensities, useful for spectra  in log units), 
+#' or \code{"tot_sig"} (sum of intensities).
 #' @param na.rm logical; indicating whether missing values should be removed
 #' when calculating signal and noise. Default is \code{TRUE}.
 #' @param \ldots further arguments passed to subfunctions; currently not used.
@@ -45,7 +48,7 @@ sig_noise.default <- function(x, ...) {
 #' @rdname sig_noise
 #'
 #' @export
-sig_noise.OpenSpecy <- function(x, metric = "sig_over_noise",
+sig_noise.OpenSpecy <- function(x, metric = "run_sig_over_noise",
                                 na.rm = TRUE, ...) {
   vapply(x$spectra, function(y) {
     if(length(y[!is.na(y)]) < 20) {
diff --git a/R/smooth_intens.R b/R/smooth_intens.R
index c52326c3..a2c4a58c 100644
--- a/R/smooth_intens.R
+++ b/R/smooth_intens.R
@@ -60,7 +60,7 @@ smooth_intens.default <- function(x, ...) {
 #'
 #' @export
 smooth_intens.OpenSpecy <- function(x, polynomial = 3, window = 11,
-                                    derivative = 0, abs = FALSE,
+                                    derivative = 1, abs = TRUE,
                                     make_rel = TRUE, ...) {
   filt <- x$spectra[, lapply(.SD, .sgfilt, p = polynomial, n = window,
                              m = derivative, abs = abs, ...)]
diff --git a/_pkgdown.yml b/_pkgdown.yml
index c27a0f6c..b92617f1 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -3,3 +3,11 @@ template:
   bootstrap: 5
 development:
   mode: auto
+articles:
+- title: Articles
+  navbar: ~
+  contents:
+  - app
+  - sop
+  - advanced
+  - spectragryph
diff --git a/cran-comments.md b/cran-comments.md
index 72c6bdb7..299d0275 100644
--- a/cran-comments.md
+++ b/cran-comments.md
@@ -16,16 +16,15 @@
 ## Comments
 
     Possibly misspelled words in DESCRIPTION:
-      preprocessing (46:5)
-      wavenumber (47:62)
+      wavenumber (50:62)
 
-Both words are spelled correctly.
+The word is spelled correctly.
 
-    installed size is  6.5Mb
-    sub-directories of 1Mb or more:
-      doc   4.9Mb
+     installed size is  6.1Mb
+     sub-directories of 1Mb or more:
+       doc   4.5Mb
 
 We tried to reduce the vignette size as much as possible but would like to keep
-the current number of screenshots for better comprehensibility.
+the current number of figures for better comprehensibility.
 
 For further details, see NEWS.md
diff --git a/inst/extdata/fitr_nitrocellulose.jdx b/inst/extdata/fitr_nitrocellulose.jdx
index 3a09a662..357d5c98 100644
--- a/inst/extdata/fitr_nitrocellulose.jdx
+++ b/inst/extdata/fitr_nitrocellulose.jdx
@@ -1023,1027 +1023,3 @@
 607.64+66946033+67829741+67681322+66557188+64851073+63203464+62214539+62111525
 599.92+62840942
 ##END=
-##TITLE=Nitrocellulose
-##JCAMP-DX=4.24
-##DATA TYPE=INFRARED SPECTRUM
-##ORIGIN=Pacific Northwest National Laboratory Under IARPA Contract
-##OWNER=Public domain
-##DATE=March 2017
-##CAS REGISTRY NO=9004-70-0
-##MOLFORM=C 6 H 9 ( N O 2 ) O 5 ; C 6 H 8 ( N O 2 ) 2 O 5 ; C 6 H 7 ( N O 2 ) 3 O 5
-##$NIST SOURCE=IARPA-IR-S
-##$NIST DOC FILE=META.MIR.PUR.Nitrocellulose.SLD.UNKNOWN.PNNL161235.pdf
-##SPECTROMETER/DATA SYSTEM=Bruker IR Cube FTIR
-##APERTURE=6 mm
-##EXTERNAL DIFFUSE REFLECTANCE ACCESSORY=A 562-G integrating sphere
-##BEAMSPLITTER=Ge on KBr
-##DETECTOR (DIA. DET. PORT IN SPHERE)=2×2 mm, 60° field of view MCT
-##SPHERE DIAMETER=75 mm
-##ACQUISITION MODE=double-sided, forward-backward
-##SCANNER SPEED=40 kHz
-##COADDED SCANS=2048
-##PHASE RESOLUTION=32.00
-##PHASE CORRECTION=Mertz
-##ZEROFILLING=4×
-##SPECTRAL RANGE=7,500 to 600 cm-1 saved; 7500 to 600 cm-1 reported
-##STATE=solid
-##RESOLUTION=0.96450084
-##SPECTRAL RESOLUTION=4 cm-1
-##WAVENUMBER ACCURACY=± 0.4 cm-1
-##APODIZATION FUNCTION=Blackman-Harris 3-term
-##LOW PASS FILTER=Open
-##SWITCH GAIN ON=512 points
-##XUNITS=1/cm
-##YUNITS=Reflectance
-##XLABEL=Wavenumber
-##YLABEL=Diffuse-only reflectance
-##XFACTOR=1
-##YFACTOR=5.6371038e-010
-##DELTAX=-0.96450084
-##FIRSTX=7498.994
-##LASTX=599.91952
-##FIRSTY=0.60527939
-##MAXX=7498.99
-##MINX=599.916
-##MAXY=0.60527941
-##MINY=0.009793684
-##NPOINTS=7154
-##XYDATA=(X++(Y..Y))
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-726.27+45806686+44481626+43718048+43574861+43902111+44417427+44788036+44759970
-718.55+44244155+43311677+42123326+40858892+39689974+38780371+38683110+38799410
-710.84+38991338+39235635+39497752+39749315+39982342+40208105+40441753+40685931
-703.12+40925377+41134818+41293723+41397420+41458073+41494760+41521717+41543105
-695.41+41557895+41570240+41415230+41356556+41425513+41777698+42356349+43119412
-687.69+43946937+44649865+45037471+45022136+44681213+44211988+43815338+43605442
-679.97+43601050+43777957+44120361+44621495+45237088+45846341+46276426+46395938
-672.26+46212836+45895277+45699846+45858947+46485034+47514744+48731617+49873939
-664.54+50749795+51275364+51455337+51353397+51066581+50699081+50327861+49980455
-656.83+49641015+49288847+48957096+48774896+48947818+48838113+48967838+49261765
-649.11+49548714+49629364+49387826+48857595+48766163+48677777+48609124+48564999
-641.39+48393713+48224773+48056963+47888413+47716132+47537064+47350763+47315355
-633.68+47281576+47199455+47137649+47085289+47016501+46912714+46788934+46672171
-625.96+46570770+46471150+46350102+46183775+46046235+45968271+45952457+45986276
-618.25+46038999+46092426+46145888+46199322+46252715+46306076+46359482+46414016
-610.53+46527107+46762146+47367860+48110571+48813661+49185602+49373092+49482463
-602.81+49571242+49675667+49768566+49077520
-##END=
diff --git a/man/OpenSpecy-package.Rd b/man/OpenSpecy-package.Rd
index 03bcc458..2064357d 100644
--- a/man/OpenSpecy-package.Rd
+++ b/man/OpenSpecy-package.Rd
@@ -78,6 +78,9 @@ Other contributors:
   \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]
+  \item Katherine Lasdin [contributor]
+  \item National Renewable Energy Laboratory [funder]
+  \item Possibility Lab [funder]
 }
 
 }
diff --git a/man/adj_intens.Rd b/man/adj_intens.Rd
index 5007a61c..5db1a9db 100644
--- a/man/adj_intens.Rd
+++ b/man/adj_intens.Rd
@@ -38,7 +38,7 @@ Many of the Open Specy functions will assume that the spectrum is in
 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)}
+The transmittance adjustment uses the \eqn{log(1 / T)}
 calculation which does not correct for system and particle characteristics.
 The reflectance adjustment uses the Kubelka-Munk equation
 \eqn{(1 - R)^2 / 2R}. We assume that the reflectance intensity
diff --git a/man/data_norm.Rd b/man/data_norm.Rd
index 117a1dde..97bcc171 100644
--- a/man/data_norm.Rd
+++ b/man/data_norm.Rd
@@ -4,7 +4,6 @@
 \alias{adj_res}
 \alias{conform_res}
 \alias{adj_neg}
-\alias{make_rel}
 \alias{mean_replace}
 \alias{is_empty_vector}
 \title{Normalization and conversion of spectral data}
@@ -15,8 +14,6 @@ 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)
diff --git a/man/gen_OpenSpecy.Rd b/man/gen_OpenSpecy.Rd
index bca31257..f43b4f3f 100644
--- a/man/gen_OpenSpecy.Rd
+++ b/man/gen_OpenSpecy.Rd
@@ -6,6 +6,8 @@
 \alias{plot.OpenSpecy}
 \alias{lines.OpenSpecy}
 \alias{summary.OpenSpecy}
+\alias{as.data.frame.OpenSpecy}
+\alias{as.data.table.OpenSpecy}
 \title{Generic Open Specy Methods}
 \usage{
 \method{head}{OpenSpecy}(x, ...)
@@ -17,6 +19,10 @@
 \method{lines}{OpenSpecy}(x, ...)
 
 \method{summary}{OpenSpecy}(object, ...)
+
+\method{as.data.frame}{OpenSpecy}(x, ...)
+
+\method{as.data.table}{OpenSpecy}(x, ...)
 }
 \arguments{
 \item{x}{an \code{OpenSpecy} object.}
@@ -29,9 +35,11 @@
 \code{head()}, \code{print()}, and \code{summary()} return a textual
 representation of an \code{OpenSpecy} object.
 \code{plot()} and \code{lines()} return a plot.
+\code{as.data.frame()} and \code{as.data.table()} convert \code{OpenSpecy}
+objects into tabular data.
 }
 \description{
-Methods to visualize \code{OpenSpecy} objects.
+Methods to visualize and convert \code{OpenSpecy} objects.
 }
 \details{
 \code{head()} shows the first few lines of an \code{OpenSpecy} object.
@@ -56,7 +64,9 @@ 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}()}
+\code{\link[graphics]{matlines}()},
+\code{\link[base]{as.data.frame}()},
+\code{\link[data.table]{as.data.table}()}
 }
 \author{
 Zacharias Steinmetz, Win Cowger
diff --git a/man/make_rel.Rd b/man/make_rel.Rd
new file mode 100644
index 00000000..7be18bfb
--- /dev/null
+++ b/man/make_rel.Rd
@@ -0,0 +1,49 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/make_rel.R
+\name{make_rel}
+\alias{make_rel}
+\alias{make_rel.default}
+\alias{make_rel.OpenSpecy}
+\title{Make spectral intensities relative}
+\usage{
+make_rel(x, ...)
+
+\method{make_rel}{default}(x, na.rm = FALSE, ...)
+
+\method{make_rel}{OpenSpecy}(x, na.rm = FALSE, ...)
+}
+\arguments{
+\item{x}{a numeric vector or an \R OpenSpecy object}
+
+\item{na.rm}{logical. Should missing values be removed?}
+
+\item{\ldots}{further arguments passed to \code{make_rel()}.}
+}
+\value{
+\code{make_rel()} return numeric vectors (if vector provided) or an
+\code{OpenSpecy} object with the normalized intensity data.
+}
+\description{
+\code{make_rel()} converts intensities \code{x} 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{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{
+make_rel(c(-1000, -1, 0, 1, 10))
+
+}
+\seealso{
+\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/manage_spec.Rd b/man/manage_spec.Rd
index 50bd2612..063fe063 100644
--- a/man/manage_spec.Rd
+++ b/man/manage_spec.Rd
@@ -22,7 +22,7 @@ sample_spec(x, ...)
 
 \method{sample_spec}{default}(x, ...)
 
-\method{sample_spec}{OpenSpecy}(x, ...)
+\method{sample_spec}{OpenSpecy}(x, size = 1, prob = NULL, ...)
 }
 \arguments{
 \item{x}{a list of \code{OpenSpecy} objects.}
@@ -35,6 +35,10 @@ spectra having all the same wavenumber range.}
 \item{res}{defaults to \code{NULL}, the resolution you want the output
 wavenumbers to be.}
 
+\item{size}{the number of spectra to sample.}
+
+\item{prob}{probabilities to use for the sampling.}
+
 \item{\ldots}{further arguments passed to submethods.}
 }
 \value{
diff --git a/man/match_spec.Rd b/man/match_spec.Rd
index f90eb855..81572e90 100644
--- a/man/match_spec.Rd
+++ b/man/match_spec.Rd
@@ -35,6 +35,7 @@ match_spec(x, ...)
   library,
   na.rm = T,
   top_n = NULL,
+  order = NULL,
   add_library_metadata = NULL,
   add_object_metadata = NULL,
   fill = NULL,
@@ -83,6 +84,9 @@ when calculating correlations. Default is \code{TRUE}.}
 \item{top_n}{integer; specifying the number of top matches to return.
 If \code{NULL} (default), all matches will be returned.}
 
+\item{order}{an \code{OpenSpecy} used for sorting, ideally the unprocessed
+one; \code{NULL} skips sorting.}
+
 \item{add_library_metadata}{name of a column in the library metadata to be
 joined; \code{NULL} if you don't want to join.}
 
@@ -134,19 +138,23 @@ matrix as a named vector.
 }
 \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)
+cor_spec(unknown, test_lib)
 
 test_lib_extract <- filter_spec(test_lib,
   logic = grepl("polycarbonate", test_lib$metadata$polymer_class,
                 ignore.case = TRUE)
 )
 
-matches2 <- cor_spec(unknown, library = test_lib_extract)
+cor_spec(unknown, library = test_lib_extract)
+
+match_spec(unknown, test_lib, add_library_metadata = "sample_name",
+           top_n = 1)
 
 }
 \seealso{
diff --git a/man/process_spec.Rd b/man/process_spec.Rd
index 383f8c0d..c0d4317b 100644
--- a/man/process_spec.Rd
+++ b/man/process_spec.Rd
@@ -27,6 +27,7 @@ process_spec(x, ...)
   smooth_intens = TRUE,
   smooth_intens_args = list(polynomial = 3, window = 11, derivative = 1, abs = TRUE),
   make_rel = TRUE,
+  make_rel_args = list(na.rm = TRUE),
   ...
 )
 }
@@ -75,6 +76,11 @@ to the spectra.}
 \item{make_rel}{logical; if \code{TRUE} spectra are automatically normalized
 with \code{\link{make_rel}()}.}
 
+\item{make_rel_args}{named list of arguments passed to
+\code{\link{make_rel}()}.}
+
+\item{na.rm}{Whether to allow NA or set all NA values to}
+
 \item{\ldots}{further arguments passed to subfunctions.}
 }
 \value{
diff --git a/man/sig_noise.Rd b/man/sig_noise.Rd
index 3688cba2..99d0e588 100644
--- a/man/sig_noise.Rd
+++ b/man/sig_noise.Rd
@@ -10,7 +10,7 @@ sig_noise(x, ...)
 
 \method{sig_noise}{default}(x, ...)
 
-\method{sig_noise}{OpenSpecy}(x, metric = "sig_over_noise", na.rm = TRUE, ...)
+\method{sig_noise}{OpenSpecy}(x, metric = "run_sig_over_noise", na.rm = TRUE, ...)
 }
 \arguments{
 \item{x}{an \code{OpenSpecy} object.}
@@ -19,8 +19,11 @@ sig_noise(x, ...)
 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).}
+noise), \code{"run_sig_over_noise"} (absolute value of signal /
+noise where signal is estimated as the max intensity and noise is
+estimated as the height of a low intensity region.),
+\code{"log_tot_sig"} (sum of the inverse log intensities, useful for spectra  in log units),
+or \code{"tot_sig"} (sum of intensities).}
 
 \item{na.rm}{logical; indicating whether missing values should be removed
 when calculating signal and noise. Default is \code{TRUE}.}
diff --git a/man/smooth_intens.Rd b/man/smooth_intens.Rd
index afed0c95..2d2fc55f 100644
--- a/man/smooth_intens.Rd
+++ b/man/smooth_intens.Rd
@@ -14,8 +14,8 @@ smooth_intens(x, ...)
   x,
   polynomial = 3,
   window = 11,
-  derivative = 0,
-  abs = FALSE,
+  derivative = 1,
+  abs = TRUE,
   make_rel = TRUE,
   ...
 )
diff --git a/tests/testthat/test-adj_intens.R b/tests/testthat/test-adj_intens.R
index 39ed63e4..f290aa83 100644
--- a/tests/testthat/test-adj_intens.R
+++ b/tests/testthat/test-adj_intens.R
@@ -1,10 +1,16 @@
 data("raman_hdpe")
+
 raman_hdpe$spectra$intensity2 <- raman_hdpe$spectra$intensity * 2
+raman_hdpe$metadata <- rbind(raman_hdpe$metadata, raman_hdpe$metadata)
+
+test_that("adj_intens() handles input errors correctly", {
+  adj_intens(1:1000) |> expect_error()
+})
 
 test_that("adj_intens() works as expected", {
-  expect_silent(adj <- adj_intens(raman_hdpe))
-  expect_silent(adj_intens(raman_hdpe, type = "reflectance"))
-  expect_silent(adj_intens(raman_hdpe, type = "transmittance"))
+  adj <- adj_intens(raman_hdpe) |> expect_silent()
+  adj_intens(raman_hdpe, type = "reflectance") |> expect_silent()
+  adj_intens(raman_hdpe, type = "transmittance") |> expect_silent()
   expect_equal(
     cor(raman_hdpe$spectra$intensity, adj$spectra$intensity), 1,
     ignore_attr = F)
@@ -13,6 +19,7 @@ test_that("adj_intens() works as expected", {
     ignore_attr = F)
 
   expect_s3_class(adj, "OpenSpecy")
+  expect_true(check_OpenSpecy(adj))
 
   expect_equal(nrow(adj$spectra), nrow(raman_hdpe$spectra))
   expect_equal(adj$wavenumber, raman_hdpe$wavenumber)
diff --git a/tests/testthat/test-adj_range.R b/tests/testthat/test-adj_range.R
index 6e09a892..96c00bdf 100644
--- a/tests/testthat/test-adj_range.R
+++ b/tests/testthat/test-adj_range.R
@@ -1,5 +1,17 @@
 library(data.table)
 
+test_that("flatten_range() error handling", {
+  test <- as_OpenSpecy(x = 1:10, spectra = data.table(V1 = 1:10))
+
+  expect_s3_class(test, "OpenSpecy")
+  expect_true(check_OpenSpecy(test))
+
+  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)))
+})
+
 test_that("restrict_range() provides correct range", {
   test_noise <- as_OpenSpecy(x = seq(400,4000, by = 10),
                              spectra = data.table(intensity = rnorm(361)))
@@ -11,7 +23,9 @@ test_that("restrict_range() provides correct range", {
                                  max = c(1500, 2500)) |>
     expect_silent()
 
-  is_OpenSpecy(single_range) |> expect_true()
+  check_OpenSpecy(single_range) |> expect_true()
+  check_OpenSpecy(double_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)))
@@ -23,6 +37,8 @@ test_that("flatten_range() function test", {
                              make_rel = F) |>
     expect_silent()
 
+  expect_true(check_OpenSpecy(flat_sam))
+
   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))
 
@@ -30,6 +46,8 @@ test_that("flatten_range() function test", {
   flat_hdpe <- flatten_range(raman_hdpe, min = c(500, 1000),
                              max = c(700, 1500)) |>
     expect_silent()
+  expect_true(check_OpenSpecy(flat_hdpe))
+
   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),
@@ -39,6 +57,7 @@ test_that("flatten_range() function test", {
   flat_map <- flatten_range(tiny_map, min = c(1000, 2000),
                             max = c(1200, 2400), make_rel = F) |>
     expect_silent()
+  expect_true(check_OpenSpecy(flat_map))
 
   expect_false(all.equal(flat_map$spectra, tiny_map$spectra) |> isTRUE())
   expect_equal(flat_map$spectra[1:20], tiny_map$spectra[1:20])
@@ -47,12 +66,3 @@ test_that("flatten_range() function test", {
                |> 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
index 47f19d15..9383e82d 100644
--- a/tests/testthat/test-as_OpenSpecy.R
+++ b/tests/testthat/test-as_OpenSpecy.R
@@ -42,6 +42,15 @@ test_that("as_OpenSpecy() generates OpenSpecy objects", {
   expect_s3_class(ost, "OpenSpecy")
   expect_s3_class(osl, "OpenSpecy")
 
+  expect_true(check_OpenSpecy(osf))
+  expect_true(check_OpenSpecy(ost))
+  expect_true(check_OpenSpecy(osl))
+
+  expect_true(is_OpenSpecy(osf))
+  expect_true(is_OpenSpecy(ost))
+  expect_true(is_OpenSpecy(osl))
+  expect_false(is_OpenSpecy(df))
+
   expect_equal(names(osf), c("wavenumber", "spectra", "metadata"))
   expect_equal(names(ost), c("wavenumber", "spectra", "metadata"))
   expect_equal(names(osl), c("wavenumber", "spectra", "metadata"))
@@ -50,11 +59,6 @@ test_that("as_OpenSpecy() generates OpenSpecy objects", {
   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("check_OpenSpecy() work as expected", {
@@ -85,6 +89,8 @@ test_that("check_OpenSpecy() work as expected", {
 test_that("'OpenSpecy' objects are read correctly", {
   os <- as_OpenSpecy(df)
 
+  expect_true(check_OpenSpecy(os))
+
   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)
@@ -101,6 +107,8 @@ test_that("'OpenSpecy' objects are transcribed to and from 'hyperSpec' objects",
   openhyper <- as_OpenSpecy(hyper)
 
   expect_true(is_OpenSpecy(openhyper))
+  expect_true(check_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
index 0cc8b172..3526dc1b 100644
--- a/tests/testthat/test-conform_spec.R
+++ b/tests/testthat/test-conform_spec.R
@@ -27,13 +27,24 @@ test_that("conform_spec() handles errors correctly", {
 })
 
 test_that("conform_spec() conforms wavenumbers correctly", {
-  sam <- as_OpenSpecy(wn <- seq(1000, 2000, 5),
+  wn <- seq(1000, 2000, 5)
+  sam <- as_OpenSpecy(x = wn,
                       data.table(intensity = rnorm(length(wn))))
+  expect_true(check_OpenSpecy(sam))
+  
   new_wavenumbers <- c(1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900)
+  wider_wavenumbers <- seq(800, 2500, by = 100)
   conf_new <- conform_spec(sam, new_wavenumbers) |>
     expect_silent()
+  expect_true(check_OpenSpecy(conf_new))
+  
   conf_roll <- conform_spec(sam, new_wavenumbers,res = NULL, type = "roll") |>
     expect_silent()
+  expect_true(check_OpenSpecy(conf_roll))
+  
+  conf_wider <- conform_spec(x = sam,range = wider_wavenumbers, res = NULL) |>
+      expect_silent()
+  expect_true(check_OpenSpecy(conf_wider))
 
   expect_equal(length(conf_new$wavenumber), length(conf_new$spectra[[1]]))
   expect_equal(range(conf_new$wavenumber), range(new_wavenumbers))
@@ -46,4 +57,8 @@ test_that("conform_spec() conforms wavenumbers correctly", {
   conform_spec(raman_hdpe)$spectra$intensity[c(63, 143, 283, 325, 402)] |>
     round(2) |>
     expect_equal(c(78.84, 65.00, 105.73, 109.41, 116.00))
+  
+  expect_true(all(conf_wider$wavenumber <= max(sam$wavenumber)) & all(conf_wider$wavenumber >= min(sam$wavenumber)))
+  
+  
 })
diff --git a/tests/testthat/test-data_norm.R b/tests/testthat/test-data_norm.R
index fd88d10b..c3a16071 100644
--- a/tests/testthat/test-data_norm.R
+++ b/tests/testthat/test-data_norm.R
@@ -4,13 +4,6 @@ test_that("adj_neg() give correct output", {
                c(1425.14, 542.03, 1.00, 458.30, 396.34))
 })
 
-test_that("make_rel() give correct output", {
-  rel <- make_rel(c(-1000, -1, 0, 1, 10))
-  expect_equal(range(rel), c(0, 1))
-  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)
 })
@@ -24,8 +17,3 @@ 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
index 646c43b8..240bece1 100644
--- a/tests/testthat/test-def_features.R
+++ b/tests/testthat/test-def_features.R
@@ -1,8 +1,17 @@
 map <- read_extdata("CA_tiny_map.zip") |> read_any()
 
+test_that("def_features() handles input errors correctly", {
+  def_features(1:1000) |> expect_error()
+})
+
+test_that("collapse_spec() handles input errors correctly", {
+  collapse_spec(1:1000) |> expect_error()
+})
+
 test_that("features are identified when given logical", {
   map$metadata$particles <- map$metadata$y == 0
   id_map <- def_features(map, map$metadata$particles)
+  expect_true(check_OpenSpecy(id_map))
   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)
@@ -13,6 +22,7 @@ test_that("features are identified when given logical", {
 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_true(check_OpenSpecy(id_map))
   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)
@@ -38,6 +48,8 @@ test_that("the original spectrum remains unmodified and metadata is amended", {
   id_map <- def_features(map,ifelse(map$metadata$x == 1,
                                     "particle", "not_particle"))
 
+  expect_true(check_OpenSpecy(id_map))
+
   expect_equal(id_map$wavenumber, map$wavenumber)
   expect_equal(id_map$spectra, map$spectra)
   expect_contains(id_map$metadata, map$metadata)
@@ -50,15 +62,22 @@ test_that("the original spectrum remains unmodified and metadata is amended", {
 test_that("collapse particles returns expected values", {
   particles <- ifelse(map$metadata$y == 1, "particleA", "particleB")
   id_map <- def_features(map, particles)
+  expect_true(check_OpenSpecy(id_map))
+
   test_collapsed <- collapse_spec(id_map)
 
+  expect_true(check_OpenSpecy(test_collapsed))
+
   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)
+  expect_true(check_OpenSpecy(id_map))
+
   test_collapsed <- collapse_spec(id_map)
+  expect_true(check_OpenSpecy(test_collapsed))
 
   expect_equal(test_collapsed$metadata |> nrow(), 2)
   expect_equal(test_collapsed$metadata$feret_max |> round(2), c(NA, 13))
diff --git a/tests/testthat/test-gen_OpenSpecy.R b/tests/testthat/test-gen_OpenSpecy.R
index 766dceaa..b40f999c 100644
--- a/tests/testthat/test-gen_OpenSpecy.R
+++ b/tests/testthat/test-gen_OpenSpecy.R
@@ -1,5 +1,6 @@
 library(data.table)
 data("raman_hdpe")
+csv <- read_extdata("raman_hdpe.csv") |> read.csv()
 
 test_that("print() and summary() work as expected", {
   print(raman_hdpe) |> expect_output(
@@ -8,7 +9,7 @@ test_that("print() and summary() work as expected", {
   summary(raman_hdpe) |> expect_output("Length.*964.*spectra.*26")
 })
 
-test_that("head returns the first few lines of the OpenSpecy object", {
+test_that("head() returns the first few lines of the OpenSpecy object", {
   head <- head(raman_hdpe)
 
   nrow(head) |> expect_equal(6)
@@ -16,7 +17,17 @@ test_that("head returns the first few lines of the OpenSpecy object", {
                                        raman_hdpe$spectra)))
 })
 
-test_that("ploting works without errors", {
+test_that("plotting works without errors", {
   plot(raman_hdpe) |> expect_silent()
 })
 
+test_that("conversion to tables works as expected", {
+  df <- as.data.frame(raman_hdpe) |> expect_silent()
+  dt <- as.data.table(raman_hdpe) |> expect_silent()
+
+  expect_s3_class(df, "data.frame")
+  expect_s3_class(dt, "data.table")
+
+  expect_equal(df, csv)
+  expect_equal(dt, as.data.table(csv))
+})
diff --git a/tests/testthat/test-interactive_plots.R b/tests/testthat/test-interactive_plots.R
index d1c1df78..dcb22960 100644
--- a/tests/testthat/test-interactive_plots.R
+++ b/tests/testthat/test-interactive_plots.R
@@ -1,10 +1,8 @@
 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() handles input errors correctly", {
+  plotly_spec(1:1000) |> expect_error()
 })
 
 test_that("plotly_spec() generates 'plotly' object", {
@@ -13,6 +11,16 @@ test_that("plotly_spec() generates 'plotly' object", {
     expect_s3_class("plotly")
 })
 
+test_that("heatmap_spec() handles input errors correctly", {
+  heatmap_spec(1:1000) |> expect_error()
+})
+
+test_that("heatmap_spec() generates 'plotly' object", {
+  heatmap_spec(map, z = map$metadata$y) |>
+    expect_silent() |>
+    expect_s3_class("plotly")
+})
+
 test_that("interactive_plot() generates 'plotly' object", {
   interactive_plot(map, x2 = raman_hdpe, select = 2) |>
     suppressWarnings() |>
diff --git a/tests/testthat/test-io_spec.R b/tests/testthat/test-io_spec.R
index dbc972fb..e0602e3b 100644
--- a/tests/testthat/test-io_spec.R
+++ b/tests/testthat/test-io_spec.R
@@ -29,7 +29,7 @@ test_that("read_spec() gives expected output", {
   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.csv") |> read_spec() |> expect_error()
 
   read_extdata("raman_hdpe.yml") |> read_spec(share = tmp) |> expect_message()
   read_extdata("raman_hdpe.json") |> read_spec(share = tmp) |> expect_message()
@@ -38,6 +38,10 @@ test_that("read_spec() gives expected output", {
   expect_s3_class(yml, "OpenSpecy")
   expect_s3_class(jsn, "OpenSpecy")
   expect_s3_class(rds, "OpenSpecy")
+  
+  expect_true(check_OpenSpecy(yml))
+  expect_true(check_OpenSpecy(jsn))
+  expect_true(check_OpenSpecy(rds))
 
   jsn$metadata$file_name <- yml$metadata$file_name <-
     rds$metadata$file_name <- NULL
@@ -62,7 +66,7 @@ test_that("read_spec() and write_spec() work nicely together", {
   expect_equal(yml[1:2], raman_hdpe[1:2])
 })
 
-test_that("as_hyperspec function", {
+test_that("as_hyperspec() function works", {
   hyperspec_object <- as_hyperSpec(raman_hdpe)
 
   # Verify the class of the output
diff --git a/tests/testthat/test-make_rel.R b/tests/testthat/test-make_rel.R
new file mode 100644
index 00000000..e43ea4b1
--- /dev/null
+++ b/tests/testthat/test-make_rel.R
@@ -0,0 +1,21 @@
+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))
+})
+
+test_that("make_rel() gives correct output", {
+  rel <- make_rel(c(-1000, -1, 0, 1, 10))
+  expect_equal(range(rel), c(0, 1))
+  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("make_rel() gives correct output with OpenSpecy objects", {
+  data("raman_hdpe")
+  rel <- make_rel(raman_hdpe) |> expect_silent()
+
+  expect_s3_class(rel, "OpenSpecy")
+  expect_true(check_OpenSpecy(rel))
+  
+  expect_equal(range(rel$spectra), c(0, 1))
+})
diff --git a/tests/testthat/test-manage_lib.R b/tests/testthat/test-manage_lib.R
index fc57f016..e2170609 100644
--- a/tests/testthat/test-manage_lib.R
+++ b/tests/testthat/test-manage_lib.R
@@ -30,6 +30,18 @@ test_that("check_lib() finds test library", {
     expect_warning()
 })
 
+test_that("load_lib() works with test library", {
+  skip_on_cran()
+  skip_if_offline(host = "api.osf.io")
+
+  tl <- load_lib(type = "test", path = tmp) |>
+    expect_silent()
+  expect_true(check_OpenSpecy(tl))
+  expect_type(tl, "list")
+  expect_s3_class(tl, "OpenSpecy")
+  expect_identical(tl, test_lib, ignore_attr = T)
+})
+
 test_that("get_lib() downloads complete library", {
   skip_on_cran()
   skip_if_offline(host = "api.osf.io")
@@ -50,15 +62,24 @@ test_that("check_lib() finds complete library", {
     expect_warning()
 })
 
-test_that("load_lib() works as expected", {
+test_that("load_lib() works with complete library", {
   skip_on_cran()
   skip_if_offline(host = "api.osf.io")
+  skip_if_not(testthat:::on_ci(), "Not on CI")
 
-  tl <- load_lib(type = "test", path = tmp) |>
+  tl <- load_lib(type = "derivative", path = tmp) |>
     expect_silent()
   expect_type(tl, "list")
   expect_s3_class(tl, "OpenSpecy")
-  expect_identical(tl, test_lib, ignore_attr = T)
+  expect_true(check_OpenSpecy(tl))
+})
+
+test_that("rm_lib() works as expected", {
+  skip_on_cran()
+  skip_if_offline(host = "api.osf.io")
+
+  rm_lib(type = "test", path = tmp) |>
+    expect_silent()
 })
 
 # Tidy up
diff --git a/tests/testthat/test-manage_spec.R b/tests/testthat/test-manage_spec.R
index 936f9e3b..46125d98 100644
--- a/tests/testthat/test-manage_spec.R
+++ b/tests/testthat/test-manage_spec.R
@@ -18,7 +18,8 @@ test_that("c_spec() merges 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_true(check_OpenSpecy(same))
+  
   expect_equal(same$wavenumber, raman_hdpe$wavenumber)
   expect_equal(same$spectra$intensity, raman_hdpe$spectra$intensity)
 })
@@ -26,6 +27,7 @@ test_that("c_spec() merges identical files without range specification", {
 test_that("c_spec() merges different files with common range", {
   diff <- c_spec(specs, range = "common", res = 5) |>
     expect_silent()
+  expect_true(check_OpenSpecy(diff))
 
   diff$wavenumber[1:2] |> expect_equal(c(655, 660))
   diff$spectra$intensity[1:2] |> round(2) |> expect_equal(c(53.87, 59.00))
@@ -36,6 +38,8 @@ test_that("c_spec() merges different files with specified range", {
   spec <- c_spec(specs, range = c(1000, 2000), res = 5) |>
     expect_silent()
 
+  expect_true(check_OpenSpecy(spec))
+  
   spec$wavenumber |> expect_equal(seq(1000, 2000, 5))
   spec$spectra$intensity |>
     expect_equal(
@@ -47,6 +51,7 @@ test_that("sample_spec() returns a subset of the spectra", {
   tiny_map <- read_any(read_extdata("CA_tiny_map.zip"))
   sampled <- sample_spec(tiny_map, size = 5)
   expect_s3_class(sampled, "OpenSpecy")
+  expect_true(check_OpenSpecy(sampled))
   expect_equal(ncol(sampled$spectra), 5)
 })
 
diff --git a/tests/testthat/test-match_spec.R b/tests/testthat/test-match_spec.R
index c90c3a9f..baebfcc6 100644
--- a/tests/testthat/test-match_spec.R
+++ b/tests/testthat/test-match_spec.R
@@ -2,11 +2,11 @@
 tmp <- file.path(tempdir(), "OpenSpecy-testthat")
 dir.create(tmp, showWarnings = F)
 
-# Create test data for cor_spec function
 data("test_lib")
 
-unknown <- read_extdata("ftir_ldpe_soil.asp") |> read_any() |>
-  conform_spec(range = test_lib$wavenumber, res = spec_res(test_lib)) |>
+unknown <- read_extdata("ftir_ldpe_soil.asp") |> read_any()
+preproc <- conform_spec(unknown, range = test_lib$wavenumber,
+                        res = spec_res(test_lib)) |>
   process_spec(smooth_intens = T, make_rel = T)
 
 # Create a subset of test_lib for filtering
@@ -14,19 +14,24 @@ 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", {
+test_that("ai_classify() handles input errors correctly", {
+  ai_classify(1:1000) |> expect_error()
+})
+
+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)
 
+  expect_error(check_OpenSpecy(lib))
+
   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) |>
+  matches <- match_spec(x = preproc, library = lib, na.rm = T, fill = fill) |>
     expect_silent()
   nrow(matches) |> expect_equal(1)
   names(matches) |> expect_contains(c("x", "y", "z", "value", "name"))
@@ -34,13 +39,23 @@ test_that("match_spec returns correct structure with AI", {
   grepl("polyamide", matches$name) |> expect_true()
 })
 
+test_that("match_spec() handles input errors correctly", {
+  match_spec(1:1000) |> expect_error()
+})
+
 # Match_spec function
-test_that("match_spec returns correct structure", {
-  matches <- match_spec(x = unknown, library = test_lib, na.rm = T, top_n = 5,
+test_that("match_spec() returns correct structure", {
+  matches <- match_spec(x = preproc, library = test_lib, na.rm = T, top_n = 5,
                         add_library_metadata = "sample_name",
                         add_object_metadata = "col_id") |>
     expect_silent()
 
+  order <- match_spec(x = preproc, library = test_lib, na.rm = T, top_n = 5,
+                      order = unknown,
+                      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))
@@ -48,21 +63,28 @@ test_that("match_spec returns correct structure", {
     c("poly(ethylene)", "polystyrene", "poly(vinyl chloride)",
       "poly(dimethylsiloxane) (pdms)", NA)
   )
+  expect_equal(matches, order)
+})
+
+test_that("cor_spec() handles input errors correctly", {
+  cor_spec(1:1000) |> expect_error()
+  restrict_range(raman_hdpe, 300, 310) |> cor_spec(test_lib) |>
+    expect_error()
 })
 
 # 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) |>
+test_that("cor_spec() returns a data.table with correct columns", {
+  matches <- cor_spec(preproc, library = test_lib) |>
     expect_silent()
 
-  unknown2 <- unknown
-  unknown2$wavenumber[1:3] <- unknown2$wavenumber[1:3] + 1
+  preproc2 <- preproc
+  preproc2$wavenumber[1:3] <- preproc2$wavenumber[1:3] + 1
 
-  matches2 <- cor_spec(unknown2, library =  test_lib) |>
+  matches2 <- cor_spec(preproc2, library =  test_lib) |>
     expect_warning()
   inherits(matches, "matrix") |> expect_true()
   expect_identical(dim(matches), c(ncol(test_lib$spectra),
-                                   ncol(unknown$spectra)))
+                                   ncol(preproc$spectra)))
 
   top_matches <- max_cor_named(cor_matrix = matches, na.rm = T) |>
     expect_silent()
@@ -79,7 +101,7 @@ test_that("cor_spec returns a data.table with correct columns", {
     expect_silent()
 
   expect_equal(nrow(test_metadata), 1)
-  full_test <- ident_spec(matches, unknown, library = test_lib, top_n = 5,
+  full_test <- ident_spec(matches, preproc, library = test_lib, top_n = 5,
                           add_library_metadata = "sample_name") |>
     expect_silent()
 
@@ -92,24 +114,35 @@ test_that("cor_spec returns a data.table with correct columns", {
   )
 })
 
+test_that("filter_spec() handles input errors correctly", {
+  filter_spec(1:1000) |> expect_error()
+})
+
 # Write the tests for filter_spec function
-test_that("filter_spec returns OpenSpecy object with filtered spectra", {
+test_that("filter_spec() returns erroneous OpenSpecy object when removing all spectra", {
   os_filtered <- filter_spec(test_lib, logic = rep(F, ncol(test_lib$spectra))) |>
     expect_silent()
+  expect_warning(check_OpenSpecy(os_filtered))
   expect_equal(ncol(os_filtered$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", {
+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_true(check_OpenSpecy(os_filtered))
+
   expect_equal(ncol(os_filtered$spectra), 1)
   expect_equal(nrow(os_filtered$metadata), 1)
 })
 
+test_that("get_metadata() handles input errors correctly", {
+  get_metadata(1:1000) |> expect_error()
+})
+
 # Tidy up
 unlink(tmp, recursive = T)
diff --git a/tests/testthat/test-process_spec.R b/tests/testthat/test-process_spec.R
index acc934be..707bdcc9 100644
--- a/tests/testthat/test-process_spec.R
+++ b/tests/testthat/test-process_spec.R
@@ -18,16 +18,18 @@ test_that("process_spec() returns expected values", {
                                derivative = 1,
                                abs = T,
                                make_rel = T))
-
+  expect_true(check_OpenSpecy(conf))
+  
   proc <- process_spec(raman_hdpe,
                        smooth_intens = TRUE,
                        smooth_intens_args = list(
                          polynomial = 3,
                          window = 11,
                          derivative = 1
-                       )) |>
-    expect_silent()
-
+                       )) |> expect_silent()
+  
+  expect_true(check_OpenSpecy(proc))
+  
   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
index 9f54bf92..669a2441 100644
--- a/tests/testthat/test-read_envi.R
+++ b/tests/testthat/test-read_envi.R
@@ -9,6 +9,7 @@ test_that("ENVI files are read", {
     expect_message() |> expect_warning()
 
   expect_s3_class(tiny_map, "OpenSpecy")
+  expect_true(check_OpenSpecy(tiny_map))
 
   expect_equal(ncol(tiny_map$spectra), 208)
   expect_length(tiny_map$wavenumber, 427)
diff --git a/tests/testthat/test-read_ext.R b/tests/testthat/test-read_ext.R
index 6547a3f6..67b6004d 100644
--- a/tests/testthat/test-read_ext.R
+++ b/tests/testthat/test-read_ext.R
@@ -32,6 +32,8 @@ test_that("read_text() gives expected output", {
     expect_warning() |> expect_warning()
 
   expect_s3_class(csv, "OpenSpecy")
+  expect_true(check_OpenSpecy(csv))
+  
   expect_equal(names(csv), c("wavenumber", "spectra", "metadata"))
   expect_equal(csv$wavenumber, raman_hdpe$wavenumber)
   expect_equal(csv$spectra, raman_hdpe$spectra)
@@ -47,6 +49,8 @@ test_that("read_asp() gives expected output", {
     expect_message() |> expect_warning()
 
   expect_s3_class(asp, "OpenSpecy")
+  expect_true(check_OpenSpecy(asp))
+  
   expect_equal(names(asp), c("wavenumber", "spectra", "metadata"))
   expect_length(asp$wavenumber, 1798)
   range(asp$wavenumber) |> round(1) |>
@@ -65,6 +69,8 @@ test_that("read_spa() gives expected output", {
     expect_message() |> expect_warning()
 
   expect_s3_class(spa, "OpenSpecy")
+  expect_true(check_OpenSpecy(spa))
+  
   expect_equal(names(spa), c("wavenumber", "spectra", "metadata"))
   expect_length(spa$wavenumber, 1738)
   range(spa$wavenumber) |> round(1) |>
@@ -74,7 +80,7 @@ test_that("read_spa() gives expected output", {
 })
 
 test_that("read_jdx() gives expected output", {
-  suppressWarnings(jdx <- read_jdx(read_extdata("fitr_nitrocellulose.jdx"),
+  suppressWarnings(jdx <- read_jdx(file = read_extdata("fitr_nitrocellulose.jdx"),
                                    encoding = "latin1")) |>
     capture_messages() |>
     expect_match("JDX file inconsistency.*")
@@ -82,6 +88,8 @@ test_that("read_jdx() gives expected output", {
     expect_error()
 
   expect_s3_class(jdx, "OpenSpecy")
+  expect_true(check_OpenSpecy(jdx))
+  
   expect_equal(names(jdx), c("wavenumber", "spectra", "metadata"))
   expect_length(jdx$wavenumber, 7154)
   range(jdx$wavenumber) |> round(1) |>
@@ -99,6 +107,8 @@ test_that("read_spc() gives expected output", {
     expect_message() |> expect_warning()
 
   expect_s3_class(spc, "OpenSpecy")
+  expect_true(check_OpenSpecy(spc))
+  
   expect_equal(names(spc), c("wavenumber", "spectra", "metadata"))
   expect_length(spc$wavenumber, 559)
   range(spc$wavenumber) |> round(1) |>
diff --git a/tests/testthat/test-read_opus.R b/tests/testthat/test-read_opus.R
index 23dba994..d901ac27 100644
--- a/tests/testthat/test-read_opus.R
+++ b/tests/testthat/test-read_opus.R
@@ -5,8 +5,10 @@ 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() |>
@@ -14,6 +16,8 @@ test_that("opus files are read correctly", {
 
   expect_s3_class(single, "OpenSpecy")
   expect_s3_class(multi, "OpenSpecy")
+  expect_true(check_OpenSpecy(single))
+  expect_true(check_OpenSpecy(multi))
 
   expect_equal(names(single), c("wavenumber", "spectra", "metadata"))
   expect_equal(names(multi), c("wavenumber", "spectra", "metadata"))
diff --git a/tests/testthat/test-smooth_intens.R b/tests/testthat/test-smooth_intens.R
index 954f2167..4f88636b 100644
--- a/tests/testthat/test-smooth_intens.R
+++ b/tests/testthat/test-smooth_intens.R
@@ -1,11 +1,16 @@
 data("raman_hdpe")
 
+test_that("smooth_intens() handles input errors correctly", {
+  smooth_intens(1:1000) |> expect_error()
+})
+
 test_that("smooth_intens() works as expected", {
   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)
+      smooth_intens(raman_hdpe, polynomial = 1)$spectra$intensity) |>
+      round(4) |>
+    expect_equal(0.8043, ignore_attr = F)
   expect_s3_class(smt, "OpenSpecy")
   expect_equal(nrow(smt$spectra), nrow(raman_hdpe$spectra))
   expect_equal(smt$wavenumber, raman_hdpe$wavenumber)
diff --git a/tests/testthat/test-subtr_baseline.R b/tests/testthat/test-subtr_baseline.R
index 52c6b542..9c36d5c5 100644
--- a/tests/testthat/test-subtr_baseline.R
+++ b/tests/testthat/test-subtr_baseline.R
@@ -7,6 +7,8 @@ test_that("polynomial subtr_baseline() works as expected", {
       subtr_baseline(raman_hdpe, degree = 1)$spectra$intensity) |> round(4) |>
     expect_equal(0.9763, ignore_attr = F)
   expect_s3_class(poly, "OpenSpecy")
+  expect_true(check_OpenSpecy(poly))
+  
   expect_equal(nrow(poly$spectra), nrow(raman_hdpe$spectra))
   expect_equal(poly$wavenumber, raman_hdpe$wavenumber)
   expect_equal(range(poly$spectra), c(0, 1))
@@ -20,6 +22,7 @@ test_that("manual subtr_baseline() works as expected", {
 
   man <- subtr_baseline(raman_hdpe, type = "manual", baseline = bl) |>
     expect_silent()
+  expect_true(check_OpenSpecy(man))
 
   cor(raman_hdpe$spectra$intensity, man$spectra$intensity) |>
     expect_equal(1, ignore_attr = F)
diff --git a/tests/testthat/test-workflows.R b/tests/testthat/test-workflows.R
index d4456292..59d8e8e2 100644
--- a/tests/testthat/test-workflows.R
+++ b/tests/testthat/test-workflows.R
@@ -11,6 +11,7 @@ test_that("Raman batch analysis with test library", {
   is_OpenSpecy(batch) |> expect_true()
   plot(batch) |> expect_silent()
   plotly_spec(batch) |> expect_silent()
+  expect_true(check_OpenSpecy(batch))
 
   get_lib(type = "test", path = tmp) |> expect_no_error()
   check_lib(type = "test", path = tmp) |> expect_silent()
@@ -19,6 +20,8 @@ test_that("Raman batch analysis with test library", {
   filter_spec(lib, lib$metadata$SpectrumType == "Raman") |> expect_silent()
   batch2 <- conform_spec(batch, lib$wavenumber, res = spec_res(lib$wavenumber)) |>
     expect_silent()
+  
+  expect_true(check_OpenSpecy(batch2))
 
   plotly_spec(batch2) |> expect_silent()
 
@@ -27,6 +30,8 @@ test_that("Raman batch analysis with test library", {
   batch3 <- process_spec(batch2, subtr_baseline = T) |> expect_silent()
   plotly_spec(x = batch3, x2 = batch) |> expect_silent()
 
+  expect_true(check_OpenSpecy(batch3))
+  
   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") |>
@@ -52,6 +57,7 @@ test_that("Raman batch analysis with complete library", {
   batch2 <- conform_spec(batch, range = lib$wavenumber,
                          res = spec_res(lib$wavenumber)) |>
     expect_silent()
+  expect_true(check_OpenSpecy(batch2))
 
   plotly_spec(batch2) |> expect_silent()
 
@@ -60,6 +66,8 @@ test_that("Raman batch analysis with complete library", {
   batch3 <- process_spec(batch2, subtr_baseline = T) |> expect_silent()
   plotly_spec(x = batch3, x2 = batch) |> expect_silent()
 
+  expect_true(check_OpenSpecy(batch3))
+
   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") |>
diff --git a/vignettes/advanced.Rmd b/vignettes/advanced.Rmd
index 6509573f..c33b2491 100644
--- a/vignettes/advanced.Rmd
+++ b/vignettes/advanced.Rmd
@@ -39,7 +39,7 @@ 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
+# 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
diff --git a/vignettes/app.Rmd b/vignettes/app.Rmd
new file mode 100644
index 00000000..6d59da22
--- /dev/null
+++ b/vignettes/app.Rmd
@@ -0,0 +1,437 @@
+---
+title: "Open Specy App Tutorial"
+author: >
+  Win Cowger, Zacharias Steinmetz
+date: "`r Sys.Date()`"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Open Specy App Tutorial}
+  %\VignetteEncoding{UTF-8}
+  %\VignetteEngine{knitr::rmarkdown}
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  warning = FALSE
+)
+```
+
+# Document Overview 
+
+This document outlines a common workflow for using the Open Specy 
+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
+ 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. If you are looking for documentation about the R package please see the [package vignette](http://wincowger.com/OpenSpecy-package/articles/sop.html)
+
+# 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 your own computer in R. 
+If using the package, you just need to read in the library and run the command `run_app()`.
+
+```{r setup}
+library(OpenSpecy)
+```
+
+```{r eval = F}
+run_app()
+```
+
+# Reading Data
+
+Once the app is open, if you have your own data, click **Browse** at the top left hand corner of the
+Analyze Spectra tab.
+
+```{r, fig.align="center", out.width="98%", echo=FALSE}
+knitr::include_graphics("app/mainpage.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.
+
+```{r, fig.align="center", echo=FALSE}
+knitr::include_graphics("app/uploadfile.jpg")
+```
+
+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 sample_data, echo=FALSE, out.width="50%"}
+knitr::kable(head(raman_hdpe), caption = "Sample data `raman_hdpe`")
+```
+
+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.
+
+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 [Spectragryph Tutorial](http://wincowger.com/OpenSpecy-package/articles/spectragryph.html).
+
+If you don't have your own data, you can use a test dataset.
+
+```{r, fig.align="center", echo=FALSE}
+knitr::include_graphics("app/samplefile.jpg")
+```
+
+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.
+
+# Visualization
+
+## Spectra
+
+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("app/spectra_vis.jpg")
+```
+
+
+## Maps
+
+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("app/map_vis.jpg")
+```
+
+# Processing
+
+The goal of this processing is to increase the signal to noise ratio (S/N) without
+distorting the shape or relative size of the peaks. 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("app/processing.jpg")
+```
+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.
+
+
+## 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("app/snr_threshold.jpg")
+```
+
+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.
+
+```{r, fig.align="center", echo=FALSE}
+knitr::include_graphics("app/signal_settings.jpg")
+```
+
+## Intensity Adjustment
+
+```{r, fig.align='center', echo=FALSE}
+knitr::include_graphics("app/intensityadjustment.jpg")
+```
+
+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.
+
+## 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. We set the 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. 
+
+## Smoothing
+
+```{r, fig.align="center", echo=FALSE}
+knitr::include_graphics("app/smoothingpoly.jpg")
+```
+
+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.
+
+## Baseline Correction
+
+```{r, fig.align="center", echo=FALSE}
+knitr::include_graphics("app/baselinecorrectionpoly.jpg")
+```
+
+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.
+
+## Range Selection
+
+```{r, fig.align="center", echo=FALSE}
+knitr::include_graphics("app/rangeselection.jpg")
+```
+
+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.
+
+## Flattening Ranges
+
+```{r, fig.align="center", echo=FALSE}
+knitr::include_graphics("app/flattten_example.jpg")
+```
+
+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. By default it is set to flatten the CO2 region for
+FTIR spectra because that region often needs to be flattened when atmospheric
+artifacts occur in spectra. 
+
+## Min-Max Normalization
+
+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. 
+
+## Downloading Processed Data
+
+You can download a csv file of your processed data using the Your Spectra option
+with download. This will download a version of your spectra with whatever
+processing options you have selected at the time.
+
+```{r, fig.align="center", echo=FALSE}
+knitr::include_graphics("app/download_yourspec.jpg")
+```
+
+# Identifying Spectra 
+
+```{r, fig.align="center", echo=FALSE}
+knitr::include_graphics("app/identification.jpg")
+```
+
+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.
+
+## Reading Libraries
+
+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' this will black out pixels in a spectral map view if
+they are lower than the threshold.
+
+## Matches
+
+```{r, fig.align="center", echo=FALSE}
+knitr::include_graphics("app/matches.jpg")
+```
+
+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.
+
+## Selection Metadata
+
+```{r, fig.align="center", out.width="98%", echo=FALSE}
+knitr::include_graphics("app/selectionmetadata.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.
+
+## Match Plot
+
+```{r, fig.align="center", echo=FALSE}
+knitr::include_graphics("app/matchplot.jpg")
+```
+
+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.
+
+## Sharing Reference Data
+
+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.
+
+## Download Top Matches
+
+To download the top matches and associated metadata, use the top matches download option. 
+```{r, fig.align="center", echo=FALSE}
+knitr::include_graphics("app/download.jpg")
+```
+
+# Characterizing Particles
+
+```{r, fig.align="center", echo=FALSE}
+knitr::include_graphics("app/download_threshold.jpg")
+```
+
+Thresholding only works and shouldn't be used for single particles. You can
+threshold spectral maps using the signal-noise values and/or the correlation
+threshold. Whatever selection you have active will inform how your spectra is
+thresholded. It should get thresholded in coincidence with what you see on the
+spectral map. Pro tip: The file that gets downloaded is an OpenSpecy with median
+spectra for each of your thresholded particles. You can upload that back to the
+app and analyze that independently.
+
+## Additional App Features
+
+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("app/translate.jpg")
+```
+
+# 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).
diff --git a/vignettes/sop/baselinecorrectionpoly.jpg b/vignettes/app/baselinecorrectionpoly.jpg
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diff --git a/vignettes/sop.Rmd b/vignettes/sop.Rmd
index 04d4c4da..55136b0a 100644
--- a/vignettes/sop.Rmd
+++ b/vignettes/sop.Rmd
@@ -1,13 +1,11 @@
 ---
-title: "Open Specy Tutorial"
+title: "Open Specy Package Tutorial"
 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
+  Win Cowger, Zacharias Steinmetz
 date: "`r Sys.Date()`"
 output: rmarkdown::html_vignette
 vignette: >
-  %\VignetteIndexEntry{Open Specy Tutorial}
+  %\VignetteIndexEntry{Open Specy Package Tutorial}
   %\VignetteEncoding{UTF-8}
   %\VignetteEngine{knitr::rmarkdown}
 ---
@@ -23,22 +21,22 @@ knitr::opts_chunk$set(
 # Document Overview 
 
 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.
-
-# R Package and App
+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 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.
 
 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.
+talk through the R functions you can use to programatically analyze spectra. If
+you are looking for a tutorial about how to use the app see [this
+tutorial](http://wincowger.com/OpenSpecy-package/articles/app.html).
 
-## Installation
+# Installation
 
 After installing the R package, you just need to read in the library.
 
@@ -46,36 +44,34 @@ After installing the R package, you just need to read in the library.
 library(OpenSpecy)
 ```
 
-## Running the App
+# 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 your own computer in R. 
+[https://openanalysis.org/openspecy/](https://openanalysis.org/openspecy/) or
+start the Shiny GUI directly from your own computer in R. If you are looking for
+a tutorial about how to use the app see [this
+tutorial](http://wincowger.com/OpenSpecy-package/articles/app.html).
 
 ```{r eval=FALSE}
 run_app()
 ```
 
-## Reading Data
+# Read Data
 
-If you have your own data, click **Browse** at the top left hand corner of the
-Analyze Spectra tab.
+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, fig.align="center", out.width="98%", echo=FALSE}
-knitr::include_graphics("sop/mainpage.jpg")
+```{r, eval=FALSE}
+read_any("path/to/your/data")
 ```
 
-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.
+These file type specific functions will also read in spectral
+data accordingly if you have a particular format in mind.
 
-```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("sop/uploadfile.jpg")
+```{r, eval=FALSE}
+read_text(".csv")
+read_asp(".asp")
+read_opus(".0")
 ```
 
 Open Specy allows for upload of native Open Specy .y(a)ml, .json, or .rds files.
@@ -90,10 +86,6 @@ 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 sample_data, echo=FALSE, out.width="50%"}
-knitr::kable(head(raman_hdpe), caption = "Sample data `raman_hdpe`")
-```
-
 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.
@@ -103,37 +95,11 @@ so that we can work on integrating it.
 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).
-
-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, eval=FALSE}
-read_any("path/to/your/data")
-```
-
-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_opus(".0")
-```
+spectral file conversions. For instructions, see [Spectragryph
+Tutorial](http://wincowger.com/OpenSpecy-package/articles/spectragryph.html).
 
 If you don't have your own data, you can use a test dataset.
 
-```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("sop/samplefile.jpg")
-```
-
-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.
-
-The following line of code does the same to load the example file into R:
-
 ```{r}
 data("raman_hdpe")
 ```
@@ -164,7 +130,7 @@ batch_example <- read_extdata("testdata_zipped.zip") |>
 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:
+scratch if you'd like.
 
 ```{r}
 scratch_OpenSpecy <- as_OpenSpecy(x = seq(1000,2000, by = 5), 
@@ -172,6 +138,36 @@ scratch_OpenSpecy <- as_OpenSpecy(x = seq(1000,2000, by = 5),
                                   metadata = list(file_name = "fake_noise")) 
 ```
 
+Open Specy objects are lists with three components, `wavenumber` is a vector of
+the wavenumber values for the spectra and corresponds to the rows in `spectra`
+which is a `data.table` where each column is a set of spectral intensities.
+`metadata` is a data.table which holds additional information about the spectra.
+Each row corresponds to a column in `spectra`.
+
+```{r}
+# Access the wavenumbers
+scratch_OpenSpecy$wavenumber
+```
+
+```{r}
+# Access the spectra
+scratch_OpenSpecy$spectra
+```
+
+```{r}
+# Access the metadata
+scratch_OpenSpecy$metadata
+```
+
+```{r}
+# Performs checks to ensure that OpenSpecy objects are adhering to our standards;
+# returns TRUE if it passes. 
+check_OpenSpecy(scratch_OpenSpecy)
+
+# Checks only the object type to make sure it has OpenSpecy type
+is_OpenSpecy(scratch_OpenSpecy)
+```
+
 We have some generic functions built for inspecting the spectra:
 
 ```{r}
@@ -186,19 +182,46 @@ summary(scratch_OpenSpecy) # summarizes the contents of the spectra
 head(scratch_OpenSpecy) # shows the top wavenumbers and intensities
 ```
 
-## Visualization
+# Save Data
+
+Open Specy objects can be saved most accurately as .rds, .yml, or .json files.
+.rds will be the most reproducible as it is a native R file format and floating
+point errors can happen with .json or .yml.
 
-### Spectra
+```{r, eval=F}
+write_spec(scratch_OpenSpecy, "test_scratch_OpenSpecy.rds")
+write_spec(scratch_OpenSpecy, "test_scratch_OpenSpecy.yml", digits = 5)
+write_spec(scratch_OpenSpecy, "test_scratch_OpenSpecy.json", digits = 5)
+```
 
-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. 
+# Format Conversions
 
-```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("sop/spectra_vis.jpg")
+Another great spectroscopy R package is
+[hyperSpec](https://CRAN.R-project.org/package=hyperSpec). We
+actually depend on their functions for several of ours. They are currently
+making some awesome new features and we want to integrate well with them so that
+both packages can be easily used together. That is why we created the
+`as_hyperSpec` function and made sure that `as_OpenSpecy` can convert from
+`hyperSpec` objects.
+
+```{r eval=F}
+hyperspecy <- as_hyperSpec(scratch_OpenSpecy)
+
+identical(as_OpenSpecy(hyperspecy)$wavenumber, scratch_OpenSpecy$wavenumber)
+
+# There are some challenges with reproducing the names and floating point
+# numbers but the underlying data is preserved. 
+identical(unname(unlist(signif(as_OpenSpecy(hyperspecy)$spectra, 2))),
+          unname(unlist(signif(scratch_OpenSpecy$spectra, 2))))
+
+# Metadata is not perfectly preserved yet.
+identical(as_OpenSpecy(hyperspecy)$metadata, scratch_OpenSpecy$metadata)
 ```
 
+# Visualization
+
+## Spectra
+
 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.
 
@@ -209,39 +232,30 @@ plot(scratch_OpenSpecy) # quick and efficient
 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
+# This will min-max normalize your data even if it isn't already but are not
+# changing your underlying data
+plotly_spec(scratch_OpenSpecy)
 ```
 
 With interactive plots you can also plot two different datasets simultaneously
 to compare. 
 
 ```{r, fig.align="center", out.width="98%"}
-plotly_spec(scratch_OpenSpecy, rds_example) # interactive comparison
+plotly_spec(scratch_OpenSpecy, rds_example)
 ```
 
-### Maps
+## Maps
 
 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
+spatial information can be plotted as a heatmap. 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("sop/map_vis.jpg")
-```
+actually has spatial coordinates.
 
-The same plot can be created in R but the user can control what values form the
-colors of the heatmap by specifying `z`.
+In R the user can control what values form the colors of the heatmap by
+specifying `z`, it is handy to put the information you want in the metadata for
+this reason. This example just shows the x values of the spectra.
 
 ```{r, fig.align="center", out.width="98%"}
 heatmap_spec(spectral_map,
@@ -258,107 +272,216 @@ that spectrum.
 interactive_plot(spectral_map, select = 100, z = spectral_map$metadata$x)
 ```
 
-## Processing
 
-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.
+# Combining OpenSpecy Objects
+
+Sometimes you have several OpenSpecy objects that you want to combine into one.
+The easiest way to do that is by having spectra which all are in the same exact
+format with the same series of wavenumbers. The default settings of `c_spec`
+assume that is the case.
+
+```{r}
+#Same range example. 
+data("raman_hdpe")
+raman_hdpe1 <- raman_hdpe
+raman_hdpe2 <- raman_hdpe
+
+raman_hdpe1$spectra <- raman_hdpe1$spectra - 100 
+
+raman_hdpe2$spectra <- raman_hdpe2$spectra - 200 
+
+combined_spectra <- c_spec(list(raman_hdpe, raman_hdpe1, raman_hdpe2))
+
+plot(combined_spectra)
 
-```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("sop/processing.jpg")
 ```
 
-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.
+If you have different wavenumber ranges for the spectra you want to combine,
+you can set  `range = "common"` and `res` equal to the wavenumber resolution you
+want and the function will collapse all the spectra to whatever their common
+range is using linear interpolation.
+
+```{r}
+asp_example <- read_extdata("ftir_ldpe_soil.asp") |> 
+  read_any() 
+ps_example <- read_extdata("ftir_ps.0") |> 
+  read_any() # preserves some metadata
+
+combined <- c_spec(list(asp_example, ps_example), range = "common", res = 5)
+
+plot(combined)
+
+```
+
+
+# Filtering OpenSpecy Objects
+
+OpenSpecy objects can have any number of spectra in them. To create an OpenSpecy
+with a subset of the spectra that is in an Open Specy object you can use the
+`filter_spec` function. Filtering is allowed by index number, name, or using a
+logical vector. Filtering will update the `spectra` and `metadata` items of the
+`OpenSpecy` but not the `wavenumber`.
+
+```{r}
+spectral_map <- read_extdata("CA_tiny_map.zip") |> 
+  read_any() # preserves some metadata
+
+# Extract the 150th spectrum. 
+filter_spec(spectral_map, 150) |>
+  plot()
+```
+
+```{r}
+# Extract the spectrum with column name "8_5". 
+filter_spec(spectral_map, "8_5") |>
+  plot()
+```
+
+```{r}
+# Extract the spectra with column names "8_5", "10_3", or "12_10" using a
+# logical argument based on the spectra column names.
+filter_spec(spectral_map, names(spectral_map$spectra) %in% c("8_5", "10_3", "12_10")) |>
+  plot()
+```
+
+```{r}
+filter_spec(spectral_map, spectral_map$metadata$y == 1) |>
+  plot() #extracts the spectra with y values of 1 for their metadata. 
+```
+
+# Sampling OpenSpecy Objects
+
+Sometimes you have a large suite of examples of spectra of the same material and
+you want to reduce the number of spectra you run through the analysis for
+simplicity or you are running simulations or other proceedures that require you
+to first sample from the spectra contained in your OpenSpecy objects before
+doing analysis. the `sample_spec` function serves this purpose.
+
+```{r}
+spectral_map <- read_any(read_extdata("CA_tiny_map.zip"))
+sample_spec(spectral_map,size = 10) |>
+  plot()
+```
+
+# Processing
+
+The goal of this all processing is to increase the signal to noise ratio (S/N)
+of the spectra without distorting the shape, position, or relative size of the
+peaks. After loading data, you can preprocess the data using intensity
+adjustment, baseline subtraction, smoothing, flattening, and range selection.
+The default settings 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.
 
 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)
+processed <- process_spec(raman_hdpe,
+                          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)
 summary(processed)
 summary(raman_hdpe)
 ```
 
-You can compare the processed and unprocessed data in an overlay plot:
+You can compare the processed and unprocessed data in an overlay plot.
 
 ```{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.
+operations. All other processing functions can be tuned using its parameters in
+the single function and we have set defaults and nested the processing functions
+in a way that provides typically quality results. 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. See
+explanations of each processing sub-function below.
 
-#### Threshold Signal-Noise
+## 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.
+often use 5. The Run Signal Over Noise technique searches your spectra for high
+and low regions and conducts division on them to derive the signal to noise
+ratio. This is a good way to automatically calculate the signal to noise ratio.
+In the example below you can see that our signal to noise ratio is increased by
+the processing, the goal of processing is generally to maximize this. 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("sop/snr_threshold.jpg")
+```{r}
+sig_noise(processed, metric = "run_sig_over_noise") > 
+  sig_noise(raman_hdpe, metric = "run_sig_over_noise")
 ```
 
 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.
+individually. Setting the `min_sn` will threshold the heatmap image to only
+color spectra which have a `sn` value over the threshold.
 
-```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("sop/signal_settings.jpg")
-```
+```{r}
+spectral_map_p <- spectral_map |>
+  process_spec(flatten_range = T)
+
+spectral_map_p$metadata$sig_noise <- sig_noise(spectral_map_p)
 
-#### Intensity Adjustment
+heatmap_spec(spectral_map_p, sn = spectral_map_p$metadata$sig_noise, min_sn = 5)
 
-```{r, fig.align='center', echo=FALSE}
-knitr::include_graphics("sop/intensityadjustment.jpg")
 ```
 
+## Intensity Adjustment
+
 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.
+can adjust reflectance or transmittance spectra to absorbance units. 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 $\frac{(1-R)^2}{2R}$.
 
 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")
+data("raman_hdpe")
+trans_raman_hdpe <- raman_hdpe
+trans_raman_hdpe$spectra <- 2 - trans_raman_hdpe$spectra^2
+    
+rev_trans_raman_hdpe <- trans_raman_hdpe |> 
+  adj_intens(type = "transmittance")
 
-plot(raman_adj)
+plotly_spec(trans_raman_hdpe, rev_trans_raman_hdpe)
 ```
 
-#### Conforming
+## 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.
+spectrally. 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 function
+can be specified.
 
 ```{r}
 conform_spec(raman_hdpe) |> # default convert res to 5 wavenumbers.
@@ -375,64 +498,51 @@ conform_spec(spectral_map, range = ps_example$wavenumber, res = 10,
   summary()
 ```
 
-#### Smoothing
-
-```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("sop/smoothingpoly.jpg")
-```
+## Smoothing
 
-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.
+The [Savitzky-Golay
+filter](https://en.wikipedia.org/wiki/Savitzky%E2%80%93Golay_filter) is used for
+smoothing. Higher polynomial numbers lead to more wiggly fits and thus less
+smoothing, 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. You'll notice a new function we are using
+`c_spec()` which is used to combine spectral objects into one OpenSpecy object.
 
-Examples of smoothing with the R package:
+Examples of smoothing:
 
-```{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)
+```{r smooth_intens, fig.cap = "Sample `raman_hdpe` spectrum with different smoothing polynomials.", fig.width=6, fig.align="center"}
 data("raman_hdpe")
 
-none <- adj_intens(raman_hdpe, make_rel = T)
-p1 <- smooth_intens(raman_hdpe, polynomial = 1)
-p4 <- smooth_intens(raman_hdpe, polynomial = 4)
+none <- make_rel(raman_hdpe)
+p1 <- smooth_intens(raman_hdpe, polynomial = 1, derivative = 0, abs = F)
+p4 <- smooth_intens(raman_hdpe, polynomial = 4, derivative = 0, abs = F)
 
 compare_derivative <- c_spec(list(none, p1, p4))
     
 plot(compare_derivative)
 ```
 
-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.
+Derivative transformation can happen with the same function. 
 
-```{r compare_derivative, fig.cap = "Sample `raman_hdpe` spectrum with different derivatives from Cowger et al. (2020).", fig.width=6, fig.align="center"}
+```{r compare_derivative, fig.cap = "Sample `raman_hdpe` spectrum with different derivatives.", fig.width=6, fig.align="center"}
 
-none <- adj_intens(raman_hdpe, make_rel = T)
+none <- make_rel(raman_hdpe)
 d1 <- smooth_intens(raman_hdpe, derivative = 1, abs = T)
-d2 <- smooth_intens(raman_hdpe, derivative = 2)
+d2 <- smooth_intens(raman_hdpe, derivative = 2, abs = T)
 
 compare_derivative <- c_spec(list(none, d1, d2))
     
 plot(compare_derivative)
 ```
 
-#### Baseline Correction
-
-```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("sop/baselinecorrectionpoly.jpg")
-```
+## Baseline Correction
 
-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
@@ -443,16 +553,19 @@ 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.
+baseline fit below. Manual baseline correction can also be specified by
+providing a baseline `OpenSpecy` object.
 
 ```{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)
+alternative_baseline <- smooth_intens(raman_hdpe, polynomial = 1, window = 51,
+                                      derivative = 0, abs = F, make_rel = F) |>
+  flatten_range(min = 2700, max = 3200, make_rel = F)
+
+none <- make_rel(raman_hdpe)
+d2 <- subtr_baseline(raman_hdpe, type = "manual",
+                     baseline = alternative_baseline)
 d8 <- subtr_baseline(raman_hdpe, degree = 8)
 
 compare_subtraction <- c_spec(list(none, d2, d8))
@@ -460,11 +573,7 @@ compare_subtraction <- c_spec(list(none, d2, d8))
 plot(compare_subtraction)
 ```
 
-#### Range Selection
-
-```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("sop/rangeselection.jpg")
-```
+## Range Selection
 
 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
@@ -472,14 +581,13 @@ 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.
+to examine a single peak or a subset of peaks of interests. 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"}
+```{r restrict_range, fig.cap = "Sample `raman_hdpe` spectrum with different degrees of range restriction.", fig.width=6, fig.align="center"}
 data("raman_hdpe")
 
-none <- adj_intens(raman_hdpe)
+none <- make_rel(raman_hdpe)
 r1 <- restrict_range(raman_hdpe, min = 1000, max = 2000)
 r2 <- restrict_range(raman_hdpe, min = c(1000, 1800), max = c(1200, 2000))
 
@@ -490,11 +598,7 @@ compare_ranges <- c_spec(list(none, r1, r2), range = "common")
 plot(compare_ranges)
 ```
 
-#### Flattening Ranges
-
-```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("sop/flattten_example.jpg")
-```
+## Flattening Ranges
 
 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
@@ -502,15 +606,15 @@ 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
+FTIR spectra because that region often needs to be flattened when atmospheric
 artefacts occur in spectra. Like `restrict_range`, the R function can accept
 multiple ranges.
 
 ```{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)
+none <- make_rel(single)
+f1 <- flatten_range(single) #default flattening the CO2 region. 
 f2 <- flatten_range(single, min = c(1000, 2500), max = c(1200, 3000))
 
 compare_flats <- c_spec(list(none, f1, f2))
@@ -518,157 +622,227 @@ compare_flats <- c_spec(list(none, f1, f2))
 plot(compare_flats)
 ```
 
-#### Min-Max Normalization
-
-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 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)
+## Min-Max Normalization
 
-compare_rel <- c_spec(list(none, relative))
-
-plot(compare_rel)
-```
+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 package, most of the processing
+functions will min-max transform your spectra by default if you do not specify
+otherwise. This plot shows two plots that are nearly identical except for the
+min-max transformed spectrum has a y axis that ranges from 0-1.
 
-## Identifying Spectra 
+```{r make_rel, fig.cap = "Sample `raman_hdpe` spectrum with one being relative and the other untransformed.", fig.width=6, fig.align="center"}
+none <- raman_hdpe
+relative <- make_rel(raman_hdpe)
+plot(none)
+plot(relative)
 
-```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("sop/identification.jpg")
 ```
 
-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.
+# Identifying Spectra 
 
-### Reading Libraries
+## Reading Libraries
 
-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'
-
-In the R package, you'll download and load the libraries into your working
-environment.
+Reference libraries are spectra with known identities. The Open Specy library
+now has over 10,000 spectra in it an is getting so large that we cannot fit it
+within the R package size limit of 5 MB. We host the reference libraries on
+[OSF](https://osf.io/x7dpz/) and have a function to pull the libraries down
+automatically. Running get_lib by itself will download all libraries to your
+package director or you can specify which libraries you want and where you want
+them.
 
 ```{r eval=F}
-get_lib() #will load all libraries unless specified otherwise. 
-lib <- load_lib(type = "derivative")
+get_lib() 
+get_lib(type = "derivative", path = "your/path")
 ```
 
-### Matches
+After download the libraries you can load them into your active environment. You
+should load them one at a time. Libraries are also `OpenSpecy` objects so you
+can use any Open Specy object as a library.
 
-```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("sop/matches.jpg")
+```{r eval=F}
+lib <- load_lib(type = "derivative")
 ```
 
-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.
-
-The same table can be returned using the OpenSpecy package commands in the R
-console.
+## Matches
+
+Before attempting to use a reference library to identify spectra it is really
+important to understand what format the reference library is in. All the
+OpenSpecy reference libraries are in Absorbance units. `derivative` has been
+absolute first derivative transformed, `nobaseline` has been baseline corrected,
+`raw` is the rawest form of the reference spectra (not recommended except for
+advanced uses). The previously mentioned libraries all have Raman and FTIR
+spectra in them. `mediod` is the mediod compressed library version of the
+absolute first derivative for FTIR, `model` is an exception because it is a
+multinomial regression approach for FTIR to identification of absolute
+derivative spectra. All of the libraries have wavenumbers at 5 cm^-1 resolution.
+Whichever library you choose, you first need to get your spectra into a similar
+enough format to use for comparison. That means conforming the wavenumbers to
+the same range and processing the spectra using the same processing procedures.
+In this example we use the `data("test_lib")` which is a subsampled version of
+the absolute derivative library and `data("raman_hdpe")` which is an unprocessed
+Raman spectrum in absorbance units of HDPE plastic. With single spectra it is
+easy to look at the spectra but when doing in batch, also refer to the
+Thresholding Signal-Noise section for guidance on making sure your batch spectra
+are processed to quality specs.
 
 ```{r}
 data("test_lib")
+data("raman_hdpe")
 
-processed <- process_spec(ps_example, 
-                        conform_spec_args = list(range = test_lib$wavenumber, 
-                                                 res = NULL))
+processed <- process_spec(x = raman_hdpe, 
+                          conform_spec_args = list(range = test_lib$wavenumber, 
+                                                   res = NULL,
+                                                   type = "interp"
+                                                   ))
 
-test <- match_spec(processed, test_lib, add_object_metadata = "col_id",
-                   add_library_metadata = "sample_name")
+# Check to make sure that the signal to noise ratio of the processed spectra is
+# greater than 10.
+print(sig_noise(processed) > 10)
+plotly_spec(raman_hdpe, processed)
 ```
 
-### Selection Metadata
+After your spectra is processed similarly to the library specifications, you can
+identify the spectra using `match_spec()`. The `add_library_metadata` and
+`add_object_metadata` options specify the column name in the metadata that you
+want to add metadata from and `top_n` specifies how many matches you want. In
+this example we just identified a single spectrum with the library but you can
+also send an OpenSpecy object with multiple spectra. The output `matches` is a
+data.table with at least 3 columns, `object_id` tells you the column names of
+the spectra in `x`, `library_id` tells you the column names from the library
+that it matched to. `match_val` is the value of the Pearson correlation
+coefficient (default) or other correlation if specified in `...` or if using the
+model identification option `match_val` will be the model confidence. The output
+in this example returned the correct material type, HDPE, as the top match. If
+using Pearson correlation, 0.7 is a good threshold to use for a positive ID. In
+this example, only our top match is greater than the threshold so we would
+disregard the other matches. If no matches were above our threshold, we would
+proclaim that the spectrum is of an unknown identity. You'll also notice in this
+example that we matched to a library with both Raman and FTIR spectra but the
+Raman spectra had the highest hits, this is the rationale for lazily matching to
+a library with both. If you want to just match to a library with FTIR or Raman
+spectra, you can first filter the library using `filter_spec()` using
+`SpectrumType`.
 
-```{r, fig.align="center", out.width="98%", echo=FALSE}
-knitr::include_graphics("sop/selectionmetadata.jpg")
+```{r}
+matches <- match_spec(x = processed, library = test_lib,
+                      add_library_metadata = "sample_name", top_n = 5)
+print(matches[,c("object_id", "library_id", "match_val", "SpectrumType",
+                 "SpectrumIdentity")])
 ```
 
-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.
+## Library Metadata
 
-The R command for manual metadata selection using `sample_name == 5381` as
-example is:
+The libraries we have created have over 100 variables of metadata in them and
+this can be onerous to read through especially given that many of the variables
+are `NA` values. We created `get_metadata()` to remedy this by removing columns
+from the metadata which are all blank values. The function below will return the
+metadata for the top match in `matches`. Remember, similar `filter_spec()`, you
+can specify `logic` for more than one thing at a time.
 
-```{r, eval=FALSE}
-find_spec(sample_name == 5381, library = spec_lib, which = "raman")
+```{r}
+get_metadata(x = test_lib, logic = matches[[1,"library_id"]], rm_empty = T)
 ```
 
-### Match Plot
+## Plot Matches
 
-```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("sop/matchplot.png")
-```
+Overlaying unknown spectra and the best matches can be extremely useful to
+identify peaks that don't fit to the reference library which may need further
+investigation. The example below shows great correspondence between the best
+match and the unknown spectrum. All major peaks are accounted for and the
+correct relative height. There are two small peaks in the unknown spectrum near
+500 that are not accounted for which could be investigated further but we would
+call this a positive id to HDPE.
 
-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.
+```{r}
+plotly_spec(processed, filter_spec(test_lib, logic = matches[[1,"library_id"]]))
+```
 
-### Sharing Reference Data
+## Sharing Reference Data
 
 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.
+package administrator to discuss options for collaborating.
+
+# Characterizing Particles
+
+Sometimes the spectroscopy task we want to perform is to identify particles in a
+spectral map. This is especially common for microplastic analysis where a
+spectral map is used to image a sample and spectral information is used to
+differentiate microplastic particles from nonplastic particles. In addition to
+the material id, one often wants to measure the shape and size of the particles.
+In a brute force technique, one could first identify every spectrum in the map,
+then use thresholding and image analysis to measure the particles. However, more
+often than not, particles are well separated on the image surface and background
+spectra is quite different from particle spectra and therefore we can use
+thresholding a priori to identify and measure the particles, then pass an
+exemplary spectrum for each particle to the identification routine. It is
+important to note here that this is at the bleeding edge of theory and technique
+so we may be updating these functions in the near future.
+
+## Brute Force
+
+```{r eval = F}
+data("test_lib")
+test_map <- read_any(read_extdata("CA_tiny_map.zip"))
 
-## Characterizing Particles
+test_map_processed <- process_spec(test_map, conform_spec_args = list(
+  range = test_lib$wavenumber, res = NULL)
+  )
 
-### Thresholding
+identities <- match_spec(test_map_processed, test_lib, order = test_map,
+                         add_library_metadata = "sample_name", top_n = 1)
 
-### Collapse Spectra
+features <- ifelse(identities$match_val > 0.7,
+                   tolower(identities$polymer_class), "unknown")
 
-## Additional App Features
+id_map <- def_features(x = test_map_processed, features = features)
 
-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.
+id_map$metadata$identities <- features
+# Also should probably be implemented automatically in the function when a
+# character value is provided. 
 
-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.
+# Collapses spectra to their median for each particle
+test_collapsed <- collapse_spec(id_map)
+```
 
-### Download Data
+## A Priori Particle Thresholding
 
-```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("sop/download.jpg")
-```
+```{r eval = F}
+data("test_lib")
+test_map <- read_any(read_extdata("CA_tiny_map.zip"))
+
+# Characterize the total signal as a threshold value. 
+snr <- sig_noise(test_map,metric = "log_tot_sig")
+
+# Use this to find your particles and the sig_noise value to use for
+# thresholding. 
+heatmap_spec(test_map, z = snr)
+
+# Set define the feature regions based on the threshold. 400 appeared to be
+# where I suspected my particle to be in the previous heatmap. 
+id_map <- def_features(x = test_map, features = snr > 400) 
+
+# Check that the thresholding worked as expected.
+heatmap_spec(id_map, z = id_map$metadata$feature_id)
+
+# Collapse the spectra to their medians based on the threshold. Important to
+# note here that the particles with id -88 are anything from the FALSE values
+# so they should be your background. 
+collapsed_id_map <- id_map |>
+  collapse_spec()
 
-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.
+# Process the collapsed spectra. 
+id_map_processed <- process_spec(collapsed_id_map, conform_spec_args = list(
+  range = test_lib$wavenumber, res = NULL)
+  )
 
-## Conceptual diagram of Open Specy
+# Check the spectra.
+plot(id_map_processed)
 
-```{r, fig.align="center", echo=FALSE}
-knitr::include_graphics("sop/flowchart.png")
+# Get the matches of the collapsed spectra for the particles.
+matches <- match_spec(id_map_processed, test_lib,
+                      add_library_metadata = "sample_name", top_n = 1)
 ```
 
 # References
diff --git a/vignettes/sop.html b/vignettes/sop.html
index 2b89eca7..ce4fb386 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="2023-08-30" />
+<meta name="date" content="2023-09-01" />
 
-<title>Open Specy Tutorial</title>
+<title>Open Specy Package 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).
@@ -2263,60 +2263,56 @@
 
 
 
-<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">2023-08-30</h4>
+<h1 class="title toc-ignore">Open Specy Package Tutorial</h1>
+<h4 class="author"><p>Win Cowger, Zacharias Steinmetz</p></h4>
+<h4 class="date">2023-09-01</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>
+package 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 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>
 <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>
+tutorial will talk through the R functions you can use to
+programatically analyze spectra. If you are looking for a tutorial about
+how to use the app see <a href="http://wincowger.com/OpenSpecy-package/articles/app.html">this
+tutorial</a>.</p>
+</div>
+<div id="installation" class="section level1">
+<h1>Installation</h1>
 <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>
+<div id="running-the-app" class="section level1">
+<h1>Running the App</h1>
 <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 your own computer in R.</p>
+or start the Shiny GUI directly from your own computer in R. If you are
+looking for a tutorial about how to use the app see <a href="http://wincowger.com/OpenSpecy-package/articles/app.html">this
+tutorial</a>.</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>
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Bz4lJrocPIedJn8paf7Sc/pOfDWxeENTYqnFPhwzFl9Oti/KPLcQtRZIQioMBRG/G4jePNTlR54HI4ap9GjgX919tdzX82vfSvB94N0Op0+s0nh1b9PqlKmN1CnTmIq0PxHmVhbbiDu5FKkgj9mod49KGJ70sD122hizhDqVIUUpc0gFCDEuETBykETupjl7pcbLZTtHd51tR3wPvCyYccZe8GTj4080stutO8J60hxtSTgpUkx8ggjBB1R9EPwrRknwaOPIAGSf0U1nl/6NGjXK+vX6hTAygia17XrIu3hRclYszizaN68O7uitRJy7Wu6i1O1q5DjzGA9HkOQ1ltYQ6jC0FSR0iTlKsaxJqNDUCPG56FYACw7UFp5Ec8dP6QT6OWPx0aNSUK0kgmoxABo6woZST4zUUqKjhJfnqU2Ozt6bB9f8ca+ioUNKHVeN1BawhIYaU9PQFqyoqKldNy7E8sEHPoxzNGnTSRVPWNE2K+vnlaV5STInpDiQnOCOnOMqwnPq5/hqwKpTfNyJXZhWajP7c9ow7+HZ/wBR0aNZG30M+m0lc8WkI8Kk85pi2lOH6qyn2aPd6waBVaakuebMc55aSanObxy7Cek9fpPr14uuW/N/4J/lndmuzPrAQQB9vlgkHI7cc+R0aNI4+l4gobSiOHSz9ukpXKO+aENluQpZVs2x+wFVmtseinSQNuMeUU0knOc/a/hjR10ztx1fI3Y+15QzR6vRu7OX+p0aNMmnwK+KrTJKiKfJAIG0G4ZqthBHP7XPPMY/HXzrlnJPiMkpzkJ8oJnL8M7tGjSTRAqV0SuW63EYTVbYlVN0T3HXpKL1qFOecYUcIYCEubBs9DmNyvT69YeuTmJUqa7Q4MukQVPIMOOu5H6k7HQhG1wFbiwpfSKHSecPM+yCRzJo1bbJcFkfsqH1rXJSFZERJAPDsrTPXk828WwlMAxsPr9tf//ZAP/bAEMAAQEBAQEBAQEBAQEBAQEBAgEBAQEBAgEBAQICAgICAgICAgMDBAMDAwMDAgIDBAMDBAQEBAQCAwUFBAQFBAQEBP/bAEMBAQEBAQEBAgEBAgQDAgMEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBP/AABEIAZUC0AMBIgACEQEDEQH/xAAfAAEAAgEFAQEBAAAAAAAAAAAAAgcIAQQFBgkDCgv/xABnEAAABQICAwkIDQgGBwQIBgMAAQIDBAUGBxESE9EIFBUhMVFTVJEWGFJWkpSV0hciIzJBVXFyk5axwdMJMzU3YXR1tCRzgaGysyU0QleX1PAmQ2PCNkVGYoKEoqMnOERkZXakw+H/xAAdAQEAAgMBAQEBAAAAAAAAAAAAAQMCBAUGBwgJ/8QAUBEAAQIDBAUJBAUICAQHAQAAAQACAxESBBQhUQUGEzFBByJSYYGRodHwF1RxkjIzsbLBCBg2QoKU4fEVFiNyc5OVokNWYtQlNERGVdLVU//aAAwDAQACEQMRAD8A/OOLhrO59xvt60Svyt4VXvTLQ3sia7XZdFW3HisOFmh99rPWtNmRkesdQlORkZmQsbcP2bSMQt19ucLKr0dMuj3FizTIM+OsiNDiUm4+kzIyMj0VMoVkZGXteMjLMh6l4WYo4r37+UExrwDqtXfuLD2mxblo8ay3YEZcGPGojcc3HiPQJZqW0uWb5rUonCWZGniSRes0ZF0DaLXF0fpG0GHFa1j+AY1j9oA97nEANBhuByGO5fKNddYNbNA02jQFihxoDIb4kQvc+twY6GDCgtYDOIQ8EVYHASmvH2hbmjdAXPQKbdNu4RXvWrerMBNTpNWptNbkxKkwvPQda900lJPRPLIszy5Bw1o4FYyX85X2bNwzu+4XrWqKaRckeFTNVJocpSVKTHlNOKQttzJCj0TLMtEx6x7qO/r4wB3Hm4DrmElzTLOcq9FuSmVg6c0w8xWmaTV5JwmJyHG1E6hLTiCyPLNJ5Z5cmQWK5V22cOd1zihYrq7fuuDgpamINYrlKNtiZTZZFIpqZxaRKI1kTkds1GRnopIzzyzG7oZmrul9GxNKGO+HChRIkOJUWSL2RYsABhwE3xYcmg4ycBvxXkNOa96+aHtlnsDbFAiPtkjZqdqS1tUAkxm8Q2FGrLmHCh3Nlu8R3dybulo6HFu4JX+hLTC5TmdNbUaW2i0nF5E6Z5JIyzy4xVtsYd31elKuiuWnatYuCj2VTSrF21Gmx0uRrfimh1wn5JmojSnRYeVmRGeTZ8Q9Xvye2OGK+KmJGIUHEbECvXpBodiM1Klxa68w7GgPLqjDbjqSQ2nI1ILRMzzLIWFg/gqnDdn8rFartONmm0Tc/OX3aKZMU06MCrIlzoLjOZFlotSnm0qTxZNqyPINNQNC6J0RB0857xZnPoIdTUCXNY04YSL3CZyIW7ozXjWyNrHpDU63wLOdIQGQIrDDETZuY9zdoDUaqmsM2ylMg7wF41WBhdiJipUZNJw6s2vXjPgxylz2qLEJ5untKPRSt95RpabJR5knTURqMjyI8jy5+JgHjTOvSo4dRMMbvevqk0zhqoWtwaTNVjw9NtvfZJUokrZNTraScbUpJmriM+PLMLCDEi7rA3HFwQMF2KlExCrF5OVu4LjtuDwlcMKMxL1D7aGtWs0mzBZjuoWRKNLb8lREWRqLIDCTdgUPEmVh7UJVMqK8YLIw4kW7ft01CmRmKVWGJlUoyJZRjacNZHpR1SMloQltxbhknjMhXAfqw3R77da7W5phCcSQwEvpMbORL2tIdkQcCvVW5vK87WSDobQ+hYT2WpwZZa3urIeP7K0RKeaLPEe18MbntLTUOa5eW114G4xWNNoVPvDDS8LclXPUW6RbyanSjaYrEt5aW2ozD5GbRuqUpJE2aiUeeeWWZjvC9yHunmzWleB1/pNBmSyOnsZpy5c/dfgyMZ0bmrGa6MZsNMVrVxhuuVec+374tu4rDm1qO09UIj0OrR5Tmi+22ksmzZZNGl7Yt8OJI8lGQ6Bu090jjRZ26FvO3MPMWLmoVnN0qlSKZAoE2OqlIKVT23H3GlG0r361LUZko+Mz5OQdW4avNsht7Yz3tmG0tLK2u3kOGO7mnhg5sxiF5Ia0cpztOM1ciaPs8KMGPiGLEEfYRGBzGsdBdIHnOMRhBnzoMSR5plhZbO57xxvO2XLytXCq9K7a7ZOqTWKfSdZHlkwZk8cZBqJx8kGlRGbKVlmkyLMyMh1SvYaYgWva1t3vcVoVuj2jeBqTbFxTY6UUutmlCnDSwslGeZJQszSokmWgossyMh66bpLdFXXuc8aNzxb9Dqk+m4TUbDyn1K6LNokGK5w3FN5+G4hJLJPujbUds2TStskL0lZ5qMdOuO5LV3XG53xxplm02bTG8PcaoeINtUyoR2Y9WgUmsutrqStW2pTaclyq8vJKlFxJ+Qs7XoPREAR4LLQ7bQmuJBlIlrA8kSnzZVDHGYBnJa2huUHXfSETRukLRoyGdG22LCax7HPLmNixjZ2MfMgbUxDDIkC2kkYGRXlvd2G9+2TQLduO7bTrFvUS9KcupWlUaoyhhi4GCZbe10ciUajRoPMqzMi4nE84rqE4bjTaj+EiP9vwDODd54j0+9sS6RbdvtnFtjDPDWnWvS4BLJRQn34bcyUg9EiLSQlcVhRkWWcY+QYKUtebLWefvSzz5B5u1tsbI5bYYlcKQk7PAYjdhPdxlKa+raBj6atOi4cbWKziBbCXVwxjRJxAaTMzcABUQZTnLBWdFw6xEnUtFdg4eYgTqE7EVParkKxatMojsdJKUp9MxEc2VNESVGbhLNJEkzM+Ix04jIyzIyMj4yMjzIx6ZWDum8KqDgPRbDqF+3HEuGFh2/bj9JYgYwrpzUlyNJbSwRwr9jUc0Gp1KTNmnNxOM9KKpOmhXmU2RpbbSZERpbSkyLLIjIiI+Ti7CIv2ENSZ2hbLAcV2JCgO4qYAAyUIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACLQzJJGozIiSWZmZ5ERFymYtGbghjRTadPrFRwhxPgUmlW2xedUqk2warFp9No8o1lGqsh5TBIbhum24SJKzJperVko8jFVPpNceQhKSUpcdaEpM8iUZpMiI/l5P7R7/4q7t3c4XPuZMQMKKNjzf8AUL+nbmemW4ziC/YDUW8sV5So9RYLCitnwG2yzb1EW81IiTWnkPOLcPN7iNRVx3PhwTEhiZy7OrHw+GKlgDooY/BssT2gficz1SxXgKOZp9uXBVmFSaXQqxUoyFm2uRBpj8phCiLMyNaUmnMi4zLPiHDcWfEeZZ8vOPRjc1Y6YU2DJwkrd1lR5lPsS4aVVLmsmrqOOxcLcCa3JlMZmg21plJQv2ys0mbmSyyzH3HkN5NdUeUzT1v0XrbptujocCzPjQ6nQmOtERpaBBY+O9kNpIJPOM5YykHEfJOV7X7Wbk/0NY9Ias6IfbokaOyE+lsR7YLHBxMR7YTXRCJiQkJb5mZaD571Kk1WjSShVimVGkzDbS8USpwXafJNCyzQsm3Eko0qLjJWWR/AY5Ss2bd9uUm2K9cVqXNQKHe1Odq9l1muUGXSKXd8Rh3UPS6W+82lEthDp6tTzBrQSuLSzHr5+WU3cW563a164O1DAq25yTsO16jAuS7a/Ro9EqFbKe6w7GpakMKXpMRNU5kvSPjkOEgkpyI+p/lTMftyduvKthBujcD8TL4o1/KwftbCS8dyrc+F8+BQcIkW7BeYUqhXKl0qacDNeiiHGbWpxRk8Rt6bjbfyrWXRrNC6UFhg4/RqaXsiOhlzSaHPh8xxBkCWyAnJwa4OaPougLe/S2j73Fw+lSQ1zA8BwFTWxJOaHNmQHTnKYLmkOOHW4YrD9v7rvAOtxW23ZVNvg5EZLqjS2lw6bUG0LMy480Gsll+1BD0P3Nt8u0b8rTjld8eiwnXKi7eSFUx2S6cRop6aWiUSVl7c9JDjySz96T2f+yQ8ccP72rGG18Wnf1vkwqs2hXWK7AblEZxZCmVe3ZdIuPQdQbjasuPJw8uMemUvdx7nehVG5sYcPcEa7Td0Ld1DVTZ9WqjrPAcR9xtpC3nJCZB6xJGyypRtR23XyjoJZpzUY1rHoPVPScKI/TjG1u5kSovBi2eRJhCkgTqJyIDjjIkL5/r1ate7HpF39WIMSLDjWZ0OEWCGRAtO0BEWJXIhlAEyKhNgm2dJWUeJO6Vwuwj3OO5SdxM3Odu42sUWXUF2NDrdxHAbteXDlOyHZCycjPJdJ8yYJSdEstT8OeRcJufN3BflRsLd3bppVkWM5csqTSZdHsypNSKtYNEp0aO8tmjHCWZHIY1rxvKU4ZGpwuQk5JLzIxsx9trE7Azc/YbU+FcLd0YXRpRXZU6pHYaplTekspSpyIpDqlqzc01HppRxKHb9yzuhMH8KcO8WsO8XrTum7qHibPhnLptAYZXEkxGIy2nWZC1SWXEmpakGWrPkTylyDB+rWp9ot8SwvYLlEhmprokYwy9wEUlwLiJ7cVuwxdOc5kLkXTW6x6nHSl2fE0vDtX6rIe12DbTTKHMBrQ6zCkYg0yxBExnVuZN1HN3R9/4v1W68LrJtNduYAVmHAj2FUalSIynKhUG31PuE6t0/cTZSlttJkkiM8zPiGQVBxup+KW4tvTGJm3qa3ed4bi72DL/qDFQfM6gq1KfVYhPPIPlkacl1ZqMzI0LaLIiIYGWfurtxlhgd0ycLMG8QbSqd02hMtioyWWmpRS0vIM46Vk9UnCJCXD0jNBEr5eQY6YA7p6kYX4CY04LXTT6/UkX1RpaLNlUhpl+JSZk+mLgSylE44g0tKU3FcI2yUeZOe15M+paNBapWpllsmnYl5oZExMe0Oa1wLDDkC8D9UYSkSATOQK4D7Frm6LbtL6o6Nfo9u3sVEMwLO2I+F/aNtAm0Pwa54f8ATmBMCUyFXGGlxY/YL2lFxVs+HW6Ph3Xbhjw27gdituW7UqlDOS20ll0la5qQkt9t6bWiZpUtJmosiHrZhfhlYmJdQwpx5kQqpErGKeDtbl3XTXJkd3fTrW8EoNTiY6NYbSnJei64k3FEbOkZ6OR+deAG6Zw2tfCur4E4/YeTcQsMpVbO5aIqjrQVUospS0uuNmg3WVGjWEp1t1p1C0G66kyUlfFfNI3f2HVIxIoblKw8uS3sHLEwsqFg2TbVJ3m5Wt8zn6co5UhtTxNNMoagJaQhLji+NSlKUpZ6OzozR2qtLY+k3tiNiBgMN1WDg4FxLRzS0DcTMlel1u1i5X3Rolg1Xs0SzRbO+M5log7MbSzuhyhMZEcTFbEqILmNpa0tJ3Eg0GePNDu+5cPcPcOLD9jKkVXEeixK1UY8ynuVSQ2qqx0LaZREhR2SJxWqccceJ1xe92k5pSRkd07qrdAX/gniLfmA9nJob1jN28xCQqtUVqpVltus01KppJkmWkXtn3lIIveGviyLIh5x2VXoltX3aV0zGZD0K37xp1xS2IxJOU8zEnsy3EN6RknSNLaiLMyLMyzMi4x6WYgbqPcNYoXJVLxvfc831cNy1ZlEeXWJbzTEp1DDWojkaWqmhBatCUJIyT/slnmOVozReiI1mixWxWwLVWJPqiMJYWycJsxNVLAQd4a0HBoA9hrVrRrjYtL2KyRLFF0jojYOD4Oys0ZjYzYgMF9EcBjTDa+PQWSLdtELZGI8noW72tC66vjTg9asCXUr3uKvYXU6n0GIimRYE+S47OmIZiNIaJKFGaiPJSzIzM+MxDcMWleNv7o66cILtj1K1FTrPccvq2J8dCpchNLlU+pxYrxEs0o09aWktJqPVvKT/tGO+UPdyYEViJYV7YrYJ1q4cbML6NwXbNwUt5jgt9TSTSy8Tq30myaj90MnWXzZcccU2Z5kMfcEd1PEtDdNXVugsSaTU6md2U6rNTKXajbKnYbs84qYzTJPuITqWG4yW8zVpGSSPIzMx2Y8DV42+HFdaK4MR8nNLnmUMtFQe5xLySZt3kUmRwXiNG6R5TIerVqsEPRggWuywCYcRsKzsrtbYtUJ1nhQ5QWsa0NeDs20xKSDUJnEa9JcmoV+75ktxT8uVWqk6+4ZcbizkPlxF/YREXMREOl0uPJNlsyjSTLQTx72X+z9g7XPlIm1WfOaJSW5lUfmtJXlppS8+t1JKy4syJZEeXwi3qKpRMN+3XxJ5dM+YeUZChtNEMSaMBLdLgvtLrRaIoEe1Euiu5zid5ccXE9ZJKpZEeTol/RZXmy9glvaT1WV5s5sGUbFLqqm0KJl1CVkSkm68lo1EfHnkasxv10tUVnfFQmmhBmSSbhf0leZ55EatIiLkHnbTrZq1ZorbNfGPjONLWQztHl2Qayo94AHEr7DofkD5ZNMWONpU6vx7LYIMPaxLTbBc7PDhYGt0a1GFDIkZyaXOMxIGYWJu9pPVZXmzmwN7SeqyvNnNgypzpXWatzF7k36wZ0rl3zVuf8ANt+sLv6che6Wj/If5LS9lls/+d0R/qVn/wDssVt7SeqyvNnNgb2k9VlebObBlTnSuTfNW5vzbef+IM6Vy75q3P8Am2/WD+nIXulo/wAh/knsstn/AM9oj/UrP/8AZYrb2k9VlebObA3tJ6rK82c2DKnOlFyyasf7NW2X/mGmnSczydqxZ8Wl7ltEDTjDusdo/wAl3ruUDkttO52n9EA8P/EYJn8swO2Q65LFfe0nqsrzZzYG9pPVZXmzmwZUm1T3Czbqsho/gTKirLtUhRhvRlWRN1qGZ8yjeR/eZB/WLRjfrmxmH/qs8ceOzl4oOR/XWNjo+Jo+0N4GFpbRbyf2BbNoPg5gKxW3tJ6rK82c2BvaT1WV5s5sGVPB0xZf0eRGl8xRphKWr/4TMj/uHHLN9pZtu61taDyWhZqStJ/tIbth0rorSTzCsNoa94xLQRUBmWmTgOsgLzWs2omvGpsCFa9Z9ExrPAimUOK5h2MQ8RDjNnCiHMMe4jGcpFYz72k9VlebObA3tJ6rK82c2DJXWOdIvyjDWOdIvyjHT2YXjbx1LGre0nqsrzZzYG9pPVZXmzmwZK6xzpF+UYaxzpF+UYbMJeOpY1b2k9VlebObA3tJ6rK82c2DJXWOdIvyjDWOdIvyjDZhLx1LGre0nqsrzZzYG9pPVZXmzmwZK6xzpF+UYaxzpF+UYbMJeOpY1b2k9VlebObA3tJ6rK82c2DJXWOdIvyjDWOdIvyjDZhLx1LGre0nqsrzZzYG9pPVZXmzmwZK6xzpF+UYaxzpF+UYbMJeOpY1b2k9VlebObA3tJ6rK82c2DJXWOdIvyjDWOdIvyjDZhLx1LGre0nqsrzZzYG9pPVZXmzmwZK6xzpF+UYaxzpF+UYbMJeOpY1b2k9VlebObA3tJ6rK82c2DJXWOdIvyjDWOdIvyjDZhLx1LGre0nqsrzZzYG9pPVZXmzmwZK6xzpF+UYaxzpF+UYbMJeOpY1b2k9VlebObA3tJ6rK82c2DJXWOdIvyjDWOdIvyjDZhLx1LGre0nqsrzZzYG9pPVZXmzmwZK6xzpF+UYaxzpF+UYbMJeOpY1b2k9VlebObA3tJ6rK82c2DJXWOdIvyjDWOdIvyjDZhLx1LGre0nqsrzZzYG9pPVZXmzmwZK6xzpF+UYaxzpF+UYbMJeOpY1b2k9VlebObA3tJ6rK82c2DJXWOdIvyjDWOdIvyjDZhLx1LGre0nqsrzZzYG9pPVZXmzmwZK6xzpF+UYaxzpF+UYbMJeOpY1b2k9VlebObA3tJ6rK82c2DJXWOdIvyjDWOdIvyjDZhLx1LGre0nqsrzZzYG9pPVZXmzmwZK6xzpF+UYaxzpF+UYbMJeOpY1b2k9VlebObA3tJ6rK82c2DJXWOdIvyjDWOdIvyjDZhLx1LGre0nqsrzZzYG9pPVZXmzmwZK6xzpF+UYaxzpF+UYbMJeOpY1b2k9VlebObA3tJ6rK82c2DJXWOdIvyjDWOdIvyjDZhLx1LGre0nqsrzZzYG9pPVZXmzmwZK6xzpF+UYaxzpF+UYbMJeOpY1b2k9VlebObA3tJ6rK82c2DJXWOdIvyjDWOdIvyjDZhLx1LGre0nqsrzZzYG9pPVZXmzmwZK6xzpF+UYaxzpF+UYbMJeOpY1b2k9VlebObA3tJ6rK82c2DJXWOdIvyjDWOdIvyjDZhLx1LGre0nqsrzZzYG9pPVZXmzmwZK6xzpF+UYaxzpF+UYbMJeOpY1b2k9VlebObA3tJ6rK82c2DJXWOdIvyjDWOdIvyjDZhLx1LGre0nqsrzZzYG9pPVZXmzmwZK6xzpF+UYaxzpF+UYbMJeOpY1b2k9VlebObA3tJ6rK82c2DJXWOdIvyjDWOdIvyjDZhLx1LGre0nqsrzZzYG95XVZXmzmwZK6xzpF+UYaxzpF+UYBkuKXg5LGre0nqsrzZzYNd7yi5I0sv8A5ZzYMlNY50i/KMNY50i/KMRsxxS8HJY1b2k9VlebObA3tJ6rK82c2DJXWOdIvyjDWOdIvyjDZhLx1LGQAAVrY44IAAG8TRAAARAAA3bkmZzQAAEmUAABJmc1Jv36Pnl9ov20WUk0uoSE6USntE6ojLNLzp8TLX9p8Z/sSYoJB5LQZ8hKI+Ll5RkORHT6ZS6SjiU3CRU5uX+29IQS0kfzGzQX9pjiaafFtDYWhrOZPtBIcRvbCbIxXDrIIY07w57XbgV9M5N4Fk0XFtvKHpSG18DRLGRIUNwBbGtsV9NjhOBwcxr2vtMVhwiQbNFh73gjkVvOSHHHnlG466s1rWrjMzMRHXZ1eqseuSqJSqTCmuNzEwokdulPVKoS1mhvIkpQvTWtRmeSUpz48iIcY7eNYYedjSKbSI8lh1TD8aRSHI8hhxKtFTbjanCUlST9qaVERkZZGWY70KDBgQ2wYLQ1rQAABIADAAdQ3L5jbrdpHSlri6Q0hGfFjxXF73vcXOe9xLnOcSZlziSSTiSSV3UB0Y72qRcsOhFnllnTlfDyf97+0a92tT6lQ+XL9GqPk4j/AO9FsupadDl3gB0ly86mVOqkkoNC1sRuOpgzpijIjckttKzLW8ftVH8g617I1e6pb3HxF/oc/wAUQZBZCE87lbYCpPZGr3VLey5+Bjy/zRJOIdwqStaYNBUhoiN1aaIpSGtI8kmsycyTmfEWeWZ8QxrbJTsIgwkrZGuZlxc4qP2Rq91S3uTP9Dn+KHsjV3qlvehz/FE1ieKbGJuVt8WeeRZ8/wAI5hD6Kk2iLLWTcpBaMSa5yL+Emnj+EuZXwcXwCjkYhXC5p6uBQV6tBuuaFDUvVpLIjUrJziIsyzM+Isy5xydKviuSaxSKfOp1GQxUJ0ZlxJUlcN9bUlaEktteszIjS4SkqIjI+I+MhzNJ6Og6RY1zXFkdmMOIAKmO7d7Tuc081wmDwI9pqZrhpDU+0RoL4TbToy0gMtVkiE7K0QwZyMpFkVhFUGOyUSC8BzTKprrKdacZcW06hTbjajQtCuJSTL4DHzG1vGdJi20dWjL0ZzRnTlvGnTUZpUnVrMj4s9BWX9gpPuuuFJGZ1RxKUlmZm20RJIvhM9ENE2+JbrEItpaGxmlzHgTlWxxa6U8aSRU2eNJE8VGvmq1k1Z1kfYtERTEsEWHBtFme+QebPaYTI8IRJc3asbEEONTzRFY8NwCvYB12Th3uiYVJo1en4e4iUyiXGxvq36tWLUcolPrrWRK1kN2Shsnk5GSs28yyMj5OMdKrjmI9sqjJuKLWaLvwlHDXPgIaYl6GWmTTpJNtZp0k6SUqM06RZkWZDpB7DuK8bd35j12K1wFFndlyERGdSfIlFmRmw3kf/wBI07rbi+M3fomvVE1tzU3eJxI9divUBRXdbcXxm79E36od11xfGjv0TfqhW3NRsH5j12K9QFFd11xFy1R36Jv1Q7rbi+M3fom/VCpqbB+Y9divUBRXdbcXxm79E36od1txfGbv0TfqhW3NLu/MeuxXqAorutuL4zd+ib9UO624vjN36Jv1Qral3fmPXYr1AUV3W3F8Zu/RN+qHdbcXxm79E36oVtS7vzHrsV6gKK7rbi+M3fom/VDutuL4zd+ib9UK2pd35j12K9QFFd1txfGbv0Tfqh3W3F8Zu/RN+qFbUu78x67FeoCiu624vjN36Jv1Q7rbi+M3fom/VCtqXd+Y9divUBRXdbcXxm79E36od1txfGbv0TfqhW1Lu/MeuxXqAorutuL4zd+ib9UWfaVSl1SlFImua19ElbOsJJIUtJZGWeXFnxmX9gkOB3LF0JzBMkLswCkZN3XBvmQSKgtpBPrShtDSNFBEoyIizTnyEO92paWOt90ir3BZNl37dlAoBmVdr1v2m/U6HRTIiUZSpqWtQ0ZEZGZLWRpIyM8iMhBe0byshAeROYXcwFfTqNjBTIUmoz6FcceBDZORLllAZksxW08anXNXpGlBFxmsyJJFxmZEOmd1lx/GbvN+abMuPk/2QrapFniHiPXYr0AUSV3XEfJVHT+Rpr1Rr3W3Fxf6Ud4zyLNposz5vehW3NBZ4h4j12K9QFFd11xHyVRw/kbaP/yh3W3Fy8KO5cmeqa9UKmjegs7zxHrsV6gKK7rbi+M3fom/VDutuL4zd+ib9UK25qLu/MeuxXqAorutuL4zd+ib9UO624vjN36Jv1Qral3fmPXYr1AUV3W3F8Zu/RN+qHdbcXxm79E36oVtS7vzHrsV6gKK7rbi+M3fom/VDutuL4zd+ib9UK2pd35j12K9QFFd1txfGbv0Tfqh3W3F8Zu/RN+qFbUu78x67FeoCiu624vjN36Jv1Q7rbi+M3fom/VCtqXd+Y9divUBRXdbcXxm79E36od1txfGbv0TfqhW1Lu/MeuxXqAorutuL4zd+ib9UO624vjN36Jv1Qrbmpu78x67FeoCtbPr1VqNSejTpRyWziqdTpoSlSFJUkiMjIi5z4hQW62x5vrBQsP27JKhJVc6ao7UnazRkVlwt5qgIZS0laiSgv6S6ajyMz9rxkRZHhEjMhsMR+5Q2A98TZN3rMcB5YRt0tutpmF1Xxpj0O03sMKBftMwvq92lZ9MKDBr9YptVq9Npqmjd15uPw6JVX0qS2bZFDUSlpUaEq6bM3am6Mp7ECTPg2vBj1SLv6mPzMPGIjNRY0zb1zC15E4jSSpOmgzTmRlnmNc2+CMJHu7fsxC2Bo+MeI9di9gQHjf39OPOWenZBZ89kRC+8b6bu1t0VTVRUVGHa1PVNgtVOGmdh6xDVLjPp02ZDRLy02lpMlJcTmlRcZGZCP6QgngVP9HR+JHrsXsEA8fG92vuinadLq7UO1naVAksQ51Taw9Ycp8J6Ul9cZp54vaIW8mLJNtKjI1lHdNJHoKy9D9zjiPcOK2FFKvG6U00q1IrdRpchdKglTYbyIjjJNK1JGaUqydMj0ciPRI8s8zO2Da4cd9DJzVUeyRYDNo+UlegAA2VqoAACIAACKsMIKXaFdxKtK379n06j2lcc923avX6vMVT6ZbBzokmLFqsh4lFotwpLsWUo1Ho6McyUSkmaTzMXh/uP6jcNMuKi3NT49FZm0Ktos47niuQWWK1Ply3IVbXNdS641Caai0yQmKono5va5wnCPIedQf2EfNmWeQoBEpEevX4rfImJT9evw4TWbUHBDc9PreRUMUlU6E2dLbbuROI9tyGJLMiNGffqiaSTJyUokSnpVLRANZvwVQTlSVraWSS+SMJ9y3VYM+o07Eu6qUcK3JExNuTLvt6q3BUJK4FDlNrivKjxWMoSqhU0ORnMnJSqWtLa2lJWkYU5Fy5Fnnnn/ZkNeUsj4yzz4+Pk5BEzOYPr+KS9dy7piMxQI1+3dHtaPFiW41W3EUSLBnJqcOPH0UGlDUhLjiXEkZqyWTi8/CP4OljTL7MshqA3KTiZoAACIAACIAACKSffJ+cQyJqv6YqSfAJttJcxJjtEX9xDHZBZqSR8RGoiM/7RkYkjq0OPW2MndOK3GqrSMzXEebbJvSMuXQcSlKiVzmZDhW2JDsmnbHbLQZQiyLCqOAD3uguYCdwqENwBOBdS3e4A/VNV7HadPcmGser2iGmJbWWiw24w2ib32ayw7fBjva0Yu2LrXBe9omWwtpFIohPcO0YM4l0nBvdL2ZijXYlVnUeyLwcq9Qi0IiVWHELpcqGRxiORHzWhcpC8ikMKMmz0Xm1aKyzGqe7N3Odx3RVatc+CaK01UYJTKxXazhba9y3HeE5qPbtPlRXFz5kmVBiVKBSZ0Vt058yTSDm65p2ZIW7IGBrtMo0552XMpeukvqJbzrdRkME6okpTpaCVaJGeiWeXFnnxEIcBW78Tr9LyvWHo3MDjM+vU18khxzCFIPEH7Fng/urdxZLi0yNH3LKLbOVaNTo99S4NnW5V6rWJEylsPNKoUl5426W+VWqdytpnLYkaqmR6K2mMTzRuR8csb763NeLV/O3RbtJujCW326c1SKba9h4SUaHGajMv62Nvz/TaW35bDEl2EuWhLe+EUmG4tJuPPLKnOArd+J1+l5XrBwFbvxOv0vK9YYCC1rqmz7028wB+Hr+WG5VWvPgWt6RESt7w9MkmZpI9+M55Hzf9cQ3tpVu1qNTKsipxHZNYnuxtQ+/bUK4YEePHe1j0UkPPoUnfhKInHm9BxtMVKUKyeWpFkKodvKjyYx0dWqlk2l7/S0ol5NuE6nRPS4vbJL5S4hxvcfanxM96cl+sLCDVNqhsVgGK4y8bmw8uSnVdNFoz1vVRM52pRZiqPDZKrNoWhUSChDBkUfNc6oqcX7b3GnwEGpxZLUOftu8sOIcW37ffjPOQ4jD6Z9arVmUwnjeJ9+Uy4+lt5e/G0JRFbaYk5kTpuGpeistDZ9x9qfEz3pyX6wdyFqfEz3pyX6whtbXVLLaw11iXVLAevSJciEVVqjSnUVaZQYFAgt7yfQ+TRsGypze2g623vxSGyU2hcg2ElopJwrQZxFweceq1R7kWICYsmROp9HOzqVJRVGZNTgPOwU6x1zJ5xlMxrfBKQmLHcybbW4klq6t3H2p8SvenJfrDTuPtP4ld9Ny/WEtqaJCUv5II0MLhbMlWDb9brU+pSV1+kQqSlLdLlxShJr6HHaW47GZSpw1LdS4mW3ouETa2WDcUtOnoFsq7ccO68R6BVoJPnHSqgUgnH6ZGoi5C4DEKG46mFHUphhC1MqUllszShKiL4DHaO5C1PiZ7j5f9OS8z/8AqG4iWzbUGVGmxqOtEmG+mTHW7V5L7aFoPSQZoNWSiIyI8j4uIRJ1IaNwUbVkzKeK+15n/wBiJ5ZcXDKDI+XlJI+W5SlYfQt01gDLxW4MLDiNi1RHrxVW0kuitw0y0ZLmkftd7pd1CndP2urSvS9rpD732Rs2cbC89c++U9ZHxGlKloQ3n8pEo8v2jHnlzIyIyMsjIyzIx53QTmxYNotEPFj48UtOYDy2YzBLSQdxEiF9V5U2OsukdEaJjiVos+jbCyK072vfAbGocODmsita9pxa4FpkQQP2f/lTWrBlbhnGZzECLTJ7sukQzw2dlRm50mRcpzIyqSukqIlGpzR16jUwZlvbXaR6vMfkEokKZT7LvZdUiyIFu1BMKPRWZcVcONOrLc+MttyGhRESnGYXCRPLbL2rTxJWft2yPh2L+vyNEiU+NfV8R4EBsmYMBi8qozBgoItEkMMk+SG0kXESUEREXFlkOFqdZrFbfRKrVXq1Zktt6luTWKpIq0hpGeeglx5alEnPj0SPLMdoNp3L5ivbZCvyVlbwO3OeH94WlRnMVaFuR1Y54n3nhZcUTCSq1i56PZ91y3rErV2uy56Z9buGstW4wiCqksLpmoUTbslUwkIvG1dwJ+Suuqn3LdNFxpvOsYc2JgpaF4YhYhvboW2KDblpSLonuxHqyvKNIklMiqKUw3aU2PEly5NLQ0wpZvrUPzjfDn8OWQ3bNQnxos6DGnzo8GqarhODHmusQqnvdetY3yylRIe1S/bt6wlaCvbJyPjFMSA94cWRC0lxOHWTgOoAgcNwOEzLKGaCA7nACWO/CWPbI78zkF7c0rcw/koa9aWIZSd0JW7Lqdk2HaDk2+ZmO9Jrp7/rFiQrmqNaolp8FpermjcMty03qBDfN+mnCdlvvqz0Ud9nbjf8lVCerRVDGWpIlxt0nAsej0Cx92Nh/ezTtjJmUduJXJNYmFT2NZcdPk1aoLUy2vgSRFbhvavRU4v8/wBmfZyfsGmRcxchlllxcfL2iwsJeHBxl/EeUhwkThgJIZphhjsSBvPHCWMh2njPjvn+h+kblj8mDaFPrEVW6FwuyvAryw2r0+dug7cxFdOGziJacSk1W1yepZuU1DdDcqSk1OTvKqrdjTFIbdiKN9W0uz8nxuH7j3Mu6i3RO52jYvYqtYc4cFVLNpdDx1o9y0ew6zBpkubWXKrUEwYxVFqFHaj1abE0W3mY0yI2w24b2mX57M/h5D5y4j/YOdhXTdFNo1Wtum3Pc1NtyvqQuvW7Trim0+3q8beWrOdAbdTHkmjRTom+hZp0SyyyIUus0Sk0xTVIAdhPbxJzVjIjRED3N5s5y/Cf2rgufjzL4D5wD/owG2epUBAABClAAARAAARAAARAAARBYWFVqUC98Q7Rta6LphWfRK1X4dPm1idFmS0KS9LYZOM3vZl1aHXicUht5aNU2syU4pKSMxXoCWkBwJEwoImF2y+6FRrYu+4aBb9ywbupFKqj0OFX6bCmU+LLShxSdDVymmntJrLVrWaCStTalIM0Gkz7vYf6EV++uf8AlFOC47D/AEIr99c/8oyhiRl1LGMQWEgKopOe+ZOjkSt8OaJnyEekrLMftx/J8y8N6luJ8BPY5KivWvAw3iw7sYittORINbQ0fdImroPNKXzl76W+cnLSQtJmZoNJj8R0n/WZP7w5/jMcxR7qum3WpTFvXRc9vsTVEqaxQLjnUNiaZJ0SN5DDqEuGSfa5rI+Li5BUROYVh3q+cY6XQHd1ZitKwAppQbIZxjq02wKhSKcqDQ6fS0VBa98Je0CQ3T0pJ9RqcMmt7cR+0PI8stxVhxuKsWKTuh7b3Qt02ZZKqji9aR4PVKq33EwwuyZSkxMRZsyj0qtykOs0uHU5US0KdNqjzDqILc2M4ok5EY82Khd93VeK5Aqt2XVVILpkbsGp3LPqMJ3RPNOmy46pCsjIjLMuLIdd5/28v7RjEh7RlE5bsR1EH8EaS0zX6QcZdzt+SCuy87ixDbxlotPRWcW6rEqVn4O422laGHVp0uiWO6/AoMFs2zfWmpy6fEcauKFEkx3p059haGjWSBsqThN+SitG36uzblfwxrtLgV+9qDXLmvPH+gXLdlx06bKwnq9t0VqLJgaftYdRu+IzcFJbYfYVb9UQp5LktTbX5yz4+P4eTP4fkzD4c/hyyz+HnFTLM5rWM2hkAAeuUpz4yMsZZmc1MxUCRhMnwPnu3ZL9GO6iww/JxY8Vu5L5w/v3AC2K9HuNizqJYNm4z2dud8OsPLak4p39S5N4OKgUJS7hdi0enWqtqlobXO3lXmZRuSG0NkOau7AD8kxhnBuWhYcYmYa1WTTpl6MV/FG5seLaxWq1sRavgrJkUek06iPw1x6whm5WZceJLhFvyLPQ3lIQ6/H0PzaHx8vH8JZ8eX/X3hnycnFxFxchcxf3CIVlMKE2EIjiBOc+Mxx6p44Yz3mWCmLEMV1cpGc8PWGWEvhxXv8AXduQPyRVAkXzTqfujpEyY7ar8Owzg7qSgXFAokpulYny6NckuY3SUt1DhdFr2K+9QS1LlLkXkiG7INam0j8/jC1uMMOOI1brjCHHWzLI21KSRqTl+wzMv7B9sz7eM/7eUaC5rHNiF5cSCAJZSnj2iU+GGElBdNob17+OICAACxYoAACIAACIAACIAACIAACJ/wBcXKYvDFPDexbJtXC+s2tidRL2qV32uurVqmU2j1iA5HWmp1WJvlo5cRlCGSTDZjmypRvG6hxwk6paFCjwEzwkoljP1wXdrC/TTv7iv/EgYZ/lEMjdwez5NRXz+U9ZSMhmZYX6ad/cV/4kDksTMFcPMYGqIq/aZKmFby5KKTJi3IdtmzvsmDkNmrPRcz3uyrIyM06PKRKPPCLCdGgFjd6hkRsK0h7t0vwWcmEuOX5Aykfks2dyBeW6MvONife1sU/EK+7zd3Dx1jGa1LzRTJTjlLpVZKIqkSEQ5FWrlPiViWl2pMQai9HZqbLTja4nmfbH5RPc0u2NhNYuKFn4rYhQrRw8sy3G2b8sG1cWbQwRnWjaNKtip9xVuVWorhTGLobaq6Zq5ZQShqegS2o78yKl4bQ9xXudz/8AUtW4uT/8T0cX9407yvc7/EtW/wCJyBz3aOjvimK4jGWE8BLdw9SW623QGhrRPCfDOU+9fKu7vncERqLQ6jhruFrVs7EWFCnPTKhXLDtO/wC1WZsq3qmUXVU+el1h5qDXXaW6xvhg84UNaXEOLVoHzdX3d+4Ful1Ldc3Pt3tUhqbS6fToK8L7JuiuUGnUmpUNy3yp1WqEh9cOPQYNMrcVNGYbOFWG7rcRL1e8mHD4rvK9zv8AE1X5/wBZ6Bp3le53+JatzfrOQM7jHOJl6l1dU/iht0Auqxn8PXduVd7sPdp7lPFvAy9MLNzjgzUsGmr3xlt7Eyo2zCwwtez7fTIoxX5v+quVeHNfqLpzk3ZTGYtCcNcCkNUZ1MZ1W+FGfetxP/8Al+on/wDbq3/mRBuu8r3O/wAS1f8A4nIF92LYVqYbW1EtGzm4tPoUKS/NaYl3PHqcpb0lRLecceW7mo1aKCIiIiIkERFymdtjsT7PFMQyl/LqVNstrbRAEITMpAYcJz+09vFdtAS0UdapvpiJ+KGijrVN9MRPxR0lypFRAS0UdapvpiJ+KGijrVN9MRPxQSRUQEskdapv9lXifigpKkGRKy40ktJpWlxCyMsyUlRGZGRlyGRmRgixtQcLQTrUzzc/2jZW0Tf9mZZiedO8Gp/SMbBkjh1gZQLutGlXHVKzWo8mpm84UeAUduOyht9xlJe3QpRn7mZmeZF7bLLi4+8luZ7PVmSa/dCjSWSiJUMzTnxln7iP1Jq5+Rjy6606AsWsuirFANltUKHGhl1phtcYcVoewlszIlrgSDiNxxBXwDTn5VnI7q7pq16A0na4wtFmiPhRA2BEcA+G4scAQJEBwInxWGudO8Gp/SMbAzp3g1P6RjYMyu9ps74/uflz5Yf4IHuZ7PLlr90cZeFD5/6kdr8xL8oX3Gz/AL1DXKH5YvIf75H/AHeJ5LDXOneDU/pGNgZ07wan9IxsGZXe02dn+n7m5+NUMi/yQLc0Wf8AH90HxZcsP8EPzEvyhvcbP+9Q1H54vIf75H/d4nksNc6d4NT+kY2BnTvBqf0jGwZlnuaLP+P7n5c+JUP8Ead7RZ3jBc/Jl76H+CH5iX5QvuNn/eoan88XkQ98j/u8TyWGudO8Gp/SMbAzp3g1P6RjYMyj3NFnfH9z8ufEqH+CHe0Wd8f3PyZe+h/gh+Yl+UMd1hs/71DUfnjch/vkf93ieSw1zp3g1P6RjYGdO8Gp/SMbBmV3tFnfH9z8ufLD/BAtzRZxf+v7nP5VQ/wQ/MR/KG9xs/71DT88bkPP/rI/7vE8lhko42m3vcpJe29tvhSFHylllokX7eUXVbsmRES29GeWy5oZGpB8RllyGXIZfsMbLFvDamYdzaCilVKfPYq7Ly1pqLbRPMLYcaI9FSCSRpUTpcWWZGk+M8+L70b8w3837h+ZtftQdM6g6zW7UTXKAwWyBS2KwObEZz2NiNxEw4Fj2ntkcQv0NqDyg2XWPRNg5QNRLXEhscXPgRm1wYrXQ3uYXAgtexzXtcAQQeIOIXfm5NPmJLfbG83j5ZUJvNpZ5cq2c+L5UH/YNeDSc440+nvlyFnI3ss//hWRZGO2WlhLiZe9NXVrSsW5K9TEPGwdQgQM4Slp98lLi1JSo08itEz0T4jyPiHc8QdzNuicKLRpGIGJeCGKNjWJcEhESiXpctmTKfatTddIzabaqBoNnScIjNBGotPI9HSHy2FZIEC0OsegtJ7Nzd8FzmRmt6g1ztowcA0RGtG4AL7A3lb1a1mtDoGvmhLLb7c36ceBFNitjuuNsA6zRHzxMWLZHxnnGJEccFUPA0/4Ex/PGjLt0hqVGmn/ALUMv/n2fWHFNp1zi2WU651DZvuMsp1rraE5GpakJzMkkRkZqMsiIyzMS1aj/wC7Pn4kZfKN42TWYn/z0H92f/3KvGn+RZpmNWNIH46YgfhocLlOBZvhQ/P2fWDgWb4UPz9n1hw69Bs8nDQ2fM4ZIP4Of5S7SE9WosyNsyMuIy0OT/rMu0RctZ/f4P7s7/uVl/WHkV/5Xt/+sQv/AMhcrwLN8KH5+z6wcCzPCh+fs+sOGUbaVpaWppDq/eNLWlLq/mpPjP8AsEySRqNBJI1pIlKQRZrSR55GZcuR5H2GFy1n9/g/uzv+5T+sPIr/AMr2/wD1iF/+QuU4Gnn71EdZnyE3MaWo/wCwlD4rplRbP28GURc5MmtPaWY49KkLzNCm1lnxmgyURdnyj7okPt8Tb77fzHlIL+4w2GtEPEWmA/qMGIzxEZ/3Vj/SnIjaxTE0NpSzHpM0hZbQPkfo2AT/AJol1r6lBmqPRTDlmrmKOvP7Bvkxo9Oydnkl6URaTVOIyUSTz4lPqLkL/wB0uMxsDmzTLI5koy5t8Ly+0bb4c+Uz5TPjMxg+wac0iNhpKOyHA/WEEOD3jomI4gsaeNDQ8jAPbjPZsutHJlqk86U1O0baLVpMfVRNIGA6zwHcIossJrmx4rf+Ht4pgNPOfAi4AcTdbMys0uoIR7rLfNLiUmeiSzStJ5FzcRZEXwCne5av/Fr3lJ2i+AHdhWeDAhMgQGhrGgAACQAG4AZDcF8ut+ldIaUt8fSmkozo1pjPdEiRHkue97zU5znHFziSSSSSScSVQ/ctX/i17yk7Q7lq/wDFr3lJ2i+AGdAWrt35BUP3LV/4te8pO0O5av8Axa95SdovgAoCbd+QVD9y1f8Ai17yk7Q7lq/8WveUnaL4AKAm3fkFQ/ctX/i17yk7Q7lq/wDFr3lJ2i+ACgJt35BUP3LV/wCLXvKTtDuWr/xa95SdovgAoCbd+QVD9y1f+LXvKTtDuWr/AMWveUnaL4AKAm3fkFQ/ctX/AIte8pO0O5av/Fr3lJ2i+ACgJt35BUP3LV/4te8pO0O5av8Axa95SdovgAoCbd+QVD9y1f8Ai17yk7Q7lq/8WveUnaL4AKAm3fkFQ/ctX/i17yk7Q7lq/wDFr3lJ2i+ACgJt35BUP3LV/wCLXvKTtDuWr/xa95SdovgAoCbd+QVD9y1f+LXvKTtFo2lTpVNpOomN6p5clburzzNBHllmfPxDs4CQ0DcodFc4SKo6ValeKTI0YK3Um+tSXG1pNCyNRmRlx/CRj4dy1f8Ai17yk7RfACNm1Tt3qh+5av8Axa95Sdody1f+LXvKTtF8AFATbvyCofuWr/xa95Sdody1f+LXvKTtF8AFATbvyCofuWr/AMWveUnaHctX/i17yk7RfABQE278gqH7lq/8WveUnaHctX/i17yk7RfABQE278gqH7lq/wDFr3lJ2h3LV/4te8pO0XwAUBNu/IKh+5av/Fr3lJ2h3LV/4te8pO0XwAUBNu/IKh+5av8Axa95Sdody1f+LXvKTtF8AFATbvyCofuWr/xa95Sdody1f+LXvKTtF8AFATbvyCofuWr/AMWveUnaHctX/i17yk7RfABQE278gqH7lq/8WveUnaHctX/i17yk7RfABQE278gqH7lq/wDFr3lJ2h3LV/4te8pO0XwAUBNu/IKtLOoNUp9RdlTYxx2d6qaTpqI1rUpSTLIiP4Mj5R98SiI4NvZpJWUqdokosyz0IIsQdVu+g1GvxaS3TExHHIUmSuQ3JnMwVJS8iMSFJNxSSUWbKyPIzMuLi48xJGEgjHl0UPcrup+4tkXthNZeJ2Fl9wqzwxZTl33lCxLpVNwlt613UVav0VmlQay7U5Lc2VIkWtXn2jkNwWN6wkrW8264iMf1re4jl4f3VZ1v4p4rWPSo1etjEe87iPD6kzr6uC1qbhxSZdXmSm4cxFMYmtVJuLownmpKGXNMl6ZtmhxdL0Kt482xS3KHb98VelUVyjHbx0eNeUNdKRC4QkVYo6I63FIQSZsqVLSpskrQ9KfWlSTcXpdmlYnbqabW6FckrFu7Xa/a8mdKtmrnfsRM63lVSAzS6iiColkTDMiHHZiuR2yJg2myTqiIzz1yyNMy4z7McPBbrXQABVjgOO/AA95me34SuCqfk58U6ZGveuvXvhdFs21I1Yfpdw1lyr0ibdCoFOjValx00veDj7DtXjyFJj5KfQ1IiSGHlocQklYp40YL3dgNf03Di+26GuvQ6PTLhblW9UDqtHqEKrwmp8F9l1bTTpaTbuitp5pt1txtxC0JNItqNjHuuoaYSY2OGIbR06fPqsJasT233mZVUVNXPkG4t41rceVUags1OGo0KmvKRoGszFZ3szi5iTcMm7b/ALhfvG5pcZiFIrlfuqFNnrYitk1GYJWsIktMoIkNtoIkoSWREQmmJMS3Sx8+GPBYVwyJTx/j5S8eoKm9W30aPJINW30aPJId49j26Or0z09C/FD2Pbo6vTPT0L8UZ0OUVNzC6Pq2+jR5JBq2+jR5JDvHse3R1emenoX4oex7dHV6Z6ehfihQ5Km5hdH1bfRo8khz1rW8zdFzW/ba61bFrJr9ZjUbukvGeuj2lQjkupZTKqctDTq2IrZrJTrxNr1aCNRpyIzLmvY9ujq9M9PQvxQ9j66Ogpnp6F+KFDkDmZhc1jRhNNwQxLufC2sXdhlfdZtCU3Bq9w4R3am/rDXJU2lx2NEq6WGm5K2NIm3VMpU2lwlIJajSrLudvkRW1bZERERUZJERFkX+sSRWJYeXMhJJTGpSEpIkpSmuwUpSRcRERE5kRELYpkR2BSKNAkG0cmDTEx5JMvJkNJXrHXDSS05pVkTiSzSZlnnxmJY1wPOVMctIFJWQOCf6sLU/qJPw5f8A62SPbPcvWRh/emEdmTbis+yrvVS7cu630yK1SKTdSrbrkivxZcB2fTHZkeUpJxFpcQZKQlxpK20PIPPR/PbhpjfZlq2ZR7erTVwIqFM1zbpwaSU+M6lb7jyVJWSyy4nMjSZZlon8GQ7wvdFYZLy1jFzOaPIbts6Zp7XB/abQvKVyP6yciOq2q1o11sujrbZrLZKztRtGPh2XYvhubtITmkFxnN29siCCv5P6c5OuVLQ/K1rBrBZ9VLVbLLGtVpLJQyWPa+0bRjw4seCCAOGIJxCzqYw4pFsY9RMO8RqzT6NbFJvFtFz12aaIcI6O0e/nXktR5D+gqRERotxm3nHSckNtZm5xDJSXg7uTa7dLHAOJrUamV25yuxiE1f8AT6BSYFu1CRVHmqDHRNiOSY1ShR2YRKVPdVmtZNuIbNevT5BlujMNk8SW7pSRcRJTbWiRf2E5kNT3R+HCjMzK6sj41F3OqyP/AO6Pc6c5VuSnTL4Fpg8plns0ZkEQnbGNBbDe/e6KYZcZOJlIVc0CQM8V4zR3Jhyn2AR4D9RI8aG+JW0xIUUvY3hDDwGgimYJpxJmdwXsu7gBua58mk1l2/dZZVFqkDDyo1ajXnSLRgXnI3rMqK6vGqklh+ImWcKkVJL8RXv5k2kESmGpGmK+ouHWAFCxKwzp8WpRr7odw2VLm3K7dl60BFs0moPw4qokoiRUIBO8HSJE1l+kyZUVyTwSpxiSrWpbLyyXumrDcjMwlyb1XCjSFyY0JyiuuQ47jpIJ11DJvaCVrJpolLSRGom0EZmSU5bXvjcODz9rdJkfGojts8lfL7qOJZOULkwYyNBtfKnBfCcyIwN28MSqY9oeS17XVNc/aCkt5wAmQGU9SPyfcpERzIsDk/jMiBzXlwgv3h7XUgFhFJa2iRngat5dV7A1Xc77nCiQVv17FObCrCbOauKBTaDedDqCLyZnU+kTUVmlRZLbT0KHFOoyzZplSd39UY8QnG3UZLNXFVXCfcu063Ku13RT3KpR6/UWafX4+ONuyU3hDVAo6KBJKG1T3Dhx5siRVJy29B2TEagOMPGa1NqHku7uk8PX1aT6rueVoIaJT1AW8sktpJDaCNTp5JSlKUpSXElKSIiIiIhDvjsOOPIrq48zz7mzL/8A2iLLyh8mrGw79yrQojhImUeGwTwnKh7TIieDy8AmbQ1oYxsR+T7lFeTduTyKwZ7B7icpzYRgZfRpJAEyXF7nZu49WVYVgYiSbdw3ulN3W2ig02eqoJrUK4jhTn4xHUIRzoqUMvEy+leiokIVoOIJaEqJRCmRQ57o3DjP3t1Zchf9nD4v2fnRp3xuHHg3Tz/+jh/ij6roTl/5ENGaIs2j7ZrtYrRFhsa10V8eGHRHAAF7gDIFxxK8LpPkT5XbfpGNbLLqna4MN7iRDbBiFrAf1QZbh6AV8gKH743Djwbq5v8A0cP8Uad8bhx4N1fVw/xR1PzjeQf/AJtsH7wzzWj7COWQ/wDtq2f5D/JdG3TR/wBMsn9rM4v/ALkQV5Rs97oIjyM0ZEfMeXEY+mMmItBxAmW6dAZqaGKSw+mQ9U4ZQFOLfcZMkob0lH7UmszUfEemWXJmIUb8w3837h/Fj8qXWTQWt3L1rBrDqzamWmwxXQKIsM1MfRZYDHUuGBAe1zcOIK/q3+TvoDTOrHIxoTQmsFmfZ7XDbHrhvEntqtMV7agcQS1wPwK999yxiNhe/ZeEUs5VEeolpoo8a7bWenxos1koT7C6lFcYcUk83yRIUlXvXCkEeZ5mP0SflQN2juNJf5PPHOjPYq4ZX1LxYwxl2fhlYNBuCDW7mqlZmtpKkuJpTazejlTXyYmOPPIbKPvLlJeglX4ATk09Jmh6RBStB5Gl51slp/YZGeZDVMuloNSkSqahSyyUpt9pClFzGZHx/wD/AAfkHVzUSHq7pG22uz2kvh2h9Za5oqaZkyrDsRzjvHXnP2uqeqn9U7bpC0WWPtIdqcHFrmc9pm4/TBNQ553jIznOftHud93Zuc8K9zPhlhniBblevq77Nol8W5Ls9GFkVOF1XiXYxVWpLdzpVU8646+cyK2icymC81GQ4xIbkJZirHG3nuivyYFMtCE3hvuX3riu2PTLjhGm98MY9Njrbq93W7MgNPSW6uonJlOoca5YsKpKStbDk6Jpk6aVavxx4Qgdfhedt7Q4Qgdehedt7R7rZMMQxeJn4iXhOYyOK9k17mtoaMPh8PIL2OibtTch4abobHi4sHsC00vc8X7hZRKdY9gxrPRTanSbspc0oyq3HbkSnODJaqHV7ngKlMOONqkvsvk0asnEdte3Qv5JHh6dUD3OFTVCp1wwFRYNIwRZo9Eu+C65bhzVNQF19w6QmFFpdSjJjE6+cyRU5Eglsb4WSPEThCB16F523tDhCB16F523tFZszCACT3nr8/syCz28SbpgSPCW6YAwynvPXivXHBPdk7mm3sGMZsN8UbDnQqZifjRcF0qwpw2wdokjDdVErUC1qXRnmpUyacmA9bfBFSqMaIxrFSX5BNqkITIfWMg391j+SVqN1UiHN3Oqo2EFr3dXq8dixtzbTn5t5nWbdtSApwqgdaTIp7Dcul3CtuGhRkhyfBdbOOlt1A8C+EIHXoXnbe0OEIHXoXnbe0YmyQyQZkECW/qA/Dfn8AsrzEnOkb57sMTPDLf+Gayi3UWNzWN90YazYVTqNZpeHmBlr4YQ6tXaEVHuSc/T4zsmpqqDpvvLlqROnTGmZKlpTvZmMhtlhttLZY0DacIQOvQvO29ocIQOvQvO29o2GMaxtLd2PiZnxVBJdKfUO4ABbsBtOEIHXoXnbe0OEIHXoXnbe0ZKFuwG04Qgdehedt7Q4Qgdehedt7QRbsBtOEIHXoXnbe0OEIHXoXnbe0EW7AbThCB16F523tDhCB16F523tBFuwG04Qgdehedt7Q4Qgdehedt7QRbsBtOEIHXoXnbe0OEIHXoXnbe0EW7AbThCB16F523tDhCB16F523tBFuwG04Qgdehedt7Q4Qgdehedt7QRbsBtOEIHXoXnbe0OEIHXoXnbe0EW7AbThCB16F523tDhCB16F523tBFuwG04Qgdehedt7Q4Qgdehedt7QRbsBtOEIHXoXnbe0OEIHXoXnbe0EW7AbThCB16F523tDhCB16F523tBFuwG04Qgdehedt7Q4Qgdehedt7QRbsBtOEIHXoXnbe0OEIHXoXnbe0EW7AbThCB16F523tDhCB16F523tBFuwG04Qgdehedt7Q4Qgdehedt7QRbsBtOEIHXoXnbe0OEIHXoXnbe0EW7AbThCB16F523tDhCB16F523tBFuwG04Qgdehedt7Q4Qgdehedt7QRbsBtOEIHXoXnbe0OEIHXoXnbe0EW7AbThCB16F523tDhCB16F523tBFuwG04Qgdehedt7Q4Qgdehedt7QRbsBtOEIHXoXnbe0OEIHXoXnbe0EW7AbThCB16F523tDhCB16F523tBFuwG04Qgdehedt7Q4Qgdehedt7QRbsBtOEIHXoXnbe0OEIHXoXnbe0EW7AbThCB16F523tDhCB16F523tBFuwG04Qgdehedt7Q4Qgdehedt7QRbsBtOEIHXoXnbe0OEIHXoXnbe0EW7AbThCB16F523tDhCB16F523tBFuwG04Qgdehedt7Q4Qgdehedt7QRbsBtN/wOvQv7JbZ/eNylaHEkttaHEKLNK0KJST+QyBFjSM6dzbuIKxujcO6/fdOv+TbD1OmvUuk0lWC95XdTai/HW3rTerUKGqClBIcI1IjOSH4+ZLksx2CW+jBYZc4F7riq4DWPU7VoeEmFt01x+4mbko19XUuvouChutuJWbejDqMcloRo+473VFNJuOm+cxKktt1NppNXZ66lvkuBFPaqexDwhuCwcXang2Tz9cuaBcUO146nbYq9jPTpk0oxMtnTqvHjTY5aclCNKS0hKiLWJNTSkrPka3ud8aaGqkIdw9uOqrrd01Kx6edtUx+5WHqxSqjOpkqmk9HQtpT6nKdLdaQhSidYbJ1Jmg8xtJuNFyPYxLxuplGtW3rsTWFXBAp9KhzptuUmccVUZEphqdLlSFONrVvpBvvukUhKVZGgibFpyN2NidUIsmPWaHYFZerNDVa951GXRZ0SoX7S3mZaJkKpHHmNto3y7OkSnZMFEaQbyiNLqEFqzwaTS2e/j+H4+GWOTnGolu7h6+z8VSlMwgxUrKK87TsO7xfbti35V03AtVAkRU0qnwicOVKd1iU5JbJp4zSWa8mHTJJ6teVccvGRkZGWZGXGR8xjKe6N1ziTedJrVIuOh2HNRctKqFGuOc1R50KoVlia1OajpWpE0kN7xTPc3vqUINRtIN83/b6eLOZnxmeZ/CfwmEzVhukO/GfhLxUNLpY7/wCA/GfYnH/0Q04+cuwagJnJTMpx/wDRDTj5y7BqATkkynH/ANENOPnLsGoBOSTKkj3yf2qL7RddE/MtFycRcnyClEe/T84vtF10T8y18hDNhnNURfNU1N9tNmGr2yt9u5qVxmfuiuMxyE22rhplApd2VO3K/TbUrkl+FRLqqNClwLXrT0YzKSzDqTjaYz7jJkZONsuKUjI9IiyMbCWeU2YeZllNdPMuUvdFD1QwH3TOAcLDCzbaxLue6It+4V4RVmlWdeNVwkodzwbGQyh5cGi0uizKq5SblnHJnuuUqdUIVOmQGF1Bp2XJQ4yhFT3ENL5TII7sScB8P5mQNpNJaJYEgfCZaO7Eny3jyiW4w3rNN1hGqJJumt1KCaJXvTVmftdL4M8s/gBxxhlRoecZZWWRmh5xLKyzJJlmSsj4yUk//iLnIetVy7srcrVugtUeycE4mCUo7cueDblx0TBm3cTLhwkqtWqjkoqnBRPnog1NiowVKhPRpEdlynre04zqkMNoX2SFu+sBqhirbt63pZ10VugFOauq64SsGLGlVGpyIVpWBbdMt2QaloclUuKVCvM06mRCJarjiuLQpKHmhiHOIJpOHj4duMlkKeJ7uzz4T3FeOmiXwkXYGRcxdg5evyaZMr1emUSEdNosyuzZlFpqiSSqbCelvOQ4xkRmWbTK2m+IzL3PiMy4xxIyaS5ocRIkbskcAHEBaZFzF2BkXMXYNQEqFpkXMXYGRcxdg1AEWmRcxdgZFzF2DUARaZFzF2BkXMXYNQBFpkXMXYGRcxdg1AEWmRcxdgZFzF2DUARaZFzF2BkXMXYNQBFpkXMXYGRcxdg1AEWmRcxdgZFzF2DUARaZFzF2BkXMXYNQBFpkXMXYGRcxdg1AEWmRcxdgZFzF2DUARaZFzF2BkXMXYNQBFpkXMXYGRcxdg1AEWmRcxdgZFzF2DUARaZFzF2BkXMXYNQBFpkXMXYGRcxdg1AEWmRcxdgZFzF2DUARaZFzF2BkXMXYNQBFpkXMXYGRcxdg1AEWmRcxdgZFzF2DUARaZFzF2BkXMXYNQBFpkXMXYGRcxdg1AEWmRcxdgZFzF2DUARaZFzF2BkXMXYNQBFpkXMXYGRcxdg1AEWmRcxdgZFzF2DUARaZFzF2BkXMXYNQBFpkXMXYGRcxdg1AEWmRcxdgZFzF2DUARaZFzF2BkXMXYNQBFpkXMXYGRcxdg1HK0ORRotYpsm4aXNrdCYlE5VaRTawVvz6izkek01NNl7UqM8j09UvLLLRPPMg3yRcTkXMXYGiXMXYO2XhULLqNSYesa1q3aVKRDS1Jp9evAr1lvvktZqeTJKHF0EGk0J1WgrI0memelkXccMbWw+rFKxCua+sQKbbEqwbeYuG0rGk05yZUsXZ5vqQijxXSdbSyhKktLkrNaXN7rd1J60kEciWJO5QchvO5VCRJPkJJ8eXEHtCMkmaCUfvUmZEpXPkQs3F/Fa6cbcQK1iReTdDYr1bajRno9t0huhUaMxDYRFiNMx0mZFq2WmmzWo1Lc0NJalLNSjv7c44wYP0SzbiwOxesSC7beKV4QTrmKzW8+GLMp+TDbjnt4TspJxFMqcjvRXUmyqozHFNPGlDajRUZHsRzg0AkLDXIuYuwXRYpmdBQRmZkmW6lJH8BZkeRdpjisaqZhnRsULupuD1alXBhxGmMlbdUlyHJxukqIwuS21KcaZckstyFSG25LrEdbqG0qVHZM9AuUsT9Ap/fHftIZQiHYjJYRmiWKpkRUtCCI1rQglK0E6aiTpKPkSWfKZ8xcYkP0Pfkzq/uB9z9gDbmM+K26BwwtrGG9rzq1GvuBXbXdreJNo0qCcJ+PQaJG3vKNrXphNyjqa2EsvHVVtNuk4wSSNaC0vcZAepKXOeCGsbMn0T2L89k6BPpc2VTanBnUypQnjjTabUobtPqMJwsjNt5hxKXG1lmRmlaSMsy4htTIyzzIyy5cyyyGb2J+6XpW6d3adKx0xmgUKPZM+8KfT603Ot9h5U23KST0eC/XWUpJM+e5GSwcp1xOTzmSDJLaUkU6ke5BrdjTcSJERqmXs+3KlQsKaXcsm2H6m7GpUiI2ifChsrjQFyZ8KNUiappojaNZOOh1RNGbODZmEIh35ceHnu6is3CTi0evXBYO/Dl8PN8IFx8nH8g9Cqjbm4bqd4VZtqpsUGmt3LPqlIYoeJFUk21W4Trt0JpVIXJkx0bwaJiHbLzspTxONO1ZxlSkoSZtdTsSobkeh3/X41YpLFTsCmYa0muUqXdNRk3JU7quNuRHOoU3SaZQ3GzaqcpCkMp1C+AiWTpmtJHLQCZTz8P5YZ4ZocJy9Yy9dWKwh+Xi+XiDI+YejEqsbjult3C9ApVkXUdvy6vRaKpypVC0pdfpTFLvulQVxWW4mTr9TkNWtOVKeJUmKdTZdQbRNGhqu6LTNzKz3ZR3q1bM+36De96UijFVqm9Tb2uqj1Vq3I9oSabNciqQe81MVxS5j6E70Nw3nGlE4lJ4tNRl8fCX2zwQ83A7+/Py8RmFhWA77ijT7PpOIt507D+pw6xZMWuuIteo0+VInRHoakNuNoQ++Zuum0a1sqcNS0rUypSFuINC1dCAGYmpIkZIAAJUKSPfp+cX2i66J+Za+QhSiPfp+cX2i66J+Za+QhZD4qmLw+BVLzVmUyZkhZ5zHuQi6RX7RttYouRtzj4j5OP8AvFjWVhzdOJtwXXSLSjQpU22bLuXE6ronVFumttUi2IEir1d1tS+JbqIzDim2S9s4oiSXGY5idgVixFashyFY9xXOrEHDSHi5b7NkUaZe0lFCnyKtGjyprUNlxUVWnQ6kam3iI0ojmozyzMqiQDSTjv7DOXfS7uOSvGO71u8x3qodYfRudhbQ0z6JzsLaLlreBWI1JiYaSadQp96O4o4PIxyotNsOjVG7KpSKCqq1mkOPVJhiOpTJsu0SUt1ZaTKG3WTN0jUaU9CXZl4t0WhXI7Z93NW3dNQVSLYuN21qg3b1zS0qJKotNnmzqJbxGZEbUZbi8zy0cxIlOQ9GZH2ghOE8wD3gO+wg/BdX1h9G52FtDWH0bnYW0Xdf+59xTw3qWFVv3DatbO8MX7Fj35bFhw7cqvd7Dak1is0RmnTaM5FRLRPU/RJSyjNtrM2nWVEZmo0pr6TY18Q4Neqkyyb0h0y1ayi3Lpqcy0KlEptr1FxWg3TqnIWwTcSUpXtUxpKm3TMyIkGZlnE8ZD1jL7cEpOGG+XiJjwK6lrD6NzsLaGsPo3Owto75OwwxNpkCXVanhriPTKVApKa/PqtSw/rNOpkGAqQcNM5+S5GS23GVISpgpC1E2bqTRpaRGkcJWbXue3GaNIuK2bkt6PcdKRXbcfuC35tDYuKC4rRbnU9b7SCkx1HxJfYNbZnyKMJicpoQRvXXtYfRudhbQ1h9G52FtGQ1ybl3HC2KDhZV5mHV6y6pi3RrjuGgWNTrFr0nEGkwbZqbdLnTKnRzhFIYYdU62+w+SVtrYWSzUjMiPr0nc+44x6bhtV0YR4j1GDi/asm9sNXKHZVTuF27qXDmS4EqXFajMOLNLTsF9SiMtImTZeMtU804uKmnjxl24+R7jkUkfXZ5jvCprWH0bnYW0NYfRudhbRY1BwoxIuiw7+xPt6zLgrFg4WzaXTsQLngUx2TTbVdrK5aKeUtSUnopWcGQa1n7VkiQp02ycbNXX63aF3WzWWbcuW0rrtu4pDcd6Pb1xWzPoNfkImZb0W3BkMofWl/SLVGlBk5mWhpDKYnL1un9mPwUdfrL7V1nWH0bnYW0NYfRudhbRYdSwnxXoz1aj1jCrFKkyLcoarmuKNVMNq7TpNApqHFsrqU5tyIlUeITja2zlPElnTQpOnpJMi5q8cCMZrBvmjYaXXhhfNNxAuOiUy4rds9Frzp9w3DDrFPYqdPdgRGmlOyTcjyEGpDCVqacQ604SHGnUIiYnKamRyVRaw+jc7C2hrD6NzsLaLqlbnbHmLTLCq54NYpSYmJyaqdjIp1gVeq1C4FUSdIp1VbaiMx1PJdjPxJJLaWgl6DKnNHV+3G1w5wDxsxbkzYuG2FOIV4qp9LqlXmSKJZ1SmU5hqjwZ1QnoOUlk2dehmmziTGJWuddjqabbW7k2JmJkZI4FuLsPX8VT+sPo3Owtoaw+jc7C2juPcBfuptiQdiXymPe9QdpNlSF2ZVEsXlLYeKO/FpCzYynPtOmTS2YmscQv2qkkriHZ8X8HbzwRuC3LWvyK3Ar9xYa27igmlHHlQqjRIly09NRhwajGkstPMTmG1aEhhSfc3CNJKVyiC5olM7zIdZkTLuBPYgaTPDd5y+0hVPrD6NzsLaGsPo3Owto+gCVC+esPo3Owtoaw+jc7C2j6ACL56w+jc7C2hrD6NzsLaPoAIvnrD6NzsLaGsPo3Owto+gAi+esPo3Owtoaw+jc7C2j6ACL56w+jc7C2hrD6NzsLaPoAIvnrD6NzsLaGsPo3Owto+gAi+esPo3Owtoaw+jc7C2j6ACL56w+jc7C2hrD6NzsLaPoAIvnrD6NzsLaGsPo3Owto+gAi+esPo3Owtoaw+jc7C2j6ACL56w+jc7C2hrD6NzsLaPoAIvnrD6NzsLaGsPo3Owto+gAi+esPo3Owtoaw+jc7C2j6ACL56w+jc7C2hrD6NzsLaPoAIvnrD6NzsLaGsPo3Owto+gAi+esPo3Owtoaw+jc7C2j6ACL56w+jc7C2hrD6NzsLaPoAIvnrD6NzsLaGsPo3Owto+gAi+esPo3Owtoaw+jc7C2j6ACL56w+jc7C2hrD6NzsLaPoAIvnrD6NzsLaGsPo3Owto+gAi+esPo3Owtoaw+jc7C2j6ACL56w+jc7C2hrD6NzsLaPoAIvnrD6NzsLaGsPo3Owto+gAi+esPo3Owtoaw+jc7C2j6ACL56w+jc7C2hrFcfubnHy8nH/ePoAIvnpn0TnYW0NYfRuf3bR9ABF89YfRuf3bRdNiHnQU8Rl/THeI+XlIU0LmsT9Ap/fHftIWQ95+Cri/R7VTIEeR5lxGZZGZcRmXMACtWIBGZFkXJzfAAAi10lZ56R58+fGNMz5zAARa5nzn2hmfGWZ5Hy8fKNABEMzPjM8z5zAABEAABFJHv0/OL7RddE/MtfIQpRHv0/OL7RdVFPJls+ZOf9wsh8VTF4fArsW5txPs7CrFqu1TEOPcjth3thtfGDV3zbOiRqjdlvQbyoFRoKqtToch1piU9BclMSd6OvMpfQ042TralJWXpjhJuoMBrdw/u96lYvY94eYfYBJ3NllWpX7MpNPoONmNh2FceI90VRaqE3XGWoMKbNqrjP8Arc5MBo6e5JbfNa0F+Y3FrdL1bD/ES6LRj2pSakxSZySbmyajIYff1zTcg9JCS0SyN008XwJIVye7IrZmSu4SgmoiMkqOqyjUnPlyPL4ci7BpRLRBmWuMnCkbj+q6odR3uw3GeMwt5kKIOcBMEfaAD9g68MF+pCg7v/BepJt5VRnY54Gzv+xl21ytYE0WC7XKRMsjFbEW/E2ZCdVU4euotVh3rA0JSlITEn0Jg34Mpki0e92p+Uo3M9oUq1JtGw7xQpU+bjPauMNw2dTaYxPYwsn06oX+9WpVsV2bW3WFnGK9I0mhU+HS6XFgnSdW6pa3VSB+TXvya54i0P0tK2B35Nc8RaH6XlbBSIllDQyeAx49R+P6ollLCRJJy2UfwA7hL8T8ZmeEgP0PTN1XY1Jx43MF32jixjNVKRgJueHsDa3jFifhbCvi873ekyL0OXJm2u5cJuPUyXEulMF1rhhqe0xrlMPNvIZMW1d+7j3PMi0r9pFgR90JQaNHsfFnCqycFq/PTcuHGJsfE2bEmRbvu6qSqu/LTUaKtC1txpLdTkqKiW8hupIOM+6f5h+/JrniLQ/S8rYHfk1zxFofpeVsFjLTZ2Oa4HETlhmST1cT8JkjHFDCjkzl0ePRAA8AO7LBfqEv/wDKZ0m9q1iauZPxyrNq3xfW6LuBi1rnuE6hRpdMxWsmkW5ZVNqMM6itlTVMlwZLkmORLajNSDVFJxxSkjg90H+UDw9xJx83OmL9AtmTXLHwy3RTW6RuDCOtYdt2vUaFIdfto51usV96uVNqqM6mgNsMLZhUqEhMSGs4JLU6lv8AMv35Nc8RaH6XlbA78mueItD9LytgxZGszCwtOLQ0D9kADtwBJ3z+JWJgRXAtIwNX+4kn7SBkF+rG0t3duZrSu/DGqVO/N1fihUMIIldrNAxUxCtrXXDX59bxapuIUmBMoLN3tpkEVOjPwYsqoTnokOolvzg15Bspa4e393bucpdzWNfd0VXdB0mt4e27dFuWfbEOiO3FZVs78xlqOItNn7xj3RTWprkqiVU6OiJINMWmTqXDkrbnstMNNflm78mueItD9Lytgd+TXPEWh+l5WwGRrKwMDT9GZG/eQR9hIWeztFL2nc4gns3fATxMt+E5gAL9HmGG7XwwszFTdVXzMpWIce1sUd1pae6pw9sCnw479Fulm1b8r9yrtG5o6JrceKiowa40yqYhE1lt6moSuM82aNHu9mbozD2/t11+T+p1uYj4l3lSMGcbK1ctfxx3SJU+2b1rKbsvB66OCFm7U5jTMenN6caO9InIS5UKvLdZOAhbK0fmM78mueItD9Lytg0VuyK2ojSqxKCpKi0VJVVZSkqI+IyMssjIy+AZMj2RtBJmGykMeADePUBul4lYvhWh4cCN+H2n8T6AX7E8W919aOANHRh9Nu6Wm63LHt+68MY1Rw7nYu3RhtBo1tXNY0Owr5jyb+fVv11Mt2ttVCZU6uyqLcchMimNOGyR4Gq3WdkyN1DhnjxFxGxTpEFvc20bCa/I114bN4potqXEsKDY9bojEJdwxHqlRqoiHKkuToUulz2jqqltIKQ2p1f522t2JV2EJaYsC3mGk+9aYqUllpOfGeSSSREPp35Nc8RaH6XlbBhDjQIZLi6ZO8yORH2Ht47zPJ8OM8SAkMOO7EH7R4kZS/UpD3bO5Xfqdt1UqluoLbg4aM4iW/hRadZkVDFCj0eLcOIbd7UGsVpwrrgPVaUUF6pUhcGa+42xJODLefqDbCml8XcP5ROx7mxfo1RgVrHnDrCSfK3STFxRLZkNpqlsrxvqdyyaJXoNFjVNiLMn0pmsQFyEqeYVrIiyjv5Elw/y/wDfk1zxFofpaVsDvya54i0P0vK2CTGspIJO4EceO+frLIIGWgMLBuMv9u7uGH2r9WR/lJcJ1lhdFiOX1adEbsFnD6849p4Vts33hNUIOFNdwxgXtadwv3Q4zIfaVWHaqimxYNJdNMp9t6e6+yzJdwvx9xK3NmN1227cU/ETdHPP2Vg7Z2DMCtV3Dyi1OuXs3Z1hTaamuVFTtcdcZdnV2NSGDjqekKapsuS+p1ciOiO74O9+TXPEWh+l5WwO/JrniLQ/S8rYIdFsznVlxnOqfWA4A9znDrnjOQli2DGawQ2jACXZh5D7d5M/SC8o1lxK++xh9V7orlrJp9PcjVK8qHFtyvuS10+K5VGlxI0h9ommZyprMdwnDU7HZZcWlta1No6uMB+/JrniLQ/S8rYHfk1zxFofpeVsFt7gcXLG7xclnwAwH78mueItD9Lytgd+TXPEWh+l5WwL3Az8Eu0XJZ8AMB+/JrniLQ/S8rYHfk1zxFofpeVsC9wM/BLtFyWfADAfvya54i0P0vK2B35Nc8RaH6XlbAvcDPwS7RclnwAwH78mueItD9Lytgd+TXPEWh+l5WwL3Az8Eu0XJZ8AMB+/JrniLQ/S8rYHfk1zxFofpeVsC9wM/BLtFyWfADAfvya54i0P0vK2B35Nc8RaH6XlbAvcDPwS7RclnwAwH78mueItD9Lytgd+TXPEWh+l5WwL3Az8Eu0XJZ8AMB+/JrniLQ/S8rYHfk1zxFofpeVsC9wM/BLtFyWfADAfvya54i0P0vK2B35Nc8RaH6XlbAvcDPwS7RclnwAwH78mueItD9Lytgd+TXPEWh+l5WwL3Az8Eu0XJZ8AMB+/JrniLQ/S8rYHfk1zxFofpeVsC9wM/BLtFyWfADAfvya54i0P0vK2B35Nc8RaH6XlbAvcDPwS7RclnwAwH78mueItD9Lytgd+TXPEWh+l5WwL3Az8Eu0XJZ8AMB+/JrniLQ/S8rYHfk1zxFofpeVsC9wM/BLtFyWfADAfvya54i0P0vK2B35Nc8RaH6XlbAvcDPwS7RclnwAwH78mueItD9Lytgd+TXPEWh+l5WwL3Az8Eu0XJZ8AMB+/JrniLQ/S8rYHfk1zxFofpeVsC9wM/BLtFyWfADAfvya54i0P0vK2B35Nc8RaH6XlbAvcDPwS7RclnwAwH78mueItD9Lytgd+TXPEWh+l5WwL3Az8Eu0XJZ8AMB+/JrniLQ/S8rYHfk1zxFofpeVsC9wM/BLtFyWfADAfvya54i0P0vK2B35Nc8RaH6XlbAvcDPwS7RclnwAwH78mueItD9Lytgd+TXPEWh+l5WwL3Az8Eu0XJZ8AMB+/JrniLQ/S8rYHfk1zxFofpeVsC9wM/BLtFyWfADAfvya54i0P0vK2B35Nc8RaH6XlbAvcDPwS7RclnwAwH78mueItD9Lytgd+TXPEWh+l5WwL3Az8Eu0XJZ8AMB+/JrniLQ/S8rYHfk1zxFofpeVsC9wM/BLtFyWfADAfvya54i0P0vK2B35Nc8RaH6XlbAvcDPwS7RclnwLmsT9Ap/fHftIeUJbsmuGZF3C0Pj//AJeVsHpjgRcrt44X21dL8RqC9XWXJ7kNhxTzMczdWjRSpXGZZII+PnGxZ48OK4tYeH4hUWiE+GwF2a6MAAJRAAARAAARAAARAAARAAARSR79Pzi+0XVRfzDfzfuFKo9+n5xfaLqov5hv5v3CyHxVMXh8CvEXdO/rzv79+j/yUYUKL63Tv687+/fo/wDJRhQo4Ef653xXbh/Vt+AW+plOmVeowaXT2Fyp9RltwYUZsyJyS88tLbTaczIs1KUlJZmRZnyi990ZuZcUNy/iT7FOJkGnFdjVn0G85JW7McrNJaYuGkRavGZTK1SEOuMJknGeUzps6+M8TTzzZJdXj0R5cgtak1ytV6KmVW6vU6xJhx41EhyKpPdnvRIUGKzFgw2lLUZoYjstNMtNJyQ222lKSSkiIvVakatwda9Pw9Dx4hhtLXOLgATzRMAA9e9Xwmte6l3Hd6+E1W/BlQ6lL+gUHBlQ6lL+gULbzPnPtDM+c+0fbPYToj3+J8jfNbF0GaqTgyodSl/QKDgyodSl/QKFt5nzn2hmfOfaHsJ0R7/E+Rvml0GaqTgyodSl/QKDgyodSl/QKFt5nzn2hmfOfaHsJ0R7/E+Rvml0GaqTgyodSl/QKDgyodSl/QKFt5nzn2hmfOfaHsJ0R7/E+Rvml0GaqTgyodSl/QKH0Kj1U2lvlTphtNuJaW5vdWghSyWaUmeXKZIWZfNPmFr5nzn2jl45nwJP4z46tDLl/wDBnCqLyGaJY0EW+JvaPoN4kDPrS6gDeqO4MqHUpf0Cg4MqHUpf0ChbWZ859oZnzn2iwchOiCJ3+J8jfNLoM1UnBlQ6lK+gUNeDKh1KX9AoW1mfOfaGZ859on2E6I9/ifI3zU3QZqpODKh1KV9Aoa8GVDqUv6BQtrM+c+0Mz5z7Q9hOiPf4nyN80ugzVScGVDqUr6BQ14MqHUpf0ChbWZ859oZnzn2h7CdEe/xPkb5pdBmqk4MqHUpX0ChrwZUOP+hS+Isz/o6uIW1mfOfaJEZ5L4z97z/tIPYToj3+J8jfNLoM1UXBlQ6lK+gUHBlQ6lL+gULbzPnPtDM+c+0PYToj3+J8jfNLoM1UnBlQ6lL+gUHBlQ6lL+gULbzPnPtDM+c+0PYToj3+J8jfNRdBmqk4MqHUpf0Cg4MqHUpf0ChbeZ859oZnzn2h7CdEe/xPkb5pdBmqk4MqHUpf0Cg4MqHUpf0ChbeZ859oZnzn2h7CdEe/xPkb5pdBmqk4MqHUpf0ChqVLqJ8RQZZn/UKFtZnzn2ibZnpFxnyH8P7DD2E6I9/ifI3zS6DNVDwZUOpS/oFBwZUOpS/oFC28z5z7QzPnPtD2E6I9/ifI3zS6DNVJwZUOpS/oFBwZUOpSvoFC28z5z7QzPnPtD2E6I9/ifI3zS6DNVLwZUOpS/oFDTgyodSlfQKFt5nzn2hmfOfaHsJ0R7/E+Rvml0GaqXgyodSl/QKGnBlQ6lK+gULbzPnPtDM+c+0PYToj3+J8jfNLoM1UvBlQ6lL+gUNODKh1KX9AoW3mfOfaNUmeZcZ8vOHsJ0R7/ABPkb5pdBmqk4MqHUpfMfuCv7RpwZUOpSvoFC3lGfFxnyn8P7RDM+c+0QOQnREp3+J8jfNBZQeKqXgyodSl/QKGnBlQ6lL+gULbzPnPtDM+c+0T7CdEe/wAT5G+aXQZqpODKh1KX9AoODKh1KX9AoW3mfOfaGZ859oewnRHv8T5G+aXQZqpODKh1KX9AoODKh1KX9AoW3mfOfaGZ859oewnRHv8AE+Rvml0GaqTgyodSl/QKDgyodSl/QKFt5nzn2hmfOfaHsJ0R7/E+Rvml0GaqU6XUS4jgy88uQ2FDTgyodSl/QKFuqM8y4z96Xw/sIRzPnPtD2E6I9/ifI3zU3QZqpODKh1KX9AoODKh1KX9AoW3mfOfaGZ859oewnRHv8T5G+ai6DNVJwZUOpS/oFDXgyodSl/QKFtZnzn2hmfOfaHsJ0R7/ABPkb5pdBmqk4MqHUpX0ChrwZUOpS/oFC2sz5z7QzPnPtD2E6I9/ifI3zU3QZqpODKh1KV9Aoa8GVDqUv6BQtrM+c+0Mz5z7Q9hOiPf4nyN80ugzVSnS6jxf0GXxlmXuCg4MqHUpf0ChbijPJHGfvef9piOZ859oewnRHv8AE+Rvml0GaqTgyodSlfQKGvBlQ6lL+gULazPnPtDM+c+0PYToj3+J8jfNLoM1UnBlQ6lK+gUNeDKh1KX9AoW1mfOfaGZ859oewnRHv8T5G+aXQZqpODKh1KV9Aoa8GVDqUvzdQtrM+c+0Mz5z7Q9hOiPf4nyN81F0GaqTgyodSlfQKGvBlQ6lL83ULazPnPtDM+c+0PYToj3+J8jfNLoM1TrsaRGUgn2XWTXxpJ1BoNXPlmPdTcrfqFw8/hS/5h4eK12mZvQMzM/c1cvzh7Vblf8AUNh5/CV/zDw+Mab0JC1d1ltehoDy9sMNAcQATNrXYgYcVyNJNoaG9a4sAActaSAAAiAAAiAAAiAAAiAAAikj36fnF9ouqi/mG/m/cKVR79Pzi+0XVRfzDfzfuFkPiqYvD4FeIu6d/Xnf379H/kowoUX1unf1539+/R/5KMKFHAj/AFzviu3D+rb8AgsK1P0a/wDvh/4EivRYVqfo1/8AfD/wJH07kc/TeF/hxPurYgfWtXZgAB+vV00AABEAABEAABEHMR/0JP8A4tD/AMmcOHHMR/0JP/i0P/JnCi0fQb/eZ99qh24rhwABc3cFKAACUQAAEQAAEQSTyL+b95CIknkX837yBFEAAEQAAEQAAEQAAEQTb9+XyH9hiAm378vkP7DBFAAAEQAAEQAAEQAAEQap5S+UaDVPKXygikvkL5T+0QE18hfKf2iAhu4KBuQAASpQAAEQAAEQAAEUlcpfNL7CERJXKXzS+whEEQAAEQAAEQAAEQAAEUlciPm/eYiJK5EfN+8xEEQAAEQAAEQAAEQAAEXSbt/PQP6tX+Ie1W5X/UNh5/CV/wAw8PFW7fz0D+rV/iHtVuV/1DYefwlf8w8Px7r7+n2kf2PuQ157Su/tC4sAAeRWggAAIgAAIgAAIgAAIgAAIpI9+n5xfaLqov5hv5v3ClUe/T84vtF1UX8w3837hZD4qmLw+BXiLunf1539+/R/5KMKFF9bp39ed/fv0f8AkowoUcCP9c74rtw/q2/ALeU+IqfOhwkE4pcuU3FQllKVvKNxaUESCUpKTUZq4iUpJGeWZkXGWYWOuCVKwEvxFj0i4KnWo8yzqDechi4KYzR7mtmTWaY1LkUapxWnXUty4SzU06WaczyMkmg0OOYZEeR5l/eWYsO1lGqnPmZJI9+H71JJL3iPgIfTuR2Z12ggdCJ3UnDvkexbECe3BJwXZtEvDT2HsDRLw09h7BEB+vV01LRLw09h7A0S8NPYewRAEUtEvDT2HsDRLw09h7BEARS0S8NPYewNEvDT2HsEQBFLRLw09h7By8ci4En+2Sf+lonP0M79g4YcxH/Qk/8Ai0P/ACZwotH0G/3mffaoO4ridEvDT2HsDRLw09h7BEBc3cFKlol4aew9gaJeGnsPYIgJRS0S8NPYewNEvDT2HsEQBFLRLw09h7A0S8NPYewRAEUtEvDT2HsGpEWS/bJ97y8fOX7BASTyL+b95AiaJeGnsPYGiXhp7D2CIAilol4aew9gaJeGnsPYIgCKWiXhp7D2Bol4aew9giAIpaJeGnsPYGiXhp7D2CIAilol4aew9gmgi0i9sk+I+fmP9g+Qm378vkP7DBFpol4aew9gaJeGnsPYIgCKWiXhp7D2Bol4aew9giAIpaJeGnsPYGiXhp7D2CIAilol4aew9gaJeGnsPYIgCKWiXhp7D2DVKSzL26eXmPYIDVPKXygimoiPi008Rnz7BHRLw09h7AURcvOZ59oiIbuCgblLRLw09h7A0S8NPYewRASpUtEvDT2HsDRLw09h7BEARS0S8NPYewNEvDT2HsEQBFLRLw09h7A0S8NP9+wRAEX0Uks/fp96XOfwF+wR0S8NPYewFcpfNL7CEQRS0S8NPYewNEvDT2HsEQBFLRLw09h7A0S8NPYewRAEUtEvDT2HsDRLw09h7BEARS0S8NPYewNEvDT2HsEQBF9DSWSPbp97zHzn+wR0S8NPYewFciPm/eYiCKWiXhp7D2Bol4aew9giAIpaJeGnsPYGiXhp7D2CIAilol4aew9gaJeGnsPYIgCKWiXhp7D2Bol4aew9giAIul3aRE9B9sR+5q5PnEPajcr/AKhsPP4Sv+YeHirdv56B/Vq/xD2q3K/6hsPP4Sv+YeH4919/T7SP7H3Ia89pXf2hcWAAPIrQQAAEQAAEQAAEQAAEQAAEUke/T84vtF1UX8w3837hSqPfp+cX2i6qL+Yb+b9wsh8VTF4fArxF3Tv687+/fo/8lGFCi+902lSsc7+0SM8p0fiLjP8A1KMKHNCyLM05FyZnxDgR/rnfFduH9W34BQFhWp+jX/3w/wDAkV6LDtQjKnPZ9cM//oSPp3I5+m8L/DifdWxA+tauygAD9erpoAACIAACIAACIOYj/oSf/Fof+TOHDjmI/wChJ/8AFof+TOFFo+g3+8z77VDtxXDgAC5u4KUAAEogAAIgAAIgknkX837yERJPIv5v3kCKIAAIgAAIgAAIgAAIgm378vkP7DEBNv35fIf2GCKAAAIgAAIgAAIgAAIg1Tyl8o0GqeUvlBFJfIXyn9ogJKMuTmM8+0REN3BQEAAEqUAABEAABEAABFJXKXzS+whESVyl80vsIRBEAABEAABEAABEAABFJXIj5v3mIiSuRHzfvMRBEAABEAABEAABEAABF0m7fz0D+rV/iHtVuV/1DYefwlf8w8PFW7fz0D+rV/iHtVuV/wBQ2Hn8JX/MPD8e6+/p9pH9j7kNee0rv7QuLAAHkVoIAACIAACIAACIAACIAACKSPfp+cX2i6qL+Yb+b9wpVHv0/OL7RdVF/MN/N+4WQ+Kpi8PgV5IYgu3DH3Vl1SrVsSk4k1+I89Lp9n1y1+7SmVE2qA4t152lmRpkFFbS7MJDiVN6UNKloWhKkHs8e5VWkWzDYptnWtWLAhV+nLp2NNI3PULA6qT6tMtmDLq1sulEjRmn2oMlyWlOtbWte8yfQtLbxIPruPNVrNG3Rl21G3qjVKTWo1ZiLptSok52m1aI6cOMhKo77SkuIWekaSNJkftv2i4t2thhUcNZNoQnN0hdeNUGoHL07WvePcZ3Bh7UYq96VWCdQfJ6gVTekpl+nPzqBOkNlIgOtuoZUkkjhWmdRnuqP4LvwedCa0DECfhL+fgBiTgrS2471RhNS9E4rkttEjTklCTqzWRLzeNCyb4s/bmlWjy6KssjzD3QFv4QWvilXKHgpNpFRsqFTaW27ULYvR+/7SnVXg5jhd2j1KSwxMXBOTrCYKa03JNKdNxpnWJYawt5P2CxLWUaqc8ajMz34ZZmeZ+8SPpnI7jrtBA6EQ/7ZS9ZK2zy2wJXZ/c+dfYQe586+whAB+vV0lP3PnX2EHufOvsIQAEU/c+dfYQe586+whAART9z519hB7nzr7CEABFP3PnX2EOXj6HAk/LSy4WicpEX/czhwo5iP+hJ/wDFof8AkzhRaPoN/vM++1Q7cVxXufOvsIPc+dfYQgAubuClT9z519hB7nzr7CEAEop+586+wg9z519hCAAin7nzr7CD3PnX2EIACKfufOvsISLQyXxry0eYuch8hJPIv5v3kCLX3PnX2EHufOvsIQAEU/c+dfYQe586+whAART9z519hB7nzr7CEABFP3PnX2EHufOvsIQAEU/c+dfYQkjQ0iyNfIfwFzGPkJt+/L5D+wwRPc+dfYQe586+whAART9z519hB7nzr7CEABFP3PnX2EHufOvsIQAEU/c+dfYQe586+whAART9z519hDUtXmXGvl5iHzGqeUvlBF9FEjMzM1FmZ8REXFxiPufOvsIF8hfKf2iAhu4KBuU/c+dfYQe586+whABKlT9z519hB7nzr7CEABFP3PnX2EHufOvsIQAEU/c+dfYQe586+whAARfRWhmWZr96XwFzENPc+dfYQ0Vyl80vsIRBFP3PnX2EHufOvsIQAEU/c+dfYQe586+whAART9z519hB7nzr7CEABFP3PnX2EHufOvsIQAEX1PQyTxr97zFzmI+586+whorkR837zEQRT9z519hB7nzr7CEABFP3PnX2EHufOvsIQAEU/c+dfYQe586+whAART9z519hB7nzr7CEABF0u7dDXQctL82rjP5xZfePajcr/qGw8/hK/wCYeHirdv56B/Vq/wAQ9qtyv+obDz+Er/mHh+Pdff0+0j+x9yGvPaV39oXFgADyK0EAABEAABEAABEAABEAABFJHv0/OL7RdVF/MN/N+4Uqj36fnF9ouqi/mG/m/cLIfFUxeHwK8acbqu9b+6ar9djOSGpVGuem1SK/ElyoMuO4wxDdbdZejOtSEOIUlK0LYcQ4SkloqSeRjJX8p3RKZSMXbMONiliLilV5llSpFz1DE1N3ouSBNVXqqtRLK4Z8uWjXpcS860k2SRJXJ0mkqVmMYMe4c2fujrng06PUJVQmXHTokCNSYqp1UkPuMQ0NIjMJ9s46pakkhtPGpRkRcozB/KvYeWTZuOdBr9qRrpRVcRKRV7oxBl3Vhq9hbPn3G3cdTh1pTtPehxFHKbmsTGpSmmN6pebUiO8+hGsHEtDgCBxLncOoYet/XjLu2cThkjg1v2ryqFhWp+jX/wB8P/AkV9lmZ5FnzZCwbU4qc9n1w/8AAkfSuRz9N4X+HE+6roH1oXZgAB+vV00AABEAABEAABEHMR/0JP8A4tD/AMmcOHHMR/0JP/i0P/JnCi0fQb/eZ99qh24rhwABc3cFKAACUQAAEQAAEQSTyL+b95CIknkX837yBFEAAEQAAEQAAEQAAEQTb9+XyH9hiAm378vkP7DBFAAAEQAAEQAAEQAAEQap5S+UaDVPKXygikvkL5T+0QE1fB8p/aICG7goCAACVKAAAiAAAiAAAikrlL5pfYQiJK5S+aX2EIgiAAAiAAAiAAAiAAAikrkR837zERJXIj5v3mIgiAAAiAAAiAAAiAAAi6Tdv56B/Vq/xD2q3K/6hsPP4Sv+YeHirdv56B/Vq/xD2q3K/wCobDz+Er/mHh+Pdff0+0j+x9yGvPaV39oXFgADyK0EAABEAABEAABEAABEAABFJHv0/OL7RdVF/MN/N+4Uqj36fnF9ouqi/mG/m/cLIfFUxeHwK8YceSdPdIXPqJUeE8m4IDjMuXUjo8eOpEaIolrlkZGyRZZ60jI0nkZGRkQ7Xu1bgta7cZKndFl16i1a1rhVMq9u0qlU632ZFlwJNSmvQaLMm0uZK389EZW22c2a5vp0i0llxko653TajTjnfxlxHv6OZGR5GX9CjCh1OLWREtSlZcftjMxwrQZvLciSu5CMoQGYC0QZJVmeeWRlmXFkZkZEf9gy6xovzDfEnEy47xwqtt21bNqkGhx49DdtWl2OmLMg27SadVXG6ZTXXIbLcibDly0kyos99npJSrSzxDFhWp+jX/3w/wDAkfTOR3HXaEP+iIf9pH4/Yr4H1oXadJPRl5RhpJ6MvKMQAfr1dNT0k9GXlGGknoy8oxAART0k9GXlGGknoy8oxAART0k9GXlGGknoy8oxAART0k9GXlGOXYNPAk/JBEXC0T4T6GcOFHMR/wBCT/4tD/yZwotH0G/3mffaoduK4rST0ZeUYaSejLyjEAFzdwUqeknoy8ow0k9GXlGIAJRT0k9GXlGGknoy8oxAART0k9GXlGGknoy8oxAART0k9GXlGNSUn2/tCy0eTM+ch8xJPIv5v3kCLXST0ZeUYaSejLyjEABFPST0ZeUYaSejLyjEABFPST0ZeUYaSejLyjEABFPST0ZeUYaSejLyjEABFPST0ZeUYkhSdLiQWeR/7R8xj5Cbfvy+Q/sMETST0ZeUYaSejLyjEABFPST0ZeUYaSejLyjEABFPST0ZeUYaSejLyjEABFPST0ZeUYaSejLyjEABFPST0ZeUY1SpOZe5ly+EY+Y1Tyl8oIpqNOZnqy4zPL2xjTST0ZeUYL5C+U/tEBDfohQNynpJ6MvKMNJPRl5RiACVKnpJ6MvKMNJPRl5RiAAinpJ6MvKMNJPRl5RiAAinpJ6MvKMNJPRl5RiAAi+ilJz42y96X+0fMQ00k9GXlGNFcpfNL7CEQRT0k9GXlGGknoy8oxAART0k9GXlGGknoy8oxAART0k9GXlGGknoy8oxAART0k9GXlGGknoy8oxAARfQ1JyTmgj9rznxcZjTST0ZeUY0VyI+b95iIIp6SejLyjDST0ZeUYgAIp6SejLyjDST0ZeUYgAIp6SejLyjDST0ZeUYgAIp6SejLyjDST0ZeUYgAIul3caTegZJy9zVyHn/ALRD2o3K/wCobDz+Er/mHh4q3b+egf1av8Q9qtyv+obDz+Er/mHh+Pdff0+0j+x9yGvPaV39oXFgADyK0EAABEAABEAABEAABEAABFJHv0/OL7RdVF/MN/N+4Uqj36fnF9ouqi/mG/m/cLIfFUxeHwK8Rd07+vO/v36P/JRhQovrdO/rzv79+j/yUYUKOBH+ud8V24f1bfgEFhWp+jX/AN8P/AkV+kszIss/lPIhatNotWt8plKrlLqFGqceShb9OqkF6mzmUusNPNmtl1KVpJaHELSaiLSStJlmRkZ/TuRz9N4X+HE+6tmBjFC5EAAfr1dJAAARAAARAAARBzEf9CT/AOLQ/wDJnDhxzEf9CT/4tD/yZwotH0G/3mffaoduK4cAAXN3BSgAAlEAABEAABEEk8i/m/eQiJJ5F/N+8gRRAABEAABEAABEAABEE2/fl8h/YYgJt+/L5D+wwRQAABEAABEAABEAABEGqeUvlGg1Tyl8oIpL5C+U/tEBNfIXyn9ogIbuCgbkAAEqUAABEAABEAABFJXKXzS+whESVyl80vsIRBEAABEAABEAABEAABFJXIj5v3mIiSuRHzfvMRBEAABEAABEAABEAABF0m7fz0D+rV/iHtVuV/1DYefwlf8AMPDxVu389A/q1f4h7Vblf9Q2Hn8JX/MPD8e6+/p9pH9j7kNee0rv7QuLAAHkVoIAACIAACIAACIAACIAACKSPfp+cX2i6qL+Yb+b9wpVHv0/OL7RdVF/MN/N+4WQ+Kpi8PgV4i7p39ed/fv0f+SjChRfW6d/Xnf379H/AJKMKFHAj/XO+K7cP6tvwC5q263Ktq4KJcUJqI/MoNWjVqIxUIyZkF52I8iQ2l9k+JbZqbSSkHxKSZkfKMv8Z90nXN0XfMnEOpW5RreNulRbciU03nLwlJajrky1OP1KeTkl9Sn6hK0dMyJtlMdpJGlklHhMLCtT9Gv/AL4f+BI+l8kENkXXWC2IJgMiHtpktmzmUYEccFYHDknq9J+r8D8EOHJPV6T9X4H4I4UB+uLpZ+gF0aQua4ck9XpP1fgfghw5J6vSfq/A/BHCgF0s/QCUhc1w5J6vSfq/A/BDhyT1ek/V+B+COFALpZ+gEpC5rhyT1ek/V+B+CHDknq9J+r8D8EcKAXSz9AJSFzXDknq9J+r8D8EcozWpJ0WoHvek5lU4hEXAMHLjZnHyarI+T/rMx1EcxH/Qk/8Ai0P/ACZwptFls4YOYPpM++FBEhMKXDkrq9J+r8D8EOHJPV6T9X4H4I4UBcLLZyJ0BTSFzXDknq9J+r8D8EOHJPV6T9X4H4I4UAuln6ASkLmuHJPV6T9X4H4IcOSer0n6vwPwRwoBdLP0AlIXNcOSer0n6vwPwQ4ck9XpP1fgfgjhQC6WfoBKQua4ck9XpP1fgfgiRVyVkv8Ao9J974vwOcv/AARwYknkX837yC6WfoBAAFzHDknq9J+r8D8EOHJPV6T9X4H4I4UAuln6ASkLmuHJPV6T9X4H4IcOSer0n6vwPwRwoBdLP0AlIXNcOSer0n6vwPwQ4ck9XpP1fgfgjhQC6WfoBKQua4ck9XpP1fgfghw5J6vSfq/A/BHCgF0s/QCUhc1w5J6vSfq/A/BEkVuWauJikFxH/wCz8DmP/wAIcGJt+/L5D+wwNks/RCUgLl+HJPV6T9X4H4IcOSer0n6vwPwRwoBdLP0AlIXNcOSer0n6vwPwQ4ck9XpP1fgfgjhQC6WfoBKQua4ck9XpP1fgfghw5J6vSfq/A/BHCgF0s/QCUhc1w5J6vSfq/A/BDhyT1ek/V+B+COFALpZ+gEpC5rhyT1ek/V+B+CNSrknMv6PSeXxfgfgjhBqnlL5Quln6ASkLmTrkrM849I5fgt+Bl/khw5J6vSfq/A/BHEL5C+U/tEBAstnkOYFEgcVzXDknq9J+r8D8EOHJPV6T9X4H4I4UBN0s/QCmkLmuHJPV6T9X4H4IcOSer0n6vwPwRwoBdLP0AlIXNcOSer0n6vwPwQ4ck9XpP1fgfgjhQC6WfoBKQua4ck9XpP1fgfghw5J6vSfq/A/BHCgF0s/QCUhc2qtys/8AV6TyF/7PwOYv/BGnDknq9J+r8D8EcOrlL5pfYQiF0s/QCSC5rhyT1ek/V+B+CHDknq9J+r8D8EcKAXSz9AJSFzXDknq9J+r8D8EOHJPV6T9X4H4I4UAuln6ASkLmuHJPV6T9X4H4IcOSer0n6vwPwRwoBdLP0AlIXNcOSer0n6vwPwQ4ck9XpP1fgfgjhQC6WfoBKQucOuSskf0ek+98X4HOf/giPDknq9J+r8D8EcOrkR837zEQuln6AUUgLmuHJPV6T9X4H4IcOSer0n6vwPwRwoBdLP0AppC5rhyT1ek/V+B+CHDknq9J+r8D8EcKAXSz9AJSFzXDknq9J+r8D8EOHJPV6T9X4H4I4UAuln6ASkLmuHJPV6T9X4H4IcOSer0n6vwPwRwoBdLP0AlIXVr4nuzHqbrW4aCQ0sk71p7EDPNSffapCdLk4tLPLjHs7uV/1DYefwlf8w8PFW7fz0D+rV/iHtVuV/1DYefwlf8AMPD8ia9ta3X3SLWjDmfcYvP6V/ELiwAB5JaCAAAiAAAiAAAiAAAiAAAikj36fnF9ouqi/mG/m/cKVR79Pzi+0XVRfzDfzfuFkPiqYvD4FeIu6d/Xnf379H/kowoUX1unf1539+/R/wCSjChRwI/1zviu3D+rb8AgsK1P0a/++H/gSOkR6fOltvvRYcuSzFRrJLseMt5uOnI1aTikkZJLJKjzPLiI+YXbVsOrzwrrlZsXEGgTrWuujvxnqlRKlqzlxETYESoRFKNC1IMnY0uM8k0qPNLyR9O5HcNdoRP/APOJ91bED60LjwE9EvDINEvDIfrutua6NbVABPRLwyDRLwyCtuaVtUAE9EvDINEvDIK25pW1QAT0S8Mg0S8MgrbmlbVAcxH/AEJP/i0P/JnDitEvDIcqz7WiVDjI/wDSsMy4/h1M8UWhzaBj+sz7zULgQQFw4CeiXhEGiXhkLw4ASmlbVABPRLwyDRLwyCtuaVtUAE9EvDINEvDIK25pW1QAT0S8Mg0S8MgrbmlbVASTyL+b95DXRLwyEiSWS/bF737yCtuaVtXyAT0S8Mg0S8MgrbmlbVABPRLwyDRLwyCtuaVtUAE9EvDINEvDIK25pW1QAT0S8Mg0S8MgrbmlbVATb9+XyH9hhol4ZCbZJJXGZnxHyGXMYVtzSpp3L4gJ6JeGQaJeGQVtzStqgAnol4ZBol4ZBW3NK2qACeiXhkGiXhkFbc0raoAJ6JeGQaJeGQVtzStqgNU8pfKJaJeGQ1Sksy9sXKFbUratF8hfKf2iA+qiIzMs8sjPlyEdEvDIA5oAE1Ac0DFQAT0S8Mg0S8MgrbmpraoAJ6JeGQaJeGQVtzStqgAnol4ZBol4ZBW3NK2qACeiXhkGiXhkFbc0ratFcpfNL7CER9VJSZ++LkL7CEdEvDIKmpW1QAT0S8Mg0S8MgrbmlbVABPRLwyDRLwyCtuaVtUAE9EvDINEvDIK25pW1QAT0S8Mg0S8MgrbmlbVorkR837zER9VJTkj2xe94+0xHRLwyCtuaVtUAE9EvDINEvDIK25pW1QAT0S8Mg0S8MgrbmlbVABPRLwyDRLwyCtuaVtUAE9EvDINEvDIK25pW1dGu389A/q1f4h7Vblf9Q2Hn8JX/ADDw8Vbu4n4P9WrL9vth7Vblb9Q2HmfxSv8AmHh+P9fP0+0jL/o+4xcDSv4hcWAAPIrQQAAEQAAEQAAEQAAEQAAEUke/T84vtF1UX8w3837hSqPfp+cX2i6qL+Yb+b9wsh8VTF4fArxF3Tv687+/fo/8lGFCi+t07+vO/v36P/JRhQo4Ef653xXbh/Vt+AXbLevi8LXhVikW/dt02/RblYKFc1MoFwS6RCrzGg40bUtlpxLb6dB55Gi8Sk5OqLLJRkeX984jYI40Qsf74u2oKYxJXhZaVPwkcrtEKy5TlXotVolIkQ6dTLfjnSdT3MwZDanKnqzN5hpSFm4szGCg1IzLkMy+Q8hgHOAkCe/Ld3KwYKanHCMyJa+I/DMaaxzpF+UYgAVvzUKesc6RflGGsc6RflGIAFb8yinrHOkX5RhrHOkX5RiABW/Mop6xzpF+UYaxzpF+UYgAVvzKL6E44Z5axfH/AO+YywtwtzT3qd990Cnu+VK8NK0DJyunMKFvq1966rV/6I3hvPuz33vn+m774H1HuOuzxLGukrnPtE7R1JbM8OORB/CXwmpaZGZCmpbhGZaxfL4RjTWOdIvyjEAEF7yZzUKesc6RflGGsc6RflGIAFb8yinrHOkX5RhrHOkX5RiABW/Mop6xzpF+UYaxzpF+UYgAVvzKKesc6RflGL0wkjYUTLOx5TiTNpsK6G8K0SMFn58utsynbmauGhuLYYRCacjLN2kprzZ8JaDCVLaMlpc0FFRAZmXIZkFb5ETUgycHZEH4yM5HqO49S+qluEpRaayyUZZaRiOsc6RflGIAFb8yoU9Y50i/KMNY50i/KMQAK35lFPWOdIvyjDWOdIvyjEACt+ZRT1jnSL8ow1jnSL8oxAArfmUU9Y50i/KMZJblVOCLuLMBe6Hft72MY1EqEurxLlfuWOmsOtMk5Gp8F+iEciPNlrTvdiXJzhxVPHIfbkJZKM9jUAkRHieKLcPuZPOk04rVE4om9FxS06OZ5ZGZEZll8JkRnzFyD5axzpF+UYinPMsss8+LPkGZ+G24H3T2LGDrWPdkWHErGE5zFwJN2JuinNNwH25smnuNPQ9acolpksR2TSlpStOrUtOWlPik6btCMCZfFDietYZaxzpF+UYaxzpF+UY9CHvyYm6+Yqr1PXYtuHDYSbz1wJxDo3c5HbTSm7idUuVr8iU3RnSrK2TTrkwUqeNvIjIcHP8AydO6fgx4Lx0GxJz0tyMzKiUrFa3au9R9/V9Ft0xUtbMpTSETphr3s4ha0ustLdzJGiashtCAWk+PrNDIGR47utYJ6xzpF+UYaxzpF+UY9KcOPyYGOl4XlYVuXJXcObPpN2XFTqLXqvFvmlXXW7HZnqtklzJ1HakoeNERF6Wo8+klkbbdbZMzzbfJrqjn5NvdOR6PBuWdTMOqXa1VoU29KRclUxZoMKlVe2oVAm3I5dEPSka2RSlwafLW3JZbUpTjaW9USloJWJeQ6mrH1/JYsc2JKgznLuO7vWAOsc6RflGGsc6RflGMwKHuSa3dOEdoYmW9dlFM6vRrovC81XNMhWhZNhUS3q/Q7XjzH6y/J0n3p9Ur9OitR0R05KlsZKUSnDa5TEzcNYrYLUao3JihWsOqRQaLUF0Guv2zfkC/6lRalvi46ZHhS4cFa3WtbVLUrtLOQZGy1IgrNxRNqQ4sXOH0nS3eJkPHD44I1zXbjxl2rCvWOdIvyjHd8OeBF3xapXS7QCt067GOsndy6t3LlHJwtYVQ4M/p+oMuJe9Pdcj9qMlt0juc8O8M7al4g4WYnrv+zH8c69hHSCyptZjOR6Vb9s1+PParkB9UWWpwrhXGeQ0w0hp2ArRW6Sz0ML8z5xAiOng7cs3sIAnx8yPtBV0boJWFCcZsSPYJVLThCd0yDw+KS5OU6mmmSDZTnLykmkj1hJU+RLNJJMyIzyFNaxzpF+UYhmZ8p5gAc8AAuKhT1jnSL8ow1jnSL8oxAArfmUU9Y50i/KMNY50i/KMQAK35lFPWOdIvyjDWOdIvyjEACt+ZRT1jnSL8oxuoSmVy4qJz0tENchCZS4pJdkobNRE4baVKSlSiTpGRKURGZERmRcY2QCQ94M5osmd1M7ua3MQaI7uU27yi4anhlbDNRjX2h5F0N3E1SGWbiXLUp91tTj0xtchZxTTES5IcRGQllDeeNOsc6RflGIGZnymZ/KAF7uBKcAFPWOdIvyjDWOdIvyjEAEVvzKKesc6RflGGsc6RflGIAFb8yinrHOkX5RhrHOkX5RiABW/Mop6xzpF+UYkhxeknNazLPjI1mWY+QBW/NFkxjY5gU5h1gG9hWimU/EArNeiYy06hzq/WYj89pFOOLUZM2pMsJRMlLcqRP02A05Eib0aNqZJTIJLGNWsc6RflGIGZnymZ/KAF75zmgwElPWOdIvyjDWOdIvyjEACt+ZRT1jnSL8ow1jnSL8oxAArfmUU9Y50i/KMNY50i/KMQAK35lFPWOdIvyjEkuLM+Na8uYlnxj5AFb80WWtyQ9zArco2DJt2pVhrdOoviQq+oExipS2p9NXIq6WiYUbaafHhx4jdBcbW269Lky51RQ40w1GYW96Tblb9QuHn8KXy/vDw8JiM80lmeWfJnxD3Z3K36hcPP4Uv+ZeHQ0eS6M4nL8QtK3kbJo6wuMAAG8tRAAARAAARAAARAAARAAARSR79Pzi+0XVRctQ3nyZFmKVR79Pzi+0XVRfzDfzfuFkPiqYvDtWI17bnexbwuqtXHcFu1yTVqnL05T8aqSYzDmrSlpvRQktEvaIRycvGfwjqvepYWeKtx+m5ewehiOQ/lExgbNBJmWialtrigATXnj3qWFnitcXpuZsGnepYWeK1x+m5mweh4CLrA6I7gpvkXNeeHepYWeK1x+m5mwO9Sws8Vrj9NzNg9DwE3WB0R3BL5FzXnh3qWFnitcfpuZsDvUsLPFa4/TczYPQ8AusDojuCXyLmvPDvUsLPFa4/TczYHepYWeK1x+m5mweh4BdYHRHcEvkXNeeHepYWeK1x+m5mwO9Sws8Vrj9NzNg9DwC6wOiO4JfIua88O9Sws8Vrj9NzNgd6lhZ4rXH6bmbB6HgF1gdEdwS+Rc154d6lhZ4rXH6bmbBr3qWFnitcXpuZsHocAXWB0R3BL5FzXnh3qWFnircfpuXsGvepYWeK1xem5mwehwBdoHRHcl8i5+K88O9Sws8Vbj9Ny9g171LCzxWuL03M2D0OALtA6I7kvkXPxXnh3qWFnircfpuXsGvepYWeK1xem5mwehwBdoHRHcl8i5+K88O9Sws8Vbj9Ny9g171LCzxWuL03M2D0OALtA6I7kvkXPxXnh3qWFnircfpuXsDvUsLPFa4/TczYPQ8Au0DojuS+Rc/FeeHepYWeK1x+m5mwO9Sws8Vrj9NzNg9DwC6wOiO4JfIua88O9Sws8Vrj9NzNgd6lhZ4rXH6bmbB6HgF1gdEdwS+Rc154d6lhZ4rXH6bmbA71LCzxWuP03M2D0PALrA6I7gl8i5rzw71LCzxWuP03M2B3qWFnitcfpuZsHoeAXWB0R3BL5FzXniW5Swt5Sta4yMvh4bmbBalqWNWLEoca2bIvXG6z7ch1F2rxKDbGKdeoVHiy3p1CqjslEZl1LaXVzLXtqWpwi0jft+nOGelGaNOXQALNAG5oS+Rc/FYxyadfcydwnLxNx5kzuHWbnNx3FGsqjqqEahlbEeYcbS1BuN0ZJUhPuejvAt7aOp9zH0okO/rarEO4LexRx8odbp5tnT6nScUqzCkwNUclTJMElRIQTZzJSm0pSRNqfWaCSrIyyYASLPBH6vr16lNL5Fwx8d3wWLVOoN30epRKzScRseKbVoEpqZDqMPFWuMy4zrJUVLTqVk5npIK27eIjP4KHALkYby+tUo951yLUoNaxExwqsSsHUTqjM7EmrSN/Jq0JVNqjS1mekTMuItUV6Mg0sONHoG3kRZZQAMbrZzvaD2evW6SC2xQQQcuJ4LGWj0Oo21Z9qWXaci7LPg2cxXafSqvataqFDueTBuORDmVWlz5ja8pcNyRAiyEMPNnq3WyWlWaUaHT7kwvXeUKoU27rhxcuen1aaVRqkKv4h1irRalITNqVTS++hxZk44Uys1eUS1ZnrqpKX751ZnmUAk2WATMtHr1+O/FYttURok08Z789/f1rDWoYRWrUcLrcwfeteU1Zls3rVMQYSIr0tquTKpV4VLp8p6ZONSlOoTHpEJtpokoQ2SXDyUpajFdd6lhZ4rXEfyVuZsHoeAi6wOiO4LM22MQATu6/XWvPHvUsLPFa4vTczYNO9Sws8Vbj9Ny9g9DwC6wOiO4KL5FzXnj3qWFnitcXpuZsGnepYWeK1x+m5mweh4BdYHRHcEvkXNeeHepYWeK1x+m5mwO9Sws8Vrj9NzNg9DwE3WB0R3BL5FzXnh3qWFnitcfpuZsDvUsLPFa4/TczYPQ8AusDojuCXyLmvPDvUsLPFa4/TczYHepYWeK1x+m5mweh4BdYHRHcEvkXNeeHepYWeK1x+m5mwO9Sws8Vrj9NzNg9DwC6wOiO4JfIua88O9Sws8Vrj9NzNgd6lhZ4rXH6bmbB6HgF1gdEdwS+Rc154d6lhZ4rXH6bmbBr3qWFnitcXpuZsHocAXWB0R3BL5FzXnh3qWFnircfpuXsGvepYWeK1xem5mwehwBdoHRHcl8i5+K88O9Sws8Vbj9Ny9g171LCzxWuL03M2D0OALtA6I7kvkXPxXnh3qWFnircfpuXsGvepYWeK1xem5mwehwBdoHRHcl8i5+K88O9Sws8Vbj9Ny9g171LCzxWuL03M2D0OALtA6I7kvkXPxXnh3qWFnircfpuXsGvepYWeK1xem5mwehwBdoHRHcl8i5+K88O9Sws8Vbj9Ny9g171LCzxWuL03M2D0OALrA6I7gl8i5rzw71LCzxVuP03L2DXvUsLPFa4vTczYPQ4AusDojuCXyLmvPDvUsLPFa4y+H9Ny+L+4Zm4W2zEs+yaRblPivwqdSycYgR5Lyn322jcUotJavbHxqUeZ84sIBmyDDhmbBJVxLREiCTisZwABWthAAARAAARAAARAAARAAARSR79Pzi+0XVRfzDfzfuFKo9+n5xfaLqov5hv5v3CyHxVMXh8Cu0o5D+UTEEch/KJixaqAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAixnAAGuuggAAIgAAIgAAIgAAIgAAIpI9+n5xfaLqov5hv5v3Cj3nFMtOvJIlKabU6kle9M0kZ8fYOapF+1RLDeUOnH7UuVLvyH/tjJrg3eq4jS7cr/AEch/KJioUYg1Ui/1GmnmfwpeL/zifshVXqFM7HvXFlbVRsYitsBUnshVXqFM7HvXD2Qqr1Cmdj3rhW1NjEyVtgKk9kKq9QpnY964eyFVeoUzse9cK2psYmStsBUnshVXqFM7HvXD2Qqr1Cmdj3rhW1NjEyVtgKk9kKq9QpnY964eyFVeoUzse9cK2psYmStsBUnshVXqFM7HvXD2Qqr1Cmdj3rhW1NjEyVtgKk9kKq9QpnY964eyFVeoUzse9cK2psYmStsBUnshVXqFM7HvXD2Qqr1Cmdj3rhW1NjEyVtgKk9kKq9QpnY964eyFVeoUzse9cK2psYmStsBUnshVXqFM7HvXD2Qqr1Cmdj3rhW1NjEyVtgKk9kKq9QpnY964eyFVeoUzse9cK2psYmStsBUnshVXqFM7HvXD2Qqr1Cmdj3rhW1NjEyVtgKk9kKq9QpnY964eyFVeoUzse9cK2psYmStsBUnshVXqFM7HvXD2Qqr1Cmdj3rhW1NjEyVtgKk9kKq9QpnY964eyFVeoUzse9cK2psYmStsBUnshVXqFM7HvXD2Qqr1Cmdj3rhW1NjEyVtgKk9kKq9QpnY964eyFVeoUzse9cK2psYmStsBUnshVXqFM7HvXD2Qqr1Cmdj3rhW1NjEyVtgKk9kKq9QpnY964eyFVeoUzse9cK2psYmStsBUnshVXqFM7HvXD2Qqr1Cmdj3rhW1NjEyVtgKk9kKq9QpnY964eyFVeoUzse9cK2psYmStsBUnshVXqFM7HvXD2Qqr1Cmdj3rhW1NjEyVtgKk9kKq9QpnY964eyFVeoUzse9cK2psYmStsBUnshVXqFM7HvXD2Qqr1Cmdj3rhW1NjEyVtgKk9kKq9QpnY964eyFVeoUzse9cK2psYmStsBUnshVXqFM7HvXD2Qqr1Cmdj3rhW1NjEyVtgKk9kKq9QpnY964eyFVeoUzse9cK2psYmStsBUnshVXqFM7HvXD2Qqr1Cmdj3rhW1NjEyVtgKk9kKq9QpnY964eyFVeoUzse9cK2psYmStsBUnshVXqFM7HvXD2Qqr1Cmdj3rhW1NjEyVtgKk9kKq9QpnY964eyFVeoUzse9cK2psYmStsBUnshVXqFM7HvXD2Qqr1Cmdj3rhW1NjEyVtgKk9kKq9QpnY964eyFVeoUzse9cK2psYmStsBUnshVXqFM7HvXD2Qqr1Cmdj3rhW1NjEyVtgKk9kKq9QpnY964eyFVeoUzse9cK2psYmStsBUnshVXqFM7HvXD2Qqr1Cmdj3rhW1NjEyVtgKk9kKq9QpnY964eyFVeoUzse9cK2psYmStsBUnshVXqFM7HvXD2Qqr1Cmdj3rhW1NjEyXQgABSttAAARAAARAAARAAARAAARfCV/qsr92c/wAChtrTpUutyoVMgkzvmSRmlcl5MaKwhttTzrrrp8SG2223HFKPkSg+I+QbmV/qsr92c/wKG2tOrS6FKh1WCpgpEVKi0ZTKZMR5t1tbLzTzauJbbjbrja0nypWfGR5GUYVCagzlgu8N2TdD7JyqZSJdcppzVQI1YobK59JqCyUSM47uilSkmpRJJRpTmrNPvkqIvvHsC+JTJPxrUrjrRvojaZQ9WRLcSakF7YyPIyIz0j9qWXGZDlaXildNJZZRRm6FSkMtuRY6qfRkpNqMvfakREqUpR6lp2dJfbI81peWSzWo0pIu0pxtraaZIN2Cw5cUmfIlSKsbiolNW1JbajvMnFaNB6JsNG0SSWRINZuoNLgz5vH1uUEunuwXSqXhxeNWns0xmjuxJ8iOxMjxqq4mmOvMSJG9W3iJfGTZOe1UpWWRcfGQ2kKyq3MqFWppnAhyKFQyuGrrmyzbYgx1NR3S0jSlStMt9MJUkkmSFKVpmhKFqL6R79uVip1GtR5rRVCqW/3MSpW9idU5FJLKCyNRmetImG8njM15kZ55mY3zmIdXVcvdiinUJu5HVvyplS4PURSJb8p2QctDaVpS2tCXEsJQj3PUtEhaVpNRGFOE+v8Ah4b+tOfLu/Cf4yXwlYb3tAkSY06350VUUzQ46pvfEdThRd+JaStrTI1rbNOWXtdI9E1JMjy0RhpiE49LYbsy4lvQVNoltpgHpRzdQhbZK48uNLjZ8Wfv0keRmRDlHMWbydbjo3xTm97OuPoUxBNpKlPNE08a2yc1StNRKdPNGaXFqNBoI9EbBOI91EURRPU80xFEcbOktmgspMaXl+33SIwZ/sSZfCHN4IKuK+ETDi+58RubFteqOR3nYbMc1IQw7KVPN1MQmkLUSlm4bKyyIsyzRmRaSTPjV2xPbodRrr0mmMt0qtIoE+lvS1NVxiS4b2inUGjRUREw8pWiszSlszNJFln2peLl2PTGpc8qFU5LSor7ZVSmrltk/DQ83HlJaN4koeSl5ZGpokEZpQrR00ksuvsXWtFLjUWVQbenU2HKnT4rb0J5uQ2/Na1alqdS77bV6LBo0yNRFGbTp5Z5nUyNPrd/FBVMTWq7IuDUQXI8difJm0w605Sqe/vmrUyGbTb6JMxvRJDSFtvNLTms1ETqNJKTUkj2E+1LmpalpqNAqsJTZNmeuimST1q2mmslFmk9Nb7KU6JnmbqMs88xzycSLnbpEei6dLXCYbjNPrkUpD71RKGUdEXfWkZtr1aIrLWegk1oQSV6eRGXKVPFm4alHia9EJNQi3TDuQ39QlVPSqnRmotPjtR1ZmSEatLiyccXrFoaPItH2083Ej16H2KJu3Lr/cHch3VPsxMeI5Xqe1LfeZantuRXkw4Tk902Xy9o5pMtqNGj79RpSXGZDWXh7eUOezTToE+VLfixpbSIDe/EKTKYhyG06SeLTSmoRErT/sLeJJ8Y3buItyPTqXXHHWXbqpzDsRy6n2d8VmpMrjojMpkqV7VxTKEr0XVEas3DzzIiIb1zFi83GpTch+mPyJZqJ+bJpDaqhktEVDqErzIiSveUZSi0c9JviMiM0gKMJ+vQUmvh6w81w3se31ptN9yVd0n6g5SmiOEaSVIaJ1TjWeeRaJMPGajPQ9yV7biMbZdnXAzb790SIW9KNGqbtEeflLOO6iay422uHqzLPX+3UvV8ugw8o8iRx82nE26jJ5t5NImxpdPapE2BKoyHolQisplJTHeQkyUaD348ZkkyPPQMjLRIfJnEa4HSkRKtvOv0qZV5dfq1EqDGlAqMmZIjSn33EoMtBZORmdFaCSpCFOIIySsyGPN9dn8VPOy9S85LgO5avLTBUxTZEwqhTo9UjlCSck0tS1SUx9PL3qlnEke1PozHNw8NL5lVJqluW3U4Eh11hlTlQjnGjsqkk6cclK4+Nw2XEklJGZGk9Ik5Hl9qXiBUqbTJEJMKCcvuROzYNTYYNiU3FVIJ41SczUTq2yN0mlJJtSDeUalLItEz2JVzSW2ESkUaScOe/U6a6/R0a2mSJbktclyOZGREbu/XkKJRKLR0CIiNOZyaZqOdj64D8ZrrSrcrLTtdjSoLkGXbcMptZhzjKPKhpN+NGy0ePNWnKZ4iPjSrSIzIhzTGHl4yabAq0aiSpEGqohHS3mkmSKiqeuY2w0ytRE2pwjgSNNBKzbIkmrLSIaw74nMSKi9OpVCqqau3Fi1VEynaDsuNDbjIZiE4lRGhkzhRFrSktJSmcyUnSVn9KNibc9Hap0OLNgy4tPhxYcKBPjlLikmFv4mXENkstFwjqEkzdbNKzM0e2LQIBRgDv/nP8PFDVMlvrd/HwW0RYV4my2+u3KuyiSnKATsB0nKkvfDEbVMJJJmbmskNlor0cyzyM8shu1YbXkh+2o66O6hV1zSpVJdWot6nMN+RH3o46WaUu6UZw8izI05Gk1cZFz0rGW6XpDD8JqjQJSyZOquNUxt5+uS2lEpbsgjPjI8lIS0kiJDbricz0tIuss3/AFyAqk7wKj0pmiVGHUqPDh09LEWC5AXOXEQglKNRoQdSle1UozPSTmftSMDTPqmO6ePgp50jn/OX4L5NWTX1Vym29KjopVTqdHTXm2qubkAocU4j001yUm2bjakssOKNvQNfERERmZDfyMMr4jPuxVUCY9ITL3nGbioOQmpmWt01xVkWittBMrNS1GgiJJ/ClZJ2zeINbZdo9RZfYRX6JEXTIdza1a6ucTeB09thbprzM2kOPrJ3PWEt3SzLIiG2YvmvRIa4hTYq2HqYdHddlx0yJEhlS6i4em8o9JSzVVZpmszMz1ieXRIG01SckncPWa3yMNr630cWTa9Yp+qkxo8x+dDUxHp++nSZaW+rjNKSUZmrIjNBIUaiLRPLYrsi62norT1Emx0zayzQIkqW3vSG7KkKSllBuLyJJL00mSl5FkfKRkZF2dvF24kQdWTVEXU0Vd6oR6wuntqehIkLfflMNN56Kda5Jf01HmZtr0CJOWkONruJl13WzGZrsiDVtTVOF1rdhaXCTylm64cttKyafJ1xS1rNaDUpTi/bESjIObPBRzprjEWdUV3HU7ZOdRGpVHjypdSqMme5Fo0VuEyb0lS3ltE4nQJJkaVNkolFkZFyjdt4dXgdap9CkUd2BKqladt+FKnKJFMelMm5rUk+jSI0kTThktJGSyQZo0sjy+8jEGpyakzVXqRb7tRKmSKFNVJgOyolSgPRGILcJ9pbukpDLLGihSlm4ZurNS1cRF9ZmKl5zptFqEupsu1C36g7Pp0smDbW244cg0pU0lZNGTW+3ya0UJUhKyLSNKUkUTAPrPHwUyfSZD+cu7f63rjHrBu5tD0hihVGfAZZOUmpwIjrkCQyTqmde1ppQ4aDWhxJGaCP3Jw9HJCzT9H8PLyhQrin1Ogz6TFtemlU6s5UmDjJbSpTCUNI5dJxRPoUSS5CI9I0nkR/Sn4jXNS2WmmZEF9yNFYiw5lQhFNnwlMNTWEPtvKVpa5TNRmtKcXpGpD58WkSVFrNxGuGpUaXQph0d+HKhSKal0qW2idDYlvtSZbcd0le0J1xlg1nkZ+5JIjT7bNgG9fZv/mknAird+E/WS2lesa5rZXCbrVOXCOpVJylwFOkptExxpERxS2jUkiW0ZTo+Tqc0GZqIj9qY5Ct4ZXpRH3ml0d2qsx1padn0InKlBStUp2Dq9LQSvTJ9lxs06HKST40rQpXxql+12tsyEVtMaqSOECqlNlSmlG5RHzXB1yoyczIkrRT2Gjb94SVOGRZqzLmnMY78dkFOenwJFUbfU/Hq8mlokVSJrHELdQ26ozyQtDbDBpUSvcY7bZaJJzOZNBkVjz8FxTuGN9tt05xFtVKSdSffhttRGSkOR34zkhDrD2R6KFEUZ5zPM06CTPSzIyLYOWDe7Uh+I5aldRKjLjNvxVQT3y2qYvVxSNvPS90X7QjyyJftTMlcQ3KL+rzEeRAgR6LTYEqIqBwdT6OTcVhlTk1422iUpSklpz5R8aj/OF4JDlmcW70YiyGGnKOkpTutekFRGykKzmqqJo0yMiNBvqNZJUR5ZmSTTpK0gbPGRl6/h3jNJuBkZevXgutLs+tRo9bkVRESh8AvogzI9bllBlyZTjJyURIzZErWuqZSbpERkjQNJ6ft0Er7U+xLpqtGKv06kyZlLU3IUUmOy46k1RpUOGtnMkmnWqdnRyQ0ZkpZKUZFkkxJF51NbcqPPhUeqwpZwTci1CmJdbjKp8JFOZdjmRlq3N7IJpSvbJVxKNBqSky3tLxKumhOIKgSmKRCjTZcqDTIrRuwYW+50Wepsm1KMl6C4cZCFLzUltCk5+2MwFJOOXj/Pcp5+Es/BbFdgXuhEhxdqV1KIqX1vq3iakoTGJJyFEZH7YkaaeNOZHpFo6Q37mF99Nppp9zlRW5UoSZ5R0MHr4LTktyGyqSR5E2bi2lqIjMzJCVKUSSSvR+icUboRJOWlylKWlsjhk9COYmmrbfdkMPx1uuLWTkdb7ptKWpZIJeRkoiLLdLxdu01EtBW9Hdbeclx3WKI227DlyDmHJnNGajykvlPmJWsyNJpfMiQnJJpltBwO/13dsu3GTnOMmfj6859/BTbAvanuKal2vWGzTNepxLRFN5lx5iQmK4lC0maVZPLQ2RkeSjUWiaizMvidn1pNAfuJRQUR47LkxdNVOSVdOK1LKnuzCi5Z6hElRMGrS0tPP2ppI1F2ZjFy64z2tYjWy2SnEOvs8ApWxK1Lyn4aXSNzNSYq1KNlOZaOkZK0y4h1py8q25RJFCUcDUyWnYq5+8E8MIjPzSqTsNMjPiYVKIn9DRz0syJRJM0niJcUBJ7x3cVzLGGNzS3X2YbtCkqh1BVJnmmttNlTZJGhKGJBKyNC3XHEtNEeems8uIjJR8cdi1smW5Rv0U4i4jU1clustPIZQ5UW6U4Skl7Y1sSXUNvoQSjbzz4y4xzasVa+iQl6FTrehoROk1PQ4MVJU/IlvsTHXn1G4ROrTIjRnW1qTpN71ZRmaEmlWwXiHU5C/6ZR7beZcpEOgSG2qa5EWuJDlszUpbUTppbccdZJa3UpzUta1Gk1KUZzzZ9Sc7H169cVxVJs+sVifUabHVTY82mTW6dJYqNTagLW87KOIlDRKPNeTiT0tEj0S4z4hy83C+9YO9m3aQp2XMkvxY0CI5vqW6caXPhPrMiLQQhLlNlmS3FJI20Ess0mZl1mRXZj9cauEmYMepM1BNVSpiISWXJCZCpROuIMz0lGsy0j4tIkkWQ7Y/ireM0nEVSVT6wzIhqp02NVKY2+xUIy2n2VR3iSaTNBokOF7U0qzJJ6WkWZwKZCrf/L+Kk1VGW5cMmwb2U008i1K6tt7e+pUiCpZu77Mii6JFxnrTUjR4v+8bM8iWg1fWTh/eEZCNOgz1yd7vypUBlg3Z8BuO620pbrRcZpUbqFINvS0kK0uTjHNUTFOv0mdGkvRaTNjsS2XVMnT0MSEsNs0+O7EYeI/c2n2qVAQ6WSjUUcsjTpKzM4tXZHZcjst2+0yqmKojDbVEJtNPhqZ3vvWMZLJSGiazQSTNXKSjM1pStOQolx9evXHHny9ety45eGd8tNR3HbempcmPuRYUROi/LkusyTivoJKDUSDacSrTN00ERJMyM8jy2cOxbln0yNU4cNuQc6tP0GDTWnjcq0yRFbJ2USGiSaNFlJ6S9NaT0SUskmhKlFzr+LF1SXnnXmLeWmTHdgTI6qIRsT4r+euiSM3NJxtZrcNSlK1qjdWo3TMyMtlbGIEy2WZ8Fmj0KVTKrvgqnCdhLSmoIdjvtNMOmTmRstKe0kpIic0SNBOklSiOOacFPOXFHYt6JSpSrWriSQclLhrhG2ls4ekUolGZkSdWaVEZqyJRpMkmo+IbyfhxfdMelsTbXqjaoJPKkKbQ3IaIo7Lb8hSFJUZLJptxCl6GlokfGWZGRclUcVbtrMR6BWFUmqwJUJymyoMynKOFIjreKQ00bCXEtkUdwiUwpJJWgkkk1rT7UzWKt1sS5M5pNDRLlQJEBx8qOkloKUecl1B6WaXHTNSlH7wzUftOJJJTbPd6l5yQV8Vs2sO627NfgLm0KK8i5lWlEKZUzYXV5jbjSHyhoJBqcQzr2VLWZJIkupy0lZpLjGrHvF9qK8xbNYeamojLhuNxtNuUUxxbUUm1EeSjcW24RJTmotWs1EkkmZb5rECutyXZao9DkyTuFd0RH5VJJ5ylTHTZN9yIemWrS7vdkloPSL2hGnRPMxy68W7rcZjR1M0AmIkFqnxmWqUuO3HbakJkkbZIdIkLNxBHrEaK0Zq1ZtGpRmFPH1h5oauC2ScMrrXQnK8mKhbKZrNOZgMoek1KY+9l7m0hDZpM0mptOeloKNwiQtRkZFtYeG97zt8mzb01LUI3UzJDqmyjxzYfYjvkaiUekpC5LOaEaSlJUZoSsiMcmWLN5Jl79bfpDDxP69KY9EYZZRk405oJQXESD1SSNPwkaiPjMzEI+KNxQ6VHocGBbECjRHTdj02JQtXFbUTpPNnkbhmZtLShSFGekegklqcIiIBTxTnTAXEIw/vZ5SCj2tW5SXYz01h1iCtTUhhhZNuPIM8j0c1IyJREpROoMknpJz+zWHd3SKVSKzEpZzYVeiMzaVvR9L78tD8mXFSkkf7KkrhPKWSzLQbNCzPRWRjkDxUu/NhZO0lDrUyPUXnk0hvWz5ERUTez8g88lrbRBitEZEktWyRGRmalHCiYpXjb9Oi0mnTYnBsRhiKiJIgpcQpthuoNEg1kaVlpt1KUhw0KSpZarjI2kGQU4TSbsltaThre9YnwKexbtTjrqCnNCTOiriw4yGXEtPOvLMvaJQtSSPMtI9ItElZlnwvcpcu+pME6FUinQ6WVblwjZLfcaIbet16289IiJHtjTlpEWZmksjy7gjF68yVFW+9TZ6obLLTPCcJyooNUV5D0N9SHHTSb0c0JSh3LSNJZOG7yjZRsTrmhzX6hGRRW5T1ut2xrTpWscbjNJeQk0uKWbhuGl9xKlrUojToJ0dFttKHN3Jzp9S2DFgXOdRdptThtWy7HpyKrJk3RI4Ggx2HZCIjK1uGSjzcfcQylJEZ6ZmSiSSVGnR/Dy+o7qGXbSriXHVSENEUPTS6cQzTI0VEeRkky4jz9vxaGlmQ+ycQbgOaqZJbo1QQ5EdhSKfUKSh+mS0PVJdXPWskos1JlrN1CiURpyJPGnMj55/GG61I1cXeEY5dLRCr8hMdaJtxPpLPfch9taFktCslNE0aCb4y9sRmQc2XWnPBw9bv4rrkPD67JVUh0iRS3KPJnUl6vR11slwmFwo6Dceke1QtZpSRe9QhS+Mj0cszLYnZt1kzEkpt+quRqg0T9PlNRFORpzalk2hxpZcRpWakGkzy0kuIUXtVJM+QO/q85VKZV5KKVMl0h9+TBKRANDTTshTK1ve5rQsnEqjtGlaVJNOR8pKMj5w8YLyU4884dBdelr3zNdcoLRKmyiRHbbmukkySb7bcSO0hRESCQ2RGgzMzNzZDNOdVjuXXiw/vhRxklalb0pqiREScQkqkGpkpBaJGZH+bUlZmeRES0kZkakkfwjWbXZkSmPxWYz82tSHWaTQESf+0c9DLjzL0lMU0+1ZbcjvtqccUnI2lnlopNRc1ExPuiI8iShNIcloalRUzXKcpuamNN1SpMUnW3EKS0pxhl72pktLjZGSyLNJ8dCvmrw40aKuHQqimKzMhJeq1K39Ldh1A31TYDjmmRqjvKkvqUk/bkp1RpcTnkJ5iGqSgeH19FCOoqtG4EwCcNo5SqcskEsnijGky99nrTJvLLlI/gSoyTbAvinLbbn2lXoi3jImUvQTzdM1tNkScjMuNT7JFzm6nLlHKrxTvFyaqoOS4C5a3NYt5VLa0jP3XLi+AiJ5ZFlyFkXwBT8UbrprZssHR3WsmzS3Ko7byG1sIiIYdIsy9u3vJg0meZZpMzI8zIRzUNfCXqf8FtY2Gd9SzNtu3J7cg4HCrMWUjej0uMTMqQp5o15IMkohvHoqUlZnokSTz4urzqNV6W2y9UqZPgNSHXWGHJkVcdDq2VqbeQkzLI1IUlSVJ5SMh2s8SbnXHehvqpciHJKSiVFdpSDbkJlx5EV8lGRkfG3KdyMjIyPRPPiHHXhdki7qo7UH4rUdOTLMY1ZPVAmWIrEVtDz5ElKzPUqeWaUJI3pDysvbZFBl+r6xP4S8VIqqkd3r+K7thngxceKjVWVbWSzt2gKum45UyXBpFFoEDhONR2n5U2TJbQhK5UuO2biiS00Tum6622lTiedVubsV02/EuY8M8XDpk6Q8zEbRh8tyrvtx4EapPS0wSeN84iWJTJlKJJtKWo2yUa0mkuGwpxfquEtXp9yWspiLdFMZNEGpSqCzV3aYtE9NQYkw3t9MPsuEtBNuo0lMSWlLakNPtLNsZLP/lEt0TIbqCHL9dSqqVVFxTn49nsw5LlWbciPt1VpxuppOM+l+CxI1MTUwzcNzSiKQtTYxcXT5g9eupZtp/W9evPdhOhKvub8TbcgU2pXTY+IFpRava1x3nSyuy3YtuSp1NtNqO9XpLcd+Wl3KOiUwZJNJKd0zJsl5HlxUXAXE2dRWbjg4X41zrekxGJ0auwcLJU2jyGZKtFh1ElDhtqS4ZkScjzPMjyy4x3Wp7peuVeoMTJtJs/UN068KVKo8XDaNT6NVWb9Jld1FKbaqaXVqmPtb6JxDqFsvPOG0aEGlpF12x+UHxso0bD6iUt6jxLUwvlyKnTKBZ+FtMt9bceVR3reqKTfJ99LOtp0qQwb2qNKXH1PKQt1S1OYsFpdJrWgu7ZcT8VDywSLd08ZyGHNA7Zz7wFjFIwAxRiVZdBlYV42xa222065SJOFcqPUkpkFIOOrUqcJWTxRZOrMs9ZvdzQ0tExs63gjiDbVFduS48OMZbft9imRq0/XK3hfJpVIYhzHTZiylyHXEoS064RtpcM9HTLRMyPIhlvM/KMYnR7ghz7DoNh2fR6FGolJsmgrwtp91S7WgW5Edi0OE1PcltvLTFOTPeSbaWjUqoSG1GtgyZLreN27pvDGWyDw2RBK0LInMTJtxQKbCblVW4qzV6m7VK/Wn5bcmO2blSdVHS8w4w4zoxG1IShxS3FYh0fcWj1nwHHdPHMYrKUImTXYZ57u3vA38NywhNi3y/9YVw/ko8b/mx2Biz3HosOorgXyxSJyI77Fces00URTMmc7TGZBStfqjaXLYkRkrJWip6M62RmtCkl1/V2/n/rdeMzP4abEPP/APyRZKMW7sVh7QsIpWIuJUzCa3b2ViHS8NnZDJ2jDq7iUNvTWou+dFLi0oPi420rddcSgnHXFrtM+CwwVQOJ0HHG889W4pvPkz0VGWf9wiJuK1jrrmWROOqcIuXLSUasv7xAEQBYCcJcWFe9woxVV+xOGNfUZdkMS9iLFv8A3S4sf8Lrg/5MJhTI5KvQFhexFi3/ALpcWP8AhdcH/Jh7EWLf+6XFj/hdcH/JhMJI5KvQFhexFi3/ALpcWP8AhdcH/Jh7EWLf+6XFj/hdcH/JhMJI5KvQFhexFi3/ALpcWP8AhdcH/Jh7EWLf+6XFj/hdcH/JhMJI5KvQFhexFi3/ALpcWP8AhdcH/Jh7EWLf+6XFj/hdcH/JhMJI5KvQFhexFi3/ALpcWP8AhdcH/JjQ8JMWk8asJ8Vklzqwwr6S/vhhMJI5Kt5X+qyv3Zz/AAKG6w6qsKi12kVOoJd3rF1mb7EZMx+A45HeaYlNsqMkuKjuuNPpQZlmbJZGR5Dt9RwvxPi06oypWGOJsWLFgPSJMqVhxXI0aM220ta3HHFRSShCUpNSlKMiSRGZmRFmKrpWWpayPMjQkyMjzI88jEAirBQQVlWV0YUSY0Cp3LCRcU6RJRBqc1VEUzcVWfhxqS1wtJVrsm2XVpnqXFUlS5OmZrPSJWXU5l02Ml2jPUSg02jz6Vc8SsFPRQF1GOttEiorkodaceSp9gknSjbjqJsyJt1JGk+NVQI96QkM6zVVLL8D+CxpAwHrCSyRlXNhC86zVqtBXX5NWnPy5DkynPzaxHkIyQiRLkuuqcchaWZNw3UrecbJC3FLUk9Lq6qnhtLi1WSxRaRDYp9suy4NNVDcjVE6uchTbLW+Vum5MjPFINwkEgjjtxGyURcZqpYBBdMY70DZK26tcVgT7ROIijxUXMi0odIYfj27vN7fkaLEYS/vtLpJLJ1qc6+s0KVJTKZSeRtnl3CXiDhbVZmhVbYiO01uc/UqZvW2U04qa5I1eTTjaXDJbCDSSnmkpMnlo0iI8yIY6AJqxnJSAAJK+7bxAsGFRZ1t1qhzHKROumfVzhxkLl0tiO8/TVRWtQtRKWpCYrhpdX7dtCVt6KifUaeOk1/C12nvHHoUCDVjTJSwTdtPVClJQuY09x6x1DmvdZ1rLLnG1GLQMkNqM8qVAQDuSWM1fxXThDTVIdolDUdRYq7MxmpzqO83qGinPyCJhonFmSmWltMrJakpfSReAnPqlrXxTUVSTWL3KoVmUqg9yVNVFjRyRSob7EhiRJ0TRorcbQsm0NkSVGUhxWtSaE51YAmrNJCUle8a6MIyMn12y3EW04mOTTNuqfWplEt1Ud1he+Mm32mlNqddUSlPKbQjIk6ee9eu/D2mz6lr6VGkyp8qFV63NpUNM2S/LciTHJzFPqBOETCGpT8X26Dyc0X+My0SGPYAXEhQGyKvFi6cO6WxJk0+nxOG23ZblKlwbScppRFE1NbgrbWuQo0N6tcJpbRpUtThSHlKMzQPjSr4tensxYskpFUoR2rDoJWpJoZLg0hSd5qqriXNaROuynIinCfLj/pBGotJBEVKAFXUlPWr0O7MJY0oyiWVAkRcmWWlTqQ5PeJptTCiU4SnUEb+iuSl1ZaROnHa9solqH1j3fhc2RKVSCUZQ10lKZluHVH0w1NU5hSDdceM0uk21UCYU0eig5CVKJKyJaqGAC4magMlKRVpV+RaTjFIqEGLb1Nq8diNXqtS4cM5lMluLnG2zTWkocNJEiJoSZKVKzUpRtmpKyMxftZxnwrq0q6FMxn4zdcO3kRGe4xiHE39Tqg1Jk1c3jdUqOhLOcUm9FT2qiNGlWTjpHhgAmrqw9evFKd2Kutd1UByEzTjud+RNbtGFSG6tOtkmnjqTdZh1FSjfI/c40ZLTrbLqc1GgzNRe6KSngO6Cl0Ku3XPpNTcfi3JR5xw40SnkqNCemzXEIjyG1noqJmMt50lJyIluNFkRpMyrMBFUpdSzlvnx/h5LIKdiJZL/DBx49QjV6qzaTUjuxinmmnsPQZBuLYiwlmqZFjE2padFl/SUpzNJJJtoh1B+86DEvK4rkoUWqU9E4qw9EXpsqjTXJTD28W1wdUSWWkvrbWojW5xNJ0iM9LOrAETPH1P+SwoG74eCyGjYi4etOqXUaVOqc+XR6dBkVaHS2mqbDKBUY0luNGp0o3VNpNphJvLbdSl1aTQlKULUZ8G1ddjKxIeu2Wg5FPfbalsNSrddqESmTo6oHu29lPJckJcTHlmhbqicQ480t0lqSpQpUBNWM5KS3CQKu2p3VhtKgzW4VLcjVCqpUt556ivmkjU/FkOpnmiUk5a1volPNuJ0CZS60gyMkZF1CkV+jwqfBbmLdefZpDkKWlNLQo1kcp184przIlJdS6lBu5cWr5jIdBAd/V3WW3as2t9ssDGOe5tPPBIlUHTkHNE8JYzwJ44rmaW0RZtM2dtmtbnBrTVzZAzLS3eQTKTjuljKeEwe/0euUSlxoTL8h6YuIxJYW5wMlKnWlymH9QZqPjKQ2282pR5m3rfhLMScqlFmpOFIqjrzLkOmRlyZVI1D8k40lb8lTiyM9D2ji2myTmRoJtJ55ZlXwD0ELlH0vCscLR5s8EwWNopIi4t2ezIcdrzptwmZnCQk3mrkv1RsL7Q+17aIIjnVT5n0q65jmYYnhLDfzucrBfq9pSoaHFQ0FWXmFOzZUukm+ytbrrTrrZoS4nNfG8hD5HmlOjxkZloaMzbHRUKxKfjuvwpjSShwGqTq0QlZGZ6pWkRkWmSMjySZoJRKzUeR1+AzdykaSiWmHa4uj7G57JHGBMEhj2VPbXS8kPJfUDU6RdOkSluqdlZCdBZao4Dpz/tMRU5rpA0zABaKZbhMD6RnZUSu2c0b0vg7ek9Rt6hMOm6pqGpOaFLbVpmZJU2pRKTnxqIlZGeZl8kV20UI3g/TESaWc0pjbDFMOHLhoJMRCGycNwzNzRaeS65xk5mXJmK6AZjlO0y2AyziyWWls/+AOdUZurFRa6r9abZGQkBIKr+pmjto6KbRHmZf8U82ndSZTbLgQZ4umTMrv71YtJSJLCKLCLXE4kpTVNWTjZqRJSS2jNZGjjOMok6JaJksiLn2rNQt5uDDjyFInyYUJ+MyuXQzUwWlNS+RaJOEalLZNxCVqz1Sl/CWRl0oBqHlB0m6MY7rJZqqS3CFSJFzHbmuAJDmNLSQXNMy0gma2Gaq2OHDENseNIEOxiVGYDm73BxEw4gylMSBmJztR+7aE7okhb6XEzWX3pCqM2ZyzRLjvtOqRnkRxmW3Y6Uf7ZOcxmNpAuKiwnIP9PkSI8aozZb0dyjGlOofUpLcVrJWWipJk4ol5pNw0pUWgk9KtgHSi8rGska1ttsSFB2jZAENiACl7X7hFAMy0VTBqxLpuc4nUZqPomHBNnZEiUmfFh3tLd5ZMYHCUpbhJvNX2kIjtumiLIXKYShGg+5GOIpZmhJrLQMzy0VGpOeeSiSRlkR5F8QAfMiZma9iBISCAACFKAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAi7NTa7DixIdPmUpibFZkPSZJuISt15S8tTo5no5IyMj0izMj4jLkG9OvWzqn0otNhLq2iJla3talpeisjM8/gzNBlxH708/gGzosGLUobsRUSKmY7Uo8NqpuvPk4wl5Elaz0CcJs1EUciTmnjM8jzzzL6HQachvXnUpLjJsvySOPGbdUhDDcZS2zVpkk3NKSSM0+09oZ58ZEPWwNStL2ywQdJ2MsfCiNnOsMpNVJa6umZBlOmoSc0zxw40TWCwWa0vsceYiNO6U5iQdMSnIEbpy3HqW8cuK3tdGVGtxEWM1NdkSGEtsO65C47jCSzVnnkbhK0VHoFqyMizMzL6U67oNGk1A4Vvw5NPnxY+lT5LzjMVbzSHs9ckszdZzkOEbSjTnq21EpJpIxspVtR2UzSZmyZDsYpKGWjioYXIXEeebkKLNeWglLWno56zIz9qeiYiqhQ+DYUlb5tusxHZtYKOtMqS0lcdyXFTqTMiQZoaNOajyM1kfwZDas2p+tGjbXtoLWsiMbUCXNc0hztnIEVMnS4vkSP7MF3RDqI2ntD2mzjbElhIBFJBEhVMjB0ptpEgZukBxlZUXFu2m6NFhHhnbUOp0lqS9ArMCOyc6oPvrQbZS3NUhSEIIjM1NmpRm00TZMlrNbUdSqzFQqU6ezTItMjzHEKTTIREiFHShKC0UkREWR6PNnx8ZnxmfKz6BHpcKsLW5IlSGYiZER3e2riNI3+iN7ZZKz1hkS805GREvLPPjEZVJi77mRkQ5MeHSWUuLmRmnJk2qko2EpUlKlE2ekbxOFoZESDy9txGfPbyc6R0XAg2AQ2sdDa6ltbnO/to0Z7w5xqE6ocR83OkGhrWkTLRvxNboOkbRa7bEeSbQ9rohpa2boLAGlokCANvKloxc5xcJBpHAlJiFof0Mjy0dMzMvbZGRnxft48/lGpSopkRHDzMk6GalEo9EiMss+zj/AGDn3bXZZN5tU6Qt6GwuXNJiES0m2k5paLBaeanP6GfEoiItZy+0PPkZNu00pBJQ3JQS3ozMZsmVZ5uO09pe+EmvMiPfCj9zMjIzPj5MuhC5OtYXwnRntayktEi8EmbST9Ge6mRBkZkCW8rROtOi2xGwmEmYnMNOYA3y3zBEsJAldCcUhbi1IRq0KVmlGeeiQgO2nbsZDT770qYhuFGOXMNEJJtukqKqWhMVRrIl5JToKNWREZkfGQ2VWoqKaybjch2SbU5cKSo2UstMGSnNURkZ6ektDelmRGg/bESs0nno2vUvWCw2OJbrRBAhwxN3PYSMQDgHTMjMTAlzXGchNXQNP6MtMZkGG81OlLmkDEA75S3EceIX9QDHmq3dT7EcTNu+tTZbeKktuNMhynqMpiGbMhyLE0WnPbEy2pLZrM83DQajIjPIsPO6m6fGq6frNP8AxQAfL6ivYp3U3T41XT9Zp/4od1N0+NV0/Waf+KABUfQCmad1N0+NV0/Waf8Aih3U3T41XT9Zp/4oAFR9AJNO6m6fGq6frNP/ABQ7qbp8arp+s0/8UACo+gEmndTdPjVdP1mn/ih3U3T41XT9Zp/4oAFR9AJNO6m6fGq6frNP/FGTe5fr1fmXNdKZdfr8tLduNLbRLrkuUhB78QWaSW4ZEeWZZlzgAmo+gk1vN1m9fEnC3dXOLviWqwou4YvRt+w3qeT5yay/TrmU3WE1I3NagkRYy4qomgaFm4hzSSpsiV/LSt4i4Kpp/wD7COX/ANpAAOvYRIH4BaVr/V7fwXZ0e9ISABvrTQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEUkrWgyNK1pyUSy0VmnI08aT4vhL4D5SH0dkSHlKW9IfdcXnprdeW4teeWeZmeZ55Fnz5FzEAC+DEiSEOo0nhMy7vXHMoGMLqiMfMhalKkpQ42mRISh5JoeQmQtKHiM8zJZEeSiMzM8jz4x8tY4emRuOe2Ikr90P3QiLLJXHx8XFkYAJ2sVxa1ziQN2Jwx4Zbz3nMqvZw2sBDRiMcPgvocmSpo2FSZCmFL1imVSFmypR5ZqNOeWfEXHlnxEJpmzUGwaJsxG9kmmMSJbiSjkZZGTeSvakfwknIAEC12ptLmxHTkBvO6Y+w4jI4jFSYMKjFo3nh1L5oefbUlbb77a2zJSFtvLQtJkZ5GRkeZZaSjLLwj5zEilSiUtZSpRLcIycXvlemslGRmRnnmeZpSZ5/CkuYgAYNtdqEE0xHDfuJ9cFAhQiXGkTAyCiqRIW2hpch9bTSTS00p9am2iV74kpM8iJXwkXL8I1VIkOoS05IfcbbUpaG3HluNoNXvjJJnkRn8JkACBarS9rg6I4ghoOJxGGBzA4DhwWMOHDEUtDRITlgMwv/9k=" 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
-license</a> 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.</p>
-<p><img src="data:image/jpeg;base64,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width="274" style="display: block; margin: auto;" /></p>
+<div id="read-data" class="section level1">
+<h1>Read Data</h1>
+<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>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
@@ -2330,41 +2326,6 @@ <h2>Reading Data</h2>
 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>
-<tr class="header">
-<th align="right">wavenumber</th>
-<th align="right">intensity</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="right">301.040</td>
-<td align="right">26</td>
-</tr>
-<tr class="even">
-<td align="right">304.632</td>
-<td align="right">50</td>
-</tr>
-<tr class="odd">
-<td align="right">308.221</td>
-<td align="right">48</td>
-</tr>
-<tr class="even">
-<td align="right">311.810</td>
-<td align="right">45</td>
-</tr>
-<tr class="odd">
-<td align="right">315.398</td>
-<td align="right">46</td>
-</tr>
-<tr class="even">
-<td align="right">318.983</td>
-<td align="right">42</td>
-</tr>
-</tbody>
-</table>
 <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
@@ -2373,24 +2334,9 @@ <h2>Reading Data</h2>
 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>
+supports many spectral file conversions. For instructions, see <a href="http://wincowger.com/OpenSpecy-package/articles/spectragryph.html">Spectragryph
+Tutorial</a>.</p>
 <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>
@@ -2415,373 +2361,522 @@ <h2>Reading Data</h2>
 <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>
+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>Open Specy objects are lists with three components,
+<code>wavenumber</code> is a vector of the wavenumber values for the
+spectra and corresponds to the rows in <code>spectra</code> which is a
+<code>data.table</code> where each column is a set of spectral
+intensities. <code>metadata</code> is a data.table which holds
+additional information about the spectra. Each row corresponds to a
+column in <code>spectra</code>.</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="co">#access the wavenumbers</span></span>
+<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a>scratch_OpenSpecy<span class="sc">$</span>wavenumber</span>
+<span id="cb8-3"><a href="#cb8-3" tabindex="-1"></a><span class="co">#&gt;   [1] 1000 1005 1010 1015 1020 1025 1030 1035 1040 1045 1050 1055 1060 1065 1070</span></span>
+<span id="cb8-4"><a href="#cb8-4" tabindex="-1"></a><span class="co">#&gt;  [16] 1075 1080 1085 1090 1095 1100 1105 1110 1115 1120 1125 1130 1135 1140 1145</span></span>
+<span id="cb8-5"><a href="#cb8-5" tabindex="-1"></a><span class="co">#&gt;  [31] 1150 1155 1160 1165 1170 1175 1180 1185 1190 1195 1200 1205 1210 1215 1220</span></span>
+<span id="cb8-6"><a href="#cb8-6" tabindex="-1"></a><span class="co">#&gt;  [46] 1225 1230 1235 1240 1245 1250 1255 1260 1265 1270 1275 1280 1285 1290 1295</span></span>
+<span id="cb8-7"><a href="#cb8-7" tabindex="-1"></a><span class="co">#&gt;  [61] 1300 1305 1310 1315 1320 1325 1330 1335 1340 1345 1350 1355 1360 1365 1370</span></span>
+<span id="cb8-8"><a href="#cb8-8" tabindex="-1"></a><span class="co">#&gt;  [76] 1375 1380 1385 1390 1395 1400 1405 1410 1415 1420 1425 1430 1435 1440 1445</span></span>
+<span id="cb8-9"><a href="#cb8-9" tabindex="-1"></a><span class="co">#&gt;  [91] 1450 1455 1460 1465 1470 1475 1480 1485 1490 1495 1500 1505 1510 1515 1520</span></span>
+<span id="cb8-10"><a href="#cb8-10" tabindex="-1"></a><span class="co">#&gt; [106] 1525 1530 1535 1540 1545 1550 1555 1560 1565 1570 1575 1580 1585 1590 1595</span></span>
+<span id="cb8-11"><a href="#cb8-11" tabindex="-1"></a><span class="co">#&gt; [121] 1600 1605 1610 1615 1620 1625 1630 1635 1640 1645 1650 1655 1660 1665 1670</span></span>
+<span id="cb8-12"><a href="#cb8-12" tabindex="-1"></a><span class="co">#&gt; [136] 1675 1680 1685 1690 1695 1700 1705 1710 1715 1720 1725 1730 1735 1740 1745</span></span>
+<span id="cb8-13"><a href="#cb8-13" tabindex="-1"></a><span class="co">#&gt; [151] 1750 1755 1760 1765 1770 1775 1780 1785 1790 1795 1800 1805 1810 1815 1820</span></span>
+<span id="cb8-14"><a href="#cb8-14" tabindex="-1"></a><span class="co">#&gt; [166] 1825 1830 1835 1840 1845 1850 1855 1860 1865 1870 1875 1880 1885 1890 1895</span></span>
+<span id="cb8-15"><a href="#cb8-15" tabindex="-1"></a><span class="co">#&gt; [181] 1900 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970</span></span>
+<span id="cb8-16"><a href="#cb8-16" tabindex="-1"></a><span class="co">#&gt; [196] 1975 1980 1985 1990 1995 2000</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="co">#access the spectra</span></span>
+<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a>scratch_OpenSpecy<span class="sc">$</span>spectra</span>
+<span id="cb9-3"><a href="#cb9-3" tabindex="-1"></a><span class="co">#&gt;      runif.n...201.</span></span>
+<span id="cb9-4"><a href="#cb9-4" tabindex="-1"></a><span class="co">#&gt;   1:     0.89334648</span></span>
+<span id="cb9-5"><a href="#cb9-5" tabindex="-1"></a><span class="co">#&gt;   2:     0.07308823</span></span>
+<span id="cb9-6"><a href="#cb9-6" tabindex="-1"></a><span class="co">#&gt;   3:     0.76352810</span></span>
+<span id="cb9-7"><a href="#cb9-7" tabindex="-1"></a><span class="co">#&gt;   4:     0.14088465</span></span>
+<span id="cb9-8"><a href="#cb9-8" tabindex="-1"></a><span class="co">#&gt;   5:     0.98389020</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; 197:     0.46765023</span></span>
+<span id="cb9-11"><a href="#cb9-11" tabindex="-1"></a><span class="co">#&gt; 198:     0.44759757</span></span>
+<span id="cb9-12"><a href="#cb9-12" tabindex="-1"></a><span class="co">#&gt; 199:     0.66183394</span></span>
+<span id="cb9-13"><a href="#cb9-13" tabindex="-1"></a><span class="co">#&gt; 200:     0.12407570</span></span>
+<span id="cb9-14"><a href="#cb9-14" tabindex="-1"></a><span class="co">#&gt; 201:     0.98535463</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="co">#access the metadata</span></span>
+<span id="cb10-2"><a href="#cb10-2" tabindex="-1"></a>scratch_OpenSpecy<span class="sc">$</span>metadata</span>
+<span id="cb10-3"><a href="#cb10-3" tabindex="-1"></a><span class="co">#&gt;    x y  file_name                          file_id         col_id</span></span>
+<span id="cb10-4"><a href="#cb10-4" tabindex="-1"></a><span class="co">#&gt; 1: 1 1 fake_noise 07b1f338633fd67b4899f09a1a58d77d runif.n...201.</span></span></code></pre></div>
+<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">check_OpenSpecy</span>(scratch_OpenSpecy) <span class="co">#Performs many checks to ensure that OpenSpecy files are adhering to our standards. Returns TRUE if it passes. </span></span>
+<span id="cb11-2"><a href="#cb11-2" tabindex="-1"></a><span class="co">#&gt; [1] TRUE</span></span>
+<span id="cb11-3"><a href="#cb11-3" tabindex="-1"></a></span>
+<span id="cb11-4"><a href="#cb11-4" tabindex="-1"></a><span class="fu">is_OpenSpecy</span>(scratch_OpenSpecy) <span class="co">#Checks only the object type to make sure it has OpenSpecy type. </span></span>
+<span id="cb11-5"><a href="#cb11-5" tabindex="-1"></a><span class="co">#&gt; [1] TRUE</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 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">print</span>(scratch_OpenSpecy) <span class="co"># shows the raw object</span></span>
+<span id="cb12-2"><a href="#cb12-2" tabindex="-1"></a><span class="co">#&gt;      wavenumber runif.n...201.</span></span>
+<span id="cb12-3"><a href="#cb12-3" tabindex="-1"></a><span class="co">#&gt;   1:       1000     0.89334648</span></span>
+<span id="cb12-4"><a href="#cb12-4" tabindex="-1"></a><span class="co">#&gt;   2:       1005     0.07308823</span></span>
+<span id="cb12-5"><a href="#cb12-5" tabindex="-1"></a><span class="co">#&gt;   3:       1010     0.76352810</span></span>
+<span id="cb12-6"><a href="#cb12-6" tabindex="-1"></a><span class="co">#&gt;   4:       1015     0.14088465</span></span>
+<span id="cb12-7"><a href="#cb12-7" tabindex="-1"></a><span class="co">#&gt;   5:       1020     0.98389020</span></span>
+<span id="cb12-8"><a href="#cb12-8" tabindex="-1"></a><span class="co">#&gt;  ---                          </span></span>
+<span id="cb12-9"><a href="#cb12-9" tabindex="-1"></a><span class="co">#&gt; 197:       1980     0.46765023</span></span>
+<span id="cb12-10"><a href="#cb12-10" tabindex="-1"></a><span class="co">#&gt; 198:       1985     0.44759757</span></span>
+<span id="cb12-11"><a href="#cb12-11" tabindex="-1"></a><span class="co">#&gt; 199:       1990     0.66183394</span></span>
+<span id="cb12-12"><a href="#cb12-12" tabindex="-1"></a><span class="co">#&gt; 200:       1995     0.12407570</span></span>
+<span id="cb12-13"><a href="#cb12-13" tabindex="-1"></a><span class="co">#&gt; 201:       2000     0.98535463</span></span>
+<span id="cb12-14"><a href="#cb12-14" tabindex="-1"></a><span class="co">#&gt; </span></span>
+<span id="cb12-15"><a href="#cb12-15" tabindex="-1"></a><span class="co">#&gt; $metadata</span></span>
+<span id="cb12-16"><a href="#cb12-16" tabindex="-1"></a><span class="co">#&gt;    x y  file_name                          file_id         col_id</span></span>
+<span id="cb12-17"><a href="#cb12-17" tabindex="-1"></a><span class="co">#&gt; 1: 1 1 fake_noise 07b1f338633fd67b4899f09a1a58d77d runif.n...201.</span></span></code></pre></div>
+<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="fu">summary</span>(scratch_OpenSpecy) <span class="co"># summarizes the contents of the spectra</span></span>
+<span id="cb13-2"><a href="#cb13-2" tabindex="-1"></a><span class="co">#&gt; $wavenumber</span></span>
+<span id="cb13-3"><a href="#cb13-3" tabindex="-1"></a><span class="co">#&gt;  Length Min. Max. Res.</span></span>
+<span id="cb13-4"><a href="#cb13-4" tabindex="-1"></a><span class="co">#&gt;     201 1000 2000    5</span></span>
+<span id="cb13-5"><a href="#cb13-5" tabindex="-1"></a><span class="co">#&gt; </span></span>
+<span id="cb13-6"><a href="#cb13-6" tabindex="-1"></a><span class="co">#&gt; $spectra</span></span>
+<span id="cb13-7"><a href="#cb13-7" tabindex="-1"></a><span class="co">#&gt;  Number Min. Intensity Max. Intensity</span></span>
+<span id="cb13-8"><a href="#cb13-8" tabindex="-1"></a><span class="co">#&gt;       1   1.429324e-05      0.9864365</span></span>
+<span id="cb13-9"><a href="#cb13-9" tabindex="-1"></a><span class="co">#&gt; </span></span>
+<span id="cb13-10"><a href="#cb13-10" tabindex="-1"></a><span class="co">#&gt; $metadata</span></span>
+<span id="cb13-11"><a href="#cb13-11" tabindex="-1"></a><span class="co">#&gt;   Min. Max.</span></span>
+<span id="cb13-12"><a href="#cb13-12" tabindex="-1"></a><span class="co">#&gt; x    1    1</span></span>
+<span id="cb13-13"><a href="#cb13-13" tabindex="-1"></a><span class="co">#&gt; y    1    1</span></span>
+<span id="cb13-14"><a href="#cb13-14" 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="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" tabindex="-1"></a><span class="fu">head</span>(scratch_OpenSpecy) <span class="co"># shows the top wavenumbers and intensities</span></span>
+<span id="cb14-2"><a href="#cb14-2" tabindex="-1"></a><span class="co">#&gt;    wavenumber runif.n...201.</span></span>
+<span id="cb14-3"><a href="#cb14-3" tabindex="-1"></a><span class="co">#&gt; 1:       1000     0.89334648</span></span>
+<span id="cb14-4"><a href="#cb14-4" tabindex="-1"></a><span class="co">#&gt; 2:       1005     0.07308823</span></span>
+<span id="cb14-5"><a href="#cb14-5" tabindex="-1"></a><span class="co">#&gt; 3:       1010     0.76352810</span></span>
+<span id="cb14-6"><a href="#cb14-6" tabindex="-1"></a><span class="co">#&gt; 4:       1015     0.14088465</span></span>
+<span id="cb14-7"><a href="#cb14-7" tabindex="-1"></a><span class="co">#&gt; 5:       1020     0.98389020</span></span>
+<span id="cb14-8"><a href="#cb14-8" tabindex="-1"></a><span class="co">#&gt; 6:       1025     0.60706594</span></span></code></pre></div>
+</div>
+<div id="save-data" class="section level1">
+<h1>Save Data</h1>
+<p>Open Specy objects can be saved most accurately as .rds, .yml, or
+.json files. .rds will be the most reproducible as it is a native R file
+format and floating point errors can happen with .json or .yml.</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">write_spec</span>(scratch_OpenSpecy, <span class="st">&quot;test_scratch_OpenSpecy.rds&quot;</span>)</span>
+<span id="cb15-2"><a href="#cb15-2" tabindex="-1"></a><span class="fu">write_spec</span>(scratch_OpenSpecy, <span class="st">&quot;test_scratch_OpenSpecy.yml&quot;</span>, <span class="at">digits =</span> <span class="dv">5</span>)</span>
+<span id="cb15-3"><a href="#cb15-3" tabindex="-1"></a><span class="fu">write_spec</span>(scratch_OpenSpecy, <span class="st">&quot;test_scratch_OpenSpecy.json&quot;</span>, <span class="at">digits =</span> <span class="dv">5</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>
+<div id="format-conversions" class="section level1">
+<h1>Format Conversions</h1>
+<p>Another great spectroscopy R package is <a href="https://cran.r-project.org/web/packages/hyperSpec/index.html">hyperSpec</a>.
+We actually depend on their functions for several of ours. They are
+currently making some awesome new features and we want to integrate well
+with them so that both packages can be easily used together. That is why
+we created the <code>as_hyperSpec</code> function and made sure that
+<code>as_OpenSpecy</code> can convert from hyperSpec objects.</p>
+<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" tabindex="-1"></a>hyperspecy <span class="ot">&lt;-</span> <span class="fu">as_hyperSpec</span>(scratch_OpenSpecy)</span>
+<span id="cb16-2"><a href="#cb16-2" tabindex="-1"></a></span>
+<span id="cb16-3"><a href="#cb16-3" tabindex="-1"></a><span class="fu">identical</span>(<span class="fu">as_OpenSpecy</span>(hyperspecy)<span class="sc">$</span>wavenumber, scratch_OpenSpecy<span class="sc">$</span>wavenumber)</span>
+<span id="cb16-4"><a href="#cb16-4" tabindex="-1"></a></span>
+<span id="cb16-5"><a href="#cb16-5" tabindex="-1"></a><span class="co">#There are some challenges with reproducing the names and floating point numbers but the underlying data is preserved. </span></span>
+<span id="cb16-6"><a href="#cb16-6" tabindex="-1"></a><span class="fu">identical</span>(<span class="fu">unname</span>(<span class="fu">unlist</span>(<span class="fu">signif</span>(<span class="fu">as_OpenSpecy</span>(hyperspecy)<span class="sc">$</span>spectra, <span class="dv">2</span>))), <span class="fu">unname</span>(<span class="fu">unlist</span>(<span class="fu">signif</span>(scratch_OpenSpecy<span class="sc">$</span>spectra, <span class="dv">2</span>))))</span>
+<span id="cb16-7"><a href="#cb16-7" tabindex="-1"></a></span>
+<span id="cb16-8"><a href="#cb16-8" tabindex="-1"></a><span class="co">#Metadata is not perfectly preserved yet.</span></span>
+<span id="cb16-9"><a href="#cb16-9" tabindex="-1"></a><span class="fu">identical</span>(<span class="fu">as_OpenSpecy</span>(hyperspecy)<span class="sc">$</span>metadata, scratch_OpenSpecy<span class="sc">$</span>metadata)</span></code></pre></div>
+</div>
+<div id="visualization" class="section level1">
+<h1>Visualization</h1>
+<div id="spectra" class="section level2">
+<h2>Spectra</h2>
 <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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+8IN/gILDc3V4DAgsGgy+XKzMw0Go2RTgWLC4G1t7f7/f6CggL2LWSvRe8hMI/HI5fLOyWw5uZmnJX3+/1RKLD169dff/31PavAcnNz46XAdDqdVquN0WGz2WzREdiuXbseeeQRkS/2+XxFRUVarfbkyZPwiMPhyM/P//nnn8PhcHl5OVZgv2UL0fX9th+M1z/52Kw+/2UOefZFK+6ZYdnzXXk8/Tz50Ivn5h7+x+1PfLzrx31f/P22p3ZJ5btffHJzQwAhFDL9sOqJde3jZk3SxfEj4wQYWWInMHZTbbvdHqn6YcDr9ba0tLAtRFBgAlPB7HZ7cnKyRCLp16/f2bNnI/pQl8ul1+tjJDCTyZSWloa4rsBeCwEC42yiERGBWa1WrVYrnsCMRqPwlsPhcHNzcywKrLy83Ol0Tps2rccJLF4KTKvVJicnxyhQrFZrdJ1QvF6v+Kl7Xq9XqVROmTJl79698IjD4ZgyZcqxY8eqqqqSk5OhP9lvXIEFfL6QXKlkyh6ZTIqCwWBc7bykKY998Oz4M39fMmPiBZc/dmTc6n0b71O9u2BA7sARQ/r2m7ry6MhHV/2+X6+LcJybL1VZWRnLRvx+/9SpUxnexe233/7555/Hstn6+nqDwRCFhWi1WlNSUhBC/fr1i7QM5nK5cnJyIrop9ng8jLJBe3s7dENPFAILhUJ+v59z8i/sP5t7IlVg6enpERGY8BhqsVg8Hg9JYJEqsO+++27u3LkajaZnLcR4KTCHwwEEFqNAgVu3KMpgPp9PvNng8/lUKtWkSZMOHDgAjzidzpycnD59+qxfvx7kF4oqxDFnzhyRix70HvDxQsqkWaXN/37uzTLiC4fth15es0szftLQ+PZClBgmLP/yROOZn/fv//Vs2cc3jZy6ctv+jx679oLSadc9vObbw1seGRuHftPxh81mGzp0aIwKDFonMEactra2GG8t6+rqBg8ezGjF5Ha7O7UQbTabXq9HPAR29913C1wDQGARKbD33nvv4YcfJh9JOAXGx1ICTzHmgb300kt4MGLDZrNlZGSIJ7DU1FRhAgNnOBYF1trampOTo1are1aBZWdnu1yuQCBWQwgUmE6ni1GgAJtG4SJGtOyOz+dTKpV9+vRpaWmBRxwOh06nGz169AcffIAJjDPEIdCcEyF06tSpurq6SHe+Z8FHYNL+t734xJDvl40dNPayhzbUmve/tmzJjBEznq6a8cwTC1K6YEeUxn6jJk0anqNGCCGJfvjVD//r7fdef/5/ls7uF1MkOw5oaGjgrO7abLbRo0fHqMDgxGUwjdVqjfFmsK6url+/fgqFghzLYldgH3/8Mb5s2AACi6in3KlTpxivx+tRsT2Q3okoCMzj8UgkEjz679y58/Dhw3zbt1qt4hWY1+vtlMCam5tRRwKLVIG1t7enpaUplcpgMBhj8CFqQOEqJSUl9tQrthBjV2DJycndQ2ApKSn4wgECGzVq1JEjR0gFxjhn4NthkmaLV6/XK5xP7oXgd+Y0pQ9sObLzpZuGhsyB1Exn+U8tfa76+7ZD628dILSGWNwROPz3BRMm3/mfVhG2pd1u38aPYDAYXWFp3rx5O3bsYD9us9lGjRoVI4HBYNEVBJaXl8e4JsXUwKxWK1Zg7BqYw+EQuNKiUGCVlZWM0bYrLMS33nrrkUce6aKhNjoCS05OxgTmcDj4Bg6guuTk5DhaiE1NTRKJBG/Q7/cHAgG4Mfd4PAqFQiSBIYR6UIS5XC6tVms0GmMvg8WxBlZUVBQFgfl8PvGnOtTAUlJS8N6CBTp69GiEEKnAGNt0Op0pKSkwIBw6dGjkyJEMNebz+RKOwHjIKGzb99qzpyY/+btbn516K/F48PjHT3xk/OPyuX26rSYVcNssVodPDPOcPHnyb3/7G9+zfr8/ihO0pqbm119/5ZQdNputpKSkpqYmGAzKZFF2J+k6Auvfvz+4IpmZmfizOlVgNpsNFNiYMWOgY41SqYSnAoEAWTthw+Vy9enT59dffxW/n2fOnGGs/9sVFuL333//+eefl5eXf/jhhzpdnPNAwgRGUgUGI2rhcDj4RmH4OcSLUZEWIulJwh8QwPF4PHq9vtPDziCwuB9SMQDWMRgMpJKora1NS0uLdIlL2JTb7Y6dwIYNGxZFDUxg3Tg2oAam0+kYCmz06NESiYRUYIyTyul0FhUVwXdsbW2tra397LPPlixZQu5G4hNY0Hy2vMYWbNn43uofHYtGS5LIJ0PWLe+8/Mbwi/7cfQQmH//YrhOPiXvtmDFjtm7dyvdscnIyaIuI8M033yAeX9tms2VmZqanp9fV1RUUFES6ZUDXEdiMGTPYCkytVmdnZ7e1tfGRLlZgaWlpgwYN+v7776dPnw5PwaY6VWARNSM4c+ZMUlKHc8xkMg0ePBjFlcBaW1vXrFnz2WefPfnkk6tWrYrLNjHgF+QjMM4AIUMnCSgw+DkiJTDhYai5ublv374MAnO73Xq93u12G43GhFBgMPuYocAeeeSRiy666KabbhJ+Lz7J8aZ0Op3X643dQoy7Avv222+nTZtGXiNsCxH2Pysra+/evcnJyfAgI8Th8XhkMpnRaAQCM5vN2dnZL7zwAklgiajAmDwUtn5x97hRo0Zf9NcDju0PTRzVEaXTH96eNGHSwG41EXsWGzduHDJkCB+BpaSkFBcXx+IisgkM5uVwXkvt7e2vvPKKmM2ChcioS4MCk8lker2e70zFIQ6E0IUXXvjtt9/ip2CX+EZSCONlZWWJtxCbm5sdDgfbQoTBMY41sJaWltzc3EsuuaS1tTUuGyQhrMA43T+2AhP4OSJVYGIsRE4Cg7eLVGCgm3vcQmQoMJvN1qmKCoVCBQUF5PUVlxCH1+sNhUL5+flxrIGFw+Hrr7++rKyM8WJ2DUyr1SKEJk+ejF/GCHEAyeE7WrPZvGjRIqfTiTvhBYPBQCCQ8AQmMVy5tvz06VP7/jJJN/dfx04zUHm24fS6qzLiP6k42PbLF6sf/eM1l0yfUDpy6JChI0snzlp44z3Pvr+rusdyTgi53e7du3cvWbJEmMBiCSKyQxxAAJwEdvLkyRdffFHMZmtra/lqYAghtVrNN8ZhCxEhdNFFF5GKVliBuVwutVod0TywM2fOZGRkMPYEhzjiqMBaWloyMzO1Wm1XrJAZO4E5nc54WYhiQhxNTU35+flsCxEh5Ha7DQZDoigwdg3M4XB0+hPX1NQwHI641MBA1aWlpcWRwM6ePdvW1sa4oMBC1Gq1uHUIWIiM9zLOGQZJm83mtLS0e+6555///CfeLEIo4QkMSZOzi4qL+4+55aUNzy0eUsxAUUGmJu7mYbhlywNTBo254sG39zVIsgaVTpoxc/rEUcUGz6mN/7h15uARi9863kNp6p07d44aNaq4uJjtaweDQXD/i4qKKisr7Xb7O++8E8VHsBWYAIE5nU6IkAnD5/OBRcCpwBBCAjN4cAoRITRp0qQTJ07gC1JYgblcLo1GEymBDR8+nDHidEWIo7W1NTMzU6PR9AiBsb9FRApMvIUIgfKUlJRYFFinBOb3+8FvRD1NYBqNhqHAxBBYRUUF6jgJLy7zwDCBxXEeGEyuYMR0wUKUSCS4DMZJYIzLh/EdzWaz0Wi8/PLLcU9FeHHCERifGajKHzu7j732yJ4TLa6O85YlSX3HThuSGj8R5vx2xR9Wt89d89Mrt4wyMqoyvuZ9Ly9dfM9tr0777t74L+LSKTZt2nTJJZdw3lXZ7XadTieRSIqLi9esWTN16tQTJ07ccsstkX5ERAQGiz4AVQhss6GhITs7WyqVCigwAQLDFqJSqZw2bdr27dsXL16MRCiwSAmssrJyxIgRhw4dIh/EIY549UJ0u90+ny8lJaWbFVgwGAyHwwI1MPgJAoGA2+0WqIGBAhNzKOD31Wg0DALbv3//pEmT8PoDjBoYRNFIBSb8We3t7UajEbbW4xYiW4F1SkJsAgN1EgqFelCBgYMnl3cYkA8ePIh4CAwhBEFEo9EI9iBjm2wFptPpSAU2bNgwtVpN9nRGCUhgvHLKX/7qgkFFpdMunHtxR8y74ZVf4tlKKnBs+3emqQ/8cymLvRBCyqwp9zx/16AjO/ZHMLcofjh69OjChQs5T0pstRUXF+/Zs+e6666LrmDDSWAGg4GPwNC5eTwCgAIYQohPgQmMO6SFiDqWwcQoMIPBEKkCE7AQ41IDA/8QIaTVarui87LAbGWVSsUpnrxeLyYwp9OpVCrjUgOD35ftD1966aX4nAkGg+3t7bm5uThC7fP58KQ0YFZhBYb9Q9RzBBYKhbxer1qtjkKBnThxAnERWFwsxPT09OhCHIjr1vDgwYNDhw5lEBicVwghXAbDNTASjJseTgVGqrQEVWB8BOb77l9Pbddes/ZArcnu6AD7yRemx7MTh0SbrA2bWtv45uj429tsMo0mvs0/ROK7774bPHgw50mJB/rS0tKff/75oYceksvlUQy4nDWw3NxcTGAWiwWn3iMlsChqYIyA1uzZs3fv3g1/i1FgCoVCoVCIFDpnzpwZOHCgXC7HIyYEWGAH4mUhtra2ZmRkIIS6WYEJEBipwBwOR3p6eigU4qQNbCEKNFAgN6tSqRgKLBQK2Ww2vPG2tjaj0ahWq0kLUafTiVdgWCKjniMwl8uVlJQkkUiiqIFVVFTIZDJOAhNpIVZUVLDbf8SowBDrygoGg0eOHJk5cyafAktOTha2EIVrYEajUalUBgIBmB0Lp8H5QmDhttOn7SN/v+LmcXlGnbYDNGplXKtgsiFXLRld8exVS5793wPVNuK0CPtMp/ase/TyG15zL7p+dg8uySyswGQy2ciRI1G0lhenAiMJ7J133nn22Wfh704J7Pnnn7/wwgv/+c9/xqLASALLzc0lO9agzggMISTeRaysrIS+2njANZvNBoMB7Kl4ERipwLqIwDgnewkTmF6vh7EDRh++7DtWYGIOhdfrBQuR/HGtVivoFfhvU1NTVlYWuVc+n89gMIivgXWbAqurq+OrJ4F/iBCKrgY2aNAgNoGJTyHeeOON+K4OAwgsJSUFLGsx28HgvAcqKyvLy8vLy8sTsBBhQQy3282pwDhTiCSBkS/zer3Z2dkWi4Xde7o3g4+LVDqdrL2hsTs6+ciG3PvxJ8vyDzx19YRCg1qXlpNfUNA3L9uo1aQPnP77N1pmvfT1ywviWHOLGDqdDgoV5IMMqw1FO+B2SmAmkwlfV/AyPgILh8Mvv/zyjTfeuHTpUqjG8SkwkSEOhBBM3Yd7NDEWIhJNYG6322w25+bmkooBJzhQDAT2yy+/kBdhNxCYRqOJlMCSkpKSkpLcbjcQmNFo5CQwXAMTbyEyFBiM75iTmpubs7OzGQSm1+uxAoPpzAKf0m0E9vjjj69bt47zKUhwIIRIBQa9/4VVlMVicblcxcXF5HdkDO6dwmazQSGNBBCYRCJJTU2NVIRxWogHDx4cN26cXq8nCczv98tkMqlUis4RGIhReISEGAsRIaRSqfBERp1Ol5SUFFEruB4HT4hDknbVypXrLr/z5n7/evr3MwYYWW3p47sT+Zf+9du5D5z6fte+IxVVTRaHN6hQ6zPy+w8fP31qSY6655cCAxEGsgbASWBRWIgejyclJYVBYDk5OdDkVyqVWiwWfE26XC6ZTEYSmN/vv+GGG55//vm+ffsePnxYo9GQszhjV2BSqVSr1drtdr1eH18Fdvbs2YKCAqlUSoYDSXsqunlge/bsmTlz5okTJ4qLi+GRbrAQObPynRIY/ApAYBqNhjNJD6eZ3W6PmsBgs6QC4yQwt9sdCoWCwWCn88C6jcDKysoGDBhAPmKxWDZu3LhkyRLQTKijAoPzU/gnPnHixKBBgxi7DVtTKpUiLUSHwwGFNBLYe4eKQ05OjphNATgtxAMHDowfP56c74UI+YXOERinf4h45oGRFqLBYEDEbSJMLwMnIIqGDz0FvlZSLRteeuesp6by/rmfPSBTadT/XRVM2ue2r479Y1rca1Jy44CpiwZMjfdm4wNOAhwb5/IAACAASURBVGP8zFErsNTUVPKqgwYfUKbS6XQWiwU/63K58vPzSQL7y1/+snHjxoKCglWrVn355ZeXXXYZufHoUogMYoYxAghMKpV2qsAYrg4fwD9EHbMVOMGBojqeFovlxhtv1Gq1JpMJExhWYElJST6fD24LItqsMOJCYHK5XMBCFNlqiDPEAb+FAIH5/X6DweByudxut0qlSkpK6g0KLBwOHz9+/OKLLyYfLCsre/rpp5csWYItRFKBwWUiTGAVFRWDBw+WSqVsAgsEAiIVGCeB2Wy2Pn36IJ6KgzD4FNjSpUubm5vJ20FOAmP7h4h1/0cqML/fDyctQigpKQkbmCqVCgissLAwov3vQfApMN3oxcsfvoArWCHRDu2BQHsPg53jiJeF6PV609LSGApswIABOp0OhjYGgRUWFmIC279//9tvv719+/ZLL730iSee+PLLL1evXk1unFRgUHaGnC5fiMPtdkulUsg4YWBF5XA4BCr8kSqwM2fOAIHF0UK8/fbbL7vssoqKClLNtLa2QoM4iUSiVqudTidutxMXREdgKpUKfgW4NVar1XwWIphIAgS2adOmYDA4f/58vFmPxxMOh6GUyCawvLw8TgUGncY6Pezt7e1YGHUdgdXW1trtdsaeeDweWAsGW4gMBaZUKjslsEGDBtXV1UUdow+Hw06nk1OBDRkyBCEUxVQwzhrYiRMnhg4d6na7SQUGOgn+BmkuoMDIo0fW+eB+lFFpBmrstA9ZbwPfPDDN4Hm/G9yte9Krwb6rstlsjHEwagWWlpZG0gmMWUBgCCE2gZ06dQr+e8stt7z22mvjx4+fNm3a008/3dDQMHHiRHLjpALD8gvxjztsVkYdCSwtLS1eNbAjR45MmjQJIcRnIUZ6PD0ez4YNGywWy80330xehC0tLWAhonNqrysIjJ0SjEiB6fV6IN1AIODz+fA8PzEx+n379nk8nvnz50OIA25B3G43bIRhITY3N48dO5azBgbLxeGiCB8YCiz27lyk7MYoLy9HrIVdPB6P2Wx2u93YQlQqlWD9wfWSlZXVqYV4/fXXt7e34/Mf8p8ajQbSEJ0KdLfbrVAompub3W63Wv3fpZ7IJqKRKjCv1yuXy8mz3efz+f1+WC+GYSHi+8uUlJT6+nrOSWCIK8SRmZkJdT5cAEMdLURo8JFYBNYLFzrujeAksHhZiGwFxkdgTqcTK7DGxkaLxbJw4UKE0LJly1atWnXppZcy+vOSCgyGJ/ibL8TByNAD8E2uw+FITU3l+474plgkge3duxdat5EKjBzLIq2BtbW1paWlJSUlpaamkgoMW4ioa8pgUSgwYBqSwPCd75o1ax555BH8SjEE5vF44IBzhnQYIQ72BnEKERQY9pT4EF8L0ev19u3bl/14eXm5RqNh7An8t6mpCVuIiDg/7XZ7VlaWcB0LLERyt3EiXyqVgkAX3mGHw5GcnFxUVHTy5EmEUGVlZWNjI2LVwMR9+/+Hz+djdGzBWxOogUGMns9CFOjEwUlgCarAmAT2/rXFBaMf2u1tfv/a4nxOFIxfsb/zKSnnGdi2QBxTiIwaGJvAyBBHUVEREFhZWRleOmH69OmlpaVXXHEFY+NksEqkAmMTGEOB8X1HfMvPuOQ40dzc3N7ePnToUNSxBkYOjpEeTxzWYMwNgj5S8HePEBj+FsePH4c/cNqCTWBlZWWM7rR6vV6hUAgQmNvtZhMYPqQMCxHuYxQKhd/vh6wmQ4F1cw3Mbrc7nU72tysvLy8pKWErMIRQY2MjvltCxM/tdDqzsrJcLhdfEDwQCFRVVfXv3x/yn/AgFnOo4/XCB/i9Bg0aBC7ifffdB/21ORWYxWIZOHDgoEGD5s+fL3BRsFuOWSwWCFlEHeIQ6MQhoMASjsCYFmLJlXf/uW1YgVxnXHzv/ZM4a2C6kr6/uRpYWlpabW0t+UgcCSw7O1uAwMxmM24w43K5srOzfT6f2+0mCQwhtG/fPnxmY+CNIHEExk5woI4EVlRUJGAhwrhmMBjg5lQA+/btmzx5Mng1fAos0uPZ1taGCYxcv41hIfaUAqutrZ09ezbMSWdYiMnJyXgUPnHiBOmnwWkmHHDlVGDk1DpEEBi8RiKRyOVyv98PU6QjrYHFkcBwdJBx9paXl5eWljIGUyCwpqYmknXwDASHwwGTvuGLsD+rsrIyLy8PyoTYJGAQWKdBRJLAXC7X1q1boaMuSWDHjh2DF7/++uvjx49/9NFHL7vssqqqKpgwyga0OiN/Yrw1Rowet+FAERIYVmB8BIYVGFQZEwUsArv6nhL464o/jej2vem1SE9PP3LkCPlIl4Y49Hq9VqvFeSG8WXBOMjMzW1paysrKSktL8bvY7IUEFRhniEOMhchnD0ZUAwMCg7/JGlgsIY7W1tb09HSEkNFoxNN0bDabXC7Hd+td0c+3UwLD7RKwLmQQWHZ2Nr7zPXHiREnJ/1+CLpdLLpcrFArxFiKMbuTva7FYdDodljJ4cIdtwr+gwPDsNAEFFg6HyZuMeBGYy+XCQyqgvLx86dKlsBof+U0RQo2NjaSFiB1jGMrhHoWTwCBDD7uNvyNJYGLmMmMC27p167Zt23Jzc3/55RfEpcD8fv/q1au//vrrIUOG5OTkCERzvV5vZmYmpwJTKpWQmYRvxFZgfDUwzhAHuLLQjYXxMqzAGKu39HLQGpgocNbA4qXAGAQGrhGIJziPZTIZnu+s0WiysrKam5sZCowTSUlJsEwXiiHEQbZcY4c4Pvvssy1btqAIa2D79u274IIL4G9GjD7qeWBYgZE1MNI/RLEpsPr6es4FB/gIDOgEiydgCDhD2CEOIDCbzdbQ0ID3EDu6wocCW4hQWkMsCzE7O5uhwGCbcGLgGhiMkuwQxwsvvIBDdzabLSkpCY+hsRMYZ/a9rq5Oo9Hk5OSwU4gSiYTPQiQJjPOzoADG2G2SACK1EL/44ou77rrLZrOZTCZMYNnZ2ceOHaurq/vkk08GDx4MtyPCc0vYFiJ5K0m6iBHF6MlgEXxNiUSi1Wpra2vP2xoYBSfiRWCff/75H/7wB/IRqIG53W4w7gOBgMfjgcArJjCdTgfXJKicrKyspqYmMQSGiJtK0leJY4hjx44d27dvR5EoMJfLdezYsXHjxuGdiYuFSCowTGCkf4hiI7Ddu3dHRGAMCxG+I+n1kQQGPtjJkyflcjk+GtjRjSLEQVqIfATm8/nA/oJeiFiBMQ77unXrbrnlFngl6R+iuCow8sHy8vKhQ4eytaDH48nIyGBbiAwC47MB+Qgsagvxm2++Wbhw4ciRIw8ePBgIBOD8Hzdu3G233TZ27NjHH398+fLl8C5hAgMLkVOBoY4EJj5GDy4xPm0wzyUnJ3MSGJ7IzLc0Xe/EebW28unTp9euXcv3LJSOottyvEIcra2tjNgxLjxACAI2C4v9YALz+XxOpxPS9lqtNisr66efftJoNOzwMRtwTaampsYrRs/4jmazGa4u8ROZDx48OGLECJJN8U1fLBZiW1sb1BgYBMZQYFE3pK+qquIkP4/HExGBGY1GqVQqk8kYCsxsNsPUH/wpdrsdEv8iFRhfCnHIkCGkhUgSGNx3w+uxAvP5fHgaGUKovb09JSXlpZdeuvfee7uIwBgHFgiM3QHS6/UWFhY2NjYqlUpOAsvIyBBWYEuXLkX8BCbeQoSGyNnZ2f369SspKdm9ezfmG4lE8sgjj8ycOfP999/HE7HjqMDYNTCYQM0GdokRITR1Ol1NTc2cOXPgNWQrqfMhxJHQSEpKYjjpJCApG92W46XA2OsVge0DV51Go8EnLklgTqcT36hqNJrs7OwdO3aIkV+oowITQ2DkcA8AAoPpMkajkTGSms1m+NbiCWzv3r1TpkzB/8WkAuMpnqSlUCgCgQA5kgqDU4HhaCL+rKgV2NmzZznJL1IFxqhUwYBoMBjsdnt5efno0aO/++472AK+ue5UgblcLr/fD3Y0Em0hwgFXKBRwPuB8B4Qe8UDZ3t7+1VdfXXjhhQsWLOg2BVZaWsrWgh6Pp1+/fidPnkxPTycJDEqeDoejsLAQ2xVscCow0oITbyEihAYPHjx9+nSEUElJybvvvsuwLiZOnEhOyuxUgTFOITEWIuwtnwJD59ohwrOkAqupqRFQYOcRgYXMv37+zr+3/XK2VT7riVcvNm+q7nvhtH492Be+E+Tl5T300EN8zz799NOMHhPiAQt04RXnwuEwhMfI14gkMMbVBaMGDKwZGRnYNdLpdG1tbUBgVquVtBAzMzMPHDhwxx13iNlz7IqICXHY7fb+/fuzvzvMReOMqJnNZjjjMYExUuxs/Prrr4sWLcL/xaMtuVIiQghGUlzX6RRkjB5fhGwFFjWB8SmwKAiMbOgFAxCsPvrjjz8uWLAAJxfwXXOnCgx1pEaGhZiVlYVHTwaBQRARXo+TAuDdwaZgbm9JScnjjz9+3XXX3Xnnnd2gwGpray+//HJ2Nc7j8RQWFu7evbuoqEigBsZpAzY3N8vlcth5gRi9SAsRIbRixQqgw5KSEjAVBN5lMBjq6uo4n4L+vOTakgghi8UCfWoQP4HJ5XKlUtnS0sJZA0Md2yHir5mcnPzzzz9jAiNbSanV6oQjMH5F4ilfs2j42MWPvbvz4Pdbtx5tC9RuWD5nxJS7vmoIdeP+9RJIpVJyWLTb7RqNhqHnxBCY0+kUIDBE3HmRCgyuSa/XK5VK5XI5JOm7SIGxu1SAAoPrlp3nNplMDQ0N4XAYExgsac9eMAmjqqqKbLaGk4FkggMQUX9kHOIANwYq2AwCiyWFKKDAdDodrL/MeJxNYBaLBVMySWAIodTU1AMHDpSWluJPEa/AJBKJ1Wplhzi8Xm8wGCQrl5wWIqnAEGErIcLU/dOf/lRQULBixYr4Ehi+MyMfNJlMRqORU4Hl5+e3t7fbbDaBGhjnT4wjiCg2CxHfVVx44YX5+fkIoeHDh0MTZIF3CSgwkD6M0YNUYGSSnozRI4RSUlIaGhoEFBicNoFAwO/3w90JnKt8IY6kpCSZTNYVPa+7CHwEFqp5954H9xY/tPVU7bFPb+0vQ0h5war9X/0+9M4dK7cmzLeLJ0gXkbNWFJ2FCLe6fAQGMzbAFcEMkZWVhRASSWCcCowvxCFQA8MExlZggUDAZDLh3ZNIJHq9XsAwqa6uLigowP/Foy32ADEiKoPht0skEoPBAINavBRYMBisra3lU2AqlYo91xjoRKQCQwilpqY6HA6YvYvXr8ENk4QJzGg0MraM+dJgMOA0hM/ng/IbYtXAGAoMH3bSM3zrrbfUanU3WIhmszk1NZVTgWm12rS0tKqqKs55YAIEhv1DJBjiEG8hYqjV6oEDB0ZNYODWMn5iRogDp6JIBQZPNTY2CluI7O+IEOKL0SOEEkuE8RGY6/ttPxivf/KxWX3+24denn3RintmWPZ8V857c30eg8xxxEJgjKuLrIGhjgTmdDqxAgMCg7MwKytLIpF0kQJjfy+49uC6ZVxmsNrvgAED6uvrMYEhwcsVmpNmZ2fjR3ANDEsoDPEEFgqFzGYzHlvxXXlNTQ3ZqShqAmtoaEhPT/f7/RDGI8EgKvJxYQsRbiPwHX1qamqfPn1SUlLwryNSgbnd7qysLMaWSQLDh5E8BwQUGBn/IwlMr9dv3bqVjNF2kYUIYVS2AoNDnZ2dXVVVhU82xjwwvhoYg8DIeWBRxOgZD5aUlMSiwGC6RaQxeniqqamJj8CwgcFQmYiLwPCWzw8CC/h8IbmStQyYTCZFwWAwkZbsjBfipcCithAxQ+Tn51933XUi1+zhq4GJJzCtVuv3+81mM1uBWa1WrVZbUFBQV1dHEphAGay6ujo/P5/MZQgoMPFTwUwmU0pKCm5Zgu/Kq6qq+vXrR36X6Ajs7NmzhYWFjKW2AHw9D9kEJpfL2ToJDy5GoxE8LuxzircQs7Oz2dSIzq1bSBIYDn+SBCaXy8GExBYipwJDCBUWFpKLCgHHCK/h+/nnnx8+fJjvWWifQR7VUChktVqBd9kKLCkpKScnx+/3R2ohdp0CQwiVlpYyTl0GIrUQxcTo4Sno+cu5ZXzaMEhaLpfjSgFbgRUWFibQosx8BJYyaVZp87+fe7OM8LvC9kMvr9mlGT9paNwXA0sAkDMkOAd6Meu+O51OqIrjR8QQGMNC1Gg0H374ocjdjkiB2e129vdC5/pegwIjvyNYPbm5ufX19bgXIhIkMAajIGK8ZiQGUSQKjPFe+LG8Xm97ezsZMo46Rg+7zTk4iiewrKwsm81GEpjJZFKpVODppaamAoHhnSRDHOxu9xikAmOEOLACAybgVGAKhQKdm8xAhjjgZQwCY0AikXTavf6LL77AuUo22C3k4a5ILpdz1sCAwOAowYPADZCrEghxdCmBLV++/K9//avAuzq1EMUrMEYNDBGHggG8TTJpCZFXfAfJVmAbNmwYNWqUwHfpVeAjMGn/2158Ysj3y8YOGnvZQxtqzftfW7ZkxogZT1fNeOaJBRxD3PkP0pqIRYFB3gE/AsMcHsTxltkKjOw+IB6cCgzuuNk39ZzfCyFkMBiAwBipCrjBz83NraysVCgUuBe+sAIjC2Coo4UYdQ2M8V7YgZqamtzcXLJDPycDlZeXf/DBB8LbxwqMj8A4a2BkM1+Xy5WTk8NQYK2trXg0nDFjxvz58xGXApNIJDKZjJPDgsFgKBRKT08XsBAxE5DnAOwwpBBhZ8xmM1uBsZM1DHTqIrrdboHu7A6HIzMzk7GENMRGgLZJNUASGL4WQL5AdZlPgbnd7ubmZnznxEdgMDVY4LsgHgKTyWRwH8AHgdn9XlbTZ8SvwBgWIgipSBUYOdeIrcASC/wpRE3pA1uO7HzppqEhcyA101n+U0ufq/6+7dD6WwecV3PHRIO8MDiVikgCw/+ic+sASaVSzI58FiJZA4sInAoM8eQ4OFOICCG9Xo8JjKHAgMBOnTpFkivOULDBqcCwhRgvBQYExkmW5OjmdrtHjBhxySWXLF26VNj3xwosUguRbCXVp08fi8VCzgNrbW3Fv+nVV199ySWXIILRyRtnPhcRflaYa8FOIbItRFKB+f1+PCBGp8CQCAJzuVwMAgsGg9DUGJ1bp4r8UXA3FolEwhjW4ZBCAZW8FuDnFqiBnTlzpl+/fvhWhnQ+o54HFhEgSchpzcFPQN4ahsNhcoQRroEhQQJjKzA+AmNsOVEgOLFXkTv11mff37T/l4qTJ47t+eKt5+6YnvtbdA8RQiwCY580YjLfTqdTKpXi7ZAjDoPAQHVxWogRgVOBIa5xB9LYnHdhmMAYwyiMNXl5eSdPniT3zWg08hkmbFLB350d4hBfA2O8F0Y0AbsS0NTUZLPZqqqqcnNzhSdfgwKL0UIUVmDsnSTHSr5DAdFBGOP4UoidhjjQOT+z0xoYG1EQ2O7du5csWQJ/sxWYQDsxrMCkUil5MhuNxtbWVpjJxPkbMdbMJJ1P0nXoOgKTy+VqtZpz4+wQh91uV6vVuKArHKOHOWScH0qGOPA+QxsR8jVwHM47BRa2/fT60gv69bn5Cy9CCHm/uiUzfeAlj35d002LgQVP/+9fHnrn5wi6uXYtyAuDswO0SAVGtu7FA4pAjJ4d4ogIfAqMPe7w+Yfo3DRMAQVWWVnJILCIFJjH4wmHw7HE6BnvhRAH+7MYo5vFYoFxDRSMwPZhUwIWIrtMxUdg5Dww3CiBsZNYgXVKYPCzgkMlkEIUqIF1tQJjW4hmsxk3VIMaGIPAyGm2ZIENE5hGoyFzQEajsba2Fo4VJ4GRjhzeMuw2+RQsESnwXVC0BIb4y2DsEAejJamwAhNwZUgLEb9swoQJN910E37N+arAwpZv7l1091fBix64qVSBEEKKqfevubfk7EvXXvNCWbek6EO1O9eu/vpEr1k7k7wwODtAiyQwsmTNJjB89UJJCRY+AAUWdQ2Mj8AYbpgAgWEFJpfLQ6EQDqFgAiMTHCjCGphEIoHRJJYYPWeIQwyBweAlPHHN7/c3NTXl5+ezLcRQKBQIBJRKZacKzOl0shUY4vJ/MP2Q90kCCowkMEaIA34gXAMjV+VmKzCyBhZfC5Fhz9rtdvwIKDDyR8E1MMSvwBhGd2pqam1tLVySnCEONoHhJD2DwLpIgSF+AsPzwPA3Zeyt8DwwgZ3hDHEUFxdj+Ys61sASkcD46lnunR+tdy9a8/Xr16TBjY40a/wNT3xUKp849pPPK+4fNjx+a1qGm9+/bvKj+1gXp9fS6An+aVD+AxKknPLM/o9uyhLVEq+rwFBg7J6BfKPt22+/XVhYOHPmzHA47Ha7ycuVTWCnTp3CzZxAhIErErUCy87OhnoDOXghrnGHL4KIENLr9c3NzXCpwNeEwRcq/GlpaWq1WgyBeb1ek8nE7j0KyibGEMeYMWMYO9De3s4mMJKB8EghrMBqa2tzcnLkcjn77h4PKNFZiIiHwMRbiJCMBwIjHWlSXgjH6HEKETdr4JvIzAm+tmQYMEWafATWH4G/hS1EtgJTqVQ5OTmwAAJGFAoMn//k6o6wXFYwGCSDPwx0hQIja6VIUIGxLUSBneEMcTBAtpJKRAuRh8DCLrPZnzW6v74jZ8gL+veTWc3WEELxIzBJ6tRrFuRtX33AO2jh7y4bdE7YhGq+fe0z5wXXXj5EgeTFg7hd3m4EeWcXkQLbtm1bSUnJzJkzXS5XUlIS2W8N3zLDVdfY2CiXy7GSAMWDn40uxFFcXHzmzBkkzkLkTHAghAwGQzAYxJFuTGBms3nAgAEIodzcXEaIg/NahVggu6WyRqNpamqCTjbk42JmJgA4QxzxUmBQAENcnajwgBJHAosoxMFQYLBlnU4Hrl2nFiKZQkQICU9k5kQUFqLNZvP5fMAEnCEOyBkiHgWGEBoyZAi5QZLAOEMc7HWCYLcDgYDb7canPawCYbfbDQbDsWPHFi1aFA6HBwwYsHnzZngB/ATRKRUBBcawENkKLGoLka3AGDhPFZjEMKIk/8yGD/beXzrDiEksUPXJv3dLhqyKcw5RUXj5i7vGzn3qlltf2VE7552Xbx9nlCLk32n/94bGa//y7LW9o3sww0IUXwNra2vDEy1hoS92iAM2DqtI4DfqdDo8HkUd4jAYDHK5vLW1tdMUooCFSIadyFtFfPfKIDA+Bcb2DwFarba6upo9FVR8L0R2iKOpqamtrY2h9uRyuUwmw6wjUoFVV1cDEbItxIgIDISFxWLpIgWGf+L+/fvr9frNmzczLEThGhjeJXwmw5xigRUeUMeuFpwABUaevWDTmUwmKH9mZGQwYvS4ywxDgXl5OjsbjcYffviBocCsVqvdbodp19CSn7HbsAwNrF6EHwcX0WAwnD59uqioaM2aNePHj29qaoK3Ry2/kAgFxlcDU6lUEokEfjsGgaWnpwvcXpAhDnxPwH5NQiswvhqYfMyfn73O8+olo6bf/PCqV9auffUfT957zcTxt+8ouPexxZnx9/KU+fOe2nL4i985X7iwdMEz2xt6TekLI+oQR2trK1+nAIaFyCYwGF5jsRDROREWS4gD50oYXzNSAmNLIoBGo6murmYUwFAMIY7U1NTKykrGJDAAefxFKrD6+vrc3FwUswLTaDQGg6GpqQkLHZhBwd7DKEIc0CYYdkYikTz88MPPPvtsRClExFJgZrMZcm58RwaJU2CM7kSgJ0wmEyxxwJDFfCnEcDjMl5QDBQZCCjsla9euXblyJbyAz0JkP47LYCaTKS8vr6ioaMqUKXv37oVnu4jAoIbKp8AQIcIYOmny5MlfffUV3yfic0bAXEl0BcabQpRmL3rz+50vLDIe//Rv99/+x2Ur/vVFdcHv1u7ZunJCNIOoGMgyp96//tCOh9M+vmr0zOWfnXL1qn4mUYc4sAID2ouCwGIJcSCEioqKKisryfoHijDEgXcDdRxJceQkRgUGBMapwMTXwBgKLBQK8ZElJ4ExFNhHH3106NAh+BsTmBgFtnv37rKyMvyUQqGAucZAYFBNJFuiCCgwMjwmHKPX6/VtbW0wpxAev+aaa+rr68+ePSuSwBgKDAisU/8QdVYDC4VCXq83NzeXdBExQ+BbOjExeijXcS7pZzQaGxoacIgDjl5ZWRnOOvJZiMIEBuf21KlT9+zZA892BYHhThx8NTBEJOnZOklgsSF89DhXWme85jxTYAghJMucfPu/vjhwpt0dCAbc7Wd++N9VN40ydG2SQmIo/eP7P3z/4qh9d9/5Xq9auCVqBUZaiIxWN4wamIACi9pCRF2swGCsGTBgAMkfer3ebreTHbMAfAoMLES2AhNZA3M4HBKJhDw4Go1GpVLxfRabwNiDy0cffbR161b4u6GhARNYpwrsww8/XL9+PfkU9LyAoKZer29pacEjBSeBwYDudruhSyE+FAIKTKvVhsNh8veVyWQPPvhgMBg0GAwKhSIUCgWDQfEKDA67SAITUGBQoktPTycJzGazqVSq9vZ2uCIYupYvRs84gUkYjcZAIABHEoIYoVDo+PHjuPs2X4yeU+sAgeFzu6sJrFMLEREKLKKwOz5nRBJYIiowoWpW0F579MiJFlfH3r2SpL5jpw1J7Uoe0w6+bvWeqTdtO9ySMUbUWobdgegUmN1u93g8ZA1Mq9Xi85hRA6uvr2cQGLb1wUKMIsSBECoqKtq7d2+nNTDhFCLiUmDYQrz11lvJ18tkMq1Wa7PZGKNDfX092QqW3JmamhpymWaAyBpYa2srOxRqNBrFExhbgVVXV+Ndra+vh1qaGAvRbrfX19cznrLb7bCWm16vP336NKnA2L8pfApjrBQOcUgkErKXMeDmm28+c+YMhAzh5GS3kiJTiIigsYgUmDCBaTSa1NRUBoHl5+eDhQiFXmjzD14lX4we3+2xASchbrulVqudTufx48dx3YvPQgSCJx9nWIgIodLS0srKSuCAWAhMr9eDID0U+AAAIABJREFUNGeAHeKwWq14NUsATtJHSmCM2aVskBZiIiowXgLzl7+6cM6fNzcGmD6eNO+ubWdWz+zqjhzKvPGX5CGEwqayHT/UaIfPmtC3Z49udAqsra1NJpMxlnuAAQ51tBBhpjCs9QXAc+bVarXf77fZbFErsPfff1+MAmPTAIC0EPHXDAQCLpeLj/PARWSMDoyGCBhgIZLLNAMYh/Sdd97JysqCfkskGhoa2NH81NRUPgLDhpWAAquqqoLlClFHBcawy/CoShIYjIAkgVksFiAqMRYifArjJolBYJs2bRo3blx6ejp2hvV6PWOpF5VK9dxzz+G/vV6v2+3GvwjMvCZTiFKpFP7Ghx2vZikAYQID2yAtLY2sgdnt9n79+mELESjH7XbDoWBYiFiB8SU4EELwenwktVrtyZMn/X6/gAKDT3Q6nYyICttCVCgU48aN279//7x58wTifJ1CWIExamB8FmJENKNSqeCw/wYVmO+7fz21XXvN2gPPXT7E2GFVFYlUkdSN/aQCR1+/5bLX+798dtsdfTpTffv371+2bBnfs+AYRL0j5OAlXoG1trYWFBSIqYF5PJ7x48eT7zUajbgspNVqW1tbY6yBxcVCxFca8BOZ4GLsvNlsJldeRvwEptVqm5qaOEMcZC1t8+bNHo9HJIHl5+fj7uOMz+pUgZlMJpvNVl1djRCCpWSA2sVYiHa73Waz4QnO6ByBwW+n1+vJCXkCNTBhBbZq1ap77rln4cKFeGuMRUkYACbgtBDx3GdQcihyBSZwWblcLlgDk6HARo8eDQQG1xF8ZZ1O53K5gM/gleSMNGELEXUksEOHDo0bN27fvn0g7DhrYB6Px+VysRUYUAUpBKdOnbp7924gsK6ogTF6IbL3FjcX7QoLEfrg4FuZxALfPLC206ftI3+/4uZxefGb8BUVZENu+tf7k5PHGUV4lmPGjHn99df5np02bRrjZI0IkMCGqygiBda/f/8dO3aEQiFsIZIEhmtgCCHSP0QIPf744zgAptVqW1paoiOwvLy89vb2YDDIIDBG0wFhApNKpbCT+EojZ4CywZnj4HuLRqMJh8PsEAejBlZbW3vkyBEy2gDgJDA8d4cBMTUwqNXV1NQghBobGzMzMyE7IMZCdDgc9fX15ECjUqlIAkNE4V1AgTHOMQaB2e12YFwBBUYCmIBzIjPk09RqNd6rSEMcjY2NfM+ChZiWloa798LOFxQUnD17FvMBvjskC2CoowITIDC4kSIJ7ODBg8OHDz969KjZbE5PT48uhUgS2OOPP466rAbGWOeBvVf4vRERmPgQh9/vhxUqxH+dXgI+BabS6WTtDY0+NLiHZxBLM8cuWjJW3GtVKhXZjoG5Ja78UkSAsU8mk4VCIbaQVygUgUAgHA6T50Fra2tWVpZOp7NarZwhDrgm5XK5SqViEBgZGtTpdDU1NdHZF1KptKCg4OTJk+Q+q9Xq5uZm8mUCBKZSqfbv3w8lFnxVCBMY+3L1+Xxer5czzguDO6cCI0ft2trawsLCLVu2XHHFFeTLsMUnBmIUWHV1dUlJyc6dOy0WC8mOYlKIdru9tbXVbrfjo81QYIggsEcffZR9xopRYCSBiVRgjBoYO8SBnyJDHCUlJcLHs1MLERTY0aNH8YM2m61fv34//fQT/o74zoDUPUi0AoMVGsl+tQcPHvzjH/8I4RG1Wq1QKBjjPiYwhkznJLCJEyf+8ssvXq+360IcEolELpfDz8GpwOC9EYXd4ScOBAJer5dv6FAqlcFg0O12J2IBDPGmECVpV61c2f+rO2/+x7cnzb5uirMHHQ3HD+zc9OX6Tz/5dP2Xm3f/XNkuKkLdbYCxj88HZ6/+gM6tUwVyBO6pOScyw8YZBMb4aK/XG50CQwgVFxcrlUqSwiOyEBFCEyZMgD/wSMq4WWaArcAECA++l3CMPhAItLa23n777Rs2bGC8jFOB8QGPlcFg0OVyAaEyCAwUWN++fWtqanCGHokOcahUqrNnz4ohsPnz57NdATE1MExgEKOHLQsnqvksRBziII27rgtxBAIBn8+Xl5eHQxyoowIjCYxRAxMYZKFlKPyt1WrLy8uHDBmSnp7e1tbGFjQokhg9HJyBAwf+/PPPXWchIsJvYO8VvpoiCrvDT8yerM2ASqWy2+2J6B8iXgILt2x46Z2znpr/3D93UJpGrUv+L/SD7tsd72nG7tNfPH7V6D7peUMnzLpk4ZXXXHvNlQvnTS8dkJVRPOuO1w+YesmEMCAwgcZibBcRWhyR6xVx1sAQQpMnTxa428U3qtHteVFREWN0iyiFSIJUYAIVfvjKHo+nX79+8EF8BTB0zkEVnsjc2NiYkZFx5ZVXbty4MRDo0E8apwTFgOzUkJycDKSuVCoVCgX+XYDACgoKampqGAqMTWBwYPGClna7feDAgWfOnBFDYJyAVrxRKDCBzWILUaCVFGkhxitGzw5x2O325ORkeIStwBgniUgFhlgEFggEhgwZkpaW1tbWxmmgwW6zb6qAwILBoNPpJN81YcKEAwcOxJhC5FwSDBMz9hsEFFgUFqKAf4hfZrPZEpTA+FpJ6UYvXv7wBVyWukQ7dEBc62Lhps+WzljyjX7BHf949MLxQwtzjDqlNOCxtdVXHt2/6f1Xl88+3Lpr14rSng/Uw/glkUg6bSyG0dbWVlxcjAkMamCkhYjPLYEZ9ejcEB9HAouoFyIJPJKKqYHt3Lmzurq6qampsLBQWIEpFAr2ZUYq2rq6ury8vD59+vTv33/Xrl2zZ8/GL4tIgWESYtznwhgBx7mqqmrmzJlsBSZsIUJ2w+12d0pgwjfRWOjzEVgoFHI6nViBiSGwTi1EUoFFWgMTYyFiBQanGfTmwCoTH1jGSQJjK/wtTGBz5szBXbC1Wm1qamp2djZYiBkZGWwFxjcPDAjMbDZD3Rc/Pn78+K1bt+r1es5oqxjgJcEYt4nYFYTfCO4qGCNMdASGFVinBEY63okFvhqYZvC833FEuFDQ4/BItbEWk0iEzvz7xQ1Jt36x/5ULO7QOzurTd8DISRcvuWHSNaPueWXn/W/N63EGwwTW6RKoGNAhQkCBiTxvYiSw4uJiMQQWqQITroHV1dV9/fXXCKGWlpbCwkIBBabRaNLT09kuB1kDq62thVz7kiVLli1b9uabb06ePBmeipTAyEa3+HFwEYGroGNIfn4+KDDcmq9TCxFG5Ly8vBgVGFvok6eWw+EIh8OMEEdKSorAudQpgY0ePRpn7kH3BAKBmpoaznl7JMRYiCSBwQiOCaxv375IUIHhbhrCBPb888/jv7VaLXT7BQUWkYUIE5nZ9viECROeeeaZSZMmRa3A0DkeYlxl2BWE34jTmSdrYBHF6M97BdYJFYV8bpfzv7DXfrZ08JjHDsRzQbBgc32zZMjEcXpuj1aSMWFSf1djo6MX2IjCNTDEYyEyamAkgQlMbWEAckqMmariMXjwYAZ54A5Ab7zxxqlTp6CQK+bixCOpACGhc0tKfv3110VFRS0tLUiQ8LRaLbsAhjoeT1BgCKFly5atXLly8eLFL7zwAkLIbreHw2Ex1Is/S0CBwd+MGpjIEIff74elunNzcxkEZjabI7UQGat+kwoMRAnDQjQYDJ0qMIH1wLRaLRa1UAPbv39/cXEx5+9CQoyFCCt0Q2cWUGBqtVoikbS0tHRaAxMzkZkBnU4H5WRQYFHUwNj2+KBBg9ra2qqrq2MnMMaD5Cnk9XrZiwqhjjUwqsBI8BJY2LTnqYsLDdA+/RxS+l73kbVPfkY8FZh8YOkI5d63Xztk46KoYOuuVz840mfE8C5uYSUKUdTA2AqMDHEI31QyPjpq+YUQGjhw4A8//EA+gsedV1555csvv4ThUkyOVnyMfu/evXK5fPr06UBgAoQHq2JyfhY+nliBIYSuvvrqzz777MMPP0QRyi/ET2A4xwEjhdFoBAIjI464ZwR+F0OBQYGHTWBYgcEnCv/ocrlcLpebTCYGgeEVnyFlwAhxLFiw4N577+XbJnCSgAIjAYd948aN7Cl3bLCLqSRg9+RyuU6ng7EbC/3U1NSamhpcA+OM0YtsJcXApEmT5s+fj84pML4amMfj4SQwWK6Mca5KpdKxY8finvfRgZPA8E8AVxYngcEboR8YhG7EQDyBJa4C47ujDx5fs/yvPxfd+9aLJadfvvv99MdfvaWf9ad3Hn8t/Nj7dxbFk8Ak6Vc/9Zd3Zy2f0v/j2ZfOHj+0MCdVp5QEvfa2+sqj+7d8s7s+77ZP7hsX3xVcogOMfeFwODoFBtJNpVIFg8FAICCXy8Vfk9AyLpadZ5ygMO74/f6KioqDBw8uXrxYpIjB37FTBVZfX79s2TK1Wg0ukADhTZ8+feLEiZz7TBIY2WuqpKSkoqLC7/dHlKFHhFvFp8Bwx2EIcVgsFpIg4e34WPERWF1dHZ7ErVQqm5ubxSsw+JTm5mZy1SuoscHfcLfBUGA5OTl8S2YgwkLkW9CSBNDGxo0b33jjDeH9RJ018scNPCG1kZqaiotADALjjNGTF5R4u+Lqq6+GP4QVmMViCQQCjGsZFBjnuT1hwoRt27bFSGDsyZFkiINPgcHJGWmzDPEWYuIqMN4VmX/9qSLr2m/+ctM0hc31yep3lWMump81d3Z++5jbXvrx2lWT4snWyuHLvjky+r2X1ny8ef3LH9eaPaEwkkgUuuyi4RPmPPqfu2+/bHByL9BfBIEJKDAyKhYIBBwOh9FoNBqNjGmbDofDYDB0mwJjAxTYiRMnlErlwYMHRSY4kOgYPQwZCxYs+OWXX6B1lslkKi4u5ns95/VDHk9sIQI0Gk3fvn0rKioiVWDp6ekwAY5PgeGOw3369IGlT0hqB7OLJDAYHRgEFgwGo66BoXPz1vksRFjmCmiDsdA2H/h6IXL2X1CpVC0tLV6vd9y4cZ1umbFUCgO41QWUwfr374/PtLS0tOPHjzNCHOwaWBQKDAPXwNhsBPOv2cSGCYx9bkOjnFgIDKxUxoOMEAdjXQW8V7CmWkQE9ltQYHwxeo/LHdbotBKEkKZoQNaZ46cCCCH1+IumWDZ9cyyeNTCEEELKPlNvfe7D7Ueq2t1+r9tud7h9XltDxf7PX3lgYS9hL8QTDyPBUGBtbW1Go1EqlZI1MES4WBGFOLqCwI4ePXrxxRebTKazZ89GocAEImqZmZkZGRnTpk3LzMzENbBOG+vxfRbqaCECRo0adeTIkUgJbPjw4ceOHUP8CgwTmFwuz8nJYcg7RpKeU4H16dNHIpFwEphSqXz33Xc7HSw0Gk2nBMYIcQhDYB4Ye2dg4cS5c+cKrwQGSE5O9ng82N5kAEIc6ByBoY4WItlCHo5qS0sLOXwzamCREhgoMD4LkY/AHA4HnwJDMRMYJnu32w3czAhxcCowiUQC6xhESmBer7fTcFZCKzC+icz6IcNyqr75+HtzGMn6DxvQ+t324wGEwpaGBofX16Uzm6WKJJ1Oq5L3FtYiEGmIA5+LpIWIiFXPIwpxRN1FlBMQ4jh69OjIkSPHjBmzY8cOkQRGKjBhC7Gurk6pVGICE1ZsnMDH0+/3t7e3MxbVBQKLaBIYQigzM1OhUDQ0NHSqwBBCffv2ZWycEUTkJDC1Wp2amopHBLKVFELo5ptv7nQntVptc3OzAIFlZ2dDK16RCoxzHpjf7+cjMITQvHnzOt0sQkgikXA6YwBsIeK5zKSFiM7xASiwUChUWVk5YMAAck/iosA4LURwQRiPy+VyhUJRX1/PPrezsrKuvfZavobXYkDO7n/mmWcghcQ4hTgJDCFkMBiam5sjtRB/qwoMKcbf/uDMhhdmDFi60W+cc+X0mr8tmnvT0ivn3r81Y+68Ed3YzLc3AS8sKV6Bwe0kGeJAHRVYz1qIR48eHTFixNixY7dv3x7fGhg6V3WLRYHhGlhDQ0NWVhZDEESnwBBC0CVPgMDwqpt9+/ZlKzAyiMhJYAih3NxcUoFhISISGo3GbDaTpxnppsKnwBIb4hWYcIyehEKhMBgMc+fOFbm3Ai4izAND52pgiJhuSBIY3BZUV1enpaWRN2rRpRDJHbNYLCaTiZPA0Dmjm4Hk5OTq6mrOm62PPvooRgWGCay5ubmurg51DHHwKTDYVXIlOTGIyEI8vxQYQrLiWz8/su+jvy0eKJP2ufGNz1ZOlZ0sMxXe/Mb6Z2bEUwkkEiJVYHide6PR2NbW5vP54LLBc5nFE5jRaIylEzEbkML69ddfR4wYMW7cuKNHj4pXYNAUTqFQiDnpSQUWhYUIozajAAaImsBGjBhx7NgxPgvx0KFDo0aNggcLCgqisBARi8BQhHP4tFptKBTiayUFnwKMK/IUUqlULpcLN8hHggSGEGpqaup0CjMG6YwB8DCNmTs9PR2yPKSFiDoqsBMnTjA6E0YX4sCQy+UpKSmwLDXjKTz3gP2u5OTkmpqaSM9VMSAJrK2trampCbFCHHjQYCA6BSYmxJGUlJS4raSEwn3KPhMXL4U/s2Y9+N6sB7tlj3ox4D4RG/ds4Ott9erVV111FanAYMlzyKlHUQO79NJLp0+fHrdvghAs/mQymQoLCxUKhfipVEAq4tkoIyOjra0tHA4Lx+75PguOJ7sAhhDKyspSqVRHjhyJQoHt3buXU4GdPXs2HA7j5QSXL1/OeK8YCxHFTGDwYgELERMYjtELIykpiVyHDCGhFCLqrFcIA6mpqaSF2NjYOGzYsD179gwbNgxbiEVFRdBrBh8iOH/wDH2Xy1VRUcEgsBgtRIRQWlpaVVUVZw0M8RPYiRMnuprA2tvboSMaO8TBSWBGozGKGphIBdbc3Cz+fqVXQSgQH7Ye+9/n7rpq9oSRQ4eVTJh91V3PfvarmblK/G8JYCGKUWAvvPDCnXfeiW+m9Ho9GdiNogYmkUhEpgTFQ61WDx8+XCqV9u3bNysrKyILUTyBKZVKnU5nMpliJDDOlhCjRo1yuVxxsRBBge3du/eCCy7AD6anpzNGk+gsRBS5AkMdCQz3WkTnOMBgMESkwCwWC5vA4rIKFMNCXLZsmdVqBXmBLcSBAweeOnUKdVRgWMTDbQGbwGIMcSCE0tPT/X4/n4XIeUKmpKS43e5uIDA4RDjEIaYGFqmFKD5Gn6AKjH8ic8s3d00af/UT/zklLxw3e84FQwyt3z533bgxSz48y7vo0PkO8SnE1tbWo0ePrlu3Ds5FmUym1+vJZqOR1sC6Amq1esSIEfD3uHHjIorRR+QHZmZmVlZWqlSqSC8SXAOrq6tjKzCE0KhRo1JTUyM9hsOGDauoqGCURkDQ7Nu3j5xtxoZIBfa73/3uD3/4A/4WKGYFxqiBpaSk6PV6s9kssj25SqWyWq0MAuMLcUQK0kL8/PPPjx07Nn/+fFgNGVuIAwYMOHXqVDgcJkMc5BXRdQoMcSktlUollUr5FBji4bYYQUpVTGBi5oGhczWwSBVYIBBgr+/MQELXwPgsxMBPLz6wtmXKqh/W3zf6XIzdX//VsgsX37Pii/kfXiF0QM5bAPEoFAphAoMJwh988MGUKVNwJpjRLbuXENjw4cPh73vvvVdkvCpSBYYQysjIiM6TUSgUoVAIwmkzZ85kv2DUqFGRyi+EkE6ny8rKOnPmDKcCu+222wTey1Bg2ATGvRBh+CNbvkanwKRSKekNclqIcEsupn9KUlKSxWIhNyiRSGQymdPpjIsCw+Pyvffeu27duo8//hgqXliBGQwGSK6TIQ58ReAQx6BBg8gtxxjiQAilp6erVCpOl1WtVgsQWFcoMBzXDIfDJpNJqVRarVYyxGE2m0OhEOfwYjQaI1VgsMZYW1vbb0+BhS2/HqnKvu7RZaOJSViK3Pkr75nmOrC/PN7LqSQIRIY4oPQ1YcKENWvWTJo0CZ4yGo34XVGEOLoCGo1m5MiR8PesWbMwmQkjOgV24sSJ6G5pwfvau3fvtGnT2M/OmTNnxYoVUWx2xIgRMpmMFJ16vb61tbWurk54CUdGiIPh/2AFRiI6BYYrpngjnAQm8vxhKzDYZrwIDBSYy+VqbW2dOnUqLMSFCAWGzokwbCEOGDDgscceg6e0Wm1DQ4Pb7Wbcjqg6rgcWnQLjG74FCEyn03XFgA6pSISQ1WpVq9V5eXmNjY04WaNSqerr69mzmAFRKDDYZjAYFE5Ono8KTKJJS9OEAwFmxSsYCkp0+uR4tpKKI8LhsEBXm9gBgxc0duN8Ac4RwVmITSSEkNFoxNVy3NEjLhWIqPHuu+9iAhOPKBRYZmZmRUVFdLe0KpXq66+/HjNmDOfbU1NTFy9eHMVmhw8fvmfPHpIhdDpdKBSaMGGC8OxdhoXIUGDxIjBoQcrYCCeBiUlwoHMExriHAAIT31uPD7i009DQAO2sMjIyKioqEDEPDCE0cODAkydP4kOk0WhuvfVWeEqj0ZhMJmh1QUL8emB8SE9P54vvChBYV/iHCCGVSgWqF9apyc7OrqmpwcdfqVTW19fzdU+GGpjAmrecUCqVcrlcWKNDQjVBFRjvcipz7vxjxuUrHls4+JmL8+CbhW1H1977tx8GLf3n4LiuBxY/7Nq1i7HYPAmn0ynQ80YMgHhkMpmAAnM6nZjASJCXhE6na25uhvSRGP+ni1BaWhrFu6AuZTKZOu1TjpGZmbl3796BAwdG93GffvrpokWLonivAIYPH84YvCQSSUpKinABDCGk1WrJmyRcwICQRRwVmACBgQuXkpJy9OhR8QrMYrEwmiUqzy0ELH7HOIEVGO5LiRUYthDRuRwHZ2MIuKAYBTAUjxBHWloaH4ElJSXxEVhX+IcAIPv29vb09PSsrKzq6mpywrswgTkcjiiqyJ1KK3wHFtGWewmYBLb5icWv/woOYUinO/aPS/q/23/EsKIMhb2u4mh5vUPR/xLHyargiP69kcNmzJghQFGxn5eYwAQUmMlk4iQwg8GA2+2Ahdiz/mHUwDF68YSUmZl56tQpzna9Yj5u69atr776ahTvFcDEiRNnzZrFeNBgMJARRE6InAdGAq9XIn73xCgwuCUXqcCSkpLYFqJCoZDL5eSyjdEBExjuioJnfTEsxLVr15JNtjDgNYwCGEJIKpXK5XKoEkVXA4O1IDifuuGGG/AamCS6h8CwAiMJrKGhgX0Q8BtR5DSjUqmEC2DoHIGdJxaiQq3V6f6/1aFu0uUFk849kZGWXVSCEEJSt+M3WgL7/8FLKpUK18D4FBgu/sN2EpfAorAQvV5v1DWw4cOHw7KHcUS/fv3efPNNxoPr1q3rtH0tO4UIP6IwgclksogGCGEFhi3ExsZGkdN3oBbCroHF5b6btBAxgcHMfWAgeNnAgQN/+uknzrCrTCZLSkpiKzB0rgwGBBbF9XLxxRfzFXcff/xxzse7gcDa2tqAwI4fP45/AuihLKDAUOQ0o1QqRRLYeaLAZj/8zmyEULh58zOP7xv26F8u79tL6109AYVCIZFI2AMBhsBc+vz8fOhUhM4R2Lp16zij4b0c0YU4ULSxLpVKtXDhwijeGAXwEs8CgBRidXX16NGjrVYrLojCYQmFQuwBWqVSRdoGTHwNTORSMjBIdRGBkRYiEBjMXmdMsu7fv397ezuZzySh0Wg4CQyXwaILcSQlJXHKLAFMnz49imirSDAU2ObNm8n5gqFQSJjAukKBwVE9TxTY/0Oil9V9s6b8ovsv7/ubDMzzAnr88D2LCYx9ld599934b51Ot3Xr1rKysm3btnXRfnYdolNgKNqJNaNGjYouptFFgJuPp59++q677lq5ciVexhoS/5w3N0ql8v/au++AJs4+DuDPZZMESCCRqVgEBSmKLBFBQMBtXbi3qLXaKu5qtWqt41VbR9XWotVate4tbisigoqooIKKCDJU9g6QS+79IxAjkLAC5MLv85fmCZfnkst98zz3PM/VN8D8/Pyq3HpGHmClpaWyVVT09fXruAwHqjw9VXmyGltgeXl5BEGkp6c7OzujylV0i4uLFXdcNu5O2XTDAwcO1Nh7Jh+I2Gw9Fp06dVLWj9d4itfAZF2I8p2SfUbqDTCtb4Epa1+xfH/aH5i2adZvt98WttqJyzWo/tNYkWyAQ41diIrMzc0dHR1DQ0NrXF1Cw8nOpNnZ2fWaB4Ya2gI7dOhQjT/MWwqbzY6Pjz979uyCBQuqXA1VNkGwAQHG5XLlc8zlG5EFmLyXso63FpORPa16C6zxQxARQnQ6XbaenrwFxmKxZAsUVYlMa2trZQu+DB48uMbxn/IWGEm73Kuo0gJLS0tTvAaGlAeYjo4Oi8VqugAjaQtM2TywjBPLl176mHJhnk8Hng5Hn/+Jod3isNZ6EaymixOKZAMcMjIyVE8KtrOzu337tpGRURNUsMnJW2B1XzzN0NCQRqM13XWF5sRms5OTk7/77rvqDUoGg1Fj84JOpzf+TgJ0Ol02CEgeYLKf5I1sganrtCXrRUxLS5P/JhMIBMnJyVV2vGPHjnVcsUyxktoXYLKrDLJ74lRZckzFb18ej1ffd6AuASafhVavLWsIZV2IXPuhs791qanthXHtOmjiEMTmUeuUwNLSUmWLwWgHBoNRWFhIp9PreOpECGEYJhAImmhuTTPj8XgCgSAoKKh6kbIAa8A1sBo3IjuPywNMlgR1H0aPqp2k1NUCQ5UB9v79e/lIfYFAkJKSUmXHra2tld05TBnZbROkUqniUvrkxefzX716JeuBFwqFVCpVcRAHUt4CQwjxeLz6fl5MJrPWXwzyUUj12rKGUDoPrPOg6fWbMtc6VFkfoQoVoxC1hmw8W32bj4sXL67vtXTN1KFDh4SEhBpPCsoCzMnJaefOnY18XVn3mkQiUZwIzGAw6j6MHjXZIA6EEJ/PT0xMVOwsFQqF7969q1K90aNH17iiigqy75R8ugLZKXYh0mg02UpXsqJaA4zP5zfdMHotCzCZkuQ7Z05evR+flidmGrbv4j2K8ONtAAAflklEQVRs9MCuAtV/ouVqDbCioqLi4mLtaG3UiEqlNqA/sPp9SchL2RlBWYCxWKzqa0w0QPWR+vr6+vVqgTVdgBkYGDx79kxx8J5AIHj37l2VFpipqWl9B/gxq92Kk9QUB3EghIyNjRUDDMMwFT3zPB6vvinOYrFqPRdp4zUwhFD567/HdbX1nrz671uxiUnxEed2LhrmZOuzKjSXaMb6aRrVgzhkc+kNDQ1bcH2NZsBgMLTjgpZ6KQswdTEzM0tKSiJXgNW9n1kZ2YL02hdgsqAyNjZW7ELU09NT8YnweLz6fl7bt2+vdRaKVrbApCkHvpt7gTv1eNymEdYcDCGE8MzInYEBS6ev7fPs157qTmtJ1tOLR4+HhD2OT/qQWyjCaTp6grbWXdz8h40b6WXR2C+B+siWCVdWymQyP3z4UN/1ykiHyWRCgFXX1AHm4uLy8OFDsVgs78DU19evY0LQ6XTZZOEqD6rrGhifzw8NDe3evbv8EVmANb7fWPtaYKmpqahy8RFjY2P5asVMJlP1tXNHR8cqkytqVWXxsBqROsCUnYuL7l4N1x2/aUtARXohhGhCt7m7l7mnX7/yAldrHYiMq4t7dnIavuSv8HTMqJNjD28fLzeHDrzS1yG/zPCxsR+1L65MrS/YCLW2wCQSiRZfAJOBFliNmiHAoqKiGtYCQwgxmcwmbYG9efNGsQUmFAo/fPjQ+NEr8hYYSfu4quDz+bJOGtl/FVtgNjY2f/75p4q/XbRo0YABA9ReJVJ3ISppgRHi0lIpR1+vys8zii6PRykVlaq1E7H42orpO7P7/vFo11QHfpXxjeUfw38LHBU0c3ev2/OtNWHsI4fDkS+NU53sIND6AGMymSS9AXmTauoAc3Z2Pn78uFAoVAywuvfRNXWA4TiuuCyIQCCQSqWN70KUD+LQmhaY4nIbxsbG2dnZsn8zGIzq63M2A21sgWH63Zwsk4/tPJOi2Ngqit615z+6g7ONOsdx4M9u3s7xXLw1sFp6IYQYRj2DNs/p9OTWvQI1vmQjdOnSRcUtSFpJgEELrEZNHWCOjo6xsbE5OTnyV+Hz+XVv4lS/r6N6RyEihKpcA0P1XIO/Rlp2DUw2p0L++2/UqFGKa/S0CG1cSgrRun67YcqxgLFdH+0b5OtgacQu//D89vkLD0rcNu8azlfnCAWMo8shcjKzJMioxsqIs7MKqGy2errqG23ixIkqSmsdCKsd4BpYjZo6wDgcjqWl5b1796ZOnSp75Keffqr7vGAWi9WkLTDUNAGmZdfAEEJ8Pl8eYA0Ylql22tgCQwhrM2j3vbv7ZjmKY0IO7t6++9DNZMHAny48uDTfXr07SrUNGNctfkPAuA2nHiQXKDT4iPKc12H//DBswu+ioeN963EvipbTSlpgEGA1auoAQwg5OzsrLuhuZmZW91ds6i5E9HmAyb4FaulC1KZrYOjzANME2ngNDCGEEMXQefL6Q5PXN3EVqLbzjx7LDZyzdmT3HxCNzRfwuQxMUlaYk5UvklIF3SbuuLh9sAEphqVDgLVmyu7wq0YuLi779+9vWEzWGGBqHIVIoVAUx7zx+XwqlaqWLsSysjKRSKRNLTCN6qSh0+my5aFbuiINoSrAihNunYtm+490E0pTQ9Ys2Hg1XbfryGXrv/MQqPkWK7S2A9df67v4dURo+JP4pA95RWUSuo6+sK3Vl65enl1NdEgRXgghRKPRqFSq1gfYtm3bqiw1CxBC+/bta4YWGEKoYa8yaNCgKjdJUGMLzMjIKDAwUDEOKRSKgYGBWlpgd+7cCQ4OXrp0aSM3pSE0rQWGENq6dWvjP6kWoTTAyqPX+/qsfNzp58cjukuOfDdx40O70X7iW8sHjqJE3/iug/rvEkbjW3sOtfZU+3abGZPJ1PoAU8u6EtqnqZtfCKEuXbowmcyGBdi6deuqPKLGAGMymdWHgAsEgsa3wDgcTkxMzN9//92nT59GbkpDODo62tnZtXQtPjN37tyWrkIDKQsw0Y2d257Zr31wdVlnSsb+ozek/XedODjJMKa9s/u/Z9/OWdgECVYzPHrLsG9Ot118bleAsLaWWE5Ozs2bN5VuCcdxXL0z2GrA5XKNjY2b+lVA68RkMmfPnm1hYaGWrZmbm6uYE9J4QqGw8QE2c+bMadOm1XcNe022atWqlq6C9lA2D6wgLa24vU9/Ow5CxZH/3SfcN/dvgyHM2s6aciAlXYKaLcAQLirIyy8qV3oTSQUpKSknTpxQVkqlUpvhaxAfH6/FCyGCFvfrr7+qa1MTJkxQ16ZqtHLlSkdHx0ZupPERCLSYstup6BoZ6Xx4+aqAcKBcPXa52HGFtyGGUHl8TDyu11m3GS9K0VxXhr5cWbfndu3a9fjx48pKe/ToYWlpqa56KQPpBYCMn59fS1cBaDmlt1PxDZzA7/ONp+8fxOO7uM/vwyyIt+dXLli67WXbmX1sW/WC9AAAADSB0ijieP3v2sk2Gw5GZI5Yu23NlHYUyY2bp+OMxuzautq9CSYMNM9ivufOnYuJian780NCQlSv3qvFcBzPzs4m6W2jGy8nJ4fFYrXa/qu0tDTFdaFaFZFIVFJSomkDBZtNRkaGn59frXcRUyRfDav5YQTR4ndHITKuLvlq3NaHIqGtk2PnL0z4XAYFLy3ISnvz7NHjhOK2ATsv/BNo2+jU3Lt378OHD+v1JwcPHjQzMyPpDIlGKikpyczMVNd4AdJ5//59M8zr0lhxcXE2NjbafVcgZfLz84uLi1t8gYyW8u7du549e1aZcaEai8Vau3Ztywy0IZST5sWe3DB7RG9Xe9vOXVx7j5i9/vjTHImKP2iYoiszzFlWY4Mf5+DVyso+3N0y0JTr8eur6mXNwNra+tWrVy3xyi0vLCzMw8OjpWvRYqZPnx4cHNzStWgxVCoVx1vkO9fy/v7770mTJrV0LVqMj4/PrVu3WroWdaW0c4zIuDSnh+vIVSdf075w8fXzsOVlXts41sVp3OG3ErUmKLkW8wUAAKAhlF0Dwx9tW7w3o+emyNMLu1UOOhSnXZjrPypoxblBh4fXo4O0FuRazBcAAICGUNICI/JiniQZj/1hbjeFIfN0s0Grg3qVPLj3QqzGGmjTYr4AAACajbJ5YGxDQzaB41WnD0ukEoyrr6vWUXlatJgvAACAZqN0Hpjf7K+Fw1asHGKzrp+5bBAeURC7d/7/IjsFbrVR872RtWYxXwAAAM2maoBdWTVqT4ysh1DK5T77ZYDVASt7O0shvTA1PvZFWhHdakDRqySJvZWaM0xrFvMFAADQPKoGGF2Hw+VWXIji9hhm0aOyQGhobNkVIYQooiJ1XgIDAAAAGqJqgPl+v99X1fMlpUWlFI7am18aikajNely3ZqsNe87QohGo6nrZo9kxGKxWucsZoQQnU6HI7+la1FXtazEIS0XlYql8mdIcy7M6rHa/NSz/3VvFR9wdnZ2q11RhiCI3NzcVnvn5cLCQiaT2ToXYUGt+8gXi8UikUibbuBSLzk5OXw+nyw/X5QGGJET9vO4SZtvJhfinz0B4/rseHrjW8vWuDwgAAAAzaEshyRxfyxY/9jy231njq70FVqM/u3SlQtH1g23Mh+66+BsSC8AAAAtTekdmWMexRuNubRmUi96QcmxnQcYTn0GGfX1bZvtNHPH/TGberTSnhUAAACaQtlKHKUlIoLN5WAIIbaltVFi3GscIaTj2qdn3uVLz/Ca/woAAABoLkoCDNO3tTNJunQ0IpdAVCs768zbN+NwhIi89PSisvLyFr8DCwAAgNZO6SAOyZvgr3rMuiKdfCF9r8O/w+y/iXUI8NGPPXuxYNzVmN98YGlCAAAALUrVMPry9Mizl/Mcp/Szwj7e2rJk4+mX5abuU1atntxVjxxDLAEAAGgvTbgjMwAAAFBvMCAeAAAAKUGAAQAAICUIMAAAAKTU+gJMWvw+/lFk5OOErLLqZSXv457GfxRVvY9ng4vIRJyf+iL68cv0whpm+YlzE2Nik/Jrmv+noohUpEXpLx49ep5aUH1PJIUpz2ISsstquFqsokhLEKWZr54+Ty2S1KuIVMS5b2MeRD58nlpYfU+0/8gn+UmPaEWk+Q+2j+jEpWAIIYQxzLwXnUksryjDU87PdzdiYAghjGnqvezqBwnRqCKNVRaxtv+kA8mKFS1POPaNi4BW8b4Yeyw8n4JXFEkLonaM6MSlYghhVL3O44JjS4jaizQXHrdz5NBfn4oVH5MWRO8e21mPiiGEME6nkb8/ke9IafzBqQ58GoYQRmFbDt4ckSetQ5HGyr+80Oe7c3k1F4pf75/eb9bhT0eGNOu/n/q008EwhDC6wGX2Cfm3RVWR5qrhyBcnnw3qKfsCI4yiZzd2x/3cio9Ry458SfqRwN4rQz/7mLTgpNeaAqz45pz2NH23Bcej0wvyk0O3D7Og6bhtfI4TBCFJ2tNXn24x6o8HqZnJd7d9ZUITjDj8QUo0uEhjiZP2DzVidt/0Cpc/hL/e6slm2kw5+PhjYUFa5O8BFjT9AftSJQRBEMWhQdZ0XbclF15mfHhxal43DqPLiqhSopYijSXNuBBoybBaFKHwPZbmXJpuQRf4rrnyMjPnTciS7rqsriujygiCIMpjfnZmsTrPPBL7PiPh6ipvPq39rKv5RC1FGksUu9lLjzVwf3ZNh2hZ7CZPXYzusiGu4siQZp0ab0wT9t3w39vMtOj9k6wYXK/tr/FaijRXDUe+JHFnby69fcD2W69zirJiTwa56tHMppzPkRLaduRLC8KXdmXxJ19QqKZWnPRaUYCVhkwT0jsvf1h57pJm/RvAZ3RbG4sT+Iv1zgy9oYeyZJ+D9H1wPzbLa0eShGhgkQaSZv33y9cBXh15VAzRFb/G0oy9/ZhM39/TKw5C/OVGVwZn5PEigiAKzkwQ0Dsvj6p4z0rD5negm826LlJdpIlKov6cM8b/SyEDQ9TPAkyStNOHze2/t3L3S0MX2gmdV0WJCaIsLMiSbjrjSrGsBI9b78zQH/FvjlRlkQaSvLuwZtqQHu25FAwxawywkqjVLjwOm/4pwKTpwX3ZTI+tiRWHc8GZCQL6lyujxSqLNJHSI1/y5hd3ht7Qf7Iq34/y6JVd6PpjThZq0ZGPxx1ZNHGAoykLQ9hnAaYdJ73Wcw2MKMgR6bbv1cOu8l5tmF7bdnxUVFAkJfIfhD/DXPv2NpBN0Mba+PZxlEbffShCDSvSSBida2zjOeqb0Q6cz+ahYwwOh0Hk5+ZXdGdLCwqKCBabTUVI/Pze/QKT3n3sK94zpnNfH4PMiHuvJaqKNBJG1RFadR86Y6K74LNjnsi9feUB8gwYbFzxpjB7bXmW8XC1Ew1JkiMiU9m9+rqzZSVUK39/S9H9u09wVUUaCaPrmdv7Tpj9VccaV+8uCl897TfGgh+H6386Msqjw6Nw2z5+7SreLq5X356M1/ciMwhVRRpJ2ZEvyciTmHf3dOJVPkpr296MUlZYWE5o0ZFPYfLbOw6YOt2/7Wf3IdaSk16ruC8lQgghTDj+yJvxCg+URJ27mqrr6NyRJkl5myLhOLY1qDyQKSbtzOhlyW/TJZKShhQha827ZzVm0HPm6p4IlV/9sO9UrGKJ/sCFC7oN2DJpNmfxkA6SF8c37ProvnxebxZCopS3qcjUwky+N3TTtiboVOI7HFkpL7LXvJ1HiOUwYZUDQnjU8jOHTig8Lnnz4pW4TX8relLo0SvRH2nm9h69vWwNqQghScrbFKKNpxmr8rlU03ZmWGbSuyJCRRHia94yNRTzvkFr+iJUeDh2x+WqGUvk3VgW+JfhyrD5nbbskD8qzUxKLsTM25lVxj3GNW9rQFx9+w6XSpUWIVMNvJWv0iOf7vZT5BuF/0vTLpyNJDrN66aH4Vpz5FM7Dl/+03AkTdoWGvxQ4XEtOem1ngD7jDjt5qbpk7elu234d6gBRrwsKsH0ePqffppT9Hi66EVRMUGIGlLUrPvSeLrdJs4bfHhycNDY/TSE4/Qvvzkx7ksdhIiSohIpps/Tk+8ips/XR6VFRRIVRS2zDw0lzcnOkUoerHLrGi1qb21QlBj3juK68uzFHz30iZLi4s8+YEyfp48ROYVFhIoiTQwwFYjMiwu/PtZ+bcQcG8YNxcdLiksQw5THlu8Nps/TQ8WFxYSKomatuloRBc/+WTQh6KrBxJPfdKEROVp/5BMlWnHSaz1diJUkWZG7p7ra913/xm1TyJkF9gyEMBqdhsrLFBbZJ8pKywgqlYo1rKhZd6ixiMzzs/2m3rLZGJpaVFqan3h1WZuTI/0W3SwgMDqdhhGKu0iUlZYijEqlqChqmb1oKEJcjktSY8tGhbxJiol68iYxbKXNk3UzNj4UVz8siLLSMgKr8bP/VNQyu9FA0vcn5s6+9OX/9szsUOWnLEaj0xBeVvbprEyUlZYRlMqdr7moueqtXiUJZ3/oZ+8SeJYTePTarsFtMNQKjnwtOemR601vLCInfMMAB6/FYeZzTz19cjLITdYSphgKDVBBbt6nOQ1l+XkizEBggDWsqBl3qdGIj2d2/fvBY8UfQZ6mLAqF095/xZ4ljm//2n2pALEEAi7Ky8379F3Nz81HfIEBRVURqWAcLpsqGLliuYeAghDCeK4L5/Znvrl+I0FCMRQYovwqu0jQDQT6mIqiFtmLBioMWT7vvOHI0e2TwkJDQ+/EvseJwrcP74RFp5RQDAQGFGlebsGnXczLzZcd+MqLWmY3GqM84eiMHo4j/8zrty3seeivwyyZCCHUCo58LTnpketNbxw8/reRg9ckuO+4F31+9RArtryAYmbXmV8eE/2i8vKAOPZRrMTYzq4NpWFFzblXjSXNz82TMtoYffr+UdqYtKGU5+UWSWk29jYoITqm8lRFZD998o5ia29DRyqKSIX2RUdLGp3J/NR/T9fV1cEIqRRRrextGR+fPEmr/LaKYqLjkLW9LUtVEYkQog/vc0ue7hzf29vb29vbf0lInjj+z0m9fb8+lCzhdrb/gngeHVM53V+S+CQmn93Z/gsaUlFELkTOlXkDppzifHPhyd09X7sJP9Vf+498LTnpkepc2ziFlzeuu2u+6NjBr6vdDobpPnSAYcLpw5FFCCFE5N0+fCHVZNAQV3pDi0iEatHd1aTk5j8nUyq6hPC3/x66I+nQ3dWIQmnb/ysn/PbhU+8kCCGEvzl+JBzrMaSfMYZUFJEKpV2/wQ555/48nS7LImnaqf0heWaeXlZUjOc7xJsVdfRoXDlCCEnfnz98vchm8GAbKlJRRCJYm+khJbhcyaWpAobzumdlpfe/t6XS7AcPap8dcvhKJoEQQqKnR47H6PoP9eYipKKIVCSvglcfyB342+mN/cyqfGlbwZGvJSe9Fhq+3/zKwxd2oLE79Z00RVHgitPJEoIg8Nd7BggZpp4zVv68fJJrG7ppwKHKGfsNK9JYZVemt6kynTP1xKQOTIZJj7Hzvl/6bYCTkM7uPPtShpQgCEKad2u+nY6e3bCFa9fMHWDN5jitiCiU/ZmKIs0lfrjMlvn5RGaiIOx7BzbT3GPi/KXzJ3qYM1nWgWfSZR+jKHpdDz0dy77frVm7KKALj9lxVkhmxaQhFUUaq+DQEFbN88AIgiDKLgcKGJ8mMhOS9JMTLRgC5/HLfv5xpndbhkHvbc/Lay3SXFWPfGnmvv5MqrHbyM/OCFNm7LxfRmjdkS95u9WD+flEZq046VFXr17dUtnZvPDEiOtvxTp4fp6iAlqn/oO7GVAoBk7DhzmgNw8in6bSHCZt2btxqEVFh0LDijQWkfsq8o2Oe8CALyvboZhe5yHjB1gzclNfxSeJhC6jlu7ctbCnrFMbY33hP8rPKCsm4uGrQhP/JX/8Mc+l4u9UFGkuovDtoxd4lyHDnYXyzgdmO98xAzuUJ8XEJOTpdRm6YPvuZT4VPSI0E4+Rg6xK4iPvP8vQd5+9I3hF78q+EhVFGkuS8TwyTeg/1s+KWUOpNOdlZKKO+4iB9rIreZhu50EBPTlp0RGPEsVWw9cEb59S2UWqokhzVT3ype+fhsbkUqkln50R8ooE7mN9vqBp25FfmvokOveLAWM8Pk0H04aTHtzQEgAAAClp+m9GAAAAoEYQYAAAAEgJAgwAAAApQYABAAAgJQgwAAAApAQBBgAAgJQgwAAAAJASBBgAAABSggADAABAShBgAAAASAkCDAAAAClBgAEAACAlCDAAAACkBAEGAACAlCDAAAAAkBIEGAAAAFKCAAMAAEBKEGAAAABICQIMAAAAKUGAAQAAICUIMAAAAKQEAQYAAICUIMAAAACQEgQYAAAAUoIAAwAAQEoQYABoKyJrX3+28cxr5S1dEQCaBgQYAAAAUoIAA0BDSCUSov5/hIulTVCXSnhJcVkTbh6ARoEAA+Azw3RZXjuSpQghJHn+syODwh3yTw6BEELi8IUdGCYzr5YShTH/zB/k0Janw2Dqm3UdMP9InAghJLo+y5zRYUG4uHJTkrj1zkyDUcdzCYSIwtiD8/vbm+jq6Brb9p7xW3iWLHck8Rtdud4bzm4L6MRn0pl6pl2/WnExGUcIIfG9RVZsz21JlQGF31/Skd3zlzfSir/yWnNktZ85l0lnGVj3WRaSmhm+aYS9EYfNa+syfnd0kbwW76+vHeFgqqvDaWPj/+2BmMLKlFRapec/O3L9dj+9sS7Aqb3bT0/wJn7HAWgoCDAAPuPvgj2+E1GIECJyH0TE44Towd2ocoSQNDkiIpXdy78HfmvpkGkHczx/OHD+8rk9879M/jNw6o54CdLxGD5AmHr5YuUZX5Jw7mys3oBx/fiY5O1fY31nHCvvs/rgqUPrh7OvL+7Xf21UacVL4k+2fL1TPCH4evj1PZN4ERsnBB3NrL0tJo7YsOC6/fpLd28Gj9eP2DzGscu0MIdVJ66dXOWaeXzhgr9kwUfkngyacoI1ZtPBQztm2STund532tF0KUJIdZUkyftnLb1vM3vX7hm2VHW/xQCoCwEAUPD8Zyem2Tc3ygii9Mp0E2Ov3l10uqx6IiakOYeGctn9gj/gif983X/i789w2fOlGcF9WQZTLpYSBFF8eboJo+vqp2KCIAj81SY3pvG0S0UEIbo1x4JhPe92YcVrlEat6MLgjTqeTxB43AYXOtU66E6JrEgcudiaabvsoZggysMXdtDx2PpWQnwq0nHfkiCp+CuWz85kWVnh8ZFcapvxp3OkBEEQeNx6Z6bprBtl0sy9/ZgY1//3pIpNiOM3u+swnH5+jqus0rO13eg02yWRoiZ+qwFoJGiBAfAZ694+bbPu3Y3H8ZcRD4tdJn7nbZQQHplJlD+6+0Ds4OcjpH4x4Y+Qg7PsUElWUmzElUPbjz8VEwSBEELsXsP6G8RfCkmQICRNOn/2iXDoWB8OwuNuhb4XevXuVJYtU2Tu5dNO9PRRvKythhn06ttdB1X8W2hAqdicalRzJ0cT2ReYwTPg0m27O+ljCCFE4RnwMYlEInsaw33k8HYV33Nax4mTPbEX9x8V1FolfTfvbiy1vakANAlaS1cAAM1Cd/D14u0Ku/chkxaZ2Mm7p7e1G/PQ3QcFPZ5G5HT8yteCgqQfb28OWvzbxccfkcDC2q6LHlv+O5DtNbw/78ily8lLOqILZx8bD1/vqYNQeebHDDxt7xCTvYovRDHJK5BddKLwDQ0a8FOSSqViCv+l0elY9SdhOsbG+vLHMZ6pMVv6Lr8Ir61KbYyF8OsWaDo4RgH4nI6bnwcjOjT0zr0n/O5ulnpunt1K74fdCgt/bezj15lGZB6dPWJVdKdV15Ly8j8kRN88MP3LT78DOV7D+ulFX7ySknTxTLR5wBh3JkII43C59MqeRTlJ+h9+DNlfYTUkT3XSgvzCeo84JEQfP+bL23NEQUamiG4o5FFrrRIFzg1A48FBCkAV+r38XcrC9uwOL3Nyd2BQjNx7dki9teNoFNfL35mB8NfRT4stRwRN7WHOoSCEyhNfJyuM0+P6DOvLeXD+r/2nH7UfOcaFjhBCdDt3F86ra1cTKp8nfff3hG7dF14rVl0RDMOkeTm5FaFV9uTm3az6j7Mvv3fybGrFJiQJh/8OxZx6dec0tEoAaBIIMACqwIy8fe0ywu4k2bq76CFEs/XswXt6O1zcs09PHYRoX9jZMN+c/HXPtajHEZf3LRk0csdraWnyi+cZIoQQQtzew/qwbm/eEmk1anTXiqYZb9CiuXZPVw8Z9eO+szdunN4eOGTuWeQ3oidHZT1oFradmHHBS9edjogKP7t50sSdr+qfXwgVX180eNovxy5d+Gft6IHLI00mfz/RgtLAKgGgUSDAAKiK2qF3b0sa1ay7mwUFIcRw9nTVwZiu/l76GEKYycRdhxfbPl8/ysdv/Iqzkimn7/052uThjwNXhsqWbNL1He7PKZPYjx5lJ+9aZDmvvBiy1j3r36XjBg+f83uiw48XLv3kXktYYKaTdh5c2C3998m9XD2Gr33hsXa+O72++0LvNi94rUvSH3NGjV14KL3r/KNXt/XjYw2tEgAaBavTeCcAQMshyguyiukCvk6dLpUB0GpAgAEAACAl6EIEAABAShBgAAAASAkCDAAAAClBgAEAACAlCDAAAACkBAEGAACAlCDAAAAAkBIEGAAAAFKCAAMAAEBKEGAAAABICQIMAAAAKUGAAQAAICUIMAAAAKQEAQYAAICUIMAAAACQEgQYAAAAUoIAAwAAQEoQYAAAAEgJAgwAAAApQYABAAAgJQgwAAAApAQBBgAAgJQgwAAAAJDS/wFIbAgQdmA6EAAAAABJRU5ErkJggg==" style="display: block; margin: auto;" /></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">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>
+<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" 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="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" tabindex="-1"></a>                               <span class="co"># if it isn&#39;t already but are not changing your</span></span>
+<span id="cb19-2"><a href="#cb19-2" 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 class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" 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 id="maps" class="section level3">
-<h3>Maps</h3>
+<div id="maps" class="section level2">
+<h2>Maps</h2>
 <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">$</span>x) <span class="co"># will show more advanced example </span></span></code></pre></div>
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-<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" tabindex="-1"></a>                                          <span class="co"># later in tutorial</span></span></code></pre></div>
+addition the spatial information can be plotted as a heatmap. It is
+important to note that when multiple spectra are uploaded in batch they
+are prescribed <code>x</code> and <code>y</code> coordinates, this can
+be helpful for visualizing summary statistics and navigating vast
+amounts of data but shouldn’t be confused with data which actually has
+spatial coordinates.</p>
+<p>In R the user can control what values form the colors of the heatmap
+by specifying <code>z</code>, it is handy to put the information you
+want in the metadata for this reason. This example just shows the x
+values of the spectra.</p>
+<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" tabindex="-1"></a><span class="fu">heatmap_spec</span>(spectral_map,</span>
+<span id="cb21-2"><a href="#cb21-2" tabindex="-1"></a>             <span class="at">z =</span> spectral_map<span class="sc">$</span>metadata<span class="sc">$</span>x) <span class="co"># will show more advanced example </span></span></code></pre></div>
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+<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" tabindex="-1"></a>                                          <span class="co"># later in tutorial</span></span></code></pre></div>
 <p>An interactive plot of the heatmap and spectra overlayed can be
 generated with the <code>interactive_plot()</code> function. A user can
 hover over the heatmap to identify the next row id they are interested
 in and update the value of select to see that spectrum.</p>
-<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" tabindex="-1"></a><span class="fu">interactive_plot</span>(spectral_map, <span class="at">select =</span> <span class="dv">100</span>, <span class="at">z =</span> spectral_map<span class="sc">$</span>metadata<span class="sc">$</span>x)</span></code></pre></div>
-<div class="plotly html-widget html-fill-item-overflow-hidden html-fill-item" id="htmlwidget-f412c9891a1a4791365b" style="width:98%;height:288px;"></div>
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+<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" tabindex="-1"></a><span class="fu">interactive_plot</span>(spectral_map, <span class="at">select =</span> <span class="dv">100</span>, <span class="at">z =</span> spectral_map<span class="sc">$</span>metadata<span class="sc">$</span>x)</span></code></pre></div>
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+</div>
+</div>
+<div id="combining-openspecy-objects" class="section level1">
+<h1>Combining OpenSpecy Objects</h1>
+<p>Sometimes you have several OpenSpecy objects that you want to combine
+into one. The easiest way to do that is by having spectra which all are
+in the same exact format with the same series of wavenumbers. The
+default settings of <code>c_spec</code> assume that is the case.</p>
+<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" tabindex="-1"></a><span class="co">#Same range example. </span></span>
+<span id="cb24-2"><a href="#cb24-2" tabindex="-1"></a><span class="fu">data</span>(<span class="st">&quot;raman_hdpe&quot;</span>)</span>
+<span id="cb24-3"><a href="#cb24-3" tabindex="-1"></a>raman_hdpe1 <span class="ot">&lt;-</span> raman_hdpe</span>
+<span id="cb24-4"><a href="#cb24-4" tabindex="-1"></a>raman_hdpe2 <span class="ot">&lt;-</span> raman_hdpe</span>
+<span id="cb24-5"><a href="#cb24-5" tabindex="-1"></a></span>
+<span id="cb24-6"><a href="#cb24-6" tabindex="-1"></a>raman_hdpe1<span class="sc">$</span>spectra <span class="ot">&lt;-</span> raman_hdpe1<span class="sc">$</span>spectra <span class="sc">-</span> <span class="dv">100</span> </span>
+<span id="cb24-7"><a href="#cb24-7" tabindex="-1"></a></span>
+<span id="cb24-8"><a href="#cb24-8" tabindex="-1"></a>raman_hdpe2<span class="sc">$</span>spectra <span class="ot">&lt;-</span> raman_hdpe2<span class="sc">$</span>spectra <span class="sc">-</span> <span class="dv">200</span> </span>
+<span id="cb24-9"><a href="#cb24-9" tabindex="-1"></a></span>
+<span id="cb24-10"><a href="#cb24-10" tabindex="-1"></a>combined_spectra <span class="ot">&lt;-</span> <span class="fu">c_spec</span>(<span class="fu">list</span>(raman_hdpe, raman_hdpe1, raman_hdpe2))</span>
+<span id="cb24-11"><a href="#cb24-11" tabindex="-1"></a></span>
+<span id="cb24-12"><a href="#cb24-12" tabindex="-1"></a><span class="fu">plot</span>(combined_spectra)</span></code></pre></div>
+<p><img 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" /><!-- --></p>
+<p>If you have different wavenumber ranges for the spectra you want to
+combibne, you can set <code>range = &quot;common&quot;</code> and <code>res</code>
+equal to the wavenumber resolution you want and the function will
+collapse all the spectra to whatever their common range is using linear
+interpolation.</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>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="cb25-2"><a href="#cb25-2" tabindex="-1"></a>  <span class="fu">read_any</span>() </span>
+<span id="cb25-3"><a href="#cb25-3" 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="cb25-4"><a href="#cb25-4" tabindex="-1"></a>  <span class="fu">read_any</span>() <span class="co"># preserves some metadata</span></span>
+<span id="cb25-5"><a href="#cb25-5" tabindex="-1"></a></span>
+<span id="cb25-6"><a href="#cb25-6" tabindex="-1"></a>combined <span class="ot">&lt;-</span> <span class="fu">c_spec</span>(<span class="fu">list</span>(asp_example, ps_example), <span class="at">range =</span> <span class="st">&quot;common&quot;</span>, <span class="at">res =</span> <span class="dv">5</span>)</span>
+<span id="cb25-7"><a href="#cb25-7" tabindex="-1"></a></span>
+<span id="cb25-8"><a href="#cb25-8" tabindex="-1"></a><span class="fu">plot</span>(combined)</span></code></pre></div>
+<p><img 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" /><!-- --></p>
 </div>
+<div id="filtering-openspecy-objects" class="section level1">
+<h1>Filtering OpenSpecy Objects</h1>
+<p>OpenSpecy objects can have any number of spectra in them. To create
+an OpenSpecy with a subset of the spectra that is in an Open Specy
+object you can use the <code>filter_spec</code> function. Filtering is
+allowed by index number, name, or using a logical vector. Filtering will
+update the <code>spectra</code> and <code>metadata</code> items of the
+<code>OpenSpecy</code> but not the <code>wavenumber</code>.</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></span>
+<span id="cb26-2"><a href="#cb26-2" 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="cb26-3"><a href="#cb26-3" tabindex="-1"></a>  <span class="fu">read_any</span>() <span class="co"># preserves some metadata</span></span>
+<span id="cb26-4"><a href="#cb26-4" tabindex="-1"></a></span>
+<span id="cb26-5"><a href="#cb26-5" tabindex="-1"></a><span class="fu">filter_spec</span>(spectral_map, <span class="dv">150</span>) <span class="sc">|&gt;</span></span>
+<span id="cb26-6"><a href="#cb26-6" tabindex="-1"></a>    <span class="fu">plot</span>() <span class="co">#extracts the 150th spectrum. </span></span></code></pre></div>
+<p><img src="data:image/png;base64,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" /><!-- --></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><span class="fu">filter_spec</span>(spectral_map, <span class="st">&quot;8_5&quot;</span>) <span class="sc">|&gt;</span></span>
+<span id="cb27-2"><a href="#cb27-2" tabindex="-1"></a>    <span class="fu">plot</span>() <span class="co">#extracts the spectrum with column name &quot;8_5&quot;. </span></span></code></pre></div>
+<p><img src="data:image/png;base64,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" /><!-- --></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">filter_spec</span>(spectral_map, <span class="fu">names</span>(spectral_map<span class="sc">$</span>spectra) <span class="sc">%in%</span> <span class="fu">c</span>(<span class="st">&quot;8_5&quot;</span>, <span class="st">&quot;10_3&quot;</span>, <span class="st">&quot;12_10&quot;</span>)) <span class="sc">|&gt;</span></span>
+<span id="cb28-2"><a href="#cb28-2" tabindex="-1"></a>    <span class="fu">plot</span>() <span class="co">#extracts the spectra with column names &quot;8_5&quot;, &quot;10_3&quot;, or &quot;12_10&quot; using a logical argument based on the spectra column names.</span></span></code></pre></div>
+<p><img 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" /><!-- --></p>
+<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">filter_spec</span>(spectral_map, spectral_map<span class="sc">$</span>metadata<span class="sc">$</span>y <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">|&gt;</span></span>
+<span id="cb29-2"><a href="#cb29-2" tabindex="-1"></a>    <span class="fu">plot</span>() <span class="co">#extracts the spectra with y values of 1 for their metadata. </span></span></code></pre></div>
+<p><img src="data:image/png;base64,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" /><!-- --></p>
 </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
+<div id="sampling-openspecy-objects" class="section level1">
+<h1>Sampling OpenSpecy Objects</h1>
+<p>Sometimes you have a large suite of examples of spectra of the same
+material and you want to reduce the number of spectra you run through
+the analysis for simplicity or you are running simulations or other
+proceedures that require you to first sample from the spectra contained
+in your OpenSpecy objects before doing analysis. the
+<code>sample_spec</code> function serves this purpose.</p>
+<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" tabindex="-1"></a>spectral_map <span class="ot">&lt;-</span> <span class="fu">read_any</span>(<span class="fu">read_extdata</span>(<span class="st">&quot;CA_tiny_map.zip&quot;</span>))</span>
+<span id="cb30-2"><a href="#cb30-2" tabindex="-1"></a><span class="fu">sample_spec</span>(spectral_map,<span class="at">size =</span> <span class="dv">10</span>) <span class="sc">|&gt;</span></span>
+<span id="cb30-3"><a href="#cb30-3" tabindex="-1"></a>    <span class="fu">plot</span>()</span></code></pre></div>
+<p><img 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" /><!-- --></p>
+</div>
+<div id="processing" class="section level1">
+<h1>Processing</h1>
+<p>The goal of this all processing is to increase the signal to noise
+ratio (S/N) of the spectra without distorting the shape, position, or
+relative size of the peaks. After loading data, you can preprocess the
+data using intensity adjustment, baseline subtraction, smoothing,
+flattening, and range selection. The default settings 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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" 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>
+simultaneously and really quickly.</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>
+<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" tabindex="-1"></a>processed <span class="ot">&lt;-</span> <span class="fu">process_spec</span>(raman_hdpe,</span>
+<span id="cb31-2"><a href="#cb31-2" tabindex="-1"></a>                          <span class="at">active =</span> <span class="cn">TRUE</span>,</span>
+<span id="cb31-3"><a href="#cb31-3" tabindex="-1"></a>                          <span class="at">adj_intens =</span> <span class="cn">FALSE</span>,</span>
+<span id="cb31-4"><a href="#cb31-4" tabindex="-1"></a>                          <span class="at">adj_intens_args =</span> <span class="fu">list</span>(<span class="at">type =</span> <span class="st">&quot;none&quot;</span>),</span>
+<span id="cb31-5"><a href="#cb31-5" tabindex="-1"></a>                          <span class="at">conform_spec =</span> <span class="cn">TRUE</span>,</span>
+<span id="cb31-6"><a href="#cb31-6" tabindex="-1"></a>                          <span class="at">conform_spec_args =</span> <span class="fu">list</span>(<span class="at">range =</span> <span class="cn">NULL</span>, <span class="at">res =</span> <span class="dv">5</span>, <span class="at">type =</span> <span class="st">&quot;interp&quot;</span>),</span>
+<span id="cb31-7"><a href="#cb31-7" tabindex="-1"></a>                          <span class="at">restrict_range =</span> <span class="cn">FALSE</span>,</span>
+<span id="cb31-8"><a href="#cb31-8" tabindex="-1"></a>                          <span class="at">restrict_range_args =</span> <span class="fu">list</span>(<span class="at">min =</span> <span class="dv">0</span>, <span class="at">max =</span> <span class="dv">6000</span>),</span>
+<span id="cb31-9"><a href="#cb31-9" tabindex="-1"></a>                          <span class="at">flatten_range =</span> <span class="cn">FALSE</span>,</span>
+<span id="cb31-10"><a href="#cb31-10" tabindex="-1"></a>                          <span class="at">flatten_range_args =</span> <span class="fu">list</span>(<span class="at">min =</span> <span class="dv">2200</span>, <span class="at">max =</span> <span class="dv">2420</span>),</span>
+<span id="cb31-11"><a href="#cb31-11" tabindex="-1"></a>                          <span class="at">subtr_baseline =</span> <span class="cn">FALSE</span>,</span>
+<span id="cb31-12"><a href="#cb31-12" tabindex="-1"></a>                          <span class="at">subtr_baseline_args =</span> <span class="fu">list</span>(<span class="at">type =</span> <span class="st">&quot;polynomial&quot;</span>, <span class="at">degree =</span> <span class="dv">8</span>, <span class="at">raw =</span> <span class="cn">FALSE</span>, <span class="at">baseline =</span></span>
+<span id="cb31-13"><a href="#cb31-13" tabindex="-1"></a>                            <span class="cn">NULL</span>),</span>
+<span id="cb31-14"><a href="#cb31-14" tabindex="-1"></a>                          <span class="at">smooth_intens =</span> <span class="cn">TRUE</span>,</span>
+<span id="cb31-15"><a href="#cb31-15" tabindex="-1"></a>                          <span class="at">smooth_intens_args =</span> <span class="fu">list</span>(<span class="at">polynomial =</span> <span class="dv">3</span>, <span class="at">window =</span> <span class="dv">11</span>, <span class="at">derivative =</span> <span class="dv">1</span>, <span class="at">abs =</span> <span class="cn">TRUE</span>),</span>
+<span id="cb31-16"><a href="#cb31-16" tabindex="-1"></a>                          <span class="at">make_rel =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb31-17"><a href="#cb31-17" tabindex="-1"></a><span class="fu">summary</span>(processed)</span>
+<span id="cb31-18"><a href="#cb31-18" tabindex="-1"></a><span class="co">#&gt; $wavenumber</span></span>
+<span id="cb31-19"><a href="#cb31-19" tabindex="-1"></a><span class="co">#&gt;  Length Min. Max. Res.</span></span>
+<span id="cb31-20"><a href="#cb31-20" tabindex="-1"></a><span class="co">#&gt;     579  305 3195    5</span></span>
+<span id="cb31-21"><a href="#cb31-21" tabindex="-1"></a><span class="co">#&gt; </span></span>
+<span id="cb31-22"><a href="#cb31-22" tabindex="-1"></a><span class="co">#&gt; $spectra</span></span>
+<span id="cb31-23"><a href="#cb31-23" tabindex="-1"></a><span class="co">#&gt;  Number Min. Intensity Max. Intensity</span></span>
+<span id="cb31-24"><a href="#cb31-24" tabindex="-1"></a><span class="co">#&gt;       1              0              1</span></span>
+<span id="cb31-25"><a href="#cb31-25" tabindex="-1"></a><span class="co">#&gt; </span></span>
+<span id="cb31-26"><a href="#cb31-26" tabindex="-1"></a><span class="co">#&gt; $metadata</span></span>
+<span id="cb31-27"><a href="#cb31-27" tabindex="-1"></a><span class="co">#&gt;   Min. Max.</span></span>
+<span id="cb31-28"><a href="#cb31-28" tabindex="-1"></a><span class="co">#&gt; x    1    1</span></span>
+<span id="cb31-29"><a href="#cb31-29" tabindex="-1"></a><span class="co">#&gt; y    1    1</span></span>
+<span id="cb31-30"><a href="#cb31-30" tabindex="-1"></a><span class="co">#&gt; [1] &quot;x&quot;                 &quot;y&quot;                 &quot;user_name&quot;        </span></span>
+<span id="cb31-31"><a href="#cb31-31" tabindex="-1"></a><span class="co">#&gt; [4] &quot;spectrum_type&quot;     &quot;spectrum_identity&quot; &quot;organization&quot;     </span></span>
+<span id="cb31-32"><a href="#cb31-32" tabindex="-1"></a><span class="co">#&gt; [7] &quot;license&quot;           &quot;session_id&quot;        &quot;file_id&quot;</span></span>
+<span id="cb31-33"><a href="#cb31-33" tabindex="-1"></a><span class="fu">summary</span>(raman_hdpe)</span>
+<span id="cb31-34"><a href="#cb31-34" tabindex="-1"></a><span class="co">#&gt; $wavenumber</span></span>
+<span id="cb31-35"><a href="#cb31-35" tabindex="-1"></a><span class="co">#&gt;  Length   Min.    Max. Res.</span></span>
+<span id="cb31-36"><a href="#cb31-36" tabindex="-1"></a><span class="co">#&gt;     964 301.04 3198.12 2.54</span></span>
+<span id="cb31-37"><a href="#cb31-37" tabindex="-1"></a><span class="co">#&gt; </span></span>
+<span id="cb31-38"><a href="#cb31-38" tabindex="-1"></a><span class="co">#&gt; $spectra</span></span>
+<span id="cb31-39"><a href="#cb31-39" tabindex="-1"></a><span class="co">#&gt;  Number Min. Intensity Max. Intensity</span></span>
+<span id="cb31-40"><a href="#cb31-40" tabindex="-1"></a><span class="co">#&gt;       1             26            816</span></span>
+<span id="cb31-41"><a href="#cb31-41" tabindex="-1"></a><span class="co">#&gt; </span></span>
+<span id="cb31-42"><a href="#cb31-42" tabindex="-1"></a><span class="co">#&gt; $metadata</span></span>
+<span id="cb31-43"><a href="#cb31-43" tabindex="-1"></a><span class="co">#&gt;   Min. Max.</span></span>
+<span id="cb31-44"><a href="#cb31-44" tabindex="-1"></a><span class="co">#&gt; x    1    1</span></span>
+<span id="cb31-45"><a href="#cb31-45" tabindex="-1"></a><span class="co">#&gt; y    1    1</span></span>
+<span id="cb31-46"><a href="#cb31-46" tabindex="-1"></a><span class="co">#&gt; [1] &quot;x&quot;                 &quot;y&quot;                 &quot;user_name&quot;        </span></span>
+<span id="cb31-47"><a href="#cb31-47" tabindex="-1"></a><span class="co">#&gt; [4] &quot;spectrum_type&quot;     &quot;spectrum_identity&quot; &quot;organization&quot;     </span></span>
+<span id="cb31-48"><a href="#cb31-48" 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>
+plot.</p>
+<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" 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>
+most processing operations. All other processing functions can be tuned
+using its parameters in the single function and we have set defaults and
+nested the processing functions in a way that provides typically quality
+results. 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. See explanations
+of each processing sub-function below.</p>
+<div id="threshold-signal-noise" class="section level2">
+<h2>Threshold Signal-Noise</h2>
 <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>
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" width="400" style="display: block; margin: auto;" /></p>
+spectra of that good quality and more often use 5. The Run Signal Over
+Noise technique searches your spectra for high and low regions and
+conducts division on them to derive the signal to noise ratio. This is a
+good way to automatically calculate the signal to noise ratio. In the
+example below you can see that our signal to noise ratio is increased by
+the processing, the goal of processing is generally to maximize this.
+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>
+<div class="sourceCode" id="cb33"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1" tabindex="-1"></a><span class="fu">sig_noise</span>(processed, <span class="at">metric =</span> <span class="st">&quot;run_sig_over_noise&quot;</span>) <span class="sc">&gt;</span> <span class="fu">sig_noise</span>(raman_hdpe, <span class="at">metric =</span> <span class="st">&quot;run_sig_over_noise&quot;</span>)</span>
+<span id="cb33-2"><a href="#cb33-2" tabindex="-1"></a><span class="co">#&gt; intensity </span></span>
+<span id="cb33-3"><a href="#cb33-3" tabindex="-1"></a><span class="co">#&gt;      TRUE</span></span></code></pre></div>
 <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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" 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+through spectra individually. Setting the <code>min_sn</code> will
+threshold the heatmap image to only color spectra which have a
+<code>sn</code> value over the threshold.</p>
+<div class="sourceCode" id="cb34"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb34-1"><a href="#cb34-1" tabindex="-1"></a>spectral_map_p <span class="ot">&lt;-</span> spectral_map <span class="sc">|&gt;</span></span>
+<span id="cb34-2"><a href="#cb34-2" tabindex="-1"></a>    <span class="fu">process_spec</span>(<span class="at">flatten_range =</span> T)</span>
+<span id="cb34-3"><a href="#cb34-3" tabindex="-1"></a></span>
+<span id="cb34-4"><a href="#cb34-4" tabindex="-1"></a>spectral_map_p<span class="sc">$</span>metadata<span class="sc">$</span>sig_noise <span class="ot">&lt;-</span> <span class="fu">sig_noise</span>(spectral_map_p)</span>
+<span id="cb34-5"><a href="#cb34-5" tabindex="-1"></a></span>
+<span id="cb34-6"><a href="#cb34-6" tabindex="-1"></a><span class="fu">heatmap_spec</span>(spectral_map_p, <span class="at">sn =</span> spectral_map_p<span class="sc">$</span>metadata<span class="sc">$</span>sig_noise, <span class="at">min_sn =</span> <span class="dv">5</span>)</span></code></pre></div>
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12 , z:  5.51839794871479 <br>snr:  5.5 ","row:  126 <br>x:  7 , y:  13 , z:  5.22972527203411 <br>snr:  5.2 ","row:  127 <br>x:  7 , y:  14 , z:  6.73906259464869 <br>snr:  6.7 ","row:  128 <br>x:  7 , y:  15 , z:  NA <br>snr:  2.7 ","row:  129 <br>x:  8 , y:  0 , z:  NA <br>snr:  4.5 ","row:  130 <br>x:  8 , y:  1 , z:  NA <br>snr:  3.9 ","row:  131 <br>x:  8 , y:  2 , z:  5.33671542673158 <br>snr:  5.3 ","row:  132 <br>x:  8 , y:  3 , z:  5.25432194851142 <br>snr:  5.3 ","row:  133 <br>x:  8 , y:  4 , z:  NA <br>snr:  4.9 ","row:  134 <br>x:  8 , y:  5 , z:  NA <br>snr:  3.3 ","row:  135 <br>x:  8 , y:  6 , z:  NA <br>snr:  3.5 ","row:  136 <br>x:  8 , y:  7 , z:  NA <br>snr:  3.6 ","row:  137 <br>x:  8 , y:  8 , z:  NA <br>snr:  4.1 ","row:  138 <br>x:  8 , y:  9 , z:  NA <br>snr:  5 ","row:  139 <br>x:  8 , y:  10 , z:  NA <br>snr:  4.7 ","row:  140 <br>x:  8 , y:  11 , z:  NA <br>snr:  5 ","row:  141 <br>x:  8 , y:  12 , z:  NA <br>snr:  4.9 ","row:  142 <br>x:  8 , y:  13 , z:  NA <br>snr:  4.7 ","row:  143 <br>x:  8 , y:  14 , z:  NA <br>snr:  3.7 ","row:  144 <br>x:  8 , y:  15 , z:  NA <br>snr:  2.8 ","row:  145 <br>x:  9 , y:  0 , z:  NA <br>snr:  3.7 ","row:  146 <br>x:  9 , y:  1 , z:  5.22560158832018 <br>snr:  5.2 ","row:  147 <br>x:  9 , y:  2 , z:  NA <br>snr:  4.9 ","row:  148 <br>x:  9 , y:  3 , z:  NA <br>snr:  4.7 ","row:  149 <br>x:  9 , y:  4 , z:  NA <br>snr:  4.7 ","row:  150 <br>x:  9 , y:  5 , z:  NA <br>snr:  4.5 ","row:  151 <br>x:  9 , y:  6 , z:  NA <br>snr:  4.6 ","row:  152 <br>x:  9 , y:  7 , z:  NA <br>snr:  4.6 ","row:  153 <br>x:  9 , y:  8 , z:  NA <br>snr:  4.6 ","row:  154 <br>x:  9 , y:  9 , z:  NA <br>snr:  4.2 ","row:  155 <br>x:  9 , y:  10 , z:  NA <br>snr:  4 ","row:  156 <br>x:  9 , y:  11 , z:  5.11009272295247 <br>snr:  5.1 ","row:  157 <br>x:  9 , y:  12 , z:  NA <br>snr:  4.8 ","row:  158 <br>x:  9 , y:  13 , z:  NA <br>snr:  4.7 ","row:  159 <br>x:  9 , y:  14 , z:  NA <br>snr:  3.3 ","row:  160 <br>x:  9 , y:  15 , z:  NA <br>snr:  2.8 ","row:  161 <br>x:  10 , y:  0 , z:  NA <br>snr:  2.9 ","row:  162 <br>x:  10 , y:  1 , z:  5.43125303270911 <br>snr:  5.4 ","row:  163 <br>x:  10 , y:  2 , z:  5.13932993515307 <br>snr:  5.1 ","row:  164 <br>x:  10 , y:  3 , z:  NA <br>snr:  4.8 ","row:  165 <br>x:  10 , y:  4 , z:  5.87943875533017 <br>snr:  5.9 ","row:  166 <br>x:  10 , y:  5 , z:  NA <br>snr:  4.7 ","row:  167 <br>x:  10 , y:  6 , z:  NA <br>snr:  4.3 ","row:  168 <br>x:  10 , y:  7 , z:  NA <br>snr:  4.2 ","row:  169 <br>x:  10 , y:  8 , z:  NA <br>snr:  3.5 ","row:  170 <br>x:  10 , y:  9 , z:  NA <br>snr:  3.8 ","row:  171 <br>x:  10 , y:  10 , z:  NA <br>snr:  3.9 ","row:  172 <br>x:  10 , y:  11 , z:  NA <br>snr:  4.5 ","row:  173 <br>x:  10 , y:  12 , z:  NA <br>snr:  4.6 ","row:  174 <br>x:  10 , y:  13 , z:  NA <br>snr:  4.4 ","row:  175 <br>x:  10 , y:  14 , z:  NA <br>snr:  3.1 ","row:  176 <br>x:  10 , y:  15 , z:  NA <br>snr:  2.6 ","row:  177 <br>x:  11 , y:  0 , z:  NA <br>snr:  3.1 ","row:  178 <br>x:  11 , y:  1 , z:  NA <br>snr:  4.9 ","row:  179 <br>x:  11 , y:  2 , z:  NA <br>snr:  4.7 ","row:  180 <br>x:  11 , y:  3 , z:  NA <br>snr:  3.9 ","row:  181 <br>x:  11 , y:  4 , z:  6.02622735147376 <br>snr:  6 ","row:  182 <br>x:  11 , y:  5 , z:  NA <br>snr:  4.6 ","row:  183 <br>x:  11 , y:  6 , z:  5.54642203970174 <br>snr:  5.5 ","row:  184 <br>x:  11 , y:  7 , z:  NA <br>snr:  3.4 ","row:  185 <br>x:  11 , y:  8 , z:  NA <br>snr:  4.2 ","row:  186 <br>x:  11 , y:  9 , z:  NA <br>snr:  4.2 ","row:  187 <br>x:  11 , y:  10 , z:  NA <br>snr:  4.5 ","row:  188 <br>x:  11 , y:  11 , z:  NA <br>snr:  4.3 ","row:  189 <br>x:  11 , y:  12 , z:  NA <br>snr:  4.7 ","row:  190 <br>x:  11 , y:  13 , z:  NA <br>snr:  3.9 ","row:  191 <br>x:  11 , y:  14 , z:  NA <br>snr:  3.5 ","row:  192 <br>x:  11 , y:  15 , z:  NA <br>snr:  3.1 ","row:  193 <br>x:  12 , y:  0 , z:  NA <br>snr:  3.3 ","row:  194 <br>x:  12 , y:  1 , z:  NA <br>snr:  3 ","row:  195 <br>x:  12 , y:  2 , z:  NA <br>snr:  4 ","row:  196 <br>x:  12 , y:  3 , z:  5.36146022078896 <br>snr:  5.4 ","row:  197 <br>x:  12 , y:  4 , z:  NA <br>snr:  5 ","row:  198 <br>x:  12 , y:  5 , z:  NA <br>snr:  4.9 ","row:  199 <br>x:  12 , y:  6 , z:  5.43547391409824 <br>snr:  5.4 ","row:  200 <br>x:  12 , y:  7 , z:  NA <br>snr:  4.4 ","row:  201 <br>x:  12 , y:  8 , z:  5.47360242071904 <br>snr:  5.5 ","row:  202 <br>x:  12 , y:  9 , z:  NA <br>snr:  3.6 ","row:  203 <br>x:  12 , y:  10 , z:  NA <br>snr:  4.8 ","row:  204 <br>x:  12 , y:  11 , z:  NA <br>snr:  3.8 ","row:  205 <br>x:  12 , y:  12 , z:  NA <br>snr:  3.8 ","row:  206 <br>x:  12 , y:  13 , z:  NA <br>snr:  3.4 ","row:  207 <br>x:  12 , y:  14 , z:  NA <br>snr:  3.7 ","row:  208 <br>x:  12 , y:  15 , z:  NA <br>snr:  2.8 "],"xaxis":"x","yaxis":"y","frame":null}],"highlight":{"on":"plotly_click","persistent":false,"dynamic":false,"selectize":false,"opacityDim":0.20000000000000001,"selected":{"opacity":1},"debounce":0},"shinyEvents":["plotly_hover","plotly_click","plotly_selected","plotly_relayout","plotly_brushed","plotly_brushing","plotly_clickannotation","plotly_doubleclick","plotly_deselect","plotly_afterplot","plotly_sunburstclick"],"base_url":"https://plot.ly"},"evals":[],"jsHooks":[]}</script>
 </div>
-<div id="intensity-adjustment" class="section level4">
-<h4>Intensity Adjustment</h4>
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width="390" style="display: block; margin: auto;" /></p>
+<div id="intensity-adjustment" class="section level2">
+<h2>Intensity Adjustment</h2>
 <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. The transmittance adjustment uses the <span class="math inline">\(\log_{10} 1/T\)</span> calculation which does not
+units. 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>
+adjustment use the Kubelka-Munk equation <span class="math inline">\(\frac{(1-R)^2}{2R}\)</span>.</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 class="sourceCode" id="cb35"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb35-1"><a href="#cb35-1" tabindex="-1"></a><span class="fu">data</span>(<span class="st">&quot;raman_hdpe&quot;</span>)</span>
+<span id="cb35-2"><a href="#cb35-2" tabindex="-1"></a>trans_raman_hdpe <span class="ot">&lt;-</span> raman_hdpe</span>
+<span id="cb35-3"><a href="#cb35-3" tabindex="-1"></a>trans_raman_hdpe<span class="sc">$</span>spectra <span class="ot">&lt;-</span> <span class="dv">2</span> <span class="sc">-</span> trans_raman_hdpe<span class="sc">$</span>spectra<span class="sc">^</span><span class="dv">2</span></span>
+<span id="cb35-4"><a href="#cb35-4" tabindex="-1"></a>    </span>
+<span id="cb35-5"><a href="#cb35-5" tabindex="-1"></a>rev_trans_raman_hdpe <span class="ot">&lt;-</span> trans_raman_hdpe <span class="sc">|&gt;</span> </span>
+<span id="cb35-6"><a href="#cb35-6" tabindex="-1"></a>                            <span class="fu">adj_intens</span>(<span class="at">type =</span> <span class="st">&quot;transmittance&quot;</span>)</span>
+<span id="cb35-7"><a href="#cb35-7" tabindex="-1"></a></span>
+<span id="cb35-8"><a href="#cb35-8" tabindex="-1"></a><span class="fu">plotly_spec</span>(trans_raman_hdpe, rev_trans_raman_hdpe)</span></code></pre></div>
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 </div>
-<div id="conforming" class="section level4">
-<h4>Conforming</h4>
+<div id="conforming" class="section level2">
+<h2>Conforming</h2>
 <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>
+be highly resolved spectrally. 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 function can be specified.</p>
+<div class="sourceCode" id="cb36"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb36-1"><a href="#cb36-1" 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="cb36-2"><a href="#cb36-2" tabindex="-1"></a>  <span class="fu">summary</span>()</span>
+<span id="cb36-3"><a href="#cb36-3" tabindex="-1"></a><span class="co">#&gt; $wavenumber</span></span>
+<span id="cb36-4"><a href="#cb36-4" tabindex="-1"></a><span class="co">#&gt;  Length Min. Max. Res.</span></span>
+<span id="cb36-5"><a href="#cb36-5" tabindex="-1"></a><span class="co">#&gt;     579  305 3195    5</span></span>
+<span id="cb36-6"><a href="#cb36-6" tabindex="-1"></a><span class="co">#&gt; </span></span>
+<span id="cb36-7"><a href="#cb36-7" tabindex="-1"></a><span class="co">#&gt; $spectra</span></span>
+<span id="cb36-8"><a href="#cb36-8" tabindex="-1"></a><span class="co">#&gt;  Number Min. Intensity Max. Intensity</span></span>
+<span id="cb36-9"><a href="#cb36-9" tabindex="-1"></a><span class="co">#&gt;       1       41.65339       662.0608</span></span>
+<span id="cb36-10"><a href="#cb36-10" tabindex="-1"></a><span class="co">#&gt; </span></span>
+<span id="cb36-11"><a href="#cb36-11" tabindex="-1"></a><span class="co">#&gt; $metadata</span></span>
+<span id="cb36-12"><a href="#cb36-12" tabindex="-1"></a><span class="co">#&gt;   Min. Max.</span></span>
+<span id="cb36-13"><a href="#cb36-13" tabindex="-1"></a><span class="co">#&gt; x    1    1</span></span>
+<span id="cb36-14"><a href="#cb36-14" tabindex="-1"></a><span class="co">#&gt; y    1    1</span></span>
+<span id="cb36-15"><a href="#cb36-15" tabindex="-1"></a><span class="co">#&gt; [1] &quot;x&quot;                 &quot;y&quot;                 &quot;user_name&quot;        </span></span>
+<span id="cb36-16"><a href="#cb36-16" tabindex="-1"></a><span class="co">#&gt; [4] &quot;spectrum_type&quot;     &quot;spectrum_identity&quot; &quot;organization&quot;     </span></span>
+<span id="cb36-17"><a href="#cb36-17" tabindex="-1"></a><span class="co">#&gt; [7] &quot;license&quot;           &quot;session_id&quot;        &quot;file_id&quot;</span></span>
+<span id="cb36-18"><a href="#cb36-18" tabindex="-1"></a></span>
+<span id="cb36-19"><a href="#cb36-19" tabindex="-1"></a><span class="co"># Force one spectrum to have the exact same wavenumbers as another</span></span>
+<span id="cb36-20"><a href="#cb36-20" tabindex="-1"></a><span class="fu">conform_spec</span>(asp_example, <span class="at">range =</span> ps_example<span class="sc">$</span>wavenumber, <span class="at">res =</span> <span class="cn">NULL</span>) <span class="sc">|&gt;</span> </span>
+<span id="cb36-21"><a href="#cb36-21" tabindex="-1"></a>  <span class="fu">summary</span>()</span>
+<span id="cb36-22"><a href="#cb36-22" tabindex="-1"></a><span class="co">#&gt; $wavenumber</span></span>
+<span id="cb36-23"><a href="#cb36-23" tabindex="-1"></a><span class="co">#&gt;  Length     Min.     Max.     Res.</span></span>
+<span id="cb36-24"><a href="#cb36-24" tabindex="-1"></a><span class="co">#&gt;    1736 651.8728 3998.025 1.928618</span></span>
+<span id="cb36-25"><a href="#cb36-25" tabindex="-1"></a><span class="co">#&gt; </span></span>
+<span id="cb36-26"><a href="#cb36-26" tabindex="-1"></a><span class="co">#&gt; $spectra</span></span>
+<span id="cb36-27"><a href="#cb36-27" tabindex="-1"></a><span class="co">#&gt;  Number Min. Intensity Max. Intensity</span></span>
+<span id="cb36-28"><a href="#cb36-28" tabindex="-1"></a><span class="co">#&gt;       1   0.0009781419      0.5156938</span></span>
+<span id="cb36-29"><a href="#cb36-29" tabindex="-1"></a><span class="co">#&gt; </span></span>
+<span id="cb36-30"><a href="#cb36-30" tabindex="-1"></a><span class="co">#&gt; $metadata</span></span>
+<span id="cb36-31"><a href="#cb36-31" tabindex="-1"></a><span class="co">#&gt;   Min. Max.</span></span>
+<span id="cb36-32"><a href="#cb36-32" tabindex="-1"></a><span class="co">#&gt; x    1    1</span></span>
+<span id="cb36-33"><a href="#cb36-33" tabindex="-1"></a><span class="co">#&gt; y    1    1</span></span>
+<span id="cb36-34"><a href="#cb36-34" tabindex="-1"></a><span class="co">#&gt; [1] &quot;x&quot;          &quot;y&quot;          &quot;file_name&quot;  &quot;license&quot;    &quot;session_id&quot;</span></span>
+<span id="cb36-35"><a href="#cb36-35" tabindex="-1"></a><span class="co">#&gt; [6] &quot;file_id&quot;    &quot;col_id&quot;</span></span>
+<span id="cb36-36"><a href="#cb36-36" tabindex="-1"></a></span>
+<span id="cb36-37"><a href="#cb36-37" tabindex="-1"></a><span class="co"># Specify the wavenumber resolution and use a rolling join instead of linear</span></span>
+<span id="cb36-38"><a href="#cb36-38" tabindex="-1"></a><span class="co"># approximation (faster for large datasets). </span></span>
+<span id="cb36-39"><a href="#cb36-39" tabindex="-1"></a><span class="fu">conform_spec</span>(spectral_map, <span class="at">range =</span> ps_example<span class="sc">$</span>wavenumber, <span class="at">res =</span> <span class="dv">10</span>,</span>
+<span id="cb36-40"><a href="#cb36-40" tabindex="-1"></a>             <span class="at">type =</span> <span class="st">&quot;roll&quot;</span>) <span class="sc">|&gt;</span> </span>
+<span id="cb36-41"><a href="#cb36-41" tabindex="-1"></a>  <span class="fu">summary</span>()</span>
+<span id="cb36-42"><a href="#cb36-42" tabindex="-1"></a><span class="co">#&gt; $wavenumber</span></span>
+<span id="cb36-43"><a href="#cb36-43" tabindex="-1"></a><span class="co">#&gt;  Length Min. Max. Res.</span></span>
+<span id="cb36-44"><a href="#cb36-44" tabindex="-1"></a><span class="co">#&gt;     329  720 4000   10</span></span>
+<span id="cb36-45"><a href="#cb36-45" tabindex="-1"></a><span class="co">#&gt; </span></span>
+<span id="cb36-46"><a href="#cb36-46" tabindex="-1"></a><span class="co">#&gt; $spectra</span></span>
+<span id="cb36-47"><a href="#cb36-47" tabindex="-1"></a><span class="co">#&gt;  Number Min. Intensity Max. Intensity</span></span>
+<span id="cb36-48"><a href="#cb36-48" tabindex="-1"></a><span class="co">#&gt;     208      -1.317072       1.168225</span></span>
+<span id="cb36-49"><a href="#cb36-49" tabindex="-1"></a><span class="co">#&gt; </span></span>
+<span id="cb36-50"><a href="#cb36-50" tabindex="-1"></a><span class="co">#&gt; $metadata</span></span>
+<span id="cb36-51"><a href="#cb36-51" tabindex="-1"></a><span class="co">#&gt;   Min. Max.</span></span>
+<span id="cb36-52"><a href="#cb36-52" tabindex="-1"></a><span class="co">#&gt; x    0   12</span></span>
+<span id="cb36-53"><a href="#cb36-53" tabindex="-1"></a><span class="co">#&gt; y    0   15</span></span>
+<span id="cb36-54"><a href="#cb36-54" 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="cb36-55"><a href="#cb36-55" tabindex="-1"></a><span class="co">#&gt;  [5] &quot;description&quot;   &quot;samples&quot;       &quot;lines&quot;         &quot;bands&quot;        </span></span>
+<span id="cb36-56"><a href="#cb36-56" 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="cb36-57"><a href="#cb36-57" 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>. 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 id="smoothing" class="section level2">
+<h2>Smoothing</h2>
+<p>The <a href="https://en.wikipedia.org/wiki/Savitzky%E2%80%93Golay_filter">Savitzky-Golay
+filter</a> is used for smoothing. Higher polynomial numbers lead to more
+wiggly fits and thus less smoothing, 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. 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>
+<p>Examples of smoothing:</p>
+<div class="sourceCode" id="cb37"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb37-1"><a href="#cb37-1" tabindex="-1"></a><span class="fu">data</span>(<span class="st">&quot;raman_hdpe&quot;</span>)</span>
+<span id="cb37-2"><a href="#cb37-2" tabindex="-1"></a></span>
+<span id="cb37-3"><a href="#cb37-3" tabindex="-1"></a>none <span class="ot">&lt;-</span> <span class="fu">make_rel</span>(raman_hdpe)</span>
+<span id="cb37-4"><a href="#cb37-4" 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 class="at">derivative =</span> <span class="dv">0</span>, <span class="at">abs =</span> F)</span>
+<span id="cb37-5"><a href="#cb37-5" 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 class="at">derivative =</span> <span class="dv">0</span>, <span class="at">abs =</span> F)</span>
+<span id="cb37-6"><a href="#cb37-6" tabindex="-1"></a></span>
+<span id="cb37-7"><a href="#cb37-7" 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="cb37-8"><a href="#cb37-8" tabindex="-1"></a>    </span>
+<span id="cb37-9"><a href="#cb37-9" 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)." />
+<img 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" alt="Sample `raman_hdpe` spectrum with different smoothing polynomials." />
 <p class="caption">
 Sample <code>raman_hdpe</code> spectrum with different smoothing
-polynomials from Cowger et al. (2020).
+polynomials.
 </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>
+<p>Derivative transformation can happen with the same function.</p>
+<div class="sourceCode" id="cb38"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb38-1"><a href="#cb38-1" tabindex="-1"></a></span>
+<span id="cb38-2"><a href="#cb38-2" tabindex="-1"></a>none <span class="ot">&lt;-</span> <span class="fu">make_rel</span>(raman_hdpe)</span>
+<span id="cb38-3"><a href="#cb38-3" 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="cb38-4"><a href="#cb38-4" 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 class="at">abs =</span> T)</span>
+<span id="cb38-5"><a href="#cb38-5" tabindex="-1"></a></span>
+<span id="cb38-6"><a href="#cb38-6" 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="cb38-7"><a href="#cb38-7" tabindex="-1"></a>    </span>
+<span id="cb38-8"><a href="#cb38-8" 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)." />
+<img src="data:image/png;base64,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" alt="Sample `raman_hdpe` spectrum with different derivatives." />
 <p class="caption">
-Sample <code>raman_hdpe</code> spectrum with different derivatives from
-Cowger et al. (2020).
+Sample <code>raman_hdpe</code> spectrum with different derivatives.
 </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
-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.</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 id="baseline-correction" class="section level2">
+<h2>Baseline Correction</h2>
+<p>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. Manual baseline correction can also be
+specified by providing a baseline OpenSpecy object.</p>
+<div class="sourceCode" id="cb39"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb39-1"><a href="#cb39-1" tabindex="-1"></a><span class="fu">data</span>(<span class="st">&quot;raman_hdpe&quot;</span>)</span>
+<span id="cb39-2"><a href="#cb39-2" tabindex="-1"></a></span>
+<span id="cb39-3"><a href="#cb39-3" tabindex="-1"></a>alternative_baseline <span class="ot">&lt;-</span> <span class="fu">smooth_intens</span>(raman_hdpe, <span class="at">polynomial =</span> <span class="dv">1</span>, <span class="at">window =</span> <span class="dv">51</span>, <span class="at">derivative =</span> <span class="dv">0</span>, <span class="at">abs =</span> F, <span class="at">make_rel =</span> F) <span class="sc">|&gt;</span> <span class="fu">flatten_range</span>(<span class="at">min =</span> <span class="dv">2700</span>, <span class="at">max =</span> <span class="dv">3200</span>, <span class="at">make_rel =</span> F)</span>
+<span id="cb39-4"><a href="#cb39-4" tabindex="-1"></a></span>
+<span id="cb39-5"><a href="#cb39-5" tabindex="-1"></a>none <span class="ot">&lt;-</span> <span class="fu">make_rel</span>(raman_hdpe)</span>
+<span id="cb39-6"><a href="#cb39-6" tabindex="-1"></a>d2 <span class="ot">&lt;-</span> <span class="fu">subtr_baseline</span>(raman_hdpe, <span class="at">type =</span> <span class="st">&quot;manual&quot;</span>, <span class="at">baseline =</span> alternative_baseline)</span>
+<span id="cb39-7"><a href="#cb39-7" 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="cb39-8"><a href="#cb39-8" tabindex="-1"></a></span>
+<span id="cb39-9"><a href="#cb39-9" 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="cb39-10"><a href="#cb39-10" tabindex="-1"></a></span>
+<span id="cb39-11"><a href="#cb39-11" 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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CcgEQuIDFKRGr+9CwEAgA6kLe+t/MOVl5cHBwfL2ioUCoVCYVvW01rs4imlOP3loJrF7V0JAAB0HP9TAVZbW/vhwwdZWyUSiUDQORezEItpjeTaD43It70rAQCADuOrASapTbxzLuh5Ql4Vycv/6ODaRwX6A/sa0tuqtu9mZWV14sQJWVuDgoLo9I5b/FcUm9TxMFHJ8Rr065/PTJo0aenSpc7Ozu1aFwAAtCfZY2DNqcdH2TqO33Q+/H30s2dJ1aKiuysG2Lktul8KC0K0MREmpJEJ69zsPz7z/v373NzcdiwJAADanawAkxSeX7Ym0uSXZ1lFydfnmBIRovTZF3XfT3JuwZZn3DYtERCSqKwiZsKYP/s/cRwvLi6uqqpq36oAAKB9yQowXvTzN0qTt23y0v60Dj1Jc9DGZR51r16kwppGbSoN8evlFI3j/lyJo7y8vLm5ubq6un2rAgCA9iUrwEQCgYREoWBfPE0kEpBYLP7BNwgDfyW05XBNmmwj/7wfWGlp6YrjwyTi0vatCgAA2pesAFNw8epREbTnVErjp+dwTsyh4y9pvVysYVG+NkXERGR5Sobmn7+LqqoqtTSMUgaXhQEAujRZsxAJpnMP+gd7/+xocdbLHi+qxY/9PGnLo3tRfK+jF0cotGmJwOMlQ8mNdXkCdy5CCKHKykriGz5Vs6WVKgEAoMuQPQuR1mP1k/jwgGnWklqRsjo39UOl9tj9z2NuzzH7n7p2rDMgiCRMIjP7bpH0YVlFWe0gqlgVAgwA0KV9NYzIOu5zdrvPkT7gc3lEOg3Cqx3kePMpqo0JqzPRMYQQYgvKq8ZgeeH17V0XAAC0J9ktMLzhw4lZfQy1p9/jI4QQ//5MdVXzoRseFLbRakzi7FtbfzkX1zmXzmhdIrJEnkC66jcex3GEEK2Q2yucVRRd1951AQBAe5IVYHjdw+WjltwXD1o9rQcZIYTI7quOL++eF+A74UBKm8yilxSFnz78IKNTLl7YyiSXuXo0y+d+PLFYjBDCani2KeoVsTXtXRcAALQnWV2CTeFXbjeNOv7gxAQV6VR6gkavKf5XepB6O167k77Kxrb17mmJV1yc2NLdn/l1Zc3ixRZ6qzFEcdsZdWWaxpeT+ruK25X5U/QNeobS+bZ8EokkojfXGAs9+DrtXRcAALQnGQGG82prhRoOpqy/ZgbJwNSQWF9bL0Go9QIMU3afMEI39PA7vsXIGT9Z/P9qhZLCp8ducPv4jrYiI5KJBbXVztf5GC1Qr5Vj93ulwJvMo9PpPEvhe8dKRQul9q4LAADak4wAwxTtuuvl3r0UuaqHh9LHEBPlXwuKwKz2tfI8RLLR6IMvHb23z5xzJKxowLlD852UCAgJwzlBd8t8t+727ZQL8LYmmjyRTqN9UKvvzeWqqakVRFUuMJ2cGfkC+bR3ZQAA0H5kjYGRei7dPbH56FD7ftPX7jty+vTR37Ytn9C71/wwg+Wbxqu3fl8eRW/I9iex92ZwDwzsMWJnaCkMff0/iUQy7ZqqRbP6k3mNXB4XIRQSlujYfahJulwnvb0ZAAC0CpltKYLmqFPR4b127j17fe/1wjohWVG/u8eM06fXT7Wn/aBaiOruq27HeJ38efpYh0czf5/GgxWrEEJ1dXU0MoVAIVeFNPK8eQ0NDTou6nyGuMwe5/F4LBarvQsEAID28bXOQKK66/w/7s3/o82KQQhhij3mXXzT78ov0xcvjKklj2rLc3dMNTU1L+Y2dydwOE9FTe5N5eXlvWdZZRDzQoc0DIQAAwB0YV8LMDGnKCk+o5L317V7MXl9x75Wyj9yRiDdcuLhV+7TnsdWqvWU/4Hn6RRqa2tF8ohAoqyyN2mqravAsIHxKla99LICy3nree1dHQAAtBuZASZMPTpywNLHZaIv+/EIuoue5x72/NHr+VJ0ew3VRQjh7JSwN4V0Wy9nfbkffMqOqaamxuSEvPJ1nedj692KKqobOTa5TBZdrfG9gMeDAAMAdF2yAkzw4o/tofQJp9/tGW2l9Je7qmAEsnwbrkYvSjox86cTpofyni/Q/qdWX3R09JIlS2RtbWpqqqvrfKtXVFRURCoS58hRbGLoOFWYV51H695sz6T/NMSEx4VbiwIAui5Z14FVZ2dzuvltnO6k23oXfP0rRKtpf1x0ZTopfUOfpYODw4kTJ2Rt7du3r6KiYmvW1iYqayopM5sQQt3fKWTbNxfm5+UvYo+iyVWNwzi17PauDgAA2o2sFpgcg0GsKS0TIMt2voKYoO44apLjt+0rLy/fs2dPmUciyF74saPCcbygKI/iTEQIFRvg5Rx2YlLSiqwRmAOhf4iC0Ka5vQsEAIB2I+tCZpWxW7YEjl443fCPHX4eZkp/uzXzjyBuLM1Mzcgvr+U0iUhUBVU9MxtrE5WuOfL1pxMnToSfv3mNMxP1QZFDm+tDC5KSU4YNC0cImSdS8/WhCxEA0HXJ6kKsvBtwLq+5MGeV943VRDkalfwxwQjac+8n/9a3VYfBmrLv7V675cSDhEr+53NGMCLTqO/kNXt2zu31Q2c9dlyhoaGL/WYpyashhEzE1r8HH7ZyssiXZNkg+0zrZnETp70LBACAdiOrBcZwGL9ibZ+WbpmI0a3NWnVcDC+/Mctj0kPWiAW/bRjYy9pIS4lBIYiaG6pLcpKiHl08uqJ/bNXLlxt7dMUJ9cnJyfOmD7pLTFiMkIfKwDXpG3329o8qeWJjaR/m0WhWCAEGAOi6ZI2B0SyHzLBs4Xlxc2Mzgd6ag0mS3KCDd+Xn3Is6MvAvSwdraOubdXMZPGmKywT7ZUfCV50Z0gUTrLS0lCKHyeNUhBA5OSY/9Gl9aWFzjgRZInKSMiK08wwbAABoR/8QRRJBE4/7CafoxizLnpveteYNwcQVJRWYVW8nVsudhJias4spr6ysseutK9XY2CgWi3tp9Z2kOBUhFKmeHiuMUywRGjEtEULMChVBI6yFCADoumQGGM5+tX2wkSKdTmd8oqA/8Uq9tp5aa7bASOY97CiRZ4/FNLQUUeKql0cvxWvb2Sp2vUGwsrIyLS0teVtzWq9uCCEboZlGLf2NXCLSUUEIaSpyiaKG9q4RAADajawuRHHa8RW74oyXnznYPfvQkouqm4/ONKz/cG7zMXzTxYXGrRlgmOq47VvPe61wM73af1j/XtZGWsoMCibmc6pLcpKinjyMKNGde22lU+vewaVTKCoqmmxqd/TDRmfTYT1ZLjYUG352zsne+V46ygihOpMauYK/3QUUAAC6DJl3ZE78kK7h+3DrtL7kBt61w+cpPQcN1/Dur1fTc27AW999LpRWrIFi+/PDeIcLAcevPr596GpRbbMERxhGZmga2zoP2HBzyfyfLJldr/2FUGFhoStLg4SbaMnpIoQkOkpllbwZwjF0eUWEkEWaclVtS7NsAACga5A1jb6Z14TTGHQMIUQzNtPITcsSIQ0ytdcgt7pVD5N3ufRo3RYRRdt9zh73OXsQQhJhM48vJsvT5EhdMbU+ExgY6DfQYKRmf4a8HkKoUg/dIqRvMf9dulWrilXfACtxAAC6LhmdgRjLykYr/+HV6FocEU1tzKpehKaJEMLrSksb+QLBj5xQQSDLMxj0Lp9ebDY7Ojr6vVsDn/nnVEMDqvEYrakfd6jRFPIUYCUOAEDXJWs0i9xr/hrP0gMeZrNChEoDxvQr3DvKe9qsMd6rnql5D7Frw8V8u6qMjIxudnYjwrWUWdrSZygEOTtmj487pDoIU40b26k6AABofzKnYxBN5tyJf31l73hzIkF76skbW9yJmSlso+knb+/0oLdlhV1UWlqahaWl17rfMdRyW1RLZFGX3prXMwAAQOfytaEsinbv8bOkX2p4rbngtaZNKuracBz39/ffunVrQkKCTU/r6MZXrkqeLe6pgenwivhtXB4AAHQcX5sQj9cn39qzaGx/527WNt2d+49dtPtGYq2kzUrrkoqLi7dv337o0KFr164ZOOpE1b6QuWtzuYl2F7y4AAAA/iT7QubKh4tceo3zv5lFMnLqP6CPlWLV0z0TnXpOupwHc7d/nKysLITQtm3btLS0+jWqD8puaT0vhBBClVgewx4CDADQdcl6BxR9OLj6dKXbvje3Vzr8/0VYwpL7Pw8cv2zjveGXfVhtVmHXkpOTY21tnZqaeu3KlZdJVwabTZa1pwlXty4uoy1rA+B75eXlGRoaYlgXn1MMfhQZLTC8LjE+X3Pihp8dPruEmKwzfMuyvrx3UamwBN+PUlhYOGHChGHDhpnraIV51DIGusnaU5OgzsiDDl3QoXl5eeXk5LR3FeB/lqzbqdBUVGi4SPTlG6RYIsYYLGYHvbWxUChMTk6WSFp+W5f1fMchEAhevXo1Y8aMzZs383MLp0b1JMq+iQymIcc2gKWkQIfGZrPr6+vbuwrwP0vm7VQGLJynNnrjppGWOwfrSteNwhuSTi/f+8Zi1u+WHfQuHm/fvl22bJmsrc3NzbW1tW1Zz/eKj49/+fLlli1bEEJyxvp9jDd+ZWe2LfWNfX1zc7O8fBe8zwxoCwKBICYmxtXV9d+9XCwWczgcDgfuWgd+lC8D7LH/+BOJ0h5CCYOR/NtQ0/OmdjbGamROcXpSakkj2XRoY2a+2M60I2ZYnz59YmJiZG1lMplKSkptWc/3KigoGDxokAMXIYSSOXFUIs2EZiFrZ0sVh4LQ2vIR5YaGhm1WIehSHj16NHny5LKyMiaT+S9e3tDQgOM4BBj4cb4MMDKVzmD8eXksw2W0gcv/b1BT0TTujhBChCa4C9UPUlBQ4GJpU/colMRiRBBvOlgO/kqAschK1CqF4uJiCDDwgzx58oTL5UZERAwbNuxfvLyuro6hTeM0QoCBH+XLAOu/9lx/hBBe8Xjn5tc2G7aO1u+g413/izIyMgYaWZxzSfIufuVUw7Jzdv7Kzhx2+dD+JtnZ2X369GmzCkGXkp+fr66uXlVV9e9eXldX533MpabuX74cgH8kczFfYvHD4zfew2entvT+/XuaOaGcxb1m+Kapj6E8gfqVnUsq0hpd+dnZ2W1WXqdWUlLS3iV0PuXl5TY2NtXV1f/u5XV1dQ6BRFTWCuvFPHz4sLERVv4EX5LVvpLvv+3crJJ98w+9yOPAhcttJCcnR4VIHVrj5JyuZqJh//WdteT0eseqpaamtk1tHVNUVNSNGzekX+M4/nGSzpEjR/j8T++bZ86cMTExqaqqio+PT0lJaYdCO5Xnz5/v2LEDIVReXm5ra1tTU/PvjlNXV+fdrCOua4WPwevXr3/9+nWLm/Lz8xctWvTfTwE6I1nXgVXeWP/Lw4qi+0s9TRSpdJbSJyo2q1/BIFjrEwgEAoHA0dlnqP2ciTMO62hafX1/OlO5ey7zw4cPAsG3TqaPiorq4CPqzc0ybxBTUlLi5eWFEJJIJK9fv165cuWdO3fCw8N/+eUXAwODsrKykJAQY2PjgoKCWbNmLV68+NKlS6tWrYqPjzc2Nl65ciWfz1+yZImDg8PGjRu5XG5xcfHAgQN9fHwyMjIMDQ05HE5FRcWzZ89mzZqFEEpISKirq2u7b7uDuXfv3s6dO0tLS9m1bMMeev+6BVZeXRbixy2Ra/jvJdXW1ubn57e4KS0t7cyZM1/5ywH/w2RdB8awG7VwsVNLbS+MYWPSEacgdnZsNltZSamRLmYpqH3L/kQWM9G42sDA4MyZMwsWLPiWl/j4+HA4nJEjR3p5ec2ePfu/1dv6+Hy+hoZGfn6+dLJoQEAAh8PZsGGDdOv+/fvDw8Pv3LlDpVKHDBmCECouLlZTU8vLy3N0dJw3bx5CiEgkBgQEhISETJs2bePGjdXV1efPn+dwOIqKin5+fufOnUMI3b17d9iwYSkpKXPnzj158qSHh0dDQ4OXl5eamlppaak04BcvXjxjxgxpmCGECgoKEELa2tppaWkkEsna2vpjzaWlpUKh0MDAoE1/Uj9YdHS0mZnZzp07zT2NCmxSYv5I4HK5dPp334SirLqsuBefkNEK1658JcAqK8eTWpgAACAASURBVCv5fH5MTAwMBndBMq8Dsx4+21rGNvAjBAYGDtAz3ZqxYqHNZnP6P//sMQo5ZFj9RHXfYwHHtLS0LCws5OXljYyMZO1fWVlZUVFhbm5+9epVGo2Wk5OzefNmKvVrw2z/qKCggMPh2Nra/uOeOI7X19crKiq2uDUnJ0cgEJDJ5IaGBn9//4cPH65atSokJITP5xcUFHh6er548eLevXtOTk4+Pj40Go1AICxcuPDw4cNycnIIoYsXL7q7uxsaGr569crNzY1IJPr7+9+7d8/Ly2v58uXv3r1TUFBYunTp8uXLBw4cWFNT09jYuHv37tmzZzc3N79582bevHmbN29OTExUVVVls9kVFRVxcXH6+vqamppisTg8PPzmzZtlZWX29va2tra1tbWXLl2qqakJDAx0c3Pz9/fX1dW9cuUKQojD4cjLy5PJX7tbXmJiorGxMYPB+PhMZmamtrZ29+7d161b1xE+VbDZ7KysrKtXr44YMWKF35SRkUY3Kp4+evRo7Nix33uoyoIq3glUX1L5H0sSCASNjY15eXktbq2oqEAI5eTkQIB1RfjXcPNfXtq/fsns6VOnz13mf/hufJXwq/t3aAwG49SpU+1dxScVFRVcLlcikcyaNcvZ2VlOTm7TwJ8SA48IJYJvPEI0+2VcfJz096inp7dq1aq/79PU1CQWi/Pz81etWjVy5Egcx6dOnerq6oph2Pv376X75ObmSr84e/bskiVLvn7SpqYm6RdhYWG+vr7jxo0rLS3NyMiQPikWi6dMmfLkyZMRI0bU1NR8PP6hQ4e0tbUvXLiQmpoqkUikz1+7dm3YsGHu7u7W1tZubm4nT57EMEy6bh6BQFBRUVFTU6PT6Q4ODvPnz8/IyAgJCRkwYMDgwYOzs7M/Dv5t3LgRx/GEhIS0tDQcx9esWbN9+3ZZxVdVVVVVVX182NDQkJ+fL/3aw8Nj4sSJmpqaGIZJQ4hA+EsHu6amJp1O19LScnZ21tfXZzAYvXv3llY4cODA5ORkb2/vuXPnNjU1BQcHx8bGzpgxY8uWLY8fP37x4sWdO3d8fHy0tLRIJJKdnZ2ZmVlKSkpISAiO4xYWFmvXrkUIzZw5U1bZN2/elP4wP/7wP/r4u2st169fHzZsmFAo9PPzSws4W33h5p69e+bNmzdx4sQPHz5816FGjhx5a8XGUd2d/mNJ5eXlLCOGo3PPFreuXLmSRqN95ZcOfrRu3bolJCS0y6llBxg/8/xEUypGZOjYOLu7O9sZqchjRNU+m1+wJT+wHomAW19TUV5RU89r7azsCAHG5/O3bt0aFhaWlJRkZWX1888/nzt3jkQiEYnEUaNGHfWfG/b48HcdUCKRWFhYSDvQrK2tTUxM9u3bx+Vyr127lpeXFxMT4+jo2K9fPzqdbmRkVFFRgeN4fHw8kUhECI0fPz4vL4/H41EolPHjx/v6+jKZzJ49exYWFlZVVR04cODNmzcLFy6srq5evnz5nDlzfHx8li5dqqamFhoa6uPjY2JighDS0dFZsGCBk5PTw4cPd+3aJW0CMhgMJSUlGo02atSogwcPkslkVVVVeXl56aIhK1asWLNmzahRoxBCRkZGz5498/Pzk4bEoEGDBg8e7OHh4ejoGBwcnJSUtG7duha/caFQeObMGTqd/r3vqrIEBQXdvXt39erVY8eO3bVrV0ZGRkREhLKysoGBAZlMNjMzW7dunYGBQUhICELI3d1969atZDJ51apVurq6CgoKdDpdQ0PD1NSUSqVSKBQ5OTlHR8fJkyerqKhoa2s7OjquXbv21KlTW7du7devn/TnTyAQ9uzZgxBSV1d3dna2sbG5du3a0KFDzc3N9+/fj+M4n8/n8/kikUhRUdHAwKBPnz6mpqYlJSVXrlwpKyubNWtWenq6srJyWFjYnj17Pn4saPFnVV5eLv26vr4+JCRk3bp1zs7OI0aM+PvOc+fOPXjwoPTr29c2nI7Zejvzirq6OpFI/HvEstlsLpfb4km5XG63/jbbnsycMaa3SCT6rt/FmzdvPn+YlpY2/nR/68HGLX6P05dMHr/de+7cud91CtCKOmCAiQtPeCsw7BfeyGz8/78ZYWX07yN0KKbLI5tbvYymgueHlo1xNdegk/5cPBjDKAq63QfN3HolgS1ulXO0TYCJRKKUlBSxWFxXV8dms+fOnXv69GkPD49r165dvHjR0tJSX1/fxcXF3v7PSYbKysr79+/fuXNnZWXlxVsr7sZ8R4AlvLubnxqF4ziXy122bJmzs7P0gtOxY8cqKiqSSCQymfxxIfDDh/88cn19vYKCQvfu3d3c3BwcHMaNG/dFo5zJZNrZ2SGEpK+dO3euoqKiiooKgUCQPiM9MpFInDBhgo+PD4lEQghJmxc9evQYMmRIv379zp8/T6fT1dXVNTQ0jh49Gh0d/ezZsxs3bsyePZtEIjk6OqqpqQ0bNuz06dM4jhcWFkqPUFxcjON4WlraNzYswsLC+Hz+d/+SvlldXV1tbW1tbS2O46WlpbGxsWKx2MDAICIiAsfx/fv3FxYWDho0aP369UFBQadOnQoODlZQUPDx8blw4UJJSQmO43fu3ElJSfnisMeOHRs7duzp06eZTObw4cMRQo8ePXJzc5P+eBkMBpPJXLlypYqKiqKioomJiY2NzeTJk6W/ER0dHQKB8Pmom3S596VLl0oPvn379gcPHuA4fuPGjV27ds2fP3/IkCG9evWqqqpatmzZnTt39PT06HS6p6cnQqi8vDw+Pj4oKGjv3r2DBw/u0aOHoaHhxzej1ZETL77dGVH0ICcnJzAw0Nzc/MmTJ1lZWR+/kVmzZv3666+ff2tisfjIkSM4jl+8eHHmihF+cSMX7xzysYH+LQQCAYlEKisr+/hMVFTU6c2TRno7FhUV/X3/mT8P2XHd19PT89tPAVpXBwyw+iAfhs68p7y/PisuOuxBs90Y27qNI17s3r7KRKpu77GLNu47ciYw6Nq1q5cvnPxj1y+zh3dXp1DNZ98ta4UM+0EBxuPxcBxPT0+3tbVdsGDB+PHjWSzWrFmzlJSU1NTUlJWV3dzcpB+3pQM2UkuXLl2zZo2ysrKlpWVhYaH0UIWBlxvZFd9+6suhW64/2/b5M1wud8eOHUwmMykpicfj9e/fX/pOFxgYKBR++q3V1NSIxWI+ny8dlKJSqdK3s+vXr9+6dWvJkiUIISKRuHjx4uHDh6urq4eHh8+aNYvBYCxatMjf35/FYlVXV7979056tISEhDt37uA4Xl1d/fEUfD4/JCREOrXy8wqrq6uTk5NxHM/Pz/+8Ny8yMvL58+ff/r23o5qams+bAoGBga9fv/74MCsrKzEx8duPVlBQcPLkSenX9vb2dnZ2EyZMuHXrlr29vba2tpWVla6u7pYtWyorKy9evGhgYECn07OysiZPnty/f39p17GZmdnTp0+1tbX79+8/depUJpNpbW29Z88eX19fAoHAYrEwDGMwGM7OzlQq1cPDAyHUo0ePtLQ0U1PTTZs2SXNRW1vbzMyMQqEwmUyhSBhX/xbH8bfHd1cevlh74xGO4wKBwNHRESFkYWHx7t07Nputra1taGg4ffp0sVh8+PBhaSMvKSkJw7CDBw/26dPnxtY9MXPn/TFjysKFCyMjIxMTE7/STAwICJBGfm5uLkIoNDT046bTp0///NJnoc/YK1eufP6SiooKDoejram64+5E91H2ERER0r/qkpKSzMzMb/8VgP+o4wWYpPrsUJr5mjdfJlXdpVEM05VR3zpI8y0k5eeGKygP+C2hscWt7NfrnRhmq6L/+yn/dYAVFhZmZmZyOJyHDx9evHgxNze3srLSz8/v4cOH/v7+Ghoaenp6H1tUY8aMWbNmDZVKnTZtmr29PY1GkzZQpA0XV1fXAQMGxMXF4TjOZrODg4P/y3eUG/k4/uSBvz/f3PxnG3nx4sUrV66k0+n19fUtHmHixInz5s0rLy8vLi4mEonSxesyMzN37dq1YcMGHMd37tw5efJkHMcPHTpkbm7+xfFB62pubo6Kinr8+LH04b1794KCghITEzkcjvSZ/fv3u7m5Sb/m8Xjz588fPny49GFhYeHTp0/9/f0/fPggzTYKheLu7m5qahoVFfXw4UM/P78dO3ZgGGZjYxMUFITj+MSJE83MzCwsLI4dO3bq1Kno6OhHjx6tW7dOLBEvT5lZ1lyM43h9cGjFhevSUwQHB2MYRiKRCASCgoLCx09jJ06c0NXVtbS09PT0dHd3l35cwzAs7/r9vLGLHyxYI21eYxg2d+5cX1/f1NTUzZs3Ozs7Z2ZmpqWlrVq1Kjs7m0gknjp1isfjSXuhfXx8du7cefbs2fnz51taWi5/MSbg7B4bG5tz585FREQEBARIe7wnTZqkp6d39taSY+c3DB8+fMOGDf379/f19fX29sZx/L+3zn9o+/5zUVFRubm50h6I/y46OrpVjvONOl6A4cK4TbZyBlOuF34eYZwPO91ZSmOvtOoomCByhTFtxMVaWdvFuQf6MIecqfrP5/wXAXbkyBErKysCgUAgEJSVlbW1tTEMIxKJBgYG48ePt7CwQAjR6fTu3bs7ODgMGzbsxo0b0gZZXV2d9N+CggJXV1fpPmvWrElLS3v27Jms050oaCGNvoKfV1yyfOdXdigvL6+urpb2gLWorKzs41SLv/d04TjOZrNLS0txHI+JiZEmWYci5vJENXUtPM9r+mK3tqrox5JIJNI/LanKysqPM3E+l56eLl1CXjqx5aMXL14ghB4+fCh9GBoaSiQSr169+sXLD+RsfRK0Lbs09lnVgw+vrqx/PU2CS3Acr6mpmTJlirW1NYVC8fLycnV1NTU1RQj169dP2vkpnfwyePBg6bybW+F7Ht/e/aDgytatW1kslqqqqjTwhg8fbmBgYG5ubmRkJP0/tX79egqFgmGYNHqdnJw0NDRoNJqurq6amhqFQnl98fedGWv27Nnz+XFGjhyJENp2Y8O7a4dKHoUrKioqKSnp6elJu15zcnKUlZWTk5MnTJjw+Xfn6+sbFhaG43hycnJRUdH79++TkpKEQqF0iHf06NEfZ8oUFhaqq6vLyjCRSJSamorjeHZ2tvR//ddt2rTpK7ni4uKioKBAo9HKyspWr179j0drEZfLDQgImDp1qjadOWzYsO8devzXOmCA4ZKK+/PM5YhKVt5TF//iv33rukXjXfSoRBXPA4mt+5lEnHewL9Vw9sOalhNKXBLoo6E5+/F//8z/LwLMzc1t+fLl/v7+qamp4eHh0tXupZ/ycBwXi8WvX7+urKwUi8VfdJR9Ljk5+e3btwUFBV8/l1AinJ8w/rvKkwgEl47PSUkP+8b9rzzaXFddLBEIJfzWbEO3o/oH4ZX7Tzc8fYXjuLCyhn3pHi6RNKVmF0xbJSgqE5T8OXOh+Odt5dsOC4rKKvefbnj0UvokLy6VF58mEQjq7sr8SNF51dXV7dq164s/y+bm5lu3bn3+TGFh4RdvcxJcsjxhWtWZqw+e/na+4HD5++jgSxs+3yEoKCgvLw/H8cDAwDNnzqSlpdnZ2VlaWh48eDAgIIDFYkl7AnEcD4hYcfvl/g3pi3EcFwgEoaGhKioq0gk7jx49mj9/PovF0tHR0dDQIJPJs2fPdnd3p1Kpe/bsCQsLmzlzpvQDU01Nzfnz57lN9fcTTuI4vnz58gMHDiCEWCwWh8O5c+fOybitLzNu4ThubGw8b96858+fnzt3btq0aT179iSRSNIudHd39ydPnhgZGV26dEn6aXLOnDkEAkFbW7t3797y8vKfXyTw+++/4zi+bt066TSloKCg4OBgY2Nj6XSV0NBQX1/fgwcPuru7SxeK1NfX//nnn3k83vPnzz/2kW7btu3p06cHDhzIy8srKCgICQkxMDDw9fX9fB6p9CefmppqZ2enrKyckJDg4+Pj5OSEEMrKyrp48WJERER9ff21a9diYmK+/uuuqanZtm3b1ClTrFiqCKEPo+Zc7Dcq5v87+Xk83scxRbFY/I/vRd+rIwYYjuPi6vfn10326m6spaaqoWfpPGL+ryE5rf9RVlJ5d6YBmW7ivXjv+Xvh7xLTs3Nzc7LS4qOf3zq5za+PjryC6+74Vuiz+hcB9rTs/ooUv8zG1F1ZaznChv9eQ4skIjEukQjKKlPXb/7e194rv/qg+No3tjAOXp6y5cOCky/Wxd45/f1ltoXaGyH4/78FCErK+fmfelSaEtPZl//sceW+SyxevEVQVskOCs4btyRv7OLGiPf1IS/yxizivkso/WVf4Zz17KDgonkb624/aXgckTduSXbgOfbFO3ljFlXsPYHjOPvC7aJ5Gwumr+a8fJc3drG44S8d2PycQmEVu62+6Y5FzOUVTFnJS0hPnrmw4FKgsIrd8DSSJ2p5qqEUn8//OI77efcyT9AoFgkLm/I+7hYdHV1WVnbz5k0cx5OSkqKjo/l8vlgszsjIkGat9DjB5deqqis/dpyWNhXtSf2laMGn/x0XLlz4mAS5x0/ER14/kLO1oKDgYxhXVlYOHDhwxYoVvXv3JhAIJBKJwWDMmDHD0tLSzs7uzJkz0omd0oYjnU63t7eXjg4qKipiGLZ27dpu3bohhCZNmmRoaCgdCCCTyf7+/pqamtJRw379+qmrq2tqas6aNcvc3Fw61L1o0aI+ffr8/vvvTCZTQ0NDOpasrKwsnQOlpaVlamqal5dnbW09YMAARUVF6eClv79/UkpMfXJq6Y2HS5Ys8fT0HD16NIZhAwcOdHR0NDU1nTx58vXr11etWpWXlxcZGblx48b6+vqoqKhNmzb98ssvDQ0N5ubm3bp1G6Jjku6zIGDT1tw717PHLhrSrWdAQMCIESO8vb2HDh3K4/ESEhL2rliz28VbLBa3YvusHQMMw3EctTt+/qP9G7Yev/u+hCf5rByMomI9YNrqnf7T7FnYfz4Jk8n8/fffv+taUQkuiW94Z8vs8bTqXk9FFwomR8AISmSV/1zLX1QH3ZNU1RKp8oKcQq29a77rtSJceD1sJ7OR1I/iTmTQ6O5OsvYUcxqL528uWtYzNfGp2FZv6Ftd3TnTif/tQubWJeE2FU5frXtkC0lDFSHEPndT0sxXXTBZurVs4wGFIR6CghJar26cxxHCsiqmt3v126iywhTL3sMFBSVU3wFpic8MYoVUR9sXRll9aZ6Ney9iZDJpzdj7JVfjmDkL4vtllLxVFNH7zfavOX5FZc6E+rvPWGO8qwMu4kKh5taltZeDaY52VAdr9sU7RAUma9SAj7XxYpLwZr6ouhYjEhVGeH18nvPkFS4SKwzz+E/fOY5X7DjKHNSH5tz9Px3nm5U2F2Xz0skY2UXJ44tNTbEp9ffDNDYurD54XuGn/nJmhgihbZkrtdJF/cx8zK36fb6zQCJACKcQ5NDfSD8bY2K8OuCC2gq/7ypv9Ydpq233qstpSR8W8nJDCq70OF5i/eseBkXhi53LtwRQfbxO0K+tMN5MwsgIoSYxjyfm0iVMhJBEIklJSamtrd22bdvLly9xHOdwOEpKSpmZmfHx8SdPngwNDQ0ICPDz8zt06FBmZuaRI0eOHj26d+9eCoWirq7+9u3buLi4/v37+/n56erqnj592snJydfXt7i4eN68eQ8ePEhMTNywYcOtW7fGjRu3devW27dv9+vXT3rt/Js3b6ZMmRIZGWlhYcHj8ZKSksLCwgYNGqSiopKRkcHj8WbNmnXjxo2xY8ceP378SPp21dBilzdMjU2L6hXpU6ZM6Wtuffr8+elLF/s6u0+a7VfPkPP09ExLS+Pz+UVFRTQarby8XCwWOzo6ZmZmbug/YtSiuTf3/D5R34LvaXmsW7hbuh4pQ2Ha7q2nevRXoNKOpn9ooBBy2FWXnYdpUKjhTobr9+2ZamFvamxs59VPJEfq6zuWy+XGHr8grGJ7/7b1u35f3bt3DwwMlEZ+G5O1EgdCCHGzw+7F0gaO660mKQ7ZumLPk1Jm93Hrdi3po9rat1iRMxyy4fKQ9Xx2QWZmfnldI19MprLU9EzNTTQZ7blsFQEj9GD1RggN1xiHENqWuXKYxtieLJd/et03EeNiAkbAEPbYJiu58i2RLGeurDL9Ow9CwsicJrZxKiPeLrGAUzJeYkfGyATs0w8tmROnLqeV2BDT3MzpM3KAgdNQtZsZTf36nrA6NVXoakXthhDK4WUY08wx9N0fErJTXxEb+Ea9B/zzrl8lLK1EYrEESZ7/xJ9SXVu26aDOwQ0cQUNjbYkqQhJeE/v87UxGSY1Bcs+AD/ys/BzlqkxfhvfF0ByF0sfTmxeYmRmzfspoTLli9nZ14xDWCK/8/GgyPTl5sWCM/qRdRTu01HQ96N7lg1VCSmut0wW2NJ5k7egV2StX+22vl7A1Vk6WvEkvXuBP1FaNLQz3MtpH1lJrik0V13OILCY3Ok6QW9T0IRkRCJgqS9LIYwxwFVWxOY9eyttacF/HIgxJA0xUWUOg0wh0mZ8JBIWlZC11jEjg5xRKgwEhVPTgHsXeIqBf1Mpk9X8RYPtz/efoLWMLq41oZt/+qufVD5slTVnc1I8BJsKFjyrv1AiqCvgJU7p5YUSi2spZH/efWzc67dX545rHt4l6MEifbm75kv2kVsger/WXP9uHlTdtmQ5va18pEFmD1Ue9EkY5NXroMYy/XhJH1CBHkKcQKAjHRwcxWWuISOPPTRrF2KjLtDu9mutLHg80Gi99UoJLEMLFSHzTPHaB3vRJm4iEfSKcRqoRVBU3F7yoebzMaJN0T2mn3KBBg6QPpQuVmZubm5ubjxo1SldXt1evXnQ6/eeffxYIBFQqdeXKlQsWLJBIJAwGAxeKevXqlZWVJW14+fn5MZlMCkV6j3o0YsSIESNGIIR++umnt2/fOjk5SWd1SuE4jmGYQCCgUChcLvdM3BFcQ3j+/PkZM2b4+/srKCjY29tfvXp16XQ/fmZeX51hpvMtQ3qdNDl44Oq8pjs3r9Qs2j1o2XoTQ0PujWcLu/UeELBDqZpjPnb41KlTJ0yY8PLard5C8uZ3oQ9+PXTv5q3eXMzAzmbh/TPxmc+e4GGjNCfex66vHDrXPyenZyMJM9Tpxqkao6x/uDAZG9a3NC039vzVX7fvsIzPE9fUKTx49aqhcvhcPzKZvKyby2BlnW/+O2p/ROkN7P9OELvL03Xm8UzjqbP6iINmDFjzVr2fI+HVoX1PWeOnOiv/9wbR32EkqqKGrqGJqbm5uamxvpYKg0JASCJoauJLMDLpv6bm7t27pVe6/LuX4wgnYkRnpb5/5O00oBq/qHlcxi/Soeq/rg0zoJqIBM2FiVGKWgY4X8B7n0hWVxFX1xIYtKIT50iIWKMixKIyBLlFJeeDKPK0xvA33IiYS+gGL+yNJkHD0NJF7kmKnLVxjbLIRfO7w0D7ZDxj4pBDjEuUWkExN6+QWGZCt6iNjI5DSVxS872kE3HFLxLr3teRud59FyGEVAd6lpSnRRM/2GcqqetYYGTS/uxNlsiEGF90u+hcIjHdTqFno6hBmF3YQBMSiSQSRmqKTREWlycx8lQoam8iAjXUTUhkucqnz4LzAyVETKeR1ZycKWesL/0pXX26RSO2/sHbo9bWXhiJhBCqv/uMYqDDe5fIexvfFJciZ2aIkUmSpmb2mRtkHU0ik5795HbhkwcCkvipdqKj5fDy9A9VcvX1bjphzeHGxr0anr8m1TW9HCKw4hsqFQkTdCsSh1C16fqC1wl5vanpyuX2Sr1TOYk9Wc5D1H0YdlZhNY8GqY3UpxrVIHYftYHdFHr8pDHBhmlvTDfvptAzkZpNpyoqk1VtmQ4actq7sn/RZBncoocaN6g1GzAvuCXaro+53Ddd91ml6E2KnJdj6r7dcSjpvWlF3njdqvLsROuGbvQeFVsP8XMKEcLVls2oDrqjOMwLw7A3h7Y2vHij0t2h5uhlfnpubeBdgaOBUNDUcPByfeRb9tErxWnvrhm+MeVq3gzdpVIvn6VcqSlRPZq5q15cK2HISSrYxg6eDY9eNsWlylubIuzT/7GiVTtvqkWwRLTomwHKtt2oRBrexK+//SRelFBD5XKFnLu5F7y0R/z9b0OEiwgY4TU7rFHUoCaniRDKr05JLIocUGOvve+1U5Iia5CHAPHPFh0qL03PqPhQTmJzuTVDNMbIa2p8fhw8KV9VXoMiz7ghuavDYdWSuQpkxdKy1ApCjRwmZ0q3QgiV80voJCZC6GzR4Zj6qMK6TMdYhq5t79fFIXwqMlS1rhFW88RcOYIcASNmclMpBLlmMU+IC+UIcgihGzG/VYqrNBn6D6tuVYkrKRSanJpKdO1LJVyhKSWTwBUw5Fg0IYVpYFRTW/y25sW9govnK06a4IbvGl/3t50iyC8pKoin6uttzlvZ6x2jqbHOytg9rOaRjrz+rbJAW6YDjvA6IZtKpN0pD2IQmSyyYnzDOzV5jb59+tn2tBHigq25K+rLcjSTmujGRmQKpUJcWldTfCFkjbVJXwKDeL/iug3TnkajVQrLBPEZFIKciNt4OXJHd5P+N/JPl0nK3A3cE9KeqqkZE7A/36kqy7NevTprKNEmqSk/efmHOD8rQ5I5sNpg6tpVvfWMzb3s6xoydszbkBJ9OV0ur6/FWDKBnIyl1/fRJZEpjKsfwh0rVayMxBqMuH4Sj3oNjWEeFacvLR3s4+nWW93F2uZ9PqOUPWD+BEwOaT9PLJ1inqheElMflYoy7BQdvdVG6lENteX1nKzsFPq7FnqrMvqq6r9vGLlxtfnY4SpWZnLKihMWz1ce5qo+pK/W6KEu86ZPnjx5ypQpI5bNy0h6pNu7P1muhVa1LMePH/fx8ZF2mbYxWQHW9GSD7xHS+jfP19tRqm6sX/bY4kDENf85HsIb2+7JTZzp+kMSrCXCiOXmVuMeacya5sT8p3NmfquUtAAAIABJREFUZ2fv27fvuQyvX7/28vJy/updImWRcJvqbz5SzxQ0J2bUNVXW1ZdZ6bslNMRYES3+yN5mGyF+lnM1khcRT8mQpBUUBt950RROz+OITdX3igO0HxQdVr1plSbHu/78j+lZamKlV4UPBEpkjhKSJOVHCaKtrL3sXEd3U3exV3MlEyjfWxvDszdLU5+OaMOyrKgGusH1t/NTXitciY1mpuhqWA3l9TH6Lc5V3KPHwGkKJJb0JRV37ncXWegm8m/XXs/LemOQSy6vzr4ivtVcW5NGznEOI+9r/kP4IPqm8kt5MuNd1JUPScHM10WnjZ47p6vfqb7alJJFrG/6TTXI5h0h14EokMPxqy+fECPq4uMxMR5CCrcMLHk2uFGLqsuqRmEvT7wpeaYSW7vDKEjxSTaBQHppnN3cUC/8+WhVTd5Jg8cqSnq1TP4TcsRA1pBu2m4P+A/5kubbKq/6aQ+la+vWCCvv0yMGD1nppNY3m1AQbJUib2PmrNjHjGEdqP9SqEkfpjnOSdH1VvklKpGmLa8X1/D+Bfuxt9pIKpFmy3TAEMYiK0m/awxhSmQVFxUPI5oZk8RSl9MkYARzhrUds2ejmJOnxcnUqXMR2FvYD7zCfFw3wNB21PT3DVGPjFJq9cgDmd6pCsW+buvLlHiGmt2Omj3RGjAo0a6pEqsJMowkiwgK1xNOOr9z0HCjybOeKn8ozfhgYuF2RPmGIlKQ0EinrCOcfRaiwT2asCYbbdfr/FuaoWX3DGM1Yzjaac1Gug7dNF2vEYIHGow9k/FrHOd9Q3lRHD1Lh6vYdPfly4J7IcYpuI7S+/KXtU2Vwfz7vVQ9jkWtr8pOfqgZ40MeqRqSw3xfrlQkfMyMLhIX61D1byceKYmJVH5Xta/pt4b74aGq8RolhEuFx9Tfsp+W3n7EeO1iPVour1ZAw4IMIvkSflVZpvLNNJfH2NCBKz04Dowedthfl9GSMzWgOdiYG7um1MYWRzwv5mRXpsSeFV8uF5bZPubebL6Dx+cebT7Zg2N6jXedlF3pl9rHJUJe16a3nLG+akxtakPcfcKzyNrQ2sr8uJRHzJjyI+JTNlEoWj7+RWOoMd0iMOU3UWyWQn5TNCcilpCsIKa/l8RSmKyEhvcVybGB1GBvpeG6bv1V5TXOcs/lvH/Kzcq2f0XsZzzaxtyjB95dXkVVzsJ4u+BX7TOJPYfOpPNJhhey48SJ73jRjroDHlbeNHknzEwO+1UQ0O2lpLQ8rVCl3ppqFxi7l52WTOCWB9ZfNMkkKRtbFmS9fUZ+nZv9xk7TJU+UdyP/NFbHk0+pUDA2Da9/ahqHc958+FV8WBSV/FAjRjcXe0B5ofWgULuYmG7YYBRWf7n+QklRkk50/RO5yNTm5NToYE5xfrBSFEYkFr4LV26mD2EOOyY6K8jM1M4U/qF4hV4lZB0Mf+HZ4O44VVleDSFkzexuzrRxVuqLBOJ8U1E/+0kZzFKMJm9GMrut9OKdbbVlYHGCUfUF8u3+hmPIRrq/aVykmhja2Xrf1XzbT82bSqSP1PTtptATIaRK0RDhomOc45gSg4ARGuX4tg5Df1U4o0hWvt8crNXD1Jhm/luufwVe6aDmmsfL+iB+bahtxJRjHVII0shX1DOWeS/4v2vHAJMxBoZXnBps/EevyPjtDiRu8DSDSTW/5j2YqYbxbozTWK0TknXQ/WsrlrYmceaV9b9FaEzcvdxD8Z8CrLi4+PLly7K2Hjhw4Ny5c0OHDv0XVUia+ZynkUgikT4k62nRetpKn098eUOXr5IjV6pj3SuLVqJWT8UKqzLwHM9uk3EF+YSEhz2KdbLtJYZK1uSonBwXqpwIy0iNMOvuaU2xLkiNSsLTveynft4t8x+VlaWnpDx3U/IQVtYwPJwJVHludBy9t/3nn+gbw6JpTt1wsSQ85jxCqI/uUI4SFpP1yIPmUczLV6smx9Kzeiu6l1iRzOhWIRGHVDFla13XUo0m3TRJdWOpuLGRoqOVb9DskKMWrZ/PklPWjeFFE+N0BWoOPUcRmfSmpAyRvT6VSEVlde+T71YxmvrrjsxSKDd824QcTaPF701oFtpv6/m6jPfCWG+bqRjCct49M3X2RgjF1b81rVXLwwsNdLuzyEo4wuPq3/VgOSOEeGJuHi/LhmmPEJLgkriGt6Y0SwWyIoawakElCSMpkpUbRPUSXKxIVv7en1tRcz4ZI2vK6SCEGkR1UexwR0VXKpFOwkhEjCgdXPnoNTtMhaJW0JRrRDMjIIIiWYlVhbL4mdbG7kJcEFb9iFUmcjIe/Fb4zkXJA0PYa3aYBcNGlfLn//Dm5EyygTbOkBPEZ1L0tEmqShKJKKY4rJf+oOLi5Mb/a+8+A5o62z6A3ydkJxAS9gZBpgiIIFMUEEdtrTjqrtrWx2pr3VarVYvV1ravVm3VR2tbq1Z9rHuvOhgKigxBlL23QAghJDnnfj8EEG3Figic9Pp9Uq6M+8rJyT9n5D65OWobcRG3KlgnQH0zJZ2fxzY28vAYWZWVVlOR38AnvT1HnbuxzctqkG4dg2djJbsRryPWU9fUJXhJ+boSX3HQxfR9omK1u9yugFPeIGZ4+LxdlhyfXpsYWO/+iFukcDfxMPbnFcuVDHU0916QOJSZVakuq2KZGXEc7dq+T/5W4r2TZoWMclZNnS3XzsZbcD0nmnvPXmGhYDT1dY64rfvAulpPP60OIUI3PIAh5KtKyrNSrpV76epxJXYV+g9zo+0bzatYUnOlAc/fs1D4WMI2jM464Vtlz61WlfOkVmFD1SUVJYXp3H7ONjx7eUNNauwxn4AJDAEfIZTXmG2UqaTKqgkWUzjYr+3A4u4esmRbWbkHIIRk127XyEpZfRxMbfsghGTX48maukJOpZlSwrG0YHn1ZmN2xYWLCqqxglXLopi9dV0EQf0fF2UXF97PU2QNGzCbyecn51y1KeMT1XJRRDDBYTfE3WusKEvn53voet+2Lx0gDpY9fMTLqhWF+Ovo66mKyuoL82uqCw3Vonj3WkOxleRWuZ7YrMQe2YidWHk1pFTG9+6TdP9sXWl+kM+UJl0Gj2LXX7gpCPTWEYte+P5sIGUYU4y7uTzvPplNGb0FrrheXpGTZurh2869oh9fseBat+5eTpImsBkcJqFTp6r10Q+sUpbrswyYBLNYUZAsveOh19+Ca33oyHchgWNNzV7iAgvdeAzseSdxyE9Os56p+OHRofGMY1N6Ty5cmX7tEzuGMnGVd+CJyLjEtZ7tHTzrmfz9/Tdt2uTn5/fimwIAAPhneuBJHPyw96aIIz4MDtuB70WrB28fbYNzT65auGzzQ6tZES6vL72wSl5fL2tUM3m6eno8+qUkAACALvLciBCEfH3xiPGGvXGVY6I2r51uzSAvXzn6wGTCD5vWBLzE4b1/SFFwZfem7b+fjU7OqWhQY4QQQbB1LVz8hkTOmP/x+L7izjnv8cSJEykpKZ3yUK84DLFYTLxoXw0dFRcXa+ZW6O6BdL6SkhLNfEjdPZDOV1paamJi8szlY7RDaWmpZir97h5I5ysvLx8+fHgHLjTa6aqrq7vrqXvC78Aa720cFr4ige/zxqhwX1c7M7GQzVArpFXF2amx509efKQ/7ffrO0eZvuratXv37oSEhE4Z8Svas2dPr169tHKlyszMtLOza53+UZtkZWVpLq3S3QPpfNnZ2ZaWlpyXOfGMLnJyciwsLLSytby8vLCwMHNz8+4eCOJyuVFRUW2nx+w67fzImapNPbJhzphQX3cX176+oWPmrD/cWZc2afssXTWZb89haGjYdiJ2bWJubt46k5CWsbW11UyhpH2cnJwyMjK6exSvhbu7+0tdHIBGvL29XzjFlNZ77mYNrjgz19933OojmUw7n7DwIBf9yotfTfTxnrQ/l+zUBFVnpaarA6fN7Pu3W8KE2O/9iZ5lael13b6hCAAAoCd53q4e9d3NS3ZXBG68dXSRV8vvr1TFp+YNGT9/5YmR+yNffNrnP6VjYW2Jj96IfTxlxN/9uIwqi4nOFFhYvPBHYAAAAP5VnhNguDYlKc904o/zvNoEB8ti5Jr5A/d+E5uuivTvtCMBDJtJCydsGjfet3DGrIlDfF3tzCRCNkE21VcVZ6fGntm780CK/eobg7RwHzYAAIBX8JwAI/gGBnysVlPP/J2kSEIo0u3Us5UIo1Hbr52w/2ztjrUzf/ibyXx3XF89zQPyCwAAwFOe+zuw8Dn/MRq9ctUo5y+HWWpmN8LS1N0Lvr7l9N4m584+fa6nTuYLAACgx3o2wM6vHr8zRYUQQogSCu9/N8LhFwd3t15GrPqijNT0YhnLYYTsUR7p7vAacoXgSGzd/WzdO/+RAQAAaJ1nA4zFEwiFas2/hf6jbVqvHGJkYNrLAyGEGI0yVZcNDwAAAPh7zwZY2Kc/h7V3e1IhUzAEsFvvlTCZTK38qS+C1ugJWqMjLW7tn3vBTByUslGhenJeBfX41Gz/NZZ/3P96wL/9hXsV1dXVBgadfFnnHgJaoyNojY60uLV/7rkBhh/fXDdp2jdX8uvVT92AEA7eknz5o15aOG0aAAAAGnleDpEPdixcf6/XRz8dO7gqzMjmna1nzp868GWkg+XbP+ydA+kFAACguz1vT2Bjyt0Mkwln1k4byJLKD237he0dMdJkaJhVtfesLbcnbPR/6esGAwAAAJ3pOdtSWCFvxHyhgEAI8Xv1Nsl5kKlGCPF8IwJrz525r+7KIQIAAAB/9ZwAI0QubmZ5Zw7G1WCk4+DWu/LalQdqhHBtSYmsSamEiXUBAAB0s+eexEFm73rLf/Z56t1TJbs9fx/t/mGq59jBotTjp6WTLqRsHdz9F1EDAADwr9beafTKklvHz9X2mz7MgSi/+u3Sr44+VJoHTF+95l0PPZgaHgAAQPfqCVdkBgAAAF4anBAPAACAliDAAAAA0BIEGAAAAFqCKQ07h6omN/1RWZPAwtnZWu+ZFxUrKjMfVvDtnS3/cnGzjpW6WFNlZnpOFSmyc3My5bWevtNUnByfVdvmiqcMkYOvp0XrlUdp0RrVUPooI7+WYejg6mD4zEVTKXnpw8w6iaOjCe/Zr3kdK/UEWJZ3NzG/oc1xb4ahS4CbcetyoG9rGtqy1KjK9Jj0yjarFyGw7udtJ2xd/1Q1OQ+KGDYutqK/fIa3U9I+GLwisvT8pyFmbEKDZzMs6noV1Vyjqv78IsKaRxAIESxDnzn/y1HiVyp1scb0X2d4ipkEQggRTEOfWb/eb9BUVHdXuj29erCDNuWSLxp/z2mNqov/foyTkKHpjW0xaPGx1rGoC08uCDBhEwghgmM+aPmFMhK/Uql7kCUH3gtddb3tK9x0cZbp0x/R3FH7pJoazVvTlqWGMcaYzP9+4NOzHTFdlieoMMYYU9I7W8Y4CXUIhAgdPddJu1LlLXdrp6SlIMBeEVV9ZJIxyzpya1yJvEmac2FlkD7TatYFGcYYU1V/TDZlGg3d8GduZXHiz9Mc2MKQ7zPVHS91MdW9NZ4cntt7e2LzautLE36e7splOS+OkWOMsfzYJBF/2OZ7Ga0eFjxW0qY13HBlri1T5LfwcGKJtC7/+vejbZg8v6/S1BhjMm/nUBHLZvyO+KLK/OjNb5kxDcfsL6Nwh0vdg5LGLPPgit89pWjzt7L/RvBMJv2a+mSxPSqWkhjTvzUtWWrNlDc+seX6rb7x4MnqlVvZiDHGuOH6/N4sXb+lpx5WlKX/8YmXgN135R3Fi0raCgLsFclPTpVwAv8vp+Vrm+L8+yYsx6W3VBhTJbuG8jlBm1pq0mNTDFl9ViV2uNTFVPHLnNjmH5xvaPmD7NJsa5b1x9eUGJPZ3wXyNG0+gxatYcXZmUYs1xUJLV/hqarfx4rZXlGpaqxOX9+frff2vubNaKp01zA+N2RLHok7WOpy6gcHFk8d0c+cSyDi6U95VfynTjz/b7P+Oiq6t0b/pfaUmr1v8Q1mnPmb8JEem2LIcl1xp/mdq7i5wJ5lMftSY/slrdVD9vjSF2URsXDVJxFWrS+kjo4OweFyEELKxJg7apeIcOvmmjBkaCA7M/ZWBe5gqYuRVTJkGRTkwWv5A9fKxoSol8oohMj8nHxka28lL3mQeO9hWQPZei9atIaljxt1bQf6u7Ga/0DoWVmLkUwqo3BdfMx9wndoqERzuIEwDovoRyVGJzSijpW6AYMjtu03Ysb7Q6yeOcbYlJ9TwrK1M6ovuJ+YnF2laD3GQvfWtGGptUEW5ORjW3trZUVmUmJ6Uf2TyWdVabG3pWahEe7N71xO/6GDJZVxsZlkeyXtBQH2igT9pnz22Tg3JpaVpCdGn/rx42UHm4I+mNiHiajKvPx6wtLaouU1JoSWVhJckFug7lipq1vjDN+WkX1ommnLcWPVoxOn0lhevu4shKV5uVXkw61hNlZu3v2czcXmfh8dzlYi1MGuu7o1wmjygexH20e2ZrP8zokLRbr9+jsyycLcQlJgaSVpaZthZm3BairILSE7VurSxhBCCOk4Rq74Iirqi0XDbZ4OMLI4J78Jx6/2tLB19/Z0MBbbhK04X0IihOjemhYstbbUBTkFqtIDE+wtnLy83azFRu5Td6U0IISQujC3CJnbWLR2zzK3MkMFOQXq9kraCwKskyj//DSwf/Bbc3/K8/hozURHJkJY3iBHbJE+v/XEIUKkr4ca6htwx0pd3VJbZPXtbZNHrUl2/HjtVGsGIgty8tWEjstH54rqG6oenl/nW7Jz+jtfJ6k62HX3NNVMVXzlyzHjNpf4Lf/sbQmB5TI5oacverJmMPT0dVGDrAF3rNSlvbSPzM/JJyme/9rYCrmsLPXowl6pGydM35VLIbq3pl1LjSrPzW/AlOWk37Pq5DW517eNUB2ZO2b5nzKE5TI5RYj09VqHTIjEIqSQych2St3TRZeAAOsknDf2lNZVZMfsHl3zzYgRG5JUiGCymEjd1PTk7YObFE2YoaNDdKzUpf08gaWpv80LcQteFGe37PiZdUG6CCGm29JrxaX3D871MxPwDRwjluzZOFqQ/Mtv8SpatYbIqls/zvB1H7o+22/j2WML3dlIs9iUTW2uuICbFE1Yp6WBly51aUPtYw/8Ormk+M7OqX0NeQKTPm9H/fp5sOr6nkOZJN1b066lxjCfebSwIuvMijAbXZ6+bfDsndtmWOQd+PmqnGCxmARuO2TcpFAgQkeH0U6pe7roEtrcW9diMLm6Rr0CZmz5ciwn6cCRVDVDYihhULU10idvqNqaOkJiKCE6VurqjhBCSJa8Y7z3gA+Osif/nHD//Opw8+b9E2xdQ2Pxk9/MEAaBwW6MkrxCJX1aw49jNozwDFly03LeH8lJR+b7aYbBMDCSIGlNm1+4NdXVNmoa6FCpC1t6IY7I2FjEaR0SwzIwuBcqzCsk6d6ali01pkBiLBE8+ZmK0H9gP1Z9QX4VxTU0FKLamton61BdTR0SG0oYqJ2S9tLm3rqC8spcK0H/dfefbFWwRPoC1CiXYyR0dbfDaYkpTc0VMicppY7v6m7H7GCpq1FFB2YMnxdtv/pq0uXvJvcVta7VVPG13Zt/ul7yZMXH9fUypGcgYdGkNaTO2DruzbVZAVtiE0+uGeXAby0wLNxcxcqUxPSWIweq1LuppKmbmzGjY6Wu7Kp9ZPa5bZsPJNS02eCQSWVIYiCmfWtatdTw4/j9m384l/3k2BWW1csolliix2A6uzujrMSUlm+BuDo5qYDh4u7MQu2UtFh3nwZJc1TZnjd0uf1WJ7ac8Fr35zxHlv7Y36spjJviljiyLacdq6Awxlh+d5UXx2DcwSoKd7TUtZSxix3YNrPO1/7lqamq/ZEilvP8a3XNpx/XXP7IkW0y5VhNh7vuYtKT7xqz3T9L+LtTjKWnZ5ixXRberMcYY6rm4ocObOs5l+UdL3UTMndTEKftueZkzuaBXN6AdcnNXZOlhyaYsezn31RguremPUsNY4zlF2aZsyyn/FHevGY03F3jw9Md9t9CEmN1xkZ/nnjErjw1xhirMreE6gkGb80lcbslrQUB9qpkcav6C5kSz8g5y1Ysfn+Yo5BpFL45VbNmkSVHptqwDftPXr7u81mDrNiS0M1pzT/S6FipK6kffuXLYtoOmjK9rZnzf01TY9x4b+NAsY7I9c0Ply1fOD2sF59tM2G/ZsWhQWtYGbPInsl3Gjrtqd7eW3k0n8QYqzN3jjBimwd/sGrdimm+xizzsfvymz8FOlbqHn/9lKdqry31FLAMvcfNW/7pJ5MCLTh817lnNV8naN6a1iw1zaiyfnrLjMnvFf7ekuWLZ73hKmKaDN16X9MtVXt1gRtPz230oqi180b05gu8V8bV4xeVtJXOmjVrunsjkN7YloMmjurDrsxKT88qU1uEfvjtro0Temvm1SN0XUeODRQUJ8bdzVE5RK7d9f10Fy56hVJXogruXH7YyMbS2rbqVNahY/3N2KaBE9/xF9dlp97Pker2Gfnxd9s/H2bBpElrSJ0TdylXxVPXPdWblOk0/E0vCYMh8Y4c7Ymy428lFzE9p327+6u3bZr3cnas1E0URUmJNXYjJgS1/GaK4NqGT4n05FU+TEkraDT0Hrt0y7ZFgQaaXcP0bq2j4+95rWlG5TV2UriFIv9+6qMqtkPYB+t3bBjv0PyhwrUbMj7cpColLuFRvdmQpTt2fOLTfIXhdkraCi5oCQAAgJZ60HFYAAAA4J+DAAMAAEBLEGAAAABoCQIMAAAALUGAAQAAoCUIMAAAALQEAQYAAICWIMAAAADQEgQYAAAAWoIAAwAAQEsQYAAAAGgJAgwAAAAtQYABAACgJQgwAAAAtAQBBgAAgJYgwAAAANASBBgAAABaggADAABASxBgAAAAaAkCDAAAAC1BgAEAAKAlCDAAAAC0BAEGAACAliDAAAAA0BIEGAAAAFqCAANAW+Gqn4bzTWddVHb3QAB4PSDAAAAA0BIEGAA9BEWS+OXvpFZRr2EsLdTyhqbX+PAAvBIIMACeMlqXG7Iln0IIITJtXT82Qzjqt8cYIYRUMYvs2WazLihwfcpvC0Z6Wunz2ByRhceIBQceNCKEGi/NtmTbL4xRtTwU+WB9f45k/OEajBCuT927YLi7mS5P19Ql9IOtMVWa3CEzvvIVDtpwfPNYJzGHxdEz93hr5el8NUIIqWIXO/CDN+e1BJT69lJHfuB32VTzvULWHlgTbinksLiS3hHLzxZVxmwc424i4Otb+Uz+MVHWOorSS1FjPM11eQJj5yEf/ZJS35KSzx1S2rp+wvAfky9/Odbb1u+LJPVrfsUB6CgIMACeMsSHuHcjrh4hhGvi4zLUuDE++o4SIUTlx8UV8QcO8VdfXTZq5t7HwZ/9cvLciZ0L+uT/970ZWzJIxAuKHGFUdO50yyc+mXXieKreiEnDxASZu2di2AeHlBFr9v6xb30k/9KSYcOj7iian1Kd9O1/tqmm7LoUc2nnNP24r6bMP1j54m0xVdyGhZfc15+JvrJrsijumwn9+s686bn6fxePrPatPLxo4R5N8OGaI/On/487YePefVtmO+fsfn/ozIMlFEKo/SGR+T/PXnbbec4PP37gotPZLzEAnQUDANpIW+fNsfjwchPGivPvm5mGhPbl9V2dpMLU431vC/nDdpWpc377z/Cp2++rNbenKnYN5Uqmn1ZgjBvOvW/G9liTrMIYY/WjjX4c05lnZBg3Xp1rw+79ybX65udQ3FnZl60//nAdxuoHG3xYOr3n35BrSqpbS3pzXJYnqDBWxiyy5wVtyiXxkxIv4Nsssvle3MHb8jW1+sPjhDrGk48+pjDGWP1gfX+O+ezLTVTl7mEcQjhke17zQ6gyvgngsb3XpanbHdL9KC8W02XprcbX/FID8IpgCwyAp/QOHWxVFRudoVY/jEto8Jn68SCTrJhblVh5Nzpe5Rk+2EjHbsqOs3tnuyF5VV5q3Pl93x9OVmGMEUKIP3D0cEnGmbNZJEJU3snjSUZvTxwsQOoHV6+XGoWEOjVVa8gsQwZbNybfzdBsqxGSgUMH8FDzv40kjOaHa5+OpXc/M80KzNaXCFkuA7xFBEIIMfQlYoIkSc3N2AHjIq2b13Om49R3g4n023elLxySyG+QF7fTXlQAXgtmdw8AgJ6F5RkWov/DzdiySuatHKdBgYN6+3H2RcdL/ZPjHju+FWbDQFT5tW/mL9l6+l45MrTp7dZXj9/6PZAfEjlc/8CZc/lLHdGp4/dMI9cH8xBSVpZXqIt3jzLb3faJGGa1Us1BJ4bYQNKBr5I6OjpEm/8yWSzirzcieKamota/E/rmpnyqoE6mftGQjE2N4Nst6OngPQrA03h+4UHsxOvXb8QmiQf49dLzC/ZS3L559WZMpungcFcmrjw4Z8zqRKfVF/Nq68qyEq/88n6fJ98DBSGjh+klnj5fmHf6WKLl2AkBHIQQIRAKWS17FluRJTvC2Zp7EX+TPH9FSevqX/qMQ9xYXl7Xuj2HpRWVjSwDI32dFw6JAZ8NoMeDNykAzxANHOLTdHPnjzFN3gGebIZJQKB90dUtB+8IQ4b0ZyN1ZmJyQ68x82f4WwoYCCFlTmZ+m/P0hINHDxXEn9zz89G7tuMm+LAQQojlFuAjeHTxQlbL7aiCX6d4DVh0saH9gRAEQdU+rmkOraakK9FVL3+evTL2yPGi5ocgs/b/ep3wHjhA0NEhAdCTQIAB8AzCZFCYW8XNG3kuAT56CDFdgv31k6/FqAIjAnkIMe3cnDnZR/5v58U79+LO/bR05LgtmZQiPz2tohEhhJAwdHQE99o3395yGP+OR/Ommf7IxfPckteMGv/5T8cvXz76/Xuj5h1H4WMCBe2Og2nj4sR5sGvZl0fj7sQc/2ba1G2PXj6/EGq4tPjNmd8dOnPqt6h33lhxy+zdT6faMDo4JAB6FAj7raTKAAABWklEQVQwAJ6lYx8a2oupYzHAz4aBEGL3D/blERzfISEiAiHCbOoP+5e4pK0fPzh88srj5PSjsf99xyzh8zdWXddM2aQbFjlE0ES6vzPerXXXIrf/qtNnowKqfl826c3IudtzPD8/deaLgBeEBWE+bdveRV4l298d6BsUGZUeFLUggPWyvbC8PtkV5ZO3Y+74iYv2lXgsOHhh8zAx0dEhAdCjEP/ofCcAQPfBSmlVA8tQzPtHh8oA+NeAAAMAAEBLsAsRAAAALUGAAQAAoCUIMAAAALQEAQYAAICWIMAAAADQEgQYAAAAWoIAAwAAQEsQYAAAAGgJAgwAAAAtQYABAACgJQgwAAAAtAQBBgAAgJYgwAAAANASBBgAAABaggADAABASxBgAAAAaAkCDAAAAC1BgAEAAKAlCDAAAAC0BAEGAACAliDAAAAA0BIEGAAAAFqCAAMAAEBL/w/YP9QUhvmEYgAAAABJRU5ErkJggg==" alt="Sample `raman_hdpe` spectrum with different degrees of background subtraction (Cowger et al., 2020)." />
+<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="range-selection" class="section level4">
-<h4>Range Selection</h4>
-<p><img 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width="380" style="display: block; margin: auto;" /></p>
+<div id="range-selection" class="section level2">
+<h2>Range Selection</h2>
 <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
@@ -2789,31 +2884,29 @@ <h4>Range Selection</h4>
 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>
+peak or a subset of peaks of interests. Multiple ranges can be specified
+simultaneously.</p>
+<div class="sourceCode" id="cb40"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb40-1"><a href="#cb40-1" tabindex="-1"></a><span class="fu">data</span>(<span class="st">&quot;raman_hdpe&quot;</span>)</span>
+<span id="cb40-2"><a href="#cb40-2" tabindex="-1"></a></span>
+<span id="cb40-3"><a href="#cb40-3" tabindex="-1"></a>none <span class="ot">&lt;-</span> <span class="fu">make_rel</span>(raman_hdpe)</span>
+<span id="cb40-4"><a href="#cb40-4" 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="cb40-5"><a href="#cb40-5" 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="cb40-6"><a href="#cb40-6" tabindex="-1"></a></span>
+<span id="cb40-7"><a href="#cb40-7" 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="cb40-8"><a href="#cb40-8" tabindex="-1"></a><span class="co"># Common argument crops the ranges to the most common range between the spectra</span></span>
+<span id="cb40-9"><a href="#cb40-9" tabindex="-1"></a><span class="co"># when joining. </span></span>
+<span id="cb40-10"><a href="#cb40-10" tabindex="-1"></a></span>
+<span id="cb40-11"><a href="#cb40-11" 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)." />
+<img 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B397L1D4snfns8u1Rvuo7+9pj+KXg+wr3h3pjnN3b1AWjrabKENJoxD+C2bg3tU9AIA2mbHedziTzc1p20tuRgPACYHPuPCnIsNmZ9o7OqPUheXtLySvk4CGFOSmqpPmPn2U12DlRLxfURCXgu7LvZh6qLCOm5/cFX3n41uVttAIJ6IUpcR3koA4IUGursvdcbYbBiPSxtMlFbv7r64BgPMytS3AKC/1zAcc9nHaH+vYTKu3J8f2AI/c5y9iXyJhFDn5be496M6Fsr8W/4PdXrJ1MBZIsLDvvAizQ+pLuOoPLX6DIbjuESM8biMze7uvrgGwzC5lkwAuKb7z0pbXNXsI74TuBjvudD5LXCjZycBDPOasHx51F8vP/XJ/tsaWyMUn7erb+z//UQWW4aXKv5v6+qFLzz55PNvfLT5RK714Z++RgsilokJaZ1ekm/N+bvwt4fUHwSpNYYXHgIAhsNnPG4iznve04L46OYUwHAMXxP/PQDsKfo903zHVc2mGG8BAAMMxbS4YuXOphCLdn2+Kd2SteP14bFeIqFEeo889rXjrv17onJ2ze4W2X7kKz8nkwB09vYZ3XpPX77lxO20/3asfn5gx1EfX3ZrkRQFV/VMyCu1f36pveSM5mg7WeeH1yUEqQ1h+zjl9DEAYE1Ot6Z6WJo1mzNJyCUYp/nkJrDXwIb5jA7kB7ukQYqh/pf2DgCc0xzfnve9S9r0IE7+MjBJx0kLFvWpLp5j4jbRrkzGZTS7Fj7/rXbYupOfz+rJA+vR1Yt2YJO3Jm6cHMYHRnP+o/Ejls//dtzhuRFuufSWbkr5q/C3WaFzuRiXh/Nr85ICSy4D0Es54CF3DUFqy3O3BFPNnOjuLriMkTJ8n71uTthiF1Z+wjH8Eb/xAODD888ye+S2pQ3h7KuNqPXIp1s7ecy1yFunzxn6LfzspZ7+GACdc/FicdxTr08I4wMAYMpur6+YsXHk4dP6uRFumcu30lYvns/+4j+5OO9R32q2JmKAOVTyzxDvRxz3tJa0CxdF39RfiZd2aMSeIkhljJ1kdzT2xLXMhe9/6T1nBiGv2+x9U2alLUXWfABIMyXLOQpvnl/D28QAe9xvCgBEimNdu0+mR3B/QiGu8lZhmtIy9vIXJlXICQyv3C33bQHdWtL2iaDn4qXtpUT126DpSd2+4l0V78ExXEiIPkv/gGTIRukjglSvYNlae14hAOBiIWNxWdZA47Bn5zP2ZvU/SMX1fifmYwA4ozl2VfefS9qkGSrNlMwe25kWl3VXOVJsmRLZquObx62FW6ZEhlSrVbe3T7vyGhgRPWF6t5TV0+f8eFlNAuYzasaIwm8/2J5uBQBgtBc/Xb6luPOgnu76GsZeF40Rx/f3GlbtE2Qc+crW6yvesyP/x2xzxlj/aXgT+H5QP1nmO0mGGwDAANMCc3ObDUpdhvF4ACDu11X2+FB3d6duaJsd43P1B07p/jrs7r64DHsNrLuib2tJO5c0WGDN25z9JQCYKdPiWy+7pE0PUnkKsf34OfNK4ltxJMpJ81/vWe01MEn7UJcWpCGiZ2/5KWPyrJmdv3s1on3H+FaiQMs/MxJOfdIpkpd//XKWZNTav1+IdFco2JzzZVd57wRZ5wJrri8voNrVG2bKxME5AlzI3kw23Oih7DfSd2zj9tSpEluhjKOo5QU81snSwzKuorWk7R3j7dVpSx/zm/io38QCa96FspOd5D2CBKEPr7eIC1E6AyGTAAAuEuIiobu7UzeM1YbzeIzZQmq07u6LaxRYc/cW7Xo6ZHa0OM5Vbcq5yn5eQwGAh/N9+QGuatZTVAlgE19tzx6Ne8U13xBqgQh5bM2pO7OP7vpj7/FLiWnZ3FYdOgJXLPKOmbDinadmjIyVurXyooKrAoCfcr8Z5TOujbQ9AJTZSzPMqR1k3QBgU/YXaltRrKSto8L0gohlQkKUYUr14vlKOdVPPDamb7I+nRAwI0bcpvYvGe7zOLuOLVIcuz7hF5qhAYCH8+y0TW0rChKEXtX9903WmsmBz/RTDS2w5iYbbibIOiu5Xg/rZ0DqjrZYAcc9dyMSXCTAeFyMz2s2afRG0qCxqwGgyFZgIHUu2UBZTEiGej8GAARGLIx8r+ENepYmk5+KSSIHPvnGwCfd3Y/KZoXMZQ96KQc6apdp7OpdBdvYAFZsK+yp7F9xtpB92hH13ihxa9eWna6fGHF8Xf+rePF8HMcYYARGAICK6z0uoPwX1F7WZXXcN1yMCwB22n7HdFvF81Zyvc5ojv5Z+OvkwGc6yLoWWfNzLVlx0gTH2BRpVDQtaFv+e6eNZu2fB5VTH3Nvj+okeP37gGHNaR1YpDj2lfBFAHBLfy3DnOqSAGahzcXWwhBhGADYGRs7RdlyNPmLNNSd3R++uXjDGZ2b8jgciRi9lAPCRdHscbgo+tXwpezxwsj3+qqG9FYNYm/mW3N+yPkKAHoo+4UJoxq9vwAAFENtzPpUS2rYmxMCpm/J+ZqpdSYMA8zPuRsf+DQxIWGnJUOEYc+EvNJO2gkAeij7zw1fEiuOB4ASe/Hx0gN3jLcB4Jh6/0epi++YbgOA2lacZkymW96iy0aGi4R+i19kj2mL1XDknHv7U1d314FJ2UTK5gEr0uUv+The2r6Hor9LGrylv7a7cBt7/FHK4roWvfN0TT6A0dmHNnyy5tcrhlo898iRIwRBYE4YDIbS0tI6ndxMmd5Kms0ekwyZZ8lmjwuteSLivjrZuZYs9qDIWsAWiYmTJLBfiwCAZBr1K6Se1GJ3f7M39VdstC3VmFRsLQAABpgH5kYaScOFspP1OzUGWAA/mB2DtpEkzAt/m5107aHsNz5guj8/CABSjInb879PM90GgCPqvV9mrGL/12lJTQusRto4cLGQNrq1HEB9CTvFez0/xd29cI1kw81d6l9pvdGb5xcriXdJmwGCkC7y3uxxkLAVjnnSjjkN1+QDGLfne+ey0n99yr8Wl8EGDhxIURTjhEQiUalUdTq5jbGGCMPZ43xrzoasT9jjH3PWHyj5c1ved6c1R3/J2wQAq1LfMlIGAEiQdZ4ZMhcATJSRLfFipkwLb73QmAMOBVf1bOg8OUdJM/T6zI8ttPmNyHd9+P4AcEy9f1P2uppfLuZI3oz8wLVd4uOCGHEb9rpaD2X/t6JWsdexO8q69VD2k3BkAHCm9Oj/0t7JtmQAwOGSf37K/UZHagHASBlcWDiuBaFphir/q8MFfCApx82mjyrTFX7wlbt74WJaUqMjtYS30kyZXJVG788P7KHsxx7PCpnr44q1ZR6kxgBGa679vmbRS09NnjBrw3Vr5vFdxzOMjdUxB57cPzDQS+SWSCvnKF8JW8Qe+/ECOsq6s8etpQntpJ1v6a8ZKT2bkjfIu3y/dgwwdleebHPGzoKfAeCG/nK0OE5P6erXh3xLzj9Ff2zN/bY2T6YZ6sec9WwovWW4dkZzdGrQLBlHruR6YYABgIKrHOk7ruZGMMACBK6pc/NACq6qs7ynmJAAwAjfsaviNoQIwgAgVtI2kB/CvpN/Fv6yIHGm2lYMAIdL/v2r8FcbbYVGH9d6HP3+k2W/7HHcxCUixuzuuqK1RukMlLrM3b1wsa6K3pMFE7n+PgXW3D8Lf3FJm1q7hl0cDQAkQ7L5Vi2H88llS+L6SUPn7ilTRbfi5Rt8ZpPxfy0YslH23NZ/1j0W+BDCia34+rGDJy8nZRRo9GaSI5R5h0QndB80qHu4zG2DYpqhARh2VM7D+WP8p7L3syU5lsWsIe4O2B0PfZu1dqTvuCBBaIgwrI9qMABsyfn6w9ZfVUpHzDLf+bdo50jfsaHCiJr7cFl3Tk/qbhtvphqTosQPqI5ytuy4xl7KDnTkHOWm7C9Xx20AABNl/DFn/QutXmMTT2p2UXuGwIjaPPPhCRKEOpL1pwbOmho4iz0OFITcNt5kF+dtyFyTZLi+Mm69mJCcKD0IAL2Vg1y4S4WnI0u17CIwlv+KebjENTsoNgLGZmfzJ+15RZotO30XveDuHrkABpg4JkocEyWhLa5aZnO27LjWrpkU+DQA/J7/oz8/yNmK1WbJWQCjsza/uvBk5JsHtr/Tr3Rl98cKgddn9em/qGFjX1o+etA3w126VYg9feeip+d8eSLPxhF7+/spJTyctOhKCgrLLCBrM+m9zV+/0kXujkz6fcW7aaAeuVtBKst8x58fxME42ZaMVsJIAiMcaT9GymChzDKuPM1024fnDwAiQtxLORAApge/KOXIci1ZFZdPGUh9G2n7AEEwxVBEjdPWvZQDORhnjP9UHs6/UHbq++x1CyKWVV1Hwvakl3Ige1IACBSELIspn/MU4MIb+suHSvZ0lvfMtWRd1p17MsjpJ8Itw/VgQau6vleNo7WkrWPr9Nlhbzrefy7GTTenUkDiwFuZ+pae0q6IWcvBOOfLTgoJEZtg0tLQegMv4t7+Gtxgfzd2pq4cAYwhSbJI7e7uuMa5shMmytjmyxTVzIldgnu5pM0IUYz9bqkBX36AgGhZGb/Ovq6azhw8q3zi3aWDArmOyMHxH/b2qwPKThxNdGl9F/L66klPbNQMXvXP9UK9rigrNTkx8dbtO7mlBm32hd8Wxpx/8/FXdpW4JQmRg3PYvAPW7sJfbuqvpJtS2SS9dFPK0uR57EM39Jd35G/hYryPWn/Fw8u/9l7RXWCA6aboAwDfZ69LNtx0NNVG2r6vashV3X8/5W6ouQ8KrkrCkfFxAQZYe1nXD1p/4RiHpRmT000pAPB34W/zbz5TXCUDQnw30wTH8PdiP99ZsJWL84IEoY6Uk2qN9pvsKZWIHUnDPZT9pwbOYm++Efnuwoj32OnHElvR+bKTbAbmR6mL19xZwT7/mu5itjnDPZ1+OKypmZUuGlE6IyGVOHt+E4fhOLuFNMbj0rZmUgumzF5qIHUAYFdrzmqOuaTNaHEcmycFAAO9RnRX9HVJs57CWQAjbTaaw+NVHvYQBA4URbkympA3f//1Ztslv34/b0S8D7/iCXFxUOexS7ZteVn817bDjX/xDQCGej9WsW50L+UAb76flCtnRzkhwrAZwS+xD0WJWkeIYthVig4/5Ww4WPw3m2I32m+yI7AdLvknw5QKAIH8kKpz1n8W/nKg5C/2uMCat7twu+MhHs5Tcb3Zq1kAsK94d6E1HwD6qoZ80ua7mq/fyrnKJVErxYREwVXVnKMh48jrVLajqSEwQs5VssejfMfNCpnLvmNzwpY4piKv6C78U/Q7lG+Su2RLTnkxsBTjrTJ73VJVmwiOt9KSmMpQ9/6cOCo5J9DXcVO/97jtTk1fXJoUfusInwUzAQDncRlbM6mIONT70b7nlObrt+1G3fcPyqWqpQJrnmPBDANMS0t3chbAZD0HdSrcunLjzQrp64z+v3Xrj4m69Wzj0rX9VosVkyvkznrCUapklNnSFP6EO8t7hgjCfHn+A7yGAwAH47aRJLAPefF8GGAqXZgd4z/1lOYI++fVXtbFsYzseOlB9lJNoCCk4k5jNEN/n72uj2pwkuEGO2hIMlzXk5Xr6KxKK894HOk7tpO8BwDIuUo+Lqi58xhgFTdsZVuoeswAszHr01q9HZ5GwpE6klNmBL/4QqvXAAAD7KmQl/uoBgEAyZD/FP2+Le87ALDSls/TP2CDHADkWbKb+EcDoZBxvBRk3r1RuGrWRF5ooOOmLT3HeifbHV2rJ4wgAAATCXFxM5kWwzGC0RmBYXCd9alg19Qt3F+8+4r2Ant8VXeBXYTacjgLG3jU82uXxZ2Z2yW2y+g3d2VrTn89d9qAdgPezxjwwbLHXFodidN22BDfE6vmfHG6oMpEAWPKPPDRvHW3Ow/t7f6KTAA22ppuSsm1ZJmoagaEfFwwzHt0xXv6qAY/GfR8pKh8xi/DnHbbeBMA5oYvceRuWGmLYxCWZUnPMWeouN5zwhazg4aeyv7j/adXOpGYkBRYcwEgXBTtGNXViZbULE56+Zh6PwDsKdqx+NZLjsVhelLHlvFtOQL4wWxNBA7GmRf+9kut3pmAAaUAACAASURBVAAAHs4f5jOaXYpupAzfZq1lvzIbSN23WZ+d0RxlX+v48tsU8FoF2TJznT3qoVuC4QJ+0KdvubsXrrGvePd1/h2uvw9wOY7SBw3UVtoxjNPKcjMFAFRcbzlH6ZJmPYazVVMMwzC2nOPfLJo+omdCbHRMfNfBU1776miOraYX1A9dcvjtvr4cjKsI79h/1LgpT86YMf2JyWOG92kXJCEwcesntyRbXXAaiUSycePGhrSQYUpdnrzg0zvvXtNdrM3z002phZY8x81b+mvLkl+t9Jyfcr45rj7AHlM0pbVrHA+tSn0rUX/VWeOHiv9JN6bUoff309o1+ZYchmHyLTkm0ljxoVJbSb2bbfYomjyrOX5Ze45hmBJr0YKbM7/N+oxhmDJb6fbc72/e/X1RNNloXaJJKv+dtbTVZr6ZYs3MvddVo6ni03R7jxd/9VOj9aqBDCcvlv2+1929cLGfczbu/eU9/ZGzDMNcKjtrpkwPfEltWDNzc+a955Km6ichIeHqVaefVA9VjTVauEF9n/uo73PsDavRRIhFD6OmC+Y18L2jydP2/7Zj76krSRlZKZlWiiuU+4T0mjFh6dhJj3cNaCLlvQL4wT2V/bk4r5UwsjbPv1R2hkcIHNtgtpa0mx784ra873oo+jmmE2PEbRxzUziGyzgKx8vH+j+RbLwZd3eWsiKSIY+q90VL6l/TWsZRsOdis1TMlMlR6RHV5K0BjhGO6+RePJ9P2nzHHvMJgZLrZaaMAJBtzng/ZeEwn9HjA54stZec05xoK+3oKMvicpZrSQxFYTyuoM290mW00ZS34MPgDe877uFFhhqOeUw1KVKtoQ0eWTqkBpMCn9aadnG8lQCwr3i3mCOJETe0HkemOc3P358sUrObl1b8j9wiOA1ttPa/9TN7twqYscvCMAxj+W2iWB49cslfmQ9hDNYoGj4Cq6tCS16eJbvSnUuSZlc7xMkxZ67P+LiWLS+69VKm6U5D+1fh1CuSF7DHp0uPXCg75aqWWyyKJu20nWEYjU39S97m85qTDMMk6W/MuzHjQPHfDMOUWItOqg+5ZLCr3X1Qven3Snfacgpy5qy47y6atheXNvx0jUPz2z+abX+zx7nzP6DMFvf2x+Wuav/T27UNb+d/qUvP/70xfcIr1vRsE2lccHNmw9usKzeOwJxdA2PK9swfM+cvatgbMzpxAQC4fV9fP799+udTJq+52RQSKtzktjGRLRBVG778gAB+5ZIWK2LWVhrilNpLAOCO6Xbtt/OJFcfXvHqsTvz4gaq75edTjUkWyvOulDQ1OEawefwKrmpSwFNdFb0BIFYS/27s531VgwHAQpuTjDfY6saXtefeTp5zUXsGADR29TXdRQtdh18BWVzK8S2vkWY8fYmx2gCA1hvxSjn0GMZ+9/cIjnVgAEAZjIypOfxN/pK3Kd2UYsvKK1ixLkHWWeKKvZa6Kvp45zIYjtsy83g4v6Xt1edsStB8ZNsf5jHr/94w2YvN2cb9uj25bFsnTo8uv+xMej2+bcsqGenwb9EfGnvp8pg19W6B/Vyr6LP0958PXVCnjVeeDpld7w5U2yVHxawJATM8Ooe+iZNx5OxBkCDUsVNPe1lXX34Ae/m92FZwqGQPyZCd5N3PlZ04pt43xn9qjDhea9do7OpQYXjVaq0MRTmyDfV7jxNSiaBdDKU3EDKXlhtoXI51YACANZdM+mJbgcVuwjgySq3JNKdxMG7D401/r2FF2o3cxwbxo1oRGLEgYplLuuopnAQwxqTR2P06RlWqf8FpFRVGaDVaGqCFBrCBXiN0VfLaG6i7op9rG6wHO2MzUUYxIW1ZE+hNA47hjg+yGHF8TET5dZEOsq5Sjpy9SHnbmLin6PfxAU+2k3Y6rTlyTXdxtN/kQEGIgdRhzwwRcL3Zl/BaBdkycwTtYnCRkB9b+Uqt5Vaa5VqSYvIjjfWT1Z9iyqOOY5zHax5rmV9o9VrxG2uYV6ZTeuMV3QWaocf6T2tgmynGW2KTSda+NTfIDwBMlJGtJNdCOJlCxBTt2ofc2fXTSU3FNctkxi9bj2Nx7aKbz/Y8dZUg68JWOHShUb7j3D7wTzbc/Dbrs2W3X2Vr5iJNAR8XtJEksLk2XRW9l8esYWtixUs6dJB1E3MkAHC27MQHKW+mGpMA4FTp4b/ik0vy0wAAiwsRP155xymMyzFduNbYP0aDEUoZuzeYp+NiPEqrxyUi2mTuKuvVSd694W1+k7XGICYJZfnIfnXa2y1qSzBnoYjTed5HU3+dPKrDxYlTH+0WpcK0eSnn//x5Z3KrxQcn+bqjLqH7GI6dE8THeND1g3oIF0VLOXI5R1FxL2akaZJzlY4dNIZ4PzLEu3xEFS6K1imyyewcAPir8NeDJX8vi14TIAg+ozlaai8Z7P0Iv1WQraC44uUlj+C39JUHP8kTfJO1pifXECQW8sND/DmBLtmoc6DXyIjXHuNgXMORs6KeHSNFsUSVixTNmNMfFfcfs/HMkW4frPr+11W/ZpXZuYrQ9gOe/vbbJdM7tLQ5Jt3uQ5RaKx/XnGs8iwnJ86Hz3d0LpM5smbmaLTvZj/hAQUhA9HTL+GQAGCefOMF/OmAYACi53gXWPDttF3CFuyfoUm899W6bdUqu13/a01bK0kPZ34UJQa6iXr9NMrgnPzrM3R1xpQJrHknZcT4vYOUbBlKfpktuL+vSwDZH3d0dyXDkLMdbNb3diw3upiepae8JwrfXi5/tPn9HbSYp0qy+c/b31TM6KFrW6AsYirYXlgi7tK3Da2g65+Vl9ryih9YpBClnzy3ERfcqLWEELmwfBwAl67aYryWzd7aWtB3rP43d0OfJ4mHvGuewebAUQ6WbU9jN1T5P/3Bp8lwzZQKAK7oLN/VXGv9nqcheWMJYPGb3slpaEvGBvyIMcBwA8q05e4p2NLxNtrgP3K3DYqUtVCPunet2NQ02KX329SvJRab7a/digtAu/eJULSWOUSWlhFJWsabcA9lyCsjiUvX6rf7vvvrwOoYgAEAWlXJ8qtlnnCrT48JqsklVMydgePl/3u6Kvo5F2S+HvVFm17ApPAXW3HRTSqwknoNxP73zLg30/PB3cAy/Zbgm5cgbZ6udivOc6o2/iLq0E3Zs0wjnfag4BD9g1UIAYCgqVBg+0qehW4KRDPlx2vLlmzsGrlnCDQ20pmTsyf85UBDScrYEcxrA7IlfPT5k3t58snLleTx49sE7Xwz0pDn0hiC8Vb4Ln6/TS2ypmYK20facypubeDRab9TvPykfP9zdHUHuQxareSGVlw+ar96ypmTgsmr2UsGI6iddOBjXm1deun6EzxjH/c+Gvqq2F7O1py9qzxZZ8+dHvIMBtjb9PSXXi61Im2FKVXK9HDsAuARjtTl242RsdqqsnhuaNymf3Fk+J2wxD+eXfveboF1sx54NTeIgMOJRxRhKcx0AuCH+xmPng4XtWlQWorMAZjv62XuHxJO/Pb9ybJzyvl1VMJwraCnRCwAwAueFBlKlWu3fh1UzavWNyZqaKeqaoLn9J2224MIHFIn3FOqNv+Dilnb10wNgPC4vvPJieUGbKEG7WM7dzLSGkHJkjs3Enwy6901uUsDTOrIMABhgdhVupxjytYjlNEOtz/wkVBj+qN9EACi2FSq4SseebXVD04SqvP8Yj8uuzvZ0OZZMhqSABxiHQ5VqT5YeamBKMwbYKN7wAlEKAPAjWylnjOmrCndRZz2Ds3VgJamp+oSZbz/VNbjJXd517vz582+95bRwtdls1mrruYQLl4gMB08rxg+v+iFuPHOZ1uqFneI5vnfrazCMoG0M19+HLCip+uHS+Ci9AcPxhoQf44n/bNn5gasXurBXiEuonhpX9U6My/VfNsfZS4pWbVBMHMmLaNDKjUBBSCCEAAAG2Kvhb7N34hgx0HsEW9vTRtu+zFgp5yjnR7xjoc2/5m2OErdmN9KrTb2+wDVL7v04fB5tszekt03EB6JlZSu/83tnDi4R0QbjT7k/dFP0aUjdAJqhUvSJcpEAADAuhx8TTjO0lba0nNWczkZgfImEUOfl26C1B23F07Zt20WLFjFM9fttnjp1Siarc+2W0i07FeNH4GKhoG20+dJNcd+uAGC5nqzff9LntVkAoP/nKHAIe0GJ6unyjxKvF6YCgHRo7yayj5Hp1CXrnWzvl5+o38sprb508+9+b8/GuC1o5N2MESqFJelOAwOYM47a0zyctzymfGM5HsaPEsexNWh0pPad5LnR4jazw940Uob9xX/GiNvESzvU0CbHR8XYm0MlDkx7t2y3VGzPLXwmZA63XtshOeRbc3+2bJvrfe/du6K78J/21POhCxrUUc/hJIBhXhOWL/9x7MtPhX32/swB0coqWzM3SSKRaPBgp0NygiAwrI4/B8Po951QTBoFAIK4KOvtDDaA4XKpJTHVnldIyGW2jFyf12eVbfu70kulI8pX6jB2kjaZCbm0bqduMLY6NQCIurXXbP+beW5S/SIQxuF4vTiNFx5sy8pj7CQ/smUVW2viGLu9rr9WfmQou31Uo8ExvJdyAHss48jXxv/AHnMxrgAXslORRdb8d27P668aPjVolp7UndUcay1px5bwl42svCLbE1EMtZbeMEsQBwDcQF+qVOvIoKk3BVc1MOBR3zfLFwLq9x736uLtzfWt+VXNiZM0eqZo1+eb0i1ZO14fHuslEkqk98hjXzveHIbztUEWqQmZBBfwAYAfHWYvLGHv54UGSof21u89br6WxI+LFMRF2bLzy4uoWqwMeV8aq37f8aKP1jdyz62pmXlvrGSsNrJEQ6jkvPAQ86WbAGC+eqtse+VYWzNcLBR1bQcA1uQ7hv0nH0p3kXqxpmUVfvB1XV/Fiwy1pmU9jP7UFQ/nj/Qd21M5AAB8+QFftN06PuBJANAfO5t/62KGORUAMsxp8xOf2Ve8GwB0ZNkZzVGNXe3WXtcTydiLoQTj8wFA2D5O+cToi9ozDaxLJyYkg73vFQYznrvql0+MC3iyoX31HM5GYJKOkxYs6lPdegJM3Cbag66LNYgtp4Ab7M8e81tH+L7xnOMh6bA+ea+tBJwQdYrHeFxeq0BrWpagTVTpd78J27cW9+nCULQtJYPfOsJ4+rJ0ZOVqh4zVZr2dbk3JkI7oV3EdT1WWxFRbaqZsdK0u9lqS0kxnLquemcCPasWPDstf8gkhl/i9M0fSp4vx5EVBQmv111tpk0UyqOe9K3bVKVq1wef1Z9k93R24Ab7G4xdq0w2kcZD5RUR1qYY14wYH1ONVjYCDcQDjAAD/TtmY4IEyVX8ACBNGLo9ewy61NlGm6/pLJEP2VQ1JNFz7Je/7kT7jeij76Uhttjk9QhTTlK/98HHBcnIBHZDvuOdU6WECIzrIutW7TTNlKrYVhgrLEzc4Shmp0Rkpg5hoir/fh8HZNTBR65FPt67mfspisODimpY/NydkQUnFHGV2Ro6x2sxXbom6tw/8ZDEuFrJ3KqeN5gb4kIUl5os3FBNHAgCtNxT9b2PAqoVkQbG4T9eKzdJmS+4rK7gBvrTNzgsPqXmBi+7vw6Iu7QCg7Jc99rxCe26RatZEQdx9dVoZO2lLyzIcO2+6eMPruUnsnV7PTspf8ons0UEAIO7TGRMJ2G4Tcpn5erJ0cC9nZ7Rn51vTsko+/8Fn/kwA0B86LenXDeNyuIF+aHV2k1JxI5Xawwi8ia9QJAuLRZ3v7fQoMeBFqzZIVr7hzw90XN2Jk7SbFTKXLRRZbCv4t+iPXqpBvZQDruguHCrZM9JnbBtpewOp19jVQYKQqiX83ULWtSN07cgeM3ZyuM8Yf0EdFphWdctw/XjSbzMLR7JfcAml3KJRr7j14rq2P7mgu57gAVWzaJvZYqcdSRF06V8v9lwe/PuNVd1bRLktyaAeVe+0JN3R/XtM1L29I80XAARtY4Cm899ZKx8/gh3cEHIpY7WZrySK+3RmF9/QRjOb1mG5liRoG+Mz/5mqjev3nzQcPuP1/GT2GjulM1hupnjPmQEA3CA/bpCfPbBQ/8/RSgFMu/ug+b/rgnaxQWvfcoznMD4v8JPFjmNxz44AwP5bM/OVW6Iu7YzHL9BmCy7gazb/Ie7VCeNyCKWMsdkdPwXidmSJpuoisNozHL+g33OEYUA6tLd0aG8XdqyByIISjr+34ybG4djzK9eYxgALFUawx5Gi2Ncj32WP4yTtOBjHjx8IAKmmpD/yfxrtP7mLvNcl7dmL2jMjfMaGCMOstMVCm9n9axqN1q75OXfjy2ELAYCx23NeXh678YMGthkkCEkoDmKo8qkyQiln1JpYSTwDDAYekbfQUE7jEFN64v1pM/53KFN//1JmTDLwc5+WMgKrtIrLcOw8mV+ECQS8sGqS4/WHzmBcrmzU3QvOGMbx9+GFh0iH9AYAskhdsPTTgI8XEVKJqFt7YZd2VVtg7PayHf9Kh/Up/HC99+wnhR3bGI9fEHVJYLsh7tMFAGizRbtzP3uQv3BV4NqlGIErJoxQTBhRv5+R1hvNN26Le3SAuxku5iu3pCP62u5k27PzCS8lJuSXvw8Y5jP/GUzg+t3CPK68bBOBS0S8iJD6vbZo5QayRKOc/jghFROuWDHmQpiAx/G5N8WNS0SMxcpQVKU57WrxcUFbafm3tA6yrh1k5ZMfkeJYC21m5xjPao7vKtj2bOi8eGmHS9qzKcZbQ30eU3G9SYbEAHtIlSEttFltKwKGAQzDOBzaaMrSpdoIMkpc3VRX7fjxAzl5AXir8i+U4n5daaP5lYBqVlY0V84CGHVr/YIPL0fM/25t+9R1c7Z4v/PVM2Hai5ve+ZpZuuXliJYSwCohFDLDoTOEUirsVE1pRFHXBHGvjlAh0ZEb4EMWFLNpexxfL3GfLuoN231ffxYwzPFfkSEpoCiMzwMA2mSRjegvHzdM9shAjMMBAFtmrnR4n4pnwYUC5bTRAGA4eJoXEeqssMIDUaXa3Pkf8EICbFl5GI/LC/bn3v0uT5aUCtrGmM5fs2XlcSw2bqCf41XCTvFO2qsn44kLZb/tBZoO+qJFbMTHWG3Gkxclg3u6pDXl1Mfq/VrpqAGC+KjahITGF/jx4vtuYxguEdF6I6Go/xbGco6SXYUGAP29hjmKLfnzg4pthezamz2FO/4p+mNx1IdhoiiNXV1p5/QG8uMHvnC2uyH/vKR/d8AwQiW/WfKfmq9vSADT2NVFTEmoKIq9ScgkhExipAw8nFfP9eOextnHn/naxSS/KctWzHh88uuz+ug0vM7DHp265KdNk5JWfn6uOSyKrw9+RIgtI8eWnsOv7msvoZBWWiwsGdrHkQMCAIopj5L5xfoD9yXyGU9fUn/3W3kLcilb8x4XCthLa96zn+THVLO0niEp3Z4j8jFD6/2zECp58BfL5OOGBW94L3j9e9wKM1FBny3FhQJeeDBZVErmFbIb5T0khLfKe850hqZtWS1iEyPd3uMl32yjjWZ3dwSECbH3vkVRNKXVu7c/NeOFBQFNP4yWAwUhw30eZ3cRetx/yvqEX8JEUTRDLUly5abnLFxrhrszexyVop05ooeyQSsEzpWdOB+cRdy/09Pfhb8dUx9oSLMexFkavcVkZkQSMQYAoohovzu3UkgAEHYb1rvs3z03msOiwnrApWJcLJQOuy8s1UCYEMtrFeS4iXE53nNn6O/PRBe0iTJfvEHrjXX6UDMcOcsN8m9gmQ9cKhZ2iseFgmp3C5QO76cYP1zYKV72yEDHnVSp1nDkbLWtmS8n2u7UOTlbEBfJjw4TdWlnTUyteL/x1CWqtKyurVVG002thh430I8fHmK5nuyS1mijySXtWG6mlHzxo0uacgGahiq1CPyWvkKoFI1wcvbSEY4RneU9GKi+JEL9ZJnTd4ZfwgXlAyN+bIQX4R0himlIm5Gi2O79p1e8Iq7ZsjNK3NpR/avZc7YjszwuPiBjz/YzGgaIqPjo4qOHbpEATFlensFqs7ny9+pZhO3j+LERUNcF0XfxWgUF/m9RxXs43kpCJc95eZn5SmLt2+EG+zsKf7gEQ1GM1UYWl5Lq8rCBETjG53F8vbiB99ZFUjq97q/D915VYcWbfv8JsqgUAExnr9T1y7Jy+uOVZtVMF65VXmzLMFVHaZWW3FViPHMl/601DPVQvrnXj6hrO3H/btbb6Q1vyp5XWLDss4a3AwDcQF9bRq5Lmmq4st/+1e874e5ewLOhr9YmD+K6/lItGzRSej1hYteBAYDyidF0tP8ZzdF69xAAosVxrSX3XU3XHzjVkdexNkukSaY5LOd1NoXI7fbiwoF5awZEz/rHrhwyvn/WqjHDZ8waP/z1Az7DR7Z7KNfbaVNRVlaxqconkk1XXFRmbiIfQl4vTnX5Jnves58M/HiRuHfn2r9EEBfJbUD6WVXmS4lFH39b+OHXNY+iuAG+9oJixm4HAIai8hf9z3SufOMoa1oWL6oVAGj/Pmy6dLM2J81b8CFtMAEAxuVWKifB8fNyLBtnMXaycPnnFbPRyCJ19qxFtkynn7zmSzeoUq35wrXadKbRyIb3lU8a1fB2yOJSV5V34XgrGbu9icwi2nMKcKn7lzFp7ZoHbqx1RL137Z33LHSt5k7iJAlPZPWpuLG7xq7+u2FbgiUZbtiZ+67oEEo5pdGqbZWTNitJNtz8OvPjhpy6iXCaAkBEPrfzyqltqybFEHjg9G9+W96XuH2zNPypb/74YICrq/VT+QdWPBqrkvu3auWnCu0z+8dbFWZGbAfmtQ4dv7mg+Y76eGHBHD/vBz/vYRImxFoSU3mhAaKuCY471V9v1Wz9s+LTMD5P1L1D6fe/A4Dh0BmysMR85RYAUKVlQNHsf07pkN6GA6ceeEbaYrUXllTKyKfK9AXvrCULS7h+3mTB3QBG0wxFYzyu7LFBZdv+cjy5dNPv3ABfzZadzk5hy873XzGXHx9VizfgoTNfSy4fvOI47opMTrJEQ3i5LBGc1yrIltkkLkPaC+/LoWfp/jqs3X2wMbuxLe+7q7oHLNvPNmd0U/TBobaJMH4vzKh49cGHVk0OqGYtTe1tzvky8Z0V7BdKFqGUGUqLVtx+QC1Ef0FghCi6IaduImpazsUL7DFpFnvoN2jhD4MeUi1yKu3rJye8f6Pts6veGeJf+t+u79bPHJhLnf396bCmmCDVTGF8ns+rzwja3Le8DDiE+VIim/To4PX85IJln1GlZeLenXitAku+3goAtoxcdvgFAOKeHUt/+IMs0XC8a/p4tecWcgN9HZOxlsQUzc9/UWoNWyWEU6Ihj55nHyrbsRcXCWWPDpSNGpA7911rWhY/MpQ2WxibzX/FPMuttIrNkuoyjlf5xZLA1W/W+w1xOcu1JNfurUOpy2p+h+tE3LeL4/KMe5EFJdyq3+cIgrx/RP6wxUkSpJwHrC6YFvQsgRG1XHG1Lfe7zooeMeLyPF5bek7pph0JDVtRPsTrEWnqPsDvfVISSjlPZ28f0rWGVwGAnKN8xHdCQ07dRNSUhM1ob/y+cvaEwd0T2sS37z54wuyPfrumcflUHpW648fTXrN+/OerBU+Mnzbno1+O//mq774FL3+f2URmDVsKUdd2lbIoeaGB3JDK6Sq4UBC4+k1CpcDFIn5shM+CmQDAj4v0mjWRfQLG50n6dDGe/I+9aU3NpPQG9YZtlS782HPyucH3ZkF5IYG0Tu/1wlTFpFGAYfyYcOX0x9mHbGlZ7NpwjM+TTxyp/WMf2w2/pa9gfJ6wQ5yjEYai8uZ/QGn1tNlS+x9cf+Ck9o/9tX9+/dgyc7lh5Rk91rSsotXfNLBBQiHjx7ps8yfpkN7V5rs2MoaieOHBuKRyRShCLqH1hsbsSX+vYdHiuJqfw8E4BlKfaKjVHHWBNZdL3Rsw4GIhWVx6uORfmqn/B90gyRAOwau4lkY57TFhh7hZIXNrfuGm7C/Ol51syKmbCKcBjCnaM7tnt4nLdqRwwrsOHtInTlG8f+XUrp2n/Zz+gInhOqKLC4qJjgN6y8u/xmCyvsu/fNn/8PK3dhU332lDjyAZ3Mvr+Sk1P4cXGggAuFBQsbii4onR5UkZDFO85nuqpEzYKb7k8y2lW3bmLVxlu5MNAEDRgjb3JvdwqTho3TJHVS2My3Fca7Rl5bFnAQDpkN5ez0121hlbSibH39v83w31+m0VY1j+4o/tuU43yDYcOVv7pQK02eLIHGEoqvaR0pae40hJ5Qb6Wm6k1DaHkKZN56v5iJQO6yNs/4BPWI+DEYT/inlV78elEkrXqAHMzti0dk0NTzBRxpWpbxVYc//Ir1XdpjnhSzgLfnDsC0OoFFSZbn/Jn+mmuu0MkGi4VmTNBwC1rThTl4SJ7hvWc3y9cJFQY1ezu7I5k2lO+yn3mxxLZp1O3QQ5C2DkxbVvfFvUe/XZ1Cv7tn+37rMvN/9+LDFx50zB7lff3t2gAsqVEQHBAdSVk2crXEAW91m67jnJjnnzfsvz+G8IngzjEPWb9cIFfEIqAQDz1SRcIuaFB4u6JkiG9MJFAq9nJ7HZ/5JBPWtTvog2mWmDieN3LzpWXc1quZVW9PG3AGC+kSxsFyvq1dF8LSlvwYeOJwjiowxHz1XbPqkus+cXC7tUszK9WuoN2013s0JKv9+h3Vl5wY31dnrV3EjGagMcd8z44UKBqEcH3Z6jtTmj5qfdRau/cQRg46mLpXcXDrqWNTWzav56E8HxVdVc89rlbuqv/pRb0yhZR5YBMGGiyGE+o2t4mgMHCEpncCxZwTgELha+4v2qvyCo5hc6lNiKzpWdSDbcOKU5AgDnyo6fN53nBlWzpGdv0a6TpYdqaOrt6NWj/SY1g5q/ztaBlV27kuE/9a25HaUVCksEPbr81X6m86cTXZl/iYePm96vdMO0kS9/un3/hUwDAwCYYvBH3y/w+XPm0Blr/0nSuHbIM/RJbAAAIABJREFUh7gSQ9GF73+p+XFX1Ydos0X7xz5HlJKPGaqYMJIfE17LRQiGo+fsOQWMxSru1anml/AjQizXkymdwXLttqBtDC4USAf35N+9JgcAkv7djScuVPvpbDp3RdSlXc0FKfQHTlKa8m9t1qQ0NgCbLyeaLyfKx963ltx44kLxJ98xVYZlGJ8X/OXyivcoxg3X/3ucNlWfwGa+covtreHIWdP5a0Fr32YXM+j+PYbxeWySJ1lS0/igHtTrt9nSc9hjW2ZuI19zYtEWa7VrMLj+PhX3gmgEgYKQVndrLVbLnx+0KOpDLsbrpuhTw9NYJbaitXfew7icin/Jws5tAzgBYkKSaU6recDE+iZrjZW29Pca1lneAwBixW17+Q+rtPU2VarV7twfJ01gl2Y7w8G4Q7wfrfk5HsHZOjCRl5eIIcnKf0oUTWESudSlpaTw8Od/3PluL/XWN6aNmvLZ1fIRtqT3B/v/WRJ5dun4N3Zrm+jXQgQAI3B7XpG9oJq0XcZqY0hK3LdLlQcYUl3m7LPbwZqaab6WTKgUXi9Ne0Af+Dxhp3jT6UuCttHstKRi8qNeL051PIEbEoDLpObrt6u+ltcqSP74EHt2vu7P6r+xMhSt+elPtgw/WVzKkCTX38d44r+Sr372nv1kxRGq6dyV0i27/Ja+gkvL83Stt9PL50vvbmXgwPH3VkweRZutVc9oS88p+fInhqRoi1Xz85++i17gBvsDhtEWa9n2PfyoMMZmt6Zm5i92cRo0Pzbccnc5ednWv/S1SCV1OfX6baaLNxr/vFX58vwf9ZtY9f7dhdsrDW6SDTfzLOW/ZRtt/SHnq6pZ7FpSQ9A4Lrwv+9T7pSfY6YR/i3ZeeVDGIwDMDVvSTzVUxfVmqxhHimPZDT8ros0Ww5FzHWRdK+7SkmvJWl8haf62MfGn3A1GylBgbSqL/+rNWSgSDXn5BZ+dby/dm+NYZcDorn87f9XZ2PFjWrs4PRD3HbB4561idfatf1/v4Phvjnn3W/Jnck7S0d83fz67l6xF1Fb2SMKE1hWHOw6EQhbw4WtVU8ZJdVnu3HcL3/uy5ma5ft5kUW0HAZK+XQ3HLygmjWKrSmJcTqWEFMWkkVDdJWtBfDQ32B8IQrfnSLUt29IyOb4qQXw0AFiT7vBjIwGA8FbKHx8iiI+mtPrSTTsAgCrVlqzf5vfWSxXzpPWHTtew97F0RD9HwmRFmp92KyaMwLgcXMAP/vpdR4PGo+cEbSIJhVSYEGs4fMble3wL2kQ7ekubzLaMHNe2Xxv23AJnFTdqXrT+MORbq3kHTKRRzlUCwM+5GxP1VwEg2XjjbNlx9tECa+513aX3Ul4nmfvKFUWKYmf7z+dHhVV7oj6qwX68By/rlNytr3Fac6TYVni6ukXQHF8VqdbQdluupXxBJwPM8dIDanux45Jeia2QhwsS9Vd3Fmx94EmbuMoBbO+ySWPHjh07duyTq29IJDc+GRUVFNN1wIhRQ3snhAZ3fOG3gsAAw+2Mh/KXxJUHRccEiu8PVIQ8ssfIidMfiff4ydrmS/XMeNljtdpvk8XxVvLCgjk+D9jIiuPnTRaW1HKiTNC+dc2JGKIu7diUB9OF68YTlb/tcgN9AcerTfQwX7nlyJXAJSLJwO4AIIiLlD06EABwkcBw+CxtNNMWq+qpcbywYNpidXz0k/nF7HxjwbLPqNIHXzpm7Pa8BR+SRSWSIeW7tTnGbbTRpP7uN8mAHgAg7t2ZLC51YQ49SxAfZbmVyk5d+syfSRaXPvAlxWs3W1MyXNYDhiHzi7kB9+a15s2bd/x4eWzIX/w/l8+a1uz9lIU2uvIQeWrQrHBhtI22ldgK2Tz73spBXeTlv69gQdjb0asHez/CTgkW2wpTjUnsQ7hU7LvohYpNsYNsAGgr7RgmqrxaMcV4q+LNK7oLB0rKF0Gmm1JPlh7alvut8dTF0h/+qPg0jMvlBviUZaatTnubvUdnLztfdvKtqFVs3AWAXsqBkwKeihDFtJG0r8fb0qRUXgfGFYolkruzeD3HtnLU9/Hx8o9oDwCAmw2NWYLEfuL11oM3RK1LP/hS4IMGYVlZWdu2bXPakt1usdQhuxqpJXbQUyeKccNoywNqQnP8vMji0rzXPgre8N4DV/5iBOE9u1Y7qXOD/IpWrrckplI6Ay4Web/8BHu/ICHWfC2pahTEJWJBm0hKZwCaqbr1KMbl8ttEma8liXt2ZC9T2e5kl23/m90x0p5fzPH3Yex2a2omXt0myNbUTKAofmwEAJgvJwo7tvF6+QmMw6l6TQ4Xi3xfn8Ummwg7xZNFalt2QW1+3tojFDLF5PL96QmVvHJJ+OpYricL28W6qjYNVabD+LyKyRq7du1SqVT9+vUDAIzDoUpdufTtgXorB0J1a7wOqfdQDDUvvDxCePF8vKA86OIYruCqHvObBAAau7rQmvd34Y5FUR98l/15b+Wg1pL7coWMpy9Zb6awM+RXdf+1l92bbLfR1i8zVr4VvdqHV/4HmWFKdew3Pcp3nJ22BQtaUenaqvUaeRGh3OyyHjHlZYJlXMWCiGUAUGYvtTE2X56/o9uOkvyeq3IAG7xo02AAYAr3fvDOqfi3VowNdfPWKXhAzymz7H5xtdkq3G63azROv6OJxeLg4AZVv0VcRdj5wVl/vNBA5bTRJeu3uqRuhQM30Ddg1cLSzb8LYiOkI/s57hd1jDeeuwojK1cHZzd4Mxw6Y7mV6v3K9KoNCjvEmS8nOnYK5fioyCI1ADAkxZgtHC9F2Y693ACfaism29JzrCkZ/NgI2mguXvN96I8fVzsZW97D7h0cx4SXQljrTbyuXbsWFBTk5fXgzUFkI/sDALsIvdJFu6qoMh3QTLWbvj4Qu/ygUo4rQzOiChuulpSUZGVlXb58mb2Jyxo7k35aUOW0EQttXnH7tfnhS9lLVtaUDN1fh30WzDxVeri1pJ0Xz+eHnK9G+01Wcr0Olvx9UXvmtYgV7BjOQOokhIQhqYp/BoRMQqo1AMAAsz7z47Xxm/m4gH3ystvzZwS/VHFnslG+4x032X1efPkBZaY9uKhynrBq+hjgcqYKygcfGGAhgjAAyDCn7Sna8VbUqo9SF78StljKkaUYb0WJW3v01pdO/kYxOZGzZ33isNfHhrp5qzsiZuqqDVMf/DwAgMjIyJUrVzp79NixY/7+taoijzQJGAYMU7Gcv6vgQoH3S09UulPUo4Ows9PdzgQJsZqtf9rzCivujlb+wk7xhiP3cvQ5XgpKq2c/qoI+XwoYhhE4u792VbxWgYaDpwDAlpVXp+KWFSt+1Uyj0QwdOvSFF1549913a/N82mzJW/Bh6A+rH5gsSukMgrbR9Stsrf19L220eL1w3ypDjpfC69lJjpuXLl0KCwtzBDBCJqEbN4CpbcUSjpQNKiwzZfLm+fryA1pL2q1Nf/+ZxB6m81dpsyXDnGaijEN9HrusPT8p4GkA8OL6PBvyKgfjdJL3AIB54W8bjp0vTTlf8QckVHK2djYG2JTAmY74pLGX9lIOZF/owMPvm+fYVbCtu7IvkZnL7nNbEZtDpCPLLLTFl+d/VnPch+cXKY5NkHbW2NUAoCXL2MHcd1mfLYhc7hiTeSJn4yvB4Hc3zcpd/eK6o+n6xrx2ythNutKiwqJSnbmF7tmC3GPLvLeEuRFUKigMANakO+xVCo6PipBLjcf/q/oqjp93wIev3buN44SXgr2AxOYjyMcOq/RJ7cANDbTl/L+9+wxoImkDADwhCaGETugIIoiCithRVBAs2FHsBRU9y3kKYvms2HvlxHJ6Byr2XkGxoRRRQKogIr23hPS2O9+PYIgIyHEoROb5BTu7m8nuZN+d2dmZEoDjotxCRbMf8k03bdpkY2PTSNN6HYzLDyhWZoBA4H/IrDzV2FaKHYxovl7Ny5Uov6SRuqZEXFzcpEmTmExmRUUFAEDRzFg6lPuPhvP4ZQfOPiy78Zbx1eRHWmQdX4utAAC2mKmnqK/mMpDS2YKfmD5Wz0MSb3Z3CZDEBnuN/pJO6gxRleTZleBTDtlQT3ZvJB1N6ZxBQ3VGkAg1xc9U2Xyy4WwAQFx1tKQvhhAXHMyqO+MrS8yEIrGSTf1DGqawEu6UXAIAxFZHVYsZAAAFgoKzzigAwN4uJwnVXCjGRtDGqyi09Mi2P1dD74GVXd+w7mFp/v2Vzp00lVU1tGrp2K553eIPwfh5z477eAyyNlBToWro6Bvo62iqqmqY9hy5YPuVHzB8FSIfiLpaTWlsbEE4m1v7uhiE5f7nsC8dBzRnjlN1rH/GgDrNgxrjXYmygyHJTMBdh4IShailLiqtFOYVkX9AqI6Li7t9+/atW7eIROK7d9/vqA0A4CWkKXXvAgAgqlP5KfW8eMBPzoCC2ueX5UeD+F/P5dYQQWaudNhZUVGpdBws6XwC4kqG7Lt6cXFxffv27dmzp6QSpj7WWXVQr6Z80H8nyi/mxiTYkLpKH0FJ4BCTTBJmo2Y303iRAlVFfZwzIBE1yFqScKVCrBsPCATCzeIL+z9vFn7Ok0zOLqWgqqLU3VryN11Umc5OAQDgEC/mF0gXPi6/CwBgiOk8jAsAgBjODAnH2dyJBjM6q9rob1xG1KynM2rVPzc6c40lM61MNfTsoV5bbh+W3agQlpUfCyrZcsRJ2YlKauG+rD9ZQ02I1O4Tly3vW1/di0C17dSy3eh57/ePct3wTqXvmAmLJtp0NNSiKiqI+cyKws/JUaEBngMuPrscfnqCQSs/jEN+PuqQ74xJ2uIqz1xR7t1d8rn8D5kKShRpL3aVPt0b3bSW2ghHAED1rSdKNpaULo29DAsAoHnPJ9G0VXp3I5u3/APaFStW7N27V0tLa/r06ZcvX+7b9/vHU2vWeMm4zGQjPYzBxHn8Ok+qmKGvVBnVqoP7QpGYQCaRdDQF6Vmyo4JJSFJl/odl+/7SWTJDpXc3AADG5JAMaAAA/ofMihPBJse3Mm6EcqPiaT7zpU2p8fHxu3btsre3f//+/fDhNS+M4zw+VlXdjFnCxRV0krYGUGjSZYRkSCMb6XWrMFIy6FwpLH9Z+dhBa6iRkumN4gtGSqaO2rV9bqVtuc8rQj5xPthr9K/zXrMGScvTZNkHVqIwL/fbGWj1Vi+U/FHEzw8pu9WF2u0tIyK2Omq5+f8AAE46oyqEZQAAPUWD9ZZ7xBX08iOBorwigGHqY4c1kn+czVH6WDFk2HAAgB7lq6ZpMS6OrHpmn5lH0tcpyUwmWBsbKslzzwDY2vCSwLHq2q6HEtn1plZFbuhLtVodLfzPHzRgwIDo6Oj/vBvkV8aJTS5csxfiOISw7Ghg9cOXzd5V8cbDvNSMlsvavxYZGWllZYVhGIQwPT3dyMhI8nfTFa0/wPuQKfkb4/GrLtyBEDKfRpYdCYQYljd/HcRxdmRc6f4zdTbkf8rJX7oFF9d+nCC7IHvy7/Trj2r2xuFCCMuPXyj4fSv79TsIYcn2P7M9lmNcnmSFyspKdXV1DMPOnz8/bdo06X6EeUV5Xv8TVzJqlxSXCfOLG/8iotKKvAX/E1XQm/7dmSHh7PSPJfzC+yXXVqUuOJ17GEJ4rSgo+eMzQU4BhJDzNpH55LV0/dCyO/MTJiRUv6t3bxibW+5/HkL48ePHAQMG4DheJ3tcVmUcIxpCmMSMy2B/qHcn9GuPGHef8tM+lx8LghBizHovmRBCWH3/WcWZq1mcjAJe7tWiINkkHOLc7NyClTsqA2+EPjsWmHe8acejMT169EhMTPzv+2mGxu9HuLmvLh7auGLRvLnzFvtsDbibWNHyD6bEmckfxIPmLuhRb1ssQWvAwhk9S1I/oNE4kJ9ApZetAkWR9fg1zuPz4j9QhzanCohzefy0z6KScrLBTxqqJy0tbdmyZaNHj5Y8LpI4cuTIihUrFBQUAADW1taGhoYvX778V7tV6mYtmXEUAMC4dF8y46WKvS0vMU1YUKKgqgwIBIpFB+HnumPCCtKzxGWV0slOAQBEDarWXHfFL7VMSV95spGeAlVFMpWr9nwPipWZtLYXHx/fs2dPBQWFXr16SftxAADIpoZqIwZXnK59OFd9PbRkq38j8wlADC8/dk5j4nBRYanshKiNUxs1pNBEfK7gpKO2y7pOO+3V+wEAphh66ofkVf51FQAgHWEZY7AEmbkuumP8bc93w60Fn3JwvqDOuGUKqsq6f8wBAISGhr558+b58+eyqaynUYLQGMlTtO5qvWRHwRdB4Y5Pa87mHfvM+ag5xU1jvAuli4XuCk8AQKHvHskZKSsrS0n5avgSRQtTYVb+66pnt0su5fOyC5Zukb4GTgAE8cdcinVHRTNj80yyrVpPIM8aDmDCT+dm2nV18tx67nlyVk569N3jq917d3X2C6e3bCghGncwgcmvoqrq3y1eEhnxSdXYWE2Ou3oi8oNA0Fk8g3HtEcCh/ubf6wzn0USigpLK05dxHp/YhJ7uEMMqT1/hJX1sxgdJnDx50tnZmUajdevWbdiwYWVlZQCA7Ozsly9fzps3T7qat7f3ggULPnz40PQ9a80cp9K3OwBA8CmHE/1e23MSAICorUGi6bCfRpE7GAMASPo6JJo2gJAdHsOJjJNsKCou05jgKvuuOlFLQ2O8S51mWPXxrvp+f0j6MZJNDAx3r5YmxcXF9e7dGwBgbW1dWFjIYtUO9q0xeSRGr5aOdKX7xxyjA+tYz6NYT77qcCHFi0sBEKqPdeYnpsnGVCnG9RDGlYeyS1hhERDDDCjGNtQemmRtPYphX81BAAAxFAk/50neYRd8zJK8wCcqKqk6e41EIFFJ6uxnUZyo+OrrIfTLD7jvkgu9d9YZ2vHFixdOTk6nTp2SXajUrTMvIS2uOjq48LR0YfWdMCgQkgmK2mTdZFacGumrMaxFJeUEBYJkNJbDhw8PGDAgPDxcmqrY0RSrYgzWdhlBG+9JnE4gEgkkIvvFG8n4lkRdLeqQfooWpupZbMn3kmMN1MywvNMj1ak9l13PYH+p7IrKoo+MM1a09Ingt2glEC+7M9+MrNpp5PJ9QXdfvE1Kz8zK+vwpLSH66c2/ti9wNFZSH7gnoQU+snlNiHw+f8aMGRs3bmSzG6ywi0Qi2X85HM68efMCAwObkck2C8MwJpPZ2rn4SQQ5BfDrRp5/RUyvzp78e+GafU1aG8dzZvpIWueaISUlhUajZWbWNPRt27bNwsLi4MGD8+fPX7t2bZ2VL1y4oK+v//r1629202gGxeJCn13siFjpEv6nnIq/rtCvPpRdjR0VX7zpcM0mInEjO6Rfvs9NTG/8Q6dOnXrhwgXJ3/3796+TZ0FuYene07JLRCXl+b/7SVom6yg/foEZFgEhZD2LKj9+4dsVuJJ2YykMy5nhjfMFgtzCsLJ7b+kREMJP7LRMdvra1EW5s30hhomrWblzV0sKCS7G8hdvEnzOgxAWrT/ITUgT06vz5q3LX7qF/ylHskthYQmEUCwWa2trZ2RkaGtrFxUVST8QFwpzZ/u+KHqQykyozbb/edlSwU/7DGVagKsfvig7WtM22Llz54MHD+rp6YWHh9fu88spYD55Xf7neQgh8/HrorX7cHHtqcHYnBTme5ao+qujIeZ8e4ga14pNiA0FsOpLk6jGi5/UKQ5Y/nEnlW6b4kX1b9Rs/OxHO2f2N1FR+LqaRVDUsR3tE/Se0fxriYxmBDCxWOzh4eHu7j579mwTE5OQkBDJ8oiIiHXr1n348IFOp//222+KioqHDh2StGsXFxf369dvypQp+vr6EREREMKCgoIXL16IxfX8pDmc75cVDMOio6MLCwubkmEcx/Pz80NDQ48dO/bkyZPq6urQ0NBZs2Zt2LChsrISQpiXl5ecnPzd/aSlpeXk1Pz2Pn/+7OHhoaOjo6amFhMT05RstHc4njPTp5FHFHUUrT/AfvW2GZ/D5/Pt7Oz++ecf2YUvXrxYuHBhp06d8vPzv90kLCxMT0/v2rVrTf8UMZNVdfFenYWMW495qZ9kl0iuwhi7nhAi+JzLiU2GEPI/5YgrGUUbDtXZto6cnBwajZaVlSX5d9myZfv2Ne1uoN78VzEkl35++uei9QckC5kh4aLyqpqci8W5c1aLmSzJv6Li8vwlmyGEBSt37E5ZncvNwkXieyVX/8o9fCr7ADsqXrKr0j2npB/BuPu0/FiQmMnKnbMaF4oghIKsfIxV8+vGReLcWatwMRYbG2tjYwMhXLx48Y4dO2QzWbIzgPOmJnpx3iRgXJ6YXp03f52woKQmh7N9pQ8IIYTFW/2ZTyIghMnJyWZmZjiOP3361NDQkMViye72UsHZ9DMnJfEbQli691RV8F3ZFc7ln3xSVntyBRh/zYffivkF/+oIt2IAa6AXIhTx+biqhnqd92IU1DQ1Ffg8fks/j6KYu2286LZBUJWbkZFTwmALMLKyBs3UsnMnA2oLDxz8r/j6+jIYjAcPHlAolNevX0+dOnX//v3a2toLFiyYMWOGi4sLl8udM2dOfHz8ggULnj59CgCIjIxcs2bNpk2bHj9+PHXq1GnTpp0/f97U1LS4uNjd3d3V1dXc3Dw4OPjChQuVlZWKiorjx48/cOCAubk5hDAqKurixYuZmZnGxsZmZmbm5uYikejIkSMQwoqKCgKBQKFQuFyui4uLr68vhmGBgYHSxwMYhlVVVZWUlOjq6trY2FhZWd28efPt27fdunWbN29ecnKytbW1np5eeXm5ioqKurq6u7u7np6ekpISl8sVCoUeHh5mZmYAAIFAsHv37oCAABUVlRcvXmhqarq5uc2ePfvYsWPx8fGTJk2Kiorq0KGe13JxHK+oqNDW1iaRvjOCgxSHwwkNDaXT6Wpqah4eHkSZvuYQQkKz3pBtEwgEEk0bYzClw9JLlZeXf/z40dHxq45qOr/NIBnoNuNztmzZYmVlNX/+fNmFTk5OTk5ODW3i6uoaFhY2bty4kJAQgUAgaW+kUqn+/v6mpqb1bkJUo2rNHFdnoYZ73VGICGQypWsnfvJHlQG1j1UYVx6ISisBjkuGm+JExBK1Net9H1xKKBROnTp1/fr1HTvW9LOfM2fOzJkzfX19iY3OeiOBsziM64+0F9QOJC9tyCUb6UtmFYAYTr98n2SoJxmYikAkKtlYClIzJTmXZo9sYrCMMUKdZFK4ckffP8ZzTO06qVoDcwAAoFhbyA5sqOYysOB3P0rnjkrdrSV9L2s7HOJ4/qINJH1dAlHhxYsXw4YNAwAsWbJkwoQJa9euVVSseT1ZY/JIoqoKAEBcXlVx6pLJcT+iprrGpJGVZ68ZbFkuzMwlGdJku4PSVs2XPEe8ffu2u7s7gUBwcXFxdXXdv3+/9I11KBJzMBYZUJW/dNbXWTqraPVeZfuu0rfHRtDG8bHaeSFYYuZgbRcDSsuPHvCjNBDYRO83d6OYzb6WJ1vZYsXtGqyh5XG5qkVqRD9bM2pgN2/elL2jSUtLMzMzMzAwkFREBAKBtJrC5/OPHTt28+ZNSUVHIiAgYNmyZZLKU2Zm5qFDh8aMGWNlZbVhwwbJhlwud8eOHbq6uh06dFBSUurWrduePXtCQkL+/vtvPz+/uXPnenh4PH36VLK3oqKi/Pz8kpKSo0ePduzYsWvXrvv373/37l1sbGxsbGx8fHxubq5AIGjou+Tm5r5//x7DMBzHw8PDN23atGzZsvnz5y9fvnzJkiU6Ojrz5s2bOnUqjUZzd3cvLCw8ffp0x44dHRwc1q1bJ93J0aNHu3Tp8ueff8bExAQGBnp6el66dAlCWFVV5eTkpK6urqCg0LlzZ39/f+lx43A4ly9ffvPmTZ2GVh6P5+TkNGTIEC8vL0dHR3t7+3v37l27dm379u3Ozs4qKirSikVmZua2bdtGjRo1bNiw48ePp6WlZWdnf/z48ejRo1OmTDl48OCnT7W385GRkWFhYZIGTx8fHzs7u507d+bl5TVyloVCIZ/fss3ikB0Ri7G/ql6npKSMHDlSU1OzU6dObm5u0pLTbHFxcfr6+qWlpc3YtqCg4NChQxcuXAgLCwsLC9u+fbuFhUV2dvZ/zBIr/C3zyWvGjVDm45oWP1wgrPz7erbHckkbGuvFm5Ltx3Pnrm5kJ3/88Ye7u3udfnqDBg1qYq0RF4oaqghCCLnxqRBC/sesQt/dkiVVF++JSiuEhSViek1LWvWDF5V/X4MQ0q89qrp4t/r+89xZqypOXxZgfDHeYNOosLBUVFYpqTDVUR4QXB4QDCF0c3O7efOmZOGECRP27NlTZ02MzS1af5B+vaalBxdj5ccvYEw240Zo5blb9X6uvb29tOUwLy9PR0enoKCm/lS0/oAgt27LDS8tU3IQIITc2GTGjdCGvlHTtcEmRIiX3l/cmULU6jpyzvJ1fju2rf99qoOpMlHH+XBSg9fItq1FutGXlJTk5ua2SH6kysvLc3NzeTze91f9YSorK/ft2xcUFCR7rff39/fy8qpzKbl27ZqXl1ePHj2mTZt2/PhxW1tbDw8PGxsbSaUQQhgZGTllyhR1dXXJCEY0Gm3UqFE9evTQ0NDQ09NTU1NzdnYOCQlxd3efOXOmtGP31atXHR0dJ02atHbt2gcPHsTGxtJotKSkpA8fPhgZGfn4+Ny9e/fevXuenp6WlpZmZmYdOnTw8vIKDAxcvHixoaHh6NGjX7165e3tbWJi0rt3744dO5qami5YsOD58+dLly7V0dHx9PRMTk6uqqqqqqqSfiMMw/755x9TU1NjY+P79+/Xe2T4fP7JkyffvXsHIWQymTNnzrS3t1+3bt2hQ4fGjh3r6OgoKVR8Pv/hw4fbt2/39PSUBtS8vLzY2FgIYXx8vKGh4YkTJyT13T179mhra8+aNSs0tPbyUVVVlZycXFZWFhcXt379+okTJ65atcrPz2/cuHG2trabNm1KSkry8/Oj0Whz5szbaNBfAAAeuElEQVQpKyuzt7c/d+5cC51/GBAQYGZmFh8fL/n3+vXrTfmx1Bv4S7b5c+NSZJdIY4MgpyBn2oriLUcb2uH58+etrKwYDEad5bdu3erfv/9381OTgR3HOTGJEEKMzSnw3vntCvTrIZVBNyGEuEDS7PnVrYaouFxYXAYh5MQkluw+yX71lpf8sWRnwLn8E2Fh/s14OIpxeWImSygUamhoSG9wP3/+rKurKw02ktUKV++pDLzx7UcUb/XnxtbT8p+VlaWvry/7eGL9+vWenp6SvysDb0pjYb14yR+LNh5KrH6XxckQ4aIrhf/UeR7WRG0xgEEIsYp3QetnDbOzMKTp6pt26T9uyYFHn+u/sZEH6D2wH4HH423atOnkyZN1ljMYjPv37x84cODz58+SJZWVlaWlpXQ6PTg42M7ObuLEiUJhY2/3BQcHd+7c2djYODg4uPE8CASCkydPWlpazpgxQ3KBiIuLk4QcaWZ27tzZoUMHyWAySkpKtra2nTt3plKpQ4YMefPmzcuXLzt16uTh4VGnhERHR9va2o4cOdLQ0NDLy8va2nrp0qURERFbtmxZunTptWvXgoODjYyM3NzcdHR0hgwZ8r///c/Hx6dPnz5CobCkpKRDhw7m5uY9evTQ19e/deurO+iKiorjx4937drVx8cHx/Hs7Gxzc3MbGxtdXV0rK6t169Zdu3Zt//79mzZtunHjRkxMzMqVK42Njb28vBISElauXEmlUkeMGPG9k/PvXLlyRU9Pb8+ePZMmTbKxsdHT05ONrxIsFis1teb+PSMjw8TEZOHChV/VrXE8d27t8yRZPB4PF4tzZvrgfMHTp09PnDghqabn5eU9e/aMz+e/fftWT09Pun9ZGIZZWlpKWyMax7j7tOLMVQgh4/aTsqOB0uXSfDJDwiXdKyqDbhZvrieaYhgWHh4uZrJkXwSMKnoSv2KFbHR58ODB0qVLm/h8+sGDB/b29rJLNm/ePH36dOk9nJjOlDzW+hY7Kh4XCCGEOI6XlNRU8nAcX7ly5aJFi2TXrK6u7tatm6+vL47jvNSMQt89eH1P35OTk4OCgnC+IG/B/x5nXg7KD3hVGXYy54AYF/M/5eD8f1dJaaMB7BeDApjc2bx588WLF1t8t2w2Ozk5OT09XbZ9mM1mHz582MLColevXn5+fleuXBk+fLixsbGk5YpOp69fv/7y5cvf7o1Op587d05ac8VxfNy4catWrRo8eLCfnx+O4y9fvmyo8wudTu/Xr5+Xl1fHjh0DAgKa/hViY2OlF7IWlJ2dPWbMmI0bN/L5/KioKH19/a1btz548ODx48erV6/u2bMnlUqVBOzHjx+bmpr++eefo0ePdnNze/To0dmzZyMiIoT5xQW/bw0ODnZ0dDx27BidTocQYhgWEBCgrq6+atUqVkTsm6goPT29iRMn6urq9u7dW1dXt3///pqamrq6unfuNNgb89KlS9ra2hQKRXIbceLEiVWrVrm5ubm6uo4ePXr16tXBwcFhYWExMTHMj5+LNh4SV7Py5q8TFpXFxcUtXLjQyspKVVX17b1HnDfvIYSSWnjR+gOMO2HPnz8PDw9nR8VzvtRyfH19SSTS9u3bZTPATUgr9ju2a9euhQsXhoaGbtmyxdTUdOXKlTQa7eDBg5Ja4/PnzwcPHjxr1qzg4OCLFy9u3779woULHA7nzp07enp6jx8/lt0hh8Pp3bs3hUKxsrLy9/eXRjIWixUcHLxv376kpCTpypGRkZKVNTQ0+vTpc/ny5TFjxjg4OMjW4SSqqqoGDRo0e/bsajojd97a4nfvPTw8xo4du3379mfPnnG53KtXr9JoNEtLy40bN9KvPiw6fb6EXxODOW+T8rzWy74n3hStGMAIEDbYI4OT+fxuvMrwKQNoeMGjbav2Pi5Ss5uyfvcfjrpyOaqTg4PDkSNHBgxozuwPSDuB4/irV69CQkKSkpKmTZs2c+ZM6WP2pisvL7ezs+vXr9+tW7cUvjd2UXV19dSpUydMmLBs2bLm5vpHSU1NDQwMTElJEQgEkj4Cknezjh07tmPHjsOHD3t5eYnF4nXr1qWmphoZGT179myO83A7str/nt/btWvXvXv37t69q6urSyaTjYyM9u/fv3r1ahMTk9evX589e9bNzS0rKysvL8/R0ZFEIlVWVhYUFNjZfWeKRYFAkJ6e/uzZs9TUVGtra1tbWwqFwufzU1JS4uPj6XR6RUUFi8UKOvt318zSwuKibQmvY2JivL29R40alZ+f//fqjQcmzozpbrJ8+XJzc/Nlc+c9fRX+LjFBLBYvHzrSg6StM2t89OOnK+5evH37toeHx7BhwwQCwaNHj1xdXTf2G/Ym/NXJ/A8eHh43b95UU1MLCgrS19fPyMjYvHlzWFhY9+7d8/Ly9uzZw2Qyw8LCiESihYVFYmJidHS0srLy3bt3+/SpO3K85Bulpqb6+PiIxWIHB4fk5OSYmBhHR0dzc/NHjx5hGObi4qKmpnb9+nV/f/8xY8YoKSndv3//6NGjAwYM2L59e719png83vLly0NDQ9ctWnLw77/menr27t07JiYmIiIiISFBX1//5s2bJiYmI0eOdLTvvcqip9mGZQAAXmJ62dHA4jEO9uPcKP9m3GQ7O7sLFy706NHUGRJaUkORTRC3q7+6gmLf3alivPj8RG2y8eDZnsM7qag7+2f+u/Fo2gpUA0N+muzs7Ka8I/GLYbFY3t7eEydOLC8vlywRiUTZ2dnv3r2T1Hg4HI6Hh8fp06cb3c1/df/+fWNjY0/bPk79Bxw9epTLrX3ucfFYQMzY+X2794iJiQkJCZk2bdq2bdt4PB6TyVyyZMlAY/P7rtNCRs6StHuXlZXNnTv3wIEDKSkpW7duPeE4ZvPkmbJ7k1VSUnLp0qV6T3pxcbH0gDQEx/GgoKD9+/c/evSooqJCujw9PT0gIGD16tXNqHC/fft20qRJDx9+/boemy193E6n01esWKGrq+vg4LDRafT78QudzTsPGzas8U5P32qDNTDeowVmUzO8Ix9vsFMpCxzbaRUpIP3OXJ2krX0GPpmTFOHbSf4qYagGhiDtBJPJZDKZ9Uxgi+PZM3x0Ny9T62Zd74YMOp0gEGkY6H2bJBaLm/6KiBwRCATR0dGaYqBv3sHQ8jtjT9erFWtgDb0Hxiws5Jg7u9mqAsB58yIGDjzgpkcABCtbK4Wg/CIMyGEAQxCknVBXV1dXV68nQUFBz3ueStf659ACAGhqaTWU9EtGLwAAhUJp5MXBNq6BOERQ09dXLvmYwYSQ8fhqCKfXCCcdAgDC9KR0sboGGpcQQRD5pOpgTyCi++9fREP3FCouXrO1Riwd7HIKvo8QO590N4PZ9zavWnf0o+lvI7r+mnciCIIgiBxpMBSpDt335IbenvPR5ZN3HN02r4MC9vTZrTT96QFHtg78SfN6t7y7d+8mJSU1Zc1bt27RaD9pLow2iMlkEggENTX5nq31vygsLDQ2lp8BdVoag8EgkUhUKrW1M9I6IIRFRUXtuQBUVVX16tWrodHF6qisrPzR+WlIY93ofzFnz55t4qzqAIC//vqrS5cucjwc339TXl5OIBB0dZszQN8vAMfxT58+WVvX/5y/PSgtLSWTydra2t9f9VckFouzs7OtrBp8VPbLKy4utrS07NWrV1NWVlJS2rFjR/0PHX+wxgIYrE65dfLk5bDYjGI2Qc3Aqo/rtMVLJvfQag/tx0QiUSQSffclnl/V5s2bKRTKpk2bWjsjrYPL5dJoNA6H09oZaTU+Pj5mZmbe3t6tnZHWUVZW1r1799LS0tbOSKtZtGhR//79Fy5c2NoZ+Y4GL9Cw7OHvDv2m+N34ROrY18XVsatm+ZO9M/r2nnkxG/uZGUQQBEGQ+jT0DEwcd3TN2bJB+9/c8rX/0ulQVHh/xfCp3pvujr046fszzSIIgiDID9RADQwykhJyDGZsXGEv02WebDx2q/cQ7tuoD6KflDsEQRAEaUBD74Gp6OioQLEYr7McwzECVUOtnT4ZQhAEQdqOhkKRiuuyxbTbmzaHFgi/LILM5LM++95YT57YpTVnSUYQBEEQ8O0zsFC/qaeTJC2EOJWacmi0ZZBld1sLGplVkJ78oZBNthzNzsjBuluiGIYgCIK0proBjKysSqWKJX9THdzNHL4k0HQMLOwAAECBx0aPwBAEQZDWVjeAufwv0KWx9TE+m6+g+utXv5SUlNrtW8wAABKJRCaTWzsXrYZIJDZjGrBfSTsvACQS6VcdureJ5KUAfGckDlzI44tw6Rp41f0lDltNbqbs6/+Ln93KykodHZ3WzkWr4XK5BAJBWVm5tTPSatp5AWCz2WQy+V/NaviLaecFgMViUSiUtn8b12AAg1Wvd86ce+BZLkv81QoEqrN/4tPlFqgjIoIgCNKaGopDWNqpVbvfWyz/+/aVzS40s2l/Pgy9f2nXJEuTiQHnl6HohSAIgrS2hloCeUlx6frTH26bO4TM5F49HqTYe8RY/ZEuppW9f/OPmb7foa3XLBEEQZBfXEMjcfC5PKhCVSUAAFQsrPSz0j6JAQDK/UYMYoQ8TBH/zCwiCIIgyLcaGolDo6utYc7DK9F0CIiWtlblL5+liQGAjKIitkAobC8zsCAIgiBtVoOdOLDPZ8Y7LAnFPe8Xne152b370uSeHs4ayXceMGc+TvrTWfUn5xNBEARBvtJYN3ph0Zs7IYxe80ZZEkqfH1y799ZHodHAeX5bPe3U2+8bUgiCIEjb0I5mZEYQBEF+JahDPIIgCCKXUABDEARB5BIKYAiCIIhcak8BTFD+6X1MdGx6Ce+bx36QX56RmFrAxr7dqnlJckbMLs54H5+aX13PGxIYKz8lKbNSUM+z0kaS5I2wKivhXUJmBb+FysYvpF0UgBa/OMib5p3l1i8AsF3gfTg3v6cWiQAAAASSbt/fzqVwapLwihfbR3RQJhAAIJB1+y67niWE/ympLROnHZ8y8XCiSGYRVhSy3tmYUnNotO0XXkjnf0njp5+vOWwEBRWLcQeiGXgTktqy6hBf5z/uMr5aJs5/sHaoEYUAACCQ9B3XPCrCalLaRQGohdOfb3Mfuyuy9pu0iwLQ4heHtqy+AtC8s9w2CkC7CGCi91t7UpRtvf6JymGwit8FzrNRIndZHcmFEOIVN2cZkGgj97zILi+MD5xrqUgdeuyTGDY7qS3Dy+57WSharo6u/anhJcHu2iTTiX9GFTDZZYmXFtoqKfff+0EMIYTCpJ19lJRsfruUXFyW+djPSYtkvuRxNfxOUlvGSz4wVF1pTGClzE9N9OGgo5pSl/mB7wqqSmNPTzEj6025XILDdlIAZBPvLTAnEdRn3/1yA9MuCkCLXxzasvoKQPPOclspAO0hgInerrNWNFoU+uW2CrLDlnQgd/jjpRDiRWdGqlAcj2TV3HMzb8/WJXfbHC+CzUxqo7ixf/0+fXg3miIBEL8qvvy7szXIPbcn1/z08PK/3ZQUnQMKMAgFr70tyLWHTZy2u4+ixuTLVXijSW0Tlnd/24IJDuZUBQKgfHX94j1bZqpotSqCV7NiwZlx+obTL1fh7aQAfIEVXZ1uQlVVUagNYO2iALT4xaGNarAANO8st5kC0B6egWEVbGDi6GgnndxKydRMn8BisnEgjI+MFXcd4dqh5jhQh44cpPgp6k0ZbGZSG0UgKtMs+09cNGeg7tdnnKBCVQEsOqNmdEvIZrJwkooKhQCw3Og3BSpDRg5UkSQRLYcPt+DFRCSIG0tqqwhkdZPuLrOXje/89fDV4sTHT0tNx03upyT5X8F44b2SosvTtQjtpABIYLnnl3rHOO/4w44ks6w9FIAWvzi0UQ0VgOad5bZTAH7xeSkBAABQ3I6nf5b5X5Rx934q2X5edzJenpPLIph0MP5yTglUE1Nt+Dg7T4zjzUkCRm1zElOlnrP9egIgjt1wO/i6bILikOXrnW9vXDxff+PM7pScR4d2J9kuvzNRhwBE+dn5UG+wsdKXVYlGHYwJ5Tl5bIg1nAS02uQgLQomI723jQSAdTHZP0T2R8bOSMsjdOpiVpV4/+HLTJ62VZ+hw/p3UAEANK9syF0BAAAA8afTv61NHX8+alya+zrp0kbO8i9UAFr64iBvBaB5Z7ntFID2EMBkYZUxJ5fO2ZrYeWXonA4KMIPDBYpGmirSY07Q0FQHHBYHQm5zkn721/nvFK0n+0z7Z9Kf6+feJBNEImA+/Z+FAzQJAEAuh0NQ19SQ3q8RNDQ1CLCKxYaNJLXR61dD8KqKKhwwL03v4fNRo7MpueTjR4bR9DOhQbMsCO2kAABhylGvzfkzblwbqVOZJrO8XRSAr7TExaF1ct58zTvLbacAtIcmxBqQmXxhxVDbwb7RHdfdebjTUQ0AAolMAmKBoLYXLBTwBVCBSCQ0L+mnfqGWwHm9YcTkYPVVjzKruTxWYcThnlGLXBZeL8Ylx0YoqO1XDwV8ASQQvxyA+pNa51s0GxQLRVD4IdvkYEJuRnxcak7qxcmiq8t9LpXAdlIAeHF7F+xmLDyzy1mjTubbRQH4osUuDq2R+f+ieWe57RSA9hLA2Imnpvbuv+iW4qzAdymhfq5GRAAAUNDW1VbAGXRm7Ylg0KsJ2rrahOYl/dwv9d9xn5z8K6Oz9/GtoyxUiQpKhgOX/7V9BP1mwI18XEFHVwdU0xm1X7KaXg3J2roahEaSWudrNBtBhapKIPf7Y+csS2UAAFA0m7za04bz8vEbQbsoAFj6n8v35Q+e6ySOCw8PD3/7uRpipSmvX0WmV8J2UQAAAC17cWiN/P8XzTvLbacAtIsAhhdcmu+2IqKT3/OEp4dm9ZA5yFSb7h1hanySoOZ/LCshqVrFpntHUjOT5Avk0hl8BT19PeKXJQR1AwNVSK+sxomW3bsqliYkFOI1Sbyk+DRg1b2rEmgkSc4o0Cw7aSpQKLUTjCuoqVMJEELYLgoAXlFcKii5t2a0k5OTk9OwGQEpIk7YxuFObjsjhO2iALT8xUHONO8st50C0B4CmCjmmN89xflBV9d+0wWL1H3cWPPKRxdDyyEAAPASL11LUhs+0Yna3CT5QtDqM6ArjL50MV0oWYCXPbgQUqXbz8GSRNB0meCkFHvlSpoQAADw4nsXw9hdxo3rQgSNJMkbisOE0Vpvg87GcyX/c+OCLr5XdHDqS2kXBYDseDhTKP5CmHFwoKL6rNscEeP8BEr7KAAtfnGQM807y22oAPzsfvs/n/jj3n5kkrnT7HmyFnifSxVDCLGiG3PMFHX7zFq/c8tvTqaK2sOOpta8JNG8pLZM9G59V8rXLzJXhq2yUyXp9Jz0+9r/rZzlaEJRNJsWnCN5vYUXv8tBXdli5B/bdqz26KFJ6bzkUXnNix6NJLVlzOAJSl+/BwbF2UETDUkatuOWrlmzZJytBlHH6UA8F0LYPgqALOzz4UGKMi8yt4MC8CMuDm1ZfQWgeWe5jRQA4tatW3960Py58LzYpx95ipDJkFUt6jDMw8FQgaBmM9ZjkGphfHRclshy0rYzx+Z9qQc3L6ktg6zsuA/iHhMm9aHV3G0SlC1GzHTvQWUXZaZ9Zqh1G7/yyCm/UZKHAIBk6DhlrCU3/U1MSpnGwGX+ZzYN06vZrpGktgwrS31TSBs+w9WS8mWRgmZPdw8H9Yr0hA+FsMPQBTtPHZjTVfJWUHsoAF+l8gsS3tM7jp4x2LS9FIAfcXFoy+orAM07y22kAKAJLREEQRC5JAc3TQiCIAjyLRTAEARBELmEAhiCIAgil1AAQxAEQeQSCmAIgiCIXEIBDEEQBJFLKIAhCIIgcgkFMARBEEQuoQCGIAiCyCUUwBAEQRC5hAIYgiAIIpdQAEMQBEHkEgpgCIIgiFxCAQxBEASRSyiAIQiCIHIJBTAEQRBELqEAhiAIgsglFMAQBEEQuYQCGIIgCCKXUABDEARB5BIKYAiCIIhcQgEMQRAEkUsogCEIgiByCQUwBEEQRC6hAIYgCILIJRTAEORXBSv+dlMx+O2JsLUzgiA/BgpgCIIgiFxCAQxB2ggcw+C/30gswn9AXr4QczmCH7h7BPlPUABDkK+4qykN9c/FAQAAS93ZS1GBOuFCFQQAAFGkbydFw98e8yEr6YLP2J6mmsqKFA1ju9E+l9J4AABe2BITxU6rIkVfdoWl7e5D0Z56jQ4BgKzk8z5u3Q3VlNUMug5b9GdkhSTuYOl7+1Gd9tw56mGtRSFT1I3sxm96kCsGAABR1GpLlcFHc74EKHHM2s4qgw59xmu2Grrt0lZXEyqFrKRtNWL9o4LyyP2Tu+urqmia9p11Ip4tzUVx2I7JPY3UlFX1ugxfHpTE+hIlG8xS6s5eVNcTiU93efQ2H7A9QfyDjziCNBcKYAjyleF9Ce9fRbMAAJD+NjpdDHlvI2KFAAA8Nzq6QGXIcAfx83UTFpyvGrwx6F7I3dM+3XL/8prvn44BZcdJo2kFIQ++XPGxzLt3ktVHzxylRcCy/5nhsuiqcMTW8zeDd09SCVszym1HLL/mI8UJBxcfF80+ExYZdnquZvTe2d5Xyr9fFxNF71kV1n33w4hnZ2ZpRB+Y3qvHgtc9/a4/ueHXr/ya76p/JIEP0m94z7uuNH3/+WD/JV2yzi4cueBKEQ4AaDxLWG7gknUxXZYFnFjUldjShxhBWgpEEERG6s7eFOOlTwUQ8kMXGhoMHdZDuYdfggjiVcETqSqjzpSIsy4sdptzMkUsWR8vOzNSSXveAz6EkBOy0FDRbmuiCEIIxRn7B1AMFjxkQ8h7/ruZotXKl6yaz+DHbuqhqDn1WjWE4rQ9fclEK+9XXEmS6M0aK0rX9e9EEAojfTspOx7JxmBtkvLAg5lYzVZKzsdzJWmsa1OoRL1Zt6pwCCEUp+3uQzFa8lSAl58dRSFQh5/MqdmFKP3AQGXF3jtTxY1mKWWHPZnUde0b3g8+1AjyH6EaGIJ8xWqYs2lFVES6WPwx+h2n75w/nPQzI9+UQ2FcxFtRT1dnGrHj7FOPzi+xBdyKnOTo0OBj1xJFEEIAAFAZ4u6mnf7wUSYGAJ5z704CbeIMZ1UgTnseXkwbOsxaUCnBNhnq3IGXGJcuqasRtIeM7K8Mav6maSvU7K5xRJPevQwlP2BFTW0quWv/3hoEAABQ0NTWImAYJllNceCUSR1qfuekznM8BxM+xMQxv5sljQFO9kotdlAR5IcgtXYGEKRtIfd0GaoZ8DqqpJz0JsvaaZCT1QBKcMRbpkNidFXn8S5mCgAvfXnAe82fD96XAl0zK9se6irS+0CVoZPcNC89DMld2xncv/PeYNLuwcoACMtLy8SFZycYnpX9IAVDBlPy0ElBS0e7GbeSRCKRIPMviUwmfLsSQdnAQEO6nKBpZKCC51Wzxd/Lkp4BDd3dIm0dKqMI8jXlAa6OivHh4a+iErT6D7BQHzDYnh/z+vnryE8Gzq42JFh+Zdlkv3hrvyc5jOqSzPhnQQu71d4Hqg51H6Ue/yA0P+fB7XgTj+kDKQAAgiqVSv7SsiiFFZ1yVZRsRagn8nwLZ1az/nWPQ8grLa2W1ucgs6ycR9ahaRK/myUFdG1A2jxUSBGkDo0hw/sKXp8+ESnoPbCnooL+wEGdCp77X4mlDh3eRxGIP8Unciwme893MFFVAAAIsz7lyvTTozq7j1R9e++fwFtx5lOm9yUDAADZdmBf1YwnjzO/rIfnnZtt39/3CafxjBAIBJxRRa8JWoKEZxEV/76fvTDqxp2Cml1gmRfPhRN6D+mv2twsIUhbggIYgtRB0HdysS17/Sqn68C+6gCQug520Ex8GSkaNGKQMgCkjrZdKJ9vHD79JPZ9dMjfa8dO8f+E83M/pJbxAAAAUIe5j1B6eeDgG8up0+xqqmaaY1evsE3cOmHqlr/vPH1665jXhBV3gOvkQaqN5oNk1tWaknZm3a5b0bGRdw7MnXM849/HLwA4YavHLTh09eH9CzumjdnwxtDzf3PMFJqZJQRpU1AAQ5C6iJ2GDbMgEY37DzBTAAAo9hncT5lA6Td8qAYBAILhnICLa7qm7p7q7Dpr0x1s3q2ov6YZvtsyZnO4ZMgmNZdJw1UFWPdpU22lTYtKfTY/eLRjYMXldTPHTfr9ZFbPLfcfbh/4nWBBMJp7/LyvfdFJzyH9HCft+OC4w2cg+d9+F7L9yjM7+uac+n3qDN/gIjufK4+PjtIiNDdLCNKmEJrU3wlBkNYDhcwKDllXS7lJj8oQpN1AAQxBEASRS6gJEUEQBJFLKIAhCIIgcgkFMARBEEQuoQCGIAiCyCUUwBAEQRC5hAIYgiAIIpdQAEMQBEHkEgpgCIIgiFxCAQxBEASRSyiAIQiCIHIJBTAEQRBELqEAhiAIgsglFMAQBEEQuYQCGIIgCCKXUABDEARB5BIKYAiCIIhcQgEMQRAEkUsogCEIgiByCQUwBEEQRC6hAIYgCILIJRTAEARBELmEAhiCIAgil1AAQxAEQeTS/wGwZg/xZxVblgAAAABJRU5ErkJggg==" alt="Sample `raman_hdpe` spectrum with different degrees of range restriction." />
 <p class="caption">
-Sample <code>raman_hdpe</code> spectrum with different degrees of
-background subtraction (Cowger et al., 2020).
+Sample <code>raman_hdpe</code> spectrum with different degrees of range
+restriction.
 </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>
+<div id="flattening-ranges" class="section level2">
+<h2>Flattening Ranges</h2>
 <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
@@ -2822,18 +2915,18 @@ <h4>Flattening Ranges</h4>
 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
+flattened when atmospheric 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="sourceCode" id="cb41"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb41-1"><a href="#cb41-1" 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="cb41-2"><a href="#cb41-2" tabindex="-1"></a>                                         <span class="co"># number or name or a logical vector. </span></span>
+<span id="cb41-3"><a href="#cb41-3" tabindex="-1"></a>none <span class="ot">&lt;-</span> <span class="fu">make_rel</span>(single)</span>
+<span id="cb41-4"><a href="#cb41-4" tabindex="-1"></a>f1 <span class="ot">&lt;-</span> <span class="fu">flatten_range</span>(single) <span class="co">#default flattening the CO2 region. </span></span>
+<span id="cb41-5"><a href="#cb41-5" 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="cb41-6"><a href="#cb41-6" tabindex="-1"></a></span>
+<span id="cb41-7"><a href="#cb41-7" 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="cb41-8"><a href="#cb41-8" tabindex="-1"></a></span>
+<span id="cb41-9"><a href="#cb41-9" 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">
@@ -2842,143 +2935,239 @@ <h4>Flattening Ranges</h4>
 </p>
 </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 id="min-max-normalization" class="section level2">
+<h2>Min-Max Normalization</h2>
+<p>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
+package, most of the processing functions will min-max transform your
+spectra by default if you do not specify otherwise. This plot shows two
+plots that are nearly identical except for the min-max transformed
+spectrum has a y axis that ranges from 0-1.</p>
+<div class="sourceCode" id="cb42"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb42-1"><a href="#cb42-1" tabindex="-1"></a>none <span class="ot">&lt;-</span> raman_hdpe</span>
+<span id="cb42-2"><a href="#cb42-2" tabindex="-1"></a>relative <span class="ot">&lt;-</span> <span class="fu">make_rel</span>(raman_hdpe)</span>
+<span id="cb42-3"><a href="#cb42-3" tabindex="-1"></a><span class="fu">plot</span>(none)</span></code></pre></div>
 <div class="figure" style="text-align: center">
-<img 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hwaSVxZkvfhXUzo9d2THE/cOfUgcEg7vrWmQCDgcDjVz4HNmjVr2bJlAwcObMFaAQBAy6o3wKSF0Zf+Pnn7ZXIuxXXVHs/Ca6n6fXsZshu3Bnj2mf+tj7Xb8iRogU3tTW/cErrCw2Pxtmn9tzq225H7lZWVXC63eoAlJSXl5ua2YJUAAKDFye7WVMbtG9rJYdTKI/ciwm7diskTpwctcLfuMTs4o3GP6IkTY+LEPXym1pFeCCFMydF3rG3Wq7jidjyjcGVlJYfDqTqEWFFRkZ2dDQEGAGjnZAWYNO2I35LHxktvvU2PPTvdhIwQreeW0OCp0r9/8b9V3pg1IOvo6+IxD0ML6k4oaVbI47dsHR0uVmdxu0AcQqwKsPfv3+M4DgEGAGjnZB1C5IfdfqI0/t+VrtpUScGndTX7rfDrc2jr/TixR9dGO3lGMhi3YMxfI0d1S58yY2zfbpYdtJQ5NEwiKM378C4m9MrRwJPRxqse9qE31v7kENEDqzqEmJ2djRCCAAMAtHOyckgsFEopNFrNbg+ZTEISiaRRD+dhakP23r9s/Pvqfaun7uZLq20bo6lYuvvse7DKp3N7zq+P58CqemDZ2dlUKrW0tLRlawUAAC1LVoApOLnaZW/edGDqyVlVk9Ljpc8C9j1gdZtm2djDKeiGXr+f8FouKEh98yYlq6hMIKEyeWp6JqbGmhyYtqpmDywrK0tfX7+8vFGP5AIAgLyRFWAkkxnbV/3rMc/B7LCrLZ5eiO+dN87/2uVQgeueo4MUmqYuGF3ZsJO9ukFpWYWYwuQqKDBbwyD/VqDGObDc3FxDQ0MIMABAOyd7FCLLbvGNqHs7fSylhWJl9fK45znaI/64/ezi9I5NECtwIXN9ahxCzMvL09fX5/P5LVsrAABoWfWGEVXHefpG5+nEA0E5n8xmNUWfCC5k/orKykomk4lhmEQiIZPJBQUFJiYmYWFhLV0vAABoSbIDCS95vn/+rxuvGe9I/mcIHQmCp6j7RvWa/ec+/4H6jXkOrBEvZMZxvKioqJ7SH6xqSxEIBHQ6nUajCYVCJpNZWFioq6sLhxABAO2crADDi67MHzo3WHfyMh87KkIIUZ0X7Zu/df0fY0YrPH201KrxemLEhcz+9V7IHHArrhh3VP3apWAPHz4cNmyYrKDi8/kFBQV1FrVyFRUVDAaDyWRWVFQwmcz8/Hw9PT0IMABAOycriCrunbpYMXTff4GjVYjYIGl0m7DqlB3F0eHMpdeLrDo12uBAso6+Ln7xYWjBhP7KdSTU91zI3Lt37/z8fFmlXC5XWVn5R6raUvh8PovF4nA45eXlysrKRA8MzoEBANo5GQGG8wsLRRpdTHhfxgbFwMSQXFxYLEWo0QIMLmSuH47jBQUFysrKLBaLCK2cnBwDAwORSEScEmvpCgIAQMuQEWCYonVnvaSg448X2fVRqgoxccqZkw8xiy2NOw4RLmSuV2lpKYPBoNFobDa7rKyMz+eLxWIFBQUiz7hcbktXEAAAWoasKKLY/7px7NnR/W2fjxw7sJuJMlac8Tb83xOXEgyW3R6l3tjzEsKFzLLl5eWpqKgghIjEyszM1NLSqnoIAQYAaLdk9qVImkMPhN3rtn7z4bObz6YViaiK+p37TD54cPlEW1YT1QWjKxtaOxpaE4/wvOhb9yMlXl112neEFRQUEAFGnAPLzs7W0NBACLHZbBjHAQBoz+o7GEhW7z5zx+WZO5qtMl8Qx+yfNjpza86FMY18AzI5k5+fTwQYkVjECA6EEIvFggADALRn9QWYpDQ9Jiohh//l3L0YQ9+hl0VdAwYbCC+8s3nBkVfiGoul2REFwordvhOvUBDFavKfS92U2uUdVbKzszU1NdGnY4apqanGxsbEw4qKipauHQAAtBiZASaK2zPE/dfrmeKaF1WRdGffTtrl0njXMmNUeuXrq6eeFjL1bDppMz8txkuKxbgwOylRTEJUxWJJo+1PzuTk5KirqyOEOBxOaWlpcnKyo6MjgkOIAIB2T1aACe/vWHuHPfpg+CZvC6Uv7qqCkaiMxp2NntPT/36kw+opPwfmm0/d85dvF0UMISS6N8doYOaau+36EKJUKo2Pjzc3N0cIaWhoZGdnR0dH+/r6IjiECABo92RMMIjnJSaW2kxdMamrrhKH/QUWk9b4sxLSDQZuuPX8wtj8jS72Qzc/yKp5PLG9CggIOHz4MHEIUVtbOzk5OT4+3tbWFiHEZrPhWmYAQHsmK4voHA45PyNT2IxVIWv2+S3o+a2FnH+GdHFfevldpbxOXdiI7t69u2XLluHDhyOEtLW1r169amFhwWQyEfTAAADtnowAw1RG+PubBM+atO3mm0Jh8yUJSanrrBPhoVst7kx19DmT2+4jLD4+fsiQISwWCyFkZmamrq7u7e1NFEEPDADQzsmaSionaOffyZVp7xZ5nFtMprOY1KrTYCTtGcGx23o19k2Zq+FYTtj7uPuAXYF3c8zNmnA/ciAjI0NHR4f428TEJC4urqqIwWBUVla2UL0AAKDlyZpKitNl1ILfetY19A9jW3Zs+kuLGUYDF20e2OS7adUKCwupVCqbXfcgFjqdLhAImrlKAADQesgahcgy95psXsdySWVZJYndfu8t2ZwyMjK0tbVllUIPDADQzn0liqTCCn75Z6Xp56aZ268Mh0GCzSE1NVVfX19WKfTAAADtnMwAwwserfXsoMhmszmfKeiPPVWsracGPbDmkJqaamBgIKuUTqdDDwwA0J7JiiJJ/L4FG14YzTl06fRKNzWD0QFXrgefXD/MRHfo7qOzjCDAmhyO46tWraonwJhMJgQYAKA9k3lH5ujnrzXGXFnt04tawj+z6wjNvt9ADQ83vXz7GTufjtniRGvWWrZDaWlpJBJpypQpslaAHhgAoJ2TNRNHJb8CZ3HYGEKIZdRRIyn+rRghxOzWr0fRtSuxcA6sycXHx9vY2BBzcNSJwWDAOTAAQHsm60JmnoWVVsqV02GFOCKbWHXMvX8nXowQXpSRUSYQNuOVze0QEUvR0dGdOnWqZzUYhQgAaOdknc2idpu5xCXjzz4dp10VKbkP7522eaiHz7ThHotuqXl4Wbfvq4ubEp/P19TUzMrKevToETHnoSwwChEA0M7JHI5BNp5+KSrk1OZRpmSS9sT95/ydyW9eFXSYtP/i+j7teHr4pvb27duioqIxY8Zcu3bN3d29njWhBwYAaOfqu6ElTdtx1DTiTw3XJf+4LmmWGrVvb968odFoDx48WLduXT1XMSPogQEA2r36Agwvjr24d++pW8/eZJZhXM2ODu6jf5453Eap1Q6iDwkJ+fXXX2WVVlRUFBYWNmd9GiA5OXny5MnHjx8fOPArE2kxGAy4IzNo5aZOnbp161YVFZWWrghom2QGGJ5zZXafkfveca37uHR102CUvo+9uWnswQMjDt85Mb5D00+G2BBdu3YNDAyUVdqrVy8lJaXmrE8DpKamWltbh4eHW1hY1L+mmppabm5u89QKgIb577///Pz8IMBAE5EVYOLn2xcfzOmx5cnFhV24H2eiF30Intd3lN+KywNPDOM1Ww2/A41Gs7e3l1VKIrXaruNHiYmJ+/btCwoKsrKy+urKmpqaubm5Uqm09bcLtFtFRUWlpaUtXQvQZsm6DqwoOipFc+zv86rSCyFE1Rno79eLHx4aJ2qm2rUzb968MTExcXR0/JaVKRSKkpISdMJA04mKijI3N5dKpQ17ellZmUgkggADTUfWdWAsFRUWLhbX/M+VSCUYh8eFn/xNIiUlxdXVVU1N7RvX19LS+vDhQ5NWCbRnFy9eTEhICA0NbdjTi4qKEEIQYKDpyIoilvusn9UurVh5/b3w0yK8JObg/M1PzIYPNW+dp8DkXmJiopGR0bevb2RklJiY2HT1Ae1cdHQ0m81OTU1t2NOJMVMQYKDp1DwHdn3VqMBo4gihlMOJ3dbf5IiJtZWRGrX0/euYuA9lVJP+ZW9SJNYmkGGNLyIiYs2aNd++vqmp6du3b5uuPqCdy8zM7NSpU15eXsOeTvTASkpKfrwmaWlpOjo6ZDJ87YAv1OyBUZlVt09RMHDyHj9+pOdP5npqKppGnfsMGTt+/IifVCrK4BRY04iNjbWxsfn29c3NzePj45uuPq1fYmLi06dPay8PCQmpfuYmNDTUyMiovLy8tLS0oKCgGSsol2JiYs6fP48QysrKsra2bnCANWIPbPjw4SEhIXUW5eXl7d2798d3AeQSXidp1rW1M1ZcTJXUXSyXOBzOgQMHWroWMolEIgqFIpF8x0seFRVlYWEhlUq/cf20tLRvX7m1KSwsnDVrFvF3VlZWYGDgo0ePNm3aZG9vP2jQoNLS0rCwsM6dO+fl5a1fv55EIgUHB+/Zsyc1NXXIkCEaGhoIoaVLlyooKEybNk0ikZSXl8+fP3/p0qUZGRmDBw+WSCQCgSA6OnrNmjU4jmdmZgoEghZtbktasmSJkpJScXExnU7fvn37zJkzG7adgwcPIoQWL17841UyNDQ8fPhwnUW3bt1SUFAQiUQ/vhfQMDY2Ni9fvmyRXcsYRo/xyO+v7Ivrt8hbv1UOmG+DCgoKFBUVv2tMvKWlJYZhJ0+eHD9+/Les7+zsTCaTJ0yYYG9vP3jw4IbWtKmIxWIbG5uIiAg2m40QOnXqVFlZ2fTp04nSgICAPXv2+Pr6isViR0dHqVQ6Y8YMDofz/Pnz/Px8Pz8/qVT69u3bvXv3Hjx40N3dfeHChe/fv9+3b19MTIyCgoKbm9uff/4pEokOHTrE5/OfPn3auXPne/fuXb9+PTo62tvbm8lkZmVlZWVlrVy50sfHZ9KkSVWvalFREZlM5nK5aWlpZDJZR0enqs6lpaUSiURRUbH5X66mExoayuFw/vzzTx6Pp62tff78eaFQSKN99y2UMjMzVVRUGuUQYkFBQXJycp1F2dnZJSUlL1686Nq164/vCMgZWckmyb65tFe3MTvvJZWImzNRm04r74EdPHjQzMzse58VEBDQs2fPBw8epKSkZGZm1rNmfn4+QogY4jhjxoyAgIAf72RkZ2cnJSV948r17C4jIyM1NZX4hvL39+/du/fZs2cHDx7cr18/f3//O3furFixQk1NjQhsogkjR45ECCkoKCCEnjx5wuFwTExM7t69q6yszOPxoqKiKBSKtbX17t27Z8+evWTJksrKyqCgIGVlZRKJpK2t7e/vj+O4r6+vra3tli1bFBUVGQyGqqoqk8nk8/k8Hm/GjBlPnjwJCQnZuHGjtbW1oqLigAEDZsyYMW7cOIFAkJOTExgY+PTp0wEDBkydOpVohVAo/GoHNzU1VSgU1mi7QCDo3r37uXPnvvGVbFIlJSVcLvf06dN0On3EiBEvXrzgcrnBwcEN2NSsWbM8PT379+//g1USiUQYho0fP77O0m3btiGEjh079oN7AQ3Wgj0wWYcQs09P69LJTIuNIYxEZSkofqZsueihsO5ntW6tLcDy8vIqKytxHF+wYMGAAQM4HE737t2/dyMREREIITqdbmZm9ttvv9VegYiNjIyMNWvWuLi4lJeXe3l59e7dm0wmv3jxglgnIyOD+OP06dPLly+vf49VOfT06dNp06aNGzcuLy8vLS2NWCiVSufNmxcSEjJjxoySkpKq7f/zzz/Gxsbnz59PSUmp2tT169d9fHyGDBni4ODQt2/fY8eOIYSIE/UUCkVFRUVJSYnFYunq6g4fPjwsLOzMmTN2dnY2NjYPHz6MiYkhfoH9/PPPOI7fuXPnyZMnOI5Pnz59/vz5OI7n5+fXrnxMTEz1T1p2dvbz589xHC8sLOzRo8eIESPU1NTodDpRBxKJVH3UgLKyspKSkpKSUr9+/UxNTRUVFV1dXYnPxOjRo5OTk4cOHbp48WKBQHD//v2EhIQFCxbs3Lnz8ePHz58/v3fvnq+vb6dOnRgMRo8ePZycnJKSkh49eoTjuLW1NTFyZ/r06bJe8xs3bhAvZu0fAVXvXWMJCgrq27dvZWXlgAEDiBquW7fOzxgdzXcAACAASURBVM9v9uzZr169+q5NeXt7r1u3rgE/y2rIyclBCHXr1q3O0iVLljAYjPXr1//gXkCDtb4Aw8tfBR/YV6fA448+yOWpsVYSYOvXr3/48GF8fLytre2SJUuOHz9OJpNJJJK7u/uMGTO+d2tSqdTQ0HDChAkIIRsbGysrqz179vD5/ODg4PT09JcvX3bv3r1///48Hk9XVzc1NRXH8RcvXlAoFITQxIkT09PTKyoqmEzmlClTJk+erKys3LVr1w8fPhQXFwcGBkZFRS1ZsqSgoGDFihV+fn7jx49fsmSJjo5OWFjY2LFjzczMEEJ6enp+fn49e/a8c+fOjh07LC0tEUKKiooKCgoKCgoTJkzYv38/g8FQUlKi0WhcLhchtHLlylWrVo0ZMwbDMD09vTNnzhDdKQzD3NzcnJ2dnZycrKysTp48+eTJkzlz5tTZcIFAsG3bNiaT+ezZsx99S3Acx/FDhw6dPn165syZ/fv3X7FiRVhYWHBwsIKCgra2NolE0tPTW7x4sba29sWLFxFCDg4OCxYsIJPJ8+bN09LSYjAYNBpNTU3N0NBQUVGRRqOx2WwrK6uBAweqqalpaGiYmZnNmjVr69at8+fPd3BwwDCMRCJRKJQdO3YghLS0tGxtba2trYODg729vW1sbPbs2VNVMYlEoqysbGZm5u7ubmFhkZeX9++//+bk5MybNy8tLU1VVfXx48cBAQH1NE0sFhcWFhJ/l5aW3rt3b/Xq1X379h0xYkTtlX/55Zdt27ZVX/L27Vui5zpt2rQaK5eWlhI/wmrj8/kmJiYPHjxgMBhi8fcdxYmKiqr+8PXr1+rq6jwer86VJ02a5OjoSPyOAS2iFQZYG9Q8ASYWi2NjYyUSSWFhYX5+/s8//3zo0CEXF5czZ84cP37c0tJSX1+/e/fuXbp0IX7Xq6qqbt68efXq1dnZ2Q3bo0AgKCkpmTNnTpcuXTw8PIjDazwej0KhUKlUDPs4lcr27duJ9YuKirhcbqdOnbp162ZnZzdq1KgaR5W5XG7nzp3Rp8m3fv75Z0VFRRUVFRKJRGyNSqVSKBQymTxs2LABAwYQ3RR1dXUKhdK5c+d+/fr16NHj4MGDLBZLVVVVVVX1r7/+evTo0dWrVw8fPjxp0iQymWxjY6OiouLp6RkYGIjjeHp6OkJIU1OTiNjY2Ng3b958S9tv3Lgh6wu0wcRicdVQmry8vLy8vNzcXIFAkJ6eHhERIRaL9fT07t+/j+P4xo0bk5OT+/btu2zZsqNHj+7du/fSpUtcLnfo0KEHDhwg2nL27Nmqni5BJBIFBAQMGTJkz549bDbby8sLIRQcHOzk5ES8/hwOh8vlLlq0SF1dXUVFpWPHjhYWFmPGjCEyXldXl0QidejQoer9Iq4dJPqdOI6vX7/+ypUrOI6fP39+w4YNv/zyy4ABAxwdHXNzc+fPnx8UFKSvr89ms52dnRFCmZmZkZGRJ0+e3LJli5eXl4ODQ4cOHWrkB47j8fHxhw8fNjU1vXHjRvW3xtfXd+vWrdXXlEgkRPoeO3bM3d2d+IH1je8mQSgUUiiUrKysqiWhoaGOjo6ampofPnyovb6zs/OiRYtcXFy+fRegcbXaACtPeXD8j+VzfSdNnDTDb9WuoKhcOR7q0xQBJpFI8vLycBx/9eqVpaWlr6/v4MGDiZ6HoqKiqqqqkpJS9+7dia8eOp1e9aXj5+e3ZMkSFRUVS0vLquNvP668vHzdunU8Hi8mJqa8vNzNzc3Q0BAhdOjQoeqnXnJzc8VisUAgIGY3ZrPZbm5uCKETJ06cP39+7ty5xKG8uXPnenp6qqur371719fXl8PhzJkzZ/Xq1UpKSjk5OY8fPya2FhERcfHiRRzHq3+/VFZWXr16VSAQ1DjqlZOTQ3yhJyYmVs/su3fv3rp1q7FehyaVmZlZ/VzXsWPHQkNDqx6+ffv22z/MYrE4LS1t//79xMMuXbrY2NiMGTPmwoUL9vb2Ojo6FhYWenp6a9asyczM/OeffwwMDNhs9qtXryZOnNivXz+EkL6+vrm5+c2bN3V0dOzt7UeNGsXlci0sLP73v/+NGjUKwzAFBQUSiaSgoODg4MBisXr16oUQsrOzi4+PNzExWb58OfHPqa2tbWpqSqfTFRQU6uwwCYXCbt26IYQ6dux4//79Dx8+aGhoGBgYjB07ViQS/fHHH8TBzOjoaAzDNmzY4OTkRCSZt7f31KlT79y5ExYWVs9Ywc2bN6enp+M4/u7dO4TQ7du3q4r2798/YcKEwYMHnzhxovpTUlNTi4qKFBQU0tLSOnTocO/ePZFIVFJSkpKSEhMT841vAfhxrTLABG+OjDVhYmSOjtVPzs4/WXdQYWBk1Z7/u18gpwOxGxxgL1++fPLkSVZW1rlz53bu3PnixYu0tLTRo0efOHFiwYIFKioqPB7P3NycSKaxY8euXr1aQUHhl19+cXR05PF4VedRaDRanz59PD09X79+jeN4UVHR1atXG7uVOI7jVZ2SefPmLV26VFFRsep0VA0+Pj6zZs3KycnJzMykUqllZWU4jicmJm7ZsmXDhg04jm/cuNHHxwfH8d27d5ubm9fYPmhcAoEgIiLi3r17xMNr165dvHgxNja2vLycWBIQENC7d2/i74qKitmzZw8ePJh4mJWVFRERsWnTppiYGKJXR6FQ+vXr16VLl/Dw8Fu3bs2ZM2fTpk3E8c/z58/jOD5hwgRDQ8POnTsfOXLk+PHj4eHh9+7dW7dunazqXblyhUQiEd16FotV9Wts165d2traenp6tra2dnZ2RCKSSCQikDZu3Fg1emjs2LFeXl7R0dF+fn4WFhaRkZERERG+vr6vX78mkUjbt28vKSkh1hw4cOBvv/32559/jh071sjI6MiRIwcPHjQxMQkICLh58+b69es9PT3pdPrQoUMNDQ1xHD927Jinp+f8+fN79uzp7e3t6uqK43jVgdMG+/EtfKNbt27FxMR871nGOkml0ib6YpGlFQaYJC3QQ4FjO+vcm7JPeSXKCftrkA7NZP5j+fz6akCAbdiwQU1NDcMwFRUVZWVlZWVlMplMDIQj8gkhpKKi0qdPH3d396lTp96+fZvo6BBf8eXl5bm5ua6urjwez8nJaf369UlJSdV/rTe1wsLCkpKSevKmoKCgtLSU+LvOjmBpaSkxGiImJubXX39tonqCb1f93SwsLKzzKy85OZk4mFzj2N3jx4+rd25CQ0MZDAbRe/4WRUVF8+bNIw7/jhw50tXVtVOnThQKheiZIYSIVBs9erSXl1fVaCCxWBwQEGBkZGRqakqs1qtXL3Nz806dOmlpaamqqiKE5s2bx2Qyia4hiUTq2bOnjo4Oi8WysbExNjZmsVgFBQUSiSQwMJDFYnE4HGI7Pj4+GIYdP34cx/G8vDwul6ukpGRqaqqpqamsrBwdHc1kMsPCwogwI+qP47iHhwcxqPLmzZuvXr0iuv58Pt/Z2XncuHHOzs7Ezzg+nx8bG8vlcvl8ftVza7wRxE+NJ0+eFBQUfPXVmzdvXvVuZQ329vY0Go1Op799+3bSpEnf+I7UUFhYuGLFCk9PT4TQTz/91GzXMrbCACs+OYyj8/NN/pdLJem7+rA6rYiUywOJDQgwLy+vP//8c+/evampqZGRkSNHjkxPT9+7d2/ViN6EhISvdkeSk5PfvHmTm5vbwHoD8P34fP6hQ4dqHAwUiUQPHz6ssdr3bvnq1avEP3NQUNC5c+cKCgrc3Nzs7OxOnjx58uRJMzMz4qB6bY8ePdLS0iIGv4SEhBDjX+zt7e3s7Mhk8tKlS0eOHKmionLkyJHw8PBZs2YR162Xlpb++++/VRvZtGnT8ePHEUKmpqaVlZXEOEmCpaXl77//Hh4efunSpZkzZ3bo0IHNZisrKxMHWo8ePaqkpLR27VoGg2FoaNi3b1+EEI/Hs7a2plAoPXv2rOpQLly4UCqVzpkzh+gLbtq0KTAwUEVFJT4+HsfxEydOODo6zp8/39jYWFFR8dWrV6qqqqNHj87IyNi/fz9xQpq4SPH48eNz5869d+9eWFjYwYMHNTU1PT09q0bhCoVC4idjaGiogoKCjo5ORkbGtGnTjI2NEULR0dH+/v5BQUEpKSkbN268efNm/W9KQkLClClTiEYxmcwBAwaoq6tXHeTPycmpOqbK5/Mb/Td06wswad7h/izTJU9qJlXR8aEck4WhMIweAPBZRUXFV9eRSqUpKSnFxcXh4eE4jqelpSUmJhJF5eXlxGnFqu0IhcJ6xi4+ePCg9sLqYczn86dMmbJ+/Xpvb28ul8vlcikUyqpVqzw9PV1cXO7cuaOnp9elSxdiOK62tranp+eQIUOoVKqVlRWXyx08eDAxNGbZsmVdu3YlkUhsNhvDsNGjR/N4PAzDMAybOnWqkZERh8NZv369k5OTqakphULx8vIyMDDw9fWl0+lVPUUmk8lisSgUirm5uaam5suXL2k0momJCZPJPHToEI/H++eff4geXnl5+V9//TV69Ohu3bpRKBQ7OztDQ8NevXoNGjRo9erVAwcODAkJuXDhwtChQ9+8ebN///6xY8cOHTo0Li5OS0uL6Hj169ePOKW6dOlSNpvt5+dnbGxsbW3dp0+f9+/fBwcHjx07VlFRsbKy8t27dyUlJY0yg0nrCzBc9GJlJ7rBhLNp1ZtX+ny9M09pxCn5PAsGAQZAO5SdnZ2QkFBjQqySkpJ79+798ssvJBLpyJEjOI7v2bOHuJLy7Nmz3bp169Gjx5QpUyQSyZs3b4yNjQMCAoKDg/v06bNq1ap3794RHdkXL15cuHABx/E7d+5wOJzTp0/7+PgEBgbOmjVr8eLFTk5O+/btGzNmjL+//9y5c83NzUtKSuzt7TU0NPT09Nhs9urVqxkMxrJly2pUmM/nz5s3b8+ePSQS6fjx48Solt69e2/atMnU1FRfX9/a2prNZmtoaNDpdF9fXwqFMnfuXOLKiqqNVFZWPnz4kEwm02g0IoOpVCqZTLa3t+/evbuvry+TyaTRaHZ2dmvXrl2zZk1kZGRISMjixYtnz579va9wKwwwXJod/LMpnaxk4TFxztJVa1cvmz3KSY9JVnH5M1pOJ4mDAAMA1GBgYBAXF/fj26l9kqw6sVickJCA43hKSkrv3r3PnTv38OHDpKSknj17Vr9goAbioCWO4z4+PsRZQDc3txMnTqSlpZ09ezY4ONjLywvH8UuXLsnayMGDB2NjY8vKyry9vTU0NDw9PaOios6dO0elUh8+fHjlypUlS5YMGzZs/Pjx2tra2trao0eP1tPT+962t2CAYTiOIxmk+c+Obdt+9GpYfEaJlKFqaNtnxC+LZ3sZMWU9oXXjcrl//fWXr69vS1cEANBaFBUVtbGpLGUpLi7m8XgIIRzHExISqgZOEyQSSWVlJYvF4vF4aWlp3/WadO7c+dixY991J43GImMyX4QQQiQVh0kbjk/a0Dw1EebGPLj9+MXrlKzC0goxhamgqtfR5idX1586KMBNgAAATaKdpBdCiEgvhBCGYTXSCyFEJpOJSbStra3fvXtnb2/f3PVrkPoCrDzx7uVIVt+RjmrS91dXL9h0I4PbeeSyDXN7qn7HjOnfQpR86bfJc3c/yhBS2KqaGkocGklcWZKXlV1UiRQsR609sneOAw9r3H0CAACo6b///pOjUJcZRcLIDW72faf8cS9XimednDtxUygyNRHdXT5g1O53UllPahBxzJZR4w8Uum2+GpNdWpKTlpgQFxf/JulDQVlxesS5JabhS4fMCcqTeaATAABAI1FSUqqaf671k9UDq7i9a3us9drwG8ssSTl/n74t9dp97qiPSrShQ/dTQcmzFxo3WidM/OrC2Vedlkce/tW8xqFCElvH3nv5KY0C6/6n7pZ7j+J8bVOJiYnEPfTqJBQKKyoqGlbJ8tBIYVL6xwcYpjDAhazIRQjxn0QJElNrL/9ifRKmMMCFzKu1vK7tSMv5GJXKG+ZBLC+7/1SQkIQQkvIrSCyW4uj+ZEWFz8vJZBKD/sV+QyKFydW2P/DTflvb8rAXwndpTbmd58Lk959f/4GuZAVOfev/SH2qb785X88W2y9JYaBLHfut63XGhSKMTlMYVNfrT8IUBrmSuXVtp87lGFb3dqovl7X+tywnkRQGuXxlv/K4/HvbVW391k9GgOElHz6UG7p4WbERKn9y7ynefauXOoawjlYdSUfSMySo8QIMCSoFGE+RJ2uDFCVlBUlCpfgbtkTMei6rVFFRkbhIsAFIbBaJ82nuHAzDqB9fN4zJqFqOkckY7eNyEof9eX0SqWr96surr4+xmMRyioYqwhBGpxLLKZqquEiMECKxGHilEKN/vKkgVUsNF4lxsRgXCBGJVG2/LBn7ZX25X+q3L//e7cha/4vln9pbYzlZgfN925exHRK32nZIJIxCrr0+RiJ93r6s7Xxjuz5tv3r9G7Kdz69D3f8/NZd/3q+s/7fvfF++ZTmGfX49ZbwOVa8nRqPhYnG194vx5fqflrOZ37Rc1nY+15NZd31qbl/G8k+zvpG4db/OMpd/w/YxMvlzPb+xPg3eTo16kuvafvX3scbyancRau1kjE4svzxRRWXk6XyptPDCOFWG8/YkCY7jgucrOjGs//eiUWfiKL8z25BuOHxnSGat8fnS8pSba/tqcnvvSPzxO7g4OjqGhYX98GYAAAB81oLD6GUdQmS5TZug1O8XZ7d9+IvHYpe93gZ48r8rFyzdnqA3o59FfUM/vhvLZfXhxdFjFvTU/59hp84WHbSUOTRMIijN+/AuNiouA+84fu/lmY3Y4wMAANAWyIwidu/NN8+rbzwaljt87fbVk/VJktt3LsZrjNn9l393uqwnNQym4rL2fsK4m+fOXw+Jep2S9jZVIKEyeWp63X1GrPQeNaSrFq2R9nT58uXo6OhG2tgPVUO+zpR+uw8fPmhra7fJpmVkZGhpabXJpmVmZmpoaBC3f2tjMjMz1dXVyXJ0TOybZWdne3l5EWPfW1Z+fn5L7bq+C5nbmIMHD0ZERLR0LRBC6PDhw0ZGRm3yQ/X27dsOHTpQKI3aR28dEhMTDQwMqFRqS1ek8b17905XV7f6/erajKSkJB0dnTbZtJSUFDc3N21t7ZauCGIwGGvXrlVQUGj+XdcXYHhx7MW9e0/devYmswzjanZ0cB/988zhNkpt8Hda81JTU4uPjyduJNHG6OjoREREtIYPVaMjbplI3CC0jTE3N798+bKZmVlLV6Tx2djYnDhxwtrauqUr0vgcHBwCAwPl5YrjJiIzjPCcK7Oduo1cdf4tpUNXN/eeFoq5NzeN7Wo/7kSypDkrCAAAANRF1qEe8fPtiw/m9Njy5OLCLtyPR/1FH4Ln9R3lt+LywBPDeM1WQwAAAKAOMnpgeFF0VIrm2N/nVaUXQoiqM9Dfrxc/PDRO1Ey1AwAAAGSQEWAYS0WFhYvFNSeNkkglGIfHhbNgAAAAWpisKGK5z/pZ7dKKldffCz8twktiDs7f/MRs+NCacz4BAAAAza3mObDrq0YFRhNHCKUcTuy2/iZHTKytjNSope9fx8R9KKOa9C97kyKxNoEMAwAA0JJqBhiVyeZwPs47yHHyNnD6VKCmomnUGSGESBVlcAoMAABAS6sZYG6//e1W3/qSyrJKEhu6Xz+EQqG0yUt9ETRNPkHT5FEbbtq3+8pMHFJhRaVIWrWGtCB4ppO/7oXYzT+19xfuR+Tn56uoqLR0LZoENE0eQdPkURtu2reTGWB4waN143y23kktFX+xAsZx2fny9hwjGIgIAACgJcnKIUn8vgUbXhjNOXTp9Eo3NYPRAVeuB59cP8xEd+juo7MgvQAAALQ0mXdkjn7+WmPMldU+vagl/DO7jtDs+w3U8HDTy7efsfPpmC1OjTU/PAAAANAgsmbiqORX4CwOG0MIsYw6aiTFvxUjhJjd+vUounYl9ltujwwAAAA0IVkzcfAsrLRSrpwOK8QR2cSqY+79O/FihPCijIwygVDYXu7AAgAAoNWSOYhD8u7AYKeZ16WTgjMO2p7ytv4lxnaECy8m6L+ScTeiA1xa/iZqAAAA2rX6htELM54EXSuym+xpgmXf/WPJposJQu3uk1f5T+qs0AbvSgsAAECutKM7MgMAAGhLYEA8AAAAuQQBBgAAQC5BgAEAAJBLMKVh4xAVJse9yRKwdczN9RVqvKh4Ze7bhByWsbkup+YsyA0ramaC3LdxSXkSXgcrM01m1fAdwYeX4YlF1e54SuKZdLPVoX96KBdNk5ZnvnmdWkRSNbE0UaXXKONnJrwtVjY11WDW/JnXsKLWAC9LeR6ZWl7tvDdJ1aK7lXrV+yC/TSO0lXdNmhsXEpdb7eOFsfXt7Dtwqj5/osKk+PckAwtDXq3v8HqK2h4c/CBJ5vXfemvRMALTwHPtgzzpxzJp3r01/fSZGIYQRlXtOutckhD/oaJmVhH3zxRbJQqGEEIYRbXrjH9iy4kS0fMVVl9+PGg9/0qWfK3+radp0uLwHcPNOCSibTSdPosuVdVFnP7v/O4aNAwhhNG1+yy7kSXBf6ioZUgyTk5zXfmg+issuDlD88uvaMaQ4yVEmZw3ra28aziO47gkdUevL2c7olgsixDhOI7j0pJnO4ebccgYQhhZwXLcgRj+p6fVU9RGQYD9IGn++XHqVP1hAWEZfEFJ0o0VPRUpejNulOE4jkvzLozXpKh5bLyXnPsh8m8fExqn94634oYXNTPRC39bOtNq2uHQlKLSzIi/J1syqOaLQvg4juP8S+N4LM/tL15XSUgrEMpN0/DyO7MNKTzHBWcjM0qKUx/s8DagMB03vRLjOC5JCfTgUQ1G7Qt/n5v6ePtgLYrq8BNZUrzBRS1DWhKytDNDaVJwZbVlWfv7MTXG/RPz+W1786FEguPy37Q28q59JHz4qyHDcdXD+M8fr+TcChzHcbz8gV9HKtdxSXBCTlbchV+7sGk2K55Vfq2orYIA+0H8fycq03v8mfTpZ1vldV8NqumSJyIcl2Yc8GDRe/71qazk0gRVaqeVkQ0uamai8KVmNO3p18s/LSi7NVOfqj/3vhDHJe+29WASzaxBLpqGV16dqka1XB7x6Se8NO/UCCVal7UxYlwct8GBpjD0+MdutDTzgCeL0XtnigRvYFGzE8efXDSxv502A0PYl9/yovDfzJhOfyTWrpW8N03+37UvFB4dzFKZcqWO8Cm5NEGVarn82cf/3MpH842pOjNvVdRf1Ga1kiO+8kuq02/Byl/76VW9kGQyGaMz6AghYWTIM7FFP3f9j2Wc3h49aG9Dn+TgDSxqZpK8MqTbs2dn5qcFDD0DDay0pEyKkCQ1KRUZGuvxM+IjXyRklUuqniUXTcNLCiq4hr2crKgfF2AKevpKqKykTIoXh4fEYt08XJWJ0w2Yuls/O2nk44gK1LCiFkCiKxna9Z/i21evxjlGQWpSBtWwg1ppWmzky3d5lVXnWOS9aW3hXatGkpaUihsa6wtz3kZFxr0v/Tz5rOhV6NMSLdd+1h//c+kOHi7KuWGhbyX1FbVdEGA/iG034fffR1pR8LKMuMjHwXvmLj0t6Dl9bCcKkuampJZiuvo6n15jjKOrp4ynJaeJG1bU3E2je+16/e6Mj+an88aiN5eDX1G7dLOmIrwkJTlPkhDgZqBnZW9nrq2k7Tjn7DshQg1sdXM3DVMbf/Ldm70Dq7KZ/+zyjfdcOwdTiiQ9OV3C1tVT/tRskpa+DlWQlpwhaVhRszYMIYQQ2XTY8jVr165Z6GXwZYBJPiSlCvDwVbY6htb2tibqSgZuy69nSBBC8t60NvCuVSdOS0oTZZ4cY6xj1sXeSl9JzXrigehyhBASpye/R9oGOlWtp2rraaG0pDRxfUVtFwRYIxHe+62Hg/Pg2YdSOs/xH2tKQQjnl/MRjafIqho4hPEUFVB5aTnesKLmblJ1kvynu8YP8X9pOnf1RH0SkqQlpYoxssWca+9Ly/MSrq/rlhE4efTmKFEDW90yjfpI9OHO+uEjt2c4Lvt9qDKG88v4mIIi7/Mng6SgyEXlZeV4w4qatS31k6QmpUqkTKfVoTn8sqyYiwuMYraMmXwgWYrkvWlt612TZienluNS3XGnEov5hckPdvUXnZ89fNm9MoTzy/hSjKeoUFVljKfEQ5VlZZJ6ilqmFc0CAqyR0AcczizOeRdy0Ltwa//+G6NECKNQKUgsEHz+98EFlQKcRCZjDStq1vZ8hpfEHJvX28p5YViHpUFX1vXkIoQoVkvuf8iMPT3bUYvNUjHtt/jwFm/2yyPHwkVy1TQkyXuyZ0o3a48N7xy3XL20wJqGiLdNKKh2xwVcUCnAyZ8a8N1Fzdqg+tF6bX6Z8eFZ4EQbVSZbo9PQtf/8z1n04PCZtxJ5b1rbetdI2lMvpuckXlnuZsBlKho6zwzcNUUn5eTfd/kYlUrB8OpVxgWVlQgjk0n1FLVMK5pFW25b8yJRGFw1o+5Tdq4fQY86eT5GTFJWVSZJiwpLPv9DFRUWY8qqyljDipq7RQghVPZy3yj7n6ZfpI3/OyL2+ip37Y/HJ2hcVXWlz9fMYCo9nK1IGSnpQvlpGl4QsrG/be/Fj3TnXXgZdd7PkagGSUVNGZUUVrvCTVBcVEE0oEFFzdikr6Lz1NV59KoqkXR7OBuh9JR0ibw3rY29axS2sroy+/NlKhynXnbU0rTUPClDVZWDigqLPn+GiguLkZKqMgnVU9R2teW2NQfhndl6bId1sZ97FVSeIhtV8Pk44lhad8BfRUYLPpZIkqKii1mW1h0oDSxqbtL3J6d4zXtsvOpu1O1t4214VZ9q6Yf7B7cfepDx+YOPl5aWIQUVZaqcNA2JXweMHLQ6sfvO0Mh//YeYsKoKSDpWlkrC6Mi4T2cORDHPYySaVlbqpIYVNWer6id5d23X2TxBogAACvdJREFU9pMRhdU6HGUlZUhZRUnum9am3jW8IPzE9t3X3n0+d4WXlZZJqUrKCiSKubU5SoyM/vQrEM9/GZVGsrA2p6J6itqwlh4GKeekWYcHcBl2qyI/DXgtvjfPlKo44lS+FMcFYYtNabo+l3KkOI7j/Ocru9BVRp7Ok+INLWpewtBFJjSDGdeLau1amndiGI9q7ne/+OPw48Lbc0xpGhMuFTa41c2s5N9J6jTr3yPqGmJc8t8ULZrFgkelOI7j0sKbv5jQ9Gfd5je8qIVIkv/qSa8+1lyStL0Xg/nTupcfWy3JPDNGi2rs96gSl/emtZ13DcdxnH9jhjZVd8KF7I+fjPLn/l2ZXM/96RIcF7/e4sRU6n8gRYzjOC56u9NVge0SkCzB6y1qsyDAflRZ2EoHDkXZdtispcsX+Xqacihq7ttjiE+WJOP8RAOaqsP4Zev+N6OPHk3ZdfurjxdpNKyoOYkTNnWjUgz7TJhc3VS/f16JcbzixZZeSmSe5aBfli5bMNnNiEUzGHOC+ODIQdNwYchCYwrLzMPni7ZNW3ExVYLjuPhtYH81mrbz9JXrlvt0U6dqjzie+vFboGFFLaP2t7y06P4SWzZV1X7kvGW//Tquhw6dZTn7KvFzQs6b1mbeNaJWiYcGa1FYRu7TFi9bNGOAJY+i4REQS7RWWnR3vhVTwcp74drV8/p3ZLHtV4SV4l8raqvI/v7+Ld0JlG803T5jh3Si5SbGxSVmiXVcf/njwJYxHYl59TCu5cARPdgfIsOeJ4lMhq0+sGOyBQP9QFFzkqY9u51QQcNLiqorFum7jnDSomn2GDvaSan4XUxsUgm308C52/b+z1OHIidNQ+KksFvJIqa4+Iu2lVDMvAZ1USaRlO2Heduid+FPXr6n2Pr8cXDTUIOPRzkbVtRCKt9HRRZ26D+m56drpjCGofuEYbbM3IToV2kVqvYjluzctbCHCnFoWL6b1tD6t76mEbXqMmKcu05lamzMmzyaidv0Dfs2jjL5+KXC6NB3lLtGXnRYxJtSrb5L9u37tevHOwzXU9RWwQ0tAQAAyKVWdB4WAAAA+HYQYAAAAOQSBBgAAAC5BAEGAABALkGAAQAAkEsQYAAAAOQSBBgAAAC5BAEGAABALkGAAQAAkEsQYAAAAOQSBBgAAAC5BAEGAABALkGAAQAAkEsQYAAAAOQSBBgAAAC5BAEGAABALkGAAQAAkEsQYAAAAOQSBBgAAAC5BAEGAABALkGAAQAAkEsQYAAAAOQSBBgAAAC5BAEGAABALkGAAQAAkEsQYAC0VXjeIS+W5oybwpauCABNAwIMAACAXIIAA6CVkEok+Pc/SSySNkFdPhHzywVNuHkAfggEGABf8OYyeu9MlSKEkOTVOjsaiTPkWAGOEEKikIXGNK0ZNyrx0uhj8wfa6ikyaXSeTuf+80/GVyCEKm7N1KUZLwgRfdqUJH6DA1151NlCHCG8NObofC9rLS6Tq2nhOj0gJI/IHcnrTd04fTYGbR9hpkSn0hW0Ow9e8V+qGCGERKGLTFjO21M+BZT46RJTVo9t76Qfn9V79Ul/d10OncpQ7thv2dX3uSFbhltrsFmKel3H74ksq6pF5q21w221uUy2unnfOUeiSz+lpMwqvVpnx3Hf8/L2+hH2ho5rosRN/IoD0FAQYAB8oW9X7MXDsFKEEF4YHvZajFeEP34mRAhJU8PC3rN69XUS3106ZOrRAuffj/x77XLg/E6p+6dN2flagpg9h/VXe3/tv0/f+JLEy0ExCv3HeSphkuTDY92mnxH28z964fiGYaxbiz291j6r/LhLcdQfP+8STThwK+RWoI9i2KYJfqdzv94XE4VtXHDLesOVx3cOjOeFbR1jZzP1ke2qczfPr+qWe3bhgsNE8OGF5/0mn2OM2XL0+M6Z5kkHfT2mns6QIoTqr5Ik9e+ZS5+az9q9Z7oFubFfYgAaCw4AqObVOnu6zi+3BTheed1XS7O3qw3TZlWUCJcWHB/KYXkeyBInHfvZa+LeWDGxvjTngAdDefJ/lTiOl1/z1aJ19n8pwnEcF7/Z4kjXnHqlDMcr7s42oHX89X7px31UPlthQ1McdbYYx8XxG7tSyR39HvKJItGTxR3pFssiRDguDFlozOz5V7IE/1zE7P5HouTjsxguu1KJstKzIzlk9fEXC6Q4juPi+A0OdO2ZtwXS3IOedIzTd2/Kx02IXm/tzqTZr3slrrdKsWu7UCkWS55UNPFLDcAPgh4YAF/o6Oqilxf6+LVYnBAWUd514tw+GokhT3Jx4fPH4SJbdxc1cocJ+64enWmF+HkpMWHXj+84+1KE4zhCCLF6eXspv75yNVGCkDTl36AotaFjXdhIHH/3QaZab1czQT6hTLe3i37Fy+evib4aptzL4ycm+vi3mjLp4+bqR9a1t9MiPsA0RWUO1eInex6GEEIkRWUlTCKREKvRuo8cpv/xc04xnTjJGYt7+rzkq1XiOfbpwmi0FxWAJkFp6QoA0LpQbd16K+5+FJqVS3mSZNanR5+OjvTjj8NLnF6GFZgOdjMgIWn2/a1+iwP+e5GNVA06WtkosKp+B7J6D/NSPHnlWuoSUxQc9EJz2AZnJkLC3Owc8YeDQ7QOVt8RSauohDjpRFJSUW7AT0kymYxVe0ihUrHaK2FMTU1e1XJMUVuTJU0rLhN/rUrqmmrw6xa0dvA/CsCXmI7uPWmRDx48DI1S+snRSMHRuUvl00d3H4W81XRxt6TguadnDV8VabbqZkpRcVZi5J0jvp0+/w5k9/b2VIj873p6yn+XInVHjOlORwhhbA6H+unIYhVJxj53GvEsrI7kqU1aUlz63SMO8Yrs7OKq/hxekpNbQVVRUyR/tUok+G4ArR78kwJQA69X366CR4F7QgT23W1pJI3uPYzf3915+hmnd18HGhK/jXxZbjTcb4qTLpuEEBImvU2tNk6P4+LtwQ7/9/DfF58bjhzTlYoQQlSr7l3Zb27eSPy0njTtnwldflp4s7z+imAYJi0qKPwYWoKoO4/zvn+cvTD0fND7j5uQJJ745wFm3+sndkOrBEBrAgEGQA2YRh83q5xHD1MsundVQIhi4eyk+PJ+iKhHvx5MhCgdrMzp787/GXjz2Yuwa4eWDBy58620MjXuVU4FQgghjqt3P8b9rX88MRk1uvPHrpniwEXzrF76Dxn1v0NBt29f3DFtyLwg5D68B7veelAMLMzo8QeWrr8Y9iwkaKvPxF1vvj+/ECq/tWjQ1G1nrgQfWzt6wPInWpN+m2hAamCVAGhVIMAAqIls7OpqRCHr/ORoQEII0RycuzExere+vXkYQpjWxN0nFv+/nft3hTiMAzjeDVckw5WF/0ASVxiuSwmlZLnhLhR2g64bLO4G3zIb1IlNiknKjzJaLGZGmW66SRmoi0FZHQufvF5/wfNM7/o8T5/+u63ixNTixmlr5eRmr9R7W5utXn+sbOqeLEx3vbQGS8WBz9Fix0j1/DLJNY/WF+YKq/WH4drZxWbui1ik+pZ2DirZRn15fCxfSO7zSTmX/u5d0tm1/WT0cXe1OF85bAyVj6+2ZzKpnx4J/pRUW/+dgN/z9vrUfE73ZDrbeiqDf0PAAAjJCBGAkAQMgJAEDICQBAyAkAQMgJAEDICQBAyAkAQMgJAEDICQBAyAkAQMgJAEDICQBAyAkAQMgJAEDICQBAyAkAQMgJAEDICQBAyAkAQMgJAEDICQBAyAkAQMgJDeAWwawHgOnDQRAAAAAElFTkSuQmCC" alt="Sample `raman_hdpe` spectrum with different degrees of background subtraction (Cowger et al., 2020)." />
+<img 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" alt="Sample `raman_hdpe` spectrum with one being relative and the other untransformed." />
 <p class="caption">
-Sample <code>raman_hdpe</code> spectrum with different degrees of
-background subtraction (Cowger et al., 2020).
+Sample <code>raman_hdpe</code> spectrum with one being relative and the
+other untransformed.
+</p>
+</div>
+<div class="sourceCode" id="cb43"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb43-1"><a href="#cb43-1" tabindex="-1"></a><span class="fu">plot</span>(relative)</span></code></pre></div>
+<div class="figure" style="text-align: center">
+<img 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" alt="Sample `raman_hdpe` spectrum with one being relative and the other untransformed." />
+<p class="caption">
+Sample <code>raman_hdpe</code> spectrum with one being relative and the
+other untransformed.
 </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 id="identifying-spectra" class="section level1">
+<h1>Identifying Spectra</h1>
+<div id="reading-libraries" class="section level2">
+<h2>Reading Libraries</h2>
+<p>Reference libraries are spectra with known identities. The Open Specy
+library now has over 10,000 spectra in it an is getting so large that we
+cannot fit it within the R package size limit of 5 MB. We host the
+reference libraries on <a href="https://osf.io/x7dpz/">OSF</a> and have
+a function to pull the libraries down automatically. Running get_lib by
+itself will download all libraries to your package director or you can
+specify which libraries you want and where you want them.</p>
+<div class="sourceCode" id="cb44"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb44-1"><a href="#cb44-1" tabindex="-1"></a><span class="fu">get_lib</span>() </span>
+<span id="cb44-2"><a href="#cb44-2" tabindex="-1"></a><span class="fu">get_lib</span>(<span class="at">type =</span> <span class="st">&quot;derivative&quot;</span>, <span class="at">path =</span> <span class="st">&quot;your/path&quot;</span>)</span></code></pre></div>
+<p>After download the libraries you can load them into your active
+environment. You should load them one at a time. Libraries are also
+<code>OpenSpecy</code> objects so you can use any Open Specy object as a
+library.</p>
+<div class="sourceCode" id="cb45"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb45-1"><a href="#cb45-1" 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 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" 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="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 id="matches" class="section level2">
+<h2>Matches</h2>
+<p>Before attempting to use a reference library to identify spectra it
+is really important to understand what format the reference library is
+in. All the OpenSpecy reference libraries are in Absorbance units.
+<code>derivative</code> has been absolute first derivative transformed,
+<code>nobaseline</code> has been baseline corrected, <code>raw</code> is
+the rawest form of the reference spectra (not recommended except for
+advanced uses). The previously mentioned libraries all have Raman and
+FTIR spectra in them. <code>mediod</code> is the mediod compressed
+library version of the absolute first derivative for FTIR,
+<code>model</code> is an exception because it is a multinomial
+regression approach for FTIR to identification of absolute derivative
+spectra. All of the libraries have wavenumbers at 5 cm^-1 resolution.
+Whichever library you choose, you first need to get your spectra into a
+similar enough format to use for comparison. That means conforming the
+wavenumbers to the same range and processing the spectra using the same
+processing procedures. In this example we use the
+<code>data(&quot;test_lib&quot;)</code> which is a subsampled version of the
+absolute derivative library and <code>data(&quot;raman_hdpe&quot;)</code> which is
+an unprocessed Raman spectrum in absorbance units of HDPE plastic. With
+single spectra it is easy to look at the spectra but when doing in
+batch, also refer to the Thresholding Signal-Noise section for guidance
+on making sure your batch spectra are processed to quality specs.</p>
+<div class="sourceCode" id="cb46"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb46-1"><a href="#cb46-1" tabindex="-1"></a><span class="fu">data</span>(<span class="st">&quot;test_lib&quot;</span>)</span>
+<span id="cb46-2"><a href="#cb46-2" tabindex="-1"></a><span class="fu">data</span>(<span class="st">&quot;raman_hdpe&quot;</span>)</span>
+<span id="cb46-3"><a href="#cb46-3" tabindex="-1"></a></span>
+<span id="cb46-4"><a href="#cb46-4" tabindex="-1"></a>processed <span class="ot">&lt;-</span> <span class="fu">process_spec</span>(<span class="at">x =</span> raman_hdpe, </span>
+<span id="cb46-5"><a href="#cb46-5" 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="cb46-6"><a href="#cb46-6" tabindex="-1"></a>                                                   <span class="at">res =</span> <span class="cn">NULL</span>,</span>
+<span id="cb46-7"><a href="#cb46-7" tabindex="-1"></a>                                                   <span class="at">type =</span> <span class="st">&quot;interp&quot;</span></span>
+<span id="cb46-8"><a href="#cb46-8" tabindex="-1"></a>                                                   ))</span>
+<span id="cb46-9"><a href="#cb46-9" tabindex="-1"></a></span>
+<span id="cb46-10"><a href="#cb46-10" tabindex="-1"></a><span class="fu">print</span>(<span class="fu">sig_noise</span>(processed) <span class="sc">&gt;</span> <span class="dv">10</span>) <span class="co">#Check to make sure that the signal to noise ratio of the processed spectra is greater than 10.</span></span>
+<span id="cb46-11"><a href="#cb46-11" tabindex="-1"></a><span class="co">#&gt; intensity </span></span>
+<span id="cb46-12"><a href="#cb46-12" tabindex="-1"></a><span class="co">#&gt;      TRUE</span></span>
+<span id="cb46-13"><a href="#cb46-13" tabindex="-1"></a><span class="fu">plotly_spec</span>(raman_hdpe, processed)</span></code></pre></div>
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+<p>After your spectra is processed similarly to the library
+specifications, you can identify the spectra using
+<code>match_spec()</code>. The <code>add_library_metadata</code> and
+<code>add_object_metadata</code> options specify the column name in the
+metadata that you want to add metadata from and <code>top_n</code>
+specifies how many matches you want. In this example we just identified
+a single spectrum with the library but you can also send an OpenSpecy
+object with multiple spectra. The output <code>matches</code> is a
+data.table with at least 3 columns, <code>object_id</code> tells you the
+column names of the spectra in <code>x</code>, <code>library_id</code>
+tells you the column names from the library that it matched to.
+<code>match_val</code> is the value of the Pearson correlation
+coefficient (default) or other correlation if specified in
+<code>...</code> or if using the model identification option
+<code>match_val</code> will be the model confidence. The output in this
+example returned the correct material type, HDPE, as the top match. If
+using Pearson correlation, 0.7 is a good threshold to use for a positive
+ID. In this example, only our top match is greater than the threshold so
+we would disregard the other matches. If no matches were above our
+threshold, we would proclaim that the spectrum is of an unknown
+identity. You’ll also notice in this example that we matched to a
+library with both Raman and FTIR spectra but the Raman spectra had the
+highest hits, this is the rationale for lazily matching to a library
+with both. If you want to just match to a library with FTIR or Raman
+spectra, you can first filter the library using
+<code>filter_spec()</code> using <code>SpectrumType</code>.</p>
+<div class="sourceCode" id="cb47"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb47-1"><a href="#cb47-1" tabindex="-1"></a>matches <span class="ot">&lt;-</span> <span class="fu">match_spec</span>(<span class="at">x =</span> processed, <span class="at">library =</span> test_lib,</span>
+<span id="cb47-2"><a href="#cb47-2" tabindex="-1"></a>                   <span class="at">add_library_metadata =</span> <span class="st">&quot;sample_name&quot;</span>, <span class="at">top_n =</span> <span class="dv">5</span>)</span>
+<span id="cb47-3"><a href="#cb47-3" tabindex="-1"></a><span class="fu">print</span>(matches[,<span class="fu">c</span>(<span class="st">&quot;object_id&quot;</span>, <span class="st">&quot;library_id&quot;</span>, <span class="st">&quot;match_val&quot;</span>, <span class="st">&quot;SpectrumType&quot;</span>, <span class="st">&quot;SpectrumIdentity&quot;</span>)])</span>
+<span id="cb47-4"><a href="#cb47-4" tabindex="-1"></a><span class="co">#&gt;    object_id                       library_id match_val SpectrumType</span></span>
+<span id="cb47-5"><a href="#cb47-5" tabindex="-1"></a><span class="co">#&gt; 1: intensity 0031bb13faea1e04b52ffbeca009e8ab 0.9741244        Raman</span></span>
+<span id="cb47-6"><a href="#cb47-6" tabindex="-1"></a><span class="co">#&gt; 2: intensity 0b10e6cc01c8e53f2221647a9c828164 0.4946028        Raman</span></span>
+<span id="cb47-7"><a href="#cb47-7" tabindex="-1"></a><span class="co">#&gt; 3: intensity 101b6ae86864958ddd95445d0ab01fe4 0.3833523         FTIR</span></span>
+<span id="cb47-8"><a href="#cb47-8" tabindex="-1"></a><span class="co">#&gt; 4: intensity 00002e4e3fac430aa1fdfea6e26f85e4 0.3225426         FTIR</span></span>
+<span id="cb47-9"><a href="#cb47-9" tabindex="-1"></a><span class="co">#&gt; 5: intensity 0062d71f901713f2ac7a89c72fb186d4 0.3169888        Raman</span></span>
+<span id="cb47-10"><a href="#cb47-10" tabindex="-1"></a><span class="co">#&gt;     SpectrumIdentity</span></span>
+<span id="cb47-11"><a href="#cb47-11" tabindex="-1"></a><span class="co">#&gt; 1:              HDPE</span></span>
+<span id="cb47-12"><a href="#cb47-12" tabindex="-1"></a><span class="co">#&gt; 2: Polyvinylchloride</span></span>
+<span id="cb47-13"><a href="#cb47-13" tabindex="-1"></a><span class="co">#&gt; 3:           Nitrile</span></span>
+<span id="cb47-14"><a href="#cb47-14" tabindex="-1"></a><span class="co">#&gt; 4:                PS</span></span>
+<span id="cb47-15"><a href="#cb47-15" tabindex="-1"></a><span class="co">#&gt; 5:      polyisoprene</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
-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.</p>
-<p>The R command for manual metadata selection using
-<code>sample_name == 5381</code> as example is:</p>
-<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 id="library-metadata" class="section level2">
+<h2>Library Metadata</h2>
+<p>The libraries we have created have over 100 variables of metadata in
+them and this can be onerous to read through especially given that many
+of the variables are <code>NA</code> values. We created
+<code>get_metadata()</code> to remedy this by removing columns from the
+metadata which are all blank values. The function below will return the
+metadata for the top match in <code>matches</code>. Remember, similar
+<code>filter_spec()</code>, you can specify <code>logic</code> for more
+than one thing at a time.</p>
+<div class="sourceCode" id="cb48"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb48-1"><a href="#cb48-1" tabindex="-1"></a><span class="fu">get_metadata</span>(<span class="at">x =</span> test_lib, <span class="at">logic =</span> matches[[<span class="dv">1</span>,<span class="st">&quot;library_id&quot;</span>]], <span class="at">rm_empty =</span> T)</span>
+<span id="cb48-2"><a href="#cb48-2" tabindex="-1"></a><span class="co">#&gt;     x y                      sample_name                      SpectrumID</span></span>
+<span id="cb48-3"><a href="#cb48-3" tabindex="-1"></a><span class="co">#&gt; 1: 37 1 0031bb13faea1e04b52ffbeca009e8ab HDPE_Sarah_#18_1s_20ac_10x_25mW</span></span>
+<span id="cb48-4"><a href="#cb48-4" tabindex="-1"></a><span class="co">#&gt;      Organization SpectrumType SpectrumIdentity Polymer.Category LibraryType</span></span>
+<span id="cb48-5"><a href="#cb48-5" tabindex="-1"></a><span class="co">#&gt; 1: J. Lynch, NIST        Raman             HDPE      polyolefins    Polymers</span></span>
+<span id="cb48-6"><a href="#cb48-6" tabindex="-1"></a><span class="co">#&gt;                polymer_class plastic_or_not</span></span>
+<span id="cb48-7"><a href="#cb48-7" tabindex="-1"></a><span class="co">#&gt; 1: Polyolefins (POLYALKENES)        plastic</span></span>
+<span id="cb48-8"><a href="#cb48-8" tabindex="-1"></a><span class="co">#&gt;                                                   url_polymer_class</span></span>
+<span id="cb48-9"><a href="#cb48-9" tabindex="-1"></a><span class="co">#&gt; 1: https://www.polymerdatabase.com/polymer%20index/polyalkenes.html</span></span>
+<span id="cb48-10"><a href="#cb48-10" tabindex="-1"></a><span class="co">#&gt;           polymer                                                url_polymer</span></span>
+<span id="cb48-11"><a href="#cb48-11" tabindex="-1"></a><span class="co">#&gt; 1: POLY(ETHYLENE) https://www.polymerdatabase.com/polymers/polyethylene.html</span></span>
+<span id="cb48-12"><a href="#cb48-12" tabindex="-1"></a><span class="co">#&gt;                                                               url_more_info</span></span>
+<span id="cb48-13"><a href="#cb48-13" tabindex="-1"></a><span class="co">#&gt; 1: https://www.polymerdatabase.com/polymer%20classes/Polyolefin%20type.html</span></span>
+<span id="cb48-14"><a href="#cb48-14" tabindex="-1"></a><span class="co">#&gt;    snr_deriv                          file_id</span></span>
+<span id="cb48-15"><a href="#cb48-15" tabindex="-1"></a><span class="co">#&gt; 1:  1339.684 77f837c1640910f2879184d61ef1df48</span></span></code></pre></div>
 </div>
-<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
-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.</p>
+<div id="plot-matches" class="section level2">
+<h2>Plot Matches</h2>
+<p>Overlaying unknown spectra and the best matches can be extremely
+useful to identify peaks that don’t fit to the reference library which
+may need further investigation. The example below shows great
+correspondence between the best match and the unknown spectrum. All
+major peaks are accounted for and the correct relative height. There are
+two small peaks in the unknown spectrum near 500 that are not accounted
+for which could be investigated further but we would call this a
+positive id to HDPE.</p>
+<div class="sourceCode" id="cb49"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb49-1"><a href="#cb49-1" tabindex="-1"></a><span class="fu">plotly_spec</span>(processed, <span class="fu">filter_spec</span>(test_lib, <span class="at">logic =</span> matches[[<span class="dv">1</span>,<span class="st">&quot;library_id&quot;</span>]]))</span></code></pre></div>
+<div class="plotly html-widget html-fill-item-overflow-hidden html-fill-item" id="htmlwidget-ce1ed4ff6ae9e53f4ee2" style="width:288px;height:288px;"></div>
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 </div>
-<div id="sharing-reference-data" class="section level3">
-<h3>Sharing Reference Data</h3>
+<div id="sharing-reference-data" class="section level2">
+<h2>Sharing Reference Data</h2>
 <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
+please contact the package 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 id="characterizing-particles" class="section level1">
+<h1>Characterizing Particles</h1>
+<p>Sometimes the spectroscopy task we want to perform is to identify
+particles in a spectral map. This is especially common for microplastic
+analysis where a spectral map is used to image a sample and spectral
+information is used to differentiate microplastic particles from
+nonplastic particles. In addition to the material id, one often wants to
+measure the shape and size of the particles. In a brute force technique,
+one could first identify every spectrum in the map, then use
+thresholding and image analysis to measure the particles. However, more
+often than not, particles are well separated on the image surface and
+background spectra is quite different from particle spectra and
+therefore we can use thresholding a priori to identify and measure the
+particles, then pass an exemplary spectrum for each particle to the
+identification routine. It is important to note here that this is at the
+bleeding edge of theory and technique so we may be updating these
+functions in the near future.</p>
+<div id="brute-force" class="section level2">
+<h2>Brute Force</h2>
+<div class="sourceCode" id="cb50"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb50-1"><a href="#cb50-1" tabindex="-1"></a><span class="fu">data</span>(<span class="st">&quot;test_lib&quot;</span>)</span>
+<span id="cb50-2"><a href="#cb50-2" tabindex="-1"></a>test_map <span class="ot">&lt;-</span> <span class="fu">read_any</span>(<span class="fu">read_extdata</span>(<span class="st">&quot;CA_tiny_map.zip&quot;</span>))</span>
+<span id="cb50-3"><a href="#cb50-3" tabindex="-1"></a></span>
+<span id="cb50-4"><a href="#cb50-4" tabindex="-1"></a>test_map_processed <span class="ot">&lt;-</span> <span class="fu">process_spec</span>(test_map, <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 class="at">res =</span> <span class="cn">NULL</span>))</span>
+<span id="cb50-5"><a href="#cb50-5" tabindex="-1"></a></span>
+<span id="cb50-6"><a href="#cb50-6" tabindex="-1"></a>identities <span class="ot">&lt;-</span> <span class="fu">match_spec</span>(test_map_processed, test_lib, <span class="at">add_library_metadata =</span> <span class="st">&quot;sample_name&quot;</span>, <span class="at">top_n =</span> <span class="dv">1</span>)[<span class="fu">order</span>(<span class="fu">factor</span>(object_id, <span class="at">levels =</span> <span class="fu">colnames</span>(test_map<span class="sc">$</span>spectra)))]<span class="co">#Should integrate this sorting into match_spec, tried but couldn&#39;t get it. </span></span>
+<span id="cb50-7"><a href="#cb50-7" tabindex="-1"></a></span>
+<span id="cb50-8"><a href="#cb50-8" tabindex="-1"></a>features <span class="ot">&lt;-</span> <span class="fu">ifelse</span>(identities<span class="sc">$</span>match_val <span class="sc">&gt;</span> <span class="fl">0.7</span>, <span class="fu">tolower</span>(identities<span class="sc">$</span>polymer_class), <span class="st">&quot;unknown&quot;</span>)</span>
+<span id="cb50-9"><a href="#cb50-9" tabindex="-1"></a></span>
+<span id="cb50-10"><a href="#cb50-10" tabindex="-1"></a>id_map <span class="ot">&lt;-</span> <span class="fu">def_features</span>(<span class="at">x =</span> test_map_processed, <span class="at">features =</span> features)</span>
+<span id="cb50-11"><a href="#cb50-11" tabindex="-1"></a></span>
+<span id="cb50-12"><a href="#cb50-12" tabindex="-1"></a>id_map<span class="sc">$</span>metadata<span class="sc">$</span>identities <span class="ot">&lt;-</span> features <span class="co">#also should probably be implemented automatically in the function when a character value is provided. </span></span>
+<span id="cb50-13"><a href="#cb50-13" tabindex="-1"></a></span>
+<span id="cb50-14"><a href="#cb50-14" tabindex="-1"></a>test_collapsed <span class="ot">&lt;-</span> <span class="fu">collapse_spec</span>(id_map) <span class="co">#Collapses spectra to their median for each particle. </span></span></code></pre></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 id="a-priori-particle-thresholding" class="section level2">
+<h2>A Priori Particle Thresholding</h2>
+<div class="sourceCode" id="cb51"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb51-1"><a href="#cb51-1" tabindex="-1"></a><span class="fu">data</span>(<span class="st">&quot;test_lib&quot;</span>)</span>
+<span id="cb51-2"><a href="#cb51-2" tabindex="-1"></a>test_map <span class="ot">&lt;-</span> <span class="fu">read_any</span>(<span class="fu">read_extdata</span>(<span class="st">&quot;CA_tiny_map.zip&quot;</span>))</span>
+<span id="cb51-3"><a href="#cb51-3" tabindex="-1"></a></span>
+<span id="cb51-4"><a href="#cb51-4" tabindex="-1"></a>snr <span class="ot">&lt;-</span> <span class="fu">sig_noise</span>(test_map,<span class="at">metric =</span> <span class="st">&quot;log_tot_sig&quot;</span>) <span class="co">#Characterize the total signal as a threshold value. </span></span>
+<span id="cb51-5"><a href="#cb51-5" tabindex="-1"></a></span>
+<span id="cb51-6"><a href="#cb51-6" tabindex="-1"></a><span class="fu">heatmap_spec</span>(test_map, <span class="at">z =</span> snr) <span class="co">#Use this to find your particles and the sig_noise value to use for thresholding. </span></span>
+<span id="cb51-7"><a href="#cb51-7" tabindex="-1"></a></span>
+<span id="cb51-8"><a href="#cb51-8" tabindex="-1"></a>id_map <span class="ot">&lt;-</span> <span class="fu">def_features</span>(<span class="at">x =</span> test_map, <span class="at">features =</span> snr <span class="sc">&gt;</span> <span class="dv">400</span>) <span class="co">#Set define the feature regions based on the threshold. 400 appeared to be where I suspected my particle to be in the previous heatmap. </span></span>
+<span id="cb51-9"><a href="#cb51-9" tabindex="-1"></a></span>
+<span id="cb51-10"><a href="#cb51-10" tabindex="-1"></a><span class="fu">heatmap_spec</span>(id_map, <span class="at">z =</span> id_map<span class="sc">$</span>metadata<span class="sc">$</span>feature_id) <span class="co">#Check that the thresholding worked as expected. </span></span>
+<span id="cb51-11"><a href="#cb51-11" tabindex="-1"></a></span>
+<span id="cb51-12"><a href="#cb51-12" tabindex="-1"></a>collapsed_id_map <span class="ot">&lt;-</span> id_map <span class="sc">|&gt;</span></span>
+<span id="cb51-13"><a href="#cb51-13" tabindex="-1"></a>    <span class="fu">collapse_spec</span>() <span class="co">#Collapse the spectra to their medians based on the threshold. Important to note here that the particles with id -88 are anything from the FALSE values so they should be your background. </span></span>
+<span id="cb51-14"><a href="#cb51-14" tabindex="-1"></a></span>
+<span id="cb51-15"><a href="#cb51-15" tabindex="-1"></a>id_map_processed <span class="ot">&lt;-</span> <span class="fu">process_spec</span>(</span>
+<span id="cb51-16"><a href="#cb51-16" tabindex="-1"></a>collapsed_id_map, <span class="at">conform_spec_args =</span> <span class="fu">list</span>(<span class="at">range =</span> test_lib<span class="sc">$</span>wavenumber, <span class="at">res =</span> <span class="cn">NULL</span>)) <span class="co">#Process the collapsed spectra. </span></span>
+<span id="cb51-17"><a href="#cb51-17" tabindex="-1"></a></span>
+<span id="cb51-18"><a href="#cb51-18" tabindex="-1"></a><span class="fu">plot</span>(id_map_processed) <span class="co">#check the spectra. </span></span>
+<span id="cb51-19"><a href="#cb51-19" tabindex="-1"></a></span>
+<span id="cb51-20"><a href="#cb51-20" tabindex="-1"></a>matches <span class="ot">&lt;-</span> <span class="fu">match_spec</span>(id_map_processed, test_lib, <span class="at">add_library_metadata =</span> <span class="st">&quot;sample_name&quot;</span>, <span class="at">top_n =</span> <span class="dv">1</span>) <span class="co">#Get the matches of the collapsed spectra for the particles. </span></span></code></pre></div>
 </div>
 </div>
 <div id="references" class="section level1">
diff --git a/vignettes/sop/deselection.jpg b/vignettes/sop/deselection.jpg
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diff --git a/vignettes/sop/download.jpg b/vignettes/sop/download.jpg
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diff --git a/vignettes/sop/flowchart.png b/vignettes/sop/flowchart.png
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index 6b8dda43..00000000
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diff --git a/vignettes/sop/hoverexample.jpg b/vignettes/sop/hoverexample.jpg
deleted file mode 100644
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diff --git a/vignettes/sop/matchplot.png b/vignettes/sop/matchplot.png
deleted file mode 100644
index 0c8d13a4..00000000
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diff --git a/vignettes/spectragryph.Rmd b/vignettes/spectragryph.Rmd
index 83c50d6d..7c6a8742 100644
--- a/vignettes/spectragryph.Rmd
+++ b/vignettes/spectragryph.Rmd
@@ -1,5 +1,5 @@
 ---
-title: "File conversion in SpectraGryph"
+title: "File Conversion in SpectraGryph"
 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{File conversion in SpectraGryph}
+  %\VignetteIndexEntry{File Conversion in SpectraGryph}
   %\VignetteEncoding{UTF-8}
   %\VignetteEngine{knitr::rmarkdown}
 ---
@@ -20,6 +20,9 @@ knitr::opts_chunk$set(
 )
 ```
 
+[Spectragryph](https://www.effemm2.de/spectragryph/) supports many spectral file
+conversions which facilitate data import to Open Specy.
+
 1. Download Spectragryph from
 [https://www.effemm2.de/spectragryph/down.html](https://www.effemm2.de/spectragryph/down.html)
 
@@ -31,14 +34,10 @@ knitr::include_graphics("spectragryph/spectragryph-1.png")
 ```
 
 3. Click File, Save/export data, save data as, and save it as an spc
-file. ¸
+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")
-```