blob_id
stringlengths 40
40
| directory_id
stringlengths 40
40
| path
stringlengths 2
327
| content_id
stringlengths 40
40
| detected_licenses
listlengths 0
91
| license_type
stringclasses 2
values | repo_name
stringlengths 5
134
| snapshot_id
stringlengths 40
40
| revision_id
stringlengths 40
40
| branch_name
stringclasses 46
values | visit_date
timestamp[us]date 2016-08-02 22:44:29
2023-09-06 08:39:28
| revision_date
timestamp[us]date 1977-08-08 00:00:00
2023-09-05 12:13:49
| committer_date
timestamp[us]date 1977-08-08 00:00:00
2023-09-05 12:13:49
| github_id
int64 19.4k
671M
⌀ | star_events_count
int64 0
40k
| fork_events_count
int64 0
32.4k
| gha_license_id
stringclasses 14
values | gha_event_created_at
timestamp[us]date 2012-06-21 16:39:19
2023-09-14 21:52:42
⌀ | gha_created_at
timestamp[us]date 2008-05-25 01:21:32
2023-06-28 13:19:12
⌀ | gha_language
stringclasses 60
values | src_encoding
stringclasses 24
values | language
stringclasses 1
value | is_vendor
bool 2
classes | is_generated
bool 2
classes | length_bytes
int64 7
9.18M
| extension
stringclasses 20
values | filename
stringlengths 1
141
| content
stringlengths 7
9.18M
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
8a037c47d4e6822a6068c31ce9d0098d47df8eb2
|
b7cc95efa28c2f230c2ff27b369e9de5ccc67eec
|
/dashboardCovid/acumulados/Japan.R
|
82ef25f4129c8f47853a6aaaabcb442fe98836e4
|
[] |
no_license
|
carlosal1015/R
|
9e273346796e9d786ec013bb07956d79ce13701f
|
14ba296d3dc8627a858009023e62625824766894
|
refs/heads/master
| 2022-11-03T17:46:58.197806
| 2020-06-13T05:24:06
| 2020-06-13T05:24:06
| 272,256,011
| 0
| 0
| null | 2020-06-14T18:00:29
| 2020-06-14T18:00:00
| null |
UTF-8
|
R
| false
| false
| 2,127
|
r
|
Japan.R
|
#------------------ Packages ------------------
library(flexdashboard)
library(coronavirus)
data(coronavirus)
`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "purple"
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "red"
#------data-----------
df_daily <- coronavirus %>%
dplyr::filter(country == "Japan") %>%
dplyr::group_by(date, type) %>%
dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
dplyr::arrange(date) %>%
dplyr::ungroup() %>%
dplyr::mutate(active = confirmed - death - recovered) %>%
#dplyr::mutate(active = confirmed - death) %>%
dplyr::mutate(
confirmed_cum = cumsum(confirmed),
death_cum = cumsum(death),
recovered_cum = cumsum(recovered),
active_cum = cumsum(active)
)
#---------plot data-------
plotly::plot_ly(data = df_daily) %>%
plotly::add_trace(
x = ~date,
# y = ~active_cum,
y = ~confirmed_cum,
type = "scatter",
mode = "lines+markers",
# name = "Active",
name = "Confirmed",
line = list(color = confirmed_color),
marker = list(color = confirmed_color)
) %>%
plotly::add_trace(
x = ~date,
y = ~death_cum,
type = "scatter",
mode = "lines+markers",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color)
) %>%
plotly::add_trace(
x = ~date,
y = ~active_cum,
type = "scatter",
mode = "lines+markers",
name = "Active",
line = list(color = active_color),
marker = list(color = active_color)
) %>%
plotly::add_trace(
x = ~date,
y = ~recovered_cum,
type = "scatter",
mode = "lines+markers",
name = "Recovered",
line = list(color = recovered_color),
marker = list(color = recovered_color)
) %>%
plotly::layout(
title = "",
yaxis = list(title = "Número acumulado de casos en Japón"),
xaxis = list(title = "Fecha"),
legend = list(x = 0.1, y = 0.9),
hovermode = "compare"
)
|
830f386594a16545df19a9464185102a68173b00
|
e322c90299a454dc197321ffa8325df3c9d1c761
|
/sub_code/process_facebook_embeddings.R
|
54229f7ec4cef57cbcdc75dc889047e4b141d123
|
[
"Apache-2.0"
] |
permissive
|
JonnoB/SETSe_assortativity_and_clusters
|
e6cbcd7c36f28511f5b696dcadf08dbb543620f6
|
46af943c19ccebba2110b7c85383412457abf97e
|
refs/heads/master
| 2021-04-20T12:40:28.144222
| 2020-10-27T14:11:52
| 2020-10-27T14:11:52
| 249,684,929
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,172
|
r
|
process_facebook_embeddings.R
|
#this loads the facebook embeddings then saves each list element as a separate file so as not to clog up the memory
if(!(length(list.files(file.path("/home/jonno/setse_1_data/facebook_embeddings",
"processed_embeddings")))==7)){
file_paths <- list.files("/home/jonno/setse_1_data/facebook_embeddings/HPC_embeddings", full.names = T)
facebook_embeddings_data <- 1:length(file_paths) %>%
map(~{
print(.x)
file_name <- basename(file_paths)[.x]
readRDS(file_paths[.x]) %>%
flatten() %>%
map(~{
Out <- .x %>% mutate(file_name = str_remove(file_name, ".rds"))
return(Out)
})
}) %>% transpose()
embeddings_names <-names(facebook_embeddings_data)
embeddings_names %>%
walk(~{
facebook_embeddings_data[[.x]] %>%
bind_rows() %>%
saveRDS(., file.path("/home/jonno/setse_1_data/facebook_embeddings",
"processed_embeddings", paste0("facebook_", .x, ".rds")))
})
rm(embeddings_names)
rm(facebook_embeddings_data)
rm(file_paths)
}
|
52ba90ec30f1664cb410d951f008097ad7023521
|
1e45d64203edd6d5125980bf23db3daedc9da89d
|
/sources/framework/visioneval/tests/models/StagedModel/Stage-2/run_model.R
|
3c769686af37054047c8b12f3314f839ed957cb0
|
[
"Apache-2.0"
] |
permissive
|
VisionEval/VisionEval-Dev
|
5c1600032307c729b96470355c40ef6cbbb9f05b
|
701bf7f68d94bf1b4b73a0dfd622672a93d4af5f
|
refs/heads/development
| 2023-08-19T17:53:55.037761
| 2023-08-15T12:33:50
| 2023-08-15T12:33:50
| 144,179,471
| 6
| 34
|
Apache-2.0
| 2023-09-07T20:39:13
| 2018-08-09T16:44:22
|
R
|
UTF-8
|
R
| false
| false
| 1,671
|
r
|
run_model.R
|
#===========
#run_model.R
#===========
#This script tests the VisionEval staged model with the second half of the model run
#Load libraries
#--------------
library(visioneval)
writeLog('Running Stage 2')
#Initialize model
#----------------
initializeModel(
ModelScriptFile = "run_model.R",
ParamDir = "defs",
RunParamFile = "run_parameters.json",
GeoFile = "geo.csv",
ModelParamFile = "model_parameters.json",
LoadDatastore = TRUE,
DatastoreName = "../Stage-1/Datastore",
SaveDatastore = FALSE
)
#Run second stage modules
#---------------------------------
for(Year in getYears()) {
for (i in 1:2) {
runModule("CalculateRoadDvmt", "VETravelPerformance", RunFor = "AllYear", RunYear = Year)
runModule("CalculateRoadPerformance", "VETravelPerformance", RunFor = "AllYears", RunYear = Year)
runModule("CalculateMpgMpkwhAdjustments", "VETravelPerformance", RunFor = "AllYears", RunYear = Year)
runModule("AdjustHhVehicleMpgMpkwh", "VETravelPerformance", RunFor = "AllYears", RunYear = Year)
runModule("CalculateVehicleOperatingCost", "VETravelPerformance", RunFor = "AllYears", RunYear = Year)
runModule("BudgetHouseholdDvmt", "VETravelPerformance", RunFor = "AllYears", RunYear = Year)
runModule("BalanceRoadCostsAndRevenues", "VETravelPerformance", RunFor = "AllYears", RunYear = Year)
}
runModule("CalculateComEnergyAndEmissions", "VETravelPerformance", RunFor = "AllYears", RunYear = Year)
runModule("CalculatePtranEnergyAndEmissions", "VETravelPerformance", RunFor = "AllYears", RunYear = Year)
}
writeLog('Completed Stage 2')
|
ce793813daf0bffec220381eb7442023c7d78e3f
|
ab1b963df9e33c2c9601b9df8a7cb525de408ebb
|
/R/describe.R
|
27279703c2a09d1a2830723beb39d26fa69907f8
|
[] |
no_license
|
javierluraschi/sparkhail
|
1e707047690523347bec2ea6cc35ca05e7055942
|
39598e78582f38520a0b152069026589aa65ca53
|
refs/heads/master
| 2020-06-18T20:50:48.750330
| 2019-07-03T16:31:29
| 2019-07-03T16:31:29
| 196,443,127
| 0
| 0
| null | 2019-07-11T18:02:34
| 2019-07-11T18:02:34
| null |
UTF-8
|
R
| false
| false
| 2,298
|
r
|
describe.R
|
#' Describe a matrixTable object
#'
#' @param jobj
#'
#'
#' @examples
#'
#' @export
describe <- function(jobj){
cat(" Global Fields:", "\n",
paste0(" ", mt_globals_fields(jobj), "\n"),
"Column Fields:", "\n",
paste0(" ", mt_col_fields(jobj), "\n"),
"Row Fields:", "\n",
paste0(" ", mt_row_fields(jobj)[[1]], "\n"),
" Info:", "\n",
paste0(" ", mt_row_fields(jobj)[[2]], "\n"),
"Entry Fields:", "\n",
paste0(" ", mt_entry_fields(jobj), "\n"),
"Column Key:", mt_col_key(jobj), "\n",
"Row Key:", mt_row_key(jobj))
}
#' @importFrom sparklyr %>% invoke
#' @export
mt_globals_fields <- function(jobj){
jobj %>%
invoke("globals") %>%
invoke("value") %>%
invoke("toString")
}
#' @importFrom sparklyr %>% invoke
#' @export
mt_str_rows <- function(jobj){
str_row <- jobj %>%
invoke("rowKeyStruct") %>%
invoke("parsableString")
parse_struct(str_row)
}
#' @importFrom sparklyr %>% invoke
#' @export
mt_row_fields <- function(jobj){
row_fields <- jobj %>%
invoke("rowType") %>%
invoke("parsableString") %>%
strsplit(",info:")
list(fields = parse_struct(row_fields[[1]][1]),
info = parse_struct(row_fields[[1]][2]))
}
#' @importFrom sparklyr %>% invoke
#' @export
mt_col_fields <- function(jobj){
col_fields <- jobj %>%
invoke("colType") %>%
invoke("parsableString")
parse_struct(col_fields)
}
#' @importFrom sparklyr %>% invoke
#' @export
mt_entry_fields <- function(jobj){
entry_fields <- jobj %>%
invoke("entryType") %>%
invoke("parsableString")
parse_struct(entry_fields)
}
parse_struct <- function(str){
str <- sub("Struct{", "", str, fixed = TRUE)
gsub("}", "", str, fixed = TRUE) %>%
strsplit( ",") %>%
unlist()
}
parse_arrayseq <- function(str){
str <- sub("ArraySeq(", "", str, fixed = TRUE)
sub(")", "", str, fixed = TRUE) %>%
strsplit( ",") %>%
unlist()
}
#' @importFrom sparklyr %>% invoke
#' @export
mt_row_key <- function(jobj){
row_key <- jobj %>%
invoke("rowKey") %>%
invoke("toString")
parse_arrayseq(row_key)
}
#' @importFrom sparklyr %>% invoke
#' @export
mt_col_key <- function(jobj){
jobj %>%
invoke("colKey") %>%
unlist()
}
|
a26660222d7784d81d13ee1284c595a5f06f332f
|
d92043c1e880559f479d25a7b50cfedcee3f48df
|
/man/f_plot_profit_bars_plus_area.Rd
|
194a4815a7ba0f526dcc7cebf18e42abbd17d7fe
|
[] |
no_license
|
erblast/oetteR
|
f759d69121361007136499706687fae6cd9274ef
|
02f59f2c0562ae224b798a2115c2061608e8e6f7
|
refs/heads/master
| 2021-06-21T13:36:58.536874
| 2019-05-07T08:39:10
| 2019-05-07T08:39:10
| 104,708,489
| 7
| 1
| null | null | null | null |
UTF-8
|
R
| false
| true
| 2,346
|
rd
|
f_plot_profit_bars_plus_area.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/f_plot_profit.R
\name{f_plot_profit_bars_plus_area}
\alias{f_plot_profit_bars_plus_area}
\title{plot revenues cost and profit development over time with bars for
revenue and costs and an area chart for profit.}
\usage{
f_plot_profit_bars_plus_area(data, col_revenue, col_cost, col_time,
now = max(data[, col_time]), unit_time = "years",
unit_value = "CHF", title = "", alpha_past = 1,
alpha_future = 0.5, alpha_past_area = 0.9, alpha_future_area = 0.7)
}
\arguments{
\item{data}{datafram}
\item{col_revenue}{character vector denoting revenue column}
\item{col_cost}{character vector denoting cost column}
\item{col_time}{character vector denoting time column}
\item{now}{integer denoting a time which should be regarded as the
breakpoint, Default: max(data[, col_time])}
\item{unit_time}{character vector, will label y-axis, Default: 'years'}
\item{unit_value}{character vector, will label x-axis, Default: 'CHF'}
\item{title}{character vector, will be title label, Default: ''}
\item{alpha_past}{double between 0 and 1 will determine alpha value for fill
under the curve before the breakpoint, Default: 1}
\item{alpha_future}{double between 0 and 1 will determine alpha value for
fill under the curve after the breakpoint, Default: 0.5}
\item{alpha_past_area}{as alpha_past but for area only, Default: 0.9}
\item{alpha_future_area}{as alpha_future but for area only, Default: 0.7#'}
}
\value{
plot (to some extent plotly compatible)
}
\description{
the function can graphically devide the chart into two periods
e.g. past and future.
}
\details{
to some extent plotly compatible
}
\examples{
data = tibble( time = c(0,1,2,3,4,5,6,7,8,9,10,11,12)
, revenue = - time^2 + time * 12
, cost = revenue * 0.4 * -1
)
data[1,'cost'] = -10
data
print( f_plot_profit_bars_plus_area( data, 'revenue', 'cost', 'time') )
print( f_plot_profit_bars_plus_area( data, 'revenue', 'cost', 'time', now = 5) )
#clv figure for presenation
p = f_plot_profit_bars_plus_area( data, 'revenue', 'cost', 'time', now = 5, alpha_past_area = 0) +
theme( panel.grid.major = element_blank()
, panel.grid.minor = element_blank()
, axis.text = element_blank()
)+
labs(x = '', y = '')
print(p)
}
|
03a6018d878c27187913ccdba5bcbe6b5661e67a
|
d0613e3f380dd0c051200a503f1330abb20c83d8
|
/misc/installPackage.R
|
dae4121c53a681715b9e5f4bf70aa640d2401631
|
[] |
no_license
|
JasonGregory/dlearn
|
db560214165124e911a60fbe33fced59cd7131bd
|
5716785c23064d799dd309d56e44372b871d3977
|
refs/heads/master
| 2021-07-08T19:52:26.084207
| 2017-09-30T14:11:24
| 2017-09-30T14:11:24
| 102,974,849
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 272
|
r
|
installPackage.R
|
library(roxygen2)
devtools::document()
# install package from computer
#devtools::install("~/Documents/R/Packages/dataFun")
#devtools::install("//Co.ihc.com/sh/User/jgregor1/GitHub/dataFun")
#install package from Github
devtools::install_github("JasonGregory/dlearn")
|
1518c27741d89180ee11932ee42d28f36892bc78
|
ae46a28c8eb6c7ac2214fcd0cda47dc0e5840607
|
/graphics_R_scripts_code/correlation_plot/t_stat_gene_expression/quad_colored_scatter_plot_RINT.R
|
ce9e4e9a52544825d4fc813ad72a90497052df72
|
[] |
no_license
|
sariya/CUMC_taub
|
a8c7079fe6f73e3c259767489472864056a91550
|
d3b83adc97ea3fb31b5223d00e2b898989554c56
|
refs/heads/master
| 2021-11-20T08:45:00.951631
| 2021-08-26T19:15:20
| 2021-08-26T19:15:20
| 135,483,729
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,131
|
r
|
quad_colored_scatter_plot_RINT.R
|
#
#Sanjeev Sariya
#06 14 2021
.libPaths(c( "/home/ss5505/libraries_R/R_LIB4.0",.libPaths()))
library(dplyr)
library(ggplot2)
library(ggpubr)
df_zf_human_orthologues<-read.table("/mnt/mfs/hgrcgrid/shared/GT_ADMIX/model_organisms/zebra_fish/DEG_files_ZF/human2zebrafish.txt", header=TRUE)
load(file = "/mnt/mfs/hgrcgrid/shared/GT_ADMIX/model_organisms/zebra_fish/DEG_files_ZF/dre_AB42vsCTL.rda")
dre_AB42vsCTL$ENS_id<-rownames(dre_AB42vsCTL)
df_mayo_deg<- read.table("/mnt/mfs/hgrcgrid/shared/GT_ADMIX/model_organisms/zebra_fish/DEG_files_human/STable4_MAYO_SumStats.csv", header=TRUE,sep=",")
df_ch_deg<- read.table("/mnt/mfs/hgrcgrid/shared/GT_ADMIX/model_organisms/zebra_fish/DEG_files_human/STable1_CH_SumStats.csv", header=TRUE,sep=",")
df_rosmap_deg<- read.table("/mnt/mfs/hgrcgrid/shared/GT_ADMIX/model_organisms/zebra_fish/DEG_files_human/STable3_ROSMAP_SumStats.csv", header=TRUE, sep=",")
colnames(df_mayo_deg)<- paste0("mayo_",colnames(df_mayo_deg))
colnames(df_rosmap_deg)<- paste0("rosmap_",colnames(df_rosmap_deg))
colnames(df_ch_deg)<- paste0("CH_",colnames(df_ch_deg))
merge_tstats<-function(temp_df1, temp_df2, col1){
orthol_merged<- merge( temp_df1, df_zf_human_orthologues, by.x = col1, by.y="Gene_humanstable_ID" )
return ( merge(orthol_merged, dre_AB42vsCTL , by.x ="Gene_ZFstable_ID", by.y="ENS_id"))
}
##function ends
##merge using the functions
ch_mergeded<-merge_tstats(df_ch_deg, dre_AB42vsCTL,"CH_gene" )
mayo_mergeded<-merge_tstats(df_mayo_deg, dre_AB42vsCTL,"mayo_gene" )
rosmap_mergeded<-merge_tstats(df_rosmap_deg, dre_AB42vsCTL,"rosmap_gene" )
mayo_mergeded_filtered<-mayo_mergeded[which( !is.na(mayo_mergeded$stat) & !is.na(mayo_mergeded$mayo_t )),]
rosmap_mergeded_filtered<-rosmap_mergeded[which( !is.na(rosmap_mergeded$stat) & !is.na(rosmap_mergeded$rosmap_t )),]
ch_mergeded_filtered<-ch_mergeded[which( !is.na(ch_mergeded$stat) & !is.na(ch_mergeded$CH_t)),]
ch_mergeded_filtered_pvalue<-ch_mergeded_filtered[which(ch_mergeded_filtered$CH_P.Value <= 0.05),]
rosmap_mergeded_filtered_pvalue <-rosmap_mergeded_filtered[which(rosmap_mergeded_filtered$rosmap_P.Value <= 0.05), ]
mayo_mergeded_filtered_pvalue<-mayo_mergeded_filtered[which(mayo_mergeded_filtered$mayo_P.Value <=0.05),]
cor.test( ch_mergeded_filtered_pvalue$CH_t, ch_mergeded_filtered_pvalue$stat, method="spearman")
cor.test( mayo_mergeded_filtered_pvalue$mayo_t, mayo_mergeded_filtered_pvalue$stat, method="spearman")
cor.test( rosmap_mergeded_filtered_pvalue$rosmap_t, rosmap_mergeded_filtered_pvalue$stat, method="spearman")
ch_mergeded_filtered_pvalue_zf<-ch_mergeded_filtered[which(ch_mergeded_filtered$CH_P.Value <= 0.05 & ch_mergeded_filtered$pvalue <=0.05),]
rosmap_mergeded_filtered_pvalue_zf <-rosmap_mergeded_filtered[which(rosmap_mergeded_filtered$rosmap_P.Value <= 0.05 & rosmap_mergeded_filtered$pvalue<=0.05), ]
mayo_mergeded_filtered_pvalue_zf<-mayo_mergeded_filtered[which(mayo_mergeded_filtered$mayo_P.Value <=0.05 & mayo_mergeded_filtered$pvalue<=0.05),]
cor.test( ch_mergeded_filtered_pvalue_zf$CH_t, ch_mergeded_filtered_pvalue_zf$stat, method="spearman")
cor.test( mayo_mergeded_filtered_pvalue_zf$mayo_t, mayo_mergeded_filtered_pvalue_zf$stat, method="spearman")
cor.test( rosmap_mergeded_filtered_pvalue_zf$rosmap_t, rosmap_mergeded_filtered_pvalue_zf$stat, method="spearman")
ch_mergeded_filtered_pvalue_zf_quad <- ch_mergeded_filtered_pvalue_zf %>%
mutate(quadrant = case_when(stat > 0 & CH_t > 0 ~ "Q1", stat< 0 & CH_t < 0 ~ "Q1", stat< 0 & CH_t > 0 ~ "Q4", stat> 0 & CH_t < 0 ~ "Q2" ) )
rosmap_mergeded_filtered_pvalue_zf_quad <- rosmap_mergeded_filtered_pvalue_zf %>%
mutate(quadrant = case_when(stat > 0 & rosmap_t > 0 ~ "Q1", stat< 0 & rosmap_t < 0 ~ "Q1", stat< 0 & rosmap_t > 0 ~ "Q4", stat> 0 & rosmap_t < 0 ~ "Q2" ) )
mayo_mergeded_filtered_pvalue_zf_quad <- mayo_mergeded_filtered_pvalue_zf %>%
mutate(quadrant = case_when(stat > 0 & mayo_t > 0 ~ "Q1", stat< 0 & mayo_t < 0 ~ "Q1", stat< 0 & mayo_t > 0 ~ "Q4", stat> 0 & mayo_t < 0 ~ "Q2" ) )
bitmap("CH_quad_colored_RINTed.tiff")
ggplot(data=ch_mergeded_filtered_pvalue_zf_quad,aes(x = RNOmni::RankNorm( CH_t, k=3/8), y = RNOmni::RankNorm( stat, k=3/8), color = quadrant)) +
geom_point() + theme_bw() +
theme( plot.title = element_text( size=15, face="bold", hjust = 0.5),
legend.background = element_rect(size=0.5,linetype="solid", colour ="black"),
legend.text=element_text(size=14),legend.title=element_text(size=16),
axis.title.x = element_text(size=20,face="bold"), legend.position = "none",
axis.title.y = element_text(size=20,face="bold"),
axis.text.x=element_text(size=18),axis.text.y=element_text(size=18)
) +labs(title="" ,y="ZF t-Stat", x = "CH t-Stat" )
dev.off()
bitmap("rosmap_quad_colored_RINTed.tiff")
ggplot(data=rosmap_mergeded_filtered_pvalue_zf_quad,aes(x = RNOmni::RankNorm( rosmap_t, k=3/8), y = RNOmni::RankNorm( stat, k=3/8), color = quadrant)) +
geom_point() +
theme_bw() +
theme( plot.title = element_text( size=15, face="bold", hjust = 0.5),
legend.background = element_rect(size=0.5,linetype="solid", colour ="black"),
legend.text=element_text(size=14),legend.title=element_text(size=16),
axis.title.x = element_text(size=20,face="bold"), legend.position = "none",
axis.title.y = element_text(size=20,face="bold"),
axis.text.x=element_text(size=18),axis.text.y=element_text(size=18)
) +labs(title="" ,y="ZF t-Stat", x = "ROSMAP t-Stat" )
dev.off()
bitmap("mayo_quad_colored_RINTed.tiff")
ggplot(data=mayo_mergeded_filtered_pvalue_zf_quad,aes(x = RNOmni::RankNorm( mayo_t, k=3/8), y = RNOmni::RankNorm( stat, k=3/8), color = quadrant)) +
geom_point() +
theme_bw() +
theme( plot.title = element_text( size=15, face="bold", hjust = 0.5),
legend.background = element_rect(size=0.5,linetype="solid", colour ="black"),
legend.text=element_text(size=14),legend.title=element_text(size=16),
axis.title.x = element_text(size=20,face="bold"), legend.position = "none",
axis.title.y = element_text(size=20,face="bold"),
axis.text.x=element_text(size=18),axis.text.y=element_text(size=18)
) +labs(title="" ,y="ZF t-Stat", x = "ROSMAP t-Stat" )
dev.off()
|
38e60777d4e61fa26b588fa288830bcca7b235b8
|
a63301c6573cf86c0d2b59ff32616efea115616d
|
/code/rstat/functions.R
|
5a1ae5742fd76a0098c0c76c2cc79316c5fc795a
|
[] |
no_license
|
skasberger/grazwahl2012
|
307936642ef586091bc08089c1a8acb4da2b01d6
|
a9bde45cc2120d05d5c6a59e41adc5799ec1bbd8
|
refs/heads/master
| 2021-01-10T19:37:59.279834
| 2013-05-20T20:15:41
| 2013-05-20T20:15:41
| 6,854,066
| 2
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 13,636
|
r
|
functions.R
|
######################################################
#
# Title: functions_grw2012.R
# Description: Functions of the analyses of the
# Grazer Gemeinderatswahlen 2012
#
#
# Author: Stefan Kasberger
# Date: 02.12.2012
# Version: 1.0
# Language: 2.15.2
# Software: RStudio 0.97.311
# License: FreeBSD (2-clause BSD)
#
######################################################
#
# Extracts the district out of the parish number
#
# variables
# data: the whole dataframe;
# colParish: name of the column for the parish
# colDistrict: name of the column for the district
#
ExtractDistrict <- function(data, colParish="numParish", colDistrict="numDistrict") {
library(stringr)
temp <- data
temp[[colParish]] <- NULL
data <- as.character(data[[colParish]])
length <- str_length(data)
district <- str_sub(data, end = length - 2)
data <- data.frame(as.numeric(data), as.numeric(district), temp)
names(data)[1] <- colParish
names(data)[2] <- colDistrict
rm(temp, length, district)
data
}
#
# reduce rows into one row per parish and transform the rows with votes per party into columns
#
# variables
# data: the comlete table (dataframe)
# colVotes: column name with the votes
# colParty: column name with the party acronym
#
TransformVotes <- function(data, colVotes, colParty, numParties) {
# save numbers of parishes and districts
tmp <- data[, c("numParish", "numDistrict")]
tmp <- tmp[!duplicated(tmp),]
# save
data <- data[, c("acrParty", "votes")]
rows <- length(data[[colVotes]]) / numParties
new <- data[[colVotes]]
dim(new) <- c(numParties, rows)
new <- data.frame(t(new))
colNames <- lapply(data[1:numParties, colParty], paste0, "abs")
names(new) <- colNames
data <- cbind(tmp, new)
}
#
# DESCRIPTION
#
# variables
#
SaveJSON <- function(data) {
filename <- paste0(environment[["folderDataJSON"]], "/", names(data), ".json")
write(data[[1]],filename)
}
#
# DESCRIPTION
#
# variables
#
Boxplot <- function(data, filename, colors, names, title, yaxis, legend=F, output=T, svg=F, pdf=F, png=F) {
# output to the console
if(output) {
boxplot(data, col=colors, names=names, ylab=yaxis)
mtext("Datenquelle: Stadt Graz - data.graz.gv.at, CC-by", side=1, line=3, adj=1)
if(legend) {
legend("topright", names, fill=colors)
}
title(title)
dev.off()
}
# export png
if(png) {
png(file=paste0(filename, ".png"), height=400, width=600)
boxplot(data, col=colors, names=names, ylab=yaxis)
mtext("Datenquelle: Stadt Graz - data.graz.gv.at, CC-by", side=1, line=3, adj=1)
if(legend) {
legend("topright", names, fill=colors)
}
title(title)
dev.off()
}
# export svg
if(svg) {
svg(file=paste0(filename, ".svg"), height=4, width=6, onefile=TRUE)
boxplot(data, col=colors, names=names, ylab=yaxis)
mtext("Datenquelle: Stadt Graz - data.graz.gv.at, CC-by", side=1, line=3, adj=1)
if(legend) {
legend("topright", names, fill=city[["partycolors"]])
}
title(title)
dev.off()
}
# export pdf
if(pdf) {
boxplot(data, col=colors, names=names, ylab=yaxis)
mtext("Datenquelle: Stadt Graz - data.graz.gv.at, CC-by", side=1, line=3, adj=1)
if(legend) {
legend("topright", names, fill=colors)
}
title(title)
dev.copy2pdf(file=paste0(filename, ".pdf"))
}
}
#
# DESCRIPTION
#
# variables
#
VotesColumnChart <- function(data, filename, colors, names, title, yaxis, shift, legend=F, output=T, png=F, svg=F, pdf=F) {
# output to the console
if(output) {
bp <- barplot(data, col=colors, names=names, main=title, ylab=yaxis)
text(x = bp, y=data + shift , labels=as.character(round(data, digits=2)), xpd=TRUE)
mtext("Datenquelle: Stadt Graz - data.graz.gv.at, CC-by", side=1, line=3, adj=1)
if(legend) {
legend("topright", names, fill=colors)
}
title(title)
dev.off()
}
round
# export png
if(png) {
png(file=paste0(filename, ".png"), height=400, width=600)
bp <- barplot(data, col=colors, names=names, main=title, ylab=yaxis)
text(x = bp, y=data + shift , labels=as.character(round(data, digits=2)), xpd=TRUE)
mtext("Datenquelle: Stadt Graz - data.graz.gv.at, CC-by", side=1, line=3, adj=1)
if(legend) {
legend("topright", names, fill=colors)
}
title(title)
dev.off()
}
# export svg
if(svg) {
svg(file=paste0(filename, ".svg"), height=4, width=6, onefile=TRUE)
bp <- barplot(data, col=colors, names=names, main=title, ylab=yaxis)
text(x = bp, y=data + shift , labels=as.character(round(data, digits=2)), xpd=TRUE)
mtext("Datenquelle: Stadt Graz - data.graz.gv.at, CC-by", side=1, line=3, adj=1)
if(legend) {
legend("topright", names, fill=colors)
}
title(title)
dev.off()
}
# export pdf
if(pdf) {
bp <- barplot(data, col=colors, names=names, main=title, ylab=yaxis)
text(x = bp, y=data + shift , labels=as.character(round(data, digits=2)), xpd=TRUE)
mtext("Datenquelle: Stadt Graz - data.graz.gv.at, CC-by", side=1, line=3, adj=1)
if(legend) {
legend("topright", names, fill=colors)
}
title(title)
dev.copy2pdf(file=paste0(filename, ".pdf"))
}
}
#
# DESCRIPTION
#
# variables
#
Histogram <- function(data, filename, colors, title, xaxis, yaxis, output=T, png=F, svg=F, pdf=F) {
# output to the console
if(output) {
hist(data, col=colors, breaks=100, main=title, xlab=xaxis, ylab=yaxis)
mtext("Datenquelle: Stadt Graz - data.graz.gv.at, CC-by", side=1, line=4, adj=1)
dev.off()
}
# export png
if(png) {
png(file=paste0(filename, ".png"), height=400, width=600)
hist(data, col=colors, breaks=100, main=title, xlab=xaxis, ylab=yaxis)
mtext("Datenquelle: Stadt Graz - data.graz.gv.at, CC-by", side=1, line=4, adj=1)
dev.off()
}
# export svg
if(svg) {
svg(file=paste0(filename, ".svg"), height=4, width=6, onefile=TRUE)
hist(data, col=colors, breaks=100, main=title, xlab=xaxis, ylab=yaxis)
mtext("Datenquelle: Stadt Graz - data.graz.gv.at, CC-by", side=1, line=4, adj=1)
dev.off()
}
# export pdf
if(pdf) {
hist(data, col=colors, breaks=100, main=title, xlab=xaxis, ylab=yaxis)
mtext("Datenquelle: Stadt Graz - data.graz.gv.at, CC-by", side=1, line=4, adj=1)
dev.copy2pdf(file=paste0(filename, ".pdf"))
}
}
#
# DESCRIPTION
#
# variables
#
DensityPlot <- function(data, filename, color, title, yaxis, output=T, png=F, svg=F, pdf=F) {
# output to the console
if(output) {
plot(data,lwd=3,col=color, main=title, ylab=yaxis)
mtext("Datenquelle: Stadt Graz - data.graz.gv.at, CC-by", side=1, line=4, adj=1)
dev.off()
}
# export png
if(png) {
png(file=paste0(filename, ".png"), height=400, width=600)
plot(data,lwd=3,col=color, main=title, ylab=yaxis)
mtext("Datenquelle: Stadt Graz - data.graz.gv.at, CC-by", side=1, line=4, adj=1)
dev.off()
}
# export svg
if(svg) {
svg(file=paste0(filename, ".svg"), height=4, width=6, onefile=TRUE)
plot(data,lwd=3,col=color, main=title, ylab=yaxis)
mtext("Datenquelle: Stadt Graz - data.graz.gv.at, CC-by", side=1, line=4, adj=1)
dev.off()
}
# export pdf
if(pdf) {
plot(data,lwd=3,col=color, main=title, ylab=yaxis)
mtext("Datenquelle: Stadt Graz - data.graz.gv.at, CC-by", side=1, line=4, adj=1)
dev.copy2pdf(file=paste0(filename, ".pdf"))
}
}
#
# DESCRIPTION
#
# variables
#
CalculateCorrelation <- function(dataParish, dataDistrict, corrMethod="pearson", folder, colors, namesIT, namesAT, yaxis, legend=F, output=T, png=F, svg=F, pdf=F) {
if(dim(dataParish)[2] & length(names) & dim(dataDistrict)[2]) {
numParties <- dim(dataParish)[2]
corCoefPar <- array(NA, dim=c(numParties, numParties))
corCoefDis <- array(NA, dim=c(numParties, numParties))
for(i in seq_along(1:numParties)) {
for(j in seq_along(1:numParties)) {
if(i != j ) {
corCoefPar[i, j] <- cor(dataParish[, i], dataParish[, j], method=corrMethod)
corCoefDis[i, j] <- cor(dataDistrict[, i], dataDistrict[, j], method=corrMethod)
}
}
}
for(i in seq_along(1:numParties)) {
corCoefDis[i, i] <- 1
corCoefPar[i, i] <- 1
}
if(corrMethod == "pearson") {
methName <- "Pearson"
methAcr <- "Pear"
}
if(corrMethod == "spearman") {
methName <- "Spearman"
methAcr <- "Spear"
}
# plot correlations as barplots for every party
for(i in seq_along(1:numParties)) {
# parish
CorrelationColumnChart(corCoefPar[i,],
filename=paste0(folder, "barCorr", namesIT[i], methAcr, "ParAbs"),
colors=colors,
names=namesAT,
title=paste0(methName, " Korrelationen von ", namesAT[i], " nach Sprengel"),
legend=legend, output=output, png=png, svg=svg, pdf=pdf)
# district
CorrelationColumnChart(corCoefDis[i,],
filename=paste0(folder, "barCorr", namesIT[i], methAcr, "DisAbs", methAcr),
colors=colors,
names=namesAT,
title=paste0(methName, " Korrelationen von ", namesAT[i], " nach Bezirke"),
legend=legend, output=output, png=png, svg=svg, pdf=pdf)
}
} else {
print("Error: Length of names vector is not the same as number of columns in the dataset!")
}
}
#
# DESCRIPTION
#
# variables
#
CorrelationColumnChart <- function(data, filename, colors, names, title, legend=F, output=T, png=F, svg=F, pdf=F) {
colText <- data
colText[data<0] <- 0
# output to the console
if(output) {
bp <- barplot(data, col=colors, names=names, main=title, ylab="Korrelationskoeffizient")
abline(h=mean(data), col="gray", lwd=2)
text(x = bp, y=colText + 0.05 , labels=as.character(round(data, digits=2)), xpd=TRUE)
mtext("Datenquelle: Stadt Graz - data.graz.gv.at, CC-by", side=1, line=3, adj=1)
if(legend) {
legend("topright", names, fill=colors)
}
title(title)
dev.off()
}
# export png
if(png) {
png(file=paste0(filename, ".png"), height=400, width=600)
bp <- barplot(data, col=colors, names=names, main=title, ylab="Korrelationskoeffizient")
abline(h=mean(data), col="gray", lwd=2)
text(x = bp, y=colText + 0.05 , labels=as.character(round(data, digits=2)), xpd=TRUE)
mtext("Datenquelle: Stadt Graz - data.graz.gv.at, CC-by", side=1, line=3, adj=1)
if(legend) {
legend("topright", names, fill=colors)
}
title(title)
dev.off()
}
# export svg
if(svg) {
svg(file=paste0(filename, ".svg"), height=4, width=6, onefile=TRUE)
bp <- barplot(data, col=colors, names=names, main=title, ylab="Korrelationskoeffizient")
abline(h=mean(data), col="gray", lwd=2)
text(x = bp, y=colText + 0.05 , labels=as.character(round(data, digits=2)), xpd=TRUE)
mtext("Datenquelle: Stadt Graz - data.graz.gv.at, CC-by", side=1, line=3, adj=1)
if(legend) {
legend("topright", names, fill=colors)
}
title(title)
dev.off()
}
# export pdf
if(pdf) {
bp <- barplot(data, col=colors, names=names, main=title, ylab="Korrelationskoeffizient")
abline(h=mean(data), col="gray", lwd=2)
text(x = bp, y=colText + 0.05 , labels=as.character(round(data, digits=2)), xpd=TRUE)
mtext("Datenquelle: Stadt Graz - data.graz.gv.at, CC-by", side=1, line=3, adj=1)
if(legend) {
legend("topright", names, fill=colors)
}
title(title)
dev.copy2pdf(file=paste0(filename, ".pdf"))
}
}
#
# DESCRIPTION
#
# variables
#
WriteCSV <- function(data, filename, folder = "QGIS", enc = "UTF-8") {
write.csv(data, paste0(folder, "/", filename, "_comma[", enc, "].csv"), fileEncoding = enc)
write.csv2(data, paste0(folder, "/", filename, "_semicolon[", enc, "].csv"), fileEncoding = enc)
}
BoxplotLR <- function(data, colSeg, filename, colors, names, title, yaxis, legend=F, output=T, svg=F, pdf=F, png=F) {
# output to the console
if(output) {
boxplot(data ~ colSeg, col=colors, names=names)
mtext("Datenquelle: Stadt Graz - data.graz.gv.at, CC-by", side=1, line=3, adj=1)
title(title)
dev.off()
}
# export png
if(png) {
png(file=paste0(filename, ".png"), height=400, width=600)
boxplot(data ~ colSeg, col=colors, names=names)
mtext("Datenquelle: Stadt Graz - data.graz.gv.at, CC-by", side=1, line=3, adj=1)
if(legend) {
legend("topright", names, fill=colors)
}
title(title)
dev.off()
}
# export svg
if(svg) {
svg(file=paste0(filename, ".svg"), height=4, width=6, onefile=TRUE)
boxplot(data ~ colSeg, col=colors, names=names)
mtext("Datenquelle: Stadt Graz - data.graz.gv.at, CC-by", side=1, line=3, adj=1)
if(legend) {
legend("topright", names, fill=city[["partycolors"]])
}
title(title)
dev.off()
}
# export pdf
if(pdf) {
boxplot(data ~ colSeg, col=colors, names=names)
mtext("Datenquelle: Stadt Graz - data.graz.gv.at, CC-by", side=1, line=3, adj=1)
if(legend) {
legend("topright", names, fill=colors)
}
title(title)
dev.copy2pdf(file=paste0(filename, ".pdf"))
}
}
|
5da5dc8b5a766e86af331fb62539548c04e87d9f
|
dfbd727cb3a08510b13bad71744a0d4121a686c0
|
/man/add_tags.Rd
|
ef3367b1cc4a79cf6de33aedb76931cf7352923b
|
[
"MIT"
] |
permissive
|
pommedeterresautee/fastrtext
|
e6fa126b61f243a58bc69e0e9ac8c14733369b9b
|
b63c5de9a5168378e8e1abfc4a50be7292002bfb
|
refs/heads/master
| 2021-01-02T08:23:41.922483
| 2019-10-28T08:35:17
| 2019-10-28T08:35:17
| 99,001,176
| 77
| 17
|
NOASSERTION
| 2019-03-12T07:17:59
| 2017-08-01T12:54:04
|
C++
|
UTF-8
|
R
| false
| true
| 1,074
|
rd
|
add_tags.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/API.R
\name{add_tags}
\alias{add_tags}
\title{Add tags to documents}
\usage{
add_tags(documents, tags, prefix = "__label__", new_lines = " ")
}
\arguments{
\item{documents}{texts to learn}
\item{tags}{labels provided as a \link{list} or a \link{vector}. There can be 1 or more per document.}
\item{prefix}{\link{character} to add in front of tag (\code{fastText} format)}
\item{new_lines}{Character that replaces new lines (\code{\\r\\n}), default is space.}
}
\value{
\link{character} ready to be written in a file
}
\description{
Add tags in the `fastText`` format.
This format is require for the training step. As fastText doesn't support newlines inside documents
(as newlines are delimiting documents) this function also ensures that there are absolutely no
new lines. By default new lines are replaced by a single space.
}
\examples{
library(fastrtext)
tags <- list(c(1, 5), 0)
documents <- c("this is a text", "this is another document")
add_tags(documents = documents, tags = tags)
}
|
a46643329bc27d8fbedcc7b15d1f60cb0225fb3f
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/funrar/examples/distinctiveness.Rd.R
|
2421dd23466f6a24f6bf6d759de051f9c527de34
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 847
|
r
|
distinctiveness.Rd.R
|
library(funrar)
### Name: distinctiveness
### Title: Functional Distinctiveness on site-species matrix
### Aliases: distinctiveness
### ** Examples
data("aravo", package = "ade4")
# Site-species matrix
mat = as.matrix(aravo$spe)
# Compute relative abundances
mat = make_relative(mat)
# Example of trait table
tra = aravo$traits[, c("Height", "SLA", "N_mass")]
# Distance matrix
dist_mat = compute_dist_matrix(tra)
di = distinctiveness(pres_matrix = mat, dist_matrix = dist_mat)
di[1:5, 1:5]
# Compute distinctiveness for all species in the regional pool
# i.e., with all the species in all the communities
# Here considering each species present evenly in the regional pool
reg_pool = matrix(1, ncol = ncol(mat))
colnames(reg_pool) = colnames(mat)
row.names(reg_pool) = c("Regional_pool")
reg_di = distinctiveness(reg_pool, dist_mat)
|
f1fd6643077da3eb24a2d9c6c2bfa8d3ff197977
|
eda751fd8916aafb27e6a7ec01287615f0a6b220
|
/Scripts/ML_ReinforcementLearning.R
|
f9d41552b0c037036d407907e84f7b943818682b
|
[
"MIT"
] |
permissive
|
Miyake-Diogo/Artificial_Inteligence_and_MachineLearning_Formation-Udemy
|
fb9df3cfa64b79d9b8e871a1625213fdadd0ac4e
|
bc715e831e7d07bc72c01d4d2b4f01a8063992a6
|
refs/heads/master
| 2020-03-22T03:51:33.989826
| 2018-07-06T12:06:09
| 2018-07-06T12:06:09
| 139,456,460
| 0
| 0
| null | null | null | null |
ISO-8859-1
|
R
| false
| false
| 1,064
|
r
|
ML_ReinforcementLearning.R
|
# Formação IA e ML - UDEMY
# Reinforcement Learning
# instalação e importação do pacote
install.packages("ReinforcementLearning")
library(ReinforcementLearning)
# Criação do ambiente, usando a função gridworldEnvironment, do próprio pacote
ambiente <- gridworldEnvironment
# Visualização do ambiente
print(ambiente)
# Criação dos estados e ações que serão tomados no ambiente
estados <- c("s1", "s2", "s3", "s4")
acoes <- c("up", "down", "left", "right")
# Geração de amostras de experiências a partir das funções
dados <- sampleExperience(N = 1000, env = ambiente, states = estados, actions = acoes)
head(dados)
# Geração do modelo de predição
# (amostras, estados, amostras, recompensas, novo estado, controle (taxa de aprendizado, fator de desconto, fator de exploração))
modelo <- ReinforcementLearning(dados, s = "State", a = "Action", r = "Reward",
s_new = "NextState", control =list(alpha = 0.1, gamma = 0.5, epsilon = 0.1))
# Mostrar o modelo e a melhor politica
modelo
policy(modelo)
|
b04cb1045aff17a0fdb3ca324aebf1f7003a9c8b
|
54cd1abbd80c5193d9f3227e9af7083bff965d35
|
/R/xxirt_ic.R
|
ead106d6b8bcc2f7d90234aa8b2b9b37febbd8ae
|
[] |
no_license
|
isoyturk/sirt
|
fdc049b008e9e8779179f59474ac490448778afd
|
6f7804c61ffb8c6d7fc3ad26bfcfefdd0811d822
|
refs/heads/master
| 2022-04-17T09:35:44.016913
| 2020-04-18T14:06:31
| 2020-04-18T14:06:31
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 423
|
r
|
xxirt_ic.R
|
## File Name: xxirt_ic.R
## File Version: 0.184
#-- information criteria xxirt
xxirt_ic <- function( dev, N, par_items, par_Theta, I,
par_items_bounds )
{
# Information criteria
ic <- list( "deviance"=dev, "n"=N, "I"=I )
# ic$np.item <- length(par_items)
ic$np.items <- sum(par_items_bounds$active)
ic$np.Theta <- length(par_Theta)
ic <- xxirt_ic_compute_criteria(ic=ic)
return(ic)
}
|
f1def545445eed907adbaf77324590923021f314
|
d5430fd76d18d78bc27de2f510f4ed5256396a81
|
/Gas_Station.R
|
217df0afc660e189b46ee59ad1dfc728169b310b
|
[] |
no_license
|
Raseshgarg/Queuing-Model
|
34498ff92c35d17005d29812f0befbdfa968d696
|
38589dd975899b364ddffa6c4a9911adfdd18b32
|
refs/heads/master
| 2022-06-21T15:19:02.480414
| 2020-05-06T03:52:57
| 2020-05-06T03:52:57
| 260,820,993
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,939
|
r
|
Gas_Station.R
|
#install.packages('simmer')
#install.packages('simmer.plot')
#install.packages('xlsx')
#install.packages('parallel')
library(simmer.plot)
library(simmer)
library(dplyr)
library(tidyr)
library(parallel)
library(xlsx)
### Setting initial parameters of the model
max_wait_time = 15
simulation_time = 60*7 # 7 hours, say friday 5-12 pm
mean_service_time = 4
customer_inter_arrival_time = 2/3 # i.e 3 cars coming every 2 minutes
no_of_simulations = 500
output_file_name = "Model_C.xlsx"
### Setting up the system (customer trajectory and environment)
customer <-
trajectory("Customer's path") %>%
log_("Here I am") %>%
set_attribute("start_time", function() {now(gas)}) %>%
renege_in(max_wait_time,
out = trajectory("Reneging customer") %>%
log_(function() {
paste("Waited", now(gas) - get_attribute(gas, "start_time"), "I am off")
})) %>%
simmer::select(c("counter1", "counter2","counter1"), policy = "shortest-queue") %>%
seize_selected() %>%
renege_abort() %>%
log_(function() {paste("Waited: ", now(gas) - get_attribute(gas, "start_time"))}) %>%
timeout(function() {rexp(1,1/mean_service_time)}) %>%
release_selected() %>%
log_(function() {paste("Finished: ", now(gas))})
####
gas <-
simmer("gas") %>%
### Adding resources (counters with respective number of servers)
add_resource("counter1",2) %>% ### 2 is for number of servers per queue
add_resource("counter2", 2) %>%
add_resource("counter3", 1) %>%
## generating customers
## If number of customers follow poisson distribution, then the interarrival time follows a exponential distribution.
add_generator("Customer", customer, function() {c(0, rexp(1000, 1/customer_inter_arrival_time), -1)})
## WSimulating above set up many times
envs <- mclapply(1:no_of_simulations, function(i) {
gas %>% run(until = simulation_time) %>%
wrap()
})
#======================================================
## Estimating Target Parameters
# ---- waiting time
waiting_time <- function(x) {
a = get_mon_arrivals(x) %>% filter(finished == TRUE, activity_time > 0 ) %>%
mutate(waiting_time = end_time - start_time - activity_time)
return(mean(a$waiting_time))
}
x = lapply(envs, waiting_time )
waiting = c('Waiting_time', mean(unlist(x)),mean(unlist(x)) - 1.96*sd(unlist(x)),mean(unlist(x)) + 1.96*sd(unlist(x)))
# ----- % customers served
customers_served <- function(x) {
a = get_mon_arrivals(x) %>% filter(finished == TRUE)
y = (length(which(a$activity_time > 0))/nrow(a))*100
return(y)
}
x = lapply(envs, customers_served )
srvd_cust = c('Percentage_of_customers_served', mean(unlist(x)),mean(unlist(x)) - 1.96*sd(unlist(x)),mean(unlist(x)) + 1.96*sd(unlist(x)))
# -------- mean time in system
system_time <- function(x) {
a = get_mon_arrivals(x) %>% filter(finished == TRUE, activity_time > 0) %>%
mutate(sys_time = end_time - start_time) %>%
filter(finished == TRUE, activity_time > 0)
return(mean(a$sys_time))
}
x = lapply(envs, system_time )
system= c('Time_spent_in_system', mean(unlist(x)),mean(unlist(x)) - 1.96*sd(unlist(x)),mean(unlist(x)) + 1.96*sd(unlist(x)))
#### -------- Queue Length
qu_length <- function(x) {
a = get_mon_resources(x)
return(mean(a$queue))
}
x = lapply(envs, qu_length )
qu = c('Queue_length', mean(unlist(x)),mean(unlist(x)) - 1.96*sd(unlist(x)),mean(unlist(x)) + 1.96*sd(unlist(x)))
#### -------- utilisation
utilisation <- function(x) {
a = get_mon_resources(x)
return((sum(a$server)/sum(a$capacity))*100)
}
x = lapply(envs, utilisation)
ifelse for start time, ifelse for end time
util_res = c('resource_utilization', mean(unlist(x)),mean(unlist(x)) - 1.96*sd(unlist(x)),min(100, mean(unlist(x)) + 1.96*sd(unlist(x))))
##### Combining all metrices into a dataframe
final = data.frame(rbind(waiting, system,srvd_cust, qu , util_res ))
colnames(final) = c('Parameter', 'Expected mean', "95%_CI_lower_bound", "95%_upper_bound")
final[,-1] = apply(final[,-1], 2,as.numeric)
final[,-1] = round(final[,-1],1)
rownames(final) = NULL
##### Writing output to a Excel File
customer_arrival_monitor <- envs[1] %>% get_mon_arrivals()
customer_arrival_monitor <- customer_arrival_monitor[order(customer_arrival_monitor$start_time),]
resource_monitor <- envs[1] %>% get_mon_resources() %>% select(-c(queue_size,limit))
### Writing monitor data for a single simulation
require(openxlsx)
list_of_datasets <- list("customer_arrival_monitor" =customer_arrival_monitor, "resource_monitor" = resource_monitor, "summary_post_1000_simulation" = final)
write.xlsx(list_of_datasets, file = output_file_name )
########## Evolution of waiting time plot
arrivals <- get_mon_arrivals(envs)
plot(arrivals, metric = "waiting_time")+
labs(subtitle="Includes all customers (both served and not served)",
y="Waiting Time (mins)",
x="Simulation Time (mins)",
title="Waiting Time Evolution")
|
401fb7b8fc53bf88f71d2e0d741d2c8a13e7d015
|
6e08f0dd4d56945e4dea19cfbf4217d93c6dd101
|
/scripts/04_eda.R
|
f588df86830254ad55d5928e53f2e4340b197c51
|
[] |
no_license
|
dhicks/chlorpyrifos
|
bb9f3c22c4da615960254302df7119403865af3d
|
5d05831c702364315354995e8ec618f3838ea193
|
refs/heads/master
| 2021-08-07T16:35:36.623445
| 2020-04-01T17:04:08
| 2020-04-01T17:04:08
| 135,846,776
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 16,510
|
r
|
04_eda.R
|
#' This document conducts an EDA on census data only, first at the tract level and then at the places level.
library(tidyverse)
library(sf)
library(spdep)
library(tidycensus)
data_dir = '~/Google Drive/Coding/EJ datasets/CA pesticide/'
load_sf = function(rds_file) {
rds_file %>%
str_c(data_dir, .) %>%
read_rds() %>%
## Remove locations w/ total population or total employed 0
filter(total_popE != 0, total_employedE != 0) %>%
## Population proportions
mutate(womenP = womenE / total_popE,
womenPM = moe_prop(womenE, total_popE, womenM, total_popM),
whiteP = whiteE / total_popE,
whitePM = moe_prop(whiteE, total_popE, whiteM, total_popM),
blackP = blackE / total_popE,
blackPM = moe_prop(blackE, total_popE, blackM, total_popM),
indigenousP = indigenousE / total_popE,
indigenousPM = moe_prop(indigenousE, total_popE, indigenousM, total_popM),
asianP = asianE / total_popE,
asianPM = moe_prop(asianE, total_popE, asianM, total_popM),
hispanicP = hispanicE / total_popE,
hispanicPM = moe_prop(hispanicE, total_popE, hispanicM, total_popM),
noncitizensP = noncitizensE / total_popE,
noncitizensPM = moe_prop(noncitizensE, total_popE,
noncitizensM, total_popM),
childrenP = childrenE / total_popE,
childrenPM = moe_prop(childrenE, total_popE, childrenM, total_popM),
poverty_combE = povertyE + extreme_povertyE,
poverty_combM = sqrt(povertyM^2 + extreme_povertyM^2),
poverty_combP = poverty_combE / total_popE,
poverty_combPM = moe_prop(poverty_combE, total_popE,
poverty_combM, total_popM),
hisp_povertyP = hisp_povertyE / hispanicE,
hisp_povertyPM = moe_prop(hisp_povertyE, hispanicE, hisp_povertyM, hispanicM),
ag_employedP = ag_employedE / total_employedE,
ag_employedPM = moe_prop(ag_employedE, total_employedE, ag_employedM, total_employedM)
) %>%
## Population densities
mutate_at(vars(womenE, whiteE, blackE, indigenousE, asianE, hispanicE,
noncitizensE, childrenE, poverty_combE,
hisp_povertyP, ag_employedP),
funs(D = . / units::drop_units(area)))
}
#' # Tracts #
tracts_sf = load_sf('02_tracts_sf.Rds')
## Gives a warning about NaNs;
## but there aren't any in the output
# as.data.frame() %>%
# select(-geometry) %>%
# transmute_all(funs(is.nan)) %>%
# summarize_all(sum)
glimpse(tracts_sf)
## Distributions of proportions ----
tracts_sf %>%
as.data.frame() %>%
select(ends_with('P')) %>%
gather(key = variable, value) %>%
ggplot(aes(value, color = variable, fill = variable)) +
geom_density() +
geom_rug() +
facet_wrap(~ variable, scales = 'free') +
scale_x_continuous(labels = scales::percent_format())
#' There are a few tracts with a modest proportion of Asian and Black residents ($> 20\%$); but only a few. Almost no tracts have more than 5% Indigenous residents, and none have more than about 20%. Children are typically ~5-12% of the population. The poverty rate varies dramatically, with a median somewhere around 30% and some values above 50%. Hispanic and White proportions are the most diverse. Very little variation in proportion of women, though there are a few extreme tracks with values < 25% or > 80%
tracts_sf %>%
mutate(w_plus_h = whiteP + hispanicP) %>%
ggplot(aes(w_plus_h)) +
geom_density() +
geom_rug()
#' In most tracts, a supermajority of people are either White or Hispanic.
ggplot(tracts_sf, aes(hispanicP, poverty_combP)) +
geom_point() +
geom_smooth(method = 'lm')
#' A greater Hispanic proportion is associated with a greater poverty rate.
## Correlations ----
tracts_sf %>%
as_tibble() %>%
select(densityE, ends_with('P')) %>%
cor() %>%
as.data.frame() %>%
rownames_to_column(var = 'var1') %>%
as_tibble() %>%
gather(key = 'var2', value = 'cor', -var1) %>%
mutate(cor.print = round(cor, digits = 1)) %>%
ggplot(aes(var1, var2, fill = cor, label = cor.print)) +
geom_tile() +
geom_text() +
scale_fill_gradient2()
tracts_sf %>%
as_tibble() %>%
select(densityE, ends_with('P'), -whiteP) %>%
cor() %>%
as.data.frame() %>%
rownames_to_column(var = 'var1') %>%
as_tibble() %>%
gather(key = 'var2', value = 'cor', -var1) %>%
filter(abs(cor) > .4, var1 < var2) %>%
arrange(desc(abs(cor)))
#' White proportion has moderate to very strong negative corelations with every other variable (except Indigenous). Very strong correlation between Hispanic and noncitizen proportion and between Hispanic and general poverty. Strong correlations between agricultural employment and noncitizens and Hispanic. Moderate correlations between each pair of poverty, children, noncitizens, and Hispanic, and between agricultural employment and poverty.
ggplot(tracts_sf, aes((densityE), hispanicP)) +
geom_point() +
geom_smooth()
#' No indication of a relationship between population density and Hispanic, linear or nonlinear.
## White/Hispanic segregation ----
## Evenness/dissimilarity
tracts_sf %>%
as.data.frame() %>%
mutate(w_h_dissim = abs(hispanicE/sum(hispanicE) - whiteE/sum(whiteE))) %>%
summarize(w_h_dissim = .5 * sum(w_h_dissim))
#' Evenness is moderate, at 47%
ggplot(tracts_sf, aes(abs(hispanicP - whiteP))) +
geom_density() +
geom_rug()
## Exposure/interaction
tracts_sf %>%
as.data.frame() %>%
mutate(w_h_exposure = abs(whiteE/sum(whiteE) * hispanicE / total_popE),
h_w_exposure = abs(hispanicE/sum(hispanicE) * whiteE/total_popE)) %>%
summarize(w_h_exposure = sum(w_h_exposure),
h_w_exposure = sum(h_w_exposure))
#' Exposure is moderate-low, at about 30% in both directions
## Correlation
ggplot(tracts_sf, aes(hispanicP, whiteP)) +
geom_point()
with(tracts_sf, cor(hispanicP, whiteP))
#' Very strong negative correlation between the two proportions. But I guess, in this kind of two-group context, we would get a very strong negative correlation even if dissimilarity were low and exposure were high.
tracts_sf %>%
as.data.frame() %>%
as_tibble() %>%
summarize_at(vars(whiteE, hispanicE), funs(sum)) %>%
mutate(hw_ratio = hispanicE / whiteE)
#' About 20% more Hispanics than Whites
## Spatial weights ----
coords_tracts = tracts_sf %>%
st_centroid() %>%
st_coordinates()
## Contiguity
weights_tracts_contig = tracts_sf %>%
pull(geometry) %>%
as_Spatial() %>%
poly2nb() %>%
nb2listw(style = 'W')
## KNN
weights_tracts_knn = 3:8 %>%
set_names() %>%
map(~ {knearneigh(coords_tracts, k = .x) %>%
knn2nb() %>%
nb2listw(style = 'W')})
## Inverse spatial weights w/in 50 km
dnn_tracts = dnearneigh(coords_tracts, d1 = 0, d2 = 50 * 1000)
weights_tracts_d = nbdists(dnn_tracts, coords = coords_tracts) %>%
map( ~ 1/.) %>%
nb2listw(dnn_tracts, glist = ., style = 'W',
zero.policy = TRUE)
weights_tracts = c(weights_tracts_knn,
contiguity = list(weights_tracts_contig),
distance = list(weights_tracts_d))
plot(tracts_sf, max.plot = 1)
plot(weights_tracts$contiguity, coords = coords_tracts,
add = TRUE, col = 'blue')
## Moran's I ----
moran.i = function(vec, weights, ...) {
moran.test(vec,
weights, ...)$estimate['Moran I statistic']
}
## Moran's I for overall density
moran_i_tracts = weights_tracts %>%
map_dfr(~moran.i(log10(tracts_sf$densityE), .)) %>%
gather(key = 'k', value = 'I')
moran_i_tracts
#' Moderate population clustering, ~.40-.45 for KNN
weights_tracts %>%
tibble(weights = ., k = names(.)) %>%
crossing(tibble(variable = c('womenE_D',
'whiteE_D',
'blackE_D',
'indigenousE_D',
'asianE_D',
'hispanicE_D',
'noncitizensE_D',
'childrenE_D',
'poverty_combE_D'))) %>%
rowwise() %>%
mutate(var_value = {tracts_sf %>%
as.data.frame() %>%
pull(variable) %>%
{. + 1} %>% log10() %>%
list()},
moran_i = moran.i(var_value, weights)) %>%
select(k, variable, moran_i) %>%
arrange(desc(moran_i)) %>%
mutate(variable = fct_inorder(variable)) %>%
ggplot(aes(variable, moran_i, color = k, group = k)) +
geom_point() +
geom_line() +
geom_hline(data = moran_i_tracts,
aes(yintercept = I, color = k),
linetype = 'dashed') +
coord_flip()
#' The 6 KNN neighborings all give similar values of Moran's $I$, with slightly lower values as $K$ increases. The dashed lines correspond to the values of $I$ for total population density, calculated above. Distance-based weights have consistently lower values of Moran's $I$, but order the groups in basically the same way. Continuity weights have consistently higher values of I, with almost no difference between different groups.
#'
#' Asian and black residents have moderate-high clustering. White, Hispanic, and noncitizen residents have moderate clustering. Children and impoverished residents seem to have clustering values the same as or just above the overall population. Indigenous people have weak positive clustering.
#'
#' # Places #
places_sf = load_sf('02_places_sf.Rds')
glimpse(places_sf)
## Distributions of proportions ----
places_sf %>%
as.data.frame() %>%
select(ends_with('P')) %>%
gather(key = variable, value) %>%
ggplot(aes(value, color = variable, fill = variable)) +
geom_density() +
geom_rug() +
facet_wrap(~ variable, scales = 'free') +
scale_x_continuous(labels = scales::percent_format())
#' Slightly higher proportions across the board. But no dramatic differences.
places_sf %>%
mutate(w_plus_h = whiteP + hispanicP) %>%
ggplot(aes(w_plus_h)) +
geom_density() +
geom_rug()
#' Again, white+Hispanic supermajority
ggplot(places_sf, aes(hispanicP, poverty_combP)) +
geom_point() +
geom_smooth(method = 'lm')
#' Again, greater Hispanic proportion is associated with a greater poverty rate.
## Correlations ----
places_sf %>%
as_tibble() %>%
select(densityE, womenP, whiteP, blackP, childrenP, hispanicP, indigenousP, noncitizensP, poverty_combP, whiteP) %>%
cor() %>%
as.data.frame() %>%
rownames_to_column(var = 'var1') %>%
as_tibble() %>%
gather(key = 'var2', value = 'cor', -var1) %>%
mutate(cor.print = round(cor, digits = 1)) %>%
ggplot(aes(var1, var2, fill = cor, label = cor.print)) +
geom_tile() +
geom_text() +
scale_fill_gradient2()
places_sf %>%
as_tibble() %>%
select(densityE, ends_with('P'), -whiteP) %>%
cor() %>%
as.data.frame() %>%
rownames_to_column(var = 'var1') %>%
as_tibble() %>%
gather(key = 'var2', value = 'cor', -var1) %>%
filter(abs(cor) > .4, var1 < var2) %>%
arrange(desc(abs(cor)))
#' White is still anticorrelated with everything except Indigenous. Strong or moderate correlations between noncitizens, poverty, or Hispanic. (Not children.) Moderate correlations between Hispanic and density, and moderate-weak between Hispanic and children and density and noncitizens.
ggplot(places_sf, aes((densityE), hispanicP)) +
geom_point() +
geom_smooth()
#' Monotonic nonlinear relationship between density and Hispanic.
## White/Hispanic segregation ----
## Evenness/dissimilarity
places_sf %>%
as.data.frame() %>%
mutate(w_h_dissim = abs(hispanicE/sum(hispanicE) - whiteE/sum(whiteE))) %>%
summarize(w_h_dissim = .5 * sum(w_h_dissim))
#' 36% dissimilarity, lower than with tracts
ggplot(places_sf, aes(abs(hispanicP - whiteP))) +
geom_density() +
geom_rug()
## Exposure/interaction
places_sf %>%
as.data.frame() %>%
mutate(w_h_exposure = abs(whiteE/sum(whiteE) * hispanicE / total_popE),
h_w_exposure = abs(hispanicE/sum(hispanicE) * whiteE/total_popE)) %>%
summarize(w_h_exposure = sum(w_h_exposure),
h_w_exposure = sum(h_w_exposure))
#' Slightly higher White-Hispanic exposure, but still moderate-low
## Correlation
ggplot(places_sf, aes(hispanicP, whiteP)) +
geom_point()
with(tracts_sf, cor(hispanicP, whiteP))
#' Very strong negative correlation between the two proportions. But I guess, in this kind of two-group context, we would get a very strong negative correlation even if dissimilarity were low and exposure were high.
places_sf %>%
as.data.frame() %>%
as_tibble() %>%
summarize_at(vars(whiteE, hispanicE), funs(sum)) %>%
mutate(hw_ratio = hispanicE / whiteE)
#' Hispanic-white ratio slightly higher, at 27%
## Spatial weights ----
library(spdep)
coords_places = places_sf %>%
st_centroid() %>%
st_coordinates()
## Contiguity
weights_places_contig = places_sf %>%
pull(geometry) %>%
as_Spatial() %>%
poly2nb() %>%
nb2listw(style = 'W', zero.policy = TRUE)
## KNN
weights_places_knn = 3:8 %>%
set_names() %>%
map(~ {knearneigh(coords_places, k = .x) %>%
knn2nb() %>%
nb2listw(style = 'W')})
## Inverse spatial weights w/in 50 km
dnn_places = dnearneigh(coords_places, d1 = 0, d2 = 50 * 1000)
weights_places_d = nbdists(dnn_places, coords = coords_places) %>%
map( ~ 1/.) %>%
nb2listw(dnn_places, glist = ., style = 'W', zero.policy = TRUE)
weights_places = c(weights_places_knn,
contiguity = list(weights_places_contig),
distance = list(weights_places_d))
plot(places_sf, max.plot = 1)
plot(weights_places$contiguity, coords = coords_places,
add = TRUE, col = 'blue')
## All systems of neighbors produce an archipelago of tight clusters and longer connections. Neither seems to produce ridiculously extended "neighbor" connections.
##
## Contiguity weights produce large numbers of islands: 246/397 (62%) have no neighbors
weights_places$contiguity$neighbours
## Moran's I ----
moran.i = function(vec, weights, ...) {
moran.test(vec,
weights, ...)$estimate['Moran I statistic']
}
## Moran's I for overall density
moran_i_places = weights_places %>%
map_dfr(~moran.i(log10(places_sf$densityE), .,
zero.policy = TRUE)) %>%
gather(key = 'k', value = 'I')
moran_i_places
#' Higher moderate population clustering, .45-.53. Distance weights are more consistent w/ KNN here. Contiguity weights are much lower.
weights_places %>%
tibble(weights = ., k = names(.)) %>%
crossing(tibble(variable = c('womenE_D',
'whiteE_D',
'blackE_D',
'indigenousE_D',
'asianE_D',
'hispanicE_D',
'noncitizensE_D',
'childrenE_D',
'poverty_combE_D'))) %>%
rowwise() %>%
mutate(var_value = {places_sf %>%
as.data.frame() %>%
pull(variable) %>%
{. + 1} %>% log10() %>%
list()},
moran_i = moran.i(var_value, weights,
zero.policy = TRUE)) %>%
select(k, variable, moran_i) %>%
arrange(desc(moran_i)) %>%
mutate(variable = fct_inorder(variable)) %>%
ggplot(aes(variable, moran_i, color = k, group = k)) +
geom_point() +
geom_line() +
geom_hline(data = moran_i_places,
aes(yintercept = I, color = k),
linetype = 'dashed') +
coord_flip()
#' With places, most groups have low and below-average clustering. Impoverished people, noncitizens, and Hispanics have moderate clustering, and Hispanics and noncitizens are above the overall average. Distance values are generally similar to but a bit lower than the KNN. Contiguity values are generally quite a bit lower.
|
5759b39176326283217f5d5647a1266c8f7d46d6
|
b03601e864642b89c448c529447fff6d0f853ace
|
/R/tsht.R
|
15a51222433b12439adef2555be206bf917396a2
|
[] |
no_license
|
shearer/simboot
|
db813dd25c3b1d1a38b6c3d925a1f516a5134717
|
cb540d814b5356ee6fcc671f27d7979e27f0c23e
|
refs/heads/master
| 2021-01-13T01:53:50.982124
| 2018-08-22T20:04:44
| 2018-08-22T20:04:44
| 11,773,008
| 3
| 1
| null | 2017-03-08T22:16:28
| 2013-07-30T19:01:54
|
R
|
UTF-8
|
R
| false
| false
| 3,980
|
r
|
tsht.R
|
tsht <-
function(X, f, theta, cmat, conf.level, alternative, R, args)
{
bargs <- args
XOBS <- as.data.frame(X)
estindsum <- function(X, f, cmat, theta)
{
estsum <- theta(X = X, f = f)
SE <- sqrt(estsum$varest)
estC <- (cmat %*% estsum$estimate)
varC <- (cmat^2) %*% (estsum$varest)
teststat <- estC/ sqrt(varC)
return(
list(
teststat = teststat,
estC = estC,
varC = varC,
cmat = cmat
)
)
}
EST <- estindsum(X = XOBS, f = f, cmat = cmat, theta = theta)
teststat.org <- EST$estC / sqrt(EST$varC)
OBS <- EST$estC
BTeststat <- function(X, i, f, cmat, obs)
{
XNEW <- as.data.frame(X[i, ])
est <- estindsum(X = XNEW, f = f, cmat = cmat, theta = theta)
Teststat <- (est$estC - obs)/ sqrt(est$varC)
return(Teststat)
}
bargs$data <- as.data.frame(X)
bargs$statistic <- BTeststat
bargs$strata = f
bargs$f <- f
bargs$cmat <- cmat
bargs$obs <- OBS
bargs$R <- R
if(is.null(bargs$R))
{
bargs$R <- 999
}
if(is.null(bargs$sim))
{
bargs$sim <- "ordinary"
}
if(is.null(bargs$stype))
{
bargs$stype <- "i"
}
bootout <- do.call("boot", bargs)
matraw <- matrix( c( teststat.org, bootout$t ), byrow = TRUE, ncol = ncol( bootout$t ), dimnames = NULL)
# teststat<-bootout$t
alpha <- 1 - conf.level
switch(alternative,
"two.sided" =
{
maxabsT <- apply(X = bootout$t, MARGIN = 1, FUN = function(x){
max(abs(x), na.rm = TRUE)
})
count <- sapply( lapply( X = teststat.org, FUN = function( x ){
maxabsT >= abs( x )
}), FUN = sum )
countraw <- apply( apply( X = matraw, MARGIN = 2, FUN = function( x ){
abs( x[2:length( x )] ) >= abs( x[1] )
}), MARGIN = 2, FUN = sum)
pval <- count / R
pvalraw <- countraw / R
quant <- quantile(maxabsT, probs = 1 - alpha, na.rm = TRUE)
LOWER <- EST$estC - quant * sqrt(EST$varC)
UPPER <- EST$estC + quant * sqrt(EST$varC)
},
"less" =
{
maxT <- apply(X = bootout$t, MARGIN = 1, FUN = function(x){
max(x, na.rm = TRUE)
})
count <- sapply( lapply( X = teststat.org, FUN = function( x ){
maxT >= x
}), FUN = sum )
countraw <- apply( apply( X = matraw, MARGIN = 2, FUN = function( x ){
x[2:length( x )] >= x[1]
}), MARGIN = 2, FUN = sum)
pval <- count / R
pvalraw <- countraw / R
quant <- quantile(maxT, probs = 1-alpha, na.rm = TRUE)
LOWER <- NA
UPPER <- EST$estC + quant * sqrt(EST$varC)
},
"greater" =
{
minT <- apply(X = bootout$t, MARGIN = 1, FUN = function(x){
min(x, na.rm = TRUE)
})
count <- sapply( lapply( X = teststat.org, FUN = function( x ){
minT <= x
}), FUN = sum )
countraw <- apply( apply( X = matraw, MARGIN = 2, FUN = function( x ){
x[2:length( x )] <= x[1]
}), MARGIN = 2, FUN = sum)
pval <- count / R
pvalraw <- countraw / R
quant <- quantile(minT, probs = alpha, na.rm = TRUE)
LOWER <- EST$estC + quant * sqrt(EST$varC)
UPPER <- NA
})
conf.int <- cbind(EST$estC, LOWER, UPPER)
colnames(conf.int) <- cbind("estimate", "lower", "upper")
p.value <- matrix(c( pval, pvalraw ), ncol = 2, dimnames = list(dimnames(cmat)[[1]], c("adj. p", "raw p")))
return(list(conf.int = conf.int, p.value = p.value, conf.level = conf.level, alternative = alternative))
}
|
a5be4ebc194588e2e65540c47d69a6f7ec5742bd
|
d17f6fda2c41536d3eb7e60c3429f9630cb7697f
|
/man/getItem.Rd
|
af33065a372f51ff548f1c8e9b87f5006445bccc
|
[] |
no_license
|
klmedeiros-ag/DGEobj
|
049ba274ab76b31eaf2c178467e18b3af39871fc
|
fd68063215058b50ab0c12e7dd018abf15b5aa73
|
refs/heads/develop
| 2023-01-10T07:09:38.089778
| 2020-11-03T03:25:42
| 2020-11-03T03:25:42
| 304,167,313
| 0
| 0
| null | 2020-11-03T03:25:43
| 2020-10-15T00:25:22
|
R
|
UTF-8
|
R
| false
| true
| 519
|
rd
|
getItem.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get.R
\name{getItem}
\alias{getItem}
\title{Function getItem}
\usage{
getItem(dgeObj, itemName)
}
\arguments{
\item{dgeObj}{A class DGEobj created by function initDGEobj()}
\item{itemName}{Name of item to retrieve}
}
\value{
The requested data item
}
\description{
Retrieve an item from a DGEobj by item name.
}
\examples{
\dontrun{
MyCounts <- getItem(DGEobj, "counts")
}
}
\author{
John Thompson
}
\keyword{DGEobj}
\keyword{RNA-Seq,}
|
e28b9eea13fe1b83177e67a515be33fc80488619
|
38d64d099cfef6f39fa08aa6364b0464a988102d
|
/bipartite/man/moduleWeb-class.Rd
|
612c284398fb1eab1ad75593aa59ae064f666670
|
[] |
no_license
|
biometry/bipartite
|
004b458f73c25f64de5bda3c4c9e2c861aec983a
|
2fb52577d297480a3a1c1c707a3549ac97e5d08c
|
refs/heads/master
| 2023-06-23T12:37:01.423686
| 2023-03-01T15:22:14
| 2023-03-01T15:22:14
| 24,846,853
| 37
| 16
| null | 2020-05-27T11:07:11
| 2014-10-06T13:26:44
|
R
|
UTF-8
|
R
| false
| false
| 2,728
|
rd
|
moduleWeb-class.Rd
|
\encoding{UTF-8}
\name{moduleWeb-class}
\docType{class}
\alias{moduleWeb-class}
\title{Class "moduleWeb"}
\description{
This class is the output of an application of the function \code{computeModules} to a graph. It consists of the matrix representing the original graph which has been passed to \code{computeModules} in order to compute modules, a matrix representing the same graph but permutated according to the identified modules, two vectors indicating the permutation of row and column indices, respectively, and information about the modules themselves.
}
\section{Objects from the class}{
Objects from the class should only be created by using the function \code{computeModules}.
}
\section{Slots}{
\describe{
\item{\code{likelihood}:}{Contains a number with the likelihood-equivalent of the final proposed module structure. This value is the same value as Q (or M), the modularity as given by Newman or Guimerà & Amaral (2005). }
\item{\code{originalWeb}:}{Object of class \code{"matrix"} representing the original bipartite graph in which modules have been computed.}
\item{\code{moduleWeb}:}{Object of class \code{"matrix"} representing the original bipartite graph but reordered such that plotting modules is possible.}
\item{\code{orderA}:}{Object of class \code{"vector"} representing the permutation of the rows of the original graph.}
\item{\code{orderB}:}{Object of class \code{"vector"} representing the permutation of the columns of the original graph.}
\item{\code{modules}:}{Object of class \code{"matrix"} containing for each module the information about its depth and involved nodes. The first row is just a consecutive number, so of no information; the first two columns can also be ignored. This matrix shows ALL network players (in the sequence of the original matrix, starting with rows), so first rows, then columns. There are as many rows as modules. Each row writes a number if a species is in that module, or a 0 if it isn't. For the modules of Safariland (\code{mod <- computeModules(Safariland); mod@modules[-1, -c(1,2) ]}), the third module are species 3 and 24, i.e. \emph{Schinus patagonicus} (third row) and Ichneumonidae4 (24 - 9 column).}
}
}
\section{Methods}{
Objects of this class are used in following functions:
listModuleInformation(moduleWebObject)
printoutModuleInformation(moduleWebObject)
plotModuleWeb(moduleWebObject, plotModules=TRUE, rank=FALSE, weighted=TRUE, displayAlabels=TRUE, displayBlabels=TRUE, labsize=1, plotsize=12, xlabel="", ylabel="", square.border="white", fromDepth=0, upToDepth=-1)
}
\author{Rouven Strauss}
\examples{
showClass("moduleWeb")
}
\keyword{classes}
\keyword{modules}
\keyword{moduleWeb}
\keyword{modularity}
|
fd3c7284887b90ae60ac2efb3263e362a052e2c9
|
a88e78c568076609192341a7428d2379668f5a15
|
/Code/Data Collection.R
|
4e051e2a99509a1618d568cf24a6e13ee43fa49c
|
[] |
no_license
|
almutaz12/Honors-calculations
|
c03e933a77312d8e19af59391e61dfc65a0e3179
|
5b598093e6aaf5c4f1100bc0a506f881212e9f98
|
refs/heads/master
| 2021-01-10T23:49:00.340310
| 2017-02-01T18:23:45
| 2017-02-01T18:23:45
| 70,094,145
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,339
|
r
|
Data Collection.R
|
# This file is using the honors_stocks code to download stock data
honors_stocks <- function(symbols='F',
what=c("prices","daily","weekly", "monthly", "dividends"),
start_year=1986,end_year=2016) {
if (! require(RCurl)) stop("Must install RCurl package.")
if (! require(dplyr)) stop("Must install dplyr package.")
what <- match.arg(what)
yahooCode <- switch(what,
prices = ,
daily = "d",
weekly = "w",
monthly = "m",
dividends = "v",
"unknown")
stockURL <- "http://real-chart.finance.yahoo.com/table.csv?s=%s&a=05&b=1&c=%d&d=01&e=25&f=%d&g=%s&ignore=.csv"
output <- NULL # for collecting output
for (symbol in symbols) {
thisURL <- sprintf(stockURL, symbol, start_year, end_year, yahooCode)
con <- try(textConnection(getURLContent( thisURL )), silent = TRUE)
if (inherits(con, what = "try-error")) {
message(paste("Symbol", symbol, "not found in years",
start_year, "to", end_year, "on Yahoo finance."))
} else {
res <- read.csv(con)
res$company <- symbol
close(con)
output <- rbind(output, res)
}
}
output <- output%>%
mutate(date = lubridate::ymd(Date)) %>%
select(-Date)
if (yahooCode == 'v') {
output <-
output %>%
rename(dividends = Dividends)
} else {
output <-
output %>%
rename(open = Open, high=High, low=Low, close=Close,
volume = Volume, adj_close = Adj.Close)
}
output
}
# Listed Companies in three indexes:
# NYSE & NASDAQ source:http://www.nasdaq.com/screening/company-list.aspx
# S&P 500 source:http://data.okfn.org/data/core/s-and-p-500-companies#readme
########################################
#NYSE
#Companies <- read.csv("NYSE_companylist.csv", stringsAsFactors = FALSE, na.strings = "n/a")
#Symbols <- Companies$Symbol[ ! is.na(Companies$Sector)]
########################################
#Data Source: Yahoo Finance
# f1 <- honors_stocks(symbols = Symbols[1:100])
#f2 <- honors_stocks(symbols = Symbols[101:200])
#f3 <- honors_stocks(symbols = Symbols[201:600])
#f4 <- honors_stocks(symbols = Symbols[601:1200])
#f5 <- honors_stocks(symbols = Symbols[1201:1900])
#f6 <- honors_stocks(symbols = Symbols[1901:2235])
#NYSE <- rbind(f1, f2,f3,f4,f5,f6)
#save(NYSE, file = "NYSE_Stock_Data.Rda")
########################################
#NASDAQ
#Companies <- read.csv("NASDAQ_companylist.csv", stringsAsFactors = FALSE, na.strings = "n/a")
#Symbols <- Companies$Symbol[ ! is.na(Companies$Sector)]
########################################
#NASDAQ<- honors_stocks(symbols = Symbols[1:2785])
#save(NASDAQ, file = "NASDAQ_Stock_Data.Rda")
########################################
#S&P 500
#Companies <- read.csv("S&P 500_companylist.csv", stringsAsFactors = FALSE, na.strings = "n/a")
#Symbols <- Companies$Symbol[ ! is.na(Companies$Sector)]
########################################
#SP_500<- honors_stocks(symbols = Symbols[1:504])
#save(SP_500, file = "SP_500_Stock_Data.Rda")
########################################
#S&P 500 historical companies
Symbols=SP_marketCap$company
SP_H_Comp<- honors_stocks(symbols = Symbols)
save(SP_H_Comp, file = "SP_H_Data.Rda")
|
5c4cdef472bf69cded18fc0523f478fbdfd6a2d2
|
f488f38e7d64ca808c18414c4a40c87d4d2e4e3d
|
/wordcloud/src/main_wordcloud.R
|
48a7a9c758b0df4f4c3329b7edc8e3f16a349d9c
|
[] |
no_license
|
maite828/Text_Mining
|
c73dcb78cb422a57feb4317ba623b1c7b849fec7
|
8a1b98722e11d02c107ca84efee55fb15a201cde
|
refs/heads/master
| 2021-06-18T19:17:57.841630
| 2017-06-30T20:54:14
| 2017-06-30T20:54:14
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,702
|
r
|
main_wordcloud.R
|
###############################################################################
## Wordcloud generator from tweets ##
###############################################################################
#
#Copyright Telefonica Digital
#Author: User Modelling Analytics Team
#Mantainer: Susana Ferreras <susanaf@tid.es>
#Version: 1.1
#Date: 28/05/2013
#Purpose of program: generate wordclouds about topics from Twitter data
#Dependencies: XML, tm, wordcloud, RColorBrewer, stringr, R.utils, rjson,
# ggplot2, reshape packages
#Inputs: Text files with tweets in 3rd field (one per row)
#Output: PDF file with individual and common wordclouds
#Config: in wordcloud.json file
#Run: wordcloud.sh or wordcloud.bat to be executed in batch mode
#
#Configuration
args = (commandArgs(TRUE))
if (length(args) == 0) {
print("-----------------------------")
print(" Error: missing arguments ")
print("-----------------------------")
q()
} else {
for (i in 1:length(args)) {
eval(parse(text=args[[i]]))
}
}
if (!file.exists(configfile) == 'TRUE') {
print("-------------------------------------")
print(" Error: missing configuration file ")
print("-------------------------------------")
q()
}
#Import libraries
library(XML)
library(tm)
library(wordcloud)
library(RColorBrewer)
library(stringr)
library(R.utils)
library(rjson)
library(ggplot2)
library(reshape)
options(warn=-1)
#Add wordcloud functions
source("../Wordcloud/src/functions_wordcloud.R")
#Import parameters in JSON (choose either local file or parameter)
#From local file
#config <- fromJSON(paste(readLines("wordcloud.json"), collapse=""))
#From parameter
config <- fromJSON(file=configfile)
tit <- config$input$title
subt <- config$input$subtitle
num.par <- config$input$number
path <- config$input$path
files <- config$input$files
names <- config$input$names
pal.ind <- config$layout$palette_ind
pal.comp <- config$layout$palette_comp
pal.all <- config$layout$palette_all
bg.colour <- config$layout$bg_colour
tx.colour <- config$layout$tx_colour
ti.colour <- config$layout$ti_colour
rp.main <- config$layout$report_title
tx.main <- config$layout$text_title
tx.date <- config$layout$text_date
out.name <- config$output$name
#Read input data
cumm <- data.frame()
tweets <- data.frame()
for (i in 1:num.par) {
wc <- read_input(paste(path, files[i], sep=""), names[i])
tweets[i, 1] <- names[i]
tweets[i, 2] <- nrow(wc)
tweets[i, 3] <- num.par - i
call <- get_words(wc, names[i], filt, ftype, del, lang, stopw)
cumm <- rbind(cumm, call)
}
colnames(tweets) <- c("words", "count", "num")
#Create report
pdf(file=out.name, width=9, height=6, family="Helvetica-Narrow")
par(bg=bg.colour, mar=c(1,1.2,1.2,1.2), col.main=tx.colour, cex.main=1.8)
plot(0:10, type='n', bty='n', xaxt='n', yaxt='n', xlab='', ylab='')
text(6, 7, tit, font=2, col=ti.colour, cex=3)
text(6, 6, subt, font=2, col=ti.colour, cex=3)
text(6, 4, tx.date, font=2, col=ti.colour, cex=2)
#Summary
ggplot(tweets, aes(x=reorder(words, num), y=count, label=count)) +
geom_bar(stat="identity", fill=brewer.pal(num.par, pal.comp)[num.par:1],
colour=tx.colour) +
theme_clean() +
coord_flip() +
ggtitle(rp.main) +
theme(plot.title=
element_text(hjust=0.5, face="bold", colour=tx.colour, size=25),
plot.background=element_rect(fill=bg.colour),
axis.text.x=element_blank(),
axis.text.y=
element_text(face="bold", size=15,
colour=brewer.pal(num.par, pal.comp)[num.par:1]),
panel.grid=element_blank(),
axis.ticks=element_blank()) +
geom_text(size=3.5, hjust=-0.2, colour=tx.colour, fontface=2)
#Individual wordclouds
for(n in 1:num.par) {
wo <- cumm[cumm[, "topic"] == names[n], ]
wordcloud(wo$word,wo$freq, scale=c(5,1), min.freq=1, max.words=100,
random.order=FALSE, random.color=FALSE, rot.per=0,
colors=brewer.pal(7, pal.ind), use.r.layout=FALSE)
title(main=paste(tx.main, names[n], tx.date))
par(fig=c(0,1,0,1), new=TRUE)
add_legend(brewer.pal(7, pal.ind))
if (n == 1) {
leg <- "* Bigger sizes and higher \ncolours in palette
show more importance"
par(fig=c(0,1,0,1), new=TRUE)
plot(0:25, type="n")
text(22, 1, leg, col=tx.colour, cex=0.8)
}
}
#Multi wordclouds
if (num.par > 1){
mat <- get_matrix(cumm)
comparison.cloud(mat,scale=c(5,1),max.words=2000,random.order=FALSE, rot.per=0,
use.r.layout=FALSE, title.size=2,
colors=brewer.pal(ncol(mat), pal.comp))
title(main=paste('Multi wordcloud', tx.date))
leg <- "* If a word is related \nto some topics,\nit will be associated
to the one with \nmore importance (in %)"
par(fig=c(0,1,0,1), new=TRUE)
plot(0:25, type="n")
text(24, 1.2, leg, col=tx.colour, cex=0.8)
all <- list()
for (i in 1:nrow(mat)) {
all[i] <- sum(mat[i, 1:num.par] > 0)
}
if (any(all == num.par)) {
commonality.cloud(mat, comonality.measure=min, scale=c(5,1),
random.order=FALSE, random.color=FALSE, rot.per=0,
colors=brewer.pal(7, pal.all), use.r.layout=FALSE)
title(main=paste('Wordcloud - Words in all topics', tx.date))
par(fig=c(0,1,0,1), new=TRUE)
add_legend(brewer.pal(7, pal.all))
}
commonality.cloud(mat, comonality.measure=max, scale=c(5,1),
random.order=FALSE, random.color=FALSE, rot.per=0,
colors=brewer.pal(7, pal.all), use.r.layout=FALSE)
title(main=paste('Wordcloud - Most important words', tx.date))
par(fig=c(0,1,0,1), new=TRUE)
add_legend(brewer.pal(7, pal.all))
}
dev.off()
rm(list=ls())
|
bc8f54fa04e2c6af2bc906d5a6b9d0f5a41dd92c
|
229ca63ae86118ac32365a5320a008a0c073110e
|
/subfunctie_df_gebied.R
|
c21b18015c242d113110d17b52e21aa616294b67
|
[] |
no_license
|
Jolien-GGD/Uitdraai_Tabellen
|
4a2050ed746372be2d1f5582c75b8bb246152ad4
|
fbe4e282dbe5dde45abcf9283bcd816c10423d01
|
refs/heads/master
| 2021-06-22T16:57:22.182763
| 2021-06-08T17:36:42
| 2021-06-08T17:36:42
| 223,177,918
| 1
| 2
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,781
|
r
|
subfunctie_df_gebied.R
|
subgebied <- function(df_gebied, gebiedskolom, ci_level, n_vraag_afkapwaarde, data, var_df, survey_design){
# Initialiseer regelnummer op 1, voor het wegschrijven van de uitgerekende data
regelnummer <- 1
for (gebied in unique(data[[gebiedskolom]])) {
# survey_design_sub <- subset(survey_design, cbs == gemeente) geeft hele rare output, doordat gemeente als R variabele wordt ingevoerd ipv tekst.
# Subset geeft geen foutmelding, maar wordt gedaan op verkeerde gemeente. Onderstaande werkt wel
tekst <- paste0("subset(survey_design, ", gebiedskolom, " == {gebied})")
survey_design_sub <- eval(parse(text = glue(tekst)))
data_sub <- data[data[[gebiedskolom]] == gebied & !is.na(data[[gebiedskolom]]),] # Ook hier geen subset() gebruiken, geeft verkeerde data terug
for (varcode in var_df$V1){
if (!all(is.na(data[[varcode]]))){
varlabels <- attr(data[[varcode]], "labels") # value labels. # value labels. Gebruik data ipv data_sub, omdat je anders labels mist als een bepaald antwoord niet voorkomt bij de eerste gemeente
# loopen met survey package wil niet op normale manier. Daarom methode met glue en eval om tb en betrouwbaarheidsintervallen te krijgen.
# tb <- svytable(formula = ~data_sub[[varcode]] , design = survey_design_sub) # Hiermee mis ik de antwoordopties die niemand heeft gekozen
string_tb <- paste0("svytable(formula = ~data[['{varcode}']], design = survey_design)") # Deze bevat populatieaantallen. Prima als je tb alleen voor labels gebruikt en niet de gewogen n per gemeente wil weten
expr_tb <- glue(string_tb)
tb <- eval(parse(text = expr_tb))
# tb <- svytable(formula = ~data[[varcode]] , design = survey_design)
for (j in 1:length(tb)){ # Voor het aantal niet-missing antwoordopties uit de vraag
val <- names(tb)[j] # val is de numerieke code van de huidige antwoordoptie
## bereken betrouwbaarheidsintervallen
string <- paste0("svyciprop(~I({varcode}=={val}), survey_design_sub, method='xlogit', na.rm=TRUE, level = ", ci_level, ")")
expr <- glue(string)
ci <- eval(parse(text = expr)) # confidence intervals
# Schrijf info weg naar dataframe
temp_varcode <- varcode # variabelenaam
temp_waarde <- names(tb)[j] # numerieke waarde van huidige antwoordoptie
temp_label <- names(varlabels)[varlabels == as.numeric(names(tb)[j])] # tekstlabel van huidige antwoordoptie
# Als variable label ontbreekt, zet dan naar lege string
if (length(temp_label) == 0) {
temp_label = ""
}
# temp_n <- round(tb_regio[[j]]) # Populatie n / gewogen n <= is populatie van hele regio, niet van subset.
temp_percentage <- ci[[1]] * 100 # Estimate/percentage
temp_CIlower <- attr(ci, "ci")[1] * 100 # CI lower
temp_CIupper <- attr(ci, "ci")[2] * 100 # CI upper
temp_n_unweighted <- sum(survey_design_sub[["variables"]][varcode] == as.integer(names(tb)[j]), na.rm = TRUE) # sample n / ongewogen n
temp_gebied <- gebied
# Schrijf data weg naar dataframe
df_gebied[regelnummer,] <- c(temp_varcode, temp_waarde, temp_label, temp_percentage, temp_CIlower, temp_CIupper, temp_n_unweighted, temp_gebied)
# Print huidige regelnummer om idee te krijgen hoe lang script nog zal runnen
print(paste0(regelnummer, " van ", aantal_verwachte_rijen))
# Hoog regelnummer met 1 op om de volgende regel in de dataframe te vullen met de volgende indicator/antwoordoptie
regelnummer <- regelnummer + 1
}
}
}
}
# Zet naar numeric
df_gebied$waarde <- as.numeric(df_gebied$waarde)
df_gebied$percentage <- as.numeric(df_gebied$percentage)
df_gebied$CIlower <- as.numeric(df_gebied$CIlower)
df_gebied$CIupper <- as.numeric(df_gebied$CIupper)
df_gebied$n_unweighted <- as.numeric(df_gebied$n_unweighted)
# Tel aantal geldige antwoorden per vraag op.
df_gebied <- df_gebied %>%
group_by(varcode, gebied) %>%
mutate(n_vraag = sum(n_unweighted)) %>%
ungroup()
df_gebied$percentage[df_gebied$n_vraag < n_vraag_afkapwaarde] <- NA
df_gebied$CIlower[df_gebied$n_vraag < n_vraag_afkapwaarde] <- NA
df_gebied$CIupper[df_gebied$n_vraag < n_vraag_afkapwaarde] <- NA
# Maak combi van variable en value aan
df_gebied$varval <- paste0(df_gebied$varcode, df_gebied$waarde)
# Maak koppelkolom aan
df_gebied$gebied_varval <- paste0(df_gebied$gebied, df_gebied$varval)
beep(sound = 3)
return(df_gebied)
}
|
681595bde7de42e06fbb74a5630d4241c1ab1523
|
9acd1e1d8d00bfb9c0e256bd5b5f6b690dea0d07
|
/tests/testthat.R
|
d8adbe45436d9a4e5b9577ebb1f02e0de48b50f9
|
[
"MIT"
] |
permissive
|
Reckziegel/DynamicStrategies
|
712bcdac026d879466feebfaa3775089387823f7
|
a088f9ec4e3845af9e6bc39cd25aa01f45c202d5
|
refs/heads/main
| 2023-08-22T02:37:26.071589
| 2021-09-21T18:33:20
| 2021-09-21T18:33:20
| 399,301,405
| 1
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 78
|
r
|
testthat.R
|
library(testthat)
library(DynamicStrategies)
test_check("DynamicStrategies")
|
7be0143de9c7219a0b1c7ea76421b3ee5af36fc6
|
3bef70f4b3d6283f2b2bfb44ccdfbf9b28c6429d
|
/inst/extdata/scripts/plot_data.R
|
fbb626cbf0ad9c336350e8a20274756385dad89b
|
[
"MIT"
] |
permissive
|
KWB-R/dwc.wells
|
4c1594ea66b1792c6c955b98418982edf80675c1
|
45e8670647c4771fe70d59db0f7cfd1e80242361
|
refs/heads/main
| 2023-04-10T01:24:40.973815
| 2022-07-12T13:42:20
| 2022-07-12T13:42:20
| 351,021,733
| 0
| 0
|
MIT
| 2022-10-16T09:17:19
| 2021-03-24T09:35:15
|
R
|
WINDOWS-1252
|
R
| false
| false
| 16,051
|
r
|
plot_data.R
|
# load package, paths and variable sets from global.R --------------------------
source("inst/extdata/scripts/global.R")
# MAIN 0: histogram of static water level measurements and data origin ---------
if (FALSE) {
# plot histogram
plot_histogram <- function(df) {
ggplot2::ggplot(df, ggplot2::aes(
x = W_static, fill = forcats::fct_rev(W_static.origin), alpha = full_data_set)) +
ggplot2::geom_histogram(position = "stack", boundary = 0) +
ggplot2::scale_fill_manual(values = RColorBrewer::brewer.pal(3, "Set2")) +
ggplot2::scale_alpha_manual(values = c(0.4, 1.0)) +
sema.berlin.utils::my_theme() +
ggplot2::labs(fill = "Type", alpha = "Data complete?", x = "static water level")
}
plot_histogram(df_Q_W_new)
ggplot2::ggsave("W_static_interpolation_m20k1_f15k8_i64k2.png", dpi = 600, width = 5, height = 3)
# histogram of Qs_rel vs. n_rehab ----------------------------------------------
ggplot(df, aes(x = Qs_rel, fill = factor(n_rehab))) +
geom_histogram(position = "fill", boundary = 0, binwidth = 0.1) +
#scale_x_continuous(limit = c(-0.5, 3)) +
scale_x_continuous(limit = c(0, 1), labels = scales::percent, breaks = scales::pretty_breaks()) +
scale_y_continuous(labels = scales::percent, breaks = scales::pretty_breaks()) +
#ggplot2::scale_fill_manual(values = RColorBrewer::brewer.pal(8, "Set2")) +
sema.berlin.utils::my_theme() +
labs(fill = "n_rehab:")
ggsave("plot_Qs_rel_vs_n_rehabs.png", dpi = 600, width = 5, height = 3)
}
# MAIN 1: plots of data distribution -------------------------------------------
if (FALSE) {
# Version 1: plot distribution of well characteristics ---
if (TRUE) {
load(file.path(paths$data_prep_out, "well_feature_data.RData"), verbose = TRUE)
df_well_features <- Data %>%
dplyr::filter(well_function == "Betriebsbrunnen") %>%
dplyr::select(-well_function)
# or select wells represented in model data
if (FALSE) {
load(file.path(paths$data_prep_out, "well_feature_data.RData"), verbose = TRUE)
df_well_features <- Data
load(file.path(paths$data_prep_out, "model_data.RData"), verbose = TRUE)
df <- Data
df_well_features <- df_well_features %>%
dplyr::filter(well_id %in% df$well_id)
}
nums <- unlist(lapply(df_well_features, is.numeric))
df_well_features_num <- df_well_features[, nums] %>%
dplyr::select(- c(well_id, well_id_replaced, operational_start.year))
df_well_features_cat <- df_well_features[, !nums] %>%
dplyr::select(-c("well_name", tidyr::ends_with("date")))
well_features_num <- model_features_with_plot_names[names(df_well_features_num)]
well_features_cat <- model_features_with_plot_names[names(df_well_features_cat)]
plots_cat <- lapply(names(well_features_cat), function(x) {
plot_frequencies(df_well_features, x, well_features_cat[x], 0.1)
})
names(plots_cat) <- names(well_features_cat)
# prepare plots for numerical variables
if (FALSE) {
# simple version with only title
plots_num <- lapply(names(well_features_num), function(x) {
plot_distribution(df_well_features, x, title = well_features_num[x],
vertical_x_axis_labels = FALSE)
})
}
# plot with xaxis label
plots_num <- lapply(names(well_features_num), function(x) {
split_label <- unlist(stringr::str_split(well_features_num[x], "\\["))
title_label <- split_label[1]
xaxis_label <- ifelse(is.na(split_label[2]), "", paste0("[", split_label[2]))
plot_distribution(df_well_features, x, title = title_label,
vertical_x_axis_labels = FALSE) +
labs(x = xaxis_label)
})
names(plots_num) <- names(well_features_num)
# combine plots in desired order
plot_list_tmp <- c(plots_num, plots_cat)
plot_list <- vector("list", length = length(plot_list_tmp))
names(plot_list) <- well_features[well_features %in% names(plot_list_tmp)]
for (var in names(plot_list)) { plot_list[[var]] <- plot_list_tmp[[var]] }
# cowplot
plots <- cowplot::plot_grid(plotlist = plot_list,
nrow = 6, ncol = 7, align = "hv", axis = "tblr",
scale = 0.9)
# save overview plot
ggplot2::ggsave("well_characteristics_distribution_Betriebsbrunnen_with_xlabels.png",
plot = plots, width = 28,
height = 25, dpi = 600)
}
# Version 2: plot distribution of model features, including Qs_rel ---
if (TRUE) {
# load data
#load(file.path(paths$data_prep_out, "model_data.RData"), verbose = TRUE)
df <- model_data
# select variables
df_well_features <- df %>% select(Qs_rel, all_of(model_features))
nums <- unlist(lapply(df_well_features, is.numeric))
df_well_features_num <- df_well_features[, nums]
df_well_features_cat <- df_well_features[, !nums]
# prepare named list of model features, add target variable Qs_rel
well_features_num <- model_features_with_plot_names[names(df_well_features_num)]
well_features_num[[1]] <- "Specific capacity [%]"
names(well_features_num)[[1]] <- "Qs_rel"
well_features_cat <- model_features_with_plot_names[names(df_well_features_cat)]
# prepare plots for categorical variables
plots_cat <- lapply(names(well_features_cat), function(x) {
plot_frequencies(df_well_features, x, well_features_cat[x])
})
names(plots_cat) <- names(well_features_cat)
# prepare plots for numerical variables
if (FALSE) {
# simple version with only title
plots_num <- lapply(names(well_features_num), function(x) {
plot_distribution(df_well_features, x, title = well_features_num[x],
vertical_x_axis_labels = FALSE)
})
}
# plot with xaxis label
plots_num <- lapply(names(well_features_num), function(x) {
split_label <- unlist(stringr::str_split(well_features_num[x], "\\["))
title_label <- split_label[1]
xaxis_label <- ifelse(is.na(split_label[2]), "", paste0("[", split_label[2]))
plot_distribution(df_well_features, x, title = title_label,
vertical_x_axis_labels = FALSE) +
labs(x = xaxis_label)
})
names(plots_num) <- names(well_features_num)
# combine plots in desired order
plot_list_tmp <- c(plots_num, plots_cat)
plot_list <- vector("list", length = length(plot_list_tmp))
names(plot_list) <- c("Qs_rel", model_features)
for (var in names(plot_list)) { plot_list[[var]] <- plot_list_tmp[[var]] }
# adapt colour of Qs_rel plot
plot_list[[1]] <- plot_list[[1]] +
ggplot2::geom_histogram(fill = "orange2", boundary = 0, binwidth = NULL)
# cowplot
plots <- cowplot::plot_grid(plotlist = plot_list, align = "hv",
axis = "tblr",
nrow = 6, ncol = 7, scale = 0.9)
# save overview plot
ggplot2::ggsave("model_feature_distribution.png",
plot = plots, width = 28,
height = 25, dpi = 600)
}
}
# MAIN 2: correlation of Qs vs. other variables plots --------------------------
if (FALSE) {
df <- model_data
correlation_plots <- lapply(model_features, function(x) {
split_label <- unlist(stringr::str_split(model_features_with_plot_names[x], "\\["))
title_label <- split_label[1]
xaxis_label <- ifelse(is.na(split_label[2]), "", paste0("[", split_label[2]))
correlation_plot(df = df, x = x, title = title_label) +
labs(x = xaxis_label)
#correlation_plot(df = df, x = x, title = model_features_with_plot_names[x])
})
multiplots <- cowplot::plot_grid(plotlist = correlation_plots,
nrow = 6, ncol = 6, axis = "tblr",
align = "hv", scale = 0.9)
ggplot2::ggsave("correlation_plots_with_xlabels.png", multiplots, dpi = 600, width = 25, height = 25)
# save individual plots
lapply(correlation_plots, function(x) {
ggplot2::ggsave(filename = paste0(gsub("\\.", "_", names(x$labels$title)), ".png"),
plot = x,
dpi = 600,
width = 6,
height = 4)
})
}
# MAIN 3: Plots zu Qs per well over time ---------------------------------------
if (FALSE) {
# filter ---
df2 <- df %>% dplyr::filter(key2 == "pump tests") %>%
dplyr::mutate(key2 = forcats::fct_drop(key2))
length(unique(df2$site_id))
df$key2 <- "pump tests"
df$facet_lab <- paste0("well id: ", df$well_id, " (year: ", df$construction_year,
ifelse(!is.na(df$well_id_replaced),
paste0(", old well id: ", df$well_id_replaced),
""),
")")
pdf("Qsrel_over_time_pump_tests.pdf", 16, 9)
pdf("Qsrel_over_time_all.pdf", 16, 9)
pdf("Qsrel_over_time_with_old_well_info.pdf", 16, 9)
for (i in seq(1, length(unique(df$well_id)), 12)) {
print(plot_Qs_over_time(df[df$well_id %in% unique(df$well_id)[i:(i + 11)], ]) +
facet_wrap(~ facet_lab, scales = "free", ncol = 4)
#facet_wrap(~ well_id, scales = "free", labeller = label_both, ncol = 4)
)
print(paste("pdf page", (i+11) / 12, "printed."))
}
dev.off()
# plots for selected well ids
plot_Qs_over_time(df[df$well_id %in% c(1081, 3258, 1084, 3259), ], xmax = 15) +
facet_wrap(~ facet_lab, scales = "free_x", ncol = 2, dir = "v") +
ggplot2::theme(strip.text.x = ggplot2::element_text(size = 9))
ggsave("example_replaced_wells.png", dpi = 600, width = 8, height = 6)
old_well_ids <- unique(df$well_id_replaced)
old_well_ids[old_well_ids %in% df$well_id]
a <- df[, c("well_id", "construction_year", "well_id_replaced", "Qs_rel")]
# plot two wells in comparison ---
library(dplyr)
library(dwc.wells)
site_ids <- c(4060070, 11020030)
well_ids <- c(1161, 5837)
df2 <- df %>%
dplyr::filter(site_id %in% site_ids) %>%
droplevels()
df$n_rehab <- as.factor(df$n_rehab)
plot_Qs_over_time(df2, xmax = 40, legend_position = "right") +
facet_wrap(~well_id, scales = "free", labeller = label_both, nrow = 1)
ggsave("Qs_over_time_two_example_wells_v2.png", dpi = 600, width = 8, height = 3)
}
# MAIN 4: plot Qs-data for all wells as heatmap --------------------------------
if (FALSE) {
# parameters
group_var <- "waterworks"
n_wells_per_page <- 20
date_limits <- c("1960-01-01", "2021-12-31")
file_name <- "Qsrel_over_time_heatmap_per_waterworks.pdf"
# select data
df <- model_data %>% select(well_id, well_name, date, Qs_rel, waterworks, well_gallery)
# interpolate data
df_interpol <- interpolate_Qs(df, 1)
# group wells
well_ids_per_group <- df %>% group_by(group = .data[[group_var]]) %>%
summarise(well_id = as.character(unique(well_id)))
colours <- sema.berlin.utils::get_bwb_colours()[c(2,3,5)]
dummy_labels <- unlist(lapply((1:n_wells_per_page) - 1, strrep, x = " "))
pdf(file_name, width = 9, height = 5)
# loop 1: go through well groups
for (well_group in unique(well_ids_per_group$group)) {
well_ids <- well_ids_per_group %>% filter(group == well_group) %>% pull(well_id)
# loop 2: for each well group, go trough wells
for (i in seq(1, length(well_ids), n_wells_per_page)) {
well_ids_to_plot <- well_ids[i:(i + n_wells_per_page - 1)]
plot_data <- df_interpol %>% filter(well_id %in% well_ids_to_plot)
print(Qs_heatmap_plot(plot_data, colours, dummy_labels, date_limits,
title = well_group, n_wells_per_page))
print(sprintf("Data for %d well(s) of %s '%s' plotted.",
i + length(well_ids_to_plot) - 1,
group_var, well_group))
}
}
dev.off()
ggsave("example_plot_Qs_over_time_heatmap.png", width = 10, height = 5, dpi = 600)
ggsave("example_plot_Qs_over_time_heatmap_v2.png", width = 8, height = 5, dpi = 600)
}
# MAIN 5: plots for Qmom-Qzul relation -----------------------------------------
if (FALSE) {
# required data set: df_Q_monitoring
# distribution
p1 <- ggplot2::ggplot(df_Q_monitoring, ggplot2::aes(x = ratio_Q_admissible_discharge,
y = stat(count) / sum(stat(count)))) +
ggplot2::geom_histogram(binwidth = 0.1, fill = "grey", col = "white", boundary = 1) +
ggplot2::scale_x_continuous(limits = c(0, 2)) +
ggplot2::scale_y_continuous(name = "Percentage",
breaks = scales::pretty_breaks(),
labels = scales::percent_format(accuracy = 1)) +
sema.berlin.utils::my_theme()
plotly::ggplotly(p1)
# cumulative distribution
l <- lapply(seq(0, 2, 0.1), function(x) table(df_Q_monitoring$ratio_Q_admissible_discharge > x))
names(l) <- sprintf("%3.1f", seq(0, 2, 0.1))
df <- data.frame(do.call("rbind", l))
colnames(df) <- c("valid", "invalid")
df$threshold <- rownames(df)
df <- tidyr::pivot_longer(data = df, cols = c("valid", "invalid"))
#df$name <- factor(df$name, levels = c("valid", "invalid"))
df$name <- factor(df$name, levels = c("invalid", "valid"))
p2 <- ggplot2::ggplot(df, ggplot2::aes(x = threshold, y = value, fill = name)) +
ggplot2::geom_bar(stat = "identity", position = "fill") +
sema.berlin.utils::my_theme(legend.position = "top",
axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5, hjust = 1)) +
ggplot2::scale_fill_manual(values = c("coral", "darkseagreen3")) +
ggplot2::scale_x_discrete(expand = c(0.05, 0.05)) +
ggplot2::scale_y_continuous(labels = scales::percent_format(),
breaks = scales::pretty_breaks()) +
ggplot2::labs(x = 'Threshold "Qmom/Qzul"', y = "Percentage", fill = "")
p2
ggplot2::ggsave("Qmom_Qzul_threshold.png", p2, dpi = 600, height = 4, width = 6)
p2
cowplot::plot_grid(p1, p2)
# plot median Q_mom per well ---------------------------------------------------
# aggregate data
df_Q_agg <- dplyr::filter(df_Q_monitoring, Q < 1000) %>%
dplyr::group_by(well_id) %>%
dplyr::summarise(Q_median = median(Q, na.rm = TRUE),
Q_stddev = sd(Q, na.rm = TRUE),
number = dplyr::n()) %>%
tidyr::drop_na()
# plot Q measurements
ggplot2::ggplot(df_Q_agg, ggplot2::aes(x = Q_median)) +
ggplot2::geom_histogram(fill = "lightblue", binwidth = 5) +
sema.berlin.utils::my_theme() +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks()) +
ggplot2::scale_x_continuous(breaks = scales::pretty_breaks()) +
ggplot2::labs(x = "Q_obs_median [m³/h]", y = "Frequency [-]")
summary(df_Q_agg)
ggplot2::ggsave("histogram_Ergiebigkeit_Q_obs.png", width = 4, height = 2.5, dpi = 600)
# plots of quality measurements ------------------------------------------------
# requires df_quality_agg_long
ggplot2::ggplot(df_quality_agg_long, ggplot2::aes(x = "", y = Wert)) +
ggplot2::geom_boxplot(width = 0.3) +
ggplot2::facet_wrap(~paste0(Parameter, "\n", "[", Einheit, "]"),
scales = "free_y", nrow = 1) +
ggplot2::labs(x = "", y = "Werte") +
sema.berlin.utils::my_theme() +
ggplot2::theme(strip.text.x = ggplot2::element_text(size = 11, hjust = 0.5),
axis.ticks.x = ggplot2::element_blank())
ggplot2::ggplot(df_quality, ggplot2::aes(x = "", y = Wert)) +
ggplot2::geom_boxplot(width = 0.3) +
ggplot2::facet_wrap(id_Brunnen~paste0(Parameter, "\n", "[", Einheit, "]"),
scales = "free_y", nrow = 1) +
ggplot2::labs(x = "", y = "Werte") +
sema.berlin.utils::my_theme() +
ggplot2::theme(strip.text.x = ggplot2::element_text(size = 11, hjust = 0.5),
axis.ticks.x = ggplot2::element_blank())
ggplot2::ggsave("plot_quality_all_wells.png", width = 15, height = 5000, dpi = 600)
getwd()
}
|
92fc82329d1e53ae4fa2c60c6c5acba5d5c7630c
|
9b34b2250d39c1b05a9d44392d7fed4711d26d30
|
/man/pairs_lower.Rd
|
c858bbbb36251d30af93044face43f000b5fb831
|
[] |
no_license
|
lbraglia/lbstat
|
11bbd806dfb74e46ce332cac23c33da726541205
|
f8dc128b507bc1b1cb2741af49c171971abe658c
|
refs/heads/master
| 2023-05-11T00:24:32.746694
| 2023-04-28T12:18:40
| 2023-04-28T12:18:40
| 51,751,382
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 232
|
rd
|
pairs_lower.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pairs2.R
\name{pairs_lower}
\alias{pairs_lower}
\title{lower panel for stats::pairs}
\usage{
pairs_lower(x, y)
}
\description{
correlation coefficients
}
|
a1fc4a90a41f52d063fb21b109f628bcda27dca1
|
96cf6b7c28944616697b5efb2a0cf06ec00dcc3c
|
/cerealsClustering.R
|
7059d2e6d3cd3235660e4421872f00a102b14559
|
[] |
no_license
|
alondraSanchezM/clustering
|
82301cff806548e6ca35a7722a9838286bc4b06a
|
3f257249cb04083fd29816af8b97d9821332f1ea
|
refs/heads/main
| 2023-03-18T17:43:28.222916
| 2021-03-04T19:28:44
| 2021-03-04T19:28:44
| 344,296,061
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,194
|
r
|
cerealsClustering.R
|
#install.packages("clustertend")
library(cluster)
library(ggplot2)
library(factoextra)
#A. Calificación de clientes de cereales para el desayuno
dataCereales<-read.csv("RFiles/Cereals.csv",header = TRUE, sep = ",")
#---Exploración inicial de los datos
str(dataCereales)
View(dataCereales)
summary(dataCereales)
#----Preprocesamiento de los datos
sum(is.na(dataCereales))
dataCereales <- dataCereales[complete.cases(dataCereales),]
sum(is.na(dataCereales))
rownames(dataCereales) <- dataCereales$name #Conversion de nombres de cereales a las
dataCereales <- dataCereales[, -c(colnames(dataCereales) %in% ("name"))] #filas para mejor visualización de clusters.
View(dataCereales)
dataCereales <- dataCereales[, -c(1:2)]
View(dataCereales)
dataCereales <- scale(dataCereales)
#---Evaluación de tendencia
library(clustertend)
set.seed(124)
hopkins(dataCereales,n=nrow(dataCereales)-1) #si es cercano a 0 el dataset es agrupable
#---Calcular estabilidad de los datos
muestra<- dataCereales[sample(nrow(dataCereales), nrow(dataCereales)*0.95), ]
muestra
hopkins(muestra,n=nrow(muestra)-1)
#---Calculo distancia Euclidiana
dist <- dist(dataCereales, method = "euclidean")
#---Agrupamiento jerarquico
hc_simple <- hclust(dist, method = "single") #enlace unico 5
hc_simple
plot(hc_simple)
fviz_dend(hc_simple, cex =0.5)
hc_complete <- hclust(dist, method = "complete") #enlace completo 6
hc_complete
plot(hc_complete)
fviz_dend(hc_complete, cex =0.5)
#---Comparamos los dendogramas de ambos metodos
#install.packages("dendextend")
library(dendextend)
dend1<-as.dendrogram(hc_simple)
dend2<-as.dendrogram(hc_complete)
dend_list<-dendlist(dend1,dend2) #Se crea la lista de dendogramas.
#tanglegram(dend1,dend2)
tanglegram(dend1,dend2,
highlight_distinct_edges=FALSE,
common_subtrees_color_lines=FALSE,
common_subtrees_color_branches=TRUE,
main=paste("entanglement=",
round(entanglement(dend_list),2))
)
#---Determinamos si los dendogramas son similares o no
cor.dendlist(dend_list,method="cophenetic")
#Si su relación es más cercana a 0 que a 1 significa que no son estadisticamente similares.
#En este caso son medianamente similares
#---Comparamos los dos metodos
dend_list<-dendlist("Single"=dend1,"Complete"=dend2)
cors<-cor.dendlist(dend_list)
round(cors,2)
#install.packages("corrplot")
library(corrplot)
corrplot(cors,"pie","lower")
#---Calcular las distancias cofenéticas para checar que
#arbol es mejor >.75 son buenos
coph_simple <- cophenetic(hc_simple)
cor(dist,coph_simple) #calcular corelación de distancias
coph_complete <- cophenetic(hc_complete)
cor(dist,coph_complete)
hc_average <- hclust(dist, method = "average") #cluster con enlcae average es el mejor en este caso
coph_ave <- cophenetic(hc_average)
cor(dist,coph_ave)
#----Calcular k
#install.packages("NbClust")
library("NbClust")
fviz_nbclust(dataCereales,hcut,method = "silhouette")
#NbClust(dataCereales,distance="euclidean",min.nc=2,max.nc=10, method="ward.D")
#--Cortar el arbor observar centroides
grp_simple <- cutree(hc_simple, k = 10)
table(grp_simple)
rownames(dataCereales)[grp_simple=1] #Obtener los nombres para los miembros del cluster 1
fviz_dend(hc_simple, k=10, cex = 0.5,
k_colors = c("#D81159", "#8F2D56","#218380","#FBB13C", "#73D2DE","#5F1F30", "#9C0D38","#BE7C4D","#82A7A6", "#F06543"),
color_labels_by_k = TRUE, rect = TRUE)
grp_complete <- cutree(hc_average, k = 10)
table(grp_complete)
rownames(dataCereales)[grp_complete=1]
fviz_dend(hc_average, k=10, cex = 0.5,
k_colors = c("#D81159", "#8F2D56","#218380","#FBB13C", "#73D2DE","#5F1F30", "#9C0D38","#BE7C4D","#82A7A6", "#F06543"),
color_labels_by_k = TRUE, rect = TRUE)
#---observar centroides
cluster_Simple<-as.matrix(grp_simple)
aggregate(dataCereales,by=list(cluster_Simple),median)
dataCereales_simple <- cbind(dataCereales, cluster = cluster_Simple)
head(dataCereales_simple)
cluster_Complete<-as.matrix(grp_complete)
aggregate(dataCereales,by=list(cluster_Complete),median)
dataCereales_complete <- cbind(dataCereales, cluster = cluster_Complete)
head(dataCereales_complete)
fviz_cluster(list(data = dataCereales, cluster = grp_simple))
fviz_cluster(list(data = dataCereales, cluster = grp_complete))
#---Visualuzar estadisticas por grupo
View(dataCereales_simple)
dataCereales_simple <- as.data.frame(dataCereales_simple)
summary(dataCereales_simple[dataCereales_simple$V14==1,])
summary(dataCereales_simple[dataCereales_simple$V14==2,])
summary(dataCereales_simple[dataCereales_simple$V14==3,])
summary(dataCereales_simple[dataCereales_simple$V14==4,])
summary(dataCereales_simple[dataCereales_simple$V14==5,])
summary(dataCereales_simple[dataCereales_simple$V14==6,])
summary(dataCereales_simple[dataCereales_simple$V14==7,])
summary(dataCereales_simple[dataCereales_simple$V14==8,])
summary(dataCereales_simple[dataCereales_simple$V14==9,])
summary(dataCereales_simple[dataCereales_simple$V14==10,])
#---Visualizar conjunto
dataCereales_simple[dataCereales_simple$V14==1,]
dataCereales_simple[dataCereales_simple$V14==2,]
|
676d4493bd18ac9a81e0453e187a151c56f1828b
|
51c04b7e4481afa63a44bcb07835a18adffed8e2
|
/merge-fasta.R
|
dbe62a3490ac2d5950a8cb20dab844095aa940bb
|
[] |
no_license
|
DanielleQuinn/skate-code
|
18e1736390e2eeff6ff958d77beb20fdad176231
|
32b8d4b93f0fac1c512c554c327ed0053a06a888
|
refs/heads/master
| 2021-04-09T17:36:15.363109
| 2018-03-19T17:05:22
| 2018-03-19T17:05:22
| 125,892,201
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 841
|
r
|
merge-fasta.R
|
# Load Packages
library(tidyr)
library(seqinr)
library(dplyr)
# Read in Working Data
data<-read.delim("data-working.txt")
data$finclip<-as.character(data$finclip)
data$species_confirmed<-as.character(data$species_confirmed)
# Read in Fasta File
ffile<-data.frame(id=names(read.fasta("data-genetics.fasta")))
fdata<-separate(ffile, id, sep="_",into=c("tissue_id", "plate","sample","genus","species"))
fdata<-fdata%>%filter(!sample=="control" & !genus=="Unclear")
# Populate species_confirmed with species names
data$species_confirmed[data$finclip %in% unique(fdata$tissue_id[fdata$species=="ocellata"])]<-"winter skate"
data$species_confirmed[data$finclip %in% unique(fdata$tissue_id[fdata$species=="erinacea"])]<-"little skate"
data$species_confirmed[data$finclip %in% unique(fdata$tissue_id[fdata$species=="radiata"])]<-"thorny skate"
|
0b11c57748d271e92d2116c0bf38845f9f16364e
|
b38e79b60909104a069f3f9a8a0fab64adf07665
|
/scripts/process-cxr
|
0c3ea03ccb5a6301dbb17e9e56bff12f94bcdcd2
|
[] |
no_license
|
pyrrhicPachyderm/interaction-partitioning
|
ec8b157195f5fa853092c97b4390504425355699
|
8d08ccd69159b5b2970ddbf48d5f85df4ee10dbb
|
refs/heads/master
| 2023-04-18T19:29:47.347692
| 2023-02-25T08:32:20
| 2023-02-25T08:32:20
| 398,422,947
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,920
|
process-cxr
|
#!/usr/bin/env Rscript
suppressPackageStartupMessages(library(optparse))
suppressPackageStartupMessages(library(magrittr))
##################
#Argument parsing.
##################
usage = "%prog SPECIES_DATA_INPUT_FILE FOCAL_OUTPUT_FILE DATA_TYPE RESPONSE_OUTPUT_FILE DESIGN_OUTPUT_FILE SPECIES_DATA_OUTPUT_FILE MIN_OBS_OUTPUT_FILE"
description = "Takes the neigh_list data from the cxr package, and reshapes it into the three files desired by the C++ code.
Namely, the 0-indexed numerical focal species vector, the response variable vector, and the species density design matrix.
All are output as tab-separated matrices, without column headers, with vectors in the form column vectors.
DATA_TYPE must be 'indv', for individual response data."
option_list <- list(
make_option(
c("-m", "--minobs"), dest = "bare_min_obs", type = "integer", default = 2,
help = "The minimum number of replicate observations required for each species pair"
)
)
parser <- OptionParser(usage = usage, description=description, option_list=option_list)
arguments <- parse_args(parser, positional_arguments = 6)
data_type <- arguments$args[1]
focal_outfile <- arguments$args[2]
response_outfile <- arguments$args[3]
design_outfile <- arguments$args[4]
species_data_outfile <- arguments$args[5]
min_obs_outfile <- arguments$args[6]
attach(arguments$options)
if(data_type != "indv") {
stop("Invalid data type requested.")
}
other_name <- "Other"
other_code <- "OTHR"
###############
#Load the data.
###############
data(neigh_list, package = "cxr")
data(species_rates, package = "cxr") #With names of each species.
##############################
#Filter to the maximal clique.
##############################
species <- names(neigh_list)
#A function to get a maximum-size clique such that all pairs have at least min_obs observations.
get_max_clique <- function(min_obs) {
adj_mat <- sapply(species, function(i) {
sapply(species, function(j) {
sum(neigh_list[[i]][[j]] > 0) >= min_obs && sum(neigh_list[[j]][[i]] > 0) >= min_obs
})
})
dist_mat <- as.dist(1 - adj_mat)
cliques <- optpart::clique(dist_mat, 0)$member
clique_sizes <- sapply(cliques, length)
return(cliques[[which.max(clique_sizes)]])
}
#We want to find the highest value of min_obs such that the largest clique
#still has the same size as at the bare minimum value of min_obs we will accept.
max_clique_size <- length(get_max_clique(bare_min_obs))
min_obs <- bare_min_obs
while(length(get_max_clique(min_obs + 1)) == max_clique_size) {
min_obs <- min_obs + 1
}
included_species <- species[get_max_clique(min_obs)]
excluded_species <- species[!species %in% included_species]
#Append all data in one data frame.
#Only including cases where the included species are the focals.
#Adding a "focal" column.
#And adding one to the focal density, to include the individual itself.
data <- data.frame()
for(sp in included_species) {
neigh_list[[sp]]$focal <- sp
neigh_list[[sp]][[sp]] <- neigh_list[[sp]][[sp]] + 1 #TODO: Check this.
data <- rbind(data, neigh_list[[sp]])
}
#Collapse all excluded species into an "other" column.
data[[other_code]] <- rowSums(data[,excluded_species])
data <- data[,!names(data) %in% excluded_species]
species_codes <- c(included_species, other_code)
#################################
#Extract the relevant components.
#################################
focal_output <- data[["focal"]] %>%
match(species_codes)
focal_output <- focal_output - 1 #Make it 0-indexed.
response_output <- data[["fitness"]]
design_output <- data[,species_codes]
##############################################
#Turn the outputs into matrices, and finalise.
##############################################
focal_output %<>% matrix(ncol=1)
response_output %<>% matrix(ncol=1)
design_output %<>% as.matrix()
################################
#Create the species data output.
################################
species_data <- data.frame(
name = species_rates$species,
code = species_rates$code,
germination_rate = species_rates$germination.rate,
seed_survival = species_rates$seed.survival
)
species_data$name <- sub("([[:alpha:]])([[:alpha:]]+)_([[:alpha:]]*)", "\\U\\1\\E\\2 \\3", species_data$name, perl = TRUE) #Format binomial names properly.
species_data <- species_data[species_data$code %in% included_species,]
species_data <- rbind(species_data, list(
name = other_name,
code = other_code,
germination_rate = NA,
seed_survival = NA
))
###################
#Print the outputs.
###################
print_table <- function(table, outfile) {
write.table(table, outfile, row.names=FALSE, col.names=FALSE, quote=FALSE, sep="\t")
}
print_table(focal_output, focal_outfile)
print_table(response_output, response_outfile)
print_table(design_output, design_outfile)
write.csv(species_data, species_data_outfile, row.names = FALSE)
print_table(data.frame(min_obs), min_obs_outfile) #Outputs the single number to a file by itself.
|
|
e4667a271cced2d053ba077527f7b03a76c80a63
|
4a06e5ed7573355e478d13a7dacdb101a1016999
|
/R/cptSlopeplot.R
|
e8628e6101cadb9d1725f86983881a1c75c3eeb4
|
[] |
no_license
|
BhaktiDwivedi/GISPA
|
38f5ab0b6b0c2c5f6fb8229f6bcf4cce297fef11
|
364b9605cafac1f6a5778700d3a70328cd860312
|
refs/heads/master
| 2021-06-03T20:51:54.985446
| 2020-06-20T20:51:34
| 2020-06-20T20:51:34
| 39,084,820
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 8,407
|
r
|
cptSlopeplot.R
|
#'@name cptSlopeplot
#'@aliases cptSlopeplot
#'@title Scatterplot representation of identified change points gene set slopes
#'@description This function will plot the average slopes estimated over all gene sets within each change point by data types
#'@usage cptSlopeplot(gispa.output,feature,type,cpt)
#'@param gispa.output : A data matrix of between gene feature profile statistics for each feature with corrosponding identified changepoints. The row names should corrospond to genes or names to be displayed on y-axis
#'@param feature : Analysis type i.e., one ('1'), two ('2') or three ('3') dimensional feature analysis.
#'@param type : Type of data, e.g., EXP (default) for expression, VAR of variants, CNV for copy number change.
#'@param cpt : Change point cutoff to be highlighted. The default is 1
#'@details This function expects the output from GISPA function of GISPA package, and highlights the gene set slope profile in the selected changepoints
#'@return Scatterplot illustrating the average slopes by change point to access the best gene set profile
#'@author Bhakti Dwivedi & Jeanne Kowalski
#'@import scatterplot3d
#'@importFrom data.table data.table
#'@importFrom graphics plot
#'@importFrom graphics par
#'@importFrom graphics text
#'@importFrom stats lm
#'@examples
#'id <- 200 ## number of rows
#'s <- 3 ## number of columns
#'dm <- matrix(runif(id*s,0,10), nrow=id, ncol=s,
#' dimnames=list(paste("gene", 1:id, sep=""),
#' paste("sample", 1:s, sep="")))
#'changepoints <- sort(sample(1:2, id, replace=TRUE))
#'dm <- cbind(dm,changepoints)
#'cptSlopeplot(gispa.output=dm,feature=1,type="EXP",cpt=1)
#'@export
cptSlopeplot <- function(gispa.output, feature=1, type="EXP", cpt=1){
changepoints <- NULL # Setting the variables to NULL first
##select for the changepoint of interest
gispa.output <- gispa.output[gispa.output[,ncol(gispa.output)]!=1000,]
if(feature==1){
#data type 1
subset_data <- gispa.output[,c(1:3,ncol(gispa.output))]
#considering we only have three groups
lm.r <- lm (t(subset_data[, 1:3]) ~ I(1:3) )
#to get the slope estimates
slope <- t(t(lm.r$coeff[2,]))
colnames(slope) <- "slope"
intercept <- t(t(lm.r$coeff[2,]))
colnames(intercept) <- "intercept"
subset_data <- cbind(subset_data,intercept,slope)
#take average slope for each changepoint
dt_data <- data.table(subset_data)
avg_slope <- dt_data[,mean(slope),by=changepoints]
avg_slope$cptcolor[as.numeric(avg_slope$changepoints) <= cpt] <- "orange"
avg_slope$cptcolor[as.numeric(avg_slope$changepoints) > cpt] <- "grey"
x <- as.numeric(avg_slope$changepoints)
y <- as.numeric(avg_slope$V1)
par(bg = "white")
slopePlot <- plot(x, y,
xlim=c(1,max(x)+1),
ylim=c(min(y),max(y)),
xlab="",
ylab=paste("Mean Slope", " (", type[1], ")", sep=""),
cex.lab =1.5,
pch=16,
cex=5,
col=avg_slope$cptcolor)
text(avg_slope$V1, labels=avg_slope$changepoints,cex=1.5,pos=4,offset=0.2)
}
if(feature==2){
#data type 1
subset_data <- gispa.output[,c(1:3,ncol(gispa.output))]
#considering we only have three groups
lm.r <- lm (t(subset_data[, 1:3]) ~ I(1:3) )
#to get the slope estimates
slope <- t(t(lm.r$coeff[2,]))
colnames(slope) <- "slope"
intercept <- t(t(lm.r$coeff[2,]))
colnames(intercept) <- "intercept"
subset_data <- cbind(subset_data,intercept,slope)
#take average slope for each changepoint
dt_data <- data.table(subset_data)
avg_slope_type_1 <- dt_data[,mean(slope),by=changepoints]
#data type 2
subset_data <- gispa.output[,c(4:6,ncol(gispa.output))]
#considering we only have three groups
lm.r <- lm (t(subset_data[, 1:3]) ~ I(1:3) )
#to get the slope estimates
slope <- t(t(lm.r$coeff[2,]))
colnames(slope) <- "slope"
intercept <- t(t(lm.r$coeff[2,]))
colnames(intercept) <- "intercept"
subset_data <- cbind(subset_data,intercept,slope)
#take average slope for each changepoint
dt_data <- data.table(subset_data)
avg_slope_type_2 <- dt_data[,mean(slope),by=changepoints]
#Merge the data ####
avg_slope <- merge(avg_slope_type_1,
avg_slope_type_2,
by=c("changepoints"))
#plot the data
avg_slope$cptcolor[as.numeric(avg_slope$changepoints) <= cpt] <- "orange"
avg_slope$cptcolor[as.numeric(avg_slope$changepoints) > cpt] <- "grey"
x <- as.numeric(avg_slope$V1.x)
y <- as.numeric(avg_slope$V1.y)
par(bg = "white")
slopePlot <- plot(x, y,
xlim=c(min(x),max(x)+1),
ylim=c(min(y),max(y)+1),
xlab=paste("Mean Slope", " (", type[1], ")", sep=""),
ylab=paste("Mean Slope", " (", type[2], ")", sep=""),
cex.lab =1.5,
pch=16,
cex=5,
col=avg_slope$cptcolor)
text(avg_slope$V1.x, avg_slope$V1.y,
labels=avg_slope$changepoints,
cex=1.5,pos=4,offset=1.2)
}
if(feature==3){
#data type 1
subset_data <- gispa.output[,c(1:3,ncol(gispa.output))]
#considering we only have three groups
lm.r <- lm (t(subset_data[, 1:3]) ~ I(1:3) )
#to get the slope estimates
slope <- t(t(lm.r$coeff[2,]))
colnames(slope) <- "slope"
intercept <- t(t(lm.r$coeff[2,]))
colnames(intercept) <- "intercept"
subset_data <- cbind(subset_data,intercept,slope)
#take average slope for each changepoint
dt_data <- data.table(subset_data)
avg_slope_type_1 <- dt_data[,mean(slope),by=changepoints]
#data type 2
subset_data <- gispa.output[,c(4:6,ncol(gispa.output))]
#considering we only have three groups
lm.r <- lm (t(subset_data[, 1:3]) ~ I(1:3) )
#to get the slope estimates
slope <- t(t(lm.r$coeff[2,]))
intercept <- t(t(lm.r$coeff[2,]))
subset_data[,c("intercept","slope")]<-rbind(intercept,slope)
#take average slope for each changepoint
dt_data <- data.table(subset_data)
avg_slope_type_2 <- dt_data[,mean(slope),by=changepoints]
#data type 3
subset_data <- gispa.output[,c(7:9,ncol(gispa.output))]
#considering we only have three groups
lm.r <- lm (t(subset_data[, 1:3]) ~ I(1:3) )
#to get the slope estimates
slope <- t(t(lm.r$coeff[2,]))
colnames(slope) <- "slope"
intercept <- t(t(lm.r$coeff[2,]))
colnames(intercept) <- "intercept"
subset_data <- cbind(subset_data,intercept,slope)
#take average slope for each changepoint
dt_data <- data.table(subset_data)
avg_slope_type_3 <- dt_data[,mean(slope),by=changepoints]
#Merge the data ####
avg_slope_1_2 <- merge(avg_slope_type_1,
avg_slope_type_2,
by=c("changepoints"))
avg_slope <- merge(avg_slope_1_2,
avg_slope_type_3,
by=c("changepoints"))
#plot the data
avg_slope$cptcolor[as.numeric(avg_slope$changepoints) <= cpt] <- "orange"
avg_slope$cptcolor[as.numeric(avg_slope$changepoints) > cpt] <- "grey"
x <- as.numeric(avg_slope$V1.x)
y <- as.numeric(avg_slope$V1.y)
z <- as.numeric(avg_slope$V1)
par(bg = "white")
slopePlot <- scatterplot3d(x, y, z,
xlim=c(min(x),max(x)+1),
ylim=c(min(y),max(y)+1),
zlim=c(min(z),max(z)+1),
xlab=paste("Mean Slope"," (",type[1],")",sep=""),
ylab=paste("Mean Slope"," (",type[2],")",sep=""),
zlab=paste("Mean Slope"," (",type[3],")",sep=""),
cex.lab =1.5,
pch=19,
cex.symbols = 5,
type="h",
color=avg_slope$cptcolor)
# convert 3D coords to 2D projection
slopePlot.coords <- slopePlot$xyz.convert(x, y, z)
text(slopePlot.coords$x, slopePlot.coords$y,
labels=avg_slope$changepoints,
cex=1.5,
pos=4,
offset=1.2)
}
return (slopePlot)
}
|
3f86a4f3faf513ea749cb2caccda7fbfa03fbaad
|
1868d1380a70a8d6d3be239a553a214817a3f4fa
|
/R/dataset_documentation.R
|
56c10b69619041dbf902bcf69f31be06be310a85
|
[] |
no_license
|
MalteThodberg/coRe
|
6f8bae1c1970cb21a7c3782849748b16ae4eb744
|
fa0cddbce9d46035e97c4639dfe35a92024cb2a0
|
refs/heads/master
| 2021-01-21T04:31:39.120875
| 2016-07-04T09:29:21
| 2016-07-04T09:29:21
| 38,311,979
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,187
|
r
|
dataset_documentation.R
|
#' Common Packages
#'
#' Names of basic packages that should always be loaded.
#'
#' @format Character vector
#' @author Malte Thodberg
#' @details
#' The list of packages includes:
#'
#' Data manipulation: magrittr, readr, tidyr, dplyr
#'
#' Special data formats: stringr, lubridate
#'
#' Plotting: grid, gridExtra, ggplot2, GGally, ggthemes, ggExtra, ggrepel, RColorBrewer, VennDiagram, pheatmap, wesanderson
#'
#' Performance: matrixStats, parallel
"core_packages"
#' Bioconductor Packages
#'
#' Names of biconductor packages that should always be loaded.
#'
#' @format Character vector
#' @author Malte Thodberg
#' @details
#' The list of packages includes:
#'
#' Installer: BiocInstaller
#'
#' Genomic Arithmetic: Biostrings, IRanges, GenomicRanges, rtracklayer
#'
#' Differential Expression: limma, edgeR, DESeq2
"bioc_packages"
#' Development Packages
#'
#' Names of biconductor packages, which should be loaded when developing.
#'
#' @format Character vector
#' @author Malte Thodberg
#' @details
#' The list of packages includes:
#'
#' Development: Rccp, devtools, roxygen2, pryr, profr
#'
#' RStudio: rstudioapi, manipulate
#'
#' Terminal: setwidth, colorout
"code_packages"
|
6023b1c354b3c485c67b18b236d3d2b25030253b
|
71db4a78c8a989b58a0d839a77d58d1774dbec5f
|
/Code/R/Likelihood_Function.R
|
dd52206882be31a29317bce8b7f7fff68d771284
|
[] |
no_license
|
saulmoore1/MSc_CMEE
|
906a7bdf09528c39c0daf6e37f2d722b8ad7bd3d
|
5bfd0a5f696c59a092aa9df5536169d905d7ab69
|
refs/heads/master
| 2022-04-30T20:14:59.660442
| 2022-03-30T11:28:15
| 2022-03-30T11:28:15
| 158,312,708
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 13,790
|
r
|
Likelihood_Function.R
|
#!/usr/bin/env R
binomial.likelihood <- function(p){
choose(10,7)*p^7*(1-p)^3
}
binomial.likelihood(p=0.1) # Reutrns: 8.748e-06
p <- seq(0,1,0.01)
likelihood.values <- binomial.likelihood(p)
plot(p, likelihood.values, type="l")
abline(h=max(likelihood.values), lty=4)
abline(v=p[likelihood.values==max(likelihood.values)[1]], lty=4)
grid(nx = NULL, ny = NA)
log.binomial.likelihood <- function(p) {
log(binomial.likelihood(p=p))
}
p <- seq(0,1,0.01)
log.likelihood.values <- log.binomial.likelihood(p)
plot(p, log.likelihood.values, type="l")
abline(h=max(log.likelihood.values), lty=4)
abline(v=p[log.likelihood.values==max(log.likelihood.values)[1]], lty=4)
grid(nx = NULL, ny = NA)
max(log.likelihood.values)
# Point at which the function is maximised remains the same (p = ~0.7)
optimize(binomial.likelihood, interval = c(0,1), maximum = TRUE)
# Maximum = x at max y (0.6999843), Objective=max(likelihood.values) (0.2668279)
# Not exactly 0.7 because of rounding errors - theoretical = 0.7
recapture.data <- read.csv("../Data/recapture.csv", header=T)
plot(recapture.data$day, recapture.data$length_diff, xlab="Day", ylab="Difference in length", pch=4)
# THE LOG-LIKELIHOOD FOR THE LINEAR REGRESSION
# PARAMETERS HAVE TO BE INPUT AS A VECTOR
regression.log.likelihood<-function(parm, dat)
{
# DEFINE THE PARAMETERS parm
# WE HAVE THREE PARAMETERS: a, b, sigma. BE CAREFUL OF THE ORDER
a <- parm[1] # Slope
b <- parm[2] # Intercept
sigma <- parm[3] # Variance of the errors
# DEFINE THE DATA dat
# FIRST COLUMN IS x, SECOND COLUMN IS y
x <- dat[,1]
y <- dat[,2]
# DEFINE THE ERROR TERM
error.term <- (y-a-b*x)
# REMEMBER THE NORMAL pdf?
density <- dnorm(error.term, mean=0, sd=sigma, log=T)
# log=T for LogLikelihood
# THE LOG-LIKELIHOOD IS THE SUM OF INDIVIDUAL LOG-DENSITY
return(sum(density))
}
# JUST TO SEE WHAT THE LOG-LIKELIHOOD VALUE IS WHEN a=1, b=1, and sigma=1
# YOU MAY TRY ANY DIFFERENT VALUES
regression.log.likelihood(c(1,1,1), dat=recapture.data)
# -452.6903
# TO OPIMISE THE LOG-LIKELIHOOD FUNCTION IN R - to find peak
# optimize() IS ONE-DIMENSIONAL,
# optim() GENERALISES TO MULTI-DIMENSIONAL CASES
optim(par=c(1,1,1), regression.log.likelihood, method='L-BFGS-B',
lower=c(-1000,-1000,0.0001), upper=c(1000,1000,10000),
control=list(fnscale=-1), dat=recapture.data, hessian=T)
# sigma cannot be negative - lowest value = 0.0001
# par=c(1,1,1) - Initial values for the parameters
# log.likelihood.regression - The function you wish to be optimised
# method=‘L-BFGS-B’ - Optimisation algorithm
# lower=c(-1000,-1000,0.0001) - Lower bound of your parameter space
# upper=c(1000,1000,10000) - Upper bound of your parameter space
# control=list((fnscale=-1)) - fnscale=-1 means to maximise
# REGRESSION WITH THE BUILT-IN lm()
m<-lm(length_diff~day, data=recapture.data)
summary(m)
n<-nrow(recapture.data)
sqrt(var(m$residual)*(n-1)/n)
# You always need to provide an initial parameter vector by par=
# Choice of method can be tricky for advanced users: See R help for details. If you use L-BFGS-B as your method, then you need to specify the upper and lower bound of the parameter values for searching for the maximum. No need to specify if you use Nelder-Mead
# If you wish to maximise a function, put fnscale=-1 in your control list, default is to minimise. You can put multiple control parameters in the control list.
# Precision can be adjusted by tolerance or maximum number of iterations, say maxit or abstol within control
# The Hessian matrix provide information about the variance-covariance
# structure of your parameter estimates
# Try multiple sets of initial parameters and ensure they all converge to the same estimates
# “Stumble around” the parameter space towards the best parameters, just like a drunkard trying to stumble home (the best place).
# Not every step is in the right direction, and it takes some time to go home.
# Ideal if the drunkard find his place. But also he may get stuck at the local maximum.
# THE LOG-LIKELIHOOD FUNCTION FOR M1 WITHOUT AN INTERCEPT
regression.no.intercept.log.likelihood<-function(parm, dat)
{
# DEFINE THE PARAMETERS
# NO INTERCEPT THIS TIME
b <- parm[1]
sigma <- parm[2]
# DEFINE THE DATA
# SAME AS BEFORE
x<-dat[,1]
y<-dat[,2]
# DEFINE THE ERROR TERM, NO INTERCEPT HERE
error.term <- (y-b*x)
# REMEMBER THE NORMAL pdf?
density<-dnorm(error.term, mean=0, sd=sigma, log=T)
# LOG-LIKELIHOOD IS THE SUM OF DENSITIES
return(sum(density))
}
regression.no.intercept.log.likelihood(c(1,1), dat=recapture.data)
# PERFORMING LIKELIHOOD-RATIO TEST
M1<-optim(par=c(1,1), regression.no.intercept.log.likelihood,
dat=recapture.data, method='L-BFGS-B',
lower=c(-1000,0.0001), upper=c(1000,10000),
control=list(fnscale=-1), hessian=T)
M2<-optim(par=c(1,1,1), regression.log.likelihood,
dat=recapture.data, method='L-BFGS-B',
lower=c(-1000,-1000,0.0001), upper=c(1000,1000,10000),
control=list(fnscale=-1), hessian=T)
# THE TEST STATISTIC D
D<-2*(M2$value-M1$value)
D # Likelihood-ratio test statistic = 3.047676
# CRITICAL VALUE
qchisq(0.95, df=1) # df=3-2 => 3.841459
# So we accept the hypothesis that the intercept is zero at α = 0.05 (Same conclusion is drawn from lm() using anova table)
# rchisq = rnorm family => generate random numbers from chisq distribution
# dchisq = dnorm/dbinom family => density
# qchisq = quantile, eg. value at 0.95 (95% CI)
regression.non.constant.var.log.likelihood<-function(parm, dat)
{
b<-parm[1]
sigma<-parm[2]
x<-dat[,1]
y<-dat[,2]
error.term<-(y-b*x)
density<-dnorm(error.term, mean=0, sd=x*sigma, log=T) # REMEMBER THE NORMAL pdf - Look up BOX-COX transformation???
return(sum(density))
}
regression.non.constant.var.log.likelihood(c(1,1), dat=recapture.data)
# MAXIMISE THE LOG-LIKELIHOOD
# HOW ABOUT CALLING IT M4?
M4<-optim(par=c(1,1), regression.non.constant.var.log.likelihood,
dat=recapture.data, method='L-BFGS-B',
lower=c(-1000,0.0001), upper=c(1000,10000),
control=list(fnscale=-1))
M4
# Confidence interval
# DEFINE THE RANGE OF PARAMETERS TO BE PLOTTED
b<-seq(2, 4, 0.1)
sigma<-seq(2, 5, 0.1)
# THE LOG-LIKELIHOOD VALUE IS STORED IN A MATRIX
log.likelihood.value<-matrix(nr=length(b), nc=length(sigma))
# COMPUTE THE LOG-LIKELIHOOD VALUE FOR EACH PAIR OF PARAMETERS
for (i in 1:length(b))
{
for (j in 1:length(sigma))
{
log.likelihood.value[i,j]<-
regression.no.intercept.log.likelihood(parm=c(b[i],sigma[j]),
dat=recapture.data)
}
}
# WE ARE INTERESTED IN KNOWING THE LOG-LIKELIHOOD VALUE
# RELATIVE TO THE MAXIMA
log.likelihood.value<-log.likelihood.value-M1$value
# FUNCTION FOR 3D PLOT
persp(b, sigma, log.likelihood.value, theta=30, phi=20,
xlab='b', ylab='sigma', zlab='log.likelihood.value',
col='blue')
# CONTOUR PLOT - ie. as viewed from above
contour(b, sigma, log.likelihood.value, xlab='b', ylab='sigma',
xlim=c(2.5, 3.9), ylim=c(2.0, 4.3),
levels=c(-1:-5, -10), cex=2)
# DRAW A CROSS TO INDICATE THE MAXIMUM
points(M1$par[1], M1$par[2], pch=3)
contour.line1<-contourLines(b, sigma, log.likelihood.value,
levels=-1.92)[[1]] # Rule of thumb is -1.92
lines(contour.line1$x, contour.line1$y, col='red',
lty=2, lwd=2)
grid(nx = NULL, ny = NA)
grid(nx = NA, ny = NULL)
# 95% CI for sigma is [2.23, 3.74]
# 95% CI for b is [2.75, 3.57]
# 95% CI for 1 parameter = 0.5*chisq1 = 1.92
abline(h=max(contour.line1$x, contour.line1$y), lty=4, col="red")
abline(h=min(contour.line1$x, contour.line1$y), lty=4, col="red")
print(paste("CI for parameter sigma is:",max(contour.line1$x, contour.line1$y),",",min(contour.line1$x, contour.line1$y)))
abline(v=max(contour.line1$x), lty=4, col="red")
abline(v=min(contour.line1$x), lty=4, col="red")
print(paste("CI for parameter b is:",max(contour.line1$x),",",min(contour.line1$x)))
# 3.737046
# 2.23367
# 3.577263
# 2.749373
# But we do not know the joint confident region for both (b,sigma)
# 95% CI for 2 joint parameters = 0.5*chisq2 = 2.99
contour.line2<-contourLines(b, sigma, log.likelihood.value,
levels=-2.99)[[1]] # Rule of thumb is -1.92
lines(contour.line2$x, contour.line2$y, col='blue',
lty=2, lwd=2)
# The joint confidence region is wider than the confidence interval for 1 parameter alone, this is because of 'multiple comparison'
# Point [3.5, 2.3] lies outside of the joint 95% CI for both parameters (b given sigma), despite lying within the bounds of the 95% CI when considering each individual parameter in turn. ie. when both parameters are considered together (variance-covariance), the confidence interval is constrained such that the point actually lies outside of the true 95% CI for both parameters.
points(3.5,2.3, pch=4)
# This point lies actually outside of the confidence region when both parameters are considered together.
abline(h=max(contour.line2$x, contour.line2$y), lty=4, col="blue")
abline(h=min(contour.line2$x, contour.line2$y), lty=4, col="blue")
abline(v=max(contour.line2$x), lty=4, col="blue")
abline(v=min(contour.line2$x), lty=4, col="blue")
max(contour.line2$x, contour.line2$y) # 4.041608
min(contour.line2$x, contour.line2$y) # 2.116497
max(contour.line2$x) # 3.691743
min(contour.line2$x) # 2.635445
# optim() GENERALISES TO MULTI-DIMENSIONAL CASES
# WITH HESSIAN MATRIX
result<-optim(par=c(1,1), regression.no.intercept.log.likelihood,
method='L-BFGS-B',
lower=c(-1000,0.0001), upper=c(1000,10000),
control=list(fnscale=-1), dat=recapture.data, hessian=T) # Hessian = TRUE returns the Hessian matrix
# GET BACK THE HESSIAN MATRIX
result$hessian
# THE VARIANCE-COVARIANCE MATRIX IS THE NEGATIVE OF
# THE INVERSE OF THE HESSIAN MATRIX.
# BY solve() FUNCTION
variance.matrix<-(-1)*solve(result$hessian) # Return inverse Hessian matrix - all second order partial derivatives
variance.matrix
# Q3 (iii)
coin.log.likelihood <- function(p,n,y){
# p is the parameter
# n is the number of trials
# y is the number of heads
return(lchoose(n,y) + y*log(p) + (n-y)*log(1-p))
}
H0 <- coin.log.likelihood(p=0.5, n=50, y=35)
H0 # Log likelihood value of -6.215
H1 <- coin.log.likelihood(p=0.7, n=50, y=35)
H1 # As expected, a higher log likelihood value of -2.1
D <- 2*(H1-H0)
D # D-statistic is 8.228
# Df = H1-H0 = 1-0 = 1
qchisq(0.95, df=1)
# 8.228 > 3.841, therefore we can reject H0 and accept H1, that the coin is unfair (loaded)
# This demonstrates the effect of sample size!
p <- seq(0,1,0.01)
coin.log.likelihood.value <- sapply(p, coin.log.likelihood, n=50, y=35) # Sapply works with vectors
coin.log.likelihood.value <- coin.log.likelihood.value - max(coin.log.likelihood.value) # Zero stanardise the plot
plot(p, coin.log.likelihood.value, type="l", lwd=2)
abline(h=max(coin.log.likelihood.value), lty=4)
abline(v=p[coin.log.likelihood.value==max(coin.log.likelihood.value)[1]], lty=4)
grid(nx = NULL, ny = NA)
grid(nx = NA, ny = NULL)
plot(p, coin.log.likelihood.value, type="l", lwd=2, xlim=c(0.4, 0.9), ylim=c(-3,0))
abline(h=-1.92, col="red", lty=4)
# Lower 95% CI
uniroot(function(p){
coin.log.likelihood(p=p, n=50, y=35) - coin.log.likelihood(p=0.7, n=50, y=35) + 1.92},
interval = c(0.01, 0.7)
) # 0.5652006
# Upper 95% CI
uniroot(function(p){
coin.log.likelihood(p=p, n=50, y=35) - coin.log.likelihood(p=0.7, n=50, y=35) + 1.92},
interval = c(0.7, 1)
) # 0.8148278
# Question 4
flowering <- read.table("../Data/flowering.txt", header = TRUE)
flowering
names(flowering)
par(mfrow=c(1,2))
plot(flowering$Flowers, flowering$State)
plot(flowering$Root, flowering$State)
# TWO ARGUMENTS: parm IS A VECTOR OF PARAMETERS,
# dat IS THE INPUT DATASET
logistic.log.likelihood<-function(parm, dat) {
# DEFINE PARAMETERS
a<-parm[1]
b<-parm[2]
c<-parm[3]
# DEFINE RESPONSE VARIABLE, WHICH IS THE FIRST COLUMN OF dat
State<-dat[,1]
# SIMILARLY DEFINE OUR EXPLANATORY VARIABLES
Flowers<-dat[,2]
Root<-dat[,3]
# MODEL OUR SUCCESS PROBABILITY
p<-exp(a+b*Flowers+c*Root)/(1+exp(a+b*Flowers+c*Root))
# THE LOG-LIKELIHOOD FUNCTION
log.like<-sum(State*log(p)+(1-State)*log(1-p))
return(log.like)
}
# TRY
logistic.log.likelihood(c(0,0,0), dat=flowering)
M1 <- optim(par=c(0,0,0), logistic.log.likelihood,
dat=flowering, method='L-BFGS-B',
lower=c(-1000,-4,-1000), upper=c(1000,1000,1000),
control=list(fnscale=-1), hessian = FALSE)
M1
# Parameters: a = 0.9614547, b = -0.1064155, c = 6.6003380
# Associated log-likelihood value: -27.03405
logistic.log.likelihood.int<-function(parm, dat)
{
# DEFINE PARAMETERS
a<-parm[1]
b<-parm[2]
c<-parm[3]
d<-parm[4]
# DEFINE RESPONSE VARIABLE, WHICH IS THE FIRST COLUMN OF dat
State<-dat[,1]
# SIMILARLY DEFINE OUR EXPLANATORY VARIABLES
Flowers<-dat[,2]
Root<-dat[,3]
# MODEL OUR SUCCESS PROBABILITY
p<-exp(a+b*Flowers+c*Root+d*Flowers*Root)/(1+exp(a+b*Flowers+c*Root+d*Flowers*Root))
# THE LOG-LIKELIHOOD FUNCTION
log.like<-sum(State*log(p)+(1-State)*log(1-p))
return(log.like)
}
logistic.log.likelihood.int(c(0,0,0,0), dat=flowering) # -40.89568
M2 <- optim(par=c(0,0,0,0), logistic.log.likelihood.int,
dat=flowering, method='L-BFGS-B',
lower=c(-1000,-4,-1000,-Inf), upper=c(1000,1000,1000,1000),
control=list(fnscale=-1), hessian = FALSE)
M2
# Parameters: a = -2.95495534, b = -0.07888641, c = 25.11688994, d = -0.20865900
# Associated log-likelihood value: -18.56412
M2[2][[1]]
M1[2][[1]]
# Likelihood ratio test
D <- 2*(M2[2][[1]]-M1[2][[1]])
D # 16.93986
# Df = 4 - 3
qchisq(0.95, df=1) # 3.841459
# D-statistic > chisq value, therefore the interaction is significant
|
7bcfd9e55ecef260bad38f40654075e8ba513df0
|
c6e7f411826df81b26253e89ea67b0da7ef01bf9
|
/R/GeoDistPSU.R
|
ef86a3e44a1bacab9052979eeed8ea023f6cb01b
|
[] |
no_license
|
cran/PracTools
|
2572c63a788e6cb46258de844ae30bf732f733d0
|
c3c4a5626e2458d533f4d184fe2afb17180ab201
|
refs/heads/master
| 2023-05-27T15:15:10.763794
| 2023-05-23T06:10:16
| 2023-05-23T06:10:16
| 17,681,595
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,258
|
r
|
GeoDistPSU.R
|
GeoDistPSU <- function(lat, ## Latitude variable. Must be in decimal
long, ## Longitude variable. Must be in decimal
dist.sw, ## Distance: miles or kilometers (kms)
max.dist, ## Maximum distance within PSU
Input.ID = NULL ## ID variable from input file
) {
## Confirm latitude and longitude are numeric and have no missing values
if(is.numeric(lat) == FALSE)
stop("Latitude must be numeric.\n")
if(any(is.na(lat)) == TRUE)
stop("Latitude has missing values, which are not allowed.\n")
if(is.numeric(long) == FALSE)
stop("Longiitude must be numeric.\n")
if(any(is.na(long)) == TRUE)
stop("Longitude has missing values, which are not allowed.\n")
## Confirm distance switch is "miles" or "kms"
if(dist.sw != "miles" & dist.sw != "kms")
stop("Distance switch must be miles or kms (kilometers).\n")
## Confirm distance is numeric and positive
if(is.numeric(max.dist) == FALSE)
stop("Maximum distance must be numeric.\n")
if((max.dist > 0) == FALSE)
stop("Maximum distance must be greater than zero.\n")
## Create distance matrix from latitude and longitude
geodf <- data.frame(long, lat)
## Create "As the crow flies" distance with lat and long
d <- geosphere::distm(geodf, fun = geosphere::distHaversine)
## Distance of d is in meters
## There are 1609.344 meters per mile
d <- if(dist.sw == "miles") d/1609.344 else
d/1000
## Convert d to distance matrix
dist <- as.dist(d)
## Perform hierarchical clustering using maximum distance between two cluster objects
hc <- hclust(dist, method = "complete")
## Cut dendogram by maximum distance for PSU assignment
psuID <- cutree(hc, h = max.dist)
## Create plot of PSU centers
PSU.Mean.Latitude <- tapply(lat, psuID, mean)
PSU.Mean.Longitude <- tapply(long, psuID, mean)
## Calculate maximum distance between units within cluster
PSU.Max.Dist <- NULL
for(i in 1:length(unique(psuID))){
PSU.Max.Dist[i] <- max(geosphere::distm(geodf[psuID == i, ],
fun = geosphere::distHaversine))}
PSU.Max.Dist <- if(dist.sw == "miles") PSU.Max.Dist/1609.344 else
PSU.Max.Dist/1000
## Calculate number of SSUs in each PSU
Number.SSUs <- table(psuID)
## Carry Input.ID through
Input.file.ID <- if(is.null(Input.ID)) seq(1, length(psuID)) else
Input.ID
## Create data frame with Input file ID and PSU Cluster ID
PSU.ID <- cbind(Input.file.ID,
psuID)
PSU.ID <- as.data.frame(PSU.ID)
## Create data frame with PSU Centroids, Number of SSUs, and Maximum Cluster Distance
PSU.Info <- cbind(Number.SSUs,
PSU.Mean.Latitude,
PSU.Mean.Longitude,
PSU.Max.Dist)
PSU.Info <- as.data.frame(PSU.Info)
## Output PSU ID and PSU Information data frames
out <- list(PSU.ID,
PSU.Info)
## Name data frames in list
names(out) <- c("PSU.ID", "PSU.Info")
## Return output
return(out)
}
|
c20681776322524c760e79f91b92668aeb1530fa
|
c3bd69562a080767188a3df5c202a5b600e2c81a
|
/R/phase.R
|
9ba5681b7f9383ee3ef6ba2dcf0dae23e2dff379
|
[] |
no_license
|
EricArcher/strataG
|
007bde64c4f999ddea609e0e18333c9bfe87d155
|
d89348cb390379522202beed20be49fa77cd5eae
|
refs/heads/master
| 2023-02-27T16:01:24.665136
| 2023-02-09T21:11:11
| 2023-02-09T21:11:11
| 34,808,785
| 25
| 16
| null | 2020-04-21T16:36:11
| 2015-04-29T17:46:44
|
HTML
|
UTF-8
|
R
| false
| false
| 13,035
|
r
|
phase.R
|
#' @name phase
#' @title PHASE
#' @description Run PHASE to estimate the phase of loci in diploid data.
#'
#' @param g a \linkS4class{gtypes} object.
#' @param loci vector or data.frame of loci in 'g' that are to be phased. If a
#' data.frame, it should have columns named
#' \code{locus} (name of locus in 'g'),
#' \code{group} (number identifying loci in same linkage group), and
#' \code{position} (integer identifying location of each locus in a
#' linkage group).
#' @param positions position along chromosome of each locus.
#' @param type type of each locus.
#' @param num.iter number of PHASE MCMC iterations.
#' @param thinning number of PHASE MCMC iterations to thin by.
#' @param burnin number of PHASE MCMC iterations for burnin.
#' @param model PHASE model type.
#' @param ran.seed PHASE random number seed.
#' @param final.run.factor optional.
#' @param save.posterior logical. Save posterior sample in output list?
#' @param in.file name to use for PHASE input file.
#' @param out.file name to use for PHASE output files.
#' @param delete.files logical. Delete PHASE input and output files when done?
#' @param ph.res result from \code{phase.run}.
#' @param thresh minimum probability for a genotype to be selected (0.5 - 1).
#' @param keep.missing logical. T = keep missing data from original data set.
#' F = Use estimated genotypes from PHASE.
#'
#' @note PHASE is not included with \code{strataG} and must be downloaded
#' separately. Additionally, it must be installed such that it can be run from
#' the command line in the current working directory. See the vignette
#' for \code{external.programs} for installation instructions.
#'
#' @details
#' \tabular{ll}{
#' \code{phase} \tab runs PHASE assuming that the executable is installed
#' properly and available on the command line.\cr
#' \code{phaseWrite} \tab writes a PHASE formatted file.\cr
#' \code{phaseReadPair} \tab reads the '_pair' output file.\cr
#' \code{phaseReadSample} \tab reads the '_sample' output file.\cr
#' \code{phaseFilter} \tab filters the result from \code{phase.run} to
#' extract one genotype for each sample.\cr
#' \code{phasePosterior} \tab create a data.frame of all genotypes for each
#' posterior sample.\cr
#' }
#'
#' @return
#' \describe{
#' \item{phase}{a list containing:
#' \tabular{ll}{
#' \code{locus.name} \tab new locus name, which is a combination of loci
#' in group.\cr
#' \code{gtype.probs} \tab a data.frame listing the estimated genotype
#' for every sample along with probability.\cr
#' \code{orig.gtypes} \tab the original gtypes object for the
#' composite loci.\cr
#' \code{posterior} \tab a list of \code{num.iter} data.frames
#' representing posterior sample of genotypes for each sample.\cr
#' }}
#' \item{phaseWrite}{a list with the input filename and the
#' \linkS4class{gtypes} object used.}
#' \item{phaseReadPair}{a data.frame of genotype probabilities.}
#' \item{phaseReadSample}{a list of data.frames representing the
#' posterior sample of genotypes for one set of loci for each sample.}
#' \item{phaseFilter}{a matrix of genotypes for each sample.}
#' \item{phasePosterior}{a list of data.frames representing the posterior
#' sample of all genotypes for each sample.}
#' }
#'
#' @references Stephens, M., and Donnelly, P. (2003). A comparison of Bayesian
#' methods for haplotype reconstruction from population genotype data.
#' American Journal of Human Genetics 73:1162-1169. Available at:
#' \url{http://stephenslab.uchicago.edu/software.html#phase}
#'
#' @author Eric Archer \email{eric.archer@@noaa.gov}
#'
#' @examples \dontrun{
#' data(bowhead.snps)
#' data(bowhead.snp.position)
#' snps <- df2gtypes(bowhead.snps, ploidy = 2, description = "Bowhead SNPS")
#' summary(snps)
#'
#' # Run PHASE on all data
#' phase.results <- phase(snps, bowhead.snp.position, num.iter = 100,
#' save.posterior = FALSE)
#'
#' # Filter phase results
#' filtered.results <- phaseFilter(phase.results, thresh = 0.5)
#'
#' # Convert phased genotypes to gtypes
#' ids <- rownames(filtered.results)
#' strata <- bowhead.snps$Stock[match(ids, bowhead.snps$LABID)]
#' filtered.df <- cbind(id = ids, strata = strata, filtered.results)
#' phased.snps <- df2gtypes(filtered.df, ploidy = 2, description = "Bowhead phased SNPs")
#' summary(phased.snps)
#' }
#'
#' @export
#'
phase <- function(g, loci, positions = NULL, type = NULL,
num.iter = 100000, thinning = 100, burnin = 100000, model = "new",
ran.seed = NULL, final.run.factor = NULL, save.posterior = FALSE,
in.file = "phase_in", out.file = "phase_out", delete.files = TRUE) {
if(getPloidy(g) != 2) stop("'g' must be diploid")
# check loci format
if(!is.data.frame(loci)) {
if(!(is.character(loci) & is.vector(loci))) {
stop("'loci' must be a data.frame or character vector")
}
if(is.null(positions)) positions <- rep(1, getNumLoci(g))
if(length(positions) != length(loci)) {
stop("'positions' must be same length as 'loci'")
}
loci <- data.frame(locus = loci, position = positions, group = 1)
}
loci$group <- as.character(loci$group)
loci$position <- as.numeric(loci$position)
if(is.null(type)) type <- rep("S", length(unique(loci$group)))
if(length(type) != length(unique(loci$group))) {
stop("'type' must be same length as number of locus groups")
}
names(type) <- unique(loci$group)
result <- lapply(unique(loci$group), function(grp) {
lets <- paste(sample(c(0:9, letters), 10, replace = TRUE), collapse = "")
in.file <- paste("phase_in_", lets, sep = "")
out.file <- paste("phase_out_", lets, sep = "")
# Write input file
group.df <- loci[loci$group == grp, ]
locus.type <- rep(type[grp], nrow(group.df))
in.file.data <- phaseWrite(
g,
loci = group.df$locus,
positions = group.df$position,
type = locus.type, in.file
)
# Set parameters
M.opt <- switch(model, new = "-MR", old = "-MS", hybrid = "-MQ", "")
S.opt <- ifelse(is.null(ran.seed), "", paste("-S", ran.seed, sep = ""))
X.opt <- ifelse(
is.null(final.run.factor),
"",
paste("-X", final.run.factor, sep = "")
)
s.opt <- ifelse(save.posterior, "-s", "")
in.file.opt <- paste("\"", in.file, "\"", sep = "")
out.file.opt <- paste("\"", out.file, "\"", sep = "")
iter.params <- paste(trunc(num.iter), trunc(thinning), trunc(burnin))
phase.cmd <- paste(
"PHASE", M.opt, S.opt, X.opt, s.opt,
in.file.opt, out.file.opt, iter.params
)
# Run Phase
err.code <- system(phase.cmd)
if(err.code == 127) {
stop("You do not have PHASE installed.")
} else if(!err.code == 0) {
stop(paste("Error running PHASE. Error code", err.code, "returned."))
cat("\n")
}
# Read output
opts <- options(warn = -1)
gtype.probs <- phaseReadPair(paste(out.file, "_pairs", sep = ""))
if(is.null(gtype.probs)) {
alleles <- rep(NA, nrow(g$genotypes))
gtype.probs <- data.frame(
id = getIndNames(g), a1 = alleles, a2 = alleles, pr = rep(1, getNumInd(g))
)
}
new.locus.name <- paste(group.df$locus, collapse = "_")
alleles <- paste(new.locus.name, 1:2, sep = ".")
colnames(gtype.probs)[1:3] <- c("id", alleles)
rownames(gtype.probs) <- NULL
options(opts)
locus.result <- list(
locus.name = new.locus.name, gtype.probs = gtype.probs,
orig.gtypes = in.file.data$gtypes
)
if(save.posterior) {
file <- paste(out.file, "_sample", sep = "")
l.type <- paste(locus.type, collapse = "")
locus.result$posterior <- phaseReadSample(file, l.type)
for(i in 1:length(locus.result$posterior)) {
colnames(locus.result$posterior[[i]]) <- c("id", alleles)
}
}
if(delete.files) {
file.remove(c(dir(pattern = in.file), dir(pattern = out.file)))
}
locus.result
})
names(result) <- lapply(result, function(x) x$locus.name)
class(result) <- c("phase.result", class(result))
result
}
#' @rdname phase
#' @export
#'
phaseReadSample <- function(out.file, type) {
if(!file.exists(out.file)) return(NULL)
post.file <- scan(file = out.file, what = "character",
sep = "\n", quiet = TRUE)
iter.start <- grep(type, post.file) + 1
lapply(iter.start, function(start) {
num.samples <- as.integer(post.file[start - 3])
end <- start + (num.samples * 3) - 3
as.data.frame(t(sapply(seq(start, end, by = 3), function(i) {
id <- strsplit(post.file[i], " ")[[1]][2]
hap1 <- gsub(" ", "", post.file[i + 1])
hap2 <- gsub(" ", "", post.file[i + 2])
c(id, hap1, hap2)
})), stringsAsFactors = FALSE)
})
}
#' @rdname phase
#' @export
#'
phaseReadPair <- function(out.file) {
if(!file.exists(out.file)) return(NULL)
pair.file <- scan(file = out.file, what = "character",
sep = "\n", quiet = TRUE)
id.start <- grep("IND:", pair.file)
gtype.probs <- lapply(1:length(id.start), function(i) {
id.end <- ifelse(i == length(id.start),
length(pair.file),
id.start[i + 1] - 1)
id <- sub("IND: ", "", pair.file[id.start[i]])
t(sapply((id.start[i] + 1):id.end, function(j) {
line.split <- unlist(strsplit(pair.file[j], " , "))
names(line.split) <- c("hap1", "hap2", "pr")
c(id = id, line.split)
}))
})
gtype.probs <- as.data.frame(do.call(rbind, gtype.probs),
stringsAsFactors = FALSE)
gtype.probs$pr <- as.numeric(as.character(gtype.probs$pr))
gtype.probs
}
#' @rdname phase
#' @export
#'
phaseWrite <- function(g, loci, positions = NULL,
type = rep("S", length(loci)), in.file = "phase_in") {
if(getPloidy(g) != 2) stop("'g' must be diploid")
# Make sure locus.names and locus.positions are sorted properly
if(is.null(positions)) positions <- rep(1, length(getNumLoci(g)))
asc.order <- order(positions)
loci <- loci[asc.order]
positions <- positions[asc.order]
sub.g <- g[, loci, ]
write(c(
getNumInd(sub.g),
length(loci),
paste("P", paste(positions, collapse = " ")),
paste(type, collapse = ""), ""
), file = in.file)
g.mat <- as.matrix(sub.g, ids = TRUE, strata = FALSE)
ids <- g.mat[, "id"]
g.mat <- g.mat[, -1]
g.mat[is.na(g.mat)] <- "?"
for(i in 1:nrow(g.mat)) {
write(c(
ids[i],
paste(g.mat[i, seq(1, ncol(g.mat) - 1, 2)], collapse = " "),
paste(g.mat[i, seq(2, ncol(g.mat), 2)], collapse = " ")
), file = in.file, append = TRUE)
}
invisible(list(filename = in.file, gtypes = sub.g))
}
#' @rdname phase
#' @export
#'
phasePosterior <- function(ph.res, keep.missing = TRUE) {
if(!"phase.result" %in% class(ph.res)) {
stop("'ph.res' is not a result from 'phase.run'.")
}
num.iter <- length(ph.res[[1]]$posterior)
lapply(1:num.iter, function(iter) {
do.call(cbind, lapply(1:length(ph.res), function(locus) {
ph.res <- ph.res[[locus]]
post.df <- ph.res$posterior[[iter]]
if(keep.missing) {
for(i in 1:nrow(post.df)) {
ids <- which(getIndNames(ph.res$orig.gtypes) == post.df[i, 1])
if(any(is.na(as.array(ph.res$orig.gtypes, ids = ids)))) {
post.df[i, 2:3] <- NA
}
}
}
cols <- if(locus == 1) {1:3} else {2:3}
post.df[, cols]
}))
})
}
#' @rdname phase
#' @export
#'
phaseFilter <- function(ph.res, thresh = 0.5, keep.missing = TRUE) {
if(!"phase.result" %in% class(ph.res)) {
stop("'ph.res' is not a result from 'phase.run'.")
}
filtered <- lapply(ph.res, function(x) {
gtype.probs <- x$gtype.probs
pr.vec <- unique(gtype.probs[, 1])
locus.filtered <- do.call(rbind, lapply(pr.vec, function(i) {
this.id <- gtype.probs[gtype.probs[, 1] == i, ]
max.index <- which.max(this.id$pr)
if(length(max.index) == 0) return(this.id[1, ])
kept.line <- this.id[max.index, ]
if(as.numeric(kept.line$pr) < thresh) kept.line[, 2:3] <- c(NA, NA)
kept.line
}))
rownames(locus.filtered) <- NULL
if(keep.missing) {
for(i in 1:nrow(locus.filtered)) {
ids <- setdiff(getIndNames(x$orig.gtypes), locus.filtered[i, 1])
id.mat <- as.matrix(x$orig.gtypes, strata = FALSE)
id.mat <- id.mat[id.mat[, "id"] %in% ids, , drop = FALSE]
if(any(is.na(id.mat))) locus.filtered[i, 2:3] <- NA
}
}
locus.filtered
})
ids <- data.frame(
id = sort(unique(unlist(lapply(filtered, function(x) x$id))))
)
filtered <- as.matrix(do.call(cbind, lapply(filtered, function(x) {
merge(ids, x, by = "id", all.x = TRUE)[, 2:3]
})))
rownames(filtered) <- ids$id
colnames(filtered) <- paste(rep(names(ph.res), each = 2), ".", 1:2, sep = "")
filtered
}
|
f42ac8f3af487508497f6b820103546bcee34742
|
4f88f602a464420e278482f4b1036645f9fce164
|
/R/DF0_COUNTY_DATA.R
|
96ecd7715010a51fda8421673beeae357e567fa1
|
[] |
no_license
|
DavidSoSiZoch/SitRep
|
61d95b127e748be557e5b52007ccd8d6024a0cc2
|
4083dcf9de99fc5c2b884d96f8818743980e2bf9
|
refs/heads/main
| 2023-09-03T00:51:03.189970
| 2021-10-21T07:17:01
| 2021-10-21T07:17:01
| 393,357,742
| 0
| 0
| null | 2021-10-15T12:09:15
| 2021-08-06T11:34:45
|
R
|
UTF-8
|
R
| false
| false
| 1,868
|
r
|
DF0_COUNTY_DATA.R
|
DF0_COUNTY_DATA <- function(matching_key){
# This function depends on the matching key, for this test application
# specifically the healthauthority_county_key.
# This function creates a dataframe, with the first 3 columns equal to
# the three columns of the matching key. The rest of the dataframe is filled
# with zeros.
# Each observation is one county.
# The varialbles for each county can be seen from the list of colnames below.
# The created dataframe is used later on to be updated with data from the
# sormas_persons on the the county level values for each variable
# listed below.
#############################################################################
df0 <- data.frame(matrix(0, ncol = 23, nrow = nrow(matching_key)))
colnames(df0) <- c("state",
"county",
"health_authority",
"population",
"case_category_confirmed",
"case_category_none",
"case_category_suspected",
"hospitalized_FALSE",
"hospitalized_NA",
"hospitalized_TRUE",
"died_FALSE",
"died_NA",
"died_TRUE",
"new_case_category_confirmed",
"new_case_category_none",
"new_case_category_suspected",
"new_hospitalized_FALSE",
"new_hospitalized_NA",
"new_hospitalized_TRUE",
"new_died_FALSE",
"new_died_NA",
"new_died_TRUE",
"n")
df0$county <- matching_key$county
df0$state <- matching_key$state
df0$health_authority <- matching_key$health_authority
return(df0)
}
|
cf65a0346ee2373a138360f7f31a2ed2027ab74b
|
5154a4f1cf8569e007604f40737473477e20ddc9
|
/plot2.R
|
9f459b6f430ade2cf95e2a6248a926af3a424a98
|
[] |
no_license
|
Williambrunzell/ExData_Plotting1
|
73c799322df3421682cf316ae1fee22a9eceec87
|
fc7fd411e9f87dc2705fa13cf6c42cac4d3f260a
|
refs/heads/master
| 2022-11-12T18:37:06.889234
| 2020-06-29T15:40:44
| 2020-06-29T15:40:44
| 274,715,230
| 0
| 0
| null | 2020-06-24T16:21:37
| 2020-06-24T16:21:36
| null |
UTF-8
|
R
| false
| false
| 177
|
r
|
plot2.R
|
#Code for Plot 2
plot(subdata$datetime, subdata$Global_active_power,
type="l", ylab = "Global Active Power (kilowatts)",
xlab=NA)
dev.copy(png,'plot2.png')
dev.off()
|
fae3bed65805ca7e78e1d331e0da747d98d547e4
|
3fce68c7d6f45822e4a3294de46cd6935d392d06
|
/man/bernieGrob.Rd
|
0009f797167a52fd93c882497c0c77a2786a44d4
|
[
"MIT"
] |
permissive
|
murrayjw/ggbernie
|
388867ccb5812333aecc6df34fd558ddf9a842f4
|
f97915109ae3f9fa233795b418e29fe18aa50e0d
|
refs/heads/master
| 2023-02-21T11:07:43.432905
| 2021-01-21T17:01:53
| 2021-01-21T17:01:53
| 331,683,214
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 217
|
rd
|
bernieGrob.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/geom_bernie.R
\name{bernieGrob}
\alias{bernieGrob}
\title{bernie grob}
\usage{
bernieGrob(x, y, size, theme)
}
\description{
bernie grob
}
|
346aa06dd187a5c31d0ec8c6115e0fb4134f857f
|
48cbb955ea27365c1266b6bedd1f2f56288615d1
|
/R/prepare_ecologist_r_code.R
|
53fb4a57042475208f95312b35f3cf3873e84ec5
|
[
"CC0-1.0"
] |
permissive
|
fschirr/sampling_r_package
|
015dc1ed44834c043d57f742998133735d789d9c
|
4703c8c2dc8b2cdd7d5d1f773fa96eb3444081e1
|
refs/heads/master
| 2021-01-17T04:48:23.200150
| 2016-06-13T13:38:07
| 2016-06-13T13:38:07
| 38,422,415
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,245
|
r
|
prepare_ecologist_r_code.R
|
#' Rewrite the dataset into a usable format.
#'
#' \code{PrepareDataset} rewrites the dataset to make it useable for the
#' Sampling function of this package. The function comibines the different
#' columns and creates a new dataset. If there are now data fro a column the
#' Column will be filled with zeros.
#'
#' @param data The input dataset.
#' @param plot
#' @param num.of.individuals
#' @param species
#' @param x.coord
#' @param y.coord
#' @param visit.year
#'
#' @export Dataset in which the columns in the right order.
#'
PrepareDataset <- function(data, plot, num.of.individuals, species, x.coord,
y.coord, visit.year, visit.month, visit.day) {
column.plot <- ifelse (plot > 0, data[plot], rep(0, length(data[, 1])))
column.num.of.individuals <- ifelse (num.of.individuals > 0,
data[num.of.individuals],
rep(0, length(data[, 1])))
column.species <- ifelse (species > 0, data[species], rep(0,
length(data[, 1])))
column.x.coord <- ifelse (x.coord > 0, data[x.coord], rep(0,
length(data[, 1])))
column.y.coord <- ifelse (y.coord > 0, data[y.coord], rep(0,
length(data[, 1])))
column.visit.year <- ifelse (visit.year > 0, data[visit.year],
rep(0, length(data[, 1])))
column.visit.month <- ifelse (visit.month > 0,data[visit.month],
rep(0, length(data[, 1])))
column.visit.day <- ifelse (visit.day > 0, data[visit.day],
rep(0, length(data[, 1])))
data <- data.frame (column.plot, column.num.of.individuals, column.species, # other name than data
column.x.coord, column.y.coord, column.visit.year,
column.visit.month, column.visit.day)
colnames (data) <- c("plot", "num.of.individuals", "species",
"x.coord", "y.coord", "year", "month", "day")
return (data)
}
#' Creates a vector with the behaviour of an ecologist.
#'
#' \code{CreateEcologist} creates a vector with characteristics of the behaviour
#' of an ecologist in the field. The characteristics are: The sampled area of a
#' plot, the detection probability, the identification error, the probability
#' of missed vistis and the costs. The first four characteristics are expected
#' to be in percent.
#'
#' @param sampling.area A number in percent.
#' @param detection.probability A number in percent.
#' @param identification.error A number in percent.
#' @param propability.missed.visits A number in percent.
#' @param costs A number in monetary units.
#'
#' @export A vector with the five characteristics of an ecologist.
CreateEcologist <- function (sampling.area, detection.probability,
identification.error, probability.missed.visits,
costs) {
ecologist <- c(sampling.area, detection.probability, identification.error,
probability.missed.visits, costs)
return (ecologist)
}
|
59b45707edaa7654d639000a1684526259683cf0
|
e541db64d64f43a9a21dcbfa2d190b02118ec0ff
|
/analysis/s2_analysis.R
|
80b4e3f0378a69b7e2ff32b0451e8197e067c3c6
|
[] |
no_license
|
amyxli/kidbandit_revision
|
1e9b38719523c9584b6a002c4dafc235391a9abb
|
21b5f076752c171f69b4b42beb580bf4500c1264
|
refs/heads/main
| 2023-09-05T21:29:14.364998
| 2021-10-27T06:03:56
| 2021-10-27T06:03:56
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 12,753
|
r
|
s2_analysis.R
|
## Last edited 19/10/21 AXL
## This script analyzes data from replication adult and child participants.
## ESS added analysis on post-tests 1/24/2020.
library(tidyverse)
library(ggthemes)
library(ggplot2)
library(ggpubr)
library(here)
library(effsize)
data_sum <- read_csv(here("data_tidy", "study2_data_sum.csv"))
age_info<-read_csv(here("data_tidy","study2_ageinfo.csv"))
data_sum<-merge(data_sum,age_info, by.x="subjID")
#-------------------------------------------------------------------------------------#
## Analysis for switching and explore* and stars ####
## *Note that explore is called 'non-maximizing' in the paper ##
#-------------------------------------------------------------------------------------#
library(BayesFactor)
data_sum = data_sum[data_sum$group %in% c("child", "adult"),]
data_sum$group = factor(data_sum$group)
################################ Switching ################################
plot(switch ~ group, data = data_sum, main = "% switching choices")
#* reported: paper*#
switchBF = ttestBF(formula = switch ~ group, data = data_sum)
switchBF ## [1]Alt., r=0.707 : 7.620799e+36 ±0%
switchChains= posterior(ttestBF(formula = switch ~ group, data = data_sum),iterations=1000)
mean(switchChains[,2]) # mean difference -0.6184066
quantile(switchChains[,2],probs=c(0.025,0.975)) # mean difference CI -0.6846195 -0.5512568
mean(switchChains[,4])# effect size estimite -3.021579
quantile(switchChains[,4],probs=c(0.025,0.975)) # effect size CI -3.479296 -2.565163
############################# 'Explore' choices #############################
plot(explore ~ group, data = data_sum, main = "% 'explore' choices")
exploreBF = ttestBF(formula = explore ~ group, data = data_sum)
exploreBF ## 2.588539e+37 ±0%
exploreChains= posterior(ttestBF(formula = explore ~ group, data = data_sum),iterations=1000)
mean(exploreChains[,2]) # mean difference -0.492433
quantile(exploreChains[,2],probs=c(0.025,0.975)) # mean difference CI -0.5479371 -0.4349280
mean(exploreChains[,4])# effect size estimite -3.033899
quantile(exploreChains[,4], probs=c(0.025,0.975)) # effect size CI -3.508251 -2.563191
################################ Stars Won ################################
aggregate(data = data_sum, totalEarn~group, FUN = "mean")
rewardBF = ttestBF(formula = totalEarn ~ group, data = data_sum)
rewardBF ## [1] Alt., r=0.707 : 8.857099e+32 ±0%
starChains= posterior(ttestBF(formula = totalEarn ~ group, data = data_sum),iterations=1000)
mean(starChains[,2]) # mean difference 116.0546
quantile(starChains[,2],probs=c(0.025,0.975)) # mean difference CI 101.9744 131.4045
mean(starChains[,4])# effect size estimite 2.746324
quantile(starChains[,4],probs=c(0.025,0.975)) # effect size CI 2.301249 3.208194
#########################################################################################
################ Between-group comparisons for post-test performance ##################
#########################################################################################
### 8-star ####
# Preliminary glimpse at diff in proportion of correctly identifying 8-star option between
# adult and child groups
# % of participants child vs adult who correctly ID'ed 8-star monster
dataDynamic <- data_sum %>% filter(condition == "dynamic")
dataStatic <- data_sum %>% filter(condition == "static")
dataDynamic %>% #* reported: paper*#
group_by(group) %>%
summarise(mean(correct_8)) %>%
ungroup()
# group `mean(correct_8)`
# <fct> <dbl>
# 1 adult 0.349
# 2 child 0.771
dynamic <- xtabs( ~ correct_8 + group, dataDynamic ) #* reported: paper*#
contingencyTableBF(dynamic,sampleType = "poisson")
# Non-indep. (a=1) : 585.2952 ±0%
### Overall ####
# mean prop overall correct in posttest, broken up by condition and age group
aggregate(data = data_sum, correct~ condition + group, FUN = "mean") #* reported: paper*#
# across both conditions
subset(data_sum, group == "child")$correct %>% mean() #* reported: paper*# 0.864
subset(data_sum, group == "adult")$correct %>% mean() #* reported: paper*# 0.7756522
# mean for study 2, dynamic, adults, EXCLUDING 8-star question
study2post <- read_csv(here("data_tidy","study2_posttest.csv"))[-1]
study2post_long <- study2post %>%
rename(correctProp = correct) %>%
pivot_longer(
cols = c(6:10),
names_to = "question",
names_prefix = "correct_",
values_to = "correct"
)
study2post_long$question <- ifelse(study2post_long$question == "1", paste0(study2post_long$question, " star"), # if "1" then "1 star"
paste0(study2post_long$question, " stars")) # if not "1" then "X stars" (e.g., "8 stars")
tmp <- subset(study2post_long, question != "8 stars" & group == "adult" & condition == "dynamic") %>%
group_by(subjID) %>%
summarise(correctProp = mean(correct)) %>%
ungroup()
tmp$correctProp %>% mean() # 0.7209302
##################################
##### LINEAR MODELS ####
##################################
dataDynamic$group<-as.factor(dataDynamic$group)
groupBF<-lmBF(correct_8~group, dataDynamic) #* reported: paper*# group : 100.4961 ±0%
groupBF
switchBF<-lmBF(correct_8~switch, dataDynamic) # reported switch: 13216616 ±0.01%
switchBF
exploreBF<-lmBF(correct_8~explore, dataDynamic) # reported explore : 1632918 ±0.01%
exploreBF
switchgroupBF<-lmBF(correct_8~switch+group, dataDynamic)
exploregroupBF<-lmBF(correct_8~explore+group, dataDynamic)
segBF<-lmBF(correct_8~switch+explore+group, dataDynamic)
allBF<-c(switchBF,exploreBF,groupBF,switchgroupBF,exploregroupBF,segBF)
allBF[1]/allBF[3] # How much better switch is than group [1] switch : 70239.28 ±0.01%
allBF[2]/allBF[3] # How much better explore is than group [1] explore : 8678.091 ±0.01%
# Comparing the child-only model to one that also includes switching
allBF[4]/allBF[3] # switch+group vs group-only.
# Check for relationship between age and exploration within children
dataChild<- data_sum %>% filter(group == "child")
plot(dataChild$AgeYear,dataChild$explore)
plot(dataChild$AgeYear,dataChild$switch)
switchAgeBF<-lmBF(switch~AgeYear, dataChild)
switchAgeBF # AgeYear : 0.4546462 ±0%
exploreAgeBF<-lmBF(explore~AgeYear, dataChild) # [1] AgeYear : 0.3908611 ±0%
####################################################################
##### DATA VIZ FOR PAPER ####
####################################################################
labels <- c(dynamic = "Dynamic condition", static = "Static condition")
theme_custom <- theme(strip.text.x = element_text(size = 28),
axis.title.y = element_text(size = 28, angle = 90),
axis.title.x = element_text(size = 28),
axis.text.x = element_text(size=24),
axis.text.y = element_text(size=24)
)
##### EARNINGS ######
earn2<-ggplot(data_sum, aes(x=group,y=totalEarn,fill=group))+
geom_dotplot(binaxis='y', stackdir='center', dotsize=.5, alpha=.3)+
geom_boxplot(alpha=.5)+
theme_bw()+
scale_fill_manual(values = c("#f4d221", "#e5263a"))+
ylab("Stars won")+
xlab(" ")+
theme(legend.position="none")+
stat_summary(fun.y=mean, geom="point", shape=23, size=4)+
facet_grid(~condition, labeller=labeller(condition = labels))+
ylim(0,500)+
scale_x_discrete(labels = c("Adults", "Children"))+
theme_custom
earn2
# ggsave(here("plots","exp2_Stars.png"), width = 9.15, height = 5.66)
#### EXPLORE CHOICES ####
explore2<-ggplot(data_sum, aes(x=group,y=explore,fill=group))+
#geom_jitter(size = 3, alpha = 0.3, width = 0.15, aes(fill=group)) +
geom_dotplot(binaxis='y', stackdir='center', dotsize=.5, alpha=.3)+
geom_boxplot(alpha=.5)+
scale_x_discrete(labels = c("Adults", "Children"))+
theme_bw()+
scale_fill_manual(values = c("#f4d221", "#e5263a"))+
ylab("Proportion of \n non-maximizing choices")+
xlab(" ")+
theme(legend.position="none")+
stat_summary(fun.y=mean, geom="point", shape=23, size=4)+
facet_grid(~condition, labeller=labeller(condition = labels))+
theme_custom +
ylim(0,1)
explore2
# ggsave(here("plots","exp2_Explore.png"), width = 9.15, height = 5.66)
### SWITCH CHOICES ###
switch2<-ggplot(data_sum, aes(x=group,y=switch,fill=group))+
geom_dotplot(binaxis='y', stackdir='center', dotsize=.5, alpha=.3)+
scale_x_discrete(labels = c("Adults", "Children"))+
#geom_jitter(size = 3, alpha = 0.3, width = 0.15) +
geom_boxplot(alpha=.5)+
theme_bw()+
scale_fill_manual(values = c("#f4d221", "#e5263a"))+
ylab("Proportion of switch choices")+
xlab(" ")+
theme(legend.position="none")+
stat_summary(fun.y=mean, geom="point", shape=23, size=4)+
facet_grid(~condition, labeller=labeller(condition = labels))+
theme_custom
switch2
# ggsave(here("plots","exp2_Switch.png"), width = 9.15, height = 5.66)
#### Post test ###
eight2<- ggplot(data_sum, aes(x = group, y = correct_8, fill=group)) +
stat_summary(fun.y=mean, geom="bar",alpha=.6, colour="black") +
theme_bw() +
# stat_summary(fun.data="mean_cl_boot", geom="errorbar", aes(width=0.1)) +
scale_fill_manual(values = c("#f4d221", "#e5263a"))+
ylab("Proportion of participants \n correct about 8-star monster")+
xlab(" ")+
scale_x_discrete(labels = c("Adults", "Children"))+
theme(legend.position="none") +
facet_grid(~condition, labeller=labeller(condition = labels))+
theme_custom +
ylim(0,1)
eight2
# ggsave(here("plots", "exp2_8Star.png"), width = 9.15, height = 5.66)
# # Final Plot for paper #
# library(patchwork)
(switch1|switch2)/(explore1|explore2)/(earn1|earn2)/(eight1|eight2)
##########################################################
# Within-condition analysis for the figures ####
##########################################################
dataDynamic <- data_sum %>% filter(condition == "dynamic")
dataStatic <- data_sum %>% filter(condition == "static")
### Switching Dynamic ###
switchBF = ttestBF(formula = switch ~ group, data = dataDynamic)
switchBF ## [1] Alt., r=0.707 : 2.803773e+13 ±0%
switchChains= posterior(ttestBF(formula = switch ~ group, data = dataDynamic),iterations=1000)
mean(switchChains[,2]) # mean difference -0.579253
quantile(switchChains[,2],probs=c(0.025,0.975)) # mean difference CI
mean(switchChains[,4])# effect size estimite -2.341969
quantile(switchChains[,4]) # effect size CI
### Switching Static ###
switchBF = ttestBF(formula = switch ~ group, data = dataStatic)
switchBF ## [1] Alt., r=0.707 : 1.426302e+20 ±0%
switchChains= posterior(ttestBF(formula = switch ~ group, data = dataStatic),iterations=1000)
mean(switchChains[,2]) # mean difference -0.6092656
quantile(switchChains[,2],probs=c(0.025,0.975)) # mean difference CI
mean(switchChains[,4])# effect size estimite -3.860834
quantile(switchChains[,4]) # effect size CI
### Non-max Dynamic ###
exploreBF = ttestBF(formula = explore ~ group, data = dataDynamic)
exploreBF ## [1] Alt., r=0.707 : 1.067564e+14 ±0%
exploreChains= posterior(ttestBF(formula = explore ~ group, data = dataDynamic),iterations=1000)
mean(exploreChains[,2]) # mean difference -0.4729586
quantile(exploreChains[,2],probs=c(0.025,0.975)) # mean difference CI
mean(exploreChains[,4])# effect size estimite -2.416524
quantile(exploreChains[,4]) # effect size CI
### Non-max Static ###
exploreBF = ttestBF(formula = explore ~ group, data = dataStatic)
exploreBF ## [1] Alt., r=0.707 : 6.424851e+19 ±0%
exploreChains= posterior(ttestBF(formula = explore ~ group, data = dataStatic),iterations=1000)
mean(exploreChains[,2]) # mean difference -0.4687977
quantile(exploreChains[,2],probs=c(0.025,0.975)) # mean difference CI
mean(exploreChains[,4])# effect size estimite-3.812591
quantile(exploreChains[,4]) # effect size CI
### Reward dynamic ###
rewardBF = ttestBF(formula = totalEarn ~ group, data = dataDynamic)
rewardBF ## [1] Alt., r=0.707 : 1.080903e+14 ±0%
starChains= posterior(ttestBF(formula = totalEarn ~ group, data = dataDynamic),iterations=1000)
mean(starChains[,2]) # mean difference 105.374
quantile(starChains[,2],probs=c(0.025,0.975)) # mean difference CI
mean(starChains[,4])# effect size estimite 2.396081
quantile(starChains[,4]) # effect size CI
### Reward static ###
rewardBF = ttestBF(formula = totalEarn ~ group, data = dataStatic)
rewardBF ## [1] Alt., r=0.707 : 4.486329e+18 ±0%
starChains= posterior(ttestBF(formula = totalEarn ~ group, data = dataStatic),iterations=1000)
mean(starChains[,2]) # mean difference 98.46568
quantile(starChains[,2],probs=c(0.025,0.975)) # mean difference CI
mean(starChains[,4])# effect size estimite 141.9224
quantile(starChains[,4]) # effect size CI
|
bf5d5e55f61588a34324df3ff53eff0c2093129b
|
d08284a960a69fe39e7f46ce82097d537300db26
|
/R/ml.est.R
|
84e229ea569157fb30fae67b4696e80289e1c94d
|
[] |
no_license
|
cran/SeleMix
|
01978514d14c599f439cee2359b5547b71d3d86a
|
dca94e4ba9b3a16c34051a326ab5be56042e91cd
|
refs/heads/master
| 2021-01-25T08:55:23.315314
| 2020-11-29T00:30:03
| 2020-11-29T00:30:03
| 17,693,652
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 9,664
|
r
|
ml.est.R
|
ml.est <- function (y, x=NULL, model = "LN", lambda=3, w=0.05, lambda.fix=FALSE, w.fix=FALSE, eps=1e-7, max.iter=500, t.outl=0.5, graph=FALSE)
{
#------------------------------------------------------------------------------
# Individuazione degli outlier basata su un modello mistura di 2 gaussiane
#------------------------------------------------------------------------------
# PARAMETRI
# y = matrice ( o data.frame) - Variabili dipendenti (con possibili errori)
# x = matrice ( o data.frame) - Variabili indipendenti (dati esatti. P.e. da archivio amministrativo)
# model = Indica se i dati osservati hanno distribuzione log-normale (LN) o normale (N).
# w = proporzione dei dati contaminati (peso a priori)
# max.iter = numero massimo di iterazioni per la convergenza EM
# eps = soglia di accettazione
# lambda = fattore di inflazione della varianza
# graph = visualizzazione dei grafici durante l'elaborazione
#------------------------------------------------------------------------------
ris<-list(
ypred = NA,
B=NA,
sigma=NA,
lambda=Inf,
w=NA,
tau=NA,
outlier = NA,
n.outlier =0,
pattern= NA,
is.conv = NA,
n.iter =NA,
sing=NA,
bic.aic = NA,
msg="",
model=model
)
#------------------------------------------------------------------------------
# Copio i dati di input su aree di appoggio
#------------------------------------------------------------------------------
memo.y <- y <- as.matrix(y)
memo.x <- x
#------------------------------------------------------------------------------
# CONTROLLI SUI PARAMETRI
# Eliminazione dei record contenenti missing per la stima dei parametri
#------------------------------------------------------------------------------
ind.NA<- which(rowSums(is.na(y)) >0)
#------------------------------------------------------------------------------
if (length(ind.NA) > 0 ) {
warning(paste("Input matrix y contains", length(ind.NA), " (%",length(ind.NA)*100/nrow(y) ,
") rows with missing values not included in parameter's estimation\n" ))
y <- y[-ind.NA,,drop=FALSE]
if (!is.null(x)) {
x <-as.matrix(x)
x <- x[-ind.NA,,drop=FALSE]
}
}
#------------------------------------------------------------------------------
# CONTROLLI SUI PARAMETRI
#------------------------------------------------------------------------------
vars <- check.vars(y,x,model,parent="ml.est")
if (vars$ret == -9) {
stop(vars$msg.err)
}
if (vars$ret != 0) {
warning(vars$msg.err)
}
y <- as.matrix(vars$y)
x <- as.matrix(vars$x)
#y <- as.matrix(y, dimnames = NULL)
p <- ncol(y)
n <- nrow(y)
omega <- rep(1,n)
# x <- as.matrix(cbind (rep(1,n),x))
q <- ncol(x)
#------------------------------------------------------------------------------
#--------------------- DEFINIZIONE VARIABILI ---------------------
conv <- FALSE
continua <- TRUE
lik<-NA
lik0 <- 10
oldlik <- 0
iter <- 0
sing <- FALSE
if (ncol(x)+ ncol(y) < 3) # INSERIRE BOXPLOT
graph=FALSE
if (graph ) {
lambda_all <- lambda
if (ncol(y) >= 2) {
lab <- names(y)[1:2]
Var <- y[,1:2]
}
else if (ncol(y) == 1 & ncol(x) > 1) {
lab <- c(names(x)[2],names(y)[1])
Var <- cbind(x[,2],y[,1])
}
par(mfrow=c(2,1))
}
# B ha q (ncol(x)) righe e p (ncol(y)) variabili
B <- solve((t(x) %*% x) + (10e-8* diag(rep(1,q)))) %*% t(x) %*% y
# B <- try(solve(t(x) %*% x) %*% t(x) %*% y, TRUE)
B0 <- B
sigma <- (t(y - x%*%B) %*% (y - x%*%B)) / (n-1)
sigma <- sigma + (10e-8* diag(rep(1,p)))
sigma0 <- sigma
sigma2 <- (1 + lambda) * sigma
w1 <- 1-w
#------------------------------------------------------------------------------
# CALCOLO DEL BIC per il modello normale da usare per i confronti
#------------------------------------------------------------------------------
# N. parametri per il modello normale
k1 <- ncol(x) * p + (p*(p+1))/2 # p=ncol(y)
# N. parametri per il modello di contaminazione
k2 <- k1 + 2 - w.fix - lambda.fix
if (n < k2) {
warning(paste("Input data are fewer than the number of model parameters\n" ))
}
#------------------------------------------------------------------------------
# Calcolo della verisimiglianza normale
#------------------------------------------------------------------------------
dati<-cbind(x,y)
norm.mv<-function(u){dmvnorm(u[q+1:p], t(B0)%*%u[1:q], sigma0, log=TRUE)}
lik.n <- sum(apply(dati,1,norm.mv))
BIC.n <- -2*lik.n + k1*log(n)
#************************ INIZIO CICLO EM ************************************
while (iter < max.iter & continua == TRUE)
{
iter <- iter + 1
# print(paste("E-step",iter))
#*********************** E - STEP ************************************
tau1 <- post.prob(y, x, B, sigma, w1, lambda)
tau2 <- 1 - tau1
#*********************** M - STEP ************************************
# print(paste("M-step",iter))
#*********************** calcolo dei pesi **********************************
if (!w.fix)
w1 <- sum(tau1)/n;
#*********************** omega **********************************
omega <- as.vector(tau1 + tau2 / (1+lambda))
#*********************** B **********************************
appo <- t(x) %*% (omega * x)
appo <- solve(appo)
B <- appo %*% t(x) %*% (omega * y)
#*********************** sigma **********************************
dif <- y - x%*%B
sigma <- (t(dif) %*% (omega * dif)) / n
if (det(sigma) < 10e-10) {
ris$sing <- TRUE
ris$is.conv <- FALSE
ris$msg <- "Covariance matrix quasi singular: essentially perfect fit"
warning(ris$msg)
}
s1 <- solve(sigma)
#*********************** lambda **********************************
# q1 <- matrix(diag(dif %*% solve(sigma1) %*% t(dif)),n,1) ## DIM n,1
# q2 <- matrix(diag(dif %*% solve(sigma2) %*% t(dif)),n,1) ## DIM n,1
if (!lambda.fix) {
appo <- t(dif) %*% (as.vector(tau2) * dif) %*% s1
lambda <- sum(diag(as.matrix(appo))) / (p * sum(tau2)) -1
# if (lambda > 1e+06) {
# sing <- TRUE
# continua <- conv <- FALSE
# warning (paste("lambda =" ,lambda,": iterations stopped because of essentially perfect fit", sep=""))
# break
# }
if (lambda < 0.5) {
continua <- conv <- FALSE
warning (paste("lambda parameter lower than 0.5. Possible lack of model identification.", sep=""))
break
}
}
#*********************** CONVERGENZA **********************************
s2 <- s1 / (1 + lambda)
sigma2 <- (1+lambda)* sigma
q1 <- matrix(tensorizza (dif, s1),n,1)
q2 <- matrix(tensorizza (dif, s2),n,1)
rm (s1,s2)
q1 <- -0.5*q1
q2 <- -0.5*q2
ll <- w1 * exp(q1) / sqrt(2*pi*det(sigma)) + (1-w1) * (exp(q2)) / sqrt(2*pi*det(sigma2))
lik <- sum(log(ll))
if (graph) {
plot(Var, col = "lightgrey", main= "EM IN ACTION...\n Identifying outliers", xlab=lab[1], ylab=lab[2] )
points(Var[tau2 > t.outl, ],pch=21,col="blue",bg=paste("cyan",sample(1:4,1),sep=""))
lambda_all <- c (lambda_all, lambda)
plot( lambda_all, xlab="n. iterations", ylab="lambda")
}
BIC.mix <- -2*lik + k2*log(n)
continua <- (abs(lik-oldlik) > eps*abs(lik-lik0) )
conv <- !continua
if (iter > round(max.iter/5) & BIC.n < BIC.mix ) {
continua <- conv <- FALSE
warning (paste("EM stopped because BIC value " ,BIC.mix,"for the contamination model is greater than BIC value",
BIC.n, " for the Gaussian model", sep=""))
break
}
#alpha <- sqrt((lambda+1) )
oldlik <- lik
if (iter == 1)
lik0 <- lik
}
#************************ FINE CICLO EM ************************************
if (iter >= max.iter)
conv <- FALSE
# CALCOLO DEI VALORI PREVISTI
yprev <- pred.y(y=memo.y, x=memo.x, B, sigma,
lambda, w=1-w1, model = model, t.outl=t.outl)
############### calcolo di BIC e AIC per i due modelli #############
BIC.n <- -2*lik.n + k1*log(n)
BIC.mix <- -2*lik + k2*log(n)
AIC.n <- 2*k1 - lik.n
AIC.mix <- 2*k2 - lik
ris$ypred <- as.matrix(yprev[,1:(ncol(yprev)-3)])
ris$B <- B
ris$sigma <- sigma
ris$lambda <- lambda
ris$w <- 1-w1
ris$tau <- yprev$tau
ris$outlier <- yprev$outlier
ris$n.outlier <- sum(yprev$outlier)
ris$pattern <- yprev$pattern
ris$is.conv <- conv
ris$n.iter <- iter
ris$sing <- sing
ris$bic.aic <- c(BIC.norm=BIC.n, BIC.mix=BIC.mix, AIC.norm=AIC.n, AIC.mix=AIC.mix)
class(ris) <- c(class(ris), "mlest" )
if (conv == FALSE)
warning (paste("EM algorithm failed to converge: stop after", iter, "iterations"))
ris
}
|
803dd784b1f18d7e462abe80b0eb9dde80dd5108
|
c28bdbe50f95ce0d7ba0b313d33246ab4d1e62ec
|
/simpleMean.R
|
3fed0811cdb07cc778b5204521ab7b21625a3ae0
|
[] |
no_license
|
wangbinzjcc/bayesian00
|
626253cdefef20d3e7d4bcd772dc4768845a94b1
|
21218253b8fd9d683b2e048c8e0ceae6fad70886
|
refs/heads/master
| 2021-01-22T23:54:25.798740
| 2014-01-20T16:03:25
| 2014-01-20T16:03:25
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,848
|
r
|
simpleMean.R
|
# Simple normal mean model in LaplacesDemon
# Generate two samples of body mass measurements of male peregrines
y1000 <- rnorm(n = 1000, mean = 100, sd = 10) # Sample of 1000 birds
###==========================================================
mean(y1000)
###==========================================================
lm0 <- lm(y1000~1)
sd(y1000)
summary(lm0)
###=========================================================
population.sd <- 1
for(i in 1:10){
mu <- 1:3000
la00 <- sapply(mu,function(xx)sum(dnorm(y1000, xx, population.sd, log=TRUE)))
mu <- mu[which.max(la00)]
population.sd <- 1:100
d01 <- sapply(population.sd,function(xx)sum(dnorm(y1000, mu, xx, log=TRUE)))
population.sd <- population.sd[which.max(d01)]
}
c(mean=mu,sd=population.sd)
plot(la00)
plot(d01)
###===============================================================
### Random walk MCMC for binomial proportion
############################################
# Parameters
parm <- c(1,10)
population.mean <- parm[1]
population.sd <- parm[2]
# Prior density
population.mean.prior <- dunif(population.mean, 0, 5000)
population.sd.prior <- dunif(population.sd, 0, 100)
# Log-Likelihood
LL <- sum(dnorm(y1000, population.mean, population.sd, log=TRUE))
# Log-Posterior
LP <- LL + population.mean.prior + population.sd.prior
Modelout <- list(LP=LP, Dev=-2*LL, Monitor=c(LP),
yhat=rnorm(length(y1000), population.mean, population.sd),
parm=c(rnorm(1,600), runif(1, 1, 30))
)
###===============================================================
# Load library
library(LaplacesDemon)
# Model specification
Model <- function(parm, Data)
{
# Parameters
population.mean <- parm[1]
population.sd <- parm[2]
# Prior density
population.mean.prior <- dunif(population.mean, 0, 5000)
population.sd.prior <- dunif(population.sd, 0, 100)
# Log-Likelihood
mu <- population.mean
LL <- sum(dnorm(Data$mass, mu, population.sd, log=TRUE))
# Log-Posterior
LP <- LL + population.mean.prior + population.sd.prior
Modelout <- list(LP=LP, Dev=-2*LL, Monitor=c(LP), yhat=rnorm(Data$N, mu, population.sd), parm=parm)
return(Modelout)
}
# Prepare the data
parm.names <- c("population.mean", "population.sd")
Data <- list(mass=y1000, N=length(y1000), mon.names=c("LP"), parm.names=parm.names)
# Initial values
Initial.Values <- c(
rnorm(1,600), # population.mean
runif(1, 1, 30) # population.sd
)
# MCMC settings
ni <- 50000 # Number of draws from posterior (for each chain)
st <- 1000 # Steps when status message should be given
nt <- 50 # Thinning rate
# Run LaplacesDemon
out <- LaplacesDemon(Model, Data=Data, Initial.Values, Iterations=ni, Status=st, Thinning=nt)
# Have a look at some summary statistics
out
# Plotting output
plot(out, BurnIn=100, Data, PDF=T, Parms=c("population.mean", "population.sd"))
|
9f6ee031ede80d5b0c9d709d023ca741e5327ce4
|
91ffdf0cea4024d9368c7599a19f852e3bb3e2c1
|
/man/gcol.Rd
|
7e83d3d3e6ff2b7f4764a418dcb91f39bfaacb55
|
[] |
no_license
|
qianlin-qz/AnalyseDD
|
a7c4e21c491402fb2b7e018c96ff3d458a66f281
|
2795cfb2f256cb32e6f5ea4e787b0507b2ac7462
|
refs/heads/master
| 2021-01-19T19:12:38.835024
| 2017-04-16T10:22:50
| 2017-04-16T10:22:50
| 88,406,021
| 0
| 0
| null | null | null | null |
WINDOWS-1250
|
R
| false
| true
| 351
|
rd
|
gcol.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/analysed.R
\name{gcol}
\alias{gcol}
\title{Centre gravite de colonnes}
\usage{
gcol(data)
}
\arguments{
\item{data:}{valeur d'origine}
}
\value{
un vector de centre de gravite
}
\description{
Le centre de gravite de profils-lignes affect¨¦s avec des poids(frequences)
}
|
368aa5164f80e002c157c2abf5bcb6676e3084f6
|
2bb142f602ad3f563818b272cfa90c98bf59f4b0
|
/bayesian statistics/01-beta.R
|
be2bf7e50569b2b032c1051e15ff45fcf7afe33f
|
[] |
no_license
|
sercandogan/lessons
|
253ba4c1da7db8900c8e266caf636424f6bc56cb
|
f497a50ffa9c58274f5489a69681c992c5245282
|
refs/heads/master
| 2021-09-05T03:15:14.570580
| 2018-01-23T23:33:46
| 2018-01-23T23:33:46
| 107,280,290
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 952
|
r
|
01-beta.R
|
# 90% positive of 10 ratings
o1 <- 9
o0 <- 1
M <- 100
N <- 100000
m <- sapply(0:M/M,function(prob)rbinom(N,o1+o0,prob))
v <- colSums(m==o1)
df_sim1 <- data.frame(p=rep(0:M/M,v))
df_beta1 <- data.frame(p=0:M/M, y=dbeta(0:M/M,o1+1,o0+1))
# 80% positive of 500 ratings
o1 <- 400
o0 <- 100
M <- 100
N <- 100000
m <- sapply(0:M/M,function(prob)rbinom(N,o1+o0,prob))
v <- colSums(m==o1)
df_sim2 <- data.frame(p=rep(0:M/M,v))
df_beta2 <- data.frame(p=0:M/M, y=dbeta(0:M/M,o1+1,o0+1))
ggplot(data=df_sim1,aes(p)) +
scale_x_continuous(breaks=0:10/10) +
geom_histogram(aes(y=..density..,fill=..density..),
binwidth=0.01, origin=-.005, colour=I("gray")) +
geom_line(data=df_beta1 ,aes(p,y),colour=I("red"),size=2,alpha=.5) +
geom_histogram(data=df_sim2, aes(y=..density..,fill=..density..),
binwidth=0.01, origin=-.005, colour=I("gray")) +
geom_line(data=df_beta2,aes(p,y),colour=I("orange"),size=2,alpha=.5)
|
ff26280eb50595570e7bdcac08076ed4e6505936
|
e6eadf086af79f7ccea1b3c765a909157c77255d
|
/man/errorGen.Rd
|
9a0697914a2c17cbe8309921d65db2b6a97ae5a2
|
[] |
no_license
|
jsta/ipdw
|
dc76bc00c32725c953b9870edcf40dffe836fe49
|
630a20a635b9a21b3838270c1f5ec02b1399fc0d
|
refs/heads/master
| 2023-03-16T07:09:08.043013
| 2023-03-09T19:11:17
| 2023-03-09T19:11:17
| 20,983,236
| 12
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,281
|
rd
|
errorGen.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/errorGen.R
\name{errorGen}
\alias{errorGen}
\title{Generate interpolation error stats from validation datasets}
\usage{
errorGen(
finalraster,
validation.sf_ob,
validation.data,
plot = FALSE,
title = ""
)
}
\arguments{
\item{finalraster}{RasterLayer object}
\item{validation.sf_ob}{sf object with points geometry}
\item{validation.data}{data.frame}
\item{plot}{logical. Plot comparison?}
\item{title}{Plot labels}
}
\value{
List of error statistics
}
\description{
Generate error statistics from validation point datasets overlaid on a raster surface
}
\examples{
library(sf)
validation.data <- data.frame(rnorm(10, mean = 0.2, sd = 1))
names(validation.data) <- c("validation")
validation.sf_ob <- validation.data
validation.data <- as.numeric(unlist(validation.data))
xy <- data.frame(x = c(0:9), y = rep(1, 10))
validation.sf_ob <- st_as_sf(cbind(validation.sf_ob, xy), coords = c("x", "y"))
m <- matrix(NA, 1, 10)
out.ras <- raster(m, xmn = 0, xmx = ncol(m), ymn = 0, ymx = nrow(m))
out.ras[] <- validation.data + rnorm(ncell(out.ras), mean = 0.01, sd = 0.2)
valid.stats <- errorGen(out.ras, validation.sf_ob, validation.data, plot = TRUE,
title = "Validation Plot")
valid.stats
}
|
4f624bc8f27708d14dd8257d9641769fb054fb5d
|
d56cff14262b0c58733898164659a27e2739d97d
|
/R/rstan_generics.R
|
a7f271565093c09ac6c761a3ec73a741ffe0e98c
|
[] |
no_license
|
cran/idealstan
|
6aeffb800be1490c1f2f969313e3f79d57eb5c5d
|
daa29ce7e203c63fbba916aa258d53b48ea430b2
|
refs/heads/master
| 2021-05-02T03:26:00.009381
| 2019-07-10T14:00:03
| 2019-07-10T14:00:03
| 120,898,012
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 16,545
|
r
|
rstan_generics.R
|
# These functions are implemented for compatibility with the
# rstantools package (and rstanarm)
#' Generic Method for Obtaining Posterior Predictive Distribution from Stan Objects
#'
#' This function is a generic that is used to match the functions used with \code{\link[bayesplot]{ppc_bars}} to calculate
#' the posterior predictive distribution of the data given the model.
#'
#' @param object A fitted \code{idealstan} object
#' @param ... All other parameters passed on to the underlying function.
#' @export
#' @return \code{posterior_predict} methods should return a \eqn{D} by \eqn{N}
#' matrix, where \eqn{D} is the number of draws from the posterior predictive
#' distribution and \eqn{N} is the number of data points being predicted per
#' draw.
#' @export
setGeneric('id_post_pred',signature='object',
function(object,...) standardGeneric('id_post_pred'))
#' Posterior Prediction for \code{idealstan} objects
#'
#' This function will draw from the posterior distribution, whether in terms of the outcome (prediction)
#' or to produce the log-likelihood values.
#'
#' This function can also produce either distribution of the
#' outcomes (i.e., predictions) or the log-likelihood values of the posterior (set option
#' \code{type} to \code{'log_lik'}.
#' For more information, see the package vignette How to Evaluate Models.
#'
#' You can then use functions such as
#' \code{\link{id_plot_ppc}} to see how well the model does returning the correct number of categories
#' in the score/vote matrix.
#' Also see \code{help("posterior_predict", package = "rstanarm")}
#'
#' @param object A fitted \code{idealstan} object
#' @param draws The number of draws to use from the total number of posterior draws (default is 100).
#' @param sample_scores In addition to reducing the number of posterior draws used to
#' calculate the posterior predictive distribution, which will reduce computational overhead.
#' Only available for calculating predictive distributions, not log-likelihood values.
#' @param type Whether to produce posterior predictive values (\code{'predict'}, the default),
#' or log-likelihood values (\code{'log_lik'}). See the How to Evaluate Models vignette for more info.
#' @param output If the model has an unbounded outcome (Poisson, continuous, etc.), then
#' specify whether to show the \code{'observed'} data (the default) or the binary
#' output \code{'missing'} showing whether an observation was predicted as missing or not
#' @param ... Any other arguments passed on to posterior_predict (currently none available)
#'
#' @export
setMethod('id_post_pred',signature(object='idealstan'),function(object,draws=100,
output='observed',
type='predict',
sample_scores=NULL,...) {
#all_params <- rstan::extract(object@stan_samples)
n_votes <- nrow(object@score_data@score_matrix)
if(object@stan_samples@stan_args[[1]]$method != 'variational') {
n_iters <- (object@stan_samples@stan_args[[1]]$iter-object@stan_samples@stan_args[[1]]$warmup)*length(object@stan_samples@stan_args)
} else {
# there is no warmup for VB
n_iters <- dim(object@stan_samples)[1]
}
if(!is.null(sample_scores) && type!='log_lik') {
this_sample <- sample(1:n_votes,sample_scores)
} else {
this_sample <- 1:n_votes
}
if(type!='log_lik') {
these_draws <- sample(1:n_iters,draws)
} else {
these_draws <- 1:n_iters
draws <- n_iters
}
print(paste0('Processing posterior replications for ',n_votes,' scores using ',draws,
' posterior samples out of a total of ',n_iters, ' samples.'))
y <- object@score_data@score_matrix$outcome[this_sample]
# check to see if we need to recode missing values from the data if the model_type doesn't handle missing data
if(object@model_type %in% c(1,3,5,7,9,11,13) & !is.null(object@score_data@miss_val)) {
y <- .na_if(y,object@score_data@miss_val)
}
if(object@use_groups) {
person_points <- as.numeric(object@score_data@score_matrix$group_id)[this_sample]
} else {
person_points <- as.numeric(object@score_data@score_matrix$person_id)[this_sample]
}
bill_points <- as.numeric(object@score_data@score_matrix$item_id)[this_sample]
time_points <- as.numeric(factor(object@score_data@score_matrix$time_id))[this_sample]
remove_nas <- !is.na(y) & !is.na(person_points) & !is.na(bill_points) & !is.na(time_points)
y <- y[remove_nas]
if(is.factor(y)) {
miss_val <- which(levels(y)==object@score_data@miss_val)
y <- as.numeric(y)
}
max_val <- max(y)
bill_points <- bill_points[remove_nas]
time_points <- time_points[remove_nas]
person_points <- person_points[remove_nas]
model_type <- object@model_type
latent_space <- model_type %in% c(13,14)
inflate <- model_type %in% c(2,4,6,8,10,12,14)
# we can do the initial processing here
# loop over posterior iterations
L_tp1 <- .extract_nonp(object@stan_samples,'L_tp1')[[1]]
A_int_free <- .extract_nonp(object@stan_samples,'A_int_free')[[1]]
B_int_free <- .extract_nonp(object@stan_samples,'B_int_free')[[1]]
sigma_abs_free <- .extract_nonp(object@stan_samples,'sigma_abs_free')[[1]]
sigma_reg_free <- .extract_nonp(object@stan_samples,'sigma_reg_free')[[1]]
pr_absence_iter <- sapply(these_draws, function(d) {
if(latent_space) {
# use latent-space formulation for likelihood
pr_absence <- sapply(1:length(person_points),function(n) {
-sqrt((L_tp1[d,time_points[n],person_points[n]] - A_int_free[d,bill_points[n]])^2)
}) %>% plogis()
} else {
# use IRT formulation for likelihood
pr_absence <- sapply(1:length(person_points),function(n) {
L_tp1[d,time_points[n],person_points[n]]*sigma_abs_free[d,bill_points[n]] - A_int_free[d,bill_points[n]]
}) %>% plogis()
}
return(pr_absence)
})
pr_vote_iter <- sapply(these_draws, function(d) {
if(latent_space) {
if(inflate) {
pr_vote <- sapply(1:length(person_points),function(n) {
-sqrt((L_tp1[d,time_points[n],person_points[n]] - B_int_free[d,bill_points[n]])^2)
}) %>% plogis()
} else {
# latent space non-inflated formulation is different
pr_vote <- sapply(1:length(person_points),function(n) {
sigma_reg_free[d,bill_points[n]] + sigma_abs_free[d,bill_points[n]] -
sqrt((L_tp1[d,time_points[n],person_points[n]] - B_int_free[d,bill_points[n]])^2)
}) %>% plogis()
}
} else {
pr_vote <- sapply(1:length(person_points),function(n) {
L_tp1[d,time_points[n],person_points[n]]*sigma_reg_free[d,bill_points[n]] - B_int_free[d,bill_points[n]]
}) %>% plogis()
}
return(pr_vote)
})
rep_func <- switch(as.character(model_type),
`1`=.binary,
`2`=.binary,
`3`=.ordinal_ratingscale,
`4`=.ordinal_ratingscale,
`5`=.ordinal_grm,
`6`=.ordinal_grm,
`7`=.poisson,
`8`=.poisson,
`9`=.normal,
`10`=.normal,
`11`=.lognormal,
`12`=.lognormal,
`13`=.binary,
`14`=.binary)
# pass along cutpoints as well
if(model_type %in% c(3,4)) {
cutpoints <- .extract_nonp(object@stan_samples,'steps_votes')[[1]]
cutpoints <- cutpoints[these_draws,]
} else if(model_type %in% c(5,6)) {
cutpoints <- .extract_nonp(object@stan_samples,'steps_votes_grm')[[1]]
cutpoints <- cutpoints[these_draws,,]
} else {
cutpoints <- 1
}
out_predict <- rep_func(pr_absence=pr_absence_iter,
pr_vote=pr_vote_iter,
N=length(person_points),
ordinal_outcomes=length(unique(object@score_data@score_matrix$outcome)),
inflate=inflate,
latent_space=latent_space,
time_points=time_points,
item_points=bill_points,
max_val=max_val,
outcome=y,
miss_val=miss_val,
person_points=person_points,
sigma_sd=.extract_nonp(object@stan_samples,'extra_sd')[[1]][these_draws],
cutpoints=cutpoints,
type=type,
output=output)
# set attributes to pass along sample info
attr(out_predict,'chain_order') <- attr(L_tp1,'chain_order')[these_draws]
attr(out_predict,'this_sample') <- this_sample
if(type=='predict') {
class(out_predict) <- c('matrix','ppd')
} else if(type=='log_lik') {
class(out_predict) <- c('matrix','log_lik')
}
return(out_predict)
})
#' Plot Posterior Predictive Distribution for \code{idealstan} Objects
#'
#' This function is the generic method for generating posterior distributions
#' from a fitted \code{idealstan} model. Functions are documented in the
#' actual method.
#'
#' This function is a wrapper around \code{\link[bayesplot]{ppc_bars}},
#' \code{\link[bayesplot]{ppc_dens_overlay}} and
#' \code{\link[bayesplot]{ppc_violin_grouped} that plots the posterior predictive distribution
#' derived from \code{\link{id_post_pred}} against the original data. You can also subset the
#' posterior predictions over
#' legislators/persons or
#' bills/item sby specifying the ID of each in the original data as a character vector.
#' Only persons or items can be specified,
#' not both.
#'
#' If you specify a value for \code{group} that is either a person ID or a group ID
#' (depending on whether a person or group-level model was fit), then you can see the
#' posterior distributions for those specific persons. Similarly, if an item ID is passed
#' to \code{item}, you can see how well the model predictions compare to the true values
#' for that specific item.
#'
#' @param object A fitted \code{idealstan} object
#' @param ... Other arguments passed on to \code{\link[bayesplot]{ppc_bars}}
#' @export
setGeneric('id_plot_ppc',signature='object',
function(object,...) standardGeneric('id_plot_ppc'))
#' Plot Posterior Predictive Distribution for \code{idealstan} Objects
#'
#' This function is the actual method for generating posterior distributions
#' from a fitted \code{idealstan} model.
#'
#' This function is a wrapper around \code{\link[bayesplot]{ppc_bars}},
#' \code{\link[bayesplot]{ppc_dens_overlay}} and
#' \code{\link[bayesplot]{ppc_violin_grouped} that plots the posterior predictive distribution
#' derived from \code{\link{id_post_pred}} against the original data. You can also subset the
#' posterior predictions over
#' legislators/persons or
#' bills/item sby specifying the ID of each in the original data as a character vector.
#' Only persons or items can be specified,
#' not both.
#'
#' If you specify a value for \code{group} that is either a person ID or a group ID
#' (depending on whether a person or group-level model was fit), then you can see the
#' posterior distributions for those specific persons. Similarly, if an item ID is passed
#' to \code{item}, you can see how well the model predictions compare to the true values
#' for that specific item.
#'
#' @param object A fitted idealstan object
#' @param ppc_pred The output of the \code{\link{id_post_pred}} function on a fitted idealstan object
#' @param group A character vector of the person or group IDs
#' over which to subset the predictive distribution
#' @param item A character vector of item IDs to subset the posterior distribution
#' @param ... Other arguments passed on to \code{\link[bayesplot]{ppc_bars}}
#' @export
setMethod('id_plot_ppc',signature(object='idealstan'),function(object,
ppc_pred=NULL,
group=NULL,
item=NULL,...) {
this_sample <- attr(ppc_pred,'this_sample')
# create grouping variable
if(!is.null(group)) {
if(object@use_groups) {
group_var <- factor(object@score_data@score_matrix$group_id, levels=group)
} else {
group_var <- factor(object@score_data@score_matrix$person_id, levels=group)
}
grouped <- T
} else if(!is.null(item)) {
group_var <- factor(object@score_data@score_matrix$item_id, levels=item)
grouped <- T
} else {
grouped <- F
}
y <- object@score_data@score_matrix$outcome[this_sample]
# check to see if we need to recode missing values from the data if the model_type doesn't handle missing data
if(object@model_type %in% c(1,3,5,7,9,11,13) & !is.null(object@score_data@miss_val)) {
y <- .na_if(y,object@score_data@miss_val)
}
if(object@use_groups) {
person_points <- as.numeric(object@score_data@score_matrix$group_id)[this_sample]
} else {
person_points <- as.numeric(object@score_data@score_matrix$person_id)[this_sample]
}
bill_points <- as.numeric(object@score_data@score_matrix$item_id)[this_sample]
time_points <- as.numeric(object@score_data@score_matrix$time_id)[this_sample]
remove_nas <- !is.na(y) & !is.na(person_points) & !is.na(bill_points) & !is.na(time_points)
y <- y[remove_nas]
bill_points <- bill_points[remove_nas]
time_points <- time_points[remove_nas]
person_points <- person_points[remove_nas]
if(!is.null(group)) {
group_var <- group_var[remove_nas]
# create a second one for the grouping variable
remove_nas_group <- !is.na(group)
}
if(!is.null(item) && !is.null(group))
stop('Please only specify an index to item or person, not both.')
if(attr(ppc_pred,'output')=='all') {
y <- as.numeric(y)
if(grouped) {
bayesplot::ppc_bars_grouped(y=y[remove_nas_group],yrep=ppc_pred[,remove_nas_group],
group=group_var[remove_nas_group],...)
} else {
bayesplot::ppc_bars(y=y,yrep=ppc_pred,...)
}
} else if(attr(ppc_pred,'output')=='observed') {
# only show observed data for yrep
y <- .na_if(y,object@score_data@miss_val)
to_remove <- !is.na(y)
y <- y[to_remove]
if(!is.null(group)) {
group_var <- group_var[to_remove]
remove_nas_group <- !is.na(group_var)
}
y <- as.numeric(y)
if(attr(ppc_pred,'output_type')=='continuous') {
ppc_pred <- ppc_pred[,to_remove]
#unbounded observed outcomes (i.e., continuous)
if(grouped) {
bayesplot::ppc_violin_grouped(y=y[remove_nas_group],yrep=ppc_pred[,remove_nas_group],
group=group_var[remove_nas_group],
...)
} else {
bayesplot::ppc_dens_overlay(y=y,yrep=ppc_pred,...)
}
} else if(attr(ppc_pred,'output_type')=='discrete') {
ppc_pred <- ppc_pred[,to_remove]
if(grouped) {
bayesplot::ppc_bars_grouped(y=y[remove_nas_group],yrep=ppc_pred[,remove_nas_group],
group=group_var[remove_nas_group],...)
} else {
bayesplot::ppc_bars(y=y,yrep=ppc_pred,...)
}
}
} else if(attr(ppc_pred,'output')=='missing') {
y <- .na_if(y,object@score_data@miss_val)
y <- as.numeric(is.na(y))
if(grouped) {
bayesplot::ppc_bars_grouped(y=y[remove_nas_group],yrep=ppc_pred[,remove_nas_group],
group=group_var[remove_nas_group],...)
} else {
bayesplot::ppc_bars(y=y,yrep=ppc_pred,...)
}
}
})
#' Helper Function for `loo` calculation
#'
#' This function accepts a log-likelihood matrix produced by `id_post_pred` and
#' extracts the IDs of the MCMC chains. It is necessary to use this function
#' as the second argument to the `loo` function along with an exponentiated
#' log-likelihood matrix. See the package vignette How to Evaluate Models
#' for more details.
#'
#' @param ll_matrix A log-likelihood matrix as produced by the \code{\link{id_post_pred}}
#' function
#' @export
derive_chain <- function(ll_matrix=NULL) {
attr(ll_matrix,'chain_order')
}
|
d705f7d7ef096c97472fb22981d95fe2eea28e37
|
030e413aebffc20fe1243ebe264755d7f8d5cee5
|
/Census NAICS Trade.R
|
b10203aef90434a0f807b0cb77dd7f85f2866db0
|
[] |
no_license
|
szmsp/bilateral-trade-in-goods-naics
|
63e2d79663996620fb319a0ff7c95465a677d4b9
|
dbad0737fe78c0d77f08c227b2e94f245ad0e4d5
|
refs/heads/master
| 2020-04-28T01:44:14.120594
| 2019-03-24T16:58:22
| 2019-03-24T16:58:22
| 174,869,220
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 12,681
|
r
|
Census NAICS Trade.R
|
###############################################################################################################################
# README
# Project: Goods trade by three-digit NAICS code between the US and other countries
# Objective: From the Census API, download two years of the US Census Bureau bilateral trade by three-digit NAICS codes
# US Census API Setup
# Census API Key request here: http://api.census.gov/data/key_signup.html
# In the API call: months must be specified as "\d\d" ("01", "02")
# The as.yearqtr function converts the underlying date value to the first day-month of the quarter
# For help on Census international trade data API
# https://www.census.gov/foreign-trade/reference/guides/Guide%20to%20International%20Trade%20Datasets.pdf
# To run, replace "[key]" and "[path]" below
###############################################################################################################################
# Run before every program to clear your workspace
rm(list=ls())
cat("\014")
# Root paths
folder <- "[path]"
analysis <- paste(folder, "Analysis\\", sep = "")
# Set dates to download
# First date
first_year <- "2013"
first_month <- "01"
# Final date
final_year <- "2018"
final_month <- "12"
### PATHS AND PARAMETERS ###
# Census key, obtained from Census website above
census_key <- "[key]"
# Set analysis parameters and vars you want to grab. Currently set to Germany
# Parameters
ctry_code <- 4280
# Vars
import_vars <- paste("CTY_CODE","CTY_NAME","NAICS","NAICS_LDESC","GEN_VAL_MO","GEN_VAL_YR", sep=",")
export_vars <- paste("CTY_CODE","CTY_NAME","NAICS","NAICS_LDESC","ALL_VAL_MO","ALL_VAL_YR", sep=",")
###############################################################################################################################
### PULL DATA FROM CENSUS API###
# Set location
Sys.setlocale("LC_ALL","C")
# Set working directory
setwd(folder)
# Load libraries
library("tidyverse")
library("RJSONIO")
library("sqldf")
library("ggthemes")
library("zoo")
library("dplyr")
library("tools")
library("lubridate")
library("reshape2")
library("formattable")
#### PULL DATA ####
# Function to extract imports data
getImports <- function(census_key, year_month, import_vars, ctry_code) {
imp_resURL <- paste("https://api.census.gov/data/timeseries/intltrade/imports/",
"naics?get=",import_vars,"&COMM_LVL=","&COMM_LVL=NA3","&time=",year_month,
"&CTY_CODE=",ctry_code,"&key=",census_key,sep="")
imp_lJSON <- fromJSON(imp_resURL) # convert JSON content to R objects
imp_lJSON <- imp_lJSON[2:length(imp_lJSON)] # keep everything but the 1st element (var names) in lJSON
imp_lJSON.cc <- sapply(imp_lJSON,function(x) x[1]) # extract country code
imp_lJSON.cn <- sapply(imp_lJSON,function(x) x[2]) # extract country name
imp_lJSON.nc <- sapply(imp_lJSON,function(x) x[3]) # extract three-digit NAICS code
imp_lJSON.nd <- sapply(imp_lJSON,function(x) x[4]) # extract NAICS description
imp_lJSON.avm <- sapply(imp_lJSON,function(x) x[5]) # extract all value month
imp_lJSON.avy <- sapply(imp_lJSON,function(x) x[6]) # extract all value year
imp_lJSON.t <- sapply(imp_lJSON,function(x) x[8]) # extract time
imp_df <- data.frame(as.Date(paste(imp_lJSON.t,"-02", sep=""), format="%Y-%m-%d"), imp_lJSON.cc,
as.character(imp_lJSON.cn), imp_lJSON.nc, imp_lJSON.nd,
as.numeric(imp_lJSON.avm),as.numeric(imp_lJSON.avy))
# put in dataframe
names(imp_df) <- c("year_month", "country_code", "country_name", "naics_code", "naics_desc",
"monthly_import_value", "ytd_import_value")
# name the vars in the data frame
return(imp_df)
}
# API calls split by year for efficiency
date_list <- seq(as.Date(paste(first_year, "/", first_month, "/1", sep = "")), as.Date(paste(final_year, "/", final_month, "/1", sep = "")), "years")
year_list <- year(date_list)
# Call imports data
for (y in year_list){
if((y == first_year)){
month_imports <- getImports(census_key, year_month = (paste("from+", y,"-01+to+", y,"-12", sep = "")), import_vars, ctry_code)
}
if(exists("month_imports") && (y != first_year) && (y != final_year)){
temp_data <- getImports(census_key, year_month = (paste("from+", y,"-01+to+", y,"-12", sep = "")), import_vars, ctry_code)
month_imports <- rbind(month_imports, temp_data)
rm(temp_data)
}
if(exists("month_imports") && (y == final_year)){
temp_data <- getImports(census_key, year_month = (paste("from+", y,"-01+to+", y,"-",final_month, sep = "")), import_vars, ctry_code)
month_imports <- rbind(month_imports, temp_data)
rm(temp_data)
}
}
View(month_imports)
check_imports <- sqldf("select year_month, naics_code, count(country_code) as naics_count from month_imports group by 1, 2")
View(check_imports)
# Check for one observation per three-digit NAICS code per month
stopifnot(check_imports$naics_count == 1)
month_imports <- month_imports[order(month_imports$naics_desc, month_imports$year_month),]
# Function to extract exports data
getExports <- function(census_key, year_month, export_vars, ctry_code) {
exp_resURL <- paste("https://api.census.gov/data/timeseries/intltrade/exports/",
"naics?get=",export_vars,"&COMM_LVL=","&COMM_LVL=NA3","&time=",year_month,
"&CTY_CODE=",ctry_code,"&key=",census_key,sep="")
exp_lJSON <- fromJSON(exp_resURL) # convert JSON content to R objects
exp_lJSON <- exp_lJSON[2:length(exp_lJSON)] # keep everything but the 1st element (var names) in lJSON
exp_lJSON.cc <- sapply(exp_lJSON,function(x) x[1]) # extract country code
exp_lJSON.cn <- sapply(exp_lJSON,function(x) x[2]) # extract country name
exp_lJSON.nc <- sapply(exp_lJSON,function(x) x[3]) # extract three-digit NAICS code
exp_lJSON.nd <- sapply(exp_lJSON,function(x) x[4]) # extract NAICS description
exp_lJSON.avm <- sapply(exp_lJSON,function(x) x[5]) # extract all value month
exp_lJSON.avy <- sapply(exp_lJSON,function(x) x[6]) # extract all value year
exp_lJSON.t <- sapply(exp_lJSON,function(x) x[8]) # extract time
exp_df <- data.frame(as.Date(paste(exp_lJSON.t,"-02", sep=""), format="%Y-%m-%d"), exp_lJSON.cc,
as.character(exp_lJSON.cn), exp_lJSON.nc, exp_lJSON.nd, as.numeric(exp_lJSON.avm),
as.numeric(exp_lJSON.avy))
# put in dataframe
names(exp_df) <- c("year_month", "country_code", "country_name", "naics_code", "naics_desc",
"monthly_export_value", "ytd_export_value")
# name the vars in the data frame
return(exp_df)
}
# Call exports data
for (y in year_list){
if(!exists("month_exports") && (y == first_year)){
month_exports <- getExports(census_key, year_month = (paste("from+", y,"-01+to+", y,"-12", sep = "")), export_vars, ctry_code)
}
if(exists("month_exports") && (y != first_year) && (y != final_year)){
temp_data <- getExports(census_key, year_month = (paste("from+", y,"-01+to+", y,"-12", sep = "")), export_vars, ctry_code)
month_exports <- rbind(month_exports, temp_data)
rm(temp_data)
}
if(exists("month_exports") && (y == final_year)){
temp_data <- getExports(census_key, year_month = (paste("from+", y,"-01+to+", y,"-",final_month, sep = "")), export_vars, ctry_code)
month_exports <- rbind(month_exports, temp_data)
rm(temp_data)
}
}
View(month_exports)
check_exports <- sqldf("select year_month, naics_code, count(country_code) as naics_count from month_exports group by 1, 2")
View(check_exports)
# Check for one observation per three-digit NAICS code per month
stopifnot(check_exports$naics_count == 1)
month_exports <- month_exports[order(month_exports$naics_desc, month_exports$year_month),]
#### CREATE LAGGED VALUES ####
# Merge import and export dataset to single dataset to create single set
trade_data <- merge(month_imports, month_exports, by = c("year_month", "country_code", "country_name", "naics_code", "naics_desc"), all = TRUE)
# Proper case NAICS fields
trade_data$naics_desc <- tolower(trade_data$naics_desc)
trade_data$naics_desc <- toTitleCase(trade_data$naics_desc)
# Create quarterly date-time variable in trade data
trade_data$yq <- as.yearqtr(trade_data$year_month, format = "%Y-%m-%d")
format(trade_data$yq, format = "%y 0%q")
View(trade_data)
# Create new dataset to sum trade by quarter-NAICS
quarterly_data <- sqldf("select country_name, yq, naics_code, naics_desc, sum(monthly_import_value) as yq_import_value, sum(monthly_export_value) as yq_export_value from trade_data group by 1, 2, 3, 4")
# YTD lagged values
ytd_data <- sqldf("select naics_code, naics_desc, year_month, ytd_import_value, ytd_export_value from trade_data group by 1, 2, 3")
ytd_data <-
ytd_data %>%
group_by(naics_desc) %>%
mutate(lag.ytd_import_value = dplyr::lag(ytd_import_value, n = 12, order_by = naics_desc, default = NA))
ytd_data <-
ytd_data %>%
group_by(naics_desc) %>%
mutate(lag.ytd_export_value = dplyr::lag(ytd_export_value, n = 12, order_by = naics_desc, default = NA))
# Quarterly lagged values
quarterly_data <- sqldf("select country_name, naics_code, naics_desc, yq, yq_import_value, yq_export_value from quarterly_data group by 1, 2, 3, 4")
quarterly_data <-
quarterly_data %>%
group_by(naics_desc) %>%
mutate(lag.yq_import_value = dplyr::lag(yq_import_value, n = 4, order_by = naics_desc, default = NA))
quarterly_data <-
quarterly_data %>%
group_by(naics_desc) %>%
mutate(lag.yq_export_value = dplyr::lag(yq_export_value, n = 4, order_by = naics_desc, default = NA))
#### FIND YEAR-ON-YEAR CHANGE ####
pct_change <- function(new, old) {(new - old)/old}
quarterly_data$change_m_yoy <- pct_change(quarterly_data$yq_import_value, quarterly_data$lag.yq_import_value)
quarterly_data$change_x_yoy <- pct_change(quarterly_data$yq_export_value, quarterly_data$lag.yq_export_value)
ytd_data$change_m_yoy <- pct_change(ytd_data$ytd_import_value, ytd_data$lag.ytd_import_value)
ytd_data$change_x_yoy <- pct_change(ytd_data$ytd_export_value, ytd_data$lag.ytd_export_value)
#### VIEW CURRENT QUARTER AND YTD SUBSETS ####
current_quarter <- as.yearqtr(paste(final_year, "-", final_month, "-02", sep=""), format = "%Y-%m-%d")
format(current_quarter, format = "20%y Q%q")
current_month <- as.Date(paste(final_year, "-", final_month, "-02", sep=""), format = "%Y-%m-%d")
quarter_subset <- subset(quarterly_data, yq == current_quarter)
ytd_subset <- subset(ytd_data, year_month == current_month)
#### CHART TRENDS BY INDUSTRY ####
# Set theme
theme_set(theme_bw())
gross_charts <- function(df, output_loc){
# Create list of industries
naics_list <- unique(df$naics_desc)
# Produce by-industry plots
for(i in seq_along(naics_list)){
# Create plots
plot <-
ggplot(subset(df, naics_desc == naics_list[i]), aes(x = year_month, y = gross_value, color = trade_type)) +
geom_line() +
xlab("Period") +
scale_y_continuous("Value ($)", labels = scales::dollar) +
ggtitle(paste("U.S.-Germany Trade in ", naics_list[i], " Products, 1/2013 to 12/2018", sep = "")) +
theme(legend.position = "bottom") +
theme(legend.title = element_blank()) +
theme(legend.spacing.x = unit(0.25, 'cm')) +
labs(caption = paste("Source: U.S. Census, U.S. Trade in Goods with Germany.", sep = ""))
# Save plots
ggsave(filename = paste(naics_list[i], " Gross Chart.pdf", sep = ""), plot, path = output_loc)
# Print to screen
print(plot)
}
}
# Monthly Gross Charts
monthly_gross_trends <- trade_data[, c(1, 3, 5:6, 8)]
monthly_gross_trends$ex_less_im <- monthly_gross_trends$monthly_export_value - monthly_gross_trends$monthly_import_value
names(monthly_gross_trends) <- c("year_month", "country_name", "naics_desc", "U.S. Monthly Imports", "U.S. Monthly Exports", "U.S. Monthly Exports Less Imports")
monthly_gross_trends$year_month <- as.Date(monthly_gross_trends$year_month)
monthly_gross_trends2 <- melt(monthly_gross_trends, id.vars = c("year_month", "country_name", "naics_desc"), variable.name = "trade_type", value.name = "gross_value")
gross_charts(df = monthly_gross_trends2, output_loc = paste(analysis, "Monthly Charts\\", sep = ""))
|
dde988f8f181b4bc4cf9be888dc141982f1507f8
|
300ba207fa8ce6bde43e7b7a5c9232b3868fc7cd
|
/plot4.R
|
9b10c1f6fbfc0fdbe80600968a726da46c61539a
|
[] |
no_license
|
ShrutiVij/Coursera_ExploratoryDataAnalysis
|
5de23afe14bce658408773440bef04cebe6221b3
|
a0a98369b021c5c15e8e93d1372e5351ed7f83d8
|
refs/heads/master
| 2021-01-13T07:57:23.853206
| 2016-10-23T03:59:26
| 2016-10-23T03:59:26
| 69,839,127
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 705
|
r
|
plot4.R
|
setwd("./Desktop/Coursera/ExploratoryDataAnalysis/Assignment")
library(ggplot2)
library(dplyr)
# Read the RDS Summary and Source Classification code RDS files
sData <- readRDS("summarySCC_PM25.rds")
scCode <- readRDS("Source_Classification_Code.rds")
coalSource <- subset(scCode, EI.Sector %in% c("Fuel Comb - Electric Generation - Coal","Fuel Comb - Industrial Boilers, ICEs - Coal","Fuel Comb - Comm/Institutional - Coal"))
allData <- merge(sData,coalSource,by = "SCC")
grpData <- allData %>% group_by(year) %>% summarise(Emissions = sum(Emissions))
png("plot4.PNG")
qplot(year,Emissions,data=grpData,geom = "line", color = "red")+ ggtitle("Coal combustion Emissions between 1999 to 2008")
dev.off()
|
b0b18608c34f6247d994c0c4eaf8b3683684b211
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/spind/examples/GEE.Rd.R
|
78edb6712d46137fc1c4d07fa0ab70812e3ebaf0
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 731
|
r
|
GEE.Rd.R
|
library(spind)
### Name: GEE
### Title: GEE (Generalized Estimating Equations)
### Aliases: GEE plot.GEE predict.GEE summary.GEE
### ** Examples
data(musdata)
coords<- musdata[,4:5]
## Not run:
##D mgee <- GEE(musculus ~ pollution + exposure,
##D family = "poisson",
##D data = musdata,
##D coord = coords,
##D corstr = "fixed",
##D scale.fix = FALSE)
##D
##D summary(mgee, printAutoCorPars = TRUE)
##D
##D pred <- predict(mgee, newdata = musdata)
##D
##D library(ggplot2)
##D
##D plot(mgee)
##D
##D my_gee_plot <- mgee$plot
##D
##D # move the legend to a new position
##D print(my_gee_plot + ggplot2::theme(legend.position = 'top'))
##D
## End(Not run)
|
2f6d5b058ef6eac30d0ee7575e8b0f2b20f3b4ed
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/BosonSampling/examples/permanents.Rd.R
|
27a336b03877abfbc9d2cf707572c9ec61503418
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 398
|
r
|
permanents.Rd.R
|
library(BosonSampling)
### Name: Permanent-functions
### Title: Functions for evaluating matrix permanents
### Aliases: Permanent-functions cxPerm rePerm cxPermMinors
### ** Examples
set.seed(7)
n <- 20
A <- randomUnitary(n)
cxPerm(A)
#
B <- Re(A)
rePerm(B)
#
C <- A[,-n]
v <- cxPermMinors(C)
#
# Check Laplace expansion by sub-permanents
c(cxPerm(A),sum(v*A[,n]))
|
e6fae73f65ff79a832cc73756f17e9af23a6776a
|
e05051fb108db2ab20c430c74f95fac6a893dc29
|
/R/ons-ts-collision.R
|
fcea50222aa4b957b045dd127b933459f217e114
|
[] |
no_license
|
mhoehle/naming
|
7d6baef51150be4350014adcca1a33b229e09bbd
|
df7a59ceceac1964f66832df5f808bb3b92370db
|
refs/heads/master
| 2023-01-29T05:30:27.153488
| 2017-09-20T22:02:47
| 2017-09-20T22:02:47
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,533
|
r
|
ons-ts-collision.R
|
######################################################################
## Author: Michael Höhle <http://www.math.su.se/~hoehle>
## Date: 2017-04-23, modified 2017-09-20 to include 2016 data.
##
## Description:
## Create bonus material plot containing the time series of the UK baby
## name collision probability. All data files specified in the
## file filenames.txt are
######################################################################
library("readxl")
library("dplyr")
library("ggplot2")
##devtools::install_github("hoehleatsu/birthdayproblem")
library("birthdayproblem")
######################################################################
## ONS Data files have to be manually downloaded from
## https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/livebirths/datasets/babynamesenglandandwalesbabynamesstatisticsgirls
## and
## https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/livebirths/datasets/babynamesenglandandwalesbabynamesstatisticsboys. Alternatively,
## one can run the following R code:
######################################################################
filenames <- readLines("filenames.txt")
for (filename in filenames) {
##Extract year and sex
sex <- gsub("^([0-9]{4})(girls|boys).*","\\2",filename)
year <- gsub("^([0-9]{4})(girls|boys).*","\\1",filename)
##Download the file if it doesn't exist
destfile <- file.path("..","Data",filename)
if (!file.exists(destfile)) {
download.file(paste0("https://www.ons.gov.uk/file?uri=/peoplepopulationandcommunity/birthsdeathsandmarriages/livebirths/datasets/babynamesenglandandwalesbabynamesstatistics",sex,"/",year,"/",filename),destfile=destfile)
}
}
##Loop over all available files in the Data2 directory. Some warnings
##are to be expected, because the excel file contains text passages at
##the end informing about special removals etc.
names <- NULL
for (sex in c("Girls","Boys")) {
files <- list.files(file.path("..","Data"), pattern=paste0("[0-9]{4}",tolower(sex)))
for (i in seq_len(length(files))) {
name <- files[i]
filePath <- file.path("..","Data",name)
##Find name of the sheet containing the NAME & COUNT information
sheets <- readxl::excel_sheets(path=filePath)
sheetName <- grep(paste0(sex," names - E&W"),sheets, value=TRUE)
if (length(sheetName) == 0) {
sheetName <- tail(grep("Table [0-9]",sheets, value=TRUE),n=1)
}
##Read the data from the excel file
x <- readxl::read_excel(path=filePath,sheet=sheetName,skip=4) %>%
select(Rank, Name, Count)
##Add column containing info about the year and sex
x <- x %>% filter(!is.na(Rank)) %>%
mutate(year=substr(name,1,4),sex=tolower(sex))
##Debug information
##cat("Sex = ", sex, "\t file= ",name, "\tncol = ", ncol(x),"\n")
##print(names(x))
names <- rbind(names, x)
}
}
##Add empirical relative frequencies per year
names$Name <- gsub("[ ]+$","",names$Name)
names <- names %>% group_by(year) %>% mutate(p = Count/sum(Count))
######################################################################
##Compute and visualize the collision probablity
######################################################################
## Compute collision prob for different group sizes
collision <- names %>% group_by(year) %>% do({
n <- 27L
p <- sapply(n, function(n) birthdayproblem::pbirthday_up(n=n, .$p ,method="mase1992")$prob)
data.frame(n=n, p=p)
})
collision <- collision %>% mutate(type=as.character("Names occurring > 2 times"))
collision <- bind_rows(collision, data.frame(year=c("2015","2016"),n=27,p=c(0.458,0.429), type="All names"))
p <- ggplot(collision, aes(x=as.numeric(year),y=p,colour=type)) + geom_line(size=1.2) + xlab("Year of birth") + ggtitle("Probability of a name clash in a group of 27 kids born in year YYYY in the UK and Wales") + ylab("Probability")+ scale_y_continuous(limits=c(0,1),labels=scales::percent) + scale_colour_discrete(name ="n") + scale_x_continuous(breaks=seq(min(as.numeric(collision$year)),max(as.numeric(collision$year)),2))
p + theme(#axis.text.x = element_text(angle = 45, hjust = 1),
legend.direction = "horizontal", legend.position = "bottom") +
scale_colour_discrete(name="Data basis: ")
##Store to file.
ggsave(filename="timeseries.png", dpi=300, width=8, height=4, bg = "transparent")
######################################################################
## Word clouds
######################################################################
require("wordcloud")
#source("mywordcloud.R")
names2015 <- names %>% filter(year==2015)
boys2015 <- names %>% filter(year==2015, sex == "boys")
girls2015 <- names %>% filter(year==2015, sex == "girls")
# set.seed(123)
# pdf(file="wordcloud-girls.pdf",width=10,height=10)
# par(mar=c(0,0,0,0))
# pal <- brewer.pal(9, "PuRd")[-c(1:2)]
# wordcloud(girls2015$Name,girls2015$Count,colors=pal,min.freq=50,random.order=FALSE)
# dev.off()
# pdf(file="wordcloud-boys.pdf",width=10,height=10)
# par(mar=c(0,0,0,0))
# pal <- brewer.pal(9, "Blues")[-c(1:2)]
# wordcloud(boys2015$Name,boys2015$Count,colors=pal,min.freq=50,random.order=FALSE)
# dev.off()
set.seed(123)
png(file="wordclouds.png",width=800,height=400,res=72,bg = "transparent")
par(mar=c(0,0,0,0), mfcol=c(1,2))
pal <- brewer.pal(9, "PuRd")[-c(1:2)]
wordcloud(girls2015$Name,girls2015$Count,colors=pal,min.freq=50,random.order=FALSE)
pal <- brewer.pal(9, "Blues")[-c(1:2)]
wordcloud(boys2015$Name,boys2015$Count,colors=pal,min.freq=50,random.order=FALSE)
dev.off()
|
2980af67f77a4a56b9e914bce8aae05680cde989
|
a1a1661d8f42f8005f4a41de6ba333eec0b096a7
|
/ERA5_grib_data_extraction.R
|
ac0c9afc55e4e41294f232c38f6371467dc98cb9
|
[] |
no_license
|
jihadrashid/UsedRcodeForMyWork
|
5db49e15b245f29d1f7ea2b76ba0494536e8b1ab
|
1b37b9c524464e0fb1b9e87b4a9a4444d494003a
|
refs/heads/main
| 2023-01-31T05:57:38.302766
| 2020-12-15T06:05:05
| 2020-12-15T06:05:05
| 321,569,013
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 826
|
r
|
ERA5_grib_data_extraction.R
|
library(rNOMADS)
library(ra/ster)
x=raster("D:/Downloads/cape.grib")
grib=brick("D:/Downloads/cape.grib")
shp=shapefile("D:/Downloads/shp.shp")
shp=spTransform(shp, CRSobj = "+proj=longlat +a=6367470 +b=6367470 +no_defs")
grib=crop(grib,shp)
grib_array= as.array(grib)
pointCoordinates=read.csv("D:/Downloads/station.csv")
coordinates(pointCoordinates)= ~ LONG+ LAT
cape=extract(grib, pointCoordinates)
combinePointValue=cbind(pointCoordinates,cape)
write.table(combinePointValue,file="D:/Downloads/combinedPointValue.csv", append=FALSE,
sep= ",", row.names = FALSE, col.names=TRUE)
data=data.frame(combinedPointValue)
View(data)
cape_ts=t.data.frame(data)
View(cape_ts)
cape_ts=cape_ts[c(1:493), ]
cape_ts=as.data.frame(cape_ts)
writexl::write_xlsx(cape_ts, "D:/Downloads/cape_data.xlsx")
|
13e6fe8a8734f3c4dc6f652fcc47991f9b8d4b0c
|
547898da61b4dae81aaa0636e5db1bc964c1651f
|
/test/inst/shiny_app/server.R
|
e7adaacdaaa0c70b3feb2fadd40f1e5da9341b86
|
[] |
no_license
|
sergeitarasov/ontoFAST
|
2e48156d3196033ae4e8b5e48db6b1cbf04cc9d5
|
c4e12584fde3ea4ccb4928374066f954a56a65d2
|
refs/heads/master
| 2022-09-02T00:54:23.513397
| 2022-08-04T13:02:00
| 2022-08-04T13:02:00
| 96,386,296
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 10,102
|
r
|
server.R
|
server <- function(input, output, session) {
########### Network
output$network <- renderVisNetwork({
withProgress(message = "Creating Network", value = 0.9, {
# minimal example
nodes <- data.frame(id = 1:3)
edges <- data.frame(from = c(1,2,3), to = c(2,3,1))
visNetwork(nodes, edges, width = "100%", height = "100%", main = "Ontology Network",
submain = "Select terms or IDs above to begin visualization") %>%
visNodes( size=30, shadow =T)
})
})
##### Selectize
updateSelectizeInput(session, "selectize", label = NULL, choices=srch_items, selected = FALSE,
options = list(openOnFocus=F, maxOptions=100, placeholder="Search Ontology term or ID and click Expand to visualize"
),
server = TRUE
)
####
####### Safe file
observeEvent(input$savefile_btn, {
print("sdfds")
save(shiny_in, file="OntoFAST_annotation_shiny_in.RData")
}
)
############
makeRadioButton=function(n=1){fluidRow(column(10,
h3(paste(shiny_in$c1[n], shiny_in$c2[n], sep=" "), style='padding-left: 12px;'),
#verbatimTextOutput(paste0("ids_selec", n)),
textInput(paste0("ids_in", n), label = "", value = "", placeholder="Enter your ID"),
actionButton(paste0("add_btn", n), label = "Add",
icon = icon("glyphicon glyphicon-download", lib="glyphicon")),
checkboxGroupInput(paste0("checkbox",n),label=NA, choices=shiny_in$c5[[shiny_in$c1[n]]],
selected=shiny_in$terms_selected[[shiny_in$c1[n]]]),
hr()
))}
output$WidgetVectorDisplay <-renderUI(
withProgress(message = "Creating character statements", value = 0.1, {
incProgress(0.3)
incProgress(0.9)
{lapply(X = 1:nchar, FUN = makeRadioButton)}
})
)
### Show descnedants upon button
observeEvent(
input$select_descen,
{
term2show<-input$selectize
output$network <- renderVisNetwork({
withProgress(message = "Creating Network", value = 0.1, {
#dt=get_part_descen(hao.obo, term2show, is_a=c("is_a"), part_of=c("BFO:0000050"))
if (input$des_chk=="descendants"){
dt=get_part_descen(hao.obo, term2show, is_a=links_chk_map[[input$links_chk]][2], #### HAO.obo to ontology index!!!!
part_of=links_chk_map[[input$links_chk]][1],
all_links=F, incl.top.anc=T, highliht_focus=T)
}
if (input$des_chk=="ancestors"){
dt=get_part_anc(hao.obo, term2show, is_a=links_chk_map[[input$links_chk]][2],
part_of=links_chk_map[[input$links_chk]][1],
all_links=F, incl.top.anc=T, highliht_focus=T)
}
if (input$des_chk=="both"){ #WORK on duplicated terms
dt1=get_part_descen(hao.obo, term2show, is_a=links_chk_map[[input$links_chk]][2], #### HAO.obo to ontology index!!!!
part_of=links_chk_map[[input$links_chk]][1],
all_links=F, incl.top.anc=T, highliht_focus=T)
dt2=get_part_anc(hao.obo, term2show, is_a=links_chk_map[[input$links_chk]][2],
part_of=links_chk_map[[input$links_chk]][1],
all_links=F, incl.top.anc=T, highliht_focus=T)
dt<-list(nodes=rbind(dt1$nodes, dt2$nodes), edges=rbind(dt1$edges, dt2$edges))
dt$nodes<-dt$nodes[!duplicated(as.character(dt$nodes$id)),] #select unique nodes
}
incProgress(0.3)
## Legend data
lnodes <- data.frame(label = c("Selected term"),
color = c("orange"),
id = term2show)
ledges <- data.frame(color = c("blue", "red"),
label = c("part_of", "is_a"), arrows =c("from", "from"))
####
visNetwork(dt$nodes, dt$edges, height = "65vh", width ="100%", main = NULL, submain =NULL) %>%
visNodes(borderWidthSelected=4)%>%
visOptions(highlightNearest = TRUE, nodesIdSelection = T)%>%
visLegend(addEdges = ledges, addNodes = lnodes, useGroups = F, position = "right", width=0.09) %>%
#visLayout(randomSeed = 12) %>%
visLayout(randomSeed = 12, hierarchical=F) -> visNt # HIERRACHICAL TRUE can be an option!!!
incProgress(0.9)
visIgraphLayout(visNt, layout="layout_with_gem")
})
})
# visNetworkProxy("network") %>%
# visFocus(id = input$selectize, scale = 2)
}
)
#######
####### Add button actions
observe({
lapply(map_btn_check, function(x) {
observeEvent(
input[[x]],
{
term_id<-input[[paste0("ids_in", names(map_btn_check)[which(map_btn_check==x)])]]
print(term_id)
## Input from Selectize
if ((term_id=="") & (input$selectize!="")){ #if term field is empty then use Selectize input that should not be empty too
############## update checkboxes and ontology index
term_id<-input$selectize
term_name<-shiny_in$c6[which(names(shiny_in$c6)==term_id)]
if (length(term_name)==0){ #check if term is found in ontology
term_name="TERM NOT FOUND!!!"
}
term_id_name<-paste(term_name, term_id, sep=", ")
CHAR_id<-paste0("CHAR:", names(map_btn_check)[which(map_btn_check==x)])
if (term_id_name %in% shiny_in$c5[[CHAR_id]]){#check for duplication
updateTextInput(session, paste0("ids_in", names(map_btn_check)[which(map_btn_check==x)]),
label = "You are trying to add the same term twice")
}
if (!term_id_name %in% shiny_in$c5[[CHAR_id]]){#check for duplication
#update terms selected
shiny_in$terms_selected[[CHAR_id]] <<- c(shiny_in$terms_selected[[CHAR_id]], term_id_name)
#update terms all
shiny_in$c5[[CHAR_id]] <<- c(shiny_in$c5[[CHAR_id]], term_id_name)
#update checkbox
updateCheckboxGroupInput(session, paste0("checkbox", names(map_btn_check)[which(map_btn_check==x)]),
label=NA, choices=shiny_in$c5[[CHAR_id]],
selected=shiny_in$terms_selected[[CHAR_id]]
)
updateTextInput(session, paste0("ids_in", names(map_btn_check)[which(map_btn_check==x)]), value = "", label = "")
term_id<-c("")
}
###########
}
### Manual input
############## update checkboxes and ontology index
if (term_id!=""){ # term fiels must be non-empty
term_id<-gsub(" ", "", term_id) # remove white spaces
term_name<-shiny_in$c6[which(names(shiny_in$c6)==term_id)]
if (length(term_name)==0){ #check if term is found in ontology
term_name="TERM NOT FOUND!!!"
}
term_id_name<-paste(term_name, term_id, sep=", ")
CHAR_id<-paste0("CHAR:", names(map_btn_check)[which(map_btn_check==x)])
if (term_id_name %in% shiny_in$c5[[CHAR_id]]){#check for duplication
updateTextInput(session, paste0("ids_in", names(map_btn_check)[which(map_btn_check==x)]),
label = "You are trying to add the same term twice")
}
if (!term_id_name %in% shiny_in$c5[[CHAR_id]]){#check for duplication
#update terms selected
shiny_in$terms_selected[[CHAR_id]] <<- c(shiny_in$terms_selected[[CHAR_id]], term_id_name)
#update terms all
shiny_in$c5[[CHAR_id]] <<- c(shiny_in$c5[[CHAR_id]], term_id_name)
#update checkbox
updateCheckboxGroupInput(session, paste0("checkbox", names(map_btn_check)[which(map_btn_check==x)]),
label=NA, choices=shiny_in$c5[[CHAR_id]],
selected=shiny_in$terms_selected[[CHAR_id]]
)
updateTextInput(session, paste0("ids_in", names(map_btn_check)[which(map_btn_check==x)]), value = "", label = "")
}
}
###########
}
)
})
})
####
###### Add checkbox actions
observe({
lapply(map_checkbox, function(x) {
observeEvent(
input[[x]],
{
CHAR_id<-paste0("CHAR:", names(map_checkbox)[which(map_checkbox==x)])
#update terms selected
shiny_in$terms_selected[[CHAR_id]] <<- input[[x]]
})
})
})
########
################ Node selection
observeEvent(
input$network_selected, {
#print(input$network_selected)
updateSelectizeInput(session, "selectize", label = NULL, choices=srch_items, selected = input$network_selected,
options = list(openOnFocus=F, maxOptions=100, placeholder="Enter term or ID"
),
server = TRUE
)
})
########### Selectize change
observeEvent(
input$selectize,
{ #print(input$selectize)
output$id_txt<-renderText({input$selectize})
output$def_txt<-renderText({
ontology$def[which(names(ontology$def)==input$selectize)]})
output$syn_txt<-renderText({
ontology$parsed_synonyms[which(names(ontology$parsed_synonyms)==input$selectize)]})
})
#####
}
###############################################################################################################################
|
ee1b44e815b06e3fbc87a8b7211012fb4ef0c5dc
|
d1a9b4bcf7b3c71c8ba79eb481f343bf2eef9850
|
/man/aggiungiRisultatiModulo.Rd
|
7ccd601001c3f2b5d41f590ec1aa07ba62059c53
|
[] |
no_license
|
kendomaniac/BBBMGU
|
1be5aa2af0a703b5747297e4f704ec3c23953d0a
|
59d19ad075ad4c2d2db6d40163559d2048e0fb68
|
refs/heads/master
| 2022-02-26T06:33:08.160285
| 2022-02-03T05:13:12
| 2022-02-03T05:13:12
| 170,837,631
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,600
|
rd
|
aggiungiRisultatiModulo.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/aggiungiRisultatiModulo.R
\name{aggiungiRisultatiModulo}
\alias{aggiungiRisultatiModulo}
\title{Una funzione che aggrega output di aggiornaStudentiVoti.}
\usage{
aggiungiRisultatiModulo(
excel.esame,
input.voti,
output.voti,
modulo = c("BB", "BM", "GU")
)
}
\arguments{
\item{excel.esame, }{e' l'excel scaricato per un modulo dalle prove parziali su SS3.}
\item{input.voti, }{output di aggiornaStudentiVotiModuli.}
\item{output.voti, }{output di aggiornaStudentiVoti per Genetica Umana, update studenti.}
\item{modulo, }{selezione del modulo per il quale i voto vanno aggiunti}
}
\value{
Il file tab delimited vuoto, studenti_voti.txt, ma con l'inserimento di tutti gli studenti indipendentemente dall'anno di corso.
}
\description{
Una funzione che aggrega output aggiornaStudentiVoti per i tre moduli.
}
\examples{
\dontrun{
aggiungiRisultatiModulo(excel.esame="basi_biologiche.xls",
input.voti="studenti_voti.txt",
output.voti="studenti_votiBB.txt", modulo="BB")
aggiungiRisultatiModulo(excel.esame="biologia_molecolare.xls",
input.voti="studenti_votiBB.txt",
output.voti="studenti_votiBBBM.txt", modulo="BM")
aggiungiRisultatiModulo(excel.esame="genetica_umana.xls",
input.voti="studenti_votiBBBM.txt",
output.voti="studenti_votiBBBMGU.txt", modulo="GU")
}
}
\author{
Raffaele Calogero, raffaele.calogero [at] unito [dot] it, University of Torino, Italy
}
|
f15a3972c135800e05c57c1bc0bfa738063d8bfe
|
ee07a8960aa9623207d1f0b592d5438d33a99864
|
/2_comparison_DHvsBL/2.06_FreqFocusHaps_inBL.r
|
4e4e2896aaddc9914ad895919fb7f3007af2fb10
|
[] |
no_license
|
DilanSarange/GWAS_DHs_landraces
|
4896e4acf58604f8a80b8abfb24c049c3f2ff3c1
|
dc9b765edbcc3c0e1e68e4a5223c2cf6eebc5597
|
refs/heads/master
| 2022-12-30T16:41:46.394929
| 2020-10-15T06:29:41
| 2020-10-15T06:29:41
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 38,999
|
r
|
2.06_FreqFocusHaps_inBL.r
|
###################################################################
###################################################################
####
#### assess frequencies of focus haplotypes (identified with GWAS in DHs)
#### in breeding lines
#### Plot frequencies, distinguishing favorable, unfavorable, random haplotypes
#### identify favorable haplotypes absent in BL
#### identify unfavorable haplotypes with high frequencies in BL
####
#### Manfred Mayer (Technical University of Munich, Plant Breeding)
#### manfred.mayer@tum.de
####
#### date: 17.04.2020
###################################################################
###################################################################
# general settings
options(stringsAsFactors=FALSE)
options(scipen=99)
options(warn = 1)
set.seed(212)
# packages
library(synbreed)
# arguments
nSNPs <- 10
steps <- 10
p_thresh <- 0.01
FDR <- "15"
minCount <- 3
# graphical parameters
cex <- 0.6
cex.axis <- 1.6
cex.lab <- 1.6
# functions
# function for calculating haplotype similarity (probability of two randomly chosen gametes to show identical haplotypes)
HapSim_f <- function(x, exCount = 0) {
allele_counts <- table(x)
allele_freqs <- allele_counts/sum(allele_counts)
expHet <- 1 - sum(allele_freqs^2)
HapSim <- 1 - expHet
return(HapSim)
}
# function for calculating Hudson and Kaplan's minimum number of historical recombination events
HudsonKaplan_Rec_f <- function(geno, pos, n_min){
fourGamete_pair <- function(x, n_min){
test <- paste(x[1 : floor(length(x)/2)], x[(floor(length(x)/2) + 1) : length(x)], sep = "_")
test <- table(test)
test <- test[which(test >= n_min)]
if(length(test) == 4){
return(1)
} else {
return(0)
}
}
# first, exclude all monomorphic markers
mono <- which(apply(geno, 2, function(x) {sd(x) == 0}))
if(length(mono > 0)){
geno <- geno[ , -mono]
pos <- pos[-mono]
}
if(length(pos) > 1){
col_pairs <- cbind(colnames(geno)[sequence(1:ncol(geno))],colnames(geno)[rep(1:ncol(geno),1:ncol(geno))])
col_pairs <- col_pairs[which(!(col_pairs[,1]==col_pairs[,2])),]
if(length(col_pairs) > 2){
start_bp <- pos[col_pairs[,1]]
end_bp <- pos[col_pairs[,2]]
test_mtx <- rbind(geno[, col_pairs[,1]], geno[ , col_pairs[,2]])
D <- apply(test_mtx, 2, fourGamete_pair, n_min = n_min)
D_info <- cbind(start_bp, end_bp, D)
D_info <- D_info[which(D_info[ , "D"] == 1), ]
if(length(D_info) == 3){
n_rec <- 1
}
if(length(D_info) < 3){
n_rec <- 0
}
if(length(D_info) > 3){
D_info <- D_info[order(D_info[,1], D_info[,2]), ]
# first step of the algorithm
ex <- NULL
for(i in 1:nrow(D_info)){
temp1 <- D_info[i,1] <= D_info[-i,1]
temp2 <- D_info[i,2] >= D_info[-i,2]
if(any(temp1 & temp2)){
ex <- c(ex, i)
}
}
if(is.null(ex) == FALSE){
D_info <- D_info[-ex, ]
}
}
if(length(D_info) == 3){
n_rec <- 1
}
if(length(D_info) > 3){
# second step of the algorithm
i <- 0
repeat{
i <- i + 1
ex <- NULL
temp1 <- D_info[(i+1):nrow(D_info),1] > D_info[i,1]
temp2 <- D_info[(i+1):nrow(D_info),1] < D_info[i,2]
if(any(temp1 & temp2)){
ex <- c(ex, (which(temp1 & temp2) + i))
}
if(is.null(ex) == FALSE){
D_info <- D_info[-ex, ]
i <- 0
}
if(length(D_info) == 3){
break
}
if(i == (nrow(D_info)-1)){
break
}
}
}
if(length(D_info) == 3){
n_rec <- 1
} else {
n_rec <- nrow(D_info)
}
} else {
test_vec <- c(geno[, col_pairs[1]], geno[ , col_pairs[2]])
D <- fourGamete_pair(test_vec, n_min = n_min)
if(D == 1){
n_rec <- 1
} else {
n_rec <- 0
}
}
} else {
n_rec <- 0
}
return(n_rec)
}
# function for generating Infolist of the corresponding blocks
makeInfolist_genet_f <- function(x, genet_map) {
genet_map_noNA <- genet_map[which(is.na(genet_map[,3]) == FALSE), ]
x2 <- x[which(x[,2] >= genet_map_noNA[1,2]), ]
x2 <- x2[which(x2[,2] <= genet_map_noNA[nrow(genet_map_noNA),2]), ]
genet_InfoList <- NULL
for(i in 1:nrow(x2)){
# start pos
dif <- x2[i,2] - genet_map_noNA[,2]
dif <- dif[order(abs(dif))][1:2]
phys <- genet_map_noNA[sort(names(dif)),2]
genet <- genet_map_noNA[sort(names(dif)),3]
genet_start <- genet[1] + (((x2[i,2] - phys[1]) * diff(genet)) / diff(phys))
# end pos
dif <- x2[i,3] - genet_map_noNA[,2]
dif <- dif[order(abs(dif))][1:2]
phys <- genet_map_noNA[sort(names(dif)),2]
genet <- genet_map_noNA[sort(names(dif)),3]
genet_end <- genet[1] + (((x2[i,3] - phys[1]) * diff(genet)) / diff(phys))
# size
genet_size <- genet_end - genet_start
genet_InfoList <- rbind(genet_InfoList, c(x2[i,1], genet_start, genet_end, genet_size, x2[i,5]))
}
colnames(genet_InfoList) <- c("Chr", "Pos_Start_cM", "Pos_End_cM", "Size_cM", "n_Markers")
rownames(genet_InfoList) <- rownames(x2)
return(list(genet_InfoList = genet_InfoList, phys_InfoList = x2))
}
###################################################################
# load data of DHs
load(paste("geno_InfoList_DH_m", nSNPs, "s", steps, ".RData", sep = ""))
str(InfoList)
str(geno)
# load genetic map (genetic and physical position for each marker)
# according to PH207 x EP1 mapping population
# I:\Projekte\MAZE\Science\Genetic_Data_Analyses\GenMapPH207vsEP1_interpolated_600kArray_Positions
load("map_chr_phy_gen_PH207vsEP1_SCAMmpiInterpol_B73v4.RData")
str(map_chr_phy_gen)
# write out each start and end position of each haplotype as genetic as well as physical positions
Info_Lists <- list()
for(CHR in 1:10){
print(paste("CHR", CHR))
InfoList_chr <- InfoList[which(InfoList[,1] == CHR), ]
# only consider first haplotype per window (start and end is the same for each haplotype in a window anyways)
InfoList_chr <- InfoList_chr[which(substr(rownames(InfoList_chr), nchar(rownames(InfoList_chr))-1, nchar(rownames(InfoList_chr))) == "_1"), ]
genet_map_chr <- map_chr_phy_gen[which(map_chr_phy_gen[,1] == CHR), ]
Info_Lists[[CHR]] <- makeInfolist_genet_f(x = InfoList_chr, genet_map = genet_map_chr)
}
str(Info_Lists)
save(Info_Lists, file = "Info_Lists_genet_phys.RData")
###################################################################
dir.create("comp_BL_DHs")
dir.create("comp_BL_DHs/focusHaps")
traits <- c("TILL", "LO",
"MF", "FF",
"PH_final",
"PH_V6", "PH_V4",
"EV_V6", "EV_V4"
)
traits <- c("EV_V4", "EV_V6", "PH_V4", "PH_V6", "TILL", "LO")
for(TRAIT in traits){
infolder_haps <- paste("FavUnfav_Stability/", TRAIT, sep = "")
infolder_haps
dir.create(paste("comp_BL_DHs/focusHaps/", TRAIT, sep = ""))
outFolder <- paste("comp_BL_DHs/focusHaps/", TRAIT, sep = "")
outFolder
# load defined haplotypes
load(paste(infolder_haps, "/haps_all_", TRAIT, ".RData", sep = ""))
haps_Fav_info
haps_Unfav_info
haps_Inter_info
all_markers <- c(haps_Fav_info$qtl_i, haps_Unfav_info$qtl_i, haps_Inter_info$qtl_i)
all_markers
# if in biallelic case, haplotypes were added, we have to do it here also for geno and InfoList
if(any(unique(all_markers) %in% colnames(geno) == FALSE)){
temp_x <- unique(all_markers)
temp_x <- temp_x[which(temp_x %in% colnames(geno) == FALSE)]
for(temp_xi in temp_x){
temp_xix <- geno[ , which(substr(colnames(geno), 1, 13) == substr(temp_xi, 1, 13))]
temp_xix[which(temp_xix == 0)] <- 99
temp_xix[which(temp_xix == 2)] <- 0
temp_xix[which(temp_xix == 99)] <- 2
geno <- cbind(geno, temp_xix)
colnames(geno)[ncol(geno)] <- temp_xi
InfoList_xi <- InfoList[which(substr(rownames(InfoList), 1, 13) == substr(temp_xi, 1, 13)), ]
InfoList <- rbind(InfoList, InfoList_xi)
rownames(InfoList)[nrow(InfoList)] <- temp_xi
print(tail(InfoList, n = 10))
}
}
###############################################################################################################
###
###
### compare ferquencies
###
###
###
### search for haplotypes in breeding lines
###
# load check genotypic data
# SNP data of BL
load("gpBL.RData")
str(gpBL)
# SNP data of LR
load(paste("gpDH.RData", sep=""))
str(gpDH)
# generate elite line haplotype dataset for "favorable" haplotypes
geno_BL_fav <- NULL
nR_BL_fav <- NULL
HH_BL_fav <- NULL
cM_BL_fav <- NULL
kb_BL_fav <- NULL
for(hap_i in haps_Fav_info$qtl_i){
chr_qtl <- InfoList[hap_i, "Chr"]
start_qtl <- InfoList[hap_i, "Pos_Start_bp"]
end_qtl <- InfoList[hap_i, "Pos_End_bp"]
IDs_qtl <- rownames(geno)[which(geno[, hap_i] == 2)]
map_i <- gpBL$map[which(gpBL$map$chr == chr_qtl), ]
map_i <- map_i[which(map_i$pos >= start_qtl), ]
map_i <- map_i[which(map_i$pos <= end_qtl), ]
seq_qtl <- gpDH$geno[ , rownames(map_i)]
seq_qtl <- apply(seq_qtl, 1, paste0, collapse = "")
seq_qtl <- unique(seq_qtl[IDs_qtl])
haps_BL <- gpBL$geno[ , rownames(map_i)]
# nR
pos_temp <- map_i$pos
names(pos_temp) <- rownames(map_i)
nR_BL_fav <- c(nR_BL_fav, HudsonKaplan_Rec_f(geno = haps_BL, pos = pos_temp, n_min = 1))
haps_BL <- apply(haps_BL, 1, paste0, collapse = "")
# HH
HH_BL_fav <- c(HH_BL_fav, 1 - HapSim_f(haps_BL, exCount = 0))
# cM
genet_pos <- Info_Lists[[chr_qtl]][[1]]
genet_pos <- genet_pos[which(substr(rownames(genet_pos), 1, 13) == substr(hap_i, 1, 13)),]
cM_BL_fav <- c(cM_BL_fav, genet_pos["Size_cM"])
# kb
phys_pos <- InfoList
phys_pos <- phys_pos[which(substr(rownames(phys_pos), 1, 13) == substr(hap_i, 1, 13)),]
kb_BL_fav <- c(kb_BL_fav, phys_pos[1 , "Size_bp"] / 1000)
haps_BL <- ifelse(haps_BL == seq_qtl, 2, 0)
geno_BL_fav <- cbind(geno_BL_fav, haps_BL)
}
if(is.null(geno_BL_fav) == FALSE){
colnames(geno_BL_fav) <- haps_Fav_info$qtl_i
str(geno_BL_fav)
names(nR_BL_fav) <- haps_Fav_info$qtl_i
names(HH_BL_fav) <- haps_Fav_info$qtl_i
names(cM_BL_fav) <- haps_Fav_info$qtl_i
names(kb_BL_fav) <- haps_Fav_info$qtl_i
print(summary(nR_BL_fav))
print(summary(HH_BL_fav))
print(summary(cM_BL_fav))
print(summary(kb_BL_fav))
save(geno_BL_fav, nR_BL_fav, HH_BL_fav, cM_BL_fav, kb_BL_fav, file = paste(outFolder, "/info_BL_fav_", TRAIT, ".RData", sep = ""))
}
# generate elite line haplotype dataset for "unfavorable" haplotypes
geno_BL_unfav <- NULL
nR_BL_unfav <- NULL
HH_BL_unfav <- NULL
cM_BL_unfav <- NULL
kb_BL_unfav <- NULL
for(hap_i in haps_Unfav_info$qtl_i){
chr_qtl <- InfoList[hap_i, "Chr"]
start_qtl <- InfoList[hap_i, "Pos_Start_bp"]
end_qtl <- InfoList[hap_i, "Pos_End_bp"]
IDs_qtl <- rownames(geno)[which(geno[, hap_i] == 2)]
map_i <- gpBL$map[which(gpBL$map$chr == chr_qtl), ]
map_i <- map_i[which(map_i$pos >= start_qtl), ]
map_i <- map_i[which(map_i$pos <= end_qtl), ]
seq_qtl <- gpDH$geno[ , rownames(map_i)]
seq_qtl <- apply(seq_qtl, 1, paste0, collapse = "")
seq_qtl <- unique(seq_qtl[IDs_qtl])
haps_BL <- gpBL$geno[ , rownames(map_i)]
# nR
pos_temp <- map_i$pos
names(pos_temp) <- rownames(map_i)
nR_BL_unfav <- c(nR_BL_unfav, HudsonKaplan_Rec_f(geno = haps_BL, pos = pos_temp, n_min = 1))
haps_BL <- apply(haps_BL, 1, paste0, collapse = "")
# HH
HH_BL_unfav <- c(HH_BL_unfav, 1 - HapSim_f(haps_BL, exCount = 0))
# cM
genet_pos <- Info_Lists[[chr_qtl]][[1]]
genet_pos <- genet_pos[which(substr(rownames(genet_pos), 1, 13) == substr(hap_i, 1, 13)),]
cM_BL_unfav <- c(cM_BL_unfav, genet_pos["Size_cM"])
# kb
phys_pos <- InfoList
phys_pos <- phys_pos[which(substr(rownames(phys_pos), 1, 13) == substr(hap_i, 1, 13)),]
kb_BL_unfav <- c(kb_BL_unfav, phys_pos[1 , "Size_bp"] / 1000)
haps_BL <- ifelse(haps_BL == seq_qtl, 2, 0)
geno_BL_unfav <- cbind(geno_BL_unfav, haps_BL)
}
if(is.null(geno_BL_unfav) == FALSE){
colnames(geno_BL_unfav) <- haps_Unfav_info$qtl_i
str(geno_BL_unfav)
names(nR_BL_unfav) <- haps_Unfav_info$qtl_i
names(HH_BL_unfav) <- haps_Unfav_info$qtl_i
names(cM_BL_unfav) <- haps_Unfav_info$qtl_i
names(kb_BL_unfav) <- haps_Unfav_info$qtl_i
print(summary(nR_BL_unfav))
print(summary(HH_BL_unfav))
print(summary(cM_BL_unfav))
print(summary(kb_BL_unfav))
save(geno_BL_unfav, nR_BL_unfav, HH_BL_unfav, cM_BL_unfav, kb_BL_unfav, file = paste(outFolder, "/info_BL_unfav_", TRAIT, ".RData", sep = ""))
}
# generate elite line haplotype dataset for "neutral" haplotypes (in the same window as the significant haplotypes but without significant effect)
geno_BL_neut <- NULL
nR_BL_neut <- NULL
HH_BL_neut <- NULL
cM_BL_neut <- NULL
kb_BL_neut <- NULL
names_haps_neut <- NULL
for(hap_ii in c(haps_Fav_info$qtl_i, haps_Unfav_info$qtl_i)){
# don't take the actual qtl, but all the others which are not significant
haps_neut_ii <- colnames(geno)[which(substr(colnames(geno), 1, 13) == substr(hap_ii, 1, 13))]
haps_neut_ii <- haps_neut_ii[which(haps_neut_ii %in% c(haps_Fav_info$qtl_i, haps_Unfav_info$qtl_i, haps_Inter_info$qtl_i) == FALSE)]
for(hap_i in haps_neut_ii){
names_haps_neut <- c(names_haps_neut, hap_i)
chr_qtl <- InfoList[hap_i, "Chr"]
start_qtl <- InfoList[hap_i, "Pos_Start_bp"]
end_qtl <- InfoList[hap_i, "Pos_End_bp"]
IDs_qtl <- rownames(geno)[which(geno[, hap_i] == 2)]
map_i <- gpBL$map[which(gpBL$map$chr == chr_qtl), ]
map_i <- map_i[which(map_i$pos >= start_qtl), ]
map_i <- map_i[which(map_i$pos <= end_qtl), ]
seq_qtl <- gpDH$geno[ , rownames(map_i)]
seq_qtl <- apply(seq_qtl, 1, paste0, collapse = "")
seq_qtl <- unique(seq_qtl[IDs_qtl])
haps_BL <- gpBL$geno[ , rownames(map_i)]
# nR
pos_temp <- map_i$pos
names(pos_temp) <- rownames(map_i)
nR_BL_neut <- c(nR_BL_neut, HudsonKaplan_Rec_f(geno = haps_BL, pos = pos_temp, n_min = 1))
haps_BL <- apply(haps_BL, 1, paste0, collapse = "")
# HH
HH_BL_neut <- c(HH_BL_neut, 1 - HapSim_f(haps_BL, exCount = 0))
# cM
genet_pos <- Info_Lists[[chr_qtl]][[1]]
genet_pos <- genet_pos[which(substr(rownames(genet_pos), 1, 13) == substr(hap_i, 1, 13)),]
cM_BL_neut <- c(cM_BL_neut, genet_pos["Size_cM"])
# kb
phys_pos <- InfoList
phys_pos <- phys_pos[which(substr(rownames(phys_pos), 1, 13) == substr(hap_i, 1, 13)),]
kb_BL_neut <- c(kb_BL_neut, phys_pos[1 , "Size_bp"] / 1000)
haps_BL <- ifelse(haps_BL == seq_qtl, 2, 0)
geno_BL_neut <- cbind(geno_BL_neut, haps_BL)
}
}
if(is.null(geno_BL_neut) == FALSE){
colnames(geno_BL_neut) <- names_haps_neut
str(geno_BL_neut)
names(nR_BL_neut) <- names_haps_neut
names(HH_BL_neut) <- names_haps_neut
names(cM_BL_neut) <- names_haps_neut
names(kb_BL_neut) <- names_haps_neut
print(summary(nR_BL_neut))
print(summary(HH_BL_neut))
print(summary(cM_BL_neut))
print(summary(kb_BL_neut))
save(geno_BL_neut, nR_BL_neut, HH_BL_neut, cM_BL_neut, kb_BL_neut, file = paste(outFolder, "/info_BL_neut_", TRAIT, ".RData", sep = ""))
}
# generate elite line haplotype dataset for "random" haplotypes
set.seed(232)
rand_haps <- sample(colnames(geno), size = 500, replace = FALSE)
rand_haps <- sort(rand_haps)
rand_haps
geno_BL_rand <- NULL
nR_BL_rand <- NULL
HH_BL_rand <- NULL
cM_BL_rand <- NULL
kb_BL_rand <- NULL
for(hap_i in rand_haps){
chr_qtl <- InfoList[hap_i, "Chr"]
start_qtl <- InfoList[hap_i, "Pos_Start_bp"]
end_qtl <- InfoList[hap_i, "Pos_End_bp"]
IDs_qtl <- rownames(geno)[which(geno[, hap_i] == 2)]
map_i <- gpBL$map[which(gpBL$map$chr == chr_qtl), ]
map_i <- map_i[which(map_i$pos >= start_qtl), ]
map_i <- map_i[which(map_i$pos <= end_qtl), ]
seq_qtl <- gpDH$geno[ , rownames(map_i)]
seq_qtl <- apply(seq_qtl, 1, paste0, collapse = "")
seq_qtl <- unique(seq_qtl[IDs_qtl])
haps_BL <- gpBL$geno[ , rownames(map_i)]
# nR
pos_temp <- map_i$pos
names(pos_temp) <- rownames(map_i)
nR_BL_rand <- c(nR_BL_rand, HudsonKaplan_Rec_f(geno = haps_BL, pos = pos_temp, n_min = 1))
haps_BL <- apply(haps_BL, 1, paste0, collapse = "")
# HH
HH_BL_rand <- c(HH_BL_rand, 1 - HapSim_f(haps_BL, exCount = 0))
# cM
genet_pos <- Info_Lists[[chr_qtl]][[1]]
genet_pos <- genet_pos[which(substr(rownames(genet_pos), 1, 13) == substr(hap_i, 1, 13)),]
cM_BL_rand <- c(cM_BL_rand, genet_pos["Size_cM"])
# kb
phys_pos <- InfoList
phys_pos <- phys_pos[which(substr(rownames(phys_pos), 1, 13) == substr(hap_i, 1, 13)),]
if(length(phys_pos) > 5){
kb_BL_rand <- c(kb_BL_rand, phys_pos[1 , "Size_bp"] / 1000)
} else {
kb_BL_rand <- c(kb_BL_rand, phys_pos["Size_bp"] / 1000)
}
haps_BL <- ifelse(haps_BL == seq_qtl, 2, 0)
geno_BL_rand <- cbind(geno_BL_rand, haps_BL)
}
colnames(geno_BL_rand) <- rand_haps
str(geno_BL_rand)
names(nR_BL_rand) <- rand_haps
names(HH_BL_rand) <- rand_haps
names(cM_BL_rand) <- rand_haps
names(kb_BL_rand) <- rand_haps
print(summary(nR_BL_rand))
print(summary(HH_BL_rand))
print(summary(cM_BL_rand))
print(summary(kb_BL_rand))
save(geno_BL_rand, nR_BL_rand, HH_BL_rand, cM_BL_rand, kb_BL_rand, file = paste(outFolder, "/info_BL_rand_", TRAIT, ".RData", sep = ""))
}
################################################
###
### summarize all early traits
###
################################################
name_set <- "earlyTraits"
dir.create(paste("comp_BL_DHs/focusHaps/", name_set, sep = ""))
outFolder <- paste("comp_BL_DHs/focusHaps/", name_set, sep = "")
outFolder
traits <- c("EV_V4", "EV_V6", "PH_V4", "PH_V6")
traits
geno_BL_fav_all <- NULL
geno_BL_unfav_all <- NULL
geno_BL_rand_all <- NULL
nR_BL_fav_all <- NULL
nR_BL_unfav_all <- NULL
nR_BL_rand_all <- NULL
HH_BL_fav_all <- NULL
HH_BL_unfav_all <- NULL
HH_BL_rand_all <- NULL
cM_BL_fav_all <- NULL
cM_BL_unfav_all <- NULL
cM_BL_rand_all <- NULL
kb_BL_fav_all <- NULL
kb_BL_unfav_all <- NULL
kb_BL_rand_all <- NULL
for(TRAIT in traits){
infolder <- paste("comp_BL_DHs/focusHaps/", TRAIT, sep = "")
load(paste(infolder, "/info_BL_fav_", TRAIT, ".RData", sep = ""))
load(paste(infolder, "/info_BL_unfav_", TRAIT, ".RData", sep = ""))
load(paste(infolder, "/info_BL_rand_", TRAIT, ".RData", sep = ""))
infolder_haps <- paste("FavUnfav_Stability/", TRAIT, sep = "")
load(paste(infolder_haps, "/haps_all_", TRAIT, ".RData", sep = ""))
all_markers <- c(haps_Fav_info$qtl_i, haps_Unfav_info$qtl_i, haps_Inter_info$qtl_i)
# if in biallelic case, haplotypes were added, we have to do it here also for geno and InfoList
if(any(unique(all_markers) %in% colnames(geno) == FALSE)){
temp_x <- unique(all_markers)
temp_x <- temp_x[which(temp_x %in% colnames(geno) == FALSE)]
for(temp_xi in temp_x){
temp_xix <- geno[ , which(substr(colnames(geno), 1, 13) == substr(temp_xi, 1, 13))]
temp_xix[which(temp_xix == 0)] <- 99
temp_xix[which(temp_xix == 2)] <- 0
temp_xix[which(temp_xix == 99)] <- 2
geno <- cbind(geno, temp_xix)
colnames(geno)[ncol(geno)] <- temp_xi
InfoList_xi <- InfoList[which(substr(rownames(InfoList), 1, 13) == substr(temp_xi, 1, 13)), ]
InfoList <- rbind(InfoList, InfoList_xi)
rownames(InfoList)[nrow(InfoList)] <- temp_xi
print(tail(InfoList, n = 10))
}
}
geno_BL_fav_all <- cbind(geno_BL_fav_all, geno_BL_fav)
nR_BL_fav_all <- c(nR_BL_fav_all, nR_BL_fav)
HH_BL_fav_all <- c(HH_BL_fav_all, HH_BL_fav)
cM_BL_fav_all <- c(cM_BL_fav_all, cM_BL_fav)
kb_BL_fav_all <- c(kb_BL_fav_all, kb_BL_fav)
geno_BL_unfav_all <- cbind(geno_BL_unfav_all, geno_BL_unfav)
nR_BL_unfav_all <- c(nR_BL_unfav_all, nR_BL_unfav)
HH_BL_unfav_all <- c(HH_BL_unfav_all, HH_BL_unfav)
cM_BL_unfav_all <- c(cM_BL_unfav_all, cM_BL_unfav)
kb_BL_unfav_all <- c(kb_BL_unfav_all, kb_BL_unfav)
}
# random are the same in every run
geno_BL_rand_all <- cbind(geno_BL_rand_all, geno_BL_rand)
nR_BL_rand_all <- c(nR_BL_rand_all, nR_BL_rand)
HH_BL_rand_all <- c(HH_BL_rand_all, HH_BL_rand)
cM_BL_rand_all <- c(cM_BL_rand_all, cM_BL_rand)
kb_BL_rand_all <- c(kb_BL_rand_all, kb_BL_rand)
summary(nR_BL_fav_all)
summary(nR_BL_unfav_all)
summary(nR_BL_rand_all)
summary(HH_BL_fav_all)
summary(HH_BL_unfav_all)
summary(HH_BL_rand_all)
summary(cM_BL_fav_all)
summary(cM_BL_unfav_all)
summary(cM_BL_rand_all)
summary(kb_BL_fav_all)
summary(kb_BL_unfav_all)
summary(kb_BL_rand_all)
haps_fav_all <- colnames(geno_BL_fav_all)
head(sort(table(haps_fav_all), decreasing = TRUE), n = 10)
length(haps_fav_all)
length(unique(haps_fav_all))
haps_unfav_all <- colnames(geno_BL_unfav_all)
head(sort(table(haps_unfav_all), decreasing = TRUE), n = 10)
length(haps_unfav_all)
length(unique(haps_unfav_all))
haps_rand_all <- colnames(geno_BL_rand_all)
head(sort(table(haps_rand_all), decreasing = TRUE), n = 10)
length(haps_rand_all)
length(unique(haps_rand_all))
# filter for non-overlapping haplotypes
geno_BL_fav_all <- geno_BL_fav_all[ , unique(colnames(geno_BL_fav_all))]
geno_BL_unfav_all <- geno_BL_unfav_all[ , unique(colnames(geno_BL_unfav_all))]
geno_BL_rand_all <- geno_BL_rand_all[ , unique(colnames(geno_BL_rand_all))]
str(geno_BL_fav_all)
str(geno_BL_unfav_all)
str(geno_BL_rand_all)
#
# calculate frequencies
#
str(geno_BL_fav_all)
str(geno_BL_unfav_all)
str(geno_BL_rand_all)
#
# calculate frequencies including
#
AF_f <- function(x){
y <- sum(length(which(x==2))*2+length(which(x==1)))/(2*(length(x)-length(which(is.na(x)))))
return(y)
}
### elite
freq_fav_elite_all <- apply(geno_BL_fav_all, 2, AF_f)
freq_unfav_elite_all <- apply(geno_BL_unfav_all, 2, AF_f)
freq_rand_elite_all <- apply(geno_BL_rand_all, 2, AF_f)
summary(freq_fav_elite_all)
summary(freq_unfav_elite_all)
summary(freq_rand_elite_all)
### LR
freq_fav_LR_all <- apply(geno[ ,colnames(geno_BL_fav_all)], 2, AF_f)
freq_unfav_LR_all <- apply(geno[ ,colnames(geno_BL_unfav_all)], 2, AF_f)
freq_rand_LR_all <- apply(geno[ ,colnames(geno_BL_rand_all)], 2, AF_f)
summary(freq_fav_LR_all)
summary(freq_unfav_LR_all)
summary(freq_rand_LR_all)
##
## permutation test for significant differences between fav/unfav/rand in elite panel
##
# permut est function (two.sided)
permut.test_f <- function(x, y, n = 10000, alternative = "two.sided"){
# remove NAs
x <- na.omit(x)
y <- na.omit(y)
# count entries per vector
nx <- length(x)
ny <- length(y)
# calculate parameter of interest (here difference in means)
stat <- mean(x) - mean(y)
# generate permuted samples (in columns)
perm_matrix <- replicate(n, sample(c(x, y)))
stat_f <- function(x, nx){
mean(x[1:nx]) - mean(x[(nx+1):length(x)])
}
# apply test to permuted samples to generate null distribution
null_distr <- apply(perm_matrix, 2, stat_f, nx = nx)
# calculate p-value
n_NULL.larger <- length(which(null_distr > stat))
n_NULL.lower <- length(which(null_distr < stat))
n_NULL.equal <- length(which(null_distr == stat))
if (alternative == "two.sided")
p_value <- (2 * (min(n_NULL.larger, n_NULL.lower) + 0.5 * n_NULL.equal)) / (n+1)
if (alternative == "less")
p_value <- (n_NULL.lower + 0.5 * n_NULL.equal) / (n+1)
if (alternative == "greater")
p_value <- (n_NULL.larger + 0.5 * n_NULL.equal) / (n+1)
p_value <- min(p_value, 1)
# generate output object
perm_out <- list(stat = stat,
null_distr = null_distr,
p_value = p_value,
original_x = x,
original_y = y,
nx = nx,
ny = ny
)
return(perm_out)
}
set.seed(2121)
summary(freq_fav_elite_all)
summary(freq_unfav_elite_all)
summary(freq_rand_elite_all)
permTest_favRand <- permut.test_f(x = freq_fav_elite_all, y = freq_rand_elite_all)
permTest_favUnfav <- permut.test_f(x = freq_fav_elite_all, y = freq_unfav_elite_all)
permTest_unfavRand <- permut.test_f(x = freq_unfav_elite_all, y = freq_rand_elite_all)
str(permTest_favRand)
str(permTest_favUnfav)
str(permTest_unfavRand)
save(freq_fav_elite_all, freq_unfav_elite_all, freq_rand_elite_all, file = paste(outFolder, "/info_freqs_", name_set, "_nonDup.RData", sep = ""))
################################################
###
### plotting
###
################################################
##
## generate vectors of "independent" haplotypes (dist > 1Mb and/or r2<0.8)
##
#
# check overlapping regions between traits
#
traits <- c("PH_V4", "PH_V6", "EV_V4", "EV_V6")
traits
HAP_list <- list()
for (TRAIT in traits){
infolder_haps <- paste("FavUnfav_Stability/", TRAIT, sep = "")
load(paste(infolder_haps, "/haps_all_", TRAIT, ".RData", sep = ""))
haps_fav_i <- haps_Fav_info$qtl_i
haps_unfav_i <- haps_Unfav_info$qtl_i
haps_inter_i <- haps_Inter_info$qtl_i
haps_all <- c(haps_fav_i, haps_unfav_i, haps_inter_i)
haps <- list()
haps[["fav"]] <- haps_fav_i
haps[["unfav"]] <- haps_unfav_i
haps[["inter"]] <- haps_inter_i
haps[["all"]] <- haps_all
HAP_list[[TRAIT]] <- haps
}
str(HAP_list)
HAP_list_fav <- unlist(lapply(HAP_list, function(x) {x[[1]]}))
str(HAP_list_fav)
HAP_list_unfav <- unlist(lapply(HAP_list, function(x) {x[[2]]}))
str(HAP_list_unfav)
HAP_list_fav[which(HAP_list_fav %in% names(freq_fav_elite_all) == FALSE)]
HAP_list_unfav[which(HAP_list_unfav %in% names(freq_unfav_elite_all) == FALSE)]
names(freq_fav_elite_all)[which(names(freq_fav_elite_all) %in% HAP_list_fav == FALSE)]
names(freq_unfav_elite_all)[which(names(freq_unfav_elite_all) %in% HAP_list_unfav == FALSE)]
new_favorable_haps <- names(freq_fav_elite_all)[which(freq_fav_elite_all == 0)]
new_favorable_haps
for(i in names(HAP_list)){
print(i)
print(HAP_list[[i]]$fav[which(HAP_list[[i]]$fav %in% new_favorable_haps)])
}
common_unfavorable_haps <- names(freq_unfav_elite_all)[which(freq_unfav_elite_all > 0.25)]
common_unfavorable_haps
for(i in names(HAP_list)){
print(i)
print(HAP_list[[i]]$unfav[which(HAP_list[[i]]$unfav %in% common_unfavorable_haps)])
}
common_favorable_haps <- names(freq_fav_elite_all)[which(freq_fav_elite_all > 0.25)]
common_favorable_haps
for(i in names(HAP_list)){
print(i)
print(HAP_list[[i]]$fav[which(HAP_list[[i]]$fav %in% common_favorable_haps)])
}
#
# now load the according associated genomic regions
#
REG_list <- list()
for (TRAIT in traits){
regs <- read.table(paste(TRAIT, "/QTLregs/finalQTL/finalQTLregs_", TRAIT, ".csv", sep = ""),
sep = ";",
dec = ".",
header = TRUE)
rownames(regs) <- regs$qtl_i
REG_list[[TRAIT]] <- regs
}
str(REG_list)
#
# calculate for traits overlapping associations, defined as:
# focus haplotypes within 1Mb and with r2 > 0.8
#
#
# filter for the 899 gphenotyped ones
load("pheno_perEnv_list_DHs.RData")
phenotyped <- pheno_perEnv_list[[1]]$Geno
ID_set <- intersect(rownames(geno), phenotyped)
# generate pairs
traits_2 <- combn(traits, 2)
traits_2
#
# for favorables
#
direction <- "fav"
# pairs
pairs_overlap_list_fav <- list()
for(trait_i in 1:ncol(traits_2)){
TRAIT1 <- traits_2[1,trait_i]
TRAIT2 <- traits_2[2,trait_i]
print(paste(TRAIT1, "vs", TRAIT2))
info_r2 <- NULL
# get positions of focus haplotypes
regs1 <- InfoList[HAP_list[[TRAIT1]][[direction]], 1:3]
regs2 <- InfoList[HAP_list[[TRAIT2]][[direction]], 1:3]
# extend for 0.5 Mb up and downstream
regs1_ext1Mb <- regs1
regs1_ext1Mb[, 2] <- regs1_ext1Mb[,2] - 2500000
regs1_ext1Mb[, 3] <- regs1_ext1Mb[,3] + 2500000
regs2_ext1Mb <- regs2
regs2_ext1Mb[, 2] <- regs2_ext1Mb[,2] - 2500000
regs2_ext1Mb[, 3] <- regs2_ext1Mb[,3] + 2500000
# check for pairs within 1Mb distance and in case, calculate r2
for(regs1_i in 1:nrow(regs1_ext1Mb)){
regs2_chr <- rownames(regs2_ext1Mb)[which(regs2_ext1Mb[,1] == regs1_ext1Mb[regs1_i,1])]
if(length(regs2_chr) > 0){
haps2 <- regs2_chr[which((regs2_ext1Mb[regs2_chr, 2] < regs1_ext1Mb[regs1_i,3]) & (regs2_ext1Mb[regs2_chr, 3] > regs1_ext1Mb[regs1_i,2]))]
if(length(haps2) > 0){
haps1 <- rownames(regs1_ext1Mb)[regs1_i]
r2 <- (cor(geno[ID_set, haps1], geno[ID_set, haps2]))^2
info_r2_i <- data.frame(TRAIT1 = rep(haps1, length(haps2)),
TRAIT2 = haps2,
r2 = t(r2),
chr = regs1_ext1Mb[regs1_i,1],
start = min(c(regs1[haps1,2], regs2[haps2, 2])),
end = max(c(regs1[haps1,3], regs2[haps2, 3])),
stringsAsFactors = FALSE)
info_r2 <- rbind(info_r2, info_r2_i)
}
}
}
if(is.null(info_r2) == FALSE){
colnames(info_r2) <- c(TRAIT1, TRAIT2, "r2", "chr", "start_bp", "end_bp")
rownames(info_r2) <- 1:nrow(info_r2)
print(info_r2[which(info_r2$r2 > 0.4),])
}
pairs_overlap_list_fav[[paste(TRAIT1, TRAIT2, sep = ".")]] <- info_r2
}
#
# for unfavorables
#
direction <- "unfav"
# pairs
pairs_overlap_list_unfav <- list()
for(trait_i in 1:ncol(traits_2)){
TRAIT1 <- traits_2[1,trait_i]
TRAIT2 <- traits_2[2,trait_i]
print(paste(TRAIT1, "vs", TRAIT2))
info_r2 <- NULL
# get positions of focus haplotypes
regs1 <- InfoList[HAP_list[[TRAIT1]][[direction]], 1:3]
regs2 <- InfoList[HAP_list[[TRAIT2]][[direction]], 1:3]
# extend for 0.5 Mb up and downstream
regs1_ext1Mb <- regs1
regs1_ext1Mb[, 2] <- regs1_ext1Mb[,2] - 2500000
regs1_ext1Mb[, 3] <- regs1_ext1Mb[,3] + 2500000
regs2_ext1Mb <- regs2
regs2_ext1Mb[, 2] <- regs2_ext1Mb[,2] - 2500000
regs2_ext1Mb[, 3] <- regs2_ext1Mb[,3] + 2500000
# check for pairs within 1Mb distance and in case, calculate r2
for(regs1_i in 1:nrow(regs1_ext1Mb)){
regs2_chr <- rownames(regs2_ext1Mb)[which(regs2_ext1Mb[,1] == regs1_ext1Mb[regs1_i,1])]
if(length(regs2_chr) > 0){
haps2 <- regs2_chr[which((regs2_ext1Mb[regs2_chr, 2] < regs1_ext1Mb[regs1_i,3]) & (regs2_ext1Mb[regs2_chr, 3] > regs1_ext1Mb[regs1_i,2]))]
if(length(haps2) > 0){
haps1 <- rownames(regs1_ext1Mb)[regs1_i]
r2 <- (cor(geno[ID_set, haps1], geno[ID_set, haps2]))^2
info_r2_i <- data.frame(TRAIT1 = rep(haps1, length(haps2)),
TRAIT2 = haps2,
r2 = t(r2),
chr = regs1_ext1Mb[regs1_i,1],
start = min(c(regs1[haps1,2], regs2[haps2, 2])),
end = max(c(regs1[haps1,3], regs2[haps2, 3])),
stringsAsFactors = FALSE)
info_r2 <- rbind(info_r2, info_r2_i)
}
}
}
if(is.null(info_r2) == FALSE){
colnames(info_r2) <- c(TRAIT1, TRAIT2, "r2", "chr", "start_bp", "end_bp")
rownames(info_r2) <- 1:nrow(info_r2)
print(info_r2[which(info_r2$r2 > 0.4),])
}
pairs_overlap_list_unfav[[paste(TRAIT1, TRAIT2, sep = ".")]] <- info_r2
}
#
# for changing sign
#
direction <- "inter"
# pairs
pairs_overlap_list_inter <- list()
for(trait_i in 1:ncol(traits_2)){
TRAIT1 <- traits_2[1,trait_i]
TRAIT2 <- traits_2[2,trait_i]
print(paste(TRAIT1, "vs", TRAIT2))
info_r2 <- NULL
# get positions of focus haplotypes
regs1 <- InfoList[HAP_list[[TRAIT1]][[direction]], 1:3]
regs2 <- InfoList[HAP_list[[TRAIT2]][[direction]], 1:3]
# extend for 0.5 Mb up and downstream
regs1_ext1Mb <- regs1
regs1_ext1Mb[, 2] <- regs1_ext1Mb[,2] - 2500000
regs1_ext1Mb[, 3] <- regs1_ext1Mb[,3] + 2500000
regs2_ext1Mb <- regs2
regs2_ext1Mb[, 2] <- regs2_ext1Mb[,2] - 2500000
regs2_ext1Mb[, 3] <- regs2_ext1Mb[,3] + 2500000
# check for pairs within 1Mb distance and in case, calculate r2
for(regs1_i in 1:nrow(regs1_ext1Mb)){
regs2_chr <- rownames(regs2_ext1Mb)[which(regs2_ext1Mb[,1] == regs1_ext1Mb[regs1_i,1])]
if(length(regs2_chr) > 0){
haps2 <- regs2_chr[which((regs2_ext1Mb[regs2_chr, 2] < regs1_ext1Mb[regs1_i,3]) & (regs2_ext1Mb[regs2_chr, 3] > regs1_ext1Mb[regs1_i,2]))]
if(length(haps2) > 0){
haps1 <- rownames(regs1_ext1Mb)[regs1_i]
r2 <- (cor(geno[ID_set, haps1], geno[ID_set, haps2]))^2
info_r2_i <- data.frame(TRAIT1 = rep(haps1, length(haps2)),
TRAIT2 = haps2,
r2 = t(r2),
chr = regs1_ext1Mb[regs1_i,1],
start = min(c(regs1[haps1,2], regs2[haps2, 2])),
end = max(c(regs1[haps1,3], regs2[haps2, 3])),
stringsAsFactors = FALSE)
info_r2 <- rbind(info_r2, info_r2_i)
}
}
}
if(is.null(info_r2) == FALSE){
colnames(info_r2) <- c(TRAIT1, TRAIT2, "r2", "chr", "start_bp", "end_bp")
rownames(info_r2) <- 1:nrow(info_r2)
print(info_r2[which(info_r2$r2 > 0.4),])
}
pairs_overlap_list_inter[[paste(TRAIT1, TRAIT2, sep = ".")]] <- info_r2
}
#
# for all
#
direction <- "all"
# pairs
pairs_overlap_list_all <- list()
for(trait_i in 1:ncol(traits_2)){
TRAIT1 <- traits_2[1,trait_i]
TRAIT2 <- traits_2[2,trait_i]
print(paste(TRAIT1, "vs", TRAIT2))
info_r2 <- NULL
# get positions of focus haplotypes
regs1 <- InfoList[HAP_list[[TRAIT1]][[direction]], 1:3]
regs2 <- InfoList[HAP_list[[TRAIT2]][[direction]], 1:3]
# extend for 0.5 Mb up and downstream
regs1_ext1Mb <- regs1
regs1_ext1Mb[, 2] <- regs1_ext1Mb[,2] - 2500000
regs1_ext1Mb[, 3] <- regs1_ext1Mb[,3] + 2500000
regs2_ext1Mb <- regs2
regs2_ext1Mb[, 2] <- regs2_ext1Mb[,2] - 2500000
regs2_ext1Mb[, 3] <- regs2_ext1Mb[,3] + 2500000
# check for pairs within 1Mb distance and in case, calculate r2
for(regs1_i in 1:nrow(regs1_ext1Mb)){
regs2_chr <- rownames(regs2_ext1Mb)[which(regs2_ext1Mb[,1] == regs1_ext1Mb[regs1_i,1])]
if(length(regs2_chr) > 0){
haps2 <- regs2_chr[which((regs2_ext1Mb[regs2_chr, 2] < regs1_ext1Mb[regs1_i,3]) & (regs2_ext1Mb[regs2_chr, 3] > regs1_ext1Mb[regs1_i,2]))]
if(length(haps2) > 0){
haps1 <- rownames(regs1_ext1Mb)[regs1_i]
r2 <- (cor(geno[ID_set, haps1], geno[ID_set, haps2]))^2
info_r2_i <- data.frame(TRAIT1 = rep(haps1, length(haps2)),
TRAIT2 = haps2,
r2 = t(r2),
chr = regs1_ext1Mb[regs1_i,1],
start = min(c(regs1[haps1,2], regs2[haps2, 2])),
end = max(c(regs1[haps1,3], regs2[haps2, 3])),
stringsAsFactors = FALSE)
info_r2 <- rbind(info_r2, info_r2_i)
}
}
}
if(is.null(info_r2) == FALSE){
colnames(info_r2) <- c(TRAIT1, TRAIT2, "r2", "chr", "start_bp", "end_bp")
rownames(info_r2) <- 1:nrow(info_r2)
print(info_r2[which(info_r2$r2 > 0.4),])
}
pairs_overlap_list_all[[paste(TRAIT1, TRAIT2, sep = ".")]] <- info_r2
}
#
# for all detected pairs, delete randomly one haplotype
# how many "unrelated" haplotypes are left
#
fav_haps_all <- names(freq_fav_elite_all)
unfav_haps_all <- names(freq_unfav_elite_all)
str(fav_haps_all)
str(unfav_haps_all)
set.seed(232)
r2_thresh_i <- "0.8"
haps_pairs <- NULL
r2_thresh <- as.numeric(r2_thresh_i)
ex <- NULL
for(i in 1:ncol(traits_2)){
haps_i <- pairs_overlap_list_fav[[i]]
haps_i <- haps_i[which(haps_i[,1] != haps_i[,2]), ]
haps_i <- cbind(haps_i[which(haps_i$r2 >= r2_thresh), 1], haps_i[which(haps_i$r2 >= r2_thresh), 2])
if(nrow(haps_i) > 0){
for(ii in 1:nrow(haps_i)){
if(any(haps_i[ii,] %in% ex) == FALSE){
ex <- c(ex, sample(haps_i[ii,], size = 1))
haps_pairs <- rbind(haps_pairs, haps_i[ii,])
}
}
}
}
freq_fav_elite_all[haps_pairs]
ex
fav_haps_indep <- fav_haps_all[which(fav_haps_all %in% ex == FALSE)]
haps_pairs <- NULL
r2_thresh <- as.numeric(r2_thresh_i)
ex <- NULL
for(i in 1:ncol(traits_2)){
haps_i <- pairs_overlap_list_unfav[[i]]
haps_i <- haps_i[which(haps_i[,1] != haps_i[,2]), ]
haps_i <- cbind(haps_i[which(haps_i$r2 >= r2_thresh), 1], haps_i[which(haps_i$r2 >= r2_thresh), 2])
if(nrow(haps_i) > 0){
for(ii in 1:nrow(haps_i)){
if(any(haps_i[ii,] %in% ex) == FALSE){
ex <- c(ex, sample(haps_i[ii,], size = 1))
haps_pairs <- rbind(haps_pairs, haps_i[ii,])
}
}
}
}
freq_unfav_elite_all[haps_pairs]
ex
unfav_haps_indep <- unfav_haps_all[which(unfav_haps_all %in% ex == FALSE)]
str(fav_haps_indep)
str(unfav_haps_indep)
summary(freq_fav_elite_all)
summary(freq_unfav_elite_all)
summary(freq_fav_elite_all[fav_haps_indep])
summary(freq_unfav_elite_all[unfav_haps_indep])
summary(freq_rand_elite_all)
length(which(freq_unfav_elite_all[unfav_haps_indep] > quantile(freq_rand_elite_all, 0.75)))
length(which(freq_unfav_elite_all[unfav_haps_indep] > quantile(freq_rand_elite_all, 0.75))) / length(freq_unfav_elite_all[unfav_haps_indep])
length(which(freq_fav_elite_all[fav_haps_indep] == 0))
length(which(freq_fav_elite_all[fav_haps_indep] == 0)) / length(freq_fav_elite_all[fav_haps_indep])
##
## test difference between distributions
##
# Mann-Whitney
wilcox.test(x = freq_fav_elite_all[fav_haps_indep], y = freq_rand_elite_all, alternative = "two.sided")
wilcox.test(x = freq_unfav_elite_all[unfav_haps_indep], y = freq_rand_elite_all, alternative = "two.sided")
wilcox.test(x = freq_fav_elite_all[fav_haps_indep], y = freq_unfav_elite_all[unfav_haps_indep], alternative = "two.sided")
##
## draw frequency plot
##
name_set <- "earlyTraits"
freq_fav_elite_all_plot <- freq_fav_elite_all[fav_haps_indep]
freq_unfav_elite_all_plot <- freq_unfav_elite_all[unfav_haps_indep]
pos_vec <- freq_fav_elite_all_plot
neg_vec <- freq_unfav_elite_all_plot
rand_vec <- freq_rand_elite_all
str(rand_vec)
str(pos_vec)
str(neg_vec)
xlim1 <- c(0,1)
xlim2 <- c(-0.5,1)
d_rand <- density(rand_vec)
str(d_rand)
d_rand$y <- d_rand$y[which(d_rand$x > 0)]
d_rand$x <- d_rand$x[which(d_rand$x > 0)]
d_rand$y <- d_rand$y[which(d_rand$x < 1)]
d_rand$x <- d_rand$x[which(d_rand$x < 1)]
d_rand$x <- c(0, d_rand$x, 1)
d_rand$y <- c(0, d_rand$y, 0)
d_pos <- density(pos_vec)
str(d_pos)
d_pos$y <- d_pos$y[which(d_pos$x > 0)]
d_pos$x <- d_pos$x[which(d_pos$x > 0)]
d_pos$x <- c(0, d_pos$x)
d_pos$y <- c(0, d_pos$y)
d_neg <- density(neg_vec)
str(d_neg)
d_neg$y <- d_neg$y[which(d_neg$x > 0)]
d_neg$x <- d_neg$x[which(d_neg$x > 0)]
d_neg$x <- c(0, d_neg$x)
d_neg$y <- c(0, d_neg$y)
png(paste(outFolder, "/density_RandFavUnfav_", name_set, "_indep.png", sep =""), width = 2100, height = 1800, res = 300)
par(mar = c(4, 4, 0, 0) + 0.1, mgp = c(2.9, 1, 0))
plot(d_rand, col = rgb(0.2, 0.2, 0.2, alpha = 1), xlim = xlim1, lwd = 2, ylim = c(0, max(c(d_rand$y, d_pos$y, d_neg$y), na.rm = TRUE)),
main = "", xlab = paste("Haplotype frequency in breeding lines", sep =""), bty = "n",
cex.axis = 1.2, cex.lab = 1.5)
polygon(d_rand, col = rgb(0.2, 0.2, 0.2, alpha = 0.2), border=NA)
points(d_neg, col = "red", xlim = xlim2, lwd = 2, type = "l")
polygon(d_neg, col = rgb(1, 0, 0, alpha = 0.15), border=NA)
points(d_pos, col = "blue", xlim = xlim2, lwd = 2, type = "l")
polygon(d_pos, col = rgb(0, 0, 1, alpha = 0.15), border=NA)
legend("center", pch = 22, col = c("blue", "red", rgb(0.2, 0.2, 0.2, alpha = 1)),
pt.bg = c(rgb(0, 0, 1, alpha = 0.15), rgb(1, 0, 0, alpha = 0.15), rgb(0.2, 0.2, 0.2, alpha = 0.2)),
pt.lwd = 2, pt.cex = 2,
text.col = c("blue", "red", rgb(0.2, 0.2, 0.2, alpha = 1)), cex = 1.2,
legend = c("Favorable", "Unfavorable", "Random"),
bty = "n")
dev.off()
###
###################################################################
|
1ef05e0dd2fa3b81667ac0c29980bb6715efda25
|
1c0a7c18e1dbe868f5ef11592384c8cb62f22bc4
|
/man/AllPreds_E.Rd
|
0e9c726e78076b1f30bcdf681fbd19ccdc0e9f04
|
[
"MIT"
] |
permissive
|
SimonDedman/gbm.auto
|
de1851a0729800fc01713dc43d85b926b69bfd81
|
ee6217a76f6ace8b250108daca5ed2ddc3cb59a1
|
refs/heads/master
| 2023-09-03T23:32:34.257690
| 2023-09-01T16:01:21
| 2023-09-01T16:01:21
| 23,468,620
| 13
| 5
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,060
|
rd
|
AllPreds_E.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{AllPreds_E}
\alias{AllPreds_E}
\title{Data: Predicted abundances of 4 ray species generated using gbm.auto}
\format{
A data frame with 378570 rows and 7 variables:
\describe{
\item{Latitude}{Decimal latitudes in the Irish Sea}
\item{Longitude}{Decimal longitudes in the Irish Sea}
\item{Cuckoo}{Predicted abundances of cuckoo rays in the Irish Sea, generated using gbm.auto}
\item{Thornback}{Predicted abundances of thornback rays in the Irish Sea, generated using gbm.auto}
\item{Blonde}{Predicted abundances of blonde rays in the Irish Sea, generated using gbm.auto}
\item{Spotted}{Predicted abundances of spotted rays in the Irish Sea, generated using gbm.auto}
\item{Effort}{Irish commercial beam trawler effort 2012}
}
}
\usage{
data(AllPreds_E)
}
\description{
Predicted abundances of 4 ray species generated using gbm.auto, and
Irish commercial beam trawler effort 2012.
}
\author{
Simon Dedman, \email{simondedman@gmail.com}
}
\keyword{datasets}
|
3add8f1ce6ed5ddafd9b360f2aa54b335b630b05
|
e08feba647b37a30417755c52c8cb7971d92036d
|
/R/yao_utils.r
|
68d193c6d5241095e0d4a43ed14a4801ccac3404
|
[] |
no_license
|
skranz/YamlObjects
|
17e984850a7c0acba003916503c211d3e3c9014d
|
295eb4fa8db053df10e29362535f629e006cc309
|
refs/heads/master
| 2021-01-10T18:58:09.827847
| 2015-10-17T03:45:59
| 2015-10-17T03:45:59
| 26,432,913
| 1
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 8,554
|
r
|
yao_utils.r
|
# mark the encoding of character vectors as UTF-8
mark_utf8 <- function(x) {
if (is.character(x)) {
Encoding(x) <- 'UTF-8'
return(x)
}
if (!is.list(x)) return(x)
attrs <- attributes(x)
res <- lapply(x, mark_utf8)
attributes(res) <- attrs
res
}
#' Compute the depth of a nested list
list.depth <- function(this,thisdepth=0, add.vector=FALSE){
#restore.point("list.depth")
if(!is.list(this)){
return(thisdepth + add.vector*(length(this)>1))
}else{
depths = unlist(lapply(this,list.depth,thisdepth=thisdepth+1, add.vector=add.vector))
#restore.point("list.depth1")
return(max(depths))
}
}
int.seq = function(from, to) {
if (from > to)
return(NULL)
from:to
}
examples.splice = function() {
v = "searchA"
splice(summarise(dt, mean.v=mean(v, na.rm=TRUE)), v=v, eval=FALSE)
}
display= function (..., collapse = "\n", sep = "")
{
str = paste(paste(..., collapse = collapse, sep = sep),
"\n", sep = "")
invisible(cat(str))
}
intersect.list <- function(li) {
Reduce(intersect, li)
}
robust.rbindlist = function(li) {
restore.point("robust.rbindlist")
cols = intersect.list(lapply(li, function(li) names(li)))
ili = lapply(li, function(li) li[cols])
rbindlist(ili)
}
na.as.zero = function(x) {
x[is.na(x)] = 0
x
}
rowProds = function(mat, cols = 1:NCOL(mat), default=NA) {
if (length(cols) == 0)
return(rep(default,NROW(mat)))
if (is.numeric(cols)) {
com = paste0("mat[,",cols,"]", collapse="*")
} else {
com = paste0("mat[,'",cols,"']", collapse="*")
}
parse.eval(com)
}
rows_along = function(x) {
if (NROW(x)==0)
return(integer(0))
return(1:NROW(x))
}
#' Like paste0 but returns an empty vector if some string is empty
sc = function(..., sep="", collapse=NULL) {
str = list(...)
restore.point("str.combine")
len = sapply(str,length)
if (any(len==0))
return(vector("character",0))
paste0(...,sep=sep,collapse=collapse)
}
any.field = function(li, field, val) {
any(sapply(li, function(el) isTRUE(el[[field]] == val)))
}
all.fields = function(li, field, val) {
all(sapply(li, function(el) isTRUE(el[[field]] == val)))
}
nlist = function (...)
{
li = list(...)
li.names = names(li)
names = unlist(as.list(match.call())[-1])
if (!is.null(li.names)) {
no.names = li.names == ""
names(li)[no.names] = names[no.names]
}
else {
names(li) = names
}
li
}
#' Like paste0 but returns an empty vector if some string is empty
str.combine = function(..., sep="", collapse=NULL) {
str = list(...)
restore.point("str.combine")
len = sapply(str,length)
if (any(len==0))
return(vector("character",0))
paste0(...,sep=sep,collapse=collapse)
}
remove.list.elements = function(li, remove=NULL) {
#restore.point("remove.list.elements")
if (length(remove)==0)
return(li)
if (is.character(remove)) {
remove = which(names(li)==remove)
}
if (length(remove)==0)
return(li)
return(li[-remove])
}
#' Does an environment / list contain the objects named as names
contains = function(env,names, inherits=FALSE,...) {
if (is.environment(env))
return(sapply(names, exists, where=env, inherits=inherits, ...))
return(names %in% names(env))
}
str.ends.with = function(txt,pattern) {
substring(txt,nchar(txt)-nchar(pattern)+1,)==pattern
}
#' Returns a string constisting of times spaces, vectorized over times
str.space = function(times, space=" ") {
space.str = paste0(rep(space,max(times)),collapse="")
substring(space.str,1,last=times)
}
example.str.space = function() {
str.space(0:4)
}
#' An operator that is true if the string str starts with the substring key
str.starts.with = function(str,key) {
substring(str,1,nchar(key))==key
}
is.true = function(val) {
if (length(val)==0)
return(FALSE)
val[is.na(val)] = FALSE
return(val)
}
is.false = function(val) {
if (length(val)==0)
return(FALSE)
val[is.na(val)] = TRUE
return(!val)
}
path.parts = function(path,sep=".") {
str.split(path,sep)
}
common.and.distinct.path.parts = function(opath, npath,sep=".") {
restore.point("common.and.distinct.path.parts")
op = str.split(opath,sep)[[1]]
np = str.split(npath,sep)[[1]]
len = length(np)
if (len == 0)
return(list(common=NULL,distinct=NULL))
op = fill.vec(op,len,"")[1:len]
common = op == np
if (all(common))
return(list(common=np,distinct=NULL))
ind = which(!common)[1]-1
if (ind==0)
return(list(common=NULL,distinct=np))
return(list(common=np[1:ind], distinct=np[(ind+1):len]))
}
examples.common.and.distinct.path.parts = function() {
opath = "a.b.cd.e"
npath = "a.b.e.f"
common.and.distinct.path.parts(opath,npath)
opath = ""
npath = ".stages.intensityChoice.actions.intensityA"
common.and.distinct.path.parts(opath,npath)
}
#' Cuts away early stuff from a tree path
cut.to.sub.tree.path = function(tree.path, after) {
pos = str.locate.first(tree.path, after)
substring(tree.path,pos[,2]+1)
}
#' Index a list tree with a tree path
at.tree.path = function(li, tree.path) {
restore.point("get.from.tree.path")
tree.path = str.replace(tree.path,".","$")
code = paste0("list(",paste0("li",tree.path,collapse=","),")")
return(eval(parse(text=code,srcfile=NULL)))
}
intersect.vector.list = function(li, init) {
if (missing(init))
return(Reduce(intersect,li))
else
return(Reduce(intersect,li,init))
}
#' Gets game variants that correspond to a tree path
variants.from.tree.path = function(tree.path) {
restore.point("variants.from.tree.path")
variants = str.extract.all(tree.path,"_if_variant_.*`")
variants = lapply(variants, function(str) str.replace(str,"_if_variant_",""))
variants = lapply(variants, function(str) str.replace(str,"`",""))
variants = lapply(variants, function(str) str.split(str,"_"))
variants = lapply(variants, intersect.vector.list)
variants
}
#' Adapts whisker render for different whisker formats
custom.whisker.render = function(template,data,...,whiskers=c("<<",">>")) {
library(whisker)
if (!is.null(whiskers)) {
template = str.replace(template,whiskers[1],"{{")
template = str.replace(template,whiskers[2],"}}")
}
whisker.render(template,data,...)
}
#' Comverts a list of vectors into a matrix, shorter vectors will be filled up
vec.list.to.matrix = function(li,fill=NA, transpose=TRUE) {
restore.point("vec.list.to.matrix")
cols = max(sapply(li,length))
ret = sapply(li, fill.vec, len=cols, fill=fill)
if (transpose)
return(t(ret))
return(ret)
}
#' fill a vector up to a specified length with fill
fill.vec = function(vec,len=length(vec),fill=NA) {
if (len == length(vec))
return(vec)
if (len > length(vec))
return(c(vec,rep(fill,len-length(vec))))
return(vec)
}
#' Returns all variable names in an R expression
var.in.expr.str = function(expr.str, envir=baseenv(), union = TRUE) {
if (length(expr.str)>1) {
vars = lapply(expr.str,var.in.expr.str,envir=envir)
if (union) {
return(unique(unlist(vars)))
} else {
return(vars)
}
} else {
return(var.in.expr(expr.str=expr.str, envir = envir))
}
}
union.of.list = function(li) {
unique(unlist(li))
}
#' Returns all variable names in an R expression
var.in.expr = function(expr,expr.str, envir=baseenv()) {
library(codetools)
if (!missing(expr.str)) {
if (length(expr.str)==0)
return(NULL)
expr = parse(text=expr.str,srcfile=NULL)
}
f <- function() {} # a dummy function
body(f) <- expr # containing the expression as its body
codetools::findGlobals(f,merge=FALSE)$variables
}
examples.var.in.expr = function() {
var.in.expr(quote(x*y+2+sin(z)))
var.in.expr(parse(text="x*y+2+sin(z)"))
var.in.expr(expr.str = "x*y+2+sin(z)")
}
#' Names lists are used to recursively store order of columns
as.names.list = function(names) {
li = vector("list",length(names))
names(li) = names
li
}
#' Names lists are used to recursively store order of columns
flatten.names.list = function(li,name="") {
if (length(li)==0)
return(name)
sub.names = sapply(seq_along(li), function(i) flatten.names.list(li[[i]],names(li)[i]))
if (nchar(name)>0) {
ret.names = paste0(name,"_",sub.names)
ret.names[nchar(sub.names)==0] = name
} else {
ret.names = sub.names
}
return(unlist(ret.names))
}
flatten.names.list.examples = function() {
li = list(A=list(B1=list(),B2=list()),C=list(),list(D=list()))
flatten.names.list(li)
as.names.list(c("A","B","C"))
}
|
0c8fe3d8e2211fb48bd1d04be44ead7698dc633b
|
0a333b063b6275ca1c278db7d3bf1be69e91bcff
|
/man/rotation.Rd
|
3817731d866dde798a4c784be63ed1bcbdd90133
|
[] |
no_license
|
md0u80c9/huxtable
|
6e82ee1b09e9b6477da7da2077dc7ef0731a0218
|
8e595d9a3fb64f7c2b1bd5a53b814b250a863ac8
|
refs/heads/master
| 2020-03-26T12:51:52.863480
| 2018-08-15T23:07:05
| 2018-08-15T23:07:05
| 144,829,038
| 0
| 0
| null | 2018-08-15T08:48:05
| 2018-08-15T08:48:04
| null |
UTF-8
|
R
| false
| true
| 1,401
|
rd
|
rotation.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/properties.R
\name{rotation}
\alias{rotation}
\alias{rotation<-}
\alias{set_rotation}
\title{Text rotation}
\usage{
rotation(ht)
rotation(ht) <- value
set_rotation(ht, row, col, value, byrow = FALSE)
}
\arguments{
\item{ht}{A huxtable.}
\item{value}{A numeric vector. Anti-clockwise from the x axis, so 0 is left to right, 90 is going up, etc. Set to \code{NA} to reset to the default, which is
\code{0}.}
\item{row}{A row specifier. See \code{\link{rowspecs}} for details.}
\item{col}{An optional column specifier.}
\item{byrow}{If \code{TRUE}, fill in values by row rather than by column.}
}
\value{
For \code{rotation}, the \code{rotation} property.
For \code{set_rotation}, the \code{ht} object.
}
\description{
Functions to get or set the \emph{text rotation} property of huxtable cells.
}
\details{
You will probably need to set \code{\link[=col_width]{col_width()}} and \code{\link[=row_height]{row_height()}} explicitly
to achieve a nice result, in both HTML and LaTeX.
}
\examples{
ht <- huxtable(a = 1:3, b = 1:3)
rotation(ht) <- 90
rotation(ht)
ht <- huxtable(a = 1:3, b = 3:1)
ht2 <- set_rotation(ht, 90)
rotation(ht2)
ht3 <- set_rotation(ht, 1:2, 1, 90)
rotation(ht3)
ht4 <- set_rotation(ht, 1:2, 1:2, c(90, 270), byrow = TRUE)
rotation(ht4)
ht5 <- set_rotation(ht, where(ht == 1), 90)
rotation(ht5)
}
|
ef7da63f5e167094b0dd2b42e660bd8ba936a8dd
|
a0ab4687753a2d8ff741b054a323c6e401d51a2f
|
/man/gr95Resid.Rd
|
8e628a0c2e313be3c57f294aaa4e61053b850f11
|
[] |
no_license
|
lakin-p/respsurf
|
1e3e170d37ff5e7d54992c0ba3f35ffa4e7113ea
|
8849d1e165a540e06d9ecf14e29d9b239916f113
|
refs/heads/master
| 2016-09-01T19:58:33.536933
| 2013-06-03T21:11:53
| 2013-06-03T21:11:53
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 811
|
rd
|
gr95Resid.Rd
|
\name{gr95Resid}
\alias{gr95Resid}
\title{Returns the residuals of the Greco 1995 model for a given
set of parameters versus concentration data and observed
endpoint data.}
\usage{
gr95Resid(param, dlist, evec, mpos)
}
\arguments{
\item{mpos}{A logical variable indicating whether the
dose slopes are positive. Usually false.}
\item{param}{A list of numeric values, always of the form
B, Econ, C50.1, C50.2, m1, m2, alpha.}
\item{dlist}{A 2-column matrix of points at which the
response surface is to be constructed.}
\item{evec}{A vector of observed endpoint values. Should
be the same length as nrow(dlist).}
}
\description{
Returns the residuals of the Greco 1995 model for a given
set of parameters versus concentration data and observed
endpoint data.
}
\author{
Paul Lakin
}
|
f24275e0abd0dd1c090ea9cc95251c9679e82c3c
|
8924983f788d5cdce430505b523f1be6b2a2a00d
|
/1lesson.R
|
4c96d1440fd9acce44c91795b9f162e7b78d373a
|
[] |
no_license
|
Maxim1488/matmod
|
778f422bf08f913e0f446d651af27a443755b3b5
|
392e5b9d0287fa126aa1713afa3b9aaf38bdd6cd
|
refs/heads/master
| 2021-01-10T16:19:27.770387
| 2016-02-05T13:14:34
| 2016-02-05T13:14:34
| 51,139,725
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 304
|
r
|
1lesson.R
|
t=c(T,TRUE)
f=c(F,FALSE)
# commentary as numeric log()
?as `logical-class`()
?as logi
# tip peremens
# func (a,b,c)
plot(density(rnorm(1:100)),col="blue")
# operator (=)
t=25^.5
# log operatory "==" logical oper
z==4
z>4=4
z<4=4
z=c(1,4,10)
# seqi(from=
)
A=c("A","B","C")
B=1:5
C=c(T,F)
#names func
#f
|
73aa3b2adfc940409ab0907fcf33ea2a48ba84fd
|
e6294d8e2099ac9bc6efbb803e7032e4d6c922db
|
/man/helperpeak.Rd
|
ace2351f5ee351142922188f31a93585d5083a07
|
[] |
no_license
|
cran/peakPick
|
670a26602eb50a763ab2b48a6d37f1f1e571ca7a
|
1737a1884e0eb08238cd253b1224b025929675c9
|
refs/heads/master
| 2021-01-10T13:15:04.134978
| 2015-12-04T15:40:34
| 2015-12-04T15:40:34
| 48,085,628
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 745
|
rd
|
helperpeak.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/peakpicking.R
\name{helperpeak}
\alias{helperpeak}
\title{helper function for small peak elimination}
\usage{
helperpeak(thepos, vec, nsd, npos)
}
\arguments{
\item{thepos}{integer position of peak in vector vec}
\item{vec}{vector of values for peakdetection}
\item{nsd}{numeric minimum number of standard deviations for a peak to rise
above the mean of its immediate vicinity in order to be considered for a
peak call}
\item{npos}{integer value, peak size will be estimated plus/minus npos
positions from peak}
}
\value{
boolean TRUE if the peak at position thepos is to be deleted
}
\description{
helper function for small peak elimination
}
\keyword{internal}
|
c1d868b98da46890d676f2332677d49b75f3b9d8
|
081c62f36f7703d7987218c1c22931e083198e73
|
/myelo/inst/doc/papers/craigGCSF/Qdb.R
|
0fd37d330ab518eb50fe6db324e2457779467662
|
[] |
no_license
|
radivot/myelo
|
be7ed23a6d1772e55310ced91270aa1d09da6735
|
2498bed404c98f096fcda4075c34a2881265e24b
|
refs/heads/master
| 2022-12-15T00:11:22.751773
| 2022-12-04T14:24:36
| 2022-12-04T14:24:36
| 6,070,078
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,451
|
r
|
Qdb.R
|
rm(list=ls())
library(tidyverse)
library(deSolve)
library(myelo)
(x0=craigIC[c(1,8)])
(parsQ=craigPars[c("Qss","Aqss","tauS","fQ","the2","s2")])
parsQ["kapDel"]=craigPars["kapss"]+craigPars["kapDel"]
parsQ
attach(as.list(parsQ))
fbeta=function(Q) fQ/(1+(Q/the2)^s2)
betaSS=fbeta(Qss)
(kapDel=(Aqss-1)*betaSS)
detach(as.list(parsQ))
(StrTimes=seq(0,80,14))
(StpTimes=StrTimes+5)
nc=length(StpTimes)
(events=tibble(var=rep("Aq",2*nc),
time=sort(c(StrTimes,StpTimes)),
value=rep(c(0.0*parsQ["Aqss"],parsQ["Aqss"]),nc),
method=rep("rep",2*nc)))
events2=events
events2$time=events2$time+150
(eventsdat=as.data.frame(bind_rows(events,events2)))
(f=file.path(system.file(paste("libs",Sys.getenv("R_ARCH"),sep=""), package = "myelo"),
paste("myelo",.Platform$dynlib.ext,sep="")))
dyn.load(f)
(parsQdb=c(tauS=2.8, fQ = 2*betaSS, thresh = 0.1, betaSS = betaSS, kapDel = kapDel))
times <- seq(-20,500,by=0.01)
(x05=c( Q = 1.10216835127605, S1 = 0.0330752837276347,
S2 = 0.0330752837276349, S3 = 0.0330752837276351, S4 = 0.0330752837276354,
Aq = 1.5116))
system.time(yout5 <- dede(x05,times = times, func = "derivsQdb", parms = parsQdb,
dllname = "myelo",initfunc = "parmsQdb",
events=list(data=eventsdat),method="lsoda",
nout = 1, outnames = c("beta")) )
D5=data.frame(yout5)
head(D5,2)
tail(D5,2)
gx=xlab("Days")
sbb=theme(strip.background=element_blank())
cc=coord_cartesian(xlim=c(-2,125))
tc=function(sz) theme_classic(base_size=sz)
d5=D5%>%select(time,Q,Aq)%>%gather(key="Lab",value="Value",-time)
d5%>%ggplot(aes(x=time,y=Value))+facet_grid(Lab~.,scales = "free")+geom_line(size=1)+gx+tc(14)+sbb#+cc
ggsave("~/Results/myelo/Qdb2x6.pdf",width=5, height=5)
(parsQdb1=c(fQ = 2*betaSS, thresh = 0.1, betaSS = betaSS, kapDel = kapDel))
times <- seq(-20,500,by=0.01)
(x0=c( Q = 1.10216835127605, Aq = 1.5116))
system.time(yout <- dede(x0,times = times, func = "derivsQdb1", parms = parsQdb1,
dllname = "myelo",initfunc = "parmsQdb1",
events=list(data=eventsdat),method="lsoda",
nout = 1, outnames = c("beta")) )
D=data.frame(yout)
head(D,2)
tail(D,2)
d=D%>%select(time,Q,Aq)%>%gather(key="Lab",value="Value",-time)
d%>%ggplot(aes(x=time,y=Value))+facet_grid(Lab~.,scales = "free")+geom_line(size=1)+gx+tc(14)+sbb#+cc
ggsave("~/Results/myelo/Qdb1_2x6.pdf",width=5, height=5) #flat steps => slightly more killing (better)
### Qdb1 is twice as fast
#
# library(rbenchmark)
# benchmark("db" = {
# dede(x05,times = times, func = "derivsQdb", parms = parsQdb,
# dllname = "myelo",initfunc = "parmsQdb",
# events=list(data=eventsdat),method="lsoda",
# nout = 1, outnames = c("beta"))
# },
# "db1"={
# dede(x0,times = times, func = "derivsQdb1", parms = parsQdb1,
# dllname = "myelo",initfunc = "parmsQdb1",
# events=list(data=eventsdat),method="lsoda",
# nout = 1, outnames = c("beta"))
# },
# "db1ode"={
# ode(x0,times = times, func = "derivsQdb1", parms = parsQdb1,
# dllname = "myelo",initfunc = "parmsQdb1",
# events=list(data=eventsdat),method="lsoda",
# nout = 1, outnames = c("beta"))
# },
#
# replications = 25,
# columns = c("test", "replications", "elapsed",
# "relative", "user.self", "sys.self")
# )
#
#
|
8095626306e34eac87156351bf6f25b4e4797bf0
|
f32dbf645fa99d7348210951818da2275f9c3602
|
/man/Zdate.Rd
|
21a19080183246cfc7725a0bff70107f076dfd27
|
[] |
no_license
|
cran/RSEIS
|
68f9b760cde47cb5dc40f52c71f302cf43c56286
|
877a512c8d450ab381de51bbb405da4507e19227
|
refs/heads/master
| 2023-08-25T02:13:28.165769
| 2023-08-19T12:32:32
| 2023-08-19T14:30:39
| 17,713,884
| 2
| 4
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,193
|
rd
|
Zdate.Rd
|
\name{Zdate}
\alias{Zdate}
\alias{dateList}
\alias{dateStamp}
\title{Date functions}
\description{
Make character vector from dates
}
\usage{
Zdate(info, sel=1, t1=0, sep=':')
dateList(datevec)
dateStamp(datelist, sep=':')
}
\arguments{
\item{info}{info structure from trace structure}
\item{sel}{selection of which ones to extract,
default=1:length(info$jd) }
\item{t1}{ time offset, seconds, default=0 }
\item{sep}{ character for separating the components in the string, default=":" }
\item{datevec}{ vector with yr, jd, mo, day, hr, mi, sec }
\item{ datelist}{ output of dateList }
}
\details{
Format date stamp for plotting and identification. Used for STAMP.
}
\value{
character strings
}
\note{
If using Zdate to create a file name, becareful about the separator. A colon
in the file name on PC and MAC systems can be confusing for the OS.
}
\author{Jonathan M. Lees<jonathan.lees.edu>}
\seealso{swig, dateStamp, ghstamp, filedatetime}
\examples{
data("GH")
sel <- which(GH$COMPS == "V")
ftime <- Zdate(GH$info, sel[1:5], 1)
dvec <- c(2009, 134, 5, 14, 10, 32, 24.5, 0)
A <- dateList(dvec)
dateStamp(A)
dateStamp(A, sep="_")
}
\keyword{misc}
|
f05ebde65b61aa141dac26c372c09b85db8c4db3
|
f77708703a51a8ff8a15504d6c8d7e6340d815bb
|
/man/make.quartiles.Rd
|
d9a58d59c2cd8bdde720d443e9800753880d3379
|
[] |
no_license
|
syyang93/yangR
|
7533fa60814ce7e9917464728455b3499c3a9640
|
dfe4cc8f9037024ff25a862e653c5630bc591ae4
|
refs/heads/master
| 2021-06-14T21:12:42.469432
| 2021-02-19T14:45:07
| 2021-02-19T14:45:07
| 143,451,657
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| true
| 573
|
rd
|
make.quartiles.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/make.quartiles.R
\name{make.quartiles}
\alias{make.quartiles}
\title{Function to make quartiles from a column within a dataframe --> taken from fashaR}
\usage{
make.quartiles(test)
}
\arguments{
\item{test}{data that needs to be divided into quartiles}
}
\value{
output Dataframe with quartiles (categories and by number, 4 = highest quartile)
}
\description{
Function to make quartiles from a column within a dataframe --> taken from fashaR
}
\examples{
test2=makequartiles(test$resid.mtDNA)
}
|
fd2e99b5d274eaa3bd66729e9cb6338662695cae
|
117936196834fbda370de297d6f5a77846bf45e9
|
/old/update1_jan18/newFigures/testplot.R
|
23e5cc8d738acefbab5f995f9c6c17d5e0b403b9
|
[] |
no_license
|
javirudolph/testingHMSC
|
a79dc2ffcdec967ed45d23e46151044d1365ab51
|
61c3e1b035b8095c45755833d2ab0ebc1179a6fb
|
refs/heads/master
| 2021-06-16T04:27:22.878177
| 2021-03-11T18:46:51
| 2021-03-11T18:46:51
| 170,368,566
| 4
| 2
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,404
|
r
|
testplot.R
|
datFig2 %>% filter(., scenario == "Fig2a") %>%
arrange(desc(r2)) %>%
ggtern(aes(x = env, z = spa, y = codist)) +
scale_T_continuous(limits=c(0,1.0),
breaks=seq(0,1,by=0.1),
labels=seq(0,1,by=0.1)) +
scale_L_continuous(limits=c(0.0,1),
breaks=seq(0,1,by=0.1),
labels=seq(0,1,by=0.1)) +
scale_R_continuous(limits=c(0.0,1.0),
breaks=seq(0,1,by=0.1),
labels=seq(0,1,by=0.1)) +
theme_showarrows() +
labs(xarrow = "Environment",
yarrow = "Codistribution",
zarrow = "Spatial") +
geom_point(aes(size = r2, shape = iteration, color = nicheOpt), alpha = 0.5) +
scale_color_viridis_c() +
#theme_minimal() +
guides(size = guide_legend(order = 1,
title = expression(paste(R^{2}))),
shape = guide_legend(order = 2,
title = NULL,
override.aes = list(size = 4)),
col = guide_colourbar(title = "Niche optima",
order = 3)) +
theme(legend.position = "bottom",
legend.box = "vertical",
axis.title = element_text(colour = "white"),
panel.grid = element_line(colour = "darkgrey"),
panel.border = element_rect(colour = "darkgrey"),
panel.background = element_rect(fill = "white"))
|
7bf9317f1afb852bae998b10c3ff3fe030841683
|
bd808eb9a1c233ba0723120a636c1b3bfa057ef3
|
/R/getGenbank.R
|
3a2df413384b4a5ec636eb289f97ecbc76114f28
|
[] |
no_license
|
wind22zhu/rDNAse
|
1165dcdcdc0840f8148c0da41647cf2221450f98
|
829fe7ebc5abe710bde3539f9e164cdd9091aa47
|
refs/heads/master
| 2021-01-17T06:33:09.818121
| 2016-07-14T00:51:17
| 2016-07-14T00:51:17
| 63,228,252
| 2
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,322
|
r
|
getGenbank.R
|
#' Get DNA/RNA Sequences from Genbank by GI ID
#'
#' Get DNA/RNA Sequences from Genbank by GI ID
#'
#' This function get DNA/RNA sequences from Genbank by GI ID(s).
#'
#' @param id A character vector, as the GI ID(s).
#'
#' @return A list, each component contains one of the DNA/RNA sequences.
#'
#' @keywords Genbank
#'
#' @aliases getGenbank
#'
#' @author Min-feng Zhu <\email{wind2zhu@@163.com}>
#'
#' @seealso See \code{\link{readFASTA}} for reading FASTA format files.
#'
#' @export getGenbank
#'
#' @examples
#' \donttest{
#' # Network latency may slow down this example
#' # Only test this when your connection is fast enough
#' require(RCurl)
#'
#' ids = c(2, 11)
#' getGenbank(ids)}
getGenbank = function (id) {
id = as.character(id)
n = length(id)
dna = vector('list', n)
for (i in 1:n) {
url = paste('http://www.ncbi.nlm.nih.gov/sviewer/viewer.cgi?tool=portal&sendto=on&log$=seqview&db=nuccore&dopt=fasta&sort=&val=', id[i],
'&from=begin&to=end&maxplex=1', sep = '')
genb = RCurl::getURL(url)
sequences = strsplit(genb[[1]], split = "\n")[[1]]
start = 2
end = length(sequences)
dna[[i]]= paste(sequences[start:end], collapse = "")
}
gi_name = lapply(1:n, function(i) paste('gi', id[i], sep = "_"))
names(dna) = gi_name
return(dna)
}
|
c029f1eab3af1df2e69b0aa9b8a5576a2ce49fe6
|
1fdce84d0fadf95c5908553ac84efee4ea1aafe0
|
/R/permKS.R
|
e9c3c54eee9a9a89adc0c71bbe9a4898a8ced73a
|
[] |
no_license
|
cran/perm
|
351a16efefd17be5340873d1b0487b0210f6d976
|
4b6d9b252ebd7e18f022271a1d5c55180d1081d3
|
refs/heads/master
| 2023-09-03T05:22:50.930937
| 2023-08-24T21:00:02
| 2023-08-24T23:30:45
| 17,698,470
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,558
|
r
|
permKS.R
|
`permKS` <-
function (x, ...){
UseMethod("permKS")
}
`permKS.formula` <-
function(formula, data, subset, na.action, ...){
## mostly copied from wilcox.test.formula
if (missing(formula) || (length(formula) != 3) || (length(attr(terms(formula[-2]),
"term.labels")) != 1))
stop("'formula' missing or incorrect")
m <- match.call(expand.dots = FALSE)
if (is.matrix(eval(m$data, parent.frame())))
m$data <- as.data.frame(data)
m[[1]] <- as.name("model.frame")
m$... <- NULL
mf <- eval(m, parent.frame())
DNAME <- paste(names(mf), collapse = " by ")
groupname<-names(mf)[2]
names(mf) <- NULL
response <- attr(attr(mf, "terms"), "response")
g <- factor(mf[[-response]])
resp <- mf[[response]]
out <- do.call("permKS", c(list(x=resp,g=g), list(...)))
out$data.name <- DNAME
out
}
`permKS.default` <-
function(x, g, exact = NULL, method=NULL, methodRule=methodRuleKS1,
control=permControl(),...){
cm<-control$cm
nmc<-control$nmc
seed<-control$seed
digits<-control$digits
p.conf.level<-control$p.conf.level
setSEED<-control$setSEED
if (!is.numeric(x) | (!is.character(g) & !is.factor(g))) stop("x must be numeric and g must be character or factor vectors")
if (is.null(method)) method<-methodRule(x,g,exact)
method.OK<-(method=="pclt" | method=="exact.mc")
if (!method.OK) stop("method not one of: 'pclt', 'exact.mc'")
mout<-switch(method,
pclt=ksample.pclt(x,g),
exact.mc=ksample.exact.mc(x,g,nmc,seed,digits,p.conf.level,setSEED))
p.values<-mout$p.values
PVAL<-p.values["p.twosided"]
if (method=="pclt") METHOD<-"K-Sample Asymptotic Permutation Test"
else if (method=="exact.mc") METHOD<-"K-Sample Exact Permutation Test Estimated by Monte Carlo"
xname<-deparse(substitute(x))
gname<-deparse(substitute(g))
if (length(xname)>1) xname<-c("x")
if (length(gname)>1) gname<-c("g")
DNAME <- paste(xname, "and", gname)
chisq<-mout$chisq.value
if (!is.null(chisq)) names(chisq)<-"Chi Square"
df<-mout$df
if (!is.null(df)) names(df)<-"df"
if (method!="exact.mc") nmc<-NULL
OUT <- list(statistic = chisq, parameter=df, estimate=NULL,
p.value = as.numeric(PVAL), method = METHOD,
data.name = DNAME, p.conf.int=mout$p.conf.int, nmc=nmc)
if (method=="exact.mc"){ class(OUT) <- "mchtest"
} else class(OUT) <- "htest"
return(OUT)
}
|
853d4d1c63e87061ffb0f3d2840f167433ee0e75
|
fc3ef1e0f0fcc246981349c65a88300c8918bab2
|
/kmeans-old.R
|
a201a566dd32de13a0898052fe7adf97b1f03abd
|
[] |
no_license
|
CuriousPICTians/lifematters
|
bac1d52cd3f55cb3f23d486c28b08d228d818453
|
ef2bb3faf265596a73c6e1b81944cb80ba848a9e
|
refs/heads/master
| 2021-01-19T07:20:47.449293
| 2017-06-06T03:58:29
| 2017-06-06T03:58:29
| 87,537,594
| 1
| 4
| null | 2017-06-06T03:58:30
| 2017-04-07T11:09:54
|
PHP
|
UTF-8
|
R
| false
| false
| 1,670
|
r
|
kmeans-old.R
|
#!/usr/bin/env Rscript
#Usage :
# 1) From Command line : $ cd /var/www/html/lifematters; Rscript kmeans.R '<email_id>'
# 2) From PHP : exec("Rscript kmeans.R <email_id>", $out);
library(rJava)
library(RMongo)
i <- commandArgs(TRUE)
#i <- '1018@hotmail.com'
rootkea <- mongoDbConnect('organ')
donors <- dbGetQuery(rootkea, 'donorinfo', '{"approved": "1"}', skip = 0, limit = 10000)
receivers <- dbGetQuery(rootkea, 'receiverinfo', '{"approved": "1"}', skip = 0, limit = 20000)
donor_ds <- donors[, c("email", "organ", "blood")]
receiver_ds <- receivers[, c("email", "organ", "blood")]
donor_clusters <- kmeans(donor_ds[, c("organ", "blood")], 8)
receiver_clusters <- kmeans(receiver_ds[, c("organ", "blood")], 8)
j <- 1
ans <- 0
for (email in donor_ds$email)
{
if (email == i)
{
ans <- j
break;
}
j <- j + 1
}
cd_cluster <- donor_clusters$cluster[ans]
organ_diff <- receiver_clusters$centers[,"organ"] - donor_clusters$centers[cd_cluster,"organ"]
blood_diff <- receiver_clusters$centers[,"blood"] - donor_clusters$centers[cd_cluster,"blood"]
ans_cluster_l <- which.min(abs(organ_diff) + abs(blood_diff))
temp <- receiver_ds[receiver_clusters$cluster == ans_cluster_l, ]
final <- temp[(temp$organ == donor_ds[ans, ]$organ) & (temp$blood == donor_ds[ans,]$blood),]
#print(final)
print(final$email)
#for (email in final$email)
#{
# query <- gsub(' ', '', paste('{"email":"', email, '"}'))
# print(query)
# dbInsertDocument(rootkea, "result", query)
#}
#k <- 1
#rows <- nrow(final)
#while (k <= rows)
#{
# docs <- gsub('[]\\[]', "", toJSON(final[k,], dataframe = c("rows")))
#print(docs)
# dbInsertDocument(rootkea, "result", doc = docs)
# k <- k + 1
#}
|
c3599ffdf1219b4f5e724d786a239d20bdd81e62
|
9c59572fe0a298f89a54221d68ee1728524db215
|
/motCorr_topUp/motionCorrect.afni.blip_singleVolume.R
|
79bc77a5043ab92790878ebe55808e1f67ac9ed7
|
[] |
no_license
|
AlessioPsych/AnalysisAfni
|
f2f2f9a3117ccff12b97bd014a9e869c7f6b5197
|
735678954d517031804ee42d92fe43ce1b7dd1a6
|
refs/heads/master
| 2023-06-28T07:00:06.160686
| 2023-06-23T08:50:17
| 2023-06-23T08:50:17
| 156,407,302
| 3
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,457
|
r
|
motionCorrect.afni.blip_singleVolume.R
|
args <- commandArgs(T)
print( args )
#args <- c( 'EPI/', 'TOPUP/', '2', '2' )
#setwd('/media/alessiofracasso/storage2/SpinozaTest/HighRes/AF_HighRes_04112016/topUpDataset2')
# get actual dir
mainDir <- getwd()
# get EPIs
setwd( args[1] )
epiFiles <- dir( pattern=sprintf('*.nii') )
# get TOPUPs
setwd( mainDir )
setwd( args[2] )
topFiles <- dir( pattern=sprintf('*.nii') )
# get selected EPI
selectedEpi <- as.numeric(args[3])
if (is.numeric(selectedEpi)==FALSE) { # selected epi is not numeric!
msg <- sprintf( 'the third argument MUST be a number (which EPI do you want to realign all the data to??)' )
warning( msg )
stopifnot(flagDir)
}
# get selected TOP UP
selectedTop <- as.numeric(args[4])
if (is.numeric(selectedTop)==FALSE) { # selected TOP is not numeric!
msg <- sprintf( 'the third argument MUST be a number (which TOP UP do you want to realign all the data to??)' )
warning( msg )
stopifnot(flagDir)
}
# get EPI n of TRs
setwd( mainDir )
instr <- sprintf('3dinfo %s%s > infoEPI.1D',args[1],epiFiles[selectedEpi])
system( instr )
trString <- scan( file='infoEPI.1D', what=c('character'), skip=17, nlines=1 )
nTRs <- as.numeric( trString[6] )
system('rm infoEPI.1D')
# get TOPUP n of TRs
setwd( mainDir )
instr <- sprintf('3dinfo %s%s > infoTOPUP.1D',args[2],topFiles[selectedTop])
system( instr )
trString <- scan( file='infoTOPUP.1D', what=c('character'), skip=17, nlines=1 )
nTRsTOPUP <- as.numeric( trString[6] )
system('rm infoTOPUP.1D')
# motionCorrect EPIs
print('##################')
print('##################')
print('motionCorrect EPIs')
print('##################')
print('##################')
setwd( args[1] )
for ( nEpi in 1:length(epiFiles) ) {
filename <- strsplit(epiFiles[nEpi],'.nii')[[1]][1]
prefixName <- sprintf('pb.%s.volreg+orig', filename)
motion1DfileAff <- sprintf('pb.%s.volreg', filename)
motion1DfileLin <- sprintf('pb.%s.lin.volreg', filename)
instr <- sprintf('3dvolreg -verbose -zpad 1 -base %s[%1.0f] -1Dfile %s -1Dmatrix_save %s -prefix %s -Fourier %s ', epiFiles[selectedEpi], nTRs-1, motion1DfileLin, motion1DfileAff, prefixName, epiFiles[nEpi] )
print( instr )
system( instr )
}
setwd( mainDir )
targetDir <- 'motionCorrectEpi'
flagDir <- dir.create( file.path(mainDir, targetDir) )
if (flagDir==FALSE) { # directory already exists!
msg <- sprintf( 'Remove the directory %s_folder to proceed', targetDir )
warning( msg )
stopifnot(flagDir)
}
setwd( args[1] )
filesToMove <- dir(pattern='*volreg*')
setwd( mainDir )
for ( nFiles in 1:length(filesToMove) ) {
instr <- sprintf('mv %s%s %s/%s', args[1], filesToMove[nFiles], targetDir, filesToMove[nFiles] )
system( instr )
}
# EPI for Top Up
print('####################')
print('####################')
print('copy EPI for top up')
print('####################')
print('####################')
setwd( args[1] )
instr <- sprintf( '3dTcat -prefix ../epiForTopUp.nii %s[%1.0f]', epiFiles[selectedEpi], nTRs-1 )
system( instr )
setwd( mainDir )
# TOP UP for Top Up
print('##################################')
print('##################################')
print('copy top up volume EPIs for top up')
print('##################################')
print('##################################')
setwd( args[2] )
instr <- sprintf( '3dTcat -prefix ../topUp.nii %s[%1.0f]', topFiles[selectedTop], 0 )
system( instr )
setwd( mainDir )
# Compute non-linear transformation
print('#########################')
print('#########################')
print('compute top up transform')
print('#########################')
print('#########################')
setwd( mainDir )
print('estimate non-linear transformation, it might take a while...')
instr <- sprintf('3dQwarp -source epiForTopUp.nii -base topUp.nii -prefix warpTop -verb -iwarp -pblur 0.05 0.05 -blur -1 -1 -noweight -minpatch 9 -plusminus' )
print( instr )
system( instr )
# Apply non-linear transformation, create target dir
print('#######################')
print('#######################')
print('apply top up transform')
print('#######################')
print('#######################')
targetDir <- 'motionCorrect_topUp_Epi'
flagDir <- dir.create( file.path(mainDir, targetDir) )
if (flagDir==FALSE) { # directory already exists!
msg <- sprintf( 'Remove the directory %s_folder to proceed', targetDir )
warning( msg )
stopifnot(flagDir)
}
# apply non-linear transformation to the files and save them in target dir
setwd('motionCorrectEpi')
motionCorrAffine <- dir(pattern='*aff12.1D')
setwd( mainDir )
for ( nEpi in 1:length(epiFiles) ) {
namePrefix <- strsplit( epiFiles[nEpi], '.nii' )[[1]][1]
filenameBlip <- sprintf( 'pb.%s.volreg+orig', namePrefix )
instr <- sprintf('3dNwarpApply -master %s%s -source %s%s -nwarp motionCorrectEpi/%s warpTop_PLUS_WARP+orig -interp wsinc5 -prefix %s/%s', args[1], epiFiles[nEpi], args[1], epiFiles[nEpi], motionCorrAffine[nEpi], targetDir, filenameBlip)
print( instr )
system( instr )
}
# storage directory
print('###################')
print('###################')
print('clean up transform')
print('###################')
print('###################')
targetDir <- 'topUpDir'
flagDir <- dir.create( file.path(mainDir, targetDir) )
if (flagDir==FALSE) { # directory already exists!
msg <- sprintf( 'Remove the directory %s_folder to proceed', targetDir )
warning( msg )
stopifnot(flagDir)
}
system('mv warpTop* topUpDir/')
system('mv epiForTopUp.nii topUpDir/')
system('mv topUp.nii topUpDir/')
|
ace3b64fb701211ca6fdde426d16ccd93887b349
|
0d10da12b402ed6b1af64c1992f48f4bd553719e
|
/predictions_on_na.R
|
9750ccd490b28c406bdbe1573c0f713565ea7610
|
[] |
no_license
|
edples/Predictions-of-8-levels
|
6ea504c564f54cbf583db6440bca364628a7c065
|
fe5ceb6a9a8be31f6954368d2240ef8a87920f61
|
refs/heads/master
| 2020-06-17T01:46:45.622859
| 2020-01-06T12:30:43
| 2020-01-06T12:30:43
| 195,759,105
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,152
|
r
|
predictions_on_na.R
|
setwd("C:/comply")
m_data <- read.csv("M.csv")
profile_data <- read.csv("ProfileMetadata.csv")
library(tidyverse)
class(m_data)
class(profile_data)
str(m_data)
str(profile_data)
glimpse(m_data)
glimpse(profile_data)
head(m_data)
colSums(is.na(m_data)) #no NA's, the n.a.'s are characters
colSums(is.na(profile_data)) # the same as above
head(profile_data)
summary(m_data)
summary(profile_data)
# The problem is clasification type , the target variable m_data$M having 8 levels.
library(stringr)
sum(str_count(profile_data$Number.of.Years.of.Births.in.profile , "n.a.")) #count the number of n.a.'s
sum(str_count(profile_data$Difference.in.Years.between.Max.Min.Year.of.Birth , "n.a."))
# as the last 2 columns of the profile_data have 84025 of "n.a." from 96737 observations, they are no longer relevant to the case.
profile_data <- profile_data[, 1:9] #slice the data, dropping the above 2 columns
glimpse(profile_data)
identical(m_data$Profile_id, profile_data$Profile_id)#check if the"Profile_id" columns of the both data sets are identical,
# meaning that the order is the same in both datasets.
profile_data$m_var <-m_data$M #addin the M column to the relevant dataset, which the model will train and predict on.
na_profile_data <-subset(profile_data, m_var == "n.a.") # choose only the n.a. rows,this will be dataset that the model will make predictions on.
glimpse(na_profile_data)
profile_data<- subset(profile_data, !m_var == "n.a.")# choose only the rows without n.a.,this will be the training dataset
glimpse(profile_data)
profile_data$has_year_of_birth<- as.factor(profile_data$has_year_of_birth)#convert the relevant columns of the datasets to factors
profile_data$has_country <- as.factor(profile_data$has_country)
profile_data$is_sanction <- as.factor(profile_data$is_sanction)
profile_data$is_pep <- as.factor(profile_data$is_pep)
profile_data$is_adverse_media <- as.factor(profile_data$is_adverse_media)
profile_data$Number.of.Source.Docs.for.Profile <- as.factor(profile_data$Number.of.Source.Docs.for.Profile)
na_profile_data$has_year_of_birth<- as.factor(na_profile_data$has_year_of_birth)
na_profile_data$has_country <- as.factor(na_profile_data$has_country)
na_profile_data$is_sanction <- as.factor(na_profile_data$is_sanction)
na_profile_data$is_pep <- as.factor(na_profile_data$is_pep)
na_profile_data$is_adverse_media <- as.factor(na_profile_data$is_adverse_media)
na_profile_data$Number.of.Source.Docs.for.Profile <- as.factor(na_profile_data$Number.of.Source.Docs.for.Profile)
profile_data$m_var <- as.numeric(profile_data$m_var)
library(vtreat)
library(dplyr)
library(magrittr)
# I consider the below variables significant as predictors.
vars_test <- c("has_year_of_birth", " has_country ", "is_sanction ", "is_pep", "is_adverse_media", "Number.of.Source.Docs.for.Profile")
treatplan <- designTreatmentsZ(profile_data,vars_test)#design a treatment plan for the variables which handles the missing values, to be used in XGBoost
(scoreFrame <- treatplan %>%
use_series(scoreFrame) %>%
select(varName, origName, code))
(newvars <- scoreFrame %>%
filter(code %in% c("clean", "lev")) %>%
use_series(varName))
trainingframe.treat <- prepare(treatplan, profile_data, varRestriction = newvars) # this makes the data compatible to XgBoost
testframe.treat <-prepare(treatplan, na_profile_data, varRestriction = newvars)
library(xgboost)
set.seed(123) # for reproducibility
model <- xgb.cv(data = as.matrix(trainingframe.treat), #perform cross-validation to find the optimal number of trees
label = profile_data$m_var,
nrounds = 100,
num_class = 8,
nfold = 5,
objective = "multi:softmax",# multiclass target
eta = 0.3,
max_depth = 6,
early_stopping_rounds = 10,
verbose = 0 # silent
)
(evlog <- model$evaluation_log)
evlog %>%
summarize(ntrees.train = which.min(train_merror_mean), # find the index of min(train_merror_mean)
ntrees.test = which.min(test_merror_mean)) # find the index of min(test_merror_mean)
#from the above lines, optimal ntrees= 1
ntrees <- 1
m_var_xgb <- xgboost(data = as.matrix(trainingframe.treat), # training data as matrix
label = profile_data$m_var, # column of outcomes
nrounds = ntrees,# number of trees to build
num_class = 8,
objective = "multi:softmax", # objective
eta = 0.3,
depth = 6,
verbose = 0 # silent
)
na_profile_data$predicted <- predict(m_var_xgb, as.matrix(testframe.treat)) #predict on the test set and add the predictions column
# to the na_profile_data set
head(na_profile_data) #check the structure of the data set
summary(na_profile_data$predicted)
unique(na_profile_data$predicted) # to see which distinct values were predicted
unique(profile_data$m_var)
library(funModeling)
df_status(na_profile_data)
|
c6254221570f2e29495aa4bbb8bb3307a9e05bad
|
2cb2bc953975540de8dfe3aee256fb3daa852bfb
|
/thisweek_masuipeo/q711/tyama_codeiq711_next.R
|
57225b9e753eb6ec5127c5d1d9789c6ca8da342d
|
[] |
no_license
|
cielavenir/codeiq_solutions
|
db0c2001f9a837716aee1effbd92071e4033d7e0
|
750a22c937db0a5d94bfa5b6ee5ae7f1a2c06d57
|
refs/heads/master
| 2023-04-27T14:20:09.251817
| 2023-04-17T03:22:57
| 2023-04-17T03:22:57
| 19,687,315
| 2
| 4
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,043
|
r
|
tyama_codeiq711_next.R
|
#!/usr/bin/Rscript
next_permutation<-function(env,name,n=NA){
a=get(name,env)
if(is.na(n))n<-length(a)
if(n<0||length(a)<n)return(FALSE)
i<-0
a<-c(a[1:n],rev(a[-n:0]))
for(i in rev(1:(length(a)-1)))if(a[i]<a[i+1])break # r doesn't go beyond the range
if(a[i]>=a[i+1]){
assign(name,rev(a),env)
return(FALSE)
}
k<-i
for(i in rev((k+1):length(a)))if(a[k]<a[i])break
l<-i
z<-a[k];a[k]<-a[l];a[l]<-z
assign(name,a<-c(a[1:k],rev(a[-k:0])),env)
return(TRUE)
}
env<-new.env()
N<-6
e0<-1:(N*2)
f0<-1:(N*2)
i<-0
r<-0
for(i in 1:N){
e0[i]=f0[i]=0
e0[N+i]=f0[N+i]=1
}
assign("e0",e0,env)
assign("f0",f0,env)
repeat{
e0=get("e0",env)
repeat{
f0=get("f0",env)
flg<-0
zero1<-0
zero2<-N
one1<-0
one2<-N
for(i in 1:(N*2)){
if(e0[i]==0)zero1=zero1+1
if(e0[i]==1)one1=one1+1
if(f0[N*2+1-i]==0)zero2=zero2-1
if(f0[N*2+1-i]==1)one2=one2-1
if(zero1==zero2)flg=flg+1
if(one1==one2)flg=flg+1
}
if(flg>=2)r=r+1
if(!next_permutation(env,"f0"))break
}
if(!next_permutation(env,"e0"))break
}
cat(r)
cat("\n")
|
c144dc18a12a94f55208bb609cdb8f315f7f05ea
|
5b1c24cc6be830fa9a8b084acb40929d26122166
|
/man/getStartingData.Rd
|
47ec377f5eb7164cff044682429b78ede7b8fc87
|
[
"MIT"
] |
permissive
|
mathewroy/ynabr
|
bf4e995daa16940bbb67aabee85b93967c70e68c
|
098a4c13db4cf1b1f0191ae03bac8b9c7e73420b
|
refs/heads/master
| 2023-01-28T11:27:08.043989
| 2023-01-19T23:30:33
| 2023-01-19T23:30:33
| 159,608,851
| 14
| 1
| null | null | null | null |
UTF-8
|
R
| false
| true
| 589
|
rd
|
getStartingData.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getStartingData.R
\name{getStartingData}
\alias{getStartingData}
\title{Retrieves user or budget names}
\usage{
getStartingData(i, param.token.code, param.token.env)
}
\arguments{
\item{i}{name of endpoint}
\item{param.token}{Your YNAB API personal access token}
}
\description{
Gets the following YNAB data for a YNAB subscriber:
"user", "budgets"
}
\examples{
endpoint <- "budgets"
mytoken <- "1234567890ABCDE"
df_budgets <- getStartingData(i = endpoint, param.token = mytoken)
}
\keyword{getStartingData}
|
1e3ef57c8c7dd700f96d4da7d75aa9326d03e833
|
34cc9bb4242a1aa4d873b726230a1c1b82203fcb
|
/merge_ele_data.R
|
b0eb6a42b3f957a536ee533f3b857ce6ca49b1b4
|
[] |
no_license
|
secs-lab/hackathon-2020
|
d820db7440b518a41dacd3b42895bfad352e47bc
|
69a478a10642221674c55e27949e46dbe45cfac6
|
refs/heads/master
| 2020-11-26T10:42:52.075188
| 2020-01-15T15:51:19
| 2020-01-15T15:51:19
| 229,047,729
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,908
|
r
|
merge_ele_data.R
|
library(reshape2)
setwd(dirname(rstudioapi::getSourceEditorContext()$path))
#read in caluclated elephant poaching and population datasets
load("data/raw/SH_AnnualModelPreds.Rdata")
ele_sums <- readRDS("data/raw/ele_sums.Rdata")
#read in raw populaion survery data, select relevant columns
#and filter to relevant years
pop.dat <- read.csv("data/raw/Copy of all_popn_estimates_mikesites_140401.csv", header = TRUE)
pop.dat <- pop.dat[-154,] # remove duplicate survey in same year
pop.dat <- pop.dat[pop.dat$year > 2001, c("sitecode", "year", "area", "est", "var", "dens")]
#read in poaching survey data, select relevant columns
#and filter to relevant years
pike.stats <- read.csv("data/raw/Copy of 170810_PikeStatsUpTo2016.csv", header = TRUE)
pike.stats <- pike.stats[pike.stats$year > 2001, c("siteid", "year", "totcarc", "illegal")]
names(pike.stats)[1] <- "sitecode"
#'convert arrays to data frames
ele.dat <- as.data.frame.table(site_quants.pike.full)
ele.sums <- as.data.frame.table(ele_sums)
#expand quantile values to individual columns
ele.dat.wide <- dcast(ele.dat, Var2 + Var3 ~ Var1, value.var="Freq")
ele.sums.wide <- dcast(ele.sums, Var2 + Var3 ~ Var1, value.var="Freq")
#rename columns
names(ele.dat.wide) <- c("sitecode", "year", "poach_q5", "poach_q50", "poach_q95")
names(ele.sums.wide) <- c("sitecode", "year", "pop_q5", "pop_q50", "pop_q95")
#create list of datafraes to be merged
df.list <- list(ele.dat.wide, ele.sums.wide, pop.dat,
pike.stats)
#merge all dataframes in the list in turn, keeping only records with matches
#in the poaching rate dataset
ele.all <- Reduce(function(x, y)
merge(x, y, by = c("sitecode", "year"),
all.x = TRUE, all.y = FALSE),
df.list, accumulate = FALSE)
#export combined dataframe as a csv
write.table(ele.all, "data/processed/ele_data.csv",
sep=",", row.names = FALSE)
|
8f9c039e4d0dc04b00418bc594b39f3252993d31
|
ff10ad9933ad8d63bb824ddd0dc7527a279325f5
|
/cachematrix.R
|
bf302f732273e2c5df93c602fb11c92d3226278d
|
[] |
no_license
|
lemenendez/ProgrammingAssignment2
|
b2dc40efa6b3c5ea672308d22b61c8dbd6be5f1f
|
60979011e7537cfdbb459ec7675faec64a0de5ce
|
refs/heads/master
| 2021-07-13T20:57:09.172316
| 2017-10-19T04:44:33
| 2017-10-19T04:44:33
| 107,472,738
| 0
| 0
| null | 2017-10-18T23:06:10
| 2017-10-18T23:06:09
| null |
UTF-8
|
R
| false
| false
| 933
|
r
|
cachematrix.R
|
## source file contains two functions to handle the mechanims
## to calculate the inverse of a matrix in a very efficient way by caching
## the result if does not change
## object for working whit a cacheable inverse of a matrix
makeCacheMatrix <- function(x = matrix()) {
i <- NULL # inverse
set <- function(y) { # set data and clear cache
x <<- y
i <<- NULL
}
get <- function() x # get data
setinv <- function(inv) i <<- inv # set inverse
getinv <- function() i # get inverse
list(set = set, get = get,
setinv = setinv,
getinv = getinv)
}
## Write a short comment describing this function
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
i <- x$getinv()
if(!is.null(i)) {
message("getting cached data")
return(i)
}
data <- x$get()
i <- solve(data)
x$setinv(i)
i
}
|
a3660517e7b95412a5768b9fb6a6df1231864ff8
|
df6279f728136d1201b18c940ce7f16c9c2bfcf7
|
/man/plot.fitcurve.Rd
|
23d84462c859387106d0b12dcb741c80704a2a6a
|
[] |
no_license
|
cran/WindCurves
|
b298f70c08252c37428657e957461aa892ea1425
|
d6b14115982540accdd7370db73783fcf0ed77ad
|
refs/heads/master
| 2022-06-04T15:25:06.495082
| 2022-05-01T03:50:02
| 2022-05-01T03:50:02
| 120,623,905
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| true
| 656
|
rd
|
plot.fitcurve.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fitcurve_plot.R
\name{plot.fitcurve}
\alias{plot.fitcurve}
\title{A function to plot the curves fitted with fitcurve() function}
\usage{
\method{plot}{fitcurve}(x, ...)
}
\arguments{
\item{x}{is object returned by fitcurve() function}
\item{\dots}{Additional graphical parameters given to plot function.}
}
\value{
Plot the curves fitted with fitcurve() function
}
\description{
A function to plot the curves fitted with fitcurve() function
}
\examples{
s <- pcurves$Speed
p <- pcurves$`Nordex N90`
da <- data.frame(s,p)
x <- fitcurve(da)
plot(x)
}
|
c097000f69908e415f72157d1bf94aba40c7c534
|
bcfc2d522327afe96f503871df7892fbbac94697
|
/www/lib/ionic/js/ionic-angular.min-compiled.js.map
|
3c493c83607051bf628a0b2059731bb560833767
|
[] |
no_license
|
derrickwilliams/phymoo
|
15b674b507d289f119bf4049434ec84cd6ebc06c
|
88d3053056aa9d10506cb037b403c43625b4426c
|
refs/heads/master
| 2021-01-10T11:56:25.237743
| 2015-06-03T13:12:01
| 2015-06-03T13:12:01
| 36,804,271
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 341,594
|
map
|
ionic-angular.min-compiled.js.map
|
{"version":3,"sources":["/Users/derwilliams/workspace/me/projects/phymoo/www/lib/ionic/js/ionic-angular.min.js"],"names":[],"mappings":"aAcA,CAAC,CAAA,UAAU,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,CAAC,qBAAqB,CAAC,CAAC,CAAC,aAAa,EAAC,CAAC,CAAC,cAAc,CAAC,IAAE,CAAC,EAAE,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,OAAO,CAAC,GAAC,CAAC,UAAU,CAAC,CAAC,EAAC,KAAK,CAAC,CAAC,EAAC,MAAM,CAAC,CAAC,EAAC,qBAAqB,CAAC,+BAAS,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,UAAU,IAAE,CAAC,IAAE,CAAC,KAAG,CAAC,KAAG,CAAC,CAAC,KAAK,IAAE,CAAC,KAAG,CAAC,CAAC,MAAM,CAAA,AAAC,CAAC,QAAO,CAAC,CAAC,KAAK,GAAC,CAAC,EAAC,CAAC,CAAC,MAAM,GAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAA,CAAA,CAAC,EAAC,yBAAyB,CAAC,mCAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,MAAM,GAAC,CAAC,IAAE,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,UAAU,CAAC,QAAO,CAAC,CAAC,UAAU,GAAC,CAAC,CAAC,MAAM,EAAC,CAAC,CAAC,CAAC,CAAA,CAAA,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,OAAO,CAAC,KAAG,CAAC,GAAC,IAAI,CAAC,CAAC,CAAC,cAAc,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,aAAa,CAAC,CAAC,EAAC,UAAU,CAAC,CAAC,EAAC,SAAS,CAAC,CAAC,EAAC,kBAAkB,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,kBAAkB,CAAC,IAAE,OAAO,KAAG,CAAC,CAAC,kBAAkB,CAAA,AAAC,EAAC,aAAa,CAAC,CAAC,EAAC,YAAY,CAAC,CAAC,EAAC,KAAK,CAAC,CAAC,EAAC,UAAU,CAAC,CAAC,CAAC,UAAU,EAAC,UAAU,CAAC,CAAC,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,OAAO,CAAC,OAAO,CAAC,CAAC,CAAC,SAAS,CAAC,aAAa,CAAC,oCAAoC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,EAAE,CAAC,MAAM,CAAC,IAAI,CAAC,CAAC,CAAC,SAAS,CAAC,UAAU,EAAC,SAAS,CAAC,CAAC,CAAC,OAAO,KAAK,CAAC,OAAO,CAAC,QAAQ,CAAC,CAAC,EAAC,CAAC,CAAC,EAAE,CAAC,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAE,CAAC,CAAA,CAAC,CAAC,CAAC,CAAC,GAAC,OAAO,CAAC,OAAO,CAAC,oDAAkD,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,UAAU,IAAE,CAAC,CAAC,QAAQ,CAAC,YAAY,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,OAAO,CAAC,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,GAAC,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,IAAG,CAAC,KAAG,CAAC,CAAC,QAAQ,IAAE,CAAC,CAAC,QAAQ,CAAA,AAAC,IAAE,CAAC,EAAE,EAAC,CAAC,IAAE,CAAC,CAAC,QAAQ,CAAA,CAAC,CAAC,IAAG,CAAC,CAAC,KAAK,GAAC,CAAC,CAAC,MAAM,EAAC,CAAC,CAAC,CAAC,KAAK,CAAA,CAAC,MAAM,IAAI,KAAK,CAAC,uEAAsE,GAAC,CAAC,GAAC,qIAAkI,CAAC,CAAA,CAAC,KAAI,CAAC,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,QAAQ,KAAG,CAAC,CAAC,KAAK,GAAC,CAAC,CAAC,QAAQ,EAAE,CAAA,AAAC,CAAC,GAAG,CAAC,IAAE,CAAC,CAAC,QAAQ,CAAC,CAAC,IAAG,CAAC,CAAC,KAAK,GAAC,CAAC,CAAC,KAAK,EAAC,CAAC,CAAC,CAAC,KAAK,CAAA,CAAC,MAAM,IAAI,KAAK,CAAC,sEAAqE,GAAC,CAAC,GAAC,oIAAiI,CAAC,CAAA,CAAC,KAAI,CAAC,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,QAAQ,KAAG,CAAC,CAAC,KAAK,GAAC,CAAC,CAAC,QAAQ,EAAE,CAAA,AAAC,CAAC,CAAC,EAAE,CAAC,aAAa,EAAE,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,MAAM,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,EAAE,CAAC,KAAK,CAAC,YAAY,CAAC,KAAG,CAAC,GAAC,IAAG,GAAC,CAAC,GAAC,IAAG,CAAA,AAAC,EAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,eAAe,EAAC,EAAE,CAAC,CAAC,IAAI,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,MAAM,IAAE,CAAC,oBAAoB,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,IAAG,CAAC,CAAC,SAAS,GAAC,CAAC,EAAC,CAAC,CAAA,CAAC,CAAC,IAAI,CAAC,CAAC,QAAQ,CAAC,CAAC,EAAE,CAAC,CAAC,GAAG,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,GAAC,GAAG,CAAC,CAAC,CAAC,QAAQ,GAAC,CAAC,KAAG,CAAC,CAAC,UAAU,CAAC,OAAO,IAAI,CAAC,KAAK,CAAC,CAAC,GAAC,CAAC,CAAC,cAAc,CAAC,CAAA,CAAC,CAAC,UAAU,CAAC,OAAO,IAAI,CAAC,KAAK,CAAC,CAAC,GAAC,CAAC,CAAC,aAAa,CAAC,CAAA,CAAC,CAAA,CAAC,KAAK,CAAC,CAAC,KAAK,GAAC,CAAC,CAAA,CAAC,KAAK,CAAC,CAAC,OAAO,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,QAAQ,GAAC,CAAC,KAAG,CAAC,CAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,MAAM,IAAE,GAAG,KAAG,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,CAAC,IAAI,CAAC,KAAK,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAC,GAAG,GAAC,CAAC,CAAC,cAAc,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,MAAM,IAAE,GAAG,KAAG,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,CAAC,IAAI,CAAC,KAAK,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAC,GAAG,GAAC,CAAC,CAAC,aAAa,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAA,CAAC,CAAA,CAAC,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,IAAE,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,IAAI,EAAE,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,eAAe,CAAC,mBAAmB,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAE,EAAE,CAAA,CAAE,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,OAAO,EAAE,EAAC,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,gBAAgB,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,GAAC,QAAQ,CAAC,CAAC,CAAC,KAAK,CAAC,EAAC,CAAC,CAAC,MAAM,GAAC,QAAQ,CAAC,CAAC,CAAC,MAAM,CAAC,EAAC,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,CAAA,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,UAAU,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,OAAO,CAAC,6CAA2C,CAAC,CAAC,CAAC,CAAC,CAAC,IAAG,CAAC,CAAC,UAAU,CAAC,YAAY,CAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,UAAU,IAAE,CAAC,CAAC,OAAO,CAAC,UAAU,CAAA,CAAC,MAAM,IAAI,KAAK,CAAC,8EAA8E,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,gBAAgB,EAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,qEAAqE,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,MAAM,IAAI,KAAK,CAAC,qGAAqG,GAAC,CAAC,CAAC,gBAAgB,GAAC,IAAI,CAAC,CAAC,IAAI,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,gBAAgB,IAAE,CAAC,CAAC,oBAAoB,EAAC,CAAC,CAAC,OAAO,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,UAAU,IAAE,CAAC,CAAC,oBAAoB,EAAC,CAAC,CAAC,CAAC,CAAC,SAAS,IAAE,CAAC,CAAC,mBAAmB,EAAC,CAAC,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,QAAQ,CAAC,EAAE,CAAC,eAAe,EAAC,CAAC,CAAC,EAAC,OAAO,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,QAAQ,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,mBAAmB,EAAC,KAAK,CAAC,sBAAsB,CAAC,UAAU,CAAC,CAAC,CAAC,UAAU,CAAC,MAAM,EAAE,EAAC,CAAC,EAAE,CAAA,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,gBAAgB,CAAC,CAAC,EAAC,SAAS,CAAC,CAAC,CAAC,IAAG,CAAC,GAAC,CAAC,KAAG,CAAC,GAAC,EAAE,CAAA,AAAC,EAAC,CAAC,OAAO,CAAC,OAAO,CAAC,CAAC,CAAC,CAAA,CAAC,MAAM,IAAI,KAAK,CAAC,2CAA2C,GAAC,CAAC,GAAC,eAAe,GAAC,OAAO,KAAK,CAAC,CAAC,CAAC,CAAC,YAAY,CAAC,UAAU,CAAC,CAAC,EAAE,CAAC,OAAO,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,yBAAyB,CAAC,CAAC,CAAC,IAAE,CAAC,EAAE,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,UAAU,EAAC,UAAU,CAAC,OAAO,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,QAAQ,EAAC,CAAC,CAAC,EAAC,CAAC,EAAE,EAAC,CAAC,CAAC,QAAQ,IAAE,CAAC,CAAC,QAAQ,CAAC,GAAG,CAAC,eAAe,EAAC,CAAC,CAAC,EAAC,CAAC,IAAE,CAAC,CAAC,UAAU,IAAE,CAAC,CAAC,UAAU,CAAC,WAAW,CAAC,CAAC,CAAC,EAAC,CAAC,IAAE,CAAC,CAAC,QAAQ,EAAE,EAAC,CAAC,GAAC,CAAC,GAAC,IAAI,EAAC,CAAC,IAAE,CAAC,CAAC,OAAO,EAAE,EAAC,CAAC,GAAC,IAAI,CAAA,CAAC,CAAC,CAAC,IAAI,CAAC,EAAC,CAAC,CAAA,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,QAAQ,CAAC,IAAG,EAAC,UAAU,CAAC,SAAS,EAAC,KAAK,CAAC,CAAC,CAAC,EAAC,OAAO,CAAC,gBAAgB,EAAC,IAAI,CAAC,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,EAAC,YAAY,CAAC,CAAC,EAAC,WAAW,CAAC,CAAC,EAAC,aAAa,CAAC,CAAC,EAAC,cAAc,CAAC,CAAC,CAAC,CAAC,OAAO,SAAS,CAAC,CAAC,CAAC,SAAS,CAAC,EAAE,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,IAAI,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,EAAE,CAAC,cAAc,EAAE,EAAC,CAAC,CAAC,CAAC,GAAC,EAAE,CAAC,iBAAiB,CAAC,EAAE,CAAC,iBAAiB,CAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,GAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,EAAC,CAAC,GAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,MAAM,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,EAAC,CAAC,CAAC,GAAC,CAAC,IAAE,CAAC,GAAC,CAAC,CAAA,KAAI,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,EAAC,OAAO,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,GAAC,CAAC,CAAC,CAAA,AAAC,CAAC,IAAI,CAAC,GAAC,CAAC,EAAC,CAAC,IAAE,CAAC,EAAC,CAAC,EAAE,EAAC,CAAC,IAAE,CAAC,CAAC,MAAM,IAAE,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,GAAG,EAAE,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,KAAK,EAAE,CAAC,IAAI,CAAC,EAAA,CAAA,AAAC,EAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,GAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,KAAK,EAAC,CAAC,CAAC,MAAM,GAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,GAAC,CAAC,KAAG,CAAC,EAAC,CAAC,CAAC,KAAK,GAAC,CAAC,KAAG,CAAC,CAAC,MAAM,GAAC,CAAC,EAAC,CAAC,CAAC,OAAO,GAAC,EAAE,CAAC,CAAC,MAAM,IAAE,CAAC,CAAC,KAAK,CAAA,AAAC,EAAC,CAAC,CAAC,IAAI,GAAC,EAAE,CAAC,CAAC,KAAK,GAAC,CAAC,MAAI,CAAC,GAAC,CAAC,CAAA,AAAC,CAAA,AAAC,EAAC,CAAC,CAAC,cAAc,IAAE,KAAK,CAAC,KAAK,CAAC,cAAc,CAAC,CAAC,CAAC,KAAK,CAAC,EAAC,CAAC,GAAC,EAAE,CAAC,aAAa,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,YAAY,KAAG,CAAC,CAAC,YAAY,IAAE,CAAC,CAAC,UAAU,KAAG,CAAC,CAAC,UAAU,CAAA,KAAI,CAAC,CAAC,IAAI,CAAC,KAAK,CAAC,KAAK,CAAC,GAAG,CAAC,SAAS,CAAC,GAAC,CAAC,CAAC,OAAO,CAAC,CAAC,EAAC,CAAC,CAAC,UAAU,GAAC,CAAC,CAAC,UAAU,CAAC,CAAC,OAAO,CAAC,CAAC,EAAC,CAAC,CAAC,YAAY,GAAC,CAAC,CAAC,YAAY,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,CAAC,aAAa,KAAG,CAAC,CAAC,aAAa,IAAE,CAAC,CAAC,WAAW,KAAG,CAAC,CAAC,WAAW,CAAA,KAAI,CAAC,CAAC,IAAI,CAAC,KAAK,CAAC,OAAO,GAAC,CAAC,CAAC,IAAI,CAAC,KAAK,CAAC,OAAO,CAAC,OAAO,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,WAAW,GAAC,CAAC,CAAC,WAAW,CAAA,GAAE,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,EAAC,CAAC,CAAC,aAAa,GAAC,CAAC,CAAC,aAAa,CAAC,CAAC,CAAA,AAAC,CAAA,AAAC,CAAC,IAAI,CAAC,KAAG,CAAC,CAAC,MAAM,GAAC,CAAC,KAAG,CAAC,GAAC,EAAE,CAAC,aAAa,CAAC,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,IAAE,CAAC,EAAC,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,GAAG,CAAC,SAAS,CAAC,GAAC,CAAC,CAAC,OAAO,CAAC,CAAC,EAAC,CAAC,CAAC,UAAU,GAAC,CAAC,CAAC,WAAW,CAAC,CAAC,OAAO,CAAC,CAAC,EAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,MAAM,GAAE,CAAC,GAAC,CAAC,CAAC,GAAG,EAAE,EAAC,CAAC,CAAC,KAAK,CAAC,UAAU,CAAC,wBAAwB,CAAC,EAAC,KAAK,CAAC,KAAK,CAAC,eAAe,CAAC,CAAC,CAAC,KAAK,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,KAAK,CAAC,KAAK,CAAC,GAAG,CAAC,SAAS,CAAC,GAAC,gCAAgC,EAAC,CAAC,CAAC,UAAU,GAAC,CAAC,CAAC,YAAY,GAAC,IAAI,CAAC,GAAG,CAAC,CAAC,IAAI,CAAC,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,MAAM,EAAC,CAAC,GAAC,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,EAAE,EAAC,GAAG,CAAC,CAAC,MAAM,CAAC,IAAI,IAAI,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,MAAM,EAAC,CAAC,GAAC,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,EAAE,EAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,GAAC,CAAC,EAAC,CAAC,CAAC,GAAG,GAAC,CAAC,CAAA,CAAC,GAAG,CAAC,CAAC,IAAI,IAAI,CAAC,CAAC,CAAC,CAAC,OAAO,EAAC,CAAC,CAAC,MAAM,GAAE,CAAC,GAAC,CAAC,CAAC,GAAG,EAAE,EAAC,CAAC,IAAE,CAAC,CAAC,KAAK,CAAC,OAAO,EAAE,CAAC,KAAK,CAAC,EAAE,CAAA,CAAC,CAAC,SAAS,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,OAAO,KAAG,CAAC,CAAC,OAAO,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,UAAU,CAAC,IAAI,IAAI,CAAC,CAAC,CAAC,CAAC,OAAO,EAAC,CAAC,CAAC,MAAM,GAAE,CAAC,GAAC,CAAC,CAAC,GAAG,EAAE,EAAC,CAAC,CAAC,OAAO,KAAG,CAAC,IAAE,CAAC,CAAC,KAAK,CAAC,OAAO,EAAE,CAAA,AAAC,CAAC,CAAC,CAAC,OAAO,GAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,KAAK,GAAC,CAAC,CAAC,IAAI,EAAE,EAAC,IAAI,CAAC,EAAE,GAAC,MAAM,GAAC,CAAC,EAAE,EAAC,CAAC,CAAC,IAAI,CAAC,KAAK,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,GAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,IAAI,CAAC,wBAAwB,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,GAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,KAAK,CAAC,KAAK,CAAC,GAAG,CAAC,SAAS,CAAC,GAAC,gCAAgC,EAAC,CAAC,CAAC,IAAI,CAAC,KAAK,CAAC,OAAO,IAAE,2BAA2B,EAAC,KAAK,CAAC,KAAK,CAAC,eAAe,CAAC,CAAC,CAAC,KAAK,CAAC,EAAC,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,IAAI,CAAC,EAAC,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,CAAC,CAAC,CAAC,oBAAoB,CAAC,KAAK,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,IAAI,CAAC,kBAAkB,GAAC,CAAC,EAAC,IAAI,CAAC,oBAAoB,GAAC,CAAC,EAAC,IAAI,CAAC,cAAc,GAAC,UAAU,CAAC,OAAO,IAAI,CAAC,GAAG,CAAC,CAAC,EAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,WAAW,GAAC,CAAC,EAAC,CAAC,CAAC,cAAc,GAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,IAAI,CAAC,gBAAgB,GAAC,UAAU,CAAC,IAAI,CAAC,iBAAiB,GAAC,CAAC,CAAC,cAAc,EAAC,IAAI,CAAC,mBAAmB,GAAC,CAAC,CAAC,aAAa,EAAC,IAAI,CAAC,oBAAoB,GAAC,CAAC,EAAC,IAAI,CAAC,sBAAsB,GAAC,CAAC,EAAC,IAAI,CAAC,oBAAoB,GAAC,CAAC,IAAE,IAAI,CAAC,KAAK,CAAC,CAAC,CAAC,aAAa,GAAC,CAAC,CAAC,IAAE,CAAC,CAAA,CAAC,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,IAAI,CAAC,kBAAkB,GAAC,CAAC,EAAC,IAAI,CAAC,oBAAoB,GAAC,CAAC,EAAC,IAAI,CAAC,cAAc,GAAC,UAAU,CAAC,OAAO,IAAI,CAAC,GAAG,CAAC,CAAC,EAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,YAAY,GAAC,CAAC,EAAC,CAAC,CAAC,eAAe,GAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,IAAI,CAAC,gBAAgB,GAAC,UAAU,CAAC,IAAI,CAAC,iBAAiB,GAAC,CAAC,CAAC,aAAa,EAAC,IAAI,CAAC,mBAAmB,GAAC,CAAC,CAAC,cAAc,EAAC,IAAI,CAAC,oBAAoB,GAAC,CAAC,EAAC,IAAI,CAAC,sBAAsB,GAAC,CAAC,EAAC,IAAI,CAAC,oBAAoB,GAAC,CAAC,IAAE,IAAI,CAAC,KAAK,CAAC,CAAC,CAAC,cAAc,GAAC,CAAC,CAAC,IAAE,CAAC,CAAA,CAAC,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,IAAI,CAAC,wBAAwB,GAAC,SAAS,CAAC,CAAC,CAAC,OAAO,CAAC,GAAC,IAAI,CAAC,oBAAoB,GAAC,IAAI,CAAC,sBAAsB,CAAA,CAAC,EAAC,IAAI,CAAC,sBAAsB,GAAC,SAAS,CAAC,CAAC,CAAC,OAAO,IAAI,CAAC,KAAK,CAAC,CAAC,GAAC,IAAI,CAAC,oBAAoB,CAAC,GAAC,IAAI,CAAC,oBAAoB,CAAA,CAAC,EAAC,IAAI,CAAC,iBAAiB,GAAC,SAAS,CAAC,CAAC,CAAC,OAAO,IAAI,CAAC,KAAK,CAAC,CAAC,GAAC,IAAI,CAAC,oBAAoB,CAAC,GAAC,IAAI,CAAC,oBAAoB,CAAA,CAAC,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,IAAI,CAAC,wBAAwB,GAAC,UAAU,CAAC,OAAO,CAAC,CAAA,CAAC,EAAC,IAAI,CAAC,sBAAsB,GAAC,SAAS,CAAC,CAAC,CAAC,OAAO,CAAC,GAAC,IAAI,CAAC,oBAAoB,CAAA,CAAC,EAAC,IAAI,CAAC,iBAAiB,GAAC,SAAS,CAAC,CAAC,CAAC,OAAO,IAAI,CAAC,KAAK,CAAC,CAAC,GAAC,IAAI,CAAC,oBAAoB,CAAC,CAAA,CAAC,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,IAAI,CAAC,cAAc,GAAC,UAAU,CAAC,OAAO,IAAI,CAAC,sBAAsB,CAAC,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,GAAC,IAAI,CAAC,oBAAoB,GAAC,CAAC,GAAC,CAAC,CAAA,CAAC,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,IAAI,CAAC,aAAa,GAAC,SAAS,CAAC,CAAC,CAAC,QAAO,CAAC,CAAC,UAAU,GAAC,IAAI,CAAC,sBAAsB,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,YAAY,GAAC,IAAI,CAAC,wBAAwB,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,WAAW,GAAC,IAAI,CAAC,oBAAoB,EAAC,CAAC,CAAC,aAAa,GAAC,IAAI,CAAC,sBAAsB,EAAC,CAAC,CAAA,CAAA,CAAC,EAAC,IAAI,CAAC,iBAAiB,GAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,GAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAC,IAAI,CAAC,iBAAiB,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,MAAM,GAAC,CAAC,EAAC,IAAI,CAAC,iBAAiB,CAAC,CAAC,CAAC,GAAC,IAAI,CAAC,oBAAoB,GAAC,CAAC,CAAC,EAAC,CAAC,GAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAC,IAAI,CAAC,sBAAsB,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,IAAI,CAAC,sBAAsB,CAAC,CAAC,CAAC,GAAC,IAAI,CAAC,oBAAoB,CAAA,CAAC,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,GAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,IAAE,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,EAAE,EAAC,CAAC,GAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,IAAE,CAAC,EAAC,CAAC,CAAC,WAAW,GAAC,CAAC,CAAC,kBAAkB,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,aAAa,GAAC,CAAC,CAAC,mBAAmB,EAAC,CAAC,CAAC,UAAU,GAAC,CAAC,CAAC,UAAU,GAAC,CAAC,CAAC,WAAW,EAAC,CAAC,CAAC,YAAY,GAAC,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,GAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,IAAE,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,EAAE,EAAC,CAAC,GAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,IAAE,CAAC,EAAC,CAAC,CAAC,aAAa,GAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,oBAAoB,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,mBAAmB,CAAC,EAAC,CAAC,CAAC,YAAY,GAAC,CAAC,CAAC,YAAY,GAAC,CAAC,CAAC,aAAa,EAAC,CAAC,KAAG,CAAC,IAAE,CAAC,CAAC,YAAY,GAAC,CAAC,CAAC,aAAa,GAAC,CAAC,CAAC,mBAAmB,EAAE,CAAC,CAAC,YAAY,GAAC,CAAC,EAAC,CAAC,CAAC,WAAW,GAAC,CAAC,CAAC,kBAAkB,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,UAAU,GAAC,CAAC,CAAC,UAAU,GAAC,CAAC,CAAC,cAAc,EAAC,CAAC,CAAC,aAAa,GAAC,CAAC,EAAC,CAAC,CAAC,cAAc,GAAC,CAAC,CAAC,WAAW,CAAA,EAAG,CAAC,CAAC,WAAW,GAAC,CAAC,CAAC,kBAAkB,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,UAAU,GAAC,CAAC,CAAC,UAAU,EAAC,CAAC,CAAC,aAAa,GAAC,CAAC,CAAC,aAAa,EAAC,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,CAAC,cAAc,GAAC,CAAC,CAAC,cAAc,GAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,CAAC,cAAc,EAAC,CAAC,CAAC,WAAW,CAAC,EAAC,CAAC,CAAC,cAAc,GAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,WAAW,EAAC,CAAC,CAAC,cAAc,CAAC,CAAA,AAAC,CAAA,CAAC,IAAI,CAAC,EAAC,CAAC,CAAC,IAAI,EAAC,CAAC,CAAC,KAAK,CAAC,QAAQ,CAAC,CAAC,EAAC,EAAE,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,EAAE,CAAC,IAAI,CAAC,cAAc,GAAC,UAAU,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,OAAM,CAAC,CAAC,CAAC,UAAU,GAAC,CAAC,CAAC,WAAW,IAAE,CAAC,CAAA,GAAE,IAAI,CAAC,sBAAsB,CAAC,CAAC,CAAC,MAAM,GAAC,CAAC,GAAC,CAAC,CAAC,GAAC,CAAC,GAAC,CAAC,CAAA,CAAC,EAAC,IAAI,CAAC,SAAS,GAAC,UAAU,CAAC,CAAC,CAAC,MAAM,GAAC,CAAC,CAAA,CAAC,EAAC,IAAI,CAAC,aAAa,GAAC,UAAU,CAAC,IAAI,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,GAAC,CAAC,CAAC,MAAM,EAAC,CAAC,GAAC,CAAC,CAAC,MAAM,EAAC,CAAC,GAAC,CAAC,EAAC,CAAC,EAAE,EAAC,CAAC,CAAC,IAAI,CAAC,EAAE,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,CAAA,CAAC,EAAC,IAAI,CAAC,eAAe,GAAC,UAAU,CAAC,CAAC,GAAC,CAAC,CAAC,CAAA,CAAC,EAAC,IAAI,CAAC,aAAa,GAAC,SAAS,CAAC,CAAC,CAAC,QAAO,CAAC,GAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,KAAG,CAAC,GAAC,GAAE,GAAC,CAAC,CAAC,MAAM,EAAE,CAAC,CAAC,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,MAAM,GAAC,CAAC,EAAC,CAAC,EAAE,CAAA,EAAG,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,EAAC,CAAC,EAAE,CAAA,CAAC,AAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAA,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,iBAAiB,GAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,IAAG,IAAI,CAAC,aAAa,CAAC,CAAC,GAAC,IAAI,CAAC,iBAAiB,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,KAAG,CAAC,IAAE,CAAC,KAAG,CAAC,CAAA,CAAC,CAAC,GAAC,CAAC,CAAC,KAAK,GAAG,CAAC,IAAE,CAAC,CAAC,IAAI,CAAC,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,MAAM,EAAC,CAAC,GAAC,CAAC,IAAE,EAAE,CAAC,CAAC,GAAC,IAAI,CAAC,aAAa,CAAC,CAAC,CAAC,CAAA,IAAG,CAAC,CAAC,UAAU,GAAC,CAAC,CAAC,cAAc,IAAE,CAAC,CAAA,AAAC,EAAC,CAAC,EAAE,EAAE,KAAK,IAAI,CAAC,GAAC,CAAC,EAAC,CAAC,IAAE,CAAC,EAAC,CAAC,EAAE,EAAC,GAAG,CAAC,CAAC,GAAC,IAAI,CAAC,aAAa,CAAC,CAAC,CAAC,CAAA,IAAG,CAAC,CAAC,UAAU,IAAE,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,CAAC,CAAC,MAAK,CAAC,CAAC,GAAC,IAAI,CAAC,GAAG,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,KAAG,CAAC,CAAC,IAAI,CAAC,aAAa,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,GAAC,CAAC,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,MAAM,EAAC,CAAC,GAAC,CAAC,EAAC,CAAC,EAAE,EAAC,GAAG,CAAC,CAAC,GAAC,IAAI,CAAC,aAAa,CAAC,CAAC,CAAC,CAAA,IAAG,CAAC,CAAC,UAAU,GAAC,CAAC,CAAC,cAAc,GAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,IAAI,CAAC,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,GAAC,CAAC,IAAE,CAAC,CAAC,GAAC,IAAI,CAAC,aAAa,CAAC,CAAC,GAAC,CAAC,CAAC,CAAA,CAAE,UAAU,KAAG,CAAC,CAAC,UAAU,GAAE,CAAC,EAAE,CAAC,MAAK,CAAC,CAAC,GAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,KAAG,CAAC,CAAC,CAAC,CAAC,GAAC,IAAI,CAAC,aAAa,CAAC,CAAC,CAAC,CAAA,CAAE,UAAU,IAAE,CAAC,CAAC,cAAc,IAAE,CAAC,CAAC,WAAW,CAAA,AAAC,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAA,CAAC,CAAA,CAAC,IAAI,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,cAAc,EAAC,CAAC,CAAC,CAAC,CAAC,aAAa,EAAC,CAAC,CAAC,CAAC,CAAC,kBAAkB,EAAC,CAAC,CAAC,CAAC,CAAC,UAAU,EAAC,CAAC,CAAC,CAAC,CAAC,SAAS,EAAC,CAAC,CAAC,CAAC,CAAC,aAAa,EAAC,CAAC,CAAC,CAAC,CAAC,YAAY,EAAC,CAAC,CAAC,CAAC,CAAC,KAAK,EAAC,CAAC,CAAC,CAAC,CAAC,UAAU,EAAC,CAAC,CAAC,CAAC,CAAC,UAAU,EAAC,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,IAAE,UAAU,CAAC,OAAO,CAAC,CAAC,KAAK,CAAA,CAAC,EAAC,CAAC,CAAC,WAAS,CAAC,EAAC,CAAC,CAAC,CAAC,QAAO,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,EAAC,CAAC,CAAC,MAAM,GAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAA,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,IAAE,UAAU,CAAC,OAAO,CAAC,CAAC,KAAK,CAAA,CAAC,EAAC,CAAC,CAAC,WAAS,CAAC,EAAC,CAAC,CAAC,CAAC,QAAO,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,EAAC,CAAC,CAAC,MAAM,GAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAA,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,UAAU,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,KAAK,KAAG,CAAC,CAAC,aAAa,CAAC,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,KAAK,KAAG,CAAC,CAAC,cAAc,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,CAAC,OAAO,EAAC,CAAC,CAAC,SAAS,EAAC,CAAC,CAAC,WAAW,EAAC,CAAC,CAAC,CAAC,CAAC,sCAAsC,CAAC,sCAAsC,EAAC,CAAC,CAAC,CAAC,CAAC,wCAAwC,CAAC,wCAAwC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,aAAa,CAAC,IAAI,EAAC,IAAI,EAAC,IAAI,EAAC,EAAE,CAAC,cAAc,EAAE,EAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,aAAa,CAAC,IAAI,EAAC,IAAI,EAAC,EAAE,CAAC,cAAc,EAAE,EAAC,IAAI,EAAC,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,EAAE,CAAC,CAAC,CAAC,IAAI,CAAC,EAAA,CAAC,IAAI,CAAC,EAAA,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAE,IAAI,CAAC,EAAE,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAE,IAAI,CAAC,EAAE,CAAC,CAAC,IAAI,EAAE,CAAC,CAAC,CAAC,kBAAkB,CAAC,iBAAiB,EAAC,EAAE,CAAC,CAAC,CAAC,OAAO,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,EAAE,CAAC,GAAC,OAAO,CAAC,IAAI,CAAC,EAAE,EAAC,EAAE,CAAC,cAAc,CAAC,EAAC,CAAC,CAAC,WAAW,GAAC,CAAC,CAAC,UAAU,EAAC,CAAC,CAAC,UAAU,GAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,cAAc,EAAE,CAAC,CAAC,CAAC,CAAC,KAAG,CAAC,IAAE,CAAC,GAAC,EAAE,CAAC,iBAAiB,GAAC,CAAC,IAAE,CAAC,GAAC,CAAC,CAAA,IAAG,CAAC,EAAE,EAAC,CAAC,CAAC,WAAW,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,CAAC,IAAI,EAAE,CAAC,CAAC,CAAC,EAAC,EAAE,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,aAAa,GAAC,UAAU,CAAC,CAAC,CAAC,MAAM,EAAE,CAAC,GAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,EAAG,CAAC,GAAC,GAAG,EAAC,CAAC,GAAC,GAAG,CAAA,AAAC,CAAC,IAAI,CAAC,CAAC,gBAAgB,CAAC,CAAC,CAAC,IAAE,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,iBAAiB,IAAE,gBAAgB,CAAC,CAAC,CAAC,iBAAiB,CAAC,IAAE,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,gBAAgB,IAAE,gBAAgB,CAAC,CAAC,CAAC,gBAAgB,CAAC,IAAE,EAAE,CAAC,CAAC,GAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,OAAO,CAAC,CAAC,IAAE,CAAC,CAAA,IAAG,CAAC,IAAE,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,YAAY,CAAC,CAAC,IAAE,CAAC,CAAA,AAAC,IAAE,CAAC,IAAE,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAC,cAAc,CAAC,aAAa,CAAC,CAAC,IAAE,CAAC,CAAA,AAAC,EAAC,CAAC,GAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,EAAG,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,YAAY,CAAC,CAAC,MAAM,KAAK,CAAC,OAAO,CAAC,QAAQ,CAAC,CAAC,CAAC,SAAS,EAAC,CAAC,GAAC,CAAC,CAAC,YAAY,CAAC,EAAE,IAAI,CAAC,CAAC,CAAC,CAAC,sBAAsB,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,gBAAgB,CAAC,CAAC,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAC,cAAc,CAAC,aAAa,CAAC,IAAE,CAAC,CAAC,CAAC,IAAG,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,GAAG,CAAC,SAAS,CAAC,GAAC,CAAC,CAAC,OAAO,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,IAAE,CAAC,EAAC,CAAC,CAAC,cAAc,IAAE,CAAC,CAAC,aAAa,KAAG,CAAC,CAAC,aAAa,GAAC,CAAC,CAAC,WAAW,CAAC,WAAW,EAAC,CAAC,CAAC,cAAc,GAAC,CAAC,CAAC,WAAW,CAAC,YAAY,CAAA,AAAC,EAAC,CAAC,EAAE,CAAC,eAAe,IAAE,OAAO,CAAC,IAAI,CAAA,EAAG,EAAC,EAAE,CAAC,gBAAgB,EAAE,EAAC,CAAC,EAAE,EAAC,CAAC,EAAE,CAAA,CAAC,IAAI,IAAI,CAAC,CAAC,IAAI,CAAC,GAAG,CAAC,EAAE,EAAC,CAAC,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,EAAC,CAAC,EAAE,EAAC,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,EAAA,CAAC,CAAC,EAAE,GAAC,CAAC,CAAC,EAAC,EAAE,IAAE,EAAE,KAAG,CAAC,CAAC,CAAC,YAAY,GAAC,CAAC,CAAC,eAAe,IAAE,CAAC,CAAC,WAAW,GAAC,CAAC,CAAC,cAAc,CAAA,IAAG,CAAC,CAAC,MAAM,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,EAAC,IAAI,CAAC,OAAO,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,EAAC,CAAC,EAAE,CAAC,aAAa,IAAE,OAAO,CAAC,IAAI,CAAA,EAAG,EAAC,EAAE,GAAC,CAAC,CAAC,CAAA,CAAC,EAAC,IAAI,CAAC,OAAO,GAAC,UAAU,CAAC,CAAC,CAAC,SAAS,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,QAAQ,EAAE,EAAC,CAAC,CAAC,KAAK,GAAC,CAAC,CAAC,OAAO,GAAC,CAAC,CAAC,IAAI,GAAC,CAAC,CAAC,MAAM,GAAC,IAAI,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,MAAM,GAAC,CAAC,EAAC,CAAC,GAAC,EAAE,EAAC,CAAC,CAAC,OAAO,CAAC,EAAE,CAAC,GAAC,EAAE,EAAC,CAAC,CAAC,UAAU,GAAC,CAAC,CAAC,WAAW,EAAC,CAAC,CAAC,MAAM,EAAE,EAAC,CAAC,EAAE,CAAC,SAAS,IAAE,OAAO,CAAC,IAAI,CAAA,EAAG,CAAA,CAAC,CAAA,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAM,CAAC,eAAe,EAAC,QAAQ,EAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,WAAW,EAAE,CAAC,OAAO,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,WAAS,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,UAAU,EAAC,UAAU,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,OAAM,CAAC,sBAAsB,EAAC,SAAS,CAAC,CAAC,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,IAAI,CAAC,cAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,MAAM,EAAC,CAAC,EAAE,IAAE,CAAC,GAAE,CAAC,GAAG,CAAC,CAAC,SAAS,CAAC,QAAQ,CAAC,QAAQ,CAAC,IAAE,CAAC,CAAC,OAAO,CAAC,KAAK,CAAC,wBAAwB,CAAC,IAAE,CAAC,CAAC,iBAAiB,EAAC,OAAO,CAAA,CAAC,GAAC,CAAC,CAAC,UAAU,CAAA,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,OAAO,CAAC,OAAO,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,MAAM,CAAC,OAAO,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,qBAAqB,EAAE,CAAC,KAAK,CAAC,OAAO,CAAC,YAAY,CAAC,CAAC,CAAC,KAAK,EAAC,CAAC,CAAC,KAAK,EAAC,CAAC,CAAC,IAAI,EAAC,CAAC,CAAC,GAAG,GAAC,EAAE,EAAC,CAAC,CAAC,IAAI,GAAC,CAAC,CAAC,KAAK,EAAC,CAAC,CAAC,GAAG,GAAC,CAAC,CAAC,MAAM,CAAC,IAAE,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,MAAM,IAAE,CAAC,CAAC,WAAW,KAAG,KAAK,CAAC,EAAE,CAAC,KAAK,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,UAAU,EAAC,UAAU,CAAC,KAAK,CAAC,GAAG,CAAC,KAAK,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAM,CAAC,WAAW,EAAC,UAAU,EAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,UAAU,CAAC,iBAAiB,EAAC,OAAO,CAAC,iBAAS,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,MAAM,CAAC,UAAU,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAA,CAAC,EAAC,SAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,OAAO,CAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,OAAO,CAAC,eAAe,CAAC,CAAC,CAAC,CAAC,UAAU,GAAC,CAAC,IAAE,CAAC,CAAC,EAAC,CAAC,CAAC,aAAa,GAAC,CAAC,IAAE,CAAC,EAAC,CAAC,CAAC,KAAK,CAAC,iBAAiB,EAAC,CAAC,CAAC,aAAa,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,UAAU,EAAC,UAAU,CAAC,OAAO,CAAC,CAAC,UAAU,EAAC,OAAO,CAAC,CAAC,aAAa,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,KAAK,EAAE,EAAC,CAAC,CAAC,GAAG,CAAC,oBAAoB,EAAC,UAAU,CAAC,KAAK,CAAC,qBAAqB,CAAC,UAAU,CAAC,CAAC,CAAC,KAAK,EAAE,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,EAAG,CAAC,CAAC,MAAM,CAAC,UAAU,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAA,CAAC,EAAC,SAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,OAAO,CAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,OAAO,CAAC,eAAe,CAAC,CAAC,CAAC,CAAC,UAAU,GAAC,CAAC,IAAE,CAAC,CAAC,EAAC,CAAC,CAAC,aAAa,GAAC,CAAC,IAAE,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,UAAU,EAAC,UAAU,CAAC,OAAO,CAAC,CAAC,UAAU,EAAC,OAAO,CAAC,CAAC,aAAa,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,UAAU,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,UAAU,EAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,QAAO,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,gBAAgB,CAAC,gBAAgB,CAAC,EAAC,CAAC,CAAC,UAAU,CAAC,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,CAAC,sBAAsB,CAAC,UAAU,CAAC,CAAC,MAAM,IAAE,CAAC,CAAC,QAAQ,CAAC,cAAc,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAA,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,YAAY,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,eAAe,EAAE,CAAA,CAAC,IAAI,CAAC,CAAC,OAAO,CAAC,MAAM,CAAC,OAAO,EAAC,CAAC,WAAW,EAAC,YAAY,EAAC,WAAW,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,MAAM,EAAC,CAAC,CAAC,OAAO,CAAC,OAAO,EAAC,CAAC,CAAC,OAAO,CAAC,SAAS,EAAC,CAAC,CAAC,OAAO,CAAC,QAAQ,EAAC,CAAC,CAAC,OAAO,CAAC,QAAQ,EAAC,CAAC,CAAC,OAAO,CAAC,OAAO,EAAC,CAAC,CAAC,OAAO,CAAC,IAAI,CAAC,CAAC,CAAC,OAAO,CAAC,mBAAmB,EAAC,CAAC,YAAY,EAAC,UAAU,EAAC,UAAU,EAAC,UAAU,EAAC,sBAAsB,EAAC,gBAAgB,EAAC,YAAY,EAAC,qBAAqB,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAE,MAAM,CAAC,IAAI,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,mBAAmB,GAAC,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,MAAM,CAAC,CAAC,EAAC,wBAAwB,CAAC,CAAC,EAAC,aAAa,CAAC,CAAC,EAAC,qBAAqB,CAAC,CAAC,EAAC,OAAO,CAAC,EAAE,EAAC,mBAAmB,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,IAAE,EAAE,CAAC,CAAC,IAAI,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,OAAO,CAAC,MAAM,EAAC,CAAC,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,eAAe,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,OAAO,GAAC,CAAC,CAAC,iFAA6E,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,uBAAuB,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,mBAAmB,CAAC,CAAC,CAAC,GAAG,CAAC,qBAAqB,EAAC,UAAU,CAAC,CAAC,CAAC,MAAM,EAAE,CAAA,CAAC,CAAC,CAAC,CAAC,CAAC,QAAO,CAAC,CAAC,WAAW,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,KAAG,CAAC,CAAC,OAAO,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,WAAW,CAAC,iBAAiB,CAAC,EAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,WAAW,CAAC,mBAAmB,CAAC,CAAA,CAAC,EAAC,GAAG,CAAC,EAAC,CAAC,CAAC,qBAAqB,EAAE,EAAC,CAAC,EAAE,EAAC,CAAC,CAAC,WAAW,CAAC,CAAC,EAAC,QAAQ,CAAC,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC,CAAC,QAAQ,EAAE,EAAC,CAAC,CAAC,MAAM,EAAE,EAAC,CAAC,CAAC,MAAM,CAAC,MAAM,GAAC,CAAC,GAAC,IAAI,EAAC,CAAC,CAAC,IAAE,CAAC,CAAA,EAAG,CAAA,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,EAAC,CAAC,CAAC,SAAS,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,KAAG,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,mBAAmB,CAAC,EAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,EAAC,QAAQ,CAAC,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,IAAE,CAAC,CAAA,EAAG,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,QAAQ,CAAC,iBAAiB,CAAC,CAAA,CAAC,EAAC,EAAE,EAAC,CAAC,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,EAAC,CAAC,CAAC,qBAAqB,GAAC,CAAC,CAAC,wBAAwB,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,WAAW,CAAC,EAAC,CAAC,CAAC,MAAM,GAAC,UAAU,CAAC,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,MAAM,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,aAAa,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,IAAE,CAAC,CAAC,WAAW,EAAE,CAAA,CAAC,EAAC,CAAC,CAAC,wBAAwB,GAAC,UAAU,CAAC,CAAC,CAAC,wBAAwB,EAAE,KAAG,CAAC,CAAC,IAAE,CAAC,CAAC,WAAW,EAAE,CAAA,CAAC,EAAC,CAAC,CAAC,SAAS,EAAE,EAAC,CAAC,CAAC,MAAM,CAAC,MAAM,GAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAA,CAAA,CAAC,OAAM,CAAC,IAAI,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,QAAQ,GAAC,SAAS,CAAC,CAAC,CAAC,IAAI,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,IAAE,UAAU,IAAE,CAAC,IAAE,kBAAkB,IAAE,CAAC,CAAC,IAAI,CAAC,GAAC,CAAC,EAAC,CAAC,GAAC,IAAI,CAAC,MAAM,EAAC,CAAC,EAAE,EAAC,IAAG,CAAC,GAAC,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,YAAY,CAAA,CAAC,GAAG,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,GAAC,CAAC,IAAE,CAAC,CAAC,SAAS,CAAC,GAAG,CAAC,CAAC,CAAC,SAAS,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,KAAI,CAAC,IAAI,CAAC,GAAC,CAAC,GAAG,IAAE,CAAC,CAAC,YAAY,CAAC,OAAO,CAAC,IAAE,EAAE,CAAA,AAAC,GAAC,GAAG,CAAA,CAAE,OAAO,CAAC,SAAS,EAAC,GAAG,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,KAAK,CAAC,GAAG,CAAC,EAAC,CAAC,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,MAAM,EAAC,CAAC,EAAE,EAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,EAAE,EAAC,CAAC,CAAC,KAAG,CAAC,CAAC,OAAO,CAAC,GAAG,GAAC,CAAC,GAAC,GAAG,CAAC,KAAG,CAAC,IAAE,CAAC,GAAC,GAAG,CAAA,AAAC,CAAC,CAAC,CAAC,YAAY,CAAC,OAAO,EAAC,CAAC,CAAC,IAAI,EAAE,CAAC,CAAA,CAAC,OAAO,IAAI,CAAA,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,WAAW,GAAC,SAAS,CAAC,CAAC,CAAC,IAAI,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,CAAC,IAAI,CAAC,GAAC,CAAC,EAAC,CAAC,GAAC,IAAI,CAAC,MAAM,EAAC,CAAC,EAAE,EAAC,IAAG,CAAC,GAAC,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,YAAY,CAAA,CAAC,GAAG,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,GAAC,CAAC,IAAE,CAAC,CAAC,SAAS,CAAC,MAAM,CAAC,CAAC,CAAC,SAAS,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,KAAK,IAAI,CAAC,GAAC,CAAC,CAAC,KAAK,CAAC,GAAG,CAAC,EAAC,CAAC,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,MAAM,EAAC,CAAC,EAAE,EAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,YAAY,CAAC,OAAO,EAAC,CAAC,GAAG,IAAE,CAAC,CAAC,YAAY,CAAC,OAAO,CAAC,IAAE,EAAE,CAAA,AAAC,GAAC,GAAG,CAAA,CAAE,OAAO,CAAC,SAAS,EAAC,GAAG,CAAC,CAAC,OAAO,CAAC,GAAG,GAAC,CAAC,CAAC,IAAI,EAAE,GAAC,GAAG,EAAC,GAAG,CAAC,CAAC,IAAI,EAAE,CAAC,CAAC,OAAO,IAAI,CAAA,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,gBAAgB,EAAC,CAAC,WAAW,EAAC,UAAU,EAAC,OAAO,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,EAAE,EAAC,CAAC,KAAG,CAAC,KAAG,CAAC,CAAC,QAAQ,CAAC,SAAS,CAAC,EAAC,CAAC,CAAC,UAAU,CAAC,CAAC,IAAE,CAAC,IAAE,CAAC,CAAC,QAAQ,CAAC,QAAQ,CAAC,CAAA,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,KAAG,CAAC,KAAG,CAAC,CAAC,WAAW,CAAC,QAAQ,CAAC,EAAC,CAAC,CAAC,UAAU,CAAC,CAAC,KAAG,CAAC,IAAE,CAAC,CAAC,WAAW,CAAC,SAAS,CAAC,CAAA,CAAC,EAAC,GAAG,EAAC,CAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,GAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,OAAO,CAAC,CAAA,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,0BAAwB,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,QAAO,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,MAAM,CAAC,CAAC,EAAC,OAAO,CAAC,CAAC,EAAC,UAAU,CAAC,CAAC,EAAC,QAAQ,CAAC,CAAC,CAAC,CAAA,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,YAAY,EAAC,CAAC,QAAQ,EAAC,cAAc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yBAAwB,GAAC,CAAC,GAAC,2CAAyC,GAAC,CAAC,CAAC,CAAC,CAAC,GAAC,qEAAoE,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,GAAC,SAAS,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,CAAA,CAAC,CAAC,CAAC,CAAA,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,YAAY,EAAC,CAAC,WAAW,EAAC,SAAS,CAAC,CAAC,CAAC,OAAM,CAAC,QAAQ,CAAC,mBAAU,CAAC,IAAI,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,SAAS,CAAC,MAAM,EAAC,CAAC,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,SAAS,CAAC,GAAG,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,IAAI,CAAA,CAAC,EAAC,WAAW,CAAC,sBAAU,CAAC,IAAI,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,SAAS,CAAC,MAAM,EAAC,CAAC,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,SAAS,CAAC,MAAM,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,IAAI,CAAA,CAAC,EAAC,WAAW,CAAC,qBAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,KAAK,CAAC,SAAS,CAAC,KAAK,CAAC,IAAI,CAAC,SAAS,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,QAAO,CAAC,CAAC,IAAI,CAAC,QAAQ,CAAC,KAAK,CAAC,IAAI,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,WAAW,CAAC,KAAK,CAAC,IAAI,EAAC,CAAC,CAAC,EAAC,IAAI,CAAA,CAAA,CAAC,EAAC,MAAM,CAAC,gBAAS,CAAC,CAAC,CAAC,QAAO,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,WAAW,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,IAAI,CAAA,CAAA,CAAC,EAAC,GAAG,CAAC,cAAU,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,kBAAkB,EAAC,CAAC,WAAW,EAAC,YAAY,EAAC,UAAU,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,cAAc,EAAE,EAAC,CAAC,CAAC,eAAe,EAAE,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,KAAG,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,MAAM,CAAC,CAAC,CAAC,EAAE,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,KAAK,CAAC,EAAC,CAAC,CAAC,SAAS,GAAC,aAAa,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,gBAAgB,CAAC,YAAY,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,gBAAgB,CAAC,WAAW,EAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,GAAC,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,IAAE,CAAC,CAAC,SAAS,CAAC,GAAG,CAAC,CAAC,CAAC,CAAA,CAAC,IAAI,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,kBAAkB,CAAC,OAAM,CAAC,IAAI,CAAC,cAAS,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,IAAI,CAAC,IAAI,EAAC,CAAC,IAAE,GAAG,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,EAAE,CAAA,CAAC,EAAC,IAAI,CAAC,eAAU,CAAC,CAAC,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,EAAC,CAAC,EAAE,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,eAAe,EAAC,CAAC,UAAU,CAAC,OAAM,CAAC,EAAE,CAAC,YAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,OAAO,MAAM,CAAC,KAAK,CAAC,SAAS,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,EAAC,GAAG,CAAC,aAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,OAAO,MAAM,CAAC,KAAK,CAAC,UAAU,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,eAAe,EAAC,CAAC,YAAY,EAAC,QAAQ,EAAC,WAAW,EAAC,SAAS,EAAC,UAAU,EAAC,oBAAoB,EAAC,uBAAuB,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,IAAI,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,IAAI,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,CAAC,IAAI,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,IAAI,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,QAAO,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,SAAS,CAAC,KAAG,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,SAAS,CAAC,GAAC,CAAC,SAAS,CAAC,CAAC,CAAC,SAAS,EAAC,eAAe,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,OAAO,CAAC,CAAC,SAAS,EAAC,KAAK,CAAC,EAAE,EAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAA,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,GAAE,CAAC,GAAG,CAAC,CAAC,cAAc,CAAC,YAAY,CAAC,EAAC,OAAM,CAAC,SAAS,CAAC,CAAC,CAAC,UAAU,EAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,GAAC,CAAC,CAAC,OAAO,CAAA,CAAC,OAAM,CAAC,SAAS,CAAC,MAAM,EAAC,KAAK,CAAC,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,WAAW,GAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,QAAQ,GAAC,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,EAAC,CAAC,CAAC,WAAW,GAAC,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,IAAE,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,OAAO,CAAC,IAAI,CAAC,CAAC,IAAG,CAAC,GAAC,CAAC,CAAC,OAAO,CAAC,IAAI,EAAC,CAAC,CAAC,MAAM,CAAA,CAAC,IAAI,IAAI,CAAC,IAAI,CAAC,CAAC,MAAM,EAAC,CAAC,CAAC,MAAM,CAAC,cAAc,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,KAAG,CAAC,IAAE,GAAG,GAAC,CAAC,GAAC,GAAG,GAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAA,AAAC,CAAC,OAAO,CAAC,CAAA,CAAC,OAAO,KAAK,CAAC,KAAK,CAAC,OAAO,EAAE,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,IAAE,CAAC,CAAC,MAAM,CAAC,IAAI,IAAI,CAAC,IAAI,CAAC,CAAC,MAAM,EAAC,CAAC,CAAC,MAAM,CAAC,cAAc,CAAC,CAAC,CAAC,KAAG,CAAC,GAAC,CAAC,IAAE,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAA,AAAC,CAAC,OAAO,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,IAAE,CAAC,CAAC,MAAM,IAAE,0BAA0B,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,OAAO,CAAC,IAAE,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,OAAO,CAAC,IAAI,CAAC,YAAY,KAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAE,OAAO,KAAG,CAAC,CAAC,IAAI,CAAC,gBAAgB,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,IAAI,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,aAAa,EAAC,CAAC,CAAC,SAAS,EAAC,CAAC,CAAC,UAAU,EAAC,CAAC,CAAC,aAAa,EAAC,CAAC,CAAC,MAAM,EAAC,CAAC,CAAC,SAAS,EAAC,CAAC,CAAC,OAAO,EAAC,CAAC,CAAC,MAAM,EAAC,CAAC,CAAC,MAAM,EAAC,CAAC,CAAC,MAAM,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,IAAI,CAAC,CAAC,SAAS,CAAC,MAAM,EAAC,eAAe,CAAC,IAAI,EAAC,KAAK,CAAC,EAAE,EAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,KAAK,CAAC,EAAE,EAAC,QAAQ,CAAC,IAAI,EAAC,WAAW,CAAC,IAAI,EAAC,WAAW,CAAC,IAAI,CAAC,EAAC,CAAC,CAAC,YAAU,EAAE,CAAC,QAAO,CAAC,CAAC,SAAS,CAAC,UAAU,GAAC,SAAS,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,IAAI,IAAI,CAAC,IAAI,CAAC,EAAC,IAAI,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,IAAI,CAAA,CAAC,OAAO,IAAI,CAAA,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,EAAE,GAAC,UAAU,CAAC,GAAG,IAAI,CAAC,SAAS,CAAC,OAAO,CAAC,CAAC,EAAE,CAAC,IAAI,CAAC,SAAS,EAAC,IAAI,CAAC,WAAW,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,IAAE,IAAI,CAAC,GAAG,KAAG,CAAC,CAAC,GAAG,EAAE,CAAC,CAAC,GAAG,CAAC,CAAC,QAAQ,KAAG,IAAI,CAAC,OAAO,CAAC,CAAC,OAAO,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,WAAW,KAAG,IAAI,CAAC,OAAO,CAAC,CAAC,OAAO,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,IAAI,CAAC,GAAG,CAAC,CAAA,CAAC,OAAO,IAAI,CAAA,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,OAAO,GAAC,UAAU,CAAC,IAAI,CAAC,KAAK,KAAG,IAAI,CAAC,KAAK,CAAC,QAAQ,IAAE,IAAI,CAAC,KAAK,CAAC,QAAQ,EAAE,EAAC,IAAI,CAAC,KAAK,GAAC,IAAI,CAAA,AAAC,CAAA,CAAC,EAAC,CAAC,QAAQ,CAAC,k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eAAe,KAAG,CAAC,CAAC,SAAS,CAAC,CAAC,GAAC,CAAC,EAAE,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,EAAC,CAAC,IAAE,CAAC,CAAC,eAAe,KAAG,CAAC,CAAC,eAAe,KAAG,CAAC,GAAC,CAAC,CAAA,AAAC,CAAA,AAAC,CAAA,AAAC,CAAC,KAAK,GAAG,CAAC,IAAE,CAAC,CAAC,OAAO,KAAG,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,MAAM,EAAC,CAAC,GAAC,CAAC,CAAC,SAAS,EAAC,CAAC,GAAC,CAAC,EAAC,CAAC,CAAC,SAAS,KAAG,CAAC,CAAC,SAAS,CAAC,CAAC,GAAC,CAAC,CAAC,CAAC,KAAG,CAAC,GAAC,CAAC,EAAC,CAAC,CAAC,SAAS,KAAG,CAAC,CAAC,eAAe,CAAC,CAAC,GAAC,CAAC,EAAE,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,EAAC,CAAC,IAAE,CAAC,CAAC,eAAe,KAAG,CAAC,CAAC,eAAe,KAAG,CAAC,GAAC,CAAC,CAAA,AAAC,CAAA,AAAC,CAAA,AAAC,EAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,IAAE,CAAC,CAAC,KAAK,KAAG,CAAC,CAAC,KAAK,CAAC,UAAU,GAAC,CAAC,CAAC,SAAS,EAAC,CAAC,GAAC,CAAC,CAAC,SAAS,CAAA,AAAC,CAAC,KAAK,GAAG,CAAC,IAAE,CAAC,CAAC,SAAS,KAAG,CAAC,IAAE,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,IAAE,CAAC,CAAC,KAAK,CAAC,MAAM,GAAC,CAAC,IAAE,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,KAAK,CAAC,MAAM,IAAE,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,OAAO,KAAG,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,MAAM,EAAC,CAAC,GAAC,CAAC,CAAC,SAAS,EAAC,CAAC,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,EAAC,CAAC,IAAE,CAAC,CAAC,eAAe,KAAG,CAAC,CAAC,CAAC,GAAC,CAAC,EAAE,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,IAAE,CAAC,CAAC,eAAe,KAAG,CAAC,CAAC,SAAS,KAAG,CAAC,GAAC,CAAC,CAAA,AAAC,CAAA,AAAC,EAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,EAAC,CAAC,IAAE,CAAC,CAAC,SAAS,KAAG,CAAC,CAAC,SAAS,KAAG,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,UAAU,GAAC,CAAC,CAAC,MAAM,CAAA,AAAC,CAAA,CAAC,KAAI,CAAC,IAAG,CAAC,GAAC,CAAC,CAAC,aAAa,CAAC,CAAC,CAAC,EAAC,IAAI,CAAC,aAAa,CAAC,CAAC,EAAC,CAAC,CAAC,CAAA,EAAC,OAAM,CAAC,MAAM,CAAC,cAAc,EAAC,SAAS,CAAC,CAAC,EAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAA,IAAG,CAAC,GAAC,KAAK,CAAC,KAAK,CAAC,OAAO,EAAE,EAAC,CAAC,CAAA,CAAC,CAAC,IAAG,CAAC,CAAC,aAAa,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,EAAC,CAAC,IAAE,CAAC,CAAC,OAAO,KAAG,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,SAAS,KAAG,CAAC,CAAC,SAAS,KAAG,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAA,AAAC,CAAA,CAAC,CAAC,IAAI,CAAC,GAAC,CAAC,CAAC,KAAK,CAAC,MAAM,GAAC,CAAC,EAAC,CAAC,IAAE,CAAC,CAAC,KAAK,EAAC,CAAC,EAAE,EAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,OAAO,EAAE,EAAC,CAAC,CAAC,KAAK,CAAC,MAAM,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,GAAC,CAAC,CAAC,SAAS,CAAA,CAAC,CAAC,CAAC,SAAS,KAAG,CAAC,CAAC,SAAS,CAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,SAAS,KAAG,CAAC,CAAC,SAAS,KAAG,CAAC,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,EAAC,CAAC,IAAE,CAAC,CAAC,eAAe,KAAG,CAAC,CAAC,eAAe,CAAC,CAAC,GAAC,CAAC,EAAE,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,eAAe,CAAC,EAAC,CAAC,IAAE,CAAC,CAAC,SAAS,KAAG,CAAC,CAAC,SAAS,KAAG,CAAC,GAAC,CAAC,CAAA,AAAC,CAAA,AAAC,CAAA,AAAC,CAAA,CAAC,KAAK,CAAC,GAAC,CAAC,CAAC,CAAC,GAAC,CAAC,KAAG,CAAC,GAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,GAAC,IAAI,CAAC,UAAU,CAAC,CAAC,MAAM,CAAC,CAAC,EAAC,KAAK,CAAC,CAAC,CAAC,KAAK,CAAC,MAAM,EAAC,SAAS,CAAC,CAAC,CAAC,SAAS,EAAC,UAAU,CAAC,CAAC,IAAE,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,MAAM,CAAC,IAAI,EAAC,aAAa,CAAC,IAAI,EAAC,OAAO,CAAC,CAAC,EAAC,SAAS,CAAC,IAAI,CAAC,gBAAgB,EAAE,EAAC,WAAW,CAAC,CAAC,EAAE,EAAC,GAAG,CAAC,CAAC,EAAC,YAAY,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,KAAK,CAAC,IAAI,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,IAAG,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,EAAC,CAAC,CAAA,CAAC,CAAC,IAAG,CAAC,CAAC,cAAc,KAAG,CAAC,GAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,WAAW,KAAG,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,UAAU,GAAC,IAAI,CAAA,AAAC,EAAC,CAAC,CAAC,WAAW,CAAA,CAAC,CAAC,IAAI,CAAC,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,KAAK,CAAC,MAAM,EAAC,CAAC,EAAE,EAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,MAAM,KAAG,CAAC,EAAE,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,KAAK,GAAC,CAAC,EAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,UAAU,GAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,aAAa,GAAC,IAAI,CAAA,CAAE,OAAO,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,KAAK,GAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,GAAC,IAAI,CAAA,CAAC,IAAG,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,QAAQ,IAAE,CAAC,IAAE,CAAC,CAAC,QAAQ,CAAC,SAAS,IAAE,CAAC,IAAE,CAAC,CAAC,QAAQ,CAAC,OAAO,IAAE,CAAC,IAAE,CAAC,CAAC,QAAQ,CAAC,GAAG,CAAA,CAAC,IAAI,CAAC,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,KAAK,CAAC,MAAM,EAAC,CAAC,EAAE,EAAC,GAAG,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,MAAM,IAAE,CAAC,CAAC,CAAC,CAAC,GAAC,QAAQ,EAAC,CAAC,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,KAAG,CAAC,CAAC,KAAK,CAAC,CAAC,GAAC,CAAC,CAAC,CAAC,aAAa,GAAC,IAAI,CAAA,AAAC,EAAC,CAAC,CAAC,WAAW,GAAC,IAAI,EAAC,CAAC,CAAC,WAAW,CAAC,KAAK,GAAC,CAAC,CAAC,QAAQ,CAAC,KAAK,EAAC,CAAC,CAAC,WAAW,CAAC,UAAU,GAAC,CAAC,CAAC,QAAQ,CAAC,UAAU,EAAC,CAAC,CAAC,QAAQ,GAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,EAAC,CAAC,CAAC,KAAK,CAAC,MAAM,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,MAAK,CAAC,QAAO,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,WAAW,CAAC,KAAK,EAAC,CAAC,MAAM,CAAC,CAAC,EAAC,MAAM,CAAC,CAAC,EAAC,SAAS,CAAC,CAAC,EAAC,SAAS,CAAC,CAAC,EAAC,UAAU,CAAC,IAAI,CAAC,WAAW,CAAC,CAAC,CAAC,WAAW,CAAC,EAAC,aAAa,CAAC,CAAC,KAAG,CAAC,CAAC,WAAW,CAAC,KAAK,EAAC,GAAG,CAAC,CAAC,CAAC,CAAA,CAAA,CAAC,EAAC,eAAe,CAAC,yBAAS,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,GAAC,KAAK,CAAC,KAAK,CAAC,OAAO,EAAE,CAAA,CAAC,EAAC,UAAU,CAAC,oBAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,EAAA,CAAC,OAAO,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,WAAW,CAAC,CAAC,EAAC,WAAW,CAAC,sBAAU,CAAC,OAAO,CAAC,CAAA,CAAC,EAAC,WAAW,CAAC,qBAAS,CAAC,CAAC,CAAC,QAAO,SAAS,CAAC,MAAM,KAAG,CAAC,CAAC,WAAW,GAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,WAAW,CAAA,CAAA,CAAC,EAAC,gBAAgB,CAAC,2BAAU,CAAC,OAAO,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,WAAW,CAAC,SAAS,CAAC,IAAI,CAAA,CAAC,EAAC,YAAY,CAAC,sBAAS,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,WAAW,EAAE,SAAS,CAAC,MAAM,KAAG,CAAC,CAAC,WAAW,CAAC,KAAK,GAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,WAAW,CAAC,KAAK,CAAA,CAAE,KAAK,CAAC,CAAA,CAAC,EAAC,QAAQ,CAAC,kBAAS,CAAC,CAAC,CAAC,QAAO,SAAS,CAAC,MAAM,KAAG,CAAC,CAAC,QAAQ,GAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,QAAQ,CAAA,CAAA,CAAC,EAAC,SAAS,CAAC,mBAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,IAAE,CAAC,CAAC,QAAQ,CAAC,OAAO,CAAC,IAAE,CAAC,CAAC,KAAK,CAAA,CAAC,EAAC,WAAW,CAAC,qBAAS,CAAC,CAAC,CAAC,QAAO,SAAS,CAAC,MAAM,KAAG,CAAC,CAAC,WAAW,GAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,WAAW,CAAA,CAAA,CAAC,EAAC,gBAAgB,CAAC,2BAAU,CAAC,OAAO,CAAC,IAAE,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,OAAO,CAAC,IAAI,CAAC,IAAI,CAAA,CAAC,EAAC,qBAAqB,CAAC,+BAAS,CAAC,CAAC,CAAC,OAAM,CAAC,EAAE,CAAC,IAAE,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,OAAO,CAAC,KAAK,IAAE,CAAC,CAAC,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,EAAC,eAAe,CAAC,yBAAS,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,IAAE,CAAC,CAAC,KAAK,CAAC,MAAM,CAAC,CAAC,GAAG,CAAC,CAAC,WAAW,IAAE,CAAC,CAAC,WAAW,CAAC,MAAM,KAAG,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,MAAM,EAAC,OAAO,CAAA,CAAC,GAAC,CAAC,MAAM,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,MAAM,EAAC,MAAM,CAAC,CAAC,EAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,EAAE,EAAE,CAAA,CAAC,CAAC,CAAC,EAAC,MAAM,CAAC,gBAAS,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,KAAG,CAAC,CAAC,CAAC,GAAG,CAAC,GAAC,CAAC,CAAC,EAAC,OAAO,CAAA,IAAI,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,IAAI,CAAC,gBAAgB,EAAE,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,MAAM,GAAC,CAAC,GAAC,CAAC,CAAC,CAAC,GAAC,CAAC,KAAG,CAAC,GAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,MAAM,GAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,IAAI,IAAI,CAAC,CAAC,CAAC,GAAC,CAAC,EAAC,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,EAAC,CAAC,KAAG,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,MAAM,CAAC,EAAC,CAAC,EAAE,EAAC,EAAE,CAAC,IAAE,CAAC,CAAC,KAAK,CAAC,MAAM,CAAA,AAAC,CAAA,AAAC,GAAE,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,MAAM,IAAE,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,GAAG,CAAC,CAAA,CAAC,CAAC,CAAC,QAAQ,IAAE,CAAC,CAAC,QAAQ,CAAC,EAAE,EAAE,CAAA,CAAC,EAAC,WAAW,CAAC,qBAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,OAAM,EAAE,CAAC,CAAC,IAAE,CAAC,CAAC,SAAS,KAAG,CAAC,CAAC,SAAS,CAAA,AAAC,CAAA,CAAC,EAAC,YAAY,CAAC,uBAAU,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,SAAS,EAAC,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,GAAG,CAAC,CAAC,IAAI,IAAI,CAAC,IAAI,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,KAAG,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,GAAC,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,IAAE,CAAC,CAAC,SAAS,KAAG,CAAC,EAAE,CAAC,CAAC,UAAU,GAAC,CAAC,CAAC,aAAa,GAAC,IAAI,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,IAAI,CAAC,CAAC,CAAC,CAAA,CAAE,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,EAAE,CAAC,IAAI,IAAI,CAAC,IAAI,CAAC,CAAC,KAAK,EAAC,CAAC,KAAG,CAAC,CAAC,MAAM,IAAE,OAAO,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAA,CAAC,EAAC,UAAU,CAAC,oBAAS,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,UAAU,CAAC,OAAO,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,EAAC,eAAe,CAAC,yBAAS,CAAC,CAAC,CAAC,QAAO,SAAS,CAAC,MAAM,KAAG,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,EAAC,IAAI,KAAG,CAAC,CAAC,CAAC,GAAC,CAAC,EAAE,CAAC,GAAC,CAAC,IAAE,EAAE,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,KAAG,CAAC,GAAC,CAAC,CAAC,UAAU,CAAC,CAAC,GAAC,IAAI,CAAA,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,CAAA,AAAC,CAAA,AAAC,CAAA,AAAC,EAAC,CAAC,CAAA,CAAA,CAAC,EAAC,aAAa,CAAC,uBAAS,CAAC,EAAC,CAAC,CAAC,CAAC,OAAO,CAAC,IAAE,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,OAAO,CAAC,IAAI,SAAY,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC,CAAA,AAAC,CAAA,CAAC,EAAC,aAAa,CAAC,uBAAS,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAC,OAAM,CAAC,CAAC,CAAC,CAAA,IAAI,IAAI,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,gBAAgB,EAAE,EAAC,CAAC,GAAE,CAAC,GAAG,CAAC,CAAC,cAAc,EAAC,OAAM,CAAC,CAAC,CAAC,CAAA,IAAG,CAAC,CAAC,IAAE,CAAC,CAAC,cAAc,CAAC,YAAY,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAA,CAAC,CAAC,GAAG,CAAC,CAAC,cAAc,CAAC,YAAY,CAAC,IAAE,CAAC,IAAE,CAAC,CAAC,UAAU,EAAC,OAAM,CAAC,CAAC,CAAC,CAAA,GAAG,CAAC,CAAC,cAAc,CAAC,kBAAkB,CAAC,IAAE,CAAC,IAAE,CAAC,CAAC,gBAAgB,CAAC,CAAC,GAAG,CAAC,CAAC,cAAc,CAAC,YAAY,CAAC,EAAC,OAAM,CAAC,CAAC,CAAC,CAAA,GAAG,CAAC,CAAC,EAAC,OAAM,CAAC,CAAC,CAAA,CAAA,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,cAAc,CAAC,kBAAkB,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,GAAC,CAAC,CAAC,OAAO,CAAA,CAAC,OAAO,CAAC,CAAC,MAAM,IAAE,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAA,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,YAAY,EAAC,QAAQ,EAAC,WAAW,EAAC,WAAW,EAAC,gBAAgB,EAAC,eAAe,EAAC,qBAAqB,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC,QAAO,CAAC,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,QAAQ,CAAC,OAAO,EAAE,EAAC,CAAC,CAAC,cAAc,EAAE,EAAC,CAAC,CAAC,CAAA,CAAA,CAAC,CAAC,CAAC,GAAG,CAAC,wBAAwB,EAAC,UAAU,CAAC,KAAK,CAAC,QAAQ,IAAE,KAAK,CAAC,QAAQ,CAAC,IAAI,IAAE,KAAK,CAAC,QAAQ,CAAC,IAAI,EAAE,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,sBAAsB,EAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,OAAO,IAAI,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,WAAW,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,IAAI,CAAC,GAAG,CAAC,IAAE,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,IAAE,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,KAAK,CAAC,MAAM,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,OAAO,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,CAAC,GAAG,IAAE,CAAC,CAAC,MAAM,KAAG,CAAC,CAAC,GAAG,GAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,MAAM,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,GAAG,KAAG,CAAC,KAAG,CAAC,CAAC,GAAG,CAAC,OAAO,CAAC,GAAG,CAAC,KAAG,CAAC,CAAC,GAAG,GAAC,CAAC,CAAC,GAAG,CAAC,OAAO,CAAC,GAAG,EAAC,EAAE,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,GAAG,KAAG,CAAC,CAAC,GAAG,EAAE,IAAE,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,CAAC,CAAA,AAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,YAAY,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,uBAAuB,EAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,KAAK,KAAG,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,GAAC,CAAC,CAAC,KAAK,CAAA,AAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,wBAAwB,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,QAAQ,CAAC,cAAc,EAAC,UAAU,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAC,CAAC,EAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAC,EAAE,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAC,EAAE,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,IAAI,CAAC,IAAI,CAAC,EAAC,CAAC,IAAE,CAAC,IAAE,CAAC,CAAC,cAAc,CAAC,CAAC,CAAC,KAAG,OAAO,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,CAAC,CAAC,GAAC,EAAE,CAAA,AAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,CAAC,CAAC,GAAC,IAAI,CAAA,AAAC,CAAA,AAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,OAAO,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,GAAC,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,GAAG,GAAC,CAAC,CAAC,CAAA,CAAE,CAAC,CAAC,CAAC,CAAC,GAAC,SAAS,CAAC,CAAC,CAAC,GAAG,SAAS,CAAC,MAAM,CAAC,QAAO,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,EAAC,CAAC,CAAA,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAC,KAAK,CAAC,QAAQ,CAAC,QAAQ,EAAE,GAAC,CAAC,GAAC,GAAG,GAAC,CAAC,CAAC,CAAC,OAAO,CAAC,IAAE,CAAC,KAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAC,SAAS,GAAC,CAAC,GAAC,GAAG,GAAC,CAAC,CAAC,CAAA,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,KAAK,CAAC,GAAG,CAAC,CAAC,IAAI,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,MAAM,EAAC,CAAC,EAAE,EAAC,CAAC,GAAG,CAAC,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,OAAO,IAAI,CAAC,CAAA,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,OAAO,CAAC,CAAA,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,QAAQ,GAAC,EAAE,CAAC,IAAI,CAAC,CAAC,UAAU,EAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,QAAQ,CAAC,CAAC,EAAC,YAAY,CAAC,CAAC,EAAC,UAAU,CAAC,CAAC,EAAC,gBAAgB,CAAC,CAAC,EAAC,iBAAiB,CAAC,CAAC,CAAC,EAAC,MAAM,CAAC,CAAC,UAAU,CAAC,CAAC,EAAC,sBAAsB,CAAC,CAAC,EAAC,wBAAwB,CAAC,CAAC,EAAC,UAAU,CAAC,CAAC,CAAC,EAAC,UAAU,CAAC,CAAC,IAAI,CAAC,CAAC,EAAC,IAAI,CAAC,CAAC,EAAC,iBAAiB,CAAC,CAAC,CAAC,EAAC,IAAI,CAAC,CAAC,QAAQ,CAAC,CAAC,EAAC,MAAM,CAAC,CAAC,CAAC,EAAC,SAAS,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,EAAC,IAAI,CAAC,CAAC,KAAK,CAAC,CAAC,EAAC,QAAQ,CAAC,CAAC,CAAC,EAAC,SAAS,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,EAAC,QAAQ,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,EAAE,CAAC,EAAC,CAAC,CAAC,SAAS,EAAC,CAAC,KAAK,CAAC,CAAC,QAAQ,CAAC,EAAE,EAAC,YAAY,CAAC,CAAC,CAAC,EAAC,UAAU,CAAC,KAAK,EAAC,gBAAgB,CAAC,CAAC,CAAC,EAAC,iBAAiB,CAAC,EAAE,CAAC,EAAC,MAAM,CAAC,CAAC,UAAU,CAAC,QAAQ,EAAC,sBAAsB,CAAC,MAAM,EAAC,wBAAwB,CAAC,OAAO,EAAC,UAAU,CAAC,MAAM,CAAC,EAAC,UAAU,CAAC,CAAC,IAAI,CAAC,oBAAoB,EAAC,IAAI,CAAC,MAAM,EAAC,iBAAiB,CAAC,CAAC,CAAC,CAAC,EAAC,IAAI,CAAC,CAAC,QAAQ,CAAC,QAAQ,EAAC,MAAM,CAAC,OAAO,CAAC,EAAC,SAAS,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC,EAAC,IAAI,CAAC,CAAC,KAAK,CAAC,UAAU,EAAC,QAAQ,CAAC,QAAQ,CAAC,EAAC,SAAS,CAAC,CAAC,WAAW,CAAC,EAAE,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,KAAK,EAAC,EAAE,CAAC,EAAC,CAAC,CAAC,SAAS,EAAC,CAAC,KAAK,CAAC,CAAC,UAAU,CAAC,SAAS,EAAC,gBAAgB,CAAC,CAAC,CAAC,CAAC,EAAC,MAAM,CAAC,CAAC,UAAU,CAAC,MAAM,EAAC,sBAAsB,CAAC,OAAO,EAAC,wBAAwB,CAAC,OAAO,CAAC,EAAC,UAAU,CAAC,CAAC,IAAI,CAAC,wBAAwB,EAAC,IAAI,CAAC,CAAC,CAAC,EAAC,iBAAiB,CAAC,CAAC,CAAC,CAAC,EAAC,IAAI,CAAC,CAAC,QAAQ,CAAC,QAAQ,EAAC,MAAM,CAAC,OAAO,CAAC,EAAC,IAAI,CAAC,CAAC,KAAK,CAAC,SAAS,EAAC,QAAQ,CAAC,KAAK,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,cAAc,EAAC,EAAE,CAAC,EAAC,CAAC,CAAC,WAAW,GAAC,CAAC,KAAK,CAAC,EAAE,EAAC,MAAM,CAAC,EAAE,CAAC,EAAC,CAAC,CAAC,WAAW,CAAC,KAAK,CAAC,GAAG,GAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,KAAK,CAAC,GAAG,CAAC,mBAAmB,CAAC,GAAC,CAAC,CAAC,aAAa,CAAC,EAAE,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,KAAG,CAAC,CAAC,SAAS,GAAC,sBAAsB,IAAE,CAAC,CAAC,aAAa,CAAC,IAAG,GAAC,CAAC,CAAC,GAAE,CAAA,AAAC,GAAC,GAAG,CAAA,AAAC,EAAC,CAAC,CAAC,KAAK,CAAC,GAAG,CAAC,SAAS,CAAC,GAAC,cAAc,GAAC,CAAC,GAAC,QAAQ,EAAC,KAAK,CAAC,OAAO,CAAC,YAAY,CAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,IAAI,CAAC,CAAC,CAAC,GAAG,CAAC,aAAS,CAAC,CAAC,CAAC,SAAS,IAAE,CAAC,EAAE,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,EAAE,IAAE,CAAC,GAAC,CAAC,CAAA,AAAC,EAAC,CAAC,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,GAAE,GAAC,CAAC,EAAC,CAAC,EAAE,GAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAA,CAAE,MAAM,IAAE,CAAC,EAAE,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,GAAE,IAAE,CAAC,GAAC,CAAC,CAAA,AAAC,EAAC,CAAC,EAAE,IAAE,CAAC,GAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,GAAG,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,CAAA,EAAG,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,EAAC,aAAa,CAAC,CAAC,KAAG,SAAS,IAAE,CAAC,IAAE,MAAM,IAAE,CAAC,CAAA,AAAC,CAAC,CAAC,OAAO,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,WAAW,CAAC,MAAM,CAAC,GAAG,GAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,KAAK,CAAC,GAAG,CAAC,mBAAmB,CAAC,GAAC,CAAC,CAAC,aAAa,CAAC,EAAE,CAAC,KAAK,EAAC,CAAC,CAAC,OAAO,GAAC,CAAC,KAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,cAAc,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,eAAe,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,aAAa,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,KAAK,CAAC,GAAG,CAAC,SAAS,CAAC,GAAC,cAAc,GAAC,CAAC,GAAC,SAAS,EAAC,CAAC,CAAC,MAAM,CAAC,WAAW,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,KAAK,CAAC,GAAG,CAAC,SAAS,CAAC,GAAC,cAAc,GAAC,CAAC,GAAC,SAAS,EAAC,CAAC,CAAC,MAAM,CAAC,OAAO,EAAC,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,GAAG,CAAC,IAAE,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,EAAE,GAAC,CAAC,CAAC,UAAU,EAAE,CAAA,IAAG,CAAC,GAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,CAAC,UAAU,EAAE,GAAC,CAAC,CAAC,kBAAkB,EAAE,CAAA,IAAG,CAAC,GAAC,CAAC,CAAA,AAAC,IAAE,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,GAAG,CAAC,IAAE,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,UAAU,EAAE,GAAC,CAAC,CAAC,kBAAkB,EAAE,CAAA,AAAC,GAAC,CAAC,CAAC,cAAc,EAAE,CAAA,GAAE,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,GAAG,CAAC,aAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,UAAU,EAAE,EAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,UAAU,EAAE,CAAC,MAAM,IAAE,CAAC,CAAC,SAAS,EAAE,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,CAAA,EAAG,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,EAAC,SAAS,CAAC,CAAC,EAAC,aAAa,CAAC,CAAC,KAAG,SAAS,IAAE,CAAC,IAAE,MAAM,IAAE,CAAC,CAAA,AAAC,CAAC,CAAC,OAAO,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,WAAW,CAAC,KAAK,CAAC,OAAO,GAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,KAAK,CAAC,GAAG,CAAC,mBAAmB,CAAC,GAAC,CAAC,CAAC,aAAa,CAAC,EAAE,CAAC,CAAC,EAAC,CAAC,CAAC,KAAK,CAAC,GAAG,CAAC,SAAS,CAAC,GAAC,cAAc,GAAC,CAAC,GAAC,QAAQ,EAAC,KAAK,CAAC,OAAO,CAAC,YAAY,CAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,CAAC,GAAC,CAAC,KAAG,SAAS,IAAE,CAAC,IAAE,MAAM,IAAE,CAAC,CAAA,AAAC,CAAC,IAAI,CAAC,CAAC,CAAC,GAAG,CAAC,aAAS,CAAC,CAAC,CAAC,SAAS,IAAE,CAAC,EAAE,CAAC,CAAC,CAAC,EAAC,EAAE,IAAE,CAAC,GAAC,CAAC,CAAA,AAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,GAAG,GAAC,CAAC,CAAC,CAAA,CAAE,MAAM,IAAE,CAAC,EAAE,CAAC,CAAC,CAAC,EAAC,CAAC,GAAG,IAAE,CAAC,GAAC,CAAC,CAAA,AAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,GAAG,GAAC,CAAC,CAAC,CAAA,EAAG,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,EAAC,aAAa,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,WAAW,CAAC,MAAM,CAAC,OAAO,GAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,OAAO,GAAC,CAAC,KAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,cAAc,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,eAAe,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,aAAa,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,WAAW,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,OAAO,EAAC,CAAC,CAAC,CAAA,CAAC,CAAC,OAAM,CAAC,GAAG,CAAC,aAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,EAAE,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,UAAU,EAAE,EAAC,CAAC,GAAC,CAAC,CAAC,CAAA,CAAC,EAAC,aAAa,CAAC,CAAC,KAAG,SAAS,IAAE,CAAC,IAAE,MAAM,IAAE,CAAC,CAAA,AAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,WAAW,CAAC,KAAK,CAAC,IAAI,GAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,OAAM,CAAC,GAAG,CAAC,aAAS,CAAC,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,KAAK,CAAC,OAAO,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,aAAa,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,WAAW,CAAC,MAAM,CAAC,IAAI,GAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,OAAM,CAAC,GAAG,CAAC,aAAS,CAAC,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,MAAM,CAAC,GAAG,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,WAAW,CAAC,MAAM,CAAC,OAAO,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,aAAa,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,iBAAiB,GAAC,CAAC,EAAC,CAAC,CAAC,IAAI,GAAC,UAAU,CAAC,OAAO,CAAC,CAAC,CAEvw+B,CAAA,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,kBAAkB,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,0BAA0B,CAAC,4DAA4D,CAAC,EAAC,CAAC,CAAC,2BAA2B,CAAC,+DAA+D,CAAC,CAAA,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,sEAAkE,EAAC,CAAC,CAAC,8EAA8E,EAAC,CAAC,CAAC,8EAA8E,EAAC,CAAC,CAAC,8GAA8G,CAAC,CAAC,CAAC,QAAQ,CAAC,qBAAqB,EAAC,CAAC,QAAQ,CAAC,6BAA6B,CAAC,CAAC,CAAC,OAAO,CAAC,eAAe,EAAC,CAAC,qBAAqB,EAAC,YAAY,EAAC,sBAAsB,EAAC,gBAAgB,EAAC,UAAU,EAAC,IAAI,EAAC,MAAM,EAAC,UAAU,EAAC,gBAAgB,EAAC,YAAY,EAAC,qBAAqB,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,EAAE,CAAC,QAAO,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,OAAO,CAAC,CAAC,QAAQ,CAAC,CAAC,EAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,EAAE,CAAC,CAAC,CAAC,IAAI,CAAC,SAAS,CAAC,CAAC,CAAC,QAAO,CAAC,CAAC,IAAI,GAAC,SAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,QAAQ,IAAE,CAAC,CAAC,OAAO,IAAE,EAAE,CAAC,CAAC,CAAC,CAAC,KAAK,GAAC,CAAC,CAAC,KAAK,IAAE,CAAC,CAAC,KAAK,EAAC,CAAC,CAAC,OAAO,KAAG,CAAC,CAAC,WAAW,GAAC,CAAC,CAAC,CAAC,UAAU,IAAE,CAAC,CAAC,YAAY,KAAG,CAAC,CAAC,EAAC,CAAC,CAAC,WAAW,KAAG,CAAC,CAAC,MAAM,EAAE,EAAC,CAAC,CAAC,UAAU,EAAE,CAAC,QAAQ,CAAC,kBAAkB,CAAC,CAAA,AAAC,CAAA,AAAC,EAAC,CAAC,CAAC,QAAQ,KAAG,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,eAAe,CAAC,EAAC,CAAC,CAAC,eAAe,GAAC,CAAC,CAAC,OAAO,CAAC,IAAI,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,EAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAA,AAAC,EAAC,CAAC,EAAE,EAAC,CAAC,GAAC,CAAC,CAAC,wBAAwB,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,SAAS,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,QAAQ,EAAE,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAA,CAAC,CAAC,CAAC,OAAO,KAAG,CAAC,CAAC,OAAO,CAAC,QAAQ,CAAC,SAAS,CAAC,EAAC,KAAK,CAAC,qBAAqB,CAAC,UAAU,CAAC,CAAC,CAAC,OAAO,KAAG,CAAC,CAAC,OAAO,CAAC,QAAQ,CAAC,QAAQ,CAAC,EAAC,CAAC,CAAC,QAAQ,CAAC,gBAAgB,CAAC,CAAA,AAAC,CAAA,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,GAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,IAAI,GAAC,UAAU,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,OAAO,KAAG,CAAC,CAAC,WAAW,KAAG,CAAC,CAAC,OAAO,EAAE,EAAC,CAAC,CAAC,UAAU,EAAE,CAAC,WAAW,CAAC,kBAAkB,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,OAAO,CAAC,WAAW,CAAC,QAAQ,CAAC,EAAC,CAAC,CAAC,WAAW,CAAC,gBAAgB,CAAC,EAAC,UAAU,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,OAAO,CAAC,WAAW,CAAC,SAAS,CAAC,CAAA,CAAC,EAAC,GAAG,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,eAAe,CAAC,EAAC,CAAC,CAAC,OAAO,GAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAA,CAAA,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAA,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,EAAE,EAAC,CAAC,IAAE,EAAE,EAAC,CAAC,IAAE,EAAE,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,KAAK,IAAE,CAAC,CAAC,SAAS,IAAE,CAAC,CAAC,QAAO,CAAC,EAAE,EAAC,CAAC,EAAE,EAAC,CAAC,CAAC,iBAAiB,KAAG,CAAC,GAAC,CAAC,CAAC,GAAG,CAAC,qBAAqB,EAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,GAAG,CAAC,mBAAmB,EAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,SAAS,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,IAAI,CAAC,eAAU,CAAC,QAAO,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,KAAK,CAAC,IAAI,EAAC,SAAS,CAAC,CAAA,CAAA,CAAC,EAAC,IAAI,CAAC,eAAU,CAAC,QAAO,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,KAAK,CAAC,IAAI,EAAC,SAAS,CAAC,CAAA,CAAA,CAAC,EAAC,UAAU,CAAC,oBAAS,CAAC,CAAC,CAAC,QAAO,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,EAAC,CAAC,EAAE,CAAC,IAAI,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAA,CAAC,CAAC,CAAA,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,EAAE,EAAC,CAAC,EAAE,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,EAAC,CAAC,EAAE,CAAC,IAAI,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,EAAE,CAAA,CAAC,CAAC,CAAA,CAAC,IAAI,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,IAAI,EAAE,CAAC,OAAM,CAAC,IAAI,CAAC,CAAC,EAAC,IAAI,CAAC,CAAC,EAAC,UAAU,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,aAAa,EAAC,CAAC,YAAY,EAAC,YAAY,EAAC,UAAU,EAAC,UAAU,EAAC,gBAAgB,EAAC,sBAAsB,EAAC,KAAK,EAAC,MAAM,EAAC,kBAAkB,EAAC,SAAS,EAAC,qBAAqB,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,KAAK,CAAC,OAAO,CAAC,CAAC,UAAU,CAAC,oBAAS,CAAC,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,KAAK,CAAC,SAAS,CAAC,UAAU,CAAC,IAAI,CAAC,IAAI,EAAC,CAAC,CAAC,EAAC,IAAI,CAAC,SAAS,GAAC,CAAC,CAAC,SAAS,IAAE,aAAa,CAAA,CAAC,EAAC,IAAI,CAAC,cAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,KAAK,CAAC,WAAW,EAAC,QAAO,CAAC,CAAC,KAAK,CAAC,cAAc,GAAC,CAAC,CAAC,QAAQ,GAAC,8CAA8C,GAAC,CAAC,CAAC,QAAQ,GAAC,YAAY,CAAC,EAAC,CAAC,CAAC,IAAI,EAAE,CAAA,CAAC,CAAA,CAAC,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,SAAS,CAAC,MAAM,CAAC,MAAM,CAAC,EAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,QAAQ,IAAE,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,QAAQ,GAAC,OAAO,CAAC,CAAA,CAAC,EAAC,GAAG,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,EAAE,CAAC,aAAa,KAAG,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,SAAS,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,EAAE,CAAC,CAAA,AAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,yBAAyB,CAAC,CAAC,QAAO,CAAC,IAAE,CAAC,CAAC,MAAM,EAAE,EAAC,CAAC,IAAE,CAAC,CAAC,YAAY,KAAG,CAAC,CAAC,YAAY,CAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,eAAe,GAAC,UAAU,CAAC,CAAC,CAAC,QAAQ,IAAE,CAAC,CAAC,YAAY,CAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,EAAC,KAAK,CAAC,EAAE,CAAC,QAAQ,EAAC,CAAC,CAAC,eAAe,EAAC,MAAM,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,QAAQ,CAAC,iBAAiB,CAAC,CAAC,WAAW,CAAC,0BAA0B,CAAC,EAAC,CAAC,CAAC,QAAQ,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,qBAAqB,GAAC,CAAC,CAAC,wBAAwB,CAAC,CAAC,CAAC,uBAAuB,CAAC,OAAO,CAAC,IAAI,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,KAAK,CAAC,EAAC,KAAK,CAAC,KAAK,CAAC,KAAK,CAAC,SAAS,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,QAAQ,KAAG,CAAC,CAAC,QAAQ,CAAC,iBAAiB,CAAC,EAAC,KAAK,CAAC,OAAO,CAAC,QAAQ,CAAC,EAAC,CAAC,CAAC,KAAK,CAAC,OAAO,IAAE,CAAC,CAAC,KAAK,CAAC,OAAO,CAAC,UAAU,CAAC,CAAC,CAAC,QAAQ,GAAC,QAAQ,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,EAAE,CAAC,SAAS,CAAC,GAAG,CAAC,QAAQ,CAAC,EAAC,CAAC,CAAC,KAAK,CAAC,UAAU,CAAC,oBAAoB,CAAC,CAAA,AAAC,CAAA,CAAC,EAAC,EAAE,CAAC,EAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,QAAQ,IAAE,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,OAAO,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,oBAAoB,IAAE,CAAC,CAAC,MAAM,KAAG,CAAC,CAAC,EAAE,IAAE,CAAC,CAAC,IAAI,EAAE,CAAA,CAAC,CAAC,CAAA,CAAC,EAAC,GAAG,CAAC,CAAA,CAAA,CAAC,EAAC,IAAI,CAAC,eAAU,CAAC,IAAI,CAAC,CAAC,IAAI,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,QAAO,CAAC,CAAC,IAAI,CAAC,GAAG,CAAC,EAAC,CAAC,CAAC,EAAE,CAAC,SAAS,CAAC,MAAM,CAAC,QAAQ,CAAC,EAAC,CAAC,CAAC,QAAQ,CAAC,UAAU,CAAC,EAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,QAAQ,IAAE,CAAC,CAAC,QAAQ,CAAC,iBAAiB,CAAC,CAAC,WAAW,CAAC,iCAAiC,CAAC,CAAA,CAAC,EAAC,EAAE,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,GAAG,CAAC,OAAO,CAAC,EAAC,CAAC,CAAC,QAAQ,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,KAAK,CAAC,OAAO,IAAE,CAAC,CAAC,KAAK,CAAC,OAAO,CAAC,UAAU,CAAC,CAAC,CAAC,QAAQ,GAAC,SAAS,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,qBAAqB,IAAE,CAAC,CAAC,qBAAqB,EAAE,EAAC,KAAK,CAAC,KAAK,CAAC,KAAK,CAAC,SAAS,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,YAAY,IAAE,KAAK,CAAC,GAAG,CAAC,QAAQ,EAAC,CAAC,CAAC,eAAe,EAAC,MAAM,CAAC,EAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,QAAQ,GAAC,OAAO,CAAC,EAAC,CAAC,CAAC,EAAE,CAAC,SAAS,CAAC,GAAG,CAAC,MAAM,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,SAAS,IAAE,GAAG,CAAC,CAAA,CAAA,CAAC,EAAC,MAAM,CAAC,iBAAU,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,QAAO,CAAC,CAAC,KAAK,CAAC,OAAO,IAAE,CAAC,CAAC,KAAK,CAAC,OAAO,CAAC,UAAU,CAAC,CAAC,CAAC,QAAQ,GAAC,UAAU,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,EAAE,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC,CAAC,KAAK,CAAC,QAAQ,EAAE,EAAC,CAAC,CAAC,GAAG,CAAC,MAAM,EAAE,CAAA,CAAC,CAAC,CAAA,CAAA,CAAC,EAAC,OAAO,CAAC,kBAAU,CAAC,OAAM,CAAC,CAAC,IAAI,CAAC,QAAQ,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,WAAS,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,KAAK,IAAE,CAAC,CAAC,KAAK,CAAC,IAAI,EAAE,IAAE,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,GAAC,CAAC,CAAC,QAAQ,IAAE,OAAO,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,UAAU,CAAC,CAAC,CAAC,EAAC,aAAa,CAAC,CAAC,CAAC,EAAC,UAAU,CAAC,CAAC,CAAC,EAAC,aAAa,CAAC,CAAC,CAAC,EAAC,QAAQ,CAAC,CAAC,CAAC,EAAC,WAAW,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,OAAO,GAAC,CAAC,CAAC,QAAQ,GAAC,GAAG,GAAC,CAAC,GAAC,QAAQ,GAAC,CAAC,CAAC,QAAQ,GAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,GAAC,CAAC,EAAC,CAAC,CAAC,EAAE,GAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,GAAC,CAAC,CAAC,EAAE,CAAC,aAAa,CAAC,GAAG,GAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,QAAO,CAAC,CAAC,KAAK,GAAC,CAAC,EAAC,CAAC,CAAC,KAAK,KAAG,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,GAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAA,CAAA,CAAC,CAAC,OAAM,CAAC,YAAY,CAAC,sBAAS,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,IAAE,EAAE,CAAC,CAAC,OAAO,CAAC,CAAA,CAAC,EAAC,eAAe,CAAC,yBAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,QAAO,OAAO,CAAC,UAAU,CAAC,CAAC,CAAC,KAAG,CAAC,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,SAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,IAAE,EAAE,CAAC,CAAC,QAAO,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAA,CAAA,CAAC,CAAC,CAAA,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,sBAAsB,EAAC,KAAK,CAAC,eAAe,CAAC,CAAC,OAAO,EAAC,gBAAgB,EAAC,SAAS,EAAC,OAAO,EAAC,aAAa,EAAC,UAAU,EAAC,UAAU,EAAC,MAAM,EAAC,kBAAkB,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,uBAAuB,EAAC,KAAK,CAAC,eAAe,CAAC,CAAC,YAAY,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,QAAQ,CAAC,qBAAqB,EAAC,CAAC,IAAI,CAAC,GAAG,EAAC,QAAQ,CAAC,GAAG,EAAC,KAAK,CAAC,GAAG,EAAC,WAAW,CAAC,GAAG,EAAC,KAAK,CAAC,GAAG,EAAC,OAAO,CAAC,GAAG,CAAC,CAAC,CAAC,QAAQ,CAAC,gBAAgB,EAAC,UAAU,CAAC,OAAM,CAAC,IAAI,CAAC,CAAC,IAAI,EAAC,SAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,oBAAoB,CAAC,8BAAS,CAAC,CAAC,CAAC,KAAK,CAAC,QAAQ,CAAC,KAAK,CAAC,UAAU,CAAC,QAAQ,CAAC,gBAAgB,CAAC,YAAY,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,EAAC,qBAAqB,CAAC,+BAAS,CAAC,CAAC,CAAC,KAAK,CAAC,QAAQ,CAAC,KAAK,CAAC,UAAU,CAAC,QAAQ,CAAC,mBAAmB,CAAC,YAAY,EAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,EAAC,kBAAkB,CAAC,EAAE,EAAC,wBAAwB,CAAC,kCAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,qBAAqB,KAAG,CAAC,CAAC,kBAAkB,GAAC,EAAE,EAAC,CAAC,CAAC,oBAAoB,CAAC,CAAC,CAAC,uBAAuB,CAAC,EAAC,CAAC,CAAC,qBAAqB,GAAC,CAAC,CAAC,CAAA,AAAC,CAAC,IAAI,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,OAAO,EAAE,EAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,EAAE,CAAC,CAAC,CAAC,CAAC,QAAO,CAAC,CAAC,kBAAkB,CAAC,CAAC,CAAC,EAAE,CAAC,GAAC,CAAC,EAAC,UAAU,CAAC,OAAO,CAAC,CAAC,kBAAkB,CAAC,CAAC,CAAC,EAAE,CAAC,CAAA,CAAC,CAAA,CAAA,CAAC,EAAC,uBAAuB,CAAC,iCAAS,CAAC,CAAC,CAAC,IAAI,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC,kBAAkB,EAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,kBAAkB,CAAC,CAAC,CAAC,CAAC,QAAQ,IAAE,CAAC,CAAC,QAAQ,CAAA,KAAI,CAAC,GAAC,CAAC,CAAC,kBAAkB,CAAC,CAAC,CAAC,CAAA,AAAC,CAAC,OAAO,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAC,CAAC,CAAA,CAAE,KAAK,CAAC,CAAA,CAAC,EAAC,EAAE,CAAC,YAAS,CAAC,CAAC,CAAC,OAAO,KAAK,CAAC,QAAQ,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,EAAE,CAAC,YAAS,CAAC,EAAC,CAAC,CAAC,CAAC,QAAO,KAAK,CAAC,QAAQ,CAAC,KAAK,CAAC,UAAU,CAAC,QAAQ,CAAC,gBAAgB,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,UAAU,CAAC,KAAK,CAAC,QAAQ,CAAC,KAAK,CAAC,UAAU,CAAC,QAAQ,CAAC,mBAAmB,CAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAA,CAAA,CAAC,EAAC,KAAK,CAAC,eAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,KAAK,EAAE,CAAC,QAAO,KAAK,CAAC,QAAQ,CAAC,KAAK,CAAC,UAAU,CAAC,CAAC,CAAC,OAAO,EAAE,EAAC,CAAC,IAAE,CAAC,EAAE,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAA,CAAA,CAAC,CAAC,CAAC,OAAO,CAAC,CAAA,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,eAAe,EAAC,CAAC,aAAa,EAAC,gBAAgB,EAAC,WAAW,EAAC,SAAS,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,IAAE,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,aAAa,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,cAAc,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,UAAU,EAAC,CAAC,CAAC,CAAC,CAAC,WAAW,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,IAAI,GAAC,CAAC,CAAC,KAAK,GAAC,CAAC,GAAC,CAAC,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,gBAAgB,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,GAAC,CAAC,CAAC,CAAC,CAAC,IAAI,GAAC,CAAC,CAAC,CAAC,CAAC,IAAI,GAAC,CAAC,GAAC,CAAC,GAAC,CAAC,KAAG,CAAC,CAAC,IAAI,GAAC,CAAC,GAAC,CAAC,GAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,GAAG,GAAC,CAAC,CAAC,MAAM,GAAC,CAAC,GAAC,CAAC,IAAE,CAAC,CAAC,GAAG,GAAC,CAAC,GAAC,CAAC,EAAE,CAAC,CAAC,GAAG,GAAC,CAAC,CAAC,GAAG,GAAC,CAAC,EAAC,CAAC,CAAC,QAAQ,CAAC,gBAAgB,CAAC,CAAA,EAAG,CAAC,CAAC,GAAG,GAAC,CAAC,CAAC,GAAG,GAAC,CAAC,CAAC,MAAM,EAAC,CAAC,CAAC,WAAW,CAAC,gBAAgB,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,GAAG,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,IAAI,GAAC,CAAC,CAAC,KAAK,GAAC,CAAC,GAAC,CAAC,CAAC,IAAI,CAAC,aAAa,CAAC,GAAC,CAAC,GAAC,CAAC,CAAC,IAAI,GAAC,IAAI,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,GAAC,IAAI,EAAC,IAAI,CAAC,CAAC,CAAC,IAAI,GAAC,IAAI,EAAC,UAAU,CAAC,GAAG,EAAC,OAAO,CAAC,GAAG,CAAC,CAAC,CAAA,CAAC,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,SAAS,EAAC,SAAS,CAAC,CAAC,EAAC,SAAS,CAAC,MAAM,EAAC,YAAY,CAAC,CAAC,CAAC,CAAC,OAAM,CAAC,YAAY,CAAC,sBAAS,CAAC,EAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,YAAY,CAAC,CAAC,EAAC,KAAK,CAAC,KAAK,CAAC,MAAM,CAAC,CAAC,EAAC,CAAC,IAAE,EAAE,CAAC,CAAC,CAAA,CAAC,EAAC,eAAe,CAAC,yBAAS,CAAC,EAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,eAAe,CAAC,CAAC,EAAC,KAAK,CAAC,KAAK,CAAC,MAAM,CAAC,CAAC,EAAC,CAAC,IAAE,EAAE,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,mhBAAmf,CAAC,CAAC,CAAC,OAAO,CAAC,aAAa,EAAC,CAAC,sBAAsB,EAAC,gBAAgB,EAAC,IAAI,EAAC,UAAU,EAAC,YAAY,EAAC,YAAY,EAAC,UAAU,EAAC,gBAAgB,EAAC,qBAAqB,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,CAAC,KAAK,CAAC,IAAI,EAAC,KAAK,CAAC,EAAE,EAAC,OAAO,CAAC,EAAE,CAAC,EAAC,CAAC,IAAE,EAAE,CAAC,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,QAAO,CAAC,CAAC,KAAK,GAAC,CAAC,CAAC,CAAC,KAAK,IAAE,CAAC,CAAA,CAAE,IAAI,EAAE,EAAC,CAAC,CAAC,OAAO,GAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,gBAAgB,GAAC,CAAC,CAAC,KAAK,EAAE,EAAC,CAAC,CAAC,GAAG,EAAE,CAAC,WAAW,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,KAAK,EAAC,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,EAAC,OAAO,CAAC,CAAC,CAAC,OAAO,EAAC,QAAQ,CAAC,CAAC,CAAC,QAAQ,EAAC,QAAQ,CAAC,CAAC,CAAC,QAAQ,EAAC,aAAa,CAAC,uBAAS,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,IAAE,CAAC,CAAA,CAAE,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,aAAa,IAAE,CAAC,EAAC,CAAC,CAAC,gBAAgB,IAAE,CAAC,CAAC,gBAAgB,CAAC,OAAO,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC,QAAQ,IAAE,CAAC,CAAC,OAAO,IAAE,EAAE,CAAC,CAAC,IAAI,CAAC,SAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,aAAa,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAA,CAAE,CAAC,CAAC,MAAM,EAAE,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,GAAC,UAAU,CAAC,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,OAAO,KAAG,CAAC,CAAC,OAAO,GAAC,CAAC,CAAC,EAAC,KAAK,CAAC,qBAAqB,CAAC,UAAU,CAAC,CAAC,CAAC,OAAO,KAAG,CAAC,CAAC,OAAO,CAAC,WAAW,CAAC,cAAc,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,QAAQ,CAAC,sBAAsB,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAA,AAAC,CAAA,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,EAAC,CAAC,CAAC,IAAI,GAAC,SAAS,CAAC,CAAC,CAAC,QAAO,CAAC,GAAC,CAAC,IAAE,CAAC,EAAC,CAAC,CAAC,OAAO,EAAE,CAAC,CAAC,OAAO,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,WAAW,CAAC,QAAQ,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,QAAQ,CAAC,cAAc,CAAC,EAAC,KAAK,CAAC,CAAC,CAAC,EAAC,GAAG,EAAC,CAAC,CAAC,CAAC,CAAA,CAAE,CAAC,EAAE,CAAA,CAAA,CAAC,EAAC,CAAC,CAAC,MAAM,GAAC,UAAU,CAAC,CAAC,CAAC,OAAO,KAAG,CAAC,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC,CAAC,OAAO,CAAC,MAAM,EAAE,EAAC,CAAC,CAAC,KAAK,CAAC,QAAQ,EAAE,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,GAAC,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,EAAC,CAAC,CAAA,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,gBAAgB,CAAC,OAAO,EAAE,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,IAAI,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,gBAAgB,CAAC,OAAO,CAAC,IAAI,CAAC,SAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,QAAM,CAAC,CAAC,KAAG,CAAC,IAAE,CAAC,CAAC,MAAM,CAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC,OAAO,EAAE,EAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,MAAM,IAAE,CAAC,CAAC,WAAW,CAAC,YAAY,CAAC,CAAA,CAAC,EAAC,GAAG,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,qBAAqB,IAAE,CAAC,CAAA,EAAG,CAAA,AAAC,EAAC,CAAC,CAAC,MAAM,EAAE,EAAC,CAAC,CAAA,CAAA,CAAC,CAAC,CAAA,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,YAAY,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,QAAO,CAAC,CAAC,MAAM,GAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,CAAC,IAAI,EAAE,EAAC,CAAC,GAAC,CAAC,CAAC,cAAc,CAAA,EAAG,CAAC,CAAC,QAAQ,CAAC,YAAY,CAAC,EAAC,CAAC,CAAC,MAAM,EAAE,EAAC,CAAC,CAAC,qBAAqB,GAAC,CAAC,CAAC,wBAAwB,CAAC,CAAC,EAAC,CAAC,CAAC,KAAK,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,gBAAgB,CAAC,OAAO,CAAC,KAAK,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,gBAAgB,CAAC,OAAO,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,gBAAgB,CAAC,MAAM,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,gBAAgB,CAAC,KAAK,CAAC,CAAC,EAAC,CAAC,EAAE,EAAC,CAAC,CAAC,gBAAgB,CAAC,OAAO,CAAA,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,aAAa,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,KAAK,EAAE,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,MAAM,IAAE,IAAI,EAAC,IAAI,CAAC,CAAC,CAAC,MAAM,IAAE,iBAAiB,EAAC,KAAK,CAAC,gBAAU,CAAC,OAAM,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,IAAE,EAAE,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,UAAU,IAAE,QAAQ,EAAC,IAAI,CAAC,CAAC,CAAC,UAAU,IAAE,gBAAgB,EAAC,KAAK,CAAC,gBAAU,CAAC,OAAM,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,IAAI,CAAC,CAAC,CAAC,MAAM,IAAE,IAAI,EAAC,IAAI,CAAC,CAAC,CAAC,MAAM,IAAE,iBAAiB,EAAC,KAAK,CAAC,gBAAU,CAAC,OAAM,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,IAAE,EAAE,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,GAAC,EAAE,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,QAAO,CAAC,CAAC,QAAQ,IAAE,iBAAiB,CAAC,IAAI,CAAC,CAAC,CAAC,QAAQ,CAAC,KAAG,CAAC,CAAC,KAAG,CAAC,GAAC,QAAQ,GAAC,CAAC,CAAC,QAAQ,GAAC,SAAS,EAAC,OAAO,CAAC,CAAC,QAAQ,CAAA,AAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,GAAC,2CAAwC,IAAE,CAAC,CAAC,SAAS,IAAE,MAAM,CAAA,AAAC,GAAC,mBAAiB,IAAE,CAAC,CAAC,gBAAgB,IAAE,EAAE,CAAA,AAAC,GAAC,KAAI,EAAC,KAAK,CAAC,CAAC,EAAC,OAAO,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,UAAU,IAAE,QAAQ,EAAC,IAAI,CAAC,CAAC,CAAC,UAAU,IAAE,gBAAgB,EAAC,KAAK,CAAC,gBAAU,EAAE,CAAC,EAAC,CAAC,IAAI,CAAC,CAAC,CAAC,MAAM,IAAE,IAAI,EAAC,IAAI,CAAC,CAAC,CAAC,MAAM,IAAE,iBAAiB,EAAC,KAAK,CAAC,gBAAU,CAAC,OAAO,CAAC,CAAC,IAAI,CAAC,QAAQ,IAAE,EAAE,CAAA,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,IAAE,EAAE,CAAC,CAAC,CAAA,CAAA,CAAC,IAAI,CAAC,CAAC,CAAC,cAAc,CAAC,EAAE,CAAC,EAAC,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,EAAC,KAAK,CAAC,CAAC,EAAC,OAAO,CAAC,CAAC,EAAC,MAAM,CAAC,CAAC,EAAC,YAAY,CAAC,CAAC,EAAC,WAAW,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,gBAAgB,EAAC,CAAC,WAAW,EAAC,SAAS,EAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,YAAY,CAAC,CAAC,CAAC,YAAY,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,gBAAgB,CAAC,CAAC,CAAC,gBAAgB,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAM,QAAQ,MAAI,CAAC,CAAC,CAAC,EAAC,UAAU,CAAC,IAAE,QAAQ,CAAA,AAAC,CAAA,CAAC,IAAI,CAAC,0JAAC,SAAS,CAAC,CAAC,CAAC,IAAI,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,YAAY,IAAE,CAAC,EAAC,CAAC,IAAE,CAAC,KAAG,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,GAAE,CAAC,GAAC,CAAC,CAAC,YAAY,CAAC,OAAO,CAAC,IAAE,CAAC,CAAA,CAAC,CAAA,CAAC,OAAM,CAAC,QAAQ,CAAC,kBAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,MAAM,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAC,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,KAAG,CAAC,GAAC,IAAI,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,IAAE,CAAC,CAAC,SAAS,GAAC,CAAC,CAAC,SAAS,EAAC,CAAC,CAAC,IAAI,IAAE,CAAC,CAAC,UAAU,GAAC,CAAC,CAAC,UAAU,CAAA,AAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,qBAAqB,EAAE,CAAC,OAAM,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,IAAE,CAAC,CAAC,IAAI,CAAC,aAAa,CAAC,EAAC,MAAM,CAAC,CAAC,CAAC,MAAM,IAAE,CAAC,CAAC,IAAI,CAAC,cAAc,CAAC,EAAC,GAAG,CAAC,CAAC,CAAC,GAAG,GAAC,CAAC,CAAC,GAAG,EAAC,IAAI,CAAC,CAAC,CAAC,IAAI,GAAC,CAAC,CAAC,IAAI,CAAC,CAAA,CAAC,EAAC,MAAM,CAAC,gBAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,qBAAqB,EAAE,CAAC,OAAM,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,IAAE,CAAC,CAAC,IAAI,CAAC,aAAa,CAAC,EAAC,MAAM,CAAC,CAAC,CAAC,MAAM,IAAE,CAAC,CAAC,IAAI,CAAC,cAAc,CAAC,EAAC,GAAG,CAAC,CAAC,CAAC,GAAG,IAAE,CAAC,CAAC,WAAW,IAAE,CAAC,CAAC,CAAC,CAAC,CAAC,eAAe,CAAC,SAAS,CAAA,AAAC,EAAC,IAAI,CAAC,CAAC,CAAC,IAAI,IAAE,CAAC,CAAC,WAAW,IAAE,CAAC,CAAC,CAAC,CAAC,CAAC,eAAe,CAAC,UAAU,CAAA,AAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,sBAAsB,EAAC,KAAK,CAAC,eAAe,CAAC,CAAC,QAAQ,EAAC,WAAW,EAAC,cAAc,EAAC,UAAU,EAAC,UAAU,EAAC,QAAQ,EAAC,QAAQ,EAAC,mBAAmB,EAAC,cAAc,EAAC,cAAc,EAAC,kBAAkB,EAAC,eAAe,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,wBAAwB,EAAC,KAAK,CAAC,eAAe,CAAC,CAAC,YAAY,EAAC,aAAa,EAAC,cAAc,EAAC,QAAQ,EAAC,YAAY,EAAC,aAAa,EAAC,gBAAgB,EAAC,mBAAmB,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,wBAAwB,EAAC,KAAK,CAAC,eAAe,CAAC,CAAC,QAAQ,EAAC,OAAO,EAAC,QAAQ,EAAC,aAAa,EAAC,UAAU,EAAC,MAAM,EAAC,MAAM,EAAC,UAAU,EAAC,OAAO,EAAC,cAAc,EAAC,UAAU,EAAC,aAAa,EAAC,OAAO,EAAC,MAAM,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,oBAAoB,EAAC,KAAK,CAAC,eAAe,CAAC,CAAC,QAAQ,EAAC,eAAe,CAAC,CAAC,CAAC,EAAC,CAAA,UAAU,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,OAAO,CAAC,qBAAqB,EAAC,CAAC,OAAO,EAAC,gBAAgB,EAAC,UAAU,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAM,WAAW,IAAE,OAAO,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,CAAC,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,MAAK,CAAC,IAAE,CAAC,EAAE,CAAA,AAAC,CAAA,AAAC,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,IAAG,CAAC,CAAC,SAAS,EAAE,EAAC,CAAC,GAAC,CAAC,CAAC,EAAC,CAAC,KAAG,CAAC,CAAC,MAAM,CAAA,CAAC,CAAC,IAAI,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,GAAG,EAAE,CAAA,AAAC,GAAE,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,GAAG,CAAC,CAAC,EAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,EAAE,CAAC,CAAC,CAAC,MAAM,IAAE,CAAC,CAAC,CAAC,EAAC,IAAG,CAAC,CAAA,CAAC,CAAC,IAAI,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,QAAO,CAAC,CAAC,SAAS,GAAC,CAAC,EAAC,CAAC,CAAA,CAAA,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,gBAAgB,EAAC,sBAAsB,EAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,GAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,GAAG,QAAQ,IAAE,OAAO,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,gBAAgB,KAAG,CAAC,CAAC,IAAE,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,SAAS,CAAC,WAAW,EAAE,CAAC,IAAG,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,IAAE,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,WAAW,CAAC,EAAC,OAAO,CAAC,QAAQ,CAAC,CAAC,CAAC,KAAK,CAAC,CAAA,CAAC,IAAI,IAAI,CAAC,IAAI,CAAC,CAAC,KAAK,EAAC,CAAC,GAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,gBAAgB,KAAG,CAAC,CAAC,IAAE,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,SAAS,CAAC,WAAW,EAAE,EAAC,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,IAAE,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,CAAA,CAAC,OAAO,CAAC,CAAC,IAAI,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,CAAA,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,qBAAqB,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,EAAE,CAAA,CAAC,CAAC,CAAC,CAAA,CAAC,CAAA,EAAE,EAAC,CAAC,CAAC,OAAO,CAAC,sBAAsB,EAAC,CAAC,UAAU,EAAC,aAAa,EAAC,OAAO,EAAC,IAAI,EAAC,YAAY,EAAC,gBAAgB,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,GAAG,CAAC,CAAC,EAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,SAAS,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,IAAI,IAAE,CAAC,CAAC,IAAI,CAAC,IAAI,EAAE,CAAA,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,EAAE,EAAC,WAAW,CAAC,EAAE,EAAC,KAAK,CAAC,IAAI,EAAC,UAAU,CAAC,IAAI,EAAC,MAAM,CAAC,EAAE,EAAC,QAAQ,CAAC,IAAI,CAAC,EAAC,CAAC,IAAE,EAAE,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,OAAO,CAAC,CAAC,IAAI,CAAC,SAAS,CAAC,CAAC,CAAC,IAAI,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,KAAK,IAAE,CAAC,CAAC,IAAI,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC,QAAO,CAAC,CAAC,UAAU,KAAG,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,UAAU,EAAC,CAAC,CAAC,CAAC,CAAC,MAAM,EAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,QAAQ,EAAE,CAAC,IAAI,CAAC,yBAAyB,EAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,QAAQ,IAAE,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,OAAO,CAAC,CAAC,EAAC,KAAK,CAAC,CAAC,CAAC,CAAA,CAAA,CAAC,CAAC,CAAA,CAAC,OAAM,CAAC,IAAI,CAAC,CAAC,EAAC,OAAO,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,mBAAmB,EAAC,CAAC,eAAe,EAAC,MAAM,EAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,mBAAmB,GAAC,CAAC,GAAC,0CAA0C,GAAC,CAAC,GAAC,6EAA6E,CAAC,CAAA,CAAC,CAAC,CAAC,EAAE,EAAC,EAAE,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,cAAc,CAAC,aAAa,EAAC,WAAW,CAAC,UAAU,EAAC,cAAc,CAAC,aAAa,EAAC,mBAAmB,CAAC,kBAAkB,EAAC,eAAe,CAAC,iBAAiB,EAAC,YAAY,CAAC,cAAc,CAAC,CAAC,QAAO,CAAC,CAAC,CAAC,EAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAC,UAAU,CAAC,QAAO,CAAC,CAAC,GAAG,GAAC,CAAC,EAAC,GAAG,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,IAAI,EAAC,SAAS,CAAC,CAAA,CAAA,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAA,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,oBAAoB,EAAC,CAAC,UAAU,EAAC,WAAW,EAAC,IAAI,EAAC,kBAAkB,EAAC,cAAc,EAAC,sBAAsB,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,SAAY,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,MAAM,CAAC,KAAK,CAAC,KAAK,CAAC,OAAO,EAAE,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,IAAE,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,OAAO,CAAC,IAAI,IAAE,EAAE,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,CAAC,EAAC,iBAAiB,CAAC,IAAE,CAAC,CAAC,cAAc,IAAE,CAAC,CAAC,KAAK,CAAC,UAAU,EAAE,IAAE,KAAK,EAAC,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,UAAU,EAAE,CAAC,QAAO,CAAC,GAAC,CAAC,IAAE,CAAC,CAAC,CAAC,EAAC,gBAAgB,CAAC,IAAE,CAAC,CAAC,aAAa,IAAE,CAAC,IAAE,MAAM,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,UAAU,CAAC,CAAC,EAAC,gBAAgB,CAAC,MAAM,KAAG,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,SAAS,CAAC,CAAC,EAAC,aAAa,CAAC,MAAM,KAAG,CAAC,IAAE,MAAM,KAAG,CAAC,CAAC,CAAC,CAAA,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,QAAO,CAAC,GAAC,CAAC,IAAE,EAAE,EAAC,CAAC,MAAM,CAAC,CAAC,CAAC,MAAM,EAAC,SAAS,CAAC,CAAC,CAAC,SAAS,EAAC,OAAO,CAAC,CAAC,CAAC,OAAO,EAAC,SAAS,CAAC,CAAC,CAAC,SAAS,EAAC,WAAW,CAAC,CAAC,CAAC,WAAW,CAAC,CAAA,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,OAAO,SAAS,CAAC,MAAM,GAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,IAAE,CAAC,CAAC,MAAM,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,KAAK,EAAE,CAAC,CAAC,KAAG,CAAC,CAAC,KAAK,CAAC,qBAAqB,EAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,QAAQ,EAAE,CAAA,AAAC,EAAC,CAAC,CAAC,MAAM,EAAE,CAAA,CAAC,CAAC,IAAI,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,mCAAmC,EAAC,CAAC,CAAC,UAAU,EAAC,CAAC,CAAC,aAAa,EAAC,CAAC,CAAC,QAAQ,EAAC,CAAC,CAAC,WAAW,EAAC,CAAC,CAAC,gBAAgB,EAAC,CAAC,CAAC,WAAW,EAAC,CAAC,CAAC,UAAU,EAAC,CAAC,CAAC,QAAQ,EAAC,CAAC,CAAC,QAAQ,EAAC,CAAC,CAAC,OAAO,EAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,UAAU,GAAC,KAAK,CAAC,UAAU,IAAE,EAAE,EAAC,KAAK,CAAC,UAAU,CAAC,QAAQ,GAAC,CAAC,CAAC,CAAC,IAAI,CAAC,EAAC,CAAC,CAAC,KAAK,CAAC,OAAO,CAAC,UAAU,EAAC,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,IAAI,EAAC,CAAC,CAAC,CAAC,MAAM,CAAC,gBAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,EAAE,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,cAAS,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,eAAe,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,gBAAgB,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,EAAC,UAAU,CAAC,CAAC,IAAE,CAAC,EAAE,CAAA,CAAC,CAAC,CAAA,CAAC,EAAC,gBAAgB,CAAC,0BAAS,CAAC,CAAC,CAAC,IAAI,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,eAAe,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,WAAW,EAAE,CAAC,IAAI,CAAC,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,MAAM,EAAC,CAAC,GAAC,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,IAAI,CAAC,CAAC,EAAC,CAAC,GAAC,KAAK,CAAC,KAAK,CAAC,OAAO,EAAE,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAA,CAAE,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,KAAG,CAAC,KAAG,CAAC,GAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,IAAE,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,EAAE,EAAE,CAAC,GAAC,CAAC,CAAC,CAAC,EAAC,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,GAAG,IAAE,CAAC,CAAC,aAAa,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,CAAC,EAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,IAAE,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,GAAC,IAAI,CAAA,CAAC,EAAC,MAAM,CAAC,gBAAS,CAAC,EAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,cAAc,CAAC,CAAC,CAAC,KAAK,EAAE,CAAC,CAAC,KAAI,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,SAAS,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,KAAK,CAAC,CAAC,CAAC,UAAU,CAAC,IAAE,CAAC,CAAC,WAAW,CAAC,KAAK,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,EAAC,IAAI,EAAC,CAAC,CAAC,SAAS,EAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,CAAC,EAAC,CAAC,MAAM,CAAC,CAAC,CAAC,MAAM,EAAC,SAAS,CAAC,CAAC,CAAC,SAAS,EAAC,SAAS,CAAC,CAAC,CAAC,SAAS,EAAC,WAAW,CAAC,CAAC,CAAC,WAAW,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,KAAG,CAAC,CAAC,IAAE,OAAO,KAAG,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,IAAE,OAAO,IAAE,CAAC,CAAC,IAAI,CAAC,YAAY,CAAC,IAAE,CAAC,KAAG,CAAC,CAAC,KAAK,CAAC,QAAQ,EAAE,CAAA,IAAG,CAAC,CAAC,IAAI,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,iBAAiB,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,SAAS,EAAC,OAAO,CAAC,CAAC,UAAU,EAAC,CAAC,CAAC,KAAK,CAAC,mBAAmB,EAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,EAAC,IAAI,CAAC,GAAG,EAAE,CAAC,EAAC,CAAC,IAAE,CAAC,EAAE,CAAA,CAAC,EAAC,UAAU,CAAC,oBAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,aAAa,CAAC,UAAU,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,aAAa,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,UAAU,CAAC,OAAO,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,sBAAsB,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,aAAa,IAAE,CAAC,EAAE,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,KAAG,IAAI,IAAE,CAAC,EAAE,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,IAAE,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,OAAO,EAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,IAAE,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAC,CAAC,KAAG,CAAC,KAAG,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,aAAa,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,UAAU,CAAC,OAAO,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,oBAAoB,EAAE,CAAA,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,GAAC,CAAC,GAAC,CAAC,GAAC,CAAC,GAAC,CAAC,GAAC,IAAI,CAAA,AAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,KAAG,IAAI,IAAE,CAAC,EAAE,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,aAAa,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,IAAI,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,YAAY,GAAC,CAAC,CAAC,YAAY,GAAC,CAAC,EAAC,CAAC,CAAC,SAAS,GAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,UAAU,GAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,WAAW,GAAC,CAAC,EAAC,CAAC,CAAC,SAAS,GAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,MAAM,EAAE,EAAC,qBAAqB,EAAC,CAAC,CAAC,UAAU,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,MAAM,EAAE,EAAC,oBAAoB,EAAC,CAAC,CAAC,SAAS,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,KAAK,CAAC,CAAC,CAAC,UAAU,CAAC,IAAE,CAAC,CAAC,WAAW,CAAC,KAAK,CAAC,IAAI,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,SAAS,EAAC,CAAC,CAAC,aAAa,IAAE,CAAC,IAAE,CAAC,CAAC,CAAC,IAAG,CAAC,CAAC,aAAa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gBAAS,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAA,CAAE,CAAC,EAAE,EAAC,CAAC,CAAC,aAAa,GAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,IAAI,CAAA,CAAC,CAAC,CAAA,CAAC,CAAA,CAAC,IAAE,CAAC,EAAE,CAAA,CAAC,CAAC,EAAC,IAAI,CAAC,cAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,KAAK,EAAE,CAAC,CAAC,KAAG,CAAC,CAAC,KAAK,CAAC,aAAa,GAAC,CAAC,GAAC,OAAO,EAAC,CAAC,CAAC,EAAC,OAAO,IAAE,CAAC,IAAE,CAAC,CAAC,KAAK,CAAC,kBAAkB,EAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,EAAE,CAAC,GAAC,CAAC,CAAC,KAAK,EAAE,EAAC,CAAC,KAAG,CAAC,CAAC,KAAK,CAAC,aAAa,GAAC,CAAC,GAAC,OAAO,EAAC,CAAC,CAAC,EAAC,OAAO,IAAE,CAAC,IAAE,CAAC,CAAC,KAAK,CAAC,kBAAkB,EAAC,CAAC,CAAC,CAAA,AAAC,CAAA,CAAE,CAAC,IAAE,CAAC,IAAE,CAAC,CAAC,MAAM,KAAG,CAAC,CAAC,KAAK,CAAC,gBAAgB,GAAC,CAAC,GAAC,OAAO,EAAC,CAAC,CAAC,EAAC,OAAO,IAAE,CAAC,IAAE,CAAC,CAAC,KAAK,CAAC,qBAAqB,EAAC,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,EAAC,OAAO,CAAC,iBAAS,CAAC,CAAC,CAAC,CAAC,IAAE,MAAM,IAAE,CAAC,CAAC,SAAS,IAAE,CAAC,CAAC,CAAC,KAAK,CAAC,YAAY,EAAE,IAAE,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,eAAe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sBAAU,CAAC,OAAO,CAAC,CAAA,CAAC,EAAC,UAAU,CAAC,qBAAU,CAAC,OAAO,CAAC,CAAA,CAAC,CAAC,CAAC,OAAO,CAAC,CAAA,CAAC,EAAC,aAAa,CAAC,uBAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,aAAa,EAAE,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,eAAe,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,EAAE,EAAC,CAAC,GAAC,EAAE,CAAA,CAAC,EAAC,cAAc,CAAC,wBAAS,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAA,CAAC,EAAC,aAAa,CAAC,uBAAS,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAA,CAAC,EAAC,eAAe,CAAC,yBAAS,CAAC,CAAC,CAAC,QAAO,SAAS,CAAC,MAAM,KAAG,KAAK,CAAC,UAAU,CAAC,QAAQ,GAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,EAAC,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,eAAe,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,GAAG,CAAC,CAAA,AAAC,CAAA,AAAC,EAAC,KAAK,CAAC,UAAU,CAAC,QAAQ,CAAA,CAAA,CAAC,EAAC,aAAa,CAAC,uBAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,IAAE,CAAC,CAAC,SAAS,KAAG,CAAC,CAAC,SAAS,GAAC,CAAC,CAAC,SAAS,EAAC,CAAC,KAAG,CAAC,CAAC,QAAQ,CAAC,MAAM,CAAA,AAAC,EAAE,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,GAAG,CAAC,MAAM,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAA,EAAG,CAAC,CAAC,SAAS,GAAC,MAAM,EAAC,CAAC,CAAC,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,EAAC,eAAe,CAAC,yBAAS,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,iBAAiB,CAAC,CAAC,EAAC,WAAW,CAAC,CAAC,EAAC,cAAc,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,UAAU,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,UAAU,EAAC,CAAC,WAAW,EAAC,SAAS,CAAC,CAAC,CAAC,QAAO,CAAC,CAAC,cAAc,GAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,yBAAyB,CAAC,eAAe,CAAC,QAAQ,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAA,CAAA,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,UAAU,EAAC,SAAS,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,QAAO,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,IAAI,EAAC,CAAC,CAAC,IAAI,GAAC,SAAS,CAAC,CAAC,CAAC,QAAO,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,UAAU,CAAC,IAAI,CAAC,CAAC,QAAQ,CAAC,aAAa,CAAC,iBAAiB,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,SAAS,GAAC,CAAC,CAAA,AAAC,CAAA,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAA,CAAA,CAAC,EAAC,CAAC,CAAA,CAAA,CAAC,CAAC,CAAC,SAAS,CAAC,WAAW,EAAC,CAAC,WAAW,EAAC,UAAU,EAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,UAAU,CAAC,iBAAiB,EAAC,CAAC,QAAQ,EAAC,UAAU,EAAC,QAAQ,EAAC,IAAI,EAAC,cAAc,EAAC,eAAe,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,QAAO,CAAC,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,GAAG,GAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAA,CAAC,IAAI,CAAC,CAAC,OAAO,EAAC,CAAC,CAAC,WAAW,EAAC,CAAC,CAAC,aAAa,EAAC,CAAC,CAAC,eAAe,EAAC,CAAC,CAAC,gBAAgB,EAAC,CAAC,CAAC,MAAM,EAAC,CAAC,CAAC,IAAI,EAAC,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,WAAW,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,wBAAwB,EAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,KAAK,GAAC,SAAS,CAAC,CAAC,CAAC,QAAO,SAAS,CAAC,MAAM,IAAE,CAAC,KAAG,CAAC,KAAG,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAA,CAAA,CAAC,EAAC,CAAC,CAAC,UAAU,GAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,QAAO,SAAS,CAAC,MAAM,KAAG,CAAC,GAAC,CAAC,EAAC,CAAC,IAAE,CAAC,CAAC,gBAAgB,EAAE,CAAA,AAAC,EAAC,CAAC,CAAA,CAAA,CAAC,EAAC,CAAC,CAAC,QAAQ,GAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,QAAO,SAAS,CAAC,MAAM,KAAG,CAAC,GAAC,CAAC,EAAC,CAAC,IAAE,CAAC,CAAC,gBAAgB,EAAE,CAAA,AAAC,EAAC,CAAC,CAAA,CAAA,CAAC,EAAC,CAAC,CAAC,WAAW,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,EAAC,CAAC,CAAC,gBAAgB,EAAE,CAAA,CAAC,EAAC,CAAC,CAAC,gBAAgB,GAAC,UAAU,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,IAAE,CAAC,CAAA,KAAI,CAAC,KAAG,CAAC,GAAC,CAAC,IAAE,CAAC,IAAE,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,IAAE,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,QAAQ,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,KAAG,CAAC,GAAC,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,KAAG,CAAC,CAAC,cAAc,KAAG,CAAC,CAAC,UAAU,CAAC,IAAI,EAAE,KAAG,CAAC,GAAC,CAAC,CAAC,CAAC,GAAC,QAAQ,CAAC,EAAC,CAAC,KAAG,CAAC,CAAC,cAAc,GAAC,CAAC,CAAC,UAAU,CAAC,IAAI,EAAE,EAAC,CAAC,CAAC,SAAS,GAAC,OAAO,GAAC,CAAC,CAAC,cAAc,CAAA,AAAC,CAAA,AAAC,EAAC,CAAC,CAAC,cAAc,KAAG,CAAC,CAAC,UAAU,CAAC,IAAI,EAAE,KAAG,CAAC,GAAC,CAAC,CAAC,CAAC,GAAC,aAAa,CAAC,EAAC,CAAC,KAAG,CAAC,CAAC,WAAW,GAAC,CAAC,CAAC,cAAc,GAAC,CAAC,CAAC,UAAU,CAAC,IAAI,EAAE,CAAA,AAAC,CAAA,AAAC,CAAA,AAAC,CAAA,AAAC,CAAA,CAAC,EAAC,CAAC,CAAC,cAAc,GAAC,UAAU,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,KAAK,CAAC,OAAO,CAAC,aAAa,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAC,IAAI,CAAC,GAAG,CAAC,CAAC,IAAE,CAAC,CAAC,KAAK,IAAE,EAAE,CAAC,CAAA,CAAC,OAAO,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,UAAU,GAAC,UAAU,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,cAAc,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,QAAO,CAAC,GAAC,CAAC,KAAG,CAAC,GAAC,CAAC,IAAE,CAAC,GAAC,CAAC,GAAC,CAAC,CAAA,AAAC,CAAA,AAAC,EAAC,CAAC,CAAA,CAAA,CAAC,EAAC,CAAC,CAAC,UAAU,GAAC,UAAU,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,WAAW,GAAC,CAAC,GAAC,CAAC,CAAC,UAAU,EAAE,GAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,cAAc,GAAC,UAAU,CAAC,OAAO,CAAC,GAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,kBAAkB,GAAC,UAAU,CAAC,IAAI,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,GAAE,CAAC,IAAE,CAAC,CAAC,UAAU,EAAC,CAAC,GAAC,CAAC,CAAC,aAAa,CAAC,OAAO,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,eAAe,GAAC,SAAS,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,UAAU,CAAC,iBAAi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iBAAiB,EAAE,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,WAAW,GAAC,CAAC,CAAC,SAAS,GAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,cAAc,EAAE,IAAE,CAAC,KAAG,CAAC,GAAC,CAAC,CAAA,AAAC,CAAA,CAAC,OAAO,CAAC,CAAC,eAAe,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,EAAC,CAAC,CAAC,UAAU,EAAC,CAAC,CAAC,WAAW,EAAC,CAAC,CAAC,YAAY,EAAC,CAAC,CAAC,GAAG,EAAC,CAAC,CAAC,aAAa,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,UAAU,GAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,MAAM,EAAC,CAAC,EAAE,EAAC,CAAC,IAAG,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,EAAC,CAAC,IAAE,CAAC,CAAC,QAAQ,CAAA,CAAC,CAAC,GAAG,CAAC,KAAG,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,CAAC,SAAQ,CAAC,GAAG,CAAC,CAAC,SAAS,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,SAAS,GAAG,CAAC,IAAE,CAAC,KAAG,CAAC,CAAC,CAAC,IAAI,CAAC,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,UAAU,CAAC,MAAM,EAAC,CAAC,EAAE,EAAC,IAAG,CAAC,GAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,EAAC,CAAC,IAAE,CAAC,CAAC,QAAQ,CAAA,CAAC,GAAG,CAAC,CAAC,SAAS,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,QAAQ,CAAC,MAAM,EAAC,CAAC,EAAE,EAAC,IAAG,CAAC,GAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAC,CAAC,CAAA,CAAC,CAAC,GAAG,CAAC,CAAC,SAAS,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,IAAE,CAAC,CAAC,WAAW,CAAA,CAAC,KAAI,CAAC,GAAG,CAAC,CAAC,SAAS,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,IAAE,CAAC,CAAC,WAAW,CAAA,CAAC,KAAK,CAAC,IAAE,CAAC,CAAC,WAAW,CAAC,KAAK,CAAC,IAAE,CAAC,CAAC,QAAQ,IAAE,CAAC,CAAC,SAAS,CAAC,IAAI,EAAE,KAAG,CAAC,GAAC,KAAK,CAAC,OAAO,CAAC,aAAa,CAAC,CAAC,CAAC,EAAC,CAAC,IAAE,CAAC,IAAE,CAAC,CAAC,KAAK,IAAE,CAAC,CAAA,AAAC,CAAC,CAAC,GAAC,CAAC,IAAE,CAAC,CAAC,WAAW,CAAA,CAAC,KAAK,CAAC,GAAC,CAAC,CAAC,WAAW,CAAA,CAAC,KAAK,CAAC,IAAE,CAAC,CAAC,QAAQ,IAAE,CAAC,CAAC,SAAS,CAAC,IAAI,EAAE,KAAG,CAAC,GAAC,KAAK,CAAC,OAAO,CAAC,aAAa,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,IAAE,CAAC,CAAC,KAAK,IAAE,CAAC,CAAA,AAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,CAAC,IAAE,CAAC,CAAA,CAAC,GAAG,MAAM,IAAE,CAAC,CAAC,CAAC,GAAC,YAAY,EAAC,CAAC,KAAG,CAAC,GAAC,CAAC,GAAC,EAAE,CAAA,AAAC,EAAC,CAAC,KAAG,CAAC,GAAC,CAAC,GAAC,EAAE,CAAA,AAAC,CAAC,KAAK,GAAG,OAAO,IAAE,CAAC,CAAC,CAAC,GAAC,aAAa,EAAC,CAAC,KAAG,CAAC,GAAC,CAAC,GAAC,EAAE,CAAA,AAAC,EAAC,CAAC,KAAG,CAAC,GAAC,CAAC,GAAC,EAAE,CAAA,AAAC,CAAC,KAAI,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAC,CAAC,CAAC,GAAC,EAAE,CAAC,CAAC,GAAC,EAAE,KAAG,CAAC,GAAC,CAAC,GAAC,CAAC,CAAA,AAAC,CAAA,CAAC,OAAM,CAAC,eAAe,CAAC,CAAC,EAAC,WAAW,CAAC,CAAC,EAAC,YAAY,CAAC,CAAC,EAAC,SAAS,CAAC,CAAC,EAAC,UAAU,CAAC,CAAC,EAAC,aAAa,CAAC,CAAC,EAAC,GAAG,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,eAAe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iBAAiB,EAAE,CAAA,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,QAAQ,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,IAAE,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,KAAK,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,QAAO,KAAK,CAAC,qBAAqB,CAAC,UAAU,CAAC,GAAG,CAAC,IAAE,CAAC,CAAC,WAAW,GAAC,EAAE,GAAC,CAAC,CAAC,WAAW,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,GAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,WAAW,GAAC,CAAC,GAAC,CAAC,CAAC,cAAc,EAAE,GAAC,EAAE,CAAC,CAAC,GAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,KAAG,CAAC,KAAG,CAAC,CAAC,KAAK,CAAC,KAAK,GAAC,CAAC,GAAC,IAAI,EAAC,CAAC,GAAC,CAAC,CAAA,AAAC,CAAA,CAAC,CAAC,CAAC,OAAO,EAAE,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAA,CAAA,CAAC,EAAC,CAAC,CAAC,MAAM,GAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,KAAK,CAAC,OAAO,CAAC,YAAY,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,GAAG,CAAC,UAAU,EAAC,UAAU,CAAC,IAAI,IAAI,CAAC,IAAI,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,GAAC,IAAI,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,UAAU,CAAC,oBAAoB,EAAC,CAAC,QAAQ,EAAC,QAAQ,EAAC,UAAU,EAAC,UAAU,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,EAAE,CAAC,KAAK,CAAC,qBAAqB,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,GAAG,CAAC,QAAQ,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,OAAO,CAAC,MAAM,CAAC,CAAC,CAAC,UAAU,IAAE,EAAE,CAAC,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,KAAK,CAAC,qBAAqB,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,MAAM,CAAC,QAAQ,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,WAAW,IAAE,CAAC,CAAC,UAAU,CAAC,MAAM,EAAE,EAAC,CAAC,CAAC,CAAC,WAAW,IAAE,CAAC,CAAC,UAAU,CAAC,WAAW,IAAE,CAAC,CAAC,UAAU,CAAC,WAAW,CAAC,YAAY,GAAC,CAAC,IAAE,CAAC,CAAC,CAAC,WAAW,CAAA,IAAG,CAAC,CAAC,WAAW,EAAE,CAAA,CAAC,EAAC,EAAE,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,GAAC,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,cAAc,EAAE,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,SAAS,EAAE,CAAC,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,IAAI,IAAE,CAAC,CAAC,IAAI,IAAE,CAAC,CAAC,IAAI,IAAE,CAAC,CAAC,KAAG,CAAC,CAAC,GAAG,IAAE,CAAC,CAAC,GAAG,IAAE,CAAC,CAAC,GAAG,CAAA,IAAG,CAAC,EAAE,CAAA,CAAC,KAAK,CAAC,GAAC,CAAC,CAAC,kBAAkB,EAAE,EAAC,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,IAAI,IAAE,CAAC,CAAC,QAAQ,CAAC,UAAU,IAAE,CAAC,CAAC,IAAI,GAAC,CAAC,CAAC,QAAQ,CAAC,WAAW,IAAE,CAAC,CAAC,KAAG,CAAC,CAAC,GAAG,IAAE,CAAC,CAAC,QAAQ,CAAC,SAAS,IAAE,CAAC,CAAC,GAAG,GAAC,CAAC,CAAC,QAAQ,CAAC,YAAY,CAAA,IAAG,CAAC,EAAE,CAAA,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,IAAE,MAAM,CAAA,CAAE,IAAI,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,IAAE,CAAC,GAAC,UAAU,CAAC,CAAC,CAAC,GAAC,GAAG,CAAA,AAAC,CAAC,CAAC,GAAC,UAAU,CAAC,CAAC,CAAC,CAAA,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,SAAS,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,GAAC,UAAU,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,YAAY,CAAA,CAAC,EAAC,CAAC,CAAC,OAAO,GAAC,UAAU,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,EAAE,CAAA,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,+BAA+B,EAAC,UAAU,CAAC,CAAC,EAAE,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,UAAU,EAAC,UAAU,CAAC,CAAC,CAAC,UAAU,IAAE,CAAC,CAAC,UAAU,CAAC,QAAQ,IAAE,CAAC,CAAC,UAAU,CAAC,QAAQ,CAAC,GAAG,CAAC,QAAQ,EAAC,CAAC,CAAC,WAAW,CAAC,EAAC,CAAC,CAAC,QAAQ,IAAE,CAAC,CAAC,QAAQ,CAAC,mBAAmB,IAAE,CAAC,CAAC,QAAQ,CAAC,mBAAmB,CAAC,QAAQ,EAAC,CAAC,CAAC,WAAW,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,WAAW,GAAC,KAAK,CAAC,KAAK,CAAC,QAAQ,CAAC,CAAC,EAAC,GAAG,CAAC,EAAC,CAAC,CAAC,cAAc,GAAC,UAAU,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,YAAY,EAAE,CAAC,OAAM,CAAC,IAAI,CAAC,CAAC,CAAC,UAAU,CAAC,OAAO,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,EAAC,GAAG,CAAC,CAAC,CAAC,UAAU,CAAC,OAAO,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,kBAAkB,GAAC,UAAU,CAAC,IAAI,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,QAAQ,CAAC,WAAW,EAAC,GAAG,CAAC,CAAC,CAAC,QAAQ,CAAC,YAAY,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,gBAAgB,CAAC,CAAC,CAAC,QAAQ,CAAC,IAAE,EAAE,CAAC,OAAM,CAAC,IAAI,CAAC,QAAQ,KAAG,CAAC,CAAC,SAAS,IAAE,MAAM,KAAG,CAAC,CAAC,SAAS,IAAE,QAAQ,KAAG,CAAC,CAAC,QAAQ,CAAC,KAAK,CAAC,YAAY,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,EAAC,GAAG,CAAC,QAAQ,KAAG,CAAC,CAAC,SAAS,IAAE,MAAM,KAAG,CAAC,CAAC,SAAS,IAAE,QAAQ,KAAG,CAAC,CAAC,QAAQ,CAAC,KAAK,CAAC,YAAY,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,sBAAsB,GAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,oBAAoB,EAAC,KAAK,CAAC,eAAe,CAAC,CAAC,aAAa,EAAC,YAAY,EAAC,eAAe,EAAC,oBAAoB,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,YAAY,EAAC,CAAC,QAAQ,EAAC,QAAQ,EAAC,oBAAoB,EAAC,eAAe,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,IAAI,EAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,iBAAiB,CAAC,CAAC,EAAC,CAAC,CAAC,cAAc,EAAC,UAAU,CAAC,OAAO,CAAC,CAAC,aAAa,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,UAAU,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,WAAW,GAAC,SAAS,CAAC,CAAC,CAAC,QAAO,SAAS,CAAC,MAAM,KAAG,CAAC,GAAC,CAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAA,CAAA,CAAC,EAAC,CAAC,CAAC,UAAU,GAAC,SAAS,CAAC,CAAC,CAAC,QAAO,SAAS,CAAC,MAAM,KAAG,CAAC,GAAC,CAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAA,CAAA,CAAC,EAAC,CAAC,CAAC,aAAa,GAAC,SAAS,CAAC,CAAC,CAAC,QAAO,SAAS,CAAC,MAAM,KAAG,CAAC,GAAC,CAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAA,CAAA,CAAC,EAAC,CAAC,CAAC,kBAAkB,GAAC,UAAU,CAAC,CAAC,CAAC,QAAQ,IAAE,CAAC,CAAC,QAAQ,CAAC,gBAAgB,EAAE,CAAA,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,UAAU,CAAC,cAAc,EAAC,CAAC,QAAQ,EAAC,UAAU,EAAC,QAAQ,EAAC,UAAU,EAAC,UAAU,EAAC,sBAAsB,EAAC,cAAc,EAAC,eAAe,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,OAAO,CAAC,IAAI,IAAE,OAAO,CAAC,GAAG,CAAC,CAAC,IAAE,CAAC,CAAC,IAAI,CAAC,OAAO,EAAC,mBAAmB,GAAC,CAAC,GAAC,6BAA6B,GAAC,CAAC,GAAC,UAAU,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,IAAI,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,MAAM,EAAC,CAAC,EAAE,EAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAA,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,IAAI,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,MAAM,EAAC,CAAC,EAAE,EAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAA,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,IAAE,KAAK,CAAC,OAAO,CAAC,UAAU,CAAC,CAAC,CAAC,YAAY,EAAE,EAAC,SAAS,EAAC,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,OAAO,CAAC,UAAU,CAAC,CAAC,EAAC,WAAW,EAAC,CAAC,CAAC,CAAA,CAAC,IAAI,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,MAAM,EAAC,CAAC,CAAC,sBAAsB,EAAC,CAAC,CAAC,gBAAgB,EAAC,CAAC,CAAC,kBAAkB,EAAC,CAAC,CAAC,YAAY,EAAC,CAAC,CAAC,gEAAgE,CAAC,KAAK,CAAC,GAAG,CAAC,EAAC,CAAC,CAAC,IAAI,EAAC,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,EAAE,CAAC,IAAI,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,cAAc,IAAE,QAAQ,GAAC,KAAK,CAAC,KAAK,CAAC,OAAO,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,iBAAiB,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,GAAC,UAAU,CAAC,CAAC,CAAC,QAAQ,CAAC,mBAAmB,CAAC,EAAC,KAAK,CAAC,OAAO,CAAC,UAAU,CAAC,CAAC,EAAC,oBAAoB,EAAC,CAAC,CAAC,KAAK,CAAC,UAAU,EAAE,CAAC,EAAC,CAAC,CAAC,eAAe,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,eAAe,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,KAAK,CAAC,gBAAgB,EAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,eAAe,GAAC,SAAS,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,KAAG,OAAO,KAAG,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,cAAc,IAAE,CAAC,IAAE,CAAC,IAAE,CAAC,IAAE,MAAM,IAAE,CAAC,CAAC,MAAM,CAAC,wBAAwB,EAAE,IAAE,CAAC,IAAE,CAAC,IAAE,OAAO,IAAE,CAAC,CAAC,MAAM,CAAC,sBAAsB,EAAE,EAAE,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,uCAAqC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAA,EAAG,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,sCAAoC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAA,AAAC,CAAA,AAAC,CAAA,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,+BAA6B,CAAC,CAAC,KAAK,CAAC,OAAO,CAAC,UAAU,CAAC,CAAC,EAAC,SAAS,EAAC,CAAC,CAAC,QAAQ,CAAC,QAAQ,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,UAAU,IAAE,CAAC,CAAC,MAAM,CAAC,UAAU,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,kBAAkB,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,IAAI,CAAC,aAAa,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,IAAE,CAAC,CAAC,IAAI,CAAC,eAAe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aAAa,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,EAAE,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,yBAAyB,CAAC,CAAC,CAAC,CAAC,cAAc,GAAC,CAAC,CAAC,UAAU,CAAC,IAAI,EAAE,EAAC,CAAC,CAAC,cAAc,GAAC,CAAC,CAAC,UAAU,CAAC,IAAI,EAAE,CAAC,IAAI,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,EAAC,KAAK,CAAC,eAAS,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,OAAO,CAAC,iBAAS,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,EAAC,CAAC,EAAE,OAAO,KAAG,CAAC,IAAE,CAAC,CAAC,KAAK,CAAC,EAAE,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAA,CAAE,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,UAAU,CAAC,oBAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,EAAE,CAAC,QAAQ,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,GAAC,IAAI,CAAA,AAAC,CAAA,CAAC,EAAC,YAAY,CAAC,uBAAU,CAAC,OAAO,CAAC,CAAA,CAAC,EAAC,YAAY,CAAC,uBAAU,CAAC,OAAO,CAAC,CAAA,CAAC,EAAC,UAAU,CAAC,qBAAU,CAAC,CAAC,CAAC,CAAC,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,eAAe,EAAE,CAAA,CAAC,EAAC,UAAU,CAAC,qBAAU,CAAC,OAAO,CAAC,CAAA,CAAC,EAAC,OAAO,CAAC,kBAAU,CAAC,CAAC,CAAC,CAAC,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,KAAK,EAAE,CAAC,QAAQ,EAAE,CAAC,IAAI,IAAI,CAAC,IAAI,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,GAAC,IAAI,CAAA,AAAC,CAAC,CAAC,IAAE,CAAC,CAAC,UAAU,EAAE,EAAC,CAAC,IAAE,CAAC,CAAC,UAAU,EAAE,EAAC,CAAC,CAAC,UAAU,EAAE,EAAC,CAAC,CAAC,UAAU,EAAE,EAAC,CAAC,CAAC,MAAM,EAAE,EAAC,CAAC,GAAC,CAAC,GAAC,CAAC,GAAC,CAAC,GAAC,CAAC,GAAC,IAAI,CAAA,CAAC,CAAC,CAAC,QAAO,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,CAAA,CAAA,CAAC,EAAC,CAAC,CAAC,UAAU,GAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,QAAO,CAAC,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAA,CAAC,EAAC,CAAC,CAAC,MAAM,GAAC,SAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,YAAY,IAAE,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,UAAU,GAAC,CAAC,CAAC,KAAK,CAAC,UAAU,EAAE,EAAC,CAAC,KAAG,CAAC,CAAC,SAAS,GAAC,MAAM,CAAA,AAAC,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,CAAC,EAAE,CAAC,IAAI,EAAC,CAAC,CAAC,CAAC,CAAC,UAAU,EAAE,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,UAAU,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,QAAQ,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,gBAAgB,EAAE,EAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,WAAW,IAAE,CAAC,CAAC,CAAC,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,UAAU,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,aAAa,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,EAAE,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,UAAU,GAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,EAAE,CAAC,IAAI,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,MAAM,EAAC,CAAC,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,GAAC,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,QAAQ,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,QAAQ,CAAC,EAAC,CAAC,CAAC,gBAAgB,GAAC,CAAC,GAAC,CAAC,GAAC,IAAI,CAAA,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,UAAU,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,MAAM,CAAC,CAAC,CAAC,gBAAgB,CAAC,IAAE,CAAC,CAAC,WAAW,CAAC,MAAM,CAAC,IAAI,EAAC,CAAC,CAAC,CAAC,CAAC,YAAY,CAAC,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,SAAS,EAAC,CAAC,CAAC,aAAa,IAAE,CAAC,CAAC,aAAa,CAAC,CAAC,KAAK,CAAC,OAAO,CAAC,UAAU,CAAC,CAAC,EAAC,oBAAoB,EAAC,CAAC,CAAC,gBAAgB,CAAC,EAAC,KAAK,CAAC,OAAO,CAAC,UAAU,CAAC,CAAC,EAAC,mBAAmB,EAAC,CAAC,CAAC,SAAS,CAAC,EAAC,CAAC,CAAC,aAAa,IAAE,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,EAAC,OAAO,CAAC,EAAE,CAAC,CAAC,CAAC,EAAC,UAAU,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,SAAS,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,eAAe,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,gBAAgB,GAAC,CAAC,GAAG,CAAC,aAAS,CAAC,CAAC,CAAC,CAAC,CAAC,aAAa,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,GAAC,MAAM,EAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,MAAM,CAAC,gBAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,QAAQ,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,QAAQ,CAAC,EAAC,CAAC,CAAC,aAAa,GAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,gBAAgB,GAAC,CAAC,GAAC,IAAI,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,OAAO,KAAG,CAAC,CAAC,OAAO,EAAE,IAAE,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,OAAO,CAAC,EAAC,CAAC,CAAC,cAAc,KAAG,CAAC,CAAC,cAAc,EAAE,IAAE,CAAC,CAAC,cAAc,CAAC,CAAC,CAAC,cAAc,CAAC,EAAC,CAAC,IAAE,CAAC,CAAC,MAAM,EAAE,CAAA,CAAC,EAAC,QAAQ,CAAC,kBAAS,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,aAAa,GAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,KAAK,EAAC,EAAE,CAAC,EAAC,CAAC,CAAC,GAAC,UAAU,CAAC,CAAC,KAAG,CAAC,KAAG,CAAC,CAAC,CAAC,EAAC,UAAU,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,SAAS,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,UAAU,CAAC,CAAC,IAAE,CAAC,IAAE,CAAC,CAAC,aAAa,IAAE,CAAC,EAAE,CAAA,CAAC,EAAC,CAAC,GAAC,IAAI,CAAA,AAAC,CAAA,CAAC,CAAA,EAAG,CAAA,CAAC,EAAC,CAAC,CAAC,sBAAsB,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,EAAC,CAAC,IAAE,CAAC,EAAE,CAAA,CAAC,EAAC,CAAC,CAAC,oBAAoB,GAAC,UAAU,CAAC,CAAC,IAAE,CAAC,EAAE,CAAA,CAAC,EAAC,CAAC,CAAC,OAAO,GAAC,SAAS,CAAC,CAAC,CAAC,QAAO,SAAS,CAAC,MAAM,KAAG,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,UAAU,GAAC,CAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,UAAU,CAAA,CAAA,CAAC,EAAC,CAAC,CAAC,UAAU,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,EAAE,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,KAAK,EAAE,CAAA,CAAE,CAAC,CAAC,IAAE,CAAC,IAAE,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,MAAM,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,IAAI,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,UAAU,CAAC,MAAM,EAAC,CAAC,EAAE,EAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,KAAG,CAAC,IAAE,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,cAAc,GAAC,SAAS,CAAC,CAAC,CAAC,GAAG,SAAS,CAAC,MAAM,CAAC,CAAC,IAAI,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,MAAM,EAAC,CAAC,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,EAAE,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,kBAAkB,GAAC,CAAC,CAAC,CAAC,CAAA,CAAC,OAAO,CAAC,CAAC,kBAAkB,CAAA,CAAC,EAAC,CAAC,CAAC,oBAAoB,GAAC,SAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,EAAE,CAAC,OAAO,CAAC,CAAC,SAAS,CAAC,MAAM,CAAC,CAAC,CAAC,UAAU,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,EAAE,CAAC,QAAQ,EAAE,CAAC,KAAK,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,KAAK,GAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,QAAO,CAAC,CAAC,CAAC,CAAC,KAAG,CAAC,GAAC,CAAC,IAAE,EAAE,EAAC,CAAC,GAAC,CAAC,IAAE,CAAC,EAAE,EAAC,CAAC,IAAE,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,GAAC,CAAC,EAAC,CAAC,CAAC,YAAY,CAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,MAAM,CAAA,CAAA,CAAC,EAAC,CAAC,CAAC,KAAK,GAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,IAAE,CAAC,EAAE,EAAC,CAAC,IAAE,CAAC,CAAC,UAAU,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,UAAU,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,aAAa,CAAC,CAAC,kBAAkB,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,eAAe,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,aAAa,CAAC,CAAC,uBAAuB,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,WAAW,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,kBAAkB,EAAC,YAAY,CAAC,EAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,QAAQ,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,eAAe,EAAC,YAAY,CAAC,EAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAEnw+B,EAAC,CAAC,CAAC,QAAQ,GAAC,UAAU,CAAC,QAAO,CAAC,CAAC,YAAY,EAAC,SAAS,CAAC,EAAC,CAAC,CAAC,KAAK,EAAE,CAAA,CAAA,CAAC,EAAC,CAAC,CAAC,IAAI,GAAC,UAAU,CAAC,CAAC,CAAC,QAAQ,EAAC,wBAAwB,CAAC,EAAC,CAAC,CAAC,MAAM,EAAE,CAAA,CAAC,EAAC,CAAC,CAAC,gBAAgB,GAAC,UAAU,CAAC,CAAC,CAAC,oBAAoB,EAAC,2BAA2B,CAAC,EAAC,CAAC,CAAC,MAAM,EAAE,CAAA,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,UAAU,EAAC,UAAU,CAAC,CAAC,CAAC,OAAO,CAAC,UAAU,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,IAAI,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,MAAM,EAAC,CAAC,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,EAAE,CAAC,CAAC,CAAC,MAAM,EAAE,EAAC,CAAC,GAAC,CAAC,GAAC,IAAI,EAAC,CAAC,EAAE,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,UAAU,CAAC,eAAe,EAAC,CAAC,QAAQ,EAAC,UAAU,EAAC,QAAQ,EAAC,UAAU,EAAC,aAAa,EAAC,sBAAsB,EAAC,uBAAuB,EAAC,eAAe,EAAC,oBAAoB,EAAC,cAAc,EAAC,sBAAsB,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,IAAI,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,MAAM,EAAC,CAAC,GAAC,CAAC,EAAC,CAAC,EAAE,EAAC,IAAG,CAAC,GAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAA,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,KAAK,EAAE,EAAC,CAAC,IAAE,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,IAAI,CAAC,OAAO,CAAC,MAAM,EAAC,MAAM,CAAC,EAAC,CAAC,CAAC,CAAC,MAAK,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,OAAO,CAAC,UAAU,CAAC,CAAC,EAAC,WAAW,EAAC,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,IAAE,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,IAAE,CAAC,CAAC,eAAe,CAAC,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,IAAI,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,UAAU,CAAC,MAAM,EAAC,CAAC,EAAE,EAAC,GAAG,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,gBAAgB,IAAE,CAAC,EAAC,OAAO,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,CAAA,OAAO,CAAC,CAAC,aAAa,CAAC,sBAAsB,CAAC,CAAA,CAAC,IAAI,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,QAAQ,EAAC,CAAC,CAAC,aAAa,EAAC,CAAC,CAAC,UAAU,EAAC,CAAC,CAAC,QAAQ,EAAC,CAAC,CAAC,QAAQ,EAAC,CAAC,CAAC,IAAI,EAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,KAAK,GAAC,CAAC,EAAC,CAAC,CAAC,OAAO,GAAC,CAAC,EAAC,CAAC,CAAC,IAAI,GAAC,UAAU,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,IAAI,IAAE,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,MAAM,EAAE,CAAC,aAAa,CAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,CAAC,IAAI,CAAC,EAAE,CAAC,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,GAAC,CAAC,KAAG,CAAC,GAAC,CAAC,GAAC,GAAG,GAAC,CAAC,CAAA,AAAC,CAAC,IAAI,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,EAAC,KAAK,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,SAAS,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,iBAAiB,CAAC,CAAC,EAAC,CAAC,CAAC,cAAc,CAAC,CAAC,QAAO,CAAC,CAAC,GAAG,CAAC,UAAU,EAAC,UAAU,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,aAAa,IAAE,CAAC,CAAC,gBAAgB,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,wBAAwB,EAAC,CAAC,CAAC,YAAY,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,gBAAgB,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,iBAAiB,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,wBAAwB,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,uBAAuB,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,kBAAkB,EAAC,CAAC,CAAC,EAAC,KAAK,CAAC,QAAQ,CAAC,KAAK,CAAC,UAAU,CAAC,KAAK,CAAC,QAAQ,CAAC,SAAS,EAAE,IAAE,CAAC,CAAC,KAAK,CAAC,gBAAgB,EAAE,IAAE,CAAC,CAAC,aAAa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aAAa,CAAC,uBAAuB,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,OAAO,KAAG,CAAC,IAAE,MAAM,KAAG,CAAC,CAAA,KAAI,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,OAAO,KAAG,CAAC,KAAG,CAAC,GAAC,MAAM,CAAA,AAAC,CAAA,AAAC,CAAA,AAAC,CAAA,CAAC,EAAC,CAAC,CAAC,MAAM,GAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,EAAC,UAAU,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,SAAS,EAAE,EAAC,CAAC,CAAC,UAAU,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,GAAC,IAAI,CAAA,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,WAAW,GAAC,SAAS,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,cAAc,CAAC,IAAI,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,IAAE,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,EAAE,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,WAAW,GAAC,SAAS,CAAC,CAAC,CAAC,QAAO,SAAS,CAAC,MAAM,KAAG,CAAC,GAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAA,CAAA,CAAC,EAAC,CAAC,CAAC,aAAa,GAAC,UAAU,CAAC,IAAI,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC,IAAI,CAAC,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,MAAM,EAAC,CAAC,GAAC,CAAC,EAAC,CAAC,EAAE,EAAC,CAAC,GAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,SAAS,KAAG,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,KAAG,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,KAAG,CAAC,CAAA,KAAI,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,cAAc,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,EAAC,KAAK,CAAC,KAAK,CAAC,eAAe,CAAC,CAAC,CAAC,KAAK,EAAE,CAAC,CAAA,CAAC,AAAC,CAAC,CAAC,CAAC,EAAE,CAAC,EAAC,CAAC,CAAC,aAAa,IAAE,CAAC,CAAC,gBAAgB,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,YAAY,GAAC,UAAU,CAAC,IAAI,IAAI,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,MAAM,EAAC,CAAC,GAAC,CAAC,EAAC,CAAC,EAAE,EAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,cAAc,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,UAAU,GAAC,SAAS,CAAC,CAAC,CAAC,IAAI,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC,IAAI,CAAC,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,MAAM,EAAC,CAAC,GAAC,CAAC,EAAC,CAAC,EAAE,EAAC,IAAG,CAAC,GAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAC,CAAC,CAAA,CAAC,IAAI,CAAC,GAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,MAAM,EAAC,CAAC,EAAE,EAAC,CAAC,KAAG,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,cAAc,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,cAAc,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,KAAK,EAAE,EAAC,CAAC,IAAE,CAAC,CAAC,UAAU,CAAC,uBAAuB,CAAC,CAAA,AAAC,CAAA,CAAC,EAAC,CAAC,CAAC,eAAe,GAAC,UAAU,CAAC,OAAO,CAAC,CAAC,QAAQ,EAAE,CAAA,CAAC,EAAC,CAAC,CAAC,iBAAiB,GAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,IAAI,EAAE,CAAC,GAAG,CAAC,IAAE,CAAC,CAAC,YAAY,CAAC,CAAC,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,YAAY,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC,IAAI,CAAC,yBAAyB,EAAC,CAAC,CAAC,CAAA,CAAC,QAAO,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAA,CAAA,CAAC,EAAC,CAAC,CAAC,KAAK,GAAC,SAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,IAAE,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,gBAAgB,GAAC,SAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,IAAE,CAAC,CAAC,gBAAgB,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,cAAc,GAAC,SAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,EAAE,CAAC,OAAO,CAAC,CAAC,SAAS,CAAC,MAAM,CAAC,CAAC,CAAC,oBAAoB,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,oBAAoB,EAAE,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,OAAO,GAAC,SAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,EAAE,CAAC,OAAO,CAAC,CAAC,SAAS,CAAC,MAAM,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,EAAE,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,SAAS,GAAC,SAAS,CAAC,CAAC,CAAC,QAAO,SAAS,CAAC,MAAM,KAAG,CAAC,GAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAA,CAAA,CAAC,EAAC,CAAC,CAAC,SAAS,GAAC,SAAS,CAAC,CAAC,CAAC,QAAO,SAAS,CAAC,MAAM,KAAG,CAAC,GAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAA,CAAA,CAAC,EAAC,CAAC,CAAC,aAAa,GAAC,UAAU,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,EAAC,EAAE,CAAC,GAAC,CAAC,CAAA,AAAC,CAAA,AAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,QAAQ,EAAE,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,WAAW,EAAE,CAAC,GAAG,CAAC,IAAE,CAAC,CAAC,SAAS,KAAG,CAAC,CAAC,SAAS,IAAE,CAAC,CAAC,YAAY,KAAG,CAAC,CAAC,CAAC,CAAC,CAAC,KAAG,CAAC,GAAC,MAAM,CAAC,UAAU,CAAA,AAAC,EAAC,CAAC,CAAC,aAAa,GAAC,CAAC,CAAC,gBAAgB,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,SAAS,CAAC,MAAM,CAAC,CAAC,CAAC,GAAC,EAAE,EAAC,CAAC,GAAC,CAAC,OAAO,CAAC,CAAC,CAAC,OAAO,EAAE,EAAC,cAAc,CAAC,CAAC,CAAC,cAAc,EAAE,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,gBAAgB,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,UAAU,CAAC,MAAM,EAAC,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,EAAE,EAAC,CAAC,GAAC,KAAK,CAAC,SAAS,CAAC,MAAM,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,KAAK,CAAC,SAAS,CAAC,SAAS,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,IAAE,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAG,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,GAAG,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,IAAE,CAAC,GAAC,EAAE,CAAA,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,KAAI,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,GAAG,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,EAAC,CAAC,IAAE,CAAC,CAAC,gBAAgB,IAAE,CAAC,CAAC,gBAAgB,CAAC,GAAG,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,IAAE,CAAC,IAAE,CAAC,IAAE,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,CAAC,IAAI,IAAI,CAAC,CAAC,IAAI,CAAC,GAAG,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,MAAM,GAAC,CAAC,EAAC,CAAC,IAAE,CAAC,IAAE,EAAE,CAAC,GAAC,CAAC,CAAC,CAAC,GAAC,GAAG,CAAA,AAAC,EAAC,CAAC,EAAE,EAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,MAAM,GAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,IAAE,CAAC,GAAC,CAAC,CAAC,CAAC,CAAA,AAAC,CAAC,IAAG,CAAC,GAAC,CAAC,CAAC,MAAM,EAAC,CAAC,GAAC,IAAG,GAAC,CAAC,IAAE,CAAC,GAAC,IAAG,EAAC,CAAC,KAAG,CAAC,GAAC,GAAE,IAAE,CAAC,GAAC,GAAE,CAAA,AAAC,CAAA,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,GAAC,GAAE,IAAE,IAAG,GAAC,CAAC,IAAE,CAAC,GAAC,CAAC,GAAC,EAAE,CAAC,MAAM,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,EAAE,EAAE,EAAC,CAAC,IAAE,CAAC,CAAC,gBAAgB,IAAE,CAAC,CAAC,gBAAgB,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,MAAM,CAAC,EAAC,CAAC,GAAC,IAAI,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,IAAE,CAAC,CAAC,gBAAgB,IAAE,CAAC,CAAC,gBAAgB,CAAC,MAAM,CAAC,CAAC,CAAC,EAAC,MAAM,EAAC,CAAC,CAAC,EAAC,CAAC,GAAC,IAAI,CAAA,CAAC,KAAK,CAAC,UAAU,CAAC,CAAC,EAAC,MAAM,EAAC,CAAC,CAAC,EAAC,KAAK,CAAC,UAAU,CAAC,CAAC,EAAC,SAAS,EAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,GAAC,CAAC,GAAC,IAAI,EAAC,CAAC,CAAC,aAAa,GAAC,CAAC,CAAC,gBAAgB,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,KAAK,CAAC,GAAG,CAAC,YAAY,CAAC,CAAC,CAAC,OAAO,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAM,CAAC,CAAC,GAAC,CAAC,CAAA,GAAE,CAAC,CAAA,CAAC,IAAI,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,iBAAiB,EAAE,EAAC,CAAC,CAAC,EAAE,CAAC,CAAC,GAAC,KAAK,CAAC,SAAS,CAAC,WAAW,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,UAAU,EAAC,UAAU,CAAC,KAAK,CAAC,UAAU,CAAC,CAAC,EAAC,WAAW,EAAC,CAAC,CAAC,EAAC,KAAK,CAAC,UAAU,CAAC,CAAC,EAAC,MAAM,EAAC,CAAC,CAAC,EAAC,KAAK,CAAC,UAAU,CAAC,CAAC,EAAC,SAAS,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,GAAC,CAAC,GAAC,CAAC,GAAC,IAAI,CAAA,CAAC,CAAC,CAAA,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,UAAU,CAAC,iBAAiB,EAAC,CAAC,QAAQ,EAAC,QAAQ,EAAC,UAAU,EAAC,YAAY,EAAC,UAAU,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,CAAC,IAAE,CAAC,CAAA,KAAI,CAAC,GAAC,IAAI,EAAC,CAAC,EAAE,CAAC,GAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,EAAE,CAAC,EAAE,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAA,EAAG,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,CAAA,AAAC,CAAA,EAAG,CAAC,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,AAAC,CAAA,AAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,IAAE,EAAE,CAAC,CAAC,OAAO,CAAC,MAAM,GAAC,CAAC,CAAA,AAAC,CAAC,CAAC,IAAG,IAAI,KAAG,CAAC,KAAG,CAAC,GAAC,QAAQ,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,OAAO,EAAC,EAAE,CAAC,CAAA,AAAC,EAAC,KAAK,CAAC,QAAQ,CAAC,SAAS,EAAE,IAAE,GAAG,KAAG,KAAK,CAAC,QAAQ,CAAC,OAAO,EAAE,IAAE,CAAC,KAAG,CAAC,CAAC,SAAS,KAAG,CAAC,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,cAAc,EAAE,CAAA,AAAC,EAAC,CAAC,GAAC,QAAQ,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,OAAO,EAAC,EAAE,CAAC,GAAC,CAAC,EAAC,CAAC,IAAE,CAAC,GAAC,CAAC,IAAE,CAAC,KAAG,CAAC,CAAC,SAAS,CAAA,EAAC,QAAO,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,IAAE,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAC,QAAQ,CAAC,CAAC,GAAC,CAAC,EAAC,EAAE,CAAC,CAAC,EAAC,MAAK,CAAC,KAAG,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,CAAA,CAAC,GAAC,CAAC,IAAE,CAAC,KAAG,CAAC,CAAC,SAAS,IAAE,CAAC,CAAC,KAAG,CAAC,GAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,cAAc,EAAE,EAAC,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAC,CAAC,CAAA,GAAE,CAAC,EAAC,EAAE,CAAC,CAAC,EAAC,CAAC,CAAC,IAAE,CAAC,GAAC,CAAC,EAAE,CAAC,GAAC,CAAC,CAAC,EAAC,KAAK,CAAC,qBAAqB,CAAC,CAAC,CAAC,CAAA,CAAE,CAAC,IAAE,CAAC,GAAC,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,EAAC,KAAK,CAAC,qBAAqB,CAAC,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,KAAG,CAAC,CAAC,MAAM,CAAC,SAAS,IAAE,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,GAAG,CAAC,SAAS,CAAC,GAAC,aAAa,GAAC,CAAC,GAAC,KAAK,EAAC,CAAC,GAAC,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,GAAC,CAAC,CAAC,IAAI,CAAC,CAAC,QAAQ,CAAC,WAAW,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,QAAQ,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,EAAC,MAAM,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,aAAa,CAAC,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,qBAAqB,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,SAAS,CAAC,GAAG,CAAC,YAAY,CAAC,EAAC,CAAC,EAAE,CAAA,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,SAAS,CAAC,MAAM,CAAC,YAAY,CAAC,EAAC,CAAC,EAAE,EAAC,CAAC,EAAE,CAAA,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAM,EAAE,CAAC,GAAC,CAAC,GAAC,CAAC,GAAC,CAAC,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,GAAG,EAAE,EAAC,CAAC,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAC,CAAC,CAAC,GAAC,CAAC,CAAA,GAAE,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,IAAE,CAAC,GAAC,CAAC,CAAA,AAAC,GAAC,CAAC,EAAC,EAAE,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,KAAK,CAAC,qBAAqB,CAAC,CAAC,CAAC,EAAE,CAAC,GAAC,CAAC,IAAE,CAAC,GAAC,CAAC,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,IAAE,CAAC,EAAE,CAAA,AAAC,CAAA,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,GAAG,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,KAAG,CAAC,CAAC,KAAK,CAAC,EAAE,CAAC,KAAK,KAAK,CAAC,qBAAqB,CAAC,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,KAAK,CAAC,GAAG,CAAC,WAAW,EAAC,CAAC,EAAC,CAAC,CAAC,EAAC,KAAK,CAAC,GAAG,CAAC,UAAU,EAAC,CAAC,EAAC,CAAC,CAAC,EAAC,KAAK,CAAC,GAAG,CAAC,QAAQ,EAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,GAAC,IAAI,EAAC,CAAC,GAAC,IAAI,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,GAAG,CAAC,QAAQ,CAAC,EAAC,CAAC,CAAC,UAAU,EAAE,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,WAAW,CAAC,mCAAmC,CAAC,EAAC,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,EAAC,GAAG,CAAC,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,GAAG,CAAC,YAAY,CAAC,EAAC,CAAC,CAAC,UAAU,EAAE,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,MAAM,CAAC,WAAW,CAAC,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,GAAG,CAAC,WAAW,CAAC,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,GAAG,CAAC,iBAAiB,CAAC,CAAA,CAAC,IAAI,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,IAAI,EAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,EAAC,CAAC,CAAC,IAAI,EAAC,CAAC,CAAC,IAAI,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,IAAE,CAAC,CAAC,IAAI,CAAC,aAAa,EAAC,wBAAwB,CAAC,EAAC,CAAC,CAAC,WAAW,GAAC,CAAC,CAAC,CAAC,CAAC,CAAC,cAAc,CAAC,IAAE,MAAM,IAAE,CAAC,CAAC,OAAO,EAAC,CAAC,CAAC,QAAQ,GAAC,CAAC,CAAC,CAAC,CAAC,cAAc,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,WAAW,CAAC,GAAG,EAAC,WAAW,CAAC,GAAG,EAAC,cAAc,CAAC,GAAG,EAAC,cAAc,CAAC,GAAG,EAAC,OAAO,CAAC,GAAG,EAAC,sBAAsB,CAAC,GAAG,EAAC,UAAU,CAAC,YAAY,EAAC,UAAU,CAAC,YAAY,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,wBAAwB,EAAC,UAAU,CAAC,CAAC,CAAC,UAAU,CAAC,KAAK,CAAC,qBAAqB,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,UAAU,CAAC,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,GAAC,UAAU,CAAC,IAAG,CAAC,GAAC,CAAC,CAAC,MAAM,EAAE,CAAC,MAAM,EAAE,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,MAAM,EAAE,CAAC,CAAC,CAAC,EAAC,EAAE,CAAC,IAAE,CAAC,CAAC,SAAS,CAAC,QAAQ,CAAC,cAAc,CAAC,IAAE,CAAC,IAAE,CAAC,CAAC,SAAS,CAAC,QAAQ,CAAC,QAAQ,CAAC,CAAA,AAAC,CAAA,CAAC,MAAM,IAAI,KAAK,CAAC,gEAAgE,CAAC,CAAC,KAAK,CAAC,EAAE,CAAC,WAAW,EAAC,CAAC,EAAC,CAAC,CAAC,EAAC,KAAK,CAAC,EAAE,CAAC,UAAU,EAAC,CAAC,EAAC,CAAC,CAAC,EAAC,KAAK,CAAC,EAAE,CAAC,QAAQ,EAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,UAAU,EAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,sBAAsB,GAAC,UAAU,CAAC,OAAM,CAAC,QAAQ,CAAC,CAAC,EAAC,UAAU,CAAC,CAAC,EAAC,KAAK,CAAC,CAAC,EAAC,IAAI,CAAC,CAAC,EAAC,IAAI,CAAC,CAAC,EAAC,IAAI,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,iBAAiB,GAAC,CAAC,EAAC,CAAC,CAAC,gBAAgB,GAAC,UAAU,CAAC,OAAO,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,iBAAiB,GAAC,UAAU,CAAC,OAAO,CAAC,CAAA,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,UAAU,CAAC,cAAc,EAAC,CAAC,QAAQ,EAAC,mBAAmB,EAAC,UAAU,EAAC,SAAS,EAAC,WAAW,EAAC,WAAW,EAAC,sBAAsB,EAAC,eAAe,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,SAAS,GAAC,CAAC,EAAC,CAAC,CAAC,kBAAkB,GAAC,CAAC,EAAC,CAAC,CAAC,QAAQ,GAAC,UAAU,CAAC,OAAM,CAAC,CAAC,CAAC,CAAC,eAAe,CAAA,CAAC,CAAC,IAAI,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,OAAO,GAAC,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,GAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,QAAQ,EAAE,CAAC,CAAC,CAAC,UAAU,GAAC,IAAI,KAAK,CAAC,KAAK,CAAC,YAAY,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,GAAC,IAAI,KAAK,CAAC,KAAK,CAAC,MAAM,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,MAAM,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,MAAM,EAAE,CAAC,CAAC,CAAA,CAAE,IAAI,CAAC,yBAAyB,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,iBAAiB,CAAC,CAAC,EAAC,CAAC,CAAC,cAAc,EAAC,UAAU,CAAC,OAAO,CAAC,CAAC,aAAa,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,IAAE,KAAK,CAAC,QAAQ,CAAC,KAAK,CAAC,UAAU,CAAC,CAAC,CAAC,OAAO,KAAG,CAAC,CAAC,OAAO,CAAC,QAAQ,GAAC,CAAC,CAAC,EAAC,KAAK,CAAC,QAAQ,CAAC,SAAS,EAAE,KAAG,CAAC,CAAC,OAAO,CAAC,QAAQ,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,YAAY,GAAC,IAAG,CAAA,AAAC,CAAA,AAAC,CAAA,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,OAAO,CAAC,IAAI,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,OAAO,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,QAAQ,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,WAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,aAAa,IAAE,CAAC,CAAA,CAAE,MAAM,IAAE,EAAE,CAAC,CAAC,CAAC,SAAS,IAAE,CAAC,CAAC,SAAS,CAAC,CAAC,KAAK,CAAC,CAAC,EAAC,SAAS,CAAC,CAAC,CAAC,SAAS,IAAE,CAAC,EAAC,UAAU,CAAC,CAAC,CAAC,UAAU,IAAE,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,QAAQ,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,UAAU,EAAC,UAAU,CAAC,CAAC,EAAE,EAAC,CAAC,IAAE,CAAC,CAAC,SAAS,IAAE,CAAC,CAAC,SAAS,EAAE,EAAC,OAAO,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,QAAQ,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,QAAQ,EAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,UAAU,GAAC,CAAC,GAAC,CAAC,CAAC,kBAAkB,GAAC,CAAC,CAAC,EAAE,GAAC,CAAC,CAAC,kBAAkB,CAAC,EAAE,GAAC,CAAC,GAAC,CAAC,CAAC,QAAQ,GAAC,CAAC,GAAC,IAAI,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,UAAU,CAAC,CAAC,IAAE,CAAC,CAAC,GAAG,IAAE,CAAC,CAAC,GAAG,EAAE,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,aAAa,GAAC,UAAU,CAAC,OAAO,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,iBAAiB,GAAC,UAAU,CAAC,OAAO,CAAC,CAAC,SAAS,EAAE,CAAA,CAAC,EAAC,CAAC,CAAC,MAAM,GAAC,UAAU,CAAC,OAAO,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC,IAAE,CAAC,CAAC,cAAc,CAAC,eAAe,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,SAAS,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,EAAE,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,YAAY,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,EAAE,CAAC,IAAI,CAAC,UAAU,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,YAAY,EAAE,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,IAAI,EAAC,CAAC,CAAC,GAAG,EAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,QAAQ,GAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,EAAE,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,MAAM,GAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,EAAE,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,MAAM,GAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,EAAE,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,QAAQ,GAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,EAAE,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,YAAY,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,EAAE,CAAC,IAAI,CAAC,UAAU,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,IAAI,EAAE,EAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,CAAC,cAAc,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,IAAE,CAAC,CAAC,CAAC,OAAO,KAAK,CAAC,CAAC,QAAQ,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAG,IAAI,KAAG,CAAC,KAAG,CAAC,IAAE,CAAC,CAAC,UAAU,CAAA,AAAC,EAAC,IAAI,KAAG,CAAC,KAAG,CAAC,IAAE,CAAC,CAAC,SAAS,CAAA,AAAC,EAAC,CAAC,GAAC,CAAC,CAAC,YAAY,CAAC,MAAM,CAAC,CAAC,UAAU,IAAE,CAAC,CAAC,OAAO,CAAC,UAAU,IAAE,CAAC,CAAC,YAAY,EAAE,CAAC,CAAC,QAAQ,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,YAAY,GAAC,CAAC,CAAC,MAAM,EAAC,CAAC,CAAC,gBAAgB,GAAC,SAAS,CAAC,CAAC,CAAC,IAAI,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,UAAU,CAAC,MAAM,EAAC,CAAC,EAAE,EAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,YAAY,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,aAAa,GAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,GAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,YAAY,IAAE,EAAE,CAAC,CAAC,CAAC,qBAAqB,CAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,UAAU,CAAC,iBAAiB,EAAC,CAAC,QAAQ,EAAC,QAAQ,EAAC,wBAAwB,EAAC,gBAAgB,EAAC,YAAY,EAAC,eAAe,EAAC,sBAAsB,EAAC,qBAAqB,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,CAAC,cAAc,CAAC,CAAC,CAAC,gBAAgB,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,cAAc,IAAE,CAAC,CAAC,gBAAgB,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,cAAc,GAAC,CAAC,CAAA,CAAC,IAAI,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,IAAI,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,GAAC,CAAC,EAAC,CAAC,CAAC,UAAU,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,GAAC,CAAC,CAAC,IAAI,EAAC,CAAC,CAAC,KAAK,GAAC,CAAC,CAAC,KAAK,EAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,OAAO,CAAC,EAAC,CAAC,CAAC,cAAc,GAAC,CAAC,CAAC,cAAc,IAAE,EAAE,EAAC,CAAC,CAAC,eAAe,CAAC,CAAC,CAAC,MAAM,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,UAAU,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,OAAO,GAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,MAAM,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,OAAO,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAA,CAAC,CAAA,AAAC,CAAA,CAAC,EAAC,CAAC,CAAC,UAAU,GAAC,UAAU,CAAC,OAAO,CAAC,CAAC,aAAa,EAAE,GAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,WAAW,GAAC,UAAU,CAAC,OAAO,CAAC,CAAC,aAAa,EAAE,GAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,UAAU,GAAC,SAAS,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,IAAE,CAAC,CAAC,IAAI,CAAC,SAAS,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,aAAa,EAAE,CAAC,CAAC,KAAG,SAAS,CAAC,MAAM,KAAG,CAAC,GAAC,CAAC,IAAE,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,OAAO,CAAC,eAAe,EAAE,EAAC,CAAC,CAAC,cAAc,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,WAAW,GAAC,SAAS,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,IAAE,CAAC,CAAC,KAAK,CAAC,SAAS,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,aAAa,EAAE,CAAC,CAAC,KAAG,SAAS,CAAC,MAAM,KAAG,CAAC,GAAC,CAAC,IAAE,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,OAAO,CAAC,eAAe,EAAE,EAAC,CAAC,CAAC,cAAc,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,GAAC,SAAS,CAAC,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,CAAC,CAAC,WAAW,EAAE,CAAC,CAAC,CAAC,UAAU,EAAE,CAAA,CAAC,EAAC,CAAC,CAAC,KAAK,GAAC,UAAU,CAAC,CAAC,CAAC,cAAc,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,aAAa,GAAC,UAAU,CAAC,OAAO,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,OAAO,CAAC,aAAa,EAAE,IAAE,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,YAAY,GAAC,UAAU,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,aAAa,EAAE,CAAC,OAAO,CAAC,IAAE,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC,GAAC,CAAC,CAAC,KAAK,CAAC,KAAK,CAAA,CAAC,EAAC,CAAC,CAAC,MAAM,GAAC,UAAU,CAAC,OAAO,CAAC,KAAG,CAAC,CAAC,aAAa,EAAE,CAAA,CAAC,EAAC,CAAC,CAAC,iBAAiB,GAAC,UAAU,CAAC,OAAO,GAAG,GAAC,CAAC,CAAC,YAAY,EAAE,CAAA,CAAC,EAAC,CAAC,CAAC,cAAc,GAAC,SAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,GAAC,GAAG,CAAC,CAAC,CAAC,IAAI,IAAE,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,IAAI,CAAC,KAAK,GAAC,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,IAAE,CAAC,GAAC,CAAC,IAAE,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,KAAK,CAAC,KAAK,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,WAAW,CAAC,CAAC,KAAG,CAAC,EAAC,WAAW,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,UAAU,GAAC,SAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,IAAI,IAAE,CAAC,CAAC,IAAI,CAAC,KAAK,IAAE,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,KAAK,IAAE,CAAC,CAAC,KAAK,CAAC,KAAK,IAAE,CAAC,CAAC,OAAM,CAAC,CAAC,CAAC,IAAI,IAAE,CAAC,CAAC,IAAI,CAAC,SAAS,IAAE,EAAE,CAAC,GAAC,CAAC,CAAA,AAAC,CAAA,KAAI,CAAC,CAAC,KAAK,IAAE,CAAC,CAAC,KAAK,CAAC,SAAS,IAAE,EAAE,CAAC,GAAC,CAAC,CAAA,AAAC,CAAA,AAAC,CAAC,CAAC,IAAE,CAAC,GAAC,CAAC,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,aAAa,CAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,GAAC,CAAC,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,aAAa,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,OAAO,CAAC,aAAa,CAAC,CAAC,CAAC,EAAC,MAAK,CAAC,IAAE,CAAC,EAAE,CAAC,GAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,KAAG,CAAC,CAAC,KAAK,IAAE,CAAC,CAAC,KAAK,CAAC,QAAQ,IAAE,CAAC,CAAC,KAAK,CAAC,QAAQ,EAAE,EAAC,CAAC,CAAC,IAAI,IAAE,CAAC,CAAC,IAAI,CAAC,OAAO,IAAE,CAAC,CAAC,IAAI,CAAC,OAAO,EAAE,CAAA,AAAC,CAAA,EAAG,CAAC,GAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,KAAK,IAAE,CAAC,CAAC,KAAK,CAAC,OAAO,IAAE,CAAC,CAAC,KAAK,CAAC,OAAO,EAAE,EAAC,CAAC,CAAC,IAAI,IAAE,CAAC,CAAC,IAAI,CAAC,QAAQ,IAAE,CAAC,CAAC,IAAI,CAAC,QAAQ,EAAE,CAAA,CAAC,AAAC,CAAA,AAAC,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,aAAa,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,UAAU,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,eAAe,EAAE,EAAC,CAAC,GAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,YAAY,EAAE,CAAC,GAAG,CAAC,KAAG,CAAC,CAAC,OAAO,KAAK,CAAC,CAAC,cAAc,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,GAAE,EAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,SAAS,EAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,SAAS,CAAC,CAAC,CAAC,cAAc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uBAAuB,GAAC,SAAS,CAAC,CAAC,CAAC,QAAO,SAAS,CAAC,MAAM,KAAG,CAAC,GAAC,CAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAA,CAAA,CAAC,EAAC,CAAC,CAAC,cAAc,GAAC,UAAU,CAAC,OAAM,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,WAAW,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,IAAE,CAAC,CAAC,IAAI,CAAC,SAAS,IAAE,CAAC,CAAC,KAAK,IAAE,CAAC,CAAC,KAAK,CAAC,SAAS,CAAA,KAAI,CAAC,CAAC,KAAK,EAAE,EAAC,CAAC,GAAC,CAAC,EAAC,CAAC,CAAC,IAAI,IAAE,CAAC,CAAC,IAAI,CAAC,SAAS,CAAC,CAAC,CAAC,OAAO,CAAC,aAAa,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,IAAE,CAAC,CAAC,KAAK,CAAC,SAAS,IAAE,CAAC,CAAC,OAAO,CAAC,cAAc,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,KAAK,CAAC,mBAAmB,EAAC,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,EAAC,CAAC,CAAC,mBAAmB,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,CAAC,EAAC,gBAAgB,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,QAAQ,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,KAAG,CAAC,IAAE,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,IAAI,EAAC,CAAC,GAAC,IAAI,EAAC,CAAC,GAAC,IAAI,CAAA,AAAC,CAAA,CAAC,EAAC,CAAC,CAAC,WAAW,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,WAAW,KAAG,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,OAAO,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,KAAK,EAAE,CAAC,GAAC,CAAC,CAAC,OAAO,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,KAAK,EAAC,CAAC,GAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,IAAE,IAAI,CAAC,GAAG,CAAC,CAAC,GAAC,CAAC,CAAC,GAAC,CAAC,CAAC,cAAc,KAAG,CAAC,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,gBAAgB,EAAE,EAAC,CAAC,GAAC,CAAC,CAAC,aAAa,EAAE,CAAA,AAAC,EAAC,CAAC,KAAG,CAAC,CAAC,UAAU,CAAC,CAAC,IAAE,CAAC,GAAC,CAAC,CAAA,AAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,AAAC,CAAA,AAAC,CAAA,CAAC,EAAC,CAAC,CAAC,cAAc,GAAC,SAAS,CAAC,CAAC,CAAC,QAAO,SAAS,CAAC,MAAM,KAAG,CAAC,CAAC,WAAW,GAAC,CAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,WAAW,CAAA,CAAA,CAAC,EAAC,CAAC,CAAC,aAAa,GAAC,EAAE,EAAC,CAAC,CAAC,oBAAoB,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,iBAAiB,GAAC,SAAS,CAAC,CAAC,CAAC,QAAO,SAAS,CAAC,MAAM,KAAG,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,GAAC,CAAC,EAAE,CAAC,CAAC,aAAa,GAAC,CAAC,EAAC,CAAC,CAAC,oBAAoB,GAAC,CAAC,CAAC,CAAA,CAAE,CAAC,CAAC,oBAAoB,GAAC,CAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,oBAAoB,CAAA,CAAA,CAAC,EAAC,CAAC,CAAC,iBAAiB,GAAC,SAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,oBAAoB,IAAE,CAAC,CAAC,CAAC,MAAM,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,UAAU,IAAE,CAAC,CAAC,OAAO,CAAC,UAAU,CAAC,MAAM,IAAE,CAAC,CAAC,OAAO,CAAC,UAAU,CAAC,MAAM,CAAC,KAAK,EAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,IAAE,CAAC,CAAC,aAAa,IAAE,CAAC,IAAE,CAAC,CAAC,OAAO,CAAC,OAAO,CAAC,WAAW,GAAC,CAAC,CAAC,aAAa,EAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,WAAW,EAAE,IAAE,EAAE,CAAC,OAAO,CAAC,CAAC,SAAS,KAAG,CAAC,CAAC,SAAS,CAAA,CAAC,OAAM,CAAC,CAAC,CAAC,WAAW,IAAE,CAAC,CAAC,MAAM,EAAE,CAAA,IAAG,CAAC,IAAE,CAAC,CAAC,CAAC,OAAO,CAAC,QAAQ,CAAC,gBAAgB,IAAE,CAAC,IAAE,CAAC,CAAC,CAAC,MAAM,CAAC,OAAO,CAAC,KAAK,CAAC,qCAAqC,CAAC,IAAE,CAAC,CAAC,CAAC,MAAM,CAAC,iBAAiB,IAAE,EAAE,CAAC,CAAC,MAAM,CAAC,OAAO,CAAC,CAAC,CAAC,MAAM,CAAC,OAAO,CAAC,aAAa,CAAC,MAAM,IAAE,CAAC,CAAC,MAAM,CAAC,YAAY,CAAC,qBAAqB,CAAC,CAAA,AAAC,CAAA,CAAC,EAAC,CAAC,CAAC,yBAAyB,GAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,IAAI,CAAC,CAAC,EAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,UAAU,CAAC,OAAO,CAAC,KAAG,CAAC,CAAC,aAAa,EAAE,CAAA,CAAC,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,EAAE,EAAC,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,wBAAwB,CAAC,CAAC,EAAC,CAAC,CAAC,QAAQ,CAAC,CAAA,AAAC,CAAA,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,iBAAiB,CAAC,CAAC,EAAC,CAAC,CAAC,cAAc,EAAC,UAAU,CAAC,OAAO,CAAC,CAAC,aAAa,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,UAAU,EAAC,UAAU,CAAC,CAAC,EAAE,EAAC,CAAC,EAAE,EAAC,CAAC,CAAC,MAAM,GAAC,IAAI,EAAC,CAAC,CAAC,OAAO,KAAG,CAAC,CAAC,OAAO,CAAC,OAAO,GAAC,IAAI,EAAC,CAAC,CAAC,OAAO,GAAC,IAAI,CAAA,AAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,CAAC,KAAK,CAAC,GAAG,CAAC,EAAC,KAAK,CAAC,CAAC,KAAK,CAAC,GAAG,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAA,SAAS,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,QAAQ,CAAC,aAAa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kBAAkB,EAAC,CAAC,CAAC,gBAAgB,EAAC,CAAC,CAAC,OAAO,EAAC,CAAC,CAAC,YAAY,EAAC,CAAC,CAAC,OAAO,EAAC,CAAC,CAAC,MAAM,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,EAAC,EAAE,CAAC,eAAe,EAAC,EAAE,CAAC,kBAAkB,EAAC,CAAC,CAAC,QAAQ,EAAC,EAAE,CAAC,kBAAkB,EAAC,EAAE,CAAC,mBAAmB,EAAC,CAAC,CAAC,MAAM,EAAC,EAAE,CAAC,gBAAgB,EAAC,EAAE,CAAC,aAAa,EAAC,EAAE,CAAC,cAAc,EAAC,CAAC,CAAC,WAAW,EAAC,CAAC,CAAC,QAAQ,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,mBAAmB,EAAC,EAAE,CAAC,WAAW,EAAC,IAAI,CAAC,QAAQ,EAAC,EAAE,CAAC,CAAC,EAAC,GAAG,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAC,EAAE,CAAC,CAAC,EAAC,IAAI,CAAC,CAAC,CAAC,EAAE,CAAC,YAAS,CAAC,EAAC,CAAC,CAAC,CAAC,OAAM,CAAC,EAAE,CAAC,KAAK,IAAE,CAAC,CAAC,EAAE,CAAC,EAAE,EAAC,EAAE,CAAC,KAAK,IAAE,CAAC,CAAC,EAAE,CAAC,EAAE,EAAC,CAAC,CAAC,CAAC,GAAC,UAAU,IAAE,EAAE,GAAC,CAAC,IAAE,CAAC,GAAC,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,CAAA,CAAC,AAAC,GAAC,GAAG,EAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,aAAU,CAAC,OAAM,CAAC,EAAE,CAAC,CAAC,EAAC,GAAG,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,uCAAuC,EAAC,CAAC,CAAC,EAAC,EAAE,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAC,EAAE,CAAC,GAAG,EAAC,EAAE,CAAC,EAAE,EAAC,CAAC,CAAC,EAAE,EAAC,EAAE,CAAC,EAAE,EAAC,EAAE,CAAC,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,GAAG,CAAC,CAAC,EAAC,WAAW,CAAC,CAAC,EAAC,OAAO,CAAC,CAAC,EAAE,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,YAAS,CAAC,CAAC,CAAC,OAAM,CAAC,EAAE,CAAC,EAAE,GAAC,IAAI,CAAC,GAAG,CAAC,CAAC,GAAC,IAAI,CAAC,EAAE,GAAC,CAAC,GAAC,CAAC,CAAC,EAAC,EAAE,CAAC,EAAE,GAAC,IAAI,CAAC,GAAG,CAAC,CAAC,GAAC,IAAI,CAAC,EAAE,GAAC,CAAC,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,aAAU,CAAC,OAAM,CAAC,EAAE,CAAC,GAAG,EAAC,GAAG,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,iBAAiB,EAAC,CAAC,CAAC,EAAC,EAAE,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,YAAS,CAAC,CAAC,CAAC,OAAM,CAAC,CAAC,CAAC,CAAC,EAAC,EAAE,CAAC,EAAE,GAAC,IAAI,CAAC,GAAG,CAAC,CAAC,GAAC,IAAI,CAAC,EAAE,GAAC,CAAC,GAAC,CAAC,CAAC,EAAC,EAAE,CAAC,EAAE,GAAC,IAAI,CAAC,GAAG,CAAC,CAAC,GAAC,IAAI,CAAC,EAAE,GAAC,CAAC,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,EAAE,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,aAAU,CAAC,OAAM,CAAC,EAAE,CAAC,cAAc,EAAC,GAAG,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,yBAAy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aAAU,CAAC,OAAM,CAAC,EAAE,CAAC,cAAc,EAAC,GAAG,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,qBAAqB,EAAC,CAAC,CAAC,EAAC,EAAE,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,EAAE,CAAC,aAAU,CAAC,OAAM,CAAC,EAAE,CAAC,GAAG,EAAC,GAAG,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,eAAe,EAAC,CAAC,CAAC,EAAC,EAAE,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,EAAC,EAAE,CAAC,CAAC,EAAC,IAAI,CAAC,CAAC,CAAC,EAAE,CAAC,YAAS,CAAC,CAAC,CAAC,OAAM,CAAC,EAAE,CAAC,EAAE,GAAC,EAAE,GAAC,CAAC,EAAC,EAAE,CAAC,EAAE,GAAC,EAAE,GAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,aAAU,CAAC,OAAM,CAAC,EAAE,CAAC,IAAI,EAAC,GAAG,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,gBAAgB,EAAC,CAAC,CAAC,EAAC,EAAE,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,EAAE,CAAC,aAAU,CAAC,OAAM,CAAC,EAAE,CAAC,IAAI,EAAC,GAAG,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,gBAAgB,EAAC,CAAC,CAAC,EAAC,EAAE,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,EAAE,CAAC,aAAU,CAAC,OAAM,CAAC,EAAE,CAAC,CAAC,EAAC,GAAG,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,cAAc,EAAC,CAAC,CAAC,EAAC,EAAE,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,WAAW,CAAC,SAAS,EAAC,EAAE,CAAC,CAAC,EAAC,MAAM,CAAC,CAAC,CAAC,EAAE,CAAC,YAAS,CAAC,CAAC,CAAC,OAAM,CAAC,EAAE,CAAC,EAAE,EAAC,EAAE,CAAC,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,aAAU,CAAC,OAAM,CAAC,EAAE,CAAC,GAAG,EAAC,KAAK,CAAC,CAAC,CAAC,GAAC,CAAC,GAAC,GAAG,EAAC,GAAG,CAAC,IAAI,EAAC,CAAC,CAAC,MAAM,EAAC,QAAQ,CAAC,KAAK,EAAC,UAAU,CAAC,eAAe,EAAC,QAAQ,CAAC,QAAQ,EAAC,EAAE,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,EAAE,CAAC,aAAU,CAAC,OAAM,CAAC,EAAE,CAAC,CAAC,EAAC,KAAK,CAAC,CAAC,CAAC,GAAC,CAAC,GAAC,GAAG,EAAC,GAAG,CAAC,IAAI,EAAC,CAAC,CAAC,WAAW,EAAC,EAAE,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,MAAM,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,cAAc,CAAC,CAAC,CAAC,EAAE,CAAC,KAAK,EAAC,aAAa,CAAC,gBAAgB,EAAC,EAAE,CAAC,EAAE,EAAC,EAAE,CAAC,EAAE,EAAC,EAAE,CAAC,CAAC,EAAC,EAAE,CAAC,EAAE,EAAC,IAAI,CAAC,CAAC,CAAC,MAAM,CAAC,GAAE,EAAC,OAAO,CAAC,OAAO,CAAC,EAAC,CAAC,MAAM,CAAC,CAAC,EAAC,OAAO,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAC,EAAE,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,IAAI,CAAC,CAAC,CAAC,MAAM,CAAC,WAAW,EAAC,CAAC,CAAC,uCAAuC,CAAC,EAAC,CAAC,CAAC,CAAC,uCAAuC,CAAC,CAAC,EAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,OAAO,CAAC,iBAAS,CAAC,CAAC,CAAC,SAAS,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,GAAG,EAAE,GAAC,CAAC,EAAC,GAAG,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,GAAC,EAAE,GAAC,CAAC,EAAC,CAAC,CAAC,GAAG,GAAC,GAAG,GAAC,CAAC,CAAC,CAAC,GAAC,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,EAAE,EAAC,CAAC,GAAC,GAAG,GAAE,CAAC,EAAE,GAAC,CAAC,EAAC,CAAC,GAAC,GAAG,GAAC,CAAC,CAAA,AAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,GAAG,EAAC,CAAC,EAAE,EAAC,CAAC,EAAE,EAAC,CAAC,GAAG,EAAC,EAAE,EAAC,CAAC,GAAG,EAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,IAAI,EAAC,IAAI,CAAC,GAAG,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAC,GAAG,CAAC,EAAC,GAAG,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,IAAI,EAAC,IAAI,CAAC,GAAG,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAC,GAAG,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,GAAG,EAAC,QAAQ,GAAC,CAAC,GAAC,gBAAgB,GAAC,CAAC,GAAC,aAAa,GAAC,CAAC,GAAC,SAAS,CAAC,EAAC,CAAC,IAAE,GAAG,EAAC,CAAC,GAAC,GAAG,KAAG,CAAC,GAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,CAAC,EAAC,GAAG,EAAC,SAAS,GAAC,CAAC,GAAC,SAAS,CAAC,EAAC,CAAC,IAAE,CAAC,KAAG,CAAC,EAAE,EAAC,CAAC,GAAC,CAAC,KAAG,CAAC,GAAC,CAAC,CAAA,AAAC,EAAC,CAAC,GAAC,IAAI,CAAC,GAAG,EAAE,CAAA,AAAC,EAAC,CAAC,CAAC,qBAAqB,CAAC,CAAC,CAAC,CAAA,CAAC,IAAI,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,GAAG,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,QAAQ,CAAC,CAAC,OAAO,UAAU,CAAC,CAAC,GAAC,IAAI,CAAC,GAAG,EAAE,EAAC,CAAC,EAAE,CAAA,CAAC,CAAA,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,eAAe,EAAC,CAAC,UAAU,EAAC,QAAQ,EAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,IAAI,GAAC,UAAU,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,cAAc,KAAG,CAAC,CAAC,QAAQ,CAAC,QAAQ,EAAE,KAAG,CAAC,GAAC,SAAS,CAAA,AAAC,EAAC,CAAC,GAAC,CAAC,CAAC,IAAI,IAAE,CAAC,IAAE,CAAC,CAAC,QAAQ,CAAC,QAAQ,EAAE,EAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,KAAG,CAAC,GAAC,KAAK,EAAC,CAAC,GAAC,CAAC,CAAC,GAAG,CAAA,AAAC,CAAC,IAAI,CAAC,CAAC,QAAQ,CAAC,aAAa,CAAC,KAAK,CAAC,CAAC,QAAO,CAAC,CAAC,KAAK,EAAC,CAAC,OAAO,CAAC,WAAW,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,SAAS,CAAC,EAAC,IAAI,CAAC,KAAK,EAAE,EAAC,CAAC,CAAA,CAAA,CAAC,EAAC,IAAI,CAAC,KAAK,GAAC,UAAU,CAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAA,CAAC,CAAA,CAAC,CAAC,CAAC,CAAA,CAAC,CAAA,CAAC,KAAK,CAAC,EAAC,CAAC,CAAC,UAAU,CAAC,WAAW,EAAC,CAAC,QAAQ,EAAC,eAAe,EAAC,QAAQ,EAAC,WAAW,EAAC,QAAQ,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,MAAM,GAAC,CAAC,EAAC,IAAI,CAAC,gBAAgB,GAAC,UAAU,CAAC,OAAO,CAAC,CAAC,IAAI,IAAE,CAAC,KAAG,CAAC,CAAC,IAAI,EAAE,CAAC,OAAO,CAAC,CAAC,CAAC,IAAI,CAAC,OAAO,CAAC,IAAI,EAAC,EAAE,CAAC,CAAC,OAAO,CAAC,KAAK,EAAC,EAAE,CAAC,CAAC,CAAA,CAAC,EAAC,IAAI,CAAC,gBAAgB,GAAC,UAAU,CAAC,OAAO,CAAC,CAAC,MAAM,IAAE,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,MAAM,CAAC,KAAK,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,IAAI,CAAC,mBAAmB,GAAC,UAAU,CAAC,OAAO,IAAI,CAAC,WAAW,IAAE,CAAC,CAAC,qBAAqB,CAAC,IAAI,CAAC,WAAW,CAAC,CAAA,CAAC,EAAC,IAAI,CAAC,eAAe,GAAC,UAAU,CAAC,OAAO,IAAI,CAAC,gBAAgB,EAAE,IAAE,IAAI,CAAC,gBAAgB,EAAE,IAAE,IAAI,CAAC,mBAAmB,EAAE,CAAA,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,UAAU,CAAC,YAAY,EAAC,CAAC,QAAQ,EAAC,UAAU,EAAC,eAAe,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,EAAC,CAAC,CAAC,IAAI,EAAC,CAAC,CAAC,IAAI,EAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,IAAI,GAAC,EAAE,EAAC,CAAC,CAAC,aAAa,GAAC,UAAU,CAAC,OAAO,CAAC,CAAC,IAAI,CAAC,OAAO,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,WAAW,GAAC,UAAU,CAAC,OAAO,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,mBAAmB,GAAC,UAAU,CAAC,OAAO,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,GAAG,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,eAAe,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,MAAM,GAAC,SAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,KAAG,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,YAAY,CAAC,IAAG,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAC,CAAC,KAAG,CAAC,CAAC,IAAI,CAAC,MAAM,CAAA,CAAC,CAAC,KAAI,CAAC,IAAI,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,IAAI,CAAC,MAAM,GAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,IAAI,CAAC,MAAM,CAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,QAAQ,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,YAAY,KAAG,CAAC,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,GAAC,IAAI,EAAC,CAAC,CAAC,YAAY,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,UAAU,IAAE,CAAC,CAAA,EAAG,EAAC,CAAC,CAAC,UAAU,IAAE,CAAC,CAAC,UAAU,CAAC,wBAAwB,CAAC,CAAA,AAAC,CAAA,CAAC,EAAC,CAAC,CAAC,MAAM,GAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAG,CAAC,GAAC,CAAC,EAAC,CAAC,IAAE,CAAC,CAAC,IAAI,CAAC,MAAM,CAAA,CAAC,OAAO,CAAC,GAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAA,CAAC,KAAK,CAAC,GAAC,CAAC,CAAC,IAAI,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,KAAG,SAAS,CAAC,MAAM,KAAG,CAAC,GAAC,EAAE,CAAC,CAAC,CAAC,WAAW,IAAE,CAAC,CAAC,CAAC,MAAM,CAAA,AAAC,CAAA,AAAC,EAAC,CAAC,IAAE,CAAC,CAAC,UAAU,IAAE,CAAC,CAAC,UAAU,CAAC,CAAC,IAAE,CAAC,CAAC,eAAe,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,KAAG,CAAC,KAAG,CAAC,CAAC,CAAC,CAAC,IAAI,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,EAAC,CAAC,GAAC,CAAC,EAAC,CAAC,CAAC,MAAM,IAAE,CAAC,CAAC,MAAM,CAAC,OAAO,KAAG,CAAC,CAAC,MAAM,CAAC,OAAO,CAAC,gBAAgB,GAAC,CAAC,CAAC,UAAU,CAAA,AAAC,EAAC,CAAC,CAAC,YAAY,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,QAAQ,IAAE,CAAC,CAAA,EAAG,EAAC,CAAC,IAAE,CAAC,CAAC,KAAK,CAAC,sBAAsB,EAAC,CAAC,IAAI,CAAC,KAAK,EAAC,QAAQ,CAAC,CAAC,EAAC,SAAS,CAAC,CAAC,CAAC,UAAU,EAAC,WAAW,CAAC,CAAC,CAAC,WAAW,EAAC,UAAU,CAAC,CAAC,CAAC,CAAC,CAAC,WAAW,EAAC,KAAK,CAAC,CAAC,CAAC,KAAK,EAAC,GAAG,CAAC,CAAC,CAAC,IAAI,EAAC,MAAM,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,EAAC,CAAC,CAAC,cAAc,GAAC,UAAU,CAAC,IAAI,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,IAAI,CAAC,MAAM,EAAC,CAAC,EAAE,EAAC,GAAG,CAAC,CAAC,aAAa,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,OAAM,CAAC,CAAC,CAAC,OAAM,CAAC,CAAC,CAAA,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,UAAU,CAAC,YAAY,EAAC,CAAC,QAAQ,EAAC,UAAU,EAAC,QAAQ,EAAC,UAAU,EAAC,YAAY,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,IAAE,WAAW,IAAE,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,IAAE,OAAO,CAAC,CAAC,KAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,CAAC,CAAC,cAAc,CAAC,IAAE,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,cAAc,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,cAAc,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,IAAE,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,UAAU,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,KAAG,CAAC,KAAG,CAAC,GAAC,CAAC,EAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,IAAI,IAAI,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,MAAM,EAAC,CAAC,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,GAAC,EAAE,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,EAAE,CAAC,CAAC,KAAK,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAM,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,IAAI,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,IAAI,EAAC,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,gBAAgB,EAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,eAAe,EAAE,EAAC,CAAC,GAAC,CAAC,CAAA,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,GAAC,UAAU,CAAC,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,qBAAqB,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,aAAa,CAAC,uBAAuB,CAAC,EAAC,CAAC,IAAE,CAAC,CAAC,KAAG,CAAC,CAAC,GAAG,CAAC,wBAAwB,EAAC,CAAC,CAAC,WAAW,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,uBAAuB,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,wBAAwB,EAAC,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,EAAC,CAAC,CAAC,WAAW,GAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,GAAG,CAAC,IAAE,CAAC,CAAC,CAAC,YAAY,CAAC,CAAC,CAAC,CAAC,YAAY,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,OAAO,EAAE,EAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,KAAK,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,IAAI,IAAI,CAAC,IAAI,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,KAAK,CAAC,CAAC,EAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,gBAAgB,CAAC,EAAC,WAAW,CAAC,CAAC,EAAC,cAAc,CAAC,CAAC,IAAE,IAAI,EAAC,UAAU,CAAC,CAAC,CAAC,CAAC,YAAY,CAAC,EAAC,YAAY,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,EAAE,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,UAAU,GAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAA,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,gBAAgB,EAAC,CAAC,WAAW,EAAC,SAAS,CAAC,CAAC,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,KAAK,CAAC,CAAC,CAAC,EAAC,OAAO,CAAC,CAAC,CAAC,EAAC,IAAI,CAAC,cAAS,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,WAAS,CAAC,CAAC,CAAC,EAAE,IAAE,CAAC,CAAC,KAAK,KAAG,CAAC,CAAC,MAAM,EAAE,EAAC,CAAC,CAAC,MAAM,EAAE,CAAA,AAAC,CAAA,CAAC,EAAC,CAAC,CAAC,WAAS,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,IAAE,CAAC,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,MAAM,EAAE,EAAC,CAAC,CAAC,MAAM,EAAE,CAAA,AAAC,CAAA,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,UAAU,EAAC,UAAU,CAAC,CAAC,CAAC,MAAM,EAAE,EAAC,CAAC,CAAC,MAAM,CAAC,OAAO,EAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,OAAO,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,OAAO,EAAC,CAAC,CAAC,CAAA,CAAC,EAAC,QAAQ,CAAC,qyBAA6vB,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,aAAa,EAAC,CAAC,cAAc,EAAC,SAAS,CAAC,CAAC,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,OAAO,CAAC,CAAC,CAAC,EAAC,OAAO,CAAC,UAAU,EAAC,UAAU,CAAC,CAAC,CAAC,EAAC,QAAQ,CAAC,wPAA8O,EAAC,OAAO,CAAC,iBAAS,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,IAAI,EAAC,UAAU,CAAC,CAAC,CAAC,OAAO,EAAC,UAAU,CAAC,CAAC,CAAC,OAAO,EAAC,YAAY,CAAC,CAAC,CAAC,SAAS,EAAC,aAAa,CAAC,CAAC,CAAC,UAAU,EAAC,eAAe,CAAC,CAAC,CAAC,WAAW,EAAC,gBAAgB,CAAC,CAAC,CAAC,YAAY,EAAC,WAAW,CAAC,CAAC,CAAC,QAAQ,EAAC,aAAa,CAAC,CAAC,CAAC,UAAU,EAAC,QAAQ,CAAC,CAAC,CAAC,QAAQ,CAAC,EAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,IAAI,CAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,GAAG,CAAC,WAAW,GAAC,CAAC,CAAC,IAAI,CAAC,QAAQ,EAAE,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,kBAAkB,EAAC,CAAC,CAAC,CAAC,OAAO,CAAC,yBAAyB,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,gFAAgF,EAAC,CAAC,CAAC,6BAA6B,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,GAAC,CAAC,yBAAyB,EAAC,QAAQ,EAAC,SAAS,EAAC,OAAO,EAAC,YAAY,EAAC,UAAU,CAAC,EAAC,CAAC,CAAC,OAAO,GAAC,CAAC,YAAY,EAAC,SAAS,EAAC,OAAO,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,YAAY,EAAC,CAAC,UAAU,EAAC,aAAa,EAAC,YAAY,EAAC,cAAc,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,OAAO,CAAC,cAAc,EAAC,KAAK,CAAC,CAAC,CAAC,EAAC,QAAQ,CAAC,GAAG,EAAC,OAAO,CAAC,iBAAS,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,CAAC,iBAAiB,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,UAAU,CAAC,WAAW,EAAC,UAAU,CAAC,CAAC,CAAC,UAAU,CAAC,YAAY,CAAC,CAAC,CAAA,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,IAAG,CAAC,CAAC,MAAM,CAAC,UAAU,CAAC,OAAM,CAAC,CAAC,CAAC,UAAU,CAAC,aAAa,CAAC,EAAE,CAAA,IAAG,CAAC,CAAC,aAAa,CAAC,gBAAgB,CAAC,EAAE,CAAA,AAAC,IAAE,CAAC,CAAC,UAAU,CAAC,aAAa,CAAC,EAAE,CAAA,AAAC,IAAE,CAAC,CAAC,aAAa,CAAC,gBAAgB,CAAC,EAAE,CAAA,AAAC,IAAE,CAAC,CAAC,QAAQ,CAAC,WAAW,CAAC,EAAE,CAAA,AAAC,IAAE,CAAC,CAAC,WAAW,CAAC,eAAe,CAAC,EAAE,CAAA,AAAC,CAAA,CAAC,EAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,UAAU,GAAC,CAAC,CAAC,aAAa,GAAC,CAAC,CAAC,UAAU,GAAC,CAAC,CAAC,aAAa,GAAC,CAAC,CAAC,QAAQ,GAAC,CAAC,CAAC,WAAW,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,SAAS,CAAC,WAAW,EAAC,iBAAiB,CAAC,mBAAmB,EAAC,WAAW,CAAC,GAAG,EAAC,OAAO,CAAC,GAAG,EAAC,SAAS,CAAC,GAAG,EAAC,UAAU,CAAC,GAAG,EAAC,UAAU,CAAC,GAAG,EAAC,MAAM,CAAC,GAAG,EAAC,MAAM,CAAC,GAAG,EAAC,mBAAmB,CAAC,GAAG,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,GAAC,CAAC,CAAC,SAAS,IAAE,GAAG,EAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,IAAE,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,OAAO,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAA,CAAE,WAAW,CAAC,SAAS,EAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,OAAO,KAAG,CAAC,CAAC,MAAM,CAAA,CAAC,CAAC,KAAI,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,QAAQ,CAAC,iBAAiB,CAAC,EAAC,CAAC,GAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,cAAc,CAAC,CAAC,CAAC,cAAc,EAAC,MAAM,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,MAAM,CAAC,IAAE,CAAC,EAAC,MAAM,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,MAAM,CAAC,IAAE,CAAC,EAAC,eAAe,CAAC,CAAC,CAAC,CAAC,CAAA,CAAE,CAAC,GAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,cAAc,CAAC,CAAC,CAAC,cAAc,EAAC,OAAO,CAAC,MAAM,MAAI,CAAC,CAAC,OAAO,IAAE,MAAM,CAAA,AAAC,EAAC,QAAQ,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,WAAW,CAAC,EAAC,MAAM,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,MAAM,CAAC,IAAE,CAAC,EAAC,MAAM,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,MAAM,CAAC,IAAE,CAAC,EAAC,UAAU,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,UAAU,CAAC,KAAG,CAAC,CAAC,EAAC,UAAU,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,UAAU,CAAC,KAAG,CAAC,CAAC,EAAC,UAAU,CAAC,CAAC,CAAC,SAAS,CAAC,OAAO,CAAC,GAAG,CAAC,IAAE,CAAC,EAAC,UAAU,CAAC,CAAC,CAAC,SAAS,CAAC,OAAO,CAAC,GAAG,CAAC,IAAE,CAAC,EAAC,mBAAmB,CAAC,QAAQ,CAAC,CAAC,CAAC,mBAAmB,EAAC,EAAE,CAAC,IAAE,EAAE,EAAC,iBAAiB,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,cAAc,EAAC,CAAC,MAAM,CAAC,CAAC,EAAC,iBAAiB,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,UAAU,EAAC,UAAU,CAAC,CAAC,KAAG,CAAC,CAAC,iBAAiB,GAAC,CAAC,EAAC,OAAO,CAAC,CAAC,EAAE,CAAA,AAAC,EAAC,CAAC,GAAC,IAAI,EAAC,CAAC,GAAC,IAAI,EAAC,CAAC,CAAC,SAAS,GAAC,IAAI,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,IAAI,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,6BAA6B,CAAC,EAAC,OAAO,IAAE,CAAC,CAAC,MAAM,EAAE,CAAC,GAAC,CAAC,CAAC,8BAA4B,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAA,CAAE,CAAC,CAAC,QAAQ,CAAC,sBAAsB,CAAC,CAAC,IAAI,CAAC,CAAC,MAAM,KAAG,CAAC,CAAC,cAAc,IAAE,CAAC,CAAC,CAAC,SAAS,CAAC,WAAW,EAAE,CAAC,QAAO,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,qBAAqB,CAAC,CAAA,AAAC,EAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAA,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,iBAAiB,EAAC,CAAC,SAAS,EAAC,SAAS,CAAC,CAAC,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,OAAO,CAAC,eAAe,EAAC,IAAI,CAAC,cAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,eAAe,CAAC,mBAAmB,CAAC,CAAC,CAAC,eAAe,CAAC,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,EAAC,CAAC,CAAC,mBAAmB,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,CAAC,mBAAmB,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,EAAE,CAAA,CAAC,IAAI,CAAC,CAAC,KAAK,CAAC,QAAQ,CAAC,UAAU,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,GAAG,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,EAAC,KAAK,CAAC,EAAE,CAAC,QAAQ,EAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,UAAU,EAAC,UAAU,CAAC,KAAK,CAAC,GAAG,CAAC,QAAQ,EAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,qJAAqJ,CAAC,KAAK,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,cAAc,EAAC,CAAC,EAAE,CAAC,CAAC,SAAS,CAAC,cAAc,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,cAAc,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,mBAAmB,EAAC,CAAC,UAAU,EAAC,SAAS,CAAC,CAAC,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,OAAO,CAAC,CAAC,gBAAgB,EAAC,mBAAmB,CAAC,EAAC,QAAQ,CAAC,kBAAS,CAAC,EAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,IAAI,CAAC,qEAAmE,CAAC,oDAAkD,CAAA,CAAC,EAAC,KAAK,CAAC,CAAC,CAAC,EAAC,UAAU,CAAC,oBAAoB,EAAC,IAAI,CAAC,cAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,UAAU,GAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,WAAW,GAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,UAAU,GAAC,CAAC,CAAC,UAAU,EAAC,CAAC,CAAC,aAAa,GAAC,cAAc,EAAC,CAAC,CAAC,QAAQ,CAAC,EAAE,CAAC,QAAQ,EAAC,CAAC,CAAC,WAAW,CAAC,CAAC,KAAI,CAAC,IAAI,CAAC,CAAC,KAAK,CAAC,OAAO,CAAC,wBAAwB,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,EAAC,iBAAiB,CAAC,CAAC,IAAG,CAAC,CAAC,QAAQ,GAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,MAAK,sDAAsD,CAAC,CAAC,CAAC,QAAQ,CAAC,gBAAgB,CAAC,QAAQ,EAAC,CAAC,CAAC,WAAW,CAAC,CAAA,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,cAAc,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,cAAc,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,WAAW,EAAE,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,SAAS,EAAC,CAAC,OAAO,EAAC,SAAS,CAAC,CAAC,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,UAAU,CAAC,CAAC,QAAQ,EAAC,UAAU,EAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,MAAM,GAAC,CAAC,EAAC,IAAI,CAAC,QAAQ,GAAC,CAAC,CAAA,CAAC,CAAC,EAAC,KAAK,CAAC,CAAC,CAAC,EAAC,OAAO,CAAC,iBAAS,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,EAAC,CAAC,CAAC,CAAC,IAAE,qCAAqC,CAAC,IAAI,CAAC,CAAC,CAAC,IAAI,EAAE,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,aAAa,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,cAAc,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAA,KAAI,CAAC,CAAC,IAAI,CAAC,SAAS,EAAC,aAAa,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,IAAE,CAAC,CAAC,IAAI,CAAC,QAAQ,EAAC,eAAe,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC,EAAC,CAAC,CAAC,QAAQ,CAAC,mBAAmB,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAA,CAAC,KAAK,CAAC,CAAC,QAAQ,CAAC,MAAM,CAAC,CAAC,OAAO,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,GAAC,UAAU,CAAC,OAAO,CAAC,CAAC,IAAI,IAAE,CAAC,CAAC,MAAM,CAAA,CAAC,EAAC,CAAC,CAAC,OAAO,GAAC,UAAU,CAAC,OAAO,CAAC,CAAC,MAAM,CAAA,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,eAAe,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,GAAG,CAAC,wBAAwB,EAAC,UAAU,CAAC,CAAC,IAAE,CAAC,CAAC,kBAAkB,KAAG,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,GAAG,CAAC,SAAS,CAAC,GAAC,EAAE,EAAC,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,GAAG,CAAC,UAAU,CAAC,GAAC,MAAM,EAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,GAAG,CAAC,UAAU,CAAC,GAAC,EAAE,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,kBAAkB,GAAC,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,wEAAsE,CAAC,CAAC,CAAC,SAAS,CAAC,iBAAiB,EAAC,UAAU,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,eAAe,EAAE,CAAA,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,OAAO,CAAC,CAAC,WAAW,EAAC,WAAW,CAAC,EAAC,QAAQ,CAAC,MAAM,CAAC,SAAS,EAAC,OAAO,CAAC,iBAAS,CAAC,EAAC,CAAC,CAAC,CAAC,QAAO,CAAC,CAAC,IAAI,CAAC,OAAO,EAAC,CAAC,CAAC,CAAC,OAAO,CAAC,IAAE,EAAE,CAAA,GAAE,0BAA0B,EAAC,CAAC,CAAC,CAAC,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,GAAC,CAAC,IAAE,CAAC,CAAC,UAAU,CAAC,SAAS,CAAC,EAAC,CAAC,IAAE,CAAC,CAAC,UAAU,EAAE,IAAE,CAAC,CAAC,QAAQ,CAAC,gBAAgB,CAAC,CAAA,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,QAAQ,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,oBAAoB,CAAC,EAAC,CAAC,CAAC,EAAE,CAAC,OAAO,EAAC,CAAC,CAAC,EAAC,CAAC,EAAE,EAAC,CAAC,CAAC,GAAG,CAAC,uBAAuB,EAAC,CAAC,CAAC,CAAA,CAAC,CAAA,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,mBAAmB,EAAC,UAAU,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,IAAI,CAAC,cAAS,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,iBAAiB,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,cAAc,CAAC,CAAC,GAAG,CAAC,IAAE,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,YAAU,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,SAAS,CAAC,GAAG,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,MAAM,CAAC,WAAW,CAAC,CAAA,CAAC,CAAC,CAAC,CAAC,gBAAgB,CAAC,OAAO,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,SAAS,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,OAAO,GAAC,UAAU,CAAC,CAAC,CAAC,KAAK,GAAC,CAAC,CAAC,UAAU,IAAE,EAAE,EAAC,CAAC,EAAE,CAAA,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,GAAG,CAAC,UAAU,EAAC,UAAU,CAAC,CAAC,CAAC,mBAAmB,CAAC,OAAO,EAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,8CAA4C,CAAC,CAAC,CAAC,SAAS,CAAC,iBAAiB,EAAC,CAAC,UAAU,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,eAAe,EAAE,CAAA,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,OAAO,CAAC,UAAU,EAAC,QAAQ,CAAC,MAAM,CAAC,SAAS,EAAC,OAAO,CAAC,iBAAS,CAAC,EAAC,CAAC,CAAC,CAAC,QAAO,CAAC,CAAC,IAAI,CAAC,OAAO,EAAC,CAAC,CAAC,CAAC,OAAO,CAAC,IAAE,EAAE,CAAA,GAAE,SAAS,EAAC,CAAC,CAAC,CAAC,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,gBAAgB,KAAG,CAAC,CAAC,gBAAgB,GAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,QAAQ,CAAC,MAAM,CAAC,CAAC,CAAC,gBAAgB,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,gBAAgB,CAAC,MAAM,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,QAAQ,CAAC,QAAQ,CAAC,qBAAqB,CAAC,EAAC,CAAC,CAAC,EAAE,CAAC,OAAO,EAAC,CAAC,CAAC,CAAA,CAAC,CAAA,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,uGAAmG,CAAC,CAAC,CAAC,SAAS,CAAC,kBAAkB,EAAC,CAAC,QAAQ,EAAC,SAAS,CAAC,CAAC,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,OAAO,CAAC,CAAC,UAAU,EAAC,WAAW,CAAC,EAAC,QAAQ,CAAC,MAAM,CAAC,SAAS,EAAC,OAAO,CAAC,iBAAS,CAAC,EAAC,CAAC,CAAC,CAAC,QAAO,CAAC,CAAC,IAAI,CAAC,OAAO,EAAC,CAAC,CAAC,CAAC,OAAO,CAAC,IAAE,EAAE,CAAA,GAAE,0BAA0B,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,YAAY,CAAC,qBAAqB,EAAC,CAAC,CAAC,CAAC,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,UAAU,GAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,UAAU,CAAC,CAAC,EAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,OAAO,KAAG,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,GAAC,SAAS,CAAC,CAAC,CAAC,QAAO,CAAC,CAAC,eAAe,EAAE,EAAC,CAAC,CAAC,CAAA,CAAA,CAAC,CAAA,AAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,QAAQ,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,qBAAqB,CAAC,EAAC,CAAC,IAAE,CAAC,CAAC,WAAW,EAAE,IAAE,CAAC,CAAC,QAAQ,CAAC,gBAAgB,CAAC,CAAA,CAAC,CAAA,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,gBAAgB,EAAC,UAAU,CAAC,OAAO,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,KAAK,CAAC,QAAQ,CAAC,SAAS,EAAE,IAAE,KAAK,CAAC,QAAQ,CAAC,YAAY,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,cAAc,IAAE,CAAC,CAAC,MAAM,CAAC,cAAc,CAAC,CAAC,CAAC,GAAG,CAAC,QAAQ,EAAC,CAAC,GAAC,IAAI,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,UAAU,CAAC,cAAc,CAAC,EAAC,CAAC,KAAG,CAAC,CAAC,UAAU,CAAC,WAAW,CAAC,KAAK,CAAC,MAAM,GAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAC,IAAI,CAAA,AAAC,CAAA,CAAC,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,CAAC,KAAK,CAAC,QAAQ,CAAC,SAAS,EAAE,IAAE,KAAK,CAAC,QAAQ,CAAC,YAAY,CAAA,KAAI,CAAC,CAAC,GAAG,CAAC,QAAQ,EAAC,EAAE,CAAC,EAAC,CAAC,KAAG,CAAC,CAAC,UAAU,CAAC,WAAW,CAAC,KAAK,CAAC,MAAM,GAAC,EAAE,CAAA,AAAC,CAAA,AAAC,CAAA,CAAC,KAAK,CAAC,EAAE,CAAC,qBAAqB,EAAC,CAAC,EAAC,MAAM,CAAC,EAAC,KAAK,CAAC,EAAE,CAAC,qBAAqB,EAAC,CAAC,EAAC,MAAM,CAAC,EAAC,KAAK,CAAC,EAAE,CAAC,qBAAqB,EAAC,CAAC,EAAC,MAAM,CAAC,EAAC,KAAK,CAAC,EAAE,CAAC,qBAAqB,EAAC,CAAC,EAAC,MAAM,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,UAAU,EAAC,UAAU,CAAC,KAAK,CAAC,GAAG,CAAC,qBAAqB,EAAC,CAAC,EAAC,MAAM,CAAC,EAAC,KAAK,CAAC,GAAG,CAAC,qBAAqB,EAAC,CAAC,EAAC,MAAM,CAAC,EAAC,KAAK,CAAC,GAAG,CAAC,qBAAqB,EAAC,CAAC,EAAC,MAAM,CAAC,EAAC,KAAK,CAAC,GAAG,CAAC,qBAAqB,EAAC,CAAC,EAAC,MAAM,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,SAAS,EAAC,CAAC,UAAU,EAAC,SAAS,CAAC,CAAC,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,OAAO,CAAC,CAAC,SAAS,EAAC,gBAAgB,CAAC,EAAC,UAAU,CAAC,YAAY,EAAC,OAAO,CAAC,iBAAS,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,sBAAoB,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,QAAO,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,EAAE,CAC1w+B,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAE,IAAE,CAAC,CAAC,QAAQ,CAAC,SAAS,CAAC,IAAE,CAAC,CAAC,WAAW,CAAC,QAAQ,CAAC,EAAC,KAAK,CAAC,qBAAqB,CAAC,UAAU,CAAC,CAAC,EAAE,IAAE,CAAC,CAAC,QAAQ,CAAC,QAAQ,CAAC,IAAE,CAAC,CAAC,WAAW,CAAC,SAAS,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,QAAQ,GAAC,IAAI,KAAK,CAAC,KAAK,CAAC,QAAQ,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,MAAM,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC,CAAC,CAAC,EAAC,QAAQ,CAAC,CAAC,IAAE,CAAC,CAAC,OAAO,EAAC,UAAU,CAAC,CAAC,IAAE,CAAC,CAAC,UAAU,EAAC,SAAS,CAAC,mBAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,EAAE,CAAC,CAAC,IAAE,CAAC,CAAC,UAAU,IAAE,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,EAAC,QAAQ,CAAC,mBAAU,CAAC,OAAO,CAAC,CAAC,aAAa,EAAE,CAAA,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,UAAU,EAAC,UAAU,CAAC,CAAC,KAAG,CAAC,CAAC,UAAU,IAAE,CAAC,CAAC,UAAU,EAAE,EAAC,CAAC,GAAC,IAAI,CAAA,AAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,IAAE,CAAC,CAAC,MAAM,CAAC,KAAK,GAAC,CAAC,CAAC,QAAQ,GAAC,GAAG,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,IAAE,CAAC,CAAC,MAAM,CAAC,KAAK,GAAC,CAAC,CAAC,UAAU,GAAC,GAAG,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,IAAE,CAAC,CAAC,MAAM,CAAC,KAAK,GAAC,CAAC,CAAC,WAAW,GAAC,GAAG,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,UAAU,CAAC,OAAO,CAAC,CAAC,UAAU,EAAE,CAAA,CAAC,EAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,GAAG,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,kBAAkB,EAAE,EAAC,CAAC,CAAC,aAAa,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,QAAQ,EAAE,CAAC,WAAW,CAAC,mBAAmB,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,WAAW,CAAC,wBAAwB,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,sBAAsB,CAAC,aAAa,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,UAAU,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,UAAU,CAAC,OAAO,CAAC,CAAC,WAAW,EAAE,CAAA,CAAC,EAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,GAAG,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,kBAAkB,EAAE,EAAC,CAAC,CAAC,aAAa,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,QAAQ,EAAE,CAAC,WAAW,CAAC,oBAAoB,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,WAAW,CAAC,wBAAwB,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,sBAAsB,CAAC,cAAc,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,WAAW,CAAC,CAAA,CAAC,CAAC,CAAC,CAAA,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAA,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,WAAW,EAAC,CAAC,eAAe,EAAC,SAAS,CAAC,CAAC,CAAC,OAAM,CAAC,QAAQ,CAAC,IAAI,EAAC,IAAI,CAAC,cAAS,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,OAAO,EAAC,UAAU,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,yBAAyB,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,eAAe,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,EAAC,cAAc,CAAC,CAAC,CAAC,EAAC,MAAM,CAAC,GAAG,CAAC,CAAC,EAAC,CAAC,CAAC,KAAK,EAAE,CAAA,AAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,YAAY,EAAC,UAAU,CAAC,OAAM,CAAC,QAAQ,CAAC,IAAI,EAAC,IAAI,CAAC,cAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,wBAAwB,EAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,yBAAyB,CAAC,CAAC,CAAC,CAAC,uBAAuB,EAAE,IAAE,CAAC,CAAC,QAAQ,CAAC,MAAM,CAAC,CAAA,CAAC,KAAK,CAAC,CAAC,WAAW,CAAC,MAAM,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,OAAO,EAAC,UAAU,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,yBAAyB,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,UAAU,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,UAAU,EAAC,CAAC,UAAU,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,UAAU,CAAC,CAAC,CAAC,EAAC,OAAO,CAAC,CAAC,CAAC,EAAC,UAAU,CAAC,CAAC,UAAU,EAAE,CAAC,EAAC,QAAQ,CAAC,8HAAwH,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,cAAc,EAAC,UAAU,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,OAAO,CAAC,iBAAS,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,OAAO,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,kBAAkB,EAAC,CAAC,cAAc,EAAC,WAAW,EAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,OAAO,CAAC,YAAY,EAAC,OAAO,CAAC,iBAAS,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,OAAM,WAAW,CAAC,IAAI,CAAC,CAAC,CAAC,SAAS,CAAC,CAAA,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,QAAQ,CAAC,CAAC,IAAI,IAAI,CAAC,IAAI,CAAC,CAAC,KAAK,EAAC,CAAC,CAAC,YAAY,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,YAAY,CAAC,UAAU,EAAC,gBAAgB,CAAC,EAAC,CAAC,CAAC,SAAS,GAAC,kCAAkC,IAAE,CAAC,CAAC,IAAI,CAAC,OAAO,CAAC,IAAE,EAAE,CAAA,AAAC,EAAC,CAAC,CAAC,SAAS,GAAC,CAAC,CAAC,IAAI,EAAE,IAAE,EAAE,CAAC,IAAI,IAAI,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,MAAM,EAAC,CAAC,EAAE,EAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,EAAC,CAAC,KAAG,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,QAAQ,CAAC,eAAe,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,QAAQ,CAAC,gBAAgB,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,CAAA,AAAC,CAAC,CAAC,IAAE,CAAC,KAAG,CAAC,CAAC,QAAQ,KAAG,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,IAAI,EAAE,CAAA,AAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,IAAI,EAAE,CAAC,IAAG,CAAC,CAAC,IAAE,CAAC,IAAE,MAAM,KAAG,CAAC,KAAG,CAAC,CAAC,SAAS,GAAC,kBAAiB,GAAC,CAAC,GAAC,UAAS,GAAC,CAAC,CAAC,SAAS,EAAC,CAAC,CAAC,SAAS,IAAE,eAAe,CAAA,AAAC,EAAC,CAAC,CAAC,CAAA,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,SAAS,GAAC,WAAW,EAAC,CAAC,CAAC,IAAE,CAAC,CAAC,UAAU,CAAC,IAAI,EAAE,KAAG,CAAC,CAAC,SAAS,IAAE,gCAA8B,GAAC,CAAC,CAAC,UAAU,CAAC,IAAI,EAAE,GAAC,SAAS,CAAA,AAAC,EAAC,CAAC,CAAC,IAAE,CAAC,CAAC,UAAU,CAAC,iBAAiB,EAAE,KAAG,CAAC,CAAC,SAAS,IAAE,wCAAsC,CAAA,AAAC,EAAC,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,CAAA,CAAC,QAAO,CAAC,CAAC,IAAI,CAAC,OAAO,EAAC,MAAM,CAAC,EAAC,CAAC,CAAC,KAAK,EAAE,EAAC,CAAC,GAAG,CAAC,aAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,YAAY,EAAC,CAAC,CAAC,SAAS,CAAC,EAAC,CAAC,GAAC,IAAI,CAAA,CAAC,CAAC,CAAA,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,WAAW,EAAC,UAAU,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,UAAU,CAAC,cAAc,EAAC,KAAK,CAAC,CAAC,CAAC,EAAC,IAAI,CAAC,cAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,EAAE,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,eAAe,EAAC,CAAC,WAAW,EAAC,SAAS,CAAC,CAAC,CAAC,OAAM,CAAC,OAAO,CAAC,YAAY,EAAC,QAAQ,CAAC,GAAG,EAAC,OAAO,CAAC,iBAAS,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,MAAM,CAAC,4BAA4B,CAAC,IAAI,CAAC,CAAC,CAAC,IAAI,IAAE,EAAE,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,IAAI,CAAC,WAAW,EAAE,CAAA,AAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,SAAS,GAAC,CAAC,GAAC,UAAU,EAAC,CAAC,CAAC,SAAS,GAAC,CAAC,CAAC,IAAI,EAAE,CAAC,IAAI,CAAC,CAAC,CAAC,GAAC,SAAS,CAAC,QAAO,CAAC,CAAC,IAAI,CAAC,OAAO,EAAC,MAAM,CAAC,EAAC,CAAC,CAAC,KAAK,EAAE,EAAC,CAAC,GAAG,CAAC,aAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,MAAM,EAAE,CAAC,IAAI,CAAC,oBAAoB,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,EAAC,CAAC,GAAC,IAAI,CAAA,CAAC,CAAC,CAAA,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,cAAc,EAAC,CAAC,oBAAoB,EAAC,SAAS,CAAC,CAAC,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,QAAQ,CAAC,IAAG,EAAC,IAAI,CAAC,cAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,OAAO,EAAC,UAAU,CAAC,CAAC,CAAC,aAAa,CAAC,CAAC,CAAC,YAAY,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,aAAa,EAAC,CAAC,WAAW,EAAC,SAAS,CAAC,CAAC,CAAC,OAAM,CAAC,OAAO,CAAC,YAAY,EAAC,QAAQ,CAAC,GAAG,EAAC,OAAO,CAAC,iBAAS,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,OAAO,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,MAAM,CAAC,CAAC,IAAI,IAAI,CAAC,IAAI,CAAC,CAAC,KAAK,EAAC,CAAC,CAAC,YAAY,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,QAAO,CAAC,CAAC,SAAS,CAAC,GAAG,CAAC,eAAe,CAAC,EAAC,CAAC,CAAC,SAAS,GAAC,CAAC,CAAC,IAAI,EAAE,EAAC,CAAC,CAAC,IAAI,CAAC,OAAO,EAAC,MAAM,CAAC,EAAC,CAAC,CAAC,KAAK,EAAE,EAAC,CAAC,GAAG,CAAC,aAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,MAAM,EAAE,CAAC,IAAI,CAAC,oBAAoB,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,EAAC,CAAC,GAAC,IAAI,CAAA,CAAC,CAAC,CAAA,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,eAAe,EAAC,CAAC,oBAAoB,EAAC,SAAS,CAAC,CAAC,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,QAAQ,CAAC,IAAG,EAAC,IAAI,CAAC,cAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,OAAO,EAAC,UAAU,CAAC,CAAC,CAAC,cAAc,CAAC,CAAC,CAAC,aAAa,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,YAAY,EAAC,CAAC,QAAQ,EAAC,cAAc,EAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,QAAQ,CAAC,CAAC,CAAC,EAAC,QAAQ,CAAC,IAAG,EAAC,UAAU,CAAC,CAAC,CAAC,EAAC,UAAU,CAAC,eAAe,EAAC,OAAO,CAAC,iBAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,QAAO,CAAC,CAAC,QAAQ,CAAC,gBAAgB,CAAC,EAAC,KAAK,CAAC,OAAO,CAAC,UAAU,CAAC,CAAC,EAAC,qBAAqB,EAAC,CAAC,CAAC,KAAK,CAAC,UAAU,EAAE,CAAC,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,QAAQ,IAAE,CAAC,CAAC,QAAQ,CAAC,MAAM,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,KAAG,CAAC,IAAE,CAAC,KAAG,CAAC,CAAA,AAAC,KAAG,CAAC,GAAC,CAAC,EAAC,CAAC,CAAC,KAAK,GAAC,CAAC,CAAC,OAAO,EAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,IAAI,EAAE,CAAC,CAAC,CAAC,GAAG,CAAC,qBAAqB,EAAC,UAAU,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,qBAAqB,EAAC,UAAU,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAA,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,UAAU,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,kBAAkB,EAAC,CAAC,WAAW,EAAC,SAAS,CAAC,CAAC,CAAC,QAAO,CAAC,CAAC,KAAK,EAAE,EAAC,CAAC,CAAA,CAAA,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,eAAe,EAAC,CAAC,QAAQ,EAAC,SAAS,CAAC,CAAC,CAAC,OAAO,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,OAAO,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,OAAO,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,GAAC,CAAC,CAAA,CAAC,CAAA,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,SAAS,EAAC,CAAC,eAAe,EAAC,SAAS,CAAC,CAAC,CAAC,OAAO,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,CAAA,CAAC,CAAA,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,cAAc,EAAC,UAAU,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,IAAI,CAAC,cAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,YAAY,EAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,SAAS,EAAC,UAAU,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,IAAI,CAAC,cAAS,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,MAAM,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,YAAY,EAAC,CAAC,UAAU,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,UAAU,CAAC,CAAC,CAAC,EAAC,OAAO,CAAC,CAAC,CAAC,EAAC,UAAU,CAAC,CAAC,UAAU,EAAE,CAAC,EAAC,QAAQ,CAAC,2FAAuF,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,gBAAgB,EAAC,UAAU,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,OAAO,CAAC,iBAAS,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,+BAA6B,CAAC,CAAC,EAAC,CAAC,CAAC,QAAQ,CAAC,SAAS,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,UAAU,EAAC,UAAU,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,OAAO,CAAC,CAAC,CAAC,EAAC,OAAO,CAAC,UAAU,EAAC,UAAU,CAAC,CAAC,CAAC,EAAC,QAAQ,CAAC,mOAAyN,EAAC,OAAO,CAAC,iBAAS,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,IAAE,CAAC,CAAC,QAAQ,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,eAAe,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,OAAO,CAAC,CAAC,QAAO,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,IAAI,EAAC,KAAK,CAAC,CAAC,CAAC,KAAK,EAAC,QAAQ,CAAC,CAAC,CAAC,QAAQ,EAAC,UAAU,CAAC,CAAC,CAAC,OAAO,EAAC,UAAU,CAAC,CAAC,CAAC,OAAO,EAAC,aAAa,CAAC,CAAC,CAAC,UAAU,EAAC,WAAW,CAAC,CAAC,CAAC,QAAQ,EAAC,aAAa,CAAC,CAAC,CAAC,UAAU,EAAC,QAAQ,CAAC,CAAC,CAAC,QAAQ,CAAC,EAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,IAAI,CAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,GAAC,UAAU,CAAC,OAAO,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,KAAK,CAAA,CAAC,CAAA,CAAC,CAAA,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,cAAc,EAAC,CAAC,UAAU,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,OAAO,CAAC,CAAC,CAAC,EAAC,OAAO,CAAC,CAAC,gBAAgB,EAAC,cAAc,CAAC,EAAC,UAAU,CAAC,iBAAiB,EAAC,QAAQ,CAAC,unBAA6lB,EAAC,IAAI,CAAC,cAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,QAAQ,EAAE,CAAC,CAAC,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,GAAG,CAAC,cAAc,CAAC,EAAC,CAAC,CAAC,aAAa,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,sBAAsB,EAAE,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,wBAAwB,EAAC,UAAU,CAAC,CAAC,CAAC,UAAU,CAAC,UAAU,CAAC,CAAC,CAAC,UAAU,CAAC,mBAAmB,EAAE,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,AAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,WAAW,EAAC,CAAC,UAAU,EAAC,aAAa,EAAC,YAAY,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,KAAK,CAAC,CAAC,CAAC,EAAC,UAAU,CAAC,qBAAU,EAAE,EAAC,OAAO,CAAC,iBAAS,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,SAAS,CAAC,GAAG,EAAC,MAAM,CAAC,GAAG,EAAC,SAAS,CAAC,WAAW,EAAC,MAAM,CAAC,GAAG,EAAC,UAAU,CAAC,GAAG,EAAC,UAAU,CAAC,GAAG,EAAC,OAAO,CAAC,GAAG,EAAC,OAAO,CAAC,GAAG,EAAC,OAAO,CAAC,GAAG,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,GAAC,CAAC,CAAC,SAAS,IAAE,GAAG,EAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,IAAE,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,OAAO,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,SAAS,EAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,MAAM,CAAC,KAAG,CAAC,CAAC,IAAE,CAAC,CAAC,QAAQ,CAAC,eAAe,CAAC,EAAC,CAAC,CAAC,SAAS,KAAG,CAAC,CAAC,SAAS,GAAC,GAAG,CAAA,AAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,MAAM,CAAC,KAAG,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,cAAc,CAAC,CAAC,CAAC,cAAc,EAAC,OAAO,CAAC,MAAM,MAAI,CAAC,CAAC,OAAO,IAAE,MAAM,CAAA,AAAC,EAAC,QAAQ,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,WAAW,CAAC,EAAC,MAAM,CAAC,CAAC,EAAC,UAAU,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,UAAU,CAAC,KAAG,CAAC,CAAC,EAAC,UAAU,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,UAAU,CAAC,KAAG,CAAC,CAAC,EAAC,UAAU,CAAC,CAAC,CAAC,SAAS,CAAC,OAAO,CAAC,GAAG,CAAC,IAAE,CAAC,EAAC,UAAU,CAAC,CAAC,CAAC,SAAS,CAAC,OAAO,CAAC,GAAG,CAAC,IAAE,CAAC,EAAC,OAAO,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,OAAO,CAAC,KAAG,CAAC,CAAC,EAAC,OAAO,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,OAAO,CAAC,IAAE,CAAC,EAAC,OAAO,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,OAAO,CAAC,IAAE,GAAE,EAAC,cAAc,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,eAAe,GAAC,GAAE,EAAC,CAAC,CAAC,QAAQ,GAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,cAAc,EAAC,CAAC,MAAM,CAAC,CAAC,EAAC,iBAAiB,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,QAAQ,CAAC,0BAA0B,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,8BAA4B,CAAC,CAAC,QAAO,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,EAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAA,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,aAAa,EAAC,UAAU,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,OAAO,CAAC,eAAe,EAAC,KAAK,CAAC,CAAC,CAAC,EAAC,OAAO,CAAC,iBAAS,CAAC,EAAC,CAAC,CAAC,CAAC,QAAO,OAAO,CAAC,WAAW,CAAC,CAAC,CAAC,SAAS,CAAC,IAAE,CAAC,CAAC,IAAI,CAAC,WAAW,EAAC,MAAM,CAAC,EAAC,OAAO,CAAC,WAAW,CAAC,CAAC,CAAC,KAAK,CAAC,IAAE,CAAC,CAAC,IAAI,CAAC,OAAO,EAAC,KAAK,CAAC,EAAC,CAAC,CAAC,QAAQ,CAAC,YAAY,GAAC,CAAC,CAAC,IAAI,CAAC,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,GAAC,CAAC,CAAC,IAAI,IAAE,MAAM,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,GAAC,IAAI,KAAK,CAAC,KAAK,CAAC,QAAQ,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,EAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,KAAK,EAAC,SAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,IAAE,CAAC,IAAE,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,SAAS,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,YAAY,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAA,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,oBAAoB,EAAC,CAAC,UAAU,EAAC,eAAe,EAAC,SAAS,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,OAAM,CAAC,QAAQ,CAAC,IAAI,EAAC,OAAO,CAAC,eAAe,EAAC,KAAK,CAAC,CAAC,CAAC,EAAC,OAAO,CAAC,iBAAS,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,aAAa,EAAE,EAAE,CAAC,CAAC,KAAK,EAAE,EAAC,CAAC,CAAC,OAAO,CAAC,QAAQ,CAAC,cAAc,EAAE,EAAC,CAAC,GAAC,IAAI,EAAC,CAAC,GAAC,IAAI,CAAA,CAAE,CAAC,KAAG,CAAC,GAAC,KAAK,CAAC,GAAG,CAAC,YAAY,CAAC,CAAC,CAAC,OAAO,CAAC,QAAQ,CAAC,CAAA,AAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,iBAAiB,CAAC,CAAC,CAAC,IAAE,GAAG,IAAE,CAAC,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,QAAQ,CAAC,cAAc,EAAE,CAAA,AAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,IAAE,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,OAAO,CAAC,QAAQ,CAAC,cAAc,EAAE,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,IAAI,EAAC,CAAC,GAAC,IAAI,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,EAAC,OAAO,CAAC,CAAC,CAAA,GAAG,CAAC,IAAE,CAAC,CAAC,OAAO,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,KAAK,CAAC,GAAG,CAAC,YAAY,CAAC,CAAC,CAAC,OAAO,CAAC,QAAQ,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,GAAG,CAAC,GAAG,CAAC,QAAO,IAAI,CAAC,GAAG,CAAC,CAAC,EAAC,CAAC,CAAC,GAAC,EAAE,KAAG,CAAC,GAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAA,CAAA,CAAC,CAAC,GAAC,KAAK,CAAC,GAAG,CAAC,YAAY,CAAC,CAAC,CAAC,OAAO,CAAC,QAAQ,CAAC,CAAA,CAAC,OAAM,GAAG,CAAA,CAAC,IAAI,CAAC,CAAC,IAAI,EAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,WAAW,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,cAAc,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,CAAC,CAAC,cAAc,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,iBAAiB,CAAC,IAAE,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,iBAAiB,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,iBAAiB,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,MAAM,CAAC,iBAAU,EAAE,EAAC,OAAO,CAAC,kBAAU,EAAE,EAAC,aAAa,CAAC,wBAAU,CAAC,OAAO,CAAC,CAAC,yBAAyB,IAAE,CAAC,CAAA,CAAC,EAAC,aAAa,CAAC,KAAK,CAAC,sBAAsB,CAAC,SAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,OAAO,GAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,GAAG,CAAC,SAAS,CAAC,GAAC,cAAc,GAAC,CAAC,GAAC,SAAS,EAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,yBAAyB,GAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,aAAa,CAAC,KAAK,CAAC,sBAAsB,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,GAAC,QAAQ,CAAC,CAAC,EAAC,EAAE,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,GAAG,CAAC,SAAS,CAAC,GAAC,cAAc,GAAC,CAAC,GAAC,SAAS,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,KAAK,GAAC,CAAC,CAAC,UAAU,GAAC,CAAC,GAAC,IAAI,EAAC,CAAC,CAAC,OAAO,GAAC,CAAC,CAAA,EAAG,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,GAAG,CAAC,SAAS,CAAC,GAAC,oBAAoB,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,KAAK,GAAC,EAAE,EAAC,CAAC,CAAC,OAAO,GAAC,CAAC,CAAA,AAAC,CAAA,CAAC,CAAC,EAAC,cAAc,CAAC,KAAK,CAAC,sBAAsB,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,GAAC,QAAQ,CAAC,CAAC,EAAC,EAAE,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,KAAK,GAAC,CAAC,CAAC,UAAU,GAAC,CAAC,GAAC,IAAI,EAAC,CAAC,CAAC,OAAO,GAAC,CAAC,CAAA,EAAG,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,KAAK,GAAC,EAAE,EAAC,CAAC,CAAC,OAAO,GAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,GAAG,CAAC,SAAS,CAAC,GAAC,oBAAoB,CAAA,CAAC,CAAC,EAAC,eAAe,CAAC,0BAAU,CAAC,CAAC,CAAC,gBAAgB,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,GAAG,CAAC,eAAe,CAAC,CAAA,CAAC,EAAC,gBAAgB,CAAC,2BAAU,CAAC,CAAC,CAAC,gBAAgB,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,MAAM,CAAC,eAAe,CAAC,CAAA,CAAC,EAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,qBAAqB,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,OAAO,CAAC,wBAAwB,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,iBAAiB,CAAC,KAAG,CAAC,CAAC,0BAA0B,GAAC,CAAC,MAAM,EAAC,OAAO,CAAC,CAAA,AAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,KAAK,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,WAAW,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,UAAU,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,QAAQ,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,UAAU,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,SAAS,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,UAAU,EAAC,UAAU,CAAC,CAAC,KAAG,CAAC,CAAC,OAAO,GAAC,IAAI,EAAC,CAAC,GAAC,IAAI,CAAA,AAAC,EAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAC,UAAU,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAC,WAAW,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAC,QAAQ,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAC,UAAU,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAC,SAAS,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAC,KAAK,EAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,QAAO,CAAC,CAAC,QAAQ,CAAC,mBAAmB,CAAC,EAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAA,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,cAAc,EAAC,CAAC,YAAY,EAAC,SAAS,CAAC,CAAC,CAAC,OAAM,CAAC,QAAQ,CAAC,KAAK,EAAC,UAAU,CAAC,iBAAiB,EAAC,OAAO,CAAC,iBAAS,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,uBAAuB,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,uBAAuB,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,mBAAmB,EAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,YAAY,KAAG,CAAC,CAAC,YAAY,GAAC,EAAE,CAAA,AAAC,EAAC,CAAC,CAAC,YAAY,CAAC,MAAM,GAAC,CAAC,EAAC,CAAC,CAAC,WAAW,CAAC,CAAC,EAAC,YAAY,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,wBAAwB,EAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,KAAG,CAAC,CAAC,gBAAgB,GAAC,CAAC,CAAC,SAAS,CAAA,AAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,UAAU,EAAC,UAAU,CAAC,CAAC,CAAC,WAAW,CAAC,WAAW,EAAC,YAAY,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,QAAO,CAAC,CAAC,IAAI,CAAC,OAAO,EAAC,CAAC,CAAC,CAAC,OAAO,CAAC,IAAE,EAAE,CAAA,GAAE,OAAO,CAAC,EAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAA,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,aAAa,EAAC,CAAC,UAAU,EAAC,UAAU,EAAC,wBAAwB,EAAC,eAAe,EAAC,sBAAsB,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,OAAO,CAAC,CAAC,CAAC,EAAC,UAAU,CAAC,CAAC,CAAC,EAAC,KAAK,CAAC,CAAC,QAAQ,CAAC,GAAG,EAAC,YAAY,CAAC,GAAG,EAAC,aAAa,CAAC,GAAG,EAAC,SAAS,CAAC,GAAG,EAAC,UAAU,CAAC,GAAG,EAAC,aAAa,CAAC,GAAG,EAAC,cAAc,CAAC,GAAG,EAAC,WAAW,CAAC,IAAI,CAAC,EAAC,UAAU,CAAC,CAAC,QAAQ,EAAC,UAAU,EAAC,QAAQ,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,CAAC,cAAc,CAAC,CAAC,CAAC,gBAAgB,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,cAAc,IAAE,CAAC,CAAC,gBAAgB,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,cAAc,GAAC,CAAC,CAAA,CAAC,IAAI,CAAC,CAAC,IAAI,EAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,YAAY,CAAC,KAAG,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,aAAa,CAAC,IAAE,IAAG,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,KAAK,CAAC,KAAK,CAAC,MAAM,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,IAAI,CAAC,CAAC,EAAC,UAAU,CAAC,CAAC,EAAC,UAAU,CAAC,CAAC,CAAC,WAAW,EAAC,aAAa,CAAC,wBAAU,CAAC,CAAC,CAAC,YAAY,GAAC,CAAC,CAAC,YAAY,EAAE,EAAC,CAAC,CAAC,UAAU,EAAE,CAAC,CAAA,CAAC,EAAC,QAAQ,CAAC,kBAAS,CAAC,CAAC,CAAC,CAAC,CAAC,YAAY,GAAC,CAAC,EAAC,CAAC,CAAC,cAAc,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,YAAY,EAAC,MAAM,CAAC,CAAC,CAAC,YAAY,CAAC,CAAC,EAAC,CAAC,CAAC,OAAO,CAAC,UAAU,CAAC,uBAAuB,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,WAAW,GAAC,CAAC,EAAC,CAAC,CAAC,UAAU,EAAE,CAAC,CAAA,CAAC,EAAC,MAAM,CAAC,iBAAU,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,SAAS,CAAC,oBAAU,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,aAAa,CAAC,KAAG,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,aAAa,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,oBAAoB,EAAC,UAAU,CAAC,CAAC,CAAC,IAAI,EAAE,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,oBAAoB,EAAC,UAAU,CAAC,CAAC,CAAC,IAAI,EAAE,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,mBAAmB,EAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,IAAI,CAAC,QAAQ,GAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,iBAAiB,CAAC,CAAC,EAAC,CAAC,CAAC,cAAc,EAAC,UAAU,CAAC,OAAO,CAAC,CAAC,aAAa,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,UAAU,EAAC,UAAU,CAAC,CAAC,EAAE,EAAC,CAAC,CAAC,IAAI,EAAE,CAAA,CAAC,CAAC,EAAC,IAAI,CAAC,WAAW,GAAC,UAAU,CAAC,OAAO,CAAC,CAAC,WAAW,EAAE,CAAA,CAAC,EAAC,IAAI,CAAC,YAAY,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,IAAI,EAAE,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,QAAQ,CAAC,+EAA2E,EAAC,IAAI,CAAC,cAAS,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,IAAI,EAAE,CAAC,CAAC,GAAC,CAAC,CAAC,yBAAyB,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,OAAO,CAAC,CAAA,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,KAAG,CAAC,CAAC,SAAS,GAAC,CAAC,CAAC,EAAC,CAAC,EAAE,CAAC,WAAW,CAAC,MAAM,EAAC,CAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,QAAQ,CAAC,WAAW,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,GAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,EAAC,CAAC,EAAE,CAAC,WAAW,CAAC,MAAM,EAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,IAAI,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,UAAU,EAAC,UAAU,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,OAAO,CAAC,cAAc,EAAC,OAAO,CAAC,iBAAS,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,cAAc,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,SAAS,CAAC,UAAU,EAAC,UAAU,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,OAAO,CAAC,CAAC,CAAC,EAAC,OAAO,CAAC,cAAc,EAAC,QAAQ,CAAC,+OAAmO,EAAC,IAAI,CAAC,cAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,0JAAC,SAAS,CAAC,CAAC,CAAC,IAAI,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAC,CAAC,CAAC,CAAC,CAAC,MAAM,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,EAAC,CAAC,EAAE,EAAC,CAAC,IAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,GAAG,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,MAAM,CAAC,QAAQ,CAAC,CAAA,CAAC,CAAA,CAAC,CAAC,CAAC,UAAU,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,YAAY,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,SAAS,GAAC,UAAU,CAAC,OAAO,IAAI,KAAK,CAAC,CAAC,CAAC,WAAW,EAAE,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,cAAc,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,YAAY,EAAC,UAAU,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,UAAU,CAAC,eAAe,EAAC,IAAI,CAAC,cAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,IAAI,EAAE,CAAC,CAAC,CAAC,QAAQ,CAAC,kBAAkB,GAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,QAAQ,EAAC,CAAC,UAAU,EAAC,cAAc,EAAC,YAAY,EAAC,oBAAoB,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,GAAC,CAAC,GAAC,KAAI,GAAC,CAAC,GAAC,IAAG,CAAC,EAAE,CAAA,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,OAAO,CAAC,CAAC,UAAU,EAAC,QAAQ,CAAC,EAAC,UAAU,CAAC,WAAW,EAAC,KAAK,CAAC,CAAC,CAAC,EAAC,OAAO,CAAC,iBAAS,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,IAAI,CAAC,CAAC,cAAc,GAAC,CAAC,CAAC,UAAU,EAAC,CAAC,CAAC,OAAO,CAAC,GAAC,CAAC,CAAC,OAAO,EAAC,CAAC,CAAC,KAAK,CAAC,GAAC,CAAC,CAAC,MAAM,EAAC,CAAC,CAAC,IAAI,CAAC,GAAC,CAAC,CAAC,SAAS,EAAC,CAAC,CAAC,MAAM,CAAC,GAAC,CAAC,CAAC,UAAU,EAAC,CAAC,CAAC,OAAO,CAAC,GAAC,CAAC,CAAC,OAAO,EAAC,CAAC,CAAC,KAAK,CAAC,GAAC,CAAC,CAAC,aAAa,EAAC,CAAC,CAAC,UAAU,CAAC,GAAC,CAAC,CAAC,QAAQ,EAAC,CAAC,CAAC,MAAM,CAAC,GAAC,CAAC,CAAC,UAAU,EAAC,CAAC,CAAC,QAAQ,CAAC,GAAC,CAAC,CAAC,OAAO,EAAC,CAAC,CAAC,OAAO,CAAC,CAAC,GAAC,iBAAiB,EAAC,CAAC,CAAC,QAAQ,CAAC,aAAa,CAAC,KAAK,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,MAAM,EAAC,CAAC,EAAE,EAAC,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,iBAAiB,CAAC,CAAC,CAAC,KAAK,EAAE,CAAC,IAAI,CAAC,EAAC,CAAC,CAAC,QAAO,CAAC,KAAG,cAAc,KAAG,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,OAAO,KAAG,CAAC,GAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,YAAY,CAAC,MAAM,CAAC,EAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,GAAG,CAAC,gBAAgB,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,KAAG,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,IAAE,CAAC,CAAC,SAAS,CAAC,GAAG,CAAC,MAAM,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,GAAG,CAAC,aAAa,CAAC,CAAA,AAAC,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,CAAC,eAAe,EAAE,IAAE,CAAC,CAAC,MAAM,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,EAAE,CAAC,KAAG,CAAC,GAAC,CAAC,CAAC,IAAI,EAAE,EAAC,CAAC,GAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,eAAe,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,QAAQ,CAAC,MAAM,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,GAAC,CAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,eAAe,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAA,CAAE,CAAC,IAAE,CAAC,KAAG,CAAC,CAAC,KAAK,CAAC,QAAQ,EAAE,GAAC,CAAC,CAAC,CAAC,CAAC,eAAe,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAA,AAAC,CAAA,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC,IAAE,CAAC,CAAC,QAAQ,EAAE,EAAC,CAAC,IAAE,CAAC,IAAE,CAAC,CAAC,MAAM,EAAE,EAAC,CAAC,CAAC,SAAS,GAAC,EAAE,EAAC,CAAC,GAAC,CAAC,GAAC,CAAC,GAAC,IAAI,CAAA,CAAC,IAAI,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,YAAY,GAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,QAAQ,CAAC,GAAG,EAAC,UAAU,CAAC,GAAG,EAAC,KAAK,CAAC,GAAG,EAAC,MAAM,CAAC,GAAG,EAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,UAAU,EAAC,UAAU,CAAC,CAAC,CAAC,YAAY,IAAE,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,YAAY,EAAE,CAAC,QAAQ,EAAE,EAAC,CAAC,CAAC,MAAM,EAAE,EAAC,CAAC,GAAC,CAAC,GAAC,CAAC,GAAC,IAAI,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,eAAe,CAAC,OAAO,CAAC,EAAC,CAAC,KAAG,CAAC,CAAC,WAAW,GAAC,CAAC,CAAC,WAAW,GAAC,CAAC,CAAA,AAAC,EAAC,CAAC,CAAC,GAAG,CAAC,qBAAqB,EAAC,CAAC,CAAC,EAAC,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,oBAAoB,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,IAAI,CAAC,mBAAmB,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,YAAY,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,cAAc,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,uBAAuB,EAAC,UAAU,CAAC,CAAC,CAAC,eAAe,CAAC,CAAC,EAAC,CAAC,CAAC,YAAY,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,uBAAuB,EAAC,UAAU,CAAC,CAAC,CAAC,YAAY,IAAE,CAAC,EAAE,CAAA,CAAC,CAAC,CAAA,CAAC,CAAA,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,WAAW,EAAC,CAAC,UAAU,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,OAAO,CAAC,CAAC,CAAC,EAAC,OAAO,CAAC,CAAC,UAAU,EAAC,SAAS,CAAC,EAAC,QAAQ,CAAC,iGAAiG,GAAC,+UAA2T,EAAC,KAAK,CAAC,CAAC,KAAK,CAAC,GAAG,EAAC,IAAI,CAAC,GAAG,EAAC,MAAM,CAAC,GAAG,EAAC,OAAO,CAAC,GAAG,EAAC,KAAK,CAAC,GAAG,EAAC,MAAM,CAAC,GAAG,EAAC,QAAQ,CAAC,GAAG,EAAC,UAAU,CAAC,GAAG,EAAC,OAAO,CAAC,GAAG,CAAC,EAAC,IAAI,CAAC,cAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,eAAe,CAAC,OAAO,CAAC,EAAC,CAAC,CAAC,SAAS,GAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,cAAc,EAAE,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,MAAM,EAAC,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,EAAE,CAAC,OAAO,EAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,UAAU,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,QAAQ,GAAC,UAAU,CAAC,OAAM,MAAM,KAAG,CAAC,CAAC,MAAM,IAAE,CAAC,CAAC,MAAM,KAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA,CAAC,EAAC,CAAC,CAAC,SAAS,GAAC,UAAU,CAAC,OAAO,CAAC,CAAC,MAAM,IAAE,CAAC,CAAC,IAAI,CAAA,CAAC,EAAC,CAAC,CAAC,UAAU,GAAC,UAAU,CAAC,OAAO,CAAC,CAAC,OAAO,IAAE,CAAC,CAAC,IAAI,CAAA,CAAC,EAAC,CAAC,CAAC,WAAW,GAAC,UAAU,CAAC,OAAO,CAAC,CAAC,WAAW,EAAE,KAAG,CAAC,CAAC,MAAM,CAAA,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,SAAS,EAAC,CAAC,oBAAoB,EAAC,cAAc,EAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,KAAK,CAAC,CAAC,CAAC,EAAC,UAAU,CAAC,YAAY,EAAC,OAAO,CAAC,iBAAS,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,eAAe,EAAE,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,mBAAmB,EAAE,CAAC,CAAC,IAAE,CAAC,CAAC,UAAU,CAAC,CAAC,CAAC,IAAI,CAAC,OAAO,CAAC,SAAS,EAAC,MAAM,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,iBAAiB,CAAC,CAAC,EAAC,CAAC,CAAC,cAAc,EAAC,CAAC,CAAC,cAAc,CAAC,CAAC,CAAC,CAAC,MAAM,GAAC,CAAC,EAAC,CAAC,CAAC,QAAQ,GAAC,CAAC,EAAC,CAAC,CAAC,YAAY,GAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,OAAO,CAAC,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,UAAU,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAA,CAAC,EAAC,SAAS,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,OAAO,CAAC,UAAU,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,KAAG,CAAC,CAAC,OAAO,CAAC,gBAAgB,CAAC,CAAC,CAAC,CAAC,QAAQ,GAAC,CAAC,CAAC,IAAE,CAAC,CAAC,EAAC,CAAC,CAAC,WAAW,GAAC,CAAC,IAAE,CAAC,CAAC,EAAC,CAAC,CAAC,KAAK,CAAC,gBAAgB,EAAC,CAAC,CAAC,WAAW,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,2BAA2B,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,0BAA0B,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,qBAAqB,EAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,UAAU,EAAC,UAAU,CAAC,CAAC,CAAC,YAAY,GAAC,CAAC,CAAC,EAAC,CAAC,EAAE,EAAC,CAAC,CAAC,YAAY,GAAC,CAAC,CAAC,QAAQ,GAAC,CAAC,CAAC,MAAM,GAAC,CAAC,GAAC,IAAI,EAAC,OAAO,CAAC,CAAC,QAAQ,EAAC,OAAO,CAAC,CAAC,WAAW,CAAA,CAAC,CAAC,CAAA,CAAC,SAAS,CAAC,CAAC,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,WAAW,EAAE,IAAE,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAA,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,8BAA4B,CAAC,CAAC,QAAO,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC,EAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,OAAO,GAAC,CAAC,CAAC,IAAI,CAAC,QAAQ,EAAE,GAAC,QAAQ,GAAC,CAAC,CAAC,IAAI,CAAC,KAAK,EAAE,CAAC,EAAC,CAAC,GAAG,CAAC,CAAC,EAAC,IAAI,CAAC,CAAC,CAAC,CAAA,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,WAAW,EAAC,CAAC,UAAU,EAAC,cAAc,EAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,OAAM,CAAC,QAAQ,CAAC,GAAG,EAAC,OAAO,CAAC,CAAC,CAAC,EAAC,OAAO,CAAC,UAAU,EAAC,UAAU,CAAC,CAAC,CAAC,EAAC,QAAQ,CAAC,iLAAuK,EAAC,OAAO,CAAC,iBAAS,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,OAAO,CAAC,CAAC,QAAO,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,IAAI,EAAC,UAAU,CAAC,CAAC,CAAC,OAAO,EAAC,UAAU,CAAC,CAAC,CAAC,OAAO,EAAC,YAAY,CAAC,CAAC,CAAC,SAAS,EAAC,aAAa,CAAC,CAAC,CAAC,UAAU,EAAC,eAAe,CAAC,CAAC,CAAC,WAAW,EAAC,gBAAgB,CAAC,CAAC,CAAC,YAAY,EAAC,WAAW,CAAC,CAAC,CAAC,QAAQ,EAAC,aAAa,CAAC,CAAC,CAAC,UAAU,EAAC,QAAQ,CAAC,CAAC,CAAC,QAAQ,CAAC,EAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAE,CAAC,CAAC,IAAI,CAAC,CAAC,EAAC,CAAC,CAAC,CAAA,CAAC,CAAC,EAAC,CAAC,CAAC,WAAW,IAAE,CAAC,CAAC,CAAC,CAAC,CAAC,oBAAoB,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC,GAAG,CAAC,CAAC,CAAC,WAAW,CAAC,EAAC,CAAC,CAAC,QAAQ,CAAC,SAAS,GAAC,CAAC,CAAC,IAAI,CAAC,MAAM,EAAE,CAAC,EAAC,SAAS,CAAC,EAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,oBAAoB,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,MAAM,GAAC,IAAI,KAAK,CAAC,KAAK,CAAC,MAAM,CAAC,CAAC,EAAE,CAAC,CAAC,EAAC,KAAK,CAAC,CAAC,EAAC,QAAQ,CAAC,CAAC,EAAC,MAAM,CAAC,CAAC,EAAC,QAAQ,CAAC,mBAAU,CAAC,CAAC,KAAG,CAAC,CAAC,aAAa,CAAC,CAAC,CAAC,OAAO,CAAC,EAAC,CAAC,CAAC,MAAM,EAAE,CAAA,AAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,GAAG,CAAC,UAAU,EAAC,UAAU,CAAC,CAAC,CAAC,MAAM,CAAC,OAAO,EAAE,CAAA,CAAC,CAAC,CAAA,CAAC,CAAA,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAC,EAAC,CAAC,CAAC,SAAS,CAAC,SAAS,EAAC,UAAU,CAAC,OAAM,CAAC,QAAQ,CAAC,IAAI,EAAC,QAAQ,CAAC,IAAG,EAAC,UAAU,CAAC,YAAY,EAAC,OAAO,CAAC,iBAAS,CAAC,CAAC,CAAC,QAAO,CAAC,CAAC,QAAQ,CAAC,MAAM,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,eAAe,CAAC,OAAO,CAAC,EAAC,SAAS,CAAC,EAAC,CAAC,EAAC,CAAC,EAAC,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,EAAE,CAAA,CAAC,CAAA,CAAA,CAAC,CAAC,CAAA,CAAC,CAAC,CAAA,CAAC,CAAA,EAAE,CAAC","file":"ionic-angular.min-compiled.js","sourcesContent":["/*!\n * Copyright 2014 Drifty Co.\n * http://drifty.com/\n *\n * Ionic, v1.0.0\n * A powerful HTML5 mobile app framework.\n * http://ionicframework.com/\n *\n * By @maxlynch, @benjsperry, @adamdbradley <3\n *\n * Licensed under the MIT license. Please see LICENSE for more information.\n *\n */\n\n!function(){function e(e,t,n,i,o,r){function a(i,a,c,s,l){function d(){z.resizeRequiresRefresh(w.__clientWidth,w.__clientHeight)&&g()}function f(){var e;return e={dataLength:0,width:0,height:0,resizeRequiresRefresh:function(t,n){var i=e.dataLength&&t&&n&&(t!==e.width||n!==e.height);return e.width=t,e.height=n,!!i},dataChangeRequiresRefresh:function(t){var n=t.length>0||t.length<e.dataLength;return e.dataLength=t.length,!!n}}}function h(){return T||(T=new e({afterItemsNode:N[0],containerNode:y,heightData:A,widthData:E,forceRefreshImages:!(!u(c.forceRefreshImages)||\"false\"===c.forceRefreshImages),keyExpression:B,renderBuffer:_,scope:i,scrollView:s.scrollView,transclude:l}))}function p(){var e=angular.element(w.__content.querySelector(\".collection-repeat-after-container\"));if(!e.length){var t=!1,n=[].filter.call(w.__content.childNodes,function(e){return ionic.DomUtil.contains(e,y)?(t=!0,!1):t});e=angular.element('<span class=\"collection-repeat-after-container\">'),w.options.scrollingX&&e.addClass(\"horizontal\"),e.append(n),w.__content.appendChild(e[0])}return e}function v(){R?m(R,A):A.computed=!0,L?m(L,E):E.computed=!0}function g(){var e=P.length>0;if(e&&(A.computed||E.computed)&&$(),e&&A.computed){if(A.value=V.height,!A.value)throw new Error('collection-repeat tried to compute the height of repeated elements \"'+k+'\", but was unable to. Please provide the \"item-height\" attribute. http://ionicframework.com/docs/api/directive/collectionRepeat/')}else!A.dynamic&&A.getValue&&(A.value=A.getValue());if(e&&E.computed){if(E.value=V.width,!E.value)throw new Error('collection-repeat tried to compute the width of repeated elements \"'+k+'\", but was unable to. Please provide the \"item-width\" attribute. http://ionicframework.com/docs/api/directive/collectionRepeat/')}else!E.dynamic&&E.getValue&&(E.value=E.getValue());h().refreshLayout()}function m(e,n){if(e){var i;try{i=t(e)}catch(o){e.trim().match(/\\d+(px|%)$/)&&(e='\"'+e+'\"'),i=t(e)}var r=e.replace(/(\\'|\\\"|px|%)/g,\"\").trim(),a=r.length&&!/([a-zA-Z]|\\$|:|\\?)/.test(r);if(n.attrValue=e,a){var c=parseInt(i());if(e.indexOf(\"%\")>-1){var s=c/100;n.getValue=n===A?function(){return Math.floor(s*w.__clientHeight)}:function(){return Math.floor(s*w.__clientWidth)}}else n.value=c}else n.dynamic=!0,n.getValue=n===A?function(e,t){var n=i(e,t);return n.charAt&&\"%\"===n.charAt(n.length-1)?Math.floor(parseInt(n)/100*w.__clientHeight):parseInt(n)}:function(e,t){var n=i(e,t);return n.charAt&&\"%\"===n.charAt(n.length-1)?Math.floor(parseInt(n)/100*w.__clientWidth):parseInt(n)}}}function $(){H||l(O=i.$new(),function(e){e[0].removeAttribute(\"collection-repeat\"),H=e[0]}),O[B]=(x(i)||[])[0],o.$$phase||O.$digest(),y.appendChild(H);var e=n.getComputedStyle(H);V.width=parseInt(e.width),V.height=parseInt(e.height),y.removeChild(H)}var w=s.scrollView,b=a[0],y=angular.element('<div class=\"collection-repeat-container\">')[0];if(b.parentNode.replaceChild(y,b),w.options.scrollingX&&w.options.scrollingY)throw new Error(\"collection-repeat expected a parent x or y scrollView, not an xy scrollView.\");var k=c.collectionRepeat,C=k.match(/^\\s*([\\s\\S]+?)\\s+in\\s+([\\s\\S]+?)(?:\\s+track\\s+by\\s+([\\s\\S]+?))?\\s*$/);if(!C)throw new Error(\"collection-repeat expected expression in form of '_item_ in _collection_[ track by _id_]' but got '\"+c.collectionRepeat+\"'.\");var T,B=C[1],I=C[2],x=t(I),A={},E={},V={},P=[],D=c.itemRenderBuffer||c.collectionBufferSize,_=angular.isDefined(D)?parseInt(D):S,R=c.itemHeight||c.collectionItemHeight,L=c.itemWidth||c.collectionItemWidth,N=p(),z=f();v(),s.$element.on(\"scroll-resize\",g),angular.element(n).on(\"resize\",d);var M=o.$on(\"$ionicExposeAside\",ionic.animationFrameThrottle(function(){s.scrollView.resize(),d()}));r(g,0,!1),i.$watchCollection(x,function(e){if(P=e||(e=[]),!angular.isArray(e))throw new Error(\"collection-repeat expected an array for '\"+I+\"', but got a \"+typeof value);i.$$postDigest(function(){h().setData(P),z.dataChangeRequiresRefresh(P)&&g()})}),i.$on(\"$destroy\",function(){angular.element(n).off(\"resize\",d),M(),s.$element&&s.$element.off(\"scroll-resize\",g),H&&H.parentNode&&H.parentNode.removeChild(H),O&&O.$destroy(),O=H=null,T&&T.destroy(),T=null});var H,O}return{restrict:\"A\",priority:1e3,transclude:\"element\",$$tlb:!0,require:\"^^$ionicScroll\",link:a}}function t(e,t,n){var i={primaryPos:0,secondaryPos:0,primarySize:0,secondarySize:0,rowPrimarySize:0};return function(o){function r(){return a(!0)}function a(t){if(!a.destroyed){var n,o,r,l,u,d=ee.getScrollValue(),f=d+ee.scrollPrimarySize;ee.updateRenderRange(d,f),W=Math.max(0,W-T),F=Math.min(A.length-1,F+T);for(n in Z)(W>n||n>F)&&(r=Z[n],delete Z[n],j.push(r),r.isShown=!1);for(n=W;F>=n;n++)n>=A.length||Z[n]&&!t||(r=Z[n]||(Z[n]=j.length?j.pop():G.length?G.shift():new s),K.push(r),r.isShown=!0,u=r.scope,u.$index=n,u[C]=A[n],u.$first=0===n,u.$last=n===A.length-1,u.$middle=!(u.$first||u.$last),u.$odd=!(u.$even=0===(1&n)),u.$$disconnected&&ionic.Utils.reconnectScope(r.scope),l=ee.getDimensions(n),(r.secondaryPos!==l.secondaryPos||r.primaryPos!==l.primaryPos)&&(r.node.style[ionic.CSS.TRANSFORM]=H.replace(z,r.primaryPos=l.primaryPos).replace(M,r.secondaryPos=l.secondaryPos)),(r.secondarySize!==l.secondarySize||r.primarySize!==l.primarySize)&&(r.node.style.cssText=r.node.style.cssText.replace(y,O.replace(z,(r.primarySize=l.primarySize)+1).replace(M,r.secondarySize=l.secondarySize))));for(F===A.length-1&&(l=ee.getDimensions(A.length-1)||i,m.style[ionic.CSS.TRANSFORM]=H.replace(z,l.primaryPos+l.primarySize).replace(M,0));j.length;)r=j.pop(),r.scope.$broadcast(\"$collectionRepeatLeave\"),ionic.Utils.disconnectScope(r.scope),G.push(r),r.node.style[ionic.CSS.TRANSFORM]=\"translate3d(-9999px,-9999px,0)\",r.primaryPos=r.secondaryPos=null;if(w)for(n=0,o=K.length;o>n&&(r=K[n]);n++)if(r.images)for(var h,p=0,v=r.images.length;v>p&&(h=r.images[p]);p++){var g=h.src;h.src=b,h.src=g}if(t)for(var $=e.$$phase;K.length;)r=K.pop(),$||r.scope.$digest();else c()}}function c(){var t;c.running||(c.running=!0,n(function(){for(var n=e.$$phase;K.length;)t=K.pop(),t.isShown&&(n||t.scope.$digest());c.running=!1}))}function s(){var e=this;this.scope=B.$new(),this.id=\"item\"+J++,x(this.scope,function(t){e.element=t,e.element.data(\"$$collectionRepeatItem\",e),e.node=t[0],e.node.style[ionic.CSS.TRANSFORM]=\"translate3d(-9999px,-9999px,0)\",e.node.style.cssText+=\" height: 0px; width: 0px;\",ionic.Utils.disconnectScope(e.scope),$.appendChild(e.node),e.images=t[0].getElementsByTagName(\"img\")})}function l(){this.getItemPrimarySize=P,this.getItemSecondarySize=_,this.getScrollValue=function(){return Math.max(0,Math.min(I.__scrollTop-q,I.__maxScrollTop-q-U))},this.refreshDirection=function(){this.scrollPrimarySize=I.__clientHeight,this.scrollSecondarySize=I.__clientWidth,this.estimatedPrimarySize=v,this.estimatedSecondarySize=g,this.estimatedItemsAcross=L&&Math.floor(I.__clientWidth/g)||1}}function u(){this.getItemPrimarySize=_,this.getItemSecondarySize=P,this.getScrollValue=function(){return Math.max(0,Math.min(I.__scrollLeft-q,I.__maxScrollLeft-q-U))},this.refreshDirection=function(){this.scrollPrimarySize=I.__clientWidth,this.scrollSecondarySize=I.__clientHeight,this.estimatedPrimarySize=g,this.estimatedSecondarySize=v,this.estimatedItemsAcross=L&&Math.floor(I.__clientHeight/v)||1}}function d(){this.getEstimatedSecondaryPos=function(e){return e%this.estimatedItemsAcross*this.estimatedSecondarySize},this.getEstimatedPrimaryPos=function(e){return Math.floor(e/this.estimatedItemsAcross)*this.estimatedPrimarySize},this.getEstimatedIndex=function(e){return Math.floor(e/this.estimatedPrimarySize)*this.estimatedItemsAcross}}function f(){this.getEstimatedSecondaryPos=function(){return 0},this.getEstimatedPrimaryPos=function(e){return e*this.estimatedPrimarySize},this.getEstimatedIndex=function(e){return Math.floor(e/this.estimatedPrimarySize)}}function h(){this.getContentSize=function(){return this.getEstimatedPrimaryPos(A.length-1)+this.estimatedPrimarySize+q+U};var e={};this.getDimensions=function(t){return e.primaryPos=this.getEstimatedPrimaryPos(t),e.secondaryPos=this.getEstimatedSecondaryPos(t),e.primarySize=this.estimatedPrimarySize,e.secondarySize=this.estimatedSecondarySize,e},this.updateRenderRange=function(e,t){W=Math.max(0,this.getEstimatedIndex(e)),F=Math.min(A.length-1,this.getEstimatedIndex(t)+this.estimatedItemsAcross-1),Y=Math.max(0,this.getEstimatedPrimaryPos(W)),X=this.getEstimatedPrimaryPos(F)+this.estimatedPrimarySize}}function p(){function e(e){var t,r,a;for(t=Math.max(0,n);e>=t&&(a=c[t]);t++)r=c[t-1]||i,a.primarySize=o.getItemPrimarySize(t,A[t]),a.secondarySize=o.scrollSecondarySize,a.primaryPos=r.primaryPos+r.primarySize,a.secondaryPos=0}function t(e){var t,r,a;for(t=Math.max(n,0);e>=t&&(a=c[t]);t++)r=c[t-1]||i,a.secondarySize=Math.min(o.getItemSecondarySize(t,A[t]),o.scrollSecondarySize),a.secondaryPos=r.secondaryPos+r.secondarySize,0===t||a.secondaryPos+a.secondarySize>o.scrollSecondarySize?(a.secondaryPos=0,a.primarySize=o.getItemPrimarySize(t,A[t]),a.primaryPos=r.primaryPos+r.rowPrimarySize,a.rowStartIndex=t,a.rowPrimarySize=a.primarySize):(a.primarySize=o.getItemPrimarySize(t,A[t]),a.primaryPos=r.primaryPos,a.rowStartIndex=r.rowStartIndex,c[a.rowStartIndex].rowPrimarySize=a.rowPrimarySize=Math.max(c[a.rowStartIndex].rowPrimarySize,a.primarySize),a.rowPrimarySize=Math.max(a.primarySize,a.rowPrimarySize))}var n,o=this,r=ionic.debounce(Q,25,!0),a=L?t:e,c=[];this.getContentSize=function(){var e=c[n]||i;return(e.primaryPos+e.primarySize||0)+this.getEstimatedPrimaryPos(A.length-n-1)+q+U},this.onDestroy=function(){c.length=0},this.onRefreshData=function(){var e,t;for(e=c.length,t=A.length;t>e;e++)c.push({});n=-1},this.onRefreshLayout=function(){n=-1},this.getDimensions=function(e){return e=Math.min(e,A.length-1),e>n&&(e>.9*A.length?(a(A.length-1),n=A.length-1,Q()):(a(e),n=e,r())),c[e]};var s=-1,l=-1;this.updateRenderRange=function(e,t){var n,i,o;if(this.getDimensions(2*this.getEstimatedIndex(t)),-1===s||0===e)n=0;else if(e>=l)for(n=s,i=A.length;i>n&&!((o=this.getDimensions(n))&&o.primaryPos+o.rowPrimarySize>=e);n++);else for(n=s;n>=0;n--)if((o=this.getDimensions(n))&&o.primaryPos<=e){n=L?o.rowStartIndex:n;break}W=Math.min(Math.max(0,n),A.length-1),Y=-1!==W?this.getDimensions(W).primaryPos:-1;var r;for(n=W+1,i=A.length;i>n;n++)if((o=this.getDimensions(n))&&o.primaryPos+o.rowPrimarySize>t){if(L)for(r=o;i-1>n&&(o=this.getDimensions(n+1)).primaryPos===r.primaryPos;)n++;break}F=Math.min(n,A.length-1),X=-1!==F?(o=this.getDimensions(F)).primaryPos+(o.rowPrimarySize||o.primarySize):-1,l=e,s=W}}var v,g,m=o.afterItemsNode,$=o.containerNode,w=o.forceRefreshImages,S=o.heightData,k=o.widthData,C=o.keyExpression,T=o.renderBuffer,B=o.scope,I=o.scrollView,x=o.transclude,A=[],E={},V=S.getValue||function(){return S.value},P=function(e,t){return E[C]=t,E.$index=e,V(B,E)},D=k.getValue||function(){return k.value},_=function(e,t){return E[C]=t,E.$index=e,D(B,E)},R=!!I.options.scrollingY,L=R?k.dynamic||k.value!==I.__clientWidth:S.dynamic||S.value!==I.__clientHeight,N=!S.dynamic&&!k.dynamic,z=\"PRIMARY\",M=\"SECONDARY\",H=R?\"translate3d(SECONDARYpx,PRIMARYpx,0)\":\"translate3d(PRIMARYpx,SECONDARYpx,0)\",O=R?\"height: PRIMARYpx; width: SECONDARYpx;\":\"height: SECONDARYpx; width: PRIMARYpx;\",q=0,U=0,W=-1,F=-1,X=-1,Y=-1,G=[],j=[],K=[],Z={},J=0,Q=R?function(){I.setDimensions(null,null,null,ee.getContentSize(),!0)}:function(){I.setDimensions(null,null,ee.getContentSize(),null,!0)},ee=R?new l:new u;(L?d:f).call(ee),(N?h:p).call(ee);var te=R?\"getContentHeight\":\"getContentWidth\",ne=I.options[te];I.options[te]=angular.bind(ee,ee.getContentSize),I.__$callback=I.__callback,I.__callback=function(e,t,n,i){var o=ee.getScrollValue();(-1===W||o+ee.scrollPrimarySize>X||Y>o)&&a(),I.__$callback(e,t,n,i)};var ie=!1,oe=!1;this.refreshLayout=function(){A.length?(v=P(0,A[0]),g=_(0,A[0])):(v=100,g=100);var e=getComputedStyle(m)||{},n=m.firstElementChild&&getComputedStyle(m.firstElementChild)||{},i=m.lastElementChild&&getComputedStyle(m.lastElementChild)||{};U=(parseInt(e[R?\"height\":\"width\"])||0)+(n&&parseInt(n[R?\"marginTop\":\"marginLeft\"])||0)+(i&&parseInt(i[R?\"marginBottom\":\"marginRight\"])||0),q=0;var o=$;do q+=o[R?\"offsetTop\":\"offsetLeft\"];while(ionic.DomUtil.contains(I.__content,o=o.offsetParent));var a=$.previousElementSibling,c=a?t.getComputedStyle(a):{},l=parseInt(c[R?\"marginBottom\":\"marginRight\"]||0);if($.style[ionic.CSS.TRANSFORM]=H.replace(z,-l).replace(M,0),q-=l,I.__clientHeight&&I.__clientWidth||(I.__clientWidth=I.__container.clientWidth,I.__clientHeight=I.__container.clientHeight),(ee.onRefreshLayout||angular.noop)(),ee.refreshDirection(),Q(),!ie)for(var u=Math.max(20,3*T),d=0;u>d;d++)G.push(new s);ie=!0,ie&&oe&&((I.__scrollLeft>I.__maxScrollLeft||I.__scrollTop>I.__maxScrollTop)&&I.resize(),r(!0))},this.setData=function(e){A=e,(ee.onRefreshData||angular.noop)(),oe=!0},this.destroy=function(){a.destroyed=!0,G.forEach(function(e){e.scope.$destroy(),e.scope=e.element=e.node=e.images=null}),G.length=K.length=j.length=0,Z={},I.options[te]=ne,I.__callback=I.__$callback,I.resize(),(ee.onDestroy||angular.noop)()}}}function n(e){return[\"$ionicGesture\",\"$parse\",function(t,n){var i=e.substr(2).toLowerCase();return function(o,r,a){var c=n(a[e]),s=function(e){o.$apply(function(){c(o,{$event:e})})},l=t.on(i,s,r);o.$on(\"$destroy\",function(){t.off(l,i,s)})}}]}function i(){return[\"$ionicScrollDelegate\",function(e){return{restrict:\"E\",link:function(t,n,i){function o(t){for(var i=3,o=t.target;i--&&o;){if(o.classList.contains(\"button\")||o.tagName.match(/input|textarea|select/i)||o.isContentEditable)return;o=o.parentNode}var r=t.gesture&&t.gesture.touches[0]||t.detail.touches[0],a=n[0].getBoundingClientRect();ionic.DomUtil.rectContains(r.pageX,r.pageY,a.left,a.top-20,a.left+a.width,a.top+a.height)&&e.scrollTop(!0)}\"true\"!=i.noTapScroll&&(ionic.on(\"tap\",o,n[0]),t.$on(\"$destroy\",function(){ionic.off(\"tap\",o,n[0])}))}}}]}function o(e){return[\"$document\",\"$timeout\",function(t,n){return{restrict:\"E\",controller:\"$ionicHeaderBar\",compile:function(i){function o(t,n,i,o){e?(t.$watch(function(){return n[0].className},function(e){var n=-1===e.indexOf(\"ng-hide\"),i=-1!==e.indexOf(\"bar-subheader\");t.$hasHeader=n&&!i,t.$hasSubheader=n&&i,t.$emit(\"$ionicSubheader\",t.$hasSubheader)}),t.$on(\"$destroy\",function(){delete t.$hasHeader,delete t.$hasSubheader}),o.align(),t.$on(\"$ionicHeader.align\",function(){ionic.requestAnimationFrame(function(){o.align()})})):(t.$watch(function(){return n[0].className},function(e){var n=-1===e.indexOf(\"ng-hide\"),i=-1!==e.indexOf(\"bar-subfooter\");t.$hasFooter=n&&!i,t.$hasSubfooter=n&&i}),t.$on(\"$destroy\",function(){delete t.$hasFooter,delete t.$hasSubfooter}),t.$watch(\"$hasTabs\",function(e){n.toggleClass(\"has-tabs\",!!e)}))}return i.addClass(e?\"bar bar-header\":\"bar bar-footer\"),n(function(){e&&t[0].getElementsByClassName(\"tabs-top\").length&&i.addClass(\"has-tabs-top\")}),{pre:o}}}}]}function r(e){return e.clientHeight}function a(e){e.stopPropagation()}var c=angular.module(\"ionic\",[\"ngAnimate\",\"ngSanitize\",\"ui.router\"]),s=angular.extend,l=angular.forEach,u=angular.isDefined,d=angular.isNumber,f=angular.isString,h=angular.element,p=angular.noop;c.factory(\"$ionicActionSheet\",[\"$rootScope\",\"$compile\",\"$animate\",\"$timeout\",\"$ionicTemplateLoader\",\"$ionicPlatform\",\"$ionicBody\",\"IONIC_BACK_PRIORITY\",function(e,t,n,i,o,r,a,c){function l(o){function l(e){e&&/icon/.test(e)&&(u.$actionSheetHasIcon=!0)}var u=e.$new(!0);s(u,{cancel:p,destructiveButtonClicked:p,buttonClicked:p,$deregisterBackButton:p,buttons:[],cancelOnStateChange:!0},o||{});for(var d=0;d<u.buttons.length;d++)l(u.buttons[d].text);l(u.cancelText),l(u.destructiveText);var f=u.element=t('<ion-action-sheet ng-class=\"cssClass\" buttons=\"buttons\"></ion-action-sheet>')(u),v=h(f[0].querySelector(\".action-sheet-wrapper\")),g=u.cancelOnStateChange?e.$on(\"$stateChangeSuccess\",function(){u.cancel()}):p;return u.removeSheet=function(e){u.removed||(u.removed=!0,v.removeClass(\"action-sheet-up\"),i(function(){a.removeClass(\"action-sheet-open\")},400),u.$deregisterBackButton(),g(),n.removeClass(f,\"active\").then(function(){u.$destroy(),f.remove(),u.cancel.$scope=v=null,(e||p)()}))},u.showSheet=function(e){u.removed||(a.append(f).addClass(\"action-sheet-open\"),n.addClass(f,\"active\").then(function(){u.removed||(e||p)()}),i(function(){u.removed||v.addClass(\"action-sheet-up\")},20,!1))},u.$deregisterBackButton=r.registerBackButtonAction(function(){i(u.cancel)},c.actionSheet),u.cancel=function(){u.removeSheet(o.cancel)},u.buttonClicked=function(e){o.buttonClicked(e,o.buttons[e])===!0&&u.removeSheet()},u.destructiveButtonClicked=function(){o.destructiveButtonClicked()===!0&&u.removeSheet()},u.showSheet(),u.cancel.$scope=u,u.cancel}return{show:l}}]),h.prototype.addClass=function(e){var t,n,i,o,r,a;if(e&&\"ng-scope\"!=e&&\"ng-isolate-scope\"!=e)for(t=0;t<this.length;t++)if(o=this[t],o.setAttribute)if(e.indexOf(\" \")<0&&o.classList.add)o.classList.add(e);else{for(a=(\" \"+(o.getAttribute(\"class\")||\"\")+\" \").replace(/[\\n\\t]/g,\" \"),r=e.split(\" \"),n=0;n<r.length;n++)i=r[n].trim(),-1===a.indexOf(\" \"+i+\" \")&&(a+=i+\" \");o.setAttribute(\"class\",a.trim())}return this},h.prototype.removeClass=function(e){var t,n,i,o,r;if(e)for(t=0;t<this.length;t++)if(r=this[t],r.getAttribute)if(e.indexOf(\" \")<0&&r.classList.remove)r.classList.remove(e);else for(i=e.split(\" \"),n=0;n<i.length;n++)o=i[n],r.setAttribute(\"class\",(\" \"+(r.getAttribute(\"class\")||\"\")+\" \").replace(/[\\n\\t]/g,\" \").replace(\" \"+o.trim()+\" \",\" \").trim());return this},c.factory(\"$ionicBackdrop\",[\"$document\",\"$timeout\",\"$$rAF\",function(e,t,n){function i(){c++,1===c&&(a.addClass(\"visible\"),n(function(){c>=1&&a.addClass(\"active\")}))}function o(){1===c&&(a.removeClass(\"active\"),t(function(){0===c&&a.removeClass(\"visible\")},400,!1)),c=Math.max(0,c-1)}function r(){return a}var a=h('<div class=\"backdrop\">'),c=0;return e[0].body.appendChild(a[0]),{retain:i,release:o,getElement:r,_element:a}}]),c.factory(\"$ionicBind\",[\"$parse\",\"$interpolate\",function(e,t){var n=/^\\s*([@=&])(\\??)\\s*(\\w*)\\s*$/;return function(i,o,r){l(r||{},function(r,a){var c,s,l=r.match(n)||[],u=l[3]||a,d=l[1];switch(d){case\"@\":if(!o[u])return;o.$observe(u,function(e){i[a]=e}),o[u]&&(i[a]=t(o[u])(i));break;case\"=\":if(!o[u])return;s=i.$watch(o[u],function(e){i[a]=e}),i.$on(\"$destroy\",s);break;case\"&\":if(o[u]&&o[u].match(RegExp(a+\"(.*?)\")))throw new Error('& expression binding \"'+a+'\" looks like it will recursively call \"'+o[u]+'\" and cause a stack overflow! Please choose a different scopeName.');c=e(o[u]),i[a]=function(e){return c(i,e)}}})}}]),c.factory(\"$ionicBody\",[\"$document\",function(e){return{addClass:function(){for(var t=0;t<arguments.length;t++)e[0].body.classList.add(arguments[t]);return this},removeClass:function(){for(var t=0;t<arguments.length;t++)e[0].body.classList.remove(arguments[t]);return this},enableClass:function(e){var t=Array.prototype.slice.call(arguments).slice(1);return e?this.addClass.apply(this,t):this.removeClass.apply(this,t),this},append:function(t){return e[0].body.appendChild(t.length?t[0]:t),this},get:function(){return e[0].body}}}]),c.factory(\"$ionicClickBlock\",[\"$document\",\"$ionicBody\",\"$timeout\",function(e,t,n){function i(e){e.preventDefault(),e.stopPropagation()}function o(){s&&(a?a.classList.remove(l):(a=e[0].createElement(\"div\"),a.className=\"click-block\",t.append(a),a.addEventListener(\"touchstart\",i),a.addEventListener(\"mousedown\",i)),s=!1)}function r(){a&&a.classList.add(l)}var a,c,s,l=\"click-block-hide\";return{show:function(e){s=!0,n.cancel(c),c=n(this.hide,e||310,!1),o()},hide:function(){s=!1,n.cancel(c),r()}}}]),c.factory(\"$ionicGesture\",[function(){return{on:function(e,t,n,i){return window.ionic.onGesture(e,t,n[0],i)},off:function(e,t,n){return window.ionic.offGesture(e,t,n)}}}]),c.factory(\"$ionicHistory\",[\"$rootScope\",\"$state\",\"$location\",\"$window\",\"$timeout\",\"$ionicViewSwitcher\",\"$ionicNavViewDelegate\",function(e,t,n,i,o,r,a){function c(e){return e?R.views[e]:null}function l(e){return e?c(e.backViewId):null}function d(e){return e?c(e.forwardViewId):null}function f(e){return e?R.histories[e]:null}function h(e){var t=p(e);return R.histories[t.historyId]||(R.histories[t.historyId]={historyId:t.historyId,parentHistoryId:p(t.scope.$parent).historyId,stack:[],cursor:-1}),f(t.historyId)}function p(t){for(var n=t;n;){if(n.hasOwnProperty(\"$historyId\"))return{historyId:n.$historyId,scope:n};n=n.$parent}return{historyId:\"root\",scope:e}}function v(e){R.currentView=c(e),R.backView=l(R.currentView),R.forwardView=d(R.currentView)}function g(){var e;if(t&&t.current&&t.current.name){if(e=t.current.name,t.params)for(var n in t.params)t.params.hasOwnProperty(n)&&t.params[n]&&(e+=\"_\"+n+\"=\"+t.params[n]);return e}return ionic.Utils.nextUid()}function m(){var e;if(t&&t.params)for(var n in t.params)t.params.hasOwnProperty(n)&&(e=e||{},e[n]=t.params[n]);return e}function $(e){return e&&e.length&&/ion-side-menus|ion-tabs/i.test(e[0].tagName)}function w(e,t){return t&&t.$$state&&t.$$state.self.canSwipeBack===!1?!1:e&&\"false\"===e.attr(\"can-swipe-back\")?!1:!0}var b,y,S,k,C=\"initialView\",T=\"newView\",B=\"moveBack\",I=\"moveForward\",x=\"back\",A=\"forward\",E=\"enter\",V=\"exit\",P=\"swap\",D=\"none\",_=0,R={histories:{root:{historyId:\"root\",parentHistoryId:null,stack:[],cursor:-1}},views:{},backView:null,forwardView:null,currentView:null},L=function(){};return L.prototype.initialize=function(e){if(e){for(var t in e)this[t]=e[t];return this}return null},L.prototype.go=function(){if(this.stateName)return t.go(this.stateName,this.stateParams);if(this.url&&this.url!==n.url()){if(R.backView===this)return i.history.go(-1);if(R.forwardView===this)return i.history.go(1);n.url(this.url)}return null},L.prototype.destroy=function(){this.scope&&(this.scope.$destroy&&this.scope.$destroy(),this.scope=null)},{register:function(e,t){var i,a,s,u=g(),d=h(e),$=R.currentView,L=R.backView,N=R.forwardView,z=null,M=null,H=D,O=d.historyId,q=n.url();if(b!==u&&(b=u,_++),k)z=k.viewId,M=k.action,H=k.direction,k=null;else if(L&&L.stateId===u)z=L.viewId,O=L.historyId,M=B,L.historyId===$.historyId?H=x:$&&(H=V,i=f(L.historyId),i&&i.parentHistoryId===$.historyId?H=E:(i=f($.historyId),i&&i.parentHistoryId===d.parentHistoryId&&(H=P)));else if(N&&N.stateId===u)z=N.viewId,O=N.historyId,M=I,N.historyId===$.historyId?H=A:$&&(H=V,$.historyId===d.parentHistoryId?H=E:(i=f($.historyId),i&&i.parentHistoryId===d.parentHistoryId&&(H=P))),i=p(e),N.historyId&&i.scope&&(i.scope.$historyId=N.historyId,O=N.historyId);else if($&&$.historyId!==O&&d.cursor>-1&&d.stack.length>0&&d.cursor<d.stack.length&&d.stack[d.cursor].stateId===u){var U=d.stack[d.cursor];z=U.viewId,O=U.historyId,M=B,H=P,i=f($.historyId),i&&i.parentHistoryId===O?H=V:(i=f(O),i&&i.parentHistoryId===$.historyId&&(H=E)),i=c(U.backViewId),i&&U.historyId!==i.historyId&&(d.stack[d.cursor].backViewId=$.viewId)}else{if(s=r.createViewEle(t),this.isAbstractEle(s,t))return{action:\"abstractView\",direction:D,ele:s};if(z=ionic.Utils.nextUid(),$){if($.forwardViewId=z,M=T,N&&$.stateId!==N.stateId&&$.historyId===N.historyId&&(i=f(N.historyId))){for(a=i.stack.length-1;a>=N.index;a--){var W=i.stack[a];W&&W.destroy&&W.destroy(),i.stack.splice(a)}O=N.historyId}d.historyId===$.historyId?H=A:$.historyId!==d.historyId&&(H=E,i=f($.historyId),i&&i.parentHistoryId===d.parentHistoryId?H=P:(i=f(i.parentHistoryId),i&&i.historyId===d.historyId&&(H=V)))}else M=C;2>_&&(H=D),R.views[z]=this.createView({viewId:z,index:d.stack.length,historyId:d.historyId,backViewId:$&&$.viewId?$.viewId:null,forwardViewId:null,stateId:u,stateName:this.currentStateName(),stateParams:m(),url:q,canSwipeBack:w(s,t)}),d.stack.push(R.views[z])}if(o.cancel(S),y){if(y.disableAnimate&&(H=D),y.disableBack&&(R.views[z].backViewId=null),y.historyRoot){for(a=0;a<d.stack.length;a++)d.stack[a].viewId===z?(d.stack[a].index=0,d.stack[a].backViewId=d.stack[a].forwardViewId=null):delete R.views[d.stack[a].viewId];d.stack=[R.views[z]]}y=null}if(v(z),R.backView&&O==R.backView.historyId&&u==R.backView.stateId&&q==R.backView.url)for(a=0;a<d.stack.length;a++)if(d.stack[a].viewId==z){M=\"dupNav\",H=D,a>0&&(d.stack[a-1].forwardViewId=null),R.forwardView=null,R.currentView.index=R.backView.index,R.currentView.backViewId=R.backView.backViewId,R.backView=l(R.backView),d.stack.splice(a,1);break}return d.cursor=R.currentView.index,{viewId:z,action:M,direction:H,historyId:O,enableBack:this.enabledBack(R.currentView),isHistoryRoot:0===R.currentView.index,ele:s}},registerHistory:function(e){e.$historyId=ionic.Utils.nextUid()},createView:function(e){var t=new L;return t.initialize(e)},getViewById:c,viewHistory:function(){return R},currentView:function(e){return arguments.length&&(R.currentView=e),R.currentView},currentHistoryId:function(){return R.currentView?R.currentView.historyId:null},currentTitle:function(e){return R.currentView?(arguments.length&&(R.currentView.title=e),R.currentView.title):void 0},backView:function(e){return arguments.length&&(R.backView=e),R.backView},backTitle:function(e){var t=e&&c(e.backViewId)||R.backView;return t&&t.title},forwardView:function(e){return arguments.length&&(R.forwardView=e),R.forwardView},currentStateName:function(){return t&&t.current?t.current.name:null},isCurrentStateNavView:function(e){return!!(t&&t.current&&t.current.views&&t.current.views[e])},goToHistoryRoot:function(e){if(e){var t=f(e);if(t&&t.stack.length){if(R.currentView&&R.currentView.viewId===t.stack[0].viewId)return;k={viewId:t.stack[0].viewId,action:B,direction:x},t.stack[0].go()}}},goBack:function(e){if(u(e)&&-1!==e){if(e>-1)return;var t=R.histories[this.currentHistoryId()],n=t.cursor+e+1;1>n&&(n=1),t.cursor=n,v(t.stack[n].viewId);for(var i=n-1,r=[],a=c(t.stack[i].forwardViewId);a&&(r.push(a.stateId||a.viewId),i++,!(i>=t.stack.length));)a=c(t.stack[i].forwardViewId);var s=this;r.length&&o(function(){s.clearCache(r)},600)}R.backView&&R.backView.go()},enabledBack:function(e){var t=l(e);return!(!t||t.historyId!==e.historyId)},clearHistory:function(){var e=R.histories,t=R.currentView;if(e)for(var n in e)e[n].stack&&(e[n].stack=[],e[n].cursor=-1),t&&t.historyId===n?(t.backViewId=t.forwardViewId=null,e[n].stack.push(t)):e[n].destroy&&e[n].destroy();for(var i in R.views)i!==t.viewId&&delete R.views[i];t&&v(t.viewId)},clearCache:function(e){o(function(){a._instances.forEach(function(t){t.clearCache(e)})})},nextViewOptions:function(e){return arguments.length&&(o.cancel(S),null===e?y=e:(y=y||{},s(y,e),y.expire&&(S=o(function(){y=null},y.expire)))),y},isAbstractEle:function(e,t){return t&&t.$$state&&t.$$state.self[\"abstract\"]?!0:!(!e||!$(e)&&!$(e.children()))},isActiveScope:function(e){if(!e)return!1;for(var t,n=e,i=this.currentHistoryId();n;){if(n.$$disconnected)return!1;if(!t&&n.hasOwnProperty(\"$historyId\")&&(t=!0),i){if(n.hasOwnProperty(\"$historyId\")&&i==n.$historyId)return!0;if(n.hasOwnProperty(\"$activeHistoryId\")&&i==n.$activeHistoryId){if(n.hasOwnProperty(\"$historyId\"))return!0;if(!t)return!0}}t&&n.hasOwnProperty(\"$activeHistoryId\")&&(t=!1),n=n.$parent}return i?\"root\"==i:!0}}}]).run([\"$rootScope\",\"$state\",\"$location\",\"$document\",\"$ionicPlatform\",\"$ionicHistory\",\"IONIC_BACK_PRIORITY\",function(e,t,n,i,o,r,a){function c(e){var t=r.backView();return t?t.go():ionic.Platform.exitApp(),e.preventDefault(),!1}e.$on(\"$ionicView.beforeEnter\",function(){ionic.keyboard&&ionic.keyboard.hide&&ionic.keyboard.hide()}),e.$on(\"$ionicHistory.change\",function(e,i){if(!i)return null;var o=r.viewHistory(),a=i.historyId?o.histories[i.historyId]:null;if(a&&a.cursor>-1&&a.cursor<a.stack.length){var c=a.stack[a.cursor];return c.go(i)}!i.url&&i.uiSref&&(i.url=t.href(i.uiSref)),i.url&&(0===i.url.indexOf(\"#\")&&(i.url=i.url.replace(\"#\",\"\")),i.url!==n.url()&&n.url(i.url))}),e.$ionicGoBack=function(e){r.goBack(e)},e.$on(\"$ionicView.afterEnter\",function(e,t){t&&t.title&&(i[0].title=t.title)}),o.registerBackButtonAction(c,a.view)}]),c.provider(\"$ionicConfig\",function(){function e(e,i){a.platform[e]=i,o.platform[e]={},t(a,a.platform[e]),n(a.platform[e],o.platform[e],\"\")}function t(e,n){for(var i in e)i!=r&&e.hasOwnProperty(i)&&(angular.isObject(e[i])?(u(n[i])||(n[i]={}),t(e[i],n[i])):u(n[i])||(n[i]=null))}function n(e,t,o){l(e,function(c,s){angular.isObject(e[s])?(t[s]={},n(e[s],t[s],o+\".\"+s)):t[s]=function(n){if(arguments.length)return e[s]=n,t;if(e[s]==r){var c=i(a.platform,ionic.Platform.platform()+o+\".\"+s);return c||c===!1?c:i(a.platform,\"default\"+o+\".\"+s)}return e[s]}})}function i(e,t){t=t.split(\".\");for(var n=0;n<t.length;n++){if(!e||!u(e[t[n]]))return null;e=e[t[n]]}return e}var o=this;o.platform={};var r=\"platform\",a={views:{maxCache:r,forwardCache:r,transition:r,swipeBackEnabled:r,swipeBackHitWidth:r},navBar:{alignTitle:r,positionPrimaryButtons:r,positionSecondaryButtons:r,transition:r},backButton:{icon:r,text:r,previousTitleText:r},form:{checkbox:r,toggle:r},scrolling:{jsScrolling:r},tabs:{style:r,position:r},templates:{maxPrefetch:r},platform:{}};n(a,o,\"\"),e(\"default\",{views:{maxCache:10,forwardCache:!1,transition:\"ios\",swipeBackEnabled:!0,swipeBackHitWidth:45},navBar:{alignTitle:\"center\",positionPrimaryButtons:\"left\",positionSecondaryButtons:\"right\",transition:\"view\"},backButton:{icon:\"ion-ios-arrow-back\",text:\"Back\",previousTitleText:!0},form:{checkbox:\"circle\",toggle:\"large\"},scrolling:{jsScrolling:!0},tabs:{style:\"standard\",position:\"bottom\"},templates:{maxPrefetch:30}}),e(\"ios\",{}),e(\"android\",{views:{transition:\"android\",swipeBackEnabled:!1},navBar:{alignTitle:\"left\",positionPrimaryButtons:\"right\",positionSecondaryButtons:\"right\"},backButton:{icon:\"ion-android-arrow-back\",text:!1,previousTitleText:!1},form:{checkbox:\"square\",toggle:\"small\"},tabs:{style:\"striped\",position:\"top\"}}),e(\"windowsphone\",{}),o.transitions={views:{},navBar:{}},o.transitions.views.ios=function(e,t,n,i){function o(e,t,n,i){var o={};o[ionic.CSS.TRANSITION_DURATION]=r.shouldAnimate?\"\":0,o.opacity=t,i>-1&&(o.boxShadow=\"0 0 10px rgba(0,0,0,\"+(r.shouldAnimate?.45*i:.3)+\")\"),o[ionic.CSS.TRANSFORM]=\"translate3d(\"+n+\"%,0,0)\",ionic.DomUtil.cachedStyles(e,o)}var r={run:function(i){\"forward\"==n?(o(e,1,99*(1-i),1-i),o(t,1-.1*i,-33*i,-1)):\"back\"==n?(o(e,1-.1*(1-i),-33*(1-i),-1),o(t,1,100*i,1-i)):(o(e,1,0,-1),o(t,0,0,-1))},shouldAnimate:i&&(\"forward\"==n||\"back\"==n)};return r},o.transitions.navBar.ios=function(e,t,n,i){function o(e,t,n,i){var o={};o[ionic.CSS.TRANSITION_DURATION]=c.shouldAnimate?\"\":\"0ms\",o.opacity=1===t?\"\":t,e.setCss(\"buttons-left\",o),e.setCss(\"buttons-right\",o),e.setCss(\"back-button\",o),o[ionic.CSS.TRANSFORM]=\"translate3d(\"+i+\"px,0,0)\",e.setCss(\"back-text\",o),o[ionic.CSS.TRANSFORM]=\"translate3d(\"+n+\"px,0,0)\",e.setCss(\"title\",o)}function r(e,t,n){if(e&&t){var i=(e.titleTextX()+e.titleWidth())*(1-n),r=t&&(t.titleTextX()-e.backButtonTextLeft())*(1-n)||0;o(e,n,i,r)}}function a(e,t,n){if(e&&t){var i=(-(e.titleTextX()-t.backButtonTextLeft())-e.titleLeftRight())*n;o(e,1-n,i,0)}}var c={run:function(n){var i=e.controller(),o=t&&t.controller();\"back\"==c.direction?(a(i,o,1-n),r(o,i,1-n)):(r(i,o,n),a(o,i,n))},direction:n,shouldAnimate:i&&(\"forward\"==n||\"back\"==n)};return c},o.transitions.views.android=function(e,t,n,i){function o(e,t){var n={};n[ionic.CSS.TRANSITION_DURATION]=r.shouldAnimate?\"\":0,n[ionic.CSS.TRANSFORM]=\"translate3d(\"+t+\"%,0,0)\",ionic.DomUtil.cachedStyles(e,n)}i=i&&(\"forward\"==n||\"back\"==n);var r={run:function(i){\"forward\"==n?(o(e,99*(1-i)),o(t,-100*i)):\"back\"==n?(o(e,-100*(1-i)),o(t,100*i)):(o(e,0),o(t,0))},shouldAnimate:i};return r},o.transitions.navBar.android=function(e,t,n,i){function o(e,t){if(e){var n={};n.opacity=1===t?\"\":t,e.setCss(\"buttons-left\",n),e.setCss(\"buttons-right\",n),e.setCss(\"back-button\",n),e.setCss(\"back-text\",n),e.setCss(\"title\",n)}}return{run:function(n){o(e.controller(),n),o(t&&t.controller(),1-n)},shouldAnimate:i&&(\"forward\"==n||\"back\"==n)}},o.transitions.views.none=function(e,t){return{run:function(n){o.transitions.views.android(e,t,!1,!1).run(n)},shouldAnimate:!1}},o.transitions.navBar.none=function(e,t){return{run:function(n){o.transitions.navBar.ios(e,t,!1,!1).run(n),o.transitions.navBar.android(e,t,!1,!1).run(n)},shouldAnimate:!1}},o.setPlatformConfig=e,o.$get=function(){return o;\n\n}}).config([\"$compileProvider\",function(e){e.aHrefSanitizationWhitelist(/^\\s*(https?|tel|ftp|mailto|file|ghttps?|ms-appx|x-wmapp0):/),e.imgSrcSanitizationWhitelist(/^\\s*(https?|tel|ftp|file|blob|ms-appx|x-wmapp0):|data:image\\//)}]);var v='<div class=\"loading-container\"><div class=\"loading\"></div></div>',g=\"$ionicLoading instance.hide() has been deprecated. Use $ionicLoading.hide().\",m=\"$ionicLoading instance.show() has been deprecated. Use $ionicLoading.show().\",$=\"$ionicLoading instance.setContent() has been deprecated. Use $ionicLoading.show({ template: 'my content' }).\";c.constant(\"$ionicLoadingConfig\",{template:\"<ion-spinner></ion-spinner>\"}).factory(\"$ionicLoading\",[\"$ionicLoadingConfig\",\"$ionicBody\",\"$ionicTemplateLoader\",\"$ionicBackdrop\",\"$timeout\",\"$q\",\"$log\",\"$compile\",\"$ionicPlatform\",\"$rootScope\",\"IONIC_BACK_PRIORITY\",function(e,t,n,i,o,r,a,c,l,u,d){function f(){return b||(b=n.compile({template:v,appendTo:t.get()}).then(function(e){return e.show=function(a){var s=a.templateUrl?n.load(a.templateUrl):r.when(a.template||a.content||\"\");e.scope=a.scope||e.scope,e.isShown||(e.hasBackdrop=!a.noBackdrop&&a.showBackdrop!==!1,e.hasBackdrop&&(i.retain(),i.getElement().addClass(\"backdrop-loading\"))),a.duration&&(o.cancel(e.durationTimeout),e.durationTimeout=o(angular.bind(e,e.hide),+a.duration)),y(),y=l.registerBackButtonAction(p,d.loading),s.then(function(n){if(n){var i=e.element.children();i.html(n),c(i.contents())(e.scope)}e.isShown&&(e.element.addClass(\"visible\"),ionic.requestAnimationFrame(function(){e.isShown&&(e.element.addClass(\"active\"),t.addClass(\"loading-active\"))}))}),e.isShown=!0},e.hide=function(){y(),e.isShown&&(e.hasBackdrop&&(i.release(),i.getElement().removeClass(\"backdrop-loading\")),e.element.removeClass(\"active\"),t.removeClass(\"loading-active\"),setTimeout(function(){!e.isShown&&e.element.removeClass(\"visible\")},200)),o.cancel(e.durationTimeout),e.isShown=!1},e})),b}function h(t){t=s({},e||{},t||{});var n=t.delay||t.showDelay||0;return S(),k(),t.hideOnStateChange&&(S=u.$on(\"$stateChangeSuccess\",w),k=u.$on(\"$stateChangeError\",w)),o.cancel(C),C=o(p,n),C.then(f).then(function(e){return e.show(t)}),{hide:function(){return a.error(g),w.apply(this,arguments)},show:function(){return a.error(m),h.apply(this,arguments)},setContent:function(e){return a.error($),f().then(function(t){t.show({template:e})})}}}function w(){S(),k(),o.cancel(C),f().then(function(e){e.hide()})}var b,y=p,S=p,k=p,C=r.when();return{show:h,hide:w,_getLoader:f}}]),c.factory(\"$ionicModal\",[\"$rootScope\",\"$ionicBody\",\"$compile\",\"$timeout\",\"$ionicPlatform\",\"$ionicTemplateLoader\",\"$$q\",\"$log\",\"$ionicClickBlock\",\"$window\",\"IONIC_BACK_PRIORITY\",function(e,t,n,i,o,r,a,c,l,u,d){var f=ionic.views.Modal.inherit({initialize:function(e){ionic.views.Modal.prototype.initialize.call(this,e),this.animation=e.animation||\"slide-in-up\"},show:function(e){var n=this;if(n.scope.$$destroyed)return c.error(\"Cannot call \"+n.viewType+\".show() after remove(). Please create a new \"+n.viewType+\" instance.\"),a.when();l.show(600);var r=h(n.modalEl);n.el.classList.remove(\"hide\"),i(function(){n._isShown&&t.addClass(n.viewType+\"-open\")},400,!1),n.el.parentElement||(r.addClass(n.animation),t.append(n.el));var s=r.data(\"$$ionicScrollController\");return s&&s.resize(),e&&n.positionView&&(n.positionView(e,r),n._onWindowResize=function(){n._isShown&&n.positionView(e,r)},ionic.on(\"resize\",n._onWindowResize,window)),r.addClass(\"ng-enter active\").removeClass(\"ng-leave ng-leave-active\"),n._isShown=!0,n._deregisterBackButton=o.registerBackButtonAction(n.hardwareBackButtonClose?angular.bind(n,n.hide):p,d.modal),ionic.views.Modal.prototype.show.call(n),i(function(){n._isShown&&(r.addClass(\"ng-enter-active\"),ionic.trigger(\"resize\"),n.scope.$parent&&n.scope.$parent.$broadcast(n.viewType+\".shown\",n),n.el.classList.add(\"active\"),n.scope.$broadcast(\"$ionicHeader.align\"))},20),i(function(){n._isShown&&n.$el.on(\"click\",function(e){n.backdropClickToClose&&e.target===n.el&&n.hide()})},400)},hide:function(){var e=this,n=h(e.modalEl);return l.show(600),e.el.classList.remove(\"active\"),n.addClass(\"ng-leave\"),i(function(){e._isShown||n.addClass(\"ng-leave-active\").removeClass(\"ng-enter ng-enter-active active\")},20,!1),e.$el.off(\"click\"),e._isShown=!1,e.scope.$parent&&e.scope.$parent.$broadcast(e.viewType+\".hidden\",e),e._deregisterBackButton&&e._deregisterBackButton(),ionic.views.Modal.prototype.hide.call(e),e.positionView&&ionic.off(\"resize\",e._onWindowResize,window),i(function(){t.removeClass(e.viewType+\"-open\"),e.el.classList.add(\"hide\")},e.hideDelay||320)},remove:function(){var e=this;return e.scope.$parent&&e.scope.$parent.$broadcast(e.viewType+\".removed\",e),e.hide().then(function(){e.scope.$destroy(),e.$el.remove()})},isShown:function(){return!!this._isShown}}),v=function(t,i){var o=i.scope&&i.scope.$new()||e.$new(!0);i.viewType=i.viewType||\"modal\",s(o,{$hasHeader:!1,$hasSubheader:!1,$hasFooter:!1,$hasSubfooter:!1,$hasTabs:!1,$hasTabsTop:!1});var r=n(\"<ion-\"+i.viewType+\">\"+t+\"</ion-\"+i.viewType+\">\")(o);i.$el=r,i.el=r[0],i.modalEl=i.el.querySelector(\".\"+i.viewType);var a=new f(i);return a.scope=o,i.scope||(o[i.viewType]=a),a};return{fromTemplate:function(e,t){var n=v(e,t||{});return n},fromTemplateUrl:function(e,t,n){var i;return angular.isFunction(t)&&(i=t,t=n),r.load(e).then(function(e){var n=v(e,t||{});return i&&i(n),n})}}}]),c.service(\"$ionicNavBarDelegate\",ionic.DelegateService([\"align\",\"showBackButton\",\"showBar\",\"title\",\"changeTitle\",\"setTitle\",\"getTitle\",\"back\",\"getPreviousTitle\"])),c.service(\"$ionicNavViewDelegate\",ionic.DelegateService([\"clearCache\"])),c.constant(\"IONIC_BACK_PRIORITY\",{view:100,sideMenu:150,modal:200,actionSheet:300,popup:400,loading:500}).provider(\"$ionicPlatform\",function(){return{$get:[\"$q\",function(e){var t={onHardwareBackButton:function(e){ionic.Platform.ready(function(){document.addEventListener(\"backbutton\",e,!1)})},offHardwareBackButton:function(e){ionic.Platform.ready(function(){document.removeEventListener(\"backbutton\",e)})},$backButtonActions:{},registerBackButtonAction:function(e,n,i){t._hasBackButtonHandler||(t.$backButtonActions={},t.onHardwareBackButton(t.hardwareBackButtonClick),t._hasBackButtonHandler=!0);var o={id:i?i:ionic.Utils.nextUid(),priority:n?n:0,fn:e};return t.$backButtonActions[o.id]=o,function(){delete t.$backButtonActions[o.id]}},hardwareBackButtonClick:function(e){var n,i;for(i in t.$backButtonActions)(!n||t.$backButtonActions[i].priority>=n.priority)&&(n=t.$backButtonActions[i]);return n?(n.fn(e),n):void 0},is:function(e){return ionic.Platform.is(e)},on:function(e,t){return ionic.Platform.ready(function(){document.addEventListener(e,t,!1)}),function(){ionic.Platform.ready(function(){document.removeEventListener(e,t)})}},ready:function(t){var n=e.defer();return ionic.Platform.ready(function(){n.resolve(),t&&t()}),n.promise}};return t}]}}),c.factory(\"$ionicPopover\",[\"$ionicModal\",\"$ionicPosition\",\"$document\",\"$window\",function(e,t,n,i){function o(e,n){var o=h(e.target||e),a=t.offset(o),c=n.prop(\"offsetWidth\"),s=n.prop(\"offsetHeight\"),l=i.innerWidth,u=i.innerHeight,d={left:a.left+a.width/2-c/2},f=h(n[0].querySelector(\".popover-arrow\"));d.left<r?d.left=r:d.left+c+r>l&&(d.left=l-c-r),a.top+a.height+s>u&&a.top-s>0?(d.top=a.top-s,n.addClass(\"popover-bottom\")):(d.top=a.top+a.height,n.removeClass(\"popover-bottom\")),f.css({left:a.left+a.width/2-f.prop(\"offsetWidth\")/2-d.left+\"px\"}),n.css({top:d.top+\"px\",left:d.left+\"px\",marginLeft:\"0\",opacity:\"1\"})}var r=6,a={viewType:\"popover\",hideDelay:1,animation:\"none\",positionView:o};return{fromTemplate:function(t,n){return e.fromTemplate(t,ionic.Utils.extend(a,n||{}))},fromTemplateUrl:function(t,n){return e.fromTemplateUrl(t,ionic.Utils.extend(a,n||{}))}}}]);var w='<div class=\"popup-container\" ng-class=\"cssClass\"><div class=\"popup\"><div class=\"popup-head\"><h3 class=\"popup-title\" ng-bind-html=\"title\"></h3><h5 class=\"popup-sub-title\" ng-bind-html=\"subTitle\" ng-if=\"subTitle\"></h5></div><div class=\"popup-body\"></div><div class=\"popup-buttons\" ng-show=\"buttons.length\"><button ng-repeat=\"button in buttons\" ng-click=\"$buttonTapped(button, $event)\" class=\"button\" ng-class=\"button.type || \\'button-default\\'\" ng-bind-html=\"button.text\"></button></div></div></div>';c.factory(\"$ionicPopup\",[\"$ionicTemplateLoader\",\"$ionicBackdrop\",\"$q\",\"$timeout\",\"$rootScope\",\"$ionicBody\",\"$compile\",\"$ionicPlatform\",\"IONIC_BACK_PRIORITY\",function(e,t,n,i,o,r,a,c,l){function u(t){t=s({scope:null,title:\"\",buttons:[]},t||{});var c={};return c.scope=(t.scope||o).$new(),c.element=h(w),c.responseDeferred=n.defer(),r.get().appendChild(c.element[0]),a(c.element)(c.scope),s(c.scope,{title:t.title,buttons:t.buttons,subTitle:t.subTitle,cssClass:t.cssClass,$buttonTapped:function(e,t){var n=(e.onTap||p)(t);t=t.originalEvent||t,t.defaultPrevented||c.responseDeferred.resolve(n)}}),n.when(t.templateUrl?e.load(t.templateUrl):t.template||t.content||\"\").then(function(e){var t=h(c.element[0].querySelector(\".popup-body\"));e?(t.html(e),a(t.contents())(c.scope)):t.remove()}),c.show=function(){c.isShown||c.removed||(c.isShown=!0,ionic.requestAnimationFrame(function(){c.isShown&&(c.element.removeClass(\"popup-hidden\"),c.element.addClass(\"popup-showing active\"),v(c.element))}))},c.hide=function(e){return e=e||p,c.isShown?(c.isShown=!1,c.element.removeClass(\"active\"),c.element.addClass(\"popup-hidden\"),void i(e,250,!1)):e()},c.remove=function(){c.removed||(c.hide(function(){c.element.remove(),c.scope.$destroy()}),c.removed=!0)},c}function d(){var e=y[y.length-1];e&&e.responseDeferred.resolve()}function f(e){function n(){y.push(o),i(o.show,a,!1),o.responseDeferred.promise.then(function(e){var n=y.indexOf(o);return-1!==n&&y.splice(n,1),y.length>0?y[y.length-1].show():(t.release(),i(function(){y.length||r.removeClass(\"popup-open\")},400,!1),(S._backButtonActionDone||p)()),o.remove(),e})}var o=S._createPopup(e),a=0;return y.length>0?(y[y.length-1].hide(),a=b.stackPushDelay):(r.addClass(\"popup-open\"),t.retain(),S._backButtonActionDone=c.registerBackButtonAction(d,l.popup)),o.responseDeferred.promise.close=function(e){o.removed||o.responseDeferred.resolve(e)},o.responseDeferred.notify({close:o.responseDeferred.close}),n(),o.responseDeferred.promise}function v(e){var t=e[0].querySelector(\"[autofocus]\");t&&t.focus()}function g(e){return f(s({buttons:[{text:e.okText||\"OK\",type:e.okType||\"button-positive\",onTap:function(){return!0}}]},e||{}))}function m(e){return f(s({buttons:[{text:e.cancelText||\"Cancel\",type:e.cancelType||\"button-default\",onTap:function(){return!1}},{text:e.okText||\"OK\",type:e.okType||\"button-positive\",onTap:function(){return!0}}]},e||{}))}function $(e){var t=o.$new(!0);t.data={};var n=\"\";return e.template&&/<[a-z][\\s\\S]*>/i.test(e.template)===!1&&(n=\"<span>\"+e.template+\"</span>\",delete e.template),f(s({template:n+'<input ng-model=\"data.response\" type=\"'+(e.inputType||\"text\")+'\" placeholder=\"'+(e.inputPlaceholder||\"\")+'\">',scope:t,buttons:[{text:e.cancelText||\"Cancel\",type:e.cancelType||\"button-default\",onTap:function(){}},{text:e.okText||\"OK\",type:e.okType||\"button-positive\",onTap:function(){return t.data.response||\"\"}}]},e||{}))}var b={stackPushDelay:75},y=[],S={show:f,alert:g,confirm:m,prompt:$,_createPopup:u,_popupStack:y};return S}]),c.factory(\"$ionicPosition\",[\"$document\",\"$window\",function(e,t){function n(e,n){return e.currentStyle?e.currentStyle[n]:t.getComputedStyle?t.getComputedStyle(e)[n]:e.style[n]}function i(e){return\"static\"===(n(e,\"position\")||\"static\")}var o=function(t){for(var n=e[0],o=t.offsetParent||n;o&&o!==n&&i(o);)o=o.offsetParent;return o||n};return{position:function(t){var n=this.offset(t),i={top:0,left:0},r=o(t[0]);r!=e[0]&&(i=this.offset(h(r)),i.top+=r.clientTop-r.scrollTop,i.left+=r.clientLeft-r.scrollLeft);var a=t[0].getBoundingClientRect();return{width:a.width||t.prop(\"offsetWidth\"),height:a.height||t.prop(\"offsetHeight\"),top:n.top-i.top,left:n.left-i.left}},offset:function(n){var i=n[0].getBoundingClientRect();return{width:i.width||n.prop(\"offsetWidth\"),height:i.height||n.prop(\"offsetHeight\"),top:i.top+(t.pageYOffset||e[0].documentElement.scrollTop),left:i.left+(t.pageXOffset||e[0].documentElement.scrollLeft)}}}}]),c.service(\"$ionicScrollDelegate\",ionic.DelegateService([\"resize\",\"scrollTop\",\"scrollBottom\",\"scrollTo\",\"scrollBy\",\"zoomTo\",\"zoomBy\",\"getScrollPosition\",\"anchorScroll\",\"freezeScroll\",\"freezeAllScrolls\",\"getScrollView\"])),c.service(\"$ionicSideMenuDelegate\",ionic.DelegateService([\"toggleLeft\",\"toggleRight\",\"getOpenRatio\",\"isOpen\",\"isOpenLeft\",\"isOpenRight\",\"canDragContent\",\"edgeDragThreshold\"])),c.service(\"$ionicSlideBoxDelegate\",ionic.DelegateService([\"update\",\"slide\",\"select\",\"enableSlide\",\"previous\",\"next\",\"stop\",\"autoPlay\",\"start\",\"currentIndex\",\"selected\",\"slidesCount\",\"count\",\"loop\"])),c.service(\"$ionicTabsDelegate\",ionic.DelegateService([\"select\",\"selectedIndex\"])),function(){var e=[];c.factory(\"$ionicTemplateCache\",[\"$http\",\"$templateCache\",\"$timeout\",function(t,n,i){function o(e){return\"undefined\"==typeof e?r():(f(e)&&(e=[e]),l(e,function(e){c.push(e)}),void(a&&r()))}function r(){var e;if(o._runCount++,a=!0,0!==c.length){for(var s=0;4>s&&(e=c.pop());)f(e)&&t.get(e,{cache:n}),s++;c.length&&i(r,1e3)}}var a,c=e;return o._runCount=0,o}]).config([\"$stateProvider\",\"$ionicConfigProvider\",function(t,n){var i=t.state;t.state=function(o,r){if(\"object\"==typeof r){var a=r.prefetchTemplate!==!1&&e.length<n.templates.maxPrefetch();if(a&&f(r.templateUrl)&&e.push(r.templateUrl),angular.isObject(r.views))for(var c in r.views)a=r.views[c].prefetchTemplate!==!1&&e.length<n.templates.maxPrefetch(),a&&f(r.views[c].templateUrl)&&e.push(r.views[c].templateUrl)}return i.call(t,o,r)}}]).run([\"$ionicTemplateCache\",function(e){e()}])}(),c.factory(\"$ionicTemplateLoader\",[\"$compile\",\"$controller\",\"$http\",\"$q\",\"$rootScope\",\"$templateCache\",function(e,t,n,i,o,r){function a(e){return n.get(e,{cache:r}).then(function(e){return e.data&&e.data.trim()})}function c(n){n=s({template:\"\",templateUrl:\"\",scope:null,controller:null,locals:{},appendTo:null},n||{});var r=n.templateUrl?this.load(n.templateUrl):i.when(n.template);return r.then(function(i){var r,a=n.scope||o.$new(),c=h(\"<div>\").html(i).contents();return n.controller&&(r=t(n.controller,s(n.locals,{$scope:a})),c.children().data(\"$ngControllerController\",r)),n.appendTo&&h(n.appendTo).append(c),e(c)(a),{element:c,scope:a}})}return{load:a,compile:c}}]),c.factory(\"$ionicViewService\",[\"$ionicHistory\",\"$log\",function(e,t){function n(e,n){t.warn(\"$ionicViewService\"+e+\" is deprecated, please use $ionicHistory\"+n+\" instead: http://ionicframework.com/docs/nightly/api/service/$ionicHistory/\")}n(\"\",\"\");var i={getCurrentView:\"currentView\",getBackView:\"backView\",getForwardView:\"forwardView\",getCurrentStateName:\"currentStateName\",nextViewOptions:\"nextViewOptions\",clearHistory:\"clearHistory\"};return l(i,function(t,o){i[o]=function(){return n(\".\"+o,\".\"+t),e[t].apply(this,arguments)}}),i}]),c.factory(\"$ionicViewSwitcher\",[\"$timeout\",\"$document\",\"$q\",\"$ionicClickBlock\",\"$ionicConfig\",\"$ionicNavBarDelegate\",function(e,t,n,i,o,r){function a(e,t){return c(e)[\"abstract\"]?c(e).name:t?t.stateId||t.viewId:ionic.Utils.nextUid()}function c(e){return e&&e.$$state&&e.$$state.self||{}}function d(e,t,n,i){var r=c(e),a=g||V(t,\"view-transition\")||r.viewTransition||o.views.transition()||\"ios\",l=o.navBar.transition();return n=m||V(t,\"view-direction\")||r.viewDirection||n||\"none\",s(f(i),{transition:a,navBarTransition:\"view\"===l?a:l,direction:n,shouldAnimate:\"none\"!==a&&\"none\"!==n})}function f(e){return e=e||{},{viewId:e.viewId,historyId:e.historyId,stateId:e.stateId,stateName:e.stateName,stateParams:e.stateParams}}function p(e,t){return arguments.length>1?void V(e,T,t):V(e,T)}function v(e){if(e&&e.length){var t=e.scope();t&&(t.$emit(\"$ionicView.unloaded\",e.data(C)),t.$destroy()),e.remove()}}var g,m,$=\"webkitTransitionEnd transitionend\",w=\"$noCache\",b=\"$destroyEle\",y=\"$eleId\",S=\"$accessed\",k=\"$fallbackTimer\",C=\"$viewData\",T=\"nav-view\",B=\"active\",I=\"cached\",x=\"stage\",A=0;ionic.transition=ionic.transition||{},ionic.transition.isActive=!1;var E,V=ionic.DomUtil.cachedAttr,P=[],D=1100,_={create:function(t,l,h,T,E,R){var L,N,z,M=++A,H={init:function(e,t){_.isTransitioning(!0),H.loadViewElements(e),H.render(e,function(){t&&t()})},loadViewElements:function(e){var n,i,o,r=t.getViewElements(),c=a(l,h),s=t.activeEleId();for(n=0,i=r.length;i>n&&(o=r.eq(n),o.data(y)===c?o.data(w)?(o.data(y,c+ionic.Utils.nextUid()),o.data(b,!0)):L=o:u(s)&&o.data(y)===s&&(N=o),!L||!N);n++);z=!!L,z||(L=e.ele||_.createViewEle(l),L.data(y,c)),R&&t.activeEleId(c),e.ele=null},render:function(e,n){if(z)ionic.Utils.reconnectScope(L.scope());else{p(L,x);var i=d(l,L,e.direction,h),r=o.transitions.views[i.transition]||o.transitions.views.none;r(L,null,i.direction,!0).run(0),L.data(C,{viewId:i.viewId,historyId:i.historyId,stateName:i.stateName,stateParams:i.stateParams}),(c(l).cache===!1||\"false\"===c(l).cache||\"false\"==L.attr(\"cache-view\")||0===o.views.maxCache())&&L.data(w,!0);var a=t.appendViewElement(L,l);delete i.direction,delete i.transition,a.$emit(\"$ionicView.loaded\",i)}L.data(S,Date.now()),n&&n()},transition:function(a,c,u){function v(){p(L,W.shouldAnimate?\"entering\":B),p(N,W.shouldAnimate?\"leaving\":I),W.run(1),r._instances.forEach(function(e){e.triggerTransitionStart(M)}),W.shouldAnimate||b()}function w(e){e.target===this&&b()}function b(){b.x||(b.x=!0,L.off($,w),e.cancel(L.data(k)),N&&e.cancel(N.data(k)),H.emit(\"after\",O,q),C&&C.resolve(t),M===A&&(n.all(P).then(_.transitionEnd),H.cleanup(O)),r._instances.forEach(function(e){e.triggerTransitionEnd()}),g=m=h=T=L=N=null)}function y(e){e.target===this&&S()}function S(){p(L,I),p(N,B),L.off($,y),e.cancel(L.data(k)),_.transitionEnd([t])}var C,O=d(l,L,a,h),q=s(s({},O),f(T));O.transitionId=q.transitionId=M,O.fromCache=!!z,O.enableBack=!!c,O.renderStart=E,O.renderEnd=R,V(L.parent(),\"nav-view-transition\",O.transition),V(L.parent(),\"nav-view-direction\",O.direction),e.cancel(L.data(k));var U=o.transitions.views[O.transition]||o.transitions.views.none,W=U(L,N,O.direction,O.shouldAnimate&&u&&R);if(W.shouldAnimate&&(L.on($,w),L.data(k,e(b,D)),i.show(D)),E&&(H.emit(\"before\",O,q),p(L,x),W.run(0)),R&&(C=n.defer(),P.push(C.promise)),E&&R)e(v,16);else{if(!R)return p(L,\"entering\"),p(N,\"leaving\"),{run:W.run,cancel:function(t){t?(L.on($,y),L.data(k,e(S,D)),i.show(D)):S(),W.shouldAnimate=t,W.run(0),W=null}};R&&v()}},emit:function(e,t,n){var i=L.scope();i&&(i.$emit(\"$ionicView.\"+e+\"Enter\",t),\"after\"==e&&i.$emit(\"$ionicView.enter\",t)),N?(i=N.scope(),i&&(i.$emit(\"$ionicView.\"+e+\"Leave\",n),\"after\"==e&&i.$emit(\"$ionicView.leave\",n))):i&&n&&n.viewId&&(i.$emit(\"$ionicNavView.\"+e+\"Leave\",n),\"after\"==e&&i.$emit(\"$ionicNavView.leave\",n))},cleanup:function(e){N&&\"back\"==e.direction&&!o.views.forwardCache()&&v(N);var n,i,r,a=t.getViewElements(),c=a.length,s=c-1>o.views.maxCache(),l=Date.now();for(n=0;c>n;n++)i=a.eq(n),s&&i.data(S)<l?(l=i.data(S),r=a.eq(n)):i.data(b)&&p(i)!=B&&v(i);v(r),L.data(w)&&L.data(b,!0)},enteringEle:function(){return L},leavingEle:function(){return N}};return H},transitionEnd:function(e){l(e,function(e){e.transitionEnd()}),_.isTransitioning(!1),i.hide(),P=[]},nextTransition:function(e){g=e},nextDirection:function(e){m=e},isTransitioning:function(t){return arguments.length&&(ionic.transition.isActive=!!t,e.cancel(E),t&&(E=e(function(){_.isTransitioning(!1)},999))),ionic.transition.isActive},createViewEle:function(e){var n=t[0].createElement(\"div\");return e&&e.$template&&(n.innerHTML=e.$template,1===n.children.length)?(n.children[0].classList.add(\"pane\"),h(n.children[0])):(n.className=\"pane\",h(n))},viewEleIsActive:function(e,t){p(e,t?B:I)},getTransitionData:d,navViewAttr:p,destroyViewEle:v};return _}]),c.config([\"$provide\",function(e){e.decorator(\"$compile\",[\"$delegate\",function(e){return e.$$addScopeInfo=function(e,t,n,i){var o=n?i?\"$isolateScopeNoTemplate\":\"$isolateScope\":\"$scope\";e.data(o,t)},e}])}]),c.config([\"$provide\",function(e){function t(e,t){return e.__hash=e.hash,e.hash=function(n){return u(n)&&t(function(){var e=document.querySelector(\".scroll-content\");e&&(e.scrollTop=0)},0,!1),e.__hash(n)},e}e.decorator(\"$location\",[\"$delegate\",\"$timeout\",t])}]),c.controller(\"$ionicHeaderBar\",[\"$scope\",\"$element\",\"$attrs\",\"$q\",\"$ionicConfig\",\"$ionicHistory\",function(e,t,n,i,o,r){function a(e){return C[e]||(C[e]=t[0].querySelector(\".\"+e)),C[e]}var c=\"title\",s=\"back-text\",l=\"back-button\",u=\"default-title\",d=\"previous-title\",f=\"hide\",h=this,p=\"\",v=\"\",g=0,m=0,$=\"\",w=!1,b=!0,y=!0,S=!1,k=0;h.beforeEnter=function(t){e.$broadcast(\"$ionicView.beforeEnter\",t)},h.title=function(e){return arguments.length&&e!==p&&(a(c).innerHTML=e,p=e,k=0),p},h.enableBack=function(e,t){return arguments.length&&(w=e,t||h.updateBackButton()),w},h.showBack=function(e,t){return arguments.length&&(b=e,t||h.updateBackButton()),b},h.showNavBack=function(e){y=e,h.updateBackButton()},h.updateBackButton=function(){var e;(b&&y&&w)!==S&&(S=b&&y&&w,e=a(l),e&&e.classList[S?\"remove\":\"add\"](f)),w&&(e=e||a(l),e&&(h.backButtonIcon!==o.backButton.icon()&&(e=a(l+\" .icon\"),e&&(h.backButtonIcon=o.backButton.icon(),e.className=\"icon \"+h.backButtonIcon)),h.backButtonText!==o.backButton.text()&&(e=a(l+\" .back-text\"),e&&(e.textContent=h.backButtonText=o.backButton.text()))))},h.titleTextWidth=function(){if(!k){var e=ionic.DomUtil.getTextBounds(a(c));k=Math.min(e&&e.width||30)}return k},h.titleWidth=function(){var e=h.titleTextWidth(),t=a(c).offsetWidth;return e>t&&(e=t+(g-m-5)),e},h.titleTextX=function(){return t[0].offsetWidth/2-h.titleWidth()/2},h.titleLeftRight=function(){return g-m},h.backButtonTextLeft=function(){for(var e=0,t=a(s);t;)e+=t.offsetLeft,t=t.parentElement;return e},h.resetBackButton=function(e){if(o.backButton.previousTitleText()){var t=a(d);if(t){t.classList.remove(f);var n=e&&r.getViewById(e.viewId),i=r.backTitle(n);i!==v&&(v=t.innerHTML=i)}var c=a(u);c&&c.classList.remove(f)}},h.align=function(e){var i=a(c);e=e||n.alignTitle||o.navBar.alignTitle();var r=h.calcWidths(e,!1);if(b&&v&&o.backButton.previousTitleText()){var s=h.calcWidths(e,!0),l=t[0].offsetWidth-s.titleLeft-s.titleRight;h.titleTextWidth()<=l&&(r=s)}return h.updatePositions(i,r.titleLeft,r.titleRight,r.buttonsLeft,r.buttonsRight,r.css,r.showPrevTitle)},h.calcWidths=function(e,n){var i,o,r,h,p,v,g,m,$,w=a(c),y=a(l),S=t[0].childNodes,k=0,C=0,T=0,B=0,I=\"\",x=0;for(i=0;i<S.length;i++){if(p=S[i],g=0,1==p.nodeType){if(p===w){$=!0;continue}if(p.classList.contains(f))continue;if(b&&p===y){for(o=0;o<p.childNodes.length;o++)if(h=p.childNodes[o],1==h.nodeType)if(h.classList.contains(s))for(r=0;r<h.children.length;r++)if(v=h.children[r],n){if(v.classList.contains(u))continue;x+=v.offsetWidth}else{if(v.classList.contains(d))continue;x+=v.offsetWidth}else x+=h.offsetWidth;else 3==h.nodeType&&h.nodeValue.trim()&&(m=ionic.DomUtil.getTextBounds(h),x+=m&&m.width||0);g=x||p.offsetWidth}else g=p.offsetWidth}else 3==p.nodeType&&p.nodeValue.trim()&&(m=ionic.DomUtil.getTextBounds(p),g=m&&m.width||0);$?C+=g:k+=g}if(\"left\"==e)I=\"title-left\",k&&(T=k+15),C&&(B=C+15);else if(\"right\"==e)I=\"title-right\",k&&(T=k+15),C&&(B=C+15);else{var A=Math.max(k,C)+10;A>10&&(T=B=A)}return{backButtonWidth:x,buttonsLeft:k,buttonsRight:C,titleLeft:T,titleRight:B,showPrevTitle:n,css:I}},h.updatePositions=function(e,n,r,c,s,l,p){var v=i.defer();if(e&&(n!==g&&(e.style.left=n?n+\"px\":\"\",g=n),r!==m&&(e.style.right=r?r+\"px\":\"\",m=r),l!==$&&(l&&e.classList.add(l),$&&e.classList.remove($),$=l)),o.backButton.previousTitleText()){var w=a(d),b=a(u);w&&w.classList[p?\"remove\":\"add\"](f),b&&b.classList[p?\"add\":\"remove\"](f)}return ionic.requestAnimationFrame(function(){if(e&&e.offsetWidth+10<e.scrollWidth){var n=s+5,i=t[0].offsetWidth-g-h.titleTextWidth()-20;r=n>i?n:i,r!==m&&(e.style.right=r+\"px\",m=r)}v.resolve()}),v.promise},h.setCss=function(e,t){ionic.DomUtil.cachedStyles(a(e),t)};var C={};e.$on(\"$destroy\",function(){for(var e in C)C[e]=null})}]),c.controller(\"$ionInfiniteScroll\",[\"$scope\",\"$attrs\",\"$element\",\"$timeout\",function(e,t,n,i){function o(){ionic.requestAnimationFrame(function(){n[0].classList.add(\"active\")}),s.isLoading=!0,e.$parent&&e.$parent.$apply(t.onInfinite||\"\")}function r(){ionic.requestAnimationFrame(function(){n[0].classList.remove(\"active\")}),i(function(){s.jsScrolling&&s.scrollView.resize(),(s.jsScrolling&&s.scrollView.__container&&s.scrollView.__container.offsetHeight>0||!s.jsScrolling)&&s.checkBounds()},30,!1),s.isLoading=!1}function a(){if(!s.isLoading){var e={};if(s.jsScrolling){e=s.getJSMaxScroll();var t=s.scrollView.getValues();(-1!==e.left&&t.left>=e.left||-1!==e.top&&t.top>=e.top)&&o()}else e=s.getNativeMaxScroll(),(-1!==e.left&&s.scrollEl.scrollLeft>=e.left-s.scrollEl.clientWidth||-1!==e.top&&s.scrollEl.scrollTop>=e.top-s.scrollEl.clientHeight)&&o()}}function c(e){var n=(t.distance||\"2.5%\").trim(),i=-1!==n.indexOf(\"%\");return i?e*(1-parseFloat(n)/100):e-parseFloat(n)}var s=this;s.isLoading=!1,e.icon=function(){return u(t.icon)?t.icon:\"ion-load-d\"},e.spinner=function(){return u(t.spinner)?t.spinner:\"\"},e.$on(\"scroll.infiniteScrollComplete\",function(){r()}),e.$on(\"$destroy\",function(){s.scrollCtrl&&s.scrollCtrl.$element&&s.scrollCtrl.$element.off(\"scroll\",s.checkBounds),s.scrollEl&&s.scrollEl.removeEventListener&&s.scrollEl.removeEventListener(\"scroll\",s.checkBounds)}),s.checkBounds=ionic.Utils.throttle(a,300),s.getJSMaxScroll=function(){var e=s.scrollView.getScrollMax();return{left:s.scrollView.options.scrollingX?c(e.left):-1,top:s.scrollView.options.scrollingY?c(e.top):-1}},s.getNativeMaxScroll=function(){var e={left:s.scrollEl.scrollWidth,top:s.scrollEl.scrollHeight},t=window.getComputedStyle(s.scrollEl)||{};return{left:\"scroll\"===t.overflowX||\"auto\"===t.overflowX||\"scroll\"===s.scrollEl.style[\"overflow-x\"]?c(e.left):-1,top:\"scroll\"===t.overflowY||\"auto\"===t.overflowY||\"scroll\"===s.scrollEl.style[\"overflow-y\"]?c(e.top):-1}},s.__finishInfiniteScroll=r}]),c.service(\"$ionicListDelegate\",ionic.DelegateService([\"showReorder\",\"showDelete\",\"canSwipeItems\",\"closeOptionButtons\"])).controller(\"$ionicList\",[\"$scope\",\"$attrs\",\"$ionicListDelegate\",\"$ionicHistory\",function(e,t,n,i){var o=this,r=!0,a=!1,c=!1,s=n._registerInstance(o,t.delegateHandle,function(){return i.isActiveScope(e)});e.$on(\"$destroy\",s),o.showReorder=function(e){return arguments.length&&(a=!!e),a},o.showDelete=function(e){return arguments.length&&(c=!!e),c},o.canSwipeItems=function(e){return arguments.length&&(r=!!e),r},o.closeOptionButtons=function(){o.listView&&o.listView.clearDragEffects()}}]),c.controller(\"$ionicNavBar\",[\"$scope\",\"$element\",\"$attrs\",\"$compile\",\"$timeout\",\"$ionicNavBarDelegate\",\"$ionicConfig\",\"$ionicHistory\",function(e,t,n,i,o,r,a,c){function s(e,t){var n=console.warn||console.log;n&&n.call(console,\"navBarController.\"+e+\" is deprecated, please use \"+t+\" instead\")}function d(e){return x[e]?h(x[e]):void 0}function f(){for(var e=0;e<I.length;e++)if(I[e].isActive)return I[e]}function p(){for(var e=0;e<I.length;e++)if(!I[e].isActive)return I[e]}function v(e,t){e&&ionic.DomUtil.cachedAttr(e.containerEle(),\"nav-bar\",t)}function g(e){ionic.DomUtil.cachedAttr(t,\"nav-swipe\",e)}var m,$,w,b=\"hide\",y=\"$ionNavBarController\",S=\"primaryButtons\",k=\"secondaryButtons\",C=\"backButton\",T=\"primaryButtons secondaryButtons leftButtons rightButtons title\".split(\" \"),B=this,I=[],x={},A=!0;t.parent().data(y,B);var E=n.delegateHandle||\"navBar\"+ionic.Utils.nextUid(),V=r._registerInstance(B,E);B.init=function(){t.addClass(\"nav-bar-container\"),ionic.DomUtil.cachedAttr(t,\"nav-bar-transition\",a.views.transition()),B.createHeaderBar(!1),B.createHeaderBar(!0),e.$emit(\"ionNavBar.init\",E)},B.createHeaderBar=function(o){function r(e,t){e&&(\"title\"===t?g.append(e):\"rightButtons\"==t||t==k&&\"left\"!=a.navBar.positionSecondaryButtons()||t==S&&\"right\"==a.navBar.positionPrimaryButtons()?(v||(v=h('<div class=\"buttons buttons-right\">'),f.append(v)),t==k?v.append(e):v.prepend(e)):(p||(p=h('<div class=\"buttons buttons-left\">'),m[C]?m[C].after(p):f.prepend(p)),t==k?p.append(e):p.prepend(e)))}var c=h('<div class=\"nav-bar-block\">');ionic.DomUtil.cachedAttr(c,\"nav-bar\",o?\"active\":\"cached\");var s=n.alignTitle||a.navBar.alignTitle(),f=h(\"<ion-header-bar>\").addClass(n[\"class\"]).attr(\"align-title\",s);u(n.noTapScroll)&&f.attr(\"no-tap-scroll\",n.noTapScroll);var p,v,g=h('<div class=\"title title-'+s+'\">'),m={},$={};m[C]=d(C),m[C]&&f.append(m[C]),f.append(g),l(T,function(e){m[e]=d(e),r(m[e],e)});for(var w=0;w<f[0].children.length;w++)f[0].children[w].classList.add(\"header-item\");c.append(f),t.append(i(c)(e.$new()));var y=f.data(\"$ionHeaderBarController\");y.backButtonIcon=a.backButton.icon(),y.backButtonText=a.backButton.text();var B={isActive:o,title:function(e){y.title(e)},setItem:function(e,t){B.removeItem(t),e?(\"title\"===t&&B.title(\"\"),r(e,t),m[t]&&m[t].addClass(b),$[t]=e):m[t]&&m[t].removeClass(b)},removeItem:function(e){$[e]&&($[e].scope().$destroy(),$[e].remove(),$[e]=null)},containerEle:function(){return c},headerBarEle:function(){return f},afterLeave:function(){l(T,function(e){B.removeItem(e)}),y.resetBackButton()},controller:function(){return y},destroy:function(){l(T,function(e){B.removeItem(e)}),c.scope().$destroy();for(var e in m)m[e]&&(m[e].removeData(),m[e]=null);p&&p.removeData(),v&&v.removeData(),g.removeData(),f.removeData(),c.remove(),c=f=g=p=v=null}};return I.push(B),B},B.navElement=function(e,t){return u(t)&&(x[e]=t),x[e]},B.update=function(e){var t=!e.hasHeaderBar&&e.showNavBar;e.transition=a.views.transition(),t||(e.direction=\"none\"),B.enable(t);var n=B.isInitialized?p():f(),i=B.isInitialized?f():null,o=n.controller();o.enableBack(e.enableBack,!0),o.showBack(e.showBack,!0),o.updateBackButton(),B.title(e.title,n),B.showBar(t),e.navBarItems&&l(T,function(t){n.setItem(e.navBarItems[t],t)}),B.transition(n,i,e),B.isInitialized=!0,g(\"\")},B.transition=function(n,i,r){function c(){for(var e=0;e<I.length;e++)I[e].isActive=!1;n.isActive=!0,v(n,\"active\"),v(i,\"cached\"),B.activeTransition=d=$=null}var s=n.controller(),l=a.transitions.navBar[r.navBarTransition]||a.transitions.navBar.none,u=r.transitionId;s.beforeEnter(r);var d=l(n,i,r.direction,r.shouldAnimate&&B.isInitialized);ionic.DomUtil.cachedAttr(t,\"nav-bar-transition\",r.navBarTransition),ionic.DomUtil.cachedAttr(t,\"nav-bar-direction\",r.direction),d.shouldAnimate&&r.renderEnd?v(n,\"stage\"):(v(n,\"entering\"),v(i,\"leaving\")),s.resetBackButton(r),d.run(0),B.activeTransition={run:function(e){d.shouldAnimate=!1,d.direction=\"back\",d.run(e)},cancel:function(t,o,r){g(o),v(i,\"active\"),v(n,\"cached\"),d.shouldAnimate=t,d.run(0),B.activeTransition=d=null;var a;r.showBar!==B.showBar()&&B.showBar(r.showBar),r.showBackButton!==B.showBackButton()&&B.showBackButton(r.showBackButton),a&&e.$apply()},complete:function(e,t){g(t),d.shouldAnimate=e,d.run(1),$=c}},o(s.align,16),(m=function(){w===u&&(v(n,\"entering\"),v(i,\"leaving\"),d.run(1),$=function(){w!=u&&d.shouldAnimate||c()},m=null)})()},B.triggerTransitionStart=function(e){w=e,m&&m()},B.triggerTransitionEnd=function(){$&&$()},B.showBar=function(t){return arguments.length&&(B.visibleBar(t),e.$parent.$hasHeader=!!t),!!e.$parent.$hasHeader},B.visibleBar=function(e){e&&!A?(t.removeClass(b),B.align()):!e&&A&&t.addClass(b),A=e},B.enable=function(e){B.visibleBar(e);for(var t=0;t<r._instances.length;t++)r._instances[t]!==B&&r._instances[t].visibleBar(!1)},B.showBackButton=function(t){if(arguments.length){for(var n=0;n<I.length;n++)I[n].controller().showNavBack(!!t);e.$isBackButtonShown=!!t}return e.$isBackButtonShown},B.showActiveBackButton=function(e){var t=f();return t?arguments.length?t.controller().showBack(e):t.controller().showBack():void 0},B.title=function(t,n){return u(t)&&(t=t||\"\",n=n||f(),n&&n.title(t),e.$title=t,c.currentTitle(t)),e.$title},B.align=function(e,t){t=t||f(),t&&t.controller().align(e)},B.hasTabsTop=function(e){t[e?\"addClass\":\"removeClass\"](\"nav-bar-tabs-top\")},B.hasBarSubheader=function(e){t[e?\"addClass\":\"removeClass\"](\"nav-bar-has-subheader\")},B.changeTitle=function(e){s(\"changeTitle(val)\",\"title(val)\"),B.title(e)},B.setTitle=function(e){s(\"setTitle(val)\",\"title(val)\"),B.title(e);\n\n},B.getTitle=function(){return s(\"getTitle()\",\"title()\"),B.title()},B.back=function(){s(\"back()\",\"$ionicHistory.goBack()\"),c.goBack()},B.getPreviousTitle=function(){s(\"getPreviousTitle()\",\"$ionicHistory.backTitle()\"),c.goBack()},e.$on(\"$destroy\",function(){e.$parent.$hasHeader=!1,t.parent().removeData(y);for(var n=0;n<I.length;n++)I[n].destroy();t.remove(),t=I=null,V()})}]),c.controller(\"$ionicNavView\",[\"$scope\",\"$element\",\"$attrs\",\"$compile\",\"$controller\",\"$ionicNavBarDelegate\",\"$ionicNavViewDelegate\",\"$ionicHistory\",\"$ionicViewSwitcher\",\"$ionicConfig\",\"$ionicScrollDelegate\",function(e,t,n,i,o,r,a,c,l,u,d){function f(e,n){for(var i,o,r=t.children(),a=0,c=r.length;c>a;a++)if(i=r.eq(a),A(i)==T){o=i.scope(),o&&o.$emit(e.name.replace(\"Tabs\",\"View\"),n);break}}function h(e){ionic.DomUtil.cachedAttr(t,\"nav-swipe\",e)}function p(e,t){var n=g();n&&n.hasTabsTop(t)}function v(e,t){var n=g();n&&n.hasBarSubheader(t)}function g(){if($)for(var e=0;e<r._instances.length;e++)if(r._instances[e].$$delegateHandle==$)return r._instances[e];return t.inheritedData(\"$ionNavBarController\")}var m,$,w,b,y,S=\"$eleId\",k=\"$destroyEle\",C=\"$noCache\",T=\"active\",B=\"cached\",I=this,x=!1,A=l.navViewAttr;I.scope=e,I.element=t,I.init=function(){var i=n.name||\"\",o=t.parent().inheritedData(\"$uiView\"),r=o&&o.state?o.state.name:\"\";i.indexOf(\"@\")<0&&(i=i+\"@\"+r);var c={name:i,state:null};t.data(\"$uiView\",c);var s=a._registerInstance(I,n.delegateHandle);return e.$on(\"$destroy\",function(){s(),I.isSwipeFreeze&&d.freezeAllScrolls(!1)}),e.$on(\"$ionicHistory.deselect\",I.cacheCleanup),e.$on(\"$ionicTabs.top\",p),e.$on(\"$ionicSubheader\",v),e.$on(\"$ionicTabs.beforeLeave\",f),e.$on(\"$ionicTabs.afterLeave\",f),e.$on(\"$ionicTabs.leave\",f),ionic.Platform.ready(function(){ionic.Platform.isWebView()&&u.views.swipeBackEnabled()&&I.initSwipeBack()}),c},I.register=function(t){var n=s({},c.currentView()),i=c.register(e,t);I.update(i);var o=c.getViewById(i.viewId)||{},r=b!==i.viewId;I.render(i,t,o,n,r,!0)},I.update=function(e){x=!0,m=e.direction;var n=t.parent().inheritedData(\"$ionNavViewController\");n&&(n.isPrimary(!1),(\"enter\"===m||\"exit\"===m)&&(n.direction(m),\"enter\"===m&&(m=\"none\")))},I.render=function(e,t,n,i,o,r){var a=l.create(I,t,n,i,o,r);a.init(e,function(){a.transition(I.direction(),e.enableBack,!y),b=y=null})},I.beforeEnter=function(e){if(x){$=e.navBarDelegate;var t=g();t&&t.update(e),h(\"\")}},I.activeEleId=function(e){return arguments.length&&(w=e),w},I.transitionEnd=function(){var e,n,i,o=t.children();for(e=0,n=o.length;n>e;e++)i=o.eq(e),i.data(S)===w?A(i,T):(\"leaving\"===A(i)||A(i)===T||A(i)===B)&&(i.data(k)||i.data(C)?l.destroyViewEle(i):(A(i,B),ionic.Utils.disconnectScope(i.scope())));h(\"\"),I.isSwipeFreeze&&d.freezeAllScrolls(!1)},I.cacheCleanup=function(){for(var e=t.children(),n=0,i=e.length;i>n;n++)e.eq(n).data(k)&&l.destroyViewEle(e.eq(n))},I.clearCache=function(e){var n,i,o,r,a,c,s=t.children();for(o=0,r=s.length;r>o;o++)if(n=s.eq(o),e)for(c=n.data(S),a=0;a<e.length;a++)c===e[a]&&l.destroyViewEle(n);else A(n)==B?l.destroyViewEle(n):A(n)==T&&(i=n.scope(),i&&i.$broadcast(\"$ionicView.clearCache\"))},I.getViewElements=function(){return t.children()},I.appendViewElement=function(n,r){var a=i(n);t.append(n);var c=e.$new();if(r&&r.$$controller){r.$scope=c;var s=o(r.$$controller,r);t.children().data(\"$ngControllerController\",s)}return a(c),c},I.title=function(e){var t=g();t&&t.title(e)},I.enableBackButton=function(e){var t=g();t&&t.enableBackButton(e)},I.showBackButton=function(e){var t=g();return t?arguments.length?t.showActiveBackButton(e):t.showActiveBackButton():!0},I.showBar=function(e){var t=g();return t?arguments.length?t.showBar(e):t.showBar():!0},I.isPrimary=function(e){return arguments.length&&(x=e),x},I.direction=function(e){return arguments.length&&(m=e),m},I.initSwipeBack=function(){function n(e){if(x&&(S=r(e),!(S>C))){p=c.backView();var n=c.currentView();if(p&&p.historyId===n.historyId&&n.canSwipeBack!==!1){w||(w=window.innerWidth),I.isSwipeFreeze=d.freezeAllScrolls(!0);var a={direction:\"back\"};k=[],T={showBar:I.showBar(),showBackButton:I.showBackButton()};var u=l.create(I,a,p,n,!0,!1);u.loadViewElements(a),u.render(a),s=u.transition(\"back\",c.enabledBack(p),!0),f=g(),m=ionic.onGesture(\"drag\",i,t[0]),$=ionic.onGesture(\"release\",o,t[0])}}}function i(e){if(x&&s){var t=r(e);if(k.push({t:Date.now(),x:t}),t>=w-15)o(e);else{var n=Math.min(Math.max(a(t),0),1);s.run(n),f&&f.activeTransition&&f.activeTransition.run(n)}}}function o(e){if(x&&s&&k&&k.length>1){for(var t=Date.now(),n=r(e),c=k[k.length-1],l=k.length-2;l>=0&&!(t-c.t>200);l--)c=k[l];var u=n>=k[k.length-2].x,v=a(n),g=Math.abs(c.x-n)/(t-c.t);if(b=p.viewId,y=.03>v||v>.97,u&&(v>.5||g>.1)){var S=g>.5||.05>g||n>w-45?\"fast\":\"slow\";h(y?\"\":S),p.go(),f&&f.activeTransition&&f.activeTransition.complete(!y,S)}else h(y?\"\":\"fast\"),b=null,s.cancel(!y),f&&f.activeTransition&&f.activeTransition.cancel(!y,\"fast\",T),y=null}ionic.offGesture(m,\"drag\",i),ionic.offGesture($,\"release\",o),w=s=k=null,I.isSwipeFreeze=d.freezeAllScrolls(!1)}function r(e){return ionic.tap.pointerCoord(e.gesture.srcEvent).x}function a(e){return(e-S)/w}var s,f,p,v,m,$,w,S,k,C=u.views.swipeBackHitWidth(),T={};v=ionic.onGesture(\"dragstart\",n,t[0]),e.$on(\"$destroy\",function(){ionic.offGesture(v,\"dragstart\",n),ionic.offGesture(m,\"drag\",i),ionic.offGesture($,\"release\",o),I.element=s=f=null})}}]),c.controller(\"$ionicRefresher\",[\"$scope\",\"$attrs\",\"$element\",\"$ionicBind\",\"$timeout\",function(e,t,n,i,o){function r(){(P||k)&&(E=null,k?(k=!1,T=0,B>I?(g(),f(I,A)):(f(0,A,v),C=!1)):(T=0,C=!1,d(!1)))}function a(e){if(P&&!(e.touches.length>1)){if(null===E&&(E=parseInt(e.touches[0].screenY,10)),ionic.Platform.isAndroid()&&4.4===ionic.Platform.version()&&0===b.scrollTop&&(k=!0,e.preventDefault()),V=parseInt(e.touches[0].screenY,10)-E,0>=V-T||0!==b.scrollTop)return C&&(C=!1,d(!1)),k&&l(b,-1*parseInt(V-T,10)),void(0!==B&&s(0));V>0&&0===b.scrollTop&&!C&&(T=V),e.preventDefault(),C||(C=!0,d(!0)),k=!0,s(parseInt((V-T)/3,10)),!x&&B>I?(x=!0,ionic.requestAnimationFrame(p)):x&&I>B&&(x=!1,ionic.requestAnimationFrame(v))}}function c(e){P=0===e.target.scrollTop||k}function s(e){y.style[ionic.CSS.TRANSFORM]=\"translateY(\"+e+\"px)\",B=e}function l(e,t){e.scrollTop=t;var n=document.createEvent(\"UIEvents\");n.initUIEvent(\"scroll\",!0,!0,window,1),e.dispatchEvent(n)}function d(e){ionic.requestAnimationFrame(e?function(){y.classList.add(\"overscroll\"),m()}:function(){y.classList.remove(\"overscroll\"),$(),v()})}function f(e,t,n){function i(e){return--e*e*e+1}function o(){var c=Date.now(),l=Math.min(1,(c-r)/t),u=i(l);s(parseInt(u*(e-a)+a,10)),1>l?ionic.requestAnimationFrame(o):(5>e&&e>-5&&(C=!1,d(!1)),n&&n())}var r=Date.now(),a=B;return a===e?void n():void ionic.requestAnimationFrame(o)}function h(){ionic.off(\"touchmove\",a,y),ionic.off(\"touchend\",r,y),ionic.off(\"scroll\",c,b),b=null,y=null}function p(){n[0].classList.add(\"active\"),e.$onPulling()}function v(){o(function(){n.removeClass(\"active refreshing refreshing-tail\"),x&&(x=!1)},150)}function g(){n[0].classList.add(\"refreshing\"),e.$onRefresh()}function m(){n[0].classList.remove(\"invisible\")}function $(){n[0].classList.add(\"invisible\")}function w(){n[0].classList.add(\"refreshing-tail\")}var b,y,S=this,k=!1,C=!1,T=0,B=0,I=60,x=!1,A=500,E=null,V=null,P=!0;u(t.pullingIcon)||t.$set(\"pullingIcon\",\"ion-android-arrow-down\"),e.showSpinner=!u(t.refreshingIcon)&&\"none\"!=t.spinner,e.showIcon=u(t.refreshingIcon),i(e,t,{pullingIcon:\"@\",pullingText:\"@\",refreshingIcon:\"@\",refreshingText:\"@\",spinner:\"@\",disablePullingRotation:\"@\",$onRefresh:\"&onRefresh\",$onPulling:\"&onPulling\"}),e.$on(\"scroll.refreshComplete\",function(){o(function(){ionic.requestAnimationFrame(w),f(0,A,v),o(function(){C&&(C=!1,d(!1))},A)},A)}),S.init=function(){if(b=n.parent().parent()[0],y=n.parent()[0],!(b&&b.classList.contains(\"ionic-scroll\")&&y&&y.classList.contains(\"scroll\")))throw new Error(\"Refresher must be immediate child of ion-content or ion-scroll\");ionic.on(\"touchmove\",a,y),ionic.on(\"touchend\",r,y),ionic.on(\"scroll\",c,b),e.$on(\"$destroy\",h)},S.getRefresherDomMethods=function(){return{activate:p,deactivate:v,start:g,show:m,hide:$,tail:w}},S.__handleTouchmove=a,S.__getScrollChild=function(){return y},S.__getScrollParent=function(){return b}}]),c.controller(\"$ionicScroll\",[\"$scope\",\"scrollViewOptions\",\"$timeout\",\"$window\",\"$location\",\"$document\",\"$ionicScrollDelegate\",\"$ionicHistory\",function(e,t,n,i,o,r,a,c){var s=this;s.__timeout=n,s._scrollViewOptions=t,s.isNative=function(){return!!t.nativeScrolling};var l,d=s.element=t.el,f=s.$element=h(d);l=s.isNative()?s.scrollView=new ionic.views.ScrollNative(t):s.scrollView=new ionic.views.Scroll(t),(f.parent().length?f.parent():f).data(\"$$ionicScrollController\",s);var p=a._registerInstance(s,t.delegateHandle,function(){return c.isActiveScope(e)});u(t.bouncing)||ionic.Platform.ready(function(){l.options&&(l.options.bouncing=!0,ionic.Platform.isAndroid()&&(l.options.bouncing=!1,l.options.deceleration=.95))});var v=angular.bind(l,l.resize);angular.element(i).on(\"resize\",v);var g=function(t){var n=(t.originalEvent||t).detail||{};e.$onScroll&&e.$onScroll({event:t,scrollTop:n.scrollTop||0,scrollLeft:n.scrollLeft||0})};f.on(\"scroll\",g),e.$on(\"$destroy\",function(){p(),l&&l.__cleanup&&l.__cleanup(),angular.element(i).off(\"resize\",v),f.off(\"scroll\",g),l=s.scrollView=t=s._scrollViewOptions=t.el=s._scrollViewOptions.el=f=s.$element=d=null}),n(function(){l&&l.run&&l.run()}),s.getScrollView=function(){return l},s.getScrollPosition=function(){return l.getValues()},s.resize=function(){return n(v,0,!1).then(function(){f&&f.triggerHandler(\"scroll-resize\")})},s.scrollTop=function(e){s.resize().then(function(){l.scrollTo(0,0,!!e)})},s.scrollBottom=function(e){s.resize().then(function(){var t=l.getScrollMax();l.scrollTo(t.left,t.top,!!e)})},s.scrollTo=function(e,t,n){s.resize().then(function(){l.scrollTo(e,t,!!n)})},s.zoomTo=function(e,t,n,i){s.resize().then(function(){l.zoomTo(e,!!t,n,i)})},s.zoomBy=function(e,t,n,i){s.resize().then(function(){l.zoomBy(e,!!t,n,i)})},s.scrollBy=function(e,t,n){s.resize().then(function(){l.scrollBy(e,t,!!n)})},s.anchorScroll=function(e){s.resize().then(function(){var t=o.hash(),n=t&&r[0].getElementById(t);if(!t||!n)return void l.scrollTo(0,0,!!e);var i=n,a=0,c=0;do null!==i&&(a+=i.offsetLeft),null!==i&&(c+=i.offsetTop),i=i.offsetParent;while(i.attributes!=s.element.attributes&&i.offsetParent);l.scrollTo(a,c,!!e)})},s.freezeScroll=l.freeze,s.freezeAllScrolls=function(e){for(var t=0;t<a._instances.length;t++)a._instances[t].freezeScroll(e)},s._setRefresher=function(e,t,n){s.refresher=t;var i=s.refresher.clientHeight||60;l.activatePullToRefresh(i,n)}}]),c.controller(\"$ionicSideMenus\",[\"$scope\",\"$attrs\",\"$ionicSideMenuDelegate\",\"$ionicPlatform\",\"$ionicBody\",\"$ionicHistory\",\"$ionicScrollDelegate\",\"IONIC_BACK_PRIORITY\",function(e,t,n,i,o,r,a,c){function s(e){e&&!$.isScrollFreeze?a.freezeAllScrolls(e):!e&&$.isScrollFreeze&&a.freezeAllScrolls(!1),$.isScrollFreeze=e}var l,u,f,h,v,g,m,$=this,w=!0;$.$scope=e,$.initialize=function(e){$.left=e.left,$.right=e.right,$.setContent(e.content),$.dragThresholdX=e.dragThresholdX||10,r.registerHistory($.$scope)},$.setContent=function(e){e&&($.content=e,$.content.onDrag=function(e){$._handleDrag(e)},$.content.endDrag=function(e){$._endDrag(e)})},$.isOpenLeft=function(){return $.getOpenAmount()>0},$.isOpenRight=function(){return $.getOpenAmount()<0},$.toggleLeft=function(e){if(!m&&$.left.isEnabled){var t=$.getOpenAmount();0===arguments.length&&(e=0>=t),$.content.enableAnimation(),$.openPercentage(e?100:0)}},$.toggleRight=function(e){if(!m&&$.right.isEnabled){var t=$.getOpenAmount();0===arguments.length&&(e=t>=0),$.content.enableAnimation(),$.openPercentage(e?-100:0)}},$.toggle=function(e){\"right\"==e?$.toggleRight():$.toggleLeft()},$.close=function(){$.openPercentage(0)},$.getOpenAmount=function(){return $.content&&$.content.getTranslateX()||0},$.getOpenRatio=function(){var e=$.getOpenAmount();return e>=0?e/$.left.width:e/$.right.width},$.isOpen=function(){return 0!==$.getOpenAmount()},$.getOpenPercentage=function(){return 100*$.getOpenRatio()},$.openPercentage=function(e){var t=e/100;$.left&&e>=0?$.openAmount($.left.width*t):$.right&&0>e&&$.openAmount($.right.width*t),o.enableClass(0!==e,\"menu-open\"),s(!1)},$.openAmount=function(e){var t=$.left&&$.left.width||0,n=$.right&&$.right.width||0;return($.left&&$.left.isEnabled||!(e>0))&&($.right&&$.right.isEnabled||!(0>e))?u&&e>t?void $.content.setTranslateX(t):l&&-n>e?void $.content.setTranslateX(-n):($.content.setTranslateX(e),void(e>=0?(u=!0,l=!1,e>0&&($.right&&$.right.pushDown&&$.right.pushDown(),$.left&&$.left.bringUp&&$.left.bringUp())):(l=!0,u=!1,$.right&&$.right.bringUp&&$.right.bringUp(),$.left&&$.left.pushDown&&$.left.pushDown()))):void $.content.setTranslateX(0)},$.snapToRest=function(e){$.content.enableAnimation(),f=!1;var t=$.getOpenRatio();if(0===t)return void $.openPercentage(0);var n=.3,i=e.gesture.velocityX,o=e.gesture.direction;$.openPercentage(t>0&&.5>t&&\"right\"==o&&n>i?0:t>.5&&\"left\"==o&&n>i?100:0>t&&t>-.5&&\"left\"==o&&n>i?0:.5>t&&\"right\"==o&&n>i?-100:\"right\"==o&&t>=0&&(t>=.5||i>n)?100:\"left\"==o&&0>=t&&(-.5>=t||i>n)?-100:0)},$.enableMenuWithBackViews=function(e){return arguments.length&&(w=!!e),w},$.isAsideExposed=function(){return!!m},$.exposeAside=function(e){($.left&&$.left.isEnabled||$.right&&$.right.isEnabled)&&($.close(),m=e,$.left&&$.left.isEnabled?$.content.setMarginLeft(m?$.left.width:0):$.right&&$.right.isEnabled&&$.content.setMarginRight(m?$.right.width:0),$.$scope.$emit(\"$ionicExposeAside\",m))},$.activeAsideResizing=function(e){o.enableClass(e,\"aside-resizing\")},$._endDrag=function(e){s(!1),m||(f&&$.snapToRest(e),h=null,v=null,g=null)},$._handleDrag=function(t){!m&&e.dragContent&&(h?v=t.gesture.touches[0].pageX:(h=t.gesture.touches[0].pageX,v=h),!f&&Math.abs(v-h)>$.dragThresholdX&&(h=v,f=!0,$.content.disableAnimation(),g=$.getOpenAmount()),f&&($.openAmount(g+(v-h)),s(!0)))},$.canDragContent=function(t){return arguments.length&&(e.dragContent=!!t),e.dragContent},$.edgeThreshold=25,$.edgeThresholdEnabled=!1,$.edgeDragThreshold=function(e){return arguments.length&&(d(e)&&e>0?($.edgeThreshold=e,$.edgeThresholdEnabled=!0):$.edgeThresholdEnabled=!!e),$.edgeThresholdEnabled},$.isDraggableTarget=function(t){var n=$.edgeThresholdEnabled&&!$.isOpen(),i=t.gesture.startEvent&&t.gesture.startEvent.center&&t.gesture.startEvent.center.pageX,o=!n||i<=$.edgeThreshold||i>=$.content.element.offsetWidth-$.edgeThreshold,a=r.backView(),c=w?!0:!a;if(!c){var s=r.currentView()||{};return a.historyId!==s.historyId}return(e.dragContent||$.isOpen())&&o&&!t.gesture.srcEvent.defaultPrevented&&c&&!t.target.tagName.match(/input|textarea|select|object|embed/i)&&!t.target.isContentEditable&&!(t.target.dataset?t.target.dataset.preventScroll:\"true\"==t.target.getAttribute(\"data-prevent-scroll\"))},e.sideMenuContentTranslateX=0;var b=p,y=angular.bind($,$.close);e.$watch(function(){return 0!==$.getOpenAmount()},function(e){b(),e&&(b=i.registerBackButtonAction(y,c.sideMenu))});var S=n._registerInstance($,t.delegateHandle,function(){return r.isActiveScope(e)});e.$on(\"$destroy\",function(){S(),b(),$.$scope=null,$.content&&($.content.element=null,$.content=null),s(!1)}),$.initialize({left:{width:275},right:{width:275}})}]),function(e){function t(e,i,o,r){var a,c,s,l=document.createElement(f[e]||e);for(a in i)if(angular.isArray(i[a]))for(c=0;c<i[a].length;c++)if(i[a][c].fn)for(s=0;s<i[a][c].t;s++)t(a,i[a][c].fn(s,r),l,r);else t(a,i[a][c],l,r);else n(l,a,i[a]);o.appendChild(l)}function n(e,t,n){e.setAttribute(f[t]||t,n)}function i(e,t){var n=e.split(\";\"),i=n.slice(t),o=n.slice(0,n.length-i.length);return n=i.concat(o).reverse(),n.join(\";\")+\";\"+n[0]}function o(e,t){return e/=t/2,1>e?.5*e*e*e:(e-=2,.5*(e*e*e+2))}var r=\"translate(32,32)\",a=\"stroke-opacity\",s=\"round\",l=\"indefinite\",u=\"750ms\",d=\"none\",f={a:\"animate\",an:\"attributeName\",at:\"animateTransform\",c:\"circle\",da:\"stroke-dasharray\",os:\"stroke-dashoffset\",f:\"fill\",lc:\"stroke-linecap\",rc:\"repeatCount\",sw:\"stroke-width\",t:\"transform\",v:\"values\"},h={v:\"0,32,32;360,32,32\",an:\"transform\",type:\"rotate\",rc:l,dur:u},p={sw:4,lc:s,line:[{fn:function(e,t){return{y1:\"ios\"==t?17:12,y2:\"ios\"==t?29:20,t:r+\" rotate(\"+(30*e+(6>e?180:-180))+\")\",a:[{fn:function(){return{an:a,dur:u,v:i(\"0;.1;.15;.25;.35;.45;.55;.65;.7;.85;1\",e),rc:l}},t:1}]}},t:12}]},v={android:{c:[{sw:6,da:128,os:82,r:26,cx:32,cy:32,f:d}]},ios:p,\"ios-small\":p,bubbles:{sw:0,c:[{fn:function(e){return{cx:24*Math.cos(2*Math.PI*e/8),cy:24*Math.sin(2*Math.PI*e/8),t:r,a:[{fn:function(){return{an:\"r\",dur:u,v:i(\"1;2;3;4;5;6;7;8\",e),rc:l}},t:1}]}},t:8}]},circles:{c:[{fn:function(e){return{r:5,cx:24*Math.cos(2*Math.PI*e/8),cy:24*Math.sin(2*Math.PI*e/8),t:r,sw:0,a:[{fn:function(){return{an:\"fill-opacity\",dur:u,v:i(\".3;.3;.3;.4;.7;.85;.9;1\",e),rc:l}},t:1}]}},t:8}]},crescent:{c:[{sw:4,da:128,os:82,r:26,cx:32,cy:32,f:d,at:[h]}]},dots:{c:[{fn:function(e){return{cx:16+16*e,cy:32,sw:0,a:[{fn:function(){return{an:\"fill-opacity\",dur:u,v:i(\".5;.6;.8;1;.8;.6;.5\",e),rc:l}},t:1},{fn:function(){return{an:\"r\",dur:u,v:i(\"4;5;6;5;4;3;3\",e),rc:l}},t:1}]}},t:3}]},lines:{sw:7,lc:s,line:[{fn:function(e){return{x1:10+14*e,x2:10+14*e,a:[{fn:function(){return{an:\"y1\",dur:u,v:i(\"16;18;28;18;16\",e),rc:l}},t:1},{fn:function(){return{an:\"y2\",dur:u,v:i(\"48;44;36;46;48\",e),rc:l}},t:1},{fn:function(){return{an:a,dur:u,v:i(\"1;.8;.5;.4;1\",e),rc:l}},t:1}]}},t:4}]},ripple:{f:d,\"fill-rule\":\"evenodd\",sw:3,circle:[{fn:function(e){return{cx:32,cy:32,a:[{fn:function(){return{an:\"r\",begin:-1*e+\"s\",dur:\"2s\",v:\"0;24\",keyTimes:\"0;1\",keySplines:\"0.1,0.2,0.3,1\",calcMode:\"spline\",rc:l}},t:1},{fn:function(){return{an:a,begin:-1*e+\"s\",dur:\"2s\",v:\".2;1;.2;0\",rc:l}},t:1}]}},t:2}]},spiral:{defs:[{linearGradient:[{id:\"sGD\",gradientUnits:\"userSpaceOnUse\",x1:55,y1:46,x2:2,y2:46,stop:[{offset:.1,\"class\":\"stop1\"},{offset:1,\"class\":\"stop2\"}]}]}],g:[{sw:4,lc:s,f:d,path:[{stroke:\"url(#sGD)\",d:\"M4,32 c0,15,12,28,28,28c8,0,16-4,21-9\"},{d:\"M60,32 C60,16,47.464,4,32,4S4,16,4,32\"}],at:[h]}]}},g={android:function(t){function i(){var t=o(Date.now()-r,650),u=1,d=0,f=188-58*t,h=182-182*t;a%2&&(u=-1,d=-64,f=128- -58*t,h=182*t);var p=[0,-101,-90,-11,-180,79,-270,-191][a];n(l,\"da\",Math.max(Math.min(f,188),128)),n(l,\"os\",Math.max(Math.min(h,182),0)),n(l,\"t\",\"scale(\"+u+\",1) translate(\"+d+\",0) rotate(\"+p+\",32,32)\"),c+=4.1,c>359&&(c=0),n(s,\"t\",\"rotate(\"+c+\",32,32)\"),t>=1&&(a++,a>7&&(a=0),r=Date.now()),e.requestAnimationFrame(i)}var r,a=0,c=0,s=t.querySelector(\"g\"),l=t.querySelector(\"circle\");return function(){r=Date.now(),i()}}};c.controller(\"$ionicSpinner\",[\"$element\",\"$attrs\",function(n,i){var o,r;this.init=function(){var a=null;\"windowsphone\"===e.Platform.platform()&&(a=\"android\"),o=i.icon||a||e.Platform.platform(),r=v[o],r||(o=\"ios\",r=v.ios);var c=document.createElement(\"div\");return t(\"svg\",{viewBox:\"0 0 64 64\",g:[v[o]]},c,o),n.html(c.innerHTML),this.start(),o},this.start=function(){g[o]&&g[o](n[0])()}}])}(ionic),c.controller(\"$ionicTab\",[\"$scope\",\"$ionicHistory\",\"$attrs\",\"$location\",\"$state\",function(e,t,n,i,o){this.$scope=e,this.hrefMatchesState=function(){return n.href&&0===i.path().indexOf(n.href.replace(/^#/,\"\").replace(/\\/$/,\"\"))},this.srefMatchesState=function(){return n.uiSref&&o.includes(n.uiSref.split(\"(\")[0])},this.navNameMatchesState=function(){return this.navViewName&&t.isCurrentStateNavView(this.navViewName)},this.tabMatchesState=function(){return this.hrefMatchesState()||this.srefMatchesState()||this.navNameMatchesState()}}]),c.controller(\"$ionicTabs\",[\"$scope\",\"$element\",\"$ionicHistory\",function(e,t,n){var i,o=this,r=null,a=null;o.tabs=[],o.selectedIndex=function(){return o.tabs.indexOf(r)},o.selectedTab=function(){return r},o.previousSelectedTab=function(){return a},o.add=function(e){n.registerHistory(e),o.tabs.push(e)},o.remove=function(e){var t=o.tabs.indexOf(e);if(-1!==t){if(e.$tabSelected)if(o.deselect(e),1===o.tabs.length);else{var n=t===o.tabs.length-1?t-1:t+1;o.select(o.tabs[n])}o.tabs.splice(t,1)}},o.deselect=function(e){e.$tabSelected&&(a=r,r=i=null,e.$tabSelected=!1,(e.onDeselect||p)(),e.$broadcast&&e.$broadcast(\"$ionicHistory.deselect\"))},o.select=function(t,a){var c;if(d(t)){if(c=t,c>=o.tabs.length)return;t=o.tabs[c]}else c=o.tabs.indexOf(t);1===arguments.length&&(a=!(!t.navViewName&&!t.uiSref)),r&&r.$historyId==t.$historyId?a&&n.goToHistoryRoot(t.$historyId):i!==c&&(l(o.tabs,function(e){o.deselect(e)}),r=t,i=c,o.$scope&&o.$scope.$parent&&(o.$scope.$parent.$activeHistoryId=t.$historyId),t.$tabSelected=!0,(t.onSelect||p)(),a&&e.$emit(\"$ionicHistory.change\",{type:\"tab\",tabIndex:c,historyId:t.$historyId,navViewName:t.navViewName,hasNavView:!!t.navViewName,title:t.title,url:t.href,uiSref:t.uiSref}))},o.hasActiveScope=function(){for(var e=0;e<o.tabs.length;e++)if(n.isActiveScope(o.tabs[e]))return!0;return!1}}]),c.controller(\"$ionicView\",[\"$scope\",\"$element\",\"$attrs\",\"$compile\",\"$rootScope\",function(e,t,n,i,o){function r(){var t=u(n.viewTitle)&&\"viewTitle\"||u(n.title)&&\"title\";t&&(a(n[t]),$.push(n.$observe(t,a))),u(n.hideBackButton)&&$.push(e.$watch(n.hideBackButton,function(e){f.showBackButton(!e)})),u(n.hideNavBar)&&$.push(e.$watch(n.hideNavBar,function(e){f.showBar(!e)}))}function a(e){u(e)&&e!==v&&(v=e,f.title(v))}function c(){for(var e=0;e<$.length;e++)$[e]();$=[]}function l(t){return t?i(t)(e.$new()):void 0}function d(t){return!!e.$eval(n[t])}var f,h,p,v,g=this,m={},$=[],w=e.$on(\"ionNavBar.init\",function(e,t){e.stopPropagation(),h=t});g.init=function(){w();var n=t.inheritedData(\"$ionModalController\");f=t.inheritedData(\"$ionNavViewController\"),f&&!n&&(e.$on(\"$ionicView.beforeEnter\",g.beforeEnter),e.$on(\"$ionicView.afterEnter\",r),e.$on(\"$ionicView.beforeLeave\",c))},g.beforeEnter=function(t,i){if(i&&!i.viewNotified){i.viewNotified=!0,o.$$phase||e.$digest(),v=u(n.viewTitle)?n.viewTitle:n.title;var r={};for(var a in m)r[a]=l(m[a]);f.beforeEnter(s(i,{title:v,showBack:!d(\"hideBackButton\"),navBarItems:r,navBarDelegate:h||null,showNavBar:!d(\"hideNavBar\"),hasHeaderBar:!!p})),c()}},g.navElement=function(e,t){m[e]=t}}]),c.directive(\"ionActionSheet\",[\"$document\",function(e){return{restrict:\"E\",scope:!0,replace:!0,link:function(t,n){var i=function(e){27==e.which&&(t.cancel(),t.$apply())},o=function(e){e.target==n[0]&&(t.cancel(),t.$apply())};t.$on(\"$destroy\",function(){n.remove(),e.unbind(\"keyup\",i)}),e.bind(\"keyup\",i),n.bind(\"click\",o)},template:'<div class=\"action-sheet-backdrop\"><div class=\"action-sheet-wrapper\"><div class=\"action-sheet\" ng-class=\"{\\'action-sheet-has-icons\\': $actionSheetHasIcon}\"><div class=\"action-sheet-group action-sheet-options\"><div class=\"action-sheet-title\" ng-if=\"titleText\" ng-bind-html=\"titleText\"></div><button class=\"button action-sheet-option\" ng-click=\"buttonClicked($index)\" ng-repeat=\"b in buttons\" ng-bind-html=\"b.text\"></button><button class=\"button destructive action-sheet-destructive\" ng-if=\"destructiveText\" ng-click=\"destructiveButtonClicked()\" ng-bind-html=\"destructiveText\"></button></div><div class=\"action-sheet-group action-sheet-cancel\" ng-if=\"cancelText\"><button class=\"button\" ng-click=\"cancel()\" ng-bind-html=\"cancelText\"></button></div></div></div></div>'}}]),c.directive(\"ionCheckbox\",[\"$ionicConfig\",function(e){return{restrict:\"E\",replace:!0,require:\"?ngModel\",transclude:!0,template:'<label class=\"item item-checkbox\"><div class=\"checkbox checkbox-input-hidden disable-pointer-events\"><input type=\"checkbox\"><i class=\"checkbox-icon\"></i></div><div class=\"item-content disable-pointer-events\" ng-transclude></div></label>',compile:function(t,n){var i=t.find(\"input\");l({name:n.name,\"ng-value\":n.ngValue,\"ng-model\":n.ngModel,\"ng-checked\":n.ngChecked,\"ng-disabled\":n.ngDisabled,\"ng-true-value\":n.ngTrueValue,\"ng-false-value\":n.ngFalseValue,\"ng-change\":n.ngChange,\"ng-required\":n.ngRequired,required:n.required},function(e,t){u(e)&&i.attr(t,e)});var o=t[0].querySelector(\".checkbox\");o.classList.add(\"checkbox-\"+e.form.checkbox())}}}]),c.directive(\"collectionRepeat\",e).factory(\"$ionicCollectionManager\",t);var b=\"data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7\",y=/height:.*?px;\\s*width:.*?px/,S=3;e.$inject=[\"$ionicCollectionManager\",\"$parse\",\"$window\",\"$$rAF\",\"$rootScope\",\"$timeout\"],t.$inject=[\"$rootScope\",\"$window\",\"$$rAF\"],c.directive(\"ionContent\",[\"$timeout\",\"$controller\",\"$ionicBind\",\"$ionicConfig\",function(e,t,n,i){return{restrict:\"E\",require:\"^?ionNavView\",scope:!0,priority:800,compile:function(e,o){function r(e,i,r){function l(){e.$onScrollComplete({scrollTop:c.scrollView.__scrollTop,scrollLeft:c.scrollView.__scrollLeft})}var d=e.$parent;if(e.$watch(function(){return(d.$hasHeader?\" has-header\":\"\")+(d.$hasSubheader?\" has-subheader\":\"\")+(d.$hasFooter?\" has-footer\":\"\")+(d.$hasSubfooter?\" has-subfooter\":\"\")+(d.$hasTabs?\" has-tabs\":\"\")+(d.$hasTabsTop?\" has-tabs-top\":\"\")},function(e,t){i.removeClass(t),i.addClass(e)}),e.$hasHeader=e.$hasSubheader=e.$hasFooter=e.$hasSubfooter=e.$hasTabs=e.$hasTabsTop=!1,n(e,r,{$onScroll:\"&onScroll\",$onScrollComplete:\"&onScrollComplete\",hasBouncing:\"@\",padding:\"@\",direction:\"@\",scrollbarX:\"@\",scrollbarY:\"@\",startX:\"@\",startY:\"@\",scrollEventInterval:\"@\"}),e.direction=e.direction||\"y\",u(r.padding)&&e.$watch(r.padding,function(e){(a||i).toggleClass(\"padding\",!!e)}),\"false\"===r.scroll);else{var f={};s?(i.addClass(\"overflow-scroll\"),f={el:i[0],delegateHandle:o.delegateHandle,startX:e.$eval(e.startX)||0,startY:e.$eval(e.startY)||0,nativeScrolling:!0}):f={el:i[0],delegateHandle:o.delegateHandle,locking:\"true\"===(o.locking||\"true\"),bouncing:e.$eval(e.hasBouncing),startX:e.$eval(e.startX)||0,startY:e.$eval(e.startY)||0,scrollbarX:e.$eval(e.scrollbarX)!==!1,scrollbarY:e.$eval(e.scrollbarY)!==!1,scrollingX:e.direction.indexOf(\"x\")>=0,scrollingY:e.direction.indexOf(\"y\")>=0,scrollEventInterval:parseInt(e.scrollEventInterval,10)||10,scrollingComplete:l},c=t(\"$ionicScroll\",{$scope:e,scrollViewOptions:f}),e.$on(\"$destroy\",function(){f&&(f.scrollingComplete=p,delete f.el),a=null,i=null,o.$$element=null})}}var a,c;e.addClass(\"scroll-content ionic-scroll\"),\"false\"!=o.scroll?(a=h('<div class=\"scroll\"></div>'),a.append(e.contents()),e.append(a)):e.addClass(\"scroll-content-false\");var s=\"true\"===o.overflowScroll||!i.scrolling.jsScrolling();return s&&(s=!e[0].querySelector(\"[collection-repeat]\")),{pre:r}}}}]),c.directive(\"exposeAsideWhen\",[\"$window\",function(e){return{restrict:\"A\",require:\"^ionSideMenus\",link:function(t,n,i,o){function r(){var t=\"large\"==i.exposeAsideWhen?\"(min-width:768px)\":i.exposeAsideWhen;o.exposeAside(e.matchMedia(t).matches),o.activeAsideResizing(!1)}function a(){o.activeAsideResizing(!0),c()}var c=ionic.debounce(function(){t.$apply(r)},300,!1);t.$evalAsync(r),ionic.on(\"resize\",a,e),t.$on(\"$destroy\",function(){ionic.off(\"resize\",a,e)})}}}]);var k=\"onHold onTap onDoubleTap onTouch onRelease onDrag onDragUp onDragRight onDragDown onDragLeft onSwipe onSwipeUp onSwipeRight onSwipeDown onSwipeLeft\".split(\" \");k.forEach(function(e){c.directive(e,n(e))}),c.directive(\"ionHeaderBar\",i()).directive(\"ionHeaderBar\",o(!0)).directive(\"ionFooterBar\",o(!1)),c.directive(\"ionInfiniteScroll\",[\"$timeout\",function(e){return{restrict:\"E\",require:[\"?^$ionicScroll\",\"ionInfiniteScroll\"],template:function(e,t){return t.icon?'<i class=\"icon {{icon()}} icon-refreshing {{scrollingType}}\"></i>':'<ion-spinner icon=\"{{spinner()}}\"></ion-spinner>'},scope:!0,controller:\"$ionInfiniteScroll\",link:function(t,n,i,o){var r=o[1],a=r.scrollCtrl=o[0],c=r.jsScrolling=!a.isNative();if(c)r.scrollView=a.scrollView,t.scrollingType=\"js-scrolling\",a.$element.on(\"scroll\",r.checkBounds);else{var s=ionic.DomUtil.getParentOrSelfWithClass(n[0].parentNode,\"overflow-scroll\");if(r.scrollEl=s,!s)throw\"Infinite scroll must be used inside a scrollable div\";r.scrollEl.addEventListener(\"scroll\",r.checkBounds)}var l=u(i.immediateCheck)?t.$eval(i.immediateCheck):!0;l&&e(function(){r.checkBounds()})}}}]),c.directive(\"ionItem\",[\"$$rAF\",function(e){return{restrict:\"E\",controller:[\"$scope\",\"$element\",function(e,t){this.$scope=e,this.$element=t}],scope:!0,compile:function(t,n){var i=u(n.href)||u(n.ngHref)||u(n.uiSref),o=i||/ion-(delete|option|reorder)-button/i.test(t.html());if(o){var r=h(i?\"<a></a>\":\"<div></div>\");r.addClass(\"item-content\"),(u(n.href)||u(n.ngHref))&&(r.attr(\"ng-href\",\"{{$href()}}\"),u(n.target)&&r.attr(\"target\",\"{{$target()}}\")),r.append(t.contents()),t.addClass(\"item item-complex\").append(r)}else t.addClass(\"item\");return function(t,n,i){t.$href=function(){return i.href||i.ngHref},t.$target=function(){return i.target};var o=n[0].querySelector(\".item-content\");o&&t.$on(\"$collectionRepeatLeave\",function(){o&&o.$$ionicOptionsOpen&&(o.style[ionic.CSS.TRANSFORM]=\"\",o.style[ionic.CSS.TRANSITION]=\"none\",e(function(){o.style[ionic.CSS.TRANSITION]=\"\"}),o.$$ionicOptionsOpen=!1)})}}}}]);var C='<div class=\"item-left-edit item-delete enable-pointer-events\"></div>';c.directive(\"ionDeleteButton\",function(){function e(e){e.stopPropagation()}return{restrict:\"E\",require:[\"^^ionItem\",\"^?ionList\"],priority:Number.MAX_VALUE,compile:function(t,n){return n.$set(\"class\",(n[\"class\"]||\"\")+\" button icon button-icon\",!0),function(t,n,i,o){function r(){c=c||n.controller(\"ionList\"),c&&c.showDelete()&&s.addClass(\"visible active\")}var a=o[0],c=o[1],s=h(C);s.append(n),a.$element.append(s).addClass(\"item-left-editable\"),n.on(\"click\",e),r(),t.$on(\"$ionic.reconnectScope\",r)}}}}),c.directive(\"itemFloatingLabel\",function(){return{restrict:\"C\",link:function(e,t){var n=t[0],i=n.querySelector(\"input, textarea\"),o=n.querySelector(\".input-label\");if(i&&o){var r=function(){i.value?o.classList.add(\"has-input\"):o.classList.remove(\"has-input\")};i.addEventListener(\"input\",r);var a=h(i).controller(\"ngModel\");a&&(a.$render=function(){i.value=a.$viewValue||\"\",r()}),e.$on(\"$destroy\",function(){i.removeEventListener(\"input\",r)})}}}});var T='<div class=\"item-options invisible\"></div>';c.directive(\"ionOptionButton\",[function(){function e(e){e.stopPropagation()}return{restrict:\"E\",require:\"^ionItem\",priority:Number.MAX_VALUE,compile:function(t,n){return n.$set(\"class\",(n[\"class\"]||\"\")+\" button\",!0),function(t,n,i,o){o.optionsContainer||(o.optionsContainer=h(T),o.$element.append(o.optionsContainer)),o.optionsContainer.append(n),o.$element.addClass(\"item-right-editable\"),n.on(\"click\",e)}}}}]);var B='<div data-prevent-scroll=\"true\" class=\"item-right-edit item-reorder enable-pointer-events\"></div>';c.directive(\"ionReorderButton\",[\"$parse\",function(e){return{restrict:\"E\",require:[\"^ionItem\",\"^?ionList\"],priority:Number.MAX_VALUE,compile:function(t,n){return n.$set(\"class\",(n[\"class\"]||\"\")+\" button icon button-icon\",!0),t[0].setAttribute(\"data-prevent-scroll\",!0),function(t,n,i,o){var r=o[0],a=o[1],c=e(i.onReorder);t.$onReorder=function(e,n){c(t,{$fromIndex:e,$toIndex:n})},i.ngClick||i.onClick||i.onclick||(n[0].onclick=function(e){return e.stopPropagation(),!1});var s=h(B);s.append(n),r.$element.append(s).addClass(\"item-right-editable\"),a&&a.showReorder()&&s.addClass(\"visible active\")}}}}]),c.directive(\"keyboardAttach\",function(){return function(e,t){function n(e){if(!ionic.Platform.isAndroid()||ionic.Platform.isFullScreen){var n=e.keyboardHeight||e.detail.keyboardHeight;t.css(\"bottom\",n+\"px\"),o=t.controller(\"$ionicScroll\"),o&&(o.scrollView.__container.style.bottom=n+r(t[0])+\"px\")}}function i(){(!ionic.Platform.isAndroid()||ionic.Platform.isFullScreen)&&(t.css(\"bottom\",\"\"),o&&(o.scrollView.__container.style.bottom=\"\"))}ionic.on(\"native.keyboardshow\",n,window),ionic.on(\"native.keyboardhide\",i,window),ionic.on(\"native.showkeyboard\",n,window),ionic.on(\"native.hidekeyboard\",i,window);var o;e.$on(\"$destroy\",function(){ionic.off(\"native.keyboardshow\",n,window),ionic.off(\"native.keyboardhide\",i,window),ionic.off(\"native.showkeyboard\",n,window),ionic.off(\"native.hidekeyboard\",i,window)})}}),c.directive(\"ionList\",[\"$timeout\",function(e){return{restrict:\"E\",require:[\"ionList\",\"^?$ionicScroll\"],controller:\"$ionicList\",compile:function(t,n){var i=h('<div class=\"list\">').append(t.contents()).addClass(n.type);return t.append(i),function(t,i,o,r){function a(){\nfunction o(e,t){t()&&e.addClass(\"visible\")||e.removeClass(\"active\"),ionic.requestAnimationFrame(function(){t()&&e.addClass(\"active\")||e.removeClass(\"visible\")})}var r=c.listView=new ionic.views.ListView({el:i[0],listEl:i.children()[0],scrollEl:s&&s.element,scrollView:s&&s.scrollView,onReorder:function(t,n,i){var o=h(t).scope();o&&o.$onReorder&&e(function(){o.$onReorder(n,i)})},canSwipe:function(){return c.canSwipeItems()}});t.$on(\"$destroy\",function(){r&&(r.deregister&&r.deregister(),r=null)}),u(n.canSwipe)&&t.$watch(\"!!(\"+n.canSwipe+\")\",function(e){c.canSwipeItems(e)}),u(n.showDelete)&&t.$watch(\"!!(\"+n.showDelete+\")\",function(e){c.showDelete(e)}),u(n.showReorder)&&t.$watch(\"!!(\"+n.showReorder+\")\",function(e){c.showReorder(e)}),t.$watch(function(){return c.showDelete()},function(e,t){if(e||t){e&&c.closeOptionButtons(),c.canSwipeItems(!e),i.children().toggleClass(\"list-left-editing\",e),i.toggleClass(\"disable-pointer-events\",e);var n=h(i[0].getElementsByClassName(\"item-delete\"));o(n,c.showDelete)}}),t.$watch(function(){return c.showReorder()},function(e,t){if(e||t){e&&c.closeOptionButtons(),c.canSwipeItems(!e),i.children().toggleClass(\"list-right-editing\",e),i.toggleClass(\"disable-pointer-events\",e);var n=h(i[0].getElementsByClassName(\"item-reorder\"));o(n,c.showReorder)}})}var c=r[0],s=r[1];e(a)}}}}]),c.directive(\"menuClose\",[\"$ionicHistory\",function(e){return{restrict:\"AC\",link:function(t,n){n.bind(\"click\",function(){var t=n.inheritedData(\"$ionSideMenusController\");t&&(e.nextViewOptions({historyRoot:!0,disableAnimate:!0,expire:300}),t.close())})}}}]),c.directive(\"menuToggle\",function(){return{restrict:\"AC\",link:function(e,t,n){e.$on(\"$ionicView.beforeEnter\",function(e,n){if(n.enableBack){var i=t.inheritedData(\"$ionSideMenusController\");i.enableMenuWithBackViews()||t.addClass(\"hide\")}else t.removeClass(\"hide\")}),t.bind(\"click\",function(){var e=t.inheritedData(\"$ionSideMenusController\");e&&e.toggle(n.menuToggle)})}}}),c.directive(\"ionModal\",[function(){return{restrict:\"E\",transclude:!0,replace:!0,controller:[function(){}],template:'<div class=\"modal-backdrop\"><div class=\"modal-backdrop-bg\"></div><div class=\"modal-wrapper\" ng-transclude></div></div>'}}]),c.directive(\"ionModalView\",function(){return{restrict:\"E\",compile:function(e){e.addClass(\"modal\")}}}),c.directive(\"ionNavBackButton\",[\"$ionicConfig\",\"$document\",function(e,t){return{restrict:\"E\",require:\"^ionNavBar\",compile:function(n,i){function o(e){return/ion-|icon/.test(e.className)}var r=t[0].createElement(\"button\");for(var a in i.$attr)r.setAttribute(i.$attr[a],i[a]);i.ngClick||r.setAttribute(\"ng-click\",\"$ionicGoBack()\"),r.className=\"button back-button hide buttons \"+(n.attr(\"class\")||\"\"),r.innerHTML=n.html()||\"\";for(var c,s,l,u,d=o(n[0]),f=0;f<n[0].childNodes.length;f++)c=n[0].childNodes[f],1===c.nodeType?o(c)?d=!0:c.classList.contains(\"default-title\")?l=!0:c.classList.contains(\"previous-title\")&&(u=!0):s||3!==c.nodeType||(s=!!c.nodeValue.trim());var h=e.backButton.icon();if(!d&&h&&\"none\"!==h&&(r.innerHTML='<i class=\"icon '+h+'\"></i> '+r.innerHTML,r.className+=\" button-clear\"),!s){var p=t[0].createElement(\"span\");p.className=\"back-text\",!l&&e.backButton.text()&&(p.innerHTML+='<span class=\"default-title\">'+e.backButton.text()+\"</span>\"),!u&&e.backButton.previousTitleText()&&(p.innerHTML+='<span class=\"previous-title\"></span>'),r.appendChild(p)}return n.attr(\"class\",\"hide\"),n.empty(),{pre:function(e,t,n,i){i.navElement(\"backButton\",r.outerHTML),r=null}}}}}]),c.directive(\"ionNavBar\",function(){return{restrict:\"E\",controller:\"$ionicNavBar\",scope:!0,link:function(e,t,n,i){i.init()}}}),c.directive(\"ionNavButtons\",[\"$document\",function(e){return{require:\"^ionNavBar\",restrict:\"E\",compile:function(t,n){var i=\"left\";/^primary|secondary|right$/i.test(n.side||\"\")&&(i=n.side.toLowerCase());var o=e[0].createElement(\"span\");o.className=i+\"-buttons\",o.innerHTML=t.html();var r=i+\"Buttons\";return t.attr(\"class\",\"hide\"),t.empty(),{pre:function(e,t,n,i){var a=t.parent().data(\"$ionViewController\");a?a.navElement(r,o.outerHTML):i.navElement(r,o.outerHTML),o=null}}}}}]),c.directive(\"navDirection\",[\"$ionicViewSwitcher\",function(e){return{restrict:\"A\",priority:1e3,link:function(t,n,i){n.bind(\"click\",function(){e.nextDirection(i.navDirection)})}}}]),c.directive(\"ionNavTitle\",[\"$document\",function(e){return{require:\"^ionNavBar\",restrict:\"E\",compile:function(t,n){var i=\"title\",o=e[0].createElement(\"span\");for(var r in n.$attr)o.setAttribute(n.$attr[r],n[r]);return o.classList.add(\"nav-bar-title\"),o.innerHTML=t.html(),t.attr(\"class\",\"hide\"),t.empty(),{pre:function(e,t,n,r){var a=t.parent().data(\"$ionViewController\");a?a.navElement(i,o.outerHTML):r.navElement(i,o.outerHTML),o=null}}}}}]),c.directive(\"navTransition\",[\"$ionicViewSwitcher\",function(e){return{restrict:\"A\",priority:1e3,link:function(t,n,i){n.bind(\"click\",function(){e.nextTransition(i.navTransition)})}}}]),c.directive(\"ionNavView\",[\"$state\",\"$ionicConfig\",function(e,t){return{restrict:\"E\",terminal:!0,priority:2e3,transclude:!0,controller:\"$ionicNavView\",compile:function(n,i,o){return n.addClass(\"view-container\"),ionic.DomUtil.cachedAttr(n,\"nav-view-transition\",t.views.transition()),function(t,n,i,r){function a(t){var n=e.$current&&e.$current.locals[s.name];n&&(t||n!==c)&&(c=n,s.state=n.$$state,r.register(n))}var c;o(t,function(e){n.append(e)});var s=r.init();t.$on(\"$stateChangeSuccess\",function(){a(!1)}),t.$on(\"$viewContentLoading\",function(){a(!1)}),a(!0)}}}}]),c.config([\"$provide\",function(e){e.decorator(\"ngClickDirective\",[\"$delegate\",function(e){return e.shift(),e}])}]).factory(\"$ionicNgClick\",[\"$parse\",function(e){return function(t,n,i){var o=angular.isFunction(i)?i:e(i);n.on(\"click\",function(e){t.$apply(function(){o(t,{$event:e})})}),n.onclick=p}}]).directive(\"ngClick\",[\"$ionicNgClick\",function(e){return function(t,n,i){e(t,n,i.ngClick)}}]).directive(\"ionStopEvent\",function(){return{restrict:\"A\",link:function(e,t,n){t.bind(n.ionStopEvent,a)}}}),c.directive(\"ionPane\",function(){return{restrict:\"E\",link:function(e,t){t.addClass(\"pane\")}}}),c.directive(\"ionPopover\",[function(){return{restrict:\"E\",transclude:!0,replace:!0,controller:[function(){}],template:'<div class=\"popover-backdrop\"><div class=\"popover-wrapper\" ng-transclude></div></div>'}}]),c.directive(\"ionPopoverView\",function(){return{restrict:\"E\",compile:function(e){e.append(h('<div class=\"popover-arrow\">')),e.addClass(\"popover\")}}}),c.directive(\"ionRadio\",function(){return{restrict:\"E\",replace:!0,require:\"?ngModel\",transclude:!0,template:'<label class=\"item item-radio\"><input type=\"radio\" name=\"radio-group\"><div class=\"item-content disable-pointer-events\" ng-transclude></div><i class=\"radio-icon disable-pointer-events icon ion-checkmark\"></i></label>',compile:function(e,t){t.icon&&e.children().eq(2).removeClass(\"ion-checkmark\").addClass(t.icon);var n=e.find(\"input\");return l({name:t.name,value:t.value,disabled:t.disabled,\"ng-value\":t.ngValue,\"ng-model\":t.ngModel,\"ng-disabled\":t.ngDisabled,\"ng-change\":t.ngChange,\"ng-required\":t.ngRequired,required:t.required},function(e,t){u(e)&&n.attr(t,e)}),function(e,t,n){e.getValue=function(){return e.ngValue||n.value}}}}}),c.directive(\"ionRefresher\",[function(){return{restrict:\"E\",replace:!0,require:[\"?^$ionicScroll\",\"ionRefresher\"],controller:\"$ionicRefresher\",template:'<div class=\"scroll-refresher invisible\" collection-repeat-ignore><div class=\"ionic-refresher-content\" ng-class=\"{\\'ionic-refresher-with-text\\': pullingText || refreshingText}\"><div class=\"icon-pulling\" ng-class=\"{\\'pulling-rotation-disabled\\':disablePullingRotation}\"><i class=\"icon {{pullingIcon}}\"></i></div><div class=\"text-pulling\" ng-bind-html=\"pullingText\"></div><div class=\"icon-refreshing\"><ion-spinner ng-if=\"showSpinner\" icon=\"{{spinner}}\"></ion-spinner><i ng-if=\"showIcon\" class=\"icon {{refreshingIcon}}\"></i></div><div class=\"text-refreshing\" ng-bind-html=\"refreshingText\"></div></div></div>',link:function(e,t,n,i){var o=i[0],r=i[1];!o||o.isNative()?r.init():(t[0].classList.add(\"js-scrolling\"),o._setRefresher(e,t[0],r.getRefresherDomMethods()),e.$on(\"scroll.refreshComplete\",function(){e.$evalAsync(function(){o.scrollView.finishPullToRefresh()})}))}}}]),c.directive(\"ionScroll\",[\"$timeout\",\"$controller\",\"$ionicBind\",function(e,t,n){return{restrict:\"E\",scope:!0,controller:function(){},compile:function(e){function i(e,i,r){n(e,r,{direction:\"@\",paging:\"@\",$onScroll:\"&onScroll\",scroll:\"@\",scrollbarX:\"@\",scrollbarY:\"@\",zooming:\"@\",minZoom:\"@\",maxZoom:\"@\"}),e.direction=e.direction||\"y\",u(r.padding)&&e.$watch(r.padding,function(e){o.toggleClass(\"padding\",!!e)}),e.$eval(e.paging)===!0&&o.addClass(\"scroll-paging\"),e.direction||(e.direction=\"y\");var a=e.$eval(e.paging)===!0,c={el:i[0],delegateHandle:r.delegateHandle,locking:\"true\"===(r.locking||\"true\"),bouncing:e.$eval(r.hasBouncing),paging:a,scrollbarX:e.$eval(e.scrollbarX)!==!1,scrollbarY:e.$eval(e.scrollbarY)!==!1,scrollingX:e.direction.indexOf(\"x\")>=0,scrollingY:e.direction.indexOf(\"y\")>=0,zooming:e.$eval(e.zooming)===!0,maxZoom:e.$eval(e.maxZoom)||3,minZoom:e.$eval(e.minZoom)||.5,preventDefault:!0};a&&(c.speedMultiplier=.8,c.bouncing=!1),t(\"$ionicScroll\",{$scope:e,scrollViewOptions:c})}e.addClass(\"scroll-view ionic-scroll\");var o=h('<div class=\"scroll\"></div>');return o.append(e.contents()),e.append(o),{pre:i}}}}]),c.directive(\"ionSideMenu\",function(){return{restrict:\"E\",require:\"^ionSideMenus\",scope:!0,compile:function(e,t){return angular.isUndefined(t.isEnabled)&&t.$set(\"isEnabled\",\"true\"),angular.isUndefined(t.width)&&t.$set(\"width\",\"275\"),e.addClass(\"menu menu-\"+t.side),function(e,n,i,o){e.side=i.side||\"left\";var r=o[e.side]=new ionic.views.SideMenu({width:t.width,el:n[0],isEnabled:!0});e.$watch(i.width,function(e){var t=+e;t&&t==e&&r.setWidth(+e)}),e.$watch(i.isEnabled,function(e){r.setIsEnabled(!!e)})}}}}),c.directive(\"ionSideMenuContent\",[\"$timeout\",\"$ionicGesture\",\"$window\",function(e,t,n){return{restrict:\"EA\",require:\"^ionSideMenus\",scope:!0,compile:function(i,o){function r(r,a,c,s){function l(e){0!==s.getOpenAmount()?(s.close(),e.gesture.srcEvent.preventDefault(),v=null,g=null):v||(v=ionic.tap.pointerCoord(e.gesture.srcEvent))}function d(e){s.isDraggableTarget(e)&&\"x\"==p(e)&&(s._handleDrag(e),e.gesture.srcEvent.preventDefault())}function f(e){\"x\"==p(e)&&e.gesture.srcEvent.preventDefault()}function h(e){s._endDrag(e),v=null,g=null}function p(e){if(g)return g;if(e&&e.gesture){if(v){var t=ionic.tap.pointerCoord(e.gesture.srcEvent),n=Math.abs(t.x-v.x),i=Math.abs(t.y-v.y),o=i>n?\"y\":\"x\";return Math.max(n,i)>30&&(g=o),o}v=ionic.tap.pointerCoord(e.gesture.srcEvent)}return\"y\"}var v=null,g=null;u(o.dragContent)?r.$watch(o.dragContent,function(e){s.canDragContent(e)}):s.canDragContent(!0),u(o.edgeDragThreshold)&&r.$watch(o.edgeDragThreshold,function(e){s.edgeDragThreshold(e)});var m={element:i[0],onDrag:function(){},endDrag:function(){},getTranslateX:function(){return r.sideMenuContentTranslateX||0},setTranslateX:ionic.animationFrameThrottle(function(t){var n=m.offsetX+t;a[0].style[ionic.CSS.TRANSFORM]=\"translate3d(\"+n+\"px,0,0)\",e(function(){r.sideMenuContentTranslateX=t})}),setMarginLeft:ionic.animationFrameThrottle(function(e){e?(e=parseInt(e,10),a[0].style[ionic.CSS.TRANSFORM]=\"translate3d(\"+e+\"px,0,0)\",a[0].style.width=n.innerWidth-e+\"px\",m.offsetX=e):(a[0].style[ionic.CSS.TRANSFORM]=\"translate3d(0,0,0)\",a[0].style.width=\"\",m.offsetX=0)}),setMarginRight:ionic.animationFrameThrottle(function(e){e?(e=parseInt(e,10),a[0].style.width=n.innerWidth-e+\"px\",m.offsetX=e):(a[0].style.width=\"\",m.offsetX=0),a[0].style[ionic.CSS.TRANSFORM]=\"translate3d(0,0,0)\"}),enableAnimation:function(){r.animationEnabled=!0,a[0].classList.add(\"menu-animated\")},disableAnimation:function(){r.animationEnabled=!1,a[0].classList.remove(\"menu-animated\")},offsetX:0};s.setContent(m);var $={stop_browser_behavior:!1};ionic.DomUtil.getParentOrSelfWithClass(a[0],\"overflow-scroll\")&&($.prevent_default_directions=[\"left\",\"right\"]);var w=t.on(\"tap\",l,a,$),b=t.on(\"dragright\",d,a,$),y=t.on(\"dragleft\",d,a,$),S=t.on(\"dragup\",f,a,$),k=t.on(\"dragdown\",f,a,$),C=t.on(\"release\",h,a,$);r.$on(\"$destroy\",function(){m&&(m.element=null,m=null),t.off(y,\"dragleft\",d),t.off(b,\"dragright\",d),t.off(S,\"dragup\",f),t.off(k,\"dragdown\",f),t.off(C,\"release\",h),t.off(w,\"tap\",l)})}return i.addClass(\"menu-content pane\"),{pre:r}}}}]),c.directive(\"ionSideMenus\",[\"$ionicBody\",function(e){return{restrict:\"ECA\",controller:\"$ionicSideMenus\",compile:function(t,n){function i(t,n,i,o){o.enableMenuWithBackViews(t.$eval(i.enableMenuWithBackViews)),t.$on(\"$ionicExposeAside\",function(n,i){t.$exposeAside||(t.$exposeAside={}),t.$exposeAside.active=i,e.enableClass(i,\"aside-open\")}),t.$on(\"$ionicView.beforeEnter\",function(e,n){n.historyId&&(t.$activeHistoryId=n.historyId)}),t.$on(\"$destroy\",function(){e.removeClass(\"menu-open\",\"aside-open\")})}return n.$set(\"class\",(n[\"class\"]||\"\")+\" view\"),{pre:i}}}}]),c.directive(\"ionSlideBox\",[\"$timeout\",\"$compile\",\"$ionicSlideBoxDelegate\",\"$ionicHistory\",\"$ionicScrollDelegate\",function(e,t,n,i,o){return{restrict:\"E\",replace:!0,transclude:!0,scope:{autoPlay:\"=\",doesContinue:\"@\",slideInterval:\"@\",showPager:\"@\",pagerClick:\"&\",disableScroll:\"@\",onSlideChanged:\"&\",activeSlide:\"=?\"},controller:[\"$scope\",\"$element\",\"$attrs\",function(t,r,a){function c(e){e&&!s.isScrollFreeze?o.freezeAllScrolls(e):!e&&s.isScrollFreeze&&o.freezeAllScrolls(!1),s.isScrollFreeze=e}var s=this,l=t.$eval(t.doesContinue)===!0,d=u(a.autoPlay)?!!t.autoPlay:!1,f=d?t.$eval(t.slideInterval)||4e3:0,h=new ionic.views.Slider({el:r[0],auto:f,continuous:l,startSlide:t.activeSlide,slidesChanged:function(){t.currentSlide=h.currentIndex(),e(function(){})},callback:function(n){t.currentSlide=n,t.onSlideChanged({index:t.currentSlide,$index:t.currentSlide}),t.$parent.$broadcast(\"slideBox.slideChanged\",n),t.activeSlide=n,e(function(){})},onDrag:function(){c(!0)},onDragEnd:function(){c(!1)}});h.enableSlide(t.$eval(a.disableScroll)!==!0),t.$watch(\"activeSlide\",function(e){u(e)&&h.slide(e)}),t.$on(\"slideBox.nextSlide\",function(){h.next()}),t.$on(\"slideBox.prevSlide\",function(){h.prev()}),t.$on(\"slideBox.setSlide\",function(e,t){h.slide(t)}),this.__slider=h;var p=n._registerInstance(h,a.delegateHandle,function(){return i.isActiveScope(t)});t.$on(\"$destroy\",function(){p(),h.kill()}),this.slidesCount=function(){return h.slidesCount()},this.onPagerClick=function(e){t.pagerClick({index:e})},e(function(){h.load()})}],template:'<div class=\"slider\"><div class=\"slider-slides\" ng-transclude></div></div>',link:function(e,n,i){function o(){if(!r){var i=e.$new();r=h(\"<ion-pager></ion-pager>\"),n.append(r),r=t(r)(i)}return r}u(i.showPager)||(e.showPager=!0,o().toggleClass(\"hide\",!1)),i.$observe(\"showPager\",function(t){t=e.$eval(t),o().toggleClass(\"hide\",!t)});var r}}}]).directive(\"ionSlide\",function(){return{restrict:\"E\",require:\"^ionSlideBox\",compile:function(e){e.addClass(\"slider-slide\")}}}).directive(\"ionPager\",function(){return{restrict:\"E\",replace:!0,require:\"^ionSlideBox\",template:'<div class=\"slider-pager\"><span class=\"slider-pager-page\" ng-repeat=\"slide in numSlides() track by $index\" ng-class=\"{active: $index == currentSlide}\" ng-click=\"pagerClick($index)\"><i class=\"icon ion-record\"></i></span></div>',link:function(e,t,n,i){var o=function(e){for(var n=t[0].children,i=n.length,o=0;i>o;o++)o==e?n[o].classList.add(\"active\"):n[o].classList.remove(\"active\")};e.pagerClick=function(e){i.onPagerClick(e)},e.numSlides=function(){return new Array(i.slidesCount())},e.$watch(\"currentSlide\",function(e){o(e)})}}}),c.directive(\"ionSpinner\",function(){return{restrict:\"E\",controller:\"$ionicSpinner\",link:function(e,t,n,i){var o=i.init();t.addClass(\"spinner spinner-\"+o)}}}),c.directive(\"ionTab\",[\"$compile\",\"$ionicConfig\",\"$ionicBind\",\"$ionicViewSwitcher\",function(e,t,n,i){function o(e,t){return u(t)?\" \"+e+'=\"'+t+'\"':\"\"}return{restrict:\"E\",require:[\"^ionTabs\",\"ionTab\"],controller:\"$ionicTab\",scope:!0,compile:function(r,a){for(var c=\"<ion-tab-nav\"+o(\"ng-click\",a.ngClick)+o(\"title\",a.title)+o(\"icon\",a.icon)+o(\"icon-on\",a.iconOn)+o(\"icon-off\",a.iconOff)+o(\"badge\",a.badge)+o(\"badge-style\",a.badgeStyle)+o(\"hidden\",a.hidden)+o(\"disabled\",a.disabled)+o(\"class\",a[\"class\"])+\"></ion-tab-nav>\",s=document.createElement(\"div\"),l=0;l<r[0].children.length;l++)s.appendChild(r[0].children[l].cloneNode(!0));var u=s.childElementCount;r.empty();var d,f;return u&&(\"ION-NAV-VIEW\"===s.children[0].tagName&&(d=s.children[0].getAttribute(\"name\"),s.children[0].classList.add(\"view-container\"),f=!0),1===u&&(s=s.children[0]),f||s.classList.add(\"pane\"),s.classList.add(\"tab-content\")),function(o,r,a,l){function f(){w.tabMatchesState()&&$.select(o,!1)}function p(n){n&&u?(b||(g=o.$new(),m=h(s),i.viewEleIsActive(m,!0),$.$element.append(m),e(m)(g),b=!0),i.viewEleIsActive(m,!0)):b&&m&&(t.views.maxCache()>0?i.viewEleIsActive(m,!1):v())}function v(){g&&g.$destroy(),b&&m&&m.remove(),s.innerHTML=\"\",b=g=m=null}var g,m,$=l[0],w=l[1],b=!1;o.$tabSelected=!1,n(o,a,{onSelect:\"&\",onDeselect:\"&\",title:\"@\",uiSref:\"@\",href:\"@\"}),$.add(o),o.$on(\"$destroy\",function(){o.$tabsDestroy||$.remove(o),y.isolateScope().$destroy(),y.remove(),y=s=m=null}),r[0].removeAttribute(\"title\"),d&&(w.navViewName=o.navViewName=d),o.$on(\"$stateChangeSuccess\",f),f();var y=h(c);y.data(\"$ionTabsController\",$),y.data(\"$ionTabController\",w),$.$tabsElement.append(e(y)(o)),o.$watch(\"$tabSelected\",p),o.$on(\"$ionicView.afterEnter\",function(){i.viewEleIsActive(m,o.$tabSelected)}),o.$on(\"$ionicView.clearCache\",function(){o.$tabSelected||v()})}}}}]),c.directive(\"ionTabNav\",[function(){return{restrict:\"E\",replace:!0,require:[\"^ionTabs\",\"^ionTab\"],template:\"<a ng-class=\\\"{'tab-item-active': isTabActive(), 'has-badge':badge, 'tab-hidden':isHidden()}\\\" \"+' ng-disabled=\"disabled()\" class=\"tab-item\"><span class=\"badge {{badgeStyle}}\" ng-if=\"badge\">{{badge}}</span><i class=\"icon {{getIconOn()}}\" ng-if=\"getIconOn() && isTabActive()\"></i><i class=\"icon {{getIconOff()}}\" ng-if=\"getIconOff() && !isTabActive()\"></i><span class=\"tab-title\" ng-bind-html=\"title\"></span></a>',scope:{title:\"@\",icon:\"@\",iconOn:\"@\",iconOff:\"@\",badge:\"=\",hidden:\"@\",disabled:\"&\",badgeStyle:\"@\",\"class\":\"@\"},link:function(e,t,n,i){var o=i[0],r=i[1];t[0].removeAttribute(\"title\"),e.selectTab=function(e){e.preventDefault(),o.select(r.$scope,!0)},n.ngClick||t.on(\"click\",function(t){e.$apply(function(){e.selectTab(t)})}),e.isHidden=function(){return\"true\"===n.hidden||n.hidden===!0?!0:!1},e.getIconOn=function(){return e.iconOn||e.icon},e.getIconOff=function(){return e.iconOff||e.icon},e.isTabActive=function(){return o.selectedTab()===r.$scope}}}}]),c.directive(\"ionTabs\",[\"$ionicTabsDelegate\",\"$ionicConfig\",function(e,t){return{restrict:\"E\",scope:!0,controller:\"$ionicTabs\",compile:function(n){function i(t,n,i,o){function a(e,t){e.stopPropagation();var n=o.previousSelectedTab();n&&n.$broadcast(e.name.replace(\"NavView\",\"Tabs\"),t)}var c=e._registerInstance(o,i.delegateHandle,o.hasActiveScope);o.$scope=t,o.$element=n,o.$tabsElement=h(n[0].querySelector(\".tabs\")),t.$watch(function(){return n[0].className},function(e){var n=-1!==e.indexOf(\"tabs-top\"),i=-1!==e.indexOf(\"tabs-item-hide\");t.$hasTabs=!n&&!i,t.$hasTabsTop=n&&!i,t.$emit(\"$ionicTabs.top\",t.$hasTabsTop)}),t.$on(\"$ionicNavView.beforeLeave\",a),t.$on(\"$ionicNavView.afterLeave\",a),t.$on(\"$ionicNavView.leave\",a),t.$on(\"$destroy\",function(){t.$tabsDestroy=!0,c(),o.$tabsElement=o.$element=o.$scope=r=null,delete t.$hasTabs,delete t.$hasTabsTop})}function o(e,t,n,i){i.selectedTab()||i.select(0)}var r=h('<div class=\"tab-nav tabs\">');return r.append(n.contents()),n.append(r).addClass(\"tabs-\"+t.tabs.position()+\" tabs-\"+t.tabs.style()),{pre:i,post:o}}}}]),c.directive(\"ionToggle\",[\"$timeout\",\"$ionicConfig\",function(e,t){return{restrict:\"E\",replace:!0,require:\"?ngModel\",transclude:!0,template:'<div class=\"item item-toggle\"><div ng-transclude></div><label class=\"toggle\"><input type=\"checkbox\"><div class=\"track\"><div class=\"handle\"></div></div></label></div>',compile:function(e,n){var i=e.find(\"input\");return l({name:n.name,\"ng-value\":n.ngValue,\"ng-model\":n.ngModel,\"ng-checked\":n.ngChecked,\"ng-disabled\":n.ngDisabled,\"ng-true-value\":n.ngTrueValue,\"ng-false-value\":n.ngFalseValue,\"ng-change\":n.ngChange,\"ng-required\":n.ngRequired,required:n.required},function(e,t){u(e)&&i.attr(t,e)}),n.toggleClass&&e[0].getElementsByTagName(\"label\")[0].classList.add(n.toggleClass),e.addClass(\"toggle-\"+t.form.toggle()),function(e,t){var n=t[0].getElementsByTagName(\"label\")[0],i=n.children[0],o=n.children[1],r=o.children[0],a=h(i).controller(\"ngModel\");e.toggle=new ionic.views.Toggle({el:n,track:o,checkbox:i,handle:r,onChange:function(){a&&(a.$setViewValue(i.checked),e.$apply())}}),e.$on(\"$destroy\",function(){e.toggle.destroy()})}}}}]),c.directive(\"ionView\",function(){return{restrict:\"EA\",priority:1e3,controller:\"$ionicView\",compile:function(e){return e.addClass(\"pane\"),e[0].removeAttribute(\"title\"),function(e,t,n,i){i.init()}}}})}();"]}
|
aa4aed2d8e631e234d68ae38463a6c52b2d2f0b3
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/bedr/examples/bedr.sort.region.Rd.R
|
c8c5f23ed31a8783e87c09982ace3edf159e57af
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 254
|
r
|
bedr.sort.region.Rd.R
|
library(bedr)
### Name: bedr.sort.region
### Title: sort a region file
### Aliases: bedr.sort.region
### Keywords: sort
### ** Examples
if (check.binary("bedtools")) {
index <- get.example.regions();
a <- index[[1]];
b <- bedr.sort.region(a);
}
|
d17d101a986468f864914b1cdeb7a4c06fefb85c
|
f9376bb4d345ec552ac295d4098f523f18eaacba
|
/R/Lecture2/Lecture2/OldMaterial/SQLandR.R
|
67e7122fe91ec649a788db18a54e5f3b5df6b710
|
[] |
no_license
|
StephenElston/DataScience410
|
1c201792c8c7084e699cf9397daaa658ea40ef73
|
21855687724240192592d0d4f72674f5f21f6895
|
refs/heads/master
| 2023-01-24T21:19:47.038382
| 2020-12-04T03:01:01
| 2020-12-04T03:01:01
| 115,932,652
| 10
| 15
| null | 2020-01-29T03:03:53
| 2018-01-01T16:56:19
|
Jupyter Notebook
|
UTF-8
|
R
| false
| false
| 4,535
|
r
|
SQLandR.R
|
##--------------------------------------------
##
## Using SQL from R
##
## Class: PCE Data Science Methods Class
##
##--------------------------------------------
getwd()
#setwd('C:/Users/Steve/Dropbox/UW/DataSci350/Lecture 2')
##-----Getting/Storing Data-----
# txt files
?read.table
# csv files. Is wraper on read.table
?read.csv # Note the option stringsAsFactors = FALSE
# web/html
?readLines
## Example: get a data frame
read.auto <- function(path = '.'){
require(stringr)
## Function to read the csv file
filePath <- file.path(path, 'Automobile price data _Raw_.csv')
auto.price <- read.csv(filePath, header = TRUE,
stringsAsFactors = FALSE)
## Coerce some character columns to numeric
numcols <- c('price', 'bore', 'stroke', 'horsepower', 'peak.rpm')
auto.price[, numcols] <- lapply(auto.price[, numcols], as.numeric)
auto.price
}
## Read the csv file
## Note that SQL databases don't like '.' characters in column names
Auto.Price = read.auto(path = 'C:/Users/Steve/GIT/DataScience350/Lecture1') ## Read the csv file
nams <- names(Auto.Price)
names(Auto.Price) <- gsub('\\.', '_', nams) ## replace '.' with '_'
##-----SQLite Access-----
## Set up the connection to the database
library(RSQLite)
# Name of the db
db.name = 'auto_db'
# Create the connection
db_conn = dbConnect(dbDriver("SQLite"), db_name)
# Write dataframe to a table
dbWriteTable(db_conn,"Auto_Price", Auto.Price, overwrite = TRUE)
# Simple query database
query = 'SELECT * FROM Auto_Price LIMIT 5;'
test = dbSendQuery(db_conn, query)
fetch(test)
## Query to find turbo cars. Note the excape required
## arround the '.
query = 'SELECT * FROM Auto_Price WHERE aspiration = \'turbo\';'
turbo.q = dbSendQuery(db_conn, query)
turbo = fetch(high.milage.q)
## Query to find high milage cars
query = 'SELECT * FROM Auto_Price WHERE city_mpg > 24;'
high.milage.q = dbSendQuery(db_conn, query)
high.milage = fetch(high.milage.q)
## make a plot of the high milage subset
require(ggplot2)
ggplot(high.milage, aes(city_mpg, price)) +
geom_point(aes(size = 2, color = factor(fuel_type))) +
ggtitle('Price vs. city mpg for high milage autos')
# Disconnect, because we have clean R code.
dbDisconnect(db_conn)
## Example: Use an existing database
## Create the database from the
con = dbConnect(dbDriver("SQLite"), dbname = 'nyc_flights/nycflights13.sqlite')
alltables = dbListTables(con)
alltables
## Look at a few tables
query = 'SELECT * FROM flights LIMIT 5;'
test = dbSendQuery(con, query)
fetch(test)
query = 'SELECT * FROM airports LIMIT 5;'
test = dbSendQuery(con, query)
fetch(test)
##----Try/Catch Pattern ----
#
# The tryCatch is used to create robust, produciton quality, R code
# It should be used a lot more. This pattern shows how to use it:
#
# result = tryCatch({
# your code
# },
# error=function(error_condition){
# message('Your error message here')
# message(error_condition)
# },
# warning=function(warning_condition){
# message('Your warning message here')
# message(warning_condition)
# },
# finally={
# Always execute these commands to cleanup.
# }
# )
#
execute.query <- function(query, db = 'nyc_flights/nycflights13.sqlite'){
stopifnot(is.character(query))
if(!file.exists(db)) stop('ERROR, database not found')
tryCatch({
con = dbConnect(dbDriver("SQLite"), dbname = 'nyc_flights/nycflights13.sqlite')
message('Opened database connection')
dbGetQuery(con, query)
},
error = function(error_condition){
message('ERROR: The query has failed')
message(error_condition)
},
warning = function(warning_condition){
message('WARRNING: warrning conditon for query')
message(error_condition)
},
finally = {
dbDisconnect(con)
message('\nDatabase connection closed')
}
)
}
## Test the function
query = 'SELECT * FROM flights LIMIT 5;'
execute.query(query)
## A query that fails
query = 'SELECT * FROM no_table LIMIT 5;'
execute.query(query)
|
cef6ed6ef2c4e861101ee07c03081dfc0d544f2a
|
1137633080330c3cf316af30b8bcf6a625310447
|
/06 - Predicting with unlabel dataset.R
|
aae09f01a8a9cd91f76c7f1f16ace772c01e3c3b
|
[] |
no_license
|
gloria2691/WiDS-Datathon-2021
|
a78fd6693740d3d684808fc749a3397a5e3df12a
|
c185482b07defd5b8fccb0aff2145530f87f7cd5
|
refs/heads/main
| 2023-03-11T20:06:51.022257
| 2021-03-02T16:11:36
| 2021-03-02T16:11:36
| 339,709,882
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 435
|
r
|
06 - Predicting with unlabel dataset.R
|
# Doing prediction with the unlabel dataset
require(randomForest)
#Load Data unlabel
data_unlabel <- read.csv(file.path(data_dir, "UnlabeledWiDS2021.csv"))
View(data_unlabel)
sum(is.na(data_unlabel))
# Predicting values
?predict
predictions_unlabel <- predict(modelo, newdata = data_unlabel, type = 'prob', na.action=na.fail)
# Result
View(predictions_unlabel)
#performance
?performance
?prediction
|
37412a1843e0493f6149e4a5fa1ddd5c0674be15
|
2cbfbfe385329c7c3768522af14ad383e2414c1a
|
/pollutantmean.R
|
5ad0f7490984c880922fe4308af34e8421830f46
|
[] |
no_license
|
vcerveron/datasciencecoursera
|
7e440a801091e963408bc20d953bebdfc2b94f41
|
5e40a492517bba1c09a5fcf50ef3e2f3125c7d64
|
refs/heads/master
| 2021-01-19T00:08:47.806107
| 2015-05-28T17:59:02
| 2015-05-28T17:59:02
| 33,619,445
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 249
|
r
|
pollutantmean.R
|
pollutantmean <- function(directory, pollutant, id=1:332) {
data <- NULL
for(idn in id) {
datap <- read.csv(paste0(directory,"/",formatC(idn,width=3,flag="0"),".csv"))
data <- rbind(data, datap)
}
mean(data[,pollutant], na.rm=TRUE)
}
|
0080cff0760cac0fc5089d2a08b215824025fd5d
|
073e4e7c9c2f4822e798f4a56e4ff90b11b5a85c
|
/Code/impact_CNA_pipeline.R
|
80a208816e2967c68d7f0ebbd303c6cec61208da
|
[] |
no_license
|
peteryzheng/RET_ACCESS
|
2cff82bd261beff926affd24798ac02ef2b8775a
|
ac4e3544d85c90ef723aa3dc433d468515020133
|
refs/heads/master
| 2022-12-13T08:56:32.229201
| 2020-08-06T04:19:45
| 2020-08-06T04:19:45
| 285,464,497
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,623
|
r
|
impact_CNA_pipeline.R
|
library(data.table)
library(dplyr)
library(tidyr)
master.ref <- fread('/ifs/work/bergerm1/zhengy1/RET_all/Sample_mapping/master_ref_080719.csv')
cna.dir <- paste0('/ifs/work/bergerm1/zhengy1/RET_all/Analysis_files/cna_',format(Sys.time(),'%m%d%y'))
# cna.dir <- paste0('/ifs/work/bergerm1/zhengy1/RET_all/Analysis_files/cna_',format(Sys.time(),'%m%d%y'),'_no_ret')
dir.create(cna.dir)
# manifests for tumor and normals -----------------------------------------
dir.create(paste0(cna.dir,'/manifests/'))
write.table(master.ref[!Tumor_Sample_Barcode.plasma %in% c('DA-ret-041-pl-T02_IGO_05500_FF_27','C-YP5R0K-L001-d','DA-ret-004-pl-T01_IGO_05500_FF_18',
'C-02WK6K-L001-d','C-02WK6K-L002-d',' DA-ret-028-pl-T01_IGO_05500_FF_24',
'DA-ret-028-pl-T02_IGO_05500_FF_25','C-2UW6JP-L002-d','C-2UW6JP-L003-d'),
.(BAM_path.plasma = gsub('-duplex','',gsub('duplex_bams','unfiltered_bams',BAM_path.plasma)),
Sex = ifelse(Sex == 'M','Male','Female'))],
paste0(cna.dir,'/manifests/tumor_manifest.txt'),sep = '\t',quote = F,row.names = F,col.names = F)
# low depth buffy coat (not good because pool A vs B ratio is diff --------
# write.table(master.ref[!is.na(BAM_path.normal),
# .(BAM_path.normal = gsub('-duplex','',gsub('duplex_bams','unfiltered_bams',BAM_path.normal)),
# Sex = ifelse(Sex == 'M','Male','Female'))],
# paste0(cna.dir,'/manifests/normal_manifest.txt'),sep = '\t',quote = F,row.names = F,col.names = F)
#
# # high depth normal (not perfect because no spike in ret probes) ----------
# write.table(data.frame(bam_paths = list.files('/ifs/work/bergerm1/brannona/ACCESS_M1.8/ACCESSv1-VAL-20190004/unfiltered',pattern = 'DONOR[0-9]+-T.*.bam',full.names = T),Sex = 'Male'),
# paste0(cna.dir,'/manifests/normal_manifest.txt'),sep = '\t',quote = F,row.names = F,col.names = F)
#
# curated low/no tumor content plasma bams --------------------------------
write.table(master.ref[Tumor_Sample_Barcode.plasma %in% c('DA-ret-041-pl-T02_IGO_05500_FF_27','C-YP5R0K-L001-d','DA-ret-004-pl-T01_IGO_05500_FF_18',
'C-02WK6K-L001-d','C-02WK6K-L002-d',' DA-ret-028-pl-T01_IGO_05500_FF_24',
'DA-ret-028-pl-T02_IGO_05500_FF_25','C-2UW6JP-L002-d','C-2UW6JP-L003-d'),
.(BAM_path.plasma = gsub('-duplex','',gsub('duplex_bams','unfiltered_bams',BAM_path.plasma)),
Sex = ifelse(Sex == 'M','Male','Female'))],
paste0(cna.dir,'/manifests/normal_manifest.txt'),sep = '\t',quote = F,row.names = F,col.names = F)
# running pipeline --------------------------------------------------------
system(paste0(
'bsub -sla Berger -q sol -cwd ',cna.dir,' -J ','cna_',format(Sys.time(),'%m%d%y'),' -o %J.o -e %J.e',
' -We 24:00 -R "rusage[mem=8]" -M 8 -n 1 ',
' /home/ptashkir/.conda/envs/py27/bin/python /home/ptashkir/CNV_ACCESS/cBX_pipeline/scripts/cfdna_scna.py',
' -t ',cna.dir,'/manifests/tumor_manifest.txt',
' -n ',cna.dir,'/manifests/normal_manifest.txt',
' -tr 25 -b /ifs/work/bergerm1/zhengy1/RET_all/Original_files/MSK-ACCESS-v1_0.sorted.RET.bed',
' -g /ifs/depot/resources/dmp/data/pubdata/hg-fasta/VERSIONS/hg19/Homo_sapiens_assembly19.fasta',
' -r /opt/common/CentOS_6/R/R-3.2.0/bin/R -q sol -o ',cna.dir,
' -bsub /common/lsf/9.1/linux2.6-glibc2.3-x86_64/bin/bsub -id EDD_ret',
' -l /home/ptashkir/CNV_ACCESS/cBX_pipeline/scripts/loessnormalize_nomapq_cfdna.R',
' -cn /home/ptashkir/CNV_ACCESS/cBX_pipeline/scripts/copynumber_tm.batchdiff_cfdna.R',
' -ta /ifs/work/bergerm1/zhengy1/RET_all/Original_files/MSK-ACCESS-v1_0.sorted.RET.merged.txt'
))
# bed.file <- fread('/ifs/work/bergerm1/zhengy1/RET/Original_file/MSK-ACCESS-v1_0-probe-A.sorted.RET.bed')
# write.table(bed.file[,!c('V4'),with = F],
# '/ifs/work/bergerm1/zhengy1/RET_all/Original_files/MSK-ACCESS-v1_0-probe-A.sorted.RET.bed',
# sep = '\t',quote = F,row.names = F,col.names = F)
# system('bedtools nuc -fi /ifs/depot/resources/dmp/data/pubdata/hg-fasta/VERSIONS/hg19/Homo_sapiens_assembly19.fasta -bed /ifs/work/bergerm1/zhengy1/RET_all/Original_files/MSK-ACCESS-v1_0-probe-A.sorted.RET.bed > /ifs/work/bergerm1/zhengy1/RET_all/Original_files/MSK-ACCESS-v1_0-probe-A.sorted.RET.txt')
# # using non ret bed file --------------------------------------------------
#
# system(paste0(
# 'bsub -sla Berger -q sol -cwd ',cna.dir,' -J ','cna_',format(Sys.time(),'%m%d%y'),'_no_ret -o %J.o -e %J.e',
# ' -We 24:00 -R "rusage[mem=8]" -M 8 -n 1 ',
# ' /home/ptashkir/.conda/envs/py27/bin/python /home/ptashkir/CNV_ACCESS/cBX_pipeline/scripts/cfdna_scna.py',
# ' -t ',cna.dir,'/manifests/tumor_manifest.txt',
# ' -n ',cna.dir,'/manifests/normal_manifest.txt',
# ' -tr 25 -b /ifs/work/bergerm1/zhengy1/RET_all/Original_files/MSK-ACCESS-v1_0.sorted.woRET.bed',
# ' -g /ifs/depot/resources/dmp/data/pubdata/hg-fasta/VERSIONS/hg19/Homo_sapiens_assembly19.fasta',
# ' -r /opt/common/CentOS_6/R/R-3.2.0/bin/R -q sol -o ',cna.dir,
# ' -bsub /common/lsf/9.1/linux2.6-glibc2.3-x86_64/bin/bsub -id EDD_ret',
# ' -l /home/ptashkir/CNV_ACCESS/cBX_pipeline/scripts/loessnormalize_nomapq_cfdna.R',
# ' -cn /home/ptashkir/CNV_ACCESS/cBX_pipeline/scripts/copynumber_tm.batchdiff_cfdna.R',
# ' -ta /ifs/work/bergerm1/zhengy1/RET_all/Original_files/MSK-ACCESS-v1_0.sorted.woRET.merged.txt'
# ))
#
|
20c086a77a47f1b11cd92c38d7188108f4cbb7cc
|
c3826e89c7c78acdcc4596820d03fa96c8710b38
|
/R/zzz.R
|
d9c48acd4955ef78045ace43d0d7f82139456bd4
|
[
"LicenseRef-scancode-unknown-license-reference",
"MIT"
] |
permissive
|
chen496/SomaDataIO
|
7b393fad010774e17e086555a026c2a38de06415
|
b8f00329aaa283f8243d1064a7bda19b873fdd67
|
refs/heads/master
| 2023-06-24T21:22:02.222540
| 2021-07-27T20:45:52
| 2021-07-27T20:45:52
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,835
|
r
|
zzz.R
|
#' @importFrom stringr str_glue
#' @noRd
.onAttach <- function(libname, pkgname) {
packageStartupMessage(
cli::rule(right = "Legal", line = 2, col = crayon::magenta),
"\n",
stringr::str_glue(
"
SomaDataIO\u2122
Copyright \u00A9 2021 SomaLogic, Inc.
Permission is hereby granted, free of charge, to any person obtaining
a copy of the SomaDataIO software and associated documentation files
(the \"Software\"), to deal in the Software without restriction,
including without limitation the rights to use, copy, modify, merge,
publish, distribute, sublicense, and/or sell copies of the Software,
and to permit persons to whom the Software is furnished to do so,
subject to the following conditions outlined below. Further,
SomaDataIO and SomaLogic are trademarks owned by SomaLogic, Inc. No
license is hereby granted to these trademarks other than for purposes
of identifying the origin or source of the Software.
The above copyright notice and this permission notice shall be
included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDER(S) BE LIABLE FOR
ANY CLAIM, DAMAGES, WHETHER DIRECT OR INDIRECT, OR OTHER LIABILITY,
WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
IN THE SOFTWARE.
"
),
"\n",
cli::rule(line = 2, col = crayon::magenta)
)
}
|
0f5d5d950d82e530a8fe9ce52307ba4943656096
|
56f809d92798dc2cdb1ffc47fdfcb306fdba014e
|
/R/temp_risks.R
|
1ed987e8f024dc7aa044d753c76a93f6c9bc1a76
|
[] |
no_license
|
gclawson1/computingpackage
|
1fcfe94fa9d1262a4d7df752d726b1f279f7d22f
|
e0d13be4b612f24a92f9df2c6538d4a6380c7342
|
refs/heads/master
| 2020-06-01T11:55:47.375459
| 2019-06-13T21:45:05
| 2019-06-13T21:45:05
| 190,771,358
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,391
|
r
|
temp_risks.R
|
#' temp_risks
#'
#' Compute the number of days per each location over the span of the data set where there is risk of heat stroke, comfortable weather, and freezing at 3 PM.
#' @param data data frame with columns Date, Location, Temp3pm
#' @author Gage Clawson
#' @example temp_risks(data)
#' @return Returns a table containing,
#' \describe{
#' \item{Location}{Location in Australia}
#' \item{heat_stroke_n}{Number of days for a particular location where there has been a risk of heat stroke}
#' \item{comfort_n}{Number of days for a particular location where the weather has been comfortable}
#' \item{freezing_n}{Number of days for a particular location where there has been a risk of freezing}
#' }
temp_risks = function(data){
clim_df <- data %>%
dplyr::mutate(year = lubridate::year(Date),
month = lubridate::month(Date),
day = lubridate::day(Date)) %>%
mutate(risk = case_when(
Temp3pm >40 ~ "heat stroke",
Temp3pm < 40 & Temp3pm >= 0 ~ "comfortable",
Temp3pm < 0 ~ "freezing"
) )
risk_df <- clim_df %>%
group_by(Location) %>%
summarise(heat_stroke_n = sum(risk == "heat stroke", na.rm = TRUE),
comfortable_n = sum(risk == "comfortable", na.rm = TRUE),
freezing_n = sum(risk == "freezing", na.rm = TRUE)) %>%
ungroup()
return(list(table = risk_df)
)
}
|
9b5d7b60782069e33e58f1472f3c51320f0005e7
|
46eeb7254223e1a4c7a08ac33b50f0689d6e3024
|
/algo/lcs4.R
|
4874fea4747399ed6dc3cd7e6627d2203c53bf26
|
[] |
no_license
|
moshahmed/R
|
4712f5021aa0c59665375ee3a6004feac2923413
|
2d79c7af4bab63cdbafd3776fc9f8bc6c29d6392
|
refs/heads/master
| 2022-10-15T03:58:00.930511
| 2022-10-10T17:30:40
| 2022-10-10T17:30:40
| 28,472,379
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,074
|
r
|
lcs4.R
|
# What: LCS in R
# Changes GPL(C) moshahmed@gmail.com
# See c:/doc3/algo/malgo/dynamic/lcs/lcs.htm
# See lcs3.R 2016-10-15
lcs4p <- function(B, X, i, j) {
# cat("lcs4p",i,j,"\n")
if ( i==0 || j==0 ) return()
if (B[i+1, j+1] == '/') {
lcs4p(B, X, i-1, j-1)
print (X[i])
} else if (B[i+1, j+1] == '^') {
lcs4p(B, X, i-1, j)
} else { # '<'
lcs4p(B, X, i, j-1)
}
}
lcs4 <- function(Xm, Yn) {
X <- unlist(strsplit(Xm,split=""))
Y <- unlist(strsplit(Yn,split=""))
m <- nchar(Xm)
n <- nchar(Yn)
if (m<1 || n < 1) return(0)
L <- matrix(0, nrow=m+2, ncol=n+2)
B <- L
for (i in 1:m+1) {
for (j in 1:n+1) {
if (i == 1 || j == 1) {
L[i,j] = 0
} else if (X[i-1] == Y[j-1]) {
L[i, j] = L[i-1, j-1] + 1
B[i, j] = '/'
} else if (L[i-1,j] >= L[i,j-1]) {
L[i, j] = L[i-1, j]
B[i, j] = '^'
} else {
L[i, j] = L[i,j-1]
B[i, j] = '<'
}
}
}
lcs4p(B, X, m, n)
return(list("L"=L,"B"=B))
}
Xm <- "ABABAD"
Yn <- "ACAADD"
ans <- lcs4(Xm,Yn)
|
9c3e10d0974b93f415dcaea32cc01ffd49a4a77e
|
59e30e3e196df56abca3a58f44d6fb1fd67d098c
|
/2-R Programming/A3/Hospital.R
|
f846419bddf7d600d3837765ccc15755cf28ae9e
|
[] |
no_license
|
yiapplege/Data_JHU_Cousera
|
9afdaf38ad61990e05efbe896e562d433c4119d5
|
860824f79393772f155f1bc9a131a9b03553cc98
|
refs/heads/master
| 2020-04-04T08:03:34.659730
| 2019-02-05T04:26:11
| 2019-02-05T04:26:11
| 155,769,760
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,968
|
r
|
Hospital.R
|
#R Programming
#Assignment for Week 4
#Author: Yige
#Date: Nov.6, 2018
rm(list=ls())
setwd("~/Desktop/Data Science @Coursera/Assignments/2_3")
#1 plot the 30-day mortality rates for heart attack
outcome <- read.csv("outcome-of-care-measures.csv",colClasses = "character")
names(outcome)
ncol(outcome)
nrow(outcome)
deathrates <- as.numeric(outcome[,11])
hist(deathrates)
#2 function to find best hosipital in a state
best <- function(state,outcome){
setwd("~/Desktop/Data Science @Coursera/Assignments/2_3")
dat <- read.csv("outcome-of-care-measures.csv",colClasses = "character")
dat <- dat[,c(1:10,11,17,23)]
#dat[,c(11,12,13)] <- as.numeric(dat[,c(11,12,13)])
#colnames(dat)[c(11,12,13)] <- c("heart attack","heart failure","pneumonia")
#subset hospital info & 30 day death rate of "heart attack","heart failure","pneumonia"
if(!state %in% dat$State){
stop("invalid state")
}
else if(!outcome %in% c("heart attack","heart failure","pneumonia")){
stop("invalid outcome")
}
switch(outcome,'heart attack'={col =11},'heart failure'={col=12},
'pneumonia'={col=13})
dat[, col] = as.numeric(dat[, col])
hop_state <- dat[dat$State == state,c(2,col)]
hop_state <- na.omit(hop_state)
hop_state <- hop_state[order(hop_state[,2],hop_state[,1]),]
hop_state[1,1]
#hop_state[which.min(hop_state[,2]), 1]
}
#TEST
best("TX", "heart attack")
best("TX", "heart failure")
best("MD", "heart attack")
best("MD", "pneumonia")
best("BB", "heart attack")
best("NY", "hear attack")
#3 Ranking hospital by outcome in a state
rankhospital <- function(state, outcome, num = "best"){
setwd("~/Desktop/Data Science @Coursera/Assignments/2_3")
dat <- read.csv("outcome-of-care-measures.csv",colClasses = "character")
dat <- dat[,c(1:10,11,17,23)]
if(!state %in% dat$State){
stop("invalid state")
}
else if(!outcome %in% c("heart attack","heart failure","pneumonia")){
stop("invalid outcome")
}
switch(outcome,'heart attack'={col =11},'heart failure'={col=12},
'pneumonia'={col=13})
dat[, col] = as.numeric(dat[, col])
dat_state <- dat[dat$State == state,c(2,col)]
dat_state <- na.omit(dat_state)
nhospital <- nrow(dat_state)
switch(num,'best'={num=1},'worst'={num=nhospital})
if(num > nhospital){ return(NA) }
dat_state <- dat_state[order(dat_state[,2],dat_state[,1]),]
dat_state[num,1]
}
#TEST
rankhospital("TX", "heart failure", 4)
rankhospital("MD", "heart attack", "worst")
rankhospital("MN", "heart attack", 5000)
#4 Ranking hospital in all states
rankall <- function(outcome,num){
setwd("~/Desktop/Data Science @Coursera/Assignments/2_3")
dat <- read.csv("outcome-of-care-measures.csv",colClasses = "character")
switch(outcome,'heart attack'={col =11},'heart failure'={col=17},
'pneumonia'={col=23},stop("invalid outcome"))
dat[, col] = as.numeric(dat[, col])
dat <- dat[,c(2,7,col)] #name,state,death rate
dat <- na.omit(dat)
states=unique(dat$State)
rankstate <- function(state){
dat_state <- dat[dat$State == state,]
nhospital <- nrow(dat_state)
switch(num,'best'={num=1},'worst'={num=nhospital})
if(num > nhospital){ return(NA) }
dat_order <- order(dat_state[,3], dat_state[,1]) #order by death rate/name
name <- dat_state[dat_order,][num,1]
c(name,state)
}
result = do.call(rbind, lapply(states, rankstate))
result = result[order(result[, 2]), ]
rownames(result) = result[, 2]
colnames(result) = c("hospital", "state")
data.frame(result)
}
#TEST
head(rankall("heart attack", 20), 10)
tail(rankall("pneumonia", "worst"), 3)
tail(rankall("heart failure"), 10)
|
d2231521ef11a416d1a6ee118d631cee08f9641a
|
a297edbe0a8b9895cc685fbd94cf1e49d58db807
|
/tests/testthat/test-tri-graticule.R
|
101a173d40542b8590fb0e8947bc7e7b9d1de297
|
[] |
no_license
|
mdsumner/bluegum
|
146aaa1091ad8a0f8932fdbd306028752c6c8223
|
06e9115edd0e118d87d28608c8a0815d1927321e
|
refs/heads/master
| 2020-08-01T02:59:37.781664
| 2019-10-26T03:18:26
| 2019-10-26T03:18:26
| 210,838,144
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,123
|
r
|
test-tri-graticule.R
|
test_that("tri_graticule works", {
mesh <- tri_graticule() %>% expect_s3_class( "mesh3d")
expect_s3_class(tri_graticule(xlim = c(10, 20)), "mesh3d")
expect_s3_class(tri_graticule(ylim = c(-80, -70)), "mesh3d")
expect_s3_class(tri_graticule(xlim = c(100, 150), ylim = c(-80, -70)), "mesh3d")
expect_s3_class(tri_graticule(hull = TRUE), "mesh3d")
expect_s3_class(tri_graticule(xlim = c(10, 20),hull = TRUE), "mesh3d")
expect_s3_class(tri_graticule(ylim = c(-80, -70),hull = TRUE), "mesh3d")
expect_s3_class(tri_graticule(xlim = c(100, 150), ylim = c(-80, -70),hull = TRUE), "mesh3d")
expect_s3_class(tri_graticule(hull = TRUE, sub = 1), "mesh3d")
expect_s3_class(tri_graticule(ylim = c(-80, -70), sub = 2, hull = TRUE), "mesh3d")
mesh2 <- tri_graticule(n = 15) %>% expect_s3_class( "mesh3d")
expect_true(ncol(mesh$vb) < ncol(mesh2$vb))
expect_s3_class(tri_graticule(n = 20, ylim = c(-80, -70), sub = 2, hull = TRUE), "mesh3d")
})
test_that("quad_graticule works", {
mesh <- quad_graticule() %>% expect_s3_class( "mesh3d")
expect_s3_class(quad_graticule(xlim = c(10, 20)), "mesh3d")
expect_s3_class(quad_graticule(ylim = c(-80, -70)), "mesh3d")
expect_s3_class(quad_graticule(xlim = c(100, 150), ylim = c(-80, -70)), "mesh3d")
expect_warning(expect_s3_class(quad_graticule(hull = TRUE), "mesh3d"))
expect_warning(expect_s3_class(quad_graticule(xlim = c(10, 20),hull = TRUE), "mesh3d"))
expect_warning(expect_s3_class(quad_graticule(ylim = c(-80, -70),hull = TRUE), "mesh3d"))
expect_warning(expect_s3_class(quad_graticule(xlim = c(100, 150), ylim = c(-80, -70),hull = TRUE), "mesh3d"))
expect_warning(expect_s3_class(quad_graticule(hull = TRUE, sub = 1), "mesh3d"))
expect_warning(expect_s3_class(quad_graticule(ylim = c(-80, -70), sub = 2, hull = TRUE), "mesh3d"))
mesh2 <- quad_graticule(n = 15) %>% expect_s3_class( "mesh3d")
expect_true(ncol(mesh$vb) < ncol(mesh2$vb))
expect_warning(expect_s3_class(quad_graticule(n = 20, ylim = c(-80, -70), sub = 2, hull = TRUE), "mesh3d"))
})
test_that("hull_graticule works", {
mesh <- hull_graticule() %>% expect_s3_class( "mesh3d")
expect_warning(hull_graticule(xlim = c(10, 20)))
expect_warning(hull_graticule(ylim = c(-80, -70)))
expect_warning(hull_graticule(xlim = c(100, 150), ylim = c(-80, -70)))
expect_warning(hull_graticule(hull = TRUE))
expect_warning(hull_graticule(xlim = c(10, 20),hull = TRUE))
expect_warning(hull_graticule(ylim = c(-80, -70),hull = TRUE))
expect_warning(hull_graticule(xlim = c(100, 150), ylim = c(-80, -70),hull = TRUE))
expect_warning(expect_s3_class(hull_graticule(hull = TRUE, sub = 1), "mesh3d"))
expect_warning(expect_s3_class(hull_graticule(ylim = c(-80, -70), sub = 2, hull = TRUE), "mesh3d"))
mesh2 <- hull_graticule(n_coords = 15) %>% expect_s3_class( "mesh3d")
expect_true(ncol(mesh$vb) > ncol(mesh2$vb))
expect_warning(expect_s3_class(hull_graticule(n_coords = 20, ylim = c(-80, -70), sub = 2, hull = TRUE), "mesh3d"))
expect_warning(hull_graticule(n_coords = 10, coords = geosphere::randomCoordinates(100)))
})
|
345486522ddbffed0a5dbd1572845e585e321e04
|
7b264bf1eabfaa4615bd53045397d8951de1c1f4
|
/R/gitlink-package.R
|
eec02bbc85be92ab3663c2eb53a310c59a71acf6
|
[
"MIT"
] |
permissive
|
colearendt/gitlink
|
062bf27ddd54bb693e741e2f76adadbd5bf9adf9
|
bf5f9ab2018934ba841f8b9809bf01d7938c88e8
|
refs/heads/master
| 2020-04-10T10:46:28.196527
| 2019-11-28T05:32:55
| 2019-11-28T05:32:55
| 160,975,384
| 16
| 0
|
NOASSERTION
| 2019-11-28T05:32:24
| 2018-12-08T20:20:01
|
R
|
UTF-8
|
R
| false
| false
| 237
|
r
|
gitlink-package.R
|
#' @importFrom htmltools a
#' @importFrom htmltools css
#' @importFrom htmltools div
#' @importFrom htmltools img
#' @importFrom htmltools tags
#' @importFrom htmltools tagList
#' @importFrom rlang list2
#' @keywords internal
"_PACKAGE"
|
20a5cf4830c6ea9656ce6faae361fed48a92a32c
|
fdb0f6ac3ca332d07b3a088ca53f6a7b0edc9b4c
|
/Code/Temporal_ordering_of_TF_programs.R
|
d051037269945b777514e64feed4f9e6d539c8a4
|
[] |
no_license
|
gaoweiwang/SCislet
|
f98966f4dcc63e9c0a9ee2784f5c1a48bc1d9d8d
|
dbff1869624743d4297f7d404fe3bc43cfe3729a
|
refs/heads/main
| 2023-04-16T17:45:22.847727
| 2023-03-02T21:44:34
| 2023-03-02T21:44:34
| 490,007,552
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,996
|
r
|
Temporal_ordering_of_TF_programs.R
|
library(Seurat)
library(Signac)
library(monocle3)
library(SeuratWrappers)
library(ggplot2)
library(patchwork)
library(reshape2)
library(cicero)
library(gplots)
library(dplyr)
library(plyr)
blank_theme <- theme_minimal()+
theme(
panel.border = element_blank(),
panel.grid=element_blank(),
axis.ticks = element_blank(),
plot.title=element_text(size=14, face="bold"),
panel.background = element_rect(fill = "white", colour = "grey50")
)
################ temporal order of transcriptional programs, Figure 3
###use alpha cell lineage as an example
load("/oasis/tscc/scratch/hazhu/share/upload/diff_atac_chromvar.rds")
diff_ds_sub<-subset(diff_ds,subset=anno%in%c("diff_ENP1","diff_ENPalpha","diff_alpha"))
col_dna<-c("diff_ENP1"="thistle1",
"diff_ENPalpha"="mediumorchid2","diff_alpha"="purple2")
dna_all_cds <- as.cell_data_set(diff_ds_sub)
dna_all_cds <- cluster_cells(cds = dna_all_cds, reduction_method = "UMAP")
dna_all_cds <- learn_graph(dna_all_cds, use_partition = F,close_loop = F,
learn_graph_control=list(ncenter=500,minimal_branch_len=10))
dna_all_cds <- order_cells(dna_all_cds, reduction_method = "UMAP")
######manually select alpha cell lineage
cds_subset<-choose_graph_segments(dna_all_cds)
cds_subset<-dna_all_cds[,colnames(cds_subset)]
p2<-plot_cells(dna_all_cds,
color_cells_by = "anno",
label_groups_by_cluster=FALSE,
trajectory_graph_color = "grey0",
trajectory_graph_segment_size = 1.5,
group_label_size = 5,
label_cell_groups = FALSE,
label_leaves=FALSE,
label_branch_points=FALSE,
label_roots = FALSE)+ scale_color_manual(values =col_dna )+NoLegend()
alpha.pseudotime.mtx<-cds_subset@assays@data$counts
alpha.pseudotime.mtx<-alpha.pseudotime.mtx[rownames(alpha.pseudotime.mtx)%in%rownames(ccre.umap)[ccre.umap$module%in%c("ENP1","ENP_alpha","SC_alpha")],]
alpha.pseudotime.mtx<-as.data.frame(t(alpha.pseudotime.mtx))
alpha.pseudotime.mtx<-cbind(alpha.pseudotime.mtx,"pseudotime"=pseudotime(cds_subset))
alpha.pseudotime.mtx<-alpha.pseudotime.mtx[order(alpha.pseudotime.mtx$pseudotime),]
#####cCRE pseudotime
alpha.ccre.pseudotime<-matrix(0,0,2)
for (i in colnames(alpha.pseudotime.mtx[,-5961])) {
temp.pseudotime<-alpha.pseudotime.mtx$pseudotime[alpha.pseudotime.mtx[,i]==1]
pseodotime.sumit<-density(temp.pseudotime)$x[which.max(density(temp.pseudotime)$y)]
temp<-c(i,pseodotime.sumit)
alpha.ccre.pseudotime<-rbind(alpha.ccre.pseudotime,temp)
}
rownames(alpha.ccre.pseudotime)<-colnames(alpha.pseudotime.mtx[,-5961])
alpha.pseudotime.umap<-ccre.umap[rownames(alpha.ccre.pseudotime),]
alpha.pseudotime.umap$pseudotime<-as.numeric(alpha.ccre.pseudotime[,2])
############Plot cCRE pseudotime on cCRE UMAP, fupplementary figure 3j
ccre.umap<-read.csv("/oasis/tscc/scratch/hazhu/share/upload/sc.islet.diff.ccre.umap.csv",row.names = 1)
p.umap.pseudotime<-ggplot(ccre.umap,aes(x=UMAP1,y=UMAP2))+geom_point(size=0.01,colour = "grey80")+
geom_point(data = alpha.pseudotime.umap,aes(x=UMAP1,y=UMAP2,color=pseudotime),size=0.01)+
scale_colour_viridis_c(option = "inferno")+
blank_theme
############construct RNA pseudotime
diff_rna<-readRDS("/oasis/tscc/scratch/hazhu/share/upload/diff_rna.rds")
rna_all_cds <- as.cell_data_set(diff_rna)
rna_all_cds <- cluster_cells(cds = rna_all_cds, reduction_method = "UMAP")
rna_subset<-rna_all_cds[,colnames(cds_subset)]
rm(rna_all_cds)
rm(diff_rna)
#######look for alpha genes and alpha TFs
alpha.peaks<-colnames(alpha.pseudotime.mtx[,1:(ncol(alpha.pseudotime.mtx)-1)])
alpha.genes<-sig_target_gene_predicton[sig_target_gene_predicton$peak_id%in%alpha.peaks,"gene"]
############include master TFs found in Figure 2d
master.tfs<-read.csv("/oasis/tscc/scratch/hazhu/share/upload/sc.islet.diff.module.specific.tf.sig.csv",row.names = 1)
alpha.master.TFs<-master.tfs[master.tfs$module%in%c("ENP1","ENP_alpha","SC_alpha"),"TF"]
alpha.genes<-unique(c(alpha.genes,alpha.master.TFs))
alpha.rna.pseudotime.mtx<-rna_subset@assays@data$logcounts
alpha.rna.pseudotime.mtx<-alpha.rna.pseudotime.mtx[rownames(alpha.rna.pseudotime.mtx)%in%alpha.genes,]
alpha.rna.pseudotime.mtx<-as.data.frame(t(alpha.rna.pseudotime.mtx))
alpha.rna.pseudotime.mtx<-cbind(alpha.rna.pseudotime.mtx,"pseudotime"=pseudotime(cds_subset))
alpha.rna.pseudotime.mtx<-alpha.rna.pseudotime.mtx[order(alpha.rna.pseudotime.mtx$pseudotime),]
alpha.rna.pseudotime<-matrix(0,0,2)
for (i in colnames(alpha.rna.pseudotime.mtx[,1:(ncol(alpha.rna.pseudotime.mtx)-1)])) {
temp.fit<-smooth.spline(alpha.rna.pseudotime.mtx$pseudotime,alpha.rna.pseudotime.mtx[,colnames(alpha.rna.pseudotime.mtx)==i])
temp.pseudotime<-temp.fit$x[which.max(temp.fit$y)]
temp<-c(i,temp.pseudotime)
alpha.rna.pseudotime<-rbind(alpha.rna.pseudotime,temp)
}
rownames(alpha.rna.pseudotime)<-colnames(alpha.rna.pseudotime.mtx[,1:(ncol(alpha.rna.pseudotime.mtx)-1)])
colnames(alpha.rna.pseudotime)<-c("names","pseudotime")
alpha.rna.pseudotime<-as.data.frame(alpha.rna.pseudotime)
alpha.rna.pseudotime$pseudotime<-as.numeric(alpha.rna.pseudotime$pseudotime)
alpha.rna.pseudotime$module<-NA
alpha.rna.pseudotime$origin<-rep("RNA",nrow(alpha.rna.pseudotime))
colnames(alpha.ccre.pseudotime)<-c("names","pseudotime")
alpha.ccre.pseudotime<-as.data.frame(alpha.ccre.pseudotime)
alpha.ccre.pseudotime$pseudotime<-as.numeric(alpha.ccre.pseudotime$pseudotime)
alpha.ccre.pseudotime$module<-alpha.pseudotime.umap$module
alpha.ccre.pseudotime$origin<-rep("cCRE",nrow(alpha.ccre.pseudotime))
alpha.all.pseudotime<-rbind(alpha.rna.pseudotime,alpha.ccre.pseudotime)
alpha.all.pseudotime$axis<-rep(1,nrow(alpha.all.pseudotime))
alpha.all.pseudotime<-alpha.all.pseudotime[order(alpha.all.pseudotime$pseudotime),]
alpha.all.pseudotime$rank<-c(1:nrow(alpha.all.pseudotime))
#######plot TF, TF target cCREs and TF target genes
TF.gene<-"ZNF414"
TF.bs<-C_TF[C_TF$gene==TF.gene & C_TF$stat>0.5,]
TF.bs<-TF.bs[TF.bs$peak_ID%in%rownames(alpha.all.pseudotime),]
alpha.all.pseudotime$scc<-rep(0,nrow(alpha.all.pseudotime))
for (i in TF.bs$peak_ID) {
alpha.all.pseudotime$scc[rownames(alpha.all.pseudotime)==i]<-TF.bs[TF.bs$peak_ID==i,"stat"]
}
pseudotime.TF.bs<-alpha.all.pseudotime[!alpha.all.pseudotime$scc==0,]
tf.exp<-alpha.all.pseudotime[TF.gene,]
ccre.target<-sig_target_gene_predicton[sig_target_gene_predicton$peak_id%in%rownames(pseudotime.TF.bs),"gene"]
ccre.target<-unique(ccre.target)
ccre.target.pseudotime<-alpha.all.pseudotime[ccre.target,]
p.pseudotime.2<-ggplot(alpha.all.pseudotime,aes(x=pseudotime,y=origin))+geom_point(size=0.5,colour="grey85")+
geom_point(data = ccre.target.pseudotime,aes(x=pseudotime,y=origin),size=0.5,colour="tan3")+
geom_point(data =tf.exp, aes(x=pseudotime,y=origin),size=2, colour="forestgreen")+
geom_point(data=pseudotime.TF.bs,aes(x=pseudotime,y=origin,color=scc),size=0.5)+
scale_color_gradientn(colours = c("grey90","Tomato1","red4"))+
blank_theme
|
87d5d035970e001fc28b36209a7d7c6eec898505
|
092393a8e01f95e26abb175fccfe13f509c25404
|
/man/shifts.Rd
|
21abe14ac641475815fa49dd50b03b3e42840765
|
[] |
no_license
|
bdemeshev/torro
|
8a74f3db6a5468fc9c65e76706e748d4b599e7a4
|
69c02a714e5b9afb4948a58a691fbdd20aa873a0
|
refs/heads/master
| 2021-01-19T15:16:50.032726
| 2018-03-06T06:11:09
| 2018-03-06T06:11:09
| 100,956,656
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 570
|
rd
|
shifts.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/torro.R
\docType{data}
\name{shifts}
\alias{shifts}
\title{Shifts}
\format{tibble}
\usage{
data(shifts)
}
\description{
Tibble with the following columns
}
\details{
\itemize{
\item shift_name name of the shift
\item shift_T_start starting time of the shift
\item win_expanding logical, TRUE for expanding/growing window, FALSE for moving
\item win_start_lenght length of the first window
\item n_shifts number of window shifts
}
See also toy version, \code{shifts_toy}.
}
\keyword{datasets}
|
4ea8ad027df86d3ad2bd0713756e6e7c66af39cd
|
b492ada9c3472a01aa6e6deab9551f233a0174c9
|
/gtexR_GO.R
|
d677c7552ddad1e13651a806f7f3b86120dd48d6
|
[] |
no_license
|
Parks-Laboratory/GTEX_liver_WGCNA_Human
|
f1e8ed6dabfcb25dd374836bd7ce0df09a2f3734
|
b53cd27743cf270332f4f12c711744821a4e0725
|
refs/heads/master
| 2020-05-30T00:11:03.509497
| 2019-07-24T15:08:51
| 2019-07-24T15:08:51
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,105
|
r
|
gtexR_GO.R
|
BiocManager::install('org.Mm.eg.db')
BiocManager::install('org.Hs.eg.db')
install.packages ("xml2")
BiocManager::install("biomaRt")
install.packages("sqldf")
library (org.Mm.eg.db)
library (org.Hs.eg.db)
library(biomaRt)
library (WGCNA)
library(sqldf)
load (file = "2-gtex-InfoWithModule.RData")
annot = read.csv ("GeneAnotation.csv",sep=",")
annot
probes = names (datExpr)
probes
probes2annot = match (probes,annot$GeneName)
probes2annot
ensembl.genes = annot$id [probes2annot]
# ensembl.genes <- readLines ('ensemble_id.csv')
ensembl.genes
ensembl.genes <- gsub('.{0,2}$', '', ensembl.genes)
ensembl.genes <- gsub("[^0-9A-Za-z///' ]","#" , ensembl.genes ,ignore.case = TRUE)
ensembl.genes <- gsub("#","", ensembl.genes ,ignore.case = TRUE)
values <- as.vector (ensembl.genes)
values
# values [is.na(values)] <- "ENSG00000270040"
# values
# mart <- useDataset("hsapiens_gene_ensembl", useMart("ensembl"))
mart = useMart("ensembl", dataset = "hsapiens_gene_ensembl", host="uswest.ensembl.org")
entrezgene = getBM(attributes=c('ensembl_gene_id', 'entrezgene'),
filters = 'ensembl_gene_id',
values = values,
mart = mart)
names(entrezgene)[2]<-"genenum"
entrezgene
entrezgene<- entrezgene[!duplicated(entrezgene[1]), ]
entrezgene
value_dataframe = data.frame (values)
value_dataframe
names(value_dataframe)[1]<-"ensembl_gene_id"
value_dataframe
# write.csv (entrezgene,"entrezgene.xls")
# write.csv (value_dataframe,"value_dataframe.xls")
combined_df <- sqldf("Select f.*, most.genenum
from value_dataframe f
left JOIN (select distinct ensembl_gene_id,genenum from entrezgene) as most
on f.ensembl_gene_id = most.ensembl_gene_id
")
combined_df
# allLLIDs = as.data.frame.matrix(entrezgene)
allLLIDs <- as.vector (combined_df[[2]])
allLLIDs
GOenr = GOenrichmentAnalysis (moduleColors,allLLIDs,organism="human",nBestP = 10)
tab = GOenr$bestPTerms [[4]]$enrichment
write.table (tab,"0-GOenrichementtable.csv",sep=",",quote= TRUE,row.names=FALSE)
|
1076eb47d748efffca4664c1774133d81bb1091f
|
093f4979b58388700d670906ddb4f9e839675299
|
/plotExons-old.R
|
60763222cb18561ae4f2a636ebcff17379763e34
|
[] |
no_license
|
sgschneider01/R_code
|
6ac0c871ad619635f238a655dde7c5f6e5d1a106
|
39d1db3d91673655cf799343eb884ee4caded5ad
|
refs/heads/master
| 2020-06-20T12:44:19.672021
| 2016-11-27T02:50:22
| 2016-11-27T02:50:22
| 74,863,072
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,365
|
r
|
plotExons-old.R
|
source("~/Desktop/research/notes/R_code/getExons.R")
source("~/Desktop/research/notes/R_code/getExonsIntrons.R")
source("~/Desktop/research/notes/R_code/parseExonTable.R")
plotExons <- function (egid,sp) {
exons <- getExons(egid,sp)
if (!is.null(exons)) {
sap <- apply(exons[seq(1,nrow(exons),by=2),], 1,
function (x) {
lines(c(x[1],x[2]),c(.01,.01),type="l",lwd=5) } )
sap <- apply(exons[seq(2,nrow(exons),by=2),], 1,
function (x) {
lines(c(x[1],x[2]),c(-0.01,-.01),type="l",lwd=5) } )
} else {
cat("No exon or sts information available. Bummer.\n")
}
}
plot.Exons <- function (exons) {
if (!is.null(exons)) {
sap <- apply(exons[seq(1,nrow(exons),by=2),], 1,
function (x) {
lines(c(x[1],x[2]),c(.01,.01),type="l",lwd=5) } )
sap <- apply(exons[seq(2,nrow(exons),by=2),], 1,
function (x) {
lines(c(x[1],x[2]),c(-0.01,-.01),type="l",lwd=5) } )
} else {
cat("No exon or sts information available. Bummer.\n")
}
}
plotExonsIntrons <- function (egid,sp) {
exons <- getExonTable(egid,sp)
if (!is.null(exons)) {
for (e in 1:nrow(exons)) {
lines(exons[e,],c(0,0),type="l",lwd=5)
}
} else {
cat("No exon or sts information available. Bummer.\n")
}
}
|
773bb41fc4d040c9d1fc8f740bab32204a67d882
|
4d7504edc5e0242f5f7fdb0e2735b2f99947eca5
|
/man/UScpiqs.Rd
|
17db9c45d637df1f1114ac7a6fad188ca4563310
|
[] |
no_license
|
ccrostirolla/midasr
|
45b08c3d79b405599975adf9714d6cbdd35d1185
|
bd06bbd98c9b0fa7831c37d9edf7fa39b647aa1e
|
refs/heads/master
| 2021-10-21T21:11:49.410418
| 2019-03-06T14:13:27
| 2019-03-06T14:13:27
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 387
|
rd
|
UScpiqs.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/midasr-package.R
\docType{data}
\name{UScpiqs}
\alias{UScpiqs}
\title{US quartely seasonaly adjusted consumer price index}
\format{A \code{\link{data.frame}} object.}
\source{
\href{http://www.bea.gov/national/xls/gdplev.xls}{FRED}
}
\description{
US quarterly CPI from 1960Q1 to 2017Q3s
}
\keyword{datasets}
|
2a9ddf83c9f1653fa6738957174f38ddfb7bee02
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/ridge/examples/logisticRidge.Rd.R
|
e5f90884c3c83dc0156931533425c6477eb4140c
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 362
|
r
|
logisticRidge.Rd.R
|
library(ridge)
### Name: logisticRidge
### Title: Logistic ridge regression.
### Aliases: logisticRidge coef.ridgeLogistic plot.ridgeLogistic
### predict.ridgeLogistic print.ridgeLogistic summary.ridgeLogistic
### print.summary.ridgeLogistic
### ** Examples
data(GenBin)
mod <- logisticRidge(Phenotypes ~ ., data = as.data.frame(GenBin))
summary(mod)
|
9315f043d9aad8f61416ae32f05d485a3447197f
|
8a8e37a05bd1810e0c6c46bdf3e63a8ff0a79e86
|
/r/SCEUtils/man/run_umap.Rd
|
494a29a07f19d2ea7e8e6a16ac4e5d49f68cc349
|
[] |
no_license
|
nathancfox/tools
|
3209208c45226988273af4a9a69267a49f9bfcf1
|
3f7db30b380a8fbf569d2016795e42cb853fa15e
|
refs/heads/master
| 2021-12-10T04:45:38.955354
| 2021-09-29T14:19:56
| 2021-09-29T14:19:56
| 240,432,103
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 454
|
rd
|
run_umap.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dim_red.R
\name{run_umap}
\alias{run_umap}
\title{Runs UMAP on an scRNA-seq PCA matrix.}
\usage{
run_umap(mat, n_pcs = NULL)
}
\arguments{
\item{mat}{A cells x PCs matrix.}
\item{n_pcs}{The number of PCs to include.}
}
\value{
A cells x 2 matrix holding the UMAP coordinates.
}
\description{
Runs the umap version of UMAP on a cells x PCs matrix
from an scRNA-seq dataset.
}
|
e981a60da9344529803346fc9965a9e29962bed6
|
06ea54d8727bf5be3c51ce50c98f7c6ea0320d90
|
/man/setBibliography-easyreporting-method.Rd
|
63f3c6f2e8a189cac2440304be1ac1c1303ead20
|
[] |
no_license
|
drighelli/easyreporting
|
198f76976150f07a81eaa8ea2cae86ccf96879dc
|
4b9b7dd26950557516ca6de98e4f691128a0c06b
|
refs/heads/master
| 2021-06-12T15:17:19.120986
| 2021-03-15T18:34:35
| 2021-03-15T18:34:35
| 153,299,157
| 1
| 0
| null | 2021-03-08T16:57:21
| 2018-10-16T14:24:01
|
R
|
UTF-8
|
R
| false
| true
| 571
|
rd
|
setBibliography-easyreporting-method.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/methods.R
\name{setBibliography,easyreporting-method}
\alias{setBibliography,easyreporting-method}
\title{setBibliography}
\usage{
\S4method{setBibliography}{easyreporting}(object, bibfile = NULL)
}
\arguments{
\item{object}{an easyreporting class object}
\item{bibfile}{a string with the name of the bib file}
}
\value{
none
}
\description{
add a bibfile name to the object that will be reflected into the
report as a bibliography section
}
\examples{
\dontrun{
# TBD
}
}
\keyword{internal}
|
61cdf0c111f3ad9f7079fb46639602b58841082c
|
8de3017b43a354005bbd2af4d7ea86603eb760e4
|
/man/GetLines.Rd
|
d2c38d66a714bb358e74f2b6a8e97798f0c98970
|
[] |
no_license
|
ccagrawal/sportsTools
|
de30d024a3b1e5cd3aaf0a89e3182792cf8b0d1d
|
748a6e88a4cf5922496668bcb1b9bfa757e96b24
|
refs/heads/master
| 2020-04-12T07:30:10.042890
| 2017-05-11T01:05:09
| 2017-05-11T01:05:09
| 42,074,685
| 22
| 6
| null | null | null | null |
UTF-8
|
R
| false
| true
| 595
|
rd
|
GetLines.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/GetLines.R
\name{GetLines}
\alias{GetLines}
\title{Betting lines.}
\usage{
GetLines(sport = "NBA", year, type = "Both")
}
\arguments{
\item{sport}{either "NBA", "NFL", or "WNBA"}
\item{year}{season (e.g. 2008 for 2007-08 season)}
\item{type}{either "Regular Season" or "Playoffs" or "Both"}
}
\value{
data frame with schedule and line for each game in that season
}
\description{
Betting lines.
}
\examples{
GetLines("NBA", 2014, "playoffs")
}
\keyword{betting}
\keyword{line,}
\keyword{odds,}
\keyword{schedule,}
|
6793797cd2c097bf8e9b590fd3bb643400a0598d
|
824cdb464cb7e28622532a965328ee4a2a49f986
|
/data-raw/comext_data.r
|
068ab61584d1d31aee67ed3bf3807e0817f915b5
|
[] |
no_license
|
trialsolution/ceta
|
516be3ed6cd1024ef3443ffb87cfff956ec85cce
|
69f817907e5a3c0e46401cb71231e08454c89749
|
refs/heads/master
| 2020-04-24T09:35:23.547612
| 2019-02-21T12:52:34
| 2019-02-21T12:52:34
| 171,867,062
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,295
|
r
|
comext_data.r
|
library(tidyverse)
#------
# CANADIAN EXPORTS (FOB)
canexport <- read.csv(file = "csv/canexport.csv", header = TRUE, colClasses = c(rep("character",3),"numeric",rep("character",2),"numeric"), sep = ";")
canexport <- as.tibble(canexport)
canexport$DECLARANT <- "CAN"
# rename FLOW
canexport <- canexport %>% select(-FLOW)
canexport$FLOW <- "EXPORTS"
# calculate average over three calendar years
canexport_avg <- canexport %>% group_by(DECLARANT, PARTNER, PRODUCT, FLOW, INDICATORS) %>% summarize(avg=mean(INDICATOR_VALUE, na.rm=FALSE))
# rename header for better merging with import data
colnames(canexport_avg) <- c("reporter","partner","hs6","flow","indicator","avg")
# -----
# CANADIAN IMPORTS (CIF)
canimport <- read.csv(file = "csv/canimport.csv", header = TRUE, colClasses = c(rep("character",3),"numeric",rep("character",2),"numeric"), sep = ";")
canimport <- as.tibble(canimport)
canimport$DECLARANT <- "CAN"
# rename FLOW
canimport <- canimport %>% select(-FLOW)
canimport$FLOW <- "IMPORTS"
# rename to ROW and to EU_28
canimport[canimport$PARTNER=="otherthanEUAgg",]$PARTNER <- "ROW"
canimport[canimport$PARTNER=="myEU",]$PARTNER <- "EU_28"
# calculate average over three calendar years
canimport_avg <- canimport %>% group_by(DECLARANT, PARTNER, PRODUCT, FLOW, INDICATORS) %>% summarize(avg=mean(INDICATOR_VALUE, na.rm=FALSE))
# rename header for better merging with export data
colnames(canimport_avg) <- c("reporter","partner","hs6","flow","indicator","avg")
# ------
# EU-ROW trade data (both CIB and FOB)
eurow <- read.csv(file = "csv/eu_row.csv", header = TRUE, colClasses = c(rep("character",3),"numeric",rep("character",3),"numeric"), sep = ";")
eurow <- as.tibble(eurow)
eurow <- eurow %>% select(-Statistical.Procedure)
# rename indicators
eurow <- eurow %>% mutate_if(is.character, str_replace_all, pattern = "CUM_QUANTITY_TON", replacement = "QUANTITY")
eurow <- eurow %>% mutate_if(is.character, str_replace_all, pattern = "CUM_VALUE_1000ECU", replacement = "VALUE_1000EURO")
# separate import and export flows (note that the declarant is always the EU)
euimp <- eurow %>% filter(Flow == 1)
euexp <- eurow %>% filter(Flow == 2)
# rename FLOW
euimp <- euimp %>% select(-Flow)
euimp$Flow <- "IMPORTS"
euexp <- euexp %>% select(-Flow)
euexp$Flow <- "EXPORTS"
# rename ROW and EU
euimp$Partner.Country <- "ROW"
euexp$Partner.Country <- "ROW"
euimp$DECLARANT <- "EU_28"
euexp$DECLARANT <- "EU_28"
# calculate average
euimp_avg <- euimp %>% group_by(DECLARANT, Partner.Country, PRODUCT_NC, Flow, INDICATORS) %>% summarize(avg=mean(INDICATOR_VALUE, na.rm=FALSE))
euexp_avg <- euexp %>% group_by(DECLARANT, Partner.Country, PRODUCT_NC, Flow, INDICATORS) %>% summarize(avg=mean(INDICATOR_VALUE, na.rm=FALSE))
# rename for easier merge
colnames(euimp_avg) <- c("reporter", "partner", "hs6", "flow", "indicator", "avg")
colnames(euexp_avg) <- c("reporter", "partner", "hs6", "flow", "indicator", "avg")
#-----
# EU imports and exports to CAN
euflows <- read.csv(file = "csv/eu_cif_fob.csv", header = TRUE, colClasses = c(rep("character",3),"numeric",rep("character",3),"numeric"), sep = ";")
euflows <- as.tibble(euflows)
euflows <- euflows %>% select(-Statistical.Procedure)
# rename indicators
euflows <- euflows %>% mutate_if(is.character, str_replace_all, pattern = "CUM_QUANTITY_TON", replacement = "QUANTITY")
euflows <- euflows %>% mutate_if(is.character, str_replace_all, pattern = "CUM_VALUE_1000ECU", replacement = "VALUE_1000EURO")
# separate import and export flows
# Note that the declarant is always the EU
# and the partner is CANADA
euflows <- euflows %>% filter(Partner.Country == "0404")
euflows$Partner.Country <- "CAN"
euflows$DECLARANT <- "EU_28"
euflows[euflows$Flow == "1",]$Flow <- "IMPORTS"
euflows[euflows$Flow == "2",]$Flow <- "EXPORTS"
euflows_avg <- euflows %>% group_by(DECLARANT, Partner.Country, PRODUCT_NC, Flow, INDICATORS) %>% summarize(avg=mean(INDICATOR_VALUE, na.rm=FALSE))
colnames(euflows_avg) <- c("reporter", "partner", "hs6", "flow", "indicator", "avg")
#----
# do the merge and save to disk
trade_data <- canexport_avg %>% full_join(canimport_avg) %>% full_join(euimp_avg) %>% full_join(euexp_avg) %>% full_join(euflows_avg)
save(trade_data, file = "data/trade_data.RData")
|
ddc7106aa1bf828dce5500cc1a7734fdf57c787a
|
4e5f8a57cc9e4bca1711d4d32a89cbaa691ee57f
|
/R/install.R
|
df3b85997af16ef21bfb604cf1be35a98957e587
|
[
"MIT"
] |
permissive
|
Hong-Sung-Hyun/multilinguer
|
1db6704266b91011f20c31d54acf2ad0dfa9a38c
|
6068e6377e493ad60ec95e874d5cfcee9d619207
|
refs/heads/master
| 2021-06-21T14:37:57.385129
| 2020-02-02T16:52:25
| 2020-02-02T16:52:25
| 254,499,426
| 1
| 0
| null | 2020-04-09T23:27:02
| 2020-04-09T23:27:01
| null |
UTF-8
|
R
| false
| false
| 1,220
|
r
|
install.R
|
#' Install conda
#'
#' @details
#' Download the [Miniconda](https://docs.conda.io/en/latest/miniconda.html)
#' installer, and use it to install Miniconda.
#' All function and descriptions from [reticulate package](https://github.com/rstudio/reticulate/blob/master/R/miniconda.R)
#'
#' @examples
#' \dontrun{
#' install_conda()
#' }
#' @export
install_conda <- function() {
message("Please install reticulate(>= 1.14) package and use install_miniconda() function.")
}
#' install `java`
#'
#' @description
#' install `corretto` which is one of openjdk(java) distro.
#' Case of `MacOS`, remove all java and reinstall `corretto` version 11.
#'
#' @examples
#' \dontrun{
#' install_java()
#' install_jdk()
#' }
#' @export
install_java <- function() {
os <- get_os()
dest <- crt_dest_loc()
java_download(os, dest)
loc <- crt_path(os)
crt_unc(os, dest, exdir = loc)
set_java_home(os)
post_process(
"install.packages('rJava', type = 'binary');library(rJava);.jinit();rstudioapi::restartSession()"
)
}
#' @rdname install_java
#' @export
install_jdk <- install_java
install_nodejs <- function() {
return(T)
}
install_go <- function() {
return(T)
}
install_rust <- function() {
return(T)
}
|
9feac120390c8894211955b651156687cec19df8
|
db377b98ae482c97a225d8532ffedff88010aabb
|
/tests/testthat/helper_objects.R
|
c2db4934013abf5ca5cb7909b7bf9012122a88b3
|
[
"BSD-2-Clause"
] |
permissive
|
JiaHaobo/mlr
|
d0a568480d6495c506c2dc72bd89618281fed3ce
|
17d7eac68433b5e37bc4c118d1a9056c5e4cc497
|
refs/heads/master
| 2021-01-19T11:20:27.365236
| 2017-04-11T15:27:00
| 2017-04-11T15:27:00
| 87,954,613
| 1
| 0
| null | 2017-04-11T16:10:10
| 2017-04-11T16:10:10
| null |
UTF-8
|
R
| false
| false
| 5,947
|
r
|
helper_objects.R
|
data(Sonar, package = "mlbench", envir = environment())
data(BreastCancer, package = "mlbench", envir = environment())
binaryclass.df = Sonar
binaryclass.formula = Class~.
binaryclass.target = "Class"
binaryclass.train.inds = c(1:50, 100:150)
binaryclass.test.inds = setdiff(seq_len(nrow(binaryclass.df)), binaryclass.train.inds)
binaryclass.train = binaryclass.df[binaryclass.train.inds, ]
binaryclass.test = binaryclass.df[binaryclass.test.inds, ]
binaryclass.class.col = 61
binaryclass.class.levs = levels(binaryclass.df[, binaryclass.class.col])
binaryclass.task = makeClassifTask("binary", data = binaryclass.df, target = binaryclass.target)
multiclass.df = iris
multiclass.formula = Species~.
multiclass.target = "Species"
multiclass.train.inds = c(1:30, 51:80, 101:130)
multiclass.test.inds = setdiff(1:150, multiclass.train.inds)
multiclass.train = multiclass.df[multiclass.train.inds, ]
multiclass.test = multiclass.df[multiclass.test.inds, ]
multiclass.class.col = 5
multiclass.task = makeClassifTask("multiclass", data = multiclass.df, target = multiclass.target)
multiclass.small.df = iris[c(1:3, 51:53, 101:103), ]
multiclass.small.formula = Species~.
multiclass.small.target = "Species"
multiclass.small.train.inds = c(1:2, 4:5, 7:8)
multiclass.small.test.inds = setdiff(1:9, multiclass.small.train.inds)
multiclass.small.train = multiclass.small.df[multiclass.small.train.inds, ]
multiclass.small.test = multiclass.small.df[multiclass.small.test.inds, ]
multiclass.small.class.col = 5
multiclass.small.task = makeClassifTask("multiclass", data = multiclass.small.df, target = multiclass.small.target)
multilabel.df = iris
multilabel.df[, "y1"] = rep(c(TRUE, FALSE), 75L)
multilabel.df[, "y2"] = rep(c(FALSE, TRUE), 75L)
multilabel.target = c("y1", "y2")
multilabel.train.inds = c(1:30, 51:80, 101:130)
multilabel.test.inds = setdiff(1:150, multilabel.train.inds)
multilabel.train = multilabel.df[multilabel.train.inds, ]
multilabel.test = multilabel.df[multilabel.test.inds, ]
multilabel.task = makeMultilabelTask("multilabel", data = multilabel.df, target = multilabel.target)
multilabel.formula.cbind = as.formula(paste("cbind(", paste(multilabel.target, collapse = ",", sep = " "), ") ~ .", sep = ""))
multilabel.formula = as.formula(paste(paste(multilabel.target, collapse = "+"), "~."))
multilabel.small.inds = c(1, 52, 53, 123)
noclass.df = iris[, -5]
noclass.train.inds = c(1:30, 51:80, 101:130)
noclass.test.inds = setdiff(1:150, noclass.train.inds)
noclass.train = noclass.df[noclass.train.inds, ]
noclass.test = noclass.df[noclass.test.inds, ]
noclass.task = makeClusterTask("noclass", data = noclass.df)
data(BostonHousing, package = "mlbench", envir = environment())
regr.df = BostonHousing
regr.formula = medv ~ .
regr.target = "medv"
regr.train.inds = seq(1, 506, 7)
regr.test.inds = setdiff(seq_len(nrow(regr.df)), regr.train.inds)
regr.train = regr.df[regr.train.inds, ]
regr.test = regr.df[regr.test.inds, ]
regr.class.col = 14
regr.task = makeRegrTask("regrtask", data = regr.df, target = regr.target)
regr.small.df = BostonHousing[150:160, ]
regr.small.formula = medv ~ .
regr.small.target = "medv"
regr.small.train.inds = 1:7
regr.small.test.inds = setdiff(seq_len(nrow(regr.small.df)), regr.small.train.inds)
regr.small.train = regr.small.df[regr.small.train.inds, ]
regr.small.test = regr.small.df[regr.small.test.inds, ]
regr.small.class.col = 14
regr.small.task = makeRegrTask("regrtask", data = regr.small.df, target = regr.small.target)
regr.num.df = regr.df[, sapply(regr.df, is.numeric)]
regr.num.formula = regr.formula
regr.num.target = regr.target
regr.num.train.inds = regr.train.inds
regr.num.test.inds = regr.test.inds
regr.num.train = regr.num.df[regr.num.train.inds, ]
regr.num.test = regr.num.df[regr.num.test.inds, ]
regr.num.class.col = 13
regr.num.task = makeRegrTask("regrnumtask", data = regr.num.df, target = regr.num.target)
getSurvData = function(n = 100, p = 10) {
set.seed(1)
beta = c(rep(1, 10), rep(0, p - 10))
x = matrix(rnorm(n * p), n, p)
colnames(x) = sprintf("x%01i", 1:p)
real.time = - (log(runif(n))) / (10 * exp(drop(x %*% beta)))
cens.time = rexp(n, rate = 1 / 10)
status = ifelse(real.time <= cens.time, TRUE, FALSE)
obs.time = ifelse(real.time <= cens.time, real.time, cens.time) + 1
return(cbind(data.frame(time = obs.time, status = status), x))
}
surv.df = getSurvData()
surv.formula = survival::Surv(time, status) ~ .
surv.target = c("time", "status")
surv.train.inds = seq(1, floor(2 / 3 * nrow(surv.df)))
surv.test.inds = setdiff(seq_len(nrow(surv.df)), surv.train.inds)
surv.train = surv.df[surv.train.inds, ]
surv.test = surv.df[surv.test.inds, ]
surv.task = makeSurvTask("survtask", data = surv.df, target = surv.target)
rm(getSurvData)
costsens.feat = iris
costsens.costs = matrix(runif(150L * 3L, min = 0, max = 1), 150L, 3L)
costsens.task = makeCostSensTask("costsens", data = costsens.feat, costs = costsens.costs)
ns.svg = c(svg = "http://www.w3.org/2000/svg")
black.circle.xpath = "/svg:svg//svg:circle[contains(@style, 'fill: #000000')]"
grey.rect.xpath = "/svg:svg//svg:rect[contains(@style, 'fill: #EBEBEB;')]"
red.circle.xpath = "/svg:svg//svg:circle[contains(@style, 'fill: #F8766D')]"
blue.circle.xpath = "/svg:svg//svg:circle[contains(@style, 'fill: #619CFF')]"
green.circle.xpath = "/svg:svg//svg:circle[contains(@style, 'fill: #00BA38')]"
black.line.xpath = "/svg:svg//svg:polyline[not(contains(@style, 'stroke:'))]"
black.line.xpath2 = "/svg:svg//svg:polyline[contains(@style, 'stroke: #000000')]"
blue.line.xpath = "/svg:svg//svg:polyline[contains(@style, 'stroke: #00BFC4;')]"
mediumblue.line.xpath = "/svg:svg//svg:polyline[contains(@style, 'stroke: #3366FF;')]"
red.line.xpath = "/svg:svg//svg:polyline[contains(@style, 'stroke: #F8766D;')]"
red.rug.line.xpath = "/svg:svg//svg:line[contains(@style, 'stroke: #FF0000;')]"
black.bar.xpath = "/svg:svg//svg:rect[contains(@style, 'fill: #595959;')]"
|
f5959fd1d91cb138f38ea5fe0cdabe0ec6094fc8
|
987177740c7f263151f2dd928a726300a8653857
|
/plot2.R
|
4ed35aea81ea3759ea14c2b2c5589439ed9e0d97
|
[] |
no_license
|
rajashrip/GettingCleaningData
|
5cf1ee544250944b0aa2a56f413c9325d4f768dc
|
89a13a20d4d699f5841b6ec7fed28c12d2dedffe
|
refs/heads/master
| 2021-01-01T17:21:26.165806
| 2015-03-05T05:19:21
| 2015-03-05T05:19:21
| 31,028,982
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,100
|
r
|
plot2.R
|
require(sqldf)
# set your working directory to the folder where you have the raw data file stored
# If you don't have the file,get it from here https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip
#read the file
file <- c("household_power_consumption.txt")
a2 <- read.csv.sql(file, header = T, sep=";", sql = "select * from file where (Date == '1/2/2007' OR Date == '2/2/2007')" )
#combining the Date and Time variables to create one datetime variable of class character
a2$datetime <- paste(a2$Date,a2$Time,sep = " ")
#convert the character class datetime variable into
a2$datetime <- as.POSIXct(strptime(a2$datetime,"%d/%m/%Y %H:%M:%S"))
#head(a2)
#Open the file device using png()
png("plot2.png",width = 480,height = 480,units = "px",bg = "transparent",pointsize = 12)
#windows() --use this to test, send to your screen
#plot the graph using base plotting system and function plot()
plot(a2$datetime,a2$Global_active_power,type = "l",ylab = "Global Active Power (kilowatts)",xlab = "")
# close the device connection.
dev.off()
|
50670b04614a38f2d7ae7d4cbc88d4b369ff58ed
|
da006494be33a8f6b9cd623bf4fb506e17d8c439
|
/R/vcf2eqtl.R
|
9ba5f40b8dda0d9a895bd7ce9e3a6e0d91c1e390
|
[] |
no_license
|
noahrose/vcf2eqtl
|
7b1b18223ab5cd14ac1c2e88b0f311e406b3ec49
|
e424d82310a5b09e364cb05747a3cd882eefb16d
|
refs/heads/master
| 2021-01-11T02:09:17.057783
| 2017-08-20T23:36:05
| 2017-08-20T23:36:05
| 70,826,485
| 4
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,666
|
r
|
vcf2eqtl.R
|
vcf2eqtl <-
function(vcf,
expr,
pops=NULL,
minHet=3,
minHetDP=10,
mc.cores=1,
alpha=0.05,
calculateFst=T,
outliers=T,
testDE=F,
all3=F,
hweFilter=T,
hweAlpha=0.05,
covariates=NULL,
propExplained=T,
withinPop=T,
format='bcftools',
transcripts=NULL){
if(is.null(pops)){
calculateFst=F
testDE=F
propExplained=F
withinPop=F
}
#extract SNP genotype and SNP depth data from vcf
globalFst=NULL
cat('reading vcf...\n')
currvcf<-suppressWarnings(readVcf(vcf,genome='curr'))
genoInfo<-geno(currvcf)
genos<-make012(genoInfo$GT)
cat(paste('vcf contains',nrow(genos),'variants\n'))
if(format=='freebayes'){
AOs<-apply(genoInfo$AO,2,as.numeric)
ROs<-apply(genoInfo$RO,2,as.numeric)
} else if (format=='bcftools'){
ad<-apply(geno(currvcf)$AD,c(1,2),function(l) l[[1]])
ROs<-ad[1,,]
AOs<-ad[2,,]
} else{
stop('format must be either bcftools or freebayes')
}
BOs<-AOs+ROs
AOs[genos!=1]<-NA
BOs[genos!=1]<-NA
AOs[BOs<minHetDP]<-NA
BOs[BOs<minHetDP]<-NA
rownames(AOs)<-rownames(genos)
rownames(BOs)<-rownames(genos)
cat('getting reference and alternate observations...\n')
alleleObs<-vector('list',nrow(genos))
names(alleleObs)<-rownames(genos)
for(i in 1:nrow(AOs)){
if(i%%10000==0) cat(paste(i,'\n'))
alleleObs[[i]]<-na.omit(data.frame(row.names=colnames(genos),x=AOs[i,],size=BOs[i,]))
}
cat('organizing SNP info...\n')
CHROM=as.vector(seqnames(rowRanges(currvcf)))
POS=as.numeric(as.character(start(ranges(rowRanges(currvcf)))))
REF=as.character(mcols(rowRanges(currvcf))[,'REF'])
ALT=as.character(unlist(mcols(rowRanges(currvcf))[,'ALT']))
AF=as.numeric(unlist(info(currvcf)$AC))/as.numeric(unlist(info(currvcf)$AN))
snpInfo<-data.frame(CHROM=CHROM,POS=POS,REF=REF,ALT=ALT,AF=AF,stringsAsFactors=F)
rownames(snpInfo)<-rownames(genos)
cat('organizing expression data...\n')
#organize and normalize expression data
if(is.null(transcripts)) transcripts=CHROM
expr<-expr[rownames(expr)%in%transcripts,]
designMatrix<-NULL
dmatComponents<-NULL
if(withinPop) dmatComponents ='pops'
if(!is.null(covariates)) dmatComponents =c('covariates', dmatComponents)
if(!is.null(dmatComponents)){
form<-as.formula(paste('~',paste(dmatComponents,collapse='+')))
designMatrix<-model.matrix(form)
}
voomExpr<-voom(expr,design=designMatrix)
rownames(voomExpr$weights)<-rownames(expr)
currexpr<-as.matrix(voomExpr$E[transcripts,])
currweights<-as.matrix(voomExpr$weights[transcripts,])
rownames(currexpr)<-rownames(genos)
rownames(currweights)<-rownames(genos)
#if desired, filter genotypes
if(all3){
cat('filtering for sites with all three genotypes...\n')
num_gts<-apply(genos,1,function(v) length(table(v)))
genos<-genos[num_gts==3,]
}
if(hweFilter){
cat('filtering out sites out of HWE in at least one population...\n')
hwepops=pops
hwe=rep(1,nrow(genos))
if(is.null(pops)) hwepops=rep(1,ncol(genos))
for(pop in unique(hwepops)){
hwe<-pmin(hwe,apply(genos[,which(pops==pop)],1,function(v) HWExact(table(factor(v,levels=c(0,1,2))),verbose=F)$pval))
}
genos<-genos[hwe>hweAlpha,]
}
cat('filtering out sites without minimum number of informative heterozygotes\n')
alleleObs<-alleleObs[rownames(genos)]
imbalanceInfo<-unlist(lapply(alleleObs,function(df) nrow(df)>minHet))
alleleObs<-alleleObs[imbalanceInfo]
genos<-genos[names(alleleObs),]
if(nrow(genos)==0){
stop('No sites left after filtering, check to make sure you have a freebayes VCF of biallelic SNPs with allele observations in it')
}
#subset other data sets after filtering genos
currexpr<-currexpr[rownames(genos),]
snpInfo<-snpInfo[rownames(genos),]
cat(paste(nrow(genos),'sites left after filtering, testing for eQTL status...\n'))
#single-threaded test
if(mc.cores==1){
cat('imbalance test...\n')
imb.out<-do.call(rbind,lapply(alleleObs,bbLRT))
} else{
#multithreaded test
cat('mulithreaded imbalance test...\n')
imb.out<-do.call(rbind,mclapply(alleleObs,bbLRT,mc.cores=mc.cores))
}
#single-threaded test
if(mc.cores==1){
cat('association test...\n')
assoc.out<-t(sapply(rownames(genos),associationTest,
currexpr=currexpr,currweights=currweights,genos=genos,withinPop=withinPop))
} else{
#multithreaded test
cat('mulithreaded association test...\n')
assoc.out<-do.call(rbind,mclapply(rownames(genos),associationTest,
currexpr=currexpr,currweights=currweights,genos=genos,
withinPop=withinPop,mc.cores=mc.cores))
}
#collect results and calculate p values using Fisher's method
res<-cbind(imb.out,assoc.out)
colnames(res)<-c('AImu','AIp','AIlog2fc','ASSOCz','ASSOCp','ASSOClog2fc')
rownames(res)<-rownames(genos)
res<-as.data.frame(res)
#compare alternate homozygotes for fc
combineP<-function(v){
if(NA%in%v) return(NA)
return(sumlog(v)$p)
}
res$ASSOClog2fc<-2*res$ASSOClog2fc
res$p<-apply(cbind(res$AIp,res$ASSOCp),1,combineP)
res$padj<-p.adjust(res$p,method='BH')
res$AIpadj<-p.adjust(res$AIp,method='BH')
res$ASSOCpadj<-p.adjust(res$ASSOCp,method='BH')
res$eQTL<-res$padj<alpha
res<-cbind(snpInfo,res)
#calculate Fst and call outliers using OutFLANK
if(calculateFst){
cat('calculating Fst...\n')
genosOutFLANK=genos
genosOutFLANK[is.na(genosOutFLANK)]<-9
wc.out<-MakeDiploidFSTMat(t(genosOutFLANK),rownames(genos),pops)
res$Fst=wc.out$FST
res$FstNum<-wc.out$T1
res$FstDen<-wc.out$T2
res$He<-wc.out$He
globalFst=sum(res$FstNum)/sum(res$FstDen)
if(outliers){
fl.out<-OutFLANK(wc.out,NumberOfSamples=ncol(genos),qthreshold=alpha)$results
res$FstOutlier=fl.out$OutlierFlag
res$FstOutlierP=fl.out$pvaluesRightTail
}
}
#test for differential expression using DESeq2
DEres =NULL
if(testDE){
cat('testing for differential expression with DESeq2...\n')
currdat<-data.frame(pops=pops)
cds<-DESeqDataSetFromMatrix(expr,currdat,~pops)
cds<-DESeq(cds,test='LRT',reduced=~1)
DEres <-results(cds)
res$DE<-DEres[res$CHROM,'padj']<alpha
res$DEp<-DEres[res$CHROM,'pvalue']
res$DEpadj<-DEres[res$CHROM,'padj']
}
#calculate reduction in population differentiation after accounting for eQTL
if(propExplained){
cat('calculating proportion of population differences explained by eQTLs...\n')
if(mc.cores==1){
propE<-sapply(rownames(genos)[which(res$eQTL)],propExplain,currexpr=currexpr,genos=genos,pops=pops)
} else{
cat('\tmulithreading...\n')
propE<-unlist(mclapply(rownames(genos)[which(res$eQTL)],propExplain,
currexpr=currexpr,genos=genos,pops=pops,mc.cores=mc.cores))
}
res$popDiffExplained<-NA
res$popDiffExplained[which(res$eQTL)]=propE
}
return(list(res=res,snpContigExpr=currexpr,genos=genos,alleleObs=alleleObs,globalFst=globalFst,pops=pops,DEres= DEres))
}
|
f095d70f3dafa694a94a1105a47e85db7f97a602
|
919359f24635c22a844a8ef3ddeb1181a72759dd
|
/lib/analysis/functions.R
|
74908fab49e0621e4d57ebedeeccf56e45cb4c8a
|
[] |
no_license
|
weswigham/chromium-history
|
1aa9c23ca9fcc2f3f74e1c13bdf8dabff7f76761
|
091e7e4f9dafdfac961b3ce6b594f2200453cacb
|
refs/heads/master
| 2021-01-11T18:11:48.052839
| 2016-04-05T19:36:16
| 2016-04-05T19:36:16
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 20,675
|
r
|
functions.R
|
# Define the functions
Dsquared <-function(obs = NULL, pred = NULL, model = NULL, adjust = FALSE) {
# version 1.3 (3 Jan 2015)
model.provided <- ifelse(is.null(model), FALSE, TRUE)
if (model.provided) {
if (!("glm" %in% class(model))) stop ("'model' must be of class 'glm'.")
if (!is.null(pred)) message("Argument 'pred' ignored in favour of 'model'.")
if (!is.null(obs)) message("Argument 'obs' ignored in favour of 'model'.")
obs <- model$y
pred <- model$fitted.values
} else { # if model not provided
if (is.null(obs) | is.null(pred)) stop("You must provide either 'obs' and 'pred', or a 'model' object of class 'glm'")
if (length(obs) != length(pred)) stop ("'obs' and 'pred' must be of the same length (and in the same order).")
if (!(obs %in% c(0, 1)) | pred < 0 | pred > 1) stop ("Sorry, 'obs' and 'pred' options currently only implemented for binomial GLMs (binary response variable with values 0 or 1) with logit link.")
logit <- log(pred / (1 - pred))
model <- glm(obs ~ logit, family = "binomial")
}
D2 <- (model$null.deviance - model$deviance) / model$null.deviance
if (adjust) {
if (!model.provided) return(message("Adjusted D-squared not calculated, as it requires a model object (with its number of parameters) rather than just 'obs' and 'pred' values."))
n <- length(model$fitted.values)
#p <- length(model$coefficients)
p <- attributes(logLik(model))$df
D2 <- 1 - ((n - 1) / (n - p)) * (1 - D2)
} # end if adj
return (D2)
}
prediction_analysis<- function(fit,release.next){
# Predict based on next release data.
prediction <- predict(fit, newdata=release.next, type="response")
# Use ROCR library calculate the performance.
pred <- prediction(prediction,release.next$becomes_vulnerable)
perf <- performance(pred, "prec", "rec")
# Select the relevant values
precision <- unlist(slot(perf, "y.values"))
recall <- unlist(slot(perf, "x.values"))
f_score = 2 * ((precision * recall)/(precision + recall))
mean_precision= mean(precision, na.rm=TRUE)
mean_recall = mean(recall, na.rm=TRUE)
mean_f_score = mean(f_score, na.rm=TRUE)
# Create ROC Curve,
# plot(perf, colorize=T)
# Calculate the Area under the curve
auc <- performance(pred,"auc")
auc <- unlist(slot(auc, "y.values"))
return (as.data.frame(cbind(mean_precision,mean_recall,mean_f_score,auc)))
}
release_modeling <- function(release,release.next){
options(warn=-1)
# Remove files where there were no bugs of any kind, or if it had no SLOC
# i.e. The subset must have at least on bug of ANY kind, and SLOC > 0
release <- subset(release, (release$num_pre_features !=0 |
release$num_pre_compatibility_bugs !=0 |
release$num_pre_regression_bugs !=0 |
release$num_pre_security_bugs !=0 |
release$num_pre_tests_fails_bugs != 0 |
release$num_pre_stability_crash_bugs != 0 |
release$num_pre_build_bugs != 0 |
release$becomes_vulnerable != FALSE)
& release$sloc > 0)
release.next <- subset(release.next, (release.next$num_pre_features !=0 |
release.next$num_pre_compatibility_bugs !=0 |
release.next$num_pre_regression_bugs !=0 |
release.next$num_pre_security_bugs !=0 |
release.next$num_pre_tests_fails_bugs != 0 |
release.next$num_pre_stability_crash_bugs != 0 |
release.next$num_pre_build_bugs != 0 |
release.next$becomes_vulnerable != FALSE)
& release.next$sloc > 0)
# Normalize and center data, added one to the values to be able to calculate log to zero. log(1)=0
release = cbind(as.data.frame(log(release[,c(1:19)] + 1)),
becomes_vulnerable = release$becomes_vulnerable,
was_buggy = release$was_buggy,
becomes_buggy = release$becomes_buggy,
was_vulnerable = release$was_vulnerable)
release.next = cbind(as.data.frame(log(release.next[,c(1:19)] + 1)),
becomes_vulnerable = release.next$becomes_vulnerable,
was_buggy = release.next$was_buggy,
becomes_buggy = release.next$becomes_buggy,
was_vulnerable = release.next$was_vulnerable)
# Modeling (forward selection)
# Individual Models
fit_null <- glm(formula = becomes_vulnerable ~ 1,
data = release, family = "binomial")
fit_control <- glm(formula = becomes_vulnerable ~ sloc,
data = release, family = "binomial")
fit_bugs <- glm (formula= becomes_vulnerable ~ sloc + num_pre_bugs,
data = release, family = "binomial")
# Category Based Models
fit_features <- glm (formula= becomes_vulnerable ~ sloc + num_pre_features,
data = release, family = "binomial")
fit_security <- glm (formula= becomes_vulnerable ~ sloc + num_pre_security_bugs,
data = release, family = "binomial")
fit_stability <- glm (formula= becomes_vulnerable ~ sloc + num_pre_stability_crash_bugs
+ num_pre_compatibility_bugs + num_pre_regression_bugs,
data = release, family = "binomial")
fit_build <- glm (formula= becomes_vulnerable ~ sloc + num_pre_build_bugs + num_pre_tests_fails_bugs,
data = release, family = "binomial")
#history models
fit_vuln_to_vuln <- glm(formula = becomes_vulnerable ~ sloc + was_vulnerable,
data = release, family = "binomial")
fit_bug_to_vuln <- glm(formula = becomes_vulnerable ~ sloc + was_buggy,
data = release, family = "binomial")
fit_bug_to_bug <- glm(formula = becomes_buggy ~ sloc + was_buggy,
data = release, family = "binomial")
# Experience Based Models
fit_security_experienced <- glm (formula= becomes_vulnerable ~ sloc + avg_security_experienced_participants,
data = release, family = "binomial")
fit_bug_security_experienced <- glm (formula= becomes_vulnerable ~ sloc + avg_bug_security_experienced_participants,
data = release, family = "binomial")
fit_stability_experienced <- glm (formula= becomes_vulnerable ~ sloc + avg_stability_experienced_participants,
data = release, family = "binomial")
fit_build_experienced <- glm (formula= becomes_vulnerable ~ sloc + avg_build_experienced_participants,
data = release, family = "binomial")
fit_test_fail_experienced <- glm (formula= becomes_vulnerable ~ sloc + avg_test_fail_experienced_participants,
data = release, family = "binomial")
fit_compatibility_experienced <- glm (formula= becomes_vulnerable ~ sloc + avg_compatibility_experienced_participants,
data = release, family = "binomial")
# Display Results:
cat("\nRelease Summary\n")
print(summary(release))
cat("\nSpearman's Correlation for bug metrics\n")
print(cor(release[,c(5:11)],method="spearman"))
cat("\nSpearman's Correlation for experience metrics\n")
print(cor(release[,c(14:19)],method="spearman", use = "complete"))
release_v <- release[ which(release$becomes_vulnerable == TRUE), ]
release_n <- release[ which(release$becomes_vulnerable == FALSE), ]
cat("\n% Vulnerable\n")
print(cbind(Total = length(release[,1]),
Neutral = length(release_n[,1]),
Vulnerable = length(release_v[,1]),
Percentage = (length(release_v[,1])/length(release_n[,1]))*100))
cat("\nWilcoxon:\n")
print(wilcox.test(release_v$sloc, release_n$sloc, alternative="greater"))
print(cbind(median_v = median(release_v$sloc, na.rm=TRUE),median_n = median(release_n$sloc, na.rm=TRUE)))
print(cbind(mean_v = mean(release_v$sloc, na.rm=TRUE),mean_n = mean(release_n$sloc, na.rm=TRUE)))
# For bug metrics
cat("\nFor bug metrics:\n")
print(wilcox.test(release_v$num_pre_bugs, release_n$num_pre_bugs, alternative="greater"))
print(cbind(median_v = median(release_v$num_pre_bugs, na.rm=TRUE),median_n = median(release_n$num_pre_bugs, na.rm=TRUE)))
print(cbind(mean_v = mean(release_v$num_pre_bugs, na.rm=TRUE),mean_n = mean(release_n$num_pre_bugs, na.rm=TRUE)))
print(wilcox.test(release_v$num_pre_features, release_n$num_pre_features, alternative="greater"))
print(cbind(median_v = median(release_v$num_pre_features, na.rm=TRUE),median_n = median(release_n$num_pre_features, na.rm=TRUE)))
print(cbind(mean_v = mean(release_v$num_pre_features, na.rm=TRUE),mean_n = mean(release_n$num_pre_features, na.rm=TRUE)))
print(wilcox.test(release_v$num_pre_compatibility_bugs, release_n$num_pre_compatibility_bugs, alternative="greater"))
print(cbind(median_v = median(release_v$num_pre_compatibility_bugs, na.rm=TRUE),median_n = median(release_n$num_pre_compatibility_bugs, na.rm=TRUE)))
print(cbind(mean_v = mean(release_v$num_pre_compatibility_bugs, na.rm=TRUE),mean_n = mean(release_n$num_pre_compatibility_bugs, na.rm=TRUE)))
print(wilcox.test(release_v$num_pre_regression_bugs, release_n$num_pre_regression_bugs, alternative="greater"))
print(cbind(median_v = median(release_v$num_pre_regression_bugs, na.rm=TRUE),median_n = median(release_n$num_pre_regression_bugs, na.rm=TRUE)))
print(cbind(mean_v = mean(release_v$num_pre_regression_bugs, na.rm=TRUE),mean_n = mean(release_n$num_pre_regression_bugs, na.rm=TRUE)))
print(wilcox.test(release_v$num_pre_security_bugs, release_n$num_pre_security_bugs, alternative="greater"))
print(cbind(median_v = median(release_v$num_pre_security_bugs, na.rm=TRUE),median_n = median(release_n$num_pre_security_bugs, na.rm=TRUE)))
print(cbind(mean_v = mean(release_v$num_pre_security_bugs, na.rm=TRUE),mean_n = mean(release_n$num_pre_security_bugs, na.rm=TRUE)))
print(wilcox.test(release_v$num_pre_tests_fails_bugs, release_n$num_pre_tests_fails_bugs, alternative="greater"))
print(cbind(median_v = median(release_v$num_pre_tests_fails_bugs, na.rm=TRUE),median_n = median(release_n$num_pre_tests_fails_bugs, na.rm=TRUE)))
print(cbind(mean_v = mean(release_v$num_pre_tests_fails_bugs, na.rm=TRUE),mean_n = mean(release_n$num_pre_tests_fails_bugs, na.rm=TRUE)))
print(wilcox.test(release_v$num_pre_stability_crash_bugs, release_n$num_pre_stability_crash_bugs, alternative="greater"))
print(cbind(median_v = median(release_v$num_pre_stability_crash_bugs, na.rm=TRUE),median_n = median(release_n$num_pre_stability_crash_bugs, na.rm=TRUE)))
print(cbind(mean_v = mean(release_v$num_pre_stability_crash_bugs, na.rm=TRUE),mean_n = mean(release_n$num_pre_stability_crash_bugs, na.rm=TRUE)))
print(wilcox.test(release_v$num_pre_build_bugs, release_n$num_pre_build_bugs, alternative="greater"))
print(cbind(median_v = median(release_v$num_pre_build_bugs, na.rm=TRUE),median_n = median(release_n$num_pre_build_bugs, na.rm=TRUE)))
print(cbind(mean_v = mean(release_v$num_pre_build_bugs, na.rm=TRUE),mean_n = mean(release_n$num_pre_build_bugs, na.rm=TRUE)))
# For experience metrics
cat("\nFor experience metrics:\n")
print(wilcox.test(release_v$avg_security_experienced_participants, release_n$avg_security_experienced_participants, alternative="greater"))
print(cbind(median_v = median(release_v$avg_security_experienced_participants, na.rm=TRUE),median_n = median(release_n$avg_security_experienced_participants, na.rm=TRUE)))
print(cbind(mean_v = mean(release_v$avg_security_experienced_participants, na.rm=TRUE),mean_n = mean(release_n$avg_security_experienced_participants, na.rm=TRUE)))
print(wilcox.test(release_v$avg_bug_security_experienced_participants, release_n$avg_bug_security_experienced_participants, alternative="greater"))
print(cbind(median_v = median(release_v$avg_bug_security_experienced_participants, na.rm=TRUE),median_n = median(release_n$avg_bug_security_experienced_participants, na.rm=TRUE)))
print(cbind(mean_v = mean(release_v$avg_bug_security_experienced_participants, na.rm=TRUE),mean_n = mean(release_n$avg_bug_security_experienced_participants, na.rm=TRUE)))
print(wilcox.test(release_v$avg_stability_experienced_participants, release_n$avg_stability_experienced_participants, alternative="greater"))
print(cbind(median_v = median(release_v$avg_stability_experienced_participants, na.rm=TRUE),median_n = median(release_n$avg_stability_experienced_participants, na.rm=TRUE)))
print(cbind(mean_v = mean(release_v$avg_stability_experienced_participants, na.rm=TRUE),mean_n = mean(release_n$avg_stability_experienced_participants, na.rm=TRUE)))
print(wilcox.test(release_v$avg_build_experienced_participants, release_n$avg_build_experienced_participants, alternative="greater"))
print(cbind(median_v = median(release_v$avg_build_experienced_participants, na.rm=TRUE),median_n = median(release_n$avg_build_experienced_participants, na.rm=TRUE)))
print(cbind(mean_v = mean(release_v$avg_build_experienced_participants, na.rm=TRUE),mean_n = mean(release_n$avg_build_experienced_participants, na.rm=TRUE)))
print(wilcox.test(release_v$avg_test_fail_experienced_participants, release_n$avg_test_fail_experienced_participants, alternative="greater"))
print(cbind(median_v = median(release_v$avg_test_fail_experienced_participants, na.rm=TRUE),median_n = median(release_n$avg_test_fail_experienced_participants, na.rm=TRUE)))
print(cbind(mean_v = mean(release_v$avg_test_fail_experienced_participants, na.rm=TRUE),mean_n = mean(release_n$avg_test_fail_experienced_participants, na.rm=TRUE)))
print(wilcox.test(release_v$avg_compatibility_experienced_participants, release_n$avg_compatibility_experienced_participants, alternative="greater"))
print(cbind(median_v = median(release_v$avg_compatibility_experienced_participants, na.rm=TRUE),median_n = median(release_n$avg_compatibility_experienced_participants, na.rm=TRUE)))
print(cbind(mean_v = mean(release_v$avg_compatibility_experienced_participants, na.rm=TRUE),mean_n = mean(release_n$avg_compatibility_experienced_participants, na.rm=TRUE)))
cat("\nCohensD for Bug metrics:\n")
print(cbind(
sloc = cohensD(release_v$sloc, release_n$sloc),
bugs = cohensD(release_v$num_pre_bugs, release_n$num_pre_bugs),
features = cohensD(release_v$num_pre_features, release_n$num_pre_features),
compatibility_bugs = cohensD(release_v$num_pre_compatibility_bugs, release_n$num_pre_compatibility_bugs),
regression_bugs = cohensD(release_v$num_pre_regression_bugs, release_n$num_pre_regression_bugs),
security_bugs = cohensD(release_v$num_pre_security_bugs, release_n$num_pre_security_bugs),
tests_fails_bugs = cohensD(release_v$num_pre_tests_fails_bugs, release_n$num_pre_tests_fails_bugs),
stability_crash_bugs = cohensD(release_v$num_pre_stability_crash_bugs, release_n$num_pre_stability_crash_bugs),
build_bugs = cohensD(release_v$num_pre_build_bugs, release_n$num_pre_build_bugs)
))
cat("\nCohensD for Experience metrics:\n")
print(cbind(
avg_security_experienced_participants = cohensD(release_v$avg_security_experienced_participants, release_n$avg_security_experienced_participants),
avg_bug_security_experienced_participants = cohensD(release_v$avg_bug_security_experienced_participants, release_n$avg_bug_security_experienced_participants),
avg_stability_experienced_participants = cohensD(release_v$avg_stability_experienced_participants, release_n$avg_stability_experienced_participants),
avg_build_experienced_participants = cohensD(release_v$avg_build_experienced_participants, release_n$avg_build_experienced_participants),
avg_test_fail_experienced_participants = cohensD(release_v$avg_test_fail_experienced_participants, release_n$avg_test_fail_experienced_participants),
avg_compatibility_experienced_participants = cohensD(release_v$avg_compatibility_experienced_participants, release_n$avg_compatibility_experienced_participants)
))
cat("\n# Summary Control Models\n")
cat("fit_null\n")
print(summary(fit_null))
cat("fit_control\n")
print(summary(fit_control))
cat("fit_bugs\n")
print(summary(fit_bugs))
cat("\n")
cat("# Summary\n")
cat("fit_security\n")
print(summary(fit_security))
cat("fit_features\n")
print(summary(fit_features))
cat("fit_stability\n")
print(summary(fit_stability))
cat("fit_build\n")
print(summary(fit_build))
cat("\n")
cat("# Summary History Models\n")
cat("fit_vuln_to_vuln\n")
print(summary(fit_vuln_to_vuln))
cat("fit_bug_to_vuln\n")
print(summary(fit_bug_to_vuln))
cat("fit_bug_to_bug\n")
print(summary(fit_bug_to_bug))
cat("\n")
cat("# Summary Experience Models\n")
cat("fit_security_experienced\n")
print(summary(fit_security_experienced))
cat("fit_bug_security_experienced\n")
print(summary(fit_bug_security_experienced))
cat("fit_stability_experienced\n")
print(summary(fit_stability_experienced))
cat("fit_build_experienced\n")
print(summary(fit_build_experienced))
cat("fit_test_fail_experienced\n")
print(summary(fit_test_fail_experienced))
cat("fit_compatibility_experienced\n")
print(summary(fit_compatibility_experienced))
cat("\n")
cat("# D^2 Analysys\n")
cat("Control\n")
cat("fit_control\n")
print(Dsquared(model = fit_control))
cat("For fit_bugs\n")
print(Dsquared(model = fit_bugs))
cat("\n")
cat("# Categories\n")
cat("fit_security\n")
print(Dsquared(model = fit_security))
cat("For fit_features\n")
print(Dsquared(model = fit_features))
cat("For fit_stability\n")
print(Dsquared(model = fit_stability))
cat("For fit_build\n")
print(Dsquared(model = fit_build))
cat("\n")
cat("# Summary History Models\n")
cat("fit_vuln_to_vuln\n")
print(Dsquared(model = fit_vuln_to_vuln))
cat("fit_bug_to_vuln\n")
print(Dsquared(model = fit_bug_to_vuln))
cat("fit_bug_to_bug\n")
print(Dsquared(model = fit_bug_to_bug))
cat("\n")
cat("# Summary Experience Models\n")
cat("fit_security_experienced\n")
print(Dsquared(model = fit_security_experienced))
cat("fit_bug_security_experienced\n")
print(Dsquared(model = fit_bug_security_experienced))
cat("fit_stability_experienced\n")
print(Dsquared(model = fit_stability_experienced))
cat("fit_build_experienced\n")
print(Dsquared(model = fit_build_experienced))
cat("fit_test_fail_experienced\n")
print(Dsquared(model = fit_test_fail_experienced))
cat("fit_compatibility_experienced\n")
print(Dsquared(model = fit_compatibility_experienced))
cat("\n")
cat("# Prediction Analysis\n")
cat("Control\n")
cat("For fit_control\n")
print(prediction_analysis(fit_control,release.next))
cat("For fit_bugs\n")
print(prediction_analysis(fit_bugs,release.next))
cat("\n")
cat("# Categories\n")
cat("For fit_security\n")
print(prediction_analysis(fit_security,release.next))
cat("For fit_features\n")
print(prediction_analysis(fit_features,release.next))
cat("For fit_stability\n")
print(prediction_analysis(fit_stability,release.next))
cat("For fit_build\n")
print(prediction_analysis(fit_build,release.next))
cat("\n")
cat("# Summary History Models\n")
cat("fit_vuln_to_vuln\n")
print(prediction_analysis(fit_vuln_to_vuln,release.next))
cat("fit_bug_to_vuln\n")
print(prediction_analysis(fit_bug_to_vuln,release.next))
cat("fit_bug_to_bug\n")
print(prediction_analysis(fit_bug_to_bug,release.next))
cat("\n")
cat("# Summary Experience Models\n")
cat("fit_security_experienced\n")
print(prediction_analysis(fit_security_experienced,release.next))
cat("fit_bug_security_experienced\n")
print(prediction_analysis(fit_bug_security_experienced,release.next))
cat("fit_stability_experienced\n")
print(prediction_analysis(fit_stability_experienced,release.next))
cat("fit_build_experienced\n")
print(prediction_analysis(fit_build_experienced,release.next))
cat("fit_test_fail_experienced\n")
print(prediction_analysis(fit_test_fail_experienced,release.next))
cat("fit_compatibility_experienced\n")
print(prediction_analysis(fit_compatibility_experienced,release.next))
options(warn=0)
}
|
624e32bc1ed7557545a6ead8bb0f4c5a63803b89
|
74923b9335356d7ddea1264932bad0d4851181a9
|
/R/tagtools/man/block_rms.Rd
|
a831cdb429f7dc1b29f00029d31b43452cdc8427
|
[] |
no_license
|
FlukeAndFeather/TagTools
|
29e491898266bf82edf436674beb7d82f78724c2
|
a6f3fd4eab0ddaef79cc9f1718f2801b30c67971
|
refs/heads/master
| 2020-08-04T03:07:00.111174
| 2019-07-17T18:37:20
| 2019-07-17T18:37:20
| 211,981,790
| 1
| 0
| null | 2019-10-01T00:15:49
| 2019-10-01T00:15:49
| null |
UTF-8
|
R
| false
| true
| 1,537
|
rd
|
block_rms.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/block_rms.R
\name{block_rms}
\alias{block_rms}
\title{Compute RMS of sample blocks}
\usage{
block_rms(X, n, nov = NULL)
}
\arguments{
\item{X}{A vector or a matrix containing samples of a signal in each column.}
\item{n}{The number of samples from X to use in each analysis block.}
\item{nov}{The number of samples that the next block overlaps the previous block.}
}
\value{
A list with 2 elements:
\itemize{
\item{\strong{Y: }} A vector or matrix containing the RMS value of each block. If X is a mxn matrix, Y is pxn where p is the number of complete n-length blocks with nov that can be made out of m samples, i.e., n+(p-1)*(n-nov) < m
\item{\strong{t: }} The time at which each output in Y is reported, in units of samples of X. So if t[1] = 12, then the value Y[1] corresponds to the “time” 12 samples in X. The times at which Y values are reported are the centers of the averaging windows.
}
}
\description{
This function is used to compute the RMS (root-mean-square) of successive blocks of samples.
}
\note{
Output sampling rate is the same as the input sampling rate so s and v have the same size as p.
Frame: This function assumes a [north,east,up] navigation frame and a [forward,right,up] local frame. In these frames, a positive pitch angle is an anti-clockwise rotation around the y-axis. A descending animal will have a negative pitch angle.
}
\examples{
X <- matrix(c(1:20), byrow = TRUE, nrow = 4)
block_rms(X, n = 2, nov = NULL)
}
|
0879b96cb33c6ef6df3605311833e77e0692e060
|
f439a076bc3fcac2c8d7eb72e69dc8d24a00b263
|
/Unit 5 Text Analytics/Assignment5_Spam1.R
|
6923bc23e45126e5dec60e5409c60083d43cd07b
|
[] |
no_license
|
jakehawk34/MIT-Analytics
|
73f9afb0cbfbbd8202e415f0c50c8e638aa76db1
|
daa2ca2eca44ba6c74ba5773d992f68e8c775b90
|
refs/heads/main
| 2023-05-07T13:54:40.796512
| 2021-05-21T00:31:11
| 2021-05-21T00:31:11
| 344,290,207
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,941
|
r
|
Assignment5_Spam1.R
|
# Assignment 5
# Separating Spam from Ham (Part 1)
emails = read.csv("emails.csv", stringsAsFactors = FALSE)
str(emails)
summary(emails)
table(emails$spam)
emails$text[1]
emails$text[2]
# How many characters are in the longest email in the dataset?
max(nchar(emails$text))
# Which row contains the shortest email in the dataset?
which.min(nchar(emails$text))
# Follow the standard steps to build and pre-process the corpus:
corpus = VCorpus(VectorSource(emails$text))
corpus = tm_map(corpus, content_transformer(tolower))
corpus = tm_map(corpus, removePunctuation)
corpus = tm_map(corpus, removeWords, stopwords("english"))
corpus = tm_map(corpus, stemDocument)
dtm = DocumentTermMatrix(corpus)
summary(dtm)
# To obtain a more reasonable number of terms, limit dtm to contain terms appearing in at least 5% of documents, and store this result as spdtm
spdtm = removeSparseTerms(dtm, 0.95)
summary(spdtm)
# Build a data frame called emailsSparse from spdtm, and use the make.names function to make the variable names of emailsSparse valid.
emailsSparse = as.data.frame(as.matrix(spdtm))
colnames(emailsSparse) = make.names(colnames(emailsSparse))
# What is the word stem that shows up most frequently across all the emails in the dataset?
sort(colSums(emailsSparse))
emailsSparse$spam = emails$spam
# How many word stems appear at least 5000 times in the ham emails in the dataset?
sort(colSums(subset(emailsSparse, spam == 0)))
# How many word stems appear at least 1000 times in the spam emails in the dataset?
sort(colSums(subset(emailsSparse, spam == 1))) # Do not count the dependent variable "spam"
emailsSparse$spam = as.factor(emailsSparse$spam)
set.seed(123)
split = sample.split(emailsSparse$spam, 0.7)
train = subset(emailsSparse, split == TRUE)
test = subset(emailsSparse, split == FALSE)
# Using the training set, train the following three machine learning models. The models should predict the dependent variable "spam", using all other available variables as independent variables.
# Please be patient, as these models may take a few minutes to train.
# 1) A logistic regression model called spamLog. You may see a warning message here - we'll discuss this more later.
spamLog = glm(spam ~ ., data=train, family="binomial")
predTrainLog = predict(spamLog, type="response")
# 2) A CART model called spamCART, using the default parameters to train the model (don't worry about adding minbucket or cp).
# Remember to add the argument method="class" since this is a binary classification problem.
spamCART = rpart(spam ~ ., data=train, method="class")
predTrainCART = predict(spamCART)[,2]
# 3) A random forest model called spamRF, using the default parameters to train the model (don't worry about specifying ntree or nodesize).
# Directly before training the random forest model, set the random seed to 123
# (even though we've already done this earlier in the problem, it's important to set the seed right before training the model so we all obtain the same results.
# Keep in mind though that on certain operating systems, your results might still be slightly different).
set.seed(123)
spamRF = randomForest(spam ~ ., data=train)
predTrainRF = predict(spamRF, type="prob")[,2]
table(predTrainLog < 0.00001)
table(predTrainLog > 0.99999)
table(predTrainLog >= 0.00001 & predTrainLog <= 0.99999)
# How many variables are labeled as significant (at the p=0.05 level) in the logistic regression summary output?
summary(spamLog)
# How many of the word stems "enron", "hou", "vinc", and "kaminski" appear in the CART tree?
prp(spamCART)
# What is the training set accuracy of spamLog, using a threshold of 0.5 for predictions?
table(train$spam, predTrainLog > 0.5)
(3052 + 954) / nrow(train)
# Training set AUC of spamLog
trainLogROCR = prediction(predTrainLog, train$spam)
as.numeric(performance(trainLogROCR, "auc")@y.values)
# What is the training set accuracy of spamCART, using a threshold of 0.5 for predictions?
table(train$spam, predTrainCART > 0.5)
(2885 + 894) / nrow(train)
# What is the training set AUC of spamCART?
trainCART.ROCR = prediction(predTrainCART, train$spam)
as.numeric(performance(trainCART.ROCR, "auc")@y.values)
# Training set accuracy of spamRF, using a threshold of 0.5 for predictions
table(train$spam, predTrainRF > 0.5)
(3015 + 916) / nrow(train)
# Training set AUC of spamRF
trainRF.ROCR = prediction(predTrainRF, train$spam)
as.numeric(performance(trainRF.ROCR, "auc")@y.values)
# What is the testing set accuracy of spamLog, using a threshold of 0.5 for predictions?
predTestLog = predict(spamLog, newdata=test, type="response")
table(test$spam, predTestLog > 0.5)
(1257 + 376) / nrow(test)
# Testing set AUC of spamLog
testLogROCR = prediction(predTestLog, test$spam)
as.numeric(performance(testLogROCR, "auc")@y.values)
# What is the testing set accuracy of spamCART, using a threshold of 0.5 for predictions?
predTestCART = predict(spamCART, newdata=test)[,2]
table(test$spam, predTestCART > 0.5)
(1228 + 386) / nrow(test)
# Testing set AUC of spamCART
testCART.ROCR = prediction(predTestCART, test$spam)
as.numeric(performance(testCART.ROCR, "auc")@y.values)
# What is the testing set accuracy of spamRF, using a threshold of 0.5 for predictions?
predTestRF = predict(spamRF, newdata=test, type="prob")[,2]
table(test$spam, predTestRF > 0.5)
(1291 + 387) / nrow(test)
# Testing set AUC of spamRF
testRF.ROCR = prediction(predTestRF, test$spam)
as.numeric(performance(testRF.ROCR, "auc")@y.values)
# In terms of testing set performance,
# the random forest outperformed logistic regression and CART in both measures, obtaining an impressive AUC of 0.997 on the test set.
# Both CART and random forest had very similar accuracies on the training and testing sets.
# However, logistic regression obtained nearly perfect accuracy and AUC on the training set and had far-from-perfect performance on the testing set.
# This is an indicator of overfitting.
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.